The Juno Waves instrument can be used to accurately determine the electron density inside Io’s orbit, the inner Io torus. These observations have revealed a local peak in the electron density just inside M=5 and at centrifugal latitudes above about 10º that is likely the ’cold torus’ as identified in Earth-based observations of S+ emissions. This peak or ’finger’ is separated from the more dense Io torus by a local minimum or ’trough’ at M ≥ 5. The electron densities are inferred by identifying characteristic frequencies of the plasma such as the low-frequency cutoff of Z-mode radiation at fL=0 and the low-frequency cutoff of ordinary mode radiation at fpe that depend on the electron density. The ’finger’ density ranges from about 0.2 to 65 cm-3 and decreases with increasing centrifugal latitude. The ’trough’ densities range from 0.05 to ~10 cm-3. This pattern of a density ’trough’ followed by the ’finger’ closer to Jupiter is found on repeated passes through the inner Io torus over a range of centrifugal latitudes. Using a simple model for the electron densities measured above about 10º centrifugal latitude, we’ve estimated the scale height of the ’finger’ densities as about 1.17 RJ with respect to the centrifugal equator, which is somewhat surprising given the expected cold temperature of the cold torus. The larger scale height suggests a population of light ions, such as protons, are elevated off the centrifugal equator. This is confirmed by a multi-species diffusive equilibrium model.

Ocean alkalinity enhancement (OAE) can potentially remove gigatons of CO2 from the atmosphere for durable storage in the ocean. Before implementing OAE at climate-relevant scales, questions about its safety and verifiability must be addressed. Operational deployment poses a dilemma between pursuing large detectability, essential for effective monitoring, reporting, and verification (MRV), and ensuring environmental safety and satisfying regulatory requirements. In this study, we present a computationally efficient approach, based on a high-resolution, coupled circulation-dissolution model of Halifax Harbour, to simulating the addition, transportation, dissolution, and sinking of various theoretical alkaline feedstocks for different dosages, seasons, and addition sites. Detectability and exposure risk of OAE are quantified and an approach for optimizing OAE deployment is demonstrated. Mean residence times (MRT) are calculated for different subregions and seasons. Results show that for a given amount of feedstock, summer is more favourable from the perspective of detectability but also creates higher exposure risks than other seasons because of a longer MRT. The exposure risk can be mitigated while maintaining large detectability by choosing optimal feedstocks with different characteristics for different seasons. The exposure risk can also be reduced by spreading alkalinity over multiple addition sites. The optimum allocation, where the largest detectability is sought without violating regulatory requirements, is specific to each season, dosage, and choice of feedstock. OAE deployments should be tailored taking into account local hydrography, season, dosage, and feedstock characteristics. Our approach provides a practical avenue for optimizing deployments.

The north-south seismic belt of China poses a high risk of earthquakes, necessitating the need for accurate and rapid prediction of intensity measures (IMs) to prevent and mitigate potential damage. We have developed a new multi-task model, CRAQuake, to predict IMs for the north-south seismic belt of China. Using initial arrival seismic waves recorded at a single station as input, CRAQuake simultaneously predicts six IMs without relying on pre-configured parameters such as earthquake source, path, and location. The model was trained on 4281 sets of strong motion records datasets at 822 stations and tested to show highly correlated results with the target IMs. The prediction performance continues to improve as the input initial arrival seismic wave time window increases. CRAQuake promises to enhance the accuracy and timeliness of IMs prediction in the north-south seismic belt of China.

A document by Mark Hall. Click on the document to view its contents.

• A new framework estimates how sea surface temperatures boosted by climate change influence tropical cyclone intensities • Hurricane intensities during 2019-2023 were 8.3 m s −1 faster, on average, than they would have been in a world without climate change • 84% of hurricanes during 2019-2023 had significantly higher intensities due to attributable local SST warming since 1900

Chlorophyll underpins ocean productivity yet simulating chlorophyll across biomes, seasons and depths remains challenging for earth system models. Inconsistencies are often attributed to misrepresentation of the myriad growth and loss processes governing phytoplankton biomass but may also arise from unresolved or misspecified photoacclimation or photoadaptation responses. A series of global ocean ecosystem simulations were enlisted to assess the impacts of alternative photoacclimation and photoadaptation assumptions on simulated chlorophyll, primary productivity and carbon export. Photoacclimation alternatives implicitly modulated the premium placed on light harvesting versus photodamage avoidance and other cellular functions, while photoadaptation experiments probed the impact of adding low- and high-light adapted phytoplankton ecotypes. Alternatives generated large chlorophyll responses that addressed prior model biases in ways that simple changes in growth and grazing could not. Simulations with photoadaptation, surface-skewed photoacclimation in deep mixed layers, and acclimation to light levels over mixing depths consistent with photoacclimation time scales in stratified waters were best able to match observed patterns. While chlorophyll was highly sensitive to alternative photoacclimation assumptions, primary production and carbon export were not because chlorophyll changes under near-saturating light at the ocean's surface yielded only modest phytoplankton growth changes that were counteracted by self-shading at depth. Improved photoacclimation and photoadaption constraints and reduced regional uncertainties in satellite-based ocean color estimates are needed to reduce ambiguities in the drivers of chlorophyll change and their biogeochemical implications.

Global warming is causing more extreme precipitation events and variability in rainfall patterns, which are affecting the timing of peak rainfall occurrence and impacting the hydrological cycle in evolving climates. This study examines spatial rainfall trends across India during the Indian Summer Monsoon (ISM) period from 2001 to 2020, utilizing GSMaP_ISRO data. Analysis reveals a significant rise of 2 mm/day in rainfall over west central India from 2011 to 2020. Additionally, an investigation into the timing of peak rainfall demonstrates an early (late) peak over the Indo-Gangetic Plain (central India) in recent years, likely influenced by changes in aerosol loading. The present results suggest a significant shift in rainfall peak time with potential implications for agriculture,hydrology and disaster management.

A document by Angel Farguell Caus. Click on the document to view its contents.

Fluid flow coupled with heat transfer through a vertical cylindrical Collapsible tube in the presence of a magnetic field and an obstacle has been investigated. The governing equations for this flow are the equations of continuity, motion and energy. These are non linear partial differential equations and has been transformed into non-linear ordinary differential equations by introducing a similarity transformation. The resulting equations are solved simultenously in the bvp4c MATLAB library to obtain the profiles and also the rate of heat transfer has been calculated. The effects of varying the Reynolds number, Hartmann number, Eckert number, Unsteadiness parameter and Prandtl number on fluid temperature , fluid velocity and the rate of heat transfer are presented in the form of tables and graphs and has been discussed. Variation in the various parameters is observed to change the fluid primary velocity, temperature and the rate of heat transfer. This kind of result is important due to its widespread application in physical, biological and applied sciences.

The Asian Summer Monsoon (ASM) has garnered attention in recent years for its impacts on the composition of the upper troposphere and lower stratosphere (UTLS) via deep convection. A recent observational effort into this mechanism, the Asian summer monsoon Chemical and CLimate Impact Project (ACCLIP), sampled the composition of the ASM UTLS over the northwestern Pacific during boreal summer 2022 using two airborne platforms. In this work, we integrate Lagrangian trajectory modeling with convective cloud top observations to diagnose ASM convective transport which contributed to ACCLIP airborne observations. This diagnostic is applied to explore the properties of convective transport associated with prominent ASM sub-systems, revealing that convective transport along the East Asia Subtropical Front generally contained more pollutants than from South Asia, for species ranging in lifetime from days to months. The convective transport diagnostic is used to isolate three convective transport events over eastern Asia which had distinct chemical tracer relationship slopes, indicating the different economical behaviors of the contributing source regions. One of these transport events is explored in greater detail, where a polluted air mass was sampled from convection over the Northeast China Plain. This event was largely confined to 12-15 km altitude, which may be high enough to impact the composition of the stratosphere. Overall, the presented diagnosis of convective transport contribution to ACCLIP airborne sampling indicates a key scientific success of the campaign and enables process studies of the climate interactions from the two ASM sub-system.

• New basis functions suitable for fitting ionospheric electrojet magnetic signatures are developed • The basis functions are constructed from principal components analysis of current loops in quasi-dipole geometry confined to the electrojet regions • The utility of these new basis functions is demonstrated by fitting them to multiyear Swarm satellite measurements of the ionospheric field

This paper introduces a novel framework for a process-informed, differential assessment of credibility across various spatial and point-based downscaling methodologies, including both complex and simple statistical approaches, as well as dynamical downscaling. The methods assessed include a convolutional neural network (CNN), the locally constructed analog method (LOCA), the statistical downscaling model (SDSM), quantile delta mapping (QDM), simple spatial interpolation plus bias correction, the Regional Climate Model (RegCM) and the Weather Research and Forecasting Model (WRF). For proof of concept of our framework, our study focuses specifically on the physical consistency of wet days in a location in the southern US Great Plains. We find that all downscaling methods perform credibly when the parent global climate model (GCM) performs credibly. However, complex statistical methods like CNN, LOCA, and SDSM exacerbate inaccuracies when the GCM outputs are unreliable, performing worse as the GCM’s credibility decreases. On the other hand, simpler methods like QDM and bias-correction generally retain the GCM’s credibility. Notably, dynamical models can mitigate issues inherited from GCMs, enhancing the overall credibility of the data. These results highlight the need for careful evaluation of complex statistical downscaling techniques and suggest that further scrutiny is warranted. Finally, we summarize our process-informed analysis of credibility into a relative credibility metric, offering a quantifiable way to compare different downscaling approaches, and we provide guidance on the application and expansion of our framework for future research.

jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Geophysical inversion plays a pivotal role in understanding the Earth’s internal structure. Recently generative neural networks (GNNs), such as normalizing flow models (NFMs), have gained popularity for solving Bayesian inversion problems. However, their effectiveness is often hindered by the loss function bias. This bias occurs due to the global nature of loss functions in NFMs, which lead to inaccuracies when applied to geophysical inversion, where the Earth model is unique. To address this, we propose the Iterative Normalizing Flow Model(INFM), a novel approach that mitigates loss function bias by progressively narrowing the prior distribution’s support set in each iteration, while ensuring that the posterior distribution accurately converges to the target. Our experiments, validated on high-dimensional Bayesian inversion tasks, show that INFM significantly enhances inversion accuracy without increasing network complexity or computational cost. When applied to the Earth’s 1-D structure model inversion, our method revealed key insights, such as a lower core density compared to the PREM model and the presence of anisotropy in both the mantle and core, consistent with previous studies. These findings suggest that the INFM method offer high computational efficiency and accuracy, making it well-suited for large-scale geophysical inversion problems.

There is a lack of consensus on how tropical cyclone outer winds may change, if at all, due to extratropical transition. Hence, this study examines changes in North Atlantic tropical cyclone outer size and structure using a large, multidecadal sample of cases from reanalysis data. These results suggest that tropical cyclone outer size and structure typically remain unchanged until after extratropical transition end. In those minority of cases with strong outer size growth during extratropical transition, increases in tropical cyclone outer winds begin first in the lower troposphere during ET and build upwards over time. This broadening of the azimuthal-mean outer winds is also associated with an increasingly asymmetric outer wind field with the strongest winds concentrated downstream of the tropical cyclone. These storms that grow most strongly during transition are typically smaller at transition start and eventually become embedded in more strongly baroclinic environments before extratropical transition end.

Coincident shifts in riparian ground-active arthropod diversity and soil nutrients under an introduced symbiotic N2-fixing treeBenjamin D. Duval1*, Evangelina Carabotta1, Sergio de Tomas-Marin1, David Lightfoot21Biology Department, New Mexico Institute of Mining and Technology, Socorro, NM 878012Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131*corresponding author, ORCID 0000-0001-7692-4400Biology Department801 Leroy PlaceNew Mexico Institute of Mining and Technology01-575-835-5820, [email protected] nitrogen-fixing plants such as Russian olive, can significantly impact soil chemistry and invertebrate biodiversity in riparian ecosystems. Here, the effects of Russian olive on soil chemical properties and invertebrate communities in riparian zones of the southwestern U.S. were investigated. Russian olive stands were compared to native cottonwood stands and restoration sites by analyzing soil nitrogen (N), phosphorus (P) and moisture levels, and arthropod diversity and abundance. Sites where Russian olive is present led to a net increase in soil nitrogen, a decrease in soil phosphorus, and greater soil moisture compared to both native cottonwood stands and restoration sites. Native cottonwood stands showed lower soil N and higher P levels, as well as higher arthropod diversity. This increased diversity could be linked to the soil’s nutrient stoichiometry, as there is a negative correlation between taxonomic diversity and the soil N:P ratio. Moreover, there was a greater abundance of detritivorous arthropods in Russian olive stands compared to native vegetation. Soil nitrate (NO3-) levels showed a strong positive correlation with detritivorous arthropod abundance, but only a moderate correlation with herbivores, and NO3- was unrelated to predator abundance. These results suggest that Russian olive stands can alter soil chemistry in ways that disproportionately benefit detritivores, potentially disrupting the balance of arthropod communities and reducing overall biodiversity in riparian ecosystems. The study underscores the need for careful management of invasive, symbiotic N2-fixing plant species to preserve the ecological integrity of riparian habitats.Keywords: nitrate, phosphate, Rio Grande, Russian olive, soilINTRODUCTIONA persistent question in invasive species ecology is what are the functional roles of the invaders and their effect in the biodiversity and functional structure and composition of the recipient community (Renault et al., 2022; Galán Díaz et al., 2023). Invasive species can have cascading effects through different trophic levels and alter ecosystem functions, processes and services (McCary et al., 2016; Castro Díez and Alonso Fernández, 2017). One of the main effects of invasive plant species is the disruption of nutrient cycling and availability (Weidenhamer and Callaway, 2010; Afzal et al., 2023) which is especially relevant in invasions produced by woody and N2-fixing plants (Liao et al. 2008).Trees in symbiotic relationships with N2-fixing actinobacteria are globally reliable as invasive species and known to induce substantial effects on the N cycle relative to native vegetation (Vitousek et al. 1987, Nsikani et al. 2018, Collette and Pither 2015). These trees typically increase soil mineral N pools relative to native vegetation, and should be considered part of the ongoing anthropogenic global disruption of the nitrogen (N) cycle (Galloway, 1998; Galloway et al., 2008). Given that increased N is occurring with relatively less change to phosphorus (P) cycling, ecosystem responses are best understood in the context of increased N:P of major biotic and abiotic components (Penuelas et al., 2020).The ratio of N to P in leaves has long been understood to impact decomposition processes and nutrient cycling (Koerselman and Meuleman, 1996; Güsewell and Freeman, 2005). As part of the initial decomposition processes, soil and surface-active arthropods physically and chemically alter litter, and multiple experiments have demonstrated food preferences for litter based on a variety of chemical and structural properties (David, 2014). The direct mechanisms by which microbial use of arthropod frass contributes to soil organic material cycling are not fully clear, but there is evidence that microbial litter colonization directly increases litter palatability to arthropods when microbes increase litter nutritive value (Gerlach et al., 2012), or indirectly when microbes detoxify plant secondary compounds that hinder arthropod consumption and digestion of said litter (David 2014). Another established link between arthropods and soil microbes is that N mineralization is consistently increased following arthropod litter processing (David, 2014). Therefore, it is reasonable to assume that altered litter and soil N:P are filters on arthropod communities, which is plausible given that arthropods are likely phylogenetically constrained in their stoichiometry and feeding guilds (Martinson et al., 2008; Ross et al., 2022), and different community assemblages and functional groups are expected to inhabit this chemically shifted environment (Tie et al., 2021; Deng et al., 2022; Nessel et al., 2023). If detritivores, and to some extent herbivores, increase in abundance from higher N litter, feedbacks to the soil system may promote further increases in N:P via decomposed frass and dung inputs (Wolters, 2000), and increased N mineralization rates (David 2014). Changes in litter input can also change soil moisture quantity and dynamics, which would feedback to the above scenarios (Wang et al., 2020; Liu et al., 2021). Thus, an invader with impacts on soil N dynamics may be expected to also alter arthropod communities in ways that further change soil N pools and transformations.Invasive N2-symbiont trees therefore provide a natural experiment to evaluate the effect of N:P shifts on surface-active arthropod diversity and potential feedbacks to soil. Arid riparian zones in the southwestern USA have been significantly altered by the establishment of non-native vegetation (Dukes and Mooney, 2004; Harms and Hiebert, 2006), including by a tree with N2-fixing actinobacterial symbionts, Russian olive (Elaeagnus angustifolia ; Bertrand and Lalonde 1985). This species was introduced to riparian woodlands along the Rio Grande of New Mexico, USA, between 1900 and 1915 (Hink and Ohmart 1984). A major ecological disruption in the form ofreduced disturbance was a consequence of dams, levees and diversions that altered dynamic flood regimes of the river, and the relatively static channels of the Rio Grande are less suitable habitat for vegetation communities evolved to frequent overbank flooding and sedimentation (Rood and Mahoney, 1990; Shah and Dahm, 2008). The tree rapidly spread throughout the Rio Grande and became a dominant component of riparian vegetation by 1960 (Campbell and Dick-Peddie 1964). Native N2-fixing symbiotic trees are known in the region (Shepherdia argentea ; silver buffaloberry) but are locally rare and exhibit much lower fixation rates than Russian olive (Petrides 1998, Follstad Shah et al. 2010).Several reports document N cycling alterations following Russian olive invasion, including increased N loss from litter mass decomposed compared to cottonwood (Simons and Seastedt 1999). De Cant (2008) isotopically demonstrated N2-fixation from Russian olive in a Rio Grande bosque, and ~5-fold increase in foliar N compared to adjacent cottonwoods (Populus deltoides var.wislizenii ), but the native trees did not utilize increased soil N from fixation inputs. Decomposing Russian olive litter (C:N = 13) was observed to produce spikes in N2O not observed from cottonwood litter (C:N = 22) under laboratory conditions (Duval et al. 2020). That study also reports increased N-processing enzyme (leucine amino peptidase, LAP) activity from cottonwood soils, suggesting potential microbial N limitation not found with Russian olive soils. However, Russian olive soils exhibited slightly higher acid phosphatase activity compared to cottonwood (Duval et al. 2020), suggesting P limitation.Russian olive introductions clearly have impacts on soil N inputs and cycling, and recent evidence suggests the presence or removal of the tree alters riparian tree-dwelling arthropod communities (West et al., 2023). Ground-dwelling arthropods are excellent taxa to explore invasive tree effects on ecosystem processes, because they are sensitive to fine-scale environmental conditions, and these conditions themselves are vital contributors to decomposition and nutrient cycling in riparian areas (Perry and Herms 2017). Indeed, detritivorous arthropods generally respond well with increased nitrogen and phosphorus levels, with isopods increasing in abundance (Coccia and Fariña 2022). Ellis et al. (1999) found the species composition and richness of middle Rio Grande bosque ground-dwelling arthropods to be similar between native cottonwood and saltcedar habitats, but cottonwood habitats supported greater densities of non-native isopods. However, it is generally unknown what the arthropod community looks like in areas dominated by Russian olive and areas where it has been removed. To improve our knowledge of the ecosystem-level impacts of Russian olive, we designed a study to compare ground dwelling arthropod relative abundance and diversity (taxonomic and functional) in Russian olive dominated stands versus native cottonwood woodlands, and the effect of restoration efforts by comparing Russian olive stands with plots where the tree had been mechanically removed. This allows us to evaluate longer-term (decadal) influence of an invasive tree compared to historic vegetation, as well as short-term impacts on the arthropod community driven by physical removal of plants (and associated ecosystem effects) where the soil chemistry has perhaps not yet appreciably changed.Because we are centering our work on shifting stoichiometry due to a plant invasion, we quantify functional (feeding) diversity of surface-active arthropods as well as taxonomic diversity. We hypothesize that cottonwood stands will have significantly different arthropod communities (beta diversity; taxonomic and functional diversity) than Russian olive stands, and those will be related to soil chemical changes induced by the invasive tree, such as greater N pools and increased N:P (DeCant 2008; Peñuelas et al. 2020). Stands where Russian olive has been removed are predicted to be chemically similar to extant Russian olive stands, but arthropod communities will differ between these areas due to physical character alterations from plant removal (moisture retention, soil temperature). Addressing these questions will provide additional insight into the ecosystem effects of altered N:P on biogeochemically relevant biota, and feedbacks related to invasive, symbiotic N2-fixing vegetation in an arid riparian zone.MATERIALS & METHODS

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.

Despite the growing use of Aquatic Ecosystem Models (AEMs) for lake modelling, there is currently no widely applicable framework for their configuration, calibration, and evaluation. Calibration is generally based on direct data comparison of observed vs. modelled state variables using standard statistical techniques, however, this approach may not give a complete picture of the model’s ability to capture system-scale behaviour that is not easily perceivable in observations, but which may be important for resource management. The aim of this study is to compare the performance of ‘naïve’ calibration and a ‘system-inspired’ calibration, an approach that augments the standard state-based calibration with a range of system-inspired metrics (e.g., thermocline depth, metalimnetic oxygen minima), to increase the coherence between the simulated and natural ecosystems. A coupled physical-biogeochemical model was applied to a focal site to simulate two key state-variables: water temperature and dissolved oxygen. The model was calibrated according to the new system-inspired modelling convention, using formal calibration techniques. There was an improvement in the simulation using parameters optimised on the additional metrics, which helped to reduce uncertainty predicting aspects of the system relevant to reservoir management, such as the occurrence of the metalimnetic oxygen minima. Extending the use of system-inspired metrics when calibrating models has the potential to improve model fidelity for capturing more complex ecosystem dynamics.

Here, the use of an hybrid methodology, VarDyn, combining minimal physically-based constraints with a variational scheme, is demonstrated to improve mapping capabilities of sea surface height (SSH) and temperature (SST). Synthesizing multi-modal satellite observations, VarDyn improves the accuracy of SSH and SST maps compared to operational products, both in terms of RMSE and effective spatial resolution. Expected, most improvements occur in high energetic ocean regions. Still, the accuracy of SSH maps also slightly improves in low energetic regions, which is a clear improvement compared to other methods. VarDyn SSH fields and associated geostrophic velocities further reveal strong agreements with newly available high-resolution instantaneous SWOT estimates. Remarkably, the assimilation of SST especially benefits SSH reconstruction when only 2 altimeters are available. The VarDyn method thus opens robust means to refine climate SSH records, jointly assimilating SSH from 2 altimeters and SST from microwave sensors.