Tim Woollings

Abstract:

In a warming climate, storm tracks are projected to intensify on their poleward side. Here we use large ensemble CO₂ ramp-up and -down simulations to show that these changes are not reversed when CO₂ concentrations are reduced. If CO₂ is removed from the atmosphere following CO₂ increase, the North Atlantic storm track keeps strengthening until the middle of the CO₂ removal, while the recovery of the North Pacific storm track during ramp-down is stronger than its shift during ramp-up. In contrast, the Southern Hemisphere storm track weakens during ramp-down at a rate much faster than its strengthening in the warming period. Compared to the present climate, the Northern Hemisphere storm track becomes stronger and the Southern Hemisphere storm track becomes weaker at the end of CO₂ removal. These hemispherically-asymmetric storm track responses are attributable to the weakened Atlantic meridional overturning circulation and the delayed cooling of the Southern Ocean.

Abstract:

Very large (~2000 members) initial-condition ensemble simulations have been performed to advance understanding of mean climate and extreme weather responses to projected Arctic sea-ice loss under 2 °C global warming above preindustrial levels. These simulations better sample internal atmospheric variability and extremes for each model compared to those from the Polar Amplification Model Intercomparison Project (PAMIP). The mean climate response is mostly consistent with that from the PAMIP multi-model ensemble, including tropospheric warming, reduced midlatitude westerlies and storm track activity, an equatorward shift of the eddy-driven jet and increased mid-to-high latitude blocking. Two resolutions of the same model exhibit significant differences in the stratospheric circulation response; however, these differences only weakly modulate the tropospheric response. The response of temperature and precipitation extremes largely follows the seasonal-mean response. Sub-sampling confirms that large ensembles (e.g. ≥400) are needed to robustly estimate the seasonal-mean large-scale circulation response, and very large ensembles (e.g. ≥1000) for regional climate and extremes.