Monthly Arctic sea ice prediction based on a data-driven deep learning model
Abstract 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...
| Published in: | Environmental Research Communications |
|---|---|
| Main Authors: | , , |
| Other Authors: | |
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
IOP Publishing
2023
|
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1088/2515-7620/acffb2 https://iopscience.iop.org/article/10.1088/2515-7620/acffb2 https://iopscience.iop.org/article/10.1088/2515-7620/acffb2/pdf |
| Summary: | Abstract 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. |
|---|