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Conference Paper

Exploiting optical remote sensing of tundra snow cover in evaluating climate models

Authors

Essery,  Richard
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Derksen,  Chris
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Marsh,  Philip
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Nagler,  Thomas
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Schwaizer,  Gabriele
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Tutton,  Rosamond
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Walker,  Branden
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Essery, R., Derksen, C., Marsh, P., Nagler, T., Schwaizer, G., Tutton, R., Walker, B. (2023): Exploiting optical remote sensing of tundra snow cover in evaluating climate models, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-2278


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5018486
Abstract
Seasonal snow cover extent is an essential climate variable that is, in principle, easy to measure with optical remote sensing because of the high contrast in albedo between snow and snow-free ground. In practice, however, the amount of information is severely limited by cloud cover globally and seasonal darkness at high latitudes. The European Space Agency snow cci project has now generated long-term global, daily snow cover products from AVHRR (1982-2018 at 5 km resolution) and MODIS (2000-2020 at 1 km resolution). To investigate the information content of these products and the level of uncertainty in gap filling with data assimilation for Arctic tundra snow, we take advantage of newly available in situ 1991-2022 meteorological time series for Trail Valley Creek, Northwest Territories, Canada. As a contribution to the ESM-SnowMIP project, we evaluate the ability of snow cover observations with and without gap filling to discriminate between climate model simulations submitted for CMIP6.