GLORIA

GEOMAR Library Ocean Research Information Access

feed icon rss

Ihre E-Mail wurde erfolgreich gesendet. Bitte prüfen Sie Ihren Maileingang.

Leider ist ein Fehler beim E-Mail-Versand aufgetreten. Bitte versuchen Sie es erneut.

Vorgang fortführen?

Exportieren
  • 1
    Materialart: Online-Ressource
    Seiten: 1 online resource (305 pages)
    Ausgabe: 1st ed.
    ISBN: 9783839462331
    Serie: Re-Figuration Von Räumen Series
    Sprache: Deutsch
    Anmerkung: Description based on publisher supplied metadata and other sources
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 2
    Publikationsdatum: 2023-01-30
    Beschreibung: These data are outputs from climate simulations carried out using HadGEM3-GC3.1 described in the associated journal article. The three simulations that constitute the STANDARD ensemble described in the article are u-as371, u-as372, u-as373, and the three simulations that comprise the GREASE ensemble in the article are labelled here as u-bj941, u-bn121 and u-bn122. Outputs from the sea ice and ocean components of the model are archived here separately (labelled '_ice' and '_ocean'). These data were produced by University of Otago, New Zealand, in collaboration with the UK Met Office for a project funded by the New Zealand Deep South National Science Challenge using the Monsoon system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, a strategic partnership between the Met Office and the Natural Environment Research Council.
    Schlagwort(e): climate modeling; File format; File name; File size; HadGEM3-GC3.1; Polar; Sea ice; Uniform resource locator/link to file
    Materialart: Dataset
    Format: text/tab-separated-values, 48 data points
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 3
    Publikationsdatum: 2023-09-29
    Beschreibung: In the past decade groundbreaking new satellite observations of the Arctic sea ice cover have been made, allowing researchers to understand the state of the Arctic sea ice system in greater detail than before. The derived estimates of sea ice thickness are useful but limited in time and space. In this study the first results of a new sea ice data assimilation system are presented. Observations assimilated (in various combinations) are monthly mean sea ice thickness and monthly mean sea ice thickness distribution from CryoSat-2 and NASA daily Bootstrap sea ice concentration. This system couples the Centre for Polar Observation and Modelling's (CPOM) version of the Los Alamos Sea Ice Model (CICE) to the localised ensemble transform Kalman filter (LETKF) from the Parallel Data Assimilation Framework (PDAF) library. The impact of assimilating a sub-grid-scale sea ice thickness distribution is of particular novelty. The sub-grid-scale sea ice thickness distribution is a fundamental component of sea ice models, playing a vital role in the dynamical and thermodynamical processes, yet very little is known of its true state in the Arctic. This study finds that assimilating CryoSat-2 products for the mean thickness and the sub-grid-scale thickness distribution can have significant consequences for the modelled distribution of the ice thickness across the Arctic and particularly in regions of thick multi-year ice. The assimilation of sea ice concentration, mean sea ice thickness and sub-grid-scale sea ice thickness distribution together performed best when compared to a subset of CryoSat-2 observations held back for validation. Regional model biases are reduced: the thickness of the thickest ice in the Canadian Arctic Archipelago (CAA) is decreased, but the thickness of the ice in the central Arctic is increased. When comparing the assimilation of mean thickness with the assimilation of sub-grid-scale thickness distribution, it is found that the latter leads to a significant change in the volume of ice in each category. Estimates of the thickest ice improve significantly with the assimilation of sub-grid-scale thickness distribution alongside mean thickness.
    Repository-Name: EPIC Alfred Wegener Institut
    Materialart: Article , isiRev
    Format: application/pdf
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
Schließen ⊗
Diese Webseite nutzt Cookies und das Analyse-Tool Matomo. Weitere Informationen finden Sie hier...