Abstract Increased aerosol concentrations can brighten low‐level clouds and extend their lifetimes, but aerosol–cloud interactions (ACI) remain highly uncertain and difficult to quantify. We show that part of this uncertainty is caused by topographical influences on clouds, that is, those arising from land–water contrasts. This is demonstrated using satellite retrievals in regions with extensive river networks, such as the Amazon Basin. 15 years of MODerate resolution Imaging Spectroradiometer (MODIS) satellite data show cloud formation over the Amazon River basin is suppressed by 26% with warm low clouds above the river exhibiting a 22% smaller droplet effective radius and 18% higher droplet concentration () compared to adjacent land clouds. Thus, clouds above the river may appear polluted but are actually influenced by river‐breeze circulations driven by the thermal contrast between the river and the surrounding land. These responses are robust in both wet and dry seasons, and tests using an improved MODIS retrieval product show cloud differences are unlikely due to retrieval artifacts. In situ measurements from the Green Ocean Amazon Experiment (GoAmazon) confirm that is elevated above rivers and are also higher when carbon monoxide concentrations are elevated near the large city of Manaus. Lagrangian airmass tracking over Manaus shows that regional‐scale river‐breeze circulations impact as much as the urban aerosol plume, complicating ACI attribution and highlighting the need to isolate land‐surface effects to assess ACI in continental regions.

Abstract In highly productive agricultural landscapes, human impacts combine with naturally variable climate conditions and geologic legacies to fundamentally alter critical zone (CZ) processes. For example, artificial drainage and fertilizer applications impact riverine transport of water and nutrients, and abrupt transitions in vegetation states impact land‐atmosphere fluxes of water and energy. Subsystem components may switch from being nearly independent to tightly synchronized, and drivers may elicit non‐linear or threshold responses. These shifts have implications for predictive understanding, but are challenging to identify and quantify. We present a flexible, data‐driven framework that identifies temporal regimes in CZ behavior, associated with particular environmental drivers and modes of variability. We integrate unsupervised clustering, dimensionality reduction, and information‐theoretic metrics to isolate temporal regimes and assess changes in drivers and predictability across several case studies. Specifically, we analyze high‐frequency time‐series data sets of root‐soil gases, land‐atmosphere fluxes, and river chemistry in intensively managed and more natural CZ study sites. Clusters and variability in these multivariate systems relate to rapid seasonal transitions in agricultural relative to prairie sites, and impacts of fertilizer timing on solute responses to events. Environmental drivers have variable explanatory power over system states that align with growing season, flows, or management. This study objectively detects dynamic transitions in CZ systems, and is more broadly applicable to any Earth system time‐series data or model evaluation to detect behavioral regimes and shifts in predictability of complex systems.

Abstract Machine learning (ML)‐based models have demonstrated high skill and computational efficiency, often outperforming conventional physics‐based models in weather and subseasonal predictions. While prior studies have assessed their fidelity in capturing synoptic‐scale atmospheric dynamics, their performance across timescales and under out‐of‐distribution forcing, such as +3K or +4K uniform‐warming forcings, and the sources of biases remain elusive, to establish the model's reliability for Earth science. Here, we design three sets of experiments targeting synoptic‐scale phenomena, interannual variability, and out‐of‐distribution uniform‐warming forcings. We evaluate the Neural General Circulation Model (NeuralGCM), a hybrid model integrating a dynamical core with ML‐based component, against observations and physics‐based Earth system models (ESMs). At the synoptic scale, NeuralGCM captures the evolution and propagation of extratropical cyclones with performance comparable to ESMs. At the interannual scale, when forced by El Niño‐Southern Oscillation sea surface temperature (SST) anomalies, NeuralGCM successfully reproduces associated teleconnection patterns but exhibits deficiencies in capturing nonlinear response. Under out‐of‐distribution uniform‐warming forcings, NeuralGCM simulates similar responses in global‐average temperature and precipitation and reproduces large‐scale tropospheric circulation features similar to those in ESMs. Notable weaknesses include overestimating the tracks and spatial extent of extratropical cyclones, biases in the teleconnected wave train triggered by tropical SST anomalies, and differences in upper‐level warming and stratospheric circulation responses to SST warming compared to physics‐based ESMs. The causes of these weaknesses were explored. Despite the noted weaknesses, NeuralGCM reproduces responses across experiments reasonably and performs comparably to ESMs. By integrating a dynamical core with ML, NeuralGCM shows potential for developing ML‐based ESMs.

Abstract The exponential growth of artificial intelligence (AI) has driven the rapid global expansion of data centers, raising serious concerns about their environmental impact—particularly water use. While national and global water consumption by data centers may seem modest compared to other users, their localized impacts can be significant—especially in regions already facing water stress or drought. This commentary examines the multi‐faceted water footprint of data centers, encompassing direct cooling, electricity generation, and supply chain water demands. It highlights major gaps in transparency around how much water data centers use, which undermine effective regulation, innovation, and community planning. To ensure the sustainable growth of digital infrastructure and the preservation of water resources, comprehensive monitoring and public disclosure of water use are essential. Equally important are resilient water infrastructure planning and stronger collaboration between industry and communities.

Abstract Oceanic stratification plays a key role in climate by modulating the ocean's resistance to be mixed vertically. However, the climatic variability of stratification remains little documented beyond the deepest reach of winter mixing. Here, we address this gap by performing a global investigation of the spatio‐temporal variability of stratification from the surface to the main pycnocline, using 20 years (2003–2022) of data from the Argo float array. Both near‐surface and main pycnocline stratification are found to exhibit spatially structured, vertically coherent, global‐scale variations on seasonal‐to‐decadal time scales, suggesting that stratification at both levels are rapidly‐evolving and responsive components of the climate system. Variability in stratification is organized into well‐defined patterns that replicate the spatial footprints and time scales of major climate modes, pointing to these modes of variability as important drivers of stratification changes. Our diagnosed patterns and forcings of stratification variability provide a benchmark for advancing the climate models used to understand and predict the ongoing climate change.

Abstract The continuous increase in atmospheric methane (CH 4 ) concentrations over the past few decades has become a major concern due to its strong role as a greenhouse gas contributing to climate change. In this work, we investigate the changes in the global methane budget using a global chemistry‐climate model constrained with methane and its isotopic observations. We apply spatially‐resolved isotopic signatures to better constrain the methane sources and include methane‐hydroxyl radical (OH) feedback to better represent methane sinks and lifetime in the model. While anthropogenic activities are found to be mainly responsible for the methane increase since the 1980s, the increasing OH trend simulated by the model plays a critical role in the global methane evolution. We find the observed post‐2006 shift of δ 13 CH 4 can be explained by increases in 13 C‐depleted agricultural and waste emissions in the tropics, coupled with decreasing 13 C‐enriched biomass burning emissions and an increasing OH trend. We also find post‐2006 emission increases in energy and agriculture sectors are large enough to offset the increasing sinks (due to increasing OH), and therefore are shown to contribute to the post‐2006 renewed methane growth. With CH 4 ‐OH feedback included in the model, the results show an increasing sensitivity to emission increases on methane concentrations and lifetime. Our study underscores the importance of OH in the global methane evolution. Neglecting changes in OH could potentially lead to misinterpreting emission changes with respect to the long‐term observations of methane and δ 13 CH 4 .

Abstract Cratonic lithospheres carry a long history of tectonic modifications that result in heterogeneous structures, as revealed by an increasing number of geophysical observations. The existence of cratonic basins indicates protracted periods of tectonic modification, causing subsidence within global continental interiors. An enigmatic aspect of this process is the cessation of subsidence in cratonic basins with unclear mechanisms. Here, using full‐wave ambient noise tomography, we reveal distinct seismic low‐velocity anomalies below 60 km beneath the Illinois and Michigan Basins, where subsidence terminated in the late Paleozoic to the early Mesozoic. These low‐velocity volumes, surrounded by distinctly higher velocities, are attributed to asthenospheric materials upwelling to shallow mantle depths during lithospheric foundering or delamination. This lithospheric modification may be associated with a major regional tectonic exhumation in the early Mesozoic that could have terminated basin subsidence and unroofed upper portions of basin stratigraphy. This timing coincides with the passage of this region over mantle plumes, which likely triggered lithospheric delamination and asthenospheric upwelling. Geodynamic modeling shows that the emplacement of these buoyant asthenospheric materials would lead to an uplift of about 3.5 km, sufficient to terminate the subsidence in the cratonic basins within this region. These findings document evidence of lithospheric delamination in the North American midcontinent and present important links between geodynamic drivers and geological records of the evolution of the cratonic lithosphere in North America and beyond. They also offer broader implications for understanding how deep Earth processes shape surface environments, influencing resource distribution and long‐term landscape evolution.

Abstract A recent report released by the U.S. Department of Energy concludes that U.S. tide‐gauge data in aggregate provide no evidence for relative sea‐level (RSL) acceleration above the historical mean trend. However, that conclusion rests largely on cursory analysis of a small number of tide‐gauge records that are known to be unrepresentative of large‐scale RSL behavior. Here I analyze all long active tide‐gauge RSL data records on the contiguous U.S. (CONUS) coast to make a comprehensive estimate of spatially averaged RSL changes over the CONUS (CONUS RSL) during the past 125 years. I find that long‐term rates of CONUS RSL rise doubled in the past century, from about 1.7 mm in 1900 to roughly 4.3 mm in 2024, and that recent rates are higher than the longterm historical mean rate since 1900, which is approximately 3.0 mm . That is, CONUS tide gauges give obvious evidence of RSL acceleration, which is likely related to ongoing climate change.

Abstract Understanding how compound extremes affect terrestrial ecosystems is a major challenge in Earth system science. Although the combined effects of stressors are recognized, the manner in which the prestress state determines the basic response mechanism remains unclear. In this study, we used the “natural experiment” methodology to compare two major extreme events within a monsoon‐influenced, low‐latitude highlands setting to explain mechanistic changes in land‐atmosphere interactions. By analyzing an extensive set of remote sensing and reanalysis data with nonlinear structural equation modeling, we show that the ecosystem response shifted from a traditional water‐limited paradigm during the 2010 drought to an energy‐governed paradigm during the 2019 heatwave. Our results suggest that this transition is governed by the antecedent root zone soil moisture status, which acts as a tipping point fundamentally shifting the impact of atmospheric factors on canopy evapotranspiration screens, such as temperature and vapor pressure deficit. This study highlights a possible threshold‐type state dependence non‐linearity, which is lacking in major Earth System Models. Incorporating this “hydrological memory” is crucial for minimizing uncertainties in climate projections and for correctly assessing the vulnerability of ecosystems in a warming world.

Abstract The Arctic Ocean double estuary is a “three‐legged” overturning system in which inflowing waters are converted into both lighter and denser waters before being exported equatorwards. As the northern terminus of the Atlantic Meridional Overturning Circulation (MOC), it thus both affects, and is affected by, the Atlantic MOC. Here we quantify the magnitudes of the two overturning cells in density space, and then decompose the water mass transformation rates into net pan‐Arctic contributions from surface forcing and diapycnal mixing. We use a high‐resolution, quasi‐synoptic ice and ocean hydrographic data set spanning the four main Arctic Ocean gateways—Fram, Davis and Bering Straits, and the Barents Sea Opening. Two surface flux reanalyses and a hydrographic climatology are used to generate estimates of surface water mass transformation rates by density class. A box model then determines the profiles of turbulent mixing transformation rates, and associated turbulent diffusivities. We show that turbulent mixing and surface forcing drive transformations of similar magnitudes, while mixing dominates in the upper cell and surface fluxes in the lower cell. Consideration of uncertainties and timescales leads to the tentative suggestion that our results might be representative of recent decades. We discuss the possible significance of tides and sea ice brine rejection as energy sources driving turbulent mixing. Finally, we speculate as to whether water mass transformation rates may change in future as ocean heat transport into the Arctic increases. As sea ice declines and the efficiency of atmosphere‐to‐ocean momentum transfer increases, the Arctic Ocean is expected to “spin up,” causing more intense turbulent mixing, with uncertain consequences.