Download Basic physical mechanisms for monsoon failure in past and future

Institut für Physik und Astronomie
Arbeitsgruppe Prof. A. Levermann
Basic physical mechanisms for monsoon failure in
past and future climate
Kumulative Dissertation
zur Erlangung des akademischen Grades
“doctor rerum naturalium” (Dr. rer. nat.)
in der Wissenschaftsdisziplin “Klimaphysik”
eingereicht an der
Mathematisch-Naturwissenschaftlichen Fakultät
der Universität Potsdam
von
Jacob Schewe
Potsdam, im Mai 2011
Abstract
In this work, I first present simulations with a coupled climate model of intermediate complexity,
CLIMBER-3α, that project monsoon rainfall around the world to increase quasi–linearly with global
warming in the coming centuries. While this is generally consistent with many other studies, the atmospheric component of CLIMBER-3α is based on a simplified statistical–dynamical approach, and may
not sufficiently represent all processes that are relevant for the response of monsoon circulations to rapid
and intense climate change. Therefore, this study attempts to identify those physical mechanisms that
are of first–order importance for large–scale monsoon dynamics and their response to external changes.
I perform a scaling analysis of the heat and moisture budgets of the Earth’s major monsoon systems,
based on reanalysis data and theoretical considerations. I find that, during the monsoon season, a self–
amplifying feedback involving the advection and release of latent heat is essential for sustaining monsoon
rainfall after the surface land–sea thermal contrast has ceased. I frame this moisture–advection feedback
in a minimal conceptual model and show that it leads to a threshold behaviour with respect to changes in
the system’s energy budget. In particular, when either net radiation over land or specific humidity over
the adjacent ocean region fall short of a critical value, no conventional monsoon circulation can exist.
I thus define a domain of existence for continental monsoon rainfall, and estimate the threshold values
within the restrictions of the conceptual model. I demonstrate the applicability of this concept to abrupt
and persistent monsoon shifts observed in paleoclimatic records.
To understand monsoon failure occurring on shorter timescales and within the domain of existence,
I develop a minimal theory of intraseasonal monsoon dynamics. Supported by observations and by
results from a comprehensive global climate model, the core assumption of this theory is that the positive
moisture–advection feedback and its interaction with the smaller–scale eddy field render the circulation
inherently unstable. I apply this theory to an ensemble of millennial climate simulations and show that
both multi–decadal variability and projected future trends in Indian summer monsoon (ISM) rainfall
can be reproduced with a simple stochastic model of the inherent instability, modulated by ambient
climate conditions only during the onset period. A projected increase in ISM failure in response to a
global warming scenario can thus be readily explained by a shift in central Pacific mean spring–time
climate that persistently alters the initial conditions for internal ISM dynamics. I thereby propose a
novel perspective on monsoon variability as the result of internal instabilities modulated by pre-seasonal
ambient climate conditions.
In summary, the results in this thesis offer a simplified framework for the investigation of both long–
term (permanent) and short–term (seasonal) monsoon failure, including the basic physical mechanisms
that lead to a non–linear response of the monsoon system to external changes.
Zusammenfassung
In dieser Arbeit stelle ich zunächst Zukunftsprojektionen mit dem Erdsystemmodell mittlerer Komplexität CLIMBER-3α vor, die eine weltweite Zunahme des Monsunniederschlags über die nächsten
Jahrhunderte zeigen, welche annähernd proportional zum Anstieg der globalen Mitteltemperatur verläuft.
Zwar legen auch viele andere Studien einen solchen Anstieg nahe, jedoch verwendet CLIMBER-3α
eine vereinfachte, statistisch–dynamische Atmosphärenkomponente und gibt wahrscheinlich nicht alle
Prozesse, die für die Reaktion von Monsunzirkulationen auf rasanten Klimawandel relevant sind, hinreichend wieder.
Um die physikalischen Mechanismen zu identifizieren, die für die großskalige Monsundynamik und
ihre Reaktion auf äußere Veränderungen von zentraler Bedeutung sind, unterziehe ich die Wärmeund Feuchtebilanzen der wichtigsten Monsunsysteme anhand von Reanalysedaten und theoretischen
Überlegungen einer Skalenanalyse. Es zeigt sich, dass der Monsun während der Regenzeit in erster
Linie von einem selbstverstärkenden Rückkopplungsmechanismus angetrieben wird, bei dem die Advektion und Freisetzung latenter Wärme den atmosphärischen Temperaturunterschied zwischen Land
und Ozean aufrechterhält. Ich stelle diese Feuchte–Advektions–Rückkopplung in einem minimalistischen
konzeptionellen Modell dar und zeige, dass sich aus ihr ein nichtlineares Verhalten des Monsunsystems
gegenüber Störungen der Energiebilanz ergibt: Wenn etwa die atmosphärische Strahlungsbilanz über
dem Kontinent oder die Luftfeuchtigkeit über der benachbarten Ozeanregion einen kritischen Wert unterschreiten, kann sich keine konventionelle Monsunzirkulation entwickeln. Durch diesen kritischen Wert
wird entsprechend der Parameterbereich definiert, in dem Monsunniederschlag über Land möglich ist. Ich
nehme eine Abschätzung des kritischen Wertes für verschiedene Monsunregionen vor und zeige, dass sich
das Konzept auf abrupte Monsunveränderungen anwenden lässt, wie sie anhand von in paläoklimatischen
Rekonstruktionen dokumentiert sind.
Des Weiteren entwerfe ich eine minimalistische Theorie intrasaisonaler Monsundynamik mit dem Ziel,
Monsunausfälle zu verstehen, die auf kürzeren Zeitskalen und innerhalb des durch den kritischen Wert
definierten Existenzbereiches auftreten. Die zugrundeliegende Hypothese ist, dass die positive Feuchte–
Advektions–Rückkopplung und ihre Wechselwirkung mit turbulenten Störungen auf synoptischer und
kleinerer Skala zu einer inhärenten Instabilität führen. Ich entwickle ein einfaches stochastisches Modell,
in dem die Monsundynamik von dieser Instabilität bestimmt und lediglich zu Beginn der Monsunsaison
von äußeren klimatischen Einflüssen moduliert wird. Dieses Modell vergleiche ich mit einem Ensemble
von Langzeitsimulationen eines realistischen Klimamodells und zeige, dass sowohl die multidekadische
Variabilität als auch die für die Zukunft projizierten Entwicklungen reproduziert werden können. Eine
Häufung von Monsunausfällen unter einem Klimawandelszenario kann ich auf diese Weise mit einer
6
Veränderung des zentralpazifischen Frühjahrsklimas erklären. Die vorgestellte Theorie eröffnet somit eine
neue Sichtweise auf die Variabilität des Monsunniederschlags als das Ergebnis einer instabilen internen
Dynamik, die zu Beginn der Saison durch das umgebende Klima moduliert wird.
Die in dieser Arbeit vorgestellten Ergebnisse bieten einen vereinfachten theoretischen Rahmen für
die Untersuchung von Monsunausfällen auf langen (paläoklimatischen) wie auch kurzen (saisonalen)
Zeitskalen, sowie der grundlegenden physikalischen Prozesse, die zu einer nichtlinearen Reaktion von
Monsunsystemen auf äußere Störungen führen können.
Contents
1 Introduction
9
1.1
Motivation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
1.2
Scope and contents of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
1.3
Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15
2 Original manuscripts
17
2.1
Climate change under a scenario near 1.5◦ C of global warming . . . . . . . . . . . . . . .
17
2.2
Basic mechanism for abrupt monsoon transitions . . . . . . . . . . . . . . . . . . . . . . .
31
2.3
A critical humidity threshold for monsoon failure . . . . . . . . . . . . . . . . . . . . . . .
49
2.4
More frequent future monsoon failure due to inherent instability . . . . . . . . . . . . . .
81
3 Discussion and Conclusions
125
References
131
7
1
Introduction
1.1
Motivation
Monsoon systems are among the most prominent large–scale phenomena in the Earth’s atmosphere.
They govern seasonal and year–to–year climate variability in many tropical and subtropical regions,
and the associated rainfall is the dominant natural variable that human economies in those regions are
built around. Changes in monsoon rainfall appear to have affected human societies throughout history,
as illustrated for example by the fate of ancient civilizations in the Indus Valley (Rashid et al., 2011)
or the rise and fall of dynasties and kingdoms in China (Zhang et al., 2008). Also today, agricultural
productivity in South Asia, East Asia, and Africa is closely tied to the magnitude and timing of monsoon
rainfall (Parthasarathy et al., 1988; Kumar et al., 2004; Tao et al., 2004; Haile, 2005; Gregory et al., 2005;
Auffhammer et al., 2006). Present–day monsoon rainfall, as observed over the past century, exhibits
significant intraseasonal and interannual variability (e.g. Webster, 1987b), including catastrophic floods
and droughts that pose risks to large parts of the population in those regions the affected regions (e.g.
Sikka, 2003; Webster et al., 2011).
While decadal–scale average monsoon rainfall has been relatively stable during the past century of
direct observations, rising trends have been observed in the annual number of extreme rain events in
India (Goswami et al., 2006a). The future evolution of the Indian summer monsoon, and other monsoon
systems, under a combination of anthropogenic forcing factors is unclear, according to an intercomparison
of comprehensive climate models (Meehl et al., 2007). Recent projections indicate that the response to
increased greenhouse gas (GHG) concentrations may differ in sign among major monsoon regions, and
reveal large uncertainties about the magnitude of the response (Kripalani et al., 2007a,b; Cherchi et al.,
2010). The effect of increased aerosol abundance is significant and may be counteracting that of GHGs
(Ramanathan et al., 2005; Lau & Kim, 2006), while human-induced vegetation changes feed back on
precipitation (Ganopolski et al., 1998; Claussen, 2009). Observations and modelling studies suggest a
recent regime shift in Asian monsoon convection (Turner & Hannachi, 2010) and its relation to northern
hemisphere thermal gradients (Li et al., 2009; Sun et al., 2010).
At the same time, paleoclimatic records show evidence of abrupt and strong monsoon shifts in India,
the Bay of Bengal, and East Asia, during the last two glacial cycles (Burns et al., 2003; Wang et al.,
2005a, 2008) and the Holocene (Gupta et al., 2003; Hong et al., 2003; Wang et al., 2005b; Rashid et al.,
2011). Some of these abrupt changes have been linked to climatic events in the North Atlantic for the
last glacial period (Overpeck et al., 1996; Burns et al., 2003) as well as for the Holocene (Gupta et al.,
2003; Wang et al., 2005b). A physical mechanism for this teleconnection has been suggested (Goswami
et al., 2006b), but relevant climatic signals of the North Atlantic events in Asia (such as temperature
10
1
INTRODUCTION
and moisture anomalies) are very small (Zhang & Delworth, 2005). East Asian monsoon shifts during
the last two glacial cycles have been roughly in phase with hemispheric insolation changes (Wang et al.,
2008), but the latter are far too slow to explain the rapidity of the transitions between different periods
of relatively stable monsoon strength. These observations indicate that internal feedbacks in monsoon
dynamics may have amplified the weak external forcing.
Both the unclear cause of paleoclimatic monsoon shifts and the uncertain future evolution of monsoon
rainfall in a changing climate call for an improved understanding of possible non–linearities in large–scale
monsoon dynamics. This thesis aims at contributing to such an improvement by identifying the primary
driving mechanisms of continental monsoon rainfall and examining their stability properties and their
implications for the response of the monsoon circulation to external forcing. It does not attempt to
account for all aspects of monsoon dynamics in general, but focuses on the possibility of large–scale
monsoon failure and the associated processes.
1.2
Scope and contents of the thesis
Monsoons are among the most complex components of the climate system, and they interact with various
other components on multiple timescales (Webster et al., 1998; Wang, 2005). Both spatial patterns and
temporal evolution of monsoon rainfall are influenced by a number of physical processes, such as the
El Niño–Southern Oscillation (ENSO) phenomenon (e.g. Krishnamurthy & Goswami, 2000; Goswami &
Xavier, 2005), regional (Clark et al., 2000; Kucharski et al., 2006; Yang et al., 2007) and remote sea
surface temperatures (Goswami et al., 2006b), atmospheric aerosol concentrations (Ramanathan et al.,
2005), or Eurasian snow cover (Hahn & Shukla, 1976; Dash et al., 2005), as well as by characteristics
of vegetation (Meehl, 1994; Claussen, 1997; Robock et al., 2003) and topography (Liu & Yin, 2002).
Direct observational records of monsoon rainfall, available for the last century, exhibit a high variability
on different timescales. For instance, in India (where data availability is highest among the monsoon
regions), seasonal mean rainfall amounts vary from year to year and, though correlated to some degree
with ENSO, are difficult to predict in advance (Goswami et al., 2006). Within the monsoon season,
rainfall does not occur uniformly, but is concentrated in periods of roughly a few weeks length, called
active spells, which are intercepted by drier periods, called break spells (Krishnamurthy & Shukla, 2000;
Rajeevan et al., 2010). These active and break spells vary in number and length from year to year, and
within them, daily rainfall amounts also vary greatly. In addition, there are large inhomogeneities in the
spatial distribution of rainfall over the monsoon regions.
Despite this complexity, however, there is one fundamental driving force behind large–scale continental monsoon rainfall: Namely, the atmospheric temperature contrast between land and ocean (e.g.
1.2
Scope and contents of the thesis
11
6
surface
column
Δ T (K)
4
2
0
−2
−4
2
4
6
8
month
10
12
Figure 1.1: Climatological temperature difference ∆T between land and ocean in the
Indian region, derived from NCEP/NCAR reanalysis data (Kistler et al., 2001). At
the surface (solid line), ∆T is largest is spring and then decreases due to cooling of
the land surface by precipitation. Averaged over the atmospheric column (dashed
line), ∆T is maintained throughout the rainy season.
Webster, 1987a). This temperature contrast develops in spring when the continent heats up faster
than the ocean, owing to the large differences in heat capacity; and it is maintained throughout the
rainy season, even when the rains have started to cool the land surface and sensible heating has ceased
(Fig. 1.1). It shapes the anomalous pressure system that draws strong, mainly ageostrophic winds
towards the continent in the lower troposphere, carrying the moisture that is then released in convection.
While this is far from a complete description of monsoon dynamics, it is a central and necessary condition
for monsoon rainfall to develop. Furthermore, the magnitude of the land–sea atmospheric temperature
difference is generally correlated with seasonal rainfall amounts: The stronger the difference during a
given monsoon season, the more rainfall can be expected, irrespective of where and when exactly that
rain falls. That also means that long–term changes in the temperature contrast can be expected to affect
long–term mean monsoon rainfall.
The seasonal development of the land–sea atmospheric temperature contrast, as a driving force of
monsoon rainfall, is captured by linear empirical models (e.g. Srinivasan, 2001) and also by coarse–
resolution climate models that otherwise may not have the spatial resolution and the degree of realism in
atmospheric physics necessary to capture all aspects of monsoon dynamics. Such models can therefore
12
1
INTRODUCTION
be used to make meaningful projections of the direct effect of temperature changes on large–scale
monsoon characteristics. In the first article of this thesis (Schewe et al., 2011a), I have applied the
Earth system model of intermediate complexity, CLIMBER-3α (Montoya et al., 2005), to project
the climatic consequences of the Representative Concentration Pathways, a set of recently developed
GHG concentration scenarios for use in the forthcoming assessment of the Intergovernmental Panel
on Climate Change (IPCC). I find that average monsoon rainfall in South Asia, East Asia and Africa
increases approximately linearly with the regional land–ocean surface temperature contrast, which
in turn is a direct consequence of global surface warming (due to the increase in GHG abundance.)
Depending on the region and the scenario, the projected rainfall increases are substantial; e.g. between
about 25% (South Asia) and 50% (East Asia) until the end of the 21st century under the highest scenario.
As discussed above, paleoclimatic records reveal large and abrupt shifts in monsoon intensity that
obviously cannot be explained as a linear response to external forcing. They therefore require different
concepts than a perturbation analysis around the present–day state. In the second and third articles of
this thesis, I take the approach of a non–linear empirical model to obtain a first–order understanding
of the processes that may have led to such abrupt events. The second article (Levermann et al., 2009)
sets up a minimal conceptual model of a monsoon circulation, comprising only the conservation of heat
and moisture and knowingly neglecting many other important physical processes, in order to distill the
fundamental non–linearity of monsoon dynamics. The model is based on a scaling analysis of the heat
and moisture budgets of major monsoon systems around the world, using present–day reanalysis data.
I find that sensible heating is important in establishing the atmospheric land–sea temperature contrast
prior to the rainy season, but becomes small after the onset of heavy rainfall. During the rainy season,
the temperature gradient is maintained by the release of latent heat over the continent. Thus, the
advection of moist air from the ocean and subsequent condensation of that moisture sustains the driving
force for the monsoon winds, and thereby constitutes a self–amplifying feedback (illustration in Fig. 1.2).
I show in the conceptual model that this moisture–advection feedback implies a threshold behaviour
with respect to quantities that affect the energy budget: E.g., if net radiative flux to the atmospheric
column falls below a critical value, no conventional monsoon circulation can develop. If the system was
close to the threshold, a small variation in external parameters could therefore lead to a transition from
a “normal” monsoon regime into a regime without continental monsoon rainfall.
However, considering the present–day state of the Earth’s monsoon systems, huge changes in net
radiation would be necessary to reach the threshold. In the third article (Schewe et al., 2011b), I
show that the minimal conceptual model also yields a threshold behaviour with respect to atmospheric
humidity over the ocean adjacent to the monsoon region. This quantity is more volatile than net
1.2
Scope and contents of the thesis
13
Figure 1.2: Geometry of the minimal conceptual monsoon model used in the second
and third articles of the thesis, with wind W, precipitation P, and net radiative flux R.
The tripartite loop illustrates the fundamental moisture–advection feedback; arrows
indicate the amplification of one physical process by another.
radiation, and the thresholds are relatively closer to the present–day situation. Based on reanalysis
data, I estimate the threshold values for four major monsoon regions. I apply this concept to a proxy
record of East Asian monsoon rainfall that exhibits a series of abrupt monsoon transitions during the
penultimate glacial period. Assuming that average humidity over the ocean was altered by orbital–scale
changes in hemispheric solar insolation, I show that the conceptual model can qualitatively explain
these transitions. As evaporation from the ocean surface can also be affected by a number of other
processes (e.g. wind speed, oceanic upwelling) on different timescales, the model could serve to improve
the understanding of other past monsoon events as well.
The basic dynamics captured in the conceptual model thus define a domain of existence for continental
monsoon rainfall; in the sense that a conventional monsoon can only develop within this domain, e.g.
above the humidity threshold. This however does not mean that within the domain of existence, monsoon
rainfall will be at full strength at all times. As I show in the fourth article of this thesis (submitted and
under review at Nature), even under present–day conditions, seasonal–mean Indian summer monsoon
(ISM) rainfall can fall short of its long–term average by 70% and more in individual years, according to
a comprehensive atmosphere–ocean general circulation model (AOGCM). Such dry monsoon years have
14
1
INTRODUCTION
not been observed in the last century, but an ensemble simulation that covers over 6,000 model years
shows a characteristic frequency distribution of seasonal–mean rainfall that continuously extends down
to these extremely low values, though with low frequency of occurrence.
Understanding such temporary monsoon failure again requires looking at the fundamental driving
mechanism of monsoon rainfall. Using AOGCM results and observational data, I show that the moisture–
advection feedback generally acts on a short sub–seasonal timescale (on the order of days rather than
months) and is frequently interrupted because of continuous perturbation by stochastic fluctuations from
the synoptic–scale eddy field. Moreover, during such interruptions, the large-scale monsoon circulation
does not simply slow down or come to a hold, but even tends to reverse, in the sense that convection is
replaced by subsidence of upper–tropospheric air over large parts of the subcontinent and the adjacent
ocean. Since this air is much drier than lower–tropospheric air, the subsidence effectively dries out
the monsoon winds and further inhibits a recommencement of the moisture–advection feedback. Thus,
another self–amplifying feedback is constituted that counteracts the moisture–advection feedback.
In the article, I develop a minimal theory of intraseasonal monsoon dynamics, based on the assumption
that the monsoon season is governed by a permanent interplay of those two counteracting feedbacks: Each
feedback itself tends to persist, and stochastic fluctuations tend to perturb the currently active feedback
and induce a flip into the other one. I frame this assumption in a simple, statistically predictive model of
seasonal–mean monsoon rainfall and show that it can reproduce the characteristic frequency distribution
found in the AOGCM, including the very dry years, or failures.
An important aspect of this simple model is that it is linked to external forcing only during the onset
period; for the rest of the monsoon season, only the idealized internal dynamics are at work and produce
rainy and dry periods that then add up to a seasonal average. I show that the model can reproduce a
large portion of multidecadal monsoon variability when forced by central–Pacific mean sea level pressure
(MSLP) anomalies in May, i.e. at the onset time. The central Pacific is known to have a distinct influence
on Indian monsoon climate, as is evident in the correlation between ISM rainfall and ENSO. Anomalously
low spring-time MSLP in the central Pacific is assumed to induce atmospheric conditions that favor more
subsidence over the Indian region and thus lead to more deficient monsoon onsets.
I then apply the simple model to global warming simulations using the same AOGCM, where ISM
failure is projected to become much more frequent until the end of the 22nd century. At the same time,
a shift towards lower spring-time MSLP in the central Pacific is projected. Again forcing the simple
model with central–Pacific MSLP anomalies in May, it successfully reproduces the projected trend in
average monsoon rainfall. The minimal theory presented in this work thereby offers a novel perspective
on monsoon variability as the result of internal instabilities modulated by pre-seasonal ambient climate
conditions.
1.3
Overview
1.3
15
Overview
This thesis is organized around four scientific articles which are either published or under review.
Each article provides their own introductory and concluding remarks, and references; some also carry
supplementary material. Here, a brief overview is given of the titles, contents, and author contributions
of the individual articles. The original manuscripts are included in the following section.
Article 1:
Climate change under a scenario near 1.5◦ C of global warming: monsoon
intensification, ocean warming and steric sea level rise
Jacob Schewe, Anders Levermann, & Malte Meinshausen
The first article, published in Earth System Dynamics, explores the climatic consequences of the latest
set of greenhouse gas concentration scenarios for the coming centuries. The response of the most important large–scale oceanic, atmospheric, and coupled processes is investigated in a coupled climate model of
intermediate complexity. Among other results, monsoon rainfall is projected to increase approximately
linearly with the regional land–sea temperature contrast due to global warming.
Jacob Schewe performed the climate model simulations, analyzed the results, and wrote the paper.
Malte Meinshausen provided the AOGCM emulations. All three authors participated in the interpretation of the results and the improvement of the manuscript.
Article 2:
Basic mechanism for abrupt monsoon transitions
Anders Levermann, Jacob Schewe, Vladimir Petoukhov, & Hermann Held
The second article, published in Proceedings of the National Academy of Sciences of the USA (PNAS),
presents a scaling analysis of the heat and moisture budgets of the Earth’s major monsoon systems, and
develops a minimal conceptual monsoon model capturing the essential moisture–advection feedback. It
shows that this feedback yields a threshold behaviour with respect to changes in net radiation, and
estimates the thresholds from reanalysis data.
Jacob Schewe analyzed the data; Anders Levermann, Vladimir Petoukhov and Jacob Schewe devised
the conceptual model; Hermann Held performed statistical tests of the robustness of the results; Anders
Levermann performed the computations and wrote the paper. All four authors participated in the
interpretation of the results and the improvement of the manuscript. Anders Levermann initiated the
research.
16
1
INTRODUCTION
Supporting information is included following the main manuscript.
Article 3:
A critical humidity threshold for monsoon failure
Jacob Schewe, Anders Levermann, & Hai Cheng
The third article, published in Climate of the Past Discussions, shows that the minimal conceptual
monsoon model implies a threshold behaviour with respect to specific humidity over the ocean, a quantity
which is more volatile than net radiation and exhibits threshold values closer to modern climate. The
threshold values are estimated from reanalysis data for four major monsoon regions, and the model is
applied to a paleoclimatic reconstruction of East Asian summer monsoon rainfall, yielding a series of
abrupt transitions in response to gradual insolation changes which is similar to those observed in the
proxy record.
Jacob Schewe performed the computations, analyzed data, and wrote the paper. Hai Cheng provided
proxy data. Jacob Schewe and Anders Levermann interpreted the results and improved the manuscript.
Anders Levermann initiated the research.
Article 4:
More frequent future monsoon failure due to inherent instability
Jacob Schewe & Anders Levermann
The fourth article (submitted and currently under review) projects Indian summer monsoon failure
to become considerably more frequent under a global warming scenario, using a comprehensive coupled
climate model. It presents a minimal theory of intraseasonal monsoon dynamics, based on an inherent
instability that is modulated by ambient climate merely during the onset period. Forced only by global
mean temperature and central–Pacific sea level pressure anomalies in May, this simple model reproduces
future trends as well as past multidecadal variability of monsoon rainfall as found in the comprehensive
climate model. The study proposes a novel perspective on monsoon variability as the result of internal
instabilities modulated by pre-seasonal ambient climate conditions.
Jacob Schewe analyzed data and climate model results and wrote the paper. Jacob Schewe and Anders
Levermann developed the minimal theory, interpreted the results, and improved the manuscript.
R
Supplementary Material, and the Matlab
code of the simple “day-to-day model”, are included
following the main manuscript.
2
Original manuscripts
2.1
Climate change under a scenario near 1.5◦ C of global warming
Climate change under a scenario near 1.5◦ C of global warming
2.1
Earth Syst. Dynam., 2, 25–35, 2011
www.earth-syst-dynam.net/2/25/2011/
doi:10.5194/esd-2-25-2011
© Author(s) 2011. CC Attribution 3.0 License.
19
Earth System
Dynamics
Climate change under a scenario near 1.5 ◦C of global warming:
monsoon intensification, ocean warming and steric sea level rise
J. Schewe1,2 , A. Levermann1,2 , and M. Meinshausen1
1 Earth
System Analysis, Potsdam Institute for Climate Impact Research, Potsdam, Germany
Institute, Potsdam University, Potsdam, Germany
2 Physics
Received: 30 September 2010 – Published in Earth Syst. Dynam. Discuss.: 14 October 2010
Revised: 8 February 2011 – Accepted: 4 March 2011 – Published: 8 March 2011
Abstract. We present climatic consequences of the Representative Concentration Pathways (RCPs) using the coupled
climate model CLIMBER-3α, which contains a statisticaldynamical atmosphere and a three-dimensional ocean model.
We compare those with emulations of 19 state-of-the-art
atmosphere-ocean general circulation models (AOGCM) using MAGICC6. The RCPs are designed as standard scenarios for the forthcoming IPCC Fifth Assessment Report
to span the full range of future greenhouse gas (GHG) concentrations pathways currently discussed. The lowest of the
RCP scenarios, RCP3-PD, is projected in CLIMBER-3α to
imply a maximal warming by the middle of the 21st century slightly above 1.5 ◦ C and a slow decline of temperatures
thereafter, approaching today’s level by 2500. We identify
two mechanisms that slow down global cooling after GHG
concentrations peak: The known inertia induced by mixingrelated oceanic heat uptake; and a change in oceanic convection that enhances ocean heat loss in high latitudes, reducing
the surface cooling rate by almost 50%. Steric sea level rise
under the RCP3-PD scenario continues for 200 years after
the peak in surface air temperatures, stabilizing around 2250
at 30 cm. This contrasts with around 1.3 m of steric sea level
rise by 2250, and 2 m by 2500, under the highest scenario,
RCP8.5. Maximum oceanic warming at intermediate depth
(300–800 m) is found to exceed that of the sea surface by
the second half of the 21st century under RCP3-PD. This
intermediate-depth warming persists for centuries even after surface temperatures have returned to present-day values,
with potential consequences for marine ecosystems, oceanic
methane hydrates, and ice-shelf stability. Due to an enhanced
land-ocean temperature contrast, all scenarios yield an intensification of monsoon rainfall under global warming.
Correspondence to: J. Schewe
([email protected])
1
Introduction
In December 2010, the international community agreed,
under the United Nations Framework Convention on Climate Change, to limit global warming to below 2 ◦ C
(Cancún Agreements, see http://unfccc.int/files/meetings/
cop 16/application/pdf/cop16 lca.pdf). At the same time, it
was agreed that a review, to be concluded by 2015, should
look into a potential tightening of this target to 1.5 ◦ C – in
part because climate change impacts associated with 2 ◦ C
are considered to exceed tolerable limits for some regions,
e.g. Small Island States. So far, research into climate system
dynamics under strong mitigation scenarios that keep warming below 2 ◦ C or even 1.5 ◦ C has been sparse. Individual
AOGCMs were run for scenarios stabilizing at 2 ◦ C (May,
2008) or below (Washington et al., 2009), or for idealized
CO2 rampdown experiments (Wu et al., 2010).
Here we investigate climate projections for the full range
of Representative Concentration Pathways (RCPs; Moss
et al., 2010) but focus in particular on the lowest scenario
RCP3-PD, which reflects a scenario that will peak global
mean temperatures slightly above, but close to, 1.5 ◦ C above
pre-industrial levels in our model. The RCPs were recently
developed in order to complement, and in part replace, the
Special Report on Emissions Scenarios (SRES; Nakicenovic and Swart, 2000) scenarios, and will be used in the Climate Model Intercomparison Project’s Phase 5 (CMIP5) that
is to be assessed in the forthcoming Intergovernmental Panel
on Climate Change (IPCC) Fifth Assessment Report (AR5).
The RCP3-PD scenario is characterized by a peak of atmospheric greenhouse gas (GHG) concentrations in 2040 and
a subsequent decline in GHG abundance. After 2070, CO2
emissions turn negative and remain at around −1 Gt CO2 eq yr−1 after 2100 (Meinshausen et al., 2011). Concentrations in the medium-low RCP4.5 and the medium-high RCP6
Published by Copernicus Publications on behalf of the European Geosciences Union.
20
2
J. Schewe et al.: Climate near 1.5 ◦ C warming
26
stabilize by 2150, while concentrations in the high RCP8.5
continue to rise until 2250.
In Sect. 2, we describe the models and their experimental setup for this study. Simulation results are presented in
Sect. 3, in particular for global mean temperature (Sect. 3.1),
and changes in large scale climate components like oceanic
meridional overturning circulation (Sect. 3.2), monsoon
(Sect. 3.3), global sea level (Sect. 3.4), and deep ocean temperature (Sect. 3.5). In Sect. 4 we provide the physical
mechanisms responsible for an asymmetrically slower cooling than warming under RCP3-PD. Section 5 concludes.
2 Models and experiments
Our primary model for investigating key large-scale aspects
of climate change under the RCP scenarios is the Earth system model of intermediate complexity CLIMBER-3α (Montoya et al., 2005). CLIMBER-3α combines a statisticaldynamical atmosphere model (Petoukhov et al., 2000) with
a three-dimensional ocean general circulation model based
on the GFDL MOM-3 code (Pacanowski and Griffies, 1999)
and a dynamic and thermodynamic sea-ice model (Fichefet
and Maqueda, 1997). In this study, CLIMBER-3α is used
without a carbon cycle. The atmosphere model has a coarse
horizontal resolution of 22.5◦ in longitude and 7.5◦ in latitude, and employs parameterized vertical temperature and
humidity profiles. Oceanic wind stress anomalies are computed with respect to the control simulation and added to the
climatology of Trenberth et al. (1989). The oceanic horizontal resolution is 3.75◦ × 3.75◦ with 24 variably spaced
vertical levels. The model’s sensitivity to vertical diffusivity (Mignot et al., 2006) and wind stress forcing (Schewe
and Levermann, 2010) has been investigated as well as the
model’s behaviour under glacial boundary conditions (Montoya and Levermann, 2008) and global warming (Levermann
et al., 2007). When compared to AOGCMs of the third Coupled Model Intercomparison Project (CMIP3) and previous
generations, the model shows qualitatively and quantitatively
similar results with respect to large-scale quantities (Gregory
et al., 2005; Stouffer et al., 2006b). The model version used
here features a low background value of oceanic vertical diffusivity (0.3 × 10−4 m2 s−1 ) and an improved representation
of the Indonesian throughflow as compared to the version described by Montoya et al. (2005).
We complement our CLIMBER-3α projections of global
mean temperature with emulations of 19 AOGCMs used in
the IPCC Fourth Assessment Report (AR4). These emulations were performed with MAGICC6, a reduced complexity model with an upwelling-diffusion ocean which has
been used in the past three IPCC assessment reports (Wigley
and Raper, 2001). MAGICC6 was shown to be able to
closely emulate the global and hemispheric mean temperature evolution of AOGCMs (Meinshausen et al., 2008). Our
AOGCM emulations use RCPs harmonized emission inputs
Earth Syst. Dynam., 2, 25–35, 2011
ORIGINAL MANUSCRIPTS
with default efficacies for the individual forcing agents, identical to the model’s setup for creating the default RCP GHG
concentration recommendations for CMIP5 (Meinshausen
et al., 2011). The only exception is that MAGICC6’s climate
model is calibrated and run for the range of 19 individual
AOGCMs, rather than a single median set of climate module
parameters.
Our CLIMBER-3α experiments focus on the four new
RCPs, namely RCP3-PD (van Vuuren et al., 2007), RCP4.5
(Clarke et al., 2007; Smith and Wigley, 2006; Wise et al.,
2009), RCP6 (Fujino et al., 2006), and RCP8.5 (Riahi et al.,
2007). We use the historical, 21st century and long-term
(until 2500) RCP forcing trajectories as provided on http://
www.pik-potsdam.de/∼mmalte/rcps/ and described in Meinshausen et al. (2011). These forcings arose from the process of harmonizing RCP emissions, and producing a single default set of GHG concentrations, which are the basis
for the CMIP5 intercomparison runs that extend from preindustrial times to 2300 (CMIP5, http://cmip-pcmdi.llnl.gov/
cmip5/forcing.html). The extension beyond 2300 follows the
same guiding principle as the extension up to 2300, i.e. a continuation of constant emissions for the RCP3-PD scenario
(and correspondingly dropping forcing levels) and a stabilization of GHG concentrations and forcing levels for the upper three RCPs, RCP4.5, RCP6 and RCP8.5.
For being used in CLIMBER-3α, we group our forcings on
a forcing-equivalence basis, i.e. we aggregate longwave absorbers into a CO2 -equivalence concentration (Fig. 1a and d).
The radiative forcing of agents that scatter or absorb shortwave radiation is aggregated and assumed to modulate the
incoming solar irradiance, taking into account geometry and
albedo (Fig. 1b and e). CLIMBER-3α’s climate sensitivity
is about 3.4 ◦ C, which is higher than the average climate
sensitivity of the transient AOGCM emulations of 2.9 ◦ C
(Meinshausen et al., 2008, Table 4), very close to the average of the slab–ocean GCMs of 3.26 ◦ C and still close to
the IPCC AR4 best estimate of 3 ◦ C (Meehl et al., 2007a,
Box 10.2). The transient climate response is about 1.9 ◦ C for
CLIMBER-3α, compared to about 1.8 ◦ C for the average of
IPCC AR4 AOGCMs (Meehl et al., 2007b, Table 8.2).
3
3.1
Results
Global mean temperature
Global mean surface air temperatures, normalized to the period 1980–1999, are shown in Fig. 1c and f relative to preindustrial (1860–1890) using the median observed temperature increase of 0.52 ◦ C (Brohan et al., 2006). The warming
projected by CLIMBER-3α lies well within the emulation
of the AOGCMs (Fig. 1c and f). For the highest scenario,
RCP8.5, the simulation yields a temperature increase of up
to 8.5 ◦ C, while the lowest scenario, RCP3-PD, reaches up
to 1.6 ◦ C of global warming compared to pre-industrial and
www.earth-syst-dynam.net/2/25/2011/
J. Schewe et al.: Climate near 1.5◦ C warming
2.1
9
Climate change under a scenario near 1.5◦ C of global warming
21
J. Schewe et al.: Climate near 1.5 ◦ C warming
1500
d
a
2000
1000
ppmv
ppmv
3000
27
1000
500
e
12
10
1365
1360
1355
1350
b
c
RCP
6
extension
f
RCP8.5
4
C
°
°
C
8
6
W/m2
W/m2
0
1365
1360
1355
1350
RCP6
2
4
RCP4.5
2
0
RCP3−PD
0
2000
2200
year
2400
1950
2000
2050
2100
year
Fig. 1. Forcing and global mean temperature response of the CLIMBER-3α climate model under the RCP3-PD (blue), RCP4.5 (yellow),
RCP6
andresponse
their extensions
until 2500. The
grey model
verticalunder
band the
marks
the RCP(blue),
periodRCP4.5
2005 to(yellow),
2100.
Fig.
1. (grey)
Forcingand
andRCP8.5
global (red)
meanscenarios
temperature
of the CLIMBER-3α
climate
RCP3-PD
(a) CO(grey)
concentration
(in ppmv)
longwave
absorbers
(KyotoThe
and grey
Montreal
protocol
gasesperiod
as well2005
as tropospheric
RCP6
and RCP8.5
(red) scenarios
and of
their
extensions
until 2500.
vertical
band greenhouse
marks the RCP
to 2100. (a)
2 -equivalence
ozone).
(b) Incoming
solar irradiance
(W mof−2longwave
), modified
by the radiative
of agents
active greenhouse
in the shortwave
(mainly
volcanic
CO
concentration
(in ppmv)
absorbers
(Kyoto forcing
and Montreal
protocol
gasesrange
as well
as tropospheric
2 -equivalence
and anthropogenic
aerosols)
and changes
in2surface
albedo
to land-use
change.
(c) Global
temperature
difference
ozone).
(b) Incoming
solar irradiance
(W/m
), modified
by due
the radiative
forcing
of agents
activesurface
in the air
shortwave
range(SAT)
(mainly
volcanic
et surface
al., 2006),
for the
simulations
(solid lines)
andair19temperature
AOGCM emulations
using
in ◦anthropogenic
C compared toaerosols)
pre-industrial
(Brohan in
and
and changes
albedo
dueCLIMBER-3α
to land-use change.
(c) Global
surface
(SAT) difference
◦
(the dashed
line denotes
the median,
darkfor
andthe
light
shading denotes
the 50% and
80%
range,
to (f): Asusing
(a)
inMAGICC6
C compared
to pre–industrial
(Brohan
et al.,and
2006),
CLIMBER-3α
simulations
(solid
lines)
andrespectively).
19 AOGCM(d)
emulations
to (c), but enlarged
for the
1950–2100.
MAGICC6
(the dashed
lineperiod
denotes
the median, and dark and light shading denotes the 50% and 80% range, respectively). (d) to (f): As (a)
to (c), but enlarged for the period 1950-2100.
then drops at an average rate of about −0.16 ◦ C per century.
This is about ten times slower than the currently observed
temperature rise of 0.16 to 0.18 ◦ C per decade (Trenberth
et al., 2007, section 3.4). Although the reduction in GHG
concentrations in the RCP3-PD is generally slower than the
increase before the peak, this explains only part of the warming/cooling asymmetry: The average cooling rate during the
first 100 years after the peak is 12% of the warming rate in
the 100 years before the peak; over the same period, the GHG
reduction rate is 35% of the increase rate prior to the peak.
The mechanisms responsible for this asymmetry will be discussed in Sect. 4.
3.2
Spatial warming pattern and oceanic overturning
The spatial distribution of temperature change in 2100 reflects the pattern of polar amplification (Winton, 2006),
i.e. above-average surface warming in high latitudes (Fig. 2).
In the low RCP3-PD scenario (Fig. 2a), warming in the
northern North Atlantic region is offset by the cooling effect
www.earth-syst-dynam.net/2/25/2011/
of a 20% reduction of the Atlantic meridional overturning
circulation (AMOC; Fig. 3a) and the associated reduction
in oceanic convection and heat release (compare Sect. 4).
As the AMOC recovers over the course of the 22nd and
23rd century, this offsetting effect will disappear. In the
RCP8.5 scenario (Fig. 2b), the AMOC reduction is relatively
smaller compared to the warming, and has no large offsetting effect. The recovery of the AMOC beyond 2200 is facilitated by the retreat of sea ice cover in the North Atlantic
(Levermann et al., 2007), which in the case of RCP3-PD
even leaves the AMOC stronger in the long-term than under pre-industrial conditions. The behaviour of the AMOC
under global warming in CLIMBER-3α is a robust feature
of most CMIP3 AOGCMs (Gregory et al., 2005), and the
mechanisms at play are in qualitative agreement across the
models (Levermann et al., 2007). Quantitatively, AOGCMs
differ significantly in their response. With respect to the
pre-industrial overturning strength, CLIMBER-3α is comparable to the IPCC AR4 model average and consistent with
Earth Syst. Dynam., 2, 25–35, 2011
J. Schewe et al.: Climate near 1.5◦ C warming
10
J. Schewe et al.: Climate near 1.5◦ C warming
10
22
2
ORIGINAL MANUSCRIPTS
J. Schewe et al.: Climate near 1.5 ◦ C warming
28
3
80ºN
80ºN
2
a
1
80ºS
40ºS
0
80ºN
80ºS
10
40ºN
80ºN
0º
8
b
6
40ºN
40ºS b
0º
80ºS
40ºS
100ºW
0º
100ºE
a
AMOC
AMOC
16
2
14
12
1
Sv
a
0
30
10
4
8
2
6
0
4
14
12
10
25
b 10
30
b
20
15
25
Sv
0º
40ºS
a 18
16
Sv
40ºN
0º
3
Sv
40ºN
18
SPG
20
2
10
2000
2200
2400
Fig. 2. (a) Surface air temperature anomaly for the year 2100, in
15
year SPG
◦ C, 80ºS
Fig.RCP3-PD.
2. (a) Surface
air temperature
anomaly
for
the◦year
2100,
intimes
0
for
Average
warming
south
of
60
S
is
1.60
◦
for RCP3-PD.
Average warming
of
60◦ S is 1.60 times◦
100ºW
0º south
100ºE
higherC,than
the global
mean. Average
warming
north of 60 N is
higher than the global mean. Average◦ warming north of 60◦ N is
only only
0.830.83
times
the
global
mean
(1.4
◦ C), because the cooling ef-Fig. 3.
(a) Maximum
10 AMOC strength of the Atlantic Meridtimes the global mean (1.4 C), because the cooling ef2000 (AMOC) in2200
fect of
a of
reduction
in in
thetheAtlantic
Overturning
Circula-ional Overturning Circulation
Sv (106 m3 s−1 ), 2400
for
fect
a reduction
Atlantic Meridional
Meridional Overturning
Circulayear
and RCP8.5 (red).
tionFig.
(AMOC)
for RCP8.5
(a)counteracts
Surface
air polar
temperature
anomaly
forSame
the
2100, inRCP3-PD (blue), RCP4.5 (yellow), RCP6 (grey),
tion2.(AMOC)
counteracts
polaramplification.
amplification.
(b)(b)
Same
foryear
RCP8.5
◦
◦ (with a global mean of 4.8
C).warming
The polar
amplification
are times
C,a for
RCP3-PD.
south
of 60◦ Sfactors
isfactors
1.60
(with
global
mean ofAverage
4.8 ◦ C).
The
polar
amplification
are(b) North Atlantic subpolar◦ gyre strength, in◦ Sv, computed from
velocities
55 N between
33.8 W
and the of
Labrador
1.48
in
the
south
and
1.53
in
the
north.
Fig.
3. (a) atMaximum
AMOC
strength
the Atlantic Meridhigher
theand
global
Average warming north of 60◦ N ismeridional
1.48
in thethan
south
1.53mean.
in the north.
◦
coast (62
W).3.
6 m3 s−1
Fig.
(a) Maximum
AMOC
strength inof Sv
the(10
Atlantic
Meridonly 0.83 times the global mean (1.4◦ C), because the cooling efional
Overturning
Circulation
(AMOC)
), for
6
3 −1
ional
Overturning
Circulation
(AMOC)
in
Sv
(10
s ),(red).
for
fect of a reduction in the Atlantic Meridional Overturning CirculaRCP3-PD (blue), RCP4.5 (yellow), RCP6 (grey), and m
RCP8.5
RCP3-PD
(blue), RCP4.5
(yellow),
RCP6 (grey),
andcomputed
RCP8.5 (red).
tion (AMOC)
counteracts
polar
amplification.
(b)
Same forAMOC
RCP8.5
(b)
North Atlantic
subpolar
gyre strength,
in Sv,
from
observations
(cf.
Fig. 10.15
in
Meehl
et
al.,
2007a).
(b) North Atlantic
gyre
strength,
Sv,and
computed
from
(with ain
global
mean to
of global
4.8◦ C). warming
The polar in
amplification
factorsare
are
meridional
velocitiessubpolar
at 55◦ N
between
33.8in◦ W
the Labrador
changes
response
CLIMBER-3α
◦ W).
meridional
velocities at 55◦ N between 33.8◦ W and the Labrador
1.48 in the south and 1.53 in the north.
coast
(62
dominated by changes in heat flux, as in most other CMIP3
coast (62◦ W).
models, while hydrological changes tend to have a minor,
strengthening effect (Gregory et al., 2005). Further possible
AMOC reduction due to Greenland ice sheet melting is not
accounted for in these simulations.
3.3
Monsoon intensification
Directly influenced by atmospheric temperature patterns,
large-scale monsoon circulations are arguably among the
most societally relevant atmospheric systems. Within the
limitations of the statistical-dynamical atmosphere model
and its coarse resolution, CLIMBER-3α simulates the principal patterns of monsoon dynamics and precipitation reasonably well (Fig. 4a), and its seasonal rainfall cycle compares favourably with reanalysis data (Fig. 4b) and IPCC
AR4 models (cf. Kripalani et al., 2007, Fig. 1). We find that
average monsoon rainfall in Asia and Africa intensifies under global warming (Fig. 5), consistent with many studies using more complex models (e.g. Kripalani et al., 2007). Seasonal (June–August, JJA) mean rainfall associated with the
South Asian summer monsoon (including India and the Bay
of Bengal) strengthens by 10% (RCP3-PD) to 20% (RCP8.5)
until the middle of the 21st century and, for RCP8.5, by up
Earth Syst. Dynam., 2, 25–35, 2011
to 30% during the 22nd century (Fig. 5a). Similar results
are found for the East Asian (including China, Fig. 5b) and
West African (Fig. 5c) monsoon, which both increase by up
to 50% for RCP8.5. In absolute terms, this means increases
in JJA rainfall by up to 3–5 mm day−1 for RCP8.5. The decline of the South Asian monsoon for RCP8.5 after 2150 is
due to a shift of the center of maximum precipitation out of
the South Asian region towards South China. While the magnitude and timing of this shift must be viewed in the context
of our intermediate-complexity model, observations suggest
that a displacement of the center of precipitation may be possible under global warming (Wang et al., 2009). In all regions
we find a strong quasi-linear correlation of monsoon rainfall
with the regional temperature difference between land and
ocean (Fig. 5d–f). Note that changes due to direct and indirect aerosol effects are not captured by simulations with
CLIMBER-3α and may have significant influence on monsoon rainfall and circulation which is likely to counter-act
that of global warming (Lau and Kim, 2006; Rosenfeld et al.,
2008).
www.earth-syst-dynam.net/2/25/2011/
Climate change under a scenario near 1.5◦ C of global warming
2.1
23
J. Schewe et al.: Climate near 1.5 ◦ C warming
40ºN
6
0º0º
2
2
-2
-2
-6
-6
40ºS
40ºS
-10 -10
-14
80ºS
80ºS
100ºW
mm/day
mm/day
b
10
100ºW
0º
100ºE
CLIMBER−3α
CLIMBER−3α
b
10
NCEP
5
10
NCEP
5
5
8
6
b
10
c
2000
0
2
4
6
month
8
10
12
South Asia
a
South Asia
b
e
East AsiaEast Asia
d
d
12
12
-14
100ºE
0º
14
mm/day
6
a 14
mm/day
10
40ºN
10
16
mm/day
a
14
mm/day
80ºN
14
mm/day
a
mm/day
80ºN
29
16
e
5
8
6
Africa
c
f
Africa
2200
year
2000
2400
2200
year
f
1 2 3 4
°
2400 C 1
2 3 4
°
C
Fig. 5. Average seasonal (JJA) precipitation of (a) South Asian
4
2
6
8
10
12
◦ E, 15–22.5◦ N), (b) East Asian (90–135◦ E, 22.5–
(67.5–112.5
Fig.
5.
Average
seasonal (JJA) precipitation of (a) South
month
◦ W–22.5◦ E, 0–15◦ N) ◦summer
◦
37.5◦ (67.5-112.5
N), and (c)◦ E,
African
Fig. 4.
(a) Difference between average boreal summer (JJA)
Asian
15-22.5(22.5
N), (b)
East Asian (90-135 E, 22.5◦
◦ show the
◦ respective
◦
monsoon
(mm/day).
Panels
(d-f)
regional
andFig.
winter
(DJF)
precipitation
(shading,
mm/day),
and (JJA)
av4. (a)
Difference
between
averageinboreal
summer
37.5
N),
and
(c)
African
(22.5
W-22.5
E, 0-15
N) precipitation
summer monmon- of (a) South
Fig.
5.
Average
seasonal
(JJA)
−1
soon
precipitation
versus
the
difference
in
JJA
regional
surface
air
erage
summer
(JJA)
near-surface
winds
(vectors)
in
the
control
and
winter
(DJF)
precipitation
(shading,
in
mm
day
),
and
avsoon
(mm/day).
Panels
(d-f)
show
the
respective
regional
monsoon
◦
◦
Fig. 4.
(a) Difference between average boreal summer (JJA)temperature
Asianover
(67.5-112.5
E,adjacent
15-22.5ocean.
N), (b)
East Asian
(90-135◦ E, 22.5land
and
the
Generally
thistemre(pre–industrial)
of CLIMBER-3α.
(b) Seasonal
of
erage summerclimate
(JJA) near-surface
winds (vectors)
in thecycle
control
precipitation
versus
the
difference
in
JJA
regional
surface
air
◦
◦
◦
◦
andmonthly
winteraverage
(DJF)precipitation
precipitation
(shading,
in
mm/day),
and
av37.5
N),
and
(c)
African
(22.5of Generally
W-22.5
E,
0-15
N) summer monlation shows
clear
linear
trend. A ocean.
shift
precipitation
the
in the South Asian
monsooncycle
regionof
(pre-industrial) climate of CLIMBER-3α.
(b) Seasonal
perature
over aland
and
the adjacent
thisfrom
relation
erage
summer
(JJA)precipitation
near-surface
winds
(vectors)
in NCEPthe
controlshows
south Asian
region
east Asian
region
leads
to
(mm/day).
Panels
show
the respective
regional
monsoon
average
in the
South
Asian
region
in monthly
CLIMBER-3α’s
control
climate
(solid
line)
andmonsoon
in the
asoon
clearmonsoon
linear trend.
Atowards
shift(d-f)
ofthe
precipitation
from the
south
deviations
for strong
and
doesAsian
not represent
a regional
qualitative
(pre–industrial)
climate
ofetCLIMBER-3α.
Seasonal
cycle ofAsian
in CLIMBER-3α’s
control
climate
(solid
line)(b)
and
in the
the period
NCEPNCAR
reanalysis
(Kistler
al., 2001),
averaged
over
precipitation
versus
difference
in JJA
monsoon
regionwarming
towards the
the east
region
leads
to devia-surface air temchange in this relation.
NCAR
reanalysis
(Kistler et al.,
2001),
averaged
over
the period
monthly
average
precipitation
in the
South
Asian
monsoon
regiontions
1948-2007
(dashed
line).
forperature
strong warming
and does
change this relation
over land
and not
therepresent
adjacenta qualitative
ocean. Generally
1948–2007
(dashed
line).
in CLIMBER-3α’s control climate (solid line) and in the NCEP-in this relation.
shows a clear linear trend. A shift of precipitation from the south
0
NCAR reanalysis (Kistler et al., 2001), averaged over the period
Asian monsoon region towards the east Asian region leads to deviasurface warming (Fig. 6, inset; cf. Rahmstorf, 2007). How1948-2007 (dashed line).
tions
for strongrelation
warming
and
represent
a qualitative change
ever, the
quasi-linear
fails
as does
soon not
as global
warming
3.4 Steric sea level rise
in
this
relation.
starts to decelerate, i.e. around 2100 for RCP8.5, and some
Oceanic warming yields a steric sea level rise (SLR) of nearly
0.5 m for RCP8.5 by 2100 compared to the 1980–1999 average (Fig. 6). Thus, thermal oceanic expansion under RCP8.5
in our CLIMBER-3α simulations is about 20% higher than
the upper 95% percentile (0.41 m by 2100) for the highest
SRES scenario A1FI (see Table 10.7 in Meehl et al., 2007a)
– in part because of slightly stronger anthropogenic forcing
in RCP8.5. For RCP4.5 and RCP6, steric SLR is about 0.3 m
by 2100 and thereby close to the upper 95% percentile provided in IPCC AR4 for the similar SRES B1 scenario. While
for the upper three RCPs, steric SLR continues beyond 2500,
the declining temperatures in RCP3-PD lead to a deceleration
of steric SLR, a peaking at ∼0.3 m and a gradual reversal in
the second half of the 23rd century, about 200 years after
the peak in global temperatures. Other contributions to total sea level rise, in particular from melting of the Greenland
and West Antarctic Ice Sheets, are beyond the scope of this
study.
During an initial phase, we find a quasi-linear relationship
between the rate of steric sea level rise and the global mean
www.earth-syst-dynam.net/2/25/2011/
time earlier for the lower scenarios. As suggested by Vermeer and Rahmstorf (2009), validity of semi-empirical projections of sea level change based on this relation might be
extended by taking rapid adjustment processes into account.
The horizontal distribution of steric SLR, shown in Fig. 7
for RCP3-PD, is qualitatively similar under different scenarios. By 2100 (Fig. 7a), the weakening of the AMOC maximum (cf. Fig. 3a) and of the North Atlantic current produces a southeast-to-northwest SLR gradient in the North
Atlantic via geostrophic adjustment (Levermann et al., 2005;
Yin et al., 2010). Small shifts in the northern subpolar and
subtropical gyre systems induce smaller-scale variations of
SLR. The interhemispheric sea level pattern found by Levermann et al. (2005) for an AMOC shutdown is not reflected
here because the AMOC change is largely confined to the
North Atlantic; Southern Ocean outflow, i.e. the AMOC flux
across 30 ◦ S, is only reduced by about 10% (not shown). By
2200, the AMOC has partly recovered, and the most prominent feature in the North Atlantic is a negative SLR anomaly
(Fig. 7b) due to a 60% increase in the subpolar gyre (Fig. 3b;
Earth Syst. Dynam., 2, 25–35, 2011
24
2
J. Schewe et al.: Climate near 1.5 ◦ C warming
30
2.5
2.5
mm
1.5
1.5
1
1
a=1.66 mm/yr/°C
0
a=1.66 mm/yr/°C
0
0
°
C
°
C
3
3
mm/yr
mm/yr
6
6
2
2
0
0.5
0.5
0
0
2000
2000
2200
2200
year
year
2400
2400
Fig. 6. Globally averaged steric sea level change (in m) relaFig.
Globally averaged
sea level
change
(in (yellow),
m) relative 6.
to 1980–1999,
under thesteric
RCP3-PD
(blue),
RCP4.5
Fig.to6.1980-1999,
Globally
averaged
steric
sea level
(in (yellow),
m) relative
under
the
RCP3-PD
(blue),
RCP4.5
RCP6
(grey)
and RCP8.5
(red)
scenarios
andchange
their
extensions
in
tive to(grey)
1980-1999,
under
the
RCP3-PD
RCP4.5
(yellow),
RCP6
and The
RCP8.5
scenarios
and
their
extensions
in
CLIMBER-3α.
inset(red)
shows
the rate(blue),
of steric
sea
level
rise
RCP6
RCP8.5
(red)
scenarios
andsteric
their
extensions
in
(in
mm(grey)
yr−1 , and
smoothed
a 15-year
moving
average)
between
CLIMBER-3α.
The
insetwith
shows
the rate
of
sea
level
rise
CLIMBER-3α.
Thea inset
shows
rate
of steric
sea level
rise
1800
and 2100
as
function
of the
global
surface
warming
above
(in
mm/yr,
smoothed
with
a 15-year
moving
average)
between
◦ C).a The
(in 1980–1999
mm/yr,
smoothed
15-year
average)
the
mean
(inwith
slopemoving
ofsurface
the quasi-linear
part
is
1800
and 2100
as a function
of
global
warmingbetween
above
◦ C−1
1800
andyr−1
2100
as (black
a(infunction
of slope
globalofsurface
warming
above
◦
1.66
mm
line;
cf.
Rahmstorf,
2007).
Circles
mark
the
1980-1999
mean
C).
The
the
quasi-linear
part
is
◦
the timing
1980-1999
(in emissions.
C). The slope of the quasi-linear part is
◦ mean
−1GHG
the
of
peak
1.66
mm
yr−1
−1 C
◦ −1(black line; cf. Rahmstorf, 2007). Circles mark
1.66
mm yr
(black
line; cf. Rahmstorf, 2007). Circles mark
the
timing
of peakC GHG
emissions.
the timing of peak GHG emissions.
Häkkinen and Rhines, 2004; Levermann and Born, 2007).
In the Southern Ocean, SLR patterns in 2200 are similar
to those in 2100: A strengthening of the Antarctic Circumpolar Current above the level of no motion by about 4 Sv
leads to below-average SLR around Antarctica (Fig. 7). On
top of that, strengthening of the Ross and Weddell gyres
by 5 Sv and 6 Sv, respectively, induces large horizontal SLR
anomalies. Hattermann and Levermann (2010) found that a
strengthening of those gyres may significantly enhance basal
ice shelf melting around Antarctica.
Yin et al. (2010) showed by comparison of simulated and
observed present-day dynamic sea level patterns in twelve
IPCC AR4 AOGCMs that their ensemble mean performs
better than any of the individual models. The SLR pattern
found in our analysis is in good qualitative agreement with
the ensemble mean projection of those models under the
SRES A1B scenario (Yin et al., 2010).
3.5
ORIGINAL MANUSCRIPTS
Deep ocean warming
In contrast to the sea surface, deep ocean temperatures respond to atmospheric warming on centennial time scales.
Due to its peaking characteristic, the RCP3-PD scenario is
well suited to study the propagation of the warming signal
into the deep ocean. Global average temperatures at 500 m
and 1000 m depth exhibit delayed peaks around the years
2200 and 2300, respectively, compared to a surface warming
peak in the middle of the 21st century (Fig. 8a). In the year
Earth Syst. Dynam., 2, 25–35, 2011
80ºN
80ºN
40ºN
40ºN
0º
0º
40ºS
40ºS
80ºS
80ºS
80ºN
80ºN
40ºN
40ºN
69
61
69
53
61
45
53
45
37
29
37
29
21
21
13
135
5-3
-3
-11
-11
86
a
a
b
b
0º
0º
40ºS
40ºS
80ºS
80ºS
100ºW
100ºW
0º
0º
100ºE
100ºE
86
76
76
66
66
56
56
46
46
36
36
26
26
16
166
6-4
-4
-14
160ºW -14
160ºW
Fig. 7. Horizontal pattern of steric sea level change (in cm), relative
Fig. 7. Horizontal pattern of steric sea level change (in cm), relative
to
pre-industrial,
RCP3-PD:
(a)level
Yearchange
2100,(in
(b)cm),
yearrelative
2200.
Fig.
7. Horizontalunder
pattern
of steric sea
to pre–industrial,
under RCP3-PD:
(a) Year
2100, (b)
year 2200.
The
shading emphasizes
anomalies
to the (b)
global
average
to pre–industrial,
under the
RCP3-PD:
(a)relative
Year 2100,
year
2200.
The shading emphasizes
the anomalies
relative
to the global
average
steric
SLR (about
29 cm the
in 2100
and 36relative
cm in 2200).
The shading
emphasizes
anomalies
to the global average
steric SLR (about 29 cm in 2100 and 36 cm in 2200).
steric SLR (about 29 cm in 2100 and 36 cm in 2200).
2370, about 300 years after the peak in global surface temperatures, major anomalies of up to 2 ◦ C are found in the upper
1000 m of the North Atlantic and Southern Ocean (Fig. 8b).
In the North Atlantic, substantial warming is observed even
below 2000 m depth. Despite the weakening of the AMOC
noted earlier, the northern oceanic warming pattern clearly
reflects the structure of the overturning cell.
In general, the strong deep oceanic warming signal results from outcropping of isopycnals (black lines in Fig. 8b)
at high latitudes, i.e. a lack of density stratification, which
is a characteristic and robust feature of the modern ocean
circulation. Mixing along these surfaces of constant density is strongly enhanced compared to diapycnal mixing
across these surfaces. In combination with the observed polar warming amplification, isopycnal mixing facilitates enhanced heat uptake as also observed in AOGCMs (e.g. Stouffer et al., 2006a) and is the reason for the observed deep
ocean warming. These heat anomalies spread at intermediate
depths around 500 m, with the effect that peak global-average
warming at those depths exceeds that of the ocean surface
(Fig. 8a). After surface temperatures have relaxed, oceanic
heat uptake is reduced and, after 2300, the ocean eventually
becomes a very weak heat source, further damping the decline of surface atmospheric temperatures (compare Fig. 9b).
This weak heat exchange between ocean and atmosphere
www.earth-syst-dynam.net/2/25/2011/
J. Schewe et al.: Climate near 1.5◦ C warming
13
1.5 a
1
Climate change under a scenario near 1.5◦ C of global warming
25
°C
2.1
0.5
1.5 ◦ C
J. Schewe et al.: Climate near
warming
J. Schewe et al.: Climate near 1.5◦ C warming
2
a
b
1.52 a
1.5
2.4
°C
1600
2000
2200
2.0
2ºC
RCP3−PD
0.5
1.6
surface
500m
1000m
0
1800
2000
80ºS 40ºS
b
400
0.4ºC
year
2200
0º
40ºN
2400
80ºN
0.4
2.0
Depth
eventually cools deeper oceanic layers, but this cooling is so
Fig. 8. Ocean response to the RCP3-PD scenario: (a) Global averslow
that the intermediate-depth warming persists for cenage ocean temperature difference relative to pre–industrial levels, at
turies
evensurface
after surface
temperatures
have
reached
the ocean
(black) and
at 500 m (dark
blue)
and 1000presentm (light
day
levels
of approximately
0.8 ◦ C relative
to pre-industrial.
blue)
depth.
Due to polar amplification
and outcropping
oceanic
Conversely,
oceanic peak
heatwarming
anomalies
serve atasintermedia longisopycnals atthese
high latitudes,
is stronger
ate depth
around
500
m thandischarges
at the surface.
Zonal
average ocean
term
reservoir
that
slowly
into(b)the
atmosphere
and
warming
in thecooling,
year 2370,
levels
(shaddelays
surface
ascompared
discussedtoinpre–industrial
the following
section.
ing, in ◦ C; ocean depth in m). Overlaid are contours of constant
density (isopycnals; in kg/m3 ).
Slow cooling under RCP3-PD
As mentioned in Sect. 3.1, global cooling after the temperature peak in RCP3-PD is much slower, relative to the rate of
GHG emissions, than the warming before the peak (Fig. 9a,
blue line). We find that two processes are responsible for this
asymmetry.
Generally oceanic heat uptake by vertical mixing creates
thermal inertia that delays any temperature change at the surface (Fig. 9b). In order to identify additional effects, we
www.earth-syst-dynam.net/2/25/2011/
0 2000
0.8
2ºC
Fig. 8. Ocean response to the RCP3-PD
scenario: (a) gobal average
800
1.6
ocean8.temperature
difference
to pre-industrial
the
Fig.
Ocean response
to therelative
RCP3-PD
scenario: (a) levels,
Globalataverocean
surface
(black)
and
at
500
m
(dark
blue)
and
1000
m
(light
age ocean
temperature difference relative to pre–industrial levels,
1200
1.2at
blue)
depth.
Due(black)
to polar
and
outcropping
the
ocean
surface
andamplification
at 500 m (dark
blue)
and 1000 oceanic
m (light
isopycnals
at high
peak warmingand
is stronger
at intermedi1600
0.8
blue)
depth.
Due latitudes,
to polar amplification
outcropping
oceanic
ate depth around
m than peak
at thewarming
surface. (b)
Zonal average
ocean
isopycnals
at high500
latitudes,
is stronger
at intermedi2000
0.4
warming
in
the year
to pre-industrial
levels (shadate
depth
around
5002370,
m thancompared
at 0.4ºC
the surface.
(b) Zonal average
ocean
ing, in ◦ C;
ocean
depth
incompared
m). Overlaid
are contourslevels
of constant
warming
in
the
year
2370,
to
pre–industrial
(shad2200
0
density
in kginm−3
ing,
in ◦(isopycnals;
C; ocean
depth
m).). Overlaid
contours
of constant
80ºS
40ºS
40ºN
80ºN
0º are
3
density (isopycnals; in kg/m ).
4
250
1
1.2
02.4
c
300 b
2
2400
b
400 1
1200
year
2200
0
−2
W mm
2000
11
0.5
0
surface
500m
1000m
0
Depth
W m−2
RCP3−PD
0.5
800
°C
1
300
c
2200
year
2400
Fig. 9. Slow-down of global cooling under the RCP3-PD scenario: (a) Global surface air temperature anomaly as in Fig. 1c (blue
line), compared to the result of the simple energy-balance equation
250
(1) that only
takes into account diffusive oceanic mixing (dashed
black line). Thin grey lines represent modified scenarios that are
identical to RCP3-PD until 2070, and after that have zero emissions or two, three,
four or five times
as large negative
2000
2200
2400emissions as
RCP3-PD, respectively. All curves are smoothed with an 11-year
year
running mean to remove short-term variability from solar and volcanic sources. The vertical dashed line marks the year 2110. (b)
Fig.
9. Slow-down
of flux
global
cooling
under totheocean.
RCP3-PD
sceGlobally
averaged heat
from
atmosphere
Increasing
Fig. 9.(a) Slow-down
of air
global
cooling anomaly
under theasRCP3-PD
scenario:
global
surface
temperature
in
Fig.
1c
(blue
GHG concentration results in enhanced oceanic heat uptake which
nario: compared
(a) Global surface
air temperature
anomaly
as in Fig. 1c (blue
line),
the in
result
of the simple
energy-balance
(1)
declines after thetopeak
atmospheric
warming
and vanishesEq.
around
line),
compared
to the
result diffusive
of the simple
energy-balance
equation
that
only
takes
into
account
oceanic
mixing
(dashed
black
the year 2300 after which the ocean becomes a source for atmo(1) that
only
takes
into
accountmodified
diffusive oceanic mixing
(dashed
line).
Thin
grey
lines
represent
that aresimulation,
identical
spheric
warming.
The
solid line is the scenarios
CLIMBER-3α
black
line). Thin
grey
lines
represent
modified
scenarios
that
to
RCP3-PD
until
2070,
and
after
that
have
zero
emissions
or are
two,
while the 19 AOGCM emulations using MAGICC6 are represented
identical
toorRCP3-PD
until
2070,
and after
that have
zero emisthree,
four
five
times
as
large
negative
emissions
as
RCP3-PD,
reby
the
dashed
line
(median)
and
shading
(50%
and
80%
range).
The
sions or two,
three,
fourare
or smoothed
five times as
large
negative
emissions
as
spectively.
All
curves
with
an
11-year
running
mean
onset of convection
in theAll
southern
North
Atlanticwith
appears
here as a
RCP3-PD,
respectively.
curves are
smoothed
an 11-year
to
remove
short-term
variability
solar
volcanic
sources.
distinct
drop
intoocean
heat
uptakefrom
after
2110and
(vertical
dashed
line).
running
mean
remove
short-term
variability
from
solar
and
volThe
verticalare
dashed
line marks
the year
2110. (b)
Globally
All curves
smoothed
asdashed
in (a).
(c) Average
depth
of
the averNorth
canic
sources.
The
vertical
line
marks
the
year
2110.
(b)
aged
heatocean
flux from
atmosphere
to ocean.
Increasing south
GHG of
conAtlantic
mixed
layer
in winter
(January-April)
the
Globally
averaged
heat
flux
from
atmosphere
to
ocean.
Increasing
centration
in (40
enhanced
◦
◦ oceanic◦ heat uptake which declines
latitudes
ofresults
Iceland
W-0
, 50-65 N).
Starting
around
the year
GHG
concentration
results
in
enhanced
oceanic
heat
uptake
which
after
peak dashed
in atmospheric
warming
and vanishes
around
the
2110 the
(vertical
line),
an abrupt
increase
in mixed
layer
depth
declines
after the
peak in
atmospheric
warming
and
vanishes
around
year
2300
after
which
the ocean
becomes a source for atmospheric
marks
the
onset
of
enhanced
convection.
the year 2300 after which the ocean becomes a source for atmowarming. The solid line is the CLIMBER-3α simulation, while the
spheric warming. The solid line is the CLIMBER-3α simulation,
19 AOGCM emulations using MAGICC6 are represented by the
while the 19 AOGCM emulations using MAGICC6 are represented
dashed line (median) and shading (50% and 80% range). The onby the dashed line (median) and shading (50% and 80% range). The
set of convection in the southern North Atlantic appears here as a
onset of convection in the southern North Atlantic appears here as a
distinct drop in ocean heat uptake after 2110 (vertical dashed line).
distinct drop in ocean heat uptake after 2110 (vertical dashed line).
All
curves are smoothed as in (a). (c) Average depth of the North
All curves are smoothed as in (a). (c) Average depth of the North
Atlantic
mixed layer
layer in
in winter (January-April)
(January–April)south
southofofthe
the
Atlantic ocean
ocean mixed
◦ W–0◦◦ ,winter
latitudes
50–65◦◦N).
N).Starting
Startingaround
aroundthe
theyear
year
latitudes of
of Iceland
Iceland (40
(40◦ W-0
, 50-65
2110
dashed line),
line), an
an abrupt
abruptincrease
increaseininmixed
mixedlayer
layerdepth
depth
2110 (vertical
(vertical dashed
marks
onset of
of enhanced
enhanced convection.
convection.
marks the
the onset
m
°C
1.5
2
1800a
31
13
0
Earth Syst. Dynam., 2, 25–35, 2011
26
2
J. Schewe et al.: Climate near 1.5 ◦ C warming
32
isolate this ocean mixing effect with an intentionally simple
energy-balance equation for global mean surface temperature
anomaly T (t), assuming a diffusive ocean (following Allen
et al., 2009; Hansen et al., 1985):
C
C0
Z
− a0 T − a2
0
t
dT (t 0 )
dt 0
(1)
√
dt 0
t − t0
where C(t) is CO2 concentration; C0 = 280 ppm is the initial
concentration at t = 0; a1 is the heat capacity of the oceanic
mixed layer; a2 is ocean vertical diffusivity; a3 ' 1.3◦ C is
climate sensitivity not accounting for any feedbacks; and
1/a0 is the climate feedback factor, such that a3 /a0 is the full
climate sensitivity, which is ∼3.4 ◦ C for CLIMBER-3α.
This model, with parameters a0−2 calibrated to match
CLIMBER-3α, reproduces the global mean temperature simulated by CLIMBER-3α very well until about 2100 (black
dashed line in Fig. 9a). However, at the beginning of the
22nd century, the CLIMBER-3α result deviates from the simple diffusive ocean heat uptake relationship: While the latter projects a steady cooling trend all the way until 2500,
CLIMBER-3α projects a substantial slow-down of the cooling around the year 2110 (vertical dashed line in Fig. 9). The
cooling rate thereafter remains almost 50% lower than suggested by Eq. (1) for about two centuries, consequently arriving at a significantly higher temperature. Plotted versus
CO2 -equivalent GHG concentration, this is visible as a clear
excursion from the smooth hysteresis projected according to
Eq. (1) (Fig. 10).
To test the robustness of this behaviour, we have conducted
additional simulations using a set of scenarios that are identical to RCP3-PD until 2070. Thereafter, we set CO2 emissions in RCP3-PD equal to zero or two, three, four or five
times as large negative emissions as in the original RCP3-PD,
respectively. Using these modified RCP3-PD scenarios, we
then computed radiative forcings following the same process
as in generating the recommended CMIP5 GHG concentrations of the RCPs (for details, see Meinshausen et al., 2011).
Under all these modified RCP3-PD scenarios, CLIMBER3α projects a drop in the cooling rate at the same time, near
the year 2110, i.e., some decades after global mean temperature started to decline (thin grey lines in Fig. 9a). For
zero emissions after 2070 (top grey line), this even leads to
a slow global warming until the early 24th century, despite
the net decrease in radiative forcing. Again, viewed relative to CO2 -equivalent GHG concentration, Eq. (1) yields
essentially the same hysteresis for all the scenarios (Fig. 10,
dashed grey lines), while the CLIMBER-3α projections for
the modified scenarios depart from that hysteresis soon after
the peak (solid grey lines).
This result suggests that, on the one hand, the global mean
temperature response of the coupled climate model to a peakand-decline scenario such as RCP3-PD is, up until about
70 years after the peak in GHG concentrations, mainly governed by the heat capacity of the oceanic mixed layer and
heat exchange with the deep ocean due to mixing. The inertia
Earth Syst. Dynam., 2, 25–35, 2011
2200
2300
2050
2100
2025
2400
1
C
dT
= a3 log2
dt
1.5
°
a1
ORIGINAL MANUSCRIPTS
2500
0.5
2000
1975
0
1925
1900
−0.5
300
400
500
CO2 concentration (ppm)
Fig. 10. As Fig. 9a, but plotted versus CO2 -equivalence concentration
of longwave
absorbers)
instead
time, and with
the
Fig.
10. (sum
As Fig.
9a, but plotted
versus
CO2of
-equivalence
concenfor the modified
scenarios
as and
dashed
results(sum
of Eq.of(1)
tration
longwave
absorbers)
instead shown
of time,
withgrey
the
lines. ofThis
the scenarios
transient “hysteresis”
of global
results
eq. figure
(1) forrepresents
the modified
shown as dashed
grey
warming
in figure
RCP3-PD
(blue line,
25 years)ofand
the
lines.
This
represents
the marked
transientevery
“hysteresis”
global
modified peak-and-decline scenarios, i.e. how much GHG reducwarming in RCP3-PD (blue line, marked every 25 years) and the
tion it takes to cool the surface back to a given temperature that it
modified peak-and-decline scenarios, i.e. how much GHG reduchad during the warming phase. The dashed lines show the hysteresis
tion it takes to cool the surface back to a given temperature that it
expected from the processes represented by Eq. (1), while the solid
had during the warming phase. The dashed lines show the hysteresis
lines show the hysteresis behaviour observed in CLIMBER-3α. The
expected from the processes represented by eq. (1), while the solid
convection-related slow-down of the cooling rate (marked by a blue
lines show the hysteresis behaviour observed in CLIMBER-3α. The
circle for the RCP3-PD scenario) translates into a widening of the
convection-related slow-down of the cooling rate (marked by a blue
hysteresis. The slow-down occurs at the same time under different
circle for the RCP3-PD scenario) translates into a widening of the
scenarios (at the beginning of the 21st century, see thin grey lines in
hysteresis.
The slow-down occurs at the same time under different
Fig. 9a), and at different CO2 concentrations.
scenarios (at the beginning of the 21st century, see thin grey lines in
Fig. 9a), and at different CO2 concentrations.
induced by these processes delays the cooling that results
from the decline in GHG concentrations (Stouffer, 2004). On
the other hand, another mechanism comes into play around
the year 2110 that further reduces the cooling rate, over a
period of two centuries, by almost 50%.
We find that a relatively rapid change in oceanic convection is responsible for this reduction. The depth of the wintertime oceanic mixed layer in the North Atlantic is a direct
indicator of the strength of convection associated with the
AMOC. This mixed layer depth shrinks during the warming
phase in the 21st century, but then extends strongly between
the years 2110 and 2150, which coincides with the change
in the surface cooling rate (Fig. 9c). Enhanced convection in
these latitudes results in enhanced heat loss of the ocean to
the atmosphere; thus, globally, net ocean heat uptake is reduced by this effect (Fig. 9b, solid blue line), slowing down
atmospheric cooling.
www.earth-syst-dynam.net/2/25/2011/
2.1
Climate change under a scenario near 1.5◦ C of global warming
J. Schewe et al.: Climate near 1.5 ◦ C warming
5 Discussion and conclusions
We have presented large-scale climatic consequences of the
new RCP scenarios, which are designed for the forthcoming IPCC AR5 to span the full range of future pathways
of anthropogenic GHG emissions currently discussed in the
literature (Moss et al., 2008, page i). CLIMBER-3α atmospheric temperature projections and AOGCM emulations using MAGICC6 are qualitatively and quantitatively similar
for the 21st century. CLIMBER-3α temperatures tend to be
slightly higher than the median of the AOGCM emulations
(cf. Fig. 1), owing to the difference in climate sensitivity.
While the CLIMBER-3α simulations are based on the standard settings presented in Montoya et al. (2005), the wider
range of possible climate responses is covered by the emulation ensemble with MAGICC6, spanning climate sensitivities from 1.9 ◦ C (emulation of the NCAR PCM model) to
5.7 ◦ C (emulation of the MIROC3.2 high resolution model,
see Meinshausen et al., 2008, Table 4). With respect to atmospheric quantities, the coarse resolution of CLIMBER-3α
and the limitations of the statistical-dynamical representation
must be kept in mind. On the other hand, large-scale oceanic
quantities have been shown to be in good agreement with recent AOGCM results.
Our evaluation of the peak-and-decline scenario RCP3-PD
reveals that global maximal temperatures can be expected
close to 1.5 ◦ C warming relative to pre-industrial levels. Owing to negative CO2 emissions, concentrations under this
scenario are projected to drop markedly after peaking in
2070, and induce a slow cooling. This finding is consistent with recent studies using other models of varying complexity (e.g. Solomon et al., 2009), which showed that under zero-emission scenarios temperatures are projected not
to drop substantially for several centuries. Our work goes
beyond those studies by demonstrating that in a physical climate model, cooling is not only delayed by mixing-related
heat exchange with the ocean, but that dynamical effects can
significantly add to the delay. The abrupt strengthening of
convection in the North Atlantic indicates an important role
of internal dynamical processes in the oceans, especially because the timing of the convection change seems to be independent of the rate of (negative) GHG emissions, once atmospheric temperatures have started to fall. Although the exact
timing will probably differ across models, the onset of strong
convection is likely to be a robust feature, because declining
atmospheric temperatures lead to stronger cooling of surface
waters and thus reduce the stability of the water column.
The projections of steric sea level rise presented here are
generally consistent with previous simulations. The highest scenario, RCP8.5, being warmer than the highest SRES
scenario, yields enhanced steric sea level rise of up to 2 m
by 2500. According to our simulations, thermal oceanic expansion can be halted only for emission trajectories corresponding to, or below, RCP3-PD. In this scenario we observe
an enhanced oceanic warming of intermediate depth due to
www.earth-syst-dynam.net/2/25/2011/
27
33
polar amplification in combination with the lack of oceanic
density stratification in high latitudes. The associated heat
content persists for centuries. Thus, these results will allow future studies to quantify the risk of such a mid-ocean
warming for marine ecosystems (Sarmiento et al., 2004) and
environments. For example, prolonged deep ocean warming could be sufficient to trigger the dissociation of shallow
methane hydrates trapped in ocean sediments, and thereby
release additional amounts of greenhouse gases into the atmosphere (Reagan and Moridis, 2008; Archer et al., 2009).
Furthermore, melting of Antarctic ice shelves (Holland et al.,
2008) and the initiation of oceanic anoxic events (Hofmann
and Schellnhuber, 2009; Stramma et al., 2009) could be facilitated.
Acknowledgements. This work was supported by the Heinrich Böll
Foundation, the German National Academic Foundation, and the
BMBF PROGRESS project (support code 03IS2191B). MM received support from the UFOPLAN project FKZ 370841103 by the
German Federal Environment Agency. NCEP Reanalysis Derived
data was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/.
We thank two anonymous referees for their helpful comments.
Edited by: K. Keller
References
Allen, M. R., Frame, D. J., Huntingford, C., Jones, C. D., Lowe,
J. A., Meinshausen, M., and Meinshausen, N.: Warming caused
by cumulative carbon emissions towards the trillionth tonne, Nature, 458, 1163–1166, doi:10.1038/nature08019, 2009.
Archer, D., Buffett, B., and Brovkin, V.: Ocean methane hydrates as
a slow tipping point in the global carbon cycle, P. Natl. Acad. Sci.
USA, 106, 20596–20601, doi:10.1073/pnas.0800885105, 2009.
Brohan, P., Kennedy, J., Harris, I., Tett, S., and Jones, P.: Uncertainty estimates in regional and global observed temperature
changes: A new data set from 1850, J. Geophys. Res.-Atmos.,
111, D12106, doi:10.1029/2005JD006548, 2006.
Clarke, L., Edmonds, J., Jacoby, H., Pitcher, H., Reilly, J., and
Richels, R.: Scenarios of Greenhouse Gas Emissions and Atmospheric Concentrations, Sub-report 2.1A of Synthesis and Assessment Product 2.1 by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research, US Department of Energy, p.154, 2007.
Fichefet, T. and Maqueda, M. A. M.: Sensitivity of a global sea ice
model to the treatment of ice thermodynamics and dynamics, J.
Geophys. Res., 102, 12609–12646, 1997.
Fujino, J., Nair, R., Kainuma, M., Masui, T., and Matsuoka, Y.:
Multi-gas Mitigation Analysis on Stabilization Scenarios Using
Aim Global Model, Energy J., Special Issue 3, 343–354, 2006.
Gregory, J. M., Dixon, K. W., Stouffer, R. J., Weaver, A. J.,
Driesschaert, E., Eby, M., Fichefet, T., Hasumi, H., Hu, A.,
Jungclaus, J. H., Kamenkovich, I. V., Levermann, A., Montoya, M., Murakami, S., Nawrath, S., Oka, A., Sokolov,
A. P., and Thorpe, R. B.: A model intercomparison of
changes in the Atlantic thermohaline circulation in response to
Earth Syst. Dynam., 2, 25–35, 2011
28
34
increasing atmospheric CO2 concentration, Geophys. Res. Lett.,
32, L12703, doi:10.1029/2005GL023209, 2005.
Häkkinen, S. and Rhines, P. B.: Decline of Subpolar North Atlantic
Circulation During the 1990s, Science, 304, 555–559, 2004.
Hansen, J., Russell, G., Lacis, A., Fung, I., Rind, D., and Stone, P.:
Climate response-times - dependence on climate sensitivity and
ocean mixing, Science, 229, 857–859, 1985.
Hattermann, T. and Levermann, A.: Response of Southern
Ocean circulation to global warming may enhance basal ice
shelf melting around Antarctica, Clim. Dynam., 35, 741–756,
doi:10.1007/s00382-009-0643-3, 2010.
Hofmann, M. and Schellnhuber, H.-J.: Oceanic acidification
affects marine carbon pump and triggers extended marine
oxygen holes, P. Natl. Acad. Sci. USA, 106, 3017–3022,
doi:10.1073/pnas.0813384106, 2009.
Holland, P. R., Jenkins, A., and Holland, D. M.: The response of ice
shelf basal melting to variations in ocean temperature, J. Climate,
21, 2558–2572, doi:10.1175/2007JCLI1909.1, 2008.
Kistler, R., Kalnay, E., Saha, S., White, G., Woollen, J., Chelliah,
M., Ebisuzaki, W., Kanamitsu, M., Kousky, V., van den Dool, H.,
Jenne, R., and Fiorino, M.: The NCEP/NCAR 50-year reanalysis, B. Am. Meteorol. Soc., 82, 247–267, 2001.
Kripalani, R. H., Oh, J. H., and Chaudhari, H. S.: Response of
the East Asian summer monsoon to doubled atmospheric CO2 :
Coupled climate model simulations and projections under IPCC
AR4, Theor. Appl. Climatol., 87, 1–28, doi:10.1007/s00704006-0238-4, 2007.
Lau, K. M. and Kim, K. M.: Observational relationships between
aerosol and Asian monsoon rainfall, and circulation, Geophys.
Res. Lett., 33, L21810, doi:10.1029/2006GL027546, 2006.
Levermann, A. and Born, A.: Bistability of the subpolar gyre
in a coarse resolution climate model, Geophys. Res. Lett., 34,
L24605, doi:10.1029/2007GL031732, 2007.
Levermann, A., Griesel, A., Hofmann, M., Montoya, M., and
Rahmstorf, S.: Dynamic sea level changes following changes in
the thermohaline circulation, Clim. Dynam., 24, 347–354, 2005.
Levermann, A., Mignot, J., Nawrath, S., and Rahmstorf, S.: The
role of northern sea ice cover for the weakening of the thermohaline circulation under global warming, J. Climate, 20, 4160–
4171, 2007.
May, W.: Climatic changes associated with a global “2 degrees C-stabilization” scenario simulated by the ECHAM5/MPIOM coupled climate model, Clim. Dynam., 31, 283–313,
doi:10.1007/s00382-007-0352-8, 2008.
Meehl, G. A., Stocker, T. F., Collins, W. D., Friedlingstein, P., Gaye,
A. T., Gregory, J. M., Kitoh, A., Knutti, R., Murphy, J. M., Noda,
A., Raper, S. C. B., Watterson, I. G., Weaver, A. J., and Zhao, Z.C.: Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, chap. Global Climate Projections, Cambridge University Press, Cambridge, Uk
and New York, NY, USA, 2007a.
Meehl, G. A., Stocker, T. F., Collins, W. D., Friedlingstein, P., Gaye,
A. T., Gregory, J. M., Kitoh, A., Knutti, R., Murphy, J. M., Noda,
A., Raper, S. C. B., Watterson, I. G., Weaver, A. J., and Zhao,
Z.-C.: Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to theFourth Assessment Report
of the Intergovernmental Panel on Climate Change, chap. Climate Models and their Evaluation, Cambridge University Press,
Earth Syst. Dynam., 2, 25–35, 2011
2
ORIGINAL MANUSCRIPTS
J. Schewe et al.: Climate near 1.5 ◦ C warming
Cambridge, UK and New York, NY, USA, 2007b.
Meinshausen, M., Raper, S. C. B., and Wigley, T. M. L.: Emulating coupled atmosphere-ocean and carbon cycle models with a
simpler model, MAGICC6 - Part 1: Model description and calibration, Atmos. Chem. Phys., 11, 1417–1456, doi:10.5194/acp11-1417-2011, 2011.
Meinshausen, M., Smith, S., Calvin, K., Daniel, J. S., Kainuma,
M., Lamarque, J.-F., Matsumoto, K., Montzka, S. A.,
Raper, S. C. B., Riahi, K., Thomson, A. M., Velders,
G. J. M., and van Vuuren, D.: The RCP Greenhouse
Gas Concentrations and their Extension from 1765 to 2300,
Climatic Change, http://www.pik-potsdam.de/∼mmalte/pubs/
09 GHG Concentrations&Extension 1Sep2010.pdf, last access:
7 March 2011, submitted, 2011.
Mignot, J., Levermann, A., and Griesel, A.: A decomposition of
the Atlantic Meridional Overturning Circulation into physical
components using its sensitivity to vertical diffusivity, J. Phys.
Oceanogr., 36, 636–650, 2006.
Montoya, M. and Levermann, A.: Surface wind-stress threshold for
glacial Atlantic overturning, Geophys. Res. Lett., 35, L03608,
doi:10.1029/2007GL032560, 2008.
Montoya, M., Griesel, A., Levermann, A., Mignot, J., Hofmann,
M., Ganopolski, A., and Rahmstorf, S.: The Earth System Model
of Intermediate Complexity CLIMBER-3α, Part I: description
and performance for present day conditions, Clim. Dynam., 25,
237–263, 2005.
Moss, R., Babiker, M., Brinkman, S., Calvo, E., Carter, T., Edmonds, J., Elgizouli, I., Emori, S., Erda, L., Hibbard, K.,
Jones, R., Kainuma, M., Kelleher, J., Lamarque, J. F., Manning, M., Matthews, B., Meehl, J., Meyer, L., Mitchell, J.,
Nakicenovic, N., O’Neill, B., Pichs, R., Riahi, K., Rose, S.,
Runci, P., Stouffer, R., van Vuuren, D., Weyant, J., Wilbanks,
T., van Ypersele, J. P., and Zurek, M.: Towards New Scenarios
for Analysis of Emissions, Climate Change, Impacts, and Response Strategies, http://www.ipcc.ch/pdf/supporting-material/
expert-meeting-report-scenarios.pdf (last access: 7March 2011),
2008.
Moss, R., Edmonds, J., Hibbard, K., Manning, M. R., Rose, S., van
Vuuren, D., Carter, T., Emori, S., Kainuma, M., Kram, T., Meehl,
G., Mitchell, J., Nakicenovic, N., Riahi, K., Smith, S., Stouffer,
R., Thomson, A., Weyant, J., and Wilbanks, T.: The next generation of scenarios for climate change research and assessment,
Nature, 463, 747–756, doi:10.1038/nature08823, 2010.
Nakicenovic, N. and Swart, R.: IPCC Special Report on Emissions
Scenarios, Cambridge University Press, Cambridge, 2000.
Pacanowski, R. C. and Griffies, S. M.: The MOM-3 manual, Tech.
Rep. Tech. Rep. 4, NOAA/Geophyical Fluid Dynamics Laboratory, Princeton, NJ, USA, 1999.
Petoukhov, V., Ganopolski, A., Brovkin, V., Claussen, M., Eliseev,
A., Kubatzki, C., and Rahmstorf, S.: CLIMBER-2: a climate
system model of intermediate complexity, Part I: model description and performance for present climate, Clim. Dynam., 16, 1,
2000.
Rahmstorf, S.:
A Semi-Empirical Approach to Projecting Future Sea-Level Rise, Science, 315, 368–370,
doi:10.1126/science.1135456, 2007.
Reagan, M. T. and Moridis, G. J.: Dynamic response of oceanic
hydrate deposits to ocean temperature change, J. Geophys. Res.Oceans, 113, C12023, doi:10.1029/2008JC004938, 2008.
www.earth-syst-dynam.net/2/25/2011/
2.1
Climate change under a scenario near 1.5◦ C of global warming
J. Schewe et al.: Climate near 1.5 ◦ C warming
Riahi, K., Gruebler, A., and Nakicenovic, N.: Scenarios of
long-term socio-economic and environmental development under climate stabilization, Technol. Forecast. Soc., 74, 887–935,
doi:10.1016/j.techfore.2006.05.026, 2007.
Rosenfeld, D., Lohmann, U., Raga, G. B., O’Dowd, C. D., Kulmala, M., Fuzzi, S., Reissell, A., and Andreae, M. O.: Flood
or drought: How do aerosols affect precipitation?, Science, 321,
1309–1313, doi:10.1126/science.1160606, 2008.
Sarmiento, J., Slater, R., Barber, R., Bopp, L., Doney, S., Hirst, A.,
Kleypas, J., Matear, R., Mikolajewicz, U., Monfray, P., Soldatov, V., Spall, S., and Stouffer, R.: Response of ocean ecosystems to climate warming, Global Biogeochem. Cy., 18, GB3003,
doi:10.1029/2003GB002134, 2004.
Schewe, J. and Levermann, A.: The role of meridional density differences for a wind-driven overturning circulation, Clim. Dynam., 34, 547–556, doi:10.1007/s00382-009-0572-1, 2010.
Smith, S. J. and Wigley, T. M. L.: Multi-gas forcing stabilization
with Minicam, Energ. J., Special Issue 3, 373–391, 2006.
Solomon, S., Plattner, G.-K., Knutti, R., and Friedlingstein, P.: Irreversible climate change due to carbon dioxide emissions, P. Natl. Acad. Sci. USA, 106, 1704–1709,
doi:10.1073/pnas.0812721106, 2009.
Stouffer, R.: Time scales of climate response, J. Climate, 17, 209–
217, 2004.
Stouffer, R., Broccoli, A., Delworth, T., Dixon, K., Gudgel, R.,
Held, I., Hemler, R., Knutson, T., Lee, H., Schwarzkopf, M.,
Soden, B., Spelman, M., Winton, M., and Zeng, F.: GFDL’s
CM2 global coupled climate models, Part IV: Idealized climate
response, J. Climate, 19, 723–740, 2006a.
Stouffer, R. J., Yin, J., Gregory, J. M., Dixon, K. W., Spelman,
M. J., Hurlin, W., Weaver, A. J., Eby, M., Flato, G. M., Hasumi,
H., Hu, A., Jungclaus, J. H., Kamenkovich, I. V., Levermann, A.,
Montoya, M., Murakami, S., Nawrath, S., Oka, A., Peltier, W. R.,
Robitaille, D. Y., Sokolov, A. P., Vettoretti, G., and Weber, S. L.:
Investigating the Causes of the Response of the Thermohaline
Circulation to Past and Future Climate Changes, J. Climate, 19,
1365–1387, 2006b.
Stramma, L., Visbeck, M., Brandt, P., Tanhua, T., and Wallace,
D.: Deoxygenation in the oxygen minimum zone of the eastern tropical North Atlantic, Geophys. Res. Lett., 36, L20607,
doi:10.1029/2009GL039593, 2009.
www.earth-syst-dynam.net/2/25/2011/
29
35
Trenberth, K., Olson, J., and Large, W.: A Global Ocean
Wind Stress Climatology based on ECMWF Analyses, Tech.
Rep. NCAR/TN-338+STR, National Center for Atmospheric Research, Boulder, Colorado, USA, 1989.
Trenberth, K., Jones, P., Ambenje, P., Bojariu, R., Easterling, D.,
Tank, A. K., Parker, D., Rahimzadeh, F., Renwick, J., Rusticucci,
M., Soden, B., and Zhai, P.: Climate Change 2007: The Physical
Science Basis, Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate
Change, chap. Observations: Surface and Atmospheric Climate
Change, Cambridge University Press, Cambridge, UK and New
York, NY, USA, 2007.
van Vuuren, D. P., Den Elzen, M. G. J., Lucas, P. L., Eickhout, B.,
Strengers, B. J., van Ruijven, B., Wonink, S., and van Houdt,
R.: Stabilizing greenhouse gas concentrations at low levels: an
assessment of reduction strategies and costs, Climatic Change,
81, 119–159, doi:10.1007/s10584-006-9172-9, 2007.
Vermeer, M. and Rahmstorf, S.: Global sea level linked to
global temperature, P. Natl. Acad. Sci. USA, 106, 21527–21532,
doi:10.1073/pnas.0907765106, 2009.
Wang, C., Kim, D., Ekman, A. M. L., Barth, M. C., and Rasch, P. J.:
Impact of anthropogenic aerosols on Indian summer monsoon,
Geophys. Res. Lett., 36, L21704, doi:10.1029/2009GL040114,
2009.
Washington, W. M., Knutti, R., Meehl, G. A., Teng, H., Tebaldi, C.,
Lawrence, D., Buja, L., and Strand, W. G.: How much climate
change can be avoided by mitigation?, Geophys. Res. Lett., 36,
L08703, doi:10.1029/2008GL037074, 2009.
Wigley, T. M. L. and Raper, S. C. B.: Interpretation of high
projections for global-mean warming, Science, 293, 451–454,
doi:10.1126/science.1061604, 2001.
Winton, M.: Amplified Arctic climate change: What does surface
albedo feedback have to do with it?, Geophys. Res. Lett., 33,
L03701, doi:10.1029/2005GL025244, 2006.
Wise, M., Calvin, K., Thomson, A., Clarke, L., Bond-Lamberty, B.,
Sands, R., Smith, S. J., Janetos, A., and Edmonds, J.: Implications of Limiting CO2 Concentrations for Land Use and Energy,
Science, 324, 1183–1186, doi:10.1126/science.1168475, 2009.
Wu, P., Wood, R., Ridley, J., and Lowe, J.: Temporary acceleration of the hydrological cycle in response to a CO2 rampdown,
Geophys. Res. Lett., 37, L12705, doi:10.1029/2010GL043730,
2010.
Yin, J., Griffies, S. M., and Stouffer, R. J.: Spatial Variability of Sea
Level Rise in Twenty-First Century Projections, J. Climate, 23,
4585–4607, doi:10.1175/2010JCLI3533.1, 2010.
Earth Syst. Dynam., 2, 25–35, 2011
2.2
Basic mechanism for abrupt monsoon transitions
Basic mechanism for abrupt monsoon transitions
33
SPECIAL FEATURE
2.2
Basic mechanism for abrupt monsoon transitions
Anders Levermanna,b,1 , Jacob Schewea,b , Vladimir Petoukhova , and Hermann Helda
a
Earth System Analysis, Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany; and b Institute of Physics, Potsdam University, 14473 Potsdam,
Germany
Monsoon systems influence the livelihood of hundreds of millions
of people. During the Holocene and last glacial period, rainfall
in India and China has undergone strong and abrupt changes.
Though details of monsoon circulations are complicated, observations reveal a defining moisture-advection feedback that dominates the seasonal heat balance and might act as an internal amplifier, leading to abrupt changes in response to relatively weak external perturbations. Here we present a minimal conceptual model
capturing this positive feedback. The basic equations, motivated by
observed relations, yield a threshold behavior, robust with respect
to addition of other physical processes. Below this threshold in net
radiative influx, Rc , no conventional monsoon can develop; above
Rc , two stable regimes exist. We identify a nondimensional parameter l that defines the threshold and makes monsoon systems
comparable with respect to the character of their abrupt transition.
This dynamic similitude may be helpful in understanding past and
future variations in monsoon circulation. Within the restrictions of
the model, we compute Rc for current monsoon systems in India,
China, the Bay of Bengal, West Africa, North America, and Australia,
where moisture advection is the main driver of the circulation.
Earth system | tipping element | abrupt climate change | atmospheric
circulation | nonlinear dynamics
M
onsoon rainfall shapes regional culture and the livelihoods
of hundreds of millions of people (e.g., 1, 2). The future evolution of monsoon rainfall under increasing levels of atmospheric
CO2 and aerosol pollution is highly uncertain (3). Although greenhouse gas abundance tends to increase monsoon rainfall strength
(4–6), the situation is more complex with changing aerosol distribution (7, 8). Given this large uncertainty in the future forcing of
monsoons, it is crucial to understand internal monsoon dynamics, especially with respect to self-amplifying feedbacks, which
might result in potentially strong responses to small perturbations. Zickfeld et al. (2005) found two stable states in a simple
model of the Indian summer monsoon, which in principle allows
for rapid transition between radically different monsoon circulations (9, 10) and thereby identified the Indian monsoon as a
potential tipping element of the climate system (11). Evidence for
such behavior is found in paleodata that show rapid and strong
variations in Indian and East Asian monsoon rainfall (12, 13).
These abrupt changes have been linked to climatic events in the
North Atlantic for the last glacial period (14, 15) as well as for
the Holocene (16, 17). Though a physical mechanism for this
teleconnection has been suggested (18), relevant climatic signals
of the North Atlantic events in Asia (such as temperature and
moisture anomalies) are very small (19) indicating that internal
feedbacks in monsoon dynamics may have amplified the weak
external forcing.
Both spatial patterns and temporal evolution of monsoon rainfall are influenced by a number of physical processes (7, 18, 20–28)
as well as characteristics of vegetation (29–31) and topography
(32). Though these details are crucial for the specific behavior
of different monsoon systems and their significance will vary from
region to region, there exist defining processes fundamental to any
monsoon dynamics (e.g. 33, 34). These processes are the advection
of heat and moisture during monsoon season and the associated
rainfall and release of latent heat. In accordance with Zickfeld
www.pnas.org / cgi / doi / 10.1073 / pnas.0901414106
et al. (9), we suggest the positive moisture-advection feedback (21)
as a candidate for the main cause of abrupt changes in monsoon
dynamics.
We derive a minimal conceptual model of a monsoon circulation
(Fig. 1A), comprising merely conservation of heat and moisture,
knowingly neglecting a large number of relevant physical processes
in order to distill the fundamental nonlinearity of monsoon circulations. The resulting governing equation exhibits the necessary
solution structure to explain qualitatively both strong, persistent
changes in monsoon rainfall, as observed in paleorecords, and
abrupt variablity within one rainy season. This equation’s dynamic
similitude, expressed through a single dimensionless number l,
which defines the threshold behavior and makes different monsoon systems comparable with respect to their transition, may
serve as a building block for understanding past and future abrupt
changes in monsoon dynamics.
Results
Moisture-Advection Feedback in Monsoon Dynamics. The seasonal
evolution of the continental heat budget for different monsoon
systems (Fig. 2) shows that sensible heat flux from the land surface
increases during spring and heats up the atmospheric column prior
to the rainy season. The onset of heavy rainfall (red vertical lines
in Fig. 2) is associated with a drop in surface temperature on land,
and consequently, sensible heat flux reduces drastically. During
the monsoon season, latent heat release dominates the atmospheric heat content, whereas net radiative fluxes are relatively
constant throughout the year, reflecting the stabilizing long-wave
radiative feedback. In response to the latent heat release, thermal
energy is transported out of the region through large-scale advection and synoptic processes. The main dynamical driver of the
monsoon is therefore the positive moisture-advection feedback
(Fig. 1A): The release of latent heat from precipitation over land
adds to the temperature difference between land and ocean, thus
driving stronger winds from ocean to land and increasing in this
way landward advection of moisture, which leads to enhanced precipitation and associated release of latent heat. In the following,
we seek to capture this feedback in a minimal conceptual model.
Minimal Conceptual Model for Abrupt Monsoon Transitions. For this
purpose, consider the heat-balance equation of the monsoon
season (Fig. 2, for example at blue vertical line).
L · P − Cp W · ΔT + R = 0,
[1]
where latent heat release and net radiation into the atmospheric
column, R, balance heat divergence, and the relatively weak contribution from sensible heat transport from the land surface to the
atmospheric column has been neglected. ΔT is the atmospheric
Author contributions: A.L. designed research; A.L. and V.P. performed research; A.L., J.S.,
and H.H. analyzed data; and A.L. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
1 To whom correspondence should be addressed. E-mail: [email protected].
This article contains supporting information online at www.pnas.org/cgi/content/full/
0901414106/DCSupplemental.
Early Edition
1 of 6
GEOPHYSICS
Edited by Hans Joachim Schellnhuber, Potsdam Institute for Climate Impact Research, Potsdam, Germany and approved August 18, 2009 (received for review
February 11, 2009)
34
2
ORIGINAL MANUSCRIPTS
some regions, does not alter the model behavior qualitatively. This
offset is discussed together with other possibly relevant processes
in the SI Appendix. Here we seek to capture only processes relevant to the self-amplification feedback. Neglecting the effect of
evaporation over land and associated soil-moisture processes in
the continental moisture budget, precipitation has to be balanced
by the net landward flow of moisture
W · ρ(qO − qL ) − P = 0,
[3]
where qO and qL are specific humidity over ocean and land, and
ρ = 1.3 kg/m3 is mean air density.
Note that evaporation is clearly an important process for the
moisture budget (e.g. (38)) and is omitted in Eq. 3 only for the
sake of clarity. Including evaporation does not change the model
behavior qualitatively (see SI Appendix). It does, however, shift the
value of the critical threshold, as we will show in the next section
when applying our model to data. In the minimalistic spirit of
this section, we omit the effect of evaporation here because it is
not of first order to the problem. Consistent with reanalysis data
(Fig. 4) and theoretical considerations (36, 39), continental rainfall is assumed to be proportional to the mean specific humidity
within the atmospheric column
P = βqL .
[4]
The effect of an offset between these quantities does not change
the model behavior qualitatively (see SI Appendix). This set of
assumptions (Eqs. 1–4) yields the dimensional governing equation
of the model
β
α
αβ
W3 + W2 −
(LqO β + R) · W − 2
· R = 0.
[5]
ρ
Cp
ρCp
Fig. 1. Basic mechanism of abrupt monsoon transitions. (A) Geometry of
conceptual model and fundamental moisture-advection feedback. The same
notation as in the text is used for wind W , precipitation P, net radiative influx
R, vertical scale H and horizontal scale L. Arrows in the feedback loop indicate the amplification of one physical processes by another. (B) Mechanism
of the abrupt transition. Heating by latent heat release and cooling through
heat advection compensate each other, and both decrease with decreasing
winds (or equivalently, land–ocean temperature difference ΔT ; see Eq. 2).
The resultant heating balances the negative net radiative flux as long as it is
above a threshold RC , below which no conventional monsoon exists.
temperature difference between land and ocean. Latent heat of
condensation is L = 2.6 · 106 J/kg and volumetric heat capacity
of air at constant pressure Cp = 1, 295 J/m3 /K. P is the mean
precipitation over land (in kg/m2 /s). The ratio = H/L between
vertical extent H of the lower troposphere and the horizontal scale
L of the region of precipitation (Fig. 1) enters because of the balance of the horizontal advective heat transport and the vertical
fluxes of net radiative influx R and precipitation P. A length scale
for the coastline drops out. Note that no annual cycle is included
in the model. Only budgets for the rainy season are considered.
Consequently, this model does not capture any interseasonal or
any interannual dynamics. Equations are only valid for landward
winds, W ≥ 0.
Assuming dominance of ageostrophic flow in low latitudes,
the landward mean wind W is taken to be proportional to the
temperature difference between land and ocean (33, 36, 37):
W = α · ΔT.
[2]
This assumption of a linear relation between the two quantities is supported by National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR)
reanalysis data (Fig. 3) with correlation coefficients above 50% for
all regions. There is significant scatter in some plots, reflecting the
fact that other processes may be relevant for the monsoon dynamics in the corresponding regions. A possible offset, as observed in
2 of 6
www.pnas.org / cgi / doi / 10.1073 / pnas.0901414106
Note that through the linear relation of Eq. 2, this equation can
equally be understood as an expression for the temperature difference beween land and ocean ΔT, which might be more useful
for some applications. Introduction of nondimensional variables
w ≡ W ρ/β and p = P/(qO β) results in the nondimensional
equation
w3 + w2 − (l + r)w − r = 0,
[6]
which depends on two parameters only: The dimensionless net
radiative influx r ≡ R · αρ2 /(Cp β2 ) and a measure for the relative
role of latent and advective heat transport
l ≡ (αρ2 LqO )/(Cp β) = (LqO β)/(Cp β2 /(αρ2 )).
[7]
Large l corresponds to a strong influence of moisture advection (scaling as LqO βp) on the continental heat budget compared
with heat advection by large-scale and synoptic processes (scaling
as Cp β2 w2 /(αρ2 )). The nondimensional precipitation is directly
related to the wind through p = w/(1 + w).
Solutions w(r) of Eq. 6 are determined entirely by a choice of
the only free parameter l, which can be expressed in terms of a
critical threshold of net radiative flux rc , below which no physical
solution exists (Fig. 5). The critical point (rc , wc ) will vary for different monsoon systems. It is directly linked to the only remaining
parameter l, through
wc (wc + 1)2 = l/2.
[8]
and therefore uniquely defines the solution w(r) of the model. The
critical radiation can be computed from
rc = −w2c (2wc + 1)
[9]
Thus for large l (as observed in some monsoon systems) the critical
threshold is well approximated by rc ≈ −l. Note that l is scaling
like qO α/β where α and β have clear-cut physical meaning (39).
α is essentially a function of the near-surface cross-isobar angle and
thereby a function of surface roughness and static stability of the
Levermann et al.
Basic mechanism for abrupt monsoon transitions
35
GEOPHYSICS
SPECIAL FEATURE
2.2
Fig. 2. Seasonal heat contributions to the atmospheric column over different continental monsoon regions in NCEP/NCAR reanalysis data (35). Radiative
heating of the land surface in spring enhances sensible heat flux from the ground (’Sensible’). During the rainy season, latent heat release dominates the heat
budget (’Latent’). Radiative heat flux comprises all radiative fluxes in and out of the atmospheric column (’Radiative’). The excess heat is transported out of
the continental monsoon region though large-scale advective and synoptic processes (’Convergence’). Error bars give the standard deviation from the 60 years
for which data is available (1948–2007). Regions from which values were taken are defined in the (SI Appendix). The red and blue vertical lines emphasize the
months of maximum sensible heat flux and latent heat flux, respectively.
planetary boundary layer (PBL). β is governed by the characteristic turnover (recycling) time of liquid water in the atmosphere and
thereby determined by static stability and vertical velocity in the
PBL. Any physical solution for r > rc is characterized by landward
winds w > 0 and positive precipitation p > 0.
Let us now try to understand the physical mechanism behind
the threshold behavior observed in Fig. 5. In the tropics net
radiative influx is negative, i.e. radiation cools the atmospheric
column. During monsoon season the same is true for the advection of heat by the winds because winds blow predominantly
from the colder oceanic surrounding. The release of latent heat
compensates for both of these heat-loss processes. If monsoon
winds get weaker, condensation and therefore latent heat release
through precipitation are reduced (moisture-advection feedback,
Fig. 1A). The abruptness of the transition emerges through an
additional stabilizing effect of the direct heat advection which is
cooling the atmospheric column and is also reduced for reduced
monsoon winds. Thus both advection-related processes, precipitative warming and thermal cooling, are simultaneously reduced
and partly compensate until a threshold is reached at which
condensation/precipitation cannot provide the necessary latent
heat to sustain a circulation. As a consequence, land-ocean
Levermann et al.
temperature difference ΔT and therewith monsoon winds break
down (Fig. 1B).
Estimate of Critical Threshold for Current Monsoon Systems. In order
to estimate the critical threshold of different monsoon systems
within the limitation of this very simple model, we use time series
of precipitation P, radiation R, temperature difference ΔT, and
specific humidity qO from the NCEP/NCAR reanalysis data (35)
to compute time series for α(t) = (LP + R)/(Cp ΔT 2 ) and
β(t) = ((LP +R)·ρP)/((LP +R)qO ρ−Cp ΔTP), assuming applicability of the model and stationary statistics within the observational
period (1948-2007). Via α(t) and β(t), the parameter l(t) is known
and the system is estimated for each year. As a simple test for the
model, we calculate the remaining quantity that is not used for the
computation of α(t) and β(t), the specific humidity over land
qL (t) = qO (t) −
CP ΔT(t)P(t)
.
ρ(LP(t) + R(t))
[10]
The resulting model estimate of the specific humidity qL compares
reasonably well (Fig. S2 in the SI Appendix) with the independently observed qL that was used in Fig. 4 to motivate the relation
between specific humidity and precipitation (Eq. 4).
Early Edition
3 of 6
36
2
Fig. 3. Landward zonal wind versus temperature difference between land
and ocean during monsoon season [NCEP/NCAR reanalysis data (35)]. The lines
show best linear regression with correlation r.
Via the definition of l, we compute Rc from the time series
α(t) and β(t) for each year between 1948-2007. Note that the only
quantity that is not constrained by data in this computation is
the parameter , which defines the ratio of vertical and horizontal scale. However, the critical threshold RC is independent of ,
and thus the calculation depends only on relatively robust averaged values of precipitation, net radiation, average temperature
difference between land and ocean, specific humidity over ocean,
and the natural constants ρ, L, and Cp . We interpret the resulting
distribution of the critical threshold Rc (Fig. 6, blue) as a noisy
estimate of a stationary critical threshold.
Within the limitations of the model, the observed net radiation
is higher than the critical threshold in the Bay of Bengal, West
Africa, and China. In India, North America, and Australia, the distributions have significant overlap. Incorporating evaporation into
the model shifts the distribution toward lower thresholds (Fig. 6,
red), while at the same time increasing the precipitation threshold Pc . Standard bootstrapping (see SI Appendix) reveals that the
estimates in Fig. 6 are already relative robust distributions, in view
of the simplicity of the model approach.
Discussion
A minimal conceptual model for monsoon circulations that captures the moisture-advection feedback is presented. The model is
unlikely to describe details of monsoon circulations quantitatively,
nor is it meant to capture all dynamical processes of a monsoon
circulation. Following a minimalistic philosophy, the model comprises the necessary processes for a positive feedback and thereby
demonstrates the possibility of an abrupt transition of monsoon
circulations from a state with strong rainfall to a weak precipitation state. All model equations are backed by relations found
in NCEP/NCAR reanalysis data. For India, it has been shown
that this data-set properly represents the statistics of precipitation when compared with regional observations with higher spatial
resolution (40).
4 of 6
www.pnas.org / cgi / doi / 10.1073 / pnas.0901414106
ORIGINAL MANUSCRIPTS
Because the processes represented in our model are fundamental to monsoon systems, we believe that the results strongly suggest
the possibility of abrupt monsoon transitions. Because the dominant driving process is captured, it is not impossible that the model
can provide a reasonable estimate for the critical threshold, Rc ,
once all necessary processes are incorporated. The bifurcation
structure of the model is robust with respect to incorporation of
other physical processes (see SI Appendix) and only changes qualitatively when either of these perturbations dominate the dynamics.
Thus, the applicability of our model is based on the assumption
that moisture advection is the dominant process in the heat budget
of a monsoon system.
The possibility of abrupt transition is due to the competition
of the main heat transport processes during the rainy season.
Although latent heat release through precipitation warms the
atmospheric column, direct advection of heat is cooling it. Both
processes decrease with decreasing monsoon winds and thereby
compensate each other with respect to the net heat injection into
the atmospheric column. The threshold of this stabilizing effect is
set by the radiative cooling, which is characteristic to low-latitudes
and is strongly influenced by aerosol distribution in the region.
According to our model, abrupt transitions may occur in two different ways. For net radiation above the critical threshold R > RC ,
the system is bistable. Because the model only describes the rainy
season and does not capture the annual monsoon cycle, abrupt
transitions in the bistable regime can only be interpreted intraseasonally, e.g., a month of heavy rain followed by a month of extraordinarily weak precipitation. An example could be the extremely
weak rainfall in July and September observed in India in the year
2002, in which the rest of the season exhibited average rainfall (41).
Our model does not capture the dynamics of a decline or
increase in monsoon strength over several years. Thus, paleodata in which strong variation in monsoon rainfall have been
recorded cannot be explained by the bistable regime because these
recordings show monsoon changes over several years, decades, or
Fig. 4. Precipitation versus specific humidity over land during monsoon season [NCEP/NCAR reanalysis data (35)]. The lines show best linear regression
with correlation r.
Levermann et al.
Basic mechanism for abrupt monsoon transitions
37
Fig. 6. State of current monsoon systems with respect to the critical
point computed from the conceptual model. The black distribution reflects
observed fluctuations in net radiation in the different monsoon systems. The
blue distribution provides the computed critical threshold from the conceptual model. The red distribution includes the effect of evaporation. Lines
give a Gaussian function with the same mean and standard deviation as the
corresponding discrete distribution.
Fig. 5. Solution of the nondimensional governing Eq. 6. (Top) Nondimensional landward wind for two values of the only parameter l = 0 and l = 1.
(Middle) Corresponding nondimensional precipitation. (Bottom) Nondimensional precipitation for higher values of l as observed in some monsoon
systems. The functional form of the solution is not changed qualitatively.
The critical threshold (wc (l), pc (l)) is given as the black curve in each frame.
even centuries. Such behavior would correspond to a shift of the
system across the critical threshold into the monostable regime
R < RC without a conventional monsoon. If persistent, such a
shift would be visible in paleorecords.
The reduction of the full set of model parameters to a single scaling number l, which determines the system and thereby
the critical threshold, testifies to a remarkable dynamic similitude
with respect to the atmospheric quantities α, β, and q0 . Different monsoon systems with the same l will have the same transition
behavior. As illustrated in Fig. 5, l provides a measure for the position and the sharpness of the transition, i.e., for the point (rc , wc ) in
state space. This means, in particular, that a decrease in inflowing
humidity q0 associated with, e.g., colder climate conditions (which
would decrease the threshold Rc and shift the system closer to a
collapse) could be compensated by decreasing β, representing a
lower turnover (recycling) time of moisture in the atmosphere,
which is influenced by, e.g., aerosols.
1. Auffhammer M, Ramanathan V, Vincent JR (2006) Integrated model shows that atmospheric brown clouds and greenhouse gases have reduced rice harvests in India. Proc
Natl Acad Sci USA 103:19668–19672.
2. Zhang P, et al. (2008) A test of climate, sun, and culture relationships from an 1810-year
chinese cave record. Science 322:940–942.
3. Patra PK, Behera SK, Herman JR, Akimoto S, Yamagata T (2005) The indian summer
monsoon rainfall: Interplay of coupled dynamics, radiation and cloud microphysics.
Atmos Chem Phys Discuss 5:2879–2895.
4. Meehl GA, Washington WM (1993) South Asian summer monsoon variability in a
model with doubled atmospheric carbon dioxide concentration. Science 260:1101–
1104.
5. Zwiers FW, Kharin VV (1998) Changes in the extremes of the climate simulated by CCC
GCM2 under CO2 doubling. J Climate 11:2200–2222.
6. May W (2002) Simulated changes of the Indian summer monsoon under enhanced
greenhouse gas conditions in a global time-slice experiment. Geophys Res Lett
29:1118.
7. Ramanathan V, et al. (2005) Atmospheric brown clouds: Impacts on South Asian
climate and hydrological cycle. Proc Natl Acad Sci USA 102:5326–5333.
Levermann et al.
The parameter q0 in our model can be interpreted in a rather
broad sense as a specific humidity of the vicinity influencing a
monsoon region. As an example, the years with anomalously high
snow cover over the Tibetan Plateau in spring and early summer
(20, 26) could be characterized by a decrease in q0 during midsummer, which would shift the threshold value Rc for the Indian
monsoon closer to the observed precipitation over the region, thus
increasing a possibility of monsoon breakdown in those years. Similarly, a colder climate with generally decreased humidity qO could
be closer to the critical threshold, which might be the reason for
less-stable monsoon circulations during glacial periods.
In the future, net radiation may be reduced through aerosol pollution, which will push the system qualitatively closer to the critical
threshold (7). On the Indian subcontinent, in China, and in parts
of sub-Saharan Africa, agricultural productivity is closely linked to
and limited by monsoon rainfall. Food security in these regions is
particularly sensitive to monsoon variability (42–45). Studies with
comprehensive models are necessary to confirm or reject the idea
of the existence of a threshold as well as its position.
ACKNOWLEDGMENTS. We thank B.N. Goswami, R. Krishnan, and J. Srinivasan for helpful hints and discussions; and T. Lenton for useful comments on
the manuscript. This work was funded by the Heinrich Böll Foundation, the
German National Academic Foundation, and the German Federal Ministry of
Education and Research.
8. Lau KM, Kim KM (2006) Observational relationships between aerosol and asian
monsoon rainfall, and circulation. Geophys Res Lett 33:L21810.
9. Zickfeld K, Knopf B, Petoukhov V, Schellnhuber HJ (2005) Is the indian summer
monsoon stable against global change? Geophys Res Lett 32:L15707.
10. Knopf B, Zickfeld K, Flechsig M, Petoukhov V (2008) Sensitivity of the Indian monsoon
to human activities. Adv Atmos Sci 25:932–945.
11. Lenton TM, et al. (2008) Tipping elements in the earth’s climate system. Proc Natl Acad
Sci USA 105:1786–1793.
12. Wang P, et al. (2005) Evolution and variability of the Asian monsoon system: State of
the art and outstanding issues. Quaternary Sci Rev 24:595–629.
13. Wang Y, et al. (2008) Millennial- and orbital-scale changes in the East Asian monsoon
over the past 224,000 years. Nature 451:1090–1093.
14. Overpeck JT, Anderson D, Trumbore S, Prell W (1996) The southwest Indian monsoon
over the last 18000 years. Climate Dynamics 12:213–225.
15. Burns SJ, Fleitmann D, Matter A, Kramers J, Al-Subbary AA (2003) Indian ocean climate and an absolute chronology over Dansgaard/Oeschger events 9 to 13. Science
301:1365–1367.
Early Edition
5 of 6
GEOPHYSICS
SPECIAL FEATURE
2.2
38
2
16. Gupta AK, Anderson DM, Overpeck JT (2003) Abrupt changes in the Asian southwest
monsoon during the Holocene and their links to the North Atlantic ocean. Nature
421:354–357.
17. Wang Y, et al. (2005) The Holocene Asian Monsoon: Links to solar changes and North
Atlantic climate. Science 308:854–857.
18. Goswami BN, Madhusoodanan MS, Neema CP, Sengupta D (2006) A physical mechanism for North Atlantic SST influence on the Indian summer monsoon. Geophys Res
Lett 33:L02706.
19. Zhang R, Delworth TL (2005) Simulated tropical response to a substantial weakening
of the Atlantic Thermohaline Circulation. J Climate 18:1853–1860.
20. Hahn DG, Shukla J (1976) An apparent relationship between Eurasian snow cover and
Indian monsoon rainfall. J Atmos Sci 33:2461–2462.
21. Webster PJ, et al. (1998) Monsoons: Processes, predictability, and the prospects for
prediction. J Geophys Res 103:14451–14510.
22. Krishnamurthy V, Goswami BN (2000) Indian monsoon-ENSO relationship on interdecadal timescale. J Climate 13:579–595.
23. Clark CO, Cole JE, Webster PJ (2000) Indian ocean SST and Indian summer rainfall:
Predictive relationships and their decadal variability. J Climate 13:2503–2519.
24. Kucharski F, Molteni F, Yoo JH (2006) SST forcing of decadal Indian monsoon rainfall
variability. Geophys Res Lett 33:L03709.
25. Goswami BN, Xavier PK (2005) ENSO control on the south Asian monsoon through
the length of the rainy season. Geophys Res Lett 32:L18717.
26. Dash SK, Singh GP, Shekhar MS, Vernekar AD (2005) Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth anomaly over Eurasia.
Climate Dynamics 24:1–10.
27. Wang B (2005) The Asian Monsoon (Springer, Berlin).
28. Yang J, Liu Q, Xie SP, Liu Z, Wu L (2007) Impact of the Indian ocean SST basin mode
of the Asian summer monsoon. Geophys Res Lett 34:L02708.
29. Meehl GA (1994) Influence of the land surface in the Asian summer monsoon: External
conditions versus internal feedbacks. J Climate 7:1033–1049.
30. Claussen M (1997) Modeling bio-geophysical feedback in the African and Indian
monsoon region. Climate Dynamics 54:247–257.
31. Robock A, Mu M, Vinnikov K, Robinson D (2003) Land surface conditions over Eurasia
and Indian summer monsoon rainfall. J Geophys Res 108:4131.
6 of 6
www.pnas.org / cgi / doi / 10.1073 / pnas.0901414106
ORIGINAL MANUSCRIPTS
32. Liu X, Yin Z (2002) Sensitivity of East Asian monsoon climate to the uplift of the Tibetan
Plateau. Palaeogeogr Palaeoclimatol Palaeoecol 183:223–245.
33. Webster PJ (1987) The elementary monsoon. In Monsoons, eds Fein JS, Stephens PL
(Wiley, New York), pp. 3-32.
34. Webster PJ (1987) The variable and interactive monsoon. In Monsoons, eds Fein JS,
Stephens PL (Wiley, New York), pp. 269–330.
35. Kistler R, et al. (2001) The NCEP/NCAR 50-year reanalysis. Bull Amer Meteor Soc
82:247–267.
36. Petoukhov VK (1982) Two mechanisms of temperature oscillations in a thermodynamical model of the troposphere-stratosphere system. Atmos Ocean Phys 18:
126–137.
37. Brovkin V, Claussen M, Petoukhov V, Ganopolski A (1998) On the stability of the
atmosphere- vegetation system in the Sahara/Sahel region. J Geophys Res 103:31613–
31624.
38. Eltahir EAB (1998) A soil moisture-rainfall feedback mechanism: 1. theory and
observations. Water Resour Research 34:765–776.
39. Petoukhov V, et al. (2000) CLIMBER-2: A climate system model of intermediate
complexity. Part I: model description and performance for present climate. Climate
Dynamics 16:1.
40. Goswami BN, Ramesh KV (2006) A comparison of interpolated NCEP (I-NCEP) rainfall
with high-resolution satellite observations. Geophysl Res Lett 33:L19821.
41. Fasullo J (2005) Atmospheric hydrology of the anomalous 2002 Indian summer
monsoon. Monthly Weather Rev 133:2996–3014.
42. Kumar KK, Kumar KR, Ashrit RG, Deshpande NR, Hansen JW (2004) Climate impacts
in Indian agriculture. Int J Climatol 24:1375–1393.
43. Gregory PJ, Ingram JSI, Brklacich M (2005) Climate change and food security. Philos
Trans R Soc London Ser B 360:2139–2148.
44. Haile M (2005) Weather patterns, food security and humanitarian response in
sub-Saharan Africa. Philos Trans R Soc London Ser B 360:2169–2182.
45. Tao F, et al. (2004) Variability in climatology and agricultural production in China in
association with the East Asian summer monsoon and El Niño Southern Oscillation.
Climate Res 28:23–30.
46. Hansen J, et al. (1983) Efficient three-dimensional global models for climate studies:
Models I and II. Monthly Weather Rev 111:609–662.
Levermann et al.
2.2
Basic mechanism for abrupt monsoon transitions
39
Basic mechanism for abrupt monsoon
transitions
Anders Levermann ∗ † , Jacob Schewe ∗
∗
†
, Vladimir Petoukhov ∗ , and Hermann Held ∗
Earth System Analysis, Potsdam Institute for Climate Impact Research, Potsdam, Germany, and † Institute of Physics, Potsdam University, Potsdam, Germany
Submitted to Proceedings of the National Academy of Sciences of the United States of America
Supporting Information
Addition of evaporation. To our understanding, the strongest miss-
ing process is the effect of evaporation over land. In order to estimate
Monsoon regions and definitions
the critical threshold of different monsoon systems we generalize the
NCEP/NCAR reanalysis data was obtained from http://www.cdc.noaa.gov/model by adding evaporation to the moisture budget (equation [3]). In
the reanalysis data evaporation provides a very weak feedback within
as 60-year monthly mean time series, starting January 1948. Heat flux
the dynamics and is well approximated by P − E ≈ γP − EO , with
and precipitation data are averaged over land in each monsoon region.
region-specific constants E0 and γ which is close to unity (figure S4).
∆T is the difference between the average temperatures over land and
For this purpose we replace equation [S2] by w (1 − p) − (γp − e)
ocean. Humidities qL and qO refer to the same land and ocean rewith e ≡ E0 / (βqO ). This equation can also be derived from the apgions, respectively. The near-surface, landward zonal wind velocity
proach by Hansen et al. [1] and an additional assumption of constant
W is averaged over a third region. All three regions are given in
total soil moisture within a rainy season. We obtain
table 1 and illustrated in figures S1 and S2, together with the respecw+e
tive definitions of the monsoon season that are used for the temporal
[ S3 ]
p=
averages shown in figures 3 and 4. W is averaged vertically between
w+γ
850hPa and 1000hPa. qO is averaged vertically between 600hPa and
Accordingly, the governing equation transforms to
1000hPa. All other vertical averages are over the entire atmospheric
w3 + γw2 − (l + r) w − (el + γr) = 0
[ S4 ]
column.
Robustness of Rc estimate
In order to determine the statistical stability of the estimate of the
distribution of RC , we proceeded in two steps. (1) From the time
series’ autocorrelation we decided to treat the time series of α and β
as containing virtually no memory, i.e. values from different years
can be treated as statistically independent. Note that this assumption
does not contradict the existence of interannual to decadal variability
that is forced externally. (2) Via bootstrapping we generated surrogate time series of length 60. From this time series ensemble we
found that the standard deviation of mean and standard deviation of
the RC -distribution are one order of magnitude below the standard
deviation of the shown distribution of RC -estimates. Hence the red
curves in figure 6 are already relatively robust estimates, in view of
the simplicity of the model approach.
Structural sensitivity of conceptual model
In order to analyse the structural robustness of the governing equation [6] to inclusion of further physical processes we start from the
non-dimensional forms of the unperturbed equations [1] and [3]
lp − w2 + r
w (1 − p) − p
=
=
0
0
[ S1 ]
[ S2 ]
using the same definitions of parameters l and r and non-dimensional
variables w and p as in the main text. Note that the parameter l as
computed from observations is of the order 104 . Its qualitative influence on the solution structure can, however, already been seen for
l = 1. Since 1 is the only other scale in the non-dimensional governing equation, we will use l = 1 as an example. Similarly we will
show the qualitative influence of other paramters by setting them to
0.5 without claiming this to be an observed value. Note that for some
cases the critical precipitation reduces and could in principle become
zero or negative. This would change the model behaviour qualitatively. However this can only be the case when the corresponding
process dominates the dynamics and is not merely a perturbation to
the dynamics described in the core model. Non of the processes included eliminates the bifurcation for small parameter values. In this
sense the model behaviour is robust.
www.pnas.org/cgi/doi/10.1073/pnas.0709640104
Both constant and precipitaton-dependent evaporation shift critical
radiation to lower and critical precipitation towards higher values (figure S5). As in the minimal model set-up, the critical threshold can be
computed analytically from
wc (wc + γ)2 = (γ − e) l/2
[ S5 ]
rc = 3wc2 + 2γwc − l
By additional use of the evaporation time series E(t) from
NCEP/NCAR reanalysis, the parameter e can be computed.
e = E(t)/ (βqO (t))
[ S6 ]
By assuming γ to be constant (taken from the regression in figure S4),
the critical threshold of this generalized model can be computed (figure 6).
Addition of cloud-albedo feedback. Assuming that cloud-albedo
over land increases with the atmospheric moisture content we add
a term −a′ qL to equation [1] where a′ is a constant. Consequently
the nondimensional heat equation is transformed into
(l − a) p − w2 + r = 0
`
´
where a ≡ a qO ǫα/ Cp β 2 . The governing equation
[ S7 ]
′
w3 + w2 − (l + r − a) w − r = 0
[ S8 ]
shows the same functional form with an effective shift of the original l-parameter towards lower values (figure S6). This reduces the
significance of the moisture-advection feedback for the monsoon circulation by lowering the threshold precipitation value. On the other
hand the threshold is reached at higher net radiation rc .
Reserved for Publication Footnotes
PNAS
Issue Date
Volume
Issue Number
1–10
40
2
ORIGINAL MANUSCRIPTS
Tab. S1: Regional definitions used for data analysis
Monsoon system
Land region
Ocean region
Wind region
Monsoon season
INDIA
BAY OF BENGAL
CHINA
W.AFRICA
N.AMERICA
AUSTRALIA
70 − 90◦ E
5 − 30◦ N
65 − 78◦ E
5 − 30◦ N
65 − 78◦ E
5 − 30◦ N
80 − 100◦ E
15 − 30◦ N
80 − 100◦ E
10 − 20◦ N
80 − 100◦ E
15 − 30◦ N
100 − 110◦ E
25 − 30◦ N
80 − 100◦ E
10 − 20◦ N
90 − 105◦ E
15 − 25◦ N
15◦ W − 10◦ E
2 − 14◦ N
28◦ W − 10◦ E
5◦ S − 14◦ N
15◦ W − 10◦ E
2 − 9◦ N
110 − 100◦ W
20 − 30◦ N
120 − 110◦ W
20 − 30◦ N
111 − 109◦ W
20 − 30◦ N
120 − 150◦ E
18 − 10◦ S
100 − 130◦ E
10 − 0◦ S
100 − 130◦ E
10◦ S − 0◦
JJA
JJA
Addition of constant equatorial easterlies. The effect of a constant
inflow of moist air leads to an addition of a constant wt to the winds
in the heat balance and moisture balance equations
lp − w (w + wt ) + r = 0
[ S9 ]
(w + wt ) (1 − p) − p = 0
[ S10 ]
yielding the governing equation
JJA
JAS
JJA
JFM
where σ ≡ σ ′ / (Cp β) and tO ≡ TO αǫ/β is the nondimensional
atmospheric temperature over the ocean. The resulting governing
equation
w3 +(σ + 1) w2 −(l + r − σ (σ + 1)) w −(r + σtO ) = 0 [ S13 ]
w3 +(2wt + 1) w2 −(l + r − wt (wt + 1)) w−((1 + wt ) r + lwt ) = 0 resembles the corresponding relation with additional trade winds (figure S8).
[ S11 ]
and a shift of the critical threshold towards lower radiation and precipitation values (figure S7).
Addition of threshold for precipitation. Adding a threshold moisAddition of stabilizing radiative feedback. Adding a negative conture value qth to equation [4] above which precipitation is initi′
tribution −σ TL to the heat balance may be used to parameterize a
ated does not change the governing equation after redefining p ≡
stabilizing temperature feedback due to changes in long wave radiaP/ ((qO − qth ) β). It however changes the physical quantities. Crittion. This addition transforms the non-dimensional heat balance into
ical precipitation then reduces to zero when the threshold value approaches qO .
lp − (w + σ) w − σtO + r = 0
[ S12 ]
1. Hansen J, et al. (1983) Efficient three-dimensional global models for climate studies:
Models I and II. Monthly Weather Review 111:609–662.
2
www.pnas.org/cgi/doi/10.1073/pnas.0709640104
Footline Author
2.2
Basic mechanism for abrupt monsoon transitions
41
Fig. S1. Difference in precipitation between seasons (JJA-DJF) and the different
monsoon regions studied (black boxes).
Fig. S2. Different ocean (blue), land (dark gray) and wind (red box) regions for
the different monsoon systems as used for computation of the different quantities used to motivate the conceptual model. Flow lines represent summer winds
connecting the ocean with the land region.
Footline Author
PNAS
Issue Date
Volume
Issue Number
3
42
2
ORIGINAL MANUSCRIPTS
11 Bay of Bengal
India
8
10
′
qL (g/kg)
9
7
9
6
8.5
9
9.5
10
9
8
11
N. America
8
′
qL (g/kg)
W. Africa
10.5
7
7
7.5
8
8.5
7.5
11
China
8
8.5
9
Australia
10
10
9
′
L
q (g/kg)
12
8
8
10
11
12
6.5
q (g/kg)
L
7
7.5
8
8.5
q (g/kg)
L
′ over land as computed by the model from time seFig. S3. Specific humidity qL
ries for precipitation, radiation, temperature difference and specific humidity over
the ocean versus observed mean specific humidity over land qL . The line gives
′ =q
the unit function qL
L
4
www.pnas.org/cgi/doi/10.1073/pnas.0709640104
Footline Author
P−E (mm/day)
P−E (mm/day)
P−E (mm/day)
2.2
4
Basic mechanism for abrupt monsoon transitions
7
INDIA
r=0.98
3
BAY OF BENGAL
r=0.99
6
γ=0.84
2
E =−2.5
γ=0.98
5
E =−3.8
0
1
5
4
6
0
4
8
8
9
10
11
4 N.AMERICA
W.AFRICA
r=0.99
r=0.98
3
3
γ=0.99
2
γ=0.68
2
E =−4.4
1
E =−1.2
1
43
0
6
8
10
6
0
4
6
8
6
CHINA
4 r=0.99
4
AUSTRALIA
r=0.99
2
γ=1.2
2
γ=0.83
0
E =−6.2
0
E =−3.6
0
5
10
P (mm/day)
0
5
10
P (mm/day)
Fig. S4. Scaling of precipitation minus evaporation with precipitation in NCEPNCAR reanalysis data.
Footline Author
PNAS
Issue Date
Volume
Issue Number
5
44
Wind w
1
2
γ=0
γ=0.9
ORIGINAL MANUSCRIPTS
e=0.2
e=0
0
Precipitation p
−1
0.4
γ=0.9
0.2 γ=0
0
−0.5
e=0
e=0.2
0
Radiation r
0.5
−0.5
0
Radiation r
0.5
Fig. S5. Change in solution structure due to inclusion of evaporation. Left: constant offset e = 0.2 without linear dependence on precipitation γ = 1 (equation [S4]). Right: linearly dependent evaporation γ = 0.9 without constant offset
e = 0.
6
www.pnas.org/cgi/doi/10.1073/pnas.0709640104
Footline Author
2.2
Basic mechanism for abrupt monsoon transitions
45
1
Wind w
a=0
a=0.5
0
Precipitation p
−1
0.4
a=0
a=0.5
0.2
0
−0.5
0
Radiation r
0.5
Fig. S6. Change in solution structure due to inclusion of the cloud-albedo feedback
Footline Author
PNAS
Issue Date
Volume
Issue Number
7
46
2
ORIGINAL MANUSCRIPTS
Wind w
1
wT=0
0
wT=0.5
Precipitation p
−1
wT=0.5
w =0
0.4
T
0.2
0
−0.5
0
Radiation r
0.5
Fig. S7. Change in solution structure due to inclusion of an inflow of moisture
and heat by constant trade winds
8
www.pnas.org/cgi/doi/10.1073/pnas.0709640104
Footline Author
2.2
Basic mechanism for abrupt monsoon transitions
47
1
Wind w
σ=0
0
σ=0.5
Precipitation p
−1
0.4
0.2
σ=0
0
−0.5
σ=0.5
0
Radiation r
0.5
Fig. S8. Change in solution structure due to inclusion of a stabilizing long wave
radiation feedback
Footline Author
PNAS
Issue Date
Volume
Issue Number
9
2.3
A critical humidity threshold for monsoon failure
2.3
A critical humidity threshold for monsoon failure
51
Manuscript prepared for Clim. Past Discuss.
with version 2.2 of the LATEX class copernicus discussions.cls.
Date: 4 May 2011
A critical humidity threshold for monsoon
transitions
Jacob Schewe1,2 , Anders Levermann1,2 , and Hai Cheng3,4
1
Earth System Analysis, Potsdam Institute for Climate Impact Research, 14473 Potsdam,
Germany
2
Institute of Physics, University of Potsdam, 14476 Potsdam, Germany
3
Institute of Global Environmental Change, Xi’an Jiaotong University, Xi’an 710049, China
4
Department of Geology and Geophysics, University of Minnesota, Minneapolis 55455, USA
Correspondence to: J. Schewe
([email protected])
Abstract
Monsoon systems around the world are governed by the so–called moisture–advection
feedback. Here we show that, in a minimal conceptual model, this feedback implies a
critical threshold with respect to the atmospheric specific humidity qo over the ocean
adjacent to the monsoon region. If qo falls short of this critical value qoc , monsoon rainfall
over land cannot be sustained. Such a case could occur if evaporation from the ocean
was reduced, e. g. due to low sea surface temperatures. Within the restrictions of
the conceptual model, we estimate qoc from present–day reanalysis data for four major
monsoon systems, and demonstrate how this concept can help understand abrupt
variations in monsoon strength on orbital timescales as found in proxy records.
1
52
1
2
ORIGINAL MANUSCRIPTS
Introduction
Monsoon rainfall is the major prerequisite of agricultural productivity in many tropical
and subtropical regions of the world, and its variability has been affecting the livelihoods
of a large share of the world’s population from ancient civilizations until today (e.g.
Parthasarathy et al., 1988; Kumar et al., 2004; Auffhammer et al., 2006; Zhang et al.,
2008; Rashid et al., 2011). Proxy records show evidence of abrupt and strong monsoon
shifts during the last two glacial cycles (Burns et al., 2003; Wang et al., 2005a, 2008)
and the Holocene (Gupta et al., 2003; Hong et al., 2003; Wang et al., 2005b; Rashid
et al., 2011) in India, the Bay of Bengal, and East Asia. In many instances in the
past, periods of strong monsoon rainfall thus appear to have alternated with periods of
prolonged drought, with comparatively rapid transitions between the two.
Both spatial patterns and temporal evolution of continental monsoon rainfall are influenced by a number of physical processes (Hahn and Shukla, 1976; Webster et al.,
1998; Krishnamurthy and Goswami, 2000; Clark et al., 2000; Kucharski et al., 2006;
Goswami et al., 2006; Goswami and Xavier, 2005; Dash et al., 2005; Ramanathan
et al., 2005; Wang, 2005; Yang et al., 2007) as well as characteristics of vegetation
(Meehl, 1994; Claussen, 1997; Robock et al., 2003) and topography (Liu and Yin,
2002). Though these details are crucial for the specific behavior of different monsoon systems, and their significance will vary from region to region, there exist defining
processes fundamental to any monsoon dynamics (e.g. Webster, 1987a,b). These
are the advection of heat and moisture during the monsoon season and the associated rainfall and release of latent heat. While differential heating of land and ocean
in spring is important for the initiation of the monsoon season, land surface temperatures drop substantially after the onset of heavy precipitation, diminishing the surface
temperature gradient. The monsoon circulation over the continent is thereafter predominantly sustained by the release of latent heat and subsequent warming of the
atmospheric column over land (Webster et al., 1998). Using a complex conceptual
model, Zickfeld et al. (2005) found that a monsoon circulation that is sustained by the
2
2.3
A critical humidity threshold for monsoon failure
53
moisture-advection feedback can undergo abrupt changes in response to changes in
the land surface albedo. Knopf et al. (2006) however showed that the threshold albedo
in this model is very far away from modern conditions and is highly uncertain due to
the dependence on various model parameters.
Here we apply a minimal conceptual model that comprises the heat and moisture
budgets of an idealized monsoon circulation. It reflects the dominant role of the self–
amplifying moisture–advection feedback during the monsoon season, which is supported by observations Levermann et al. (2009). We find that the model yields a
threshold behaviour with respect to the atmospheric humidity over the ocean adjacent to the monsoon region. Below the threshold, the advection and release of latent
heat is not sufficient to sustain monsoon rainfall over land. It is important to note that
globally, rainfall associated with the intertropical convergence zone will naturally be
sustained even without continental monsoon rainfall. Furthermore the seasonal reversal of cross-equatorial winds, driven by the seasonal change in hemispheric insolation,
is not affected by our analysis. The question addressed here is which conditions are
necessary in order to sustain a rainy season over land which goes beyond the zonal–
mean dynamics of the intertropical convergence zone. The basic dynamics captured
in our model thus form a necessary condition for continental monsoon rainfall to exist.
We describe the conceptual model in section 2 and analyze its implications in section
3. In section 4, the critical threshold is estimated for four major monsoon regions.
In section 5, we apply the model to abrupt monsoon changes on orbital timescales.
Section 6 concludes.
2
Conceptual model
We use the minimal conceptual model presented by Levermann et al. (2009) (see
illustration in Fig. 1). It is based on the observation that, during the rainy season, the
regional-scale moist static energy balance of the atmospheric column is dominated by
latent heating due to precipitation, which is balanced by advective as well as radiative
3
54
2
ORIGINAL MANUSCRIPTS
cooling. According to NCEP/NCAR reanalysis data (1948-2007; Kistler et al., 2001),
this holds for all major monsoon regions (Levermann et al., 2009). The moist static
energy balance can thus be approximated by
L · P − Cp W · ∆T + R = 0,
(1)
W = α · ∆T.
(2)
W · (qo − qL ) − P = 0,
(3)
where ∆T is the atmospheric temperature difference between land and ocean, and
P is the mean precipitation over land (in kg/m2 s). Latent heat of condensation is
L =2.6·106 J/kg and volumetric heat capacity of air Cp =1,295 J/m3 /K. The ratio
= H/L between vertical extent H of the lower troposphere and the horizontal scale L
of the region of precipitation enters due to the balance of the horizontal advective heat
transport and the vertical fluxes of net radiative forcing R and precipitation P . Note that
this is a model of the monsoon season only, and includes no annual cycle. The above
balance therefore neglects the contribution from sensible heating, which is important
at the onset stage but relatively weak once heavy rainfall has started to cool the land
surface (Fig. 2). Consequently, this model does not capture any interseasonal or interannual dynamics. Equations are only valid for landward winds, W ≥ 0. That means
that situations in which no solution of the model with positive W can be found will be
considered as parameter and forcing combinations for which no monsoon rainfall can
be sustained.
Assuming dominance of ageostrophic flow in low latitudes, the landward mean wind
W is taken to be proportional to the temperature difference between land and ocean
(Petoukhov, 1982; Webster, 1987a; Brovkin et al., 1998):
Reanalysis data confirm that this is a valid first–order approximation (Fig. 3). Neglecting the effect of evaporation over land (which will be discussed below) and associated
soil moisture processes, precipitation has to be balanced by the net landward flow of
moisture
4
2.3
A critical humidity threshold for monsoon failure
55
where qo and qL are specific humidity over ocean and over land, respectively. Consistent with reanalysis data (Fig. 4) and theoretical considerations (Petoukhov, 1982;
Petoukhov et al., 2000), continental rainfall is assumed to be proportional to the mean
specific humidity within the atmospheric column:
P = βqL .
(4)
Note that the proportionality constants α and β have explicit physical interpretations. α
is essentially a function of the near-surface cross-isobar angle and thereby a function of
surface roughness and static stability of the planetary boundary layer (PBL); β is governed by the characteristic turnover (recycling) time of liquid water in the atmosphere
and thereby determined by static stability and vertical velocity in the PBL (Petoukhov
et al., 2000). While equations (1) to (4) are the basic relations necessary to capture
the moisture–advection feedback, eq. (4) can be made more realistic by considering
an offsets in qL , which will be discussed further below.
3
Critical qo threshold for monsoon existence
From equations (1), (3) and (4) it follows that
β
β
Lβ qo = 1 +
Cp W · ∆T − 1 +
·R
ρW
ρW
(5)
This equation represents the heat budget of the conceptual monsoon circulation in
terms of latent heat. The two terms on the r. h. s. represent the loss of heat from the
land region by advection of warm air and by radiation, respectively (note that R < 0).
Their sum must be balanced by latent heat as provided by the inflow of humid air from
the ocean, namely Lβ qo . The term (1 + β/ρW ) incorporates the fact that the latent
heat has to be transported from ocean to land by means of advection; the lower the
advective velocity W , the higher the specific humidity qo that is necessary to provide
the required amount of latent heat to the atmospheric column over land.
5
56
2
ORIGINAL MANUSCRIPTS
Using equation
(2) and the relations w ≡ W ρ/β; r ≡ R · αρ/(Cp β 2 ),
l ≡ αρ2 Lqo /(Cp β), the non-dimensional form of eq. (5) is obtained:
l = 1 + w−1 · w2 − 1 + w−1 · r
and
(6)
where l is proportional to qo . This is the governing equation of the conceptual model.
Its solution is determined entirely by the only free parameter r. The physical part (l ≥ 0,
w ≥ 0) of the solution of eq. (6) is shown in Fig. 5 for the case r = −0.05, where the thick
red line denotes a stable solution and the thin red line an unstable one. The advective
(dashed line) and radiative (dotted line) terms are also plotted separately to show how
the solution is obtained as the sum of these two contributions (to illustrate this, the
figure is organized with the control parameter l on the y-axis). In the case r ≡ 0, only
the advective part of the solution remains (i. e. the red line would collapse onto the
dashed line). The y-axis in Fig. 5 can be interpreted as the demand in latent heating
that results from the loss of heat from the land region due to radiation and advection.
It turns out that no physical solution exists below a critical threshold lc (horizontal
line in Fig. 5), which corresponds to a critical value of specific humidity over the ocean,
qoc . When qo falls short of this value, the supply of moisture is not sufficient to maintain
the monsoon circulation driven by the moisture–advection feedback. No conventional
monsoon circulation can thus develop in a climate where qo < qoc .
Equation (6) can also be expressed in terms of non–dimensional precipitation p ≡
P/(qo β), which is directly related to the wind through
(7)
p = w/(1 + w).
The solution in terms of p has a similar shape as in terms of w (Fig. 6), while dimensional precipitation P scales approximately linearly with l as long as l is sufficiently
above the threshold lc (Fig. 7). While we do not expect to find this quasi-linear relation
perfectly reflected in observations, NCEP/NCAR reanalysis data show that seasonal
mean precipitation and specific humidity over ocean are correlated to some extent
(Fig. 8).
6
2.3
4
A critical humidity threshold for monsoon failure
57
Estimation of the critical threshold qoc
The critical point [lc , wc ] (or [qoc , wc ]) will vary for different monsoon systems. It is
determined by the non–dimensional radiation r via
−wc2 (2wc + 1) = r.
(8)
lc = 2wc (wc + 1)2 ,
(9)
The critical l can then be computed from
and the critical humidity threshold qoc via the definition of l. Within the limitations of this
minimal conceptual model, we estimate qoc for four different monsoon regions. We use
seasonal mean precipitation P , radiation R, land–ocean temperature difference ∆T ,
and specific humidity over ocean qo from the NCEP/NCAR reanalysis to compute time
2
series for α(t) = (LP + R)/ C
p ∆T , β(t) = ((LP + R) · ρP )/((LP + R)qo ρ − Cp ∆T P ),
and r(t) = R·α(t)/ Cp β(t)2 , assuming applicability of the model and stationary statistics within the observational period (1948-2007). Because the observational period is
subject to significant anthropogenic global warming, we remove a linear trend from all
reanalysis data to get a closer approximation of a stationary climate. From α(t), β(t)
and r(t), qoc can then be obtained for each year via equations (8), (9) and the definition of l. Note that qoc is independent of , the only quantity that is not constrained by
data. The resulting qoc distribution (Fig. 9, blue) is much lower than the observed distribution of qo (black) in the Bay of Bengal, West Africa and China, while in India the
distributions are closer. The blue pin marks the qoc estimate that is obtained from the
time–mean values α(t), β(t) and R(t).
The spread in the distribution of qoc is due to substantial variability in α(t), β(t) and
r(t) throughout the reanalysis period. The interannual variability in the dimensional net
radiation R is about 15–20% during the reanalysis period, depending on the region. On
longer timescales, R can be expected to be rather stable because of the negative long–
wave radiation feedback according to the Stefan–Boltzmann law. However, the factors
α and β may also vary over time. Moreover, in reality the basic relations of eq. (2) and
7
58
2
ORIGINAL MANUSCRIPTS
eq. (4) are blurred by higher–order physical processes that are not represented in our
idealized model, limiting our ability to determine α and β (cf. Fig. 3 and 4). Depending
on the relative importance of actual variability and observational uncertainty, the distribution of qoc can be interpreted either (i) as a noisy estimate of a stationary critical
threshold, or (ii) as a probability distribution of an interannually varying threshold.
In order to obtain more realistic estimates of qoc , we extend eq. (4) by an offset qLo that
terrestrial humidity qL needs to exceed before precipitation is initiated (as suggested
by the correlation in Fig. 4):
P = β (qL − qLo ).
(10)
After replacing qo by (qo − qLo ) in the definitions of l and p, the non–dimensional equations (6)–(9) remain unchanged.
As above, we estimate qoc for this refined version of the model, obtaining the parameter qLo from linear regression of the corresponding reanalysis data (Fig. 4). Note that,
due to eq. (10), qoc now also depends on . Since we have no direct observation of ,
we choose such that α(t) matches the α that we observe as the slope of the linear
regression between W and ∆T (Fig. 3). The results for qoc are shown in Fig. 9 (red),
where again the pin marks the estimate from mean quantities. The consideration of the
offset qLo generally yields a distribution of qoc which is narrower and closer to, while still
clearly seperate from, the present-day range of qo . Only for the Indian region, the distribution of qoc overlaps with that of the observed qo ; however, when considered pointwise,
qoc (t) is still lower than qo (t) for all years.
5
Application to past abrupt monsoon changes
Wang et al. (2008) presented a speleothem δ 18 O record from central China that testifies to several large and persistent changes in the strength of the East Asian summer
monsoon (EASM) during the penultimate glacial period. These changes are in phase
8
2.3
A critical humidity threshold for monsoon failure
59
with, but much more abrupt than, precession–dominated oscillations in northern hemisphere summer insolation (NHSI): While the latter follow a quasi-sinusoidal cycle, the
form of the monsoon changes rather resembles that of a step–function, with variations
around either a strong or a weak mean state, followed by a comparatively rapid transition into the other state (cf. Fig. 2b in Wang et al. (2008)). This behaviour is especially
apparent before about 160 kyr BP (Fig. 10, grey line) and suggests that non–linear
processes inherent to the monsoon system might have amplified changes in external
forcing. In particular, the abrupt transitions might have been triggered by the mean insolation crossing a certain threshold that separated two different states of the monsoon
circulation.
Our conceptual monsoon model offers a simplified but robust mechanism to explain
such sort of behaviour. It shows that the moisture–advection feedback implies a threshold qoc that seperates two regimes: One where a conventional monsoon circulation can
exist, and one where it cannot. We therefore speculate that orbital–timescale variations in NHSI and the associated surface temperature changes might have affected
evaporation at the ocean surface such that average humidity over the ocean persistently crossed the threshold, thus critically altering the moisture supply for the adjacent
monsoon region and triggering a transition between the two regimes. In the following
we apply our model to demonstrate how such variations in qo could have led to monsoon variations consistent with those observed in the proxy record. In doing so, we
assume that the values of α, β, , and qLo estimated for modern climate from reanalysis
data also hold for the penultimate glacial period; and that R was also comparable during that period to its modern value. In reality, R might have also varied in phase with
NHSI, however this variation would have been damped by the stabilizing long–wave radiation feedback. Moreover, a variation of R along with NHSI would act to exacerbate
the threshold effect, moving the threshold towards higher values when insolation, and
thus the inferred qo , is low (cf. Fig. 11). Therefore, neglecting variations in R yields a
conservative result with respect to the volatility of the system.
The solution of the conceptual model depends on the non-dimensional net radiation
9
60
2
ORIGINAL MANUSCRIPTS
r. We choose r = r(t) − σ/2 = -106.5, where σ denotes a standard deviation (Fig. 11).
This corresponds to a critical threshold qoc = 8.0 g/kg, which is in the upper part of the
estimated qoc distribution for the China region (cf. Fig. 9). We further assume qo to vary
linearly with NHSI (Fig. 12). The linear relation is chosen such that the maximum qo
is close to the range of present–day observations. Finally, we assume that when the
threshold qoc is crossed from below (i. e., coming from a no–monsoon regime), it takes
an additional increase ∆q to trigger the transition into the monsoon regime (Fig. 11).
The EASM precipitation thus resulting from the conceptual model is shown in Fig. 10
(red line). We set P to zero during periods when the model yields no physical solution,
to illustrate the idea that no conventional monsoon circulation can exist during those
periods, and no rainfall associated with that circulation would occur. However we would
expect sources of rainfall other than the large–scale monsoon circulation to play a role,
too, so that actual rainfall would not completely cease during such periods. Note that
neither the hysteresis width ∆q nor the second degree of freedom in the relation qo ∝
NHSI are constrained by data; instead they are chosen such that the result of the
conceptual model matches the transition behaviour found in the proxy record, taking
into account dating errors in the latter (grey bars in Fig. 10).
6
Discussion and conclusions
We have shown that, in a minimal conceptual model of large–scale monsoon circulation, a critical threshold qoc exists with respect to specific humidity over the ocean region
upwind of the continental monsoon region. This threshold follows from the central role
of the self–amplifying moisture–advection feedback, which governs the atmospheric
MSE balance during the monsoon season. If qo falls short of the threshold qoc , no conventional monsoon circulation can exist over land. The model neglects any processes
that are not crucial to the moisture–advection feedback, in order to isolate the consequences of this feedback. The basic dynamics captured in the model therefore form
a necessary condition for the existence of continental monsoon rainfall beyond what
10
2.3
A critical humidity threshold for monsoon failure
61
is accounted for by the zonal-mean dynamics of the intertropical convergence zone.
Our results complement those of Levermann et al. (2009), who found a threshold with
respect to the net radiation R. While qo can generally be expected to be more volatile
than R, the model allows for a superposition of changes in both quantities, with the
one either damping or amplifying the effect of the other, depending on the direction of
change.
As the model contains the physical feedback that causes the threshold behaviour,
it can be used to produce meaningful first–order estimates of the threshold values.
Within the framework of the minimal model, we have estimated the critical threshold qoc
for four major monsoon regions, using seasonally averaged reanalyses of regional precipitation, net radiation, specific humidity, and temperature for the past sixty years. The
resulting distribution of qoc can be interpreted either as a noisy estimate of a stationary
critical threshold, or as a probability distribution of an interannually varying threshold.
The degree to which either of these interpretations is valid depends chiefly on the relative importance of actual variability and observational uncertainty in the parameters
α and β (see equations (2) and (4)), the assessment of which is beyond the scope
of this study. However we have seen that the consideration of an offset in terrestrial
specific humidity in eq. (4) leads to a qoc distribution which is significantly narrower than
with the basic version of the model, suggesting that at least some of the spread in qoc
can be eliminated by making the model more realistic, and thus does not reflect actual
year–to–year variability in qoc .
Consequently, relevant second–order physical processes would have to be included
into the model in order to obtain more robust results for qoc . Probably one of the most
important missing processes is evaporation over land (e. g. Eltahir, 1998). Its effect
on the heat budget would be mainly to reduce sensible heat flux to the atmosphere,
which we have already neglected (eq. (1)) because it is comparatively small during the
rainy season; on the other hand, its effect on the moisture budget (eq. (3)) would be
to stabilize the monsoon regime by recycling a part of the atmospheric humidity that is
lost by precipitation. Therefore considering evaporation would tend to move the critical
11
62
2
ORIGINAL MANUSCRIPTS
threshold towards lower values of qo .
While the estimation of qoc could profit from a refinement of the model, the aim of
this study is to demonstrate how the simple concept that the model is based on can
help understanding past monsoon variations. The non–linear solution structure of the
model can lead to abrupt changes in the modelled monsoon rainfall in response to
smooth changes in the control parameter, qo . Changes in qo could be brought about
by various factors acting on different timescales. For instance, as wind speed over the
oceans increases due to global warming (Young et al., 2011), evaporation e.g. in the
Arabian Sea could be affected both directly and via the amount of upwelling of cold
waters at the continental margins, and thereby alter the moisture supply for the Indian
summer monsoon. For the East Asian summer monsoon (EASM), we have shown
that, assuming variations in qo along orbital–timescale insolation changes, the model
yields a series of abrupt monsoon transitions similar to that observed in a proxy record
of the penultimate glacial period. While the additional assumption of a hysteresis is not
crucial for the transition behaviour, it changes the timing of the individual transitions
such that they are all consistent, within dating errors, with those found in the proxy
record (physically, a hysteresis might be induced by inert climate components such
as e.g. large–scale oceanic circulation or Himalayan glaciation, rather than by atmospheric processes). The idea of a threshold behaviour in monsoon circulations due to
the defining mechanism of the moisture–advection feedback may thus be a useful first–
order concept for understanding past large–scale monsoon changes. The conceptual
model investigated here may also serve as a basic building block that can be made
more realistic by the inclusion of other relevant processes and by a more detailed estimation of the model parameters. For a complete understanding of monsoon variations
on multiple timescales, of course, more complex models will have to be invoked.
12
2.3
A critical humidity threshold for monsoon failure
63
Appendix A
Methods
NCEP/NCAR reanalysis data has been obtained as monthly–mean time series (January 1948 – December 2007), and regionally aggregated as indicated in Table 1. W is
averaged vertically between 850hPa and 1000hPa; qo between 600hPa and 1000hPa;
qL between 400hPa and 1000hPa; and ∆T over the entire atmospheric column, as
represented in the reanalysis data.
Acknowledgements. This work was funded by the Heinrich Böll Foundation, the German National Academic Foundation, and the BMBF PROGRESS project (support code 03IS2191B).
NCEP Reanalysis Derived data was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/.
References
Auffhammer, M., Ramanathan, V., and Vincent, J. R.: Integrated model shows that atmospheric
brown clouds and greenhouse gases have reduced rice harvests in India, Proceedings of the
National Academy of Sciences, 103, 19 668–19 672, 2006.
Brovkin, V., Claussen, M., Petoukhov, V., and Ganopolski, A.: On the stability of the
atmosphere- vegetation system in the Sahara/Sahel region, Journal of Geophysical Research, 103, 31 613–31 624, 1998.
Burns, S. J., Fleitmann, D., Matter, A., Kramers, J., and Al-Subbary, A. A.: Indian Ocean Climate
and an Absolute Chronology over Dansgaard/Oeschger Events 9 to 13, Science, 301, 1365–
1367, 2003.
Clark, C. O., Cole, J. E., and Webster, P. J.: Indian Ocean SST and Indian summer rainfall:
Predictive relationships and their decadal variability, Journal of Climate, 13, 2503–2519,
2000.
Claussen, M.: Modeling bio-geophysical feedback in the African and Indian monsoon region,
Climate Dynamics, 54, 247–257, 1997.
13
64
2
ORIGINAL MANUSCRIPTS
Dash, S. K., Singh, G. P., Shekhar, M. S., and Vernekar, A. D.: Response of the Indian summer
monsoon circulation and rainfall to seasonal snow depth anomaly over Eurasia, Climate
Dynamics, 24, 1–10, 2005.
Eltahir, E. A. B.: A soil moisture-rainfall feedback mechanism: 1. Theory and observations,
Water Resources Research, 34, 765–776, 1998.
Goswami, B. N. and Xavier, P. K.: ENSO control on the south Asian monsoon through the
length of the rainy season, Geophysical Research Letters, 32, L18 717, 2005.
Goswami, B. N., Madhusoodanan, M. S., Neema, C. P., and Sengupta, D.: A physical mechanism for North Atlantic SST influence on the Indian summer monsoon, Geophysical Research Letters, 33, L02 706, 2006.
Gupta, A. K., Anderson, D. M., and Overpeck, J. T.: Abrupt changes in the Asian southwest
monsoon during the Holocene and their links to the North Atlantic Ocean, Nature, 421, 354–
357, 2003.
Hahn, D. G. and Shukla, J.: An apparent relationship between Eurasian snow cover and Indian
monsoon rainfall, Journal of Atmospheric Sciences, 33, 2461–2462, 1976.
Hong, Y., Hong, B., Lin, Q., Zhu, Y., Shibata, Y., Hirota, M., Uchida, M., Leng, X., Jiang, H., Xu,
H., Wang, H., and Yi, L.: Correlation between Indian Ocean summer monsoon and North
Atlantic climate during the Holocene, Earth and Planetary Science Letters, 211, 371–380,
doi:10.1016/S0012-821X(03)00207-3, 2003.
Kistler, R., Kalnay, E., Saha, S., White, G., Woollen, J., Chelliah, M., Ebisuzaki, W., Kanamitsu, M., Kousky, V., den Dool, H. V., Jenne, R., and Fiorino, M.: The NCEP/NCAR 50-year
reanalysis, Bull. Amer. Meteor. Soc., 82, 247 – 267, 2001.
Knopf, B., Flechsig, M., and Zickfeld, K.: Multi-parameter uncertainty analysis of a bifurcation
point, Nonlinear Processes in Geophysics, 13, 531–540, 2006.
Krishnamurthy, V. and Goswami, B. N.: Indian monsoon-ENSO relationship on inter-decadal
timescale, Journal of Climate, 13, 579–595, 2000.
Kucharski, F., Molteni, F., and Yoo, J. H.: SST forcing of decadal Indian monsoon rainfall variability, Geophysical Research Letters, 33, L03 709, 2006.
Kumar, K. K., Kumar, K. R., Ashrit, R. G., Deshpande, N. R., and Hansen, J. W.: Climate
impacts on Indian agriculture, International Journal of Climatology, 24, 1375–1393, doi:10.
1002/joc.1081, 2004.
Levermann, A., Schewe, J., Petoukhov, V., and Held, H.: Basic mechanism for abrupt monsoon
transitions, Proceedings of the National Academy of Sciences, 106, 20 572–20 577, 2009.
14
2.3
A critical humidity threshold for monsoon failure
65
Liu, X. and Yin, Z.: Sensitivity of East Asian monsoon climate to the uplift of the Tibetan Plateau,
Palaeogeography, Palaeoclimatology, Palaeoecology, 183, 223–245, 2002.
Meehl, G. A.: Influence of the Land Surface in the Asian Summer Monsoon: External Conditions versus Internal Feedbacks, Journal of Climate, 7, 1033–1049, 1994.
Parthasarathy, B., Munot, A., and Kothawale, D.: Regression model for estimation of Indian
foodgrain production from summer monsoon rainfall, Agricultural and Forest Meteorology,
42, 167 – 182, doi:10.1016/0168-1923(88)90075-5, 1988.
Petoukhov, V., Ganopolski, A., Brovkin, V., Claussen, M., Eliseev, A., Kubatzki, C., and Rahmstorf, S.: CLIMBER-2: a climate system model of intermediate complexity. Part I: model
description and performance for present climate, Climate Dynamics, 16, 1, 2000.
Petoukhov, V. K.: Two mechanisms of temperature oscillations in a thermodynamical model
of the troposphere-stratosphere system, Izvestiya, Atmospheric and Oceanic Physics, 18,
126–137, 1982.
Ramanathan, V., Chung, C., Kim, D., Bettge, T., Kiehl, J. T., Washington, W. M., Fu, Q., Sikka,
D. R., and Wild, M.: Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle, Proceedings of the National Academy of Sciences, 102, 5326–5333, 2005.
Rashid, H., England, E., Thompson, L., and Polyak, L.: Late Glacial to Holocene Indian Summer Monsoon Variability Based upon Sediment Records Taken from the Bay of Bengal,
Terrestrial Atmospheric and Oceanic Sciences, 22, 215–228, doi:10.3319/TAO.2010.09.17.
02(TibXS), 2011.
Robock, A., Mu, M., Vinnikov, K., and Robinson, D.: Land surface conditions over Eurasia and
Indian summer monsoon rainfall, Journal of Geophysical Research, 108, 4131, 2003.
Wang, B.: The Asian monsoon, Springer-Verlag, 2005.
Wang, P., Clemens, S., Beaufort, L., Braconnot, P., Ganssen, G., Jian, Z., Kershaw, P., and
Sarnthein, M.: Evolution and variability of the Asian monsoon system: state of the art and
outstanding issues, Quaternary Science Reviews, 24, 595–629, 2005a.
Wang, Y., Cheng, H., Edwards, R. L., He, Y., Kong, X., An, Z., Wu, J., Kelly, M. J., Dykoski,
C. A., and Li, X.: The Holocene Asian Monsoon: Links to Solar Changes and North Atlantic
Climate, Science, 308, 854–857, 2005b.
Wang, Y., Cheng, H., Edwards, R. L., Kong, X., Shao, X., Chen, S., Wu, J., Jiang, X., Wang, X.,
and An, Z.: Millennial- and orbital-scale changes in the East Asian monsoon over the past
224,000 years, Nature, 451, 1090–1093, 2008.
Webster, P. J.: The Elementary Monsoon, in: Monsoons, edited by Fein, J. S. and Stephens,
15
66
2
ORIGINAL MANUSCRIPTS
P. L., pp. 3–32, John Wiley, New York, N.Y., 1987a.
Webster, P. J.: The Variable and Interactive Monsoon, in: Monsoons, edited by Fein, J. S. and
Stephens, P. L., pp. 269–330, John Wiley, New York, N.Y., 1987b.
Webster, P. J., Magaña, V. O., Palmer, T. N., Shukla, J., Tomas, R. A., Yanai, M., and Yasunari, T.: Monsoons: Processes, predictability, and the prospects for prediction, Journal of
Geophysical Research, 103, 14,451–14,510, 1998.
Yang, J., Liu, Q., Xie, S.-P., Liu, Z., and Wu, L.: Impact of the Indian Ocean SST basin mode
on the Asian summer monsoon, Geophysical Research Letters, 34, L02 708, 2007.
Young, I. R., Zieger, S., and Babanin, A. V.: Global Trends in Wind Speed and Wave Height,
Science, 332, 451–455, doi:10.1126/science.1197219, 2011.
Zhang, P., Cheng, H., Edwards, R. L., Chen, F., Wang, Y., Yang, X., Liu, J., Tan, M., Wang, X.,
Liu, J., An, C., Dai, Z., Zhou, J., Zhang, D., Jia, J., and Johnson, K. R.: A Test of Climate,
Sun, and Culture Relationships from an 1810-Year Chinese Cave Record, Science, 322,
940–942, 2008.
Zickfeld, K., Knopf, B., Petoukhov, V., and Schellnhuber, H. J.: Is the Indian summer monsoon
stable against global change?, Geophysical Research Letters, 32, L15 707, 2005.
16
2.3
A critical humidity threshold for monsoon failure
67
R
W
P
W
qo
qL
Ocean
Land
Fig. 1. Geometry of the conceptual model, illustrating wind W , precipitation P , net radiative
flux R, and atmospheric specific humidity over land (qL ) and ocean (qo ).
17
68
2
300
W/m2
200
300
INDIA
Latent
Sensible
100
0
0
−100
−100
Radiative
2
300
4
Convergence
6
8
W.AFRICA
10
W/m2
Radiative
2
300
Latent
4
6
Converg.
8
10
12
CHINA
Latent
200
Sensible
100
0
−100
Latent
Sensible
−200
12
200
100
BAY OF BENGAL
200
100
−200
ORIGINAL MANUSCRIPTS
Sensible
0
Radiative
−200
2
4
−100
Convergence −200 Radiative
6
8
Month
10
12
2
4
Convergence
6
8
Month
10
12
Fig. 2. Seasonal heat flux contributions to the atmospheric column over four major continental
monsoon regions in NCEP/NCAR reanalysis data (Kistler et al., 2001). Radiative heating of
the land surface in spring enhances sensible heat flux from the ground (’Sensible’). During
the rainy season, latent heat release dominates the heat budget (’Latent’). Radiative heat flux
comprises all radiative fluxes in and out of the atmospheric column (’Radiative’). The excess
heat is transported out of the continental monsoon region through large–scale advective and
synoptic processes (’Convergence’). Error bars give the standard deviation from the reanalysis
period (1948-2007). See Table 1 for the geographical definitions of the monsoon regions. The
red and blue vertical lines emphasize the months of maximum sensible heat flux and latent
heat flux, respectively.
18
2.3
A critical humidity threshold for monsoon failure
4.5
W (m/s)
8.5 INDIA
BAY OF BENGAL
4
8 r=0.49
3.5
7.5
r=0.57
3
7
1.8
2
α=3.62
α=1.85
2.2
2
2.5
2.4
1.6 1.8
4
6 W.AFRICA
W (m/s)
69
r=0.44
4
3
CHINA
r=0.63
2
0
0
0.2
0.4
Δ T (K)
α=3.00
2
α=9.64
0.6
2.2
0.5
1
Δ T (K)
1.5
Fig. 3. Wind W versus temperature difference between land and ocean region, ∆T , from
NCEP/NCAR reanalysis data, for the major monsoon regions of India, the Bay of Bengal, West
Africa, and China (East Asia; see Table 1). The correlation coefficient r is indicated, as well as
the slope α of a linear fit through the origin (black line).
19
2
P (mm/day)
70
11
8 INDIA
B. OF BENGAL
L
5
β=0.0265
8
P (mm/day)
10 r=0.65
7 r=0.83
qo=6.27
6
9
8
ORIGINAL MANUSCRIPTS
9
8
β=0.0218
10
10.5
11
11
CHINA
10 r=0.61
qo=4.73
9 L
o
qL=4.58
β=0.0271
6
o
qL=5.52
10
W.AFRICA
r=0.63
7
9
7.5
8
q (g/kg)
8
7
8.5 9
L
β=0.0205
10
q (g/kg)
L
Fig. 4. Precipitation P versus specific humidity over land, qL , from NCEP/NCAR reanalysis
data. The black line shows the result of a linear regression, the correlation coefficient r is
o
indicated, as well as the slope β (in kg/m2 s) and the offset in terrestrial humidity, qL
(in g/kg).
20
2.3
A critical humidity threshold for monsoon failure
71
1
parameter l ∝ qo
0.8
0.6
advection
radiation
resulting latent
heat demand
lc
0.4
0.2
0
0
0.2
0.4
Wind w
0.6
0.8
Fig. 5. Solution structure of the conceptual model as a function of the non–dimensional parameter l, which is proportional to specific humidity over the ocean, qo . For illustrative purposes, l
is plotted on the y-axis. The latent heat demand (red line; bold part indicates the stable branch)
results from heat loss due to net radiative flux (dotted) and advection of warm air out of the land
region (dashed).
21
72
2
ORIGINAL MANUSCRIPTS
0.4
0.35
Precipitation p
0.3
0.25
0.2
0.15
0.1
0.05
0
0
lc
0.2
0.4
0.6
parameter l ∝ qo
0.8
1
Fig. 6. As Fig. 5 (red line), but in terms of non–dimensional precipitation p, and organized with
the control parameter l on the x-axis.
22
A critical humidity threshold for monsoon failure
73
P (mm/day)
2.3
lc
0
0.2
0.4
0.6
parameter l ∝ qo
0.8
1
Fig. 7. As Fig. 6, but in terms of dimensional precipitation P . The shape of the solution is
different than in terms of p because the relation between p and P depends on l. Units on the
y-axis are arbitrary.
23
2
P (mm/day)
74
P (mm/day)
11
8 INDIA
BAY OF BENGAL
r=0.63
7
10
6
9
5
9
9.5
10
9 W.AFRICA
r=0.46
8
7
r=0.47
8
10.5
11
10
10.5
11
CHINA
10 r=0.51
9
8
6
5
8
ORIGINAL MANUSCRIPTS
8.5
9
qO (g/kg)
7
9.5 10
11
qO (g/kg)
12
Fig. 8.
Correlation between precipitation and specific humidity over the ocean from
NCEP/NCAR reanalysis data. Black lines show the result of a linear regression, the correlation coefficients are indicated.
24
2.3
A critical humidity threshold for monsoon failure
75
50
40
India
30
30
20
20
10
0
10
0
40
0.005
qo (kg/kg)
0
0.01
20
10
10
0.005
qo (kg/kg)
0
0.01
0.005
0.01
0.005
0.01
qo (kg/kg)
China
30
20
0
0
40
W.Africa
30
0
Bay of Bengal
40
0
qo (kg/kg)
Fig. 9. Estimate of critical specific humidity value over the ocean, qoc , from NCEP/NCAR reanalysis data for the basic minimal model (blue) and including the effect of a minimum terrestrial
o
humidity qL
required for the onset of precipitation (red). The black histogram shows the observed distribution of qo . Pins mark the estimates obtained from time–mean parameter values.
25
76
2
ORIGINAL MANUSCRIPTS
18
0
−9
O ( /00, VPDB)
5
δ
P (mm/day)
−11
−7
0
160
170
180
190
200
time (kyr BP)
210
220
Fig. 10. Grey: Speleothem δ 18 O record from central China, used as EASM proxy for the
penultimate glacial period, where more negative values indicate stronger rainfall (Wang et al.,
2008). The record is smoothed with a 5–point running average, and dating errors (± 2σ)
are shown for selected dates (grey horizontal bars). Red: Result of the conceptual model
for EASM precipitation P in response to qo variations driven by northern hemisphere (65◦ N)
summer insolation, assuming a hysteresis of 0.5 g/kg width. For illustration, we set P to zero
during periods when no monsoon circulation exists according to the model.
26
2.3
A critical humidity threshold for monsoon failure
77
10
P (mm/day)
8
6
4
r=−106.5
r=−83.8
r=−61.0
2
0
6
8
10
humidity over ocean q (kg/kg)
0
12
−3
x 10
Fig. 11. Physical solution for EASM precipitation P from the conceptual model, for three different values of the non–dimensional parameter r: r(t) (dashed), r(t) + σ/2 (dot–and–dash), and
r(t)−σ/2 (solid), where r(t) is the estimate from NCEP/NCAR reanalysis data, and σ denotes a
standard deviation. Bold lines indicate the upper, stable branch. The value r(t) − σ/2 = -106.5,
which corresponds to a critical threshold qoc = 8.0 g/kg, is used for the comparison of the model
with EASM proxy data. Vertical dashed lines mark qoc and qoc + ∆q , where we choose ∆q = 0.5
g/kg as the width of an assumed hysteresis that is thought to appear when the threshold is
crossed from either side (illustrated by arrows).
27
78
2
ORIGINAL MANUSCRIPTS
−3
10
x 10
9.5
o
q (kg/kg)
9
8.5
8
7.5
7
6.5
160
170
180
190
200
time (kyr BP)
210
220
Fig. 12. Time series of qo used for the application of the conceptual model. qo is assumed to
vary linearly with northern hemisphere (65◦ N) summer insolation. The horizontal dashed line
marks qoc .
28
2.3
A critical humidity threshold for monsoon failure
79
Table 1. Regional definitions used for data analysis. Monthly–mean NCEP/NCAR reanalysis
data has been averaged over the indicated regions and seasons; Land and Ocean indicate that
only terrestrial or oceanic grid points have been considered, respectively; and ∆T = TL − To .
The bottom row lists the values for the dimensionless parameter that have been used in the
estimation of the critical threshold (see section 4).
Quantity
P , R, qL , TL (Land)
qo , To (Ocean)
W
Monsoon season
INDIA
70-90◦ E
5-30◦ N
65-78◦ E
5-30◦ N
65-78◦ E
5-30◦ N
June–Aug.
4.5·10−3
BAY OF BENGAL
80-100◦ E
15-30◦ N
80-100◦ E
10-20◦ N
80-100◦ E
15-30◦ N
June–Aug.
2.3·10−2
29
W.AFRICA
15◦ W-10◦ E
2-14◦ N
35-15◦ W
2-14◦ N
15◦ W-10◦ E
2-9◦ N
July–Sep.
2.0·10−1
CHINA (EASM)
100-120◦ E
20-32◦ N
105-115◦ E
15-25◦ N
100-120◦ E
20-32◦ N
June–Aug.
6.7·10−2
2.4
More frequent future monsoon failure due to inherent instability
2.4
More frequent future monsoon failure due to inherent instability
More frequent future monsoon failure due to inherent instability
Jacob Schewe1,2 & Anders Levermann1,2
1
Earth System Analysis, Potsdam Institute for Climate Impact Research, Potsdam, Germany
2
Institute of Physics, Potsdam University, Potsdam, Germany
Indian summer monsoon rainfall is vital for a large share of the world’s population. Both
reliably projecting India’s future rainfall and unraveling abrupt monsoon shifts found in
paleo–records require improved understanding of its stability properties. Here we project
monsoon failure to become considerably more frequent due to global warming, and provide
a simple dynamical explanation for this trend as well as for multi–decadal rainfall variability.
Based on fundamental properties of observed monsoon dynamics and an associated inherent
instability that is modulated by ambient climate merely during the onset period, we develop
a statistically predictive model of seasonal rainfall. Forced only by global mean temperature
and central-Pacific sea level pressure anomalies in May, this simple model reproduces past
and future monsoon trends as found in a comprehensive climate model. We thereby propose
a novel perspective on monsoon variability as the result of internal instabilities modulated by
pre–seasonal ambient climate conditions.
Indian summer monsoon (ISM) rainfall is the major prerequisite of agricultural productivity in the region, and its variability severely affects the livelihoods of a large share of the world’s
population1, 2 . While average ISM rainfall has been relatively stable during the past century of di1
83
84
2
ORIGINAL MANUSCRIPTS
rect observations, rising trends have been observed in the annual number of extreme rain events3 .
The future evolution of the ISM, and other monsoon systems, under a combination of anthropogenic forcing factors is unclear4 : Recent projections indicate that the response to increased
greenhouse gas (GHG) concentrations may differ in sign among major monsoon regions, and reveal large uncertainties about the magnitude of the response5–7 . The effect of increased aerosol
abundance is significant and may be counteracting that of GHGs8, 9 , while human-induced vegetation changes feed back on precipitation10, 11 . Observations and modelling studies suggest a
recent regime shift in Asian monsoon convection12 and its relation to northern hemisphere thermal
gradients13, 14 . At the same time, paleodata testify to abrupt and strong shifts in both the Indian and
the East Asian monsoon during the last two glacial cycles15–17 and the Holocene18, 19 .
Fundamental monsoon dynamics: Persistence and self-amplification
These uncertainties call for an understanding of the internal monsoon dynamics to project their
apparently non-linear response to external perturbations. In order to assess large-scale failure of
seasonal rainfall, we focus here on fundamental monsoon dynamics which are universal across the
different regions: The onset of monsoon precipitation in spring is initiated by the development of
a tropospheric temperature contrast between land and ocean, and associated convergence of moist
air over the continent. During the rainy season, the atmospheric heat budget in the monsoon region
(cf. Supplementary Fig. S1) is then dominated by the so-called moisture-advection feedback: The
release of latent heat by precipitation enhances the sea-level pressure gradient between land and
ocean, thereby stabilizing the circulation that brings in more moist air from the ocean, which in
2
2.4
More frequent future monsoon failure due to inherent instability
85
turn maintains precipitation (Fig. 1A and B, top). In conceptual models, this self-amplification
yields a threshold behaviour which can lead to a permanent cessation of the monsoon circulation
in response to shifts in external forcing20, 21 .
Unless the threshold is crossed, however, the moisture-advection feedback stabilizes the
monsoon circulation by adding inertia to the system: Rainfall over a certain period within the
rainy season releases latent heat, thereby reinforces the circulation and increases the probability
for rainfall in subsequent days. This “memory effect”, or persistence, can be seen in direct observations, where it is reflected as a quasi-proportionality between the expectation value of daily
rainfall and the amount of latent heating accumulated during some period prior to the specific day.
Here we use daily rainfall data for 1951-2003 on a 1◦ grid from India Meteorological Department
(IMD)22 , averaged over all available data within 5-30◦ N, 70-90◦ E. For the summer months (MaySeptember), we plot each day’s precipitation against the normalized average rainfall p̃ during a
memory period τ prior to that day, such that at day t,
hP it−1
t−τ − P−
,
p̃t ≡
P+ − P−
(1)
where P is the daily precipitation rate; h...i indicate temporal averaging; and P+ and P− are maximum and minimum precipitation rates, respectively (see Methods section). The memory effect is
reflected in the correlation between Pt and p̃t for all days within the season, shown in Fig. 2A for
τ = 17 days. The correlation depends on the choice of τ , and a sensitivity test (Supplementary
Fig. S2) reveals that the rainfall memory is effective on a timescale of 2-3 weeks; after that time,
the information on the rainfall history is lost from the system. In particular, the signal-to-noise
ratio of the correlation is greater than 1 for τ ≤ 18 days. We interpret p̃ as a probability for daily
3
86
2
ORIGINAL MANUSCRIPTS
precipitation.
Although the memory effect can be seen clearly in the IMD data, the observational period is
not long enough to probe the full influence of this effect on the statistics of seasonal mean precipitation, especially with respect to extreme events. Instead, we use an ensemble of five millennial
climate simulations performed with the comprehensive Earth system model COSMOS developed
at MPI-M Hamburg23 . The model comprises the atmospheric general circulation model (GCM)
ECHAM5 at T31 (∼ 3.75◦ ) horizontal resolution, as well as an oceanic GCM, MPI-OM, and modules for terrestrial vegetation and ocean biogeochemistry. Comparison with other GCMs and with
observational data shows that ECHAM5 reproduces the Indian summer monsoon realistically24, 25 .
The five simulations each span the period 800-2005 CE and do not differ in forcing. While they
include the past century of anthropogenic GHG emissions, we consider the total of 1206 years
sufficiently homogenous to be treated as belonging to the same climate state. Using the same value
τ = 17 days as above, and averaging daily rainfall over 5-30◦ N, 70-90◦ E, we find the memory effect well represented in the COSMOS output (Fig. 2B, inset). This confirms not only the presence
of the moisture-advection feedback in the model, but also the approximate timescale on which the
feedback operates.
Additional feedback mechanism for sustained dry–state
At the same time, seasonal (June-August) mean rainfall over the total of 6030 model years shows a
characteristic frequency distribution (Fig. 2B, gray bars) which extends far towards the lower end
4
2.4
More frequent future monsoon failure due to inherent instability
of the spectrum, featuring very weak monsoon years with seasonal mean rainfall below 2 mm/day.
Latent–classes analysis suggests that this characteristic distribution represents a superposition of
two monsoon modes (Supplementary Fig. S3). While the rainy mode is dominated by the moistureadvection feedback, the dry mode is determined by a second dynamical feedback. In order to
identify this second feedback, we investigate year 1158 of the first ensemble member as one of the
driest monsoon years within the entire simulation ensemble (due to significant internal variability
in this class of climate models, this year cannot be expected to coincide with any specific historical
year, and will henceforth be referred to as the ‘dry year’). Average seasonal precipitation over India
in this year is ∼ 70% below the long-term mean of 5.7 mm/day (Fig. 3A, circles). The dynamical
South Asian monsoon index26 indicates an exceptionally large deviation of the zonal tropospheric
wind shear from the long-term mean (Fig. 3A, bars).
The dry year’s rainfall cycle (Fig. 3B) shows that large-scale monsoon precipitation is erratic
and largely suppressed during the entire summer, caused by a qualitatively different circulation
pattern compared to normal monsoon years: The initial lack of latent heating over India leaves
the regional-scale sea level pressure anomalously high throughout the summer (Fig. 3C and map
in Supplementary Fig. S4). This causes subsidence of upper-tropospheric air both over the subcontinent and over the Arabian sea (Fig. 3D and map in Supplementary Fig. S5). Since specific
humidity is lower in the upper troposphere, this large-scale subsidence effectively dries out the
monsoon region: Specific humidity in the lower troposphere (i.e. in the lower, landward branch
of the ISM circulation, Fig. 1A) substantially decreases over India and the eastern Arabian Sea,
and remains exceptionally low through June-August (Fig. 3E and map in Supplementary Fig. S6;
5
87
88
2
ORIGINAL MANUSCRIPTS
also note the spatial congruence of subsidence and moisture anomalies in Fig. S6). This acts to
further suppress precipitation and sustain the dry state, as illustrated in Fig. 1A (bottom). Note that
the dry regime is associated with a qualitatively different circulation pattern compared to the wet
regime: Instead of convective upwelling, the monsoon region is characterized by subsidence of
dry air from the upper troposphere; also, the flow direction in the upper troposphere changes from
generally westward to northward or even eastward, which is consistent with an anomalously low
zonal wind shear as reflected by the dynamical monsoon index (Fig. 3A, bars). Once initiated, this
self-amplifying feedback sustains the dry state in a similar way as the wet state is sustained by the
moisture-advection feedback (Fig. 1B).
A model of inherent instability
The dry year examined here is an extreme case in the sense that the dry feedback is dominant
throughout the season. Most years in the spectrum of the climate model (Fig. 2B, gray bars) are
less extreme, i.e. their seasonal precipitation averages somewhere between the dry state and an
upper limit of about 9 mm/day. We propose a simple statistically predictive model for seasonal
mean monsoon precipitation that explains this characteristic spectrum by an interplay between
the wet moisture-advection feedback and the dry subsidence feedback on a quasi-daily timescale.
This “day-to-day” model (illustration Fig. 1C) is based only on the two feedbacks and the memory
effect. It employs the idealized assumption of two discrete circulation states: One with weak
precipitation (we choose P− ≡ 0 mm/day in our example, Supplementary Table S1), corresponding
to the dry feedback loop in Fig. 1B, and one with maximum precipitation (P+ , here chosen to be 9
6
2.4
More frequent future monsoon failure due to inherent instability
mm/day), corresponding to the wet feedback loop in Fig. 1B. It is further assumed that the system
can flip between these two states on a daily timescale, and that the probability pt for ending up
in the wet state at time step (day) t depends on the ratio of dry and rainy days during a certain
period τ prior to t - this corresponds to the precipitation probability p̃ of equation (1): That is, the
more rainy days there were in the period τ before t, the more likely it is to have rainfall at time
t. Thus here the memory effect is represented by an exact proportionality between rainfall and
rainfall history. It acts to impede a flip between the two circulation states. Still, a flip can always
be triggered by stochastic atmospheric fluctuations, e.g. regional and local weather, that perturb
the large-scale dynamical flows comprised in the two feedback mechanisms. In the day-to-day
model, these perturbations are represented by an unbiased random process that, together with pt ,
ultimately determines the system state at each time step.
This very basic auto-regression model of internal monsoon dynamics needs to be complemented by information on the onset of the rainy season, prior to the dominance of the two positive
feedback loops. During the period of length τ at the beginning of the season, a constant initial
probability for rainfall, pinit , replaces the dynamical rainfall probability p̃. pinit represents external
factors that crucially influence the development of the monsoon circulation at a time when it is
most sensitive to external perturbations26 . In the case of India this could for example comprise the
role of El Niño/Southern Oscillation (ENSO), Indian Ocean sea surface temperatures, or Eurasian
snow cover27 , as well as the current phase of the Madden-Julian Oscillation (MJO)28 . Finally,
we introduce a maximum probability pm , such that pt cannot be greater than pm or smaller than
(1 − pm ). This prevents locking into either P− or P+ when pt becomes zero or one, which oth7
89
90
2
ORIGINAL MANUSCRIPTS
erwise could occur as an artifact of the discrete nature of the model. In this form, the day-to-day
model can reproduce the spectrum of the comprehensive climate model (Fig. 2B, blue line). The
R
Matlab
code is provided as Supplementary Information.
Monsoon failure under global warming
The COSMOS climate model simulations of the past, as used above, have been complemented
with IPCC SRES A1B scenario29 simulations until 2100, followed by constant CO2 concentrations
until 220030 . This results in an increase in global mean surface air temperature of 4.6◦ C relative to
pre–industrial by the end of the 22nd century (Fig. 4A, black line). We find that in the warm climate
of 2151-2200 CE, the frequency distribution of seasonal mean ISM rainfall is completely inverted,
in the sense that now dry years are much more frequent than wet years (Fig. 2C, gray bars). This
inversion occurs gradually over the course of the warming scenario and involves a sign change in
the distribution’s skewness at the end of the 21st century (Fig. 4B, red line). The associated change
in the expected seasonal mean rainfall, however, is not monotonic: An increase throughout much
of the 21st century is followed by a rapid decrease, falling short of the pre–industrial long–term
mean roughly by the turn of the 22nd century (Fig. 4C, red line).
These changes can be understood in the simple framework of the day-to-day model. A
warmer tropical troposphere can hold more water vapor, which tends to enhance rainfall in the
absence of counter–acting processes31 . We translate this into a linear shift of the precipitation
range [P− , P+ ] towards higher values with increasing global mean temperature. In addition to this
8
2.4
More frequent future monsoon failure due to inherent instability
thermodynamical effect, dynamical changes that affect the monsoon development during the onset
period can be represented by the day-to-day model via the parameter pinit . In order to understand
which process dominates the initiation of monsoon rainfall in the comprehensive climate model,
consider the dry year investigated above. Both during April and May, mean sea level pressure
(MSLP) was extremely low over the central Pacific Nino3.4 region (170-120◦ W, 5◦ S-5◦ N; Supplementary Fig. S7), which tends to suppress the development of the low pressure system over India,
represented by a low pinit value in the day-to-day-model. This relation can be found throughout
the 6030 years of historical climate simulations: There is a clear correlation between the two monsoon modes present in the distribution of seasonal mean rainfall and anomalous spring–time (May)
Nino3.4 MSLP (Fig. 2B, red bars; see also Supplementary Fig. S8). As a simple approximation,
we scale pinit linearly with May Nino3.4 MSLP.
Forced with decadal averages of global mean temperature and May Nino3.4 MSLP, the dayto-day model reproduces the long–term trends in mean and skewness of the ISM rainfall frequency
distribution observed in COSMOS (Fig. 4B and C, gray lines and shading). The non–trivial peak–
and–decline response in ISM rainfall is captured due to the delay in the Pacific MSLP signal
compared to the warming signal (Fig. 4A). Moreover, a significant portion of multidecadal rainfall
variability is reproduced (Fig. 4C). Similar to the historical period, the model can explain the
frequency distribution of the future period 2151-2200 CE (Fig. 2C). Note that the variation of pinit
along with decadally–averaged spring–time Nino3.4 MSLP represents the influence of a slowly
varying central Pacific mean climate on the ISM onset, rather than a direct forcing of the ISM
season by ENSO.
9
91
92
2
ORIGINAL MANUSCRIPTS
Discussion and conclusions
We conclude that major characteristics of seasonal–mean monsoon rainfall can be explained on the
basis of only two fundamental assumptions: (i) An inherent instability of the monsoon circulation
on a daily timescale, in the form of a competition between two different circulation regimes, each
of which is associated with a self-amplifying feedback. (ii) A memory effect that is induced by
those feedbacks, and determines, in a probabilistic manner, the transition between the wet and
the dry periods. Both assumptions are backed by observations and comprehensive climate model
results. The subsidence feedback not only complements the picture of the atmospheric circulation
in the temporary absence of monsoon rainfall, but constitutes an active antagonist of the moistureadvection feedback due to its self-amplifying nature. The memory effect is a logical consequence
of the dominant role of these two feedbacks, and is found in observational data as well as in climate
model results.
Monsoon rainfall exhibits intraseasonal variability on various time scales which influences
the seasonal mean. While some of this variability, in particular its slower components, is related to
interaction with larger-scale atmospheric phenomena such as the MJO28 , internally generated variability is an equally important factor in shaping the monsoon cycle and determining the seasonal
mean rainfall32, 33 . The notion of an internal instability of the large-scale monsoon circulation on
a quasi–daily timescale, incorporated here in a slim and fundamental framework, offers a first–
order explanation for the driving mechanism of internally generated intraseasonal variability. We
have demonstrated that this mechanism alone can explain the non–trivial, long–term frequency
10
2.4
More frequent future monsoon failure due to inherent instability
distribution of seasonal mean monsoon rainfall in a complex climate model.
Moreover, it can explain ISM changes on decadal to centennial time scales with a minimal
amount of external information. We have shown that the “day-to-day model” can reproduce past
ISM multidecadal variability as well as projected future changes, including a substantial increase
in monsoon failure, when driven by the decadal changes in global mean temperature and tropical
Pacific MSLP in May. While global warming is assumed to elevate the baseline of monsoon rainfall due to increased atmospheric moisture content, a shift towards lower spring–time MSLP in
the tropical Pacific is assumed to induce atmospheric conditions that favor more subsidence over
the Indian region and thus lead to more deficient monsoon onsets. In the day-to-day model, this
translates into a lower initial probability pinit . This parameter summarizes the influence of ambient
climatic factors that, to some extent, predispose the monsoon system during the onset season34 . It
does not determine the rainfall amount of an individual monsoon season, but it has strong influence on the probability distribution of seasonal mean rainfall. While in the comprehensive climate
model applied here, pinit is dominated by the influence of central Pacific climate variations, other
factors such as changing spring–time Indian Ocean sea surface temperatures might play a significant role, too35 , and could be translated into monsoon statistics using the statistically predictive
day-to-day model.
11
93
94
2
ORIGINAL MANUSCRIPTS
Methods summary
We used a gridded observational data set of daily ISM rainfall to demonstrate the memory effect. Seasonal mean rainfall statistics as well as the feedback mechanism sustaining a dry circulation regime were established using millennial historical simulations with a comprehensive climate
model. Based on those results, we developed a simple, statistically predictive model for seasonal
mean monsoon rainfall and applied it to the characteristics and trends found in the complex climate
model, including future projections under a global warming scenario.
1. Parthasarathy, B., Munot, A. & Kothawale, D. Regression model for estimation of Indian
foodgrain production from summer monsoon rainfall. Agricultural and Forest Meteorology
42, 167 – 182 (1988).
2. Auffhammer, M., Ramanathan, V. & Vincent, J. R. Integrated model shows that atmospheric
brown clouds and greenhouse gases have reduced rice harvests in India. Proceedings of the
National Academy of Sciences 103, 19668–19672 (2006).
3. Goswami, B. N., Venugopal, V., Sengupta, D., Madhusoodanan, M. S. & Xavier, P. K. Increasing trend of extreme rain events over India in a warming environment. Science 314,
1442–1445 (2006).
4. Meehl, G. A. et al. Climate Change 2007: The Physical Science Basis. Contribution of
Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate
12
2.4
More frequent future monsoon failure due to inherent instability
Change, chap. Global Climate Projections (Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA, 2007).
5. Kripalani, R. H., Oh, J. H. & Chaudhari, H. S. Response of the East Asian summer monsoon
to doubled atmospheric CO2: Coupled climate model simulations and projections under IPCC
AR4. Theor. Appl. Climatol. 87, 1–28 (2007).
6. Kripalani, R. H., Oh, J. H., Kulkarni, A., Sabade, S. S. & Chaudhari, H. S. South Asian summer monsoon precipitation variability: Coupled climate model simulations and projections
under IPCC AR4. Theor. Appl. Climatol. 90, 133–159 (2007).
7. Cherchi, A., Alessandri, A., Masina, S. & Navarra, A. Effects of increased CO2 levels on
monsoons. Climate Dynamics (online) (2010).
8. Ramanathan, V. et al. Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle. Proceedings of the National Academy of Sciences 102, 5326–5333 (2005).
9. Lau, K. M. & Kim, K. M. Observational relationships between aerosol and asian monsoon
rainfall, and circulation. Geophysical Research Letters 33, L21810 (2006).
10. Ganopolski, A., Kubatzki, C., Claussen, M., Brovkin, V. & Petoukhov, V. The influence of
vegetation-atmosphere-ocean interaction on climate during the mid-holocene. Science 280,
1916–1919 (1998).
11. Claussen, M. Late quaternary vegetation-climate feedbacks. Clim. Past 5, 203–216 (2009).
13
95
96
2
ORIGINAL MANUSCRIPTS
12. Turner, A. G. & Hannachi, A. Is there regime behavior in monsoon convection in the late 20th
century? Geophysical Research Letters 37, L16706 (2010).
13. Li, J. et al. Summer monsoon moisture variability over China and Mongolia during the past
four centuries. Geophysical Research Letters 36, L22705 (2009).
14. Sun, Y., Ding, Y. & Da, A. Changing links between South Asian summer monsoon circulation
and tropospheric land-sea thermal contrasts under a warming scenario. Geophysical Research
Letters 37, L02704 (2010).
15. Burns, S. J., Fleitmann, D., Matter, A., Kramers, J. & Al-Subbary, A. A. Indian ocean climate
and an absolute chronology over Dansgaard/Oeschger events 9 to 13. Science 301, 1365–1367
(2003).
16. Wang, P. et al. Evolution and variability of the Asian monsoon system: state of the art and
outstanding issues. Quaternary Science Reviews 24, 595–629 (2005).
17. Wang, Y. et al. Millennial- and orbital-scale changes in the East Asian monsoon over the past
224,000 years. Nature 451, 1090–1093 (2008).
18. Gupta, A. K., Anderson, D. M. & Overpeck, J. T. Abrupt changes in the Asian southwest
monsoon during the Holocene and their links to the North Atlantic ocean. Nature 421, 354–
357 (2003).
19. Wang, Y. et al. The Holocene Asian Monsoon: Links to solar changes and North Atlantic
climate. Science 308, 854–857 (2005).
14
2.4
More frequent future monsoon failure due to inherent instability
20. Zickfeld, K., Knopf, B., Petoukhov, V. & Schellnhuber, H. J. Is the indian summer monsoon
stable against global change? Geophysical Research Letters 32, L15707 (2005).
21. Levermann, A., Schewe, J., Petoukhov, V. & Held, H. Basic mechanism for abrupt monsoon
transitions. Proceedings of the National Academy of Sciences 106, 20572–20577 (2009).
22. Rajeevan, M., Bhate, J., Kale, J. A. & Lal, B. High resolution daily gridded rainfall data for
the Indian region: Analysis of break and active monsoon spells. Current Science 91, 296–306
(2006).
23. Haak, H. MPI-M Earth System Modelling Framework: millennium full forcing experiment
(ensemble members 1–5) (2008). World Data Center for Climate. CERA-DB ”mil0010”–
”mil0015”.
24. Annamalai, H., Hamilton, K. & Sperber, K. R. The South Asian summer monsoon and its
relationship with ENSO in the IPCC AR4 simulations. Journal of Climate 20, 1071–1092
(2007).
25. Li, J. & Zhang, L. Wind onset and withdrawal of Asian summer monsoon and their simulated
performance in AMIP models. Climate Dynamics 32, 935–968 (2009).
26. Webster, P. J. & Yang, S. Monsoon and ENSO: Selectively interactive systems. Quarterly
Journal of the Royal Meteorological Society 118, 877–926 (1992).
27. Charney, J. G. & Shukla, J. Monsoon Dynamics, chap. Predictability of Monsoons (Cambridge
University Press, Cambridge, United Kingdom, 1981).
15
97
98
2
ORIGINAL MANUSCRIPTS
28. Wheeler, M. & Hendon, H. An all-season real-time multivariate MJO index: Development of
an index for monitoring and prediction. Monthly Weather Review 132, 1917–1932 (2004).
29. Nakicenovic, N. & Swart, R. (eds.) IPCC Special Report on Emissions Scenarios (Cambridge
University Press, 2000).
30. Jungclaus, J. MPI-M Earth System Modelling Framework: millennium scenarios (continuation of full forcing experiment ensemble members 1–5) (2009). World Data Center for
Climate. CERA-DB ”mil0016”–”mil0020”.
31. Allen, M. & Ingram, W. Constraints on future changes in climate and the hydrologic cycle.
Nature 419, 224–232 (2002).
32. Krishnamurthy, V. & Shukla, J. Intraseasonal and interannual variability of rainfall over India.
Journal of Climate 13, 4366–4377 (2000).
33. Goswami, B. N., Wu, G. & Yasunari, T. The annual cycle, intraseasonal oscillations, and
roadblock to seasonal predictability of the Asian summer monsoon. Journal of Climate 19,
5078–5099 (2006).
34. Palmer, T. N. Chaos and predictability in forecasting the monsoons. Proc. Indian Nat. Sci.
Acad. 60, 57–66 (1994).
35. Li, T., Zhang, Y., Chang, C. & Wang, B. On the relationship between Indian Ocean sea
surface temperature and Asian summer monsoon. Geophysical Research Letters 28, 2843–
2846 (2001).
16
2.4
More frequent future monsoon failure due to inherent instability
99
36. Roeckner, E. et al. The atmospheric general circulation model ECHAM5 - Part I: Model description. Report No. 349, Max Planck Institute for Meteorology, Hamburg, Germany (2003).
37. Hagemann, S., Arpe, K. & Roeckner, E. Evaluation of the hydrological cycle in the ECHAM5
model. Journal of Climate 19, 3810–3827 (2006).
38. Raddatz, T. J. et al. Will the tropical land biosphere dominate the climate-carbon cycle feedback during the twenty-first century? Climate Dynamics 29, 565–574 (2007).
39. Marsland, S., Haak, H., Jungclaus, J., Latif, M. & Roske, F. The Max-Planck-Institute global
ocean/sea ice model with orthogonal curvilinear coordinates. Ocean Modelling 5, 91–127
(2003).
40. Wetzel, P. et al. Effects of ocean biology on the penetrative radiation in a coupled climate
model. Journal of Climate 19, 3973–3987 (2006).
41. Crowley, T. J. et al. Volcanism and the Little Ice Age. PAGES Newsletter 16, 22–23 (2008).
42. Krivova, N. A. & Solanki, S. K. Models of Solar Irradiance Variations: Current Status. J.
Astrophys. Astr. 29, 151–158 (2008).
43. Pongratz, J., Reick, C., Raddatz, T. & Claussen, M. A reconstruction of global agricultural
areas and land cover for the last millennium. Global Biogeochemical Cycles 22 (2008).
44. http://www.mpimet.mpg.de/fileadmin/ozean/drg/web page millennium nov09 model details.pdf.
45. Leisch, F. FlexMix: A general framework for finite mixture models and latent class regression
in R. Journal of Statistical Software 11, 1–18 (2004).
17
100
2
ORIGINAL MANUSCRIPTS
46. Schwarz, G. Estimating dimension of a model. Annals of statistics 6, 461–464 (1978).
Supplementary Information is linked to the online version of the paper at www.nature.com/nature.
Acknowledgements This work was funded by the Heinrich Böll Foundation, the German National Academic Foundation, and the BMBF PROGRESS project (support code 03IS2191B). The climate model simulations were performed in the framework of the MPI-M project MILLENNIUM and have been partly funded
by the German Earth System Research Partnership Program ENIGMA. We thank J. Jungclaus for access to
the model output; V. Petoukhov, V. Brovkin, and R. Krishnan for helpful comments on the manuscript; and
K. Frieler for advice on statistical methods.
Author Information The authors declare that they have no competing financial interests. Correspondence
and requests for materials should be addressed to J.S. (email: [email protected]).
18
2.4
More frequent future monsoon failure due to inherent instability
A
B
WET
101
C
pinit
pt
WET
1-pt
DRY
1-pinit
pt
DRY
1-pt
Figure 1: Illustration of idealized ISM circulation regimes (A), associated positive feedback mechanisms (B), and the simple ‘day-to-day model’ (C). In the wet regime, latent heating due to precipitation (P) creates a regional sea level pressure (SLP) low, which leads to atmospheric upwelling
and associated inflow of moist air towards the ISM region. In turn, a lack of latent heating causes a
regional positive SLP anomaly, which leads to subsidence of dry upper-troposphere air and lowers
the humidity of the monsoon winds, thereby inhibiting precipitation and sustaining a dry regime.
In the ‘day-to-day model’, the probability for a wet or dry day depends on the amount of latent
heating accumulated during some previous period, except during the onset when it is determined
by an initial probability pinit .
2
10
5
years out of 6030
0
0
0.2
0.4 ~ 0.6
p
10
400
5
200 0
years out of 250
ORIGINAL MANUSCRIPTS
A
0.5
~
p
0
1
0.8
P (mm/day)
P (mm/day)
102
1
B
0
20
C
day−to−day
model
10
COSMOS
0
0
2
4
6
mm/day
8
Figure 2: ISM rainfall statistics. (A) IMD observed daily rainfall during May-September versus
precipitation probability p̃ (see main text). The thick line connects the mean values for each of
50 bins on the p̃-axis; shading denotes ±1 standard deviation. Days with p̃ < 0.2 stem mainly
from before the monsoon onset. (B) Distribution of seasonal (JJA) mean ISM precipitation from
the COSMOS ensemble over the period 800–2005 CE (gray bars; 5 simulations, 1206 years each)
and from 6030 runs of the stochastic day-to-day model (blue line shows mean value, and error
bars show ±1 standard deviation, from 100 realizations of the model). Dark red (light red) bars
show the distribution of those COSMOS years where the Nino3.4 mean sea level pressure in May
exceeds (falls short of) the long-term mean by more than one standard deviation. Inset: As A, but
with P and p̃ diagnosed from one of the COSMOS simulations. (C) Gray bars as in B, but over the
period 2151–2200 CE in a global warming scenario (see main text). Blue line and errorbars as in
B, but from 250 runs and with a different set of model parameters (see Supplementary Table S1).
More frequent future monsoon failure due to inherent instability
mm/day, index
2.4
5
0
−5
1140
mm/day
10
1150
year
1160
B
precipitation
5
0
mb
precipitation
monsoon index
J F MA MJ J A S ON D
1010 C
g/kg
10−2 Pa/s
1005
sea level pressure
6
0
−6
12
D
subsidence
E
8
4
humidity
May Jun Jul Aug Sep
Figure 3: The dry year. (A) Seasonal mean precipitation over India (circles, in mm/day; dashed
line shows long-term mean) and South Asian monsoon index26 (bars) in a 19-year interval of the
simulation; the dry year is marked in black. (B) Annual cycle of average daily precipitation over
India during the dry year (thick) and averaged over the previous 17 years (thin; shading denotes
the 16% and 84% quantiles). See Supplementary Fig. S9 for a comparison covering the entire
simulation period. (C-E) Characteristics of the monsoon circulation over western India and the
Arabian Sea during May-September: (C) Mean sea level pressure (MSLP, in hPa); (D) vertical
velocity ω (in 10−2 Pa/s) at 500hPa, with positive values indicating subsidence of air; and (E)
near-surface (850-1000hPa) specific humidity, in g/kg. In B-E, a 3-day running mean was applied;
thick lines are for the dry year; and thin lines give the average over the previous 17 years, where
the shading denotes ± 1 standard deviation.
103
104
2
A
1011
Nino3.4 SLP May
Δ Tglobal
0
mb
2.5
°
C
5
ORIGINAL MANUSCRIPTS
1009
1 B
precip skewness
0
mm/day
−1
6
C
5
4
800
D2D model
COSMOS
precip mean
1000 1200 1400 1600 1800 2000 2200
year
Figure 4: Application of the ‘day-to-day model’ to past and future ISM variability. (A) Global
mean surface temperature (black, in ◦ C relative to the 1980-1999 mean) and May MSLP over the
Nino3.4 region (blue, in mb) over the full period of the millennial COSMOS simulations and their
continuations under a global warming scenario. Data have been averaged decadally (801–810, etc.)
and over the 5 ensemble members, such that each point represents an average over 50 model years.
(B) Skewness of the decadal frequency distribution of seasonal mean ISM rainfall in COSMOS
(red). Each point is computed from a set of 50 model years (10 years, 5 ensemble members). The
skewness computed from 50 runs each of the day-to-day model, with parameters set according to
global mean temperature and May Nino3.4 MSLP as in A, is shown in gray (thin line shows mean
value, and shading shows ±1 standard deviation, from 100 realizations of the model). (C) As B,
but for the mean of the frequency distribution (in mm/day).
2.4
More frequent future monsoon failure due to inherent instability
Methods
Memory effect in observations. For demonstrating the memory effect in the IMD observational
data set, we choose P+ = 12 mm/day and P− = 0 mm/day. P+ is chosen to approximate the
maximum observed 17-day average daily precipitation. We test the sensitivity of the memory effect
to the length of the memory period τ by computing the ‘signal-to-noise ratio’ of the correlation
for a range of τ values (Fig. S2): A linear regression of the mean values between p̃ = 0.4 and 0.6
is evaluated at p̃ = 0.2 and p̃ = 0.8, and the difference in P is divided by the average standard
deviation over the same interval. As the maximum observed average daily precipitation is higher
for shorter averaging periods and vice versa, we have varied P+ along with τ – namely, between
14 and 10 mm/day – in producing Fig. S2. This variation however only has a minor effect on the
signal-to-noise ratio, which is much more sensitive to the choice of τ .
Climate model analysis. The climate model simulations used in this study belong to the
“Millennium” simulation ensemble23 . They were performed with the comprehensive MPI-M Earth
System Model COSMOS, comprising the atmospheric GCM ECHAM536, 37 Version 5.4.01 at
T31L19 resolution; the land surface scheme JSBACH38 at T31 resolution, one soil layer, 13 vegetation types and four vegetation tiles per grid box; and the oceanic GCM MPI-OM39 Version 1.3.0,
including the ocean biogeochemistry module HAMOCC40 , at GR3.0L40 resolution. The model
is run for the years 800 to 2005 CE with time-dependent external forcing including the effects of
volcanic stratospheric sulphate aerosols41 , variation of total solar irradiance42 , land-use change43 ,
and changes in orbital parameters. Following the historical period, the simulations are continued
23
105
106
2
ORIGINAL MANUSCRIPTS
with forcing according to the IPCC SRES A1B scenario29 until 2100, and with CO2 emissions corresponding to constant CO2 concentrations from 2101 until 220030 . Further details can be found
on the MPI-M website44 .
In analyzing the climate model results, precipitation is averaged over the land area in 530◦ N, 70-90◦ E. The dynamical monsoon index26 is computed as the zonal wind shear difference
over 0-20◦ N, 60-100◦ E according to (Ū850hP a − hŪ850hP a i) − (Ū200hP a − hŪ200hP a i), where Ū is
the seasonal (May-September) average zonal wind speed, and angle brackets indicate the long term
mean over the historical simulation period. Sea level pressure is averaged over 0-25◦ N, 60-80◦ E;
vertical velocity ω in pressure coordinates is averaged over 5-30◦ N, 55-80◦ E; and specific humidity
over 5-25◦ N, 60-80◦ E. For demonstrating the memory effect in COSMOS, the same procedure is
applied as for the IMD observational data, except that we now choose P+ = 9 mm/day, which
is closer to the maximum 17-day average daily precipitation observed in the climate model. The
latent–classes analysis is performed using the flexmix routine45 , fitting a model of one, two or three
superposed Gaussian distributions to the data. According to the Bayesian Information Criterion46
(BIC), the assumption of two Gaussians improves the model substantially (from a BIC value of
21364 to 20034) as compared to only one Gaussian, while a third Gaussian yields no significant
improvement, and is not physically motivated.
Statistically predictive model. The precipitation probability used in the simple day-to-day
24
2.4
More frequent future monsoon failure due to inherent instability
model is defined as





pm
if p̃t ≥ pm ,





pt = (1 − pm ) if p̃t ≤ (1 − pm ),








p̃t
else,
107
(2)
where pm is a maximum probability, and p̃t is defined according to equation (1) in the main text;
except that t is now the index of model time steps, not days. However, the length of the season is
chosen such that a time step corresponds approximately to one day (see Supplementary Table S1).
During an initial period of length τ in the beginning of the season, pt is not (and cannot be)
determined by equation (2). Instead, we introduce an initial probability pinit which is treated as
a model parameter. This day-to-day model is integrated over l time steps, corresponding to the
length of the model season, and the seasonal mean precipitation P̄ , P− ≤ P̄ ≤ P+ , is saved. See
Supplementary Table S1 for sets of parameters used in the model integration, and the attached
R
Matlab
script for the model code.
For reproducing multidecadal trends in the historical as well as the future climate simulations
(Fig. 4 in the main paper), we run the day-to-day model for each decade, varying the parameter
pinit according to pinit = p0 · (m − m0 ) + p0 , where m is the decadally-averaged May Nino3.4
MSLP in mb; m0 = 1008.9 mb; p0 = 0.39 mb−1 ; and p0 = 0.2. The parameters P+ and P− are
varied according to P± = P 0 · ∆T + P0± , where ∆T is the decadally-averaged global mean surface
temperature anomaly in ◦ C; P 0 = 0.42 mm day−1 ◦ C−1 ; P0+ = 9 mm day−1 ; and P0− = 0 mm
day−1 .
25
2.4
More frequent future monsoon failure due to inherent instability
Supplementary Material for
More frequent future monsoon failure due to inherent instability
Jacob Schewe1,2 , Anders Levermann1,2
1
Earth System Analysis, Potsdam Institute for Climate Impact Research, Potsdam, Germany
2
Institute of Physics, Potsdam University, Potsdam, Germany
1
109
110
2
ORIGINAL MANUSCRIPTS
300
Latent
INDIA NCEP/NCAR
200
Sensible
W/m2
100
0
−100
Radiative
Convergence
−200
2
4
6
Month
8
10
12
Figure S1: Net contributions (climatology) to the annual moist static energy budget of the atmospheric column over India (5-30◦ N, 70-90◦ E), from NCEP/NCAR reanalysis data1 (1948-2007;
errorbars denote one standard deviation): Radiative fluxes (black solid line), sensible heat flux
from the ground (red), latent heat flux due to precipitation (blue), and convergence of heat due to
advection by the large-scale time-mean circulation and eddies (black dashed line). Sensible heat
flux is strongest just before the monsoon onset, but during the rainy season, latent heat release
dominates the energy budget, balanced by advective processes that transport the excess heat out of
the region. This dominance is found in all major monsoon regions2 .
2
2.4
More frequent future monsoon failure due to inherent instability
111
signal−to−noise ratio
3
2.5
2
1.5
1
0.5
0
0
10
20
τ (days)
30
40
Figure S2: Sensitivity of the memory effect to the memory period τ . The ‘signal-to-noise ratio’,
i.e. the ratio of the slope of the correlation vs. the standard deviation (cf. Fig. 2A in main paper),
is greater than one for τ ≤ 18 days, and becomes very small for τ greater than 25 days. The filled
circle marks τ = 17 days, the value used for the analysis in the main paper.
3
112
2
ORIGINAL MANUSCRIPTS
500
number of years
400
300
200
100
0
0
2
4
mm/day
6
8
Figure S3: Gray bars as in Fig. 2B in the main paper. Black dashed lines show the characteristics of
the two modes obtained from latent–classes analysis, and the solid line shows their superposition.
4
2.4
More frequent future monsoon failure due to inherent instability
113
June
May
1030
30oN
15oN
0o
1020
15oS
30oS
A
dry
B
dry
1010
30oN
15oN
1000
0o
15oS
30oS
C
average
o
40 E
o
D
o
average
o
70 E 100 E
40 E
o
70 E 100oE
990
Figure S4: (A) Monthly mean sea level pressure (in hPa) in May of the dry year; (B) same for June;
(C) as (A), but averaged over the 17 previous years; (D) same for June. Contour spacing is 4 hPa.
Due to the absence of convection and associated latent heat release, the Eurasian surface Low is
diminished over India in the dry year, which favors a northward expansion of the Indian Ocean
surface High and subsequent subsidence over India and the northwestern Indian ocean region (cf.
Fig. S5).
5
114
2
ORIGINAL MANUSCRIPTS
June
May
0.1
30oN
15oN
0o
15oS
30oS
A
dry
B
dry
0
30oN
15oN
0o
15oS
30oS
C
average
40oE
D
70oE 100oE
average
40oE
70oE 100oE
−0.1
Figure S5: (A) Monthly mean vertical velocity ω (in Pa/s) at 500hPa in May of the dry year; (B)
same for June; (C) as (A), but averaged over the 17 previous years; (D) same for June. Positive
ω (red shading) indicates downward motion of air. Contour spacing is 0.02 Pa/s. In the dry year,
central India and the Arabian Sea are characterized by subsidence instead of upwelling.
6
2.4
More frequent future monsoon failure due to inherent instability
115
June
May
0.1
30oN
15oN
0
0o
15oS
30oS
ω anomaly
A
ω anomaly
B
30oN
−0.1
3
15oN
0
0o
15oS
30oS
C
q anomaly
40oE
D
70oE 100oE
q anomaly
40oE
−3
70oE 100oE
Figure S6: (A) Anomaly of monthly mean vertical velocity ω (in Pa/s, contour spacing 0.02) at
500hPa in May of the dry year, with respect to the average over the 17 previous years; (B) same
for June; (C) anomaly of monthly mean lower troposphere (500-1000hPa) specific humidity (g/kg,
contour spacing 1 g/kg) in May of the dry year, with respect to the average over the 17 previous
years; (D) same for June. Anomalous subsidence of dry air over India and the Northern Indian
Ocean leads to moisture depletion in the monsoon region.
7
116
2
1013
ORIGINAL MANUSCRIPTS
Nino3.4 MSLP
1009
1005
Feb
Mar
Apr
May
Jun
Figure S7: Mean sea level pressure (MSLP) over the Nino3.4 region (170-120◦ W, 5◦ S-5◦ N) during
spring in the COSMOS simulation, for the dry year (thick line) and averaged over the previous 17
years (thin line; shading denotes ± 1 standard deviation).
8
2.4
More frequent future monsoon failure due to inherent instability
117
1
correlation
0.8
0.6
0.4
0.2
0
previous year
O N D J
F
M A M J
J
Figure S8: Coefficient of the correlation between seasonal (JJA) mean ISM rainfall and monthlyaveraged mean sea level pressure (MSLP) over the Nino3.4 region (170-120◦ W, 5◦ S-5◦ N) in the
COSMOS simulation. The correlation is strongest when MSLP is taken in May just before the
monsoon season (indicated by the gray shading).
9
118
2
ORIGINAL MANUSCRIPTS
10
Precipitation
mm/day
8
6
long−term mean
4
2
dry years
0
J
F
M A
M J
J
A
S
O N D
Figure S9: Annual cycle of daily precipitation over India as a long-term mean over the first 1206year COSMOS simulation (thin line; shading denotes the 16% and 84% quantiles), and averaged
over all dry years within this simulation (JJA mean precipitation lower than 2.5 mm/day, thick
line).
10
2.4
More frequent future monsoon failure due to inherent instability
Additional references
1. Kistler, R. et al. The NCEP/NCAR 50-year reanalysis. Bull. Amer. Meteor. Soc. 82, 247 – 267
(2001).
2. Levermann, A., Schewe, J., Petoukhov, V. & Held, H. Basic mechanism for abrupt monsoon
transitions. Proceedings of the National Academy of Sciences 106, 20572–20577 (2009).
11
119
120
2
ORIGINAL MANUSCRIPTS
Table S1: Parameters of the day-to-day model
Parameter
Symbol
Value used for
Value used for
Fig. 2B
Fig. 2C
length of season
l
135 time steps
length of memory period
τ
17 time steps
precipitation in wet state
P+
9 mm/day
10.9 mm/day
Value used for Fig. 4
varying linearly with
global
temperature,
see Methods text
precipitation in dry state
P−
0 mm/day
1.9 mm/day
varying linearly with
global
temperature,
see Methods text
initial probability
pinit
0.75
0.2
varying linearly with
Nino3.4 MSLP, see
Methods text
maximum probability
pm
0.8
0.82
12
0.82
2.4
More frequent future monsoon failure due to inherent instability
121
122
2
ORIGINAL MANUSCRIPTS
3
Discussion and Conclusions
In this thesis, I have offered a novel perspective on the possibility of large–scale monsoon failure, motivated
by the observation of two different types of events: The dry regimes, observed in paleoclimatic proxy
records, that persist on millennial and longer timescales; and individual years of extremely deficient
monsoon rainfall as found in present–day climate simulations with a comprehensive global climate model.
Previous studies have linked paleoclimatic monsoon failure to external influences such as remote forcing
from the North Atlantic region (Burns et al., 2003; Wang et al., 2005b), or orbital–scale changes in solar
insolation (Wang et al., 2008), but no consistent theory has been developed so far that can explain the
strong and abrupt response of monsoon rainfall to these either weak or gradual external perturbations.
Seasonal monsoon failure under present–day climate conditions, on the other hand, has been investigated
here in a realistic climate model, as a possible scenario that goes far beyond the interannual variability
found in direct observations.
In the first part of the thesis (section 2.1), I have presented simulations with a coupled climate
model of intermediate complexity, CLIMBER-3α, that project monsoon rainfall around the world to
increase quasi–linearly with global warming in the coming centuries. While this is generally consistent
with many other studies (e.g. Kripalani et al., 2007a), the atmospheric component of CLIMBER-3α is
based on a simplified statistical–dynamical approach, and may not sufficiently represent all processes that
are relevant for the response of monsoon circulations to rapid and intense climate change. Therefore,
in the subsequent work I have attempted to identify those physical mechanisms that are of first–order
importance for large–scale monsoon dynamics and their response to external changes.
The second and third part of my work (sections 2.2 and 2.3) have focused on the conditions that
are necessary in order to sustain a rainy season over land. It is important to note that globally, the
seasonal reversal of cross-equatorial winds is driven by the seasonal change in hemispheric insolation,
and in general the rainfall associated with the intertropical convergence zone will naturally be sustained
even without continental monsoon rainfall. However, the excursion of the tropical rain belt towards
high latitudes and the intense continental precipitation in monsoon regions goes beyond the zonal-mean
dynamics of the intertropical convergence zone.
I have identified the so–called moisture–advection feedback as the primary driving force of large–scale
continental monsoon rainfall (section 2.2). While differential heating of the land and ocean surfaces
is important in establishing the atmospheric land–sea thermal contrast during the onset period, the
moisture–advection feedback is crucial in maintaining this contrast after the rainy season has started.
From an energetic point of view, the advection of latent heat in the form of moisture from the ocean
to the continent is the main source of energy that keeps the monsoon circulation going during the rainy
126
3
DISCUSSION AND CONCLUSIONS
season.
I have shown in a minimal conceptual model that the self–amplifying nature of the moisture–advection
feedback implies a threshold behaviour with respect to changes in the energy budget of the monsoon system. In particular, if specific humidity over the ocean falls below a critical value, no conventional monsoon
circulation can develop according to the basic dynamics captured in the minimal model (section 2.3).
These basic dynamics thus define a domain of existence for continental monsoon rainfall, which allows
for abrupt transitions between wet and dry regimes when the threshold is crossed. Physically, the possibility for abrupt transition arises from the competition among the main heat transport processes during
the rainy season. Although latent heat release through precipitation warms the atmospheric column,
direct advection of heat is cooling it. Both processes become weaker when monsoon winds decrease, and
thereby compensate each other with respect to the net heat injection into the atmospheric column. The
threshold of this stabilizing effect is set by the net radiative cooling, which is a characteristic feature of
low latitudes.
I have demonstrated that this concept is qualitatively consistent with a series of abrupt monsoon
transitions found in a proxy record of the East Asian summer monsoon for the penultimate glacial
period. In this application, I have assumed a hysteresis that occurs when the threshold is crossed from
different directions. While this assumption is not crucial for the transition behaviour, it changes the
timing of the individual transitions such that they are all consistent, within dating errors, with those
found in the proxy record. Physically, a hysteresis on these paleoclimatic timescales might be induced
by inert climate components such as e.g. large-scale oceanic circulation or Himalayan glaciation, rather
than by atmospheric processes. While it is thus not inconsistent with the underlying physics, it remains
a hypothesis at this stage and should be further investigated in future research.
My findings suggest that the moisture–advection feedback could indeed be the main physical
mechanism within the dynamics of monsoon circulations that facilitates abrupt and persistent monsoon
failure on long timescales. Beyond these qualitative results, I have estimated the threshold humidity
values for major monsoon regions, using present–day reanalysis data. The resulting distribution of
threshold values can be interpreted either as an uncertain estimate of a stationary critical threshold, or as
a probability distribution of an interannually varying threshold. I have shown that the estimates become
more robust when further relevant processes are incorporated in the conceptual model. However, it must
be kept in mind that the conceptual model is designed in a minimalistic spirit, comprising only those
physical processes that are essential for the moisture–advection feedback, and neglecting many other
processes that are nevertheless important for a quantitative assessment. Therefore my estimates of the
critical threshold need to be considered a first attempt of quantification. For a more accurate estimation,
more complex models would be necessary that, for instance, take into account evaporation over land
and associated soil moisture processes, and regionally specific features such as orography. An extended
127
analysis of paleoclimatic reconstructions as well as detailed present–day observations could also be useful
to further constrain the results. However, the example of the Atlantic thermohaline circulation suggests
that it might be generally very difficult to quantify thresholds in the climate system with the accuracy
that would be necessary for future projection of a transition (Rahmstorf et al., 2005; Drijfhout et al., 2010).
I have also suggested a basic physical mechanism for seasonal monsoon failure under modern climate
conditions, which is again based on the moisture–advection feedback (section 2.4). Within the rainy
season, rainfall over a certain period tends to reinforce the circulation by adding heat to the continental
atmosphere, and thereby increases the probability for rainfall in subsequent days, as I have shown for
India in observational data as well as in a comprehensive AOGCM. On the other hand, the AOGCM
simulations feature years where average Indian summer monsoon rainfall is up to ∼70% below the long–
term mean; this is much lower than any drought year observed within the past century. Examining one
such dry year, I have identified a second self–amplifying feedback that counteracts the moisture–advection
feedback: Subsidence of dry air from the upper troposphere dries out the monsoon winds and thereby
further inhibits the onset of convection, leading to more subsidence, and so on. While this dry feedback is
most dominant and visible in those years with very weak rainfall, I suggest that it is a general dynamical
feature that, to a lesser extent, is also present in regular monsoon years. I have developed a minimal
theory of intraseasonal monsoon dynamics that is based on the idea of a permanent interplay between
these two, antagonistic feedbacks, where each of them tends to persist, while synoptic–scale weather
events can perturb the currently active feedback and induce a flip into the other one. The timescale
of this interplay is on the order of days, and follows from the timescale of synoptic and smaller–scale
tropospheric features and the time that the heating anomaly induced by the moisture–advection feedback
can persist in the atmosphere.
I have framed this minimal theory in a simple, statistically predictive model for seasonal mean monsoon
rainfall. The inherently dynamics of the two counteracting feedbacks produces a series of alternating
wet and dry periods of varying length and frequency, which finally add up to a total seasonal rainfall
amount. While this is a highly idealized representation of internally generated intraseasonal variability,
the fact that real monsoon systems also exhibit variability on similar timescales supports the idea that
an instability similar to the one captured in the simple model may indeed be at work in reality. This
would offer an explanation for some of the observed intraseasonal variability, on the basis of internal
monsoon dynamics. Note that these results do not contradict other sources of monsoon variability on
various timescales. For example, the Madden–Julian Oscillation (Wheeler & Hendon, 2004) plays an
important role for intraseasonal monsoon variability on a monthly timescale. A corresponding external
forcing could in principle be added to the statistically predictive model without qualitatively altering its
characteristics. On the other hand, it is a robust assumption that a major part of intraseasonal variability
128
3
DISCUSSION AND CONCLUSIONS
is generated by internal monsoon dynamics and not by external forcing (Krishnamurthy & Shukla, 2000;
Goswami et al., 2006).
I have shown that this simple conceptual model explains the characteristic, bimodal frequency
distribution of seasonal–mean Indian summer monsoon rainfall found in the AOGCM. Moreover, it can
also reproduce a large portion of multidecadal monsoon variability when forced with central–Pacific
mean sea level pressure (MSLP) anomalies in May, i.e. at the onset time; no external forcing is applied
during the rest of the rainy season. Anomalous spring-time conditions in the central Pacific are assumed
to induce atmospheric conditions that favor either convection or subsidence over the Indian region, thus
leading to either intense or deficient monsoon onsets. This initial condition is then propagated into
the seasonal–mean rainfall in a probabilistic sense; in principle however, even after a deficient onset an
overall strong monsoon season can develop according to the basic internal dynamics.
We can now delineate a common perspective for both seasonal and long–term monsoon failure. Under
present–day climate conditions, the monsoon season is governed by the inherent instability induced by
the self–amplifying moisture–advection feedback and the counteracting dry subsidence feedback. The
seasonal–mean rainfall is therefore determined mainly by stochastic internal dynamics, in conjunction with
an initial condition set by external forcing during the onset period. The resulting statistics of seasonal–
mean rainfall include very dry years, or monsoon failure, albeit with a low frequency of occurrence (or
low probability, if the frequency distribution is interpreted as a probability distribution). If however
overall climatic conditions change substantially, the system can shift into a regime where the moisture–
advection feedback cannot be established, and thus continental monsoon rainfall cannot be sustained.
Such a regime shift would go along with crossing a threshold that is associated with non–zero values of
expected seasonal–mean precipitation; in this sense, the regime shift would be abrupt, i.e. from “rain”
to “no rain”.
Of course, this is a highly simplified perspective. It cannot, and is not meant to, explain all relevant
aspects of monsoon circulations, nor to make quantitatively accurate assessments or predictions of monsoon characteristics. I have taken this simplified approach because for understanding large–scale monsoon
failure, whether seasonally or permanently, it might be a useful intermediate step to consider only the
most fundamental physical processes that are essential for the non–linearity which is dominant for large
temporal and spatial scales. My results are qualitatively consistent with abrupt monsoon transitions
found in paleo–records, as well as with monsoon variability under present–day conditions as observed in
a realistic climate model; and they offer a simple and readily understood explanation for both. This may
be seen as supporting the simplified approach.
It is important to note that, besides the quantitative weaknesses that result from the many simplifications, some of my conclusions may also depend on regional specifics. While in the first three articles
129
I have considered several major monsoon regions and set up a conceptual model that can be expected
to hold for all those regions to a similar degree, the fourth study has focused especially on India. There
is reason to believe that much of the fundamental dynamics I have extracted from the analysis of the
Indian summer monsoon are universal across different monsoon regions, but detailed analysis of those
other regions is needed to confirm this hypothesis.
The final part of this thesis lends further support to my conceptual results, and it also sheds light on a
possible risk associated with the response of unstable monsoon dynamics to future anthropogenic climate
change. Simulations with a realistic climate model project seasonal failure of Indian summer monsoon
rainfall to become much more frequent in response to global warming. The minimal, statistically predictive model closely reproduces this trend, with spring-time MSLP in the central Pacific again determining
the initial condition at the monsoon onset. Within this simplified framework, the increase in monsoon
failure is thus caused by a strong but delayed response of spring–time central Pacific MSLP to global
warming. While in my analysis these central Pacific climate variations seem to be the dominant influence
on the initial condition, other factors such as changing springtime Indian Ocean sea surface temperatures
might play a significant role, too (Li et al., 2001), and could be translated into monsoon statistics using
the statistically predictive model.
Note that I have only considered climate simulations with a single AOGCM. Comparison with other
comprehensive models is needed to test the robustness of the projected trends in Indian summer monsoon
and central Pacific climate. The interrelation between the two needs to be investigated in detail using
complex models as well as observations, in order to confirm the link suggested by my results. Considering
the potential consequences of frequent large–scale monsoon failure in the near future, such research efforts
appear worthwhile.
References
Auffhammer, M., Ramanathan, V., & Vincent, J. R. 2006. Integrated model shows that atmospheric brown clouds and greenhouse gases have reduced rice harvests in India. Proceedings of the
National Academy of Sciences, 103, 19668–19672.
Burns, S. J., Fleitmann, D., Matter, A., Kramers, J., & Al-Subbary, A. A. 2003. Indian
Ocean Climate and an Absolute Chronology over Dansgaard/Oeschger Events 9 to 13. Science, 301,
1365–1367.
Cherchi, Annalisa, Alessandri, Andrea, Masina, Simona, & Navarra, Antonio. 2010. Effects
of increased CO2 levels on monsoons. Climate Dynamics, (online), 1–19.
Clark, C. O., Cole, J. E., & Webster, P. J. 2000. Indian Ocean SST and Indian summer rainfall:
Predictive relationships and their decadal variability. Journal of Climate, 13(14), 2503–2519.
Claussen, M. 1997. Modeling bio-geophysical feedback in the African and Indian monsoon region.
Climate Dynamics, 54, 247–257.
Claussen, M. 2009. Late Quaternary vegetation-climate feedbacks. Climate of the Past, 5(2), 203–216.
Dash, S. K., Singh, G. P., Shekhar, M. S., & Vernekar, A. D. 2005. Response of the Indian
summer monsoon circulation and rainfall to seasonal snow depth anomaly over Eurasia. Climate
Dynamics, 24, 1–10.
Drijfhout, S. S, Weber, S. L, & van der Swaluw, E. 2010. The stability of the MOC as diagnosed
from model projections for pre-industrial, present and future climates. Climate Dynamics, 112.
Ganopolski, A., Kubatzki, C., Claussen, M., Brovkin, V., & Petoukhov, V. 1998. The
Influence of Vegetation-Atmosphere-Ocean Interaction on Climate During the Mid-Holocene. Science,
280, 1916–1919.
Goswami, B. N., & Xavier, P. K. 2005. ENSO control on the south Asian monsoon through the
length of the rainy season. Geophysical Research Letters, 32, L18717.
Goswami, B. N., Venugopal, V., Sengupta, D., Madhusoodanan, M. S., & Xavier, P. K.
2006a. Increasing Trend of Extreme Rain Events Over India in a Warming Environment. Science, 314,
1442–1445.
Goswami, B. N., Madhusoodanan, M. S., Neema, C. P., & Sengupta, D. 2006b. A physical
mechanism for North Atlantic SST influence on the Indian summer monsoon. Geophysical Research
Letters, 33, L02706.
132
REFERENCES
Goswami, B. N., Wu, Guoxiong, & Yasunari, T. 2006. The annual cycle, intraseasonal oscillations,
and roadblock to seasonal predictability of the Asian summer monsoon. Journal of Climate, 19(20),
5078–5099.
Gregory, P. J., Ingram, J. S. I., & Brklacich, M. 2005. Climate change and food security. 360,
2139–2148.
Gupta, A. K., Anderson, D. M., & Overpeck, J. T. 2003. Abrupt changes in the Asian southwest
monsoon during the Holocene and their links to the North Atlantic Ocean. Nature, 421, 354–357.
Hahn, D. G., & Shukla, J. 1976. An apparent relationship between Eurasian snow cover and Indian
monsoon rainfall. Journal of Atmospheric Sciences, 33(12), 2461–2462.
Haile, M. 2005. Weather patterns, food security and humanitarian response in sub-Saharan Africa.
360, 2169–2182.
Hong, YT, Hong, B, Lin, QH, Zhu, YX, Shibata, Y, Hirota, M, Uchida, M, Leng, XT, Jiang,
HB, Xu, H, Wang, H, & Yi, L. 2003. Correlation between Indian Ocean summer monsoon and North
Atlantic climate during the Holocene. Earth and Planetary Science Letters, 211(3–4), 371–380.
Kistler, R., Kalnay, E., Saha, S., White, G., Woollen, J., Chelliah, M., Ebisuzaki, W.,
Kanamitsu, M., Kousky, V., den Dool, H. Van, Jenne, R., & Fiorino, M. 2001. The
NCEP/NCAR 50-year reanalysis. Bull. Amer. Meteor. Soc., 82, 247 – 267.
Kripalani, R. H., Oh, J. H., & Chaudhari, H. S. 2007a. Response of the East Asian summer
monsoon to doubled atmospheric CO2: Coupled climate model simulations and projections under
IPCC AR4. Theoretical and Applied Climatology, 87(1-4), 1–28.
Kripalani, R. H., Oh, J. H., Kulkarni, A., Sabade, S. S., & Chaudhari, H. S. 2007b. South
Asian summer monsoon precipitation variability: Coupled climate model simulations and projections
under IPCC AR4. Theoretical and Applied Climatology, 90(3-4), 133–159.
Krishnamurthy, V., & Goswami, B. N. 2000. Indian monsoon-ENSO relationship on inter-decadal
timescale. Journal of Climate, 13, 579–595.
Krishnamurthy, V, & Shukla, J. 2000. Intraseasonal and interannual variability of rainfall over
India. Journal of Climate, 13(24), 4366–4377.
Kucharski, F., Molteni, F., & Yoo, J. H. 2006. SST forcing of decadal Indian monsoon rainfall
variability. Geophysical Research Letters, 33(3), L03709.
REFERENCES
133
Kumar, K. Krishna, Kumar, K. Rupa, Ashrit, R. G., Deshpande, N. R., & Hansen, J. W.
2004. Climate impacts on Indian agriculture. International Journal of Climatology, 24, 1375–1393.
Lau, K. M., & Kim, K. M. 2006. Observational relationships between aerosol and Asian monsoon
rainfall, and circulation. Geophysical Research Letters, 33, L21810.
Levermann, A., Schewe, J., Petoukhov, V., & Held, H. 2009. Basic mechanism for abrupt
monsoon transitions. Proceedings of the National Academy of Sciences, 106(49), 20572–20577.
Li, J., Cook, E. R., Chen, F., Davi, N., D’Arrigo, R., Gou, X., Wright, W. E., Fang, K.,
Jin, L., Shi, J., & Yang, T. 2009. Summer monsoon moisture variability over China and Mongolia
during the past four centuries. Geophysical Research Letters, 36, L22705.
Li, T, Zhang, YS, Chang, CP, & Wang, B. 2001. On the relationship between Indian Ocean sea
surface temperature and Asian summer monsoon. Geophysical Research Letters, 28(14), 2843–2846.
Liu, X., & Yin, Z. 2002. Sensitivity of East Asian monsoon climate to the uplift of the Tibetan Plateau.
Palaeogeography, Palaeoclimatology, Palaeoecology, 183, 223–245.
Meehl, G. A. 1994. Influence of the Land Surface in the Asian Summer Monsoon: External Conditions
versus Internal Feedbacks. Journal of Climate, 7, 1033–1049.
Meehl, G. A., Stocker, T. F., Collins, W. D., Friedlingstein, P., Gaye, A. T., Gregory,
J. M., Kitoh, A., Knutti, R., Murphy, J. M., Noda, A., Raper, S. C. B., Watterson,
I. G., Weaver, A. J., & Zhao, Z.-C. 2007. Climate Change 2007: The Physical Science Basis.
Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on
Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.
Chap. Global Climate Projections.
Montoya, M., Griesel, A., Levermann, A., Mignot, J., Hofmann, M., Ganopolski, A., &
Rahmstorf, S. 2005. The Earth System Model of Intermediate Complexity CLIMBER-3α. Part I:
description and performance for present day conditions. Climate Dynamics, 25, 237–263.
Overpeck, J. T., Anderson, D., Trumbore, S., & Prell, W. 1996. The southwest Indian Monsoon
over the last 18000 years. Climate Dynamics, 12, 213–225.
Parthasarathy, B., Munot, A.A., & Kothawale, D.R. 1988. Regression model for estimation
of Indian foodgrain production from summer monsoon rainfall. Agricultural and Forest Meteorology,
42(2-3), 167 – 182.
134
REFERENCES
Rahmstorf, S., Crucifix, M., Ganopolski, A., Goosse, H., Kamenkovich, I., Knutti, R.,
Lohmann, G., Marsh, B., Mysak, L. A., Wang, Z., & Weaver, A. 2005. Thermohaline circulation hysteresis: A model intercomparison. Geophysical Research Letters, 32, L23605.
Rajeevan, M, Gadgil, Sulochana, & Bhate, Jyoti. 2010. Active and break spells of the Indian
summer monsoon. Journal of Earth System Science, 119(3), 229–247.
Ramanathan, V., Chung, C., Kim, D., Bettge, T., Kiehl, J. T., Washington, W. M., Fu, Q.,
Sikka, D. R., & Wild, M. 2005. Atmospheric brown clouds: Impacts on South Asian climate and
hydrological cycle. Proceedings of the National Academy of Sciences, 102(15), 5326–5333.
Rashid, Harunur, England, Emily, Thompson, Lonnie, & Polyak, Leonid. 2011. Late Glacial
to Holocene Indian Summer Monsoon Variability Based upon Sediment Records Taken from the Bay
of Bengal. Terrestrial Atmospheric and Oceanic Sciences, 22(2, Sp. Iss. SI), 215–228.
Robock, A., Mu, M., Vinnikov, K., & Robinson, D. 2003. Land surface conditions over Eurasia
and Indian summer monsoon rainfall. Journal of Geophysical Research, 108(D4), 4131.
Schewe, J., Levermann, A., & Meinshausen, M. 2011a. Climate change under a scenario near
1.5◦ C of global warming: monsoon intensification, ocean warming and steric sea level rise. Earth
System Dynamics, 2(1), 25–35.
Schewe, J., Levermann, A., & Cheng, H. 2011b. A critical humidity threshold for monsoon transitions. Climate of the Past Discussions, 7.
Sikka, D. R. 2003. Evaluation of monitoring and forecasting of summer monsoon over India and a
review of monsoon drought of 2002. Proceedings of the Indian National Science Academy Part A,
69(5), 479–504.
Srinivasan, J. 2001. A simple thermodynamic model for seasonal variation of monsoon rainfall. Current
Science, 80(1), 73–77.
Sun, Ying, Ding, Yihui, & Da, Aiguo. 2010. Changing links between South Asian summer monsoon
circulation and tropospheric land-sea thermal contrasts under a warming scenario. Geophysical Research
Letters, 37, L02704.
Tao, F., Yokozawa, M., Zhang, Z., Hayashi, Y., Grassl, H., & Fu, C. 2004. Variability in
climatology and agricultural production in China in association with the East Asian summer monsoon
and El Niño Southern Oscillation. 28, 23–30.
Turner, A. G., & Hannachi, A. 2010. Is there regime behavior in monsoon convection in the late
20th century? Geophysical Research Letters, 37, L16706.
REFERENCES
135
Wang, B. 2005. The Asian monsoon. Springer-Verlag.
Wang, P., Clemens, S., Beaufort, L., Braconnot, P., Ganssen, G., Jian, Z., Kershaw, P.,
& Sarnthein, M. 2005a. Evolution and variability of the Asian monsoon system: state of the art
and outstanding issues. Quaternary Science Reviews, 24, 595–629.
Wang, Y., Cheng, H., Edwards, R. L., He, Y., Kong, X., An, Z., Wu, J., Kelly, M. J.,
Dykoski, C. A., & Li, X. 2005b. The Holocene Asian Monsoon: Links to Solar Changes and North
Atlantic Climate. Science, 308, 854–857.
Wang, Y., Cheng, H., Edwards, R. L., Kong, X., Shao, X., Chen, S., Wu, J., Jiang, X.,
Wang, X., & An, Z. 2008. Millennial- and orbital-scale changes in the East Asian monsoon over the
past 224,000 years. Nature, 451, 1090–1093.
Webster, P. J. 1987a. The Elementary Monsoon. Pages 3–32 of: Fein, J. S., & Stephens, P. L.
(eds), Monsoons. New York, N.Y.: John Wiley.
Webster, P. J. 1987b. The Variable and Interactive Monsoon. Pages 269–330 of: Fein, J. S., &
Stephens, P. L. (eds), Monsoons. New York, N.Y.: John Wiley.
Webster, P. J., Magaña, V. O., Palmer, T. N., Shukla, J., Tomas, R. A., Yanai, M., &
Yasunari, T. 1998. Monsoons: Processes, predictability, and the prospects for prediction. Journal of
Geophysical Research, 103, 14,451–14,510.
Webster, P. J., Toma, V. E., & Kim, H.-M. 2011. Were the 2010 Pakistan floods predictable?
Geophysical Research Letters, 38, L04806.
Wheeler, MC, & Hendon, HH. 2004. An all-season real-time multivariate MJO index: Development
of an index for monitoring and prediction. Monthly Weather Review, 132(8), 1917–1932.
Yang, J., Liu, Q., Xie, S.-P., Liu, Z., & Wu, L. 2007. Impact of the Indian Ocean SST basin mode
on the Asian summer monsoon. Geophysical Research Letters, 34, L02708.
Zhang, P., Cheng, H., Edwards, R. L., Chen, F., Wang, Y., Yang, X., Liu, J., Tan, M.,
Wang, X., Liu, J., An, C., Dai, Z., Zhou, J., Zhang, D., Jia, J., & Johnson, K. R. 2008. A
Test of Climate, Sun, and Culture Relationships from an 1810-Year Chinese Cave Record. Science,
322, 940–942.
Zhang, R., & Delworth, T. L. 2005. Simulated Tropical Response to a Substantial Weakening of the
Atlantic Thermohaline Circulation. Journal of Climate, 18, 1853–1860.
Danksagung
Mein Dank gilt zuallererst Anders Levermann für die hervorragende Betreuung, die für den Erfolg
meiner Arbeit und nicht zuletzt die Freude an derselben von unschätzbarem Wert war und mich stets
sehr motiviert hat.
Herzlich bedanken möchte ich mich bei meinen Kolleginnen und Kollegen am PIK. Malte, Hermann, Vladimir, danke für die gute Zusammenarbeit bei den jeweiligen Studien.
Arathy, Alex,
Carl, Dana, Daria, Dim, Friederike, Hendrik, Johannes, Katja, Mahé, Maria, Marianne, Matthias,
Rica, Tore, Torsten... danke für Rat und Tat, Obst und Kaffee, Wandern und Oper, für schöne drei Jahre!
Dank gebührt auch der Heinrich-Böll-Stiftung für die Finanzierung meiner Promotion und für ein
Begleitprogramm, das mir Ausblicke und Eindrücke jenseits der Naturwissenschaft ermöglicht hat; und
der Studienstiftung des deutschen Volkes für die ideelle Förderung.
Schließlich danke ich meiner Familie für alles.
“Siehst du! Jetzt fängt alles erst richtig an. Was du bisher erfahren
hast, das war doch nur eine Vorahnung vom Anfang und längst
noch nicht
alles.”
Hans Bemmann, Stein und Flöte
Diese Arbeit ist bisher an keiner anderen Hochschule eingereicht worden. Sie wurde selbständig und
ausschließlich mit den angegebenen Mitteln angefertigt.
Jacob Schewe
Potsdam, im Mai 2011
Diese Arbeit wurde durchgeführt am
Potsdam–Institut für Klimafolgenforschung