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photo of Zheng Duan on Lund webpage

Zheng Duan

Senior lecturer

photo of Zheng Duan on Lund webpage

Improved snow depth retrieval over Arctic sea ice from FY-3 satellites using machine learning and its future scenario projection

Author

  • Chang Qing Ke
  • Haili Li
  • Xiaoyi Shen
  • Zifei Wang
  • Lijian Shi
  • Ralf Ludwig
  • Zheng Duan

Summary, in English

Snowpack atop sea ice plays a vital role in the Arctic heat and energy balance by reducing the energy transfer between the atmosphere and the ocean. This study presents an improved snow depth retrieval model over Arctic multiyear ice (MYI) from the Microwave Radiometer Imager (MWRI) data of the Fengyun-3 (FY-3) series satellites using the optimal machine learning approach identified. Validation against the Operation IceBridge (OIB) data revealed our snow depth estimates had a root mean square error (RMSE) of 7.88 cm and 6.83 cm for snow depth estimates over first-year ice (FYI) and MYI, respectively. FY-3 derived snow depth estimates from 2010 to 2014 were used to evaluate snow depth outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Based on evaluation results, seven CMIP6 models were selected for snow depth predictions, namely ACCESS-ESM1-5, AWI-CM-1-1-MR, CAMS-CSM1-0, CanESM5, FGOALS-f3-L, MRI-ESM2-0, and NorESM2-MM. Cold season snow depths from 2015 to 2100 were generated under four shared socioeconomic pathway-representative concentration pathway (SSP-RCP) scenarios (SSP126, SSP245, SSP370, and SSP585). The results indicated that short-term increases in radiation forcing have minimal impact on snow depth variability; however, as time goes by and radiative forcing accumulates, the rate of snow depth decrease accelerates. Relative to the average snow depth in the baseline period (2010–2014), the percentage change in snow depth during the late 21st century (2081–2100) under the SSP585 scenario (−63.53 %) is more than twice the percentage change observed under the SSP126 scenario (−29.63 %). These findings highlight the potential for substantial snow loss over Arctic sea ice under high greenhouse gas emission scenarios.

Department/s

  • Dept of Physical Geography and Ecosystem Science
  • BECC: Biodiversity and Ecosystem services in a Changing Climate
  • MERGE: ModElling the Regional and Global Earth system

Publishing year

2024-11

Language

English

Publication/Series

Journal of Hydrology

Volume

644

Document type

Journal article

Publisher

Elsevier

Topic

  • Meteorology and Atmospheric Sciences

Keywords

  • Arctic sea ice
  • CMIP6
  • FY-3 brightness temperature
  • Machine learning
  • Snow depth
  • Spatiotemporal variation

Status

Published

ISBN/ISSN/Other

  • ISSN: 0022-1694