Bosong Zhang | Searcharxiv

arXiv2026

Remote Influences of Land Surface Temperature and their Implications for Sea Surface Temperature Patterns

The spatial pattern of sea surface temperature (SST) plays a central role in shaping the climate system, yet the influence of land surface temperature (LST) remains poorly understood. Using a state-of-the-art coupled ocean--land--atmosphere model, we examine the model's response to regional LST perturbations imposed through LST nudging and idealized time-dependent ramp warming simulations. We find that LST warming over South America strengthens the tropical Pacific zonal SST gradient, yielding a more La Ni\~na--like mean state. Enhanced LST increases the zonal contrast in diabatic heating and excites stationary Rossby wave responses, which reinforce alongshore winds and coastal upwelling in the eastern Pacific. This provides a dynamical pathway linking regional land warming to changes in the equatorial Pacific mean state. Similar responses are found for warming over North America, accompanied by North Pacific cooling, and for warming over Central Africa, coupled with tropical Atlantic cooling. In contrast, warming over the Maritime Continent or the Tibetan Plateau does not induce significant SST pattern changes. Historical simulations nudged toward observed LST exhibit cooling in the tropical southeast Pacific, with the tentative implication that uncertainty in LST may contribute to model-simulated SST biases during the historical period.

physics.ao-ph↗

arXiv2025

The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming

Machine learning models for the global atmosphere that are capable of producing stable, multi-year simulations of Earth's climate have recently been developed. However, the ability of these ML models to generalize beyond the training distribution remains an open question. In this study, we evaluate the climate response of several state-of-the-art ML models (ACE2-ERA5, NeuralGCM, and cBottle) to a uniform sea surface temperature warming, a widely used benchmark for evaluating climate change. We assess each ML model's performance relative to a physics-based general circulation model (NOAA's Geophysical Fluid Dynamics Laboratory AM4) across key diagnostics, including surface air temperature, precipitation, temperature and wind profiles, and top-of-atmosphere radiation. While the ML models reproduce key aspects of the physical model response, particularly the response of precipitation, some exhibit notable departures from robust physical responses, including radiative responses and land region warming. Our results highlight the promise and current limitations of ML models for climate change applications and suggest that further improvements are needed for robust out-of-sample generalization.

physics.ao-ph↗