Using Arctic ice mass balance buoys for evaluation of modelled ice energy fluxes
A new method of sea ice model evaluation is demonstrated. Data from the network of Arctic ice mass balance buoys (IMBs) are used to estimate distributions of vertical energy fluxes over sea ice in two densely sampled regions – the North Pole and Beaufort Sea. The resulting dataset captures seasonal...
| Published in: | Geoscientific Model Development |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2020
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/gmd-13-4845-2020 https://doaj.org/article/83d03ba3d5354b32a74870e566e98240 |
| Summary: | A new method of sea ice model evaluation is demonstrated. Data from the network of Arctic ice mass balance buoys (IMBs) are used to estimate distributions of vertical energy fluxes over sea ice in two densely sampled regions – the North Pole and Beaufort Sea. The resulting dataset captures seasonal variability in sea ice energy fluxes well, and it captures spatial variability to a lesser extent. The dataset is used to evaluate a coupled climate model, HadGEM2-ES (Hadley Centre Global Environment Model, version 2, Earth System), in the two regions. The evaluation shows HadGEM2-ES to simulate too much top melting in summer and too much basal conduction in winter. These results are consistent with a previous study of sea ice state and surface radiation in this model, increasing confidence in the IMB-based evaluation. In addition, the IMB-based evaluation suggests an additional important cause for excessive winter ice growth in HadGEM2-ES, a lack of sea ice heat capacity, which was not detectable in the earlier study. Uncertainty in the IMB fluxes caused by imperfect knowledge of ice salinity, snow density and other physical constants is quantified (as is inaccuracy due to imperfect sampling of ice thickness) and in most cases is found to be small relative to the model biases discussed. Hence the IMB-based evaluation is shown to be a valuable tool with which to analyse sea ice models and, by extension, better understand the large spread in coupled model simulations of the present-day ice state. Reducing this spread is a key task both in understanding the current rapid decline in Arctic sea ice and in constraining projections of future Arctic sea ice change. |
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