How strongly are mean and extreme precipitation coupled?

<p>Changes in mean and extreme precipitation are among the most important consequences of climate change. Here we examine the relationship between the mean and three different measures of extreme precipitation over the Australian continent, from a regional climate projection ensemble. We show that model uncertainty in mean and extreme precipitation are tightly coupled for both the present-day climate and future changes. On the continental scale the differences in mean precipitation explain 80-99% of the variance in the extremes. We also find that in most regions except along the coasts, precipitation statistics projected by regional modelling system (RCM) are highly predictable from the mean precipitation of the global model (GCM) providing the boundary conditions. In coastal regions RCMs are more accurate than GCMs and they also have more impact on present-day statistics, however, this impact disappears for future changes, suggesting that improved present-day accuracy will not carry over to future changes.</p>

Causes of the Uncertainty in Projections of Tropical Terrestrial Rainfall Change: East Africa

Understanding the causes of regional climate projection uncertainty is a critical component toward establishing reliability of these projections. Here, four complementary experimental and decomposition techniques are synthesized to begin to understand which mechanisms differ most between models. These tools include a variety of multimodel ensembles, a decomposition of rainfall into tropics-wide or region-specific processes, and a separation of within-domain versus remote contributions to regional model projection uncertainty. Three East African regions are identified and characterized by spatially coherent intermodel projection behavior, which interestingly differs from previously identified regions of coherent interannual behavior. For the “Short Rains” regions, uncertainty in projected seasonal mean rainfall change is primarily due to uncertainties in the regional response to both the uniform and pattern components of SST warming (but not uncertainties in the global mean warming itself) and a small direct CO2 impact. These primarily derive from uncertain regional dynamics over both African and remote regions, rather than globally coherent (thermo)dynamics. For the “Long Rains” region, results are similar, except that uncertain atmospheric responses to a fixed SST pattern change are a little less important, and some key regional uncertainties are primarily located beyond Africa. The latter reflects the behavior of two outlying models that experience exceptional warming in the southern subtropical oceans, from which large lower-tropospheric moisture anomalies are advected by the mean flow to contribute to exceptional increases in the Long Rains totals. Further research could lead to a useful assessment of the reliability of these exceptional projections.

A Pragmatic Approach to Build a Reduced Regional Climate Projection Ensemble for Germany Using the EURO-CORDEX 8.5 Ensemble

The application of an ensemble reduction technique to the European branch of the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) ensemble at resolution “EUR-11” (~12.5 km) under the RCP8.5 scenario is presented. The technique is based on monthly mean changes between a reference and two future time periods, calculated for eight regions in Germany, of the parameters near-surface air temperature (tas), precipitation totals (pr), contribution of precipitation from very wet days to precipitation totals (R95pTOT), near-surface specific humidity (huss), and surface downwelling shortwave radiation (rsds). The sensitivity of the reduction procedure with respect to a number of tuning parameters is investigated. When the optimal combination of tuning parameters is applied, the technique allows the reduction from 15 to 7 ensemble members, while the reduced ensemble reproduces about 94% of the spread of the full ensemble. Keeping in mind that climate projection ensembles are expected to grow substantially in the near future, this ensemble reduction technique can be useful to limit the computational efforts necessary for further processing and applications such as impact modeling.