Abstract
Global climate models (GCMs) consistently underestimate the response of September Arctic sea-ice area (SIA) to warming. Modeled SIA losses are highly correlated to global mean temperature increases, making it challenging to gauge if improvements in modeled sea ice derive from improved sea-ice models or from improvements in forcing driven by other GCM components. I use a set of five large GCM ensembles, and CMIP6 simulations, to quantify GCM internal variability and variability between GCMs from 1979–2014, showing modern GCMs do not plausibly estimate the response of SIA to warming in all months. I identify the marginal ice zone fraction (MIZF) as a metric that is less correlated to warming, has a response plausibly simulated from January–September (but not October–December), and has highly variable future projections across GCMs. These qualities make MIZF useful for evaluating the impact of sea-ice model changes on past, present, and projected sea-ice state.
Highlights
Global climate models (GCMs) consistently underestimate the response of September Arctic sea-ice area (SIA) to warming
The underestimation of September SIA loss has improved across CMIP generations, but the link between SIA and global mean temperature (GMT) makes it challenging to judge whether improvements in modeled sea ice originate in improvements in sea-ice model physics or are due primarily to improvements in the external forcing of sea ice[14], like changes to aerosol forcing[8,15,16,17] noted that the inaccuracurate representation of SIA trends in CMIP5 models can be explained as the result of inherent internal variability in modeled climate, which in this study will be referred to as “model internal variability”
As future projections of September and December marginal ice zone fraction (MIZF) differ radically between models, MIZF may be a useful way to determine in real-time whether current models make skillful predictions of future Arctic sea ice variability
Summary
Global climate models (GCMs) consistently underestimate the response of September Arctic sea-ice area (SIA) to warming. To assess inter-model differences, I create an “ensemble-mean-ensemble” (CMIP6-EME) from the ensemble-mean statistics of 8 climate models which submitted relatively large sets (10+ member) of historical simulations to CMIP6 (Table 1, bottom). These model ensembles are compared to three satellite observational products from which I derive observational uncertainty. I find that after accounting for model internal variability, inter-model variance, and observational uncertainty, modern GCMs are generally unable to produce plausible estimates of SIA sensitivity to warming at any point during the year. As future projections of September and December MIZF differ radically between models, MIZF may be a useful way to determine in real-time whether current models make skillful predictions of future Arctic sea ice variability
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