Download Summer climate and heatwaves in Europe

Summer climate and heatwaves in Europe
Annemiek Stegehuis
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Annemiek Stegehuis. Summer climate and heatwaves in Europe. Biodiversity and Ecology.
Université Paris-Saclay, 2016. English. <NNT : 2016SACLV052>. <tel-01480310>
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NTT:2016SACLV052
T HÈSE DE D OCTORAT
DE
L ’U NIVERSITÉ
PARIS -S ACLAY
PRÉPARÉE À
L ’U NIVERSITÉ
V ERSAILLES S AINT-Q UENTIN EN Y VELINES
E COLE D OCTORALE N O. 129
S CIENCES DE L ’ ENVIRONNEMENT D ’I LE - DE -F RANCE
M ÉTÉOROLOGIE , OCÉANOGRAPHIE ET PHYSIQUE DE L ’ ENVIRONNEMENT
PAR
A NNEMIEK I RENE S TEGEHUIS
S UMMER
CLIMATE AND
HEATWAVES IN E UROPE
T HÈSE PRÉSENTÉE ET SOUTENUE AU LSCE, LE 7 JUILLET 2016
PhD director :
PhD co-director :
PhD co-director :
President of the jury :
Reporters :
Examiner :
Robert Vautard
Philippe Ciais
Adriaan J. Teuling
Directeur de recherche (LSCE/CNRS)
Philippe Bousquet
Bart J. J. M. van den Hurk
Jesus Fernandez
Wolfgang Cramer
Professeur (UVSQ/IPSL)
Directeur de recherche (LSCE/CEA)
Assistant Professor (Wageningen University)
Directeur de recherche (KNMI)
Assistant Professor (Universidad de Cantabria)
Professeur / Directeur de recherche (CNRS)
Acknowledgments
Merci pour toutes ces belles années !
i
Contents
Acknowledgments
i
Contents
iii
Abstract
vii
Introduction
1
I
5
6
6
8
8
8
II
General introduction
1
European summer climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1
Oceanic influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2
Large scale circulation . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.1
North Atlantic Oscillation . . . . . . . . . . . . . . . . . . . .
1.2.2
Principal summer weather regimes . . . . . . . . . . . . . . .
1.2.3
Weather regimes associated with warm summer temperature
anomalies . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3
Land-atmosphere interactions influencing the summer climate . . . . .
1.3.1
Soil moisture-temperature feedback . . . . . . . . . . . . . .
1.3.2
Soil moisture-precipitation feedback . . . . . . . . . . . . . .
1.3.3
Regions for strong land-atmosphere feedbacks . . . . . . . .
1.3.4
Influence of vegetation . . . . . . . . . . . . . . . . . . . . .
2
Heatwaves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1
Characteristics of the 2003 heatwave . . . . . . . . . . . . . . . . . . .
2.2
Mechanisms behind the 2003 heatwave . . . . . . . . . . . . . . . . .
2.3
Consequences of the 2003 heatwave . . . . . . . . . . . . . . . . . . .
3
Impact of elevated temperatures and drought on vegetation . . . . . . . . . .
10
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Uncertainties in the future climate
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21
22
iii
iv
CONTENTS
2
3
4
5
The European climate under a 2◦ C global warming . . . . . . . . . . . . . . .
2.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3
Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.1
Temperature and precipitation change . . . . . . . . . . . . .
2.3.2
Land heat fluxes and radiation . . . . . . . . . . . . . . . . .
2.4
Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . . .
Future European temperature change uncertainties reduced by using land heat
flux observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Discussion and additional results . . . . . . . . . . . . . . . . . . . . . . . . .
4.1
Model ensemble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2
Uncertainty reduction method . . . . . . . . . . . . . . . . . . . . . . .
4.3
The choice for the 2◦ C limit . . . . . . . . . . . . . . . . . . . . . . . .
Summary and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
III Atmospheric processes
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . .
2
WRF model description . . . . . . . . . . . . . . . . . .
2.1
Microphysics . . . . . . . . . . . . . . . . . . . .
2.2
Planetary Boundary Layer . . . . . . . . . . . . .
2.3
Surface Layer . . . . . . . . . . . . . . . . . . . .
2.4
Radiation . . . . . . . . . . . . . . . . . . . . . .
2.5
Convection . . . . . . . . . . . . . . . . . . . . .
2.6
Land surface . . . . . . . . . . . . . . . . . . . .
3
An observation-constrained multi-physics WRF ensemble
pean mega heat waves . . . . . . . . . . . . . . . . . . .
4
Discussion and additional results . . . . . . . . . . . . .
4.1
The importance of the convection scheme . . . .
4.2
What triggers convection? . . . . . . . . . . . . .
5
Summary and conclusions . . . . . . . . . . . . . . . . .
IV
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The influence of soil moisture on summer temperatures
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1
Model description . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2
Control simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3
Attribution of temperature anomalies . . . . . . . . . . . . . . . . . .
3
Summer warming induced by early summer soil moisture changes in Europe
3.1
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2
Main text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Discussion and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Drought impacts on European forest species
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
Drought and heat stress on 5 main European forest tree species
2.1
Picea sp. . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2
Pinus sylvestris . . . . . . . . . . . . . . . . . . . . . . .
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CONTENTS
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Discussion & concluding remarks
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Climate models in general . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
Regarding the future climate . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
Impact studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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VI
2.3
Fagus sylvatica . . . . . . . . . . . . . . . . . . .
2.4
Quercus robur & petraea . . . . . . . . . . . . . .
2.5
Quercus ilex . . . . . . . . . . . . . . . . . . . . .
Model description . . . . . . . . . . . . . . . . . . . . . .
3.1
ORCHIDEE . . . . . . . . . . . . . . . . . . . . .
3.2
ORCHIDEE-CAN . . . . . . . . . . . . . . . . . .
3.2.1
From PFT to species . . . . . . . . . . .
3.2.2
Carbon allocation and canopy structure
3.2.3
Hydraulic architecture . . . . . . . . . .
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1
Puéchabon . . . . . . . . . . . . . . . . . . . . .
4.1.1
GPP . . . . . . . . . . . . . . . . . . . .
4.1.2
Soil hydraulics . . . . . . . . . . . . . .
4.1.3
Transpiration . . . . . . . . . . . . . . .
4.2
Other sites . . . . . . . . . . . . . . . . . . . . .
4.2.1
Brasschaat . . . . . . . . . . . . . . . .
4.2.2
Tharandt . . . . . . . . . . . . . . . . .
4.2.3
Hesse & Collelongo . . . . . . . . . . .
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1
Site-specific characteristics . . . . . . . . . . . . .
5.2
Plant hydraulics . . . . . . . . . . . . . . . . . .
5.3
Other processes . . . . . . . . . . . . . . . . . . .
Summary and concluding remarks . . . . . . . . . . . .
Bibliography
Annexes
A
v
The European climate under a 2dC global warming
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123
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Abstract
vii
Titre: Le climat estival et des vagues de chaleur en Europe
Mots clés: Vagues de chaleur en Europe, Modélisation la méthode, Forets en Europe,
Sécheresse
Résumé:
L'objectif de ce travail de thèse est de contribuer
à la compréhension des rôles joués par les
interactions sol-atmosphère et par la circulation à
grande-échelle dans la formation d’anomalies de
températures estivales en Europe. Cela constitue
un double défi du fait de la rareté des
observations d’une part et des incertitudes liées
aux
paramétrisations
des
modèles
atmosphériques et de leurs schémas de surface
d’autre part. Ce travail est centré sur 4 sujets
principaux: 1) Comment les interactions sur les
processus sol-atmosphère liés à l’eau du sol
disponible pour les plantes et aux flux d’énergie
échangés entre la surface et l’atmosphère
influencent-elles les projections climatiques de
température d’été et leurs incertitudes dans le
climat
européen?
2)
Comment
les
paramétrisations atmosphériques influencent les
simulations des vagues de chaleur extrêmes en
Europe? 3) Quelle importance joue l’humidité du
sol comparée aux circulations atmosphériques
sur les anomalies de température estivales? Et 4)
Quels sont les impacts des sécheresses et de la
chaleur sur la végétation?
1) Comment les interactions sol-atmosphère
influencent-elles les projections climatiques et
leurs incertitudes?
Il existe des incertitudes considérables dans la
simulation du climat actuel en Europe, qui
peuvent se propager dans la simulation du climat
futur et de sa sensibilité à l’augmentation des
forçages anthropiques. Cela soulève plusieurs
questions, dont deux sont traitées dans ce
chapitre, présentés dans deux parties différentes.
Premièrement je discute des incertitudes dans les
projections climatiques Européennes pour un
échauffement global moyen de 2˚C. Je présente
les incertitudes dans le climat futur liée pour
différents horizons temporels. Un accent
particulier est mis sur les températures estivales
et les flux d’énergie échangés entre la surface et
l’atmosphère. Cette étude fait partie du projet
Européen IMPACT2C et les résultats sont
publiés par Vautard et al. (2014). Je ne montre
que les résultats du travail personnel accompli
dans cette étude. L’article complet se trouve dans
l’Annexe A. Deuxièmement j’examine le lien
heuristique entre les incertitudes des simulations
des flux d’énergie échangés entre la surface et
l’atmosphère pour l’actuel et les incertitudes de
température dans un climat futur.
Les rétroactions entre surface et atmosphère
jouent un rôle important dans la genèse de vagues
de chaleur et de sécheresses en l’été dans le
climat tempéré en Europe. Cela peut également
être analysé dans un ensemble de sorties de
modèles où les rétroactions plus fortes donnent
lieu à des températures plus élevées. Stegehuis et
al. (2013b) ont montré que, en utilisant un
ensemble de modèles, les flux de chaleur sensible
les plus importants sont associés à des
températures estivales plus élevées (Fig. II.I).
Ensuite, nous avons mis en évidence de grandes
différences dans la répartition des flux d’énergie
échangés entre la surface et l’atmosphère entre
différents modèles, ce qui conduit à des
incertitudes dans les simulations pour le climat
actuel. Cependant, comment ces différences se
traduisent en différences pour le climat futur
reste mal connu.
Les incertitudes dans des simulations du climat
sont mieux représentées dans des ensembles de
modèles que dans chaque modèle pris
individuellement. Dirmeyer et al. (2006) ont
montré que rétroactions entre terre et atmosphère
sont relativement mal représentées dans chaque
modèle, alors que la moyenne de l’ensemble
montre de meilleures performances. Ceci est en
partie dû à la rareté des observations spatialement
explicites à long terme pour certaines des
variables importantes dans les rétroactions entre
surface et atmosphère, tels que les flux de chaleur
sensible et latente. Récemment, certains produits
satellitaires sont devenus disponibles comme
GLEAM (Miralles et al. 2011). Un autre produit,
basé sur une combinaison de mesures de
télédétection et des données de tours à flux du
réseau FLUXNET est le ‘Model Tree Ensemble’
de Jung et al. (2010), utilisé dans l’étude
présentée au Chapitre 2. Cependant, en utilisant
ce type de produit dérivé des observations, on
doit toujours garder à l’esprit que les données ont
également des incertitudes systématiques.
Mueller et al. (2011) ont montré que la dispersion
des produits de flux de chaleur latente dérivés
d’observations est comparable à celle des flux
simulés avec des ensembles de modèles de
surface.
En raison de la meilleure représentation des
ensembles de modèles par rapport à celle de
modèles individuels, plusieurs ensembles de
modèles, régionaux et globaux, ont été réalisés
par différents groupes de recherche. Un exemple
d’un ensemble avec des modèles climatiques
régionaux (RCM) européen est celui du projet
FP6-ENSEMBLES (Hewitt et al., 2004; Van der
Linden et al., 2009). Dans cette étude j’ai utilisé
15 RCMs, conduits par 6 modèles climatiques
globaux, du 4ème rapport du GIEC, pour le
scénario A1B.
En ce qui concerne la première question posée,
nous avons d’abord constaté que les expériences
de simulation européennes ont un réchauffement
moyen qui est supérieur à la moyenne mondiale,
dépassant 2°C au niveau régional. Le
réchauffement est le plus grand en Scandinavie,
la région méditerranéenne, la péninsule ibérique
et les Alpes. Les iles britanniques subissent un
réchauffement plus faible. Les flux de chaleur
sensible plus élevés confirment ce plus fort
réchauffement dans le sud, associé à une
diminution des flux de chaleur latente et une
augmentation des flux de chaleur sensible dans
les régions du sud pendant l’été. Dans la région
où le régime d’évapotranspiration est limité par
l’énergie (Seneviratne et al., 2010), Scandinavie,
les températures plus élevées conduisent à
l’augmentation du flux de chaleur latente
pendant l’été, alors que le flux de chaleur
sensible est diminué. Cela peut être dû à la
formation des nuages qui diminue le
rayonnement solaire et améliore le rayonnement
des ondes longues.
En ce qui concerne la deuxième question posée,
utilisant des observations de flux de chaleur
sensible de Jung et al. (2009), nous avons pu
réduire l’écart inter-modèles des RCMs sur la
prédiction du changement de température d’été
en Europe jusqu’à 40%. Les réductions
d’incertitude les plus importantes ont été trouvé
sur la France et les Balkans, des régions déjà
mentionnées pour leur importance des
rétroactions entre terre et atmosphère (e.g.
Seneviratne et al., 2006a). Dans le sud et le nord
de l’Europe, d’autres variables observées
pourraient être utilisées pour réduire l’incertitude
dans les projections de température, par exemple
les rétroactions entre les précipitations et la
température (Quesada et al., 2012) ou la
rétroaction neige-albédo-température (Hall &
Qu, 2006).
grand écart entre les modèles. La source d’une
telle surestimation n’est pas évidente. Parmi les
différents modèles, le choix d’un jeu de
paramétrisations des processus physiques
conduit à des résultats différents (Bellprat et al.,
2012). Une autre source d’incertitude dans les
RCMs peut aussi découler des forçages. En outre,
la dynamique créée par les modèles, pourrait être
une source de différenciation par la variabilité
naturelle chaotique, provoqué par l’instabilité de
l’atmosphère (Lorenz, 1963).
2) Dans le chapitre 2 l’accent était mis sur les
rétroactions entre surface et atmosphère dans des
simulations de RCMs et leur influence sur les
températures estivales futures. Ce chapitre se
concentre sur les processus atmosphériques
(physique et microphysique des nuages,
convection, transfert radiatif) dans un modèle
RCM très largement utilisé (WRF) et leurs
impacts sur les simulations des températures
estivales pendant les vagues de chaleur en
Europe. L’objectif principal est d’essayer de
répondre à la question de savoir si et comment
différentes formulations physiques différentes
disponibles pour le modèle WRF influencent la
simulation des vagues de chaleurs et l’incertitude
des températures d’été en Europe.
Dans une étude sur les phénomènes climatiques
extrêmes, Good et al. (2006) ont constaté que les
projections des modèles sont nécessairement
limitées par des incertitudes, en particulier en
raison de la paramétrisation des processus
physiques dans les modèles atmosphériques. Des
ensembles ‘multi-physiques’ ont été construits
pour estimer cette incertitude (Awan et al., 2011).
Avec cette approche, un seul modèle est utilisé,
mais des physiques différentes sont testées pour
trouver les combinaisons les plus réalistes. Bien
que cette méthode ne soit pas vraiment nouvelle
(Fernandez et al., 2007; Awan et al., 2011 ;
Evans et al., 2012, Garcia-Diez et al., 2013 ;
Mooney et al., 2013), elle n’a pas encore été
utilisée pour analyser les vagues de chaleur.
Pourtant, cela peut être d’une grande importance
en ce qui concerne leurs conséquences et leur
fréquence et gravité plus élevées attendu dans le
climat futur de l’Europe.
Il n’est pas évident à priori que les RCMs
peuvent représenter les vagues de chaleur
extrêmes dans les régions où les observations
sont rares (Perkins et al., 2013). Même si la
physique était exactement connue, des
incertitudes sont liées au manque de données, par
exemple de réseaux d’observations de stations
météorologiques. peuvent conduire à une sousestimation des températures maximales (Halenka
et al., 2006), soit trop peu de vagues de chaleurs
simulées. Cependant, Vautard et al. (2013) ont
constaté que la fréquence et l’amplitude des
vagues de chaleur sont souvent surestimées dans
les RCMs en Europe, en particulier dans la région
méditerranéenne. Toutefois ils ont montré un
Dans le chapitre 3 j’ai utilisé le modèle ‘Weather
and Research Forecast’ (WRF) (Skamarock et
al., 2008) et j’ai testé 216 combinaisons
différentes de la paramétrisation des processus
physiques atmosphériques. Ils se composent de 3
physiques de rayonnement, 4 physiques de
convection, 3 microphysiques et 6 physiques de
couche limite planétaire. J’ai simulé les vagues
de chaleur de 2003 et 2010, et l’été plus humide
de 2007. Pour mettre l’accent sur la physique
seulement, j’ai guidé les simulations au-dessus
de la couche limite atmosphérique avec des
observations. Bien que les interactions surfaceatmosphère jouent également un rôle important
dans les canicules d’été (voir Chapitre 2), seules
les physiques de l’atmosphère ont été testées en
raison du choix limité entre différents schémas de
surface dans le modèle WRF au moment de
l’étude. Les 216 simulations ont été évaluées par
rapport à différents jeux d’observations.
J’ai trouvé un fort écart de la température, la
précipitation et le rayonnement simulé avec les
observations, même si les simulations ont été
guidées. Les différences de température entre
modèle et observations étaient localement
jusqu’à 10˚C, avec les plus grandes incertitudes
en Europe centrale, probablement en raison de
représentations différentes des interactions
surface-atmosphère (Stegehuis et al., 2013a). Les
températures moyennes et maximales ont été
systématiquement sous-estimées quelle que soit
la paramétrisation des processus physiques
atmosphériques utilisée. Les précipitations et le
rayonnement solaire incident ont été, la plupart
de temps, surestimés. J’ai montré que la
convection domine la dispersion de l’ensemble
de simulations de WRF avec différentes
physiques, probablement en induisant des
incertitudes dans les nuages que affectent à la fois
l’énergie de surface et le budget de l’eau avant et
pendant les vagues de chaleur. Un petit ensemble
a été sélectionné, qui produit le meilleur accord
avec les données d’observation. Nous suggérons
que cet ensemble de configurations pourrait être
utilisé pour d’autres recherches sur les vagues de
chaleur et les températures estivales en Europe
avec le modèle WRF.
3) Les résultats des chapitres 2 et 3 ont permis de
montrer que les températures estivales en Europe
sont contrôlées par les interactions surfaceatmosphère et par la circulation de l’atmosphère
(physique). Pour les vagues de chaleur, la
circulation atmosphérique doit favoriser le
transport de l’air chaud vers le continent. Le sol
doit favoriser une rétroaction entre la baisse de
l’humidité du sol et l’augmentation des
températures, à travers la dissipation d’énergie
par les flux de chaleur sensible. Bien que des
études antérieures ont établi l’importance de ces
deux processus (Black et al., 2004 ; Miralles et
al., 2014), elles n’ont pas cherché à séparer
quantitativement leur contribution individuelle a
des anomalies de température d’été pour les
vagues de chaleur. Dans cette étude, j’ai choisi
d’étudier l’effet de humidité du sol au début de
l’été seulement sur la production d’anomalies de
température en été, car l’humidité du sol plus tard
dans la saison estivale est aussi dépendent de la
circulation
atmosphérique,
donc
moins
formellement séparable.
L’augmentation de la température prédite en
Europe à la fin de ce siècle est estimée entre 2˚C
et 5˚C, selon le scénario et la région (van der
Linden & Mitchell, 2009 ; Boberg &
Christensen, 2012 ; Stegehuis et al., 2013a).
Cette augmentation devrait être plus forte en
Europe du Sud (up to 5˚C), que dans la partie
nord (Fig. 3, Stegehuis et al., 2013a). En outre,
une plus grande variabilité des températures est
attendue, notamment en Europe centrale. Cette
augmentation globale de la température sur le
continent Européen pourrait conduire à plus de
dessèchement du sol et plus de variabilité, créant
des situations favorables pour des rétroactions
positives entre le sol et la température. Une
compréhension plus précise, et la quantification
du rôle de l’humidité du sol et de ses tendances
au cours des dernières années, pourraient donc
être critiques dans la compréhension de la
variabilité des températures estivales et
événements canicule dans le climat futur.
Les régimes de circulation atmosphérique
associés au développement des canicules sont les
deux régimes de Blocage Atmosphérique et
Atlantic Low (Cassou et al., 2005). L’apparition
des différents régimes de circulation ont changé
au cours du siècle dernier (Horton et al., 2015 ;
Coumou et al., 2015 ; Alvarez-Castro et al.,
2016). Bien que l’impact de ces régimes aient été
étudiés (van Haren et al., 2015), il y a encore
beaucoup d’incertitude quant à savoir si et
comment ils peuvent changer dans le climat futur
(Cattiaux et al., 2012). Néanmoins, une meilleure
compréhension de leur contribution aux vagues
de chaleur en été est utile pour prédire le
changement climatique.
Dans le chapitre 3, j’avais sélectionné un nombre
limité de configurations de paramétrisations des
processus physiques atmosphériques dans le
RCM WRF qui sont capables de simuler les
vagues de chaleur observées de 2003 et 2010,
ainsi que l’année d’été relativement humide de
2007. Bien que le chapitre 4 ne traite pas
uniquement des vagues de chaleur, ces
configurations seront choisies pour créer un
ensemble d’étés basés sur différentes
initialisations de l’humidité du sol au début de
l’été et différents régimes atmosphériques.
Toutes ces conditions initiales sont réalistes,
comme les valeurs choisies sont celles des années
d’été modélisées de 1980-2011.
Nous avons constaté que la contribution de
l’humidité du sol au début de l’été dans les
vagues de chaleur de 2003 et 2010 a été
importante et a contribué à environ 1˚C au
maximum. Cependant, le conditionnement par le
régime de circulation atmosphérique (facteur de
grande échelle) explique statistiquement la plus
grande partie des anomalies de température, avec
une contribution maximum de 3˚C en 2003 et
jusqu’à 6˚C en 2010. Un résultat intéressant est
que la contribution de l’humidité du sol initiale a
augmenté en importance au cours des trois
dernières décennies en Europe centrale, en
France et dans certaines parties de la Russie,
alors que celle des régimes de temps est restée à
peu près stable. Ces régions coïncident avec les
régions avec une tendance négative significative
de l’humidité du sol dans les simulations du
modèle WRF. L’influence des régimes de temps
sur les anomalies de température d’été n’a
augmenté au cours des trois dernières décennies
qu’en l’Europe de l’Est.
4) Dans les chapitres précédents l’accent a été
mis sur le climat de l’été et les vagues de chaleur
en Europe, en analysant les processus
atmosphériques et les rétroactions surfaceatmosphère. Un aspect important dans les
rétroactions liées aux processus de surface est la
végétation, qui peut soit avoir un effet
modérateur ou un effet d’amplification sur les
anomalies de température régionales. Ceci
dépend en grande partie du type de végétation et
sur la disponibilité de l’humidité du sol qui
influence l’état de la végétation couplé à l’état de
l’atmosphère par les flux de chaleur sensible et
latente. L’impact des étés chauds et des vagues
de chaleur sur l’état hydrique et le
fonctionnement de la végétation est donc un
aspect important dans la compréhension du
climat, jusqu’ici peu ou pas abordé dans les
RCMs qui ont des schémas de surface assez
simples (pas de phénologie, pas de physiologie,
pas de mortalité ou dépérissement de la
végétation).
Les forêts peuvent émettre plus de chaleur latente
dans l’atmosphère, et plus longtemps durant les
vagues de chaleur, en raison de la profondeur de
l’enracinement, et donc un meilleur accès à l’eau
souterraine,
conduisant
à
plus
d’évapotranspiration que les prairies pendant le
développement des vagues de chaleur. L’albédo
plus faible des forêts peut en revanche provoquer
un effet de réchauffement radiatif (Naudts et al.,
2016). L’effet d’albédo semble dominer l’effet
de l’évaporation pendant le début des vagues de
chaleur (Teuling et al., 2010). Cet équilibre peut
cependant être inversé pendant le développement
ultérieur des vagues de chaleur ou lorsque la
plupart des arbres ferment leurs stomates à cause
du stress hydrique. Pour séparer ces effets, les
connaissances sur la réaction de la végétation au
cours du stress de l’eau est cruciale.
Pour les effets du climat sur la végétation sur des
plus grandes échelles, les modèles de végétation
peuvent être utilisés couplés à des modèles
atmosphériques. La plupart de ces modèles ne
distinguent pas entre des espèces différentes,
mais utilisent des ‘groupes fonctionnels’ (PFT).
Cependant, une nouvelle version d’ORCHIDEE
(Krinner et al., 2005) inclut les caractéristiques
des espèces de la plupart des essences d’arbre
européennes. Cette version, ORCHIDEE-CAN
(Naudts et al., 2015), a en outre une description
explicite des potentiels d’eau dans le continuum
sol-plante-atmosphère,
que
peut
mieux
représenter en principe le stress hydrique des
plantes pendant les périodes chaudes et sèches.
Dans le chapitre 5, je présente d’abord une revue
de littérature sur les effets de la sécheresse et de
la chaleur sur le stress hydrique de la végétation.
Cette revue est concentrée sur cinq espèces
d’arbres en Europe. En outre, je présente des
résultats préliminaires de l’évaluation du modèle
de la végétation ORCHIDEE-CAN pour simuler
la baisse de transpiration et de photosynthèse du
couvert végétal pendant des épisodes de
sécheresse. Les données d’évaluation sont celles
de tours à flux (Baldocchi et al., 1996) pour 5
sites en Europe. Le site sur lequel j’ai essayé de
calibrer le modèle ORCHIDEE-CAN pour
Quercus ilex (chêne vert) est celui de Puéchabon,
soumis à un fort stress hydrique du sol au
printemps et en été. J’ai montré qu’ORCHIDEECAN ne permet pas de simuler la baisse de
photosynthèse (GPP) et de transpiration de ce
site. La GPP de la végétation est restée trop
élevée pendant l’été, probablement causée par
des niveaux d’humidité du sol trop élevé. Par
contre, pour les espèces tempérées Fagus
sylvatica, Picea sp., Pinus sylvestris et Quercus
robur et petraea la simulation de GPP a été
beaucoup mieux représenté, bien que ces espèces
n’aient pas subi beaucoup de stress hydriques
pendant les années observées. Certains processus
à l’échelle de l’arbre ou du couvert ne sont pas
représentés dans le modèle de la végétation, ce
qui peut entrainer des différences entre la
production primaire modélisé et observé.
Pour simuler l’impact du stress hydrique sur la
végétation de l’augmentation prévue de la
fréquence et de la gravité des vagues de chaleur,
certaines
adaptations
du modèle que
ORCHIDEE-CAN doivent être apportées. Tout
d’abord, les processus spécifiques qui agissent le
plus fortement en période de sécheresse doivent
être ajoutés. Un exemple est la résistance entre le
sol et la racine. Outre ces processus spécifiques à
la sécheresse, il serait également nécessaire
d’introduire des effets décalés. La perte de
conductivité (hydraulic failure) n’est pas
reproduite par ORCHIDEE-CAN pour le
moment. En réalité, la mortalité des arbres peut
se produire même plusieurs années après un
événement de sécheresse extrême. Une solution
relativement simple serait d’ajouter une forme de
mortalité lorsque l’embolie devient trop
importante, si ce processus peut être reproduit
dans le modèle utilisé. Le temps n’a pas permis
de déboucher sur un résultat finalisé, c’est à dire
une version satisfaisante d’ORCHIDEE-CAN
capable de reproduire la baisse de potentiel
hydrique du sol, et celle des différents organes de
la plante.
Title: Summer climate and heatwaves in Europe
Keywords: Heatwaves, Europe, Modeling, Forest, Summer climate
Abstract:
Through this work I aimed to improve the
understanding of the role of land-atmosphere
feedbacks and large-scale circulation that lead to
warm summer temperatures in Europe. This is
challenging due to the scarcity of observations
and the uncertainties of parameterized
atmospheric processes. I focused on four main
issues: 1) How do land-atmosphere feedbacks
affect climate projections and their uncertainties?
2) How do different physical parameterizations
affect the simulation of extreme heatwaves? 3)
How large are the roles of soil moisture and
atmospheric circulation in the development of
European summer temperature anomalies? And
4) What are the impacts of heat and drought
stress on vegetation?
Regarding the first question I found that the
different partitioning of land heat fluxes between
models leads to spatially different warming over
Europe in the future. The uncertainty of future
climate change was especially high in central
Europe, largely due to the uncertainty in heat flux
partitioning, while in Southern Europe the
models mostly agreed. The use of observationbased sensible heat fluxes allowed to reduce this
climate change uncertainty regionally up to 40%.
While
studying
different
atmospheric
parameterizations for the extreme heatwaves of
2003 and 2010, I found a large temperature
spread between the simulations. Compared to
observations,
temperature
was
mostly
underestimated. Shortwave radiation and
precipitation were generally overestimated. I
selected a reduced model ensemble of well
performing configurations compared to
observations, to perform future studies on warm
summer temperatures over Europe.
The best physics configuration was consequently
used to quantify the role of early summer soil
moisture and large-scale drivers on summer
temperature anomalies. The contribution of soil
moisture was up to maximum 1ºC during the
heatwaves of 2003 and 2010. The contribution of
large-scale drivers was larger, reaching up to 3ºC
in 2003 and up to 6ºC in 2010. However, the
contribution of early summer soil moisture to the
temperature anomalies has been increasing over
the last decades over parts of central Europe and
Russia, corresponding to the regions with a
significant negative trend of soil moisture. Largescale drivers showed an increasing importance in
the Eastern European region.
Lastly, I studied the impacts of drought and heat
stress on several European forest tree species. I
found an overestimation of modeled GPP at a
local scale in the Mediterranean region during
summer with ORCHIDEE. This indicates that the
vegetation model does not well reproduce the
complicated consequences of drought stress. To
model future, possibly more severe impacts of
drought, the model may need to be adapted with
drought-specific processes and lagged effects.
Introduction
The European summer climate is characterized by a large variability, both in space and
time. Although our understanding of this climate variability has been increasing, some important questions are still unanswered. One example is the question of whether European
summer conditions can be predicted in advance, over time periods of weeks or even decades.
Such a prediction could bring enormous benefits in many different sectors, but requires detailed and precise knowledge on underlying mechanisms. Local observations are adequate
for understanding processes at local scales. However, some of the processes and feedbacks
occur at larger regional or continental scales for which models may be a more suitable tool.
The first numerical climate models only calculated the large-scale circulation patterns on
a global scale. Nowadays climate models exist on both regional and global scale and contain numerous routines and parameterizations to describe the current state of the land and
the atmosphere. Their increasing complexity and resolution are advantageous in that more
processes can be described, but the uncertainty of these processes might lead to error compensations, which may lead to ’the right temperature for the wrong reason’. Such model
deficiencies often become apparent when looking at interannual variability. This is problematic in current-climate simulations, but might be even more of an issue for the simulation of
the future climate, as interannual variability is expected to increase (Schär et al., 2004).
The last decades have shown extreme examples out of the historical range of variability, in
the form of extreme warm and dry summers such as in 2003 in Western Europe and in 2010
in Russia (Luterbacher et al., 2004; Barriopedro et al., 2011). While warm weather does bring
advantages, these events were characterized by high financial costs, increased mortality rates
and decreased ecosystem productivity (Ciais et al., 2005; García-Herrera et al., 2010).
The importance of the ability of models to correctly simulate interannual variability and
extreme events such as heatwaves has not been recognized for some time, and there was more
focus on obtaining the right climatology. Now both our knowledge on climatic processes and
computing capacity have increased, more effort has been invested in the representation of
the above mentioned issues, especially after the two heatwave episodes.
1
Two processes central for European heatwaves are large-scale atmospheric circulation and
land-atmosphere coupling on a local to regional scale. Warm dry air imported from Eastern or
Southern Europe has the potential to reduce precipitation and increase clear sky conditions.
This increases evaporative demand resulting in enhanced soil drying. Reduced soil moisture
alters the partitioning of land heat fluxes and favors sensible over latent heat flux which
can result in a positive feedback loop promoting high temperatures. However, large-scale
observations of the most important variables of these processes, such as soil moisture and
land heat fluxes, are scarce. This limits their precise understanding and thus the simulation
of heatwave events.
Through this work, I aim to improve the understanding of the role of land-atmosphere
feedbacks and large-scale circulation that lead to warm summer temperatures in Europe.
This is challenging due to the scarcity of observations and the uncertainties of parameterized
atmospheric processes. I will first focus on some important variables of the energy budget
and land-atmosphere feedbacks, and study how these, and their uncertainties, affect climate
projections. A second step is to evaluate different models to simulate the extreme heatwaves
we have witnessed in 2003 in Europe and 2010 in Russia. I will pay special attention to
different atmospheric physics to find the uncertainty that physical formulations can induce.
After the evaluation of the models, I will study the specific mechanisms leading to higher
summer temperatures, with a direct focus on soil moisture legacy effects and atmospheric
circulation patterns and try to separate their importance.
An important part of land-atmosphere feedbacks during summer is the vegetation covering the land surface. It can either have an amplifying or a moderating effect on the climate,
depending largely on the availability of soil moisture which influences the status of the vegetation, and the vegetation type. Forests were found to have an amplifying effect on high
temperatures at the beginning of a heatwave, while grasslands have a moderating effect
(Teuling et al., 2010). The lower albedo and evapotranspiration rates of forest were responsible for these differences. On longer time scales however, this effect may be reversed due to
the higher capacity of trees to store water and extract it from the soil. To better understand
the influence of vegetation on climate, we also need to study how climate influences vegetation. Here too, models are a very suitable tool for investigating the impacts of climate on
vegetation as the processes involved can act over large scales.
A large uncertainty from the modeling of forests is upscaling of processes from single trees
and leaves to whole forest ecosystems. In addition, not all processes on leaf level might be
known, inducing more uncertainty. A start to answer the full question about the impact of
future heatwaves is the evaluation of vegetation models on the simulation of current summer stress. I will use a recently developed vegetation model with a specific description of
water potentials in the soil-plant-atmosphere continuum, and asses its capability to simulate
summer stress at different sites in Europe. This model could then be used to upscale to the
whole European region. Besides this first/limited model assessment, I will discuss different
drought-affected processes on tree-level described in the literature.
To summarize, the research questions addressed in this work are:
1) How do the important land-atmosphere processes in regional climate models lead to
different summer temperatures? How do their uncertainties affect future summer temperatures predictions? And how can present-day observations be used to reduce a part of the
2
CONTENTS
3
uncertainty in these temperature projections?
2) Can current regional climate models correctly simulate heatwaves in Europe? How do
different atmospheric parameterizations influence the simulation of heatwaves? And can we
design a model ensemble that can correctly simulate European heatwaves?
3) How large is the role of atmospheric circulation and soil moisture feedbacks in the
development of European summer temperatures? Can we quantify and separate their importance? And is the importance of land-atmosphere feedbacks becoming more important?
4) Can a state of the art vegetation model correctly simulate summer stress in forests?
What are some of the missing processes? And can the model simulate extreme stresses as
have been experienced in 2003, with a focus on green oak ecosystems in the Mediterranean
region?
C HAPTER
I
General introduction
5
6
1
CHAPTER I. GENERAL INTRODUCTION
European summer climate
Europe can be separated in broadly four climatic zones: a boreal climate in the north,
a Mediterranean climate in the south, a continental climate in the central east and a maritime or oceanic climate central west. In the Alps and the Pyrenees some regions have an
alpine climate. These different climates are characterized by different annual and seasonal
temperature and precipitation values. The Mediterranean climate has hot and dry summers
but milder and wet winters (Fig. I.1). The former is partially due to the sinking air from the
Hadley cells, whilst the latter is partially caused by the Mediterranean Sea that moderates the
temperatures during the winter and storms that form over the Atlantic Ocean. The maritime
or oceanic temperate climate in Europe is characterized by warm summers and mild winters
(Fig. I.1). The annual temperature range is relatively small as it is moderated by the relative
warm water from the ocean North Atlantic drift. Precipitation occurs the whole year round.
The continental climate of Eastern Europe has warm to hot summers and cold winters (Fig.
I.1). Most winter precipitation falls in the form of snow. The attenuating effect of the Atlantic
Ocean does not apply in this part of Europe. The boreal climate is characterized by long and
cold winters while the summers are short and cool. Winter temperatures can drop to -40◦ C.
The average summer temperature is approximately 10◦ C (Fig. I.1). Precipitation is usually
low. To better understand model processes and the different drivers for interannual summer
variability, it is important to identify these different climatic zones and to understand what
drives the differences between the varying temperatures.
In the next sections I will discuss the influences of the Atlantic Ocean and large-scale circulation on the European summer climate. This is followed by a section on land-atmosphere
feedbacks and their importance on the summer climate. Heatwaves will be discussed in section 1.2, and the last part is a concise description on the influence of summer climate on
vegetation.
Besides the influences of the ocean and land-atmosphere feedbacks, the land itself can also
play a role in the local or regional climate. For example, at high altitudes in mountainous
areas the climate is colder even in low latitudes. Snow albedo feedbacks can play a role
here. Furthermore, a difference between a north- or south-facing slope can cause a significant
difference in local temperatures. Orography also has an influence on precipitation. As air is
pushed up against a mountain slope, it loses heat with altitude and might precipitate mostly
on one side of the mountain, while the other side remains much drier throughout the year.
This however, will not be discussed in further detail.
1.1
Oceanic influence
The ocean generally has a moderating effect on the European climate through the northward transport of warm tropical water by the thermohaline circulation and the Gulf Stream.
Besides these currents, the Atlantic Multidecadal Oscillation (AMO) also exerts an influence.
This is a mode of variability of sea surface temperatures (SST) in the North Atlantic, which
is detrended for climate change. The AMO is characterized by cold and warm phases, with
a cool phase from 1905 to 1925 and from 1965 to 1990 and a warm phase between 19311960 (Sutton and Hodson, 2005). Besides its effects on the winter weather, Sutton and
Hodson (2005) have shown its influence on Europe’s summer climate. They found that during the warm phases, a relatively low pressure area is formed west of the British Isles. This
1. EUROPEAN SUMMER CLIMATE
7
Winter
(a)
Summer
(b)
C
25
20
15
10
5
0
−5
Winter
(a)
Summer
(b)
mm
5
4
3
2
1
0
Figure I.1: European winter and summer climatology. a) DJF temperature; b) JJA temperature; c) DJF precipitation; and d) JJA precipitation. Data is from E-OBS (Haylock et al.,
2008).
8
CHAPTER I. GENERAL INTRODUCTION
is linked with reduced precipitation over Western Europe and increased temperatures over
central Europe (Sutton and Hodson, 2005). Confirming these result, Mariotti and Dell’Aquila
(2012) find a positive correlation between the AMO index and air temperature patterns that
is, besides over Western Europe, extending over the Mediterranean during summer. However,
these temperature differences might also be related to dimming and brightening as described
by Wild et al. (2005).
1.2
1.2.1
Large scale circulation
North Atlantic Oscillation
The variability of the European climate is strongly influenced by large scale atmospheric
patterns. The main mode of variability over Europe and the North Atlantic Ocean is the North
Atlantic Oscillation (NAO). This index is based on the average difference in surface sea-level
pressure between the Icelandic low and the Subtropical (Azores) High, describing the largest
fluctuations of the North Atlantic flow. It has a large influence on Europe especially during the
winter. The positive phase of the NAO (NAO+ ) is characterized by a strengthening of both the
Icelandic low and the Azores High. This pattern increases the strength of westerlies, transporting humid and relatively warm air towards Northern Europe and generating warmer and
more humid winter weather than normal in temperate Northern Europe (Bladé et al., 2012;
Gimeno et al., 2003). In contrary, Southern Europe experiences drier winters. During summer, this region experiences drought conditions due to reduced precipitation in the preceding
months (López-Moreno and Vicente-Serrano, 2008). As a result, Roig et al. (2009) was able
to link tree-ring width with NAO indices in the Iberian Peninsula. Also Gouveia et al. (2008)
found reduced vegetation activity after a winter characterized by NAO+ in Southern Europe.
Another consequence of the reduced winter and spring precipitation in Southern Europe is a
possible enhancement of land-atmosphere coupling, that may favor even drier conditions due
to different positive feedback loops (further discussed in section 3.3) (Wang et al., 2011).
During the negative phase of the NAO (NAO− ), the westerlies are reduced and an opposite
effect on the weather is observed over Europe. Mediterranean winters are wetter and warmer
than average, while in Northern Europe it is drier and colder. The extreme cold weather
in March 2013 was characterized by NAO− . Opposite from the NAO+ , the NAO− reduces
precipitation over northern Europe, causing summers to be drier on average (López-Moreno
and Vicente-Serrano, 2008). The NAO index however, is based on the data of a few stations
only (Lisbon, the Azores or Gibraltar and Reykjavik), and might therefore not observe all
movement of the action centers entirely (Roig et al., 2009).
1.2.2
Principal summer weather regimes
During the summer, the main atmospheric weather regimes or circulation patterns are
studied in detail by Cassou et al. (2005). They revealed four main states of the atmosphere
by analyzing the geopotential height anomalies at 500 hPa (Fig. I.2). Both the NAO+ and
NAO− patterns were observed together with the Atlantic Low and the Atlantic Ridge. The
Atlantic Low exhibits a negative pressure anomaly above the North Atlantic Ocean and a weak
positive anomaly above Europe. The Atlantic ridge is characterized by a negative anomaly
above Scandinavia and Greenland, while an anticyclonic pattern is located over the North
1. EUROPEAN SUMMER CLIMATE
9
Figure I.2: (a-d) Four main atmospheric circulation patterns over the North Atlantic-European
sector from 1950 to 2003 at Z500 (m). The figure is taken from Cassou et al. (2005).
10
CHAPTER I. GENERAL INTRODUCTION
Atlantic Ocean east from Western Europe. Only Blocking and the Atlantic Low will be further
discussed, as they are associated with warm European summer temperatures.
1.2.3
Weather regimes associated with warm summer temperature anomalies
1.2.3.1 Blocking One of the two patterns associated with exceptional warm weather in
Europe summers is a Blocking pattern. The anticyclonic conditions over northern Europe,
associated with Atmospheric Blocking, transports air from the east into Europe. This air has
had sufficient time over land to warm up, and therefore transports warmer air especially into
central and northern Europe. Because of the strength of the positive pressure anomaly, local
convective instabilities are suppressed, diminishing wind strengths and increasing drought
and cloudless skies even more (Cassou et al., 2005). Warm and dry conditions are especially
found over the British Isles, central Europe and a part of the Baltic (Linderholm et al., 2009;
Bladé et al., 2012). In the Mediterranean region this circulation pattern has less effect because
the high pressures zone is too far north (Bladé et al., 2012; Mariotti and Dell’Aquila, 2012).
However, on general wetter conditions can be found (Linderholm et al., 2009). The extreme
heatwave in August 2003 and exceptionally warm July 2013 were characterized with an
atmospheric Blocking pattern.
1.2.3.2 Atlantic Low The Atlantic Low is the second pattern that has been related with
warm summer temperatures and heatwaves in Europe. It has most influence over the southern regions. Warm air from northern Africa and the Mediterranean basin is transported northwards (Cassou et al., 2005). The recent extreme hot summer in southern Europe of 2015 is
characterized by an Atlantic Low pressure regime.
1.3
Land-atmosphere interactions influencing the summer climate
The influence the land surface has on climate is mostly through the effect of soil moisture
controlling the partitioning of the land heat fluxes. Wet soils have the potential for evaporation and high evaporative fractions (ratio of latent heat flux to net radiation), while sensible
heat flux dominates over dry soils. Budyko defined two evapotranspiration regimes; a soil
moisture limited and an energy limited regime (Fig. I.3). In the former regime, enough
energy is available for evapotranspiration but due to a dry soil, there is very limited water accessible for latent heat flux. This regime prevails in the Mediterranean region during
summer. In the energy limited regime however, soil moisture is plenty while the energy to
evaporate the moisture is limited. This situation occurs in Northern Europe. Both regimes are
quite stable and the land surface has very limited ability to influence the atmosphere (Schär
et al., 1999; Seneviratne et al., 2010). In between these regions both an energy- or a soil
moisture limited situation can be found, where wet soils may lead to increased evapotranspiration and relative low temperatures, whilst dry soils might favor higher temperatures with
less precipitation. This can lead to the large interannual summer variability found in Europe.
The coming two sections (I.3.1 and I.3.2) describe soil moisture-temperature feedbacks
and soil moisture-precipitation feedbacks in more detail. This is followed by a section where
regions of strong land-atmosphere coupling are discussed. I.3.4 describes the role of vegetation in land-atmosphere feedbacks.
1. EUROPEAN SUMMER CLIMATE
EF=λE/Rn
EFmax
0
DRY
11
TRANSITIONAL
SOIL MOISTURE LIMITED
θWILT
WET
ENERGY LIMITED
θCRIT
Soil moisture content
Figure I.3: Definition evapotranspiration regimes. This definition follow the framework described in section I.3. EF denotes the evaporative fraction, and EFmax its maximal value. The
dotted lines indicate possible pathways, as changes in EF are not necessarily linear. Adapted
from Seneviratne et al. (2010).
1.3.1
Soil moisture-temperature feedback
Soil moisture has the potential to influence temperature through the partitioning of land
heat fluxes in latent, sensible and ground heat flux. The latter accounts only for a very small
percentage and does not exert a strong influence on air temperature. The two remaining heat
fluxes can affect temperature through multiple positive and negative feedback loops.
A very direct consequence of enhanced sensible heat flux is its immediate effect on temperature. Besides that, a lower Bowen ratio due to drier soils leads to a drier atmosphere and
reduced cloudiness increasing the net radiation. Intensified radiation and a drier atmosphere
raise the atmospheric demand and consequently induce more evapotranspiration and even
drier soils. A reduced convection may furthermore lead to a development of anticyclonic
circulation conditions in the upper air (Zampieri et al., 2009). Also, in the beginning of a
warm period, when soil moisture is still available, an increased atmospheric demand and an
increased latent heat flux may lead to more relative humidity and thus a stabilizing situation.
The positive feedback loops described above act mostly non-linearly, and a higher impacts
of soil moisture on temperatures are found during dry conditions on temperature maxima
(or a decrease of temperatures with initial wetter soil conditions) (Jaeger and Seneviratne,
2011). Ford and Quiring (2014) and Mueller and Seneviratne (2012) find similar results in
that soil moisture has most impact on extreme warm temperatures. The latter find a higher
probability on hot days after dry soil conditions, e.g. > 70% in the Iberian Peninsula, South
America and Eastern Australia; > 60% in most of North US and Eastern Europe, while it
decreased till < 30-40% after wet soil conditions. Quesada et al. (2012) and Hirschi et al.
(2011) find similar results from observations over Europe, and Lorenz et al. (2015) find a
stronger impact for soil moisture variability on the warmest temperatures.
During the 2003 heatwave a positive coupling between soil moisture and temperature is
found through reduced evaporation, a drier atmosphere and reduced cloud cover by Stéfanon
et al. (2014). They find that this feedback contributed up to 20% of the extreme temperatures in Eastern France and Western Germany, while its contribution reached up to 40% over
Western France and Northern Spain. However, although these processes can account for an
important part to the temperature anomalies during the hot summer of 2003, the authors
note that the initial cause of the extreme event was a Blocking situation over Europe.
12
CHAPTER I. GENERAL INTRODUCTION
While these feedbacks are mostly local and on shorter time scales, non-local impacts with
longer timescales are also possible. A well described mechanism is the impact of reduced
winter and spring precipitation in southern European on elevated summer temperatures in
Europe (Vautard et al., 2007; Zampieri et al., 2009; Quesada et al., 2012). A probable underlying process is the local inhibition of convective cloud formation and increased sensible heat
flux due to reduced precipitation. These warm and dry air masses can be advected over central Europe due to southerly flows. The reduced cloudiness associated with these air masses
increases atmospheric demand of soil moisture, reducing the water reservoir, enhancing sensible heat flux and higher temperatures (Zampieri et al., 2009).
A negative feedback loop can occur when latent heat flux is favored over sensible heat flux
by wetter initial soils. This might occur when temperatures start rising in the beginning of
the summer. The higher temperatures increase latent heat flux, which leads to an increased
atmospheric humidity. This in turn enhances cloud formation by lowering the dew point,
leading to reduced net radiation and decreased temperatures, reduced atmospheric demand
and thus relatively more soil moisture. The soil albedo may also play a role as it usually
bears higher values with a lower soil humidity. With an increase in longwave radiation due
to dry and warm soil, they can lead to a reduction of net radiation and consequently lower
temperatures (Eltahir, 1998). A last negative coupling may be through a destabilization of
the atmosphere due to very strong thermal activity. This can result in cloud formation when
the PBL reaches the lifting condensation level (Boé, 2013).
1.3.2
Soil moisture-precipitation feedback
Soil moisture can influence precipitation in different ways. The most direct mechanism
might be moisture recycling, in which regional evaporated soil moisture contributes to precipitation. Comparing different studies Eltahir and Bras (1996) find that 10% of the precipitation may be derived from regional recycled water over Eurasia, while in the tropics this can
be 25%-60% (Spracklen et al., 2012). Two important processes that determine the magnitude
that soil moisture has on precipitation are: 1) the amount of soil moisture that is evaporated;
and 2) the amount of evaporation that is precipitated. While the former is relatively easy to
measure, the latter is harder to quantify. It is mostly considered that the effect of soil moisture
on evaporation is larger than the effect of evaporation on precipitation (Wei and Dirmeyer,
2012; Boé, 2013). Another direct mechanism is the influence of soil moisture on the intensity of precipitation. Lorenz et al. (2015) showed that in mid-latitudes, and especially in the
Mediterranean, soil moisture variability increases heavy precipitation events both in amount
of rainfall and in duration.
Besides this direct soil moisture-precipitation feedback, three indirect feedbacks have been
described through the modification of soil moisture on boundary layer stability and on the
potential for convective precipitation by Schär et al. (1999). 1) Starting with dry soils, an
increase of the Bowen ratio and a deepening of the daytime PBL is expected. This causes
an increased entrainment of dry air. The changes in PBL and entrainment together induce
a decrease in the density of water vapor and in the moist static energy (MSE) that can lead
to reduced potential for convective rainfall. 2) An increase of longwave radiation due to dry
soils and an increase in albedo induces a decrease in net radiation. This decreases the total
turbulent energy flux transferred to the PBL that decreases the MSE. The decrease in MSE
in a deeper PBL with increased entrainment causes again a reduced potential for convective
1. EUROPEAN SUMMER CLIMATE
13
precipitation (Schär et al., 1999). 3) The level of free convection decreases due to dry soils,
also stabilizing the atmosphere and reducing the potential for convective rainfall. These
processes remain positive feedback mechanisms when starting with wet soils instead of the
dry case examples, thus enhancing the potential for convective precipitation (Schär et al.,
1999).
The indirect mechanisms have been subject of many modeling and observational studies.
Findell et al. (2011); Guillod et al. (2014) found for example that previous wetter conditions
enhance the probability of afternoon rainfall in Mexico and eastern US. In central and southwestern US however, where a soil moisture limited regime prevails, soil moisture is the driver
for positive feedbacks on precipitation. Rios-Entenza and Miguez-Macho (2014) find a large
impact of evapotranspiration on precipitation in the Iberian Peninsula, especially when there
is the potential for convection. The feedback mechanisms are thus also dependent on largescale circulation patterns (Ford et al., 2015a; Guillod et al., 2015; Boé, 2013). Soil moisture
may exert an influence on the persistence of certain large-scale circulation patterns, however,
this has so far only been demonstrated for SST anomalies (Guemas et al., 2010).
Some studies also mention negative feedbacks mechanisms between soil moisture and
precipitation. Boé (2013) find a weak but negative feedback during warm conditions with
a Blocking pattern. They assign this to a destabilization of the atmosphere over drier soils.
Likewise Taylor et al. (2011); Taylor (2015); Ford et al. (2015b); Guillod et al. (2015) find
that convective precipitation initiation has a preference over dry soils. During the 2003 heatwave many studies have investigated the soil moisture-temperature feedbacks, but Zaitchik
et al. (2006) studied the soil moisture-precipitation feedback in detail during this event. They
find evidence for a negative feedback due to a reduced local production of MSE (the second
indirect mechanism) only during April.
Non-local feedback also occur, where the advection of evaporated soil moisture affects
neighboring regions Beljaars et al. (1996); Rowntree and Bolton (1983). The latter found
that moister or drier air was advected from central Europe into Scandinavia and from southern Europe into North Africa. This has also been shown to occur for soil moisture-temperature
coupling during hot European summers, where reduced southern European precipitation during winter and spring induces warmer conditions over Europe (Zampieri et al., 2009). While
there is substantial evidence for soil moisture-precipitation feedbacks, precaution has to be
taken with the interpretation of results. Different observation-based data sets and different
convection parameterizations in climate models can change the sign and the magnitude of
the feedbacks (Hohenegger et al., 2009; Guillod et al., 2014).
1.3.3
Regions for strong land-atmosphere feedbacks
Regions of strong soil moisture-temperature and -precipitation feedbacks, as discussed in
the previous sections, have been studied extensively in the Global Land-Atmosphere Coupling
Experiment (GLACE) (Koster et al., 2004). These so called ’hot spots’ are mostly identified for
their strong feedbacks between soil moisture and precipitation. Although when soil moisture
has a substantial influence on precipitation and thus on the atmosphere, we can assume that
it is similar for temperature.
Simulations with varying and climatologically prescribed soil moisture performed with
an AGCM ensemble (GLACE), showed that strongest land-atmosphere feedback strength was
14
CHAPTER I. GENERAL INTRODUCTION
over the Great Plains in Northern US, the Sahel, equatorial Africa and India. Less coupling
strength was found over parts of the Amazon, Eastern China and some parts over Russia and
Canada. Using GCMs over Europe Koster et al. (2004) did not find land-atmosphere coupling
strength, besides a very small region north of Portugal and around Poland. Using the absolute
values of precipitation instead of its natural logarithm, as was done in Koster et al. (2004),
Koster et al. (2006) found that hot spots occurred with a very similar pattern but with a
decreased magnitude, indicating some form of robustness. Using only the eight best performing models for summer precipitation, they found that the magnitude of coupling with soil
moisture increased in most areas, including a small region in South-East Europe. With these
same eight models, Seneviratne et al. (2006a) obtained an estimate of seasonal forecasting
potential by combining the coupling strength with soil moisture memory. While the coupling
strength is not enormously high, some hot spots in Europe appear: in the Mediterranean
regions, the southern part of France and in Eastern Europe around the Danube. Including
more detailed analysis on the surface heat fluxes, Dirmeyer (2011) found positive coupling
over almost the entire Mediterranean area, while there is a negative coupling over Northern
Europe and over the Alps. The latter is probably related to late snow melt.
Using a regional climate model over Europe, Seneviratne et al. (2006b) found different
feedback hot spots than were found by Koster et al. (2006). While Koster et al. (2006) did not
find any sign of strong land-atmosphere coupling over Europe, Seneviratne et al. (2006b) find
that in the present climate strong coupling is found over the Mediterranean region, which is
more similar to Dirmeyer (2011), although the regions found by the latter is extended more
west-ward. They assign the changes to the different models, a different spatial resolution and
a different representation of SSTs. In the future the coupling hot spots might change. These
’new’ hot spots might occur more north and east-ward, extending over central and Eastern
Europe (Seneviratne et al., 2006b).
The difference in coupling strength between the separate models is mostly caused by the
different sensitivities of evaporation rate to soil moisture. Models with a stronger correlation
between these variables show stronger feedback strength (Guo et al., 2006). The ensemble
model mean of the coupling strength is shown to be fairly well simulated (Dirmeyer et al.,
2006). However, interpretations of single models must be done with care, because their
skill in correctly simulating the relation between land and atmosphere is limited. Comparing
certain model variables such as latent heat flux and surface soil moisture with observations,
Dirmeyer et al. (2006) find large biases.
1.3.4
Influence of vegetation
A very clear example of vegetation-atmosphere feedbacks is the work of Aleina et al.
(2013). They model a world starting with different vegetation fractions and different initial
deep soil moisture conditions. They find three stable equilibria: a warm desert without vegetation, a cold desert with little vegetation and a temperate vegetated state. The vegetated
state can only be reached when starting with a sufficient initial vegetation fraction and sufficient initial deep soil moisture. Whenever the initial vegetation fraction is too small, the
generated water cycle is not strong enough to maintain the vegetation, and thus the water
cycle, alive (Aleina et al., 2013). This extreme case is very unlikely to happen on a global
scale, but it illustrates the importance of impact of vegetation on the water cycle on longer
time scales. Some ecosystems seem to have multiple alternative stable states, and the loss
1. EUROPEAN SUMMER CLIMATE
15
of only a small fraction of vegetation may shift the system from a vegetated to a desert state
(Scheffer et al., 2001). This is also shown by some modeling studies (Wang and Eltahir, 2000;
Baudena et al., 2008).
Other feedback mechanisms between vegetation and atmosphere occur on much shorter
time-, and smaller spatial-scales. A well-studied topic is the difference between forests and
croplands in their effects on temperature. Intuitively forests may seem to better moderate
temperatures, but in reality this is not always the case (Teuling et al., 2010). Two processes
that can explain differences between the two vegetation types are the albedo and evapotranspiration. While evapotranspiration is mostly found to have a mitigating effect on temperature, as explained in section I.1.3.1, the lower albedo of forests might increase local and
regional temperatures (Teuling et al., 2010).
Stéfanon et al. (2012) presented the mitigating effect of evapotranspiration in June 2003,
which was increased due to enhanced spring greening. During August however, the vegetation stress and early leaf fall reduced evapotranspiration, thereby increasing sensible over
latent heat flux and amplifying the already above average temperatures Lorenz et al. (2013).
Detto et al. (2006) found similar mitigating results for Sardinia, where the natural woody
vegetation evaporated more and longer during drought periods than grasses. Vegetation is
also found to enhance evapotranspiration and accordingly the water cycle in the semi-arid
regions of Northern China (Jiang and Liang, 2013). Although the enhanced evapotranspiration may have a moderating effect on temperature, the faster depletion of the soil water
reservoir (Zhang and Schilling, 2006) may lead to enhanced sensible heat flux during longterm droughts. Although Baudena et al. (2008) find that natural vegetation can balance these
effects through the adaptation to lower soil moisture.
The negative effect of albedo is especially pronounced over the boreal regions, where a
decrease in albedo occurs by forests that cover the snow layer. This induces a rise in temperature (Snyder and Liess, 2014; Bonan, 2008). Furthermore, forests usually have a lower
albedo than grass or crop land, retaining more sunlight energy that also increases the temperature (Jackson et al., 2008; Naudts et al., 2016). This effect is also found by Teuling et al.
(2010) during the heatwave of 2003. The air temperature over grasslands was found to be
cooler than over forests due to a lower Bowen ratio. However, the different rooting depths
between trees and grasses or crops may inverse these effects during periods of prolonged
drought.
Other than the non-local effects found for both soil moisture-temperature and soil moistureprecipitation feedbacks, the effects of the biosphere on the atmosphere seem to be only local
(Pitman et al., 2009). Also Lorenz et al. (2013) found that vegetation only has a minor effect
on mean spring and summer climate in Europe.
In the future, reforestation has the potential to reduce global warming till approximately
2050. Increased CO2 concentrations can cause an increase in leaf area index which enhances
evapotranspiration. However, at the end of the 21st century the cooling by evapotranspiration
becomes less important due to CO2 fertilization (Pitman and Narisma, 2005). Similar results
are found over central Europe by Wramneby et al. (2010), although in Southern Europe
increased droughts may limit plant growth, reducing evapotranspiration and consequently
increase temperatures. Vilà-Guerau De Arellano et al. (2012) emphasizes the CO2 fertilization
effect on stomatal closure, and its possible impact on the partitioning of heat fluxes and cloud
16
CHAPTER I. GENERAL INTRODUCTION
formation.
2
Heatwaves
The processes described in the previous sections lead to interannual and seasonal variability and extreme events such as heatwaves. While definitions vary, heatwaves are usually described as prolonged periods of extreme high temperatures relative to the usual weather. The
World Meteorological Organization defines a heatwave as a period of at least five consecutive
days with a daily maximum temperature that exceeds the average maximum temperature of
the period 1961-1990 by 5◦ C.
Although all heatwaves are characterized by above normal temperatures, they differ temporally and spatially. Consequently it is impossible to describe one mechanism that describes
all heatwaves. For this reason I will discuss the recent 2003 European heatwave as an illustrative example, describe its characteristics, possible mechanisms and consequences. This
specific heatwave is selected as it is very well described in literature and because of the varying processes that resulted to the anomalous hot temperatures.
2.1
Characteristics of the 2003 heatwave
The European heatwave in 2003 broke many temperature records, both in June and August. Exceptionally warm periods with daily maxima well over 40◦ C were marked across
Spain and Portugal, 36-38◦ C over Southern and central France and 32-36◦ C over Northern
France (García-Herrera et al., 2010). The monthly average of temperature anomalies were
+2◦ C, +4.2◦ C, +2◦ C and +3.8◦ C from May to August, respectively (Black et al., 2004). The
temperature anomaly of early August was up to 8.5◦ C (Zaitchik et al., 2006). The summer
mean exceeded the climatological mean by approximately 3◦ C in some regions, corresponding to five standard deviations (Schär et al., 2004). Most exceptional temperatures were
recorded in France and Switzerland (Black et al., 2004), with 40-60 hot days, which is 5 to
6 times the long term average (Fischer et al., 2007a). The rareness of the temperatures over
Switzerland are shown in their very long return period of 46 000 year (Schär et al., 2004).
This summer was likely the warmest in the last 500 years (Luterbacher et al., 2004).
2.2
Mechanisms behind the 2003 heatwave
The main mechanisms of the heatwave were the winter and spring precipitation deficit,
an atmospheric Blocking pattern which caused anticyclonic conditions, warm SSTs and landatmosphere interactions. Teleconnections are also mentioned in several studies, but will not
be discussed in detail. For a comprehensive overview I refer to García-Herrera et al. (2010).
Precipitation deficit Over large parts of Europe, the average precipitation was more
than 50% lower in the preceding winter and spring months compared with the long-term average (Fischer et al., 2007a). This deficit probably amplified the evolution of the temperature
anomalies during spring and summer (Loew et al., 2009). The cause of the anomalous rainfall
might have been anomalous 500 hPa geopotential heights (García-Herrera et al., 2010).
2. HEATWAVES
17
Atmospheric circulation Besides, and maybe related to the precipitation deficit, there
was a persistent atmospheric Blocking pattern. This pattern was associated with anticyclonic
situations during almost the entire summer season (Loew et al., 2009; Cassou et al., 2005;
Trigo et al., 2005), being most pronounced in June, and in August over France (Black et al.,
2004). The anticyclonic conditions might have been related with the Icelandic low that was
displaced more southward and the intensification and northward shift of the Azores anticyclone (Black et al., 2004). The almost stagnant atmospheric flow resulted in the possibility
of the build-up of a warm and deep boundary layer (Black et al., 2004), remaining relatively
constant over night time (Miralles et al., 2014). Besides the promotion of land-atmosphere
feedbacks, the synoptic patterns also induced clear skies and a positive downward net radiative flux, which were preserved by the interactions between land and atmosphere (Black
et al., 2004).
SSTs Although SSTs may not be able to induce a heatwave, they can prolong or favor
certain large-scale atmospheric patterns, which can form the foundation of extreme climatic
events (Guemas et al., 2010). During the spring and summer of 2003 the Mediterranean SST
was anomalously high (Feudale and Shukla, 2007). The sea started warming in April. In
June, both the Mediterranean Sea and the Atlantic Ocean were hotter than normal (Black
et al., 2004). Feudale and Shukla (2007) suggests that global SSTs were responsible for the
anticyclonic blocking, whilst local SSTs were more important in maintaining the heatwave
(Fennessy and Kinter III, 2011). A combination of anomalously global SSTs and those in
the Atlantic Ocean may have caused a reduction in the meridional gradient, leading to a
northward shift of the polar yet, that allowed an enlargement of the anticyclone. The impact
of the SSTs on the 2003 temperatures may have been in the order of 1-2◦ C (Fennessy and
Kinter III, 2011).
Land-atmosphere coupling Land-atmosphere feedbacks have been mentioned in many
studies for their contribution to the extreme 2003 summer temperatures. The soil drying
probably contributed to local heating (Schär et al., 2004) and caused a increase of 1-2◦ C to
the surface temperature (Fennessy and Kinter III, 2011), with 40-50% to the maximum temperatures (Fischer et al., 2007a). It furthermore enhanced dry convection and entrainment
that deepened the boundary layer (Black et al., 2004; Miralles et al., 2014). Due to the stable
synoptic conditions, the dry and warm air prevailed inducing further soil desiccation. The
early vegetation green-up (Zaitchik et al., 2006) caused positive latent heat flux in the beginning of June (Black et al., 2004), dampening the heatwave (Stéfanon et al., 2012; Lorenz
et al., 2013). However, the depletion of the soil moisture caused negative evapotranspiration anomalies later in summer (Fischer et al., 2007b). This in turn increased temperatures,
leading to further decreased vegetation production and an amplification of the hot temperatures (Stéfanon et al., 2012; Lorenz et al., 2013). A change in albedo due to soil drying
could possibly have a negative effect on temperature, although this was largely masked by
the water-stressed vegetation that increased the albedo (Teuling and Seneviratne, 2008). Besides the direct effect on temperature, the drought conditions may also have amplified the
anticyclonic circulation patterns (Fischer et al., 2007b). The total soil moisture-temperature
coupling might have induced 50-80% of the number of hot days during the 2003 heatwave
(Fischer et al., 2007a), and was strongest even before the peak of the temperature in France
(Miralles et al., 2012).
18
2.3
CHAPTER I. GENERAL INTRODUCTION
Consequences of the 2003 heatwave
The impacts of the heatwave were widespread in many different sectors. The total estimated human mortality related to the event is estimated at over 40 000, particularly elderly
people (García-Herrera et al., 2010). In France alone the excess mortality was 14 800 during
August (Fouillet et al., 2006). Besides the mortality rate, the failure of the health systems to
such extreme events was evident (García-Herrera et al., 2010).
Air pollution was also problematic. Exceptionally high ozone concentrations have been
measured during an extensive period (Vautard et al., 2005). The increased UV radiation
due to decreased cloud cover and precipitation, stagnant atmospheric circulation, reduced
dry deposition, increased biogenic emissions and forest fires have been mentioned as cause
(Tressol et al., 2008). Although forest fires were not extreme (García-Herrera et al., 2010),
they resulted in an economic loss of $1.6 billion (Schär and Jendritzky, 2004). The fires in
Portugal in the beginning of August might have resulted in the highest aerosol load of this
month (Hodzic et al., 2006). The reduced dry deposition was caused by the stomatal closure
of vegetation (Emberson et al., 2000). The biogenic emission came mostly from isoprene,
emitted by trees as a defense against the high temperature and increased solar irradiation
(Solberg et al., 2008).
The agricultural sector also experienced losses both in crops and animals (García-Herrera
et al., 2010). Crop losses were unprecedented (INSEE, 2004; Zaitchik et al., 2006), accounting for an economical loss of $12.6 billion (Schär and Jendritzky, 2004). The largest losses
were mainly of non-irrigated summer crops. Some wine regions however, were positively
affected (García-Herrera et al., 2010).
The GPP of natural vegetation was severely affected and a reduction was estimated around
30% (Ciais et al., 2005; Reichstein et al., 2007). Only mountainous vegetation experienced
increased growth rates. In spring the early green-up (Zaitchik et al., 2006) led to higher GPP
in some regions, although the reduced precipitation also caused negative NDVI in other areas
(Lorenz et al., 2013). In August most vegetation activity ceased (Stéfanon et al., 2012).
3
Impact of elevated temperatures and drought on vegetation
The summer climate usually is the period of most extensive growth and production in
many parts of Europe, the Mediterranean being an exception. The higher temperatures are
favorable for most tree species if the soil water level is sufficiently high. The latter is generally
the bottleneck for vegetation development during summers in Southern Europe. Given the
predicted increase in interannual summer climate variability in central Europe (Schär et al.,
2004), this might become a relevant issue in much larger regions of Europe. Especially after
the 2003 heatwave has shown a decrease in vegetation production of 30% (Ciais et al., 2005).
Similar results were found for the Great Plains after the 2012 summer drought, although the
enhanced spring temperatures compensated the summer carbon loss (Wolf et al., 2016). In
the section below a description is given of the most common and general responses of vegetation to drought or heat stress. The focus will be on European forest tree species. Note
however, that in the various climate zones, different species occur with different responses to
water shortage. There is also considerable intra-species variability, which depends on genotypic variation and phenotypic plasticity (Corcuera et al., 2011). A more detailed description
3. IMPACT OF ELEVATED TEMPERATURES AND DROUGHT ON VEGETATION
19
of the effects of drought and heat of the most common European tree species will be given in
chapter V.
The main challenge for plants during warm and dry summers is to continue carbon assimilation while water is limited. Carbon is taken up by plants in the form of CO2 through their
stomata, but water is evaporated through the stomata at the same time. Broadly, two types
of drought response exist: drought avoidance or isohydric species with quick stomatal closure with beginning drought, and drought tolerance or anisohydric species, which decrease
stomatal conductance and water potential and can keep their stomata open for a longer time
period by increasing root water uptake. The disadvantage or the latter one is the risk of cavitation (disrupted water columns) and embolism (air-filling), or hydraulic failure (Hartmann
et al., 2013), while the problem of the first response mechanism is carbon starvation (Sevanto
et al., 2014).
Embolism is not necessary exceptional and detrimental (Lo Gullo and Salleo, 1992). It
can occur on a daily basis during summer, and refilling of the vessels can occur within minutes
and during the night (McCully et al., 1998; Salleo et al., 2004; Zufferey et al., 2011). The
problem develops when too many xylem vessels are embolized or when ’runaway embolism’
occurs (increased frequency of cavitation due to a reduced number of functioning vessels
with a constant transpiration rate) (Tyree and Sperry, 1988). Cavitation occurs more easily in
wider vessels, in where the hydraulic conductivity is larger (Tyree and Zimmermann, 2002).
A higher conductivity increases the fluency of the water transport. It is therefore believed that
a trade-off exists between the resistance to cavitation and the specific hydraulic conductivity
(Martínez-Vilalta and Piñol, 2002). Most tree species seem to operate very close to their
’catastrophic’ cavitation limit and have small safety margins (Tyree and Sperry, 1988; Choat
et al., 2012). Roots seem to act more closely to their limits than other parts of the tree (Sperry
and Saliendra, 1994; Hacke et al., 2000). When the soil is wet this reduces the resistance of
water transport, but when the soil is dry it disconnects the plant from the soil (Sperry and
Ikeda, 1997; Martínez-Vilalta and Piñol, 2002). Furthermore, root construction is relatively
cheap in carbon compared to root maintenance. Thus whenever xylem embolism becomes
too high, roots can be shed without major consequences (Bouma et al., 2001).
Isohydric species close their stomata much faster in case of droughts. Stomatal conductance may be reduced even before stem water potential decreases (Gollan et al., 1992). Roots
are believed to release ascorbic acid (ABA) that induces this process (Cochard et al., 1996b;
Beck et al., 2007). Because of carbon reserves trees can survive for a while without carbon assimilation. Prolonged droughts however, may lead to carbon starvation (McDowell
et al., 2008; Galiano et al., 2010). Although there has been some controversy against this
theory (Rowland et al., 2015). But even if trees would not die from carbon starvation, carbon reserves have been shown to decline during droughts leading to reduced growth rates
in subsequent years (Galiano et al., 2010; Gruber et al., 2012) and reduced susceptibility to
pathogens and insect attacks (Rebetez and Dobbertin, 2004; Bigler et al., 2006; Dobbertin
et al., 2007). Furthermore, trees seem to be more vulnerable to successive droughts when
growth rates have been reduced (Bigler et al., 2007). Another disadvantage of stomatal
closure are photoinhibition and photorespiration. The former occurs when an imbalance between the electron transport rate and the available CO2 occurs. Electron transport, which is
driven by light, is often increased during prolonged periods of drought and clear skies, whilst
carbon assimilation is reduced. This can lead to the formation of reactive oxygen species
20
CHAPTER I. GENERAL INTRODUCTION
(ROS) (Hendry et al., 1992), which may damage proteins in membranes or in the photosynthetic apparatus (Perdiguero et al., 2013). Antioxidants can reduce this problem by oxidizing
or scavenging ROS (Costa et al., 1998). Photorespiration occurs when Rubisco binds to O2
instead of CO2 . The plant is not able to use the formed molecule and its breakdown costs energy. Besides stomatal closure, trees can maintain water potentials with osmotic adjustment
(Osonubi and Davies, 1978; Harfouche, 2003). This is the active accumulation of solutes in
the vacuole of cells and is a mechanism to maintain turgor pressure (Thomas and Hartmann,
1996; López et al., 2009; Sergeant et al., 2011). Turgor pressure is important in among others
cell expansion and growth, and stomatal aperture (Nguyen-Queyrens et al., 2002).
Tree morphology can also be adapted in response to drought stress. Leaf thickness may
be increased while their size may be smaller to limit evapotranspiration (Bansal et al., 2015).
Increased cuticular wax leads to a similar result (Le Provost et al., 2013). Leaf abscission
has also been observed in response to drought stress to reduce evapotranspiration rates, and
to redistribute assimilates (Bréda et al., 2006). The number of trichomes can be increased
for larger sunlight reflectivity (Sardans et al., 2013). To avoid cavitation xylem vessels could
be adapted for higher cavitation resistance (Hacke and Sperry, 2001; Paiva et al., 2008).
Biomass allocation can be shifted to the roots to maximize the uptake of limiting resources
(López et al., 2009; Barthel et al., 2011; Matías et al., 2014).
C HAPTER
II
Uncertainties in the future
climate
21
22
1
CHAPTER II. UNCERTAINTIES IN THE FUTURE CLIMATE
Introduction
There are considerable uncertainties in simulating the present climate of Europe that
can propagate into the future climate. This raises several questions, of which I answer two
in this chapter with two different studies presented in two parts. The first part discusses
uncertainties of future projections for Europe for a given 2◦ C average global warming. The
uncertainties constraining the future climate by temperature instead of by time are presented.
A specific focus is placed on summer temperatures and land heat fluxes. This study is part
of the European IMPACT2C project and the results are published by Vautard et al. (2014). I
will only show the results of the work done by myself in this study. The complete paper can
be found in Appendix A. The second part of this chapter investigates how the uncertainty of
modeled present-day land heat fluxes links with uncertainties in the future climate.
Feedbacks between land and atmosphere play an important role in generating summer
heat in temperate climates. This can also be seen in models, where stronger feedbacks result
in higher temperatures. Stegehuis et al. (2013b) showed that, using an ensemble of models,
larger sensible heat flux is associated with elevated summer temperature (Fig. II.1). Furthermore they found large differences in the heat flux partitioning between the models, leading to
uncertainties in present-day simulations. However, how these differences and uncertainties
in land heat flux partitioning translate into future climate projections is unknown.
The use of model ensembles has been shown to better represent uncertainties than single model simulations. Dirmeyer et al. (2006) showed for example that land atmosphere
feedbacks are relative poorly represented in single models, while the ensemble model mean
showed much better skill. This is partly due to the scarcity of long-term gridded observationbased data of some of the important variables related to land-atmosphere feedbacks such as
the land heat fluxes. Recently some satellite-based products have become available such as
GLEAM (Miralles et al., 2011). Another product, based on a combination of remote sensing
measurements and FLUXNET data is the Model Tree Ensemble (MTE) of Jung et al. (2010).
The latter data set is used in the present study. However, by using these kind of data sets, one
always has to keep its uncertainty in mind. Mueller et al. (2011) showed that the spread in
observation based latent heat flux products was as large as that from a model ensemble.
Because of the better representation of model ensembles compared to that of single models, different regional and global model ensembles have been constructed. The European
RCM ensemble from the FP6-ENSEMBLES project (Hewitt and Griggs, 2004; van der Linden
and Mitchell, 2009) is one example on a regional scale. In this study 15 of its RCMs, driven
by 6 different GCMs from the 4th IPCC assessment report for the A1B scenario, were used.
The two studies presented below use this ensemble.
Regional European impacts of a 2◦ C average global warming In the first study, we investigate regional European effects on temperature, land heat fluxes and the radiation budget
with an average global warming of 2◦ C since the pre-industrial period. This has been done
in context of the European IMPACT2C project in the view of the 2◦ C global warming target. The target has been agreed upon during the last United Nations Conference on Climate
Change (COP21). While in climate studies usually a temporal range in the future is considered, a global temperature metric for climate change is often used as a measure of impacts
and risk of potential tipping points, which has also been looked upon in the present study.
1. INTRODUCTION
23
Figure II.1: Annual cycle of latent heat flux (a), sensible heat flux (b) and 2-m temperature
(c) over Europe, from 1961 to 2000 for RT3-models, ERA-40 and E-OBS, and from 1983 to
2008 from MTE and ERA-I. The two colored models are examples of the different feedback
strengths between models. The observations are in black. From Stegehuis et al. (2013b).
The ENSEMBLES models are used to study the above mentioned climatic consequences and
uncertainties in Europe when the global temperature modeled by the driving GCMs reaches
2◦ C. These periods range between 2014 and 2067. The full study can be found in Appendix
A (Vautard et al., 2014).
We find that Europe experiences on average a higher warming than the global average,
thus exceeding the 2◦ C regionally. Warming is largest in Scandinavia, the Mediterranean region, the Iberian Peninsula and the Alps. The British Isles experience a weaker warming. The
land heat fluxes confirm this warming in the south, by showing less latent and more sensible
heat flux in the southern regions during summer. In the energy limited region of Scandinavia,
the higher temperatures lead to higher latent heat flux during summer, while sensible heat
flux is diminished. This might be due to cloud formation that diminishes incoming shortwave
radiation and enhances longwave radiation.
Reduction of temperature change uncertainties by observational constraints In the
second study we constrain future temperature projections by using land heat fluxes. Using
the ENSEMBLES model ensemble we hypothesize that summer biases in the partitioning of
the land surface energy balance in the current climate will be amplified under future (drier)
summer conditions. Models with stronger feedbacks will then produce hotter and drier future
temperature. Information on the present-day may then help constrain temperature change
projections. This concept has been used by Hall and Qu (2006), who used the seasonal
cycle of the snow albedo feedback to reduce uncertainty in temperature predictions. Boberg
and Christensen (2012) related the future temperature bias of models to their present-day
bias to constrain projections. They found the models’ bias especially in the warm end of
the temperature distribution. Quesada et al. (2012) used a similar approach, but related
winter and spring precipitation to summer temperatures. Although land heat fluxes are a key
component of land-atmosphere feedbacks, these have not yet been used for the reduction of
temperature change projections.
Using observations of sensible heat flux we were able to constrain the ensemble and reduce the summer temperature change predictions regionally by up to 40%. Largest reductions were found over France and the Balkan, regions already mentioned for their impor-
24
CHAPTER II. UNCERTAINTIES IN THE FUTURE CLIMATE
tance of land-atmosphere feedbacks (Seneviratne et al., 2006a). In Southern and Northern
Europe, other metrics may be used to reduce uncertainty in temperature projections, such
precipitation-temperature coupling (Quesada et al., 2012) or the snow-albedo feedback (Hall
and Qu, 2006).
In the next section I discuss some of the European climatic consequences of an average
global warming of 2◦ C. This is followed by the peer reviewed paper on reducing future summer temperature change predictions by using land heat flux observations. In section II.4 the
discussion will focus on model ensembles, the used method of uncertainty reduction and the
2◦ C limit. This is followed by a summary and some concluding remarks.
2
The European climate under a 2◦ C global warming
2.1
Introduction
In most climate change studies, only the temporal scale in the future is considered and
investigated. However, impact studies are often based on temperature change above a certain
threshold instead of a period in the future. A limit of two degree global warming (since the
pre-industrial period) has been advocated by the IPCC and has now been agreed upon as
target during the last United Nations Conference on Climate Change (COP21). This limit
is based on the IPCC 2nd assessment Report (IPCC, 1995). The potential risk of disastrous
events to happen by exceeding this temperature threshold is substantial. In this section some
of the climatic consequences over Europe were studied, with a focus on land atmosphere
interactions, when the average global warming reaches 2◦ C. This has been done in the context
of the European IMPACT2C project. The results presented here are part of a peer reviewed
paper (Vautard et al., 2014 - Appendix A).
2.2
Methods
We used 15 RCMs of the FP6 ENSEMBLES project (Hewitt and Griggs, 2004; van der
Linden and Mitchell, 2009). The 2◦ C average global warming period for each RCM was based
on its driving GCM. These 30-year periods lay in the range between 2014 and 2067 (Table 1
in Appendix A). A reference period from 2031 to 2060 is taken to remove the uncertainty of
GCM sensitivity to GHG as compared with fixed time periods. A specific attention is payed to
the land heat fluxes, and the difference between the ’2◦ C’ and the control period (1971-2000).
A more detailed description of the methodology can be found in Appendix x.
2.3
2.3.1
Results and discussion
Temperature and precipitation change
The annual mean temperature increase over Europe, with a global warming of 2◦ C compared to the pre-industrial period, is higher than the 2◦ C global average, except from the
British Isles. Most warming occurs in autumn and winter, while spring warms least (Fig.
II.2). The difference of temperature change between the 2◦ C and 2031–2060 is small, but
the ensemble spread of the period between 2031–2060 is larger than the spread of the 2◦ C
period. This is expected because the models are centered on a similar temperature instead of
a similar time-period in the future.
25
15
∆TAS [°C]
0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75
2. THE EUROPEAN CLIMATE UNDER A 2◦ C GLOBAL WARMING
10
●
●
●
∆PR [%]
0
5
●
−5
●
●
●
Eastern Europe
Mediterranean
Alps
Scandinavia
Mid−Europe
France
10.0
Iberian Peninsula
●
British Isles
Eastern Europe
Mediterranean
Alps
Scandinavia
Mid−Europe
France
Iberian Peninsula
2031−2060
2−degree period
5.0
∆PR [%]
0.0
2.5
−2.5
0.00
−7.5
−5.0
2.50
1.00
0.50
∆TAS [°C]
1.50 2.00
7.5
3.00
3.50
British Isles
−15
●
−10
●
Spring
(MAM)
Summer Autumn
(JJA)
(SON)
Winter
(DJF)
Spring
(MAM)
Summer Autumn
(JJA)
(SON)
Winter
(DJF)
Figure II.2: (a) Yearly averaged change —relative to the reference period 1971–2000 — in
yearly mean temperature in the different European regions for periods corresponding to +2C
of global average change. The global temperature change (1.54◦ C) between 1971–2000 and
the 2◦ C period is marked as a dotted line. (b) Same as (a) for precipitation in % of change.
The solid red line indicates no change for precipitation and the red dotted line a change of
1.54 C for temperature, corresponding to a global warming of 2◦ C relative to pre-industrial.
(c) Spatial average over land of changes in temperature and (d) precipitation, together with
the range of changes for the GCM–RCM ensemble (median, 25–75% range and min and max
values). The open bars refer to fixed time future period (2031–2060), the gray bars to the
temperature controlled (+2◦ C) period.
26
CHAPTER II. UNCERTAINTIES IN THE FUTURE CLIMATE
Most warming is found to occur over Scandinavia (Fig. II.2). The large warming might be
partly due to a decrease in albedo due to snow melt and the general arctic warming (IPCC,
2013). Furthermore, the albedo might also be influenced by trees that cover snow. The snowalbedo feedback may explain the large winter warming signal in panel c (Fig. II.2). After
Scandinavia, most warming is found in the Alps, the Iberian Peninsula, the Mediterranean
region and Eastern Europe. Over France and central Europe it is somewhat lower. The large
spread found over central Europe might be due to the possible differences between models
in soil moisture or energy limited regimes, caused by land-atmosphere feedbacks and the
uncertainty in the partitioning of modeled land heat fluxes during summer. Least warming is
found over the British Isles. The large influence of the ocean on this region and the increases
in clouds and precipitation may make less sensible for temperature change.
Precipitation is more variable in the 2◦ C warmer world (Fig. II.2). There is a tendency
of drier summers in Southern central Europe but wetter ones in Scandinavia, although the
spread between the models is large. The other three seasons might experience more rainfall,
especially autumn and winter, with a mean increase of approximately 2.5% and 5% respectively. Also here, the difference between the 2◦ C and 2031–2060 is small, but ensemble
spread is large in both cases.
Precipitation is expected to increase most in Scandinavia, followed by mid-Europe, the
British Isles and Eastern Europe. This might be caused by an increase in evaporation due
to elevated temperatures, causing a more humid atmosphere and more cloud formation. A
precipitation decrease if found especially in the south of Europe. Drier soils result in the
opposite pattern as found in the north: less humid atmospheres and less cloud formation.
2.3.2
Land heat fluxes and radiation
Land heat fluxes show a consistent pattern of change in the future with previous studies,
indicating a clear change in North and South Europe and a disagreement in sign between
models in central Europe (Schär et al., 2004; Stegehuis et al., 2013b). In spring, latent heat
fluxes increase over almost entire Europe, although the sign of change in most of Southern
Europe can be negative, but the magnitude is small (Fig. II.3). In spring, soil moisture usually
is still non-limiting and elevated temperatures may in that case an increase in evaporation.
However, if the regime is already limited by soil moisture today, as seems to be the case in the
most southern parts of Europe, an increase in temperature would cause a decrease in latent,
and an increase in sensible heat flux (Fig. II.3). This is also evident from the radiation fluxes.
Almost all models show an increase in shortwave radiation in the south, while longwave
radiation decreases. In Northern Europe however, the average longwave radiation increases,
although due to little model agreement, the area is not colored (Fig. II.3).
In summer, the spring pattern of especially sensible heat flux is intensified, indicating
more drying in Southern Europe and a possibly wetter climate in the north (also shown by its
increase in precipitation - Fig. II.2). The increase in latent heat flux in the north causes more
cloud formation leading to an increase in longwave radiation, while the amount of shortwave
radiation decreases. Summer precipitation is also intensified. Central Europe is a region
where climate change seems to be most uncertain and the variability is highest (Seneviratne
et al., 2006b). The region can either stay in an energy limited regime as usually is the case
in spring, or change into a soil moisture limited regime when enough energy is available to
2. THE EUROPEAN CLIMATE UNDER A 2◦ C GLOBAL WARMING
27
Figure II.3: Changes, between the 1971–2000 and the +2◦ C periods, in surface energy fluxes.
Left panels: latent heat fluxes; second left panels: sensible heat fluxes; second right panels:
net short-wave fluxes; right panels: net long-wave fluxes. Upper panels: MAM, Lower panels:
JJA. Only areas with at least 12 models agreeing on change sign are colored. Areas where
at least 14 models agree on change sign are highlighted with dots (except for temperature
where almost all areas satisfy this).
cause soil drying (Seneviratne et al., 2010). As shown by Stegehuis et al. (2013b), some
models in the ENSEMBLES project overestimate land-atmosphere feedbacks based on their
performance for present day latent heat flux observations. These models will probably make
a switch between the regimes, while others might not do so.
2.4
Summary and conclusion
Using an ensemble of RCMs, I showed that with a global temperature rise to a 2◦ C, Europe will experience an overall more intense warming than the global mean. The magnitude
depends on the region and the season, with most warming in Northern and Southern Europe and during the winter season. Precipitation will increase except for the southern areas
that tend to become drier. Both latent and sensible heat flux from model simulations confirm these findings. Latent heat flux shows an increase over almost entire Europe in spring
and over Northern Europe in the summer. Whilst sensible heat flux increases especially in
Southern Europe.
Although there seems to be some robustness in the results (smaller model spread) over the
most Northern and Southern parts of Europe, large uncertainty remains over central Europe.
This can both be illustrated in the large error bars of Fig. II.2 and in the absence of color in
Fig. II.3, in both land heat fluxes and radiation.
The temperature increase in the Mediterranean area and the Iberian Peninsula, together
with a drying of these regions may lead to a shift in vegetation. Trees species in this are
adapted to hot and dry summers, but their resistance differs between species. Less droughtadapted species might disappear or shift north and to higher altitudes, while better adapted
species may expand their distribution range. A temperature increase over Scandinavia may
favor a northern expansion of broadleaf species, that could possible outcompete the native
28
CHAPTER II. UNCERTAINTIES IN THE FUTURE CLIMATE
needleleaf trees in that region.
3
Future European temperature change uncertainties reduced by
using land heat flux observations
GEOPHYSICAL RESEARCH LETTERS, VOL. 40, 2242–2245, doi:10.1002/grl.50404, 2013
Future European temperature change uncertainties reduced by using
land heat flux observations
Annemiek I. Stegehuis,1 Adriaan J. Teuling,2 Philippe Ciais,1 Robert Vautard,1
and Martin Jung3
Received 4 January 2013; revised 16 March 2013; accepted 21 March 2013; published 28 May 2013.
[1] The variability of European summer climate is expected
to increase in the next century due to increasing levels of
atmospheric greenhouse gases, likely resulting in more
frequent and more extreme droughts and heatwaves.
However, climate models diverge on the magnitude of these
processes, due to land-surface coupling processes which are
difficult to simulate, and poorly constrained by observations.
Here we use gridded observation-based sensible heat fluxes
to constrain climate change predictions from an ensemble of
15 regional climate models. Land heat flux observations
suggest that temperature projections may be underestimated
by up to 1 K regionally in Central to Northern Europe, while
slightly overestimated over the Mediterranean and Balkan
regions. The use of observation-based heat flux data
allows significant reductions in uncertainty as expressed
by the model ensemble spread of temperature for the
2071–2100 period. Maximal reduction is obtained over
France and the Balkan with values locally reaching 40%.
Citation: Stegehuis, A. I., A. J. Teuling, P. Ciais, R. Vautard,
and M. Jung (2013), Future European temperature change
uncertainties reduced by using land heat flux observations,
Geophys. Res. Lett., 40, 2242–2245, doi:10.1002/grl.50404.
1. Introduction
[2] The greenhouse gases forced increase in radiative
forcing causes global warming. However, not all regions of
the globe are expected to warm at the same rate. Europe is
a sensitive region where natural climate variability is large,
and land-atmosphere coupling is important in large parts of
the continent. In Central-Western Europe, a projected
increase in summer temperature and temperature variability
[Schär et al., 2004; Seneviratne et al. 2006; Fischer et al.
2007] has been attributed to an increase in this coupling.
In this study, we hypothesize that any summer anomaly in
the partitioning of the land surface energy balance in the
current climate will be amplified on average under (drier)
future summer climate conditions. It follows that information to reduce uncertainty on the land surface energy balance
partitioning in the current climate may help to constrain
future climate projections [Fischer et al. 2012]. This type
of approach (see Hall and Qu [2006]) is topical because
the magnitude of land surface feedback processes is uncertain and differs considerably between climate models
because the parameterization of these models are diverse,
and rather poorly constrained. Most commonly, model
ensemble means and spread are used to quantify model
ensemble predictions of future climate variables.
[3] A method that leads to local reduction in uncertainty in
the future by using present-day observations and models was
proposed by Hall and Qu [2006]. They linked empirically
the seasonal cycle of present-day snow albedo feedback to
the magnitude of this feedback in the future. Following a
similar rationale, Boberg and Christensen [2012] showed
that inter-climate models’ future summer temperature
predictions over Europe were related to the present-day
temperature bias of models, and this property was used to
constrain temperature predictions in regions with dry and
warm climate such as the Mediterranean. Quesada et al.
[2012] reduced uncertainty in future European summer
climate projections by the CMIP3 global climate model
(GCM) ensemble by selecting a subset of models that best
represent the current relationships between spring precipitation and summer temperature. They found that the CMIP3
models underpredict summer temperatures in Central
Europe. However, constraining future projections using
surface heat fluxes observations (latent or sensible) has not
been done, and yet these fluxes controlling land-atmosphere
feedbacks were identified to be a main source of model spread
and uncertainty [De Noblet-Ducoudré et al., 2012].
[4] A method to improve future projections based upon heat
flux measurements requires long-term observations, which are
generally not available; the FLUXNET network of sites where
latent and sensible heat is measured online only contains rather
few multi-annual time series. Here we used a recent gridded
observation-based dataset of sensible heat fluxes (hereafter
H) based on the time and space extrapolation of point-scale
FLUXNET observations by machine-learning algorithm
[Jung et al., 2009]. The purpose of this study is to investigate
the use of the Jung et al observation-based land sensible heat
flux dataset for selecting the most realistic climate models
among an ensemble of future regional climate model (RCM)
projections over Europe.
1
LSCE/IPSL, Laboratoire CEA/CNRS/UVSQ, Gif-sur-Yvette, France.
Hydrology and Quantitative Water Management Group, Wageningen
University, The Netherlands.
3
Max-Planck Institute for Biogeochemistry, Jena, Germany.
2
Corresponding author: A.I. Stegehuis, LSCE/IPSL, Laboratoire
CEA/CNRS/UVSQ, 91191 Gif-sur-Yvette CEDEX, France. (annemiek.
[email protected])
©2013. American Geophysical Union. All Rights Reserved.
0094-8276/13/10.1002/grl.50404
2. Data and Methods
[5] 15 RCMs of the FP6 ENSEMBLES project are used
[Hewitt and Griggs, 2004; van der Linden and Mitchell,
2009]. The RCMs are driven by GCMs used for the
4th IPCC assessment report for the A1B scenario. We use
only those RCMs that were run for the period 1961–2099
or longer. All model outputs are provided on a 25 km spatial
2242
STEGEHUIS ET AL.: EUROPEAN TEMPERATURE CHANGE UNCERTAINTY
ρ(H,ΔT)
1.0
0.8
0.6
0.4
0.2
0.0
−0.2
−0.4
Figure 1. Spatial distribution of the Pearson correlation
coefficient between simulated summer sensible heat flux
(W m2) and simulated temperature change (K) of 15 RCMs,
defined by the difference in average air temperature between
future and present-day periods. The black dot represents the
grid point center location for Figure 2.
resolution, but in this study, they have been projected onto a
common 50 km resolution grid using bilinear interpolation
to be compared to the observational-based data. Monthly
mean model output are extracted for sensible heat flux
at surface (Wm2) and 2-m mean temperature (K). The
following GCMs are used from the institutes (in parentheses)
HadCM3Q16 (C4I), ARPEGE (CNRM, DMI), ECHAM5-r3
(DMI, ICTP, KNMI, MPI, SMHI), BCM (DMI, SMHI),
HadCM3Q0 (ETHZ, HC), HadCM3Q3 (HC, SMHI), and
HadCM3Q16 (HC).
[6] We used observationally derived gridded sensible heat
fluxes [Jung et al., 2011] (hereafter MTE-H). This data
product was obtained by applying a machine learning
algorithm Model Tree Ensemble to estimate global monthly
heat fluxes from 1982 to 2008 on a resolution of 0.5 (for a
detailed description of the data product and underlying
methodology, see Jung et al., [2009]). The spread (standard
deviation, sH), between ensemble members being taken as a
measure of uncertainty on observation-derived H. The
advantage of using H instead of latent heat flux is that (1)
H is more directly linked with air temperature, and (2) the
measurement method by the eddy covariance technique is
more direct than for latent heat fluxes [Wilson et al., 2002].
[7] The spread of simulated summer temperature change
(ΔT, difference between 2071–2100 and 1971–2000) is
rather large in the FP6 ENSEMBLES ensemble of RCMs.
We will refer to this spread as the a priori spread (i.e., before
adding information from flux observations). If a tight correlation exists between RCM-modeled ΔT and H (1971–2000)
(Figure 1), then a better (a posteriori) estimate of ΔT could
be obtained through the linear regression ΔT = aH + b at
every grid point. The a posteriori uncertainty of ΔT then
depends on the uncertainty of the observation (sH), the
goodness-of-fit of the regression (through the residual
spread sres), and the slope of the regression (a) (Figure 2a).
The a posteriori uncertainty of ΔT, given sH, is given by
(see Appendix A):
sΔT jH ¼
(1)
[8] Note that adding information on H (predictor) will
only result in a reduction of the a priori uncertainty if the
observational uncertainty sH is (much) smaller than the
spread of RCM-modeled H. The a posteriori uncertainty of
ΔT in equation (1) can be reduced by taking the standard
error of the prediction of this correlation (see Appendix A).
Where correlation is low, uncertainty reduction will only
be minor. This also applies for a larger uncertainty in the
observation-derived H data product (Figure 2b).
(b)
0
0
2
2
4
4
6
6
8
8
(a)
Summer ΔT (K)
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
s2res þ a2 s2H
0
20
40
60
80
100
Present day summer H (Wm−2)
Figure 2. (a) Simulated summer sensible heat flux (W m2) versus simulated air temperature change (ΔΤ in K) in summer
for the grid box indicated in Figure 1. Vertical lines indicate the MTE-H (solid) observation derived estimate, plus and minus
one standard deviation (dashed). The gray area indicates the prediction interval for ΔΤ (sΔT|H) that is compatible with
the MTE-H observations. The red lines show the linear regression between RCM-modeled ΔΤ and H (thick solid) and the
1-s prediction interval (dashed). (b) Estimated prior and posterior normal distributions with modeled mean and SD;
SD of ΔΤ a priori (solid), SD of ΔΤ given a perfect H observation (dotted), and SD of ΔΤ a posteriori given uncertain
H observations (sΔT|H) (dashed).
2243
STEGEHUIS ET AL.: EUROPEAN TEMPERATURE CHANGE UNCERTAINTY
ΔT a priori
(a)
ΔT a posteriori
a posteriori − a priori
K
(b)
6
K
(c)
1.5
5
1.0
4
0.5
3
0.0
2
−0.5
−1.0
1
−1.5
σΔT a priori
(d)
0
σΔT a posteriori
a posteriori − a priori
K
(e)
2.0
(f)
%
20
10
1.5
0
1.0
−10
−20
0.5
0.0
−30
−40
Figure 3. (a) Mean predicted summer temperature change (ΔΤ in K) of 15 RCMs, i.e., a priori simulations, (b) predicted
temperature change that is compatible with observed sensible heat flux, i.e., a posteriori, and (c) the estimated RCM model
ΔΤ bias, diagnosed from the difference between b and a (K). Hashed areas indicate a significant difference between
observation-based H and modeled H. (d) Standard deviation of mean temperature change of 15 RCMs a priori, (e) standard
error of predicted temperature change based on the subset of RCM models that are compatible with present-day sensible heat
flux, i.e., a posteriori, and (f) the relative change of ΔΤ (s difference between d and e in percentage).
3. Results
[9] We found that there is a significant positive correlation
between simulated average summer H in the current climate
and mean summer ΔT (Figure 1) which reflects the existence
of a physical link between summer H and near-surface
temperature change. Positive values are found over most of
Europe, with exception of the Iberian Peninsula (Figure 1).
The strongest positive correlations are found over central
France and Hungary, regions which were also highlighted
as strong coupling regions in other studies on soil moisturetemperature coupling [e.g., Seneviratne et al., 2006]. The
low correlation over the Iberian Peninsula might indicate
different controls than land-atmosphere coupling behind
model differences in this region.
[10] In general, the RCM-modeled ΔT (Figure 3a), is close
to ΔT constrained by MTE-H (Figure 3b), leading us to diagnose only a small temperature bias when using MTE-H
to restrain temperature change. Over central Europe and
the southern part of Scandinavia, we found a positive ΔT
bias (Figure 3c), locally up to 1 K, suggesting an underestimation of modeled H with regard to MTE-H. Over the
southeastern part of Europe, however, we found a negative
bias, suggesting that models have somewhat unrealistic
drying, resulting into too low latent heat emissions (soil
moisture limitations) and too high H [Stegehuis et al.,
2012; Boberg and Christensen, 2012]. This is confirmed
by subtracting mean simulated H from the observation based
MTE-H (figure not shown). Areas with significant difference
(a = 0.1) between the two are indicated in Figure 3c.
[11] The ensemble spread of simulated temperature
change is largest over France and the Balkan (Figure 3d),
which are two regions with predicted high interannual
variability of summer temperature due to a strong soil
moisture-temperature coupling [Seneviratne et al., 2006;
Hirschi et al., 2011]. Also, in these regions, the correlation
between present-day H and temperature change was highest
(suggesting a strong coupling, indirectly explained by soil
moisture limitations on evapotranspiration), thus resulting
in a smaller standard error of the prediction (Figure 3e).
The difference between the a priori and a posteriori, the
uncertainty reduction of temperature change predictions, is
displayed in Figure 3 f. As can be seen, uncertainty (RCM
model spread) is reduced by up to 40% in regions with
highest correlations, by using H observations to constrain
ensemble projections. This approach is only possible
because we had access to an ensemble of RCM results with
marked differences in model skills to reproduce presentday H, allowing to establish a strong positive relationship
between H and temperature change, like in Figure 2.
4. Concluding Remarks
[12] In this study, we proposed an approach to constrain
summer temperature change predictions by using the
existing correlation between simulated present-day H and
temperature change in combination with present-day H
observation-based gridded datasets over Europe. The fact
that the uncertainty in observed H is smaller than the range
spanned by the regional models output made it possible to
reduce regional uncertainty of future climate change predictions. We chose to use sensible rather than latent heat flux as
a predictor of future temperature change in the present study,
because of the uncertainty between different observationbased data products [Mueller et al., 2011].
[13] Our results indicate that in Central and Northern
Europe, the ENSEMBLES RCM projections underestimate
future temperature change, but that they overestimate it in
Mediterranean regions. In contrast, Fischer et al. [2012]
found that an ensemble constrained by observed present-day
interannual summer variability predicts lower temperature
change over Central Europe than the ensemble of all models.
While this could be caused by an overestimation of MTE-H,
a 40% lower MTE-H would be required to obtain a similar
reduction in ΔT. Using reanalysis data from ERA-interim
2244
STEGEHUIS ET AL.: EUROPEAN TEMPERATURE CHANGE UNCERTAINTY
[Dee et al., 2011] instead of MTE-H as a reference dataset
results in an overestimation of ΔT in Central to Northern
Europe (like in Fischer et al. [2012]) of the ensemble of all
models, and an underestimation over France (similar to
MTE-H), the Balkan, and the Mediterranean regions, with
on average twice the magnitude of MTE-H (not shown). Using
our method with interannual summer variability instead of H
results in a similar difference between a priori and a posteriori
ΔT as found in Fischer et al. [2012], indicating that the difference is caused by the selection of datasets and not methodology. The apparent inconsistency can be explained by the fact
that the relations used are statistical rather than physical, since
correlation does not guarantee causality.
[14] In the case of MTE-H constraint, the reduction of
uncertainty in regional temperature change predictions has
a heterogeneous amplitude. In regions where surface fluxes
form a loose constraint on projections, other variables
might be used. In addition to snow cover [Hall and Qu,
2006], temperature-precipitation relationships [Quesada
et al., 2012], or summer temperature variability [Fischer
et al., 2012], we hypothesize that other variables such as
convective precipitation, soil moisture, moisture convergence,
wind speed and direction, and indices of weather regimes that
influence the transport of heat from North Africa can be used.
In future studies, a multi-linear empirical regression approach
could be developed where the target variable (temperature
change) is empirically related to these different predictors.
Since our study highlights the impact of land surface conditions on European summer temperatures, one key implication
is that land cover and land cover change must be better
accounted for by RCMs for robust predictions of the future
summer climate in Europe.
Appendix A
[15] The results of the regression between H and ΔT can
be written as:
E ½ΔT jH ¼ aH þ b
(A1)
[16] With the residual standard deviation independent of H:
E½ðΔT aH bÞ2 H ¼ s2res
(A2)
E ½H ¼ mH
(A3)
h
i
E ðH mH Þ2 ¼ s2H
(A4)
[17] Let:
and
and denote the a posteriori expectations of ΔT by:
E½ΔT jmH ¼ mΔT jH
2 s2ΔT jH ¼ E ΔT mΔT jH
(A5)
mΔT jH ¼ amH þ b
(A7)
(A6)
[18] With:
[19] In rewriting (A6), the cross-term cancels out because
E[ΔT aH b|H] = 0
[20] The conditional expectation E[(ΔT E[ΔT])2|H] = sΔT|H
then becomes:
sΔT jH ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
s2res þ a2 s2H
(A8)
[21] Acknowledgments. The ENSEMBLES data used in this work
was funded by the EU FP6 Integrated Project ENSEMBLES (Contract
number 505539) whose support is gratefully acknowledged. We acknowledge
financial support from The Netherlands Organization for Scientific Research
through Veni grant 016.111.002. This work was partly supported by the
FP7 CARBOEXTREME project. We thank Paul Torfs for his help on the
uncertainty propagation.
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2245
4. DISCUSSION AND ADDITIONAL RESULTS
4
33
Discussion and additional results
In this chapter we used the ENSEMBLES set of RCMs to analyze a few key aspects of the
summer climate in Europe and its change with climate warming. We first investigated the
changes in the land energy budget, precipitation and temperature over Europe under a global
warming scenario of 2◦ C. Furthermore, with the modeled relationship between sensible heat
flux and temperature change, the uncertainty of summer temperature change predictions
were reduced by using an observation-based data set of sensible heat flux. While model
ensembles are found to better represent observations than a single model (Dirmeyer et al.,
2006), they might add different uncertainty. This will be discussed in section 4.1. In section
4.2 I will discuss the method that was used to reduce temperature change uncertainties. This
is followed by a discussion on the 2◦ C limit.
4.1
Model ensemble
An objective of this study was to use the differences in land-atmosphere feedbacks between models to reduce uncertainty in future temperature changes. While I did not have as
objective to study each model in detail, it is important to keep in mind that within the ensemble, the models might not be completely independent. Although models of different institutes
were used, they might share for example similar convective calculations or parameterizations
which could cause similar biases (Knutti et al., 2013). Furthermore, the uncertainty in climate
change projections comes from both the driving and the forced models. In this study, the 15
RCMs are driven by only 6 GCMs, which might also result in similar biases. To overcome and
understand such biases, very extensive ensembles should be created and analyzed, in which
the RCMs are driven by all different GCMs. Furthermore all models should be thoroughly
studied and compared with one another to verify their independence. Whenever different
models share similar parameterizations or parts of codes, weighting could be applied to avoid
that similar biases are multiplied in the ensemble.
4.2
Uncertainty reduction method
We used the differences between the models to reduce uncertainty in temperature prediction. The models that simulate a sensible heat flux closer to the observation-based heat
flux, are considered more important than models that simulate a heat flux that is far from
the observations. The latter model values are less accounted for in the calculation of the a
posteriori temperature and uncertainty prediction. However, this method can be discussed.
All models are used for the calculation of the relationship between present-day sensible heat
flux and summer temperature change, and the models being far from the observations are
not necessarily wrong when considering changes. They might be less credible. Conversely,
models that well simulate the present climate, do not necessarily correctly simulate a future
different climate (Oreskes et al., 1994). Therefore it can be argued that a method with model
weighting might be better justified.
To ascertain a robustness in our results, we applied Bayesian Model Averaging (BMA)
(van Oijen et al., 2013b) on the same data sets. The probability of each model is calculated
based on the observation-based values of sensible heat flux. This probability is then used to
obtain a model ensemble weighted average.
34
CHAPTER II. UNCERTAINTIES IN THE FUTURE CLIMATE
Figure II.4: (a) Mean predicted summer temperature change of 15 RCMs, i.e., a priori simulations, (b) predicted temperature change that is compatible with observed sensible heat
flux, i.e., a posteriori based on Bayesian Model Averaging, and (c) the estimated RCM model
temperature ∆T bias, diagnosed from the difference between b and a (K). (d) Standard deviation of mean temperature change of 15 RCMs a priori, (e) standard deviation of predicted
temperature change based on BMA, and (f) the relative change of ∆T (σ difference between
d and e in percentage).
Although differences between both methods are found, the overall patterns of temperature change and uncertainty reduction are similar (compare Fig. II.3 with Fig. 3 from section
2.3). The estimated model ∆T bias differs most in Southern Europe. While BMA only shows
a negative ∆T bias in some of the south eastern parts, the method used in section 2.3 had
more negative and more widespread negative values. Most parts of Italy seemed to experience too much drying, whilst using the BMA weighting approach it seems to be more realistic.
Over the Iberian Peninsula the pattern is opposite: models show not enough drying, or an underestimation of modeled H compared to the observations using BMA, while almost no bias
was found previously. Regarding the uncertainty reduction of temperature predictions (Fig.
II.3 f) most difference between the two methods is found in the magnitude of the reduction.
BMA shows much more reduction, up till 80% (not shown in figure), whilst before the reduction showed maxima of 40%. The pattern is also slightly different. While previously a
larger uncertainty (positive values) was found over Scandinavia and parts of Great Britain,
BMA reduces the model prediction uncertainty also over parts of these regions. While some
differences between the two methods exist, the overall similarity in the patterns improves our
confidence in the previously obtained results.
5. SUMMARY AND CONCLUSIONS
4.3
35
The choice for the 2◦ C limit
The 2◦ C global warming limit has now been agreed upon as target during the last United
Nations Conference on Climate Change (COP21). However, this target has been debated by
Hare et al. (2011); Knutti et al. (2016). They question its safety and argue that scientific
evidence is lacking that legitimize this specific threshold.
Figure II.4 shows the average European temperature for a control period (1971-2000),
and that for both a 1.5◦ C and a 2◦ C global warming. Both future periods are GCM dependent
and their details can be found in Appendix A. In spring and summer the temperature of the
2◦ C period is higher than that of the 1.5◦ C period. However, during autumn and winter the
mean temperature is similar, while there is more spread in the 1.5◦ C period. This suggests
non-linear warming and confirms the necessity of studying different ’warming limits’. For
precipitation most differences between the two periods is found in winter, where there is
significantly less precipitation change in the 1.5◦ C period. In summer there seems to be less
drying compared to the 2◦ C period, but there is a large inter-model uncertainty.
5
Summary and conclusions
In this chapter we showed some of the patterns on the European climate under a 2◦ C
global warming. Furthermore we proposed an approach to constrain summer temperature
projections by using the strong relationship between present-day sensible heat flux and summer temperature change together with present-day observation-based data. We showed that
on average, a 2◦ C global temperature rise results in a higher European warming than 2◦ C.
Precipitation changes strongly depend on the region, with highest increases over Scandinavia
and drying over the Iberian Peninsula and the Mediterranean region. The reason for these
changes may be due to a different partitioning of the land heat fluxes. During summer, sensible heat flux increases strongly in Southern Europe, while latent heat flux decreases over this
region. This pattern is opposite in Northern Europe where the increase in latent heat flux may
favor precipitation. The largest model differences for both heat fluxes lies in central Europe,
where models do not agree on the sign of change. This translates into a large uncertainty in
the simulated temperature projections. However, using observations of the land heat fluxes
and their uncertainty, we were able to reduce the spread of models’ summer temperature
change predictions regionally up to 40%. Yet, this approach relies on strong assumptions
about the interpretation of model bias. In particular, whether a model with a smaller bias for
the present climate will be more realistic in the future remains unproven.
While temperature change will increase over entire Europe in the future, its magnitude
and regional patterns remain uncertain. While land heat fluxes helped to constrain model
spread in central Europe, in other regions different methods may need to be used, as the
sensitivity of evapotranspiration to changes is soil moisture might be too small.
Better projections of future temperature changes may be important for impact studies, for
example for the estimation of vegetation distribution and subsequent management practices.
When better predictions for climate change are available, management can be adapted for
the conservation of species, especially those that are sensitive to elevated temperatures and
grow on the southern limit of their distribution. For example for extensive thinning may
help survive Pinus sylvestris and Fagus sylvatica on xeric sites (Giuggiola et al., 2013; van der
CHAPTER II. UNCERTAINTIES IN THE FUTURE CLIMATE
−5
0
5
TAS [°C]
10
15
20
36
1971−2000
1.5−degree period
2−degree period
Spring
(MAM)
Summer
(JJA)
Autumn
(SON)
Winter
(DJF)
Figure II.5: Temperature over Europe per season from 15 RCM from the ENSEMBLES project
(spatial average over land pixels) for the control period (1971-2000) (open bars), the 1.5◦ C
period (light grey bars) and the 2◦ C period (dark grey bars).
37
0.0
−7.5
−5.0
−2.5
∆PR [%]
2.5
5.0
7.5
10.0
5. SUMMARY AND CONCLUSIONS
1.5−degree period
2−degree period
Spring
(MAM)
Summer
(JJA)
Autumn
(SON)
Winter
(DJF)
Figure II.6: Spatial average over land of changes in precipitation over Europe relative to the
control period (1971-2000), together with the range of changes for the GCM-RCM ensemble
(median, 25-75% range and min and max values). The open bars refer the 1.5◦ C period, the
grey bars to the 2◦ C period.
38
Maaten, 2013).
CHAPTER II. UNCERTAINTIES IN THE FUTURE CLIMATE
C HAPTER
III
Atmospheric processes
39
40
1
CHAPTER III. ATMOSPHERIC PROCESSES
Introduction
In the previous chapter the focus was on land-atmosphere feedbacks in models and their
influence on future summer temperatures. This chapter is concentrated on atmospheric processes and physics in models and their impact on summer temperatures and heatwaves. The
main aim is to try to answer the question how different physical formulations influence simulated heatwaves and the spread and uncertainty of modeled summer temperatures.
It is not self-evident that RCMs can well represent extreme heatwaves in regions where
they have only been scarcely observed (Butler, 2003; Perkins and Fischer, 2013). Even if the
physics of such events would be known, uncertainties could arise by the poor restriction by
observations, leading to underestimation of maximum temperatures (Halenka et al., 2006).
This would directly lead to too few simulated heatwaves. Vautard et al. (2013), however,
found that the heatwave frequency and magnitude were overestimated in RCMs, especially
in the Mediterranean region. But simultaneously they found a large spread between the
models. The source of such a spread however, is not always clear. Among various models,
different physics can be used. But changing parameters can cause different model outputs
even if the physics are similar (Bellprat et al., 2012). Another source of uncertainty in RCMs
can arise from boundary conditions, either from models or reanalyses data. Additionally, the
dynamics created by the model might be a source of differentiation through natural chaotic
variability which arises from the ’butterfly’ effect (Lorenz, 1963).
In a study on climate extremes, Good et al. (2006) found that model projections are
necessarily limited by uncertainties, particularly due to the model physics. Multi-physic ensembles have been used to reduce such this specific uncertainty (Awan et al., 2011). With
this approach, a single model is used, but different physics are tested to find the most realistic
combinations of physical formulations. Although this method is not unique (Fernández et al.,
2007; Awan et al., 2011; Evans et al., 2012; García-Díez et al., 2013; Mooney et al., 2013),
multi-physics ensembles have not yet been used for analyzing heatwaves. Yet this may be
of high importance regarding their consequences and their expected elevated frequency and
severity in the future European climate.
In this chapter we use the Weather and Research Forecast model (WRF) (Skamarock et al.,
2008) and test 216 different atmospheric physics combinations. They consist out of 3 radiation, 4 convection, 3 microphysics and 6 planet boundary-surface layer schemes. All possible
combinations were used. Both the heatwaves of 2003 and 2010 were simulated together
with the wetter summer of 2007. To focus on physics only, we nudge the model simulations
above the atmospheric boundary layer. Although land-atmosphere interactions are known to
play an important role in heatwaves, only the atmospheric physics were tested because of
the limited choice between different land surface schemes at the time of the study. All 216
simulations were evaluated against different sets of observations and the best configurations,
as compared to observations, were chosen to perform further studies.
We found a large spread in simulated temperature, precipitation and incoming shortwave
radiation, even though the simulations were nudged. Temperature differences were locally up
to 10◦ C, with the largest uncertainties in central Europe, probably due to differences in landatmosphere feedback representations (Stegehuis et al., 2013a). Mean and maximum temperatures were systematically underestimated. Precipitation and shortwave radiation were
mostly overestimated. Convection was found to dominate the ensemble spread, probably by
2. WRF MODEL DESCRIPTION
41
inducing uncertainties in clouds that affect both the surface energy and water budget prior
to, and during the heatwaves. A small ensemble was selected, constrained by different observational data sets. We suggest that this model ensemble could be used for further research on
heatwaves and summer temperatures in Europe.
In the following section I describe the model and the used physics, which could not be
included in the peer reviewed paper on the WRF multi-physics ensemble, presented in section
III.3. In the discussion I focus especially on the convection physics, as it was shown to be most
important in the model spread. The chapter is completed with a summary and concluding
remarks in section 5.
2
WRF model description
WRF (Skamarock et al., 2008) was developed by a collaboration between the National
Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (represented by the National Centers for Environmental Prediction (NCEP) and the
(then) Forecast Systems Laboratory (FSL)), the Air Force Weather Agency (AFWA), the Naval
Research Laboratory, the University of Oklahoma, and the Federal Aviation Administration
(FAA) (website WRF). It can both be used for weather forecasting as for climate research. In
this study we used it for analyzing European summer heatwaves.
WRF is a model platform that allows different physic packages to be interchanged. In
version 3.3.1, the used version for this study, more than 50 different physics options were
available. These cover microphysics, planet boundary layer physics, cumulus or convection
physics, radiation physics, land surface and surface layer physics. The aim of this study
is to analyze the spread due to model physics, and to find some that well represent European summer climate and heatwaves specifically. Due to the large number of available
physics schemes however, it was impossible to test all combinations. Possible non-linear effects between different schemes advocates not only testing the different physics, but also the
inter-physic interactions. We therefore chose to investigate 3 radiation, 4 convection, 3 microphysics and 6 planet boundary-surface layer schemes, with all possible combinations. The
selection of schemes was made to avoid variants of the same physics and to maximize the
difference of temperatures and precipitation outputs in preliminary experiments. However,
as some physics gave unrealistic results, a subset was selected within a reasonable range of
the observed variables (Fig. III.1).
In this study, a total of 216 configurations was tested. A short description with the main
characteristics and differences between the schemes is described below.
2.1
Microphysics
The microphyics scheme plays an important role in cloud representation and non-convective
rain (the convective rain is determined by the convection scheme). The cloud effects impact
the radiation physics directly and the land surface is affected by the amount and distribution of non-convective rain. Differences between microphysics schemes can lay among others
in their number of mass variables, number variables and formulation of different processes
such as saturation and nucleation. The three schemes used in this study, WRF-SM6 (Hong
and Lim, 2006), New Thompson (Thompson et al., 2008) and Morrison DM (Morrison et al.,
42
CHAPTER III. ATMOSPHERIC PROCESSES
Figure III.1: Maximum temperatures over Europe using different microphysics, PBL-surface
layer, cumulus and land surface physics. The numbers correspond to the different options of
the physics available in WRF. The black curve corresponds to the observations.
2009), have 6 particle classes: water vapor, clouds, rain, ice, snow and graupel. New Thompson and Morrison DM are both double-moment schemes, predicting both mass and number
concentrations of the particles, while the single-moment WRF-SM6 only predicts the mass of
the variables. Double-moment schemes may be more realistic but might be more difficult to
validate because of their more specific cloud properties (Lee and Donner, 2011; Molthan and
Colle, 2012).
2.2
Planetary Boundary Layer
The Planetary Boundary Layer (PBL) physics describe entrainment, vertical diffusion and
local and/or non-local mixing processes. They interact mostly with the surface layer physics,
as it impacts surface temperature, wind and exchanges of momentum and moisture fluxes.
The land heat fluxes from the surface physics affect the PBL. PBL schemes tested in this
study are Yonsei Uni (YSU) (Hong et al., 2006), Mellor–Yamada–Janjic (MYJ) (Janjić, 1994,
2002), Quasi–Normal Scale Elimination (QNSE) (Sukoriansky et al., 2005), Mellor–Yamada
2. WRF MODEL DESCRIPTION
43
Nakanishi Niino (MYNN) (Nakanishi and Niino, 2006, 2009) and the Asymmetric Convection
Model 2 Scheme (ACM2) (Pleim, 2007). Differences between schemes can come from among
others the prognostic and diagnostic variables, the formulation for vertical mixing and closure schemes, and the (non)-locality. MYJ, QNSE and MYNN have local TKE-based vertical
mixing, while YSU has non-local mixing. ACM2 describes both local and non-local mixing.
Furthermore, YSU and ACM2 have first order closure, whilst MYJ and QNSE have 1.5-order
closure and MYNN both 1.5- and second order closure schemes. This can lead for example to
differences in PBL depth, which have been found to be too deep for both YSU and ACM2 and
to shallow for MYJ, QNSE and MYNN (Cohen et al., 2015).
2.3
Surface Layer
The surface layer physics are linked very tightly to the PBL as they describe the momentum, heat and moisture fluxes from the surface into the PBL. They are often coupled to
specific PBL physics, and are therefore considered as pairs. Besides its relation with the PBL,
they interact with the radiation scheme through short- and longwave radiation, and with the
microphysics and the convection scheme through non-convective and convective rain, respectively. All four surface layer schemes are based on the Monin-Obukhov similarity theory. They
differ among others in the calculation of roughness length. The four schemes used are: 1)
MM5 (Beljaars, 1994) coupled with YSU and ACM2 PBL schemes; 2) ETA (Janjić, 1996), coupled with MYJ and MYNN; 3) QNSE (Sukoriansky et al., 2005), coupled with QNSE; and 4)
MYNN (Nakanishi and Niino, 2006, 2009), coupled with MYNN.
2.4
Radiation
Radiation physics are important for the calculation of the energy budget. Short- and
longwave radiation are calculated as well as reflection and scattering. The radiation scheme
interacts with the microphysics for the effects of clouds on radiation. It is connected with the
surface layer for the downward radiation and upward surface emission and the albedo. It
receives the cloud fraction from the convection scheme. In this study the radiation schemes
for longwave and shortwave radiation are used simultaneously. The three used schemes
are the Community Atmosphere Model (CAM) radiation (Collins et al., 2004), the RRTGM
scheme (Iacono et al., 2008) and the New Goddard radiation (Chou and Suarez, 1999).
Differences between the radiation physics can arise from e.g., a different interaction with
cloud fractions, different spectral methods and a different number of spectral bands (8, 16
and 10 bands for longwave and 19, 14 and 11 bands for shortwave radiation in CAM, RRTGM
and New Goddard, respectively) and its interaction with aerosols. Because of its effect on the
radiation, these schemes can strongly affect temperature (Evans et al., 2012).
2.5
Convection
Convection is very important in the coupling of land and atmosphere as it describes convective rainfall over land and water recycling and determines cloud cover. Furthermore it is
involved in the heat, moisture and momentum transport and cloud cover. The four convection schemes studied are Kain-Fritsch (Kain, 2004), Grell-Devenyi (Grell and Freitas, 2014),
Tiedtke (Tiedtke, 1989; Zhang et al., 2011) and the New Simplified Arakawa–Schubert (New
SAS) scheme (Han and Pan, 2011). Differences between convective parameterizations can
44
CHAPTER III. ATMOSPHERIC PROCESSES
arise among others from different closure formulations, different triggering functions and differences in entrainment and detrainment. Some of the main biases of convection physics are a
wrong diurnal cycle, missing cold pools, missing mesoscale circulations and a convection that
is triggered too easily (Lynn et al., 1995; Hohenegger et al., 2009; Hohenegger and Stevens,
2013; Hourdin et al., 2013; Marsham et al., 2013).
2.6
Land surface
The land surface scheme is the most important scheme for land-atmosphere interactions,
as it describes the soil moisture, and thereby impacts the partitioning of the land heat fluxes
and temperature (Jin et al., 2010; Zeng et al., 2011; Ramarohetra et al., 2015). In this
study only the NOAH land surface scheme was used (Tewari et al., 2004). It has four soil
layers where both soil moisture and soil temperature are predicted. It furthermore includes
vegetation effects, snow cover and canopy moisture. In the used WRF version more options
were available, but they were not appropriate for our study. Tests with the Rapid Update Cycle
(RUC) (Benjamin et al., 2004) scheme revealed unrealistic results, and technical problems
made us decide not to further use this scheme. A more detailed description of the choice of a
single land surface scheme is presented in the next section.
3
An observation-constrained multi-physics WRF ensemble for
simulating European mega heat waves
Geosci. Model Dev., 8, 1–14, 2015
www.geosci-model-dev.net/8/1/2015/
doi:10.5194/gmd-8-1-2015
© Author(s) 2015. CC Attribution 3.0 License.
An observation-constrained multi-physics WRF ensemble for
simulating European mega heat waves
A. I. Stegehuis1 , R. Vautard1 , P. Ciais1 , A. J. Teuling2 , D. G. Miralles3,4 , and M. Wild5
1 LSCE/IPSL,
Laboratoire CEA/CNRS/UVSQ, Gif-sur-Yvette, France
and Quantitative Water Management Group, Wageningen University, the Netherlands
3 Department of Earth Sciences, VU University Amsterdam, Amsterdam, the Netherlands
4 Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium
5 ETH Zurich, Zurich, Switzerland
2 Hydrology
Correspondence to: A. I. Stegehuis ([email protected])
Received: 8 September 2014 – Published in Geosci. Model Dev. Discuss.: 19 November 2014
Revised: 5 May 2015 – Accepted: 23 June 2015 – Published:
Abstract. Many climate models have difficulties in properly reproducing climate extremes, such as heat wave conditions. Here we use the Weather Research and Forecasting
(WRF) regional climate model with a large combination of
different atmospheric physics schemes, in combination with
the NOAH land-surface scheme, with the goal of detecting
the most sensitive physics and identifying those that appear
most suitable for simulating the heat wave events of 2003
in western Europe and 2010 in Russia. In total, 55 out of
216 simulations combining different atmospheric physical
schemes have a temperature bias smaller than 1 ◦ C during
the heat wave episodes, the majority of simulations showing
a cold bias of on average 2–3 ◦ C. Conversely, precipitation
is mostly overestimated prior to heat waves, and shortwave
radiation is slightly overestimated. Convection is found to be
the most sensitive atmospheric physical process impacting
simulated heat wave temperature across four different convection schemes in the simulation ensemble. Based on these
comparisons, we design a reduced ensemble of five well performing and diverse scheme configurations, which may be
used in the future to perform heat wave analysis and to investigate the impact of climate change during summer in Europe.
1
Introduction
An increasing number of simulations and studies project a
higher frequency of several types of extreme weather events
in the future (e.g., Schär et al., 2004; Meehl et al., 2004;
Della-Marta et al., 2007; Beniston et al., 2007; Kuglitsch et
al., 2010; Fischer and Schär, 2010; Seneviratne et al., 2012;
Orlowsky and Seneviratne, 2012). Since summer heat waves
are among the most impacting of such phenomena – threatening society and ecosystems – climate models used for future
projections must provide accurate simulations of these phenomena, or at least their uncertainties should be documented.
Even if climate models have been evaluated using observed
weather in past decades, it is unclear whether they will be
able to simulate extreme heat waves in future climates that
may not have analogues in the historical record. At a minimum, models should be able to reproduce the conditions
measured during recent extreme heat wave cases, some of
them having been shown to be unprecedented when considering the climate over the past 5 or 6 centuries (Chuine et al.,
2004; Luterbacher et al., 2010; García-Herrera et al., 2010;
Barriopedro et al., 2011; Tingley and Huybers, 2013).
Given the importance of forecasting summer heat waves
well in advance, many studies have analyzed their predictability, which remains poor in seasonal forecasts. For
instance, the 2003 European heat wave was not simulated
realistically (neither timing nor intensity) by the operational European Centre for Medium-Range Weather Forecasts (ECMWF) system, but improvements were clear with
Published by Copernicus Publications on behalf of the European Geosciences Union.
2
A. I. Stegehuis et al.: Multi-physics WRF ensemble for simulating European mega heat waves
the use of a new land-surface hydrology, convection and radiation schemes (e.g., Weisheimer et al., 2011; Dole et al.,
2011; Koster et al., 2010; van den Hurk et al., 2012). However, seasonal forecasting experiments do not straightforwardly allow for the assessment of a model’s physical processes underlying extreme temperatures during heat waves
because it is difficult to separate model biases due to deficiencies in the model representation from sensitivity to initial
conditions. These may inhibit the effect of the representation
of physical processes in reproducing the exact atmospheric
circulation when starting simulations at the beginning of the
season.
From a statistical perspective, extreme temperatures have
been found to be reasonably well represented in global simulations of the current climate (IPCC, 2013), as well as
in regional simulations (Nikulin et al., 2010). In recent regional modeling evaluation experiments, using an ensemble
of state-of-the-art regional models guided by re-analysis at
the boundaries of a European domain, summer extreme seasonal temperatures were shown to be simulated with biases in
the range of a few degrees (Vautard et al., 2013). Individual
mega heat waves (2003 in western Europe, 2010 in Russia)
were reproduced by most models. However, it was difficult
to infer whether these models could also simulate associated
processes leading to the extreme heat waves. The exact same
events with similar atmospheric flow and persistence could
not be reproduced due to internal variability (internal degrees
of freedom) of the models.
A comprehensive assessment of simulations of recent
mega heat waves has only been the object of a limited number of such studies. Process-oriented studies of high extreme
temperatures over Europe have focused on land–atmosphere
feedbacks (e.g., Seneviratne et al., 2006, 2010; Fischer et
al., 2007; Teuling et al., 2009; Stegehuis et al., 2013; Miralles et al., 2014) because, beyond atmospheric synoptic
circulation, these feedbacks are known to play an important role in summer heat waves. However, the sensitivity
of simulated heat wave conditions to physical processes in
models has not yet been explored in a systematic way. This
could be important because error compensation among processes that involve land–atmosphere interactions, radiation
and clouds may cause high temperatures for the wrong reasons (Lenderink et al., 2007).
The goal of the present study is threefold. First we examine the ability of a regional climate model, Weather Research
and Forecast (WRF, Skamarock et al., 2008), to simulate recent European mega heat waves with a number of different model configurations. Analysis of these experiments then
allows understanding which physical parameterizations are
prone to reproduce the build up of extreme temperatures and
thus the need for carefully constraining them in order to simulate these events properly. Finally, using observational constraints of temperature, precipitation and radiation, we select
a reduced ensemble of WRF configurations that best simulates European heat waves with different sets of physical
Geosci. Model Dev., 8, 1–14, 2015
schemes combinations. This constrained multi-physics ensemble aims therefore at spanning a range of possible physical parameterizations in extreme heat wave cases while keeping simulations close to observations.
Our multi-physics regional ensemble approach contrasts
with the classical multi-model ensembles that are constructed
by the availability of model simulations in coordinated experiments (see e.g., Déqué et al., 2007, and references therein) or
by arbitrarily configured combinations of parameterizations
selected by different groups using the same model system
(García-Díez et al., 2015). In the latter ensemble, the lack
of overall design strategy may lead the uncertainty estimation to be biased and the models to be farther from observations. In addition, the real cause of model spread is difficult to understand because of shortcomings in the representation of physical processes and their interactions. Regional perturbed-physics or multi-physics ensembles could
help understand and constrain uncertainties more effectively,
but so far they have been seldom explored. García-Díez et
al. (2015) showed that even a small multi-physics ensemble
confronted to several climate variable observations can help
diagnose mean biases of a regional climate model. Bellprat et
al. (2012) showed that a well-constrained perturbed-physics
ensemble may encompass the observations. Their perturbedphysics ensemble was designed by varying the values of a
number of free parameters and selecting only the configurations that were closest to the observations; however, the
number of combinations of different physical parameterization schemes was limited to a total of eight different configurations.
The WRF model offers several parameterization schemes
for most physical processes, and is thus suitable for a multiphysics approach. In fact, a WRF multi-physics approach has
been used in several studies (e.g., García-Díez et al., 2011;
Evans et al., 2012; Awan et al., 2011; Mooney et al., 2013),
also with its predecessor MM5, but not specifically to simulate extreme heat waves.
Here we run an ensemble of 216 configurations of WRF
physical parameterizations and compare each simulation
with a set of observations of relevant variables in order to
select a reduced set of five configurations that best represent
European summer mega heat waves. The evaluation is made
over the extreme 2003 and 2010 events. The ensemble is also
evaluated for a more regular summer (2007) in order to test
the model configurations under non-heat wave conditions.
2
2.1
Methods
Simulations and general model setup
We use the WRF version 3.3.1 and simulate the 3 summers
(2003, 2007, 2010) using an ensemble of physics scheme
combinations. We first test the time necessary to initialize the
soil moisture on a limited number of cases. Soil conditions
www.geosci-model-dev.net/8/1/2015/
A. I. Stegehuis et al.: Multi-physics WRF ensemble for simulating European mega heat waves
3
Table 1. Physics schemes used in this study (with references). All possible permutations are made, yielding a total of 216 simulations. The
numbers in the table refer to the number the schemes have in the Weather Research and Forecasting (WRF) model.
Microphysics (MP)
PBL + surface (PBLSF)
Radiation (RA)
Convection (CU)
Soil
(6) WRF-SM6 (Hong
and Lim, 2006)
(1-1) Yonsei UniMM5 (Hong et al.,
2006; Beljaars, 1994)
(3) CAM (Collins et
al., 2004)
(1)
Kain-Fritsch
(Kain, 2004)
(2) NOAH (Tewari et
al., 2004)
(8) New Thompson
(Thompson et al.,
2008)
(2-2) MYJ-ETA (Janjic, 1994, 2002)
(4) RRTMG (Iacono
et al., 2008)
(3)
Grell–Devenyi
(Grell and Devenyi,
2002)
(10) Morrison DM
(Morrison et al.,
2009)
(4-4) QNSE-QNSE
(Sukoriansky et al.,
2005)
(5) Goddard (Chou
and Suarez, 1999)
(6) Tiedtke (Tiedtke,
1989; Zhang et al.,
2011)
(14) New SAS (Han
and Pan, 2011)
(5-2)
MYNN-ETA
(Nakanishi and Niino,
2006, 2009; Janjic,
2002)
(5-5) MYNN-MYNN
(Nakanishi and Niino,
2006, 2009)
(7-1)
ACM2-MM5
(Pleim, 2007; Beljaars, 1994)
are initialized using the ERA-Interim (Dee et al., 2011) soil
moisture and temperatures; thereafter, soil moisture and air
temperature are calculated as prognostic variables by WRF.
For the August 2003 case, we find that temperatures differ
by less than 0.5 ◦ C among one another when starting experiments before 1 May. Thus, in the current study, each simulation is run from the beginning of May to the end of August for the years 2003, 2007 and 2010. The regional domain
considered is the EURO-CORDEX domain (European Coordinated Downscaling Experiment; Jacob et al., 2014; Vautard
et al., 2013) and the low-resolution setup of 50 km × 50 km
(∼ 0.44◦ on a rotated lat–long grid) is used – note that Vautard et al. (2013) recently concluded that a higher spatial
resolution did not provide a substantial improvement in heat
wave simulations. We use a vertical resolution with 32 levels for WRF. Boundary conditions come from ERA-Interim,
including sea surface temperatures, initial snow cover, and
soil moisture and temperature. In order to focus on physical processes in the boundary layer and the soil–atmosphere
interface, and to avoid chaotic evolution of large-scale atmospheric circulation, we constrain the model wind fields
with ERA-Interim re-analyses above model level #15 (about
3000 m), similar to previous studies (Vautard et al., 2014), using grid nudging, with a relaxation coefficient of 5.10−5 s−1
corresponding to a relaxation time approximately equivalent
to the input frequency (every 6 h) (Omrani et al., 2013). Tem-
www.geosci-model-dev.net/8/1/2015/
perature and water vapor were not constrained, to allow feedbacks to fully develop.
2.2
Physics schemes
We test 216 combinations of physics schemes. We consider different physics of the planetary boundary layer and
surface layer (PBL; six schemes), microphysics (MP; three
schemes), radiation (RA; three schemes) and of convection
(CU; four schemes). For each type of scheme, a few options
were selected among the ensemble of possibilities offered in
WRF. The selection was made to avoid variants of the same
scheme and to maximize the difference of temperature and
precipitation outputs in preliminary experiments. At the time
of study and model development stage, different land-surface
schemes were available in WRF: five-layer thermal diffusion
scheme (Dudhia, 1996), NOAH (Tewari et al., 2004), Rapid
Update Cycle (RUC) (Benjamin et al., 2004) and Pleim–Xiu
(Gilliam and Pleim, 2010). We decided however to only use
one, the NOAH land-surface scheme in order to focus our
study on atmospheric processes, while limiting the number
of simulations, and because the NOAH scheme is the most
widely used in WRF applications. This was also motivated
by the poor performance and extreme sensitivity of the RUC
land-surface scheme for the land latent and sensible heat
fluxes as compared with local observations in 2003. It simulates strong latent heat fluxes in the beginning of the season
Geosci. Model Dev., 8, 1–14, 2015
4
A. I. Stegehuis et al.: Multi-physics WRF ensemble for simulating European mega heat waves
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Figure 1. Time series of daily land heat fluxes in 2003 from May to the end of August on three different FLUXNET sites, with latent heat
flux (LH) on the first row, sensible heat flux (SH) on the second row, and evaporative fraction (EF – latent heat flux divided by the sum of
latent and sensible heat flux) on the last row (DOY is day of year). The three columns represent three sites, with Neustift/Stubai (Austria –
ATneu 47◦ N, 11◦ E) in the first column, Tharandt (Germany – DETha, 51◦ N, 4◦ E) in the second, and Soroe-LilleBogeskov (Denmark –
DKsor, 66◦ N, 11◦ E) in the third column. Vegetation types on the three sites are respectively grassland (GRA), evergreen needleleaf forest
(ENF), and deciduous broadleaf forest (DBF). In gray all 216 simulations with the NOAH scheme. Observational data is shown in black
(FLUXNET). The green line is one configuration with NOAH, while the blue line represents the same configuration but with RUC instead
of NOAH.
and an extreme drying at the end, while sensible heat flux is
overestimated. The NOAH scheme appeared more realistic
and robust in the tests that were done for capturing both latent
and sensible heat fluxes during the 2003 heat wave at selected
flux tower sites in western Europe (Fig. 1). Furthermore, the
Pleim–Xiu scheme is especially recommended for retrospective air quality simulations and is developed with a specific
surface layer scheme as coupled configuration (Gilliam and
Pleim, 2010). The last possible option is the five-layer thermal diffusion scheme (Dudhia, 1996) which predicts ground
and soil temperatures but no soil moisture and is therefore
also not suitable for our study. Table 1 describes the physical
Geosci. Model Dev., 8, 1–14, 2015
schemes that were combined to simulate the weather over the
3 summer seasons.
2.3 Observational data
In order to evaluate the ensemble and to rank and select its
best-performing simulations, we use gridded observed daily
temperature and precipitation from E-OBS with a 0.25◦ resolution (version 7.0) (Haylock et al., 2008). Bilinear interpolation is used to regrid E-OBS data and the model output to
the same grid. Furthermore, we use station data of monthly
global radiation from the Global Energy Balance Archive
(GEBA) network (Wild et al., 2009). For France 2003 the
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Figure 2. Domains used in this study: France, Iberian Peninsula,
Russia and Scandinavia.
data of 21 stations were available, for 2007 this number was
20. Observations over Russia were too scarce and have therefore not been considered. Model data are interpolated to these
stations using the nearest neighbor method. In addition, in
order to check land–atmosphere fluxes and the partitioning
of net radiation into sensible and latent heat fluxes, we use
the satellite observation-driven estimates of daily latent heat
fluxes from GLEAM (Miralles et al., 2011). Since the latter is not a direct measurement we do not use them to rank
the model configurations. Furthermore, latent and sensible
heat flux measurements are used from three FLUXNET sites
(Neustift/Stubai – Austria (Wohlfahrt et al., 2010); TharandtAnchor station – Germany (Grünwald and Bernhofer, 2007);
and Soroe-LilleBogeskov – Denmark (Pilegaard et al., 2011),
from the Carbon-Extreme database), for the evaluation of the
land-surface schemes.
2.4
Evaluation and ranking of model simulations
For ranking, we set up several measures of model skill based
on the differences between observed and simulated spatial averages over two domains: France for 2003 and 2007
(5◦ W–5◦ E, 44–50◦ N), and Russia for 2007 and 2010 (25–
60◦ E and 50–60◦ N) (Fig. 2). A first scheme selection is
made based on the skill to reproduce air temperature dynamics, since this is the primary impacted variable, while corresponding observations are reliable. Because we are interested in heat waves, we select only those simulations that are
within a 1 K regional average difference between simulated
and observed temperature, for heat wave periods; these pewww.geosci-model-dev.net/8/1/2015/
5
riods are defined as 1–15 August for France (in 2003), and
1 July till 15 August for Russia (in 2010). The 1 K threshold was arbitrarily chosen and is used to avoid processing
a large number of simulations that have unrealistic temperatures. Only 55 of the 216 simulations meet this criterion
and are further considered. Then, the ranking of the retained
simulations is done based on (i) the daily temperature difference between simulations and observations during the heat
wave periods (as above for 2003 and 2010), and during the
period 1–31 August for the normal year 2007; and (ii) the
root mean square error of monthly precipitation and radiation for the months July, June and August. The GEBA data
set only contains scarce radiation observations over Russia
and therefore we could not consider this region for ranking models against incoming shortwave radiation. As a final step, an overall ranking is proposed by averaging the
ranks obtained from the three variables (temperature, precipitation and radiation). From this final ranking, and in order
to select an elite of multi-physics combinations, we selected
the top-five highest-ranked configurations. Note that observational uncertainty is not considered in this study, which
was shown to potentially impact model ranking over Spain
(Gomez-Navarro et al., 2012).
3 Results
3.1 Large systematic errors found during heat wave
periods
Figure 3 shows the large temperature range spanned by the
216 ensemble members for the spatial average over the heat
wave areas. The min–max range between ensemble members
is up to 5 ◦ C during heat wave periods (Fig. 3). Locally, at
50 km resolution, the difference between the warmest and
the coldest simulation during a heat wave is larger, reaching more than 10 ◦ C in 2003 (Fig. 3d). In 2007, when summer temperatures were not extreme, the range is about twice
as small. Only a few simulations match the observed high
temperatures (Fig. 3a–c). In Fig. 3a, we select two extreme
configurations (blue and red lines) based on daily mean temperature over France during the 2003 heat wave. Interestingly, they are extreme in all regions and years, indicating
that each configuration tends to induce a rather large systematic bias. This bias, however, is different for the “warm” and
the “cold” configurations. It seems not to be due to a misrepresentation of the diurnal cycle, since they remain when
analyzing time series of maximum and minimum daily temperatures independently (see Fig. S1a–f in the Supplement).
However, minimum temperatures show a less consistent bias
than maximum daily temperatures. A systematic temperature
underestimation by WRF simulations over Europe has also
been found in other multi-physics ensemble studies over Europe (e.g., Awan et al., 2011; García-Díez et al., 2011, 2015).
Geosci. Model Dev., 8, 1–14, 2015
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Figure 3. Time series of daily mean temperature over France in 2003 (a) and 2007 (b) and Russia in 2010 (c). Every simulation is shown
in gray and observations of E-OBS in black. The blue and red lines are the coldest and the warmest simulations over France during the heat
wave. These lines have the same set of physics in all the figures (3, 4, 5). Panel (d) shows the simulated temperature min–max range during
the heat wave of 2003 (1–15 August). The range is calculated as the difference between the warmest simulation and the coldest simulation
during the heat wave period between the 216 members of the ensemble.
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Figure 4. Monthly precipitation over France in 2003 (a) and 2007 (b) and Russia 2010 (c). The box plots show the extremes, 25th, 50th, and
75th percentiles. The blue and red dots are the coldest and the warmest simulations over France during the heat wave (as in Fig. 3).
For monthly precipitation we obtain a large range of
simulated values, with most configurations overestimating
monthly summer rainfall (JJA) during heat wave years, and
to a lesser extent during the wetter 2007 season (Fig. 4a–c).
This is in line with the findings reported by Warrach-Sagi
Geosci. Model Dev., 8, 1–14, 2015
et al. (2013) and Awan et al. (2011) and with the overestimation of precipitation by many EURO-CORDEX models
shown by Kotlarski et al. (2014). The two selected extreme
configurations (based on temperature, as explained above)
are reproducing precipitation overall without a major bias.
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A. I. Stegehuis et al.: Multi-physics WRF ensemble for simulating European mega heat waves
FRANCE 2003
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Figure 5. Scatter plot of soil moisture content on 31 July, and temperature in August. Every point is one simulation. Different colors and
symbols represent different physics for convection (CU) (a), microphysics (MP) (b), radiation (RA) (c) and planetary boundary layer–surface
(PBL-SF) (d).
This suggests that the temperature bias in these two extreme
simulations is not explicitly caused by a misrepresentation of
the atmospheric water supply from precipitation. However,
soil moisture (the soil moisture over the whole column) does
show a strong relation to temperature biases in model simulations. Figure 5a–d shows soil moisture at the end of July
versus temperature in August 2003 for each model configuration. Configurations with low soil moisture level are associated with higher temperatures and vice versa, confirming
the role of land–atmosphere feedbacks during heat waves,
already pointed out by previous studies. This indicates that
the evapotranspiration from spring to summer depleting soil
moisture can be a critical process during summer for the development of heat waves, and that this process is not simply
related to summer precipitation.
For solar radiation, the mean differences between our simulations over France 2003 and 2007 reach approximately
100 W m−2 (Fig. 6a, b). Observations for France (black dots)
are found below the median value of the simulations, so a
slight overestimation of the ensemble is obtained. The first
www.geosci-model-dev.net/8/1/2015/
(warmest) extreme configuration (red dot) is associated with
an overestimated radiation of 10–50 W m−2 while the other
(coldest, blue dot) extreme configuration exhibits an underestimated radiation by about the same amount. Since the
warmest simulation agrees better with temperature observations than the coldest simulation, one may therefore suspect
that it contains a cooling mechanism that partly compensates
for the overestimated solar radiation.
3.2
Sensitivity of temperatures to physical
parameterizations and sources of spread
In order to identify the physics schemes to which the development of heat waves is most sensitive, we examine how resulting temperatures are clustered as a function of the scheme
used. We find that the spread between all simulations – both
in terms of temperature and soil moisture – is mostly due
to the differences in convection scheme (clustering of dots
with the same color in Fig. 5a). For instance the Tiedtke
scheme (blue dots) systematically leads to higher temperatures and lower soil moisture, while the Kain–Fritsch scheme
Geosci. Model Dev., 8, 1–14, 2015
8
A. I. Stegehuis et al.: Multi-physics WRF ensemble for simulating European mega heat waves
FRANCE 2003
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Figure 6. Monthly radiation over France in 2003 (a) and 2007 (b); no radiation data being available in Russia for 2010. The box plots show
the extremes, 25th, 50th, and 75th percentiles. The blue and red dots are the coldest and the warmest simulations over France during the heat
wave (as in Fig. 3).
Table 2. The five best-performing configurations of physics in ranked from the best to the fifth best.
Microphysics
PBL-surface
Radiation
Convection
Soil
Rank
Morrison DM
WRF-SM6
WRF-SM6
New Thompson
New Thompson
Yonsei Uni-MM5
MYNN-MYNN
ACM2-MM5
MYNN-MYNN
ACM2-MM5
RRTMG
RRTMG
Goddard
RRTMG
RRTMG
Tiedtke
Grell–Devenyi
Tiedtke
New SAS
Tiedtke
NOAH
NOAH
NOAH
NOAH
NOAH
1
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4
5
(green dots) leads to wetter soils and lower temperatures, inhibiting heat waves. Microphysics and radiation schemes are
also contributing to the spread of simulated temperature and
soil moisture values (Fig. 5b, c), although their effect is less
marked than for convection. Heat wave temperatures and soil
moisture seem to be least sensitive to the planetary boundary
layer and surface layer physics schemes. The sensitivity of
the convection scheme in WRF has already been mentioned
in previous studies (Jankov et al., 2005; Awan et al., 2011;
Vautard et al., 2013; García-Díez et al., 2015). Note that the
soil moisture simulated in early August 2003 is better correlated with preceding radiation than with precipitation (compare Figs. S2 and S3), indicating that the way clouds, and
particularly convective clouds, affect radiation prior to the
onset of heat waves is a major driver of the spread for the development of heat waves – higher radiation leading to drier
soils and higher temperatures during heat waves.
3.3
A constrained reduced ensemble of best simulations
Focusing only on the 55 selected simulations that differ less
than 1 ◦ C from the observations during the heat waves, we
apply the ranking method introduced in Sect. 2 based on
temperature, precipitation and radiation model–observation
comparison metrics. The five highest-ranked simulations are
given in Table 2 and are actually the numbers 1–5 in Table S1
Geosci. Model Dev., 8, 1–14, 2015
in the Supplement. Figure 7a confirms the ranking by showing that these simulations also perform well in terms of temperature, during the months prior to the heat wave. The same
is furthermore found for the years 2007 in France (Fig. S5)
and 2010 in Russia (Fig. S4), and also for other regions such
as the Iberian Peninsula and Scandinavia (Fig. S6a, d). The
selected simulations however performed less well for precipitation over France in 2003 (Fig. 7b), but do not show a large
overestimation of precipitation either. Precipitation over Russia for the five highest-ranked simulations does show good
performance (Fig. S4b), as well as for other European regions (Fig. S6). The mean radiation of the ensemble of the
five best simulations is closer to the GEBA observations than
in the case of the original ensemble (Fig. 7c).
Nonetheless, the better match of the reduced ensemble of
the five highest-ranked simulations to the observations of
temperature, precipitation and radiation is to a very large degree unsurprising: the selection was based on the fit to observations. However, it is still satisfactory to see that some
simulations are capable of matching all three variables. Conversely, we also compare simulations against another key
variable that was not used for evaluating and ranking simulations, namely the latent heat flux (Fig. 7d). Albeit somehow
reduced compared to the full-ensemble spread, the spread
of the five best simulations for the latent heat flux remains
large over the whole period, on average between 50 and
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A. I. Stegehuis et al.: Multi-physics WRF ensemble for simulating European mega heat waves
9
Table 3. Cross-comparison between France 2003 and Russia 2010. The (5, 10, 15, 20 and 25) best simulations, when only using one heat
wave to select the best configurations and vice versa, are taken and compared with their ranking for the other heat wave. If there would be no
correlation between the 2 years, the average ranking would lay approximately at half of the total number of simulations for both years that
lay within a first selection of 1 K (column eight). In bold the rankings that are lower than this number. Because observations of radiation are
lacking over Russia, we tested France with and without including radiation in the ranking.
With radiation
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Without radiation
average rank Fr–Ru
average rank Ru–Fr
average rank Fr–Ru
average rank Ru–Fr
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radiation (d). Observations are shown in black and the five best-performing runs in colors. Gray lines indicate other simulations. All figures
are a spatial average over France during summer 2003.
120 W m−2 (observed values are around 75 W m−2 ). However, during the 2003 heat wave over France, three of the
five best simulations exhibit a close resemblance to the latent
heat observations (approximately 5–10 W m−2 ) (Fig. 7d).
The two simulations that are found to considerably overestimate latent heat flux by approximately 30–40 W m−2 (as
www.geosci-model-dev.net/8/1/2015/
compared to GLEAM) are those that use a different convection scheme than the Tiedtke scheme. The overestimation of
latent heat fluxes in these schemes is however not generalized for other regions and years (Figs. S4c, S5d, S6c, f–h),
for which the latent heat flux was fairly well simulated within
the range of uncertainty of GLEAM.
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A. I. Stegehuis et al.: Multi-physics WRF ensemble for simulating European mega heat waves
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Figure 8. Sensitivity test of the initialization of soil moisture. Difference between the perturbed simulations (red indicates 20 % reduction
of initial soil moisture, blue 20 % enhancement) performed with the five highest-ranked configurations compared to their corresponding
“control” simulations. The darkest lines refer to the simulation conducted with the best-ranked configuration (1), while descending color
shade agrees with descending ranking (1–5).
A cross-comparison for the years 2003 and 2010, that is,
using only the 2010 heat wave to select schemes and verify
the performance of the selected schemes over 2003 and vice
versa, yields some promising results. Table 3 shows the average ranking of the best (5, 10, 15, 20 and 25) simulations.
When only using one heat wave to select the best configurations, they all lie in the top-ranked half, and even higher in
the ranking in the case of the 2010 heat wave over Russia being used to select the best configurations. This suggests that
the selection based upon one heat wave in one region should
also provide better simulations for other heat waves or heat
waves in other areas, i.e., that the bias of a member of the
WRF ensemble is not local but at least regional at the scale
of western Europe.
Geosci. Model Dev., 8, 1–14, 2015
4
Concluding remarks
In this study we designed and analyzed a large multi-physics
ensemble with the WRF model. It is made of all possible
combinations of a set of different atmospheric physics parameterization schemes. They were evaluated for their ability
to simulate the European heat waves of 2003 and 2010 using the regional climate model WRF based on temperature,
precipitation and shortwave radiation. Even though the simulations were constrained by grid nudging, the multi-physics
ensemble contained a large spread in temperature, precipitation and incoming shortwave radiation, the three variables we
used to create an overall configuration ranking. Most simulations systematically underestimate temperature and overestimate precipitation during heat waves, a model pattern that
was already found in previous studies dealing with much
smaller ensembles (e.g., Awan et al., 2011; García-Díez et
al., 2011; Warrach-Sagi et al., 2013). The spread among ensemble members is amplified during the two extreme heat
www.geosci-model-dev.net/8/1/2015/
A. I. Stegehuis et al.: Multi-physics WRF ensemble for simulating European mega heat waves
waves of study. Since we only considered a single landsurface scheme, it is possible that the ensemble spread would
considerably increase when incorporating the uncertainty associated with modeling land-surface processes. Nevertheless,
considering only atmospheric processes, the magnitude of
the spread still reaches 5 ◦ C during the peak of the heat
waves.
We also showed that among atmospheric process parameterizations, the choice of a convection scheme appears to
dominate the ensemble spread. We found indications that the
large differences between convection schemes seem to occur
mostly through radiation and therefore the way convective
clouds affect the surface energy and water budget prior to
and during heat waves. Changes in incoming radiation cause
changes in evapotranspiration and therefore soil moisture,
which may subsequently feed back on air temperature.
From this ensemble, we selected a small sub-ensemble
with the five best configurations of atmospheric physics
schemes based on the fit to observations. These configurations capture well the temperature dynamics during the mega
heat waves of France and Russia and they perform better than
other configurations in other regions of Europe. In addition,
they are consistent with independent latent heat flux data
used for cross-validation. This indicates that the constraints
set for the selection reduce the uncertainty across the whole
European continent and point towards the creation of an optimized ensemble of WRF configurations specific for heat
waves, with reduced error compensations. A sub-ensemble
that outperforms a larger ensemble was also found by Herrera
et al. (2010). The sub-ensemble based on mean precipitation
showed better results for extreme precipitation as well.
However, a limitation of this study is the use of only
one land-surface scheme; the five selected WRF configurations may actually all be affected by systematic errors of
the NOAH land-surface scheme. The importance of the selected land-surface scheme is further confirmed by the larger
spread of the “best” ensemble for latent heat (in W m−2 ) than
for shortwave radiation. In order to mimic radically different
land-surface processes, sensitivity tests in which the initial
absolute amount of soil moisture was artificially increased
and decreased by 20 % all along the soil column have been
conducted. Results confirm the sensitivity of the temperature simulations to soil moisture, a variable partly controlled
by the land-surface scheme (Fig. 8). The full answer to this
question is left for a future study in which different atmospheric schemes and surface schemes will be jointly permuted.
Although our ensemble is trained on only summer conditions, our results have several implications for climate modeling. First, the constrained WRF ensemble may be used in
future studies of climate change; each of the five members
may exhibit a different sensitivity to future climate change
conditions, leading to a constrained exploration of the uncertainty. Then it is important to notice that our study pinpoints
the need to carefully design or adjust the convection scheme
www.geosci-model-dev.net/8/1/2015/
11
for a proper representation of the summer climate during
heat waves. This is particularly important in order to evaluate the impacts of climate change on ecosystems, health,
carbon cycle, water and cooling capacity of thermal energy
plants, since heat waves in the mid latitudes are expected to
be one of the most impacting phenomena in a human-altered
climate. Therefore, impact studies can be designed based on
the selected configurations.
The Supplement related to this article is available online
at doi:10.5194/gmd-8-1-2015-supplement.
Acknowledgements. A. I. Stegehuis acknowledges CEA for
funding as well as the GHG-Europe FP7 project. A. J. Teuling
acknowledges financial support from the Netherlands Organisation
for Scientific Research through Veni grant 016.111.002. P. Ciais
acknowledges support of the ERC-SYG project P-IMBALANCE.
The authors acknowledge K. Pilegaard, A. Ibrom, C. Bernhofer,
G. Wohlfahrt and CarboEurope for sharing FLUXNET data. We
would like to thank the reviewers for their useful comments and
suggestions for improving the manuscript.
Edited by: A. Colette
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www.geosci-model-dev.net/8/1/2015/
Supplement of Geosci. Model Dev., 8, 2285–2298, 2015
http://www.geosci-model-dev.net/8/2285/2015/
doi:10.5194/gmd-8-2285-2015-supplement
© Author(s) 2015. CC Attribution 3.0 License.
Supplement of
An observation-constrained multi-physics WRF ensemble for simulating
European mega heat waves
A. I. Stegehuis et al.
Correspondence to: A. I. Stegehuis ([email protected])
The copyright of individual parts of the supplement might differ from the CC-BY 3.0 licence.
1
Supplementary material
2
Table S1: Final ranks.
Final rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
3
4
Physic combinations
MP
PBL
10
1
6
5
6
7
8
5
8
7
8
7
10
5
6
1
10
2
10
5
8
4
8
5
6
7
8
5
6
7
6
4
6
7
6
5
8
5
8
1
8
2
6
2
8
5
6
5
8
2
8
5
6
5
8
1
8
5
6
5
8
7
8
1
6
2
6
7
6
5
6
2
8
2
10
5
6
1
10
5
6
1
10
1
6
7
6
5
8
7
8
2
6
5
8
5
8
5
8
6
8
5
6
5
6
1
8
1
8
2
SF
1
5
1
5
1
1
2
1
2
5
4
5
1
2
1
4
1
5
5
1
2
2
2
2
2
2
5
1
5
2
1
1
2
1
5
2
2
5
1
2
1
1
1
5
1
2
5
2
5
6
2
2
1
1
2
RA
4
4
5
4
4
5
4
3
4
4
4
4
4
4
3
4
4
4
5
5
4
5
5
4
5
3
5
5
3
3
4
4
3
4
3
4
3
4
5
4
4
4
4
5
4
4
4
4
4
4
4
5
4
4
5
CU
6
3
6
14
6
6
6
6
6
6
6
3
6
14
6
6
3
14
6
6
14
6
6
14
6
6
6
3
6
6
14
14
6
14
6
14
6
1
14
1
14
1
1
1
1
1
1
1
1
1
1
1
1
1
1
SU
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
5
Figure S1a-d: Timeseries over France 2003 (a,b) and 2007 (c,d) and Russia (e,f) with maximum (a,c,e)
6
and minimum (b,d,f) daily temperatures. Every simulation is shown in gray and observations of E-OBS
7
in black. The blue and red lines are the coldest and the warmest simulations over France during the
8
heatwave. These lines have the same set of physics in all the figures.
9
10
11
12
Figure S2a-d: Scatter plot with soil moisture content at July 31st and precipitation in the preceding
13
months of June-July. Every point is one simulation. Different colors and symbols represent different
14
physics for convection (CU) (a), microphysics (MP) (b), radiation (RA) (c) and planet boundary layer-
15
surface (PBL-SF) (d).
16
17
18
19
Figure S3a-d: Scatter plot with soil moisture content at the end of July and shortwave radiation during
20
the preceding months of June-July. Every point is one simulation. Different colors and symbols
21
represent different physics for convection (a), microphysics (b), radiation (c) and planet boundary
22
layer-surface (d).
23
24
25
26
27
28
Figure S4a-c: Timeseries of temperature (a), precipitation (b) and latent heatflux (c) over Russia 2010.
29
Figure S5a-d: Timeseries of temperature (a), precipitation (b), radiation (c) and latent heatflux (d) over
30
France 2007.
31
32
33
34
35
Figure S6a-h: Timeseries of temperature (a,d), precipitation (b,e) and latent heatflux (c,f,g,h) over the
36
Iberian Peninsula 2003 (a,b,c), Scandinavia 2003 (d,e,f), the Iberian Peninsula 2010 (g) and
37
Scandinavia 2010 (h).
38
39
40
41
42
68
4
CHAPTER III. ATMOSPHERIC PROCESSES
Discussion and additional results
Convection was found to play a very important role for summer temperatures, precipitation and soil moisture, as was shown in the previous section. The specific role of the
convection however, needs further analyses. In this discussion I try to investigate how the
temperature and other variables are influenced by the different convection physics.
4.1
The importance of the convection scheme
The large spread due to the convection schemes (Fig. 5, S2 and S3 of the paper) raises
the question whether the choice of this scheme in the beginning of the summer was still of
influence for August temperatures. Low soil moisture level in the beginning of the summer
favors warm summer conditions and heatwaves (Vautard et al., 2007; Quesada et al., 2012).
If a ’dry’ convection scheme would initialize dry soils, it could possibly determine the values
of late summer temperatures through positive soil moisture-temperature feedbacks. In a
’wet’ convection scheme however, the wetter soils may not lead to positive soil moisturetemperature feedbacks, leading to cooler August temperatures. Here we investigate whether
initial dry conditions can be turned into wetter conditions by changing the convection physics.
This may elucidate the relative importance of land-atmosphere feedbacks and convection for
August temperatures.
In a sensitivity experiment on the 2003 heatwave, four control simulations were run from
May to August with different convection physics. All other physics schemes were kept constant. The four convection schemes were those used in the previous section (Kain-Fritsch,
Grell-Devenyi, Tiedtke and New-SAS). The control simulation was restarted at the end of
May, 15 and 30 June, and 15 and 30 July. At these dates the simulation was restarted with
the other three convection schemes, resulting in three new simulations at every restart. The
August temperatures were then evaluated. Changing the convection physics during a simulation may lead to inconsistencies in the simulation due to the interaction with the other physic
schemes. However, we did not find any abnormal behavior in analyzing the model outputs.
The Grell-Devenyi scheme was found to be the wettest scheme overall, with high soil moisture values and high precipitation. Tiedtke was the driest scheme, with lower values for both
soil moisture and precipitation (Fig. III.2 and figures 5, S2 and S3 of the paper). In Fig. III.2
all colored dots represent a simulation and the different colors represent different convection
physics. All grey dots represent a simulation from the sensitivity test. Their different sizes
represent different restart dates; the later the restart date, the bigger the dots. Their different
shades represents the different restart convection physics. The orange circles represent the 4
control simulations. Interestingly, the range of the four different convection physics (vertical
range of the grey dots) is almost similar. This suggests that the convection mostly determines
the temperatures at the end of the summer, and that land atmosphere feedbacks seem to be
less important in some convection schemes. Because, even when starting with very dry soils
in May, temperatures can decrease substantially, probably due to enhanced precipitation in
certain convection physics.
4. DISCUSSION AND ADDITIONAL RESULTS
69
Figure III.2: Scatter plot of soil moisture content in May, and temperature in August over
France 2003. Every point is one simulation. Different colors and symbols represent different
physics for convection. The orange circle is the original simulation. Different shades of grey
represent the convection scheme for the restart and different size shows the time of the restart
date, the later the restart, the larger the circle.
●
●●
0
20
40
60
80
Relative humidity 500 hPa (%)
0.6
0.4
0.2
Precipitation (mm)
●●
●●
●●
●●
●●
●
●
●●
●●
●●
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●
●
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●
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●
●
●●
●●
●●
●●
●●
●
●
●●
●●
●●
●●
●●
0.8
●
Kain−Fritsch
●●
●●
Grell−Devenyi
●●
●●
●
Tiedtke
●●
●● SAS
New
●●
0.0
0.015
0.010
Density
0.005
0.000
1.0
CHAPTER III. ATMOSPHERIC PROCESSES
0.020
70
100
●
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●●
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●
●
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●
●●
●●
●●
●●
0
20
40
60
80
100
Relative humidity 500 hPa (%)
Figure III.3: PDF of the relative humidity at 500 hPa (left) and precipitation as a function of
relative humidity at 500 hPa (right). The different colors correspond to different convection
schemes.
4.2
What triggers convection?
In both section 3.3 and in the sensitivity experiment the importance of the convection
scheme on temperature and humidity was emphasized. However, the question of how the
convection physics modify the temperature is still unanswered. It is not possible to solve
this issue completely without the precise knowledge of the physics in the convection schemes
and the interactions with all other physics. Here I will give some brief ideas and show some
preliminary results.
While the source of the exact differences between the schemes is uncertain and are probably multiple, some simple tests may show some possible mechanisms between the different
physics. In Fig. III.2 it was shown that even starting with a dry soil, cold August temperatures
could be obtained by changing the convection scheme. This implies that the physics have a
different sensibility regarding humidity. One can imagine for example, that if one scheme
rains more with a certain relative humidity, it may get wetter overall through feedbacks with
enhanced precipitation and soil moisture.
Fig. III.3 shows a probability density function (PDF) of the relative humidity at 500 hPa
for the 4 different convection schemes. Although the schemes are quite similar, Grell-Devenyi
exhibits a higher density between 5% and 30% relative humidity, while it shows lower values
between 50% and 70%, compared to the other three convection schemes. This fact alone
does not necessarily proves a general higher wetness of this scheme. The right panel shows
the amount of precipitation at a certain relative humidity. With values between 10% and
30%, both Kain-Fritsch and Grell-Devenyi have relatively high precipitation compared with
the other two schemes. This coincides with the higher occurrence of relative humidity in
the PDF of Grell-Devenyi. It could explain why Grell-Devenyi is wetter overall, and why
even when starting with dry initial soils, it can get much colder and wetter than with other
convection physics.
5. SUMMARY AND CONCLUSIONS
71
In a model three main questions should be answered regarding convection: 1) Will there
be convection? 2) How much convection will there be? and 3) What are the consequences of
convection; will there be cloud formation? (Hohenhegger, personal communication).
The first question depends on the trigger function which identifies the potential updraft source layers for convective clouds and determines when and where convection occurs
(Charabi, 2010; Warner et al., 2010). An often used method is the parcel lifting. A parcel with
a certain temperature is lifted until it is warmer than its environment. In this case convection
is triggered. If the parcels’ temperature remains less than the temperature of the environment, convection will not take place. Kain and Fritsch (1992) found considerable differences
between simulations where only the trigger function was changed.
The amount of convection depends on the closure scheme. Three closure schemes are
moisture convergence, convective available potential energy (CAPE) closure and boundary
layer closure. Moisture convergence is a relatively old method and is used in Grell-Devenyi
(Grell and Freitas, 2014) and in the first version of the Tiedtke scheme (Tiedtke, 1989). The
latter has been adapted with a CAPE closure (Zhang et al., 2011), a scheme that is now
widely used. This method is based on a quasi-equilibrium between convection and large
scale processes. It is also used in Kain-Fritsch (Kain, 2004), the New SAS scheme (Han and
Pan, 2011) and Grell-Devenyi (Grell and Freitas, 2014; Pohl et al., 2011). Boundary layer
closure is relatively new and not used in any of the tested convection schemes (Hohenhegger,
personal communication).
The amount of clouds is determined by the cloud model that simulates the effect of single
clouds and cloud ensembles. Arakawa (1974) developed a spectral cloud model that permitted different clouds in each grid cell of a model. However, because of its complexity simpler
cloud models exist as well. In these only one single cloud model is applied to each grid cell.
This is for example used in the original Tiedtke scheme (Tiedtke, 1989). For the entrainment
and detrainment rate different formulation exist that can have a large impact on precipitation
rates. A comparison between these formulations however, is out of the scope of this study.
5
Summary and conclusions
In this chapter a large multi-physics ensemble in WRF was designed and tested on its
ability to simulate the heatwaves of 2003 and 2010 and the relatively wet summer of 2007 to
1) identify the sensitivity to different atmospheric physics and 2) to create a well-performing
reduced ensemble for future studies especially focusing on summer climate and heatwaves.
The 216 different configurations were evaluated against observations of temperature, precipitation and incoming radiation. Despite wind nudging above the boundary layer, a large
temperature spread was found at the end of the summer between different model configurations. Almost all simulations underestimated temperature, while precipitation and shortwave
radiation were mostly overestimated. This is important as future heatwaves might be of even
larger magnitude than the ones of 2003 and 2010 that were evaluated here. A good model
structure with appropriate physics must therefore be chosen to be able to reach such extreme
temperatures or droughts.
Convection was found to be the most sensitive process leading to most model spread.
There seem to be relatively ’dry’ schemes, such as the Tiedtke scheme, that result in warmer
72
CHAPTER III. ATMOSPHERIC PROCESSES
temperature and little precipitation. On the other hand, the relatively ’wet’ schemes, such
as Grell-Devenyi, induce higher soil moisture values, more precipitation and, probably more
cloudy skies, less shortwave radiation and therefore relatively low temperatures. The positive
feedback mechanism between dry soils, less clouds and higher temperatures might not always
be of importance. Dry soils in the summer can easily be reversed to wetter soils by changing
the convection scheme. This suggests that the triggering of precipitation might be decisive
for climate conditions at the end of the summer in some model simulations.
Although the land surface physics were not changed within the 216 simulations, its sensitivity was tested. It was shown that temperature could be in- or decreased till up to 3◦ C by
perturbing the initialization of soil moisture by 20%. At the time of the study however, only
one suitable land surface scheme was available. In the current version of WRF, more schemes
are available which might be more appropriate for future studies. Though soil moisture plays
an essential role in the partitioning of fluxes which are described in the land surface parameterization, the convection scheme may be able to kill positive soil moisture-temperature
feedbacks. Thus the scheme determining eventual positive land-atmosphere feedbacks is not
only the land surface scheme, but also the convection, radiation, microphyics and boundary
layer schemes.
Evaluated and constrained by several observation-based data products, we created a 5member multi-physics ensemble to perform future heatwave studies. This reduced ensemble contains 3 different microphysic schemes, 3 PBL-surface layer combinations, 2 radiation
schemes and 3 convection schemes. The high variety of these schemes is important, as different physics might take slightly different pathways to reach similar climatic conditions. For
assessing uncertainty in future climate simulations, very similar models would not be helpful,
because the full range of uncertainty would not be displayed. Also uncertainty assessment for
impact studies may benefit from more diverse simulations. Vegetation for example, can react
very differently to the different precipitation rates and soil humidity levels that are present in
our 5-member multi-physics ensemble.
C HAPTER
IV
The influence of soil moisture on
summer temperatures
73
74
1
CHAPTER IV. THE INFLUENCE OF SOIL MOISTURE ON SUMMER TEMPERATURES
Introduction
Summer temperatures in Europe are controlled by both atmospheric circulation and landatmosphere interactions. For elevated temperatures and heatwaves, the atmospheric circulation needs to favor the transport of warm air towards the continent and stabilize atmospheric
conditions. The soil must relate to a positive soil moisture-temperature feedback mechanism
as discussed in chapter I. Although previous studies have established the importance of these
two processes (Black et al., 2004; Miralles et al., 2014), they did not try to quantify their
individual contribution to warm summer temperature anomalies. In this study we chose to
investigate early summer soil moisture only, as soil moisture later in the summer season may
be much harder to separate from the circulation. The work in the chapter was initiated by
the internship of Martha Vogel, and I contributed to the supervision. She performed most of
the simulations and made a preliminary analysis of the results.
The predicted temperature increase over Europe at the end of this century is estimated between 2◦ C and 5◦ C, depending on the scenario and the region (van der Linden and Mitchell,
2009; Boberg and Christensen, 2012; Stegehuis et al., 2013a). This increase is projected to be
stronger in Southern Europe (up to 5◦ C), than in the northern part (up to 2◦ C) (Fig. 3 Stegehuis et al., 2013a). Furthermore, a larger temperature variability is expected, especially in
central Europe. This overall increase in temperature might lead to more soil drying, creating
favorable situations for positive soil moisture-temperature feedbacks. A more precise understanding, and the quantification of the role of soil moisture and its trends over the recent
years, might therefore be critical in understanding the variability in summer temperatures
and heatwave events in the future climate.
The atmospheric circulation patterns associated with heatwaves are atmospheric Blocking
and Atlantic low, as discussed in section I.1.2.3. The occurrence of the different circulation patterns have changed over the last century (Horton et al., 2015; Coumou et al., 2015;
Alvarez-Castro et al., 2016). Although the impact of these patterns have been studied (van
Haren et al., 2015), there is still considerable uncertainty about whether and how they may
change in the future climate (Cattiaux et al., 2012). Nonetheless, a better understanding of
their contribution to extreme heatwaves can be useful in predicting climate change.
In the previous chapter we have selected a limited number of model configurations that
were able to simulate the heatwaves of 2003 and 2010 well, as well as the relatively wet
summer year of 2007. Although we do not focus solely on heatwaves in this chapter, these
configurations will be used here to create an ensemble of summers based on different initializations of soil moisture and atmospheric variables. All these initial conditions are realistic,
as the chosen values are those from modeled summer years from 1980-2011.
We find that the contribution of early summer soil moisture in the heatwaves of 2003
and 2010 was important and contributed to approximately 1◦ C at maximum. The large scale
drivers however, explains the largest part of the temperature anomaly, with up to 3◦ C in 2003
and up to 6◦ C in 2010. An interesting result is that the contribution of initial soil moisture
has been increasing in importance over the last three decennia in central Europe and France
and in parts of Russia. These regions coincide with the regions with a significant negative
trend in soil moisture. The influence of large-scale drivers on the other hand, has increased
only over Eastern Europe.
2. METHODS
75
In the following section the methods are presented, followed by the results of ongoing
work. Some additional results of the evaluation of the used control simulation are presented
afterwards. The chapter is finished with a discussion and concluding remarks.
2
2.1
Methods
Model description
We used the regional climate model Weather Research and Forecast (WRF) (Skamarock
et al., 2008) over the EURO-CORDEX domain (Jacob et al., 2014) with a spatial resolution
of 50 km. For more specific details of the model setup we refer to (Stegehuis et al., 2015).
The physics that were used in this study correspond to some of those tested for heatwaves in
the same study. The main physics used was ranked number 7, with microphysics Morission
(Morrison et al., 2009), PBL-Surface layer MY-ETA (Nakanishi and Niino, 2006), RRTGM radiation citepIacono2008, the convection scheme Tiedtke (Tiedtke, 1989; Zhang et al., 2011)
and the NOAH land surface (Tewari et al., 2004). A more detailed description of the physic
schemes can be found in chapter III.
2.2
Control simulation
The physics configuration that was used in this study, after Stegehuis et al. (2015), was
only evaluated by them for summer conditions. For a multi-year simulation however, it is
preferable that the model also performs well during other seasons. A bias in winter or spring
might still be visible in summer, as for example soil moisture conditions have a long-term
memory. The correct performance over all seasons is not imperative, as different processes
are important in different times of the year. Snow melt for example, is a typical winter/spring
process, and might influence temperature and soil moisture even over spring and summer.
In this section I present an evaluation of the control simulation. For this study configuration 7 was used from the supplementary table 1 from Stegehuis et al. (2015). Although it
was not the best performing configuration over the summer, preliminary tests showed better
performance during the winter. Boundary conditions are from ERA-Interim (Dee et al., 2011).
The control simulation performs best over the summer, the season on which the configuration was selected (Fig. IV.7). While the minimum temperature is slightly too warm in
large parts of Europe (up to ∼0.5◦ C-1◦ C), the mean and maximum temperature are slightly
too cold and thus underestimating observed temperatures. Maximum temperatures have a
cold bias in all seasons except from the autumn. The largest cold bias was found in spring,
which was similar for the minimum and mean temperatures (Fig. IV.7). Whilst a temperature
bias in spring can sometimes be attributed to a misrepresentation in snowmelt, we do not
assume this is the case here, as the cold bias is almost similar over entire Europe, and not just
over the coldest regions. The warm biases in minimum and mean temperature in Northern
Scandinavia however, are most likely due to the incorrect simulation of snow.
2.3
Attribution of temperature anomalies
Two experiments were performed in order to analyze and quantify the effect of initial soil
moisture (ESSM) and large-scale drivers (LS) on summer temperature anomalies. In the first
experiment, initial soil moisture conditions of one year were replaced in the end of June,
76
CHAPTER IV. THE INFLUENCE OF SOIL MOISTURE ON SUMMER TEMPERATURES
Figure IV.1: Minimum, mean and maximum temperature difference between the control simulation and EOBS over the winter, spring, summer and autumn averaged over 1980-2011.
with the soil moisture conditions of the 31 other years, to attribute temperature anomalies
to anomalies in soil moisture. In the second experiment, we replaced all other variables of
each of the 31 years for the attribution to large-scale drivers, which include SST, large-scale
circulation and boundary conditions. WRF was then run over the two following months, till
the end of August. The temperature response to the soil moisture anomaly can therefore be
estimated as:
Ry’y = Ty – Ty’yESSM (1)
Where Ty is the temperature of the control run for year y, and Ty’yESSM the average temperature over the 32 ensemble simulations (the 31 perturbed years plus the control year). A
similar equation could be written to express the response of the temperature anomaly to LS
contribution:
Ryy’ = Ty – Tyy’LS (2)
Where Ty is the temperature of the control year and Tyy’LS the average temperature of the
32 simulations.
We have verified that the this decomposition is almost additive, i.e. that T’y (Ty – the
long-term climatology) and T”y (Ryy’ + Ry’y ) are close to one another, meaning that there is
no strong nonlinear interaction among ESSM and initial SM effects.
3. SUMMER WARMING INDUCED BY EARLY SUMMER SOIL MOISTURE CHANGES IN
EUROPE
3
3.1
77
Summer warming induced by early summer soil moisture
changes in Europe
Abstract
Heatwaves and warm summer temperatures are often associated with a deficit in soil
moisture. Heat can develop more easily during and after drought conditions, fostered by landatmosphere feedbacks (Seneviratne et al., 2010; Stéfanon et al., 2014). Summer drought
changes have also been shown to be a major driver of the fraction of heat attributable to
climate change (Orth and Seneviratne, 2014) as for instance in the case of the Russian heatwave. However, the role of soil moisture changes in recent warming trends has not been established so far. Here we show that early summer soil moisture (ESSM) deficit explains most
of the rapid recent warming trend in Western/central Europe [∼0.5◦ C.decade-1], where extreme heatwaves took place in 2003, 2006 and 2015. This result comes in contrast to the relatively weak relative contribution of ESSM to interannual variability of summer temperature,
reaching only at most 20% on average, while more than 80% is explained by atmospheric
circulation and other large-scale drivers. Such a process attribution was made possible by
using a regional climate model and a systematic array of sensitivity experiments to estimate
and separate the temperature response of ESSM anomaly from other contributions on a seasonal basis. We show as an example that the spring drought preceding the 2003 heatwave
contributed to about 1◦ C, while large scale drivers were dominant. We also found that the
different contributions of circulation and soil moisture effects approximately add up to the
anomaly, suggesting little nonlinear interactions among the two processes. Our results illustrate the complexity of processes involved in regional warming signal, not directly attributable
to changes in large-scale drivers.
3.2
Main text
Mega-heatwaves and exceptional warm summers are very likely to become more frequent
over Europe in the coming century (Schär et al., 2004; Barriopedro et al., 2011). Such extreme climatic events can cause great damage and loss to society and ecosystems, as witnessed
in for example 2003 in Europe and 2010 in Russia (e.g. Ciais et al., 2005; Fouillet et al., 2006;
Barriopedro et al., 2011). Although adaptation can mitigate such impacts, early warnings and
seasonal predictions might be far more efficient. However, such predictions in Europe had
limited success in practice so far.
Heatwaves, and warm summers in general, are associated with specific atmospheric circulation patterns, especially atmospheric blocking and south-westerly flows (Cassou et al.,
2005), but their predictability on the seasonal time scale is very low. Hopes of seasonal
predictability come from soil moisture in early summer conditions which may precondition
temperature anomalies in subsequent months (Wang and Kumar, 1998; Kanamitsu et al.,
2003; Vautard and Yiou, 2009). In Europe, indications of such a causal link have been provided in several observational studies (Vautard et al., 2007; Hirschi et al., 2011). In particular
dry early summer soil moisture (ESSM) has been shown to make temperature more sensitive
to the occurrence anticyclonic weather regimes (Quesada et al., 2012). The establishment
of dry summer conditions induce land-atmosphere feedbacks helping amplification of heat in
such cases (Seneviratne et al., 2010; Hirschi et al., 2011; Mueller and Seneviratne, 2012).
However, the contribution of ESSM to late summer temperature interannual variability has
78
CHAPTER IV. THE INFLUENCE OF SOIL MOISTURE ON SUMMER TEMPERATURES
not been quantified so far in a systematic way. Long-term changes in ESSMs, due to greenhouse gases or natural interdecadal variability may also contribute to the summer warming
trend, which has not been estimated so far.
We use regional climate sensitivity simulations to quantify the contribution of ESSM. A
control simulation from 1979 to 2011 with ERA-Interim (Dee et al., 2011) boundary conditions and grid nudging (wind only) above the boundary layer was performed with the
Weather Research and Forecasting (WRF) (Skamarock et al., 2008) regional climate model.
The model physics were optimized for the simulation of the European summer climate (Stegehuis et al., 2015).
The sensitivity simulations consist of more than 900 additional simulations starting at
June 30 of each year from 1980 to 2011. At June 30, in year x, the soil moisture is initialized
with the values of all other 31 years, where after the simulation is run until the end of August,
keeping the other conditions as in the control experiment. A similar set of experiments is done
with the large-scale drivers (LS, here defined as winds above the lowest model layers, SST
and boundary conditions). The contribution of ESSM and LS to late summer temperature
is the difference between the temperature anomaly of year x and the ensemble mean of the
31 sensitivity simulations + year x, for all years (see methods). This method works best
in absence of feedback of initial soil moisture on later large-scale circulation and SST. This
seems realistic as no significant correlation was found (P < 0.1) between ESSM and observed
sea level pressure or global z500 in July and August (not shown).
The model reproduces well the 2003 July and August mean temperature anomaly, as the
bias over Europe remains lower than about one degree, giving confidence in the process analysis (Fig. IV.1a). Such a model skill was also found for other summers. The average summer
temperature bias over 1980 to 2011 was of similar magnitude with the exception of some
small regions as in the Alps, the Pyrenees and parts of the Northern Iberian Peninsula. Summer precipitation is mostly underestimated over Southern Europe, where rain is essentially
absent during the summer, with largest deviations (up to 60%) in the southern part of the
Iberian Peninsula, and overestimations in Scandinavia (∼20%).
In the build-up of the temperatures of the summer 2003, the contribution of ESSM was
significant but not dominant in the temperature anomaly of July and August 2003. It explained approximately 0.5 - 1◦ C of the temperature anomaly in central Europe (∼20%) (Fig.
IV.1b). This area corresponds well with the area where ESSM was most anomalously low
during summer (-10% to -50%) (Fig. IV.1d). The contribution of ESSM was of the same
magnitude in 2010 in Russia, but contributed 10% to 15% to the total temperature anomaly
(Fig. IV.1f). The importance of LS such as atmospheric circulation and SST was larger in the
heatwave area, reaching 2-3◦ C in France and central Europe in 2003 and 5-6◦ C in Russia in
2010 (Fig. IV.1c,g). An essential component of the heatwaves therefore lies in the large-scale
drivers. The sum of the two contributions is nearly equal to the modeled anomaly itself, indicating weak or no nonlinear interactions between ESSM-driven mechanisms and flow-driven
mechanisms (Fig. IV.1a,e).
Compared to the contribution of LS, the contribution of ESSM in 2003 and 2010 might
have been relatively small. However, the influence of ESSM on summer temperatures has
increased over central Europe over the last 30 years with approximately 0.2◦ C per decade
(Fig. IV.2c). Thus, the temperature warming is induced by drier initial soils, a result that is
3. SUMMER WARMING INDUCED BY EARLY SUMMER SOIL MOISTURE CHANGES IN
EUROPE
79
Figure IV.2: Difference of July + August (JA) temperature anomaly between the model and
the observations for 2003 (a) and 2010 (e). The contribution of ESSM (b,f) and LS (OTH)
(c,g) to the JA temperature anomaly of 2003 (b,c) and 2010 (f,g). Simulated JA soil moisture
anomaly of 2003 (d) and 2010 (h). The shaded area in panel a and e indicates the regions
where the joint contribution of ESSM and LS is more than 0.5◦ C different from the total
temperature anomaly.
in agreement with Seneviratne et al. (2006b), who showed an increasing importance of soil
moisture towards the end of the century. A large part of France, Germany and other parts of
central Europe have experienced a more important trend in the ESSM contribution (p-value
< 0.05) (Fig. IV.2c). In this area, the trend in ESSM-induced warming is almost equal to the
warming trend itself, despite that the model slightly underestimates this trend as compared
to observations (Fig. IV2a,b). An underestimation of the warming trend was also found in
the ENSEMBLES models (Lorenz and Jacob, 2010). A central result here is therefore that the
early summer drying trend (Fig. IV.3) explains a major part of the July and August warming
along the past three decades in many Western European areas in our WRF simulations. In
other areas where the ESSM contribution is significant (e.g. Ukraine), it explains a smaller
fraction of the overall warming.
The contribution of LS also shows an overall warming trend over large parts over Europe
(Fig. IV.2d). It is not surprising that this contribution is large as it contains the response to
the more direct effects of climate change in SST warming, temperature and humidity in the
boundary conditions of the domain. The trend in LS in complementary areas of where the
soil moisture is dominating (Fig. IV.2d). This is partly due to the trend in the SLP, that is
positive over Eastern Europe, while it is negative over central and Western Europe (Fig. IV.4).
The latter suggests that the overall global warming trend could be contra-balanced by the
negative influence of the SLP.
The contribution of ESSM to recent warming is larger than the one of circulation in Western Europe. This comes in contrast to its relative contribution in explained interannual variance of the summer temperature anomalies, which, as for the summer in 2003 and of 2010,
only reaches a small fraction (Fig. IV.1b,d). At maximum, only 20% of the interannual variance of temperature is explained by ESSM variability. As for the case of 2003, we do not find
an overall strong nonlinear interaction between ESSM-driven warming, and warming induced
80
CHAPTER IV. THE INFLUENCE OF SOIL MOISTURE ON SUMMER TEMPERATURES
Figure IV.3: Temperature trend of observations (a) and WRF (b) in July and August from
1980-2011. Trend of the contribution of soil moisture (c) and circulation (d). Shaded areas
indicate a significance level of 95%.
3. SUMMER WARMING INDUCED BY EARLY SUMMER SOIL MOISTURE CHANGES IN
EUROPE
81
65
0.001
55
0.002
60
70
mm/yr
50
0.000
45
−0.001
40
−0.002
−10
0
10
20
30
40
Figure IV.4: Modeled trend of the soil moisture level at 30 June (ESSM) over 1980-2011
(mm/yr).
by large-scale drivers as their sum nicely add up to the total simulated warming trend.
The correlation between the temperature anomaly and the ESSM contribution is relatively
low (Fig. IV.5a) compared to that between the temperature anomaly and the LS contribution
(Fig. IV.5c). The maximum of the correlation with ESSM is located in central-southern and
central-eastern areas, which are areas where strong soil-atmosphere interactions take place
in several studies (e.g. Seneviratne et al., 2006b). The correlation between the two drivers
is very weak (Fig. IV.5b), while their sum adds up to almost 1 (Fig. IV.5d). The role of
soil moisture to the temperature anomaly in France and central Russia is most important in
the warmest years, in this case 2003 and 2010 (compare Fig. IV.5a with IV.6). Which is
understandable as in these areas the regime can shift from energy- to soil moisture limited.
In colder years (Fig. IV.6), soil moisture is important in Italy and areas around the Black Sea,
which might be caused by enhanced precipitation. The high correlation between the LS and
the temperature anomaly in Russia, there were the heatwave was strongest, is especially due
to the specific 2010 heatwave, as the elimination of this year causes a decrease in correlation
from > 0.9 to 0.8 or smaller (Figs. IV.5,6). Note however, that a correlation over 10 year
cannot easily be compared with that over 32 years.
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CHAPTER IV. THE INFLUENCE OF SOIL MOISTURE ON SUMMER TEMPERATURES
70
15
60
10
5
50
0
40
−5
30
−10
−15
−40
−20
0
20
40
60
Figure IV.5: Trend of NCEP sea level pressure over 1980-2011 (hPa/yr).
4
Discussion and conclusions
In this chapter we attempted to quantify the relative role of early summer soil moisture
(ESSM) and of large-scale drivers (LS) to summer temperatures. We find that the contribution
of ESSM in the heatwaves of 2003 and 2010 was important and contributed to approximately
1◦ C at maximum. LS however, explains the largest part of the temperature anomaly, with up
to 3◦ C in 2003 and up to 6◦ C in 2010. This finding uses the assumption of no feedback from
ESSM onto subsequent circulation. Interestingly, we found that ESSM deficit explains most of
the rapid recent warming trend in Western/central Europe [∼0.5◦ C.decade-1]. Regions that
coincide with the areas with a significant negative trend in ESSM. The influence of LS on the
other hand, has increased over Eastern Europe. The relatively warm summer years of 2012 to
2015 may stabilize or strengthen the trend, but time was missing to extend the experiment.
An assumption that we made in this study was the absence of feedback of initial soil
moisture to large-scale circulation and SST. To test this, we correlated the initial soil moisture averaged over France, there were the soil moisture anomaly was largest in 2003, to
observation-based data of global sea level pressure and z500. We found no correlation and
thus concluded the validity of the assumption. Another assumption has to do with the model
settings. We nudged the wind above the boundary layer that might have blocked the circulation to develop totally freely, especially in response to ESSM anomalies. In the future a
test with a non-nudged run could be done for the validation of this second assumption. It
4. DISCUSSION AND CONCLUSIONS
83
Figure IV.6: Correlation between ESSM contribution and July-August (JA) temperature
anomaly (a), ESSM and circulation contribution (b), LS contribution and JA temperature
anomaly (c) and the added contribution of ESSM and LS with JA temperature anomaly. Note
that the JA temperature anomaly is not detrended.
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CHAPTER IV. THE INFLUENCE OF SOIL MOISTURE ON SUMMER TEMPERATURES
10 warmest years
10 warmest years without 2003 and 2010
10 coldest years
10 coldest years without 1980 and 1984
Figure IV.7: Same as Fig. IV.5 but for the 10 warmest years (averaged over the domain),
the 10 warmest years without 2003 and 2010, the 10 coldest years and the 10 coldest years
without 1980 and 1984.
4. DISCUSSION AND CONCLUSIONS
85
seems that there is only little or no feedback between soil moisture and large-scale drivers.
We can however, not be fully sure with the method we have chosen. This may partly be due
to our decision to describe these drivers as a combination of SST, boundary conditions and
large-scale circulation.
The increasing contribution of early summer soil moisture deficit to the warming trend in
Western/central Europe and parts of Russia may provide more accurate seasonal predictions
and early warning signals in the future. The large-scale circulation does not have a longterm memory, thus knowledge about its state at the beginning of the summer season is little
helpful. The increasing importance of soil moisture, and its longer-term memory, may support
the possibility of early seasonal summer predictions. Furthermore, a better insight to the
different drivers of summer temperatures could improve our understanding of impacts, such
as vegetation responses, to elevated temperatures. Vegetation can respond differently to soil
drought and atmospheric drought (Chaves, 1991; Sellin, 1997; Darlington et al., 1997; MaierMaercker, 1998). Thus knowledge on both the soil status and the atmosphere may help to
better predict future vegetation responses and distribution.
C HAPTER
V
Drought impacts on European
forest species
1
Introduction
In the previous chapters the main focus was on summer climate and heatwaves in Europe,
discussing both atmospheric processes and land-atmosphere feedbacks. An important aspect
in land-atmosphere feedbacks is the vegetation, which can either have a moderating or an
amplifying effect on the climate. This depends largely on the vegetation type and on the
availability of soil moisture which influences the status of the vegetation (Section I.1.3.4)
and can regulate the repartitioning of land heat fluxes, also through the vegetation (Section
I.1.3.1). The impact of warm summers and heatwaves on vegetation is therefore an important
aspect in understanding the climate and will be investigated in this chapter.
Forests can transport more moisture into the atmosphere due to deeper rooting depths,
and thus a better access to groundwater, leading to more evapotranspiration than grasslands
during droughts when the topsoil is dry. The lower albedo of forests may however cause
a micro-climate above forests that is warmer compared to short vegetation (Naudts et al.,
2016). The albedo effect seems to outrun the evaporation effect for starting heatwaves (Teuling et al., 2010). This may however be reversed after longer or more severe heatwaves when
most trees close their stomata due to water stress. To untangle these effects, knowledge on
the reaction of vegetation during water stress is crucial.
For the effect of climate on vegetation on larger scales, vegetation models can be used.
Most of these models do not distinguish between separate species, but use plant functional
types (PFTs). A new version of ORCHIDEE (Krinner et al., 2005) however, does include the
species characteristics of most European tree species. This version, ORCHIDEE-CAN (Naudts
et al., 2015), additionally has a more detailed description of the water potentials in the soilplant-atmosphere continuum, which may better represent the water stress that is experienced
by the plants during dry and warm summers.
Because effects on drought and heatwave on vegetation is often local and species dependent, it is important to review ecological literature about these effects before starting with
modeling. I will focus on 5 of the main forest tree species in Europe. Furthermore, I will
87
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CHAPTER V. DROUGHT IMPACTS ON EUROPEAN FOREST SPECIES
present some preliminary results of the evaluation of the vegetation model for simulating
drought and heat stress on vegetation. These are studies on a local scale, using eddy covariance data (Baldocchi et al., 1996) for the evaluation of ORCHIDEE-CAN. This can be
considered as a first step for simulating vegetation stress on a large, regional or global, scale.
For the latter, the vegetation model needs to be coupled to, or driven by, reanalyses data or a
regional or global climate model. However, these last steps have not yet been performed.
In the next section a detailed description of heat and drought stress on some of the main
tree species in Europe is presented based on a literature review. This is followed by a description of the vegetation model ORCHIDEE-CAN, which is used to evaluate the simulation
of drought and heat stress on vegetation presented in section 5.4. In the next section, I will
discuss on some of the missing processes in vegetation models for better simulating the impact of heatwaves on vegetation. The chapter is concluded by a summary and concluding
remarks.
2
Drought and heat stress on 5 main European forest tree
species
This section describes some of the drought stress-induced characteristics of five main forest tree species in Europe (Picea sp., Pinus sylvestris, Fagus sylvatica, Quercus robur & petraea,
and Quercus ilex). The literature review shows the complexity of drought stress mechanisms
in different species, highlighting the difficulty of modeling impacts of drought stress on vegetation.
2.1 Picea sp.
Spruce is an important economically dominant forest tree species in many parts of Europe.
While the genus Picea consists out of multiple species, in this study they are all grouped
together. Spruce is relatively sensitive to drought and is a water saving, or isohydric species.
The drought sensitivity is often attributed to its relatively shallow root system (Schlyter et al.,
2006). Although some studies contradict this and point out that deep vertical roots develop
at an older age (Puhe, 2003). Decline and dieback in different regions are often associated
with drought and high summer temperatures (Nillson, 1993; Modrzyński and Eriksson, 2002;
Allen et al., 2010).
Photosynthesis Photosynthesis of Picea species greatly decreases or even stops during
drought stress (Silim et al., 2001; Bigras, 2005; Zweifel et al., 2009). Stomatal closure, a
mechanism to limit water stress during drought, may occur at shoot water potentials below
-2 MPa (Bigras, 2005). Although this is also dependent on the atmospheric demand (MaierMaercker, 1998; Ditmarová et al., 2010). During less severe water stress, a significant decline
in stomatal conductance was found up to 50% (Townend, 1993; Stewart et al., 1995; Lu et al.,
1996; Tan and Blake, 1997; Silim et al., 2001; Pepin et al., 2002; Lu et al., 2007; Ditmarová
et al., 2010). It might however, not be the principal limitation of net photosynthesis (Toivonen and Vidaver, 1988; Stewart et al., 1995). The inter-cellular CO2 concentration, which is
coupled to the stomatal conductance, may be more important for the reduction in net photosynthetic rate. Stomatal conductance was found to contribute only to approximately 20% to
2. DROUGHT AND HEAT STRESS ON 5 MAIN EUROPEAN FOREST TREE SPECIES
89
the reduction in net photosynthesis (Stewart et al., 1995). The recovery of stomatal conductance is debated, and both quick (Stewart et al., 1995) and incomplete recovery (Blake and
Li, 2003) are found. The incomplete recovery may be due to membrane damage preventing
the reopening of stomata (Blake and Li, 2003). The differences found between the studies
might be dependent on species and the experimental design. Mesophyll limitation seemed to
increase with seedling age, but was not affected by soil drought (Stewart et al., 1995). Water
use efficiency (WUE) increased during drought stress (Silim et al., 2001; Duan et al., 2007).
Another method to measure photosynthetic performance is chlorophyll fluorescence. Chlorophyll fluorescence is the excess energy of absorbed light that is re-emitted by chlorophyll. Absorbed light can furthermore be used to drive photosynthesis or it can be dissipated as heat.
Drought or heat stress can influence the partitioning of absorbed light energy into these components. Some studies on photosynthetic performance nowadays use this technique (Maxwell
and Johnson, 2000). The paragraph below describes some of the results concerning Picea sp.
Chlorophyll fluorescence mostly provides information on photosystem II (PSII) because it is
generally assumed that the variability of the fluorescence of photosystem I is not significant
at room temperature.
Maximum efficiency of PSII decreased quadratically with decreasing shoot water potential (Bigras, 2005). This decrease seemed to be due to a decrease in maximal fluorescence
(Fm) rather than an increase in dark limited fluorescence, the latter being related to damage
to the reaction centers (Bigras, 2005). The decrease in Fm may be the start of photoprotective mechanisms (Demmig and Björkman, 1987). Pukacki and Kamińska-Rozek (2005);
Ditmarová et al. (2010) however, found that the decline of the maximum efficiency of PSII
only decreased significantly after ∼1 month of water stress, while mild stress did slightly,
or not at all, decrease the maximal photochemical efficiency of PSII (Eastman and Camm,
1995; Duan et al., 2005; Blödner et al., 2007; Fossdal et al., 2007; Ditmarová et al., 2010).
Effective quantum yield also decreased significantly, to about 50%, with increasing drought
(Bigras, 2005; Blödner et al., 2005; Lu et al., 2007; Fossdal et al., 2007; Blödner et al., 2007),
even before photoinhibition occurred (Eastman and Camm, 1995). The reduction however,
was largely reversible in darkness. Furthermore, the efficiency of open reaction centers decreased significantly with increasing drought (Eastman and Camm, 1995; Bigras, 2005). This
can be due to damaged reoxidation of the primary electron acceptor (QA ) of PSII by light
induced protein damage in the adjacent acceptor (QB ), the second electron accepting plastoquinone of PSII (Kyle et al., 1984). Photochemical quenching also decreased with low soil
moisture levels (Bigras, 2005), but only under high irradiances (Eastman and Camm, 1995).
Non-photochemical quenching (qN) increased (Eastman and Camm, 1995; Bigras, 2005; Lu
et al., 2007). This indicates an enhanced capacity to dissipate excess energy at PSII. The increase in qN was higher in sun than in the shade (Duan et al., 2005), which corresponds to the
location where the dissipation of energy is most necessary. After ∼3 weeks, photosynthetic
processes recovered to their pre-stress values (Pukacki and Kamińska-Rozek, 2005).
Drought stress resulted in alterations in protein composition. Several proteins involved
in photosynthesis were increased, for example those of the oxygen-evolving complex and of
Rubsico (Blödner et al., 2007). Also chitinase transcripts and dehydrins of the stressed plants
increased (Blödner et al., 2005; Fossdal et al., 2007; Eldhuset et al., 2013).
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CHAPTER V. DROUGHT IMPACTS ON EUROPEAN FOREST SPECIES
Antioxidants Plants have different antioxidants that can have a protective role during drought stress. Both chlorophyll a and b decreased in severely drought stressed Picea
seedlings, but their ratio remained constant (Wallin et al., 2002; Pukacki and KamińskaRozek, 2005; Barsi et al., 2009; Ditmarová et al., 2010). Mild stress did not affect the
chlorophyll content (Lu et al., 2007; Barsi et al., 2009; Ditmarová et al., 2010). Carotenoid
concentration also decreased (Pukacki and Kamińska-Rozek, 2005; Barsi et al., 2009). This
lead to a lower chlorophyll:carotenoid ratio that indicates a higher capacity to dissipate excess energy (Munné-Bosch and Alegre, 2000). After ∼3 weeks of recovery, chlorophyll a and
carotenoid concentrations reached their control values, while chlorophyll b concentration
remained ∼20% lower (Pukacki and Kamińska-Rozek, 2005).
Proline accumulated in stressed spruce plants after 2 weeks (Yang et al., 2010; Ditmarová
et al., 2010). This antioxidant has osmoprotective function that can help to preserve the protein structures and enzyme activities (Arndt et al., 2001). It can furthermore scavenge hydroxyl and other free radicals that may increase during drought (Smirnoff and Cumbes, 1989;
Kishor et al., 1995; Kamińska-Rozek and Pukacki, 2004), even as ascorbate peroxidase (APX),
catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD), that also increased under drought stress (Duan et al., 2005; Kamińska-Rozek and Pukacki, 2004; Lu et al., 2007;
Duan et al., 2007; Yang et al., 2010). The concentration of malondialdehyde (MDA) may reflect the degree of lipid peroxidation and membrane damage due to drought stress by active
free radicals (Menconi et al., 1995). Its concentration increased significantly with increasing stress (Kamińska-Rozek and Pukacki, 2004; Lu et al., 2007). It may cause an enhanced
membrane permeability leading to electrolyte leakage (Campos et al., 2003). Other changing
components due to droughts stress were: ABA concentration that increased (Lu et al., 2007;
Duan et al., 2007), whilst glutathione, ascorbic acid, alpha-tocopherol, guaiacol peroxidase
and flavolnoids declined (Kamińska-Rozek and Pukacki, 2004).
Xylem embolism Xylem embolism is a phenomenon where the water column in conduits is filled with air or water vapor and becomes disrupted. The xylem potential at which
photosynthesis declines, lays between -1 and -1.6 MPa (Townend, 1993; Eastman and Camm,
1995; Tan and Blake, 1997), and stomata begin to close at approximately -1.6 MPa (Grossnickle and Blake, 1987; Blake and Li, 2003), although this can fluctuate depending on the
level of soil drought and air humidity (Sellin, 1998). Significant xylem embolism occurred
at values lower than -2.5 MPa (Sperry and Tyree, 1990; Cochard, 1992; Eastman and Camm,
1995), and P50 was found to be between -2.3 and -3.5 MPa, depending on the age and the
properties of the wood (Lu et al., 1996; Mayr and Cochard, 2003; Rosner et al., 2006). Most
embolism seemed to occur in the soil and trunk compartments, as no embolism was detected
in the branches (Lu et al., 1996). Recovery of embolism may be difficult or even impossible in spruce, as conifers are not able to refill embolized xylem due to the sealing of the pit
membranes to the pit pores (Sperry and Tyree, 1990). Also Blake and Li (2003) found that
hydraulic and stomatal conductance did not recover after rehydration.
Morphological changes The maintenance of turgor is crucial for the viability of plants.
Due to water loss, the turgor can be lost. The elasticity of the cell wall can overcome this
problem and it has been found to increase with drought stress (Major and Johnsen, 1999).
Although another study reports no such changes (Blake and Li, 2003). The maintenance
of turgor can also be provided by active osmotic adjustment (Major and Johnsen, 1999).
2. DROUGHT AND HEAT STRESS ON 5 MAIN EUROPEAN FOREST TREE SPECIES
91
Although drought can also cause osmotic stress, which can damage cell membranes (Major
and Johnsen, 1999, 2001).
Drought can also cause morphological changes in wood which was also observed for Picea
sp. Water stress caused less early and late wood tracheids, decreasing the radial increase
(Jyske et al., 2010; Eldhuset et al., 2013). Furthermore the cell wall thickness was higher
due to drought, mostly in the late wood (Jyske et al., 2010). This increased wall thickness resulted in an increased wood density. This may, in the future, lead to changed wood properties
(Jyske et al., 2010). Root growth, mass and density may decrease during and after drought
(Beier et al., 1995; Blanck et al., 1995; Darlington et al., 1997; Eldhuset et al., 2013). Although increased fine root biomass and production were found by Gaul et al. (2008); Major
et al. (2012), compared to a control plot during and after drought treatment. There was
no increased dieback of fine roots during drought and post-drought periods (Blanck et al.,
1995). Leaves chlorosis increased after a drought treatment (Dambrine et al., 1993; Fan and
Blake, 1997), and needle biomass decreased during severe drought (Eldhuset et al., 2013).
Needle loss was not enhanced (Dambrine et al., 1993). Further changes were observed in the
nutrient status of the needles, with decreased calcium and magnesium concentration (Blanck
et al., 1995) and increased needle leakage (Blake and Li, 2003). Other morphological changes
were found on sub-cellular level with decreased chloroplast and mitochondria volume (Zellnig et al., 2010), and a decreased specific leaf area (Duan et al., 2011). Height and radial
increment decreased with decreasing soil water levels (Spiecker, 1990; Blanck et al., 1995;
Beier et al., 1995; Wallin et al., 2002; Dohrenbusch et al., 2002; Büntgen et al., 2006; Fossdal
et al., 2007; Chhin and Wang, 2008; Jyske et al., 2010; Duan et al., 2011; Sohn et al., 2012;
Eldhuset et al., 2013). Maybe partly due to limited carbon allocation (Sohn et al., 2012). The
limitation in height and radial increments depends on the length of the stress period (Blake
and Li, 2003). Furthermore the position and structure of the tree may also influence the
drought impacts as trees with larger crowns were found to be less affected that those with
shorter crowns (Spiecker, 1990).
Lagged effects and acclimation Although many direct drought and heat effects are
found, lagged effects from up till 5 years after the drought are observed in elevated mortality
rates and vulnerability, either due to desiccation, insect attacks (Spiecker, 1990; Berg et al.,
2006; Bigler et al., 2007), or decreased growth rates (Dohrenbusch et al., 2002). Management strategies could be applied by choosing provenance from drier and warmer sites. As
individuals from drought-tolerant populations were better able to maintain turgor and photosynthesis under drought stress (Tan and Blake, 1997; Major et al., 2012). Furthermore, the
decrease in chlorophyll due to drought was lower in plants from a dry population, and also
their antioxidant level was higher (Duan et al., 2005). Tree resilience may also be increased
by extensive thinning (Misson et al., 2003; Sohn et al., 2012; Kohler et al., 2010), as was the
case for Quercus ilex.
2.2 Pinus sylvestris
The next species that will be discussed is Pinus sylvestris, or Scots pine. Pinus sylvestris
is one of the most abundant tree species in Europe and has a geographical range from the
Mediterranean region to the boreal forests (Martín et al., 2010; Matías and Jump, 2012). It
is a drought avoiding, or isohydric species (Cochard, 1992; Irvine et al., 1998; Gea-Izquierdo
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CHAPTER V. DROUGHT IMPACTS ON EUROPEAN FOREST SPECIES
et al., 2014). Its southern limits are mostly determined by water availability and elevated
temperatures (Rebetez and Dobbertin, 2004; Brunner et al., 2009; Matías and Jump, 2012).
In the southern regions it is often outcompeted by more drought-resistant species (Sykes and
Prentice, 1995).
Photosynthesis Drought induced a quick decrease in stomatal conductance and stomatal closure in Scots pine (Irvine et al., 1998; Croisé et al., 2001; Cregg and Zhang, 2001;
Zweifel et al., 2007, 2009), thereby limiting both the occurrence on xylem embolism (Irvine
et al., 1998; Perks et al., 2004), and net photosynthesis (Holst et al., 2008). Besides the
stomatal limitations, also non-stomatal limitations to net photosynthesis were found, that
were most important during severe water stress (Kellomäki and Wang, 1996). The photochemical efficiency was only affected when water stress was very high (Pearson et al., 2013).
This may explain the rapid recovery after the period of stress (Pearson et al., 2013; Bansal
et al., 2013). Due to stomatal closure, WUE increased with increasing drought stress (Cregg
and Zhang, 2001; Bansal et al., 2013).
Xylem embolism Because of the fast stomatal closure, xylem embolism does not occur
frequently. The water potential that caused significant xylem embolism was around -2.5 MPa
and P50 was approximately 3 MPa (Cochard, 1992). Water potentials recovered and xylem
vessels were refilled in a few weeks after rewatering (Perks et al., 2004; Croisé et al., 2001).
Morphological changes Lumen diameters were larger in drought stressed trees. They
are cheaper in carbon and increased conductivity, but also more vulnerable to xylem embolism
(Eilmann et al., 2011). Radial increment was lower, and wood formation ended earlier than
in non-stressed trees (Gruber et al., 2010; Eilmann et al., 2011). Decreased soil water level
also decreased the fine root length and biomass (Brunner et al., 2009; Pearson et al., 2013;
Bansal et al., 2013), together with needle biomass. The latter mostly because of senescence of
older needles during and after the stress period (Croisé et al., 2001; Rebetez and Dobbertin,
2004; Zweifel et al., 2007; Martínez-Vilalta et al., 2008; Galiano et al., 2010). Stem diameter
increase was reduced during moisture deficit (Croisé et al., 2001; Bigler et al., 2006; Eilmann
et al., 2011; Pearson et al., 2013), and persistent drought stress resulted in smaller ring
widths on the long-term (Bigler et al., 2006). Although other studies find little or positive
effects of warm and dry summers on radial growth (Polacek et al., 2006; Bansal et al., 2013).
Moderate water stress may not cause a decrease in stored carbohydrates (Croisé and Lieutier,
1993; Gruber et al., 2012). However, Galiano et al. (2010) found a decrease in carbohydrate
reserves.
Lagged effects and acclimation Several studies have reported Scots pine die-back due
to drought stress (Bigler et al., 2006; Galiano et al., 2010; Hereş et al., 2012; Vilà-Cabrera
et al., 2014). However other factors may also determine the survival chances of stressed trees.
Manion (1991) defined three categories of stress: predisposing, inciting and contributing
factors. Predisposing factors act on the long term and could be related to site characteristics
such as poor nutrient availability. An inciting factor acts on a short time-scale, such as drought
stress. Insect attack could be a contributing factors. The latter does not kill the tree if it has
not been exposed before to the predisposing and inciting factors (Manion, 1991). In the case
of Pinus sylvestris, different studies have reported the importance of both the drought stress
2. DROUGHT AND HEAT STRESS ON 5 MAIN EUROPEAN FOREST TREE SPECIES
93
and secondary pathogens or insect outbreaks (Croisé et al., 2001; Rebetez and Dobbertin,
2004; Dobbertin et al., 2007; Polomski and Rigling, 2010; Krams et al., 2012). Thinning
may be a proper management tool to increase drought resistance of trees, especially through
diminishing competition (Bigler et al., 2006; Giuggiola et al., 2013). Acclimation may also
occur on a longer time scale, as individuals from warmer and drier sites were found to be
more drought resistant than individuals from cooler and wetter sites (Cregg and Zhang, 2001;
Martín et al., 2010; Matías et al., 2014).
2.3 Fagus sylvatica
Beech, Fagus sylvatica, is a very important broad-leaved species in Western and central
Europe (García-Plazaola and Becerril, 2000; Peuke et al., 2002). Beech is relatively drought
sensitive, but can both exhibit an isohydric or an anisohydric strategy. The species is sometimes replaced in the Mediterranean region by more drought resistant species (Aranda et al.,
2012).
Photosynthesis One of the first effects to soil water depletion of drought sensitive
species such as Fagus sylvatica (Zapater et al., 2013; Bréda et al., 2006) is a decrease of stomatal conductance Tognetti et al. (1995); Sánchez-Gómez et al. (2013), which down-regulates
water use (Chaves, 1991) and decreases net photosynthesis. Net photosynthesis was found to
decrease from about 50% to almost complete inhibition after two weeks of drought (Tognetti
et al., 1995; Gallé and Feller, 2007; Robson et al., 2009; Simpraga et al., 2011). The inhibition of the photosynthesis was probably caused by the almost complete closure of the stomata,
and an irreversible change in their structure (Gallé and Feller, 2007). However, Aranda et al.
(2012) found, although with lower water stress, that stomatal closure only contributed with
33% to the reduction in net photosynthesis, while the largest part (45%) was due to biochemical limitation. Mesophyll limitation contributed with 22%. After 4 weeks after rewatering,
after severe drought stress, (Gallé and Feller, 2007; Simpraga et al., 2011) found restored
net photosynthesis values, whilst the restoration of stomatal conductance was incomplete.
Similar results were found by Tognetti et al. (1995), indicating that the intrinsic WUE was,
and stayed, higher in stressed trees (Peuke et al., 2002; Gallé and Feller, 2007; Aranda et al.,
2012; Sánchez-Gómez et al., 2013).
The maximum photochemical efficiency of PSII was found to decrease due to water stress
(García-Plazaola and Becerril, 2000), but increased again within two weeks after rewatering
(Gallé and Feller, 2007). The decrease was attributed to the quenching of Fm and initial fluorescence (F0), where a reduction in Fm is most important in the early stages of drying, but
during more severe desiccation the increase in F0 is most critical. Gallé and Feller (2007);
Sánchez-Gómez et al. (2013) found a non-significant effect of drought on the effective quantum yield, while Robson et al. (2009) found a small but significant decrease. The efficiency
of open reaction centers also decreased slightly (Robson et al., 2009). Non-photochemical
quenching did not change significantly (Gallé and Feller, 2007), but to fight the decline of
photochemical efficiency and the risk of photooxidation, antioxidant levels were increased,
such as violoxanthin, antheraxanthin, zeoxanthin (VAZ) and a-tocopherol (García-Plazaola
and Becerril, 2000). Gallé and Feller (2007) however, found the pool of xanthophyll cycle
pigments (VAZ) to be decreased during severe drought. Chlorophyll and carotenoids decreased due to drought (Gallé and Feller, 2007; García-Plazaola and Becerril, 2000). Both
94
CHAPTER V. DROUGHT IMPACTS ON EUROPEAN FOREST SPECIES
the rate of electron transport and the maximum carboxylation rate decreased in August, although this might be due to leaf aging instead of water stress (Aranda et al., 2012).
Xylem embolism P50 is between -2.5 and -3.2 MPa (Lemoine et al., 2002; Wortemann
et al., 2011). The water potential with significant loss of hydraulic conductivity and a chance
on the occurrence of xylem embolism is in between -1.5 and -2.25 MPa (Fotelli et al., 2001;
Lemoine et al., 2002). Differences between individuals and branches can be due to different irradiance levels, where sun-exposed branches are less vulnerable than shaded branches
(Cochard et al., 1999; Lemoine et al., 2002). Furthermore they can be due to different soils
(Čermák et al., 1993). The reduction of water potential due to drought is different in the
various tree parts. Water potential was reduced more strongly in the roots than in the shoots
(Peuke et al., 2002). Irrigation caused a fast response in water stressed trees and indicates resistance of short-term water stress to cavitation (Čermák et al., 1993; Gallé and Feller, 2007).
Lemoine et al. (2002) also concluded that embolism remained low at the end of the summer.
They assigned this to the fast stomatal closure that inhibits evaporation and thus embolism.
Morphological changes Decreasing precipitation reduced the fine root biomass in different soil layers (Meier and Leuschner, 2008). The fine roots were also thinner in drier
stands. So it seems that beech trees maintain root water absorption by lowering their root
potential and thus increasing absorption per unit root surface during mild drought periods
(Meier and Leuschner, 2008). Biomass allocation to the roots was increased (Barthel et al.,
2011). Also the composition of the roots was found to be different after drought; more fibrous
roots were found in declining trees (Čermák et al., 1993). The specific leaf area decreased
under water deficit (Sánchez-Gómez et al., 2013), but nitrogen content increased. Furthermore, sclerophyllous adaption to mild drought was found (Bussotti et al., 1995a), and leaf
shedding during severe drought (Bréda et al., 2006). The growth of beech seedlings was lowered during moderate water stress (Madsen and Larsen, 1997; Fotelli et al., 2001; Piovesan
et al., 2008). Furthermore, the stem diameter can decline during summer (Ježík et al., 2011).
While regular variations in shrinkage and swelling of the stem diameter occur the whole year
around, drought causes a sharper midday decline and a depletion of internal water reserves
on the longer term (Simpraga et al., 2011). This suggests that in areas exposed to regular
drought, reserves, both in water and nutrients, are important for plant survival and resilience
(Scartazza et al., 2013).
Lagged effects and acclimation Several studies show that beech can survive moderate
and short-term droughts (Čermák et al., 1993), but that longer and more severe droughts
may decrease their survival chances, which has already been showed in the Mediterranean
region (Piovesan et al., 2008; Robson et al., 2009). This may partly be due to drought induced
nitrogen shortage (Geßler et al., 2004). The nitrogen shortage may be a result of an altered
water balance of the tree that negatively affects the capacity of mycorrhizal fungi to take up
nitrogen (Geßler et al., 2005). Although the ectomycorrhiza fungi can allow for more water
uptake during mild stress (Pena et al., 2013). Acclimation may occur as plants from different
provenances and different climates respond differently to drought stress (Peuke et al., 2002;
García-Plazaola and Becerril, 2000; Peuke and Rennenberg, 2011). Beech trees from drier
regions were on average more sensitive to drought, and so might be better adapted to future
drier conditions (Tognetti et al., 1995; Fotelli et al., 2009; Rose et al., 2009; Stojnić et al.,
2. DROUGHT AND HEAT STRESS ON 5 MAIN EUROPEAN FOREST TREE SPECIES
95
2012; Sánchez-Gómez et al., 2013). A management strategy for increased survival chances
of drought stress is thinning (van der Maaten, 2013). Also a reduced competition with other
species may favor survival (Fotelli et al., 2002; Geßler et al., 2007).
2.4 Quercus robur & petraea
Pedunculate (Quercus robur) and sessile oak (Quercus petraea) are, after beech, the most
common summer green broadleaved species in central Europe (Thomas and Gausling, 2000).
On average, Quercus species are relatively deeply rooted and relatively well resistant to
drought stress, and are isohydric (Abrams, 1990; Cochard, 1992). However, their susceptibility to defoliation by insects may decrease their overall resistance (Gieger and Thomas,
2005). Overall, Quercus petraea seems to be somewhat more drought resistant than Quercus
robur (Cochard, 1992; Dickson and Tomlinson, 1996; Demeter et al., 2014).
Photosynthesis Stomatal conductance and photosynthesis decreased with drought stress
(Epron and Dreyer, 1993b; Bréda et al., 1993; Thomas and Gausling, 2000; Grassi et al.,
2005). The reduction in photosynthesis was happening slowly and was still positive at water
potentials of -3 MPa (Epron and Dreyer, 1993a,b), whilst the reduction in stomatal conductance occurred more suddenly (Epron and Dreyer, 1993a; Cochard et al., 1996a; Čater and
Batič, 2006). The latter could have resulted in enhanced survival by avoiding xylem embolism (Cochard et al., 1996a). Epron and Dreyer (1993b); Simonin et al. (1994) did not
find a significantly decrease due to drought stress of maximum photochemical efficiency of
PSII, effective quantum yield, and the efficiency of open reaction centers. However, some
studies found these variables to decrease (Epron and Dreyer, 1993a). The inhibition of photosynthesis of severely stressed seedlings may have been a result from both excess light energy
at PSII and drought stress. This excess light energy also decreased photochemical quenching
and the primary electron acceptors of PSII (Epron and Dreyer, 1993a).
Antioxidants Active oxygen compounds were found to increase due to drought stress.
This probably led to an increased level of antioxidants, such as gamma-tocopherol (Hendry
et al., 1992). Also osmoprotectants, such as proline, methylproline and hydroxyproline, were
found to increase (Spieß et al., 2012; Hu et al., 2013). Osmotic adjustment may be greater
in natural occurring droughts that develop slowly (Collet and Guehl, 1997). Ascorbic acid
however, was found to decrease (Hendry et al., 1992), even as glutathione (Hu et al., 2013).
The latter was not detected by Hendry et al. (1992) with increased water stress. ABA concentrations did not change significantly in leaf tissues (Triboulot et al., 1996; Fort et al., 1997),
however it did increase in the root xylem (Fort et al., 1997).
Xylem embolism The vulnerability to cavitation was greater in Quercus robur than in
Q. petraea (Cochard, 1992; Bréda et al., 1993). This is also apparent in the P50 , which was
between -2.7 and -3.5 MPa in Q. robur and between -3.3 and -4.3 MPa in Q. petraea (Cochard,
1992; Higgs and Wood, 1995). Whereas cavitation becomes significantly higher around -2.5
MPa in the former species Simonin et al. (1994) and around -2 to -2.5 MPa in the latter
(Thomas and Hartmann, 1996; Cochard et al., 1996a). Most stomata were closed at about
-1.8 MPa, while net photosynthesis was almost zero at -2.8 MPa for both oak species (Vivin
et al., 1993; Čater and Batič, 2006). The somewhat higher vulnerability to cavitation of Q.
robur makes the species less drought resistant (Cochard, 1992; Higgs and Wood, 1995). The
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CHAPTER V. DROUGHT IMPACTS ON EUROPEAN FOREST SPECIES
petioles were found to be more vulnerable than the twigs (Cochard, 1992), although another
study found similar vulnerabilities (Simonin et al., 1994). The water potentials were restored
till pre-drought conditions after rewatering (Cochard, 1992).
Morphological changes Early wood seems to be most important for the transport of
water in trees and wider vessels transport water more efficiently (Hagen-Poiseulle). This may
be important for the fitness of the trees, as dead oak trees had much smaller vessel widths
than healthy oak trees (Tulik, 2014). The leaf area and biomass of drought stressed trees
was decreased (Gieger and Thomas, 2002; Arend et al., 2011), while the fine root length
and biomass were unaffected by drought (Arend et al., 2011). This resulted in a decreased
leaf-fine root ratio during drought (Thomas and Gausling, 2000). The absence of response
of the roots might be due to the relatively deep root system that enables the access to water
even during periods of drought (Arend et al., 2011). Leaf abscission increased during the
drought stress, starting some weeks after the stress period began (Rust and Roloff, 2004).
This may be a method to reduce transpiration. Growth was slowed down or even ceased
in stems and shoot during drought (Saveyn et al., 2007; Arend et al., 2011; Spieß et al.,
2012). Osmotic pressure was lowered in drought stressed trees (Thomas and Gausling, 2000).
Soluble carbohydrates can contributed to osmoregulation during drought (Epron and Dreyer,
1996; Sergeant et al., 2011). This may explain the increased level of leaf carbohydrates,
glucose, fructose, galactose, mannitol and sucrose found by (Spieß et al., 2012).
Lagged effects and acclimation Mortality of oak trees can occur from one up to several
years after the drought event (Becker and Becker, 1982; Vivin et al., 1993). Acclimation may
take place as trees from warmer and drier sites have a higher frequency of drought-tolerant
alleles. An increased frequency and severity of extreme droughts or heatwaves may thereby
lead to reduced diversity loss, making populations less adaptive to other stresses (Borovics
and Mátyás, 2013).
2.5 Quercus ilex
Holm oak, or Quercus ilex, is a widespread species occupying large parts of the Mediterranean basin (Terradas and Savé, 1992; Ogaya and Peñuelas, 2007). It is anisohydric and
highly tolerant to drought stress (Tognetti et al., 1998; Martínez-Ferri et al., 2000). This is
partly due to its sclerophylly and its deep root system (Terradas and Savé, 1992; Filella et al.,
1998; Gratani and Varone, 2004). Because of the usually very dry and hot summers, holm oak
usually has two peaks of vegetation growth, one in the spring and one in the autumn (Ogaya
and Peñuelas, 2004; Gratani et al., 2013). Due to its resprouting ability, it was planted in
large parts of Spain and Italy as coppice systems (Serrada et al., 1992).
Photosynthesis Stomatal control is efficient in Quercus ilex, and the conductance is significantly lowered during periods of water stress, together with net photosynthesis (Terradas
and Savé, 1992; Peñuelas et al., 1997; Filella et al., 1998; Llusià and Peñuelas, 1998; Peñuelas
and Llusià, 1999a; Fotelli et al., 2000; Loreto et al., 2001; Gulías et al., 2002; Martínez-Vilalta
and Piñol, 2002; Pesoli et al., 2003; Serrano and Peñuelas, 2005; Baquedano and Castillo,
2006; Gimeno et al., 2009; Limousin et al., 2010b; Misson et al., 2010; Rodríguez-Calcerrada
et al., 2011; Gratani et al., 2013; Martin-Stpaul et al., 2013; Tsonev et al., 2014). During
autumn and winter, net photosynthesis and stomatal conductance increase again (Peñuelas
2. DROUGHT AND HEAT STRESS ON 5 MAIN EUROPEAN FOREST TREE SPECIES
97
and Llusià, 1999a; Serrano and Peñuelas, 2005; Vaz et al., 2010; Misson et al., 2010; Gratani
et al., 2013), although the rate of recovery may be dependent on leaf age (Limousin et al.,
2010b). Mesophyll conductance also decreases significantly during drought (Limousin et al.,
2010b), although net photosynthesis may be more limited by stomatal than by mesophyll
conductance (Misson et al., 2010; Limousin et al., 2010b). Fleck et al. (2010); Gallé et al.
(2011) however, find that mesophyll conductance is the main factor limiting net assimilation,
but this might be dependent on the severity of the drought stress (Misson et al., 2010). Biochemical limitation was found to have the lowest impact (Limousin et al., 2010b; Gallé et al.,
2011), or similar to that of mesophyll limitation (Misson et al., 2010).
WUE is usually higher during drought due to less water loss because of stomatal closure.
This was also found for holm oak (Gratani and Varone, 2004; Gimeno et al., 2009; Limousin
et al., 2010b). Although (Pesoli et al., 2003; Serrano and Peñuelas, 2005) found a reduced
WUE under water stress. This may be due to increased respiration due to elevated temperature (Reichstein et al., 2002).
Maximum photochemical efficiency of PSII decreased during water stress (Filella et al.,
1998; Martínez-Ferri et al., 2000; Peña-Rojas et al., 2004; Baquedano and Castillo, 2006;
Ogaya et al., 2011; Valero-Galván et al., 2013). This may indicate down-regulation of the
non-cycling electron transport (Chaumont et al., 1995). Rambal et al. (1994); Méthy et al.
(1996); Valladares et al. (2005) however, find stable values for water potentials up to -4 to -5
MPa, where after a significant decrease is found. The values were measured to be close to 0
at water potentials of -7 MPa. These results suggest that stomatal regulation is responsible for
the reduced net assimilation (Rambal et al., 1994). Effective quantum yield, the non-cycling
electron transport, also showed a significant decrease during soil water depletion (MartínezFerri et al., 2000; Peña-Rojas et al., 2004; Baquedano and Castillo, 2006; Valero-Galván et al.,
2013). Although other studies find a small, non-significant decrease (Echevarría-Zomeño
et al., 2009). The efficiency of open reaction centers decreased as well during drought stress
(Peña-Rojas et al., 2004; Baquedano and Castillo, 2006). A decrease in this efficiency may
imply increased excitation energy of quenching processes in the light-harvesting antennae
of PSII (Horton et al., 1994). Such a mechanism could protect the plant from photodamage. Furthermore, photochemical quenching decreased with increasing drought stress and
decreasing effective quantum yield (Peña-Rojas et al., 2004; Baquedano and Castillo, 2006).
Non-photochemical quenching increased with decreasing maximum photochemical efficiency
and drought stress (Martínez-Ferri et al., 2000; Gulías et al., 2002).
Chlorophyll and antioxidants The change of chlorophyll concentration under drought
stress is debated, and some studies find a decrease (Baquedano and Castillo, 2006), an increase (Gulías et al., 2002), or no change (Vaz et al., 2010). Furthermore, the concentration
of carotenoids were decreased (Baquedano and Castillo, 2006). Other antioxidants, such as
alpha-tocopheral, ascorbic acid, malondialdehyde (MDA) and VAZ (violaxanthin, antheraxanthin and zeaxanthin), the latter related to non-photochemical quenching, increased during low soil moisture levels (Gulías et al., 2002; Munné-Bosch et al., 2004; Baquedano and
Castillo, 2006; Camarero et al., 2012).
Xylem embolism P50 , the pressure causing a 50% loss of hydraulic conductivity, was
found to be around -3 and -5.6 MPa (Corcuera et al., 2004; Limousin et al., 2010a; Martin-
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CHAPTER V. DROUGHT IMPACTS ON EUROPEAN FOREST SPECIES
Stpaul et al., 2013), and stomatal closure occurred at water potentials between -4 and -4.5
MPa (Pesoli et al., 2003). Water potentials of -6 MPa were found during summer (Tognetti
et al., 1998). And while no injuries occurred at potentials lower than -3 MPa (Pigott and
Pigott, 1993), longer-term exposures may lead to critical damage (Terradas and Savé, 1992).
Although there are differences in vulnerability by different tree parts (Martínez-Vilalta and
Piñol, 2002).
Morphological adaptations Biomass allocation to the roots is found to increase during
drought (Baquedano and Castillo, 2006). However Manes et al. (2006); Corcobado et al.
(2014) find no change in root:shoot ratio, root biomass and root length. Drought can also
cause morphological changes in the wood. Corcuera et al. (2004) found narrower xylem
vessels in a dry summer year. Changing leaf properties have also been observed. Although
Rodríguez-Calcerrada et al. (2011); Martin-Stpaul et al. (2013) did not find a change in
leaf mass per area (LMA) during drought stress, Ogaya and Peñuelas (2006) found a small
decrease and Limousin et al. (2010b); Camarero et al. (2012) found an increase in leaf mass
per area. These contradicting results may be due to different experimental set-ups or the use
of different subspecies. Furthermore, LMA may be a more important protection against cold
than against drought stress (Ogaya and Peñuelas, 2007). Also the position of the leaves on
the tree plays a role, as sun leaves have higher LMA, thickness and lower area than shade
leaves (Ogaya and Peñuelas, 2006). Leaf area also decreased with water stress (Baquedano
and Castillo, 2006; Limousin et al., 2010b; Martin-Stpaul et al., 2013; Rodríguez-Calcerrada
et al., 2011), as well as leaf production, resulting in a lower leaf area index LAI (Ogaya
and Peñuelas, 2006, 2007; Rodríguez-Calcerrada et al., 2011; Martin-Stpaul et al., 2013).
Furthermore, the leaf to sapwood ratio decreased (Martin-Stpaul et al., 2013). Defoliation
and leaf damage occurred after severe drought stress (Peñuelas et al., 2000; Pesoli et al.,
2003; Corcuera et al., 2004). The non-structural carbon reserves were reduced due to low
soil moisture levels (Galiano et al., 2012), although such a reduction was not found by (SanzPérez et al., 2009). Furthermore, the buds of water stressed seedlings were found to be
bigger and the bud-burst was altered (Sanz-Pérez and Castro-Díez, 2010; Misson et al., 2010).
Ogaya and Peñuelas (2004) however, did not find a change in flowering and fruit growth in
water stressed individuals.
BVOC Another process that may act on the carbon balance, although with a very limited effect, is the emission of biogenic volatile organic compounds. Quercus ilex emitted
more terpenes under moderate, but not under severe water stress. This might be because
the substances cannot be stored (Llusià and Peñuelas, 1998; Blanch et al., 2009), which
makes the emission rate more dependent on the availability of recently fixed carbon than on
temperature or soil moisture availability (Peñuelas and Llusià, 1999a; Staudt et al., 2002).
The percentage of emitted newly synthesized carbon was between 6% and 13% (Peñuelas and Llusià, 1999b). Highest concentrations of emitted substances were found to be
alpha- and beta-pinene, limonene, sabinene and myrcene (Llusià and Peñuelas, 1998; Loreto
et al., 2001; Staudt et al., 2002; Plaza et al., 2005; Blanch et al., 2009). Less emitted substances were camphene, alpha-tujene, delta3-carene, 2-carene, p-cymene, alpha- and betaphellandrene, cis and trans-beta-ocimene, gamma-terpinene, linalool, ocimene, caryophyllene and alpha-caryophyllene (Staudt et al., 2002; Blanch et al., 2007, 2009). Both alphaand beta-pinene emissions were stronger during the warm summer temperatures (Peñuelas
3. MODEL DESCRIPTION
99
and Llusià, 1999a). Terpene emissions may have a protecting role for the photosynthetic
functioning or tissue damage of environmental stresses, as has been suggested for isoprene
(Litvak et al., 1996).
Plasticity Drought stress also caused differentiation in the composition of proteins (Jorge
et al., 2006; Echevarría-Zomeño et al., 2009). The ones increasing are proteins involved in
photosynthesis, several proteins involved in storage, possibly because of increased need for
stored carbohydrates, a protein involved in carbohydrate metabolism, and a protein involved
in stress regulation (Jorge et al., 2006; Echevarría-Zomeño et al., 2009). The results may be
dependent of the duration of the water stress, as different stress protection mechanisms play
a role at different time scales (Jorge et al., 2006).
Lagged effects and acclimation Severe drought stress was found to lead to permanent
damage (Peñuelas et al., 2000; Galiano et al., 2012), long-term loss of reserves (Galiano et al.,
2012), or mortality (Filella et al., 1998; Peñuelas et al., 2000; Valladares et al., 2005; Galiano
et al., 2012) up to several years after the drought event took place. But acclimation can also
take place as plants from warm and dry regions were found to be more drought resistant
than individuals from colder and wetter climates (Bussotti et al., 1995b; Pesoli et al., 2003;
Villar-Salvador et al., 2004; Sánchez-Vilas and Retuerto, 2007; Bonito et al., 2011; Corcobado
et al., 2014). For example, osmotic adjustment (Villar-Salvador et al., 2004), acorn size
(Bonito et al., 2011), and photosynthetic capacity (Sánchez-Vilas and Retuerto, 2007) were
enhanced. Although Limousin et al. (2010b); Rodríguez-Calcerrada et al. (2011) did not find
long-term acclimation during several years of study. Intensive thinning was found to reduce
competition and improve the resistance of holm oak forests to severe water stress (Galiano
et al., 2012).
All the drought stress effects in this section are mainly based on observations at local
scales and small scale experiments. To study impacts on larger scales a different approach is
needed, such as the use of vegetation models. However, before simulating drought impacts
on the European scale, simulations on site scales are necessary to evaluate the model. This
will be done in the coming sections. First the vegetation model will be described, followed
by the simulation results at 5 different forested sites in Europe (Table V.1). The sites are
chosen for their species composition and localization. A discussion and conclusion will end
the chapter.
3
3.1
Model description
ORCHIDEE
The vegetation model used in this study is ORCHIDEE-CAN (Naudts et al., 2015), an
adapted version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE)
model (Krinner et al., 2005). ORCHIDEE consists out of two modules: a hydrological module SECHIBA and a carbon module STOMATE. The former describes the energy and water
exchanges in the soil-vegetation-atmosphere continuum. The latter simulates the vegetation
processes such as carbon dynamics and the phenology. The vegetation in the model is represented by 13 plant functional types (PFT): 1 bare ground, 2 crop types, 2 grass types and 8
forest types.
100
CHAPTER V. DROUGHT IMPACTS ON EUROPEAN FOREST SPECIES
3.2
ORCHIDEE-CAN
In ORCHIDEE-CAN some structural changes have been implemented. I will briefly discuss
the changes that are most relevant for my study. For a detailed description of all changes I
refer to Naudts et al. (2015).
3.2.1
From PFT to species
A first important adaptation is the change from PFTs to species. The main European forest
trees species have been parameterized. These are: Fagus sylvatica, Pinus pinaster, Quercus
ilex, a group with Quercus robur and Q. petraea, Betula sp., Pinus sylvestris and Picea sp. The
last three species are parameterized for both the boreal and temperate region. Furthermore,
Populus sp. was described for short rotation coppice and observations from Larix sp. were
included for a better description of boreal needleleaf deciduous forest.
3.2.2
Carbon allocation and canopy structure
In ORCHIDEE-CAN the canopy structure is much more detailed than in the standard version of ORCHIDEE (trunk). Different circumference classes are added, which supports a
social position of the trees in the canopy. This allows for intra-tree competition as dominant
trees capture more light, resulting in more photosynthesis and biomass accumulation for the
most dominant trees. Furthermore, the different height classes also allow for a more consistent calculation of the absorbed light. In the trunk version this was based on LAI only,
and not the physical height or canopy depth of the trees. The photosynthesis module in the
ORCHIDEE-CAN includes a modified Arrhenius function. This allows for a temperature dependence accounting for a decrease in electron transport and carboxylation capacity at high
temperatures.
3.2.3
Hydraulic architecture
In the trunk version of ORCHIDEE, the soil hydrology had a direct influence on the water
stress of the plant. In the new version, a specific hydraulic architecture with plant water supply is implemented in order to more realistically simulate water stress on plants. A drought
stress factor is calculated as the ratio between the transpiration based on water supply and
the transpiration based on atmospheric demand. When the former is exceeded by the transpiration based on energy supply, stomatal conductance is restricted. The water supply of
the plants is a pressure difference between the soil and the leaves, and between the total
hydraulic resistance of the roots, sapwood and leaves. Cavitation is calculated based on an
s-shaped vulnerability curve and increases the sapwood resistance. ORCHIDEE-CAN uses an
11-layer hydrology scheme.
4
Results
This section presents the preliminary results of model experiments with ORCHIDEE-CAN,
trying to answer the main question of this chapter whether this state-of-the-art vegetation
model can simulate summer drought stress on different tree species. Five sites with different
species were chosen to represent the main tree forest species of Europe (Table V.1). There is
a specific focus on the simulation of Quercus ilex, a drought-resistant tree species.
4. RESULTS
101
Table V.1: Site characteristics of the 5 sites used in this study. EB = Evergreen broadleaf, EN
= Evergreen needleleaf, DB = Deciduous broadleaf.
Puéchabon
Brasschaat
Tharandt
Hesse
Collelongo
Country
Latitude
Longitude
Altitude
Species
France
43◦ 44’N
3◦ 35’E
270 m
Quercus ilex
Germany
50◦ 95’N
13◦ 57’E
380 m
Picea abies
France
48◦ 40’N
7◦ 05’E
300 m
Fagus sylvatica
Italy
41◦ 51’N
13◦ 35’E
1550 m
Fagus sylvatica
Phenology
EB
Belgium
51◦ 18’N
4◦ 31’E
16 m
Pinus sylvestris &
Quercus robur
EN & DB
EN
DB
DB
4.1
Puéchabon
A first site that will be evaluated is Puéchabon, a forest site located in the south of France
with a Mediterranean climate. The choice of this specific site was made because of our interest
in heat and drought stress on vegetation, which occurs every summer in this area. Furthermore, the Mediterranean region is expected to experience even warmer and drier summers in
the future climate (Giorgi, 2006; Giorgi and Lionello, 2008; Somot et al., 2008; Nikulin et al.,
2011), with probable negative effects for vegetation productivity (Hickler et al., 2012; van Oijen et al., 2014). The main tree species at Puéchabon is Quercus ilex or holm oak, a broadleaf
evergreen species. The average canopy height is between 4 and 6 meters (Reichstein et al.,
2002; Allard et al., 2008; Limousin et al., 2010a; Misson et al., 2010; Rambal et al., 2014)
with a basal area of approximately 28 m2 ha−1 (Misson et al., 2010; Rodríguez-Calcerrada
et al., 2014). Holm oak is a widespread species occupying large parts of the Mediterranean
basin (Terradas and Savé, 1992; Ogaya and Peñuelas, 2007). It is highly tolerant to drought
stress as described in section V.2.5. The site of Puéchabon has a very rocky soil. More than
75% of the upper 50 cm of soil is occupied by rocks, where 90% of the roots are found (Allard
et al., 2008). Below 50 cm, rocks occupy more than 90% of the soil (J.-M. Ourcival, personal
communication). The deepest roots can reach till 4.5 m depth (Allard et al., 2008).
4.1.1
GPP
Modeled GPP is on average too low during spring, while during summer it is too high
(Fig. V.1). This suggests that the drought stress is simulated incorrectly, which can have consequences when looking at a higher frequency and severity of heatwaves in a future climate.
In reality, multiple climate extreme such as heatwaves may lead to forest die-off, but with
insufficient water stress in the model this cannot be simulated. Incorrect GPP values can also
be the result of inaccurate biomass allocation. Although, besides fruit mass, the allocation
seemed to be reasonably well simulated. A study by Rambal et al. (2014) estimated fruit
mass around 26.4 g cm−2 , while it was simulated much lower with ORCHIDEE-CAN. Increasing the fruit mass allocation in the model led to better fruit mass results (from approximately
10 to 23 g cm−2 ). The tree height slightly decreased (from ∼8 to 7.5 m), as less biomass was
available for growth, and became closer to the observed values as well (5.5 m). This indicates
that the hydraulics might be simulated incorrectly. A comparison of soil moisture with daily
measurements with a neutron probe up to 4 m soil depth confirms this (Fig. V.2). While the
Rainfall (kg m−2 s−1)
3
1
−3 −1
4e−08
20
−2e−08
Time
Time
ORCHIDEE−CAN
FLUXNET
5
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3
1
2
GPP (gC m−2 day−1)
6
7
−30
SW net (W m−2)
CHAPTER V. DROUGHT IMPACTS ON EUROPEAN FOREST SPECIES
0
Temperature (°C)
102
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2006
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Figure V.1: Temperature (blue), rainfall (light blue) and shortwave net radiation (purple)
anomalies and gross primary productivity (GPP) in Puéchabon from 2000 to 2006. Observed
GPP in red and simulated in black. The grey vertical bars represent the summer months
(June, July and August), where the model is in general not able to reproduce the observed
summer stress in GPP.
observed soil moisture level is around 90 mm in summer, the modeled soil moisture does not
reach values below 110 mm, presumably leading to a lack of modeled water stress, and too
high GPP. Two years with extreme summer stress are 2003 and 2006. Figure V.1 shows that
this is mostly due to the high temperatures. Both precipitation and shortwave net radiation
seem to have a lesser effect, as their values are not anomalous.
4.1.2
Soil hydraulics
For calculating the root water potential in ORCHIDEE-CAN, it is assumed that the root
potential is similar to the soil potential. Furthermore, there is no capacitance in the model.
This resulted in too much water stress in some broadleaf tree species, leading to the addition
of a tuning parameter (PST), to be able to reduce stress. The root water potential is therefore
a function of the root density, the soil water potential and the tuning parameter. The default
PST value is 0 MPa for Quercus ilex. We tried different values, from releasing stress (positive
PST) to adding stress (negative PST). We found no changes for PST values as low as -2
103
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Soil moisture (mm)
250
300
4. RESULTS
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2006
Time (years)
Figure V.2: Soil moisture in Puéchabon from 2004 to 2006, observed (red) and simulated
(black).
MPa. Below this value GPP started to react by showing more summer stress (lower GPP). An
additional stress of -3.3 MPa resulted in the most realistic GPP values (Fig. V.3; note that only
several PST values are shown for readability). Adding more stress resulted in zero production
(Fig. V.4 darkbrown dotted line). Testing the same parameter values at Castelporziano in
Italy, another site with Quercus ilex, led to better results with a tuning value of -3.2 MPa, thus
very similar to the one found for Puéchabon. Although the oak trees on this site suffered
much less from drought stress, probably due to its location close to the sea.
Another reason why the soil moisture and the summer stress could be estimated wrongly
is the vertical distribution of the soil moisture into the different soil layers and its evolution
after a precipitation event. If the soil moisture would penetrate too fast into the deepest
layers, the trees would not be able to subtract water from the soil. While the reverse could
also be true. Two interesting seasons for studying the soil moisture are the very wet spring
of 2004 and the very dry summer of 2006. Figure V.4 shows the water distribution per layer
after several rain events in the spring of 2004. The distribution shows to be reasonable,
with the top layer becoming wettest and drying out fastest. The lowest three layers react
differently due to the saturated hydraulic conductivity that decreases exponentially below 30
cm, that corresponds to the depth below layer 8.
The total modeled soil moisture is higher than the observations, from approximately 50
CHAPTER V. DROUGHT IMPACTS ON EUROPEAN FOREST SPECIES
8
104
PST 0 (default)
PST −3.2
PST −3.3
PST −3.4
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Figure V.3: GPP in Puéchabon, from 2000 to 2006, observed (red) and simulated. The different colors and symbols correspond to different values of a tuning parameter for root water potential (see main text for more details). The grey vertical bars represent the summer
months.
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Layer 1
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Layer 11
Rainfall
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Time (months)
Figure V.4: Soil moisture per soil layer in Puéchabon, from March to May 2004. The different
colors correspond to the 11 different soil layers of the model, layer 1 being the top layer.
Precipitation is shown in blue.
March
April
1.0e−06
Rainfall (kg m−2 s−1)
5.0e−07
0.0e+00
250
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4. RESULTS
May
Time (months)
Figure V.5: Total soil moisture in Puéchabon, from March to May 2004, in observations (red)
and the model (black). Precipitation is shown in blue.
mm in the beginning, reaching to about 100 mm at the end of the spring of 2004 (Fig. V.5).
Furthermore, the modeled soil moisture reacts less strongly to water input by precipitation
and decays slower after rainfall. This may partly be responsible for the lack of summer stress
in the trees. During the dry summer of 2006 we observe similar differences, with higher soil
water levels in the model and a reduced reaction to precipitation (Fig. V.6). Furthermore it
seems that there is a threshold value under which the soil moisture cannot go. A possibility
in future work would be to force the summer soil moisture to different levels (e.g. 70-90
mm), or increase the infiltration. This could lead to higher vegetation stress. The soil water
level with an adapted tuning parameter for water potentials is higher than that of the original
model. This is induced by the increased difficulty for trees to take up soil moisture.
4.1.3
Transpiration
Another variable to assess the model for its ability to simulate the right water stress is the
transpiration, or the sapflow. Figure V.7 shows the evolution of sapflow during 2006, from
both observations and the model. While during winter and spring the model simulates the
transpiration quite well compared to the observations, during summer the model transpires
too much. This is probably due to the lack of summer stress and the constant availability
of soil moisture. With the water potential tuning parameter, the transpiration decreases, but
is still higher than the observations. This shows that even by tuning the water potential
106
CHAPTER V. DROUGHT IMPACTS ON EUROPEAN FOREST SPECIES
Figure V.6: Total soil moisture in Puéchabon, from June to August 2006, in observations (red)
and the model (black). The brown symbols correspond to the adapted model parameter for
soil water potential (see text for more details). Precipitation is shown in blue.
parameter and thereby better simulating the GPP, the model has still a limited ability to
simulate summer drought stress. A possible improvement may be to include a more physically
based model to simulate the soil and root water potential.
4.2
Other sites
The other sites (Brasschaat, Tharandt, Collelongo and Hesse) that are studied are grouped
together, as so far there was no time to do a thorough investigation to their ecological water
balance and drought periods. The seasonal cycles are on average well simulated. The largest
differences between the model and observations occurred in Hesse and Collelongo, both sites
with Fagus sylvatica. ORCHIDEE-CAN underestimated the GPP during some summers with
over 4 gC m− 2 day− 1. By adjusting a parameter related to the photosynthesis, the GPP was
much better simulated for most summers. One exception occurred for a summer with water
stress that with the new parameterization did not show stress anymore. This shows the need
for a more in depth study to the simulation of GPP.
4. RESULTS
107
Figure V.7: GPP (upper panel) and transpiration (lower panel) in Puéchabon in 2006, in
observations (red) and the model (black). The green and brown symbols correspond to the
adapted model parameters for soil water potential (see main text for more details).
4.2.1
Brasschaat
The first site that will be discussed is Brasschaat, a FLUXNET site in Northern Belgium,
close to Antwerp. The forest is composed of a mixture with mostly Pinus sylvestris (∼40%)
and Quercus robur (∼25%). Furthermore there are some other needleleaf species (∼10%),
deciduous species (∼5%), C3 grasses (∼15%) and some bare soil (Carrara et al., 2003, 2004).
The tree height of Scots pine is ∼21 m, while pedunculate oak reaches about 17 m (Xiao et al.,
2003; Carrara et al., 2003). LAI is in between 2 and 3, depending on the species mixture and
the season (Xiao et al., 2003; Carrara et al., 2004).
Scots pine Figure V.8 shows the GPP of Pinus sylvestris from 2000-2002 in Brasschaat.
It is simulated too high in all seasons except for the spring, where the observations and the
model correspond relatively well. The overestimation in winter may be due to the LAI that
remains too high, although this should not be the main problem in the summer. The soil
texture is sandy till 1.5 m depth. Below this layer, till 2 m, is a clay layer with poor drainage
(Carrara et al., 2003). While the soil depth in ORCHIDEE-CAN is 2 m, the soil texture cannot
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Pinus sylvestris
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observed (red) and simulated (black).
be changed with increasing depth, and sand has been used as texture class throughout the
whole vertical soil column. Although this may favor the model relative to the observations
because the water may be more easily available. Lastly, the species combination may cause a
difference between the model and the observations. Brasschaat is a mixed forest, consisting
out of needleleaf and broadleaf species. Although in the simulation also different vegetation
fraction are applied, in this example I only show the results of one species which represents
40% of the vegetation cover, that may not well reflect the reality.
Oak Summer GPP of oak at Brasschaat is, even as spruce, too high (Fig. V.9). This may
be caused by a modeled overestimation of LAI. The winter GPP is slightly too low. Pedunculate
oak is a summergreen species, and loses all leaves during winter resulting in a LAI of zero.
The observations however, do not differentiate between the different tree species, leading to
a slightly higher winter GPP. Extreme drought stress was not observed. A better comparison
between the model and observations may be made by comparing a mono-culture of oak,
instead of this mixed forest, that only has a coverage of 25& for oak.
In Brasschaat the seasonal cycle of the GPP was in general quite well simulated both for
Scotch pine and oak. We found some inconsistencies with the observations during summer
where the modeled GPP was somewhat too high (∼1-2 gC m− 2 day− 1) for several years.
During winter, simulated GPP was slightly too high for Pinus sylvestris while it seemed to
4. RESULTS
109
Quercus robur
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Figure V.9: Gross primary productivity of Quercus robur in Brasschaat, from 2000 to 2002,
observed (red) and simulated (black).
be too low for Quercus robur. The latter is due to the species mixture at the site, with an
evergreen needleleaf covering ∼40% and a summergreen broadleaf species covering ∼25%. A
solution could be to test different percentages of both species at the site to study the sensitivity
of the model for species mixtures.
4.2.2
Tharandt
Tharandt is a forest site in central-East Germany. The main tree species are 120-year
old evergreen needleleaf Picea abies, which grows together with Pinus sylvestris (∼10%),
deciduous summer green trees (∼10%), C3 grasses (∼10%), leaving ∼5% bare soil. The
forest floor is mainly covered by young Fagus sylvatica and Deschampsia flexuosa (Grünwald
and Bernhofer, 2007). The average tree height is about 27 m with an LAI of ∼8.5 (Grünwald
and Bernhofer, 2007; Granier et al., 2007). Tharandt has a shallow soil of approximately 1
m, and the main rooting depth is ∼35 cm (Grünwald and Bernhofer, 2007).
Simulated GPP is too low in summer for all modeled years (1996-2006), while in winter
it is too high (Fig. V.10). Although the species is sensitive to drought, extreme summer stress
as in Quercus ilex was not observed. This may be due to the different climates between both
sites. The difference in winter GPP may be due to the species mixture which contains both
evergreen and summer green trees. The LAI during winter may stay too high in the model,
CHAPTER V. DROUGHT IMPACTS ON EUROPEAN FOREST SPECIES
Picea abies
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Figure V.10: Gross primary productivity of Picea abies in Tharandt, from 2002 to 2004, observed (red) and simulated (black).
possibly resulting in more carbon assimilation. The average modeled LAI however, is slightly
too low, possibly explaining the summer GPP deficit.
4.2.3
Hesse & Collelongo
Beech, Fagus sylvatica, is a very important broad-leaved species in Western and central
Europe (García-Plazaola and Becerril, 2000; Peuke et al., 2002) and is present at two of the
studied FLUXNET sites, Collelongo and Hesse. Hesse, located in the north-east of France, is
a deciduous broadleaf forest. Approximately 90% of the vegetation cover exists out of beech.
The remaining 10% is a mixture of Carpinus betulus, Betula pendula, Quercus petraea, Larix
decidua, Prunus avium and Fraxinus excelsior (Granier et al., 2000b). The stand age is about
45 years and the average tree height was 13 m in 2000. The understory vegetation is very
sparse (Granier et al., 2000a). After thinning in 1999, the LAI was ∼4.8 m2 m−2 (Granier
et al., 2002). Collelongo is beech forest site in central Italy, approximately 80 km east from
Rome. About 90% of the ground is covered with beech, while the remaining part is bare soil.
Gallium makes the understory vegetation. The average tree height was 25 m 2003, with an
LAI of 4 (Valentini, 2003). The stand age is ∼110 years (Valentini et al., 1996). The soil at
both sites is not very deep, with 0.8 m in Collelongo and 1 m in Hesse (Valentini, 2003).
The modeled GPP of Fagus sylvatica is too low during summer at both test sites (Fig. V.11).
5. DISCUSSION
111
Collelongo
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Figure V.11: Gross primary productivity of Fagus sylvatica in Hesse from 2003 to 2005 and in
Collelongo from 1999 to 2001. Comparison between observations (red) and ORCHIDEE-CAN
(black). The solid black curve represents the standard model, the dotted line the model with
an adapted parameter for enhanced photosynthesis.
In Hesse, the observed tree height was 13 m in 2000. The modeled tree height is 21 m. The
modeled LAI may have been slightly too high as well. This would indicate that the GPP would
be higher than observed, however, this is not the case. In Collelongo, both the tree height and
the LAI in the model are very close to the measurements. This suggests that the incorrect GPP
has another reason. By tuning a parameter for the efficiency of photosynthesis (LLALPHA
from 0.3 to 0.5) the GPP is much better simulated, implying that this may be the reason for
the too low simulated GPP. However, by increasing photosynthetic efficiency, both LAI and
height increase as well, inducing errors in these simulated variables. Furthermore, drought
stress seems not to be simulated by increasing the photosynthetic efficiency (Fig. V.11 - Hesse
year 2004). This shows that underlying physical mechanisms of drought stress need to be
better understood, before we are able to correctly model them.
5
Discussion
In this chapter we analyzed the impact of drought stress on five European tree species
in literature, observational data and in a vegetation model. Overall the model simulated the
seasonal cycle of the growth, represented by the GPP, quite well. Drought stress however,
mostly experienced by Quercus ilex in Puéchabon, was not so well simulated. The production
of the vegetation remained too high during summer, and the soil water level did not decrease
enough as compared to observations. In a future, possibly warmer climate, it is especially
important to simulate drought stress, as more severe and frequent heatwaves and droughts
are expected to come that may cause permanent damage to the vegetation. In this section we
review the model results of Quercus ilex in more detail to better understand the lack of stress
response in the simulations. Furthermore we will discuss possible model adjustments with
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some of the missing processes as described in the literature (section V.2).
5.1
Site-specific characteristics
Some characteristics of Puéchabon make the site difficult to model and should be discussed first. The rock fraction in the soil is very high. Ingelmo et al. (1994) already remarked
that rocky soil are hard to model due to their heterogeneity. In ORCHIDEE-CAN, the coarsest
stone or sand fraction is smaller than 2 mm. This has consequences for the availability of
water for the tree, the run-off and the root distribution. The modeled root distribution is
represented by an exponential decrease that is grown in a homogeneous soil. In rocky soils
however, deep roots can only grow in cracks that are not represented by the model. The
run-off and infiltration rate are also strongly affected by the presence of rocks (Poesen and
Lavee, 1994), decreasing the total soil water availability (Saxton and Rawls, 2006). This can
lead to overestimation of the available soil moisture (Cousin et al., 2003). To overcome these
issues, it would be necessary to decrease the soil depth or to include rocky soil fraction in
models, and anisotrope soils, with preferential flow. Several of such models exist, although
the more complex models may still have limited use due to the scarcity of observations for
correct parameterizations (Simunek et al., 2003). A more simplistic option may be to describe
the soil in only two layers, in where the deepest layer is only available for a small fraction
of the roots. This method seems to work relatively well for Puéchabon (S. Rambal, personal
communication).
It is important to keep in mind that ORCHIDEE-CAN is designed as a global vegetation
model. Therefore it cannot be expected that all site-specific characteristics are well represented and simulated. A better infiltration parameterization however, could be useful in
many vegetation models.
5.2
Plant hydraulics
Besides the rocky soil, a process that is missing in the current version of ORCHIDEE-CAN
is a soil-root resistance. This is an additional resistance that can act very strongly in the case
of severe droughts (H. Cochard, personal communication). When the soil dries out, it will
be more difficult for the root to subtract water. Furthermore, the root may shrink, causing a
gap between the root and the soil (Carminati et al., 2013). Bonan et al. (2014) developed a
vegetation model with a soil-to-root resistance, which adds an additional stress factor during
drought. Preliminary results indicate that the addition of such a resistance reduces the GPP
during the vegetative period, but does not explain all of the missing summer stress. This may
be caused by the soil water level that is not correctly simulated. In the model, the soil moisture
level during summer is too high and an additional resistance would only increase the water
level further, as the trees would have more difficulty to uptake water. This may be overcome
by adapting the whole tree hydraulic architecture, by adding for example capacitance, as in
Bonan et al. (2014), that is currently missing in the model. This is ongoing work by Emilie
Joetzjer (personal communication).
5.3
Other processes
Aside from the soil, different specific mechanisms exist that can help trees to survive
periods of drought. These will be discussed in the same order as presented in the literature
5. DISCUSSION
113
review in section V.2, starting with photosynthesis, BVOCs, and plasticity, lagged effects and
acclimation.
Photosynthesis When drought stress increases, stomatal conductance and photosynthesis decrease. However these processes do not only depend on the climatic factors but
can also be determined by factors related to the tree. Limousin et al. (2010b) found for
example that leaf age influences the recovery rate of photosynthesis. Such mechanisms are
often not implemented in large scale vegetation models that are based on PFTs instead of
species. In ORCHIDEE-CAN the implementation may be easier, as this model defines different
tree species and the Vcmax (maximum rate of carboxylation) is related to leaf age. Another
process influencing photosynthesis rate is the mesophyll conductance that is, opposite from
stomatal conductance, not always present in models (Naudts et al., 2016). This may be a
limiting factor in explaining gas exchange during drought stress (Keenan et al., 2010). The
WUE can also change during drought stress, and usually increases during and sometimes after
periods of stress. A changing WUE however, is not necessarily present in vegetation models
(Traore et al., 2014). Besides these relatively well known processes, some mechanisms are
still unknown or only partly known. For example those of non-photochemical quenching (Farquhar et al., 2001). Furthermore, different observational studies do not always agree on their
results. Peña-Rojas et al. (2004); Limousin et al. (2010b); Vaz et al. (2010); Misson et al.
(2010), for example, find a decrease in both the rate of electron transport and the maximum
carboxylation rate due to drought stress, but Gallé et al. (2011) did not find such a change.
These differences in results may be related to the severity of the water stress or the age of the
studied plants (Peña-Rojas et al., 2004), but complicate the modeling effort on larger scales.
Lastly, during drought stress, leaf damage can be heterogeneous (Sardans et al., 2010) and
the closure of stomata may be un-even (Terashima et al., 1988). This could also lead to an
incorrect calculation of the total gas exchange. Another process related to photosynthesis that
is missing in large-scale vegetation models is the change in chlorophyll concentrations and
antioxidant substances. Although chlorophyll concentration and antioxidants under drought
stress is debated (section V.2), this is not at all accounted for in models. These possible
missing protection mechanisms for photosynthesis may lead to an underestimation of GPP in
models during drought stress.
BVOC During drought or heat stress many species emit BVOC, such as terpenes or isoprene. Quercus ilex is a terpene emitting species. The precise mechanisms of the emissions
are not known, so they are often not represented in models or only in a very simplistic way.
As BVOC are carbon compounds, their emission may alter the carbon balance of the tree.
During drought stress, when carbon uptake is limited, this may be an important mechanisms
through which the tree loses carbon. The impact of BVOC on the carbon balance of Quercus
ilex is probably minimal. The substances cannot be stored in the tree thus newly synthesized
carbon molecules are used for BVOC. As photosynthesis is very low during drought stress,
BVOC emissions become close to zero. For other species this process may be more important,
and could possibly play a role on the carbon balance of the tree on longer time scales.
Acclimation & lagged effects Process that are mostly not accounted for in global vegetation models are plasticity, acclimation and lagged effects. During drought stress the composition of proteins may be changed. These proteins can for example be related to the pho-
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tosynthesis apparatus or stress regulation. Although such detailed genetic knowledge is very
useful for understanding and predicting impacts, it is too detailed for the use in large-scale
vegetation models. Furthermore, trees can adapt to drought stress by allocating carbon to
different plant parts such as the roots, or by defoliation. This is also currently lacking in most
models.
Regarding the future climate, maybe most important processes would be permanent damage, mortality, acclimation and management. As more frequent and more severe heatwaves
are expected in the future climate (Schär et al., 2004), such effects may have detrimental impacts that could play a significant role in the global carbon cycle. For example, severe drought
stress was found to lead to permanent damage (Peñuelas et al., 2000; Galiano et al., 2012),
long-term loss of reserves (Galiano et al., 2012), or mortality (Filella et al., 1998; Peñuelas et al., 2000; Valladares et al., 2005; Galiano et al., 2012) up to several years after the
drought event took place. Although embolism is sometimes considered in vegetation models, it may not always lead to permanent damage and carry-over effects. Acclimation is also
not accounted for while trees originating from different climates may respond differently to
drought and heat stress. Plants from warm and dry regions were found to be more drought resistant than individuals from colder and wetter climates (Bussotti et al., 1995b; Pesoli et al.,
2003; Villar-Salvador et al., 2004; Sánchez-Vilas and Retuerto, 2007; Bonito et al., 2011;
Corcobado et al., 2014). For example, osmotic adjustment (Villar-Salvador et al., 2004),
acorn size (Bonito et al., 2011), and photosynthetic capacity (Sánchez-Vilas and Retuerto,
2007) were enhanced. These results suggest acclimation, which may be important in a future warmer and drier climate (Misson et al., 2010). Although Limousin et al. (2010b);
Rodríguez-Calcerrada et al. (2011) did not find long-term acclimation during several years of
study. Lastly, management options may be included. This has already be done in ORCHIDEECAN (Naudts et al., 2015), but is not present in all vegetation models. Intensive thinning for
example, was found to reduce competition and improve the resistance of holm oak forests to
severe water stress (Galiano et al., 2012).
It is important to keep in mind that some specific events, such as extreme heatwaves
for climate models, are not easy to simulate. Furthermore, some of the uncertainties in
vegetation models may come from larger scale processes, such as the incorrect simulation of
the evapotranspiration, the absence of nutrient cycles, or other inaccuracies in the water or
carbon cycle. It may be more relevant to focus on these processes than on the small scale
processes that were mentioned previously. A next step could be an extension with mortality.
This will probably be one of the most important processes for simulating the response of
vegetation to more frequent and more severe heatwaves in a future climate.
6
Summary and concluding remarks
In this chapter I reviewed the consequences of drought and heat stress on five main forest
tree species in Europe. Furthermore, some preliminary results of simulating summer stress at
a local scale are presented. The main goal was to analyze the ability of the global vegetation
model ORCHIDEE-CAN to correctly reproduce drought stress in order to use the model to
predict future, possibly more severe impacts. At Puéchabon, the site with most recurrent
drought stress, ORCHIDEE-CAN was not able to correctly simulate the affected GPP of Quercus
ilex. The production of the vegetation remained too high during summer, probably caused by
6. SUMMARY AND CONCLUDING REMARKS
115
too high soil moisture levels. For Fagus sylvatica, Picea sp., Pinus sylvestris and Quercus robur
and Q. petraea the simulation of GPP was much better represented, although these species
did not experience much drought stress. Some processes on a tree- and leaf-scale are not
represented in the vegetation model, which may cause the differences between the modeled
and observed primary production.
To simulate the impact of drought stress on vegetation of the projected increase in frequency and severity of heatwaves, some adaptations should be made to the model. Firstly,
specific processes that act most strongly during drought conditions could be added. An example is soil-root resistance. Besides such drought-specific processes, it would also be necessary
to introduce lagged effects. Hydraulic failure is hardly possible to simulate at the moment,
as embolism is restored completely during autumn and winter. In reality, tree mortality can
occur even several years after an extreme drought event. A relatively simple solution would
be to add a form of mortality when embolism becomes too extensive. Lastly, it may be interesting to add insects or pathogens to the model. Drought stress can increase the susceptibility
of trees to insect attacks. Furthermore, increasing temperatures may favor the population
density of harmful insects. However, this may be much more complicated and may remain an
issue for a future study.
C HAPTER
VI
Discussion & concluding remarks
Through this work I aimed to improve the understanding of the role of land-atmosphere
feedbacks and large-scale circulation that lead to warm summer temperatures in Europe. This
is challenging due to the scarcity of observations and the uncertainties of parameterized atmospheric processes. I focused on four main issues: 1) How do land-atmosphere feedbacks
affect climate projections and their uncertainties? 2) How do different physical parameterizations affect the simulation of extreme heatwaves? 3) How large are the roles of soil moisture
and atmospheric circulation in the development of European summer temperature anomalies? And 4) What are the impacts of heat and drought stress on vegetation?
Regarding the first question I found that the different partitioning of land heat fluxes
between models leads to spatially different warming over Europe in the future. The uncertainty of future climate change was especially high in central Europe, largely due to the
uncertainty in heat flux partitioning, while in Southern Europe the models mostly agreed.
The use of observation-based sensible heat fluxes allowed to reduce this climate change uncertainty regionally up to 40%. While studying different atmospheric parameterizations for
the extreme heatwaves of 2003 and 2010, I found a large temperature spread between the
simulations. Compared to observations, temperature was mostly underestimated. Shortwave
radiation and precipitation were generally overestimated. I selected a reduced model ensemble of well performing configurations compared to observations, to perform future studies on
warm summer temperatures over Europe. The best physics configuration was consequently
used to quantify the role of early summer soil moisture and large-scale drivers on summer
temperature anomalies. The contribution of soil moisture was up to maximum 1◦ C during
the heatwaves of 2003 and 2010. The contribution of large-scale drivers was larger, reaching
up to 3◦ C in 2003 and up to 6◦ C in 2010. However, the contribution of early summer soil
moisture to the temperature anomalies has been increasing over the last decades over parts of
central Europe and Russia, corresponding to the regions with a significant negative trend of
soil moisture. Large-scale drivers showed an increasing importance in the Eastern European
region. Lastly, I studied the impacts of drought and heat stress on several European forest
tree species. I found an overestimation of modeled GPP at a local scale in the Mediterranean
region during summer with ORCHIDEE. This indicates that the vegetation model does not
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CHAPTER VI. DISCUSSION & CONCLUDING REMARKS
well reproduce the complicated consequences of drought stress. To model future, possibly
more severe impacts of drought, the model may need to be adapted with drought-specific
processes and lagged effects.
In the following section I discuss three topics concerning the presented work in this
manuscript. The first regards climate models in general. The second part discusses the future
climate. To conclude I will discuss the modeling of vegetation impacts.
1
Climate models in general
The use of climate models is becoming increasingly important. With the growing understanding of the functioning of the climate, new processes are added into the current models
which are becoming more sophisticated. This has both advantages and disadvantages. It
often allows for better physical mechanisms and can lead to higher spatial and temporal accuracy, but at the same time it can more easily lead to error compensation and to too complex
models for a full comprehension. The expanding number of parameters must be tuned while
observations are not always abundant. What is then the meaning of using a climate model?
How is it then possible to obtain a correct understanding of the model outputs? Chapter 3
serves as a suitable example. Starting to model the heatwave of 2003, I required the model
to give me the correct temperatures. At that point a first question emerged: which physics to
choose? Using WRF I had many options for the physics, but they did not necessarily mean a
lot to me. There I discovered that modeling is not so simple, at least, modeling and trying to
understand what I was doing. I have attempted during my work to better understand why
different configurations of the one model I used provided me with such different climates. I
think I partly succeeded, although some questions remain unanswered. I did not have the
intention to completely understand the exact differences between all physics options, and
even if I had, I am not sure I would have succeeded during my PhD. I also do not think it
is necessary to understand all processes for using a model. I want to argue here that in using models, it is important to at least consider the different physics and parameterizations
of models. To try to understand why a certain model produces higher temperatures and less
rain than another one. How otherwise, would it be possible to deduce significant information
on our climate? In this work we obtained a better understanding of some of the physics of
the WRF model and created a small well-performing multi-physics ensemble that was used to
study the summer climate in Europe. Furthermore, this ensemble could be used for studying
impacts of heat waves on vegetation for example.
Another interesting study that could be done concerns the atmospheric circulation patterns in the different physics configurations from chapter 3. As shown in chapter 4, the largescale drivers increased their influence on summer temperatures across almost all of Eastern
Europe from 1980-2011. It may be interesting if this was due to a change in atmospheric
circulation, for example, an increase in Blocking situations. This has recently been studied
by Horton et al. (2015); Alvarez-Castro et al. (2016), who found a change in atmospheric
patterns looking at reanalyses data. Although the simulations are nudged above the boundary layer, differences in strength of certain atmospheric features, due to the diversity of the
physics, may be discovered. Subsequently, the method could be applied to future simulations
to better understand possible climates that we may encounter.
2. REGARDING THE FUTURE CLIMATE
2
119
Regarding the future climate
Modeling the future climate may require different kind of questions than using a climate
model for representing and understanding the current climate. Although the issue of including a good representation of the mechanisms is still important, another question we should
ask ourselves is to what extend we put our trust in the models. Models that well simulate the
current climate, do not necessarily accurately model another climate. For example our ’best’
physics configuration, is not able to correctly simulate the monsoon rain in Africa (C. Klein,
personal communication). Also the past climate may not be well represented because other
processes may have been more important than in the present climate. So why put trust in
simulations about the future climate? A climate that is very uncertain if we consider the range
of temperature possibilities at the end of the century (IPCC, 2013). A climate for which we
have no measurements and observations. A climate with a possible sea level rise of several
meters (Hansen et al., 2016). Is it then legitimate to say we have reduced the uncertainty
of predictions (Chapter 2) by using a small set of regional climate models? Does this mean
we cannot say anything about the future climate? That all the predictions are just random
guesses? No, probably not. Our current knowledge on the climate is extensive, and with the
information on different periods of past climate, the predictions are much more than a guess,
and built on physically-based mechanisms. However, it is important to keep in mind that current processes may change and therefore generate unpredictability in the future climate that
may not be foreseen. Another issue that could be important in the simulation of the future
climate is the resolution of climate models. Although many large-scale processes can be reasonably well simulated, we do not yet perfectly understand how small-scale processes, that
are not represented in these models, influence the processes at large-scale. These small-scale
processes may arise from future land-use changes for example. At the moment, all climate
models operate on too large scales for the investigation of such issues.
The reduced ensemble of well performing physics configurations that was created in Chapter 3 could be used for studying the future climate. Although a model that is well performing
in the present climate does not necessarily correctly simulate the future, our configurations
can at least handle high summer temperatures. This may be essential for simulating the end
of the current century. Preliminary results of 150-year climate simulations, from 1951-2100,
show differences between the three ensemble members (Fig. VI.1). The numbers of the
physics correspond to the model ranking from Stegehuis et al. (2015). In total I performed 4
simulations. Two with the best performing physics, one with the second best, and one with
the third (chapter 3.3). The difference between the simulations with the best performing
physics is that one is nudged above the boundary layer, while the second one is not nudged.
All four future simulations are forced by the climate of the LMDZ model with RCP8.5.
There is an almost constant temperature difference between the three physics schemes,
with a range of almost 1◦ C over Europe during the entire simulation. The simulation that
is not nudged shows somewhat lower temperatures than the one that is nudged, but the
differences seem small. Interestingly, the different physics do not diverge much even at the
end of the century. This may suggest that the boundary conditions play a more important
role than the different physics that were used. However, the simulations need to be analyzed
in more detail. A focus could be on the summer seasons, to investigate whether there will be
more frequent and more severe heatwaves as expected. The bias of the LMDZ model should
also be investigated and taken into account. A comparison between simulations driven by the
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CHAPTER VI. DISCUSSION & CONCLUDING REMARKS
Figure VI.1: Mean annual temperature over Europe from 1950 to 2099. Different colors
represent different model configurations. The numbers of the physics correspond to the model
ranking in Stegehuis et al. (2015). All simulations were nudged above the boundary layer
except from one (black line).
3. IMPACT STUDIES
121
LMDZ model and driven by reanalyses data, from approximately 1980 to 2012, could show
some of the main biases of LMDZ. Although, of course, some bias may also arise from the
interaction between the forcing and the forced climate model.
3
Impact studies
Concerning vegetation impact studies both the uncertainty of the climate model and that
of the vegetation model should be taken into account. As discussed in chapter 5, vegetation
models are far from perfect, and many processes at both a tree- and leaf-level are not included.
However, the models are repeatedly calibrated and evaluated against observations and they
are essential for impact studies on a large spatial and temporal resolution. To increase confidence in simulations a similar approach as has been done for WRF (chapter 3) could be
meaningful: using different models of biochemical and physical processes to simulate various
events and analyze the output. This has been done for some processes such as photosynthesis
(Misson et al., 2004), but not yet in a systematic way. This may be challenging, as a model
platform such as WRF does not exist for vegetation models.
For assessing the impact of climate events on vegetation different methods have been proposed. van Oijen et al. (2013a) propose a probabilistic method to estimate the expected loss
of ecosystem variables such as net primary productivity. They define a risk as expected loss,
a hazard as a potentially damaging phenomenon and the probability of the hazard actually
occurring, and the vulnerability as the degree of loss that results if the phenomenon occurs
(van Oijen et al., 2013a). Through this approach it is possible to assess how and when extreme climate events such as heatwaves jeopardize ecosystems on a large scale. Furthermore
the method allows for the detection of the underlying causes that can either be a change in
the frequency of the climate event, or a change in the vulnerability of the ecosystem (van Oijen et al., 2013a). Another interesting reversed approach is proposed by (Zscheischler et al.,
2013), in where first the impact is detected, followed by the attribution to a climate event.
This approach allows for a deeper understanding of different causes of loss of ecosystem
productivity.
Because of the increasing temperature and the higher chance of heatwaves, it may be
interesting to use such methods for future climate projections. A multi-physics ensemble as
proposed in this work may serve as an uncertainty measure on the vegetation stress. The
different simulations (Fig. VI.1) all lead to warmer temperatures, but there may be differences in for example the partitioning of the land heat fluxes, leading to different vegetation
responses. However, in this case it may be necessary to include lagged effects in the vegetation model, as carbon starvation or hydraulic failure caused by extreme climate events may
lead to mortality on a longer time scale.
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Annexes
A PPENDIX
A
The European climate under a
2dC global warming
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2014 Environ. Res. Lett. 9 034006
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Environmental Research Letters
Environ. Res. Lett. 9 (2014) 034006 (11pp)
doi:10.1088/1748-9326/9/3/034006
The European climate under a 2 ◦C global
warming
Robert Vautard1 , Andreas Gobiet2 , Stefan Sobolowski3 , Erik Kjellström4 ,
Annemiek Stegehuis1 , Paul Watkiss5 , Thomas Mendlik2 ,
Oskar Landgren6 , Grigory Nikulin4 , Claas Teichmann7,8 and
Daniela Jacob7
1
Laboratoire des Sciences du Climat et de l’Environnement (CEA/CNRS/UVSQ), Institut Pierre-Simon
Laplace, Orme des Merisiers, Gif sur Yvette, France
2
Wegener Center for Climate and Global Change, University of Graz, Austria
3
Uni Research, Bjerknes Center for Climate Research, Bergen, Norway
4
Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
5
Paul Watkiss Associates, Oxford, UK
6
Norwegian Meteorological Institute, Oslo, Norway
7
Climate Service Center (CSC), Helmholtz-Zentrum Geesthacht, Fischertwiete 1, D-20095 Hamburg,
Germany
8
Max Planck Institute for Meteorology (MPIM), Bundesstr. 53, D-20146 Hamburg, Germany
E-mail: [email protected]
Received 22 December 2013, revised 8 February 2014
Accepted for publication 11 February 2014
Published 6 March 2014
Abstract
A global warming of 2 ◦ C relative to pre-industrial climate has been considered as a threshold
which society should endeavor to remain below, in order to limit the dangerous effects of
anthropogenic climate change. The possible changes in regional climate under this target level
of global warming have so far not been investigated in detail. Using an ensemble of 15
regional climate simulations downscaling six transient global climate simulations, we identify
the respective time periods corresponding to 2 ◦ C global warming, describe the range of
projected changes for the European climate for this level of global warming, and investigate
the uncertainty across the multi-model ensemble. Robust changes in mean and extreme
temperature, precipitation, winds and surface energy budgets are found based on the ensemble
of simulations. The results indicate that most of Europe will experience higher warming than
the global average. They also reveal strong distributional patterns across Europe, which will be
important in subsequent impact assessments and adaptation responses in different countries
and regions. For instance, a North–South (West–East) warming gradient is found for summer
(winter) along with a general increase in heavy precipitation and summer extreme
temperatures. Tying the ensemble analysis to time periods with a prescribed global
temperature change rather than fixed time periods allows for the identification of more robust
regional patterns of temperature changes due to removal of some of the uncertainty related to
the global models’ climate sensitivity.
Keywords: regional climate change, extreme events, European climate
1. Introduction
Content from this work may be used under the terms of
the Creative Commons Attribution 3.0 licence. Any further
distribution of this work must maintain attribution to the author(s) and the
title of the work, journal citation and DOI.
1748-9326/14/034006+11$33.00
Internationally, there is an ambition to limit global average
surface temperature to 2 ◦ C relative to pre-industrial levels.
1
c 2014 IOP Publishing Ltd
Environ. Res. Lett. 9 (2014) 034006
R Vautard et al
a 2◦
This is in broad alignment with Article 2 of the objectives
of the United Nations Framework Convention on Climate
Change (UNFCCC 1992), i.e. ‘stabilization of greenhouse gas
(GHG) concentrations in the atmosphere at a level that would
prevent dangerous anthropogenic interference with the climate
system’. The 2 ◦ C goal was initially advocated (WBGU 1995)
on the basis of the evidence of the IPCC 2nd Assessment
Report (IPCC 1995) with the aim of avoiding serious adverse
effects to water resources, ecosystems, biodiversity and human health. More recent IPCC assessments broadly reinforce
the goal. The Third Assessment Report (TAR: IPCC 2001)
outlined greater negative impacts and more widespread and
greater risks with rising temperature (as presented in the
reasons for concern ‘burning embers’ diagram), while the
Fourth Assessment Report (AR4: IPCC 2007) stated it was
‘very likely that all regions will experience either declines
in net benefits or increases in net costs for increases in
temperature greater than about 2–3 ◦ C’. The European Union
agreed to the proposed goal (CEU 1996, 2004, CEC 2005,
2007), and at the UNFCCC Conference of the Parties in
Cancun (UNFCCC 2010), there was international agreement
to ‘establish clear goals and a timely schedule for reducing
human-generated GHG emissions over time to keep the global
average temperature rise below two degrees’.
One element of these review assessments—and the 2 ◦ C
goal—is the potential risk of catastrophic events (global or
regional discontinuities), known as tipping points or tipping
elements (Lenton et al 2008). While information on the
likelihood of such events remain subjective, and the critical
threshold temperatures that might trigger them are highly
uncertain, previous studies (Smith et al 2008, Kriegler et al
2009) indicate potential concerns of shifting too far away from
the present climate, and especially for moving above 2 ◦ C of
warming.
However, even the achievement of the 2 ◦ C goal will
be accompanied by a significantly changed climate from
today, and will necessitate adaptation. In order to have a
comprehensive picture of the consequences of a 2 ◦ C warmer
climate for Europe, climate projections with a higher spatial
resolution than global climate projections (such as provided by
the World Climate Research Program ‘Climate Model Intercomparison Project’ CMIP3 (Meehl et al 2007) and CMIP5
(Taylor et al 2012)) are needed, with a rigorous assessment
of uncertainties. These goals can be achieved by ensembles
of climate projections using regional, limited-area models to
downscale global climate projections. Such ensembles have
been produced in recent studies dedicated to Europe as in the
EU FP5 project PRUDENCE (Christensen et al 2007) and
EU FP6 project ENSEMBLES (van der Linden and Mitchell
2009).
However, none of these downscaled studies specifically
investigated the climate associated with 2 ◦ C warming. Instead, they investigated climate change and its uncertainty in
fixed future timed periods. Also, most of the socio-economic
scenarios used for these projections (SRES A1B, Nakićenović
et al 2000) were not designed to reach a stabilized 2 ◦ C
warming and therefore, reach a warmer climate over the
century. While a small number of simulations have investigated
stabilization scenario (e.g. the ENSEMBLES E1 scenario,
van der Linden and Mitchell 2009, Jacob and Podzun 2010),
their small number does not allow for robust uncertainty
estimation. New simulations carried out in the framework of
the CMIP5 and EURO-CORDEX (Jacob et al 2013) have
used a scenario that drives to a likely warming lower than 2 ◦ C
(RCP2.6), but at the time of writing the number of simulations
using this scenario also remains too small to study uncertainty.
The identification of changes corresponding to the 2 ◦ C global
warming thus requires using scenarios overpassing this target
value with a snapshot approach.
Here we use the ENSEMBLES regional simulations of the
A1B scenario (Nakićenović et al 2000), which are now well
studied (Kjellström et al 2013, Déqué et al 2012). The GCMs
driving these regional simulations have different sensitivities
to natural and anthropogenic climate forcing and reach the
target warming at different times. Our method is to collect
changes in climate parameters associated with these different
times and a reference period for each simulation and gather
them in a ‘2 ◦ C ensemble’. This ensemble thus includes uncertainties in the simulation of regional processes simulations
and their responses to the global warming and reduces some
of the uncertainty due to driving GCM sensitivity. There are
limitations to this approach as it does not account for the
contributions to uncertainty from systems that have response
times longer than the 2 ◦ C time period. This letter reports the
most likely changes and their uncertainties calculated from this
ensemble. It also aims to estimate the part of the uncertainty
that is removed due to considering a period defined by a fixed
global warming target instead of fixed time target. We focus
on main variables such as temperature, precipitation, sea level
pressure and winds, changes in their average and extremes, and
on less classical but more explanatory variables such as surface
fluxes. By doing so, we provide a unique assessment of what
the 2 ◦ C goal might mean for Europe’s climate, overall and
across regions, and how this compares to the global average.
2. GCM and RCM simulations used
In the subsequent analysis, we analyze 15 out of 22 RCMs from
ENSEMBLES with a horizontal resolution of about 25 km. The
22 RCMs are driven by 6 different A1B GCMs, however, not
each of the regionalized climate simulations has a sufficiently
long time series to reach +2 ◦ C warming. This leaves 15 RCM
simulations driven by 6 different GCMs (table 1).
Two of the GCMs (bccr bcm2 0-r1, mpi echam5-r3) are
realizations from the CMIP3 multi-model dataset (Meehl et al
2007), three models (HadCM3Q0, HadCM3Q3, HadCM3Q16)
stem from the Hadley Centre perturbed physics GCM ensemble ‘QUMP’ (Quantifying Uncertainty in Model Predictions)
(Collins et al 2011). One GCM (ARPEGE; Salas y Mélia et al
2005) is a spectral model with a stretched grid (Fox-Rabinovitz
et al 2008).
In order to account for RCM and GCM errors, we used a
model output statistic (MOS) approach (Maraun et al 2010),
namely quantile mapping (QM) as described by Themeßl et al
(2011), based on Déqué (2007). The observational reference
was the E-OBS version 5 dataset on a regular 25 km × 25 km
2
Environ. Res. Lett. 9 (2014) 034006
R Vautard et al
Table 1. Time period for which +2 ◦ C and +1.5 ◦ C compared to pre-industrial times was reached in ENSEMBLES A1B global climate
projections.
GCM
RCM
+2 ◦ C central year
+2 ◦ C period +1.5 ◦ C central year +1.5 ◦ C period
bccr bcm2 0-r1
HadCM3Q0
HadCM3Q16
HadCM3Q3
mpi echam5-r3
ARPEGE
RCA HIRHAM
RRCMCLM HadRM
RCA HadRM
RCA HadRM
RegCM REMO HIRHAM RACMO RCA
ALADIN HIRHAM
2052
2035
2028
2047
2048
2043
2038–2067
2021–2050
2014–2043
2033–2062
2034–2063
2029–2058
grid (Haylock et al 2008) in the period 1965–2010. We used
bias corrected data whenever available, i.e. for daily mean,
minimum, and maximum temperature and daily precipitation
sum.
Themeßl et al (2012) demonstrated the successful application of QM to future scenarios of daily precipitation
and Wilcke et al (2013) for other meteorological variables.
This implementation is very stable and flexible and has been
demonstrated to have higher skill in systematically reducing
RCM biases than parametric methods (Gudmundsson et al
2012). In order to avoid the suppression of new extremes in
the future periods (i.e. values outside the calibration range),
our implementation uses the correction terms of minimum
and maximum values of the calibration range outside of the
calibration range. Although this simple heuristic extrapolation
can probably be improved by using methods of the extreme
value theory, it proved to be stable and to lead to better results
than the uncorrected model output (Themeßl et al 2012). However it has to be kept in mind that while quantile mapping is
very successful in removing biases and adjusting distributions,
it cannot substantially improve the temporal structure of time
series from RCMs (e.g., Maraun 2012 and Wilcke et al 2013) or
properly correct biases in atmospheric circulation (Eden et al
2012). Further, several studies show that bias correction can
moderately modify the climate change signal of a simulation
(Christensen et al 2008, Themeßl et al 2012, Boberg and
Christensen 2012, Dosio et al 2012). However, we conducted
a parallel analysis based on raw RCM output (not shown),
which led to similar qualitative conclusions as the presented
study for mean changes.
2039
2022
2016
2028
2035
2028
2025–2053
2008–2037
2002–2031
2014–2043
2021–2050
2014–2043
temperature rise in a 30-year running mean from 1881–1910 to
1971–2000 (figure 1). The three datasets show an average past
warming from the pre-industrial period until the base period of
0.46 K (GISS LOTI: 0.437 K, HadCRUT3: 0.475 K, NOAA
NCDC: 0.477 K). Thirty-year running means, starting from the
base period 1971–2000, are calculated for the 6 GCMs used.
The +2 ◦ C period is determined by the year when the 30-year
running mean crosses the +2 ◦ C threshold. The projected
+2 ◦ C periods show considerable spread, reaching from 2014–
2043 (HadCM3Q16) to 2038–2067 (BCM) (figure 1) with
corresponding central years at 2028 and 2052, respectively.
The subset of 6 GCMs used in this analysis (table 1) still spans
the same range for global temperature, so no considerable
information should be lost in this respect compared to the full
set of ENSEMBLES GCMs, even though possibly reducing
the range for regional variables. Also, compared to the entire
CMIP3 A1B ensemble with 53 simulations, the ENSEMBLES
GCMs miss some lower sensitivity simulations. The CMIP3
simulations project +2 ◦ C warming from 2029 up to 2075, with
a median of 2049, whereas the selected ENSEMBLES GCMs
reach +2 ◦ C around 2045. Figure 1 (bottom panels) shows for
comparison global warming in the new CMIP5 simulations
for the representative emission pathways RCP2.6, RCP4.5 and
RCP8.5. While most of the RCP2.6 simulations don’t reach
+2 ◦ C at all, the RCP4.5 simulations reach it around 2050 and
the RCP8.5 simulations around 2042 (median). This means,
except for RCP2.6, all shown emission scenarios most likely
to lead to +2 ◦ C warming in a relatively narrow time window
between 2042 and 2050, while much stronger differences
between the scenarios can be expected in the second half of
the 21st century.
3. When is global climate likely to reach a 2 ◦ C
warming?
4. Robustness assessment
In this study the +2 ◦ C period is defined as the time when
the 30-year average global mean temperature reaches +2 ◦ C,
compared to a ‘pre-industrial’ period 1881–1910. To define
the +2 ◦ C period, we analyzed past observed and future
projected temperatures. The following global observational
datasets have been analyzed for this purpose: GISS LOTI
(1880–2011) (http://data.giss.nasa.gov/gistemp/), HadCRUT3
(1850–2011) (www.cru.uea.ac.uk/cru/data/temperature/), and
NOAA NCDC (1880–2011) (www.ncdc.noaa.gov/cmb-faq/a
nomalies.php). The time period common to all datasets matching best the pre-industrial period is 1881–1910. Thus, we consider past pre-industrial warming until the base period as the
Recently a number of approaches to assess and communicate
robustness of projected climate change have been proposed
(Tebaldi et al 2011), distinguishing model agreement in sign
and some indication of statistical significance of individual
models changes. They also generally make some attempt to
show areas where the climate change signal is low relative to
internal variability, but may still contain useful information
for policy makers (e.g. a projected change is small and not
statistically significant but the models agree on the sign). Here
we simply define robustness based on agreement between
models. In order for the ensemble change to be considered
robust 12 of the 15 models at least must agree on the sign
3
Environ. Res. Lett. 9 (2014) 034006
R Vautard et al
Figure 1. Global mean temperature (30-year running mean; gray lines) for the SRES A1B ensemble (top panel) and for the RCP2.6, RCP4.5
and RCP8.5 CMIP5 simulations (bottom panels) exceeding the +2 ◦ C threshold (bold red horizontal line). The average observed
temperature compared to pre-industrial (1881–1910) is depicted in the upper panel as black line. The CMIP3 and CMIP5 ensemble median
years of reaching the 2 ◦ C target for each emission scenario are shown as black vertical lines, whereas the red vertical line represents the
median year of the six driving GCMs of this study, which are highlighted in red. Since most RCP2.6 simulations stabilize below +2 ◦ C, no
median exceedance year is shown.
(threshold based on the 95% confidence interval of a binomial
test with 50% chance of success). In subsequent figures, areas
where such an agreement is not obtained are filled by gray
color. A more in-depth investigation of uncertainty will be
carried out in a future study using a broader ensemble of
EURO-CORDEX simulations and multiple socio-economic
scenarios.
measured as the standard deviation between individual model
changes, ranges between about 0 and 1 ◦ C depending on
season (not shown). The removal of the uncertainty related
to transient climate response to radiative forcing in the GCMs
can be readily seen when comparing the spread of simulated
temperature changes at +2 ◦ C to those from a fixed time
period of 2031–2060, which has a roughly equivalent mean
temperature change (figure 2(c)).
Figure 3 (left panels) shows the mean temperature changes
simulated by the RCMs between the control period and the
+2 ◦ C period, in winter and summer separately. This provides
information on the pattern of warming across Europe. Average
changes have similar patterns as those described for fixed time
periods in several regional ensemble studies (Fischer and Schär
2010, Kjellström et al 2011): a temperature increase is found
everywhere for all models, with enhancement in North-Eastern
and Eastern parts of Europe in winter (2–3 ◦ C) and in Southern
Europe in summer (2–3 ◦ C). The regional warming exceeds
the global warming in most areas except the British Isles and
Iceland, where the influence of the moderate warming of the
North Atlantic is seen in all seasons. In summer, a relatively
small warming is also seen close to the North Sea and the
5. Changes in Europe at 2 ◦ C average global
warming
Under a 2 ◦ C global warming (+0.46 ◦ C from pre-industrial to
1971–2000 and +1.54 ◦ C from 1971–2000 to the 2 ◦ C period),
ensemble-averaged projected European regional warming generally ranges between 1.5 and 2.0 ◦ C depending on the region.
European temperatures therefore mainly exceed the global
warming after 1971–2000 (figure 2(a)). Only North-Western
Europe (British Isles and France in particular) witness a lower
relative increase in warming, i.e. below 1.54 ◦ C. The warming
is higher in winter (mean = 1.99 ◦ C, 25% = 1.65 ◦ C, 75%
= 2.34) than in summer (mean = 1.72 ◦ C, 25% = 1.45 ◦ C,
75% = 1.99) (figure 2(c)). The spread of simulated changes,
4
Environ. Res. Lett. 9 (2014) 034006
R Vautard et al
Figure 2. (a) Yearly averaged change—relative to the reference period 1971–2000—in yearly mean temperature in the different European
regions for periods corresponding to +2 ◦ C of global average change. The global temperature change (1.54 ◦ C) between 1971–2000 and the
2 ◦ C period is marked as a dotted line. (b) Same as (a) for precipitation in % of change. The solid red line indicates no change for
precipitation and the red dotted line a change of 1.54 ◦ C for temperature, corresponding to a global warming of 2 ◦ C relative to
pre-industrial. (c) Spatial average over land of changes in temperature and (d) precipitation, together with the range of changes for the
GCM–RCM ensemble (median, 25–75% range and min and max values). The open bars refer to fixed time future period (2031–2060), the
gray bars to the temperature controlled (+2 ◦ C) period.
Baltic Sea. All areas undergo robust warming (robustness
not shown for temperature because it covers all areas). The
ensemble standard deviation of the changes remains much
smaller than the amplitude of changes (about 3–10 times
smaller) everywhere (not shown).
These spatial differences are important with respect to
subsequent impacts. Higher summer temperature changes are
found in the Iberian Peninsula and the Mediterranean region,
and will thus compound existing temperature-related impacts
such as energy use for cooling (EEA 2012). However, the
higher winter warming in Northern Europe will have a mix of
positive as well as negative effects, including reduced winter
heating. This reveals important distributional consequences
5
Environ. Res. Lett. 9 (2014) 034006
R Vautard et al
Figure 3. Seasonal mean changes of temperature (left panels), precipitation (middle panels) and sea level pressure (right panels), between
the 1971–2000 and the +2 ◦ C periods. Top panels show wintertime changes and bottom panels show summertime changes. Only areas
where at least 12 models agree on the sign of the change are colored and areas where at least 14 models agree on the sign of the change are
dotted areas. For temperature the agreement on the sign of the change was found everywhere and is not shown.
in the Northeast, but the signal is not very robust. This is
however consistent with the temperature and precipitation
changes and suggests expansion of the subtropical dry zone
into Southern Europe and an enhanced hydrological cycle in
Northern Europe and Scandinavia. The summer signal is more
robust with most of the models agreeing on modest decreases
in SLP across Southern Europe. This modest response could
be indicative of localized thermal low pressure due to heating.
This is a common feature in the Iberian Peninsula under
present conditions but any future increase of said phenomenon
requires further elucidation than the present study allows. The
summer pattern also indicates an increase in the North–South
pressure gradient over the Northern part of the North Atlantic
in the domain as pressure increases over the British Isles and
decreases over Iceland. Such an increase may help to explain
the increase in precipitation in parts of Scandinavia as partly
being a consequence of enhanced moisture transport from the
North Atlantic.
across Europe, in terms of the patterns of likely impacts, even
under the 2 ◦ C goal.
A similar analysis has been undertaken for precipitation.
When averaged over the PRUDENCE regions, annual average
precipitation robustly decreases by up to about 10% in Southern sub-regions while it may increase with more than 10% in
Northern Europe (figure 2(b)). When averaged over European
land areas for each season, mean precipitation significantly
increases in autumn (mean = 3.3%, 25% = 2.1%, 75% =
4.8%) and winter (mean = 5.3%, 25% = 5.1%, 75% =
6.5%) but changes in spring (mean = 1.7%, 25% = 0.2%,
75% = 3.1%) and summer (mean = −0.5%, 25% = −2.3%,
75% = 1.1%) do not show a clear sign (figures 2(b)–(d)). In
winter, a general increase is found with maximum values in
Northern Europe, especially along many coastal areas, where
all models agree upon an increase of 10–15% (figure 3 middle
panels). In Southern Europe the models do not agree on
sign except over a few areas (Southern Italy, Greece). By
contrast, in summer, the models agree on a robust decrease
of precipitation in South-Central Europe of about 10–15%,
together with an increase in precipitation over Scandinavia.
These changes may exacerbate existing water management
issues across Europe, i.e. potentially increasing water deficits
in the South during the already heat and evaporation stressed
summer. The only area where all models agree on the sign
of change is Scandinavia (increase in both seasons) and some
smaller areas in South-Eastern Europe and the West coasts of
the Iberian Peninsula, France and the Southern British Isles
(decrease in summer).
Some climate change signal is found in sea level pressure
(figure 3, right panels) in the winter season with lower pressure
6. Extremes
Extreme temperature is here defined as the daily maximum
temperature that is exceeded on average only once every
20 years. This is termed the return value and 20 years the return
period. Changes in extreme temperature are thus, characterized
through changes in the 20-year return values. Return values
are estimated using the block maxima method for which the
generalized extreme value (GEV) distribution describes the
behavior of said maxima (Coles 2001). The approach closely
follows that of Nikulin et al (2011) and further details are
provided therein.
6
Environ. Res. Lett. 9 (2014) 034006
R Vautard et al
Figure 4. Changes, between the 1971–2000 and the +2 ◦ C periods, in the 20-year return value for Tmin in winter (upper left), Tmax in
summer (lower left), heavy precipitation in winter (top middle) and in summer (bottom middle), extreme winds (I99) in winter (top right)
and in summer (bottom right). Only areas with at least 12 models agreeing on change sign are colored. Areas where at least 14 models agree
on change sign are highlighted with dots (except for temperature where almost all areas satisfy this).
For summer daily maximum temperatures (figure 4),
the largest changes (3–4 ◦ C) are found over South-Eastern
Europe and the Iberian Peninsula. In areas where this value is
highest under today’s conditions (Iberian Peninsula, France,
the Balkans) the 20-year return value is expected to rise well
above 40 ◦ C. As increases in summer extreme heat are linked
to health impacts in the form of temperature-related mortality
(Baccini et al 2008) the pattern of changes projected under
2 ◦ C is likely to have important health impacts in the more
vulnerable regions of Europe. Conversely, the extremes of
daily minimum temperatures are reduced most notably in
Northern and Eastern areas of Europe, which will have benefits
in reducing current winter cold extremes and cold-related
mortality as well as winter heating costs (EEA 2012), though
there would also be negative impacts, such as on winter tourism
and ecosystems.
All temperature changes are found to be robust but the
spread between models is high in Central and North-Eastern
Europe. In parts of this area, notably in Northern Sweden
and Finland, there are even models indicating no increase in
extreme maximum temperatures. Discrepancies in this area
may to some extent be related to how different RCMs treat
lakes as parts of this area has a large fraction of lakes that have
an impact on the regional climate (Samuelsson et al 2010).
Extremes of wintertime daily minimum temperature (figure 4)
undergo a large positive change in winter, ranging from 2–3 ◦ C
in Central and Southern Europe to 5–8 ◦ C in Scandinavia and
Russia. These changes are robust but again a large spread in
model responses is found over Central Europe, where some
models do not even agree on the positive sign of the change in
extreme temperatures.
Changes in extremes of heavy precipitation defined as the
20-year return value calculated from extreme value theory in
the same way as outlined for temperature above are shown
in figure 4. The ensemble mean exhibits positive changes in
almost all areas both in summer and winter, with amplitude
ranging from 5% to about 15%. The increase is marked over
Eastern Europe and Scandinavia in summer and over Southern
Europe in winter. In contrast to this study, where no overall
trend in extreme summer precipitation in Southern Europe
is found, Kendon et al (2008) and Maraun (2013) found a
decrease for extreme summer precipitation in this region. The
difference in the findings might be related to the fact that
the negative trend emerges relatively late and thus might not
have emerged for some ensemble members when reaching
the +2 ◦ C period. It may also result from the way extremes
are defined and the different sets of selected simulations,
indicating a lack of robustness of a possible decrease.
The increases in heavy precipitation are an important
factor with respect to flood risks, thus the increases in heavy
precipitation found under the +2 ◦ C scenario are likely to
enhance the potential for these events. Floods are among the
most important weather-related loss events in Europe and can
have large economic consequences: the EEA (2010) reports
total losses of over e50 billion over the past decade. The
projected increase in Eastern Europe is a particular concern
because this is one of the existing flood hot spots. Uncertainties
remain large in the southernmost areas of Europe. Compared
to the changes in seasonal means the changes in extremes are
less spatially coherent and individual models exhibit patchy
structures.
7
Environ. Res. Lett. 9 (2014) 034006
R Vautard et al
Figure 5. Changes, between the 1971–2000 and the +2 ◦ C periods, in surface energy fluxes. Left panels: latent heat fluxes; second left
panels: sensible heat fluxes; second right panels: net short-wave fluxes; right panels: net long-wave fluxes. Upper panels: MAM, Lower
panels: JJA. Only areas with at least 12 models agreeing on change sign are colored. Areas where at least 14 models agree on change sign
are highlighted with dots (except for temperature where almost all areas satisfy this).
Windstorms are also among the most damaging extreme
events in Europe (ABI 2005). Extreme winds are calculated
here as the 99th percentile of the daily maximum 10-meter
wind speed for each season (I99). Figure 4 (right panels)
show the ensemble mean relative changes in I99 (in %) winds
as simulated by the RCMs, in winter and summer. Robust
increases of extreme winds of up to 10% are only seen over
small scattered areas of Central/Eastern Europe in winter. Over
most regions the change is generally positive but not robust.
of Central Europe, have decreasing latent heat due to drought
increase and soil-moisture limitations. This drying causes an
increase in sensible heat in summer in this area, with large
model agreement, while over Scandinavia sensible heat fluxes
decrease due to a wetter and cloudier climate. In spring,
sensible heat flux only increases with high model agreement
in the southernmost areas.
However, it is suspected that these fluxes are biased on
ensemble average in the ENSEMBLES simulations. Stegehuis
et al (2013) showed that several models in the ensemble
have large evapotranspiration in the spring, which induces
a dry, soil-moisture limited regime in summer, with large
sensible heat flux accompanied by a low latent heat flux.
This effect can lead to an overestimate of the ensemble mean
changes and an underestimate of the inter-annual variability
changes (Fischer et al 2012). This could also contribute to the
nonlinear temperature bias found in this dataset by Boberg and
Christensen (2012), which probably induces an overestimation
of mean summertime warming in Southern Europe.
Heat flux changes respond to the changes in net radiative
fluxes (figure 5). In the two seasons a robust increase in
short-wave radiation is found in Southern Europe and robust
increase in long-wave radiation is found in Northern Europe. In
summer, the short-wave radiation increase extends Northward
over central Europe where drying, increase of sensible heat
and temperature also occurs. This extension is consistent
with the Northward propagation of drought and heat in the
spring–summer transition described in Zampieri et al (2009).
It is noteworthy that sea level pressure, however, does not
exhibit an associated robust increase in anticyclonic weather
over Mediterranean areas in summer, which could be due to
compensation due to heating-induced thermal surface pressure
lows. In Northern Europe, wetter and cloudier weather induces
an increase in long-wave radiation.
7. Surface energy budget
Surface weather changes are influenced both by larger scale
dynamical changes and by local changes in the physics of
the vertical column above the surface. These latter changes
are due to clouds, atmospheric composition, turbulence, soil
moisture and temperature as well as land use properties. All
these processes influence the surface energy budget (SEB),
characterized by radiative and heat fluxes. Changes in the SEB
are particularly important for driving changes in summer temperature and precipitation, which involve amplifying feedback
processes (Seneviratne et al 2010, Fischer et al 2007). They
also have impacts on drought changes, river discharge, water
and energy resources, with important economic implications.
We consider net short- and long-wave fluxes, sensible
and latent upward fluxes, only in spring (MAM) and summer
(JJA) in order to analyze the evolution of heat fluxes across
the growing season. Spatial patterns of latent and sensible
heat flux changes are shown in figures 5(a)–(d). In spring,
almost all models agree on an increase of latent heat except
in southernmost areas and in other smaller areas such as
North-Western coastal areas (this is also the case in fall and
winter). This distribution is consistent with the energy-limited
nature of evapotranspiration in Europe in spring (Teuling et al
2009). In summer, the latent heat increase is restricted to
Northern areas while Southern areas, including large parts
8
Environ. Res. Lett. 9 (2014) 034006
R Vautard et al
8. Conclusions
Many of the changes—in terms of the sign and magnitude
of the change, and perhaps more importantly the spatial
location and distributional pattern across Europe—will act to
exacerbate existing and future impacts. For example, there is
higher relative warming and greater relative increase in heat
extremes in Southern Europe in summertime, which will drive
heat and temperature-related impacts such as cooling costs and
heat-related mortality. Similarly, there are higher relative (and
more robust) signals for increased precipitation and heavy
precipitation events in Eastern Europe along existing flood
risk corridors, but lower projected summer rainfall in the
Mediterranean, which will increase pressure on water resource
management. There are some exceptions (e.g. higher winter
warming in the north, with the benefit of reduced winter
mortality and winter heating demand, though there would
also be negative impacts on winter tourism and ecosystems
in these regions). However, the general findings are that the
distributional patterns of change across Europe are likely
to drive geographically specific negative impacts. This is of
policy relevance: even if the 2 ◦ C goal is achieved, Europe
will experience impacts, and these are likely to exacerbate
existing climate vulnerability. Further work on identifying key
hotspots, potential impacts and advancing carefully planned
adaptation is therefore needed.
While it does not qualitatively affect the robust patterns
of climate changes for temperature and precipitation, the bias
correction may in some areas slightly modify the amplitude of
temperature changes (e.g. Dosio et al 2012). This was deduced
from additional experiments (not reported here). Precipitation
changes appear less sensitive to bias correction.
We have identified changes in European regional climate associated with a +2 ◦ C global warming relative to pre-industrial
climate. The +2 ◦ C period was characterized using 30-year
periods of an ensemble of global climate simulations of the
SRES Scenario A1B, downscaled at a 25 km resolution by
an ensemble of regional climate models (RCMs), simulations
carried out in the framework of the FP6 ENSEMBLES project.
The robustness of these changes has been quantified by measuring model agreement on the sign of the change and by
assessing the statistical significance of the change.
The main characteristics of the changes in Europe expected for a +2 ◦ C global warming relative to a reference
period of 1971–2000 are:
(1) Europe generally experiences higher warming than the
global average, i.e. it will experience more than 2 ◦ C
of warming even if the 2 ◦ C goal is achieved. There is
also a strong distributional pattern of warming across
Europe (and thus different countries). A warming over all
European regions is found, with slightly weaker amplitude
than the global warming over North-Western Europe but
a more intense warming (up to +3 ◦ C) in Northern and
Eastern Europe in Winter and in Southern Europe in
Summer.
(2) A robust increase of precipitation over Central and Northern Europe in winter and only over Northern Europe in
summer, while precipitation decreases in Central/Southern
Europe in summer, with changes reaching 20%.
(3) A marked trend with an increased amplitude of up to more
than 4 ◦ C in the 20-year return value of the summer daily
maximum and an even larger warming (up to more than
6 ◦ C) over Scandinavia for extreme cold daily minima in
winter.
(4) A robust increase in heavy precipitation everywhere and
in all seasons, except Southern Europe in summer, with
amplitudes in the range 0–20%.
(5) A modest and marginally robust increase in extreme winds
in parts of Central Europe in winter, while in summer wind
extremes changes are not robust.
(6) Sensible and latent heat flux changes have a strong seasonality with increasing (almost everywhere) evapotranspiration in spring, while it decreases in Southern/Central
Europe; sensible heat fluxes exhibit an opposite pattern
with an even higher amplitude in Southern/Central Europe; In summer fewer clouds in this area allow more
intense net radiation input in this area. However models
may overestimate evapotranspiration in spring, leading to
an exaggerated drying and sensible heat flux increase in
summer.
(7) The analysis also led us to conclude that a +2 ◦ C change is,
on average, approximately equivalent to a change for the
2031–2060 period in the A1B scenario. Choosing the time
period reflecting a global mean change of +2 ◦ C, however,
reduces the spread in the results for temperature. A similar
reduction in spread is not seen for other variables.
Acknowledgments
The research leading to these results has received funding
from the European Union Seventh Framework Programme
(FP7/2007–2013) under the project: IMPACT2C: Quantifying
projected impacts under 2 ◦ C warming, grant agreement no.
282746. The ENSEMBLES data used in this work was funded
by the EU FP6 Integrated Project ENSEMBLES (Contract
number 505539) whose support is gratefully acknowledged.
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