arXiv subjects

Maria Rugenstein

Publications and source records attributed to Maria Rugenstein.

5 recordsLinked to original sources

arXiv2026

A Land-Sea Contrast Pattern in Surface Temperature and Atmospheric Circulation Trends in Recent Decades

Spatial patterns in observed climate trends remain poorly understood. Here we argue that a warming of land relative to ocean has shaped observed surface temperature and atmospheric circulation trends, including the negative Inter-Decadal Pacific Oscillation (IPO)-like tendency across the Pacific basin. Observed and modeled trends display an overall decline in sea level pressure over the faster-warming land relative to ocean, with a spatial pattern that resembles the seasonal cycle and the response to land heating in idealized climate model experiments. Coupled climate model simulations with historical forcing underestimate the land-sea warming ratio. It is only in the early response of abrupt CO2 quadrupling climate model simulations that climate models are able to recreate the observed land-sea warming ratio, in which case a strengthening of oceanic surface highs and a negative IPO-like surface warming pattern over the Pacific comparable to observed trends are seen. We propose that discrepancies between modeled and observed trends in many climate variables may be explained by the underestimation of the land-sea warming ratio by climate models. Determining the cause of this discrepancy has the potential to constrain projections of future climate change as the underlying mechanism causing climate models to underestimate the land-sea warming ratio discrepancy will set the persistence of this problem.

physics.ao-ph↗

arXiv2026

Recent Weakening of the Global Radiative Feedback

Earth's climate stability, characterized by the global radiative feedback parameter ($\lambda$), varies decadally due to changing surface temperature patterns. Recent variations in $\lambda$ are poorly understood as coordinated model simulations typically end in 2014. We apply a convolutional neural network trained on climate model simulations to observation-based surface temperature reconstructions to estimate variations in $\lambda$ up to 2025. We find that $\lambda$ reached a minimum (maximum stability) around the mid 1990s ($\lambda\simeq -3 {\rm Wm^{-2}/K}$), but has since weakened significantly ($\lambda\simeq -2\, {\rm Wm^{-2}/K}$). We confirm these results with climate model simulations extended to 2022. The recent $\lambda$ weakening is not significantly affected by El Ni\~no Southern Oscillation or Pacific Decadal Oscillation. Attribution reveals that warming in the subtropical Northeast Pacific is an important driver of the recently weakened feedback, confirmed by targeted experiments in E3SMv2. Our approach enables near real-time monitoring of Earth's climate stability.

physics.ao-ph↗

arXiv2025

Quantifying the radiative response to surface temperature variability: A critical comparison of current methods

Over the past decade, it has become clear that the radiative response to surface temperature change depends on the spatially varying structure in the temperature field, a phenomenon known as the "pattern effect''. The pattern effect is commonly estimated from dedicated climate model simulations forced with local surface temperatures patches (Green's function experiments). Green's function experiments capture causal influences from temperature perturbations, but are computationally expensive to run. Recently, however, several methods have been proposed that estimate the pattern effect through statistical means. These methods can accurately predict the radiative response to temperature variations in climate model simulations. The goal of this paper is to compare methods used to quantify the pattern effect. We apply each method to the same prediction task and discuss its advantages and disadvantages. Most methods indicate large negative feedbacks over the western Pacific. Over other regions, the methods frequently disagree on feedback sign and spatial homogeneity. While all methods yield similar predictions of the global radiative response to surface temperature variations driven by internal variability, they produce very different predictions from the patterns of surface temperature change in simulations forced with increasing CO2 concentrations. We discuss reasons for the discrepancies between methods and recommend paths towards using them in the future to enhance physical understanding of the pattern effect.

physics.ao-ph↗

arXiv2025

Spatial Controls of Lower Tropospheric Stability

Marine low clouds play a crucial role in Earth's radiation budget. These clouds efficiently reflect sunlight and drive the magnitude and sign of the global cloud feedback. Nevertheless, the evolution of shallow cloud decks over the last decades is not well understood. A dominant control of this low cloud cover is the lower tropospheric stability, quantified by the estimated inversion strength (EIS). We quantify how regional EIS depends on local and remote surface temperature, revealing the dynamics controlling the shallow cloud characteristics on annual timescales. We find that global EIS increases with warming in tropical regions of ascent and decreases with warming in regions of descent. In addition to the West Pacific Warm Pool, the Atlantic convection regions and the central Pacific are important predictors. Focusing on subtropical ocean upwelling regions in different ocean basins, where the low cloud decks reside, EIS increases with a fairly complex pattern of remote warming and decreases with local warming. The spatial relationship between surface temperature and EIS is robust across climate models and reanalyses, allowing us to constrain the spread in historical EIS trend estimates. In the Southeast Pacific, historical surface temperature decreased, but we attribute the observed EIS increase since 1980 entirely to remote warming. Our results challenge the canonical dominance of the West Pacific Warm Pool in controlling low cloud feedbacks in the eastern Pacific and give mechanistic insights into the spatial dependence of radiative feedbacks on surface temperature patterns.

physics.ao-ph↗

arXiv2025

Reanalysis-based Global Radiative Response to Sea Surface Temperature Patterns: Evaluating the Ai2 Climate Emulator

The sensitivity of the radiative flux at the top of the atmosphere to surface temperature perturbations cannot be directly observed. The relationship between sea surface temperature (SST) and top-of-atmosphere radiation can be estimated with Green's function simulations by locally perturbing the sea surface temperature boundary conditions in atmospheric climate models. We perform such simulations with the Ai2 Climate Emulator (ACE), a machine learning-based emulator trained on ERA5 reanalysis data (ACE2-ERA5). This produces a sensitivity map of the top-of-atmosphere radiative response to surface warming that aligns with our physical understanding of radiative feedbacks. However, ACE2-ERA5 likely underestimates the radiative response to historical warming. We compare to two additional versions of ACE and traditional climate models. We argue that Green's function experiments can be used to evaluate the performance and limitations of machine learning-based climate emulators by examining if causal physical relationships are correctly represented and testing their capability for out-of-distribution predictions.

physics.ao-ph↗