Abstract
Abstract. The Sea Ice Evaluation Tool (SITool) described in this paper is a performance metrics and diagnostics tool developed to evaluate the skill of Arctic and Antarctic model reconstructions of sea ice concentration, extent, edge location, drift, thickness, and snow depth. It is a Python-based software and consists of well-documented functions used to derive various sea ice metrics and diagnostics. Here, SITool version 1.0 (v1.0) is introduced and documented, and is then used to evaluate the performance of global sea ice reconstructions from nine models that provided sea ice output under the experimental protocols of the Coupled Model Intercomparison Project phase 6 (CMIP6) Ocean Model Intercomparison Project with two different atmospheric forcing datasets: the Coordinated Ocean-ice Reference Experiments version 2 (CORE-II) and the updated Japanese 55-year atmospheric reanalysis (JRA55-do). Two sets of observational references for the sea ice concentration, thickness, snow depth, and ice drift are systematically used to reflect the impact of observational uncertainty on model performance. Based on available model outputs and observational references, the ice concentration, extent, and edge location during 1980–2007, as well as the ice thickness, snow depth, and ice drift during 2003–2007 are evaluated. In general, model biases are larger than observational uncertainties, and model performance is primarily consistent compared to different observational references. By changing the atmospheric forcing from CORE-II to JRA55-do reanalysis data, the overall performance (mean state, interannual variability, and trend) of the simulated sea ice areal properties in both hemispheres, as well as the mean ice thickness simulation in the Antarctic, the mean snow depth, and ice drift simulations in both hemispheres are improved. The simulated sea ice areal properties are also improved in the model with higher spatial resolution. For the cross-metric analysis, there is no link between the performance in one variable and the performance in another. SITool is an open-access version-controlled software that can run on a wide range of CMIP6-compliant sea ice outputs. The current version of SITool (v1.0) is primarily developed to evaluate atmosphere-forced simulations and it could be eventually extended to fully coupled models.
Highlights
Most regional and global climate models include an interactive sea ice model, reflecting the reality that sea ice plays a fundamental role in the polar environment, by influencing air–ice and ice–sea exchange, atmospheric and oceanic processes, and climate change
The best performance on the mean ice thickness simulation is in IPSL-CM6A-LR/C for the Arctic, while for the Antarctic the best performance is in CMCC-CM2-HR4/J compared to the Environmental Satellite (Envisat) data and in Geophysical Fluid Dynamics Laboratory (GFDL)-OM4p5B/C compared to the ICESat data
The best performance on the mean snow depth simulation for the Arctic is in MIROC6/C compared to the Envisat data and in GFDL-CM4/C compared to the SnowModel-LG data, and for the Antarctic, the best performance is in NorESM2-LM/J (Fig. 7b)
Summary
Most regional and global climate models include an interactive sea ice model, reflecting the reality that sea ice plays a fundamental role in the polar environment, by influencing air–ice and ice–sea exchange, atmospheric and oceanic processes, and climate change. SITool version 1.0 (v1.0) is applied to evaluate the performance of Arctic and Antarctic historical sea ice simulations under the experimental protocols of the CMIP6 Ocean Model Intercomparison Project (OMIP, Griffies et al, 2016). Tsujino et al (2020) and Chassignet et al (2020) evaluated the impact of atmospheric forcing and horizontal resolution on the global ocean–sea ice model simulations based on the experimental protocols of OMIP provided by model groups participated in this intercomparison project Their studies focused on the evaluation of ocean components from sea surface height, temperature, salinity, mixed layer depth, and kinetic energy to circulation changes. This interpolation allows point-by-point comparison and avoids the systematic bias of sea ice extent under different grids, due to differences in land–sea masks
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