Can Transfer Learning be Used to Identify Tropical State-Dependent Bias Relevant to Midlatitude Subseasonal Predictability?

Autor:	Mayer, Kirsten J., Dagon, Katherine, Molina, Maria J.
Rok vydání:	2024
Předmět:	Physics - Atmospheric and Oceanic Physics
Druh dokumentu:	Working Paper
Popis:	Previous research has demonstrated that specific states of the climate system can lead to enhanced subseasonal predictability (i.e., state-dependent predictability). However, biases in Earth system models can affect the representation of these states and their subsequent evolution. Here, we present a machine learning framework to identify state-dependent biases in Earth system models. In particular, we investigate the utility of transfer learning with explainable neural networks to identify tropical state-dependent biases in historical simulations of the Energy Exascale Earth System Model version 2 (E3SMv2) relevant for midlatitude subseasonal predictability. Using a perfect model framework, we find transfer learning may require substantially more data than provided by present-day reanalysis datasets to update neural network weights, imparting a cautionary tale for future transfer learning approaches focused on subseasonal modes of variability. Comment: This work has been submitted for publication in Artificial Intelligence for the Earth Systems (AIES). Copyright in this work may be transferred without further notice
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.10755 Zobrazit plný text záznamu View this record from Arxiv