Small-scale ocean currents are crucial in regulating Earth’s climate, with a significant impact in the distribution of ocean properties. During the Calibration/Validation phase of the Surface Water and Ocean Topography (SWOT) satellite mission, we performed a high-resolution, multi-platform experiment to evaluate SWOT’s ability to resolve small-scale features, focusing on a ~20-25 km-radius anticyclonic eddy in the Western Mediterranean Sea. Underwater glider data identified biconvex isopycnals, classifying the eddy as an intrathermocline feature with a ~3 cm sea level anomaly. Acoustic Doppler Current Profiler (ADCP) data recorded maximum velocities of ~40 cm/s. SWOT successfully captured the sea level signal and geostrophic currents of the eddy, showing notable improvements over conventional altimetry: 33% in sea level representation compared to glider, 61% in horizontal velocity compared to ADCP, and 44% and 10% in velocity magnitude and direction compared to drifter measurements. This study highlights SWOT’s potential in resolving small-scale ocean dynamics.

The unique ecosystem of estuaries as well as their social and economic usage such as freshwater abstraction are highly dependent on local salinity. Shifts in salt intrusion can have severe consequences. The salinity dynamics are influenced by several natural factors, especially the river discharge and the tides, but also by human activities such as channel deepening. A thorough understanding of salt transport mechanisms and their response to changing conditions is essential for assessing the effects of both natural variability and human activities on salt intrusion. This study applies a detailed salt transport decomposition method to a high-resolution numerical model of the North German Weser River Estuary. The analysis of the cross-channel integrated transport showed an alternating dominance of two up-estuary salt transport mechanisms: the subtidal shear transport, driven by estuarine circulation and the tidally-correlated depth-averaged transport, such as tidal pumping. A novel decomposition method allowing for two-dimensional maps of salt transport is developed and implemented here to understand the local topographical impacts on the salt transport. Our results highlight that the cross-sectionally integrated transport can underestimate the strength of the resolved transport in specific areas, as opposing flows often occur between the channel and adjacent shoals. Furthermore, we investigate a potential future scenario involving channel deepening, finding that it increases subtidal shear transport, particularly in dredged areas. These findings offer new insights into the spatial complexity of salt dynamics and the impact of anthropogenic changes on estuarine environments.

Background mesoscale flows (BMFs) modify the local inertial frequency and affect the energy and vertical propagation speeds of near-inertial waves (NIWs). Despite their significance, the effect of BMFs on the downward energy flux (Fz) of NIWs remains underexplored. This study aimed to estimate the downward energy flux (Fz) of NIWs while accounting for BMF effects using two years of mooring data (November 2017–October 2019) from the Kuroshio Extension. By dividing the data into 11 day segments, the temporal variability of the effective near-inertial frequency and group velocity owing to the BMFs was considered. During winter, when NIWs are active—on a temporal average in anticyclonic flows—Fz increased by 50 %, whereas in cyclonic flows, Fz decreased by 45 % when BMFs were considered. Because cyclonic circulations are twice as frequent, Fz decreased by ~17 %, to 0.37 x 10-3 W m-2. This amount is ~1.8 times greater than that in the northeastern Pacific and accounts for ~28 % of the wind work rate, similar to eddy-resolving high-resolution numerical model results. Thus, the Kuroshio Extension is an important area for downward NIW energy propagation and BMFs should be considered for accurate NIW energetics.

Understanding the mechanics and rheological properties of the solid Earth requires integrating observations of earthquake cycle deformation with advanced numerical models of the underlying processes. In this article, we introduce a new numerical modeling tool designed for two-dimensional subduction zone geometries that solves the equations for mechanical equilibrium in the lithosphere-asthenosphere system to predict displacements, strains, and stresses throughout the medium. Using a Boundary Element Method (BEM) framework, we reduce the governing partial differential equations to a set of coupled ordinary differential equations, simulating periodic earthquake cycles in a viscoelastic medium with spatially variable power-law viscous rheologies and rate-dependent fault friction. Our approach overcomes key limitations of existing BEM models by ensuring (1) mechanical consistency across timescales, from individual earthquake cycles to geological periods, and (2) precise stress transfer calculations in a viscoelastic medium. We also demonstrate that for spatially heterogeneous linear viscoelastic materials, the coupled ordinary differential equations can be simplified to an eigenvalue problem, significantly enhancing computational efficiency. These advancements offer a powerful tool for predicting spatiotemporal patterns of surface displacement given complex mantle structures and lay the foundation for high-dimensional inverse problems to infer constitutive properties of the lithosphere-asthenosphere system.

Zircon U--Pb geochronology is a vital tool for dating geological events, yet interpreting discordant zircon ages---often the result of Pb loss due to fluid-rock interactions---remains challenging. Although frequently disregarded, discordant data can yield valuable geological insights. Here, we introduce a Python application that models the timing of Pb loss in zircon, enabling efficient analysis of discordant U--Pb data. Our application uses the concordant-discordant comparison test in Tera-Wasserburg concordia space to classify zircon analyses based on a customisable discordance threshold. Featuring a user-friendly interface, the application models possible Pb loss events and calculates optimal Pb loss ages by minimising the dissimilarity between concordant and reconstructed age distributions. When applied to the Mount Isa Inlier in northeastern Australia, the tool identifies a major Pb loss event around 481 Ma, corresponding to Palaeozoic tectonic activity along Gondwana's active margin, including the Delamerian Orogeny. Older Pb loss ages cluster along major NE-SW fault systems, suggesting that long-lived, reactivated structures facilitated fluid migration and subsequent Pb mobilisation in zircon. Sensitivity analyses confirm the consistency of Pb loss age estimates across various model parameters. This study provides new insights into fluid-rock interactions in the Mount Isa Inlier and offers a methodological framework for exploring Pb loss in zircon U--Pb datasets. The application is broadly applicable for future research in regions with complex tectonic histories.

The impact of aviation on climate change due to CO2 emissions is well established and is associated with much less uncertainty than the non CO2 effects. Among the non CO2 effects contrails formation and their evolution into cirrus-type clouds remain a subject of considerable uncertainty. A frequently overlooked source of uncertainty arises from the sensitivity of climate models to adjustable parameters used to represent the effect of subgrid-scale processes. The limited number of state-of-the-art climate models with an explicit contrail representation makes it challenging to evaluate model sensitivity of contrail radiative forcing to parameters and backgroud atmospheric conditions. To better characterize the contrail radiative forcing and its evolution it is therefore necessary to develop their representation within a large range of existing climate models. Here we develop and evaluate a new parameterization of contrail cirrus for the ARPEGE-Climat atmospheric model. The representation of the ice-supersaturated regions, where contrails persist, agrees well with in-situ and satellite observations, as well as the simulated contrail microphysical properties. With this parameterization, and using the ERA5 reanalysis to nudge the ARPEGE-Climat model, we estimate that for the air traffic of the year 2019 the global mean annual contrail coverage is 1.27%. In addition, the global mean annual instantaneous radiative forcing is estimated to be 66.2 mW/m2, although this result is sensitive to the choice of some key parameters of the contrail-cirrus parameterization. These findings are consistent with similar published results obtained using the same flight inventory but based on different methods of representing contrail effects.

Urban flood monitoring is crucial for understanding flood processes and implementing management strategies. However, current monitoring systems cannot comprehensively capture urban flooding dynamics. Here we explore the use of cutting-edge Large Multimodal Models (LMMs) to estimate floodwater depth from ground-level images, as alternative observational approaches. Evaluated on two urban flood image datasets, LMMs generate estimations exhibiting acceptable concordance to ground truth, with GPT-4 achieving the highest accuracy of 0.65 and a Spearman correlation coefficient of 0.88. Furthermore, a combined effect of image complexity and textual prompt is found to influence LMMs’ performance. Our study systematically demonstrates, for the first time, that LMMs can be effective tools for imaging-based urban flood monitoring, enlarging the data for flood forecasting and model calibration.

Atmospheric reaeration is a key source of dissolved oxygen in rivers, plays a critical role for assessing aquatic ecosystem health and understanding the mechanisms that support ecological functions. However, traditional water quality models often fail to capture the temporal and spatial dynamics of the reaeration coefficient (k2), thereby hindering accurate predictions of dissolved oxygen concentration distributions. This study employed a recirculating water tank to conduct experiments under various wave and flow conditions, quantitatively analyzing the impact of hydrodynamic factors-such as water depth, flow velocity, and wave action-on oxygen transfer at the air-water interface. The results indicated that as water depth increases and flow velocity decreases, efficiency of oxygen transfer declines. This trend occurs because increased depths and reduced velocities diminish interface disturbance, thereby weakening gas-liquid exchange. Conversely, wave action significantly enhances oxygen transfer, particularly under stronger wave conditions. By integrating gas transfer theory with experimental data, we developed a mapping relationship between k2 and hydrodynamic variables. This framework enabled the development of a multi-parameter dynamic simulation model that integrates hydrodynamics with dissolved oxygen transfer. The model is based on real-time data of hydrodynamic parameters and realizes the dynamic update of the whole domain complex k2, which can accurately simulate the spatial and temporal distribution and transfer of dissolved oxygen under complex hydrodynamic conditions. Driven by dynamic data, the model greatly improves the applicability and accuracy of the traditional mean generalization model, and can be used as an effective tool for predicting dissolved oxygen levels in complex aquatic environments.

We have detected and characterized traveling ionospheric disturbances (TIDs) in the mid-latitude ionosphere over the European region using Kharkiv incoherent scatter (IS) radar data acquired near solstices and equinoxes during solar cycle 24 and under magnetically quiet conditions. We analyzed the diurnal, seasonal and solar activity dependence of both large-scale (LS) and medium-scale (MS) TIDs. We studied 140 TID events and estimated their energetic (relative amplitudes), temporal (dominant periods, frequency of occurrence), and spatial (heights of maximum relative amplitudes, vertical and horizontal phase velocities and wavelengths) characteristics. We suggest that the most probable generation mechanisms of magnetically quiet LS and MS TIDs are solar terminators passages, enhanced gravity wave activity and dissipation, severe tropospheric convection, coupling processes between Es layer and F region, polarization electric fields, and Perkins’s instability. We have shown a positive correlation between the heights of the maximum relative TID amplitudes and the solar activity for LSTIDs. The TID characteristics obtained during extremely quiet conditions provide a useful context for analysing the possible TID response to localized energy releases including those of anthropogenic origin, and enable improvements in global and regional ionospheric models by clarifying the contribution of wave processes to the overall energy budget of the atmosphere and ionosphere.

The inductive component of the magnetospheric electric field, which is associated with the temporal change of magnetic field, provides an additional means of local plasma energization and transport in addition to the electrostatic counterpart. This study examines the detailed response of the inner magnetosphere to inductive electric fields and the associated electric-driven convection corresponding to different solar wind conditions. A novel modeling capability is employed to self-consistently simulate the electromagnetic and plasma environment of the entire magnetospheric cavity. The explicit separation of the electric field by source (inductive vs. electrostatic) and subsequent implementation of inductive effects in the ring current model allow us to investigate, for the first time, the effect of the inductive electric field on the kinetics and evolution of the ring current system. The simulation results presented in this study demonstrate that the inductive component of the electric field is capable of providing an additional source for long-lasting plasma drifts, which in turn significantly alter the trajectories of both thermal and energetic particles. Such changes in the plasma drift, which arise due to the inductive electric fields, further reshape the storm-time ring current morphology and alter the degree of the ring current asymmetry, as well as the timing and the peak of the ion pressure. The total ion energy is increasing at a faster rate than the supply of energetic ions to the ring current, suggesting that the inductive electric field provides effective and accumulative local energization for the trapped ring current population without confining additional particles.

The flux of dissolved organic carbon (DOC) from land to sea is an important net transfer within the global carbon cycle. The biogeochemical fate of this terrestrial DOC (tDOC) remains poorly understood and is usually neglected in ocean models. Southeast Asia accounts for around 10% of global tDOC flux, mostly from tropical peatland-draining rivers discharging onto the Sunda Shelf. We developed a new light-driven parameterization of tDOC remineralization that accounts for photochemical, microbial, and interactive photochemical–microbial degradation. Using this, we simulated the transport and remineralization of tDOC through the Sunda Shelf seas using the regional 3D hydrodynamical–biogeochemical models HAMSOM–ECOHAM. Our realistic hindcast simulations for 1958–2022 show that about 50% of riverine tDOC is remineralized before leaving the shelf. This lowers seawater pH across the entire inner Sunda Shelf by an average of 0.005 units (by up to 0.05 units in the Malacca Strait). Correspondingly, seawater pCO2 is raised, increasing CO2 outgassing from the shelf by 3.1 Tg C yr−1 (0.14 mol m−2 yr−1 ) during 2013-2022. Even regional ocean acidification trends increase, because river discharge and tDOC flux increase. Our model reveals large spatial variability with greatest inputs and remineralization of tDOC close to major peatlands, especially off Sumatra and Borneo. The interannual variability in tDOC input and the monsoonal current reversal lead to strong temporal variability in carbonate system parameters in these areas. Our results highlight the importance of representing tDOC in ocean models, and reveal the fate of tropical peatland tDOC.

Plankton influences biogeochemical and ecosystem processes, such as sequestration of atmospheric CO2, carbon export to the ocean floor, and the productivity of higher trophic levels. Body size is a proxy for many plankton functional traits, and one means of analyzing its community structure is through the distribution of biovolume across size classes (the size spectrum). To understand how climate forcing can affect plankton communities, we assessed the size spectra in the historical simulations of seven Earth System Models (ESMs) included in the 6th Coupled Model Intercomparison Project (CMIP6) and analyzed projected changes under a high emissions scenario (SSP5-8.5). We compared the historical estimates with the Pelagic Size Structure database (PSSdb), a novel size structure dataset from imaging systems. The median slope from models ranged from -1.66 to -1.07, with shallower slopes from this range falling near both the theoretical expectation and PSSdb observations (-1.05), with variations around the median representing differences in the total biovolume distribution across plankton functional groups. Consistent with the observations, most ESMs show steeper slopes and lower biovolume in oligotrophic subtropical gyres compared to productive ocean regions. There was a lack of agreement between models and observations in the size spectra seasonal cycle, possibly stemming from missing model processes and incomplete sampling. Despite these caveats, the size spectra from ESMs presented here, and their evaluation with PSSdb, provides insights on how climate change will affect ecological processes in the plankton, and highlights areas of improvement in model development and imaging data coverage.

The emergence of global convective-permitting models (GCPMs) represents a significant advancement in climate modeling, offering improved representation of deep convection and complex precipitation patterns. In this study, we evaluate the performance of the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) using its doubly-periodic configuration (DP-SCREAM) against large eddy simulations (LES) and modern observational datasets from the Atmospheric Radiation Measurement (ARM) program. We introduce several new transitional cloud regime cases, such as the transition from shallow to deep convection and from stratocumulus to cumulus, as well as cold-air outbreak scenarios. The results reveal both strengths and limitations of SCREAM, particularly in the accurate simulation of cloud transitions and mid-level convection, with varying degrees of sensitivity to horizontal and vertical resolution. Despite improvements at higher resolutions, key biases remain, including the abrupt transition from shallow to deep convection and the lack of congestus clouds. These findings underscore the need for further refinement in turbulence parameterizations and vertical grid resolution in GCPMs.

Paris Agreement-compliant / IPCC-vetted temperature trajectory modeling is shown here.  1000 TWh/year is about the electricity consumption of Japan or the Russian Federation.  This illustrates the trade-offs between carbon dioxide and methane emissions for two policy options: coalfired vs gas-fired electric power generation.  The atmospheric concentration and temperature effect of methane decays within a few decades after cessation of emissions.  Carbon dioxide lingers in the atmosphere for centuries, locking in temperature increases even after emissions stop. Coal vs LNG: Global Temperature Increase 1000 TWh/year-50 Year Powerplant Lifetime Environmental/Economics Issue: Can global greenhouse gas emissions be reduced by shipping liquefied natural gas to China, or by encouraging China to use locally-produced coal in their electric power plants? This is a trillion-dollar decision with global environmental impact. Scientific Question: How can we accurately assess the global warming effects of emitting various combinations of carbon dioxide and methane?  Carbon dioxide and methane are the two most important greenhouse gases.

Full waveform inversion (FWI), a state-of-the-art seismic inversion algorithm, comprises an iterative data-fitting process to recover high-resolution Earth’s properties (e.g., velocity). At the heart of this process lies the numerical wave equation solver, which necessitates discretization. To perform efficient discretization-free FWI for large-scale problems, we introduce physics-informed neural networks (PINNs) as surrogates for conventional numerical solvers. Unfortunately, the original PINN implementation requires additional training for the new velocity model when used in the forward simulation. To make PINNs more suitable for such scenarios, we introduce latent representation learning to PINNs. We first append the input with the encoded velocity vectors, which are the latent representation of the velocity models using an autoencoder model. Unlike the original implementation, the trained PINN model can instantly produce different wavefield solutions without retraining with this additional information. To further improve the FWI efficiency, instead of computing the FWI updates on the original velocity dimension, we resort to updating in its latent representation dimension. Specifically, only the latent representation vectors get updated while the weights of the autoencoder and the PINN model are kept fixed during FWI. Through a series of numerical tests, the proposed framework shows a significant increase in accuracy and computational efficiency compared to the conventional FWI. The improved performance of our framework can be attributed to implicit regularization introduced by the velocity encoding and physics-informed training procedures. The proposed framework presents a significant step forward in utilizing a discretization-free wave equation solver for a more efficient and accurate FWI application.

This paper is Part II of a two-part paper that documents the CM4X (Climate Model version 4X) hierarchy of coupled climate models developed at the Geophysical Fluid Dynamics Laboratory (GFDL). Part I of this paper is presented in \citeA{CM4X_partI}. 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 (CMIP6). 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 $0.25^{\circ}$ of CM4X-p25 to the $0.125^{\circ}$ 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.

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 $0.25^{\circ}$ (referred to as CM4X-p25) and the other that uses a $0.125^{\circ}$ grid (CM4X-p125). CM4X also refines its atmospheric grid from the nominally 100~km (cubed sphere C96) of CM4.0 to 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 $-0.02~\mbox{W}~\mbox{m}^{-2}$ within $\mathcal{O}(150)$ years in a pre-industrial simulation, and retains that thermally equilibrated state over the subsequent centuries. This 1850 thermal equilibrium is characterized by roughly $400~\mbox{ZJ}$ less ocean heat than present-day, which corresponds to estimates for anthropogenic ocean heat uptake between 1850 and present-day. CM4X-p25 approaches its thermal equilibrium only after more than 1000 years, at which time its ocean has roughly $1100~\mbox{ZJ}$ {\it 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 {\it mesoscale dominance hypothesis} for why CM4X-p125 shows such favorable thermal equilibration properties.

Snowpack dynamics play a key role in controlling hydrological and ecological processes at various scales, but snow monitoring remains problematic. Data assimilation techniques are emerging as promising tools to improve uncertain snowpack simulations by fusing state-of-the-art numerical models with information rich, but noisy observations. However, the occlusion of the ground below the forest canopy limits the retrieval of snowpack information from remote sensing tools. Thus, remote sensing observations in these environments are spatially incomplete, impeding the implementation of fully distributed data assimilation techniques. Here we propose different experiments to propagate the information obtained in forest clearings, where it is possible to retrieve observations, towards the sub-canopy, where the point of view of remote sensors is occluded. The experiments were conducted in forests within Sagehen Creek watershed (California, USA), by updating simulations conducted with the Flexible Snow Model (FSM2) with airborne lidar snow data using the Multiple Snow data Assimilation system (MuSA). The successful experiments improved the reference simulations significantly both in terms of validation metrics (correlation coefficient from R=0.1 to R ~0.8 in average) and spatial patterns. Both data assimilation configurations, using geographical distances or a space of topographical dimensions, managed to improve the reference run. However, those creating a space of synthetic coordinates by combining the spatiotemporal data assimilation with a principal components analysis did not show any improvement, even degrading some validation metrics. Future data assimilation initiatives would benefit from building specific localization functions that are able to model the spatial snowpack relationships at different resolutions.