Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature - S. Schöngart; S. Schöngart; L. Gudmundsson; M. Hauser; P. Pfleiderer; Q. Lejeune; S. Nath; S. I. Seneviratne; C.-F. Schleussner; C.-F. Schleussner; C.-F. Schleussner (Geoscientific Model Development)
Emulators of Earth system models (ESMs) are statistical models that approximate selected outputs of ESMs. Owing to their runtime efficiency, emulators are especially useful when large amounts of data are required, for example, for in-depth exploration of the emission space, for investigating high-impact low-probability events, or for estimating uncertainties and variability. This paper introduces an emulation framework that allows us to emulate gridded monthly mean precipitation fields using gridded monthly mean temperature fields as forcing. The emulator is designed as an extension of the Modular Earth System Model Emulator (MESMER) framework, and its core relies on the concepts of generalised linear models (GLMs). Precipitation at each (land) grid point and for each month is approximated as a multiplicative model with two factors. The first factor entails the temperature-driven precipitation response and is assumed to follow a gamma distribution with a logarithmic link function. The second factor is the residual variability in the precipitation field, which is assumed to be independent of temperature but may still possess spatial precipitation correlations. Therefore, the monthly residual field is decomposed into independent principal components and subsequently approximated and sampled using a kernel density estimation with a Gaussian kernel. The emulation framework is tested and validated using 24 ESMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). For each ESM, we train on a single-ensemble member across scenarios and evaluate the emulator performance using simulations with historical and Shared Socioeconomic Pathways (SSP5-8.5) forcing. We show that the framework captures grid-point-specific precipitation characteristics, such as variability, trend, and temporal auto-correlations. In addition, we find that emulated spatial (cross-variable) characteristics are consistent with those of ESMs. The framework is also able to capture compound hot–dry and cold–wet extremes, although it systematically underestimates their occurrence probabilities. The emulation of spatially explicit coherent monthly temperature and precipitation time series is a major step towards a computationally efficient representation of impact-relevant variables of the climate system.