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Abstract Arctic amplification (AA), the greater Arctic surface warming compared to the global average, has been widely attributed to increasing concentrations of greenhouse gases (GHG). However, less is known about the impacts of other forcings - notably, anthropogenic aerosols (AER) - and how they may compare to the impacts of GHG. Here we analyze sets of climate model simulations, specifically designed to isolate the AER and GHG effects on global climate. Surprisingly, we find stronger AA produced by AER than by GHG during the 1955–1984 period, when the strongest global AER increase. This stronger AER-induced AA is due to a greater sensitivity of Arctic sea ice, and associated changes in ocean-to-atmosphere heat exchange, to AER forcing. Our findings highlight the asymmetric Arctic climate response to GHG and AER forcings, and show that clean air policies which have reduced aerosol emissions may have exacerbated the Arctic warming over the past few decades. 

Abstract Arctic amplification (AA), defined as the enhanced warming of the Arctic compared to the global average, is a robust feature of historical observations and simulations of future climate. Despite many studies investigating AA mechanisms, their relative importance remains contested. In this study, we examine the different timescales of these mechanisms to improve our understanding of AA’s fundamental causes. We use the Community Earth System Model v1, Large Ensemble configuration (CESM-LE), to generate large ensembles of 2 years simulations subjected to an instantaneous quadrupling of CO₂. We show that AA emerges almost immediately (within days) following CO₂increase and before any significant loss of Arctic sea ice has occurred. Through a detailed energy budget analysis of the atmospheric column, we determine the time-varying contributions of AA mechanisms over the simulation period. Additionally, we examine the dependence of these mechanisms on the season of CO₂quadrupling. We find that the surface heat uptake resulting from the different latent heat flux anomalies between the Arctic and global average, driven by the CO₂forcing, is the most important AA contributor on short (<1 month) timescales when CO₂is increased in January, followed by the lapse rate feedback. The latent heat flux anomaly remains the dominant AA mechanism when CO₂is increased in July and is joined by the surface albedo feedback, although AA takes longer to develop. Other feedbacks and energy transports become relevant on longer (>1 month) timescales. Our results confirm that AA is an inherently fast atmospheric response to radiative forcing and reveal a new AA mechanism. 

Abstract This study examines the Arctic surface air temperature response to regional aerosol emissions reductions using three fully coupled chemistry–climate models: National Center for Atmospheric Research-Community Earth System Model version 1, Geophysical Fluid Dynamics Laboratory-Coupled Climate Model version 3 (GFDL-CM3) and Goddard Institute for Space Studies-ModelE version 2. Each of these models was used to perform a series of aerosol perturbation experiments, in which emissions of different aerosol types (sulfate, black carbon (BC), and organic carbon) in different northern mid-latitude source regions, and of biomass burning aerosol over South America and Africa, were substantially reduced or eliminated. We find that the Arctic warms in nearly every experiment, the only exceptions being the U.S. and Europe BC experiments in GFDL-CM3 in which there is a weak and insignificant cooling. The Arctic warming is generally larger than the global mean warming (i.e. Arctic amplification occurs), particularly during non-summer months. The models agree that changes in the poleward atmospheric moisture transport are the most important factor explaining the spread in Arctic warming across experiments: the largest warming tends to coincide with the largest increases in moisture transport into the Arctic. In contrast, there is an inconsistent relationship (correlation) across experiments between the local radiative forcing over the Arctic and the simulated Arctic warming, with this relationship being positive in one model (GFDL-CM3) and negative in the other two. Our results thus highlight the prominent role of poleward energy transport in driving Arctic warming and amplification, and suggest that the relative importance of poleward energy transport and local forcing/feedbacks is likely to be model dependent.