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European Geosciences Union, Geoscientific Model Development, 12(13), p. 6011-6028, 2020

DOI: 10.5194/gmd-13-6011-2020

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Effects of horizontal resolution and air–sea coupling on simulated moisture source for East Asian precipitation in MetUM GA6/GC2

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Precipitation over East Asia in six Met Office Unified Model (MetUM) simulations is compared with observation and ERA-Interim reanalysis. These simulations include three different horizontal resolutions, from low and medium to high, and including atmosphere-only version (Global Atmosphere 6.0; GA6) and air–sea coupling version (Global Coupled 2.0; GC2). Precipitation in simulations is systematically different from that in observations and reanalysis. Increasing horizontal resolution and including air–sea coupling improve simulated precipitation but cannot eliminate bias. Moisture sources of East Asian precipitation are identified using the Water Accounting Model (WAM-2layers) – a moisture tracking model that traces moisture source using collective information of evaporation, atmospheric moisture and circulation. Similar to precipitation, moisture sources in simulations are systematically different from that of ERA-Interim. Major differences in moisture sources include underestimated moisture contribution from tropical Indian Ocean and overestimate contribution from Eurasian continent. By increasing horizontal resolution, precipitation bias over the Tibetan Plateau is improved. From the moisture source point of view, this is achieved by reducing contribution from remote moisture source and enhancing local contribution over its eastern part. Although including air–sea coupling does not necessarily change East Asian precipitation, moisture sources show differences between coupled and atmosphere-only simulations. These differences in moisture sources indicate different types of models biases caused by surface flux or/and atmospheric circulation on different locations. This information can be used to target model biases on specified locations and due to different mechanisms.