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The region of southern Africa (SAF) is among the most exposed climate change hotspots and is projected to experience severe impacts on multiple economical and societal sectors. For this reason, producing reliable projections of the expected impacts of climate change is key for local communities. In this work we use a set of 19 regional climate models (RCMs) performed in the context of the Coordinated Regional Climate Downscaling Experiment (CORDEX) - Africa and a set of 10 global climate models (GCMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), that were used as the driving GCMs in the RCM simulations. We are concerned about the degree to which RCM simulations are influenced by their driving GCMs, with regards to monthly precipitation climatologies, precipitation biases and precipitation change signal, according to the Representative Concentration Pathway (RCP) 8.5 for the end of the 21st century. We investigate the degree to which RCMs and GCMs are able to reproduce specific climatic features over SAF and over three sub-regions, namely the greater Angola region, the greater Mozambique region and the greater South Africa region. We identify that during the beginning of the rainy season, when regional processes are largely dependent on the coupling between the surface and the atmosphere, the impact of the driving GCMs on the RCMs is smaller, compared to the core of the rainy season, when precipitation is mainly controlled by the large-scale circulation. In addition, we show that RCMs are able to counteract the bias received by their driving GCMs, hence, we claim that the cascade of uncertainty over SAF is not additive, but indeed the RCMs do provide improved precipitation climatologies. The fact that certain bias patterns over the historical period (1985-2005) identified in GCMs are resolved in RCMs, provides evidence that RCMs are reliable tools for climate change impact studies over SAF. [ABSTRACT FROM AUTHOR]

The Land Use and Climate Across Scales Flagship Pilot Study (LUCAS FPS) is a coordinated community effort to improve the integration of land use change (LUC) in regional climate models (RCMs) and to quantify the biogeophysical effects of LUC on local to regional climate in Europe. In the first phase of LUCAS, nine RCMs are used to explore the biogeophysical impacts of re-/afforestation over Europe: two idealized experiments representing respectively a non-forested and a maximally forested Europe are compared in order to quantify spatial and temporal variations in the regional climate sensitivity to forestation. We find some robust features in the simulated response to forestation. In particular, all models indicate a year-round decrease in surface albedo, which is most pronounced in winter and spring at high latitudes. This results in a winter warming effect, with values ranging from +0.2 to +1 K on average over Scandinavia depending on models. However, there are also a number of strongly diverging responses. For instance, there is no agreement on the sign of temperature changes in summer with some RCMs predicting a widespread cooling from forestation (well below -2 K in most regions), a widespread warming (around +2 K or above in most regions) or a mixed response. A large part of the inter-model spread is attributed to the representation of land processes. In particular, differences in the partitioning of sensible and latent heat are identified as a key source of uncertainty in summer. Atmospheric processes, such as changes in incoming radiation due to cloud cover feedbacks, also influence the simulated response in most seasons. In conclusion, the multi-model approach we use here has the potential to deliver more robust and reliable information to stakeholders involved in land use planning, as compared to results based on single models. However, given the contradictory responses identified, our results also show that there are still fundamental uncertainties that need to be tackled to better anticipate the possible intended or unintended consequences of LUC on regional climates. [ABSTRACT FROM AUTHOR]

The aim of this work is to investigate the recent past and future patterns of the Etesian winds, one of the most persistent localized wind systems in the world, which dominates the wind regime during warm period over the Aegean Sea and eastern Mediterranean. An objective classification method, the Two Step Cluster Analysis (TSCA), is applied on daily data from regional climate model simulations carried out with RegCM3 for the recent past (1961-1990) and future periods (2021-2050 and 2071-2100) constrained at lateral boundaries either by ERA-40 reanalysis fields or the global circulation model (GCM) ECHAM5. Three distinct Etesian patterns are identified by TSCA with the location and strength of the anticyclonic action center dominating the differences among the patterns. In case of the first Etesian pattern there is a ridge located over western and central Europe while for the other two Etesian patterns the location of the ridge moves eastward indicating a strong anticyclonic center over the Balkans. The horizontal and vertical spatial structure of geopotential height and the vertical velocity indicates that in all three Etesian patterns the anticyclonic action center over central Europe or Balkan Peninsula cannot be considered as an extension of the Azores high. The future projections for the late 21st century under SRES A1B scenario indicate a strengthening of the Etesian winds associated with the strengthening of the anticyclonic action center, and the deepening of Asian thermal Low over eastern Mediterranean. Furthermore the future projections indicate a weakening of the subsidence over eastern Mediterranean which is rather controlled by the deepening of the south Asian thermal Low in line with the projected in future weakening of South Asian monsoon and Hadley cell circulations. [ABSTRACT FROM AUTHOR]

The ability of a large ensemble of regional climate models to accurately simulate heat waves at the regional scale of Europe was evaluated. Within the EURO-CORDEX project, several state-of-the art models, including non-hydrostatic meso-scale models, were run for an extended time period (20 years) at high resolution (12 km), over a large domain allowing for the first time the simultaneous representation of atmospheric phenomena over a large range of spatial scales. Eight models were run in this configuration, and thirteen models were run at a classical resolution of 50 km. The models were driven with the same boundary conditions, the ERA-Interim re-analysis, and except for one simulation, no observations were assimilated in the inner domain. Results, which are compared with daily temperature and precipitation observations (ECA&D and E-OBS data sets) show that, even forced by the same re-analysis, the ensemble exhibits a large spread. A preliminary analysis of the sources of spread, using in particular simulations of the same model with different parameterizations, shows that the simulation of hot temperature is primarily sensitive to the convection and the microphysics schemes, which affect incoming energy and the Bowen ratio. Further, most models exhibit an overestimation of summertime temperature extremes in Mediterranean regions and an underestimation over Scandinavia. Even after bias removal, the simulated heat wave events were found to be too persistent, but a higher resolution reduced this deficiency. The amplitude of events as well as the variability beyond the 90th percentile threshold were found to be too strong in almost all simulations and increasing resolution did not generally improve this deficiency. Resolution increase was also shown to induce large-scale 90th percentile warming or cooling for some models, with beneficial or detrimental effects on the overall biases. Even though full causality cannot be established on the basis of this evaluation work, the drivers of such regional differences were shown to be linked to changes in precipitation due to resolution changes, affecting the energy partitioning. Finally, the inter-annual sequence of hot summers over central/southern Europe was found to be fairly well simulated in most experiments despite an overestimation of the number of hot days and of the variability. The accurate simulation of inter-annual variability for a few models is independent of the model bias. This indicates that internal variability of high summer temperatures should not play a major role in controlling inter-annual variability. Despite some improvements, especially along coastlines, the analyses conducted here did not allow us to generally conclude that a higher resolution is clearly beneficial for a correct representation of heat waves by regional climate models. Even though local-scale feedbacks should be better represented at high resolution, combinations of parameterizations have to be improved or adapted accordingly. [ABSTRACT FROM AUTHOR]