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The impact that disappearing Arctic sea ice will have on the atmospheric circulation and weather variability remains uncertain. In this study, results are presented from a sea ice perturbation experiment using the coupled Community Climate System Model, version 4 (CCSM4). By decreasing the albedo of the sea ice, the impact of an ice-free summertime Arctic on the coupled ocean-atmosphere system is isolated in an idealized but energetically self-consistent way. The multicentury equilibrium response is examined, as well as the transient response in an initial condition ensemble. The perturbation drives pronounced year-round sea ice thinning, Arctic warming, Arctic amplification, and moderate global warming. Even in the almost complete absence of summertime sea ice, the atmospheric general circulation response is very weak and the transient response is small compared to the internal variability. Surface temperature variability is reduced on all time scales over most of the middle and high latitudes with a 50% reduction in the standard deviation of temperature over the Arctic Ocean. The reduction is attributed to decreased temperature gradients and increased maritime influence once the sea ice melts. This reduced variability extends weakly into the variability of the midlatitude and free tropospheric geopotential height (less than 10% reduction in the standard deviation). Consistently, eddy geopotential height variability is found to decrease while geopotential isopleth meandering, which reflects Arctic amplified warming, increases moderately. The sign of these changes is consistent with recent observations, but the size of these changes is relatively small. [ABSTRACT FROM AUTHOR]

This study seeks to derive the sea surface temperature (SST) response to anthropogenic forcing from observations over the last century, using simple methods inspired from pattern scaling. As in pattern scaling, the spatial response is assumed to scale with global-mean and annual-mean surface temperature. The long-term aim of this work is to generate anthropogenically forced SST and sea ice patterns for the recent past and near-term future, and use them to force atmosphere-land climate models for attribution and prediction purposes. The present work compares estimation methodologies and, within a Monte Carlo framework based on large initial condition ensembles of climate model simulations, examines the robustness of the patterns obtained. The different methods explored here yield a similar SST spatial response, mostly reflecting the observed SST linear trend map. The different methods nevertheless provide distinctive temporal evolution of the global-mean and annual-mean SST response, which in turn affects the temporal evolution of the global-mean and annual-mean air surface temperature simulated in corresponding prescribed SST simulations. The estimated SST spatial response consists mostly of a warming of the midlatitude coasts near the western boundary currents, the tropical Indian Ocean, and the Arctic Ocean. This pattern generally agrees with previously published observational and modeling studies. Based on Monte Carlo analysis of the large ensembles, it is found that between 36% and 56% of its spatial variance results from anthropogenic forcing. Overall, the work herein provides constraints on the uncertainty associated with the spatial variability of an anthropogenically forced component of climate change derived from observations, which can potentially be used for climate attribution and prediction. [ABSTRACT FROM AUTHOR]