Modeling Antarctic Atmospheric Dynamic Extremes

Objective

During March 2022, an unprecedented heatwave caused by an atmospheric river (AR) occurred in East Antarctica that led to the largest observed temperature anomaly observed globally. This event signaled the grave risk of a similar event occurring during summer over the more vulnerable ice shelves of West Antarctica and triggering ice sheet destabilization, thus accelerating global sea level rise. Many of the elements that made the March 2022 heatwave so extreme occur on relatively small spatial scales that are often poorly represented in climate models which all leads to an underappreciation of the risk that polar extreme weather poses. To address this gap, the latest generation of high-resolution climate models can help identify which small-scale dynamical processes are most connected to polar heat extremes. Here, I propose to apply a machine-learning based AR detection algorithm to two high-resolution, convection-permitting Earth system models (IFS and ICON) and a regional climate model (ICOLMDZ-LAM) to study the small-scale dynamical processes related to the diabatic processes found in ARs while identifying the relevant model parameters needed for properly simulating the subsequent response in blocking and heat extremes around Antarctica. Then we will compare future climate simulations from these models to examine changes in Antarctic extreme weather behavior. With the expertise of the research supervisor, Benjamin Pohl, this study will assess how the explicit resolution of tropical convection within climate models translates to the depiction of heat extremes in Antarctica. Working alongside model developers at the LSCE, this work will inform future improvements in simulating polar extreme weather in the ICOLMDZ model. The project represents the beginning of a longer term research plan to assess whether polar extreme weather can act as a tipping point for ice sheet stability while helping me secure a tenured research position and create a research team.