arXiv subjects

Noelle E. Selin

Publications and source records attributed to Noelle E. Selin.

3 recordsLinked to original sources

arXiv2026

Optimal scenario design for climate emulation

As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing architectures that embed physical constraints. However, for machine-learning surrogate climate models (emulators), we show that the low structural diversity in existing scenarios commonly used to generate training data places a ceiling on predictive skill. Here, we examine whether training datasets themselves can be optimized to improve generalization. We introduce a method to create datasets that produce emulators capable of generalizing to new, structurally different scenarios absent from the training data. We use a differentiable Simple Climate Model (SCM) to calculate the sensitivity of emulator loss to perturbations in the training data, iteratively updating the training data to maximize emulator skill. For an SCM, training on one scenario optimized in this fashion outperforms an emulator trained on six standard ScenarioMIP pathways. We achieve this higher predictive skill despite training on a smaller dataset, finding that our emulator successfully isolates distinct physical behaviors of different climate forcing agents (e.g., greenhouse gases vs. aerosols) without single-forcing runs. We then demonstrate that scenarios optimized using an SCM, when used to drive an intermediate-complexity climate model, produce a training dataset that yields a more skillful emulator than training on ScenarioMIP outputs. Our results suggest that, in the compute-constrained environment of running full-scale climate models, generating a small number of dynamically rich scenarios provides greater marginal value for emulation and characterizing system responses than expanding the suite of traditional emissions pathways.

physics.ao-ph↗

arXiv2026

Radiative and Dynamical Controls on the Land-Ocean Warming Contrast in Climate Models

Surface air over land warms substantially more than over the ocean under greenhouse forcing, a phenomenon known as the land-ocean warming contrast. Current explanations for this contrast are commonly expressed either in terms of energetic constraints, from top-of-atmosphere and surface energy balance, or dynamical constraints, from large-scale atmospheric dynamics. We show that these perspectives are complementary when viewed through the lens of atmospheric moist static energy (MSE) transport, and that connecting them yields new insight into the controls of the warming contrast and the spread in climate models. We use this framework to construct an interpretable emulator that reproduces the land-ocean warming response across 22 models from the latest Coupled Model Intercomparison Project (CMIP6). We find that the strength of the land-ocean warming contrast emerges from the interplay between a model-dependent radiative baseline and a robust dynamical restoring mechanism that favors greater warming over land. This interplay leads to two broad model regimes that align with climate sensitivity. In low-climate-sensitivity models, more stabilizing radiative feedbacks over the ocean directly favor greater land warming. In high-climate-sensitivity models, radiative feedbacks alone would instead favor greater ocean warming, but a strong MSE-transport feedback (approximately 0.2 PW/K) more than compensates for this tendency. The intermodel spread in the land-ocean warming contrast is closely related to the ratio of radiative feedbacks over land and ocean, highlighting a broader connection between climate sensitivity and the land-ocean warming contrast.

physics.ao-ph↗

arXiv2024

Origin and Limits of Invariant Warming Patterns in Climate Models

Climate models exhibit an approximately invariant surface warming pattern in typical end-of-century projections. This observation has been used extensively in climate impact assessments for fast calculations of local temperature anomalies, with a linear procedure known as pattern scaling. At the same time, emerging research has also shown that time-varying warming patterns are necessary to explain the time evolution of effective climate sensitivity in coupled models, a mechanism that is known as the pattern effect and that seemingly challenges the pattern scaling understanding. Here we present a simple theory based on local energy balance arguments to reconcile this apparent contradiction. Specifically, we show that the pattern invariance is an inherent feature of exponential forcing, linear feedbacks, a constant forcing pattern and diffusive dynamics. These conditions are approximately met in most CMIP6 Shared Socioeconomic Pathways (SSP), except in the Arctic where nonlinear feedbacks are important and in regions where aerosols considerably alter the forcing pattern. In idealized experiments where concentrations of CO2 are abruptly increased, such as those used to study the pattern effect, the warming pattern can change considerably over time because of spatially inhomogeneous ocean heat uptake, even in the absence of nonlinear feedbacks. Our results illustrate why typical future projections are amenable to pattern scaling, and provide a plausible explanation of why more complicated approaches, such as nonlinear emulators, have only shown marginal improvements in accuracy over simple linear calculations.

physics.ao-ph↗