Propagation of biases in climate models from the synoptic to the regional scale: Implications for bias adjustment

Journal of Geophysical Research D: Atmospheres - Tập 121 Số 5 - Trang 2075-2089 - 2016
Nans Addor1,2, Marco Rohrer3, Reinhard Furrer4, Jan Seibert5,1
1Department of Geography, University of Zurich, Zurich, Switzerland
2Research Applications Laboratory, National Center for Atmospheric Research Boulder Colorado USA
3Oeschger Center for Climate Change Research and Institute of Geography, University of Bern, Bern, Switzerland
4Department of Mathematics, University of Zurich, Zurich, Switzerland
5Department of Earth Sciences, Uppsala University, Uppsala, Sweden

Tóm tắt

AbstractBias adjustment methods usually do not account for the origins of biases in climate models and instead perform empirical adjustments. Biases in the synoptic circulation are for instance often overlooked when postprocessing regional climate model (RCM) simulations driven by general circulation models (GCMs). Yet considering atmospheric circulation helps to establish links between the synoptic and the regional scale, and thereby provides insights into the physical processes leading to RCM biases. Here we investigate how synoptic circulation biases impact regional climate simulations and influence our ability to mitigate biases in precipitation and temperature using quantile mapping. We considered 20 GCM‐RCM combinations from the ENSEMBLES project and characterized the dominant atmospheric flow over the Alpine domain using circulation types. We report in particular a systematic overestimation of the frequency of westerly flow in winter. We show that it contributes to the generalized overestimation of winter precipitation over Switzerland, and this wet regional bias can be reduced by improving the simulation of synoptic circulation. We also demonstrate that statistical bias adjustment relying on quantile mapping is sensitive to circulation biases, which leads to residual errors in the postprocessed time series. Overall, decomposing GCM‐RCM time series using circulation types reveals connections missed by analyses relying on monthly or seasonal values. Our results underscore the necessity to better diagnose process misrepresentation in climate models to progress with bias adjustment and impact modeling.

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Journal of Geophysical Research D: Atmospheres

Tập 121 Số 5

2075-2089

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