Download SuMMaRy oF analySES CaRRIEd out FoR thE ESCRIME

Summary of analyses
carried out for
the ESCRIME project
Sandrine Bony, Laurent Bopp, Pascale Braconnot, Patricia Cadule,
Christophe Cassou, Michel Déqué, Hervé Douville, Jean-Louis Dufresne,
Pierre Friedlingstein, Christophe Genthon, Eric Guilyardi, Laurent Li,
Serge Planton, Jean-François Royer, David Salas, Pascal Terray, Laurent
Terray
A. Global modeling
and climate change
In 2004, in preparation for the IPCC Fourth Assessment Report, the Working Group on Coupled Modeling (WGCM) of
the World Climate Research Programme (WCRP) launched an
ambitious initiative under the auspices of the IPCC to encourage modeling teams to perform climate change simulations
according to a precise protocol. The results of these simulations were to be presented in a standard format and made
available to the whole of the scientific community in order
to encourage cross-analysis between several models. For the
first time, French teams have performed all the simulations required and have thus been able to provide a greater contribution to the preparation of the report.
France has two climate models, one developed by MétéoFrance and CERFACS, the other by IPSL, which differ mainly
by their atmospheric component. Since the previous IPCC
report in 2001, all the components of these climate models have been improved: the atmosphere (representation of
convection, clouds, aerosols and orography), the ocean (free
surface formulation), sea ice (rheology) and continental surfaces (land use). The resolution of the models has been increased and the coupling between components improved.
Finally, several research projects have been launched to couple these climate models to models of chemicals, aerosols
and biogeochemical cycles, etc.
The simulations performed for IPCC cover climate change
from 1860 to today, as well as projections for the 21st century (Fig. A1). For the 20th century, the temperature trends
simulated by the models are consistent with observations for
France and for the rest of the world. Numerous studies have
been carried out to characterize and assess the qualities and
limitations of the models in terms of both mean state and
variability, by comparing them with recent observations. For
the future and for the SRES-A2 scenario (continued growth
of emissions), both models simulate a fairly similar change
in temperatures (Fig. A1 and A2). For precipitation, on the
other hand, there are more considerable differences, espe-
cially over continents, and for the geographical distribution
of precipitation change.
B. Climate feedback
and variability
Cloud feedback
The climate models differ in the scale of global warming they
predict in response to a doubling of atmospheric CO2. It has
long been acknowledged that this uncertainty stems principally from inter-model differences in the radiative response of
clouds to climate change. The development of new methods
for analyzing physical feedback mechanisms in climate models has shown that these uncertainties mainly concern the response of boundary layer clouds (stratus, stratocumulus and
cumulus) (Fig. B1). This opens the way for new strategies to
assess clouds and their sensitivity in climate models.
Carbon cycle
The possibility of positive feedback between anthropogenic
climate change and the carbon cycle has only recently been
revealed: future climate change could drastically reduce
the efficiency of natural sinks, the terrestrial biosphere and
oceans in absorbing anthropogenic CO2, meaning an increase in the rate of CO2 growth and an intensification of
climate change. Estimations made using the IPSL coupled
climate-carbon model show that for the SRES-A2 scenario,
this feedback could mean an increase in the growth of CO2
emissions of 35 ppm by 2100. Studies carried out as part of
an international project comparing coupled climate-carbon
models, coordinated by IPSL, have shown that this amplification, which is always positive, varied by between 20 and
200 ppm for 2100. This could correspond to an induced
warming of 1.5°C more than estimations made using traditional climate models (Fig. B2).
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Water cycle
The response of precipitation to anthropogenic forcings, despite being crucial for many climate change impact studies,
is still uncertain in many regions. It is in fact harder to predict than that of temperatures for a number of reasons. Beyond the unknown factors linked to the different emissions
scenarios, mainly concerning the amplitude of the simulated
anomalies, projections remain highly variable from one model
to another, including at the global continental scale (Fig. B3).
Among the different methods possible for restricting model
response, validating the interannual variability of the water cycle and its relationships with sea surface temperatures could
be an interesting option to explore (Fig. B4).
Variability modes
Simulated anthropogenic climate variability and change is
characterized by considerable diversity between models. This
disparity may be explained by different spatio-temporal interactions, especially between ENSO-type interannual variability
and the seasonal cycle in the Tropical Pacific, the intra-seasonal activity of the tropical atmosphere, etc. It is also explained
by the models’ ability to correctly simulate ENSO teleconnections, which tend to be overestimated, thereby dominating
the variability of monsoons (Africa, South America, etc.) and
even extratropical latitudes. The model responses to anthropogenic forcings, especially for the water cycle, seem to be
largely controlled by the nature of these ENSO teleconnections. Scenario analysis shows that the ENSO characteristics
do not change in an altered climate (Fig. B5). Changes at mid
to high latitudes are characterized by a more zonal trend with
a pattern similar to the positive phase of the North Atlantic
Oscillation over Europe.
Cryosphere
It is vitally important to understand the current and future
evolution of continental and marine ice. Satellite observations
show that melting continental ice contributed to a sea level
rise of almost 1 mm/year over the 1993-2005 period, in addition to the 2 mm/year caused by the thermal expansion of
oceans in the warming phase. For the end of the 21st century, the models indicate that the melting of the Greenland
ice sheet should greatly accelerate. The associated sea level
rise should however be moderate due to an increase in the
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accumulation of snow in the Antarctic, in line with the rise
in temperatures in this region. Sea ice is currently rapidly receding, and according to the most recent climate models, this
trend should continue: consequently, the Arctic Ocean could
be completely ice-free in summer by the end of the 21st century (Fig. B6).
C. Regional modeling and
climate change detection
Regional modeling and extremes
An assessment of the impact of anthropogenic climate change
on the frequency of wind, temperature and precipitation phenomena over France was carried out using high resolution
simulations over Europe by the IPSL and CNRM models, based
on the A2 scenario. Emphasis was placed on the frequency
of heatwaves, storms and heavy rain or droughts over the
country. Furthermore, the impact on the frequency of tropical
cyclones in the North Atlantic was studied. Three approaches
were used to assess the impact of climate change: the direct
approach, which directly uses the model’s variables; the statistical approach, which uses observations to establish an empirical relationship between the large-scale variables observed
and the associated weather risk; and the dynamic approach,
which takes a weather situation as a whole over the North Atlantic and Europe for a given day and identifies the associated
extreme phenomena. The results reveal a very clear response
of an increase in heatwaves (Fig. C1), a moderate increase in
the risk of heavy rain in winter, and a fairly insignificant impact
on strong winds. The response of the frequency of cyclones
depends on the hypothesis of sea temperature change, but
the precipitation associated with cyclones is increasing.
Detection and attribution
The studies carried out within the French community are the
first to suggest that it is possible to detect a spatial footprint
of anthropogenic climate change at sub-regional scales in
observations of minimum summer temperatures in France
(Fig. C2). Attribution studies show that most of this warming is
due to the combined action of greenhouse gases and sulfate
aerosols. Analyses conducted seem to indicate that the nonlinearities between soil water and temperature, via changes
in evapotranspiration, are responsible for the spatial structure
of warming. Furthermore, studies on precipitation show that
it is also possible to detect an anthropogenic signal for winter
trends over the last few decades. Separating the precipitation
signal into a dynamic part at the regional scale and a residual
part shows that this dynamic component almost completely
captures the trend observed.
Summary of analyses carried out for the ESCRIME project
f i g u r e A 1 Top: Evolution of average earth surface temperature (°C)
observed (in black, from 1860 to 2004), and simulated by the CNRM model
(in blue) and the IPSL model (in red). After the year 2000, we use either the
SRES-A2 scenario (continuous line), or the SRES-B1 scenario (line with circles),
or we maintain CO2 concentration at a constant level (line with triangles).
The observations are those compiled by the CRU [Jones and Moberg, 2003].
Bottom: Evolution of the average temperature (°C) during the three summer
months (June to August) in mainland France, observed (in black, from 1880
to 2005), and simulated by the CNRM model (in blue) and the IPSL model (in
red). After the year 2000, we use the SRES-A2 model. Summer 2003 is clearly
visible. The observations come from Météo France.
F i g u r e A 2 Geographical distribution of temperature change (°C, top) and precipitation change (mm/day, bottom), between the end of the 21st and the
end of the 20th century, with the SRES-A2 scenario and calculated with the IPSL model (left) and the CNRM model (right).
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F i g u r e B 1 Sensitivity of shortwave radiative forcing of tropical clouds
to climate warming in different atmospheric circulation regimes, associated
with different types of dominant clouds (a positive sensitivity corresponds
to a reduction in the reflectivity of solar radiation by clouds). It is in atmospheric subsidence regimes (regimes where the large-scale vertical speed is
positive), characterized by the presence of low-level clouds of the stratus,
stratocumulus or small cumulus kind, that the radiative responsive of clouds
to warming differs the most between sensitive climate models (in red, the
average of simulations by models predicting high climate warming) and the
least sensitive models (in blue, the models predicting lower climate warming). (Adapted from Bony and Dufresne, Geophys. Res. Lett., 2005, see also
Bony et al., 2006, Webb et al., 2006).
F i g u r e B 2 dispersion of CO2 concentration for the C4MIP simulations
with carbon cycle around the SRES-A2 scenario (top). Global temperature
anomalies for the IPCC and C4MIP scenarios for the SRES-A2 scenario (bottom).
Annual GCP anomalies versus the GCP SST link
Observed link
Annual precipitation anomaly (mm/day)
(estimated as the global average of correlations between GCP and SST)
GCP-SST link (after removing the trend)
F i g u r e B 3 Comparative evolution of annual anomalies for global continental precipitation (GCP) in 20th century simulations and A2 scenarios of
14 models (including the French CNRM and IPSL models) and in CRU TS 2.1
observations. The anomalies are filtered (cut-off frequency at 10 years) and
are estimated in relation to the 1971-2000 period. They show considerable
differences in 21st century scenarios. Each curve corresponds to a single
simulation and although they reflect contrasting behavior, the evolutions
observed over the 20th century must be interpreted with care due to the
diversity and the relative weakness of the anthropogenic forcings imposed.
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F i g u r e B 4 Presentation of annual anomalies for global continental precipitation (GCP) in 14 models according to an indicator of the interannual
link that exists between GCP and sea surface temperatures. On the ordinate,
the anomalies are estimated over the last 30 years of the A2 scenario in relation to the 1971-2000 period. On the abscissa, the interannual link is estimated as the global average of the correlations in grid points with seas surface
temperatures after removing the trend. The scatter plot suggests that there
is a continuum between the sensitivity of GCPs at the interannual and multidecadal scales. The grey vertical bar indicates the intensity of the interannual
link observed and suggests that the strongest hydrological responses could
be exaggerated.
Summary of analyses carried out for the ESCRIME project
F i g u r e B 5 Structure of El Niño-type variability in the tropical Pacific Ocean (standard deviation of sea surface temperature in degrees Celsius). A)
Observations, b) CNRM-CM3 in the current climate, c) IPSL-CM4 in the current climate, d) CNRM-CM3 in 2100 (IPCC SRES A1B scenario), e) IPSL-CM4 in 2100
(IPCC SRES A1B scenario).
F i g u r e B 6 Average fraction of sea ice for September (minimum extension) in the Arctic, simulated by the CNRS/IPSL-CM4 (top) and Météo-France/ CNRMCM3 (bottom) models. (a), (d): 1960-1989 period. These results are very close to current satellite observations; (b), (e): 2070-2099, SRES-B1 scenario; (c), (f ):
2070-2099, SRES-A2 scenario. It thus appears that while estimations of ice quantity remaining at the end of summer vary according to the model and the SRES
scenario used, there is a very clear trend towards sea ice retreat.
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F i g u r e C 1 Average number of heatwave days per summer. Here, a heatwave is defined as a series of at least five consecutive days when the maximum
diurnal temperature exceeds the climate normal (1961-1990) by at least 5°C. Reference climate (a), average climate around 2050 seen by the IPSL model (b)
and by the Météo-France model (c).
F i g u r e C 2 Signal for change in minimum daily temperature in summer calculated from the average of three climate change scenarios performed using
the ARPEGE-Climat variable resolution model. The scale is arbitrary but the warming increases from blue to pink (left). Observation of the trend for minimum
daily temperature in summer over the 1971-2000 period. The scale is one tenth of a degree per decade (right).
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