Impact of observation-based snow albedo parameterization on global ocean simulation results
Albedo parameterization is of fundamental importance for accurate representation of high-latitude climate variability by modeling studies. Field observations show that near-infrared snow albedo decreases dramatically when surface air temperature exceeds −2 °C. This can influence reproduction of sea...
| Published in: | Polar Science |
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| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://nipr.repo.nii.ac.jp/?action=repository_uri&item_id=16018 http://id.nii.ac.jp/1291/00015906/ |
| Summary: | Albedo parameterization is of fundamental importance for accurate representation of high-latitude climate variability by modeling studies. Field observations show that near-infrared snow albedo decreases dramatically when surface air temperature exceeds −2 °C. This can influence reproduction of sea ice simulations taking into consideration the importance of the drastic change in albedo in early melt season for the seasonal change of sea ice extent. Therefore, we conducted global ocean data-assimilative simulation experiments using a modified snow albedo parameterization. The modified parameterization reduced the albedo directly and achieved a comparable indirect reduction via changes in the modeled snow and sea ice distributions (ice–albedo feedback). As a result, sea ice thickness was reduced by more than 0.4–1 cm over most of the central Arctic Ocean. Sea ice velocities were also reduced by enhanced ocean drag with weakened surface ocean circulation in the Beaufort Gyre. In the Southern Ocean, the modified parameterization caused snow thicknesses to be decreased by up to 2 cm in the Weddell Sea. These impacts, which were generally larger than the spread of ensemble experiment results and therefore robust, at least in our model, provide useful information for quantifying the results of albedo modification in climate modeling studies. |
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