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Interdecadal change in the Northern Hemisphere seasonal climate prediction skill: part II. predictability and prediction skill
Authors:XiaoJing Jia  June-Yi Lee  Hai Lin  Harry Hendon  Kyung-Ja Ha
Affiliation:1. Zhejiang University, Hangzhou, China
2. State Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
3. Pusan National University, Busan, 609-735, Korea
4. Atmospheric Numerical Weather Prediction Research, Environment Canada, Dorval, QC, Canada
5. The Centre for Australian and Climate Research, Bureau of Meteorology, Melbourne, Australia
Abstract:The interdecadal change in seasonal predictability and numerical models’ seasonal forecast skill in the Northern Hemisphere are examined using both observations and the seasonal hindcast from six coupled atmosphere-ocean climate models from the 21 period of 1960–1980 (P1) to that of 1981–2001 (P2). It is shown that the one-month lead seasonal forecast skill of the six models’ multi-model ensemble is significantly increased from P1 to P2 for all four seasons. We identify four possible reasons accounting for the interdecadal change of the seasonal forecast skill. Firstly, the numerical model’s ability to simulate the mean state, the time variability and the spatial structures of the sea surface temperature and precipitation over the tropical Pacific is improved in P2 compared to P1. Secondly, an examination of the potential predictability of the atmosphere, estimated by the ratio of the total variance to the variance due to the internal dynamics of the model atmosphere, reveals that the atmospheric potential predictability is significantly increased after 1980s which is mainly due to an increased influence of El Niño-Southern Oscillation signal over the North Pacific and North American regions. Thirdly, the long-term climate trends in the atmosphere are found to contribute, to some extent, to the increased seasonal forecast skill especially over the Eurasian regions. Finally, the improved ocean observations in P2 may provide better initial conditions for the coupled models’ seasonal forecast.
Keywords:
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