The rapid decline of Arctic sea ice, including sea ice area (SIA) retreat and sea ice thinning, is a striking manifestation of global climate change. Analysis of 40 CMIP6 models reveals a very large spread in both model simulations of the September SIA and thickness and the timing of a summer ice-free Arctic Ocean. The existing SIA-based evaluation metrics are deficient due to observational uncertainty, prominent internal variability, and indirect Arctic response to global forcing. Given the critical roles of sea ice thickness (SIT) in determining Arctic ice variation throughout the seasonal cycle and the April SIT bridging the winter freezing and summer melting processes, we propose two SIT-based metrics, the April mean SIT and summer SIA response to April SIT, to assess climate models’ capability to reproduce the historical change of the Arctic sea ice area. The selected 11 good models reduce the uncertainty in the projected first ice-free Arctic by 70% relative to 11 poor models. The chosen models’ ensemble mean projects the first ice-free year in 2049 (2043) under the shared socio-economic pathways (SSP)2-4.5 (SSP5-8.5) scenario with one standard deviation of the inter-model spread of 12.0 (8.9) years.
Arctic sea ice loss in response to a warming climate is assessed in 42 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Sea ice observations show a significant acceleration in the rate of decline commencing near the turn of the twenty-first century. It is our assertion that state-of-the-art climate models should qualitatively reflect this accelerated trend within the limitations of internal variability and observational uncertainty. Our analysis shows that individual CMIP6 simulations of sea ice depict a wide range of model spread on biases and anomaly trends both across models and among their ensemble members. While the CMIP6 multimodel mean captures the observed sea ice area (SIA) decline relatively well, an individual model’s ability to represent the acceleration in sea ice decline remains a challenge. Seventeen (40%) out of 42 CMIP6 models and 37 (13%) out of the total 286 ensemble members reasonably capture the observed trends and acceleration in SIA decline. In addition, a larger ensemble size appears to increase the odds for a model to include at least one ensemble member skillfully representing the accelerated SIA trends. Simulations of sea ice volume (SIV) show much larger spread and uncertainty than SIA; however, due to limited availability of sea ice thickness data, these are not as well constrained by observations. Finally, we find that models with more ocean heat transport simulate larger sea ice declines, which suggests an emergent constraint in CMIP6 ensembles. This relationship points to the need for better understanding and modeling of ice–ocean interactions, especially with respect to frazil ice growth.more » « less
- NSF-PAR ID:
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Climate
- Page Range / eLocation ID:
- p. 6069-6089
- Medium: X
- Sponsoring Org:
- National Science Foundation
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