Arctic precipitation and surface wind speed associated with cyclones in a changing climate

This study assesses Arctic cyclone characteristics and associated precipitation and surface wind speeds using an ensemble of regional climate model (GEMCLIM) simulations at 0.5 degrees resolution for the 1981-2099 period following the RCP8.5 scenario. Comparison of GEMCLIM simulation with observations for current climate (1981-2010) suggests that GEMCLIM realistically reproduces the spatial and seasonal variation of Arctic cyclone frequency and intensity, and associated precipitation, for winter and summer. Clear added-value is found for several regions, compared to the driving data. The pressure-wind speed relationships for each region are reasonably reproduced and more extreme winds associated with increasing cyclone intensity are realistically simulated. In addition, the spatial and temporal variations of observed extreme cyclones are well captured. In future climate (2070-2099), the winter cyclone intensity and frequency, and associated precipitation, are projected to increase and decrease over the Aleutian and Icelandic Low regions, respectively. For summer, the projected changes are relatively smaller than those for winter and vary with region. Interestingly, significant decreases in cyclone contribution to total precipitation are found for northern Canada and Eurasia regions, despite increases in cyclone-related precipitation amount. This suggests stronger influence of mesoscale systems on precipitation compared to synoptic-scale systems. Enhanced pressure-wind speed relationships are projected for Arctic Canada and the Chukchi and East Siberian Seas. The increase of extreme cyclones during autumn is primarily related to sea ice loss during summer, while for winter, large-scale circulation changes (i.e. Arctic dipole) are mostly responsible due to strong sea ice loss in the central Arctic during autumn. This study demonstrates the added-value of dynamic downscaling with respect to Arctic cyclone characteristics and associated surface variables and provides useful insights regarding their future projections for use in risk assessment studies.