Land, Food, and Environment Systems

Abstract. An intermediate-complexity moist general circulation model is used to investigate the forcing of localized storm tracks by land–sea contrast, horizontal gradients in ocean heat uptake, planetary albedo, and topography. The additivity of the response to these building blocks is investigated. Building on previous work focusing on stationary waves, the storm track patterns and strength are not simply the linear additive sum of the response to each surface inhomogeneity. As observed on Earth, the SH storm tracks are stronger than those in the NH, and also stronger over ocean basins than over continents. In this model, the most important building block for this asymmetry is land-sea contrast, however, there is substantial non-additivity both in the regional structure and also the hemispheric asymmetry. An energy budget perspective offers some insight on the causes of the non-additivity, and highlights how the net impact of each building block on outgoing longwave radiation is dependent on the existence of the other two. Relatively small changes in oceanic heat transport from the Southern Ocean to the North Atlantic have a pronounced impact on the individual terms making up the energy budget, however there is substantial cancellation between these terms leading to a small impact on the NH vs. SH asymmetry in storm track strength. The detailed structure of albedo has a weak impact on the NH vs. SH asymmetry due to substantial cancellation between the changes in individual terms making up the energy budget, even though the albedo profile has a large impact on the overall transient eddy activity in each hemisphere.

Urban heat disproportionately burdens socioeconomically vulnerable communities, yet how these disparities change over time remains poorly understood. Here we show that heat disparities between low- and high-income neighborhoods generally narrowed across 55 major cities in the United States from 1986 to 2020, although substantial inequities persisted. This narrowing coincided with faster environmental improvements in disadvantaged neighborhoods, particularly greater increases in greenness. Disparity trajectories varied across climate zones, with temperate and cold cities showing narrowing heat gaps, whereas arid cities more often showed persistent or widening gaps. At the city level, environmental disparities followed diverse pathways depending on changes in both overall environmental conditions and the distribution of change between income groups. Changes in greenness disparities were more strongly associated with changes in heat disparities than were changes in cooling efficiency. These findings highlight the dynamic nature of urban environmental inequality and the importance of equitable greening in urban climate adaptation. This study finds that heat disparities between high- and low-income neighborhoods narrowed across 55 U.S. cities from 1986 to 2020, alongside faster greening in disadvantaged neighborhoods, although substantial inequalities persisted.

The Madden-Julian oscillation (MJO) is a major source of subseasonal-to-seasonal (S2S) predictability. The MJO is commonly defined and tracked with indices such as the Real-time Multivariate MJO (RMM) index. Although the RMM provides a useful description of the MJO, its evolution can be noisy and difficult to predict. We define an MJO index using a data-driven approximation of the Koopman operator. The Koopman index captures similar tropical circulation and convection patterns to the RMM but evolves more smoothly and predictably. Skillful prediction extends to 46 days for the Koopman index compared to 11 days for the RMM under the same prediction framework. While this new approach does not recover the RMM as well as operational S2S models, which provide skillful forecasts up to 35 days, the Koopman index could complement existing MJO diagnostics in evaluating and developing extended-range forecast systems.

Extreme precipitation events are increasing their frequency and intensity across the United States, posing growing risks to urban communities through pluvial flooding. Effective adaptation against pluvial flooding through structural and nonstructural measures requires understanding the spatial distribution of flood risks across urban populations and critical infrastructures. However, most flood risk assessments focus narrowly on the direct impacts of flooding and demographic exposures, overlooking disruptions that may result from indirect exposures and impacts to essential services like healthcare, education, welfare, and public safety. This study introduces an expanded risk framework to assess pluvial flood risk by integrating social vulnerability, population exposure, and the exposure of critical infrastructures across two citywide pluvial flooding scenarios that combine different precipitation events (moderate and extreme) and sea level projections. Analyses reveal vast differences in exposure between the scenarios. The extreme rainfall hazard scenario shows flooding in 3.8 times the area of the city compared to the moderate scenario, and affecting 3.1 times as many people. Exposure indicators for critical facilities and services show similar differences. Results show multiple locations where there is both high flood exposure and high social vulnerability. We also identify several neighborhoods where high flood exposure coincides with high social vulnerability and widespread risk to infrastructure, which can compound recovery challenges. These findings demonstrate the importance of broader framings of flood risk beyond population exposure to consider also social vulnerability and potential risks to critical infrastructure. Results further emphasize the need for equity-centered climate adaptation strategies in dense urban settings.

AI weather emulators benefit from clear objectives and metrics, which have led to the rapid development of models that outperform traditional benchmarks. In contrast, long-term climate emulators must reliably reproduce forced responses over months to centuries, while relying on training objectives that span a small number of model time steps. We assess autoregressive, full-depth ocean emulators using data from the midHolocene experiment of a numerical climate model to examine their skill in responding to surface forcings from an in-distribution, out-of-sample climate. We demonstrate that these emulators generalize to new orbital forcings, reproducing the spatial structure of the large-scale response as well as changes in seasonal patterns and in the spatial structure of ocean variability, while underestimating their amplitude. Baselines that infer the ocean state directly from the boundary forcings also recover much of the large-scale pattern, but only near the surface, and capture neither the seasonal nor the variability changes, indicating that these require some representation of dynamics. Despite these successes, the emulators fail to reproduce the slow, internally driven evolution of the ocean interior. We then show that the emulators' total forced response is well reconstructed by linearly composing their independent responses to each forcing component. Tracking response across training epochs, we find that convergence on mean state metrics in the training climate does not guarantee that the emulators capture the dynamics necessary for a skillful response. Together, these experiments establish the midHolocene as a controlled, ground-truthed setting for diagnosing forced-response failures before emulators are pushed to out-of-distribution climates.

Abstract We introduce FloeNet, a graph neural network trained to emulate the Geophysical Fluid Dynamics Laboratory global sea ice model, SIS2. FloeNet is a mass‐conserving model, emulating 6‐hr mass and area budget tendencies related to sea ice and snow‐on‐sea‐ice growth, melt, and advection. We train FloeNet using SIS2 data from a reanalysis‐forced ice‐ocean simulation and test its ability to generalize to pre‐industrial and 1% CO 2 forcing conditions. FloeNet outperforms a non‐conservative model at reproducing sea ice and snow‐on‐sea‐ice mean state, trends, and inter‐annual variability, with volume anomaly correlations above 0.96 in the Antarctic and 0.76 in the Arctic, across all forcings. FloeNet also produces the correct thermodynamic‐dynamic response to forcing, enabling physical interpretability of emulator output. Finally, we show that FloeNet outputs high‐fidelity coupling‐related variables, including ice‐surface temperature, ice‐to‐ocean salt flux, and melting energy fluxes. We hypothesize that FloeNet will improve polar climate processes within existing atmosphere and ocean emulators.

Abstract Much of the outgoing longwave radiation (OLR) emitted to space can be described as a noisy “background” of random variability. A rigorous characterization of this background provides an objective null spectrum and enables the isolation of atmospheric phenomena within OLR observations, such as waves and storms. Previously, the background has only been considered in the Tropics. Here we study the background on global, subannual scales and focus on its physical origins. We define the background as isotropic fluctuations implied by the fluctuation‐dissipation theorem in response to internal atmospheric variability on small spatiotemporal scales. We use a stochastically forced energy balance climate model that generates a red spectrum in space and time, consistent with observations. By fitting the model to OLR measurements from satellites, we find that the background fluctuations have an upper bound of about 400 km and 2.5 days on their spatiotemporal (de)correlations, between meso‐scale and synoptic‐scale weather.

Abstract Global ocean models exhibit biases in the mean state and variability, particularly at coarse resolution, where mesoscale eddies are unresolved. To address these biases, parameterization coefficients are typically tuned ad hoc. Here, we formulate parameter tuning as a calibration problem using Ensemble Kalman Inversion (EKI). We optimize parameters of a neural network parameterization of mesoscale eddies in two idealized ocean models at coarse resolution. The calibrated parameterization reduces errors in the time‐averaged fluid interfaces and their variability by factors of 1.7–3.3 compared to the unparameterized model, depending on the metric and configuration. The EKI method is robust to noise in time‐averaged statistics arising from chaotic ocean dynamics. Furthermore, we propose an efficient calibration protocol that bypasses integration to statistical equilibrium by carefully choosing an initial condition. These results demonstrate that systematic calibration can substantially improve coarse‐resolution ocean simulations and provide a practical pathway for reducing biases in global ocean models.

High Resolution Image Download MS PowerPoint Slide Three Enterococcus species (i.e., casseliflavus, faecalis, and faecium) were previously found to have dissimilar decay kinetics during disinfection. It is therefore possible for the relative abundances of viable bacteria of each species to shift as the bacterial population is subjected to treatment processes, which may subsequently impact the disinfection of the enterococci population as a whole. Therefore, this study was conducted to assess the relative abundances of different enterococci species in wastewater as it flows through treatment processes. Culture-based, qPCR, and hybrid culture-qPCR methods were used to assess the distribution of E. faecalis, E. faecium and E. casseliflavus in samples of wastewater influent, primary effluent, secondary effluent, and postchlorination final effluent; grab samples were collected quarterly over a year from one facility. Using qPCR, E. faecium concentrations ranged between 1.1- and 2.6-log greater than E. faecalis and E. casseliflavus. The species distribution of culturable isolates did not change between influent and secondary effluent; however, it was significantly different postchlorination, with E. faecium being the only culturable species identified. While this study assessed just one treatment facility, consistent results over a year illustrate that culture-based methods do not provide a full picture of enterococci disinfection and persistence, given the varying relative abundances of viable Enterococcus species.

Sea state prediction is essential for operational maritime applications and coupled earth system modeling, yet current spectral wave models remain computationally prohibitive for many use cases, including online coupling to climate simulations and making probabilistic (ensemble-based) predictions. While deep learning has recently demonstrated strong performance in weather forecasting, existing AI-based wave models are predominantly deterministic and largely limited to bulk variables such as significant wave height, leaving probabilistic sea state estimation largely unexplored. In this work, we propose a diffusion-based generative model for global sea state estimation that conditions on a relatively long history (5 days) of global wind forcing. This generative model directly samples the complex conditional distribution of sea state without autoregressive time-stepping. Unlike prior approaches, our framework naturally extends beyond bulk variables to estimate partition-related variables and derived quantities, such as Stokes drift and mean square slope. Trained on a 30-year global WAVEWATCH-III hindcast, the model achieves substantial computational acceleration compared with numerical spectral models while delivering skillful predictions and a calibrated ensemble spread for the bulk variables. Our results suggest that diffusion-based sea state sampling offers a promising path toward probabilistic wave forecasting and efficient coupling of sea state information into broader earth system models.

Interpretable Neural Networks to Predict Momentum Fluxes of Orographic Gravity Waves, Haslauer, Elias Johannes, Schwabe, Mierk, Dörnbrack, Andreas, Gerber, Edwin P., Rapp, Markus, Žagar, Nedjeljka, Eyring, Veronica

Weather extremes pose major societal risks, especially in a changing climate, but due to their rarity, they are difficult to study using limited observations or complex climate models. We introduce AI+RES, a framework coupling fast AI weather forecasts with a high-fidelity physics model using a rare-event algorithm to efficiently characterize extremes. This approach enables the study of the statistics and physics of very rare events, such as once per millennium heat waves at two orders-of-magnitude lower computational cost. AI+RES can be applied broadly across climate science and other fields concerned with rare events.

Urban flooding poses significant risks to public health and community resilience, highlighting the need for collaborative approaches that integrate science, policy, and local knowledge. This paper outlines the community engagement and dissemination strategy of FloodNet NYC, a collaborative project launched in 2020 involving researchers from the City University of New York and New York University alongside New York City agencies, including the Mayor’s Office of Climate & Environmental Justice, Department of Environmental Protection, and Office of Technology & Innovation. FloodNet develops real-time urban flood monitoring tools, making flood data and community-driven resources accessible and actionable for residents, community organizations, government agencies, and researchers. Guided by community-based participatory research principles, the project co-developed a community engagement and participatory dissemination strategy that is integrated throughout the research process rather than treated as a final step. We highlight three participatory pathways — community outreach, education, and action — that ensure that data products and educational materials produced by FloodNet are created and shared collaboratively with community members in flood-prone neighborhoods. This approach yields both successes and challenges, demonstrating how co-produced and iterative dissemination deepens integration of scientific, experiential, and civic knowledge. This paper presents a practical model for public health and urban climate resilience professionals to engage communities in research.

Abstract Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land surface, and other geophysical processes. This paradigm allows for distributed development of individual components within a common framework, unified by a coupler that handles translation between realms via spatial or temporal alignment and flux exchange. Following a similar approach adapted for machine learning‐based emulators, we present SamudrACE: a coupled global climate model emulator which produces centuries‐long simulations at 1‐degree horizontal, 6‐hourly atmospheric, and 5‐daily oceanic resolution, with 145 2D fields spanning 8 atmospheric and 19 oceanic vertical levels, plus sea ice, surface, and top‐of‐atmosphere variables. SamudrACE is highly stable and has low climate biases comparable to those of its components with prescribed boundary forcing, with realistic variability in coupled climate phenomena such as ENSO that is not possible to simulate in uncoupled mode.

Ocean general circulation models (OGCMs) are essential to climate science but computationally expensive, limiting ensemble size and forcing scenarios. Neural emulators promise orders-of-magnitude speedups, yet existing ocean emulators have not combined fine spatial resolution with multi-year autoregressive rollouts. Samudra, the first autoregressive neural ocean emulator to produce multi-decade global rollouts, is limited to 1∘ resolution and exhibits two long-horizon failure modes: \emph{variance collapse}, the loss of temporal variability, and \emph{imprinting artifacts}, in which velocity patterns leak into deep-ocean fields. We present Samudra 2, which introduces a wider U-Net backbone with modified ConvNeXt-style blocks and a reduced block-internal expansion factor, together with a dynamic loss that reweights output channels according to their prediction errors, strengthening gradients for slow-evolving deep-ocean fields. At 1∘, Samudra 2 increases upper-ocean global-mean temperature R² from 0.56 to 0.87 and reduces deep-ocean temperature error by roughly sevenfold. The same architecture scales to 1/2∘ and 1/4∘ over approximately 8-year autoregressive rollouts, recovering mesoscale eddies and sharp western boundary currents. Running on a single GPU, Samudra 2 enables larger ensembles for sea-level projections, ocean heat uptake, and climate variability studies. All artifacts are publicly available: project page, code, checkpoints, documentation.

Accurate high-resolution precipitation data is critical for climate risk assessment in Sub-Saharan Africa, yet global climate models operate at coarse resolutions, failing to capture localized extremes. While deterministic deep learning models offer efficient downscaling, our evaluation shows that minimizing pointwise error inherently penalizes high-frequency features, artificially smoothing precipitation fields and suppressing extremes regardless of architectural complexity. Here, we propose a conditional denoising diffusion model for precipitation downscaling, evaluating its structural realism, physical interpretability, and resolution limits against deterministic baselines. The diffusion model successfully recovers sub-grid variability, preserving near-perfect spatial variance and heavy-tailed extreme event distributions where deterministic approaches fail. Through perturbation-based feature importance analysis, we demonstrate the generative framework acts as a context-aware emulator of atmospheric physics. It dynamically shifts predictor reliance, prioritizing low-level moisture convergence in rugged terrain and dry seasons, while favoring mid-tropospheric dynamic forcing during monsoons. Furthermore, by progressively coarsening inputs from 0.25° to 1.0°, we interrogate generative downscaling limits. As spatial constraints weaken at 1.0°, the model generates unconstrained stochastic false alarms, and the resulting ensemble smoothing directly quantifies predictive spatial uncertainty under severe information loss. A moderate 0.5° input emerges as an optimal balance, providing sufficient structural anchors to maintain both deterministic accuracy and extreme event preservation. Ultimately, this work provides a physically interpretable, resolution-aware framework for generative downscaling, offering a robust strategy for translating coarse climate projections into localized risk assessments.

Accurate emulation of multi-scale physical systems governed by PDEs demands models that remain stable over long autoregressive rollouts while preserving fine-scale structures. Deterministic emulators produce overly-smoothed predictions, while generative approaches better capture details but are costly. Latent-space generative models have emerged as a compromise but with the additional cost of separately pre-trained autoencoders. We propose Wavelet Flow Matching (WFM), a novel generative emulator that overcomes current trade-offs between cost and skill by performing optimal-transport directly in the multi-scale wavelet space. Rather than learning a latent compression, WFM leverages the hierarchical structure of a U-Net to jointly predict transport velocities of a prescribed wavelet representation. On three challenging systems of chaotic fluid dynamics, WFM achieves superior long-horizon stability, accuracy and spectral coherence compared to state-of-the-art models. Our results clearly position the wavelet space as an effective training-free representation for generative emulation of complex physical dynamics.

Improvements of Machine Learning (ML)-based radiation emulators remain constrained by the underlying assumptions to represent horizontal and vertical subgrid-scale cloud distributions, which continue to introduce substantial uncertainties. In this study, we introduce a method to represent the impact of subgrid-scale clouds by applying ML to learn processes from high-resolution model output with a horizontal grid spacing of 5 km. In global storm resolving models, clouds begin to be explicitly resolved. Coarse-graining these high-resolution simulations to the resolution of coarser Earth System Models yields radiative heating rates that implicitly include subgrid-scale cloud effects, without assumptions about their horizontal or vertical distributions. We define the cloud radiative impact as the difference between all-sky and clear-sky radiative fluxes, and train the ML component solely on this cloud-induced contribution to heating rates. The clear-sky tendencies remain being computed with a conventional physics-based radiation scheme. This hybrid design enhances generalization, since the machine-learned part addresses only subgrid-scale cloud effects, while the clear-sky component remains responsive to changes in greenhouse gas or aerosol concentrations. Applied to coarse-grained data offline, the ML-enhanced radiation scheme reduces errors by a factor of 4–10 compared with a conventional coarse-scale radiation scheme. This shows the potential of representing subgrid-scale cloud effects in radiation schemes with ML for the next generation of Earth System Models.

Abstract Neural networks offer novel ways to parameterize unresolved ocean mixing but are challenging to interpret. Here, we derive compact equations that reproduce the behavior of neural networks trained on a second‐moment closure data set. The resulting interpretable expressions employed in a physics‐based first order closure scheme match neural‐network performance in global forced simulations. They expose a structural error in the baseline physics‐based scheme and describe how surface friction velocity, buoyancy flux, rotation, and boundary layer depth regulate diffusivity. The equations reveal a shift in the mixing peak toward the surface under stabilizing conditions and toward the mid‐boundary layer depth under convective conditions. The diffusivity amplitude (set by the velocity scale) is controlled by surface shear and buoyancy flux. Equations yield a transparent, efficient, and physically grounded vertical diffusivity applicable for ocean models.

Abstract Understanding how the fractional area of different rain types, including shallow convective, deep convective, and stratiform, varies with large-scale environmental conditions is fundamental to advancing our knowledge of moist atmospheric processes. Yet, this relationship remains poorly quantified, especially at instantaneous time scales where observational uncertainty and inherent variability pose challenges. In this study, we tackle this problem by integrating satellite-derived rain area data from Tropical Rainfall Measuring Mission (TRMM) with environmental variables from ERA5, using a two-step framework that combines conditional normalizing flows (CNFs) with symbolic equation discovery. The CNF model reconstructs the full conditional joint distribution of three rain area types, capturing their codependence and reproducing observed features such as the bimodal relationship between convective and stratiform area. From these distributions, we extract the conditional means and then apply equation discovery to derive compact analytical expressions that relate rain area fractions to large-scale thermodynamic variables. The resulting equations reveal interpretable and physically consistent predictors, such as lower-tropospheric stability, free-tropospheric moisture deficit, and boundary layer buoyancy, that act as both triggering thresholds and predictors of rain area magnitude. These expressions capture well-known mechanisms in closed form, opening a path toward stochastic, physically grounded parameterizations. Our phase space analysis clarifies how rain types evolve across sea surface temperatures and stability regimes, linking environmental variability to the spatial extent of convective systems—crucial for precipitation prediction and cloud–radiation interactions. Together, this framework provides a probabilistically robust, physically based approach for modeling rain area, with implications for both process understanding and parameterization.

Pretrained diffusion models provide powerful learned priors, but in scientific sampling the target distribution often depends on physical context that is not fully represented by one generative model. We introduce Generative Gibbs for Physics-Aware Sampling (GG-PA), a training-free framework that formulates the composition of learned partial priors and explicit physical context as inference over a joint target distribution in an augmented state space. We derive a Gibbs sampler for this joint target, show that it is asymptotically exact as the diffusion time approaches zero, and prove that in settings with quadratic interactions it remains exact at finite diffusion times. We further introduce replica exchange over diffusion time to accelerate mixing. Experiments on a double-well system, a ϕ⁴ lattice model, and atomistic peptide systems show that GG-PA recovers context-induced distribution shifts and emergent collective behavior in interacting systems using partial priors without retraining. These results demonstrate GG-PA as a practical approach for combining pretrained generative priors with explicit physical context.

Abstract We present a novel, data‐driven approach to predict systematic model errors in the ocean component of a coupled general circulation model leveraging deep learning and data assimilation. We examine the skill of the proposed scheme in learning systematic model errors, including their spatial patterns, variance, scales, and test its sensitivity to different predictors and neural network architecture. The scheme utilizes local state variables such as ocean temperature, salinity, velocities, and surface fluxes to predict corrections to temperature tendency for the upper 1,000 m in the ocean on daily timescales. The performance is evaluated on the withheld test data set and compared against the empirical climatological temperature corrections that are geographically dependent. The performance is depth‐dependent, with significant improvements over the benchmark in the upper 20 m in the ocean. It degrades rapidly with depth but remains comparable to the climatology benchmark. Neural networks can capture up to of the daily variance in temperature increments in the upper 20 m relative to the benchmark's. The improvements are associated with networks predicting finer spatiotemporal scales than the benchmark. They are expected to perform better in reducing surface ocean mixed layer bias than previously used techniques. Despite being column‐local without geographical inputs, networks can sufficiently reproduce spatial patterns on daily and longer timescales. The patterns consist of corrections to regional dynamical features such as western boundary currents, equatorial undercurrents, bathymetry‐related corrections in the Southern Ocean, and warm surface increments over subtropical and midlatitude belts.

Mesoscale eddies remain poorly represented in most climate models, motivating the use of parameterizations to account for their dynamical effects on the coupled system. In this study, we implement a data-driven eddy parameterization based on Zanna and Bolton (2020; ZB20) in an idealized, fully coupled CESM configuration and assess its influence on the mean climate state. When applied within an eddy-permitting ocean model (MOM6) embedded in the coupled configuration, the ZB20 eddy momentum parameterization, which features upgradient (backscatter) momentum flux, energizes mesoscale eddies and strengthens poleward ocean heat transport. The response is particularly strong in the Southern Hemisphere, where the open circumpolar channel sustains vigorous eddy activity and is sensitive to the parameterization, further leading to a marked hemispheric asymmetry. The oceanic meridional overturning circulation also intensifies around 60°S. The resulting ocean adjustments produce a coherent dipolar temperature pattern, with cooling in mid-latitudes and warming at high latitudes, driven primarily by anomalous meridional heat transport rather than local surface fluxes, shown using a regional heat-budget analysis. The atmosphere, in turn, exhibits a compensating reduction in meridional heat transport and an equatorward shift of the mid-latitude jet, associated with the mid-latitude surface cooling and changes in the meridional temperature gradient. Together, these results highlight how a data-driven eddy momentum parameterization can affect large-scale circulation and the mean climate state, providing a reference for understanding its impacts in more comprehensive climate models.

Abstract Accurately quantifying air‐sea fluxes is important for understanding air‐sea interactions and improving coupled weather and climate models. This study introduces a probabilistic framework to represent the highly variable nature of air‐sea fluxes, which is missing in deterministic bulk algorithms. Assuming Gaussian distributions conditioned on the input variables, we use artificial neural networks and eddy‐covariance measurement data to estimate the mean and variance by minimizing negative log‐likelihood loss. The trained neural networks provide alternative mean flux estimates to existing bulk algorithms, and quantify the uncertainty around the mean estimates. A stochastic parameterization of air‐sea turbulent fluxes can be constructed by sampling from the predicted distributions. Tests in a single‐column forced upper‐ocean model suggest that changes in flux algorithms influence sea surface temperature and mixed layer depth seasonally. The ensemble spread in stochastic runs is most pronounced during spring restratification.

Recent advances in autoregressive neural surrogate models have enabled orders-of-magnitude speedups in simulating dynamical systems. However, autoregressive models are generally prone to distribution drift: compounding errors in autoregressive rollouts that severely degrade generation quality over long time horizons. Existing work attempts to address this issue by implicitly leveraging the inherent trade-off between short-time accuracy and long-time consistency through hyperparameter tuning. In this work, we introduce a unifying mathematical framework that makes this tradeoff explicit, formalizing and generalizing hyperparameter-based strategies in existing approaches. Within this framework, we propose a robust, hyperparameter-free model implemented as a conditional diffusion model that balances short-time fidelity with long-time consistency by construction. Our model, Self-refining Neural Surrogate model (SNS), can be implemented as a standalone model that refines its own autoregressive outputs or as a complementary model to existing neural surrogates to ensure long-time consistency. We also demonstrate the numerical feasibility of SNS through high-fidelity simulations of complex dynamical systems over arbitrarily long time horizons.

Abstract Rivers shape their floodplains through meander growth and cutoffs, which reorganize channel geometry. Such threshold events are thought to limit geomorphic predictability, yet whether cutoffs alone are sufficient to generate deterministic chaos remains unresolved. We test this question using a kinematic meander model formulated at fixed spatial resolution to track the divergence, measured by the Hamming distance, between trajectories that begin from infinitesimally perturbed initial channel conditions. Using a counterfactual numerical experiment that disables cutoffs, we find that trajectories with cutoffs exhibit sustained exponential divergence, whereas those without cutoffs do not. The inferred growth rate, measured by the finite-time Lyapunov exponent, converges with grid resolution, is insensitive to perturbation magnitude, and is consistent across diverse initial planforms. Notably, we find that the Lyapunov exponent scales with migration rate but remains effectively invariant to the cutoff threshold, whereas the cutoff threshold regulates the frequency of topological resets. Thus, in kinematic models, cutoffs alone produce sensitive dependence on initial conditions and define a finite predictability horizon that is bounded by the mode of cutoff formation.

Abstract Representing mesoscale turbulence in eddy‐permitting ocean models raises challenges for climate simulations; in such models, eddies and their associated energy and transport effects are resolved either marginally or only over parts of the domain. Kinetic energy backscatter parameterizations have recently shown promise as both a momentum and a buoyancy closure for partially resolved mesoscale turbulence—energizing eddies which can themselves maintain accurate large‐scale stratification by slumping steep isopycnals. However, it has not been systematically explored whether such backscatter parameterizations can also serve as a closure for tracer mixing along isopycnals. Here, we present simulations using GFDL‐MOM6 in an idealized basin‐scale configuration to assess whether isopycnal mixing is improved, at 1/2 and 1/4 eddy‐permitting resolutions, through the addition of a backscatter parameterization. We assess the representation of isopycnal mixing principally through diagnosing the three‐dimensional structure of isopycnal diffusivities via a multiple tracer inversion method. Isopycnal mixing via backscatter alone shows significant improvement and closely resembles a 1/32 eddy‐resolving simulation. Backscatter‐parameterized mixing also outperforms simulations with no mesoscale parameterization or with an isopycnal diffusion parameterization alone, with the latter damping the tracer signature of partially resolved eddy variability. Simulations that vary the magnitude of backscatter show that increases in isopycnal diffusivities largely track increases in eddy energy. Our results suggest that parameterizing backscatter can plausibly capture key mesoscale physics in a unified framework: the inverse cascade of kinetic energy, the slumping of steep isopycnals, and the along‐isopycnal mixing of tracers.

Our Editorial team reflects on the journals’ first five years and discusses our continued aspiration to be an outlet for thought provoking and effective syntheses of essential advances in urban sustainability science and policy.

A central challenge across science and engineering is to build data-driven reduced-order models of turbulent dynamical systems that reproduce stationary statistics, predict responses to external perturbations, and remain practical for real-world applications. To this end, we introduce an abstract discrete-time formulation of turbulent dynamical systems with exact energy-conserving nonlinearities. Parameterizing this structure with neural networks yields stable, physics-constrained reduced-order models. We then use the fluctuation-dissipation theorem (FDT) to validate the emulators' forced responses from unperturbed data alone, testing models beyond stationary statistics. The FDT also identifies candidate direct causal links, which are used as regularization terms only when they pass validation. We first test the framework on two idealized models of geophysical turbulence: the proposed emulators reproduce stationary statistics and accurately predict responses to weak and strong forcings, despite being trained solely on unperturbed data. We then move beyond idealized systems to model tropical climate dynamics from reanalysis data, where data scarcity, partial observability, and sensitivity to choices of stochastic parameterizations become central challenges. The resulting physics-constrained model reproduces key statistics of the El Niño-Southern Oscillation (ENSO) and qualitatively captures its cumulative responses to perturbations; augmenting it with a non-Markovian stochastic closure substantially improves quantitative agreement with the FDT benchmark. This response-validated model is then used to characterize long-term causal drivers of ENSO variability. The proposed methodology establishes a modular framework for stable reduced-order models capable of probing causal mechanisms in realistic, partially observed turbulent systems.

The critical nexus between biodiversity loss, climate change, and societal change is increasingly intertwined, requiring coordinated action, especially in urban contexts. This study examines how urban governance operationalizes the climate-biodiversity-health nexus in four European case cities through a goals-oriented framework informed by the Planetary Health approach. We conduct a qualitative analysis of urban policy documents to assess the degree of change and level of coordination across climate mitigation, adaptation, biodiversity, and health domains. While cities have employed transformative solutions like nature-based solutions (NBS), we consistently identify policy blind spots such as fragmented mid-level targets, sectoral silos, insufficient attention to indirect emissions, and reliance on soft governance tools. We conclude by offering actionable insights for transformative urban nexus governance: mainstream transformative metrics and indicators, create new institutional innovations, integrate multi-benefit NBS across sectors, expand governance toolkits to address trade-offs, and co-create a culture of innovation, learning, co-creation and leadership.

Abstract Machine learning for the parameterization of subgrid‐scale processes in climate models has been widely researched and adopted in a few models. A key challenge in developing data‐driven parameterization schemes is how to properly represent rare, but important events that occur in geoscience data sets. We investigate and develop strategies to reduce errors caused by insufficient sampling in the rare data regime, under constraints of no new data and no further expansion of model complexity. Resampling and importance weighting strategies are constructed with user defined parameters that systematically vary the sampling/weighting rates in a linear fashion and curb too much oversampling. Applying this new method to a case study of gravity wave momentum transport reveals that the resampling strategy can successfully improve errors in the rare regime at little to no loss in accuracy overall in the data set. The success of the strategy, however, depends on the complexity of the model. More complex models can overfit the tails of the distribution when using non‐optimal parameters of the resampling strategy.

Extreme events pose significant risks and are challenging to predict. Assessing climate hazards requires placing quantitative constraints on geophysical fields under observable but fluctuating conditions. We propose a framework for estimating uncertainties -- a ReLU-bias loss quantile neural network (RBLQNN) -- with two novel modifications to the loss function to enforce uniform quantile accuracy and reduce degenerate predicted probability distributions. We evaluate the RBLQNN against other probabilistic baselines on a suite of datasets: synthetic datasets, observed daily temperature maxima from 1,501 NOAA Global Surface Summary of the Day (GSOD) weather stations, and altimetry-observed precipitation from the Tropical Rainfall Measuring Mission (TRMM). On synthetic datasets, the RBLQNN accurately predicts conditional distributions where more restrictive methods like linear quantile regression (LQR) or mean-variance estimation (MVE) neural networks fail, mitigates shortcomings of some other quantile neural networks, and converges stably under a range of hyperparameters. When applied to daily temperature maxima, the RBLQNN reveals that temperature distributions are relatively well described by Gaussian statistics, though nonlinear dependencies on local sea level pressure and geopotential heights appear important. For precipitation statistics, the RBLQNN strongly outperforms both LQR and MVE baselines, demonstrating its capacity to capture highly nonlinear and non-Gaussian conditional distributions. The RBLQNN's performance across varied datasets demonstrates it is a flexible and general approach for constraining uncertainties in geophysical quantities with nonlinear or non-Gaussian conditional dependencies.

As urbanization intensifies, communities encounter increasing challenges in designing, planning, and managing urban green spaces. Co-design offers a just and participatory approach that can unite diverse interest-holders to identify challenges and devise transformative solutions to complex urban issues such as green space governance. However, while co-design is acknowledged for its potential to foster learning and systemic change, it largely remains anthropocentric, often overlooking multispecies perspectives in planning, policy, and academia. We present a case study from New York City that examines how a multispecies co-design approach may enhance social-ecological systems transformation by exploring participants’ learning experiences and perceptual shifts. The paper begins by exploring the literature on co-design and multispecies thinking within social-ecological systems and their contribution toward transformations, establishing the conceptual groundwork for integrating non-human actors into urban planning. We then present a case study of a multispecies co-design process in New York City and explore how engaging with it contributes to systems transformation. Findings from the case study reveal key motivations for participation, drivers of learning during the process, and emergent transformative solutions. By analyzing these dynamics, this research illustrates how co-design, which integrates multispecies perspectives, may refine ecological decision making and encourage deeper engagement with urban nature. The study argues that integrating multispecies approaches into co-design practices is essential for fostering sustainable societies. The paper concludes with guiding principles for researchers and practitioners seeking to implement inclusive co-design processes in social-ecological systems, particularly in complex urban environments such as New York City. Non-Technical Summary As cities grow, it becomes more difficult for communities to design and care for green spaces in ways that work for everyone. Co-design is a collaborative approach that brings different people together to identify problems and create shared solutions, but it usually focuses only on human needs. This paper explores how including non-human life, such as plants and animals, in co-design can lead to better outcomes for both people and nature. Using a case study from New York City, the research looks at how a multispecies co-design process changed how participants thought, learned, and acted in relation to urban green spaces. The findings demonstrate why people chose to participate, how learning occurs during the process, and how new, more sustainable ideas emerge. Overall, the study shows that designing cities with both humans and other species in mind may support healthier urban ecosystems and more engaged communities, offering practical guidance for doing this in complex urban settings.

Abstract Cities face complex challenges in meeting community needs while also addressing multiple sustainability issues. Yet prevailing governance approaches struggle to engage with the complexity of cities as social—ecological—technological systems (SETS). Specifically, biodiversity, culture, geography, and their interactions, are often neglected. In this chapter, we present approaches for planning and governance in the nexus of biodiversity, culture, and geography, highlighting challenges and opportunities in the context of the polycrisis: the interlinked, compounding threats posed by biodiversity loss, climate change, inequity, and injustice. We argue that research and innovation with nature in cities requires understanding and embracing the complexity of cities as urban systems; striving for a nexus understanding highlights the importance of the interrelations and interactions between elements of urban systems. We review the knowledge gaps and challenges identified by the Innovate4Cities initiative reports (see Chapter 2 and 3), and, through the nexus conceptualization, propose four ways forward: (1) adopting integrated frameworks to identify leverage points for innovating cities with nature; (2) progressing narrative shifts to more plural and decolonial understandings of innovations in cities with nature; (3) integrating cultural considerations into urban development, planning, and governance; and (4) learning from diverse urban contexts to inform localized urban research and practice.

Abstract Machine learning models can represent climate processes that are nonlocal in horizontal space, height, and time, often by combining information across these dimensions in highly nonlinear ways. While this can improve predictive skill, it makes learned relationships difficult to interpret and prone to overfitting as the extent of nonlocal information grows. We address this challenge by introducing data-driven integration kernels, a framework that adds structure to nonlocal operator learning by explicitly separating nonlocal information aggregation from local nonlinear prediction. Each spatiotemporal predictor field is first integrated using learnable kernels (defined as continuous weighting functions over horizontal space, height, and/or time), after which a local nonlinear mapping is applied only to the resulting kernel-integrated features and optional local inputs. This design confines nonlinear interactions to a small set of integrated features and makes each kernel directly interpretable as a weighting pattern that reveals which horizontal locations, vertical levels, and/or past timesteps contribute most to the prediction. We demonstrate the framework for South Asian monsoon precipitation using a hierarchy of neural network models with increasing structure, including baseline, nonparametric kernel, and parametric kernel models. Across this hierarchy, kernel models achieve near-baseline performance with far fewer trainable parameters, indicating that much of the relevant nonlocal information can be captured through a small set of interpretable integrations when appropriate structural constraints are imposed.

In recent years, analysis of remote sensing data has benefited immensely from borrowing techniques from the broader field of computer vision, including the use of shared models pre-trained on large datasets. However, satellite imagery has unique features that are not accounted for in traditional computer vision, such as the existence of multiple revisits of the same location. Here, we explore the best way to use revisits in the framework of fine-tuning pre-trained remote sensing models. As a test case, we focus on the applied research question of power substation segmentation, which is representative of applied uses of pre-trained models more generally. Through extensive tests of different multi-temporal input schemes across diverse model architectures, we find that fusing representations from multiple revisits in the model's latent space is superior to other methods of using revisits, including as a form of data augmentation. We also find that a SWIN transformer-based architecture performs better than U-Nets and ViT-based models. We verify the generality of our results on a separate building density estimation task. In total, this work equips remote sensing scientists with a general method for utilizing the unique temporal properties of their images.

ranges, indicating that environmental conditions will have a large influence on potential photodegradation rates. Consequently, identifying the relevant photodegradation mechanisms for a given chemical can be used to more accurately model environment-specific persistence, and this mechanistic approach should be integrated into regulatory frameworks.

We showcase a hybrid modeling framework that embeds machine learning (ML) inference into the Geophysical Fluid Dynamics Laboratory Seamless System for Prediction and Earth System Research (SPEAR) climate model for online sea ice bias correction during a set of global fully coupled 1-year retrospective forecasts. We compare two hybrid versions of SPEAR to understand the importance of exposing ML models to coupled ice-atmosphere-ocean feedbacks before implementation into fully coupled simulations: Hybrid CPL (couple trained; with feedbacks) and Hybrid IO (ice ocean trained; without feedbacks). Relative to SPEAR, Hybrid CPL systematically reduces seasonal forecast errors in the Arctic and considerably reduces Antarctic errors for target months May to December, with >2× error reduction in 4- to 6-month lead forecasts of Antarctic winter sea ice extent. Meanwhile, Hybrid IO suffers from out-of-sample behavior that can trigger a chain of Southern Ocean feedbacks, leading to ice-free Antarctic summers. Our results emphasize that ML can demonstrably improve numerical sea ice prediction capabilities and that exposing ML models to coupled ice-atmosphere-ocean processes is essential for generalization in fully coupled simulations.

For a transition between two stable states, the committor is the probability that the dynamics leads to one stable state before the other. It can be estimated from trajectory data by minimizing an expression for the transition rate that depends on a lag time. We show that an existing such expression is minimized by the exact committor only when the lag time is a single time step, resulting in a biased estimate in practical applications. We introduce an alternative expression that is minimized by the exact committor at any lag time. The key idea is that when trajectories enter the stable states, the times that they enter (stopping times) must be used for estimating the committor and transition rate instead of the lag time. Numerical tests on benchmark systems demonstrate that our committor and transition rate estimates are much less sensitive to the choice of lag time. We show how further accuracy for the transition rate can be achieved by combining results from two lag times. We also relate the transition rate expression to a variational approach for kinetic statistics based on the mean-squared residual and discuss further numerical considerations with the aid of a decomposition of the error into dynamic modes.

We introduce WaveSim, a multi-scale similarity metric for the evaluation of spatial fields in weather and climate applications. WaveSim exploits wavelet transforms to decompose input fields into scale-specific wavelet coefficients. The metric is built by multiplying three orthogonal components derived from these coefficients: Magnitude, which quantifies similarities in the energy distribution of the coefficients, i.e., the intensity of the field; Displacement, which captures spatial shift by comparing the centers of mass of normalized energy distributions; and Structure, which assesses pattern organization independent of location and amplitude. Each component yields a scale-specific similarity score ranging from 0 (no similarity) to 1 (perfect similarity), which are then combined across scales to produce an overall similarity measure. We first evaluate WaveSim using synthetic test cases, applying controlled spatial and temporal perturbations to systematically assess its sensitivity and expected behavior. We then demonstrate its applicability to physically relevant case studies of key modes of climate variability in Earth System Models. Traditional point-wise metrics lack a mechanism for attributing errors to physical scales or modes of dissimilarity. By operating in the wavelet domain and decomposing the signal along independent axes, WaveSim bypasses these limitations and provides an interpretable and diagnostically rich framework for assessing similarity in complex fields. Additionally, the WaveSim framework allows users to place emphasis on a specific scale or component, and lends itself to user-specific model intercomparison, model evaluation, and calibration and training of forecasting systems. We provide a PyTorch-ready implementation of WaveSim, along with all evaluation scripts, at: https://github.com/gabrieleaccarino/wavesim.

This chapter provides a comprehensive overview of stratosphere–troposphere interactions, with emphasis on the Southern Hemisphere. We introduce key concepts such as wave activity (Eliassen–Palm fluxes), the Charney–Drazin criterion for wave propagation, downward control, and radiative coupling. The chapter then explores the stratosphere’s influence on tropospheric timescales and the surface impacts of dynamical perturbations originating in the stratosphere. It delves into subseasonal to seasonal prediction, highlighting the enhanced forecasting skill derived from stratospheric dynamics. The discussion also addresses the impact of stationary waves and model biases on stratosphere–troposphere coupling. Furthermore, the chapter examines the stratosphere’s role in shaping tropospheric circulation over longer timescales, including the effects of climate change and the potential influence of the ozone hole. Finally, it investigates the interactions between the Quasi-Biennial Oscillation and the Madden–Julian Oscillation, providing a holistic view of the complex interplay between these atmospheric layers.

: Daylighting urban waterways is one form of nature-based solution (NBS) gaining attention amongst cities internationally. Urban daylighting – which involves removing historically buried waterways from underground pipes and bringing them back to the surface – is an intensive form of NBS which can address urban stormwater management, reduce combined-sewage overflow issues, improve riparian habitat, and increase biodiversity. The process of reinserting a waterway in an urban environment makes urban daylighting an important context for analyzing urban NBS trade-offs and the associated justice implications. We use a typology of trade-offs – rigid, governance and functional trade-offs – intersected with a socio-ecological justice lens to critically approach and understand the implications of trade-offs in urban daylighting projects. We apply this framework to the contemporary case study of daylighting Tibbetts Brook in The Bronx, New York City. Data was collected through interviews with stakeholders involved in the project, and street interviews, to capture the perceptions of trade-offs related to daylighting. We find governance trade-offs related to the process of including local community, and choices between accessibility and ecological functioning. Additionally, we identify functional trade-offs related to daylighting as infrastructural upgrades or maximized ecological benefits, considerations of future use and impacts, and the trade-off between novel and landscaped ecosystems. We examine the differentiated socio-ecological justice dilemmas associated with the trade-offs for diverse groups, including non-human nature. Moreover, we indicate how wider policy and social drivers interconnect and shape the trade-offs in daylighting Tibbetts Brook. Overall, we highlight the socio-ecological benefits that daylighting can provide, as well as the complexity of daylighting in cities and the interconnected infrastructural, social and ecological factors which create justice dilemmas.

By 2050, nearly 70% of the global population will live in cities (UN, 2018), increasing the demand for urban green spaces.Urban areas are facing increasing risks from climate change, including heatwaves, flooding, wildfires, and growing social inequality, which challenges urban planning and design.Urban forests form the backbone of green infrastructure supporting resilient, equitable, and sustainable cities. Importantly, their cost-effective benefits advance sustainable development, climate action, and biodiversity conservation.

Machine learning (ML) and artificial intelligence (AI) algorithms are transforming and empowering the characterization and control of dynamic systems in the engineering, physical, and biological sciences. These emerging modeling paradigms require comparative metrics to evaluate a diverse set of scientific objectives, including forecasting, state reconstruction, generalization, and control, while also considering limited data scenarios and noisy measurements. We introduce a common task framework (CTF) for science and engineering, which features a growing collection of challenge data sets with a diverse set of practical and common objectives. The CTF is a critically enabling technology that has contributed to the rapid advance of ML/AI algorithms in traditional applications such as speech recognition, language processing, and computer vision. There is a critical need for the objective metrics of a CTF to compare the diverse algorithms being rapidly developed and deployed in practice today across science and engineering.

Boundary layer turbulence, particularly the vertical fluxes of momentum, shapes the evolution of winds and currents and plays a critical role in weather, climate, and biogeochemical processes. In this work, a unified, data-driven parameterization of turbulent momentum fluxes is introduced for both the oceanic and atmospheric convective boundary layers. An artificial neural network (ANN) is trained offline on coarse-grained large-eddy simulation (LES) data representing a wide range of turbulent regimes in both fluids. By normalizing momentum flux profiles with their surface values, we exploit a self-similar structure across regimes and fluids, enabling joint training. The ANN learns to predict vertical profiles of subgrid momentum fluxes from mean wind or current profiles, capturing key physical features such as upgradient fluxes that are inaccessible to traditional first-order closure schemes. When implemented online in the Single Column Atmospheric Model (SCAM), the ANN parameterization consistently outperforms the SCAM baseline parameterization in replicating the evolution of the boundary layer wind profiles from the LES, especially under convective conditions, with errors reduced by a factor of 2-3 across regimes. ANN performance remains robust even when the surface momentum flux is biased up to 30\%, and generalization is confirmed by testing on LES cases excluded from the training dataset. This work demonstrates the potential of machine learning to create unified and physically consistent parameterizations across boundary layer systems in climate models.

Abstract Parameterizations of km submesoscale mixed layer instabilities in General Circulation Models (GCMs) represent the effects of unresolved vertical buoyancy fluxes (VBF) in the ocean mixed layer. These submesoscale flows interact non‐linearly with mesoscale and boundary layer turbulence, and it is challenging to account for all the relevant processes in physics‐based parameterizations. In this work, we present a data‐driven approach for the submesoscale parameterization, that relies on a Convolutional Neural Network (CNN) trained to predict mixed layer VBF as a function of relevant large‐scale variables. The data used for training is given from 12 regions sampled from the global high‐resolution MITgcm‐LLC4320 simulation. When compared with the baseline of a submesoscale physics‐based parameterization, the CNN demonstrates high offline skill across all regions, seasons, and filter scales tested in this study. During seasons when submesoscales are most active, which generally corresponds to winter and spring months, we find that the CNN prediction skill tends to be lower than in summer months. The CNN exhibits a dependency on the large scale strain field, a variable closely related to frontogenesis, which is currently missing from the submesoscale parameterizations in GCMs.

Abstract We propose a multiscale approach for predicting quantities in dynamical systems which is explicitly structured to extract information in both fine-to-coarse and coarse-to-fine directions. Our approach improves model accuracy and stability with minimally increased computation compared to non-multiscale approaches with analogous network architecture. We evaluate our approach on an idealized fluid subgrid parameterization (known as closure) task in which our multiscale networks correct chaotic underlying models to reflect the contributions of unresolved, fine-scale dynamics.

Climate simulations, at all grid resolutions, rely on approximations that encapsulate the forcing due to unresolved processes on resolved variables, known as parameterizations. Parameterizations often lead to inaccuracies in climate models, with significant biases in the physics of key climate phenomena. Advances in artificial intelligence (AI) are now directly enabling the learning of unresolved processes from data to improve the physics of climate simulations. Here, we introduce a flexible framework for developing and implementing physics- and scale-aware machine learning parameterizations within climate models. We focus on the ocean and sea-ice components of a state-of-the-art climate model by implementing a spectrum of data-driven parameterizations, ranging from complex deep learning models to more interpretable equation-based models. Our results showcase the viability of AI-driven parameterizations in operational models, advancing the capabilities of a new generation of hybrid simulations, and include prototypes of fully coupled atmosphere-ocean-sea-ice hybrid simulations. The tools developed are open source, accessible, and available to all.

Coastal areas face worsening storm-induced flooding due to climate change, threatening critical below-ground infrastructure like subway systems, as seen from Hurricane Sandy’s catastrophic impact on New York City (NYC)’s subway system in 2012. Stakeholders must urgently address these risks to protect infrastructure and assets. Simulating future flood scenarios is crucial for estimating flood risk and damage efficiently and for identifying reliable and effective protective measures. This article uses the GIS-based Subdivision-Redistribution (GISSR) methodology, a high-speed, physics-based flood estimation tool, that is now extended to model subway system flooding and associated economic impacts. It identifies flooded tunnels and stations and quantifies indirect economic losses from subway inoperability using an input–output model. The model is validated against observed subway flooding during Hurricane Sandy. Each analysis, covering both above- and below-ground flooding in Lower Manhattan, takes less than 90 s on a single 4-core Intel Core i7-13620H CPU machine. Scenario analyses were conducted with NYC stakeholders, incorporating sea level rise projections and various protective measures. Results, benchmarked against NYC’s ongoing resiliency projects, demonstrate the effectiveness of adaptation/protective strategies, particularly when subway system-specific and coastal measures are combined, highlighting the model’s value as a practical guide for stakeholders.

Abstract The equator-to-pole temperature gradient has traditionally been understood as the primary driver of the midlatitude storm tracks, which derive their kinetic energy in the process of transporting sensible heat down the gradient. Latent heat, however, accounts for an estimated 30%–60% of the meridional energy transport, a portion which is likely to increase in a warmer world. The contribution of latent heat to the energetics is complicated in that it is inefficient: Only a fraction of the transported latent heat is converted into kinetic energy. Currently, there is no complete theory to explain the relationship between meridional energy transport and kinetic energy generation by midlatitudes eddies. We use a two-layer moist quasigeostrophic model to develop a theory of how the energetic output of the midlatitude atmosphere depends on the relative humidity structure. By varying the surface evaporation rate, we show that the system only reaches the maximum possible energetic output in the saturated limit, producing substantially less kinetic energy at lower evaporation rates. We quantify this reduction in kinetic energy production in terms of a moist conversion efficiency. Using a moist energetic framework, we identify that precipitation dissipation and the diffusion of moisture in subsaturated regions account for the reduction in energetic output. We then show that the moist conversion efficiency can be diagnosed from the distribution of humidity. Significance Statement The impact of humidity on the strength of midlatitude storms is not well understood. Humidity will increase as the planet warms, but it is unclear whether storms will become stronger or weaker as a result. We use an idealized computer model to learn how humidity will impact the strength of storms. We focus on the effect of evaporation at the planet’s surface, with simulations ranging from a completely dry atmosphere to one with rain everywhere. In between these two limits, it is raining in only part of the atmosphere, and storms are much weaker than in the case with rain everywhere. We discuss how to connect these results to more complex models and real-world data.

Abstract Participatory urban planning is undergoing significant changes because of the rapid development of artificial intelligence (AI) tools. This study explores and compares the differences in the relational agency of Analytical, Generative and Humanised AI as perceived by urban planners. We consider relational agency with respect to the intended and unintended uses and perceived opportunities and risks of these technologies in participatory urban planning. A Delphi survey was conducted with 34 urban planners across municipalities in Finland with > 20,000 inhabitants, followed up by a focus group and survey to build convergence on respondents’ views (n = 13). Respondents commonly used Analytical AI to summarise plan information and to report on environmental variables, and saw its potential to analyse and report on citizen feedback on draft plans, including survey responses. Generative AI is currently used to undertake more creative tasks than Analytical AI, including generating images and simulations, with the potential to use it to draft plan content and summarise expert reports on plan drafts. Risks centre around data protection problems, the outsourcing of decision-making to computer tools, and the potential to create misinformation or incorrect content. Humanised AI is rarely used in urban planning in Finland, but respondents saw potential to use it to respond to community feedback on urban plans and to communicate plans more effectively to the wider public through a cautious approach that addresses the potential for emotion and opinion influencing. We discuss the implications of these findings for participatory urban planning across Europe and elsewhere.

Abstract Data‐driven methods have become popular to parameterize the effects of mesoscale eddies in ocean models. However, they perform poorly in generalization tasks and may require retuning if the grid resolution or ocean configuration changes. We address the generalization problem by enforcing physics constraints on a neural network parameterization of mesoscale eddy fluxes. We found that the local scaling of input and output features helps to generalize to unseen grid resolutions and depths offline in the global ocean. The scaling is based on dimensional analysis and incorporates grid spacing as a length scale. We formulate our findings as a general algorithm that can be used to enforce data‐driven parameterizations with dimensional scaling. The new parameterization improves the representation of kinetic and potential energy in online simulations with idealized and global ocean models. Comparison to baseline parameterizations and impact on global ocean biases are discussed.

Abstract Reliable dynamic sea level forecasts are hindered by numerous sources of uncertainty on daily to seasonal time scales (1–180 days) due to atmospheric boundary conditions and internal ocean variability. Studies have demonstrated that certain initial states can extend predictability horizons; thus, identifying these initial conditions may help improve forecast skill. Here, we identify sources of dynamic sea level predictability on daily to seasonal time scales using neural networks trained on CESM2 large ensemble data to forecast dynamic sea level. The forecasts yield not only a point estimate for sea level but also a standard deviation to quantify forecast uncertainty based on the initial conditions. Forecasted uncertainties can be leveraged to identify state-dependent sources of predictability at most locations and forecast leads. Network forecasts, particularly in the low-latitude Indo-Pacific, exhibit skillful deterministic predictions and skillfully forecast exceedance probabilities relative to local linear baselines. For networks trained at Guam and in the western Indian Ocean, the transfer of sources of predictability from local sources to remote sources is presented by the deteriorating utility of initial condition information for predicting exceedance events. Propagating Rossby waves are identified as a potential source of predictability for dynamic sea level at Guam. In the Indian Ocean, persistence of thermosteric sea level anomalies from the Indian Ocean dipole may be a source of predictability on subseasonal time scales, but El Niño drives predictability on seasonal time scales. This work shows how uncertainty-quantifying machine learning can help identify changes in sources of state-dependent predictability over a range of forecast leads. Significance Statement Uncertainty-quantifying neural networks trained to forecast dynamic sea level anomalies on daily to seasonal time scales skillfully identify sources of state-dependent predictability and forecast exceedance probabilities. We use these predicted probabilities to identify how sources of state-dependent predictability change over daily to seasonal time scales. At Guam, Rossby waves are a source of dynamic sea level predictability. In the western Indian Ocean, El Niño emerges as a source of predictability, possibly by acting as a precursor to the Indian Ocean dipole. This work may clarify how sources of predictability change between daily and seasonal time scales, which may help improve forecasts at these lead times.

Abstract We present the GFDL‐CM4X (Geophysical Fluid Dynamics Laboratory Climate Model version 4X) coupled climate model hierarchy. The primary application for CM4X is to investigate ocean and sea ice physics as part of a realistic coupled Earth climate model. CM4X utilizes an updated MOM6 (Modular Ocean Model version 6) ocean physics package relative to CM4.0, and there are two members of the hierarchy: one that uses a horizontal grid spacing of (referred to as CM4X‐p25) and the other that uses a grid (CM4X‐p125). CM4X also refines its atmospheric grid from the nominally 100 km (cubed sphere C96) of CM4.0–50 km (C192). Finally, CM4X simplifies the land model to allow for a more focused study of the role of ocean changes to global mean climate. CM4X‐p125 reaches a global ocean area mean heat flux imbalance of within years in a pre‐industrial simulation, and retains that thermally equilibrated state over the subsequent centuries. This 1850 thermal equilibrium is characterized by roughly less ocean heat than present‐day, which corresponds to estimates for anthropogenic ocean heat uptake between 1870 and present‐day. CM4X‐p25 approaches its thermal equilibrium only after more than 1000 years, at which time its ocean has roughly more heat than its early 21st century ocean initial state. Furthermore, the root‐mean‐square sea surface temperature bias for historical simulations is roughly 20% smaller in CM4X‐p125 relative to CM4X‐p25 (and CM4.0). We offer the mesoscale dominance hypothesis for why CM4X‐p125 shows such favorable thermal equilibration properties.

Abstract This paper is Part II of a two‐part paper that documents the Climate Model version 4X (CM4X) hierarchy of coupled climate models developed at the Geophysical Fluid Dynamics Laboratory. Part I of this paper is presented in Griffies et al. (2025a, https://doi.org/10.1029/2024MS004861). Here we present a suite of case studies that examine ocean and sea ice features that are targeted for further research, which include sea level, eastern boundary upwelling, Arctic and Southern Ocean sea ice, Southern Ocean circulation, and North Atlantic circulation. The case studies are based on experiments that follow the protocol of version 6 from the Coupled Model Intercomparison Project. The analysis reveals a systematic improvement in the simulation fidelity of CM4X relative to its CM4.0 predecessor, as well as an improvement when refining the ocean/sea ice horizontal grid spacing from the of CM4X‐p25 to the of CM4X‐p125. Even so, there remain many outstanding biases, thus pointing to the need for further grid refinements, enhancements to numerical methods, and/or advances in parameterizations, each of which target long‐standing model biases and limitations.

Antarctic meltwater is expected to increase throughout the coming centuries and impact sea level, ocean circulation, and the coupled climate evolution. This motivates interest in understanding the ocean response to Antarctic freshwater injection, including potential sources of uncertainty. In this study, we use idealized single-basin ocean simulations with meltwater input to examine the dependence of ocean transport and the timescales of the adjustment of regional sea level patterns on: (a) the model resolution and parameter values such as the mesoscale eddy Gent-McWilliams parameterization and vertical diffusivity, thereby partially addressing structural and parametric uncertainty; and (b) the depth of meltwater forcing, which must be prescribed both in our experiments and in most comprehensive climate model simulations, due to a lack of dynamic coupling with an ice sheet model. We find distinct sea level adjustment timescales and changes in the upper and abyssal cells depending on the depth of input, including a near total shutdown of the abyssal cell which only occurs with meltwater injection at the surface. We additionally find correlations between the ocean response to meltwater and the background stratification in each control simulation, which depends on the model resolution and parameter values. These results indicate that, in addition to uncertainty in how ocean models interact with fluxes from ice sheets, the ocean physics and simulated preindustrial state substantially influence the dynamic ocean response to projected ice shelf meltwater fluxes.

Abstract We investigate the temporal evolution of ocean heat uptake efficiency (OHUE) using observations and large ensemble model simulations. OHUE, defined as the ratio of ocean heat uptake rate to changes in global mean surface temperature anomalies, has exhibited significant variability over recent decades. We found a relatively low OHUE in the late 1980s, a peak around 2000, and a subsequent decline. A key finding is the significant influence of volcanic eruptions, particularly the 1991 Mount Pinatubo eruption, which caused abrupt cooling followed by a gradual recovery, leading recent variation of OHUE. Moreover, the Southern Ocean is becoming less efficient at absorbing heat into interior ocean in recent period associated with increased stratification. These results highlight the role of natural external forcing in climate dynamics and the importance of mid‐latitudes in global OHUE variability, underscoring the need for idealized experiments to quantify the potential effects of future volcanic eruptions on OHUE.

Abstract. Fitting models to data to obtain distributions of consistent parameter values is important for uncertainty quantification, model comparison, and prediction. Standard Markov chain Monte Carlo (MCMC) approaches for fitting ordinary differential equations (ODEs) to time-series data involve proposing trial parameter sets, numerically integrating the ODEs forward in time, and accepting or rejecting the trial parameter sets. When the model dynamics depend nonlinearly on the parameters, as is generally the case, trial parameter sets are often rejected, and MCMC approaches become prohibitively computationally costly to converge. Here, we build on methods for numerical continuation and trajectory optimization to introduce an approach in which we use Langevin dynamics in the joint space of variables and parameters to sample models that satisfy constraints on the dynamics. We demonstrate the method by sampling Hopf bifurcations and limit cycles of a model of a biochemical oscillator in a Bayesian framework for parameter estimation, and we attain performance that matches or exceeds the performance of leading MCMC approaches that require numerically integrating the ODEs forward in time. We describe numerical experiments that provide insight into the speedup. The method is general and can be used in any framework for parameter estimation and model selection.

Abstract Surface and upper-ocean measurements of mesoscale eddies have revealed the central role they play in ocean transport, but their interior and deep ocean characteristics remain undersampled and underexplored. In this study, mooring arrays, sampling with high vertical resolution, and a high-resolution global atmosphere–ocean coupled simulation are used to characterize full-depth mesoscale eddy vertical structure. The vertical structure of eddy kinetic energy, e.g., partitioning of barotropic to baroclinic eddy kinetic energy or vertical modal structure, is shown to depend partly on bathymetric slope and roughness. This influence is contextualized alongside additional factors, such as latitude and vertical density stratification, to present a global landscape of vertical structure. The results generally reveal eddy vertical structure to decay with increasing depth, consistent with theoretical expectations relating to the roles of surface-intensified stratification and buoyancy anomalies. However, at high latitudes and where the seafloor is markedly flat and smooth (approximately 20% of the ocean’s area), mesoscale eddy vertical structures are significantly more barotropic by an approximate factor of 2–5. From a climate modeling perspective, these results can inform the construction, implementation, and improvement of energetic parameterizations that account for the underrepresentation of mesoscale eddies and their effects. They also offer expectation as to a landscape of eddy vertical structure to be used in inferring vertical structure from surface measurements. Significance Statement This work addresses the question of how do ocean seafloor features (bathymetry) affect the vertical structure of ocean currents and eddies? Seafloor features modify eddies in complex ways not often accounted for in global ocean simulations. We analyze high-resolution velocity observations, find diverse structures at four mooring sites, and consider how sloping and rough bathymetry change distributions of eddy kinetic energy throughout the water column. Comparison to theory and model output reveals a relationship between vertical structure and bathymetry. These results show that vertical structures vary significantly with bathymetry, density stratification, and latitude and contribute to model development efforts to reproduce the effects of eddy turbulence without explicit representation. These results also enhance interpretations of more numerous surface observations.

Accurate atmospheric profiles from remote sensing instruments such as Doppler Lidar, Radar, and radiometers are frequently corrupted by low-SNR (Signal to Noise Ratio) gates, range folding, and spurious discontinuities. Traditional gap filling blurs fine-scale structures, whereas deep models lack confidence estimates. We present CuMoLoS-MAE, a Curriculum-Guided Monte Carlo Stochastic Ensemble Masked Autoencoder designed to (i) restore fine-scale features such as updraft and downdraft cores, shear lines, and small vortices, (ii) learn a data-driven prior over atmospheric fields, and (iii) quantify pixel-wise uncertainty. During training, CuMoLoS-MAE employs a mask-ratio curriculum that forces a ViT decoder to reconstruct from progressively sparser context. At inference, we approximate the posterior predictive by Monte Carlo over random mask realisations, evaluating the MAE multiple times and aggregating the outputs to obtain the posterior predictive mean reconstruction together with a finely resolved per-pixel uncertainty map. Together with high-fidelity reconstruction, this novel deep learning-based workflow enables enhanced convection diagnostics, supports real-time data assimilation, and improves long-term climate reanalysis.

High Resolution Image Download MS PowerPoint Slide Most data on laboratory-scale experiments evaluating E. coli and enterococci disinfection are from experiments conducted using laboratory-cultured bacteria. However, environmental bacteria, such as those in wastewater, have potential to be more resistant to disinfection than their laboratory-cultured counterparts. Additionally, most Enterococcus disinfection studies have only evaluated E. faecalis despite the diversity of Enterococcus species in the environment. In this study, we evaluated inactivation kinetics of wastewater-sourced E. coli and enterococci, laboratory-cultured E. coli, and three species of laboratory-cultured Enterococcus with exposure to free chlorine, monochloramine, UVC, and simulated sunlight. All bacteria were purified and suspended in a chlorine-demand-free buffer with minimal light attenuation to allow comparison between populations without confounding matrix effects. Laboratory-cultured bacteria were more susceptible to the oxidants than the wastewater-sourced bacteria, highlighting that research using reference-strain bacteria in the laboratory may not reflect inactivation kinetics in the environment. When exposed to the light-based disinfectants, only laboratory-cultured E. coli and E. faecalis were more susceptible than the wastewater-sourced bacteria. Notably, different laboratory-cultured Enterococcus species had different inactivation rates, with E. faecalis being the most susceptible. These findings highlight the importance of incorporating indigenous environmental bacteria in laboratory studies and assessing a variety of Enterococcus species in disinfection research.

In the 1970s and 1980s, the semi-arid Sahel, the southern edge of the Sahara Desert, experienced spatially uniform drought, from the Atlantic Ocean to the Red Sea. The recovery that ensued is projected to continue in the center and east, leaving the west out. We show that these two patterns-uniform variation and east-west contrast-are present in instrumental observations and in simulations with constant or time-varying external forcing. Uniform variation is amplified by 20th century external forcing, while a global warming-induced strengthening of the monsoon seeds the east-west contrast. This contrast is deepened by a mid-21st century transition to a North Atlantic cooling relative to the global tropical oceans, which affects the western Sahel most strongly because it is immediately adjacent to the North Atlantic, leading to a divergence in outcomes-between a progressively wetter central and eastern Sahel and an abruptly drier western Sahel-that is unparalleled in instrumental observations.

Earth system modeling presents a fundamental challenge in scientific computing: capturing complex, multiscale nonlinear dynamics in computationally efficient models while minimizing forecast errors caused by necessary simplifications. Even the most powerful AI- or physics-based forecast system suffer from gradual error accumulation. Data assimilation (DA) aims to mitigate these errors by optimally blending (noisy) observations with prior model forecasts, but conventional variational methods often assume Gaussian error statistics that fail to capture the true, non-Gaussian behavior of chaotic dynamical systems. We propose PnP-DA, a Plug-and-Play algorithm that alternates (1) a lightweight, gradient-based analysis update (using a Mahalanobis-distance misfit on new observations) with (2) a single forward pass through a pretrained generative prior conditioned on the background forecast via a conditional Wasserstein coupling. This strategy relaxes restrictive statistical assumptions and leverages rich historical data without requiring an explicit regularization functional, and it also avoids the need to backpropagate gradients through the complex neural network that encodes the prior during assimilation cycles. Experiments on standard chaotic testbeds demonstrate that this strategy consistently reduces forecast errors across a range of observation sparsities and noise levels, outperforming classical variational methods.

Nature-based Solutions for Urban SustainabilityCities across the globe face growing sustainability and resilience challenges that will require innovative solutions and collaborative partnerships.The pace of climate change, the frequency and duration of unprecedented climate-related events, threats to biodiversity, and the concentration of people and outdated infrastructure in cities demand new strategies to ensure that cities are livable, resilient, and equitable.Nature-based solutions (NbS) are at the forefront of addressing impacts of climate change, especially in urban planning, practice, and research.A growing number of cities worldwide are exploring NbS, sometimes referred to as green infrastructure, urban nature, ecosystem-based adaptations, or simply environmental restoration or conservation (1).NbS incorporate natural processes to support adaptation to climate change and urbanization, as well as the restoration of ecological functions (Fig. 1).While emphasized as a tool to address climate change in cities, NbS are also promoted for their benefits to individuals and communities, including improved public health, access to nature, and social equity (2, 3).In contrast to traditional engineered urban infrastructure that is mostly rigid and inflexible over time, NbS provide flexibility to confront the shifting impacts of climate change and uncertainty of the future (4, 5).In keeping pace with accelerating interest in urban NbS worldwide, key principles, frameworks, and classification schemes are emerging to understand and advance NbS (6-11).Not only has there been an explosion of research on NbS in the last 10 years, there have also been new local, national, and international development directives supporting NbS implementation.For example, NbS have gained strong support in local climate adaptation plans (12) and national policy agendas within the United States, where the Biden Administration Executive Order 14072 (13) centered NbS for climate adaptation and mitigation alongside biodiversity protection.NbS are also emerging in international agendas through the European Union Horizon 2020 (14), International Union of Conservation of Nature (15), United Nations (UN) (16), Intergovernmental Panel on Climate Change (17), and the World Economic Forum (18).Despite rapid uptake in policy, uncertainties remain about scaling up and scaling out of NbS to address climate change in cities, particularly in diverse social-cultural, governance, ecological-biophysical, climatic, and infrastructural contexts (19).For example, while NbS are expected to be versatile, multifunctional, and help mitigate the impact of climate risks, the full suite of NbS benefits and potential tradeoffs is uncertain, particularly under changing climate and more severe extreme weather-related events (4, 20-22).The recent frameworks on urban NbS have largely emerged from the Global North, and the application of these frameworks in other contexts, such as in the Global South, informal urban settlements, or from Indigenous perspectives, is not always appropriate (23-26).Moreover, in some global regions, such as Latin America and the Caribbean, urban NbS are predominantly seen (and reported upon) in the wealthiest or largest nations and cities; NbS are less systematically studied elsewhere in the Latin America region, despite likely broad implementation through bottom-up or un-funded initiatives (27, 28).Continued efforts studying place-based design, implementation, and functioning of NbS across diverse global contexts will provide an important solution-set for addressing future urban sustainability and resilience challenges.The objective of this paper is to introduce a Special Feature: "Nature-based Solutions for Urban Sustainability"-a collection of four articles addressing the current state of global urban NbS science and conceptual framings.The special feature explores opportunities for research and practice to scale up NbS to address equity, justice, and inclusion while enabling transformation in urbanized areas.The special feature includes a review and three perspectives papers that leverage learnings from NATURA (NAT ure-based Solutions for U rban R esilience in the A nthropocene), an international network of networks that connects academic researchers and practitioners around the world and provides a critical platform for knowledge exchange.The works in the special feature build upon the progress made through global collaborations on urban NbS within NATURA.

Nature-based solutions (NBS) are used to transform existing unsustainable and undesirable path dependencies in cities. For NBS to contribute to just urban transformations, a stronger inter- and transdisciplinary knowledge base is needed. This knowledge base is essential to engage with six complex yet crucial questions about NBS, including "for what?," "which nature?," "where?," "how?," "when," and "for whom?." To address these questions, we identify two critical opportunities to advance the knowledge of NBS. First, we argue for solidifying interdisciplinary approaches to examine how NBS can be designed, planned, and implemented for multifunctionality. Second, we argue that researchers need to work transdisciplinarily with diverse stakeholders to ensure the design, siting, and planning of NBS are appropriate to the context. In both critical opportunities, justice should be a core guiding principle from the beginning of planning the NBS, starting with the foundational understanding that NBS are not inherently just or unjust. Instead, their value depends on a holistic examination of the context in which they operate and the institutional logic that guides their planning. To center justice in the inter- and transdisciplinary research and practice of NBS, a knowledge shift from epistemological injustice to epistemological inclusivity is a critical way forward.

The latest report of the Intergovernmental Panel on Climate Change showed that upcoming decades are likely to be accompanied with an increase in climate impacts in urban areas, thereby stressing the need for empowering cities to help them address the challenges ahead by closing the urban adaptation gap. The lock-in systems in which cities are often trapped create resistance to change and leads to missed opportunities to cocreate better futures. Putting nature at the center of urban adaptation agenda is a promising vision that nature-based solutions can help realize if city leaders are adequately supported in accessing the knowledge and resources they need to take action. Despite their proven potential, important barriers to the planning, implementation, and management of nature-based solutions (NbS) for urban climate adaptation persist and need to be urgently addressed. We highlight key barriers related to knowledge gaps and dissemination, policy incentives, and financial autonomy faced by city leaders trying to integrate NbS into policy and planning. In this perspective, we propose four levers of transformative adaptation which build on examples from Europe, the United States, Africa, and Latin America and which have proven successful in supporting cities adopting nature-based adaptation actions, including to 1) produce, assess and share knowledge; 2) adopt incentive-based policies and regulations; 3) facilitate access to multiple sources of funding; and 4) create reflexive monitoring mechanisms. If these steps are taken with the goal of addressing vulnerabilities on the ground, they can unleash the potential of NbS to engage on a path of transformative adaptation.

Nature-based solutions (NbS) have emerged as a key strategy for sustainably addressing multiple urban challenges, with rapidly increasing knowledge production requiring synthesis to better understand whether and how NbS work in different social, ecological, economic, or governance contexts. Insights in this Perspective are drawn from a thematic review of 61 NbS review articles supported by an expert assessment of NbS knowledge in seven global regions to examine key challenges, fill gaps in Global South assessment, and provide insights for scaling up NbS for impact in cities. Eight NbS challenges emerged from our review of NbS reviews including conceptual, thematic, geographic, ecological, inclusivity, health, governance, and systems challenges. An additional expert assessment reviewing literature and cases in seven global regions further revealed the following: 1) Local context-based ecological knowledge is essential for NbS success; 2) Improved technical knowledge is required for planning and designing NbS; 3) NbS need to be included in all levels of planning and governance; 4) Putting justice and equity at the center of urban NbS approaches is critical, and 5) Inclusive and participatory governance processes will be key to long-term success of NbS. We synthesized findings from the NbS review results and regional expert assessments to offer four critical pathways for scaling up NbS: 1) foster new NbS research, technological innovation, and learning, 2) build a global NbS alliance for sharing knowledge, 3) ensure a systems approach to NbS planning and implementation, and 4) increase financing and political will for diverse NbS implementation.

Abstract An intermediate-complexity general circulation model is used to disentangle changes in the large-scale zonally asymmetric circulation in response to rising greenhouse gases. Particular focus is on the anomalous ridge that develops over the Mediterranean in future climate projections, directly associated with reduced winter precipitation over the region. Specifically, we examine changes in stationary waves forced by land–sea contrast, horizontal oceanic heat fluxes, and orography, following a quadrupling of CO2. The stationary waves associated with these three drivers depend strongly on the climatological state, precluding a linear decomposition of their responses to warming. However, our modeling framework still allows a process-oriented approach to quantify the key drivers and mechanisms of the response. A combination of three similarly important mechanisms is found responsible for the rain-suppressing ridge. The first is part of a global response to warming: elongation of intermediate-scale stationary waves in response to strengthened subtropical winds aloft, previously found to account for hydroclimatic changes in southwestern North America. The second is regional: a downstream response to the North Atlantic warming hole and enhanced warming of the Eurasian landmass relative to the Atlantic Ocean. A third contribution to the Mediterranean Ridge is a phase shift of planetary wave 3, primarily associated with an altered circulation response to orographic forcing. Reduced land–sea contrast in the Mediterranean basin, previously thought to contribute substantially to Mediterranean drying, has a negligible effect in our integrations. This work offers a mechanistic analysis of the large-scale processes governing projected Mediterranean drying, lending increased understanding and credibility to climate model projections.

Abstract Promoting people-nature relationships is essential for the effective adoption of Nature-Based Solutions (NBS) in cities. Across scientific domains, the potential of eXtended Reality (XR) technologies as a novel tool to support and enable people-nature relationships is increasingly highlighted. However, the application of XR in urban NBS planning remains uncertain. Through a scoping review of the literature, we found five major application areas for employing XR in the context of people-nature relationships: perception and preference assessment, spatial planning and design, education and awareness enhancement, psychological intervention, and monitoring and maintenance. In this paper, we examine how nature’s instrumental, relational, and intrinsic values are communicated through XR and explore the potential role that XR technologies can play in promoting people-nature relationships. Furthermore, we discuss the implications of adopting XR technologies for urban NBS planning.

Public spaces play a crucial role in climate action as the main setting of many urban adaptation interventions, while also critical for public life and social resilience. Public space is often contested since it can help develop justice-based urban climate adaptation, but can also drive different forms of injustice and maladaptation. Examining public space adaptation processes through the lens of climate justice is essential to identifying, learning from, and preventing maladaptation outcomes. However, there are currently no frameworks to facilitate the understanding of the relationship between climate justice and the design of public spaces or evaluation tools to assess its outcomes. To fill this gap, we developed and pilot-tested a new evaluation tool for the collective assessment of climate justice in already implemented public space design projects through a case study in Hunters Point South Park, New York City. While examining the particularities of this case, we also critically explore the potential and applicability of this assessment tool in other contexts and for broader justice assessments of urban adaptation interventions.

Effector-Triggered Immunity (ETI) is an important part of the plant immune system, allowing plants to sense and respond to harmful pathogen proteins known as "effectors." Effectors can be sensed directly or indirectly by NLR (Nucleotide-binding Leucine-rich Repeat) proteins, many of which "guard" the plant proteins targeted by effectors. Although a few effector-target-NLR interactions have been characterized, a general understanding of how these molecular interactions give rise to a functioning immune system is lacking. Here, we present a physics-based model of ETI based on protein-protein interactions. We show that the simplest physical model consistent with the biology gives rise to a robust immune sensor and explains the empirical phenomenon of effector interference as a generic consequence of molecules competing for binding partners. Using the evolutionarily conserved ZAR1 defense gene as a model, we explain how more complex interaction networks integrate multiple pathogen signals into a single response. We then examine alternatives to a guarding architecture, including direct sensing, decoys, and blended "integrated decoy" strategies, and reveal that these sensing architectures obey functional trade-offs between their sensitivity, target protection, and proteomic cost. This allows a quantitative analysis of the trade-offs between different forms of ETI. We discuss these findings in the context of the evolutionary forces shaping the plant immune system.

Cities worldwide are increasingly adopting green interventions, such as nature-based solutions(NBS), to meet urban sustainability goals. However, how and where to optimize investments to receive the most benefit for NBS implementation are largely unknown. Here, we provide a continental-scale assessment of the effect of urban green areas for cooling across 32 Chinese cities that vary in size, climate, and native biome. The relationship between green patch size and surface temperature reveals a consistent decreasing concave-up relationship across varied cities, indicating that increasing green patch size initially corresponds to a decrease in temperature. However, this pattern shifts such that cooling benefits decrease per area once patch size increases to a specific climate threshold, a green-area threshold (GAT) for cooling. The direct implication is that large green space patches will be needed in temperate and drier cities for effective cooling as a climate adaptation strategy, whereas a smaller threshold is recommended in the tropics.

Abstract The quasi-biennial oscillation (QBO) is the dominant mode of variability in the equatorial stratosphere. It is characterized by alternating descending easterly and westerly jets over a period of approximately 28 months. It has long been known that the QBO interactions with the annual cycle, e.g., through variation in tropical upwelling, lead to variations in the descent rate of the jets and, resultantly, the QBO period. Understanding these interactions, however, has been hindered by the fact that conventional measures of the QBO convolve these interactions. Koopman formalism, derived from dynamical systems, allows one to decompose spatiotemporal datasets (or nonlinear systems) into spatial modes that evolve coherently with distinct frequencies. We use a data-driven approximation of the Koopman operator on zonal-mean zonal wind to find modes that correspond to the annual cycle, the QBO, and the nonlinear interactions between the two. From these modes, we establish a data-driven index for a “pure” QBO that is independent of the annual cycle and investigate how the annual cycle modulates the QBO. We begin with what is already known, quantifying the Holton–Tan effect, a nonlinear interaction between the QBO and the annual cycle of the polar stratospheric vortex. We then use the pure QBO to do something new, quantifying how the annual cycle changes the descent rate of the QBO, revealing annual variations with amplitudes comparable to the 30 m day−1 mean descent rate. We compare these results to the annual variation in tropical upwelling and interpret them with a simple model. Significance Statement The quasi-biennial oscillation (QBO) is a periodic cycle of winds in tropical atmosphere with a period of 28 months. The phase of QBO is known to influence other aspects of the atmosphere, including the polar vortex, but the magnitude of its effects and how it behaves are known to depend on the season. In this study, we use a data-driven method (called a Koopman decomposition) to quantify annual changes in the QBO and investigate their causes. We show that seasonal variations in the stratospheric upwelling play an important but incomplete role.

Abstract Mass loss from the Antarctic ice sheet is projected to continue over the coming century. The resultant sea level change will have a regional pattern that evolves over time as the ocean adjusts. Accurate estimation of this evolution is crucial for local communities. Current state-of-the-art climate models typically do not couple ice sheets to the atmosphere–ocean system, and the impact of ice sheet melt has often been studied by injecting meltwater at the model ocean surface. However, observational evidence suggests that most Antarctic meltwater enters the ocean at depth through ice shelf basal melt. A previous study has demonstrated that the regional sea level pattern at a given time depends on meltwater injection depth. Here, we introduce a 2.5-layer model to investigate this dependence and develop a theory for the associated adjustment mechanisms. We find mechanisms consistent with previous literature on the ocean adjustment to changes in forcing, whereby a slower Rossby wave response off the eastern boundary follows a fast response from the western boundary current and Kelvin waves. We demonstrate that faster baroclinic Rossby waves near the surface than at depth explain the injection depth dependence of the adjustment in the 2.5-layer model. The identified Rossby wave mechanism may contribute to the dependence of the ocean’s transient adjustment on meltwater injection depth in more complex models. This work highlights processes that could cause errors in the projection of the time-varying pattern of sea level rise using surface meltwater input to represent Antarctica’s freshwater forcing. Significance Statement Sea level rise is expected to be larger in some locations than in others. Accurate projections of the pattern of sea level change, which changes in time as the ocean adjusts, are essential information for local communities. One of the factors that leads to uncertainty in the local sea level change due to Antarctic melt is the depth at which this meltwater is input into an ocean model. We propose a mechanism for a faster response of sea level around the basin when meltwater is injected at the ocean surface compared to when it is injected well below the surface. This mechanism has implications for projections of the regional sea level response to Antarctic melt.

Abstract Cities are increasingly adapting green interventions meant to enhance climate resilience, given their capacity to provide numerous ecosystem services (ES). Yet, little is known about ecological and social impacts of such interventions in the context of socio‐ecological framework and environmental justice. Here, we used a quantitative and spatial analytical approach to assess the changes of urban greenspace (UG) and examine the equity of changes in ES supply and demand across demographic and socioeconomic groups in Beijing and NYC between 2010 and 2017. Results showed that previously existing green space and its expansion were unevenly distributed across districts. High‐income districts in NYC had high green cover and experienced the highest increase of UG, in contrast to high green cover in areas with low population density and the greatest increase of UG in high‐income areas for Beijing, respectively. The inequitable distribution of UG resulted in a high level of mismatch in supply demand of ES, revealing a pattern of distributional ecological injustice within city. We also found more ecological supply was provided in the areas having less vulnerability, indicating that social vulnerability remained in a lower greenspace exposure after greening implementation in both cities. Our study contributes to a better understanding on how the dynamics of urban greenspace impact urban climate injustices, and provides new insights for critical urban adaptation interventions or practice on shaping ecological injustice.

Abstract Radiative forcing drives warming in Earth system, leading to changes in sea surface temperatures (SSTs) and associated radiative feedbacks. The link between changes in the top-of-the-atmosphere (TOA) net radiative flux and SST patterns is typically diagnosed by studying the response of atmosphere-only models to SST perturbations. In this work, we diagnose the pattern effect through response theory, by performing idealized warming perturbation experiments from unperturbed data alone. First, by studying the response at short time scales, where the response is dominated by atmospheric variability, we recover results that agree with the literature. Second, by extending the framework to longer time scales, we capture coupled interactions between the slow ocean component and the atmosphere, yielding a novel “sensitivity map” quantifying the response of the net radiative flux to SST perturbations in the coupled system. Here, feedbacks are captured by a spatiotemporal response operator, rather than time-independent maps as in traditional studies. Both formulations skillfully reconstruct changes in externally forced simulations and provide practical strategies for climate studies. The key distinction lies in their perspectives on climate feedbacks. The first formulation, closely aligned with prediction tasks, follows the traditional view in which slow variables, such as SSTs, exert a one-way influence on fast variables. The second formulation broadens this perspective by incorporating spatiotemporal interactions across state variables. This alternative approach explores how localized SST perturbations can alter the coupled dynamics, leading to temperature changes in remote areas and further impacting the radiative fluxes at later times.

Mesoscale eddies are a major sink of available potential energy (APE) in the ocean. When these eddies are not resolved or only partially resolved in a model, this effect needs to be parameterized to simulate a realistic large-scale state. Traditionally, the Gent-McWilliams (GM) parameterization has provided this sink of APE. However, the GM parameterization, which diffuses isopycnal heights, is not accompanied by a skillful prescription for GM diffusivity rooted in data from observations or models. Also, at eddy permitting resolutions, GM diffusion can negatively impact resolved eddies, and the only scale-aware prescription is to turn GM off in regions where eddies are permitted. Here we present a novel data-driven parameterization, as a substitute for GM, that extracts APE without overly negative impacts on the resolved flow. It is both flow-aware and scale-aware, and its magnitude can be tuned using an O(1) non-dimensional number. Features like non-dimensional inputs/outputs, lateral non-locality, flow-dependent coordinates, and range limitations improve the generalization of the data-driven scheme. Functional forms are learned via a small multi-layer perceptron, ensuring low computational cost and simple implementation in ocean models. The parameterization performs skillfully in offline evaluation, especially at scales smaller than the largest eddies. It is implemented in NOAA GFDL’s MOM6 and shown to be skillful in online tests in two-layer idealized simulations of a zonal channel and wind-driven gyre, at both eddy-permitting and non-eddying resolutions. This work suggests a path towards leveraging high-resolution simulations for the reduction of structural error and improvement in the fidelity of climate simulations.

Building-level exposure data are critical to natural hazard risk modeling, yet most global inventories describe where buildings are located rather than what they are made of. Roof material is a critical but poorly documented attribute for assessing vulnerability to wildfires, wind hazards, urban heat, floods, and earthquakes. To address this gap, we introduce RoofNet, a global dataset that maps 49,662 georeferenced building instances from 101 countries to 14 key roofing material classes using Earth observation (EO) imagery (redistributed where permitted) and associated geospatial metadata. RoofNet contributes (1) climatographically and architecturally diverse coverage of roof material labels, (2) a scalable annotation pipeline combining SME-guided manual labeling with vision-language model (VLM)-assisted classification, rule-based validation, and human-in-the-loop verification, and (3) a resource for evaluating subtle, geographically variable material-level identification in EO imagery and its implications for material-aware hazard risk modeling. Evaluation on a manually labeled hold-out set shows that zero-shot Remote Contrastive Language-Image Pre-Training (RemoteCLIP) struggles with roof material classification, while fine-tuning with RoofNet improves top-1 accuracy from 4.9% to 47.7%. We use RoofNet in an illustrative hazard case study to demonstrate how material-aware exposure data can change vulnerability estimates relative to material-naive inventories. RoofNet provides a missing material layer for global building attribute mapping and scalable hazard risk assessment.

Abstract AI emulators for forecasting have emerged as powerful tools that can outperform conventional numerical predictions. The next frontier is to build emulators for long climate simulations with skill across a range of spatiotemporal scales, a particularly important goal for the ocean. Our work builds a skillful global emulator of the ocean component of a state‐of‐the‐art climate model. We emulate key ocean variables, sea surface height, horizontal velocities, temperature, and salinity, across their full depth. We use a modified ConvNeXt UNet architecture trained on multi‐depth levels of ocean data. We show that the ocean emulator— Samudra —which exhibits no drift relative to the truth, can reproduce the depth structure of ocean variables and their interannual variability. Samudra is stable for centuries and 150 times faster than the original ocean model. Samudra struggles to capture the correct magnitude of the forcing trends and simultaneously remain stable, requiring further work.

Deep generative models hold great promise for representing complex physical systems, but their deployment is currently limited by the lack of guarantees on the physical plausibility of the generated outputs. Ensuring that known physical constraints are enforced is therefore critical when applying generative models to scientific and engineering problems. We address this limitation by developing a principled framework for sampling from a target distribution while rigorously satisfying mathematical constraints. Leveraging the variational formulation of Langevin dynamics and Lagrangian duality, we propose Constrained Alternated Split Augmented Langevin (CASAL), a novel primal-dual sampling algorithm that enforces constraints progressively through variable splitting. We analyze our algorithm in Wasserstein space and derive explicit mixing time rates. While the method is developed theoretically for Langevin dynamics, we demonstrate its applicability to diffusion models. We apply our method to diffusion-based data assimilation on a complex physical system, where enforcing physical constraints substantially improves both forecast accuracy and the preservation of critical conserved quantities. We also demonstrate the potential of CASAL for challenging non-convex feasibility problems in optimal control.

Predicting gray swan weather extremes, which are possible but so rare that they are absent from the training dataset, is a major concern for AI weather models and long-term climate emulators. An important open question is whether AI models can extrapolate from weaker weather events present in the training set to stronger, unseen weather extremes. To test this, we train independent versions of the AI weather model FourCastNet on the 1979-2015 ERA5 dataset with all data, or with Category 3-5 tropical cyclones (TCs) removed, either globally or only over the North Atlantic or Western Pacific basin. We then test these versions of FourCastNet on 2018-2023 Category 5 TCs (gray swans). All versions yield similar accuracy for global weather, but the one trained without Category 3-5 TCs cannot accurately forecast Category 5 TCs, indicating that these models cannot extrapolate from weaker storms. The versions trained without Category 3-5 TCs in one basin show some skill forecasting Category 5 TCs in that basin, suggesting that FourCastNet can generalize across tropical basins. This is encouraging and surprising because regional information is implicitly encoded in inputs. Given that current state-of-the-art AI weather and climate models have similar learning strategies, we expect our findings to apply to other models. Other types of weather extremes need to be similarly investigated. Our work demonstrates that novel learning strategies are needed for AI models to reliably provide early warning or estimated statistics for the rarest, most impactful TCs, and, possibly, other weather extremes.

The global energy budget is fundamental for understanding climate change. It states that the top-of-atmosphere imbalance between radiative forcing (which drives climate change) and radiative response (which resists the forcing) equals energy storage in Earth's heat reservoirs (i.e. the ocean, atmosphere, land, and cryosphere). About 90% of Earth's energy imbalance is stored as heat content in the ocean interior, which is poorly sampled before 1960. Here, we reconstruct Earth's energy imbalance since 1880 by inferring subsurface ocean warming from surface observations via a Green's function approach. Our estimate of Earth's energy imbalance is consistent with the current best estimates of radiative forcing and radiative response during 1880-2020. The consistency is improved in this study compared to previous ones. We find two distinct phases in the global energy budget. In 1880-1980, Earth's energy imbalance closely followed the radiative forcing. After 1980, however, Earth's energy imbalance increased at a slower rate than the forcing; in 2000-2020, the imbalance amounted to less than 50% of the forcing. In simulations of historical climate change, the model-mean energy imbalance is consistent with observations within uncertainties, but individual models with a "weak" response to anthropogenic aerosol agree better with observations than those with a "strong" response. Because the global energy budget before and after 1980 implies very different global warming in the future, further studies are required to better understand the cause of this historical variation.

As climate-driven extreme weather events continue to intensify, risk mitigation and governance are a critical aspect of urban climate change adaptation. Interdisciplinary knowledge integration is critical in order to account for varied perspectives related to the impacts of extreme weather events on urban systems. Despite advances made to integrate different strands of knowledge through systems-based approaches, few methods exist to contextualize and analyze the diversity of the knowledge being integrated. Assessing knowledge diversity exposes varying ways in which stakeholders identify and problematize the impacts of extreme weather events, uncovering knowledge gaps as well as dominant knowledge framings that might bias and/or hinder risk governance processes. This study presents a novel methodology that integrates a mental models approach with the social-ecological-technological systems (SETS) framework to assess and compare the perceptions of individual stakeholders on the impacts of extreme weather events on an urban system. By classifying system components and interactions into social, ecological, and technological domains, mental models enable the visualization of knowledge diversity, as well as the identification of potential gaps and silos in stakeholder understanding. The methodology is applied to New York City, engaging 20 expert stakeholders from diverse disciplines and sectors involved in mitigating the impacts of extreme precipitation. Findings reveal significant variability in how stakeholders emphasize SETS domains and interactions. By supporting more holistic and inclusive co-production processes, this approach provides a theoretical and empirical foundation for addressing the multifaceted challenges posed by climate change in urban environments.

Abstract This study addresses the boundary artifacts in machine‐learned (ML) parameterizations for ocean subgrid mesoscale momentum forcing, as identified in the online ML implementation from a previous study (Zhang et al., 2023, https://doi.org/10.1029/2023ms003697). We focus on the boundary condition (BC) treatment within the existing convolutional neural network (CNN) models and aim to mitigate the “out‐of‐sample” errors observed near complex coastal regions without developing new, complex network architectures. Our approach leverages two established strategies for placing BCs in CNN models, namely zero and replicate padding. Offline evaluations revealed that these padding strategies significantly reduce root mean squared error (RMSE) in coastal regions by limiting the dependence on random initialization of weights and restricting the range of out‐of‐sample predictions. Further online evaluations suggest that replicate padding consistently reduces boundary artifacts across various retrained CNN models. In contrast, zero padding sometimes intensifies artifacts in certain retrained models despite both strategies performing similarly in offline evaluations. This study underscores the need for BC treatments in CNN models trained on open water data when predicting near‐coastal subgrid forces in ML parameterizations. The application of replicate padding, in particular, offers a robust strategy to minimize the propagation of extreme values that can contaminate computational models or cause simulations to fail. Our findings provide insights for enhancing the accuracy and stability of ML parameterizations in the online implementation of ocean circulation models with coastlines.

Abstract In response to rising, chemistry‐climate models (CCMs) project that extratropical stratospheric ozone will increase, except around 10 and 17 km. We call the muted increases or reductions at these altitudes the “double dip.” The double dip results from surface warming (not stratospheric cooling). Using an idealized photochemical‐transport model, surface warming is found to produce the double dip via tropospheric expansion, which converts ozone‐rich stratospheric air into ozone‐poor tropospheric air. The lower dip results from expansion of the extratropical troposphere, as previously understood. The upper dip results from expansion of the tropical troposphere, low‐ozone anomalies from which are then transported into the extratropics. Large seasonality in the double dip in CCMs can be explained, at least in part, by seasonality in the stratospheric overturning circulation. The remote effects of the tropical tropopause on extratropical ozone complicate the use of (local) tropopause‐following coordinates to remove the effects of global warming.

Abstract. Ozone is the most significant radiatively-active gas whose number density maximizes in the interior of the atmosphere, at an altitude of around 26 km in the tropics. Textbook explanations for this interior maximum begin by invoking the Chapman Cycle, a photochemical system that reproduces the altitude of maximum ozone despite omitting leading-order sinks from catalytic cycles and transport. Yet, these textbook explanations subsequently fragment into (1) a source-controlled paradigm, explaining ozone to maximize where its production rate maximizes, between abundant photons aloft and abundant O2 below, and (2) a source/sink competition paradigm explaining ozone to maximize due to competition between the photolytic source and photolytic sink. Augmenting the Chapman Cycle with destruction by generalized catalytic cycles and transport, we demonstrate that these paradigms correspond to different regimes of ozone destruction, distinguished by whether photolysis of O3 contributes at leading order to the sink. The tropical stratosphere is estimated to occupy a photolytic sink regime above 26 km and a non-photolytic sink regime below. Paradoxically, each paradigm predicts ozone to maximize outside its altitude range of applicability, motivating a new explanation, the regime transition paradigm: the interior maximum of ozone occurs at the transition from the photolytic sink regime aloft to the non-photolytic sink regime below. An explicit solution is derived for ozone under gray radiation, which produces an interior maximum at an endogenously-determined regime transition, and elucidates the ozone response to top-of-atmosphere UV perturbations.

Abstract Approximately eight billion people are living on Earth today with more than half (55%, ∼4.2 billion) living in cities—a proportion predicted to increase to 70% (∼6.6. billion) by 2050. As the human population grows, urban residents will face increasingly extreme temperatures under future climate change, which will affect human well-being, health, and mortality. However, nature-based solutions offer promising strategies to mitigate these impacts. Here, we analyst future projections of the maximum temperature of the warmest month, as a proxy for extreme heat exposure across 5646 cities in 218 countries. We show that by mid-century, this climate metric is projected to increase by an average of +1.7 °C (± 0.5 °C), with the largest increases (∼4 °C) projected to occur in mid-to-high latitude cities of Europe, North America, and Australia. We highlight the urgent need to adopt nature-based solutions to mitigate projected increases in urban heat and contribute to net-zero CO2 emissions goals.

We introduce CAMulator version 1, an auto-regressive machine-learned (ML) emulator of the Community Atmosphere Model version 6 (CAM6) that simulates the next atmospheric state given the prescribed sea surface temperatures and incoming solar radiation. CAMulator explicitly conserves global dry air mass, moisture, and total atmospheric energy while remaining numerically stable over indefinite climate integrations. It successfully reproduces the annual CAM6 climatology and key modes of climate variability, including the El Niño-Southern Oscillation, the North Atlantic Oscillation, and the Pacific-North American pattern, with slightly muted variability. When forced with sea surface temperature (SST) outside the training distribution, CAMulator exhibits a systematic cold bias in high-latitude regions, particularly in boreal winter, likely due to the absence of interactive land and sea ice. Nonetheless, CAMulator achieves these results with a 350 times speedup over CAM6, making it an efficient alternative for generating large ensembles.

Abstract Emotions relate to climate change action in various ways. Here we elaborate on how the expansion of digital social networks and advances in artificial intelligence, ranging from recommender systems to generative AI, may affect the way people perceive and engage emotionally on climate change. We develop a simple framework that links individual and collective emotions, AI, and climate action, and suggest three critical areas in need of further investigation.