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Diagnostics of subseasonal prediction biases of the Asian summer monsoon by the NCEP climate forecast system |
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| Authors: | Xiangwen Liu Song Yang Arun Kumar Scott Weaver Xingwen Jiang |
| Institution: | 1. National Climate Center, China Meteorological Administration, Beijing, China 4. NOAA Climate Prediction Center, 5830 University Research Court, College Park, MD, 20740, USA 3. Institute of Plateau Meteorology, China Meteorological Administration, Chengdu, Sichuan, China |
| Abstract: | Biases of subseasonal prediction of the Asian summer monsoon are diagnosed using daily data from the hindcasts of 45-day integrations by the National Centers for Environmental Prediction Climate Forecast System version 2. The retrospective forecasts often show apparent systematic biases, which are mostly represented by the underestimation of the whole Asian monsoon. Biases depend not only on lead time, but also on the stage of monsoon evolution. An abrupt turning point of bias development appears around late June and early July, when ensemble spread and bias growth of winds and precipitation show a significant change over the northwestern Pacific (NWP) and the South Asian summer monsoon (SASM) region. The abrupt turning of bias development of winds, precipitation, and surface temperature is also captured by the first two modes of multivariate empirical orthogonal function analysis. Several features appear associated with the abrupt change in bias development: the western Pacific subtropical high (WPSH) begins its first northward jump and the surface temperature over the Tibetan Plateau commences a transition from warm bias to cold bias, and a reversal of surface temperature biases occurs in the eastern tropical Indian Ocean and the SASM region. The shift of WPSH position and the transition of surface thermal bias show close relationships with the formation of bias centers in winds and precipitation. The rapid growth in bias due to the strong internal atmospheric variability during short leads seems to mainly account for the weak WPSH and SASM in the model. However, at certain stages, particularly for longer-lead predictions, the biases of slowly varying components may also play an important role in bias development of winds and precipitation. |
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