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20 pages; International audience; The impact of global warming on the warmest and coldest days of the annual cycle is explored according to an A2 scenario simulated by the CNRM-CM3 climate model in the framework of the IPCC AR4 intercomparison. Given the multi-model spread in IPCC projections, a validation strategy is proposed using the NCEP/NCAR reanalysis. Validation of the late twentieth century model climatology shows that warm and cold model events are slightly too long and infrequent. Although interannual trends in the warm (cold) day occurrence were positive (negative) only for six (three) of the nine considered sub-continental regions, simulated model trends are always positive (negative). This different behaviour suggests that simulated non-anthropogenic decadal variability is small relative to anthropogenic trends. Large-scale synoptic processes associated with European regional warm and cold peaks are also described and validated. Regional cold peaks are better reproduced than warm peaks, whose intensity accuracy is limited by other physical variables. Positive (negative) winter anomalies of sea and land surface temperature lead to summers with severe (weak) temperatures. These inter-annual anomalies are generated by a persistent pressure dipole over Europe. Regarding climate change, warm (cold) events will become more (less) frequent and longer (shorter). The number of warm days will largely rise and the number of cold days will dramatically decrease. The intensity of warm days will be particularly pronounced over Europe, given the projected summer drying in this region. However, according to the limited skill of the CNRM model, these results must be considered with caution

Three ensembles of AMIP-type simulations using the Arpege-climat coupled land–atmosphere model have been designed to assess the relative influence of SST and soil moisture (SM) on climate variability and predictability. The study takes advantage of the GSWP2 land surface reanalysis covering the 1986–1995 period. The GSWP2 forcings have been used to derive a global SM climatology that is fully consistent with the model used in this study. One ensemble of ten simulations has been forced by climatological SST and the simulated SM is relaxed toward the GSWP2 reanalysis. Another ensemble has been forced by observed SST and SM is evolving freely. The last ensemble combines the observed SST forcing and the relaxation toward GSWP2. Two complementary aspects of the predictability have been explored, the potential predictability (analysis of variance) and the effective predictability (skill score). An analysis of variance has revealed the effects of the SST and SM boundary forcings on the variability and potential predictability of near-surface temperature, precipitation and surface evaporation. While in the tropics SST anomalies clearly maintain a potentially predictable variability throughout the annual cycle, in the mid-latitudes the SST forced variability is only dominant in winter and SM plays a leading role in summer. In a similar fashion, the annual cycle of the hindcast skill (evaluated as the anomalous correlation coefficient of the three ensemble means with respect to the “observations”) indicates that the SST forcing is the dominant contributor over the tropical continents and in the winter mid-latitudes but that SM is supporting a significant part of the skill in the summer mid-latitudes. Focusing on boreal summer, we have then investigated different aspects of the SM and SST contribution to climate variations in terms of spatial distribution and time-evolution. Our experiments suggest that SM is potentially an additional source of climate predictability. A realistic initialization of SM and a proper representation of the land–atmosphere feedbacks seem necessary to improve state-of-the-art dynamical seasonal predictions, but will be actually efficient only in the areas where SM anomalies are themselves predictable at the monthly to seasonal timescale (since remote effects of SM are probably much more limited than SST teleconnections).

While transient climate change experiments with coupled atmosphere–ocean general circulation models undoubtedly represent the most comprehensive tool for studying the climate response to increasing concentrations of greenhouse gases (GHGs), less computationally expensive time-slice experiments with atmospheric GCMs are still useful to test the robustness of the projected climate change. In the present study, three sets of time-slice experiments with prescribed sea surface temperature (SST) are compared to a reference climate scenario obtained with the Centre National de Recherches Météorologiques Coupled Climate Model (CCM). The main objective is to assess the sensitivity of the monsoon response to the magnitude or pattern of SST anomalies in two regions where such anomalies are highly model dependent, namely, the circumpolar Southern Ocean and the tropical Pacific Ocean. On the one hand, it is shown that the regional climate anomalies predicted by the CCM can be reproduced at least qualitatively by a pair of time-slice experiments in which the present-day SST biases of the CCM are removed. On the other hand, the results indicate that the Indian monsoon response to increasing amounts of GHG is sensitive to regional uncertainties in the prescribed SST warming. Increasing the sea surface warming in the southern high latitudes to compensate for the weak sea ice feedback simulated by the CCM around the Antarctic has a significant influence on the regional climate change simulated over India, through a perturbation of the regional Hadley circulation. Prescribing zonal mean rather than El Niño–like SST anomalies in the tropical Pacific has an even stronger impact on the monsoon response, through a modification of the Walker circulation. These results suggest that both deficiencies in simulating present-day climate (even at high latitudes) and uncertainties in the SST patterns caused by enhanced GHG concentrations (especially in the tropical Pacific) are major obstacles for predicting climate change at the regional scale.

El-Nino/Southern Oscillation (ENSO) variability and its relationship with precipitation in the tropics and subtropics are analysed using the ARPEGE-OPA ocean-atmosphere coupled model. Three 150-year simulations are considered, differing by greenhouse gases (GHG) and aerosols concentrations. The first one has constant (1950 level) concentrations, and the two others follow observed values till 1999, then the SRES B2 scenario until 2099. The model is able to reproduce most present-day features characteristic of ENSO in the Pacific. It also displays ENSO as the leading mode of sea-surface temperature (SST) variability, with spatial patterns and explained variance both quite similar to the observation. A detailed analysis of its teleconnections with rainfall variability is carried out on a seasonal basis. Patterns for the last part of the twentieth century compare favourably with the observation, with the notable exception of parts of the Atlantic sector. The overall strong rainfall response arises from the strong interannual variability of simulated ENSO, and also suggests an ability to simulate atmospheric dynamics in a realistic way. In the future climate, the model does not exhibit major changes in the ENSO/rainfall teleconnections. However, on a regional basis, there is some evidence of strengthening (e.g., in parts of Southern Africa) and weakening (e.g., East Africa) in the course of the twenty-first century. In most cases, decadal swings in the correlations suggest that these alterations may partly reflect natural changes in the teleconnections with ENSO, long-term correlation trends (possibly GHG-induced) being comparatively weaker.

Ensembles of boreal summer atmospheric simulations, spanning a 15-year period (1979–1993), are performed with the ARPEGE climate model to investigate the influence of soil moisture on climate variability and potential predictability. All experiments are forced with observed monthly mean sea surface temperatures. In addition to a control experiment with interactive soil moisture boundary conditions, two sensitivity experiments are performed. In the first, the interannual variability of the deep soil moisture is removed during the whole season, through a relaxation toward the monthly mean model climatology. In the second, only the variability of the initial soil moisture conditions is suppressed. While it is shown that soil moisture strongly contributes to the climate variability simulated in the control experiment, an analysis of variance indicates that soil moisture does not represent a significant source of predictability in most continental areas. The main exception is the North American continent, where climate predictability is clearly reduced through the use of climatological initial conditions. Using climatological soil moisture boundary conditions does not lead to strong and homogeneous impacts on potential predictability, thereby suggesting that the climate signals driven by the sea surface temperature variability are not generally amplified by interactive soil moisture and that the relevance of soil moisture for seasonal forecasting is mainly an initial value problem.

As a first step of the international Global Soil Wetness Project (GSWP), several state-of-the-art land surface models were recently forced by 1° × 1° atmospheric analyses and observations to obtain global soil moisture climatologies over the 1987–1988 period. Besides the 6-hourly atmospheric forcing, the models also used common boundary conditions (soil and vegetation parameters), also drawn from the ISLSCP Initiative I dataset. While very few in situ observations are available to validate the soil moisture fields, the simulated runoff can be compared with river discharge measurements. This strategy is employed here to assess the quality of GSWP simulations based on the ISBA land surface model. The ISBA runoff is transformed into 1° × 1° gridded stream flows by using the TRIP river routing model. The focus is on the Amazon basin where all models participating in GSWP showed a strong underestimation in the annual runoff and a significant lag in the annual cycle of the runoff. A sample of 31 gauging stations is selected to validate the gridded runoff simulated by ISBA. Sensitivity tests have been performed, that suggest that deficiencies in both the precipitation forcing and the boundary conditions provided by ISLSCP contribute to the poor simulation of the Amazon water balance. The use of alternative precipitation, soil and vegetation datasets allows ISBA to produce a more realistic annual runoff, although the amplitude of the annual cycle remains exaggerated at the downstream gauging station of Obidos. Among these experiments, the simulation leading to the best annual runoff has been used as a reference to test simple modifications in the TRIP river routing model. Tuning the parameters of TRIP or increasing the resolution of the river channel network is not sufficient to improve the annual cycle of the simulated discharge. New developments are necessary to deal more explicitly with the floodplain inundation that occurs during the rainy season over the Amazon basin, and to derive a geographically variable and time-evolving stream flow velocity. In future, considerable efforts are needed to provide more reliable global land surface and forcing datasets in the continuation of GSWP, in order to validate more efficiently the land surface models and thereby to produce more realistic soil moisture climatologies.

Long-range forecasting is a notoriously difficult area that has received increased attention in recent years. Both statistical and dynamical methods have been proposed, which are all based on the assumption that the atmospheric variability is influenced on the seasonal time scale by lower boundary forcings such as the sea surface temperature (SST) or the continental hydrology. Whereas dynamical predictions rely on the explicit simulation of major atmospheric processes, it is still unclear that they lead to better estimations of seasonal precipitation anomalies on the regional scale. In the present study, both forecasting strategies are used to predict monsoon rainfall anomalies over Central Sahel. The statistical predictions are based on linear regressions using SST and rainfall predictors observed over 1968–97, a relatively dry period compared with the whole 20th century. The dynamical predictions are based on ten-member ensembles of seasonal hindcasts using the ARPEGE atmospheric model forced by observed SST over the 1979–93 period. The results suggest that the ARPEGE precipitation forecasts are less skilful than a linear regression scheme based on only two predictors. However, the atmospheric model shows a better ability to simulate the variability of the large-scale monsoon circulation. As a consequence, a simple statistical adaptation of the ARPEGE dynamical predictions is feasible and leads to better predictions of the rainfall anomalies than the direct use of the modelled precipitation. Finally, a statistico-dynamical method is proposed, based on a multivariate regression using both observed predictors and ARPEGE dynamical outputs. Although based on too short a time-series to lead to definitive conclusions, the results are encouraging and suggest the need to combine dynamical and statistical tools to perform efficient seasonal predictions. Copyright © 2002 Royal Meteorological Society.

We attempt to quantify the Antarctica annual mass balance by estimating the seasonal water mass exchange among the main surface reservoirs (oceans, atmosphere, continents and ice sheets) using various data sources. These data include Topex-Poseidon altimetry and in situ ocean temperature data, soil moisture and snow depth data, and atmospheric surface pressure data. We calculate the seasonal (annual) variations of ocean water mass, continental waters and atmospheric water vapor contributions respectively. We find that the observed ∼9 mm annual mean sea level variation caused by water mass exchange with the other reservoirs cannot be totally explained by the combined contributions of snow, soil moisture and atmospheric water vapor. A residual signal, of 3 mm amplitude, maximum at the end of December, is observed that we attribute to the Antarctica annual mass balance. This residue is consistent with existing understanding of Antarctica precipitation.

Global soil moisture data of high quality and resolution are not available by direct observation, but are useful as boundary and initial conditions in comprehensive climate models. In the framework of the GSWP (Global Soil Wetness Project), the ISBA land-surface scheme of Meteo-France has been forced with meteorological observations and analyses in order to study the feasibility of producing a global soil wetness climatology at a 1°×1° horizontal resolution. A control experiment has been performed from January 1987 to December 1988, using the ISLSCP Initiative I boundary conditions. The annual mean, the standard deviation and the normalised annual harmonic of the hydrologic fields have been computed from the 1987 monthly results. The global maps which are presented summarise the surface hydrologic budget and its annual cycle. The soil wetness index and snow cover distributions have been compared respectively to the results of the ECMWF reanalysis and to satellite and in situ observations. The simulated runoff has been validated against a river flow climatology, suggesting a possible underestimation over some large river basins. Besides the control run, other simulations have been performed in order to study the sensitivity of the hydrologic budget to changes in the surface parameters, the precipitation forcing and the runoff scheme. Such modifications have a significant impact on the partition of total precipitation into evaporation and runoff. The sensitivity of the results suggests that soil moisture remains one of the most difficult climatological parameters to model and that any computed soil wetness climatology must be considered with great caution.

A temperate and boreal deforestation experiment has been performed at Meteo-France using the ARPEGE climate model. A first simulation was performed as a control with a present-day vegetation map, and another one with all forests north of 45 °N replaced by meadows. Prescribed monthly mean climatological SSTs were used in both integrations. The ARPEGE climate model includes a physically based land surface scheme, which has been tested both on snowfree and snow-covered sites, and has a relatively high horizontal resolution. Results of the 4-year integrations suggest that forests exert a strong influence on the surface climate of the temperate and boreal regions. Deforestation induces a significant cooling which modifies the atmospheric circulation simulated in the high latitudes, and also in the tropics. The most important impact is observed during the melting season which is delayed by the forest removal. This result is consistent with preliminary stand-alone experiments showing that the atmospheric boundary layer can be heated by the forest, even if the ground is covered by snow. The study confirms that vegetation feedbacks should be included when performing future climate studies such as doubled CO2 experiments, eventhough many uncertainties still remain with regard to other physical aspects of the climate models.

Research into land-atmosphere coupling within the African Monsoon Multidisciplinary Analysis has highlighted the atmospheric impact of soil moisture on space scales of 5 km upwards and time scales of several days. Observational and modelling studies have shown how antecedent rainfall patterns affect new storms in the Sahel. The land feedback operates through various mechanisms, including a direct link to afternoon storm initiation from surface-induced mesoscale circulations, and indirectly via a large-scale moisture transport in the nocturnal monsoon. The results suggest potential for significant improvements in weather forecasting through assimilation of satellite data. Intriguing questions remain about the importance of vegetation memory on seasonal-interannual scales.

[1] Given the strong natural variability of precipitation, detection of anthropogenic climate change has been mainly based on surface thermal indicators. It is here argued that precipitation variability, whatever its natural or anthropogenic origin, is likely to affect surface temperature and, thereby, to hamper the detection-attribution of climate change at the regional scale. This issue is illustrated over Sudan and Sahel using the outputs of eleven coupled general circulation models having participated in the latest IPCC simulations. Like in situ observations, models show a strong relationship between detrended seasonal anomalies of surface air temperature and precipitation during the summer monsoon season. Linear regressions are then used to remove the precipitation influence on observed and simulated linear trends in surface air temperature. The results indicate that this strategy is efficient to reduce the spread in the simulated surface warming and make it more consistent with the instrumental record over recent decades.