Iron (Fe) is an essential micronutrient for phytoplankton, particularly diazotrophs, which are abundant in the Western Tropical South Pacific Ocean (WTSP). Their success depends on the numerous trace metals, particularly iron, released from shallow hydrothermal vents along the Tonga Arc. This study aimed to explore the impact of hydrothermal fluids on particulate trace metal concentrations and biological activity. To identify the composition of sinking particles across a wide area of the WTSP, we deployed sediment traps at various depths, both close and further west of the Tonga Arc. Seafloor sediments were cored at these deployment sites, including at a remote location in the South Pacific Gyre. The sinking particles were composed of a large amount of biological material, indicative of the high productivity of the Lau Basin. A significant portion of this material was lithogenic of hydrothermal origin, as revealed through Al-Fe-Mn tracing. The sinking material showed similar patterns between lithogenic and biogenic fractions, indicating that hydrothermal input within the photic layer triggered surface production. A hydrothermal fingerprint was suggested in the sediments due to the high sedimentation rates and the presence of large, heterogeneous, trace metal-rich particles. The presence of nearby active deep hydrothermal sources was suspected near the Lau Ridge due to the large particle size and the significant enrichment of Fe and Mn. Overall, this study revealed that deep and shallow hydrothermal sources along with submarine volcanism have a significant influence on the biogeochemical signature of particles in the Lau Basin at large spatial and temporal scales.
Land use and land cover change (LULCC) represents a key process of human-Earth system interaction and has profound impacts on ecosystem carbon cycling. As a key input for ecosystem models, future gridded LULCC data is typically spatially downscaled from regionally LULCC projections by integrated assessment models. The uncertainty associated with different spatial downscaling methods and its impacts on subsequent model projections have been historically ignored and rarely examined. This study investigated this problem using two representative spatial downscaling methods and focused on the impacts on the carbon cycle over ABoVE domain. Specifically, we used the Future Land Use Simulation model (FLUS) and Demeter model to generate 0.25-degree gridded LULCC data with the same input of regional LULCC projections from Global Change Analysis Model, under SSP126 and SSP585. The two sets of downscaled LULCC were used to drive CLM5 to prognostically simulate terrestrial carbon cycle dynamics over the 21st century. The results suggest large spatial-temporal differences between two LULCC datasets under both SSP126 and SSP585. The LULCC differences further lead to large discrepancies in the spatial patterns of projected carbon cycle variables, which are more than 79% of the contributions of LULCC in 2100. Besides, the difference for LULCC and carbon flux under SSP126 is generally larger than those under SSP585. This study highlights the importance of considering the uncertainties induced by spatial downscaling process in future LULCC projections and carbon cycle simulations.
The Sustainable Development Goals (SDGs) provide targets for humanity to achieve sustainable development by 2030. A monitoring framework of 248 environmental, social, and economic indicators, reported nationally by 193 UN Member States, tracks progress. The framework includes 92 environmental indicators, most of which refer to environmental policies. The SDG monitoring framework provides data to assess whether, across countries, environmental policies are: 1. Addressing environmental pressures, 2. Linked to environmental improvements, and 3. Linked with societal benefits delivered by healthy environments. We use statistical analysis and a generalized linear modeling approach to test for correlations between SDG indicators related to environmental policies, environmental pressures, the state of the environment, and social impacts delivered by healthy environments. Our results show that environmental policies, particularly protected areas and sustainable forest certification, are linked with environmental improvements, mainly in forest and water ecosystems. However, we find no evidence that environmental improvements are linked with positive social impacts. Finally, environmental pressures, including freshwater withdrawal, domestic material consumption, and tourism, are linked with environmental degradation. Environmental policy responses are generally increasing across countries. Despite this, the state of the environment globally continues to decline. Governments must focus on understanding why environmental policies have not been sufficient to reverse environmental decline, particularly concerning the pressures that continue to degrade the environment. To better track progress towards sustainable development, we recommend that the SDG monitoring framework is supplemented with additional indicators on the state of the environment.
Fire is a crucial factor in terrestrial ecosystems playing a role in disturbance for vegetation dynamics. Process-based fire models quantify fire disturbance effects in stand-alone dynamic global vegetation models (DGVMs) and their advances have incorporated both descriptions of natural processes and anthropogenic drivers. Nevertheless, these models show limited skill in modeling fire events at the global scale, due to stochastic characteristics of fire occurrence and behavior as well as the limits in empirical parameterizations in process-based models. As an alternative, machine learning has shown the capability of providing robust diagnostics of fire regimes. Here, we develop a deep-learning-based fire model (DL-fire) to estimate daily burnt area fraction at the global scale and couple it within JSBACH4, the land surface model used in the ICON ESM. The stand-alone DL-fire model forced with meteorological, terrestrial and socio-economic variables is able to simulate global total burnt area, showing 0.8 of monthly correlation (rm) with GFED4 during the evaluation period (2011-15). The performance remains similar with the hybrid modeling approach JSB4-DL-fire (rm=0.79) outperforming the currently used uncalibrated standard fire model in JSBACH4 (rm=-0.07). We further quantify the importance of each predictor by applying layer-wise relevance propagation (LRP). Overall, land properties, such as fuel amount and water content in soil layers, stand out as the major factors determining burnt fraction in DL-fire, paralleled by meteorological conditions over tropical and high latitude regions. Our study demonstrates the potential of hybrid modeling in advancing fire prediction in ESMs by integrating deep learning approaches in physics-based dynamical models.
InSight’s seismometers recorded more than 1300 events. Ninety-eight of these, named the low-frequency family, show energy predominantly below 1 Hz down to ∼0.125 Hz. The Marsquake Service identified seismic phases and computed distances for 42 of these marsquakes, 26 of which have backazimuths. Hence, the locations of the majority of low-frequency family events remain undetermined. Here, we use an envelope shape similarity approach to determine event classes and distances, and introduce an alternative method to estimate the backazimuth. In our similarity approach, we use the highest quality marsquakes with well-constrained distance estimates as templates, including the largest event S1222a, and assign distances to marsquakes with relatively high signal-to-noise ratio based on their similarities to the template events. The resulting enhanced catalog allows us to re-evaluate the seismicity of Mars. We find the Valles Marineris region to be more active than initially perceived, where only a single marsquake (S0976a) had previously been located. We relocated two marsquakes using new backazimuth estimates, which had reported distances of ∼90o, in the SW of the Tharsis region, possibly at Olympus Mons. In addition, two marsquakes with little or no S-wave energy have been located in the NE of the Elysium Bulge. Event epicenters in Cerberus Fossae follow a North-South trend due to uncertainties in location, while the fault system is in the NW-SE direction; therefore, these events are re-projected along the observed fault system.
Using a three-dimensional coupled physical-biological model, this paper explores the creation of phytoplankton blooms around tropical islands in the presence of ambient currents and short-lived (~4 days) wind events. The ambient flow creates a retention zone of weak flows in the lee of the island, which is a typical feature of island wakes. Findings reveal that wind-induced upwelling effects are essential for the initial nutrient enrichment and phytoplankton growth that occur mainly in this retention zone. Oscillating flow, typical of island wakes, occasionally releases mesoscale patches of upwelled water and its phytoplankton load into the ambient ocean. The phytoplankton continues to grow within floating structures that are of up to 20 km in diameter. This mechanism complements the plankton growth associated with the formation of mesoscale eddies.
Dynamical models used in climate prediction often have systematic errors that can deteriorate predictions. In this study, we work in a twin experiment framework with a reduced-order coupled ocean-atmosphere model and aim to demonstrate the benefit of machine learning for climate prediction. Machine learning is applied to learn the model error and thus build a data-driven model to emulate the dynamical model error. Then we build a hybrid model by combining the data-driven and dynamical models. The prediction skill of the hybrid model is compared to that of the standalone dynamical model. We applied this approach to the ocean-atmosphere coupled model. The results show that the hybrid model outperforms the dynamical model alone for both atmospheric and oceanic variables. Also, we build two other hybrid models only correcting either atmospheric errors or oceanic errors. It was found that correcting both atmospheric and oceanic errors leads to the best performance.
The northeastern Pacific climate system is featured by an extensive low-cloud deck off California on the southeastern flank of the subtropical high that accompanies intense northeasterly trades and relatively low sea surface temperatures (SSTs). This study investigates climatic impacts of the low-cloud deck by turning low-cloud radiative forcing on and off only within the subtropical northeastern Pacific in a coupled atmosphere-ocean model. The low-cloud radiative forcing causes a local SST decrease of up to 3ºC on an annual average, with the response extending southwestward through the wind-evaporation-SST (WES) feedback. The SST decrease peaks in summer under the seasonally enhanced insolation and the seasonally shallow ocean mixed layer. The lowered SST suppresses deep-convective precipitation that would otherwise occur in the absence of the low-cloud deck. The resultant anomalous diabatic cooling induces a surface anticyclonic response in summer and autumn as a baroclinic Matsuno-Gill pattern. On its equatorward flank, the enhanced trade winds further cool SST as the WES feedback, leading to the southwest propagation of the coupled response. The enhanced trades accompany the intensified upper-tropospheric westerlies, strengthening the vertical wind shear that, together with the lowered SST, acts to shield Hawaii from powerful hurricanes. On the basin scale, the anticyclonic surface wind response accelerates the North Pacific subtropical ocean gyre to speed up the Kuroshio by as much as 30%. SST thereby increases along the Kuroshio and its extension, intensifying upward turbulent heat fluxes from the ocean to increase precipitation.
Grain size affects the rates of aeolian sediment transport on beaches. Sediment in coastal environments typically consists of multiple grain size fractions and exhibits spatiotemporal variations. Still, conceptual and numerical aeolian transport models are simplified and often only include a single fraction that is constant over the model domain. It is unclear to what extent this simplification is valid and if the inclusion of multi-fraction transport and spatial grain size variations affects aeolian sediment transport simulations and predictions of coastal dune development. This study applies the numerical aeolian sediment transport model AeoLiS to compare single-fraction to multi-fraction approaches for a range of grain size distributions and spatial grain size scenarios. The results show that on timescales of days to years, single-fraction simulations with the median grain size, D50, often give similar results to multi-fraction simulations provided the wind is able to mobilize all fractions within that time frame. On these timescales, vertical variability in grain size has a limited effect on total transport rates, but it does influence the simulation results on minute timescales. Horizontal grain size variability influences both the total transport rates and the downwind bed grain size composition. The results provide new insights into the influence of beach sediment composition and spatial variability on total transport rates towards the dunes. The findings of this study can guide the implementation of grain size variability in numerical aeolian sediment transport models.
Carbon dioxide (CO2) quantification is critical for assessing city-level carbon emissions and sustainable urban development. While urban vegetation has the potential to provide environmental benefits, such as heat and carbon mitigation, the CO2 exchange from biogenic sectors and its impact from the environmental perturbations are often overlooked. It is also challenging to simulate the plant functions in the complex urban terrain. This study presents a processed-based modeling approach to assess the biogenic carbon fluxes from the vegetated areas over the Chicago Metropolitan Area (CMA) using the Weather Research and Forecast - Urban Biogenic Carbon exchange (WRF-UBC) model. We investigate the change of CO2 sink power in CMA under heatwaves and irrigation. The results indicate that the vegetation plays a significant role in the city’s carbon portfolio and the landscaping management has the potential to reduce carbon emissions significantly. Furthermore, based on the competing mechanisms in the biogenic carbon balance identified in this study, we develop a novel Environmental Benefit Score metrics framework to identify the vulnerability and mitigation measures associated with nature-based solutions (NbS) within CMA. By using the generalized portable framework and our science-policy confluence analysis presented in this study, global cities can maximize the effectiveness of NbS and accelerate carbon neutrality.
The Surface Water and Ocean Topography (SWOT) satellite is expected to observe the sea surface height (SSH) down to scales of ∼10-15 kilometers. While SWOT will reveal submesoscale SSH patterns that have never before been observed on global scales, how to extract the corresponding velocity fields and underlying dynamics from this data presents a new challenge. At these soon-to-be-observed scales, geostrophic balance is not sufficiently accurate, and the SSH will contain strong signals from inertial gravity waves — two problems that make estimating surface velocities non-trivial. Here we show that a data-driven approach can be used to estimate the surface flow, particularly the kinematic signatures of smaller scales flows, from SSH observations, and that it performs significantly better than directly using the geostrophic relationship. We use a Convolution Neural Network (CNN) trained on submesoscale-permitting high-resolution simulations to test the possibility of reconstructing surface vorticity, strain, and divergence from snapshots of SSH. By evaluating success using pointwise accuracy and vorticity-strain joint distributions, we show that the CNN works well when inertial gravity wave amplitudes are weak. When the wave amplitudes are strong, the model may produce distorted results; however, an appropriate choice of loss function can help filter waves from the divergence field, making divergence a surprisingly reliable field to reconstruct in this case. We also show that when applying the CNN model to realistic simulations, pretraining a CNN model with simpler simulation data improves the performance and convergence, indicating a possible path forward for estimating real flow statistics with limited observations.
The origins of river and floodplain waters (groundwater, rainfall, and snowmelt) and their extent during overbank flow events strongly impact ecological processes such as denitrification and vegetation development. However, the long-term sensitivity of floodplain water signatures to climate change remains elusive. We examined how the integrated hydrological model HydroGeoSphere and the Hydraulic Mixing-Cell method could help us understand the long-term impact of climate change on water signatures and their spatial distribution in the protected Biebrza River Catchment in northeastern Poland. Our model relied on 20th century Reanalysis Data from 1881 to 2015 and an ensemble of EURO-CORDEX simulations for RCP 2.6, 4.5, and 8.5 from 2006 to 2099. The historical component of the simulations was subjected to extensive multiple-variable validation from 1881 to 2019. The results show that the extents of water sources were rather stable in the floodplain in the 1881-2015 period. The projected future impacts were variable with each analyzed RCP, but in all cases, different significant trends were present for the spatial distribution of water sources and for the river-floodplain mixing. However, the total volume of water from different sources was less sensitive to climate change than the dominant sources and spatial distribution of water. The simulation results highlight the impact of climate change on the extent of water sources in temperate zone wetlands with significant implications for ecological processes and management. These results also underscore the urgent need to leverage such modeling studies to inform protective and preservation strategies of floodplain wetlands.
The mixing of ocean waters on continental shelves, which is mainly driven by waves, tides, and currents, plays a key role in the physics, biogeochemistry, and ecology of coastal regions. This study focuses on four months of continuous data recorded along a telecommunication cable offshore Oregon, USA, with Distributed Acoustic Sensing (DAS). We apply a cross-correlation approach to the continuous DAS data to infer the propagation of ocean surface gravity waves in the 3 to 100 s period range and estimate near-surface ocean flows. We observe strong spatio-temporal variations of ocean flows along the cable over four months, with strong impacts from a series of storms in late October 2021. We find that our measurements capture oceanic surface motions as those measured by nearby traditional oceanographic instruments. This study demonstrates that ocean-bottom DAS can be used to infer the dynamic properties of near-shore oceans with an unprecedented spatio-temporal resolution.
In this study, we examine the influence of the mantle and large-scale tectonics on the global mid-ocean ridge (MOR) system. Using solely seismically-inferred upper mantle temperatures below the melting zone (260-600 km) and an interpretable machine learning model (Random Forest and Principal Component Analysis), we predict, with up to 90\% accuracy, the basin of origin of ridge segments without any prior geographic information. Two features provide $>$50\% of the discriminative power: the temperature difference between the mid-layer (340-500 km) and other depths, and the depth-averaged temperature. Our result implies that the large-scale geophysical and geochemical differences observed along the MOR system are reflective, not primarily of shallow processes associated with melting, but of long-term tectonic and convective processes in the mantle that determine the present-day upper mantle temperature structure.
There have been a number of theories proposed concerning the loss of relativistic electrons from the radiation belts. However, direct observations of loss were not possible on a number of previous missions due to the large field of view of the instruments and often high-altitude orbits of satellites that did not allow researchers to isolate the precipitating electrons from the stably trapped. We use measurements from the ELFIN-L suit of instruments flown on Lomonosov spacecraft at LEO orbit, which allows us to distinguish stably trapped from the drift loss cone electrons. The sun-synchronous orbit of Lomonosov allows us to quantify scattering that occurred into the loss cone on the dawn-side and the dusk-side magnetosphere. The loss at MeV energies is observed predominantly on the dawn-side, consistent with the loss induced by the chorus waves. The companion data publication provides processed measurements.
Robust in-situ magnetic field measurements are critical to understanding the various mechanisms that couple mass, momentum, and energy throughout our solar system. However, the spacecraft on which magnetometers are often deployed contaminate the magnetic field measurements via onboard subsystems including reaction wheels and magnetorquers. Two magnetometers can be deployed at different distances from the spacecraft to determine an approximation of the interfering field for subsequent removal, but constant data streams from both magnetometers can be impractical due to power and telemetry limitations. Here we propose a method to identify and remove time-varying magnetic interference from sources such as reaction wheels using statistical decomposition and convolutional neural networks, providing high-fidelity magnetic field data even in cases where dual-sensor measurements are not constantly available. For example, a measurement interval from the Parker Solar Probe outboard magnetometer experienced a 95.1% reduction in reaction wheel interference following application of the proposed technique.