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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). page 11 Escrime White Paper 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 page 12 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). page 13 Escrime White Paper 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. page 14 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. page 15 Escrime White Paper 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). page 16