Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability

Abstract

Two distinct features of anthropogenic climate change, warming in the tropical upper troposphere and warming at the Arctic surface, have competing effects on the mid-latitude jet stream’s latitudinal position, often referred to as a “tug-of-war”. Studies that investigate the jet’s response to these thermal forcings show that it is sensitive to model type, season, initial atmospheric conditions, and the shape and magnitude of the forcing. Much of this past work focuses on studying a simulation’s response to external manipulation. In contrast, we explore the potential to train a convolutional neural network (CNN) on internal variability alone and then use it to examine possible nonlinear responses of the jet to tropospheric thermal forcing that more closely resemble anthropogenic climate change. Our approach leverages the idea behind the fluctuationdissipation theorem, which relates the internal variability of a system to its forced response but so far has been only used to quantify linear responses. We train a CNN on data from a long control run of the CESM dry dynamical core and show that it is able to skillfully predict the nonlinear response of the jet to sustained external forcing. The trained CNN provides a quick method for exploring the jet stream sensitivity to a wide range of tropospheric temperature tendencies and, considering that this method can likely be applied to any model with a long control run, could lend itself useful for early stage experiment design.

Internal Variability Increased Arctic Amplification During 1980–2022

“…Following recent work showing that ML methods can effectively isolate internally generated and externally forced trends (Barnes et al., 2019; Connolly et al., 2023; Gordon & Barnes, 2022; Po‐Chedley et al., 2022), we create ML algorithms to isolate these trend contributions in observed surface air temperature during the 43‐year period from 1980 to 2022 over both the Arctic and globe. To do this, we create a training data set based on 10 CMIP6 models, of which each have at least 10 ensemble members (Table S1 in Supporting Information ).…”

Internal Variability Increased Arctic Amplification during 1980-2022

“…Following recent work showing that ML methods can effectively isolate internally generated and externally forced trends (Barnes et al, 2019;Connolly et al, 2023;Gordon & Barnes, 2022;Po-Chedley et al, 2022), we create ML algorithms to isolate these trend contributions in observed surface air temperature during the 43-year period from 1980 to 2022 over both the Arctic and globe. To do this, we create a training data set based on 10 CMIP6 models, of which each have at least 10 ensemble members (Table S1 in Supporting Information S1).…”

Exploiting a variational auto-encoder to represent the evolution of sudden stratospheric warmings

“…As each SSW manifests a predominant spatial pattern in the stratosphere (either displacement-type or split-type structure), deep learning models, especially those that are salient in extracting features from two-dimensional images, could offer a new opportunity to examine SSW morphology. Convolutional neural networks (CNNs) are a deep learning technique developed to tackle such image recognition tasks and have been widely used in multiple earth science problems to extract key spatial structures (Ham et al 2019, Connolly et al 2023. In addition, variational auto-encoders (VAEs; Kingma and Welling 2013), a typical type of generative deep learning model, have been extensively utilized in retrieving non-linear relationships in geoscience data (Vuyyuru et al 2021, Lopez-Alvis et al 2022, Tsekhmistrenko et al 2022.…”