Exploiting optical remote sensing of tundra snow cover in evaluating climate models ...
<!--!introduction!--> 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...
| Main Authors: | , , , , , , |
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| Format: | Conference Object |
| Language: | unknown |
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GFZ German Research Centre for Geosciences
2023
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.57757/iugg23-2278 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5018486 |
| Summary: | <!--!introduction!--> 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 ... : The 28th IUGG General Assembly (IUGG2023) (Berlin 2023) ... |
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