The relative contribution and physical drivers of internal variability in recent Arctic sea ice loss remain open questions, leaving up for debate whether global climate models used for climate projection lack sufficient sensitivity in the Arctic to climate forcing. Here, through analysis of large ensembles of fully coupled climate model simulations with historical radiative forcing, we present an important internal mechanism arising from low-frequency Arctic atmospheric variability in models that can cause substantial summer sea ice melting in addition to that due to anthropogenic forcing. This simulated internal variability shows a strong similarity to the observed Arctic atmospheric change in the past 37 years. Through a fingerprint pattern matching method, we estimate that this internal variability contributes to about 40–50% of observed multi-decadal decline in Arctic sea ice. Our study also suggests that global climate models may not actually underestimate sea ice sensitivities in the Arctic, but have trouble fully replicating an observed linkage between the Arctic and lower latitudes in recent decades. Further improvements in simulating the observed Arctic–global linkage are thus necessary before the Arctic’s sensitivity to global warming in models can be quantified with confidence.
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All reanalysis data used in this study were obtained from publicly available sources: ERA-Interim reanalysis data can be obtained from the ECMWF public data sets web interface (http://apps.ecmwf.int/datasets/). Simulated global circulation, temperature and sea ice under anthropogenic forcing were obtained from the CMIP5 and LENS archives accessed through the Earth System Grid Federation data portal (http://esgf.llnl.gov).
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This study was supported by NOAA’s Climate Program Office, Climate Variability and Predictability Program (NA15OAR4310162) and Ocean Observing and Monitoring Division (NA18OAR4310424), and by NSF’s Polar Programs (OPP1744598 and ARC1203425). The authors acknowledge support from the CESM Large Ensemble Community Project and supercomputing resources provided by NSF/CISL/Yellowstone.
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