Arctic amplification has altered the climate patterns both regionally and globally, resulting in more frequent and more intense extreme weather events in the past few decades. The essential part of Arctic amplification is the unprecedented sea ice loss as demonstrated by satellite observations. Accurately forecasting Arctic sea ice from sub-seasonal to seasonal scales has been a major research question with fundamental challenges at play. In addition to physics-based Earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting. Looking at the potential of data-driven approaches to study sea ice variations, we propose MT-IceNet - a UNet based spatial and multi-temporal (MT) deep learning model for forecasting Arctic sea ice concentration (SIC). The model uses an encoder-decoder architecture with skip connections and processes multi-temporal input streams to regenerate spatial maps at future timesteps. Using bi-monthly and monthly satellite retrieved sea ice data from NSIDC as well as atmospheric and oceanic variables from ERA5 reanalysis product during 1979-2021, we show that our proposed model provides promising predictive performance for per-pixel SIC forecasting with up to 60% decrease in prediction error for a lead time of 6 months as compared to its state-of-the-art counterparts.

翻译：北极放大效应在近几十年改变了区域及全球气候模式，导致极端天气事件更加频繁且强度加剧。北极放大效应的核心表现是卫星观测所揭示的前所未有的海冰损失。从次季节到季节尺度准确预报北极海冰一直是面临根本性挑战的重大研究课题。除基于物理的地球系统模型外，研究人员已应用多种统计和机器学习模型进行海冰预报。为探究数据驱动方法研究海冰变化的潜力，我们提出MT-IceNet——一种基于UNet架构的空间与多时相（MT）深度学习模型，用于预报北极海冰密集度（SIC）。该模型采用带有跳跃连接的编码器-解码器架构，通过处理多时相输入流来重建未来时刻的空间分布图。利用1979-2021年间NSIDC的双月与月卫星反演海冰数据以及ERA5再分析产品中的大气和海洋变量，我们证明所提模型在逐像素SIC预报中展现出令人期待的性能：与当前最优模型相比，其6个月提前期的预测误差降低最高达60%。