Exploring the Atmospheric Responses to Arctic Sea-Ice Loss in Google's NeuralGCM

The rapid loss of Arctic sea ice is a striking consequence of anthropogenic global warming. Itsremote impacts on mid‐latitude weather and climate have attracted scientific and media attention. In this study,we use a hybrid (dynamical plus machine‐learning) atmospheric model—Google's NeuralGCM—to investigatethe mid‐latitude atmospheric circulation responses to Arctic sea‐ice loss for the first time. We conductexperiments in which NeuralGCM is forced with pre‐industrial and future sea‐ice concentrations following theprotocol of the Polar Amplification Model Intercomparisom Project. To assess the performance of NeuralGCM,we compare the results with those simulated by two physics‐based climate models. NeuralGCM produces acomparable response of near‐surface warming to sea‐ice loss and the subsequent weakened zonal wind in mid‐latitudes. However, there is a substantial discrepancy between the two models' stratospheric responses, wheredifferent temperature responses in these models are associated with different zonal wind and geopotential heightresponses. Further investigation of North Atlantic blocking shows that NeuralGCM produces stronger, morefrequent, and more realistic blocking events. Our results demonstrate the capability of NeuralGCM insimulating the tropospheric responses to Arctic sea‐ice loss, but improvements may be needed for thestratospheric representation.