Weather stations can represent local weather variability and extremes more reliably than gridded products and are therefore better suited for local climate impact applications like calculation of the Fire Weather Index (FWI), a multivariate index for wildfire danger assessment. However, the prediction at multiple sites poses the challenge of preserving spatial consistency across locations, requiring a suitable multi-site approach. This study evaluates the potential of Convolutional Neural Networks (CNNs) for statistical downscaling (SD) of FWI predictions across the Iberian Peninsula. We compare our CNN-Multi-Gaussian (CNN-MG) model against Generalized Linear Models (GLMs) and a benchmark single-site CNN approach. Our evaluation focuses on predictive accuracy, distributional congruence, spatial coherence and extreme events reproducibility using daily FWI data from 29 locations in Spain. The CNN-MG model, which integrates the covariance structure of the predictands, outperformed other methods in representing FWI distributions across both single and multisite scales. Moreover, our model provides greater physical interpretability via eXplainable Artificial Intelligence (XAI) techniques, while also emphasizing simplicity and ease of training.

 The absolute values of VLM and VCM are mostly from +1mm/yr to-1mm/yr. The area of Poland can be considered tectonically stable.  The developed VCM model correlates with the geological and tectonic structure of the region.  The method of combining WAV sets based on different computational strategies yielded good results.

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.

A better understanding of the many interacting processes governing the evaluation of ice in natural clouds is required to improve the representation of ice clouds in global circulation models. Recent studies suggest a dominant role of vapor growth processes in determining the temperature dependence of cloud top ice sizes and shapes. Using airborne cloud remote sensing along with reanalysis data, here we show that observed cloud top ice effective radii and estimated normalized growth rates at cloud top highly correlate with an approximately linear relationship, which is consistent with a conceptual model also presented. Furthermore, significant differences in crystal shape characteristics and scattering asymmetry parameters are found between sub- and super-saturated cloud tops over ocean, although not over land. These results provide valuable observational targets for studying ice formation and evolution processes using models, while also helping interpretation of satellite observations of ice microphysical properties at cloud tops.

The assessment of volcanic hazards is crucial to develop effective emergency plans, especially for volcanoes close to urban centers or under air traffic routes. Impact assessment relies on underlying numerical codes that require eruptive source parameters as input and forecasts drastically depend on their robust reconstruction during ongoing events. We apply a novel deposit inversion workflow built on ensemble methods and data assimilation techniques to reconstruct the explosive events that occurred at Mt Etna, Italy, between 3 and 5 December 2015. Based on results from previous studies, we reconstruct this eruption using the Gaussian with Non-negative Constraints data assimilation method. Results agree well with independent observations and highlight the potential for automatized procedures in volcanic hazard assessment.

Neutral in-situ leaching (ISL) is a prominent technique for extracting uranium (U) from sandstone-type U deposits. However, the intricate biogeochemical processes and associated microbial communities in environments subjected to prolonged neutral in-situ U leaching remain insufficiently understood. Here, capitalizing on groundwater samples collected from an area subjected to two decades of ISL (Undergoing-ISL) and a background aquifer (Pre-ISL) at the Qianjiadian U mining site (the first CO2+O2 ISL site located in the Songliao Basin of northern China) we investigate the responses of aquifer microbiomes to CO2+O2 ISL using genome-resolved metagenomics resulting in a total of 315 good-quality bacterial metagenome-assembled genomes (MAGs) and 5 archaeal genomes. Five putative keystone MAGs were identified based on their high connection degree and low betweenness centrality within the co-occurrence network of reconstructed MAGs, notably highlighting the prevalence of microorganisms involved in sulfur, ammonium, and Fe(Ⅱ) oxidation, which are integral to facilitating U(Ⅳ) oxidation during CO2+O2 ISL. In particular, sulfur-oxidizing bacteria and Fe(Ⅱ)-oxidizing bacteria (Rhodoferax and SHZL01) played a key role in U mobilization during long-term neutral ISL. These findings enhance our understanding of subsurface microbial ecology and hold significant implications for bioremediation strategies and groundwater management in regions affected by mining.

During the Last Glacial Maximum, wetter and cooler conditions drove expanded pluvial lake systems throughout the southwestern United States. Here, we aim to understand the drivers of these hydroclimate changes using new biomarker records from Cumbres Bog, a high-elevation fen in the San Juan Mountains of southern Colorado. The leaf wax hydrogen isotope record, which we interpret as reflecting changes in the proportions of seasonal precipitation sources reaching Cumbres Bog, indicates that winter moisture delivery was reduced at the LGM, increased suddenly during the deglaciation at ca. 14.8 kyr, and then declined rapidly at ca. 11.5 kyr in association with exceptional early Holocene drought. Winter precipitation abruptly recovered at ca. 8.2 ka and remained high throughout the mid- to late-Holocene. BrGDGT-derived temperatures indicate that the southern Rockies were ca. 6.5°C colder during the LGM. Deglacial warming commenced at ca. 18.1 ka, reaching peak temperatures ~2°C greater than present in conjunction with the peak mid-Holocene drought. Deglacial warming was interrupted by small (~1-2°C) abrupt cooling events coincident with the YD and HS1. These deglacial and Holocene hydroclimate changes were accompanied by changes in terrestrial vegetation, lake evolution, and microbial community changes. Indices based on isoGDGT abundances reveal microbial community changes associated with primary succession and the expansion of methanogenic Archaea with deglacial warming. Past temperature variations significantly altered carbon cycling at the Cumbres Bog site and suggest that high alpine lacustrine ecosystems could be particularly vulnerable to future anthropogenic warming.

Lakes and reservoirs play a key role in the global carbon cycle, representing important carbon sinks and sources within the terrestrial landscape under different environmental conditions. Changes in climate and land use have led to increased air and surface water temperatures; increased occurrence and duration of hypolimnetic anoxia; and altered nutrient loading and hydrology, which have the potential to affect how these freshwater ecosystems receive and process carbon. To assess how interacting environmental drivers influence carbon cycling in lakes and reservoirs, we used a five-year whole-ecosystem experiment to investigate the effects of variable catchment, meteorology, and in-lake drivers on epilimnetic and hypolimnetic dissolved organic carbon in a small, freshwater reservoir. Using a combination of whole-ecosystem models and time-series analyses, we found that primary production and other internal sources contributed ~33% of the dissolved organic carbon in the reservoir’s epilimnion over the five-year period. We also found that sinking epilimnetic primary production, dissolved organic carbon from the sediments, and other factors were likely important sources of hypolimnetic dissolved organic carbon, especially during periods of anoxia. Both the epilimnion and hypolimnion, however, were found to be intermittent sinks yet net sources of dissolved organic carbon. Overall, water temperature was identified as the most important environmental predictor for water-column dissolved organic carbon, with higher concentrations observed under seasonally elevated temperatures during the late summer and early fall. Our results suggest that lakes and reservoirs may become larger sources of dissolved organic carbon to downstream ecosystems under a warmer, more anoxic future

Globigerinoides ruber (white) is a mixed layer planktic foraminifera cosmopolitan to the tropical and subtropical oceans. G. ruber (white) has two principal morphotypes: sensu stricto (s.s.) and sensu lato (s.l.). Previous geochemical studies have suggested that differences in geochemistry (δ18O, Mg/Ca) across these morphs may arise from seasonal preferences (s.l.: cold/winter-biased) or distinct calcification depth habitats (s.s.: 0-25m; s.l.: 25-50 m). Contrary to this, recent studies have demonstrated that there are no systematic or significant geochemical offsets in coeval s.s.-s.l. δ18O and δ13C. However, no such study has been conducted in the northern Indian Ocean to determine the suitability of selective versus non-selective mixtures of G. ruber for paleoceanographic reconstructions. In this study, we report individual foraminiferal analyses (IFA) measurements of δ18O and δ13C on coevally occurring s.s.-s.l. G. ruber (white) morphotypes within core-tops and late Holocene samples at three sites from the northeastern Indian Ocean (cores: IODP Site U1446 - northwestern Bay of Bengal, MGS30 - eastern Bay of Bengal, and SK343 - Gulf of Mannar). We report a total of 362 IFA measurements of s.s. (n=201) and s.l. (n=161). Our observations and statistical approach confirm that there are no significant stable isotopic offsets in coeval s.s.-s.l. morphotypes of G. ruber in this region. Hence we suggest minimal biases for the use of non-selective mixtures of G. ruber morphotypes for paleoceanographic reconstructions of mixed-layer variability in the northern Indian Ocean, at least for Holocene samples, or for periods with similar biogeochemical and oceanographic conditions.

A document by Meiyun Lin. Click on the document to view its contents.

We examine statistically the duration of substorm growth phases which occurred during the year 2010. We find that there is no strong link between the duration of a growth phase and the average dayside reconnection rate during that growth phase. Rather, there is a distribution of growth phase durations between 10 mins and 3 hours, with a mean of 80 mins. We infer that substorm onset occurs randomly within this distribution, irrespective of the level of solar wind driving. Such a “typical’ growth phase duration explains why the open magnetic flux content of the magnetosphere at substorm onset depends on average on the dayside reconnection rate. We also infer that the rate of nightside reconnection during the expansion phase closely matches the dayside rate at the time of substorm onset. This leads to a substorm intensity that is related to the open flux at onset, as has been observed in previous studies. We suggest that pseudo-breakups are failed substorm onsets and that the growth phase continues until one of these attempts to initiate nightside reconnection succeeds.

The phase and amplitude relationships between dynamical quantities and ozone within the Quasi-biennial Oscillation (QBO) are explored. An initial assessment of this is done by applying a Principal Oscillation Pattern analysis to observations (SWOOSH for Ozone) and reanalysis data (ERA5). This analysis highlights features of the ozone and temperature variability including two peaks in amplitude in the QBO region as well as more subtle phase differences that cannot be explained by a simple QBO theory. We also quantify the sizes of the ozone and temperature advection terms and show that the contribution of background upwelling on variations in ozone gradient is not negligible (~25%). A radiative-convective equilibrium and photochemical equilibrium model, with the imposed ERA5 QBO variation in upwelling and OSIRIS NOx variations, is used to further understand ozone and temperature changes. The results show that photochemistry and transport are important at all levels and it is misleading to divide the QBO into separate regimes. Prominent aspects of the variability can be reproduced if ERA5 upwelling is reduced by ~60% between 15 and 50 hPa where ERA5 is likely over-predicting the strength of the secondary meridional circulation. Finally, we demonstrate that non-locality in the vertical plays a major role in QBO dynamics. This arises from ozone transport, the dependence on column ozone of photochemical production and radiative transfer between layers.

AI-based model emulators have emerged as a pragmatic strategy for calibrating Earth System models or their components (e.g., land, atmosphere, ocean), circumventing the previously insurmountable hurdle of the process-heavy models’ computational expense. Such emulators require large, spatially diverse datasets for training, however, which – in the land/hydrology context – contrasts with parameter estimation approaches that have traditionally emphasized optimizing model performance for individual basins, followed by similarity-based transfer schemes for parameter regionalization. Compared to calibrating basins individually, direct land/hydrology process model calibration approaches typically perform worse when trained jointly to large collections of basins. Building on insights from large-sample deep learning hydrologic modeling, this study introduces a Large-Sample Emulator (LSE) approach that unifies and streamlines process model parameter calibration and regionalization. Tested across 627 basins in the continental United States using the Community Terrestrial Systems Model (CTSM), the LSE approach consistently improves runoff predictions in all basins, outperforming the Single-Site Emulator (SSE) in both single-objective and multi-objective calibration tasks. Moreover, LSE-based regionalization in unseen basins, evaluated through spatial cross-validation, achieves better results than the default parameters in most cases. This LSE framework offers a promising strategy for effective large-domain process-based model calibration and regionalization.

Recent observations from the ExoMars Trace Gas Orbiter (TGO) have revealed the presence of hydrogen chloride (HCl) in the martian atmosphere. HCl shows strong seasonality, primarily appearing during Mars’ perihelion period and rapidly decreasing afterwards faster than projected from photolysis and gas-phase chemistry. HCl also shows anti-correlation with atmospheric water ice. One candidate explanation is heterogeneous chemistry. We present the first results from a heterogeneous chlorine chemistry scheme incorporated into a Mars global climate model (GCM), with atmospheric dust/water ice parameterized as an HCl source/sink respectively. Results were compared against a Mars GCM with gas-phase only chlorine chemistry and observations from TGO’s Atmospheric Chemistry Suite (ACS). We found that the heterogeneous scheme significantly improved the modelled HCl seasonal, latitudinal, and vertical distribution, supporting a crucial role for heterogeneous chemistry in Mars’ chlorine cycle. Remaining discrepancies show that further work is needed to characterise the exact aerosol reactions involved.

A document by Roshan Jha. Click on the document to view its contents.

Modern climate change presents unprecedented challenges, posing critical crises that threaten sustainable development, human well-being, and planetary health. A significant concern is the potential for global warming to cause irreversible disruptions to the water-carbon cycle, a topic that remains underexplored. This study seeks to address a crucial knowledge gap by examining how increasing wet extremes impact ecosystem productivity. The research agenda focuses on three primary questions: 1) How do the intensity and duration of various wet extremes affect evapotranspiration across different watersheds and terrestrial biomes? 2) How do immediate and legacy responses to wet extremes vary across different biomes, and what insights do these temporal patterns provide about the causal and predictive relationships between wet extreme and evapotranspiration? 3) To what extent do watershed characteristics (such as soil properties, hydrological conditions, and vegetation factors) modulate the relationship between wet extremes and ecosystem productivity? As climate change alters precipitation patterns, understanding these complex ecosystem responses becomes crucial for developing targeted watershed management strategies and informing mitigation efforts to manage food and water resources more effectively.

Understanding the interior structures of icy moons will be crucial in untangling narratives of formation and evolution, both within our solar system and beyond it. Seismology is a proven and unparalleled methodology for investigating the deep interiors of planetary bodies but has never been deployed on an icy moon. To improve future mission design, we conduct seismic simulations for Saturn’s icy moon Enceladus which account for the unique seismic responses of icy ocean worlds. We discover that even with high surface temperatures at the south pole and 3D ice thickness models, seismic amplitudes are two orders of magnitude higher than the self-noise of mission-candidate instrumentation. We compare the effects of a 2D and 3D ice shell to determine the detail of seismic inversion for ice shell properties and how this varies with source and receiver location. We also compare the travel time differences caused by ice shell variation with potential effects from the uncertain core structure and discover that these two sources of travel time perturbation have similar magnitudes but could be distinguished through careful inversion strategy. We investigate varied source types to represent focal mechanisms likely to be present at the south pole of Enceladus. We finally make recommendations supporting landing sites between 20 - 30° from the south pole that should enable observation of a wide range of seismic phases, including core-transmitted phases that could constrain core velocities.

Predicting laboratory earthquakes using machine learning has progressed markedly recently. Previous related studies mainly focus on predicting the occurrence time and shear stress of laboratory earthquakes using acoustic emission signals. Here, based on numerical simulations, we use machine learning to show that statistical features of plate motion signals contain information about the slip duration and friction drop of laboratory earthquakes. We find that the plate motion signals during the initial slip stage contain the precursor information about the slip duration of laboratory earthquakes. While to accurately predict the friction drop, we need to incorporate the plate motion signals during the entire slip stage. The results demonstrate that the high-order moment and variance of plate motion signals are respectively among the best predictors for the slip duration and friction drop of laboratory earthquakes. Our work provides new insights for future investigations into natural earthquake prediction through machine learning.