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Ying Zhang, Burkhardt Rockel, Rolf Stuhlmann, Rainer Hollmann, and Ute Karstens

Abstract

The grid-scale cloud properties of the Regional-Scale Model (REMO) are compared with the cloud retrievals derived from the International Satellite Cloud Climatology Project (ISCCP) “DX” datasets for the Baltic Sea Experiment region in March of 1994. For the uppermost cloud layer, the layer seen by a satellite instrument, the original REMO cloud scheme gives an ice cloud cover amount of about 90%, and the water cloud cover is close to zero. As compared with ISCCP, this result is an overestimation of ice clouds by up to 60% and underestimation of water clouds by up to 40%. To improve the REMO cloud results, a sensitivity study is carried out that shows that ice heterogeneous nucleation is the most influential process in the REMO cloud scheme. Cloud properties derived from the new REMO cloud scheme show that the amount of grid-scale ice clouds has decreased by about 40% and that of grid-scale water clouds has increased by about 20% in comparison with the old version. When compared with the ISCCP cloud retrievals, the new cloud scheme shows a general improvement, but the amount of ice clouds is still up to 20% higher, and that of water clouds can still be underestimated by about 20%. The total cloud cover for the month of March is reduced to 80%, the same amount as derived from the ISCCP retrievals. In addition, REMO cloud-top heights as compared with ISCCP retrieval and the REMO precipitation as compared with ground observations have also improved in the new version of REMO.

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Frauke Feser, Burkhardt Rockel, Hans von Storch, Jörg Winterfeldt, and Matthias Zahn

An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties).

However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales.

Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.

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Markku Rummukainen, Burkhardt Rockel, Lars Bärring, Jens Hesselbjerg Christensen, and Marcus Reckermann
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Bart van den Hurk, Martin Hirschi, Christoph Schär, Geert Lenderink, Erik van Meijgaard, Aad van Ulden, Burkhardt Rockel, Stefan Hagemann, Phil Graham, Erik Kjellström, and Richard Jones

Abstract

Simulations with seven regional climate models driven by a common control climate simulation of a GCM carried out for Europe in the context of the (European Union) EU-funded Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects (PRUDENCE) project were analyzed with respect to land surface hydrology in the Rhine basin. In particular, the annual cycle of the terrestrial water storage was compared to analyses based on the 40-yr ECMWF Re-Analysis (ERA-40) atmospheric convergence and observed Rhine discharge data. In addition, an analysis was made of the partitioning of convergence anomalies over anomalies in runoff and storage. This analysis revealed that most models underestimate the size of the water storage and consequently overestimated the response of runoff to anomalies in net convergence. The partitioning of these anomalies over runoff and storage was indicative for the response of the simulated runoff to a projected climate change consistent with the greenhouse gas A2 Synthesis Report on Emission Scenarios (SRES). In particular, the annual cycle of runoff is affected largely by the terrestrial storage reservoir. Larger storage capacity leads to smaller changes in both wintertime and summertime monthly mean runoff. The sustained summertime evaporation resulting from larger storage reservoirs may have a noticeable impact on the summertime surface temperature projections.

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Jonathan Spinoni, Paulo Barbosa, Edoardo Bucchignani, John Cassano, Tereza Cavazos, Jens H. Christensen, Ole B. Christensen, Erika Coppola, Jason Evans, Beate Geyer, Filippo Giorgi, Panos Hadjinicolaou, Daniela Jacob, Jack Katzfey, Torben Koenigk, René Laprise, Christopher J. Lennard, M. Levent Kurnaz, Delei Li, Marta Llopart, Niall McCormick, Gustavo Naumann, Grigory Nikulin, Tugba Ozturk, Hans-Juergen Panitz, Rosmeri Porfirio da Rocha, Burkhardt Rockel, Silvina A. Solman, Jozef Syktus, Fredolin Tangang, Claas Teichmann, Robert Vautard, Jürgen V. Vogt, Katja Winger, George Zittis, and Alessandro Dosio

Abstract

Two questions motivated this study: 1) Will meteorological droughts become more frequent and severe during the twenty-first century? 2) Given the projected global temperature rise, to what extent does the inclusion of temperature (in addition to precipitation) in drought indicators play a role in future meteorological droughts? To answer, we analyzed the changes in drought frequency, severity, and historically undocumented extreme droughts over 1981–2100, using the standardized precipitation index (SPI; including precipitation only) and standardized precipitation-evapotranspiration index (SPEI; indirectly including temperature), and under two representative concentration pathways (RCP4.5 and RCP8.5). As input data, we employed 103 high-resolution (0.44°) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX), based on a combination of 16 global circulation models (GCMs) and 20 regional circulation models (RCMs). This is the first study on global drought projections including RCMs based on such a large ensemble of RCMs. Based on precipitation only, ~15% of the global land is likely to experience more frequent and severe droughts during 2071–2100 versus 1981–2010 for both scenarios. This increase is larger (~47% under RCP4.5, ~49% under RCP8.5) when precipitation and temperature are used. Both SPI and SPEI project more frequent and severe droughts, especially under RCP8.5, over southern South America, the Mediterranean region, southern Africa, southeastern China, Japan, and southern Australia. A decrease in drought is projected for high latitudes in Northern Hemisphere and Southeast Asia. If temperature is included, drought characteristics are projected to increase over North America, Amazonia, central Europe and Asia, the Horn of Africa, India, and central Australia; if only precipitation is considered, they are found to decrease over those areas.

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