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Intraseasonal summer rainfall variability over China in the MetUM GA6 and GC2 configurations

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Stephan,  Claudia C.
Global Circulation and Climate, The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;

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gmd-11-3215-2018.pdf
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Citation

Stephan, C. C., Klingaman, N. P., Vidale, P. L., Turner, A. G., Demory, M.-E., & Guo, L. (2018). Intraseasonal summer rainfall variability over China in the MetUM GA6 and GC2 configurations. Geoscientific Model Development, 11, 3215-3233. doi:10.5194/gmd-11-3215-2018.


Cite as: http://hdl.handle.net/21.11116/0000-0002-00B8-E
Abstract
The simulation of intraseasonal precipitation variability over China in extended summer (May-October) is evaluated based on six climate simulations of the Met Office Unified Model. Two simulations use the Global Atmosphere 6.0 (GA6) and four the Global Coupled 2.0 (GC2) configuration. Model biases are large such that mean precipitation and intraseasonal variability reach twice their observed values, particularly in southern China. To test the impact of air-sea coupling and horizontal resolution, GA6 and GC2 at horizontal resolutions corresponding to similar to 25, 60, and 135 km at 50 degrees N are analyzed. Increasing the horizontal resolution and adding air-sea coupling have little effect on these biases. Pre-monsoon rainfall in the Yangtze River basin is too strong in all simulations. Simulated rainfall amounts in June are too high along the southern coast and persist in the coastal region through July, with only a weak northward progression. The observed northward propagation of the Meiyu-Baiu-Changma rainband from spring to late summer is poor in all GA6 and GC2 simulations. To assess how well the MetUM simulates spatial patterns of temporally coherent precipitation, empirical orthogonal teleconnection (EOT) analysis is applied to pentad-mean precipitation. Patterns are connected to large-scale processes by regressing atmospheric fields onto the EOT pentad time series. Most observed patterns of intraseasonal rainfall variability are found in all simulations, including the associated observed mechanisms. This suggests that GA6 and GC2 may provide useful predictions of summer intraseasonal variability despite their substantial biases in mean precipitation and overall intraseasonal variance.