20th- and 21th-century climate changes in western China simulated with a hierarchy of global and regional climate models, including improved scenarios of human activity and reliable observational data for validation

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AKSU TARIM-CLIM is funded by the German Research Foundation (DFG) since October 2010. Since then important improvements in the regional assessment of climate change in western China and Central Asia have been achieved. Dynamical downscaling with the regional climate model REMO at different resolutions (0.5° and 0.16°) have been performed, revealing improvements in the model bias compared with the driving global climate model and re-analyses. This achievement enabled a rather detailed picture of future climate change in the mountainous region of Asia. Alternatively, a statistical-dynamical downscaling approach has been developed and compared with REMO results. The latter method first analyses the large-scale weather types affecting the region. For selected and representative episodes for each weather type, dynamical downscaling with the regional climate model CCLM at a resolution of 7 km (0.0625°) is performed. Finally, this information is recombined using the frequencies of weather types from global climate model simulations to obtain high resolution climate projections. In general, the added value of dynamical and statistical downscaling of climate projections in western China could be identified and the simulated climate data will now be applied to impact models within the AKSU TARIM project bundle, assessing changes in glacier mass balance, glacier runoff, permafrost and snow cover in the Tien Shan mountain range. Yet, some model deficiencies still exist which require further investigations in order to improve the interface between climate, glaciological and hydrological models. With this aim, we apply for an additional project year, following the current two-year project phase of AKSU TARIM-CLIM. During this project year we plan to (1) develop and apply a statistical postprocessing of the model data in order to remove systematic biases in REMO and CCLM and to adjust grid box data to local climate information required at level of the individual glaciers. (2) One of the most obvious larger-scale deficiencies in REMO consists of a remarkable summer-time warm bias over the Central Asian plain. We assume that this bias is due to the missing land-use process of irrigation and, hence, intend to carry out sensitivity studies with REMO, systematically modifying land-cover towards irrigation. This would be of particular value for the entire hydrological cycle over Central Asia, since evapo-transpiration from irrigated land surfaces represents a crucial source of water vapor and, thus, also snow accumulation in the region. (3) The statisticaldynamical downscaling has been found to be a quick and valuable option in regional climate change assessment. A new approach will be developed which applies to a spatial scale of 2.8 km, closer to the spatial scale of glaciers, to daily values and to additional meteorological variables, in particular temperature. Additionally, the statistical-dynamical downscaling will be extended by using changing weather type frequencies from different GCMs, in order to gain better estimation of the uncertainties associated with the climate projections for the target region.