Spreading and transport of the Maximum Salinity Core Layer (MSCL), defined as water with salinity higher than 36 psu, is investigated from hydrographic data sets collected along twelve cross-sections by the Sea education Association (SEA) between Tahiti and the equator from 2008 and 2015, the ARGO data objectively mapped from JAMSTEC and satellite remote sensing. Aquarius sea surface salinity data show that the MSCL occupied an area averaged over time of about 6.7 106 km² with an increase of the occupancy in 2015. The variation of the area occupied by the MSCL at the surface does not show any seasonal cycle. Hydrographic dataset are used to show the extent of the MSCL below the surface. The SEA dataset exhibits the spreading of the MSCL northward of Tahiti between isopycnals 24 σt and 25 σt and is observed as far north as 4°S. The MSCL is observed at the surface as far north as 12°S and the disappearance of the MSCL from the surface is due to input of fresher water from the equatorial region or from the west. Even tough ARGO data have a poorer vertical resolution, comparison with SEA dataset shows that the core of the MSCL is well determined. But ARGO data has a better time resolution and monthly data are available from 2001 to 2017. ARGO data allow us to visualize the whole volume of the MSCL and the westward extension of the MSCL. Geostrophic currents are calculated from ARGO data relative to the surface where surface currents are adjusted to velocities provided by the satellite derived OSCAR data set. The transport calculation shows that most of the water north of Tahiti is transported westward with the highest volume during an El Niño year.

Understanding how thermokarst lakes on arctic river deltas will respond to rapid warming is critical for projecting how carbon storage and fluxes will change in those vulnerable environments. Yet, this understanding is currently limited partly due to the complexity of disentangling significant interannual variability from the longer-term surface water signatures on the landscape, using the summertime window of optical spaceborne observations. Here, we rigorously separate perennial lakes from ephemeral wetlands on 12 arctic deltas and report distinct size distributions and climate trends for the two waterbodies. Namely, we find a lognormal distribution for lakes and a power-law distribution for wetlands, consistent with a simple proportionate growth model and inundated fractal topography, respectively. Furthermore, while no trend with temperature is found for wetlands, a statistically significant decreasing trend of mean lake size with warmer temperatures is found, attributed to colder deltas having deeper and thicker permafrost preserving larger lakes.

As a consequence of a diminishing sea ice cover in the Arctic, activity is on the rise. The position of the sea ice edge, which is generally taken to define the extent of the ice cover, changes in response to dynamic and thermodynamic processes. Forecasts for sea ice expansion due to an advancing ice edge will provide information that can be of significance for operations in polar regions. However, the value of this information depends on the quality of the forecasts. Here, we present methods for examining the quality of forecasted sea ice expansion and the geographic location where the largest expansion are expected from the forecast results. The algorithm is simple to implement, and an examination of two years of model results and accompanying observations demonstrates the usefulness of the analysis.

A significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development of satellite conjunction assessment and collision avoidance capabilities for new spacecraft. Often, the greatest source of uncertainty in predicting a satellite's trajectory in low Earth orbit originates from atmospheric neutral mass density variability caused by enhanced geomagnetic activity and solar EUV absorption. This work investigates the impacts of solar and geomagnetic index forecasting uncertainty on satellite drag and satellite maneuver decision-making. During an averaged point in the solar cycle, accurate index forecasts with reduced uncertainty are shown to provide significantly improved advance notice for dangerous conjunction events above 500 km. Below 500 km, forecast improvements are less impactful. This boundary of utility from forecast improvements shifts upward and downward during solar maximum and solar minimum, respectively. Improved index forecasts are shown to have little impact on making maneuver decisions 12-24 hours from a potential conjunction event, but are demonstrated to be very useful when trying to make maneuver decisions with more lead time. These improved forecasts of the space weather indices help in making actionable, durable conjunction predictions sooner than is currently possible.

Unfrozen water content (UWC) is a key parameter affecting a variety of soil physical-mechanical properties and processes in frozen soil systems. However, traditional estimation models suffer limitations due to oversimplified assumptions or limited applicable conditions. Given that, there is a compelling need to explore alternative modeling approaches that leverage machine learning (ML) algorithms, which have shown increasing potential in engineering fields. To this end, this study evaluated and compared six widely known ML algorithms (i.e., three ensemble models: RF, LightGBM and XGBoost; and three non-ensemble models: KNN, SVR and BPNN) for modeling UWC based on collected experimental datasets. These algorithms were optimized and evaluated using a framework combining Bayesian optimization and cross-validation to ensure model stability and generalization. The results demonstrated that the ensemble tree-based methods, particularly LightGBM and XGBoost, achieved the highest predictive accuracy and superior overall performance. On the other hand, the nonensemble methods exhibited poorer generalization abilities. Interestingly, during 10-fold cross-validation, consistent underperformance was observed for a particular fold, possibly stemming from the challenges of the data distribution in that fold after random shuffling. The present study highlights the effectiveness of ensemble learning approaches, importance of proper hyperparameter tuning and validation strategies, and intrinsic modeling challenges arising from the difference between the freezing and thawing phase change behaviors. This comprehensive ML model comparison and robust training framework provide valuable guidance on selecting suitable data-driven techniques for modeling frozen soil properties for cold regions hydrogeology and engineering practices.

The Madden–Julian Oscillation (MJO) is the dominant source of sub-seasonal variability in the tropics. It consists of an Eastward moving region of enhanced convection coupled to changes in zonal winds. It is not possible to predict the precise evolution of the MJO, so sub-seasonal forecasts are generally probabilistic. We present a deep convolutional neural network (CNN) that produces skilful state-dependent probabilistic MJO forecasts. Importantly, the CNN’s forecast uncertainty varies depending on the instantaneous predictability of the MJO. The CNN accounts for intrinsic chaotic uncertainty by predicting the standard deviation about the mean, and model uncertainty using Monte-Carlo dropout. Interpretation of the CNN mean forecasts highlights known MJO mechanisms, providing confidence in the model. Interpretation of forecast uncertainty indicates mechanisms governing MJO predictability. In particular, we find an initially stronger MJO signal is associated with more uncertainty, and that MJO predictability is affected by the state of the Walker Circulation.

Quantifying connectivity patterns in dryland ecosystems is crucial for understanding how water and sediments flow across the landscape, crucial for mitigating the impacts of climate change and land use modifications. This study quantifies the multi-scale water-mediated connectivity within grassland and shrubland hillslopes using a weighted, directed network model. By integrating high-resolution elevation, vegetation data, and modeled event-based hydrologic and sediment transport, we quantify both structural (topology) and functional (dynamical) connectivity under varying rainfall and soil moisture conditions. Our findings reveal a marked increase in local connectivity metrics in shrublands compared to grasslands, with metrics such as betweenness centrality and the weighted length of connected pathways increasing up to tenfold. Despite these substantial local changes, global properties like link density and global efficiency show less drastic variations, suggesting a robust network topology that sustains geomorphic functionality across different vegetation states. This indicates that although local connectivity is highly sensitive to vegetation changes, the overall structure and functional connectivity of water and sediment at the global scale remain relatively stable. Functional connectivity is more strongly correlated with structural connectivity for sediment than for water. This difference is particularly pronounced under high rainfall conditions, yet shows little sensitivity to variations in antecedent soil moisture, highlighting the critical role of event-driven processes in shaping connectivity patterns. The study advances our quantitative understanding of how structural changes affect functional processes in dryland ecosystems. It offers a comprehensive framework for analyzing connectivity at multiple scales, which can inform targeted management strategies aimed at enhancing ecosystem resilience.

On 15 January 2022, the Hunga Tonga-Hunga Ha’apai submarine volcano erupted violently and triggered a giant atmospheric shock wave and tsunami. The exact mechanism of this extraordinary eruptive event, its size and magnitude are not well understood yet. In this work, we analyze data from the nearest ground-based receivers of Global Navigation Satellite System (GNSS) to explore the ionospheric total electron content (TEC) response to this event. We show that the ionospheric response consists of a giant TEC increase followed by a strong long-lasting depletion. We observe that the explosive event of 15 January 2022 began at 04:05:54UT and consisted of at least 5 explosions. Based on the ionospheric TEC data, we estimate the energy released during the main major explosion to be between 9 and 37 Megatons in TNT equivalent. This is the first detailed analysis of the eruption sequence scenario and the timeline from ionospheric TEC observations.

Accurate estimation of terrestrial latent heat flux (LE) at high spatial resolution is of great vital importance for energy balance and water resource management, especially for agricultural production at field scale. However, there are relatively few LE products based on Chinese GaoFen-1 (GF-1) remote sensing data. In this study, we used GF-1 satellite data with a 16 m spatial resolution over the part regions of Ethiopia, Laos and Pakistan, and generated the LE products using Modified Satellite-based Priestley-Taylor model. LE products based on GF-1 data were aggregated to a 1 km spatial resolution to be validated by Global LAnd Surface Satellite (GLASS) LE products with the same spatial resolution as reference values. The validation results demonstrated that the 16 m GF and 1 km GLASS LE products of the three countries all presented good spatial consistency, and the coefficient of determination of LE estimates based on GF-1 data against GLASS LE products were all greater than 0.6, indicating that the accuracy of LE products based on GF-1 data was high. LE estimation based on GF-1 data is of great significance for energy balance and water resource precision management.

The mid-to-late Miocene is proposed as a key interval in the transition of the Earth’s climate state towards that of the modern-day. However, it remains a poorly understood interval in the evolution of Cenozoic climate, and the sparse proxy-based climate reconstructions are associated with large uncertainties. In particular, tropical sea surface temperature (SST) estimates largely rely on the unsaturated alkenone Uk37 proxy, which fails to record temperatures higher than 29˚C, the TEX86 proxy which has challenges around its calibration, and Mg/Ca ratios of poorly preserved foraminifera. We reconstruct robust, absolute, SSTs between 13.5 Ma and 9.5 Ma from the South West Indian Ocean (paleolatitude ~5.5˚S) using Laser-Ablation (LA-) ICP-MS microanalysis of glassy planktic foraminiferal Mg/Ca. Employing this microanalytical technique, and stringent screening criteria, permits the reconstruction of paleotemperatures using foraminifera which although glassy, are contaminated by authigenic coatings. Our absolute estimates of 24-31⁰C suggest that SST in the tropical Indian Ocean was relatively constant between 13.5 and 9.5 Ma, similar to those reconstructed from the tropics using the Uk37 alkenone proxy. This finding suggests an interval of enhanced polar amplification between 10 and 7.5 Ma, immediately prior to the global late Miocene Cooling.

Ensemble-based data assimilation of radar observations across inner-core regions of tropical cyclones (TCs) in tandem with satellite all-sky infrared radiances across the TC domain improves TC track and intensity forecasts. This study further investigates potential enhancements in TC track, intensity, and rainfall forecasts via assimilation of all-sky microwave radiances using Hurricane Harvey (2017) as an example. Assimilating GPM constellation all-sky microwave radiances in addition to GOES-16 all-sky infrared radiances reduces the forecast errors in the TC track, rapid intensification, and peak intensity compared to assimilating all-sky infrared radiances alone, including a 24-hour increase in forecast lead-time for rapid intensification. Assimilating all-sky microwave radiances also improves Harvey’s hydrometeor fields, which leads to improved forecasts of rainfall after Harvey’s landfall. This study indicates that avenues exist for producing more accurate forecasts for TCs using available yet underutilized data, leading to better warnings of and preparedness for TC-associated hazards in the future.

Tidal currents are known to influence basal melting of Antarctic ice shelves through two types of mechanisms: local processes taking place within the boundary current adjacent to the ice shelf-ocean interface and far-field processes influencing the properties of water masses entering the cavity. The separate effects of these processes are poorly understood, limiting our ability to parameterize tide-driven ice shelf-ocean interactions. Here we focus on the small-scale processes within the boundary current and we apply a one-dimensional plume model to a range of ice base geometries characteristic of Antarctic ice shelves to study the sensitivity of basal melt rates to different representations of tide-driven turbulent mixing. Our simulations demonstrate that the direction of the relative change in melt rate due to tides depends on the approach chosen to parameterize entrainment of ambient water into the plume, a process not yet well constrained by observations. A theoretical assessment based on an analogy with tidal bottom boundary layers suggests that tide-driven shear at the ice shelf-ocean interface enhances mixing through the pycnocline. Under this assumption our simulations predict an increase in melt and freeze rates along the base of the ice shelf when adding tides into the model. An approximation is provided to account for this response in basal melt rate parameterizations that neglect the effect of tide-induced turbulent mixing

Brazilian tropical woodlands, such as the wooded Cerrado, perform hydrological functions that need to be well understood by field data acquisition and mathematical modeling. Here, we aimed to assess the water partitioning behavior and variability of a wooded Cerrado fragment located in Southeastern Brazil by (i) measuring fluxes using eddy covariance; (ii) applying machine learning techniques to obtain a model to estimate the evapotranspiration (ET) using meteorological data as input: solar radiation (Rg), wind speed (WS), temperature (T), relative humidity (RH), and rainfall (P); and (iii) simulating a long-term water balance using stochastic climate generator inputs and the previously calibrated ET model. The average observed ET was 3.12±0.93 mm d) and P (1227±208 mm yr-1) have similar standard deviations; differently from the ET (1054±46 mm yr-1), which presented higher annual rates with a small variability throughout the simulation.

Shock waves in collisionless plasmas rely on kinetic processes to convert the primary incident bulk flow energy into thermal energy. That conversion is initiated within a thin transition layer but may continue well into the downstream region. At the Earth’s bow shock, the region downstream of shock locations where the interplanetary magnetic field is nearly parallel to the shock normal is highly turbulent. We study the distribution of thin current events in this magnetosheath. Quantification of the energy dissipation rate made by the MMS spacecraft shows that these isolated intense currents are distributed uniformly throughout the magnetosheath and convert a significant fraction (5%-11%) of the energy flux incident at the bow shock.

A document by Grant Ferguson. Click on the document to view its contents.

Quantifying flow and transport from hillslopes is vital for understanding surface water quality, but remains obscure because of limited subsurface measurements. A recent combination of water mass balance over a single year with the transmissivity feedback model for a lower montane hillslope in the East River watershed (Colorado) left large uncertainties in transmissivities and predicted fluxes. Because snowmelt drives subsurface flow on this hillslope, improved constraints on the transmissivity profile were obtained by optimizing flux predictions over years having large differences in precipitation minus evapotranspiration. The optimized field-scale hydraulic properties combined with water table elevations predict groundwater discharges that are consistent with wide ranges of snowmelt. As snowmelt rapidly raises the water table, solutes released primarily through bedrock weathering are largely transported out of the hillslope via its highly transmissive soil. Such pulsed water and solute exports along the soil are minimized during snow drought years. Although solute concentrations generally are lower in soils relative to the underlying weathering zone, solute exports during high recharge occur predominantly via soil because of its enlarged transmissivities under snowmelt-saturated conditions. In contrast, this shallow pathway is negligible when recharge and water table elevations are low. The multiyear calibrated subsurface properties combined with updated pore water chemistry continue to show that the weathering zone is the primary source of base cations and reactive nitrogen released from the hillslope. Subsurface export predictions can now be obtained for wide ranges of snowmelt based on measurements of water table elevation and profiles of pore water chemistry.

Rupture processes of global large earthquakes have been observed to exhibit great variability, whereas recent studies suggest that the average rupture behavior could be unexpectedly simple. To what extent do large earthquakes share common rupture characteristics? Here we use a machine learning algorithm to derive a generic model of global earthquake source time functions. The model indicates that simple and homogeneous ruptures are pervasive whereas complex and irregular ruptures are relatively rare. Despite the standard long-tail and near-symmetric moment release processes, the model reveals two special rupture types: runaway earthquakes with weak growing phases and relatively abrupt termination, and complex earthquakes with all faulting mechanisms but mostly shallow origins (<40 km). The diversity of temporal moment release patterns imposes a limit on magnitude predictability in earthquake early warning. Our results present a panoptic view on the collective similarity and diversity in the rupture processes of global large earthquakes.

Iron is a key limiting nutrient for phytoplankton. Continental shelf and slope sediments are important sources of dissolved iron (DFe). Stable iron isotopes (d56Fe) are a particularly useful tool to quantify the DFe sources and sinks in the ocean. The isotopic signature of the sedimentary DFe source is controlled by environmental factors such as bottom water redox conditions, carbon oxidation and bioturbation by burrowing fauna, but the exact relation on a global scale is poorly understood. We developed a reaction-transport model capable of tracing dissolved iron isotope fractionation in marine sediments to quantify the isotopic signature of benthic DFe fluxes under a wide range of environmental conditions. We derived fractionation factors for iron reduction (-1.3 permille), iron oxidation (+0.4 permille), iron sulphide precipitation (+0.5 permille and dissolution (-0.5 permille and pyrite precipitation (-0.7 permille) that were in line with existing literature. At bottom-water oxygen concentrations >50 µM, bioturbation increased the benthic DFe flux and increased the d56Fe signature. In contrast, at bottom-water oxygen concentrations <50 µM, a reduction in bioturbation led to a decrease in the benthic DFe flux and its d56Fe value. On a global scale, a model simulation without bioturbation decreased the sedimentary DFe release from ~158 Gmol DFe yr-1 to ~70 Gmol DFe yr-1, and decreased the variability in the d56Fe signature of the DFe flux. Finally, we find that a decrease in ocean oxygen content by 40 µM can increase global sedimentary DFe release by up to 103 Gmol DFe yr-1.