Drivers of 2016 record Arctic warmth assessed using climate simulations subjected to Factual and Counterfactual forcing

Abstract

A suite of historical atmospheric model simulations is described that uses a hierarchy of global boundary forcings designed to inform research on the detection and attribution of weather and climate-related extremes. In addition to experiments forced by actual variations in sea surface temperature, sea ice concentration, and atmospheric chemical composition (so-called Factual experiments); additional (Counterfactual) experiments are conducted in which the boundary forcings are adjusted by removing estimates of long-term climate change. A third suite of experiments are identical to the Factual runs except that sea ice concentrations are set to climatological conditions (Clim-Polar experiments). These were used to investigate the cause for extremely warm Arctic surface temperature during 2016.Much of the magnitude of surface temperature anomalies averaged poleward of 65°N in 2016 (3.2 ± 0.6 °C above a 1980–89 reference) is shown to have been forced by observed global boundary conditions. The Factual experiments reveal that at least three quarters of the magnitude of 2016 annual mean Arctic warmth was forced, with considerable sensitivity to assumptions of sea ice thickness change. Results also indicate that 30–40% of the overall forced Arctic warming signal in 2016 originated from drivers outside of the Arctic. Despite such remote effects, the experiments reveal that the extreme magnitude of the 2016 Arctic warmth could not have occurred without consideration of the Arctic sea ice loss. We find a near-zero probability for Arctic surface temperature to be as warm as occurred in 2016 under late-19th century boundary conditions, and also under 2016 boundary conditions that do not include the depleted Arctic sea ice. Results from the atmospheric model experiments are reconciled with coupled climate model simulations which lead to a conclusion that about 60% of the 2016 Arctic warmth was likely attributable to human-induced climate change. Keywords: Arctic, Climate, Extreme, Model, Attribution