A Spatial Evaluation of Arctic Sea Ice and Regional Limitations in CMIP6 Historical Simulations

17 USC 105 interim-entered record; under review.The article of record as published may be found at http://dx.doi.org/10.1175/JCLI-D-20-0491.1The Arctic sea ice response to a warming climate is assessed in a subset of models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6), using several metrics in comparison with satellite observations and results from the Pan-Arctic Ice Ocean Modeling and Assimilation System and the Regional Arctic System Model. Our study examines the historical representation of sea ice extent, volume, and thickness using spatial analysis metrics, such as the integrated ice edge error, Brier score, and spatial probability score. We find that the CMIP6 multimodel mean captures the mean annual cycle and 1979–2014 sea ice trends remarkably well. However, individual models experience a wide range of uncertainty in the spatial distribution of sea ice when compared against satellite measurements and reanalysis data. Our metrics expose common and individual regional model biases, which sea ice temporal analyses alone do not capture. We identify large ice edge and ice thickness errors in Arctic subregions, implying possible model specific limitations in or lack of representation of some key physical processes. We postulate that many of them could be related to the oceanic forcing, especially in the marginal and shelf seas, where seasonal sea ice changes are not adequately simulated. We therefore conclude that an individual model’s ability to represent the observed/reanalysis spatial distribution still remains a challenge. We propose the spatial analysis metrics as useful tools to diagnose model limitations, narrow down possible processes affecting them, and guide future model improvements critical to the representation and projections of Arctic climate change.U.S. NavyDepartment of Energy (DOE)Regional and Global Model Analysis (RGMA)Office of Naval Research (ONR)Arctic and Global Prediction (AGP)National Science Foundation (NSF)Arctic System Science (ARCSS)Ministry of Science and Higher Education in PolandDOE: 89243019SSC0036DESC0014117ONR: N0001418WX00364NSF: IAA1417888IAA160360