Maximizing crop yield is a central goal in agriculture and plant breeding. Several phenotypic traits, including leaf chlorophyll content and canopy greenness, can help predict yield across different varieties. In this study, we aimed to quantify the relationship between leaf chlorophyll concentration, canopy greenness, and crop yield within the Genomes to Fields trials. Our goal was to assess the correlation and enhance predictive modeling between these factors, evaluating the effectiveness of canopy cover and spectral reflectance in predicting yield. We accomplished this by collecting chlorophyll data from multiple leaves within each field plot and utilizing imagery captured by unoccupied aerial systems. This image data provided RGB and multispectral reflectance information on a per-plot basis throughout the growing season, allowing us to model patterns linking chlorophyll concentration and yield for each plot. We integrated data from these two methods with genotypic data from the same maize varieties to explore the fundamental relationships between chlorophyll levels, canopy greenness, and crop yield.

 Non-engineered landfills or open dumpsites are the most common solid waste disposal facilities in developing countries. The absence of proper liner systems in these non-engineered landfills causes deterioration of the nearby surface and sub-surface water resources via leachate migration beneath and around the landfill site. Therefore, it is necessary to understand the contaminant transport mechanisms to predict the long-term impact of a landfill in the subsurface groundwater system for the development of possible remedial measures. The present research aims to simulate the subsurface transport of two heavy metals (Lead and Cadmium) from a non-engineered landfill situated at Boragaon, Guwahati, in North-East India, to the nearby eco-sensitive wetland, Deepor Beel (Ramsar site no. 1207). Based on the hydrogeological information collected from the central groundwater board (CGWB) and field investigation, a single-layer conceptual model of the study area was constructed using Groundwater Modeling Systems (GMS) software. Input parameters for the contaminant transport model were determined from laboratory batch and column experiments with the soil samples collected near the vicinity of the landfill. Numerical simulation of groundwater flow and advective–dispersive transport was then performed using MODFLOW and MT3DMS codes to understand the migration pathway and spatiotemporal variation of heavy metals concentration. The simulation results produced different size plumes of heavy metal contaminated groundwater, which are predominantly expanded in the longitudinal direction, indicating advection-dominated transport mechanism. Particle-tracking simulations further revealed that travel times for the plumes of Lead (Pb)and Cadmium (Cd) to reach the wetland were about 22 and 16 years, respectively. The trend of contour lines shows that, after reaching the wetland, the concentration of heavy metals will be enough to degrade its water quality within a few years. The results obtained from the current simulation study can be used for designing and installing engineered barriers to mitigate the migration of heavy metals into the wetland.

Strong winds in Southern Ocean storms drive air-sea carbon and heat fluxes. These fluxes are integral to the global climate system and the wind speeds that drive them are increasing. The current scatterometer constellation measuring vector winds remotely undersamples these storms and the higher winds within them, leading to potentially large biases in Southern Ocean wind reanalyses and the fluxes that derive from them. This observing system design study addresses these issues in two ways. First, we describe an addition to the scatterometer constellation, called Southern Ocean Storms -- Zephyr, to increase the frequency of independent observations, better constraining high winds. Second, we show that potential reanalysis wind biases over the Southern Ocean lead to uncertainty over the sign of the net winter carbon flux. More frequent independent observations per day will capture these higher winds and reduce the uncertainty in estimates of the global carbon and heat budgets.

A document by Vanille Ritz. Click on the document to view its contents.

The increase in record-breaking extreme events caused by climate change poses a threat to human health and well-being; understanding the future impacts of such events on global populations can provide decision-making support for policies aiming to mitigate climate change. Here, we investigated the population exposure to eight climate extreme indices and drivers of exposure trajectories based on NASA Earth Exchange Global Daily Downscaled Projections Coupled Model Intercomparison Project 6 (CMIP6) and population projection data under four shared socioeconomic pathway (SSP) scenarios at a spatial resolution of 0.25°×0.25°. The results show that by the mid-21st century, most regions around the world, especially Africa and South America, will continue to experience record-breaking temperatures and compound drought and heatwaves (CDHWs). Regarding population exposure, under the worst-case scenario of SSP3-7.0 in the late 21st century, the mean value of the multimodel median expected annual exposure (EAE) of all extreme temperature indices and CDHW reaches 8.79 billion persons per year; population exposure hotspots will be concentrated in Central Africa, South Asia, Southeast Asia, and East Asia, mostly in developing countries, where 62.77%-87.42% of the EAE is found. The drivers of exposure trajectories are spatially heterogeneous; the increase in record-breaking probability contributes more than population growth to EAE growth in most regions of the world except Central Asia, the Middle East, and most of Africa. These findings highlight the necessity of using various climate extreme indices to reveal spatiotemporal patterns of population exposure, which can provide references for future adaptation decisions and risk management.

In clouds containing both liquid and ice that have temperatures between -3C and -8C, liquid droplets collide with large ice crystals, freeze, and shatter, producing a plethora of small ice splinters. This process, known as Hallett-Mossop rime splintering, can cause clouds to reflect less sunlight and to have shorter lifetimes. Here, we use a novel suite of five global cloud-resolving models, which break up the Earth’s atmosphere into columns with 2-4 km horizontal edges, to show that this microscale process has global implications. Simulations that include Hallett-Mossop rime splintering have reduced cumulus cloud cover over the Southern Ocean and reflect 12 Wm^(-2) less sunlight back to space over the same region, better matching satellite observed radiative fluxes. We evaluate simulated clouds using high-resolution visible images from the Himawari satellite, and radar reflectivities and two-dimensional images of cloud particles from the SOCRATES aircraft campaign. Cumulus clouds from simulations with Hallett-Mossop rime splintering included have more realistic cloud morphology, cloud vertical structure and ice crystal properties. We show that Hallett-Mossop rime splintering is an important control on cumulus cloud cover and cloud radiative effects over the Southern Ocean, and that including it in simulations improves model performance. We also demonstrate the key role that global cloud-resolving models can play in detangling the effects of clouds on Earth’s climate across scales, making it possible to translate the behavior of tiny cloud particles (10^(-8) m^2) to their impact on the radiative budget of the massive Southern Ocean basin (10^(14) m^2).

A document by Alex Omar Gonzalez. Click on the document to view its contents.

Karen J Wang1,2,*, Yongsong Huang1,2,*, Tyler Kartzinel2,3, Markus Majaneva4, Nora Richter1,2,5, Sian Liao2,6, Camilla S Andresen7, Flor Vermassen7Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, 02912, USAInstitute at Brown for Environment and Society, Brown University, Providence, RI, 02912, USADepartment of Ecology and Evolutionary Biology, Brown University, Providence, RI, 02912, USANorwegian Institute for Nature Research (NINA), NO-7485, Trondheim, NorwayDepartment of Marine Microbiology & Biogeochemistry, NIOZ Royal Netherlands Institute for Sea Research, 1790 AB Den Burg, the NetherlandsDepartment of Chemistry, Brown University, Providence, RI, 02912, USADepartment of Glaciology and Climate, Geological Survey of Denmark and Greenland

Thirty years (1993-2022) of concurrent satellite and in-situ observations show a long-term strengthening of the equatorial Pacific upper-ocean circulation. Enhanced southeasterly and cross-equatorial winds have caused an annual mean, basin-wide acceleration of the equatorial westward near-surface currents by ~20% and an acceleration of poleward flow north (south) of the equator by ~60% (~20%). Additional moored velocity data reveal a deepening of the EUC core at 170°W and significant shoaling at 140°W and 110°W, but no significant changes in EUC core velocity. The strongest subsurface zonal velocity trends are observed above the EUC core and occur before and after the seasonal maximum of EUC core velocity, causing enhanced upper-ocean vertical current shear. Consistent with trends of the 20°C isotherm depth along the equatorial Pacific, a significant basin-wide steepening of the equatorial thermocline is observed. Both the accelerating equatorial current system and the enhanced thermocline slope are consistent with an observed steepening of the zonal sea surface height gradient due to increased wind-driven westward mass transport at the surface. During February-March, both surface and subsurface currents show eastward velocity trends, in contrast to westward near-surface current trends during the remainder of the year. The trend reversal is attributed to both a long-term shift in equatorial Kelvin wave activity and to the impact of strong interannual variability due to El Niño Southern Oscillation and other modes of natural variability on decadal to multidecadal time scales.

We deformed samples with varied proportions of olivine and orthopyroxene in a deformation-DIA apparatus to test the applicability of subgrain-size piezometry to polymineralic rocks. We measured the stress within each phase in situ via X-ray diffraction during deformation at a synchrotron beamline. Subgrain-size piezometry was subsequently applied to the recovered samples to estimate the stress that each phase supported during deformation. For olivine, the final in-situ stresses are consistent with the stresses estimated via subgrain-size piezometry, both in monomineralic and polymineralic samples, despite non-steady state conditions. However, stress estimates from subgrain-size piezometry do not reliably record the in-situ stress in samples with grain sizes that are too small for extensive subgrain-boundary formation. For orthopyroxene, subgrain boundaries are typically sparse due to the low strains attained by orthopyroxene in olivine-orthopyroxene mixtures. Where sufficient substructure does exist, our data supports the use of the subgrain-size piezometer on orthopyroxene. These results do, however, suggest that care should be taken when applying subgrain-size piezometry to strong minerals that may have experienced little strain. Stresses estimated by X-ray diffraction also offer insight into stress partitioning between phases. In mixtures deformed at mean stresses > 5 GPa, orthopyroxene supports stresses greater than those supported by olivine. This stress partitioning is consistent with established theory that predicts a slightly higher stress within a ‘strong’ phase contained in a material consisting of interconnected weak layers. Overall, these results demonstrate that subgrain-size piezometry is a valuable tool for quantifying the stress state of polymineralic rocks.

The study of lunar plasma environment’s response to the magnetotail lobe condition is the main subject of our investigation in this report. Photoionization and charge exchange of protons with the lunar exosphere arethe ionization processes included in our model. The computational model includes the dynamics of heavy Na+ pickup and ambient magnetospheric ions. The electrons are considered as a fluid.The lunar interior is considered as a weakly conducting body. In this report we consider for the first time a formation of lunar plasma structures, wakes, and a generation of low-frequency electromagnetic waves by using a self-consistent hybrid kinetic modeling. The input parameters were taken from the ARTEMIS observations. At an early stage the Moon with exosphere and conducting core excites whistler waves in case of Sub-Alfvenic/sonic interaction. At a later stage an excitation of the Alfven wave is observed. The topology of the Alfven waves is approximately similar to the Alfven wing near the planetary moons (Io, Europa etc.). The physics of the Moon-magnetotail lobe interaction is also close to the physics of the interaction between plasma clouds (expanding and not expanding) and ambient magnetospheric plasma. The heavy pickup ions create a large structured halo with space scale of more than 10 R_{E} in the direction of the background field. The modeling also shows an excitation of the compressional waves due to expansion of heavy exospheric pickup ions. The lunar model with weaker interior conductivity excites lower levels of the wave activity. This work was supported by NASA Award (80NSSC20K0146) from Solar System Workings Program (NNH18ZDA001N-C.3-SSW2018). Computational resources were provided by the NASA High-End SupercomputingFacilities (Aitken-Ames, Project HEC SMD-20-02357875).

The propagation of the fast magnetosonic (FMS) wave in the curved magnetic field is studied. A hemicylindrical model of the magnetosphere is considered where the magnetic field lines are represented by concentric circles. An ordinary differential equation is derived describing the coupled Alfv\’en and FMS waves. Using the equation, it was demonstrated that in the curved field the propagation of the fast mode is drastically different from the propagation in the planar magnetic field. In particular, on the magnetic surface known as the reflection surface for the fast mode in the planar magnetic field, there is a wave singularity where some components of the wave’s magnetic field (the azimuthal and compressional components) as well as the plasma density have logarithmic singularity. The physical reason for this singularity is the decrease of the volume of the magnetic flux tube toward the axis of the cylinder.

As partially molten rocks deform, they develop melt preferred orientations, shape preferred orientations, and crystallographic preferred orientations (MPOs, SPOs and CPOs). We investigated the co-evolution of these preferred orientations in experimentally deformed partially molten rocks, then calculated the influence of MPO and CPO on seismic anisotropy. Olivine-basalt aggregates containing 2 to 4 wt% melt were deformed in general shear at a temperature of 1250°C under a confining pressure of 300 MPa at shear stresses of τ = 0 to 175 MPa to shear strains of γ = 0 to 2.3. Grain-scale melt pockets developed a MPO parallel to the maximum principal stress, s1, at γ < 0.4. At higher strains, the grain-scale MPO remained parallel to s1, but incipient, sample-scale melt bands formed at ~20° to s1. An initial SPO and CPO were induced during sample preparation, with [100] and [001] axes girdled perpendicular to the long axis of the sample. At the highest explored strain, a strong SPO was established, and the [100] axes of the CPO clustered nearly parallel to the shear plane. Our results demonstrate that grain-scale and sample-scale alignments of melt pockets are distinct. Furthermore, the melt and the solid microstructures evolve on different timescales: in planetary bodies, changes in the stress field will first drive a relatively rapid reorientation of the melt network, followed by a relatively slow realignment of the crystallographic axes. Rapid changes to seismic anisotropy in a deforming partially molten aggregate are thus caused by MPO rather than CPO.

The northern permafrost region has been projected to shift from a net sink to a net source of carbon under global warming. However, estimates of the contemporary net greenhouse gas (GHG) balance and budgets of the permafrost region remain highly uncertain. Here we construct the first comprehensive bottom-up budgets of CO2, CH4, and N2O across the terrestrial permafrost region using databases of more than 1000 in-situ flux measurements and a land cover-based ecosystem flux upscaling approach for the period 2000-2020. Estimates indicate that the permafrost region emitted a mean annual flux of 0.36 (-620, 652) Tg CO2-C y-1, 38 (21, 53) Tg CH4-C y-1, and 0.62 (0.03, 1.2) Tg N2O-N y-1 to the atmosphere throughout the period. While the region was a net source of CH4 and N2O, the CO2 budget was near neutral with large uncertainties. Terrestrial ecosystems remained a CO2 sink, but emissions from fire disturbances and inland waters largely offset the sink in vegetated ecosystems. Including lateral fluxes, the permafrost region was a net source of C and N, releasing 136 (-517, 821) Tg C y-1 and 3.2 (1.9, 4.8) Tg N y-1. Large uncertainty ranges in these estimates point to a need for further expansion of monitoring networks, continued data synthesis efforts, and better integration of field observations, remote sensing data, and ecosystem models to constrain the contemporary net GHG budgets of the permafrost region and track their future trajectory.

The scientific debate on the existence of possible precursors before large earthquakes has been wide and brought to not univocal conclusions. One observable alone is clearly insufficient to provide indications on the criticality of the seismic fault status. In particular, it’s fundamental to investigate how critical is the accumulation of stress on a seismogenic fault and how we can predict an incoming earthquake. Considering that the Earth is a complex system in which the geo-layers (lithosphere, atmosphere and ionosphere) interact, it seems possible to understand better the Earth’s system if we study it as a whole. This requires an integration of observations and data from several sources and instruments, from ground data to remote sensing satellites. Despite the technical obstacles, several works have been done in this direction in the last years, and they seem to confirm firstly that the geo-layers show alterations (i.e., anomalies) before earthquake occurrence; secondly, it seems that several mechanisms of coupling are possible. This last point is the present research topic, as further clarifications are required.In the presentation, several examples of couplings before the earthquakes will be shown; for example, the recent investigation of the Lushan (China) 2013 earthquake, which shows three possible couplings by different geophysical mechanisms at very different times before the earthquake (130 days, 45 days, and 2 weeks).For the more recent seismic event, it is possible to investigate the ionosphere layer with the precious data from China Seismo Electromagnetic Satellite (CSES-01), by its instruments such as Langmuir probes and magnetometers. The integration of CSES and ESA Swarm data allows us to define better the ionospheric disturbances possibly related to earthquake occurrence. These cases include Mw = 7.5 Indonesia 2018, Mw = 7.6 Papua New Guinea 2019, Mw = 7.7 Jamaica 2020 or Mw = 7.8 Turkey 2023 earthquakes.Finally, we are still far from predicting earthquakes but several steps toward a better understanding of the phenomena eventually associated with seismic events seem to be not far.  

Model error is one of the main obstacles to improved accuracy and reliability in numerical weather prediction (NWP) conducted with state-of-the-art atmospheric models. To deal with model biases, a modification of the standard 4D-Var algorithm, called weak-constraint 4D-Var, has been developed where a forcing term is introduced into the model to correct for the bias that accumulates along the model trajectory. This approach reduced the temperature bias in the stratosphere by up to 50% and is implemented in the ECMWF operational forecasting system. Despite different origins and applications, Data Assimilation and Deep Learning are both able to learn about the Earth system from observations. In this paper, a deep learning approach for model bias correction is developed using temperature retrievals from Radio Occultation (RO) measurements. Neural Networks require a large number of samples to properly capture the relationship between the temperature first-guess trajectory and the model bias. As running the IFS data assimilation system for extended periods of time with a fixed model version and at realistic resolutions is computationally very expensive, we have chosen to train, the initial Neural Networks are trained using the ERA5 reanalysis before using transfer learning on one year of the current IFS model. Preliminary results show that convolutional neural networks are adequate to estimate model bias from RO temperature retrievals. The different strengths and weaknesses of both deep learning and weak constraint 4D-Var are discussed, highlighting the potential for each method to learn model biases effectively and adaptively.

The time evolving strain field contains a wealth of information that can be used to interpret subsurface behavior. For example, injecting or removing fluids from reservoirs or aquifers causes deformation that can be used as a diagnostic signal in some cases, while it can interfere with geodetic interpretations in other cases. We've recently completed a field study that demonstrated the feasibility of measuring the strain tensor at a depth of 30 m caused by injection into a reservoir at 530 m depth. The observed strain signals were interpreted using four independent analytic and numerical methods that resulted in estimates of the poroelastic properties and geometry of the reservoir that was consistent with data from well logs. However, studies like these are only possible if these deformations can be reliably measured. Advances in optical fiber sensing systems have led to their introduction in a number of areas including quasi-static and dynamic subsuface deformation monitoring. Optical fiber-based interferometers are capable of measuring very small displacements while being completely passive in their operation. The low attenuation and significantly reduced bending loss in rare-earth doped, high numerical aperture glass optical fibers allows for the embedding of long lengths of fiber into compact, durable and exceptionally sensitive downhole sensing packages. We have expanded on years of lab and field work developing and deploying long baseline and embedded single-component borehole strainmeters to the design of three novel horizontal tensor strainmeters. Each design represents a unique embedding approach for measuring directional strain across the diameter of a borehole with differing advantages in terms of ease of fabrication and assembly, as well as directional resolution. We will present the design details along with laboratory calibration results and preliminary field data comparing their relative performances across tidal and seismic frequencies.

Designing where to plant riparian vegetation is a component of many river projects. Several mechanistic models have been developed considering biological, soil, hydrological, and hydraulic requirements that influence riparian vegetation growth. However, many models are not spatial explicit and there remains high uncertainty as to where plantings will survive or die. This study sought to determine if a machine learning (ML) algorithm could be trained to accurately characterize the complex set of site attributes that promote survival, and do so exclusively using metrics derived from airborne LiDAR. Results could then be used to guide planting strategies. The selected testbed river was 34 km of alluvial, regulated, gravel/cobble river where planting projects are common and have high mortality. The lower Yuba River, California, USA was mapped at sub-meter resolution in 2017. Our approach has four steps. First, a set of 32,000 vegetation presence/absence observations were randomly selected from LiDAR-derived polygons of naturally occurring established vegetation. Second, the river was split into 75 training, validation and test areas. Third, a set of 17 LiDAR-derived topographic potential predictors were computed at 0.91-m (3-ft) resolution. Finally, a Random Forest machine learning model was trained to best predict vegetation presence. The model results in a riparian vegetation presence probability map and has a “Area Under the Curve” (AUC) of 0.77. As probability values are difficult to interpret, a forage ratio electivity index analysis was performed with statistical bootstrapping. Results show that points with probability values > 0.8 had ~ 8.5 times more riparian vegetation present than would be likely from random chance at the 95% confidence level. Microtopographic ‘vector ruggedness’ was identified as the main driver for vegetation presence, followed by Terrain Ruggedness Index and Roughness. In conclusion, a ML model can identify where riparian vegetation planting are most likely to succeed and guide design. Our results also suggest that more attention should be paid to creating rugged microtopography under plantings to help cuttings and seedlings establish deposition critical for nutrition.