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January 23, 2026Journal of Geophysical Research Atmospheres1 citationsOpen Access

Precipitation Biases Over the Southern Ocean in CMIP6, Reanalyses and Satellite‐Based Products

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JBJoaquin BlancoSSSteven T. SiemsLALisa V. Alexander

Key Points

  • The aim is to assess the accuracy of CMIP6 models and reanalysis data in representing precipitation over the Southern Ocean.
  • Evaluated 46 CMIP6 atmospheric simulations and 5 reanalyses using satellite-based precipitation products.
  • Analyzed daily precipitation characteristics including total amount, variance, and wet day metrics.
  • Compared snowfall frequency and intensity in models against satellite observations.
  • Investigated biases in precipitation frequency distributions across specified latitude bands.
  • Most models showed a 'too frequent, too light' precipitation bias.
  • Notable shifts and shape changes in frequency distributions were documented, absent in satellite data.
  • Models displayed significant biases in snowfall frequency and intensity, with rainfall overestimations contributing to discrepancies.

Abstract

Abstract A set of gridded, satellite‐based, precipitation products has been used to assess the performance of 46 Coupled Model Intercomparison Project (CMIP6) atmospheric‐only simulations and 5 reanalyses over the Southern Ocean (SO) on daily timescales, in terms of total precipitation and variance, frequency and intensity of wet days, and seasonal changes. Besides the expected “too frequent, too light” precipitation biases in most models and reanalyses over the region, our study reveals other undocumented features such as notorious peak shifts and shape changes in the frequency distributions from 35°–50° to 50°–65° bands, which do not occur in satellite estimates. We also evaluated snowfall over mid to high latitudes and found that models have a substantial bias in frequency and intensity of snow days (>1 mm/day). The fraction of snow to total precipitation is substantially smaller for AMIP, but due to a rainfall overestimation rather than a deficit in snowfall. The intercomparison of the 7 precipitation data sets that employ remote‐sensing observations (including GPCPv3.2 and IMERGv7) is characterized by large uncertainties, which are additionally discussed. Using previous studies based on in situ data over the SO as well as CloudSat, we can also infer biases in most satellite data sets.

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Cite This Study

Blanco et al. (2026) studied this question.

synapsesocial.com/papers/69730f78c8125b09b0d1f4edhttps://doi.org/10.1029/2025jd044145
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