New classes of climate model emulators to improve paleoclimate reconstructions

Abstract

Abstract. Reconstructing spatial climate variability from proxy records requires forward models “emulators” that capture the dynamical structure of the climate system while remaining computationally efficient. Traditional emulators based on Empirical Orthogonal Functions (EOFs) and Linear Inverse Models (LIMs) face inherent limitations due to linearity and variance-based dimensionality reduction. Here we develop and evaluate a hierarchy of CMIP-class climate model emulators, for annual surface air temperature field emulation, that integrate autoencoder-based dimensionality reduction with nonlinear prediction architectures, including Reservoir Computing (RC) and Recurrent Neural Networks (RNNs). Using a comprehensive experimental protocol applied to the IPSL-CM6A-LR model and 52 CMIP6 models, we show that the combination of a prediction-oriented autoencoder (AE) latent representation with RC dynamics retaining memory and nonlinear state evolution (AERCn) yields, the most robust configuration when training data are plentiful. This improves the representation of El Niño Southern Oscillation and Atlantic Multidecadal Variability, while preserving spatial reconstruction quality and robustness across distinct CMIP6 model structures. When training data are scarce, a multimodel pre-trained AERNN provides a data-efficient and competitive alternative. These properties make the proposed architectures particularly well suited for integration into Particle Filters and Ensemble Kalman Filter PDA frameworks. Our results highlight the importance of predictability-oriented dimensionality reduction and nonlinear dynamical memory for emulator design. They provide a scalable proof of concept toward multivariate climate-field emulation for improved reconstructions of climate variability over the Common Era.