| Autor: |
Xiaohe Huan, Jielong Wang, Zhongfang Liu |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Environmental Research Communications, Vol 5, Iss 10, p 101003 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2515-7620 |
| DOI: |
10.1088/2515-7620/acffb2 |
| Popis: |
There is growing interest in sub-seasonal to seasonal predictions of Arctic sea ice due to its potential effects on midlatitude weather and climate extremes. Current prediction systems are largely dependent on physics-based climate models. While climate models can provide good forecasts for Arctic sea ice at different timescales, they are susceptible to initial states and high computational costs. Here we present a purely data-driven deep learning model, UNet-F/M, to predict monthly sea ice concentration (SIC) one month ahead. We train the model using monthly satellite-observed SIC for the melting and freezing seasons, respectively. Results show that UNet-F/M has a good predictive skill of Arctic SIC at monthly time scales, generally outperforming several recently proposed deep learning models, particularly for September sea-ice minimum. Our study offers a perspective on sub-seasonal prediction of future Arctic sea ice and may have implications for forecasting weather and climate in northern midlatitudes. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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