Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

China’s Recent Progresses in Polar Climate Change and Its Interactions with the Global Climate System

During the recent four decades since 1980,a series of modern climate satellites were launched,allowing for the measurement and record-keeping of multiple climate parameters,especially over the polar regions where traditional observations are difficult to obtain.China has been actively engaging in polar expeditions.Many observations were conducted during this period,accompanied by improved Earth climate models,leading to a series of insightful understandings concerning Arctic and Antarctic climate changes.Here,we review the recent progress China has made concerning Arctic and Antarctic climate change research over the past decade.The Arctic temperature increase is much higher than the global-mean warming rate,associated with a rapid decline in sea ice,a phenomenon called the Arctic Amplification.The Antarctic climate changes showed a zonally asymmetric pattern over the past four decades,with most of the fastest changes occurring over West Antarctica and the Antarctic Peninsula.The Arctic and Antarctic climate changes were driven by anthropogenic greenhouse gas emissions and ozone loss,while tropical-polar teleconnections play important roles in driving the regional climate changes and extreme events over the polar regions.Polar climate changes may also feedback to the entire Earth climate system.The adjustment of the circulation in both the troposphere and the stratosphere contributed to the interactions between the polar climate changes and lower latitudes.Climate change has also driven rapid Arctic and Southern ocean acidification.Chinese researchers have made a series of advances in understanding these processes,as reviewed in this paper.