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Abstract Clouds and radiation play an important role in warming events over the Southern Ocean (SO). Here we evaluate European Center for Medium‐Range Weather Forecasts Reanalysis version 5 (ERA5) and Polar Weather Research Forecast (PWRF) output through comparison to surface‐based measurements of clouds, radiation, and the atmospheric state over the SO during 2017–2023 at Escudero Station (62.2°S, 58.97°W) on King George Island. ERA5 mean monthly downward shortwave (DSW) radiative fluxes are found to be 38–50 W m−2higher than observations in summer, whereas ERA5 mean monthly downward longwave (DLW) is biased by −18 to −22 W m−2in summer and −16 W m−2on average over the year. Comparisons of temperature, humidity, and lowest‐cloud base heights between ERA5 and observations rule these factors out as large contributors to the DLW flux biases. The similarity between observed DLW cloud forcing distributions for atmospheric columns containing low‐level liquid and ice‐only clouds suggests limited influence of cloud phase errors on DLW biases. Thus the most likely explanation for DLW flux biases in ERA5 is underestimated cloud optical depth, which is also consistent with DSW flux biases. Similar biases in ERA5 are found during atmospheric river (AR) events. By contrast, PWRF flux bias magnitudes are much smaller during AR events (−12 W m−2for DSW and −2 W m−2for DLW). After bias correction, ERA5 monthly average net cloud forcing over 2017–2023 is found to be a minimum of −107 W m−2in January and a maximum of 65 W m−2in June.
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Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset
Abstract Diffuse solar radiation is an important, but understudied, component of the Earth’s surface radiation budget, with most global climate models not archiving this variable and a dearth of ground-based observations. Here, we describe the development of a global 40-year (1980–2019) monthly database of total shortwave radiation, including its diffuse and direct beam components, called BaRAD (Bias-adjusted RADiation dataset). The dataset is based on a random forest algorithm trained using Global Energy Balance Archive (GEBA) observations and applied to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) dataset at the native MERRA-2 resolution (0.5° by 0.625°). The dataset preserves seasonal, latitudinal, and long-term trends in the MERRA-2 data, but with reduced biases than MERRA-2. The mean bias error is close to 0 (root mean square error = 10.1 W m−2) for diffuse radiation and −0.2 W m−2(root mean square error = 19.2 W m−2) for the total incoming shortwave radiation at the surface. Studies on atmosphere-biosphere interactions, especially those on the diffuse radiation fertilization effect, can benefit from this dataset.
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- Award ID(s):
- 1933630
- PAR ID:
- 10383720
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Data
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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