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.

Convection-permitting dynamical downscaling (CPDD) allows for an explicit representation of the storm-scale generators of tornadoes, hail, severe thunderstorm winds, and locally heavy precipitation. Possible changes in such hazardous convective weather (HCW) due to human-induced climate change are therefore projected with higher confidence using CPDD than with analyses of relatively coarse global climate models (GCM). However, the computational resources necessary for CPDD are significant and therefore CPDD-based future projections of HCW have tended to be based on a single experiment, and thus absent of uncertainty measures otherwise determined with an ensemble of experiments via an ensemble of GCMs. Herein we present “environment-informed” CPDD as a means to efficiently generate a CPDD ensemble driven by different GCMs. This variant of CPDD is applied only to a subset of days and geographical domains over which the meteorological conditions potentially favor HCW; unnecessary model integrations on meteorologically unfavorable days and domains are thereby eliminated. The selection procedure also accounts for GCM biases.The temporal and geospatial occurrence of historical HCW over the United States is demonstrated from the perspective of environment-informed CPDD as applied to eight different GCMs and ERA5 reanalysis. The overall geographical distributions in HCW vary considerably from downscaled GCM to GCM, thus demonstrating the value of an ensemble. The efficiency in which HCW is realized in favorable environments also varies considerably across the eight downscaled GCMs.

Tomorrow.io operates a near-global, near-real time precipitation retrieval framework that extends coverage beyond traditional radar networks. This manuscript details the development and operationalization of the product's third generation, which is currently available with a refresh rate of 5 minutes and spatial resolution of 0.04° (~4 km), covering latitudes between 60°S and 72°N. Precipitation estimates are derived from a convolutional neural network that ingests observation sequences from multi-channel geostationary imagers and orbiting microwave sounders within the preceding hour. The network is trained to handle sparse data from individual instruments, delivering a spatially-complete retrieval that approximates real-time conditions. Our results demonstrate that this third generation of the Tomorrow.io near-global precipitation product improves categorical and continuous precipitation metrics by 5-10% over the previous generation, with significantly larger skill improvements over publicly available near real-time alternatives such as IMERG-Early. Verification was performed against terrestrial radar networks (USA, Europe, Japan, Brazil, and Australia) using test datasets spanning all seasons and against gauge observation networks for several case studies outside the training domains. Additionally, case studies of retrievals in impactful hurricanes during 2024 and recent operational results demonstrate the impact of incorporating microwave sounder data from two recently launched instruments in Tomorrow.io's constellation. These instruments provide more direct observations of precipitation processes than the geostationary imagers, which the network leverages to further enhance skill beyond the third-generation product's elevated baseline. The Tomorrow.io satellite constellation and precipitation retrieval framework benefit near-real time weather applications in aviation, agriculture, hazard preparedness, water resource management, and other sectors at global scales.

Ocean-bottom seismic acquisitions are gaining widespread popularity for various subsurface applications. However, the high cost of these systems often necessitates receiver geometries with large intervals between ocean-bottom cables or nodes. The upside-down Rayleigh-Marchenko (UD-RM) method has been recently proposed as an effective solution for accurate redatuming and imaging of sparse seabed data. In this paper, we present the first successful application of the UD-RM method to field data. We demonstrate that in the presence of a shallow seabed, an improved data pre-processing workflow is necessary to generate more accurate input wavefields than those produced by the workflow in the original paper. To validate the proposed processing workflow, the UD-RM method is initially tested on a synthetic dataset mimicking the Volve field data, followed by its application to a 2D line of the Volve ocean-bottom cable dataset. Subsequently, the field dataset is subsampled by retaining only 25\% of the total receivers to numerically validate the UD-RM method’s capability to handle sparse receiver arrays. The resulting images reveal that the UD-RM method, when paired with our enhanced data processing workflow, can effectively handle surface-related multiples, internal multiples, and sparse receiver arrays, producing accurate imaging results without the need for costly and labor-intensive multiple removal processes. Finally, we provide theoretical insights and numerical evidence supporting the necessity of source-side deghosting prior to redatuming. While a pre-processing workflow that omits source-side deghosting can offers some practical advantages, we show that this ultimately produces blurrier images compared to those obtained using source-side deghosted input data.

A document by Ayako Yamamoto. Click on the document to view its contents.

The ”Relámpago del Catatumbo” is a unique phenomenon that occurs over Lake Maracaibo, where the most intense lightning activity on the planet takes place. In this work, we extend the study of lightning activity in Lake Maracaibo and its surroundings by a decade, evaluating the period from 2014 to 2023. The results show that the hourly distributions of lightning activity are correlated with the variations of deep convective events in the region; while, the seasonal variations in lightning strikes are correlated with the monthly variations in the mean water temperature of Lake Maracaibo. These findings suggest that the Lake may be providing the necessary water vapor for the development of storm systems that form in the region. It was found that the diurnal and monthly variations of the lightning activity have not changed substantially over the years, as these variations are, with a high degree of significance, very similar to those found in the period 2005-2010 by Bürgesser et al. (2012). The analysis of the interannual variations in lightning strikes for the period does not show any indication of a systematic increase or decrease in lightning activity; however, it can be noted that there is an increase in lightning activity in the last two years analyzed, 2022 and 2023.

Regime dependencies and Granger causal relationships between tropical and extratropical teleconnections are inferred using Bayesian structure learning. Using ERA5 data, an examination of the differences between the learned causal structures during particular phases of the Interdecadal Pacific Oscillation (IPO) are used to infer the role of the background state on interactions between the major climate teleconnections and in particular the dependence of the ENSO autocorrelation on the phase of the background state, and the role of the Madden Julian Oscillation as being key to link the extratropical tropospheric modes (PNA, NAO) and the equatorial surface ocean forced convective modes (IOD, ENSO). These relationships are presented as posterior distributions over possible graphical representations in terms of Dynamic Bayesian Network (DBN) models constructed from time series of empirical climate indices. General circulation model simulations are used to sample from a larger distribution of possible IPO periods and their phase dependencies.

The eastern tropical North Pacific oxygen deficient zone (ETNP-ODZ) exhibits a distinct physical and biological environment compared to other oxygenated water columns, leading to a unique scenario of particulate organic matter (POM) production and vertical transport. To elucidate these biological pump processes, we present the first comparison of δ¹⁵N values of nitrate, phenylalanine (Phe), and glutamic acid (Glu) within two distinct size fractions of particles collected along a productivity gradient in the ETNP-ODZ. Low δ¹⁵NPhe and δ¹⁵NGlu values in both particle pools at the secondary chlorophyll maximum (SCM), compared to the ambient δ¹⁵N-NO₃-, suggest the presence of recycled N-utilizing primary producers distinct from those at the primary chlorophyll maximum and their contribution to export. We observed reduced ¹⁵N enrichment of Phe in suspended particles and a narrower δ¹⁵NPhe disparity between the two particle size fractions compared to the results from oxic waters, likely due to slower microbial remineralization of suspended particles. Unique δ¹⁵NPhe and δ¹⁵NGlu signatures of particles were found at the lower oxycline, potentially attributable to chemoautotrophic production and zooplankton mediation. These findings underscore the need for further investigations targeting particles generated at the SCM, their subsequent alteration by zooplankton, and the new production by chemoautotrophs. This will allow for a better evaluation of the efficiency of the biological pump in the expanding ODZs.

We applied an unsupervised clustering algorithm, initially trained on data from Magnetospheric Multiscale (MMS) mission at Earth, to MESSENGER observations at Mercury to identify three distinct plasma regions: magnetosphere, magnetosheath, and solar wind. This demonstrates the applicability of transfer learning for heliophysics, a machine learning technique where knowledge learned from one task is reused to perform a similar task, for an unsupervised learning task. We find that 72% of bow shock crossings and 84% of magnetopause crossings identified by the clustering algorithm using MESSENGER data are in agreement with published magnetopause and bow shock lists. These findings highlight the potential for utilizing a clustering algorithm across multiple planetary environments.

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

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.

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 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.

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.

 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.

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.

Clumped isotope thermometry (T(∆47)) of soil carbonates provides an estimate of soil temperature at the time of mineral formation. Historically, that temperature has been interpreted to represent a warm-season soil temperature based on modern calibration studies largely done in (very) coarse-grained soils. Additionally, T(∆47) gives us an estimate of the oxygen isotope composition of soil water (δ18Ow) in the past, but previous calibration work has not generated independent δ18Ow datasets with which to understand these archives. Here, we present a modern calibration study of pedogenic carbonate clumped isotope thermometry in three soils in Colorado and Nebraska, USA, that have a fine-medium grain size, contain clay, and are representative of many carbonate-bearing paleosols preserved in the rock record. At two of the three sites, Briggsdale, CO and Seibert, CO, T(∆47) overlaps with mean annual air temperature (MAAT), and the calculated δ18Ow overlaps within uncertainty with measured δ18Ow at carbonate bearing depths. At the third site in Oglala National Grassland, NE, mean T(∆47) is 9 – 10°C warmer than MAAT, and the calculated δ18Ow has a significantly higher isotope value than any observations of δ18Ow. At all three sites, even in the fall season, δ18Ow values at carbonate bearing depths indicate little to no evaporative enrichment of heavy isotopes. Moreover, δ18Ow values overlap with precipitation isotope values from spring precipitation. Altogether, these data indicate that soil grain size affects pedogenic carbonate formation, and highlight a need for continued research in modern systems that emulate key features of the geologic record.