Download
tc-18-1185-2024.pdf 7,19MB
WeightNameValue
1000 Titel
  • Observations and modeling of areal surface albedo and surface types in the Arctic
1000 Autor/in
  1. Jäkel, Evelyn |
  2. Becker, Sebastian |
  3. Sperzel, Tim R. |
  4. Niehaus, Hannah |
  5. Spreen, Gunnar |
  6. Tao, Ran |
  7. Nicolaus, Marcel |
  8. Dorn, Wolfgang |
  9. Rinke, Annette |
  10. Brauchle, Jörg |
  11. Wendisch, Manfred |
1000 Verlag
  • Copernicus Publications
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-03-12
1000 Erschienen in
1000 Quellenangabe
  • 18(3):1185-1205
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.5194/tc-18-1185-2024 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:p>Abstract. An accurate representation of the annual evolution of surface albedo of the Arctic Ocean, especially during the melting period, is crucial to obtain reliable climate model predictions in the Arctic. Therefore, the output of the surface albedo scheme of a coupled regional climate model (HIRHAM–NAOSIM) was evaluated against airborne and ground-based measurements. The observations were conducted during five aircraft campaigns in the European Arctic at different times of the year between 2017 and 2022; one of them was part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2020. We applied two approaches for the evaluation: (a) relying on measured input parameters of surface type fraction and surface skin temperature (offline) and (b) using HIRHAM–NAOSIM simulations independently of observational data (online). From the offline method we found a seasonally dependent bias between measured and modeled surface albedo. In spring, the cloud effect on surface broadband albedo was overestimated by the surface albedo parametrization (mean albedo bias of 0.06), while the surface albedo scheme for cloudless cases reproduced the measured surface albedo distributions for all seasons. The online evaluation revealed an overestimation of the modeled surface albedo resulting from an overestimation of the modeled cloud cover. Furthermore, it was shown that the surface type parametrization contributes significantly to the bias in albedo, especially in summer (after the drainage of melt ponds) and autumn (onset of refreezing). The lack of an adequate model representation of the surface scattering layer, which usually forms on bare ice in summer, contributed to the underestimation of surface albedo during that period. The difference between modeled and measured net irradiances for selected flights during the five airborne campaigns was derived to estimate the impact of the model bias for the solar radiative energy budget at the surface. We revealed a negative bias between modeled and measured net irradiances (median: −6.4 W m−2) for optically thin clouds, while the median value of only 0.1 W m−2 was determined for optically thicker clouds. </jats:p>
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/SsOka2VsLCBFdmVseW4=|https://frl.publisso.de/adhoc/uri/QmVja2VyLCBTZWJhc3RpYW4=|https://frl.publisso.de/adhoc/uri/U3BlcnplbCwgVGltwqBSLg==|https://frl.publisso.de/adhoc/uri/TmllaGF1cywgSGFubmFo|https://frl.publisso.de/adhoc/uri/U3ByZWVuLCBHdW5uYXI=|https://frl.publisso.de/adhoc/uri/VGFvLCBSYW4=|https://frl.publisso.de/adhoc/uri/Tmljb2xhdXMsIE1hcmNlbA==|https://frl.publisso.de/adhoc/uri/RG9ybiwgV29sZmdhbmc=|https://frl.publisso.de/adhoc/uri/Umlua2UsIEFubmV0dGU=|https://frl.publisso.de/adhoc/uri/QnJhdWNobGUsIErDtnJn|https://frl.publisso.de/adhoc/uri/V2VuZGlzY2gsIE1hbmZyZWQ=
1000 Hinweis
  • DeepGreen-ID: 80c662f1faff440b9af2161c4d60f668 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
1000 Label
1000 Förderer
  1. Deutsche Forschungsgemeinschaft |
  2. Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie |
  3. Horizon 2020 |
1000 Fördernummer
  1. -
  2. -
  3. -
1000 Förderprogramm
  1. -
  2. -
  3. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Deutsche Forschungsgemeinschaft |
    1000 Förderprogramm -
    1000 Fördernummer -
  2. 1000 joinedFunding-child
    1000 Förderer Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie |
    1000 Förderprogramm -
    1000 Fördernummer -
  3. 1000 joinedFunding-child
    1000 Förderer Horizon 2020 |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6480663.rdf
1000 Erstellt am 2024-05-23T16:01:04.158+0200
1000 Erstellt von 322
1000 beschreibt frl:6480663
1000 Zuletzt bearbeitet 2024-05-27T11:32:52.456+0200
1000 Objekt bearb. Mon May 27 11:32:52 CEST 2024
1000 Vgl. frl:6480663
1000 Oai Id
  1. oai:frl.publisso.de:frl:6480663 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

View source