01.04.2016: FB1-Seminar

11:00 h, Hörsaal, Düsternbrooker Weg 20

Abstract:

Over the last three decades, regional climate research has grown from an experimental stage to an operational activity, providing high resolution ensemble projections as input for climate impact modelling and to inform decision makers. State-of-the-art CORDEX simulations downscale CMIP5 global climate model projections for many regions of the globe at a horizontal resolution of 12km to 25km.

Yet along with the success of regional climate modeling, there is a growing concern about the limitations of these projections, in particular their readiness as a basis for regional-scale climate change adaptation. First, comparing different model generations (e.g., CMIP3 vs. CMIP5, ENSEMBLES vs. CORDEX), GCM ensembles with RCM ensembles (e.g., CMIP5 with CORDEX), or different types of downscaling
techniques reveals considerable contradictions at the regional scale. Where one ensemble projects no change, another ensemble might simulate a strong change.  Second, even the best operational global climate models have considerable biases in key processes, making their usefulness as input for regional climate models questionable. First, important small-scale processes are not well represented by
operational regional climate models, weakening our confidence in projecting, e.g., summertime heavy precipitation into the future.

In other words: it is not at all clear how much information current regional climate projections actually contain for a given application. Researchers are beginning to address these issues in the emerging field of climate information distillation. Whereas some consider climate information distillation to be a pure communication
issue between downscalers and users, it is in fact an interdisciplinary topic having the potential to join a wide range of disciplines from global climate modeling to decision making. Here I briefly present the state-of-the-art of regional climate modeling,
problems of regional climate change projections, and perspectives of climate information distillation.

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