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.

A document by Jack Sheehan. Click on the document to view its contents.

Previous statistical studies have described the distributions and properties of whistler-mode waves in Jupiter’s magnetosphere, but explaining these wave distributions requires modeling wave propagation from their generation near the magnetic equator. In this letter, we conduct ray tracing of whistler-mode waves based on realistic Jovian magnetic field and density models. The ray tracing results generally agree with the statistical wave distributions based on Juno measurements. The modeled ray paths show that high-frequency waves generated near the equator are confined within 20° magnetic latitude due to Landau damping, low-frequency waves can propagate to higher latitudes and lower M-shells, with changing wave normal angles, and a portion of low-frequency waves could propagate to high M shells at high latitudes. Our modelling results provide a theoretical interpretation of whistler-mode wave distributions and properties, providing essential insights for future radiation belt models at Jupiter.

Retrieval of ocean surface current using single-antenna synthetic aperture radar (SAR) relies on the removal of the wind and wave signal (wind-wave bias) from the observed SAR Doppler shift. This is often performed using deterministic atmospheric and wave models input into a geophysical model function, though atmospheric models are chaotic, and their predictability is flow-dependent. 251 Sentinel-1 SAR scenes, obtained in 2023 over Skagerrak and Kattegat, are analyzed to assess the uncertainty in the radial velocity estimation caused by uncertainties in the wind speed and direction provided by MEPS, an operational regional atmospheric ensemble system. Using ensemble surface wind direction and speed from MEPS, we investigate the conditions under which the radial velocity retrieval uncertainties exceed community-guided thresholds. The maximum wind speed and direction values that meet the specified thresholds are also estimated using two approaches. Our findings show that retrievals have a higher degree of uncertainty when the wind speed uncertainty exceeds 20% of the mean field, with larger uncertainties associated with low-wind speed conditions. A strong dependency between satellite antenna-look and maximum uncertainty values is reported. Employing ensemble models on radial velocity uncertainty quantification has promising potential and may be extended to operational global products.

We combine lidar temperature observations onboard a research aircraft with numerical simulations in the framework of the SOUTHTRAC (Southern Hemisphere Transport, Dynamics, and Chemistry) Campaign. Deep propagation of gravity waves (GW) from the troposphere to the lower mesosphere is studied above the Southern Andes during two flights in September 2019. We use the Weather Research and Forecasting (WRF) model with a configuration for the simulations that has been validated in a previous study of this campaign. Strong orographic GW were detected during both flights, that were conceived for different latitudes. The observational and numerical data reveal the presence of significant GW attenuation, breaking and secondary wave generation above the stratopause due to the development of convective and dynamic instability as well as conditions for wave evanescence. The GW generated by topography were not able to alter the stable structure of the stratosphere, but the scenario was quite different in the lower mesosphere. The disturbed zones in that layer were produced by the combined effect on lapse rate of the background temperature variation and the perturbations associated with GW, which together may induce large vertical gradients. As a consequence, areas of reduced stability (with low or even negative buoyancy parameter) emerge above the stratopause. The existence of these GW self-induced attenuation layers in the mesosphere where temperature perturbations produce large negative gradients may lead to an amplitude growth control mechanism for the upward propagating waves.

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.

High-resolution time series of burned area (BA) derived from Sentinel-2 can advance understanding of the determinants and dynamics of fire by incorporating small fires previously excluded from regional analyses. Here, we assessed the drivers of fire frequency, fire size, and fire seasonality across Southeastern Africa via fine (Sentinel-2 MSI) and moderate (MODIS) spatial resolution data. Twenty-six predictors of ignition patterns, fuel load, flammability, and fire spread—factors that underpin fire frequency, size, and seasonality—were incorporated into machine learning models to evaluate their predictive capacity, relative importance, and directional relationships with fire regime attributes. We found large differences between fine and moderate resolution mapping in the estimates of fire frequency, size, and to a lesser extent, seasonality, with models using Sentinel-2 having better predictive performance. However, the relationships between fire regime attributes and predictors were generally consistent between sensors. We found that high fire frequency was associated with fuel load and seasonality and that interannual stability in land cover, livestock density and human population, was associated with low fire frequency. Fire sizes were generally small in both the high and low extremes of the precipitation and vegetation productivity gradient, and in highly transformed areas. The fraction of fire outside of the fire season was higher under low seasonality conditions and under higher human influence. Our results demonstrate the applicability of existing theory of fire dynamics found on moderate resolution data to new fine scale data while providing nuanced insights into fire regime determinants in increasingly transformed landscapes.

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.

Haoran Liu1, Min Chen1 *1 Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, WI, USA*Corresponding author: Min Chen ([email protected])

A document by Jack Sheehan. Click on the document to view its contents.

A document by Samuel Fagbemi. Click on the document to view its contents.

The Nmax observations from Global-scale Observations of the Limb and Disk (GOLD) satellite report for the first time a unique transition of asymmetric Equatorial Ionization Anomaly (EIA) crest from south to north within a short duration of 1.7 hours during the main phase of 23 April 2023 geomagnetic storm. The Prompt Penetration Electric Field occurred on this day produced a superfountain, manifesting an enhancement in EIA crests. The movement of Nmax along a fixed longitude confirmed the transition rate of 650 m/s, which falls under the velocity range of Large Scale Travelling ionospheric Disturbances (LSTIDs). The wavelet periodogram of GPS-TEC over EIA crest and trough revealed an enhanced periodicity of 1.2 hour indicating the presence of LSTID during the period when EIA exhibited the transition. These unique observations indicate the importance of assessing the impact of storms on large-scale plasma processes prevailing over the equatorial/low-latitude ionosphere.

Upper atmosphere whorls or vortices produced by an extreme geomagnetic storm were reported for the first time by Evans, Correira et al (2024). Evidence of the vortices was seen in the upper atmosphere’s neutral composition, effective neutral temperature near 160 km, and ionospheric electron densities. Evans, Correira et al (2024) speculated that a combination of GOLD’s unique capabilities and the historical nature of the Gannon Storm allowed for the previously unseen phenomena to be observed. However, following a severe geomagnetic storm on 10 October 2024, evidence of similar upper atmosphere vortices were again observed in GOLD ON2 and TDISK data. The appearance of vortices following another severe geomagnetic storm during solar maximum prompted a review of the GOLD dataset for additional instances, revealing the appearance of vortices following several less intense geomagnetic storms during Solar Cycle 25.

Through its unique capacity for narrative transportation and its ability to create strong parasocial bonds with familiar and loved characters, mass media entertainment provides new and exciting ways to support and expand the landscape of climate education. Savvy representations of climate change and climate solutions in mass media can deliver informal climate education with the ability to reach diverse and potentially disengaged audiences at scale. Creative professionals in the entertainment industry are already including climate themes and sustainability in content across genres and formats. In parallel, there is a growing ecosystem of environmental non-profits, research institutions, and industry-affiliated groups that provides support to this effort. Integrating these entertainment-based efforts into existing programs of climate education, communication, and outreach could both increase the breadth of audiences for these programs, while also improving their ability to empower individuals and communities to address climate change. This poster: (i) provides a landscape analysis of key stakeholders and initiatives supporting the development, production, and distribution of climate-relevant programming within mass media entertainment; (ii) through case studies, explores how research can inform the use of entertainment to inspire climate action at scale; (iii) highlights specific examples of climate representation in popular entertainment; and, (iv) identifies empirically-grounded recommendations for narrative framing.

Changes in land-use and land cover (LULC) occur in response to economic development and the growing demand for food. However, the impact on water availability for downstream users is often overlooked in land management policies, likely because of the lack of a well-established approach for evaluating the impact of LULC changes on river flows. This study explores the use of long short-term memory (LSTM) networks trained on data from thousands of stream gauges across the United States (US). Three LSTM networks were considered, each using different levels of LULC information: none, static (constant) and dynamic (updated annually). For this study, we created the DROMEDARY US dataset that incorporates the Cropland Data Layer dataset from the US Department of Agriculture, reflecting significant human-related LULC changes, and includes significantly more basins (3,246) than existing datasets commonly used for benchmarking hydrological models. All LSTMs demonstrated good out-of-sample prediction skills across the US. However, the ones using dynamic LULC information outperformed the others by a significant margin in reproducing observed changes in flow following changes in fallowing, an agricultural practice used to let the land rest after intense cropping cycles, or to spare water during droughts. Interestingly, using static LULC information performed worse than using no LULC information, highlighting that use of inaccurate (or outdated) information can degrade model performance at reproducing the effect of change in LULC, while having good streamflow prediction skill. Scenarios involving increased fallowed land highlighted further the benefits of using dynamic, emphasizing the need for frequently updated LULC datasets.

The thermophysical properties of lunar regolith are crucial for the scientific and engineering issues in future lunar missions. In this work, we evaluate the thermophysical properties of lunar regolith between 75 • N and 75 • S from the bolometric temperature dataset of Diviner radiometer by treating the radius of lunar regolith grains as a proxy. By the ground-truth median/average grain radius and Diviner data at Apollo, Luna and Chang'E landing sites, we re-calibrate the reference rolling force for a grain radius of 47.5 m as 0.8 ± 0.1 × 10 −7 N. On the global scale, the estimated grain radius values are fairly uniform, focus mostly between 40 and 60 m and show an average value of ∼ 52.7 m. Some regions of Oceanus Procellarum, Mare Imbrium and Mare Frigoris show abnormally large grain radius 80-100 m, which are associated with the late high-Ti volcanism during 2.3-1.2 Ga within Procellarum KREEP Terrane (PKT). Mare Humorum is dated to be as old as 3.9 Ga, but still shows large grain radius values of ∼ 100 m that are far greater than those in other contemporaneous mares. Such an anomaly may indicate a distinguished evolution for the local lunar regolith. For the surficial lunar regolith on the global scale, the contact component of thermal conductivity varies mostly within 0.0008-0.0010 W/(m•K), whereas the radiative component normalized to 250 K varies mostly within 0.002-0.004 W/(m•K). The thickness of topmost loosely packed layer is estimated as 0.02-0.04 m. Besides, the thickening of loosely packed layer is more likely to be a natural consequence of the change of packing style associated with the decrease of grain radius during the space weathering.

The paper analyzes “polar” substorms observed at high geomagnetic latitudes (>70° MLAT) in the absence of substorm-like disturbances at lower latitudes. The analysis is limited to the longitudinal sector of the IMAGE network (~108-114 Mlong), in this case “polar” substorms develop over the Svalbard archipelago. The “polar” substorms are usually observed under quiet geomagnetic conditions, but the question remains open whether “polar” substorms can develop under extremely quiet conditions, when the geoeffective parameters of the space weather are extremely small. We studied 92 “extreme geomagnetic quiet” intervals in 2010- 2020, associated with the intervals of extremely slow solar wind (ESSWs, V < 300 km/s). It is found that ”polar” substorms were observed even under an extremely slow solar wind, however, with the appearance of the negative Bz component of IMF; there were established 32 such events at the longitudinal sector of the IMAGE network in the 17 ESSW intervals (~19% of all ESSW intervals). It is shown that “polar” substorms during ESSW exhibit the main features of ordinary substorms, namely they accompanied by Pi1B geomagnetic pulsations, positive subauroral or mid-latitude magnetic bays, a poleward moving of the westward electrojet and auroras during its expansion phase. Besides, it was found that the most events (~82%) of ”polar” substorms during ESSW were isolated events, developed only in the pre-midnight sector, without substorm disturbances in other longitudinal sectors. Several “polar” substorm events have been studied in detail.

A document by Michal Ostrowski. Click on the document to view its contents.