SIFM: A Foundation Model for Multi-granularity Arctic Sea Ice Forecasting

• URL: http://arxiv.org/abs/2410.14732v1
• Date: Wed, 16 Oct 2024 08:52:12 GMT
• Title: SIFM: A Foundation Model for Multi-granularity Arctic Sea Ice Forecasting
• Authors: Jingyi Xu, Yeqi Luo, Weidong Yang, Keyi Liu, Shengnan Wang, Ben Fei, Lei Bai,
• Abstract summary: We propose to cultivate temporal multi-granularity that naturally derived from Arctic sea ice reanalysis data. Our Sea Ice Foundation Model ( SIFM) is designed to leverage both intra-granularity and inter-granularity information. Our experiments show that SIFM outperforms off-the-shelf deep learning models for their specific temporal granularity.
• Score: 19.23074065880929
• License:
• Abstract: Arctic sea ice performs a vital role in global climate and has paramount impacts on both polar ecosystems and coastal communities. In the last few years, multiple deep learning based pan-Arctic sea ice concentration (SIC) forecasting methods have emerged and showcased superior performance over physics-based dynamical models. However, previous methods forecast SIC at a fixed temporal granularity, e.g. sub-seasonal or seasonal, thus only leveraging inter-granularity information and overlooking the plentiful inter-granularity correlations. SIC at various temporal granularities exhibits cumulative effects and are naturally consistent, with short-term fluctuations potentially impacting long-term trends and long-term trends provides effective hints for facilitating short-term forecasts in Arctic sea ice. Therefore, in this study, we propose to cultivate temporal multi-granularity that naturally derived from Arctic sea ice reanalysis data and provide a unified perspective for modeling SIC via our Sea Ice Foundation Model. SIFM is delicately designed to leverage both intra-granularity and inter-granularity information for capturing granularity-consistent representations that promote forecasting skills. Our extensive experiments show that SIFM outperforms off-the-shelf deep learning models for their specific temporal granularity.

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