Evaluation of the Arctic Sea-Ice Simulation on SODA3 Datasets

This study evaluates the Arctic sea-ice simulation of the SODA3 dataset driven by different atmospheric forcing fields and explores the errors of the Arctic sea-ice simulation caused by the forcing field.We find that the SODA3 data driven by different forcing fields represent a significant systematical error in the simulation of Arctic sea-ice concentration,showing a low concentration of thick ice and a high concentration of thin ice.In terms of sea-ice extent,the SODA3 data from different versions well characterize the interannual variability and declining trend in the observed data,but they overestimate the overall Arctic sea-ice extent,which is related to excessive simulation of ice in the sea-ice margin.Compared to observations,all the chosen SODA3 reanalysis versions driven by different atmospheric forcing generally tend to underestimate the Arctic sea-ice thickness,especially for thick ice in the multi-year sea-ice regions.Inaccurate simulations of Arctic sea-ice transport may partly explain the error in SODA3 sea-ice thickness in multi-year sea-ice areas.The results of different SDOA3 versions differ greatly in the Beaufort Sea,the Fram Strait,and the Central Arctic Sea.The difference in sea-ice thickness among different SODA3 versions is primarily due to the thermodynamic contribution,which may come from the diversity of atmospheric forcing fields.Our work provides a reference for using SODA3 data to study Arctic sea ice.

Evaluation of Arctic Sea-ice Cover and Thickness Simulated by MITgcm

A regional Arctic Ocean configuration of the Massachusetts Institute of Technology General Circulation Model(MITgcm)is applied to simulate the Arctic sea ice from 1991 to 2012.The simulations are evaluated by comparing them with observations from different sources.The results show that MITgcm can reproduce the interannual and seasonal variability of the sea-ice extent,but underestimates the trend in sea-ice extent,especially in September.The ice concentration and thickness distributions are comparable to those from the observations,with most deviations within the observational uncertainties and less than 0.5 m,respectively.The simulated sea-ice extents are better correlated with observations in September,with a correlation coefficient of 0.95,than in March,with a correlation coefficient of 0.83.However,the distributions of sea-ice concentration are better simulated in March,with higher pattern correlation coefficients(0.98)than in September.When the model underestimates the atmospheric influence on the sea-ice evolution in March,deviations in the sea-ice concentration arise at the ice edges and are higher than those in September.In contrast,when the model underestimates the oceanic boundaries’influence on the September sea-ice evolution,disagreements in the distribution of the sea-ice concentration and its trend are found over most marginal seas in the Arctic Ocean.The uncertainties of the model,whereby it fails to incorporate the atmospheric information in March and oceanic information in September,contribute to varying model errors with the seasons.