Monday, 13 January 2025: 2:00 PM
244 (New Orleans Ernest N. Morial Convention Center)
A wave of unprecedented extreme weather events, breaking records worldwide, has sparked a debate on the capability of existing weather and climate models to accurately forecast and prepare for the novel impacts of climate change on human life and infrastructure. Extreme wind speeds and precipitation triggered by extratropical cyclones pose an increasing threat to various stakeholders, from government policymakers to businesses in the heavily populated northern hemisphere midlatitudes. The advent of global kilometre-scale climate models offers significant potential for enhancing our understanding and projection capabilities of these extreme events.
In this study, we investigate changes in near-surface wind speeds and precipitation associated with the top 100 cyclones in the North Atlantic region during the present-day period (2019-2021) and an idealized warmer climate scenario with a 4K increase in sea surface temperatures. Using the latest version of the kilometer-scale model X-SHIELD, developed by the Geophysical Fluid Dynamics Laboratory, we track extratropical cyclones and create cyclone composites. Our findings reveal the largest increases in near-surface wind speeds and precipitation within the warm conveyor belt of these cyclones.
To highlight the benefits of kilometre-scale modelling, we compare our results with those obtained by coarsening the simulations to 25-km resolution. While the overall structure of the extratropical cyclones remains consistent across resolutions, the kilometre-scale simulations offer higher realism in surface fields and better capture peak winds. Considering that the damage caused by wind increases cubically with wind speed, the higher peak winds captured at the kilometre-scale resolution are of significant importance for projecting future damage and informing adaptation strategies in a warmer climate. Finally, the reduced reliance on model parameterizations in the X-SHIELD kilometre-scale modelling, which explicitly captures previously parameterized processes such as deep convection, enhances the robustness of our results.
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