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Larger variability, better predictability?

Authors

  • Bo Sun,

    Corresponding author
    1. Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
    2. College of Earth Science, Graduate University of Chinese Academy of Sciences, Beijing 100049, China
    3. Climate Change Research Center, Chinese Academy of Sciences, Beijing 100029, China
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  • Huijun Wang

    1. Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
    2. Climate Change Research Center, Chinese Academy of Sciences, Beijing 100029, China
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Bo Sun, Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China. E-mail: sunb@mail.iap.ac.cn

Abstract

The statistical relationship between observed interannual variability and the corresponding predictability of June–July–August mean precipitation is computed and discussed, using hindcasts from the Development of a European Multimodel Ensemble System for Seasonal to Interanuual Prediction (DEMETER) project and reanalysis datasets of the Global Precipitation Climatology Project and Climatic Research Unit. It is found that there exists a globally positive correlation between the variability and predictability, being prominent in the low latitudes and less evident in the high latitudes. Although this correlation varies with modulated verification datasets, multi-model ensembles and temporal periods, all of which are artificial factors, a significant positive correlation is found over East Asia, Australia, South America, Europe and Africa, while a negative correlation for North America, under most circumstances. Moreover, it is also found that different dynamical processes in the climate system seem to have made different contributions to building this ‘larger variability–better predictability’ relationship, possibly through enhancing the predictability over domains where there is a larger contribution to variability by the climate signals, while making an uncertain contribution to the predictability over domains that are less influenced by the signals.

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