arXiv subjects

Gregory S Elsaesser

Publications and source records attributed to Gregory S Elsaesser.

3 recordsLinked to original sources

arXiv2026

Understanding Perturbed Parameter Ensemble Sensitivities Using A Contrastive Learning Approach

Perturbed parameter ensembles (PPEs) reveal how physics parameters affect climate simulations, but interpreting parameter sensitivities across multivariate, spatially structured outputs remains challenging, particularly when calibrating models against observations. We develop an explainable contrastive learning model that maps 5 monthly cloud and radiation fields into a shared representation space. We train the model on the fields of two 100-member Community Atmosphere Model version 6 (CAM6) PPEs, spanning 34 parameters, that only differ in the warm rain microphysics scheme: KK2000, the default bulk microphysics scheme, and TAU-ML, a neural network emulator of a bin microphysics scheme. The learned representations separates two PPEs with over 94\% linear classification accuracy while preserving the seasonal variability and ensemble spread due to parameter perturbations. In the shared representation space, the representations of satellite observations occupy the same low-dimensional manifold as the PPEs but are displaced from them most strongly during boreal spring and autumn. TAU-ML PPE has a lower distance to observations compared to KK2000 in the representation space. Integrated Gradients attributions highlights the contributions in subtropical low-cloud regions, Northern and Southern Hemisphere storm track regions, and tropical convection regions to differences between PPEs and observations. Regional attributions correlate most strongly with parameters associated with cloud microphysics, boundary layer turbulence, and deep convection. These results demonstrate that explainable representations of climate fields can attribute model differences to specific variables, regions, seasons, and physical parameters.

physics.ao-ph↗

arXiv2026

A Self-Diagnosing Structural Error-Aware Parameter Estimation Method for Earth System Models

We propose a fully automated, structural error-aware, interpretable climate model parameter estimation method that leverages Perturbed Parameter Ensembles (PPEs). It is based on history matching and aligns with an increasingly-used iterative simulation-emulation-calibration methodology. The method is motivated by the negative impacts of structural error and emulator and observational uncertainties on climate model parameter estimation efforts, as well as the problems associated with sparsely-sampled PPEs. To address these challenges, the method explicitly builds simpler emulators that avoid overfitting, detect structural error, avoids compensating for structural error through inflated mismatch tolerances, and sequentially excludes structurally inconsistent variables for parameter estimation. The method decomposes the high-dimensional calibration problem into linked low-dimensional subproblems, and integrates their constraints to reconstruct the jointly plausible region of the full parameter space. The method is applied to a 100-member PPE with 34 perturbed parameters generated by a version of CAM6 with machine learning-based warm rain microphysics parameterization. Through iterative application, the method greatly reduces the ensemble spread and improves the matching between simulated and observed zonal climatologies. The method also finds ensemble members that outperform the default CAM6 configuration in root mean square error across multiple diagnostics. Controlled experiments demonstrate that overly-conservative emulator uncertainty could lead to neglect of informative observations, and tolerance of the structural error, in the context of this method, biases the estimated parameters toward compensating for structural error. Our work also emphasizes the value of interpretability for diagnosing structural error and informing parameter estimation in PPE-based calibration.

physics.ao-ph↗

arXiv2024

A simple emulator that enables interpretation of parameter-output relationships, applied to two climate model PPEs

We present a new additive method, nicknamed sage for Simplified Additive Gaussian processes Emulator, to emulate climate model Perturbed Parameter Ensembles (PPEs). It estimates the value of a climate model output as the sum of additive terms. Each additive term is the mean of a Gaussian Process, and corresponds to the impact of a parameter or parameter group on the variable of interest. This design caters to the sparsity of PPEs which are characterized by limited ensemble members and high dimensionality of the parameter space. sage quantifies the variability explained by different parameters and parameter groups, providing additional insights on the parameter-climate model output relationship. We apply the method to two climate model PPEs and compare it to a fully connected Neural Network. The two methods have comparable performance with both PPEs, but sage provides insights on parameter and parameter group importance as well as diagnostics useful for optimizing PPE design. Insights gained are valid regardless of the emulator method used, and have not been previously addressed. Our work highlights that analyzing the PPE used to train an emulator is different from analyzing data generated from an emulator trained on the PPE, as the former provides more insights on the data structure in the PPE which could help inform the emulator design.

stat.ME↗