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Seasonal predictability of summer rainfall over South America



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  • Journal Title:
    Journal of Climate
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    The seasonal predictability of austral summer rainfall is evaluated in a set of retrospective forecasts (hindcasts) performed as part of the Minerva and Metis projects. Both projects use the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) coupled to the Nucleus for European Modelling of the Ocean (NEMO). The Minerva runs consist of three sets of hindcasts where the spatial resolution of the model’s atmospheric component is progressively increased while keeping the spatial resolution of its oceanic component constant. In the Metis runs, the spatial resolution of both the atmospheric and oceanic components are progressively increased. We find that raw model predictions show seasonal forecast skill for rainfall over northern and southeastern South America. However, predictability is difficult to detect on a local basis, but it can be detected on a large-scale pattern basis. In addition, increasing horizontal resolution does not lead to improvements in the forecast skill of rainfall over South America. A predictable component analysis shows that only the first predictable component of austral summer precipitation has forecast skill, and the source of forecast skill is El Niño–Southern Oscillation. Seasonal prediction of precipitation remains a challenge for state-of-the-art climate models. Positive benefits of increasing model resolution might be more evident in other atmospheric fields (i.e., temperature or geopotential height) and/or temporal scales (i.e., subseasonal temporal scales).
  • Source:
    J. Clim. (2018) 31(20): 8181–8195
  • DOI:
  • Document Type:
  • Funding:
  • Place as Subject:
  • Rights Information:
    Other
  • Compliance:
    Submitted
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  • File Type:
    Filetype[PDF - 5.20 MB]
  • Collection(s):
  • Main Document Checksum:
    urn:sha256:dd00dfdeda29ec3132db17b259591feabd8d930f70a0403023609061f1149157
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