Disentangling Forced Trends in the North Atlantic Jet From Natural Variability Using Deep Learning - Hermoso, Alejandro; Schemm, Sebastian; id_orcid:0 000-0002-1601-5683 (American Geophysical Union, 2024)
Regional weather variability and extremes over Europe are strongly linked to variations in the North Atlantic jet stream, especially during the winter season. Projections of the evolution of the North Atlantic jet are essential for estimating the regional impacts of climate change. Therefore, separating forced trends in the North Atlantic jet from its natural variability is an extremely relevant task. Here, a deep learning based method, the Latent Linear Adjustment Autoencoder (LLAE), is used for this purpose on an ensemble of fully-coupled climate simulations. The LLAE is based on a variational autoencoder and an additional linear component. The model uses detrended temperature and geopotential to predict the component of the zonal wind associated with natural variability. The residual between this prediction and the original wind field is interpreted as the forced component of the jet. The method is first tested for the geostrophic wind for which the forced trend can be obtained analytically from the difference between geostrophic wind computed from detrended and full geopotential. Despite the large variability of the total trends, the LLAE is shown to be effective in extracting the forced component of the trend for each individual ensemble member in both geostrophic and full wind fields. The LLAE-derived forced trend shows an increase in the upper-level zonal wind speed along a southwest–northeast oriented band over the ocean and a jet extension toward Europe. These are common characteristics over different periods and show some similarities to the upper-level zonal wind speed trend obtained from the ERA5 reanalysis. ISSN:0148-0227 ISSN:2169-897X