Revisiting the performance of CMIP6 models in the Arctic: Concerns on benchmarking climate models

Climate models are known to have difficulty in simulating the present day climate in the Arctic. Many studies, including the most recent Inter-governmental Panel on Climate Change Sixth Assessment Report (IPCC AR6), report that, comparing to reanalysis datasets such as ERA5 as reference, climate models participating the past phases of the Coupled Model Intercomparison Project (CMIP) simulate a too cold Arctic. However, recent studies reveal substantial warm biases over sea ice surface in global atmospheric reanalyses due to missing representation of physical processes such as the snow layer on top of the sea-ice.

In this work we revisit the so-called long-standing climate model bias in the Arctic by using a new, satellite-derived near surface air temperature (T2m) dataset for the Arctic sea ice region as an alternative reference to the commonly used reanalysis data ERA5. This observational T2m dataset is derived from the satellite based on DMI/CMEMS daily gap-free (so called L4) sea surface temperature and sea ice surface temperature climate data record, spanning from 1^st January 1982 to 31^st May 2021, covering the Arctic region (> 58 ◦N). We show that, in comparison with the new observational dataset, the ERA5 reanalysis exhibits widespread warm biases exceeding 2℃ over sea ice in the central Arctic, particularly during winter when the warm bias may be as large as 6-10℃. In contrast, the CMIP6 model ensemble demonstrates reasonable performance, with an annual mean bias less than ±1℃ in the same region. We also find that the CMIP6 model mean slightly outperforms the ERA5 in capturing the observed warming trend over the central Arctic region where is fully covered by sea ice with concentration of more than 70%. Outside of this region, it is evident that ERA5 aligns well with observations, while CMIP6 models show large cold bias in the North Atlantic marginal ice zone, consistent with the well-documented results in the past.

Our results challenge the current assessment of climate models in the central Arctic, suggesting that relying on existing reanalyses datasets as a reference may underestimate the climate models creditability in the region. It is thus imperative to integrate new observational data for benchmarking climate models in the Arctic region.