ALBERT

Description: This study investigates the role of physical parameterization in regional climate model simulations. The authors also present a comprehensive assessment of errors arising from use of physical parameterization schemes, and their consequent impact on model performance in a region of complex topography. An error range related to the choice of physical parameterization is provided for 2-m air temperature T2m and precipitation. Two state-of-the-art nonhydrostatic mesoscale regional climate models, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) model, are used to dynamically downscale the 40-yr ECMWF Re-Analysis (ERA-40) to a spatial resolution of 10 km × 10 km in the European alpine region. Simulated T2m and precipitation are compared with gridded observational datasets. The model performance on regional and subregional scales is evaluated on daily, monthly, seasonal, and annual time scales. The results are based on a mixed physics ensemble of twenty-nine 1-yr-long hindcast simulations generated by choosing different model configurations. These results indicate that performance of both models is sensitive to the choice of physical parameterization and WRF is more sensitive than MM5. This sensitivity is higher during summer than during winter. The cumulus and microphysics scheme have the dominant effect on model performance during summer while boundary layer and radiation schemes affect the results during all seasons. It is found that annual mean error in the alpine region for T2m and precipitation lies between −2.75° and −1.08°C (−1.12° and 1.33°C) and 0.27 and 0.80 mm day−1 (0.13 and 1.51 mm day−1) for MM5 (WRF), respectively. The authors also found that the error range during winter for subregional scales is higher than the regional-scale error range, while during the other seasons the subregional error ranges are higher only in case of precipitation but not for T2m. These results suggest that significant reductions of errors can be achieved by choosing a suitable model configuration for the region of interest. The other outcome of these experiments is a suitable setup, which can be used to conduct further long-term high-resolution climate simulations.

Description: Central Asian water resources largely depend on melt water generated in the Pamir and Tien Shan mountain ranges. To estimate future water availability in this region, it is necessary to use climate projections to estimate the future glacier extent and volume. In this study, we evaluate the impact of uncertainty in climate change projections on the future glacier extent in the Amu and Syr Darya river basins. To this end we use the latest climate change projections generated for the upcoming IPCC report (CMIP5) and, for comparison, projections used in the fourth IPCC assessment (CMIP3). With these projections we force a regionalized glacier mass balance model, and estimate changes in the basins' glacier extent as a function of the glacier size distribution in the basins and projected temperature and precipitation. This glacier mass balance model is specifically developed for implementation in large scale hydrological models, where the spatial resolution does not allow for simulating individual glaciers and data scarcity is an issue. Although the CMIP5 ensemble results in greater regional warming than the CMIP3 ensemble and the range in projections for temperature as well as precipitation is wider for the CMIP5 than for the CMIP3, the spread in projections of future glacier extent in Central Asia is similar for both ensembles. This is because differences in temperature rise are small during periods of maximum melt (July–September) while differences in precipitation change are small during the period of maximum accumulation (October–February). However, the model uncertainty due to parameter uncertainty is high, and has roughly the same importance as uncertainty in the climate projections. Uncertainty about the size of the decline in glacier extent remains large, making estimates of future Central Asian glacier evolution and downstream water availability uncertain.

Description: Central Asian water resources largely depend on (glacier) melt water generated in the Pamir and Tien Shan mountain ranges, located in the basins of the Amu and Syr Darya rivers, important life lines in Central Asia and the prominent water source of the Aral Sea. To estimate future water availability in the region, it is thus necessary to project the future glacier extent and volume in the Amu and Syr Darya river basins. The aim of this study is to quantify the impact of uncertainty in climate change projections on the future glacier extent in the Amu and Syr Darya river basins. The latest climate change projections provided by the fifth Coupled Model Intercomparison Project (CMIP5) generated for the upcoming fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC) are used to model future glacier extent in the Central Asian region for the two large river basins. The outcomes are compared to model results obtained with the climate change projections used for the fourth IPCC assessment (CMIP3). We use a regionalized glacier mass balance model to estimate changes in glacier extent as a function of glacier size and projections of temperature and precipitation. The model is developed for implementation in (large scale) hydrological models, when the spatial model resolution does not allow for modelling of individual glaciers and data scarcity is an issue. Both CMIP3 and CMIP5 model simulations point towards a strong decline in glacier extent in Central Asia. However, compared to the CMIP3 projections, the CMIP5 projections of future glacier extent in Central Asia provide a wider range of outcomes, mostly owing to greater variability in precipitation projections among the latest suite of climate models. These findings have great impact on projections of the timing and quantity of water availability in glacier melt dominated rivers in the region. Uncertainty about the size of the decline in glacier extent remains large, making estimates of future Central Asian glacier extent and downstream water availability uncertain.

Description: In the current work we present six hindcast Weather Research and Forecasting (WRF) simulations for the EURO-CORDEX domain with different configurations in microphysics, convection and radiation for the time period 1990–2008. All regional model simulations are forced by the ERA-Interim reanalysis and have the same spatial resolution (0.44°). These simulations are evaluated for surface temperature, precipitation, short- and longwave downward radiation at the surface and total cloud cover. The analysis of the WRF ensemble indicates systematic biases in both temperature and precipitation linked to different physical mechanisms for the summer and winter season. Overestimation of total cloud cover and underestimation of downward shortwave radiation at the surface, mostly when using Grell–Devenyi convection and the CAM radiation scheme, intensifies the negative summer temperature bias in northern Europe (max −2.5 °C). Conversely, a strong positive downward shortwave summer bias in central (40–60%) and southern Europe mitigates the systematic cold bias in WRF over these regions, signifying a typical case of error compensation. Maximum winter cold bias is over north-eastern Europe (−2.8 °C); this location is indicative of land–atmosphere rather than cloud-radiation interactions. Precipitation is systematically overestimated in summer by all model configurations, especially the higher quantiles, which are associated with summertime deep cumulus convection. The Kain–Fritsch convection scheme produces the larger summertime precipitation biases over the Mediterranean. Winter precipitation is reproduced with lower biases by all model configurations (15–30%). The results of this study indicate the importance of evaluating not only the basic climatic parameters of interest for climate change applications (temperature-precipitation), but also other components of the energy and water cycle, in order to identify the sources of systematic biases, possible compensatory or masking mechanisms and suggest methodologies for model improvement.

Description: EURO-CORDEX is an international climate downscaling initiative that aims to provide high-resolution climate scenarios for Europe. Here an evaluation of the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble is presented. The study documents the performance of the individual models in representing the basic spatio-temporal patterns of the European climate for the period 1989–2008. Model evaluation focuses on near-surface air temperature and precipitation, and uses the E-OBS dataset as observational reference. The ensemble consists of 17 simulations carried out by seven different models at grid resolutions of 12 km (nine experiments) and 50 km (eight experiments). Several performance metrics computed from monthly and seasonal mean values are used to assess model performance over eight sub-domains of the European continent. Results are compared to those for the ERA40-driven ENSEMBLES simulations. The analysis confirms the ability of RCMs to capture the basic features of the European climate, including its variability in space and time. But it also identifies non-negligible deficiencies of the simulations for selected metrics, regions and seasons. Seasonally and regionally averaged temperature biases are mostly smaller than 1.5 °C, while precipitation biases are typically located in the ±40% range. Some bias characteristics, such as a predominant cold and wet bias in most seasons and over most parts of Europe and a warm and dry summer bias over southern and south-eastern Europe reflect common model biases. For seasonal mean quantities averaged over large European sub-domains, no clear benefit of an increased spatial resolution (12 km vs. 50 km) can be identified. The bias ranges of the EURO-CORDEX ensemble mostly correspond to those of the ENSEMBLES simulations, but some improvements in model performance can be identified (e.g., a less pronounced southern European warm summer bias). The temperature bias spread across different configurations of one individual model can be of a similar magnitude as the spread across different models, demonstrating a strong influence of the specific choices in physical parameterizations and experimental setup on model performance. Based on a number of simply reproducible metrics, the present study quantifies the currently achievable accuracy of RCMs used for regional climate simulations over Europe and provides a quality standard for future model developments.

Description: EURO-CORDEX is an international climate downscaling initiative that aims to provide high-resolution climate scenarios for Europe. Here an evaluation of the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble is presented. The study documents the performance of the individual models in representing the basic spatiotemporal patterns of the European climate for the period 1989–2008. Model evaluation focuses on near-surface air temperature and precipitation, and uses the E-OBS data set as observational reference. The ensemble consists of 17 simulations carried out by seven different models at grid resolutions of 12 km (nine experiments) and 50 km (eight experiments). Several performance metrics computed from monthly and seasonal mean values are used to assess model performance over eight subdomains of the European continent. Results are compared to those for the ERA40-driven ENSEMBLES simulations. The analysis confirms the ability of RCMs to capture the basic features of the European climate, including its variability in space and time. But it also identifies nonnegligible deficiencies of the simulations for selected metrics, regions and seasons. Seasonally and regionally averaged temperature biases are mostly smaller than 1.5 °C, while precipitation biases are typically located in the ±40% range. Some bias characteristics, such as a predominant cold and wet bias in most seasons and over most parts of Europe and a warm and dry summer bias over southern and southeastern Europe reflect common model biases. For seasonal mean quantities averaged over large European subdomains, no clear benefit of an increased spatial resolution (12 vs. 50 km) can be identified. The bias ranges of the EURO-CORDEX ensemble mostly correspond to those of the ENSEMBLES simulations, but some improvements in model performance can be identified (e.g., a less pronounced southern European warm summer bias). The temperature bias spread across different configurations of one individual model can be of a similar magnitude as the spread across different models, demonstrating a strong influence of the specific choices in physical parameterizations and experimental setup on model performance. Based on a number of simply reproducible metrics, the present study quantifies the currently achievable accuracy of RCMs used for regional climate simulations over Europe and provides a quality standard for future model developments.