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dc.contributor.authorSu, Wenying
dc.contributor.authorLiang, Lusheng
dc.contributor.authorMyhre, Gunnar
dc.contributor.authorThorsen, Tyler J.
dc.contributor.authorLoeb, Norman G.
dc.contributor.authorSchuster, Gregory L.
dc.contributor.authorGinoux, Paul
dc.contributor.authorPaulot, Fabien
dc.contributor.authorNeubauer, David
dc.contributor.authorCheca-Garcia, Ramiro
dc.contributor.authorMatsui, Hitoshi
dc.contributor.authorTsigaridis, Kostas
dc.contributor.authorSkeie, Ragnhild Bieltvedt
dc.contributor.authorTakemura, Toshihiko
dc.contributor.authorBauer, Susanne E.
dc.contributor.authorSchulz, Michael
dc.date.accessioned2022-01-27T13:29:10Z
dc.date.available2022-01-27T13:29:10Z
dc.date.created2021-10-13T20:07:33Z
dc.date.issued2021
dc.identifier.issn1942-2466
dc.identifier.urihttps://hdl.handle.net/11250/2890629
dc.description.abstractBiases in aerosol optical depths (AOD) and land surface albedos in the AeroCom models are manifested in the top-of-atmosphere (TOA) clear-sky reflected shortwave (SW) fluxes. Biases in the SW fluxes from AeroCom models are quantitatively related to biases in AOD and land surface albedo by using their radiative kernels. Over ocean, AOD contributes about 25% to the urn:x-wiley:19422466:media:jame21429:jame21429-math-0001S–urn:x-wiley:19422466:media:jame21429:jame21429-math-0002N mean SW flux bias for the multi-model mean (MMM) result. Over land, AOD and land surface albedo contribute about 40% and 30%, respectively, to the urn:x-wiley:19422466:media:jame21429:jame21429-math-0003S–urn:x-wiley:19422466:media:jame21429:jame21429-math-0004N mean SW flux bias for the MMM result. Furthermore, the spatial patterns of the SW flux biases derived from the radiative kernels are very similar to those between models and CERES observation, with the correlation coefficient of 0.6 over ocean and 0.76 over land for MMM using data of 2010. Satellite data used in this evaluation are derived independently from each other, consistencies in their bias patterns when compared with model simulations suggest that these patterns are robust. This highlights the importance of evaluating related variables in a synergistic manner to provide an unambiguous assessment of the models, as results from single parameter assessments are often confounded by measurement uncertainty. Model biases in land surface albedos can and must be corrected to accurately calculate TOA flux. We also compare the AOD trend from three models with the observation-based counterpart. These models reproduce all notable trends in AOD except the decreasing trend over eastern China and the adjacent oceanic regions due to limitations in the emission data set.en_US
dc.language.isoengen_US
dc.publisherJohn Wiley & Sons, Inc.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleUnderstanding Top-of-Atmosphere Flux Bias in the AeroCom Phase III Models: A Clear-Sky Perspectiveen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.source.volume13en_US
dc.source.journalJournal of Advances in Modeling Earth Systemsen_US
dc.source.issue9en_US
dc.identifier.doi10.1029/2021MS002584
dc.identifier.cristin1945762
dc.relation.projectNorges forskningsråd: 250573en_US
dc.relation.projectEC/H2020/820829en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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