Towards a better surface radiation budget analysis over sea ice in the high Arctic Ocean: a comparative study between satellite, reanalysis, and local‒scale observations

International audience Reanalysis datasets from atmospheric models and satellite products are often used for Arctic surface shortwave (SW) and longwave (LW) radiative budget analyses, but they suffer from limitations and require validation against local‒scale observations. These are rare in the high...

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Bibliographic Details
Published in:Journal of Geophysical Research: Atmospheres
Main Authors: Di Biagio, Claudia, Pelon, Jacques, Blanchard, Y., Loyer, Lilian, Hudson, Stephen R., Walden, V., P., Raut, Jean-Christophe, Kato, S., Mariage, Vincent, Granskog, Mats, A.
Other Authors: Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA (UMR_7583)), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), TROPO - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre ESCER, Université du Québec à Montréal = University of Québec in Montréal (UQAM), Norwegian Polar Institute, Department of Civil and Environmental Engineering Pullman (CEE), Washington State University (WSU), NASA Langley Research Center Hampton (LaRC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), ANR-10-EQPX-0013,PLANAQUA,PLAteforme expérimentale NAtionale d'écologie aQUAtique(2010), ANR-10-EQPX-0032,IAOOS,Système d'observation de la glace, de l'atmopshère et de l'océan en Arctique(2010)
Format: Article in Journal/Newspaper
Language:English
Published: HAL CCSD 2021
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Online Access:https://insu.hal.science/insu-03040609
https://insu.hal.science/insu-03040609v1/document
https://insu.hal.science/insu-03040609v1/file/dibiagio_2020.pdf
https://doi.org/10.1029/2020JD032555
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Summary:International audience Reanalysis datasets from atmospheric models and satellite products are often used for Arctic surface shortwave (SW) and longwave (LW) radiative budget analyses, but they suffer from limitations and require validation against local‒scale observations. These are rare in the high Arctic, especially for longer periods that include seasonal transitions. In this study, radiation and meteorological observations acquired during the Norwegian Young Sea Ice Cruise (N‒ICE2015) campaign over sea ice north of Svalbard (80‒83°N, 5‒25°E) from January to June 2015, cloud lidar observations from the Ice‒Atmosphere‒Ocean Observing System (IAOOS) and the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP) are compared to daily and monthly satellite retrievals from the Clouds and the Earth's Radiant Energy System (CERES) and ERA‒Interim and ERA5 reanalyses. Results indicate that surface temperature is a significant driver for winter LW radiation biases in both satellite and reanalysis data, along with cloud optical depth in CERES. In May the SW and LW downwelling irradiances are close to observations and cloud properties are well captured (except for ERA‐Interim), while SW upward irradiances are biased low due to surface albedo biases in all datasets. Net SW and LW radiation biases are comparable (⁓20‒30 Wm‒2) but opposite in sign for ERA‒Interim and CERES in May, which allows for error compensation. Biases reduce to ±10 Wm‒2 in ERA5. In June downward LW remains biased low (8‒10 Wm‒2) in all datasets suggesting unsettled cloud representation issues. Surface albedo always differs by more than 0.1 between datasets, leading to significant SW and total flux differences.