A document by Hongxiang Yan. Click on the document to view its contents.

A document by Sergio León-Ríos. Click on the document to view its contents.

Observation and measurements of ice structure and deformation made in tunnels excavated into the margin of Taylor Glacier reveal a complex, rapidly deforming basal ice sequence. Displacement measurements in the basal ice, which is at a temperature of -18°C, together with the occurrence of cavities and slickenslides, suggests that sliding occurs at structural discontinuities within the basal zone although we cannot rule out the possibility of rapid deformation in thin zones of high shear. Strain measurements show that the highest strain rates occur in ice with average debris concentrations of 26% followed by ice with debris concentrations of around 12%. The lowest strain rates occur in clean ice that has very low debris concentrations (<0.02%). Deformation within the basal ice sequence is dominated by simple shear but disrupted by folding which results in shortening of the debris-bearing ice followed by attenuation of the folds due to progressive simple shear which generates predominantly laminar basal ice structures. About 60% of glacier surface velocity can be attributed to deformation within the 4.5 m thick sequence of basal ice that was monitored for this study, and 15% of motion can be attributed to sliding. The combination of high debris concentrations and high strain rates in the debris-bearing ice means that material transported in the basal ice is exposed to a high rates of abrasion which produces heavily striated and facetted clasts typical of temperate glaciers even though the basal ice is at a temperature of -18°C.

Cloud microphysics is a critical aspect of the Earth’s climate system, which involves processes at the nano- and micrometer scales of droplets and ice particles. In climate modeling, cloud microphysics is commonly represented by bulk models, which contain simplified process rates that require calibration. This study presents a framework for calibrating warm-rain bulk schemes using high-fidelity super-droplet simulations that provide a more accurate and physically based representation of cloud and precipitation processes. The calibration framework employs ensemble Kalman methods including ensemble Kalman inversion (EKI) and unscented Kalman inversion (UKI) to calibrate bulk microphysics schemes with probabilistic super-droplet simulations. We demonstrate the framework’s effectiveness by calibrating a single-moment bulk scheme, resulting in a reduction of data-model mismatch by more than $75\%$ compared to the model with initial parameters. Thus, this study demonstrates a powerful tool for enhancing the accuracy of bulk microphysics schemes in atmospheric models and improving climate modeling.

Following the 3rd release of the “Emerging PV reports” , the best achievements in the performance of emerging photovoltaic (e-PV) devices in diverse e-PV research subjects are summarized, as reported in peer-reviewed articles in academic journals since August 2022. Updated graphs, tables and analyses are provided with several performance parameters, such as power conversion efficiency, open-circuit voltage, short-circuit current density, fill factor, light utilization efficiency, and stability test energy yield. These parameters are presented as a function of the photovoltaic bandgap energy and the average visible transmittance for each technology and application, and are put into perspective using, for example, the detailed balance efficiency limit. The 4th installment of the “Emerging PV reports” discusses the “PV emergence” classification with respect to the “PV technology generations” and “PV research waves” and highlights the latest device performance progress in multijunction and flexible photovoltaics. Additionally, Dale-Scarpulla’s plots of efficiency-effort in terms of cumulative academic publication count are also introduced.

To improve the predictive capability of ecosystem biogeochemical models (EBMs), we discuss the feasibility of formulating biogeochemical processes using physical rules that have underpinned the many successes in computational physics and chemistry. We argue that the currently popular empirically based modeling approaches, such as multiplicative empirical response functions and the law of the minimum, will not lead to EBM formulations that can be continuously refined to incorporate improved mechanistic understanding and empirical observations of biogeochemical processes. As an alternative to these empirical models, we propose to formulate EBMs using established physical rules widely used in computational physics and chemistry. Through several examples, we demonstrate how mathematical representations derived from physical rules can improve understanding of relevant biogeochemical processes and enable more effective communication between modelers, observationalists, and experimentalists regarding essential questions, such as what measurements are needed to meaningfully inform models and how can models generate new process-level hypotheses to test in empirical studies?

Braided rivers easily form wide and shallow floodplains when there is no constraints on both sides of the river. During floods, rising water level submerges the floodplain of the bifurcated channel, resulting in the Compound-Braided River. The generalized model was established based on statistical data from the braided river reach of Heilongjiang. In this paper, the flow field of the straight compound-braided river was measured in flume experiments, and then the effect of the interaction of floodplain and main channel on the flow pattern, water level, flow structure and resistance force were studied under overbank flow conditions. The split ratio variation trend is further discussed. The results show that hydraulic factors in diverge segment were mainly related to braided reach, with high longitudinal velocity observed in inner floodplain. The exchange flow between floodplain and main channel accelerates transverse flow and promotes sediment transport intensity laterally. Secondary flow of compound section within the influence range of bifurcated flow was obviously inhibited. Boundary shear stress analysis showed that the diversion ratio of the main tributary under overbank condition decreased slightly and would maintain constant values as surface rise.

A document by Xiang Yang. Click on the document to view its contents.

Mineral dust is one of the most abundant atmospheric aerosol species and has various far-reaching effects on the climate system and adverse impacts on air quality. Satellite observations can provide spatio-temporal information on dust emission and transport pathways. However, satellite observations of dust plumes are frequently obscured by clouds. We use a method based on established, machine-learning-based image in-painting techniques to restore the spatial extent of dust plumes for the first time. We train an artificial neural net (ANN) on modern reanalysis data paired with satellite-derived cloud masks. The trained ANN is applied to gray-scaled and cloud-masked false-color daytime images for dust aerosols from 2021 and 2022, obtained from the SEVIRI instrument onboard the Meteosat Second Generation satellite. We find up to 15 \% of summertime observations in West Africa and 10 \% of summertime observations in Nubia by satellite images miss dust events due to cloud cover. The diurnal and seasonal patterns in the reconstructed dust occurrence frequency are consistent with known dust emission and transport processes. We use the new dust-plume data to validate the operational forecasts provided by the WMO Dust Regional Center in Barcelona from a novel perspective. The comparison elucidates often similar dust plume patterns in the forecasts and the satellite-based reconstruction, but the latter computation is substantially faster. Our proposed reconstruction provides a new opportunity for validating dust aerosol transport in numerical weather models and Earth system models. It can be adapted to other aerosol species and trace gases.

Atmospheric nitrogen (N) deposition and climate change are transforming the way N moves through dryland watersheds. For example, N deposition is increasing N export to streams, which may be exacerbated by changes in the magnitude, timing, and intensity of precipitation (i.e., the precipitation regime). While deposition controls the amount of N entering a watershed, the precipitation regime influences rates of internal cycling; when and where soil N, plant roots, and microbes are hydrologically connected; how quickly plants and microbes assimilate N; and rates of denitrification, runoff, and leaching. We used the ecohydrological model RHESSys to investigate (1) how N dynamics differ between N-limited and N-saturated conditions in a dryland watershed, and (2) how total precipitation and its intra-annual intermittency (i.e., the time between storms in a year), interannual intermittency (i.e., the duration of dry months across multiple years), and interannual variability (i.e., variance in the amount of precipitation among years) modify N dynamics. Streamflow N export was more sensitive to increasing intermittency and variability in N-limited vs. N-saturated model scenarios, particularly when total precipitation was lower—the opposite was true for denitrification. N export and denitrification increased or decreased the most with increasing interannual intermittency compared to other changes in precipitation timing. This suggests that under future climate change, prolonged droughts that are followed by more intense storms may pose a major threat to water quality in dryland watersheds.

Estuaries in the northern California current system (NCCS) experience seasonally reversing wind stress, which is expected to impact the origin and properties of shelf water which enters NCCS estuaries (’shelf inflow’). Wind stress has been shown to affect the source of shelf inflow by driving alongshelf currents. However, the effects of wind-driven Ekman dynamics and shelf currents from larger-scale forcing on shelf inflow have yet to be explored. Variations in shelf inflow to the Salish Sea and the Columbia River estuary, two large NCCS estuarine systems, were studied using a realistic hydrodynamic model. The paths and source of shelf water were identified using particles released on the shelf. Particles were released every two weeks of 2017 and tracked for sixty days. Shelf inflow was identified as particles that crossed the estuary mouths. Mean wind stress during each release was compared with initial horizontal and vertical positions and physical properties of shelf inflow particles. For both the Salish Sea and the Columbia River estuary, upwelling-favorable wind stress was correlated with a shelf inflow source north of the estuary mouth. Depth was not correlated with wind stress for either estuary, but relative depth (depth scaled by isobath) increased during upwelling-favorable winds for both. Properties of inflow changed from cold and fresh during upwelling to warm and salty during downwelling, reflecting seasonal changes in NCCS shelf waters. These results may be extended to predict the source and properties of shelf inflow to estuaries in other regions with known wind or shelf current patterns.

A document by Aurélien Podglajen. Click on the document to view its contents.

We reprocessed and interpreted Chang’E-4 Lunar Penetrating Radar (LPR) data collected until 14th February 2023, exploiting a new Deep Learning-based algorithm to automatically extract reflectors from a processed radar dataset. The results are in terms of horizon probability and have been interpreted by integrating signal attribute analysis with orbital imagery. The approach provides more objective results by minimizing the subjectivity of data interpretation allowing to link radar reflectors to their geological context and surface structures. For the first time, we imaged dipping layers and at least 20 shallow buried crateriform structures within the regolith using LPR data. We further recognized four deeper structures similar to craters, locating ejecta deposits related to a crater rim crossed by the rover path and visible in satellite image data.

Climate risk management relies on accurate predictions of key climate variations such as El Niño-Southern Oscillation (ENSO), but the skill of ENSO predictions has recently plateaued or even degraded. Here we analyze the North American Multi-Model Ensemble (NMME) and estimate how the seasonal prediction of ENSO may benefit from reducing initial prediction errors. An analysis of predictable signals and system noises identifies a high-predictability regime and a low-predictability regime. The latter corresponds to the spring predictability barrier and is related to a rapid drop in the signal-to-noise ratio, which is caused by the comparably strong dampening of predictable signals. Reducing first-month prediction errors (FPEs) will likely reduce root-mean-square errors of the ENSO prediction. As a conservative estimate, halving the FPEs may extend the NMME’s skill by one to two months. Importantly, this study identifies the regions where reducing FPE is the most effective. Unlike the predictions initialized after the boreal spring, the March-initialized predictions of the wintertime ENSO will likely benefit the most from FPE reductions in the tropical Northwest Pacific. An opportunistic thought experiment suggests the buoy observation changes during 1995–2020 may have contributed to FPEs associated with large cold biases (>1K) in some El Niño-year predictions. While data availability prevented in-depth analyses of physical processes, the findings suggest that prioritizing modeling and observation in certain regions can improve climate predictions cost-effectively. The analytical framework here is applicable to other climate processes, thus holding wide potential for benefiting climate predictions.

Our understanding of tree stem methane (CH4) emissions is evolving rapidly. Few studies have combined seasonal measurements of soil, water and tree stem CH4 emissions from forested wetlands, inhibiting our capacity to constrain the tree stem CH4 flux contribution to total wetland CH4 flux. Here we present annual data from a subtropical freshwater Melaleuca quinquenervia wetland forest, spanning an elevational topo-gradient (Lower, Transitional and Upper zones). Eight field-campaigns captured an annual hydrological flood-dry-flood cycle, measuring stem fluxes on 30 trees, from four stem heights, and up to 30 adjacent soil or water CH4 fluxes per campaign. Tree stem CH4 fluxes ranged several orders of magnitude between hydrological seasons and topo-gradient zones, spanning from small CH4 uptake to ~203 mmol m-2 d-1. Soil CH4 fluxes were similarly dynamic and shifted from maximal CH4 emission (saturated soil) to uptake (dry soil). In Lower and Transitional zones respectively, tree stem CH4 contribution to the net ecosystem flux was greatest during flooded conditions (49.9 and 70.2 %) but less important during dry periods (3.1 and 28.2 %). Minor tree stem emissions from the Upper elevation zone still offset the Upper zone CH4 soil sink capacity by ~51% during dry conditions. Water table height was the strongest driver of tree stem CH4 fluxes, however tree emissions peaked once the soil was inundated and did not increase with further water depth. This study highlights the importance of quantifying the wetland tree stem CH4 emissions pathway as an important and seasonally oscillating component of wetland CH4 budgets.

The Sudan-Sahel region has long been vulnerable to environmental change. However, the intensification of global warming has led to unprecedented challenges that require a detailed understanding of climate change for this region. This study analyzes the impacts of climate change for Burkina Faso using eleven climate indices that are highly relevant to Sudan-Sahelian societies. The full ensemble of statistically downscaled NEX-GDDP-CMIP6 models (25 km) is used to determine the projected changes for the near (2031-2060) and far future (2071-2100) compared to the reference period (1985-2014) for different SSPs. Validation of the climate models against state-of-the-art reference data (CHIRPS and ERA5) shows reasonable performance for the main climate variables with some biases. Under the SSP5-8.5, Burkina Faso is projected to experience a substantial temperature increase of more than 4.3°C by the end of the century. Rainfall amount is projected to increase by 30% under the SSP5-8.5, with the rainy season starting earlier and lasting longer. This could increase water availability for rainfed agriculture but is offset by a 20% increase in evapotranspiration. The country could be at increased risk of flooding and heavy rainfall in all SSPs and future periods. Due to the pronounced temperature increase, heat stress, discomfort, and cooling degree days are expected to strongly increase under the SSP8.5 scenarios, especially in the western and northern parts. Under the SSP1-2.6 and SSP5-8.5, the projected changes are much lower for the country. Thus, timely implementation of climate change mitigation measures can significantly reduce climate change impacts for this vulnerable region.

Saturated hydraulic conductivity (Ks) is a crucial parameter that influences water flow in saturated soils, with applications in various fields such as surface water runoff, soil erosion, drainage, and solute transport. However, accurate estimation of Ks is challenging due to temporal and spatial uncertainties. This study addresses the knowledge gap regarding the long-term behaviour of Ks in sandy soils with less than 10% fine particles. The research investigates the changes in Ks over a long period of constant head tests and examines the factors influencing its variation. Two sandy samples were tested using a hydraulic conductivity cell, and the hydraulic head and discharge were recorded for over 50 days. The results show a general decline in Ks throughout the test, except for brief periods of increase. Furthermore, the relationship between flow rate and hydraulic head gradient does not follow the expected linear correlation from Darcy’s law, highlighting the complex nature of sandy soil hydraulic conductivity. The investigation of soil properties in three different sections of the samples before and after the tests revealed a decrease in the percentage of fine particles and a shift in specific gravity from the bottom to the top of the sample, suggesting particle migration along the flow direction. Factors such as clogging by fine particles and pore pressure variation contribute to the changes in Ks. The implications of this study have far-reaching effects on various geotechnical engineering applications. These include groundwater remediation, geotechnical stability analysis, and drainage system design.

This study investigates land loss and coastal inundation in Louisiana's Barataria Basin, a region highly susceptible to anthropogenic pressures and natural factors like land subsidence, sea-level rise, and tidal dynamics. Using high-resolution Digital Elevation Models (DEM) and water level data from the Coastal Reference Monitoring System (CRMS) stations, we analyzed changes in land area and water levels between 2007 and 2022. The attenuation coefficient magnitude of tidal intrusion, which quantifies tidal amplitude reduction as a function of landward distance from the coastline, exhibited a persistent decrease from 2007 to 2022 for O1 and K1 (the dominant tidal constituents), with an accumulated decrease of nearly 20%, signaling enhanced hydrological connectivity across the region. We also projected land area for historic years and predicted it for future years up to 2075, based on a range of displacement rates to account for uncertainties in vertical land motion. Our analyses predict that, in the absence of human intervention, the significance of tidal variations in influencing land loss will escalate; by 2045, the land area estimated based on Mean Higher High Water (MHHW) will constitute approximately 65% of the land area estimated using Mean Sea Level (MSL). Our findings underline the importance of considering the compound effects of subsidence, sea-level rise, and tidal dynamics in future land loss mapping and flood risk assessments. 

Diagnosing and predicting evaporation through satellite-based surface energy balance (SEB) and land surface models (LSMs) is challenging due to the non-linear responses of aerodynamic (ga) and stomatal conductance (gcs) to the coalition of soil and atmospheric drought. Despite a soaring popularity in refining gcs formulation in the LSMs by introducing a link between soil-plant hydraulics and gcs, the utility of gcs has been surprisingly overlooked in SEB models due to the overriding emphasis on eliminating ga uncertainties and the lack of coordination between these two different modeling communities. Therefore, a persistent challenge is to understand the reasons for divergent evaporation estimates from different models during strong soil-atmospheric drought. Here we present a virtual reality experiment over two contrasting European forest sites to understand the apparent sensitivity of the two critical conductances and evaporative fluxes to a water-stress factor (b-factor) in conjunction with land surface temperature (soil drought proxy) and vapor pressure deficit (atmospheric drought proxy) by using a non-parametric diagnostic model (Surface Temperature Initiated Closure, STIC1.2) and a prognostic model (Community Land Model, CLM5.0). Results revealed the b-factor and different functional forms of the two conductances to be a significant predictor of divergent response of the conductances to soil and atmospheric drought, which subsequently propagated in the evaporative flux estimates between STIC1.2 and CLM5.0. This analysis reaffirms the need for consensus on theory and models that capture the sensitivity of the biophysical conductances to the complex coalition of soil and atmospheric drought for better evaporation prediction.

To estimate groundwater flow and transport, lumped conceptual models are widely used due to their simplicity and parsimony - but these models are calibration reliant as their parameters are unquantifiable through measurements. To eliminate this inconvenience, we tried to express these conceptual parameters in terms of hydrodynamic aquifer properties to give lumped models a forward modelling potential. The most generic form of a lumped model representing groundwater is a unit consisting of a linear reservoir connected to a dead storage aiding extra dilution, or a combination of several such units mixing in calibrated fractions. We used one such standard two-store model as our test model, which was previously nicely calibrated on the groundwater flow and transport behaviour of a French agricultural catchment. Then using a standard finite element code, we generated synthetic Dupuit-Forchheimer box aquifers and calibrated their hydrodynamic parameters to exactly match the test model’s behaviour (concentration, age etc). The optimized aquifer parameters were then compared with conceptual parameters to find clear physical equivalence and mathematical correlation - we observed that the recession behaviour depends on the conductivity, fillable porosity, and length of the catchment whereas the mixing behaviour depends on the total porosity and mean aquifer thickness. We also noticed that for a two-store lumped model, faster and slower store represents differences only in porosities making it rather a dual porosity system. We ended with outlining a clear technique on using lumped models to run forward simulations in ungauged catchments where valid measurements of hydrodynamic parameters are available.

Fires were historically rare in tropical forests of West and Central Africa, where dense vegetation, rapid decomposition, and high moisture limit available fuels. However, increasing heat and drought combined with forest degradation and fragmentation are making these areas more susceptible to wildfire. We evaluated historical patterns of MODIS active fires in African tropical forests from 2003-2021. Trends were mostly positive, particularly in the northeastern and southern Congo Basin, and were concentrated in areas with high deforestation. Year-to-year variation of fires was synchronized with increasing temperature and vapor pressure deficit. There was anomalously high fire activity across the region during the 2015-2016 El Niño. These results contrast sharply with the drier African woodlands and savannas, where fires have been steadily decreasing. Further attention to fires in African tropical forests is needed to understand their global impacts on carbon storage and their local implications for biodiversity and human livelihoods.

A major advance in global bathymetric observation occurred in 2018 with the launch of NASA’s ICESat-2 satellite, carrying a green-wavelength, photon-counting lidar, the Advanced Topographic Laser Altimeter System (ATLAS). Although bathymetric measurement was not initially a design goal for the mission, pre- and post-launch studies revealed ATLAS’s notable bathymetric mapping capability. ICESat-2 bathymetry has been used to support a wide range of coastal and nearshore science objectives. However, analysis of ICESat-2 bathymetry in numerous locations around the world revealed instances of missing or clipped bathymetry in areas where bathymetric measurement should be feasible. These missing data were due to the ATLAS receiver algorithms not being optimized for bathymetry capture. To address this, two updates have been made to ICESat-2’s receiver algorithm parameters with the goal of increasing the area for which ICESat-2 can provide bathymetry. This paper details the parameter changes and presents the results of a two-phased study designed to investigate ICESat-2’s bathymetry enhancements at both local and global scales. The results of both phases confirm that the new parameters achieved the intended goal of increasing the amount of bathymetry provided by ICESat-2. The site-specific phase demonstrates the ability to fill critical bathymetric data gaps in open ocean and coastal settings. The global analysis shows that the area of potential bathymetry approximately doubled, with 6.1 million km2 of new area in which bathymetric measurements may be feasible. These enhancements are anticipated to facilitate a range of science objectives and close the gap between ICESat-2 bathymetry and offshore sonar data.

Redox processes, aqueous and solid-phase chemistry, and pH dynamics are key drivers of subsurface biogeochemical cycling in terrestrial and wetland ecosystems but are typically not included in terrestrial carbon cycle models. These omissions may introduce errors when simulating systems where redox interactions and pH fluctuations are important, such as wetlands where saturation of soils can produce anoxic conditions and coastal systems where sulfate inputs from seawater can influence biogeochemistry. Integrating cycling of redox-sensitive elements could therefore allow models to better represent key elements of carbon cycling and greenhouse gas production. We describe a model framework that couples the Energy Exascale Earth System Model (E3SM) Land Model (ELM) with PFLOTRAN biogeochemistry, allowing geochemical processes and redox interactions to be integrated with land surface model simulations. We implemented a reaction network including aerobic decomposition, fermentation, sulfate reduction, sulfide oxidation, and methanogenesis as well as pH dynamics along with iron oxide and iron sulfide mineral precipitation and dissolution. We simulated biogeochemical cycling in tidal wetlands subject to either saltwater or freshwater inputs driven by tidal hydrological dynamics. In simulations with saltwater tidal inputs, sulfate reduction led to accumulation of sulfide, higher dissolved inorganic carbon concentrations, lower dissolved organic carbon concentrations, and lower methane emissions than simulations with freshwater tidal inputs. Model simulations compared well with measured porewater concentrations and surface gas emissions from coastal wetlands in the Northeastern United States. These results demonstrate how simulating geochemical reaction networks can improve land surface model simulations of subsurface biogeochemistry and carbon cycling.

A document by Aryav Bhesania. Click on the document to view its contents.