Abstract
The role of sea surface temperature (SST) in determining the predictability of monthly mean precipitation over the global land is assessed by analyzing the Atmospheric Model Intercomparison Project (AMIP)-like simulations forced by observed SST, which provides a benchmark for the impact of SST on the precipitation. The correlations of monthly mean precipitation anomalies between the ensemble mean of the AMIP simulations and observations are dominated by positive values with maxima around 0.3–0.4 in the tropical North Africa along 15° N and northeastern Brazil. The SST forcing for the precipitation variability is mainly associated with the El Niño-Southern Oscillation (ENSO) and in the tropical Indian Ocean. Statistically, positive and negative SST anomalies associated with an ENSO cycle have a comparable influence on precipitation variability over the land. In addition to the spatial variations, the precipitation responses to SST also vary with season and decade. Pattern correlations are larger in boreal winter than in boreal summer in the Northern Hemisphere, and relatively larger in April-June and September–November in the Southern Hemisphere. The global average of correlation is lower during 1957–1980 and 2000–2018, and higher in between. The interdecadal fluctuation of the pattern correlations is coherent with the interdecadal variation of the amplitude of ENSO.
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We appreciate the comments and suggestions of two reviewers as well as our colleagues Drs. Peitao Peng and Caihong Wen. The scientific results and conclusions, as well as any view or opinions expressed herein, are those of the authors and do not necessarily reflect the views of NWS, NOAA, or the Department of Commerce.
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Hu, ZZ., Kumar, A., Jha, B. et al. How much of monthly mean precipitation variability over global land is associated with SST anomalies?. Clim Dyn 54, 701–712 (2020). https://doi.org/10.1007/s00382-019-05023-5
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DOI: https://doi.org/10.1007/s00382-019-05023-5