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DOE OSTI2021

Climate warming threatens the persistence of a community of disturbance-adapted native annual plants

With ongoing climate change, populations are expected to exhibit shifts in demographic performance that will alter where a species can persist. This presents unique challenges for managing plant populations and may require ongoing interventions, including in situ management or introduction into new locations. However, few studies have examined how climate change may affect plant demographic performance for a suite of species, or how effective management actions could be in mitigating climate change effects. Over the course of two experiments spanning 6 yr and four sites across a latitudinal gradient in the Pacific Northwest, United States, here we manipulated temperature, precipitation, and disturbance intensity, and quantified effects on the demography of eight native annual prairie species. Each year we planted seeds and monitored germination, survival, and reproduction. We found that disturbance strongly influenced demographic performance and that seven of the eight species had increasingly poor performance with warmer conditions. Across species and sites, we observed 11% recruitment (the proportion of seeds planted that survived to reproduction) following high disturbance, but just 3.9% and 2.3% under intermediate and low disturbance, respectively. Moreover, mean seed production following high disturbance was often more than tenfold greater than under intermediate and low disturbance. Importantly, most species exhibited precipitous declines in their population growth rates (λ) under warmer-than-ambient experimental conditions and may require more frequent disturbance intervention to sustain populations. Aristida oligantha, a C4 grass, was the only species to have λ increase with warmer conditions. These results suggest that rising temperatures may cause many native annual plant species to decline, highlighting the urgency for adaptive management practices that facilitate their restoration or introduction to newly suitable locations. Frequent and intense disturbances are critical to reduce competitors and promote native annuals’ persistence, but even such efforts may prove futile under future climate regimes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2024

Validation and Application of Satellite-Derived Sea Surface Temperature Gradients in the Bering Strait and Bering Sea

The Arctic is one of the most important regions in the world’s oceans for understanding the impacts of a changing climate. Yet, it is also difficult to measure because of extreme weather and ice conditions. In this work, we directly compare four datasets from the Group for High-Resolution Sea Surface Temperature (GHRSST) with a NASA Saildrone deployment along the Alaskan Coast and the Bering Sea and Bering Strait. The four datasets used are the Remote Sensing Systems Microwave Infrared Optimally Interpolated (MWIR) product, the Canadian Meteorological Center (CMC) product, the Daily Optimally Interpolated Product (DOISST), and the Operational Sea Surface Temperature and Ice Analysis (OSTIA) product. Spatial sea surface temperature (SST) gradients were derived for both the Saildrone deployment and GHRSST products, with the GHRSST products collocated with the Saildrone deployment. Overall, statistics indicate that the OSTIA product had a correlation of 0.79 and a root mean square difference of 0.11 °C/km when compared with Saildrone. CMC had the highest correlation of 0.81. Scatter plots indicate that OSTIA had the slope closest to one, thus best reproducing the magnitudes of the Saildrone gradients. Differences increased at latitudes > 65°N where sea ice would have a greater impact. A trend analysis was then performed on the gradient fields. Overall, positive trends in gradients occurred in areas along the coastal regions. A negative trend occurred at approximately 60°N. A major finding of this study is that future work needs to revolve around the impact of changing ice conditions on SST gradients. Another major finding is that a northward shift in the southern ice edge occurred after 2010 with a maxima at approximately 2019. This indicates that the shift of the southern ice edge is not gradual but has dramatically increased over the last decade. Future work needs to revolve around examining the possible causes for this northward shift.

Arctic↗

DOE OSTI2023

The effect of the vertical gradients of photosynthetic parameters on the CO 2 assimilation and transpiration of a Panamanian tropical forest

Terrestrial biosphere models (TBMs) include the representation of vertical gradients in leaf traits associated with modeling photosynthesis, respiration, and stomatal conductance. However, model assumptions associated with these gradients have not been tested in complex tropical forest canopies. Here we compared TBM representation of the vertical gradients of key leaf traits with measurements made in a tropical forest in Panama, and then quantified the impact of the observed gradients on simulated canopy scale CO 2 and water fluxes. Comparison between observed and TBM trait gradients showed divergence that impacted canopy scale simulations of water vapor and CO 2 exchange. Notably, the ratio between the dark respiration rate and the maximum carboxylation rate was lower near the ground than at the top-of-canopy, leaf-level water-use efficiency was markedly higher at the top-of-canopy, and the decrease in maximum carboxylation rate from the top-of-canopy to the ground was less than TBM assumptions. The representation of the gradients of leaf traits in TBMs is typically derived from measurements made within-individual plants, or, for some traits, assumed constant due to a lack of experimental data. Our work shows that these assumptions are not representative of the trait gradients observed in species-rich, complex tropical forests.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Observed Increase in Tropical Cyclone‐Induced Sea Surface Cooling Near the U.S. Southeast Coast

Abstract Tropical cyclones (TCs) induce substantial upper‐ocean mixing and upwelling, leading to sea surface cooling. In this study, we explore changes in TC‐induced cold wakes along the United States (U.S.) Southeast and Gulf Coasts during 1982–2020. Our study shows a significant increase in TC‐induced sea surface temperature (SST) cooling of about 0.20°C near the U.S. Southeast Coast over this period. However, for the U.S. Gulf Coast, trends in TC‐induced SST cooling are insignificant. Analysis of the large‐scale oceanic environments indicate that the increasing TC‐induced cold wakes near the Southeast coast have been predominantly caused by the cooling of subsurface waters in that region. This upper‐ocean change is attributed to the enhancement of surface pressure gradient across land‐sea boundary and the associated increase in alongshore winds over there. Further analysis with climate models reveals the important role of anthropogenic forcings in driving these changes in the atmospheric circulation response along the U.S. Southeast Coast.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1989

Dynamics of planetary atmospheres

The overall goal is to illuminate the mechanisms that control weather and climate on the Earth and other planets. Each planet presents its own puzzling behavior - the stability of jets and vortices in Jupiter's otherwise turbulent atmosphere, the superrotation of the Venus atmosphere, the interplay of dust, polar volatiles, and climate change in Mars, the supersonic meteorology of Io, and the counterintuitive equator-to-pole temperature gradients on the outer planets. The data sets are generally those obtained from spacecraft - cloud-tracked winds, radiometrically inferred temperatures, and the results of in situ observations where appropriate. The approach includes both data analysis and modeling, ranging from analytic modeling to time-dependent numerical modeling of atmospheric dynamics. The latter approach involves the use of supercomputers such as the San Diego Cray. Progress is generally made when a model with a small number of free parameters either fits a data set that has a large number of independent observations or applies to several planets at once.

Ingersoll, Andrew P.↗

NASA NTRS2011

Hydrological Variations in Australia Recovered by GRACE High-Resolution Mascons Solutions

Australia represents a challenging region in which to study hydrological variations as recovered by the GRACE (Gravity Recovery And Climate Experiment) mission data. Much of Australia is characterized by relatively small hydrological signals, with large precipitation gradients between the North and the South. These signals are better recovered using innovative GRACE processing techniques such as high-resolution mascon solutions, which may help overcome the deficiencies in the standard GRACE data processing and filtering methods. We will show the power of using regional and global mas con solutions to recover hydrological variations from 2003 to 2011, as well as the oceanic mass variations in the surrounding regions. We will compare the GRACE signals with state of the art hydrology and ocean general circulation models, precipitation, soil moisture and groundwater data sets. We especially emphasize the gravity signatures observed during the decadal drought in the Murray-Darling river basin and the early 2011 floods in North-Western Australia.

Carabajal, Claudia C.↗

NASA NTRS2012

Advances and Limitations of Atmospheric Boundary Layer Observations with GPS Occultation over Southeast Pacific Ocean

The typical atmospheric boundary layer (ABL) over the southeast (SE) Pacific Ocean is featured with a strong temperature inversion and a sharp moisture gradient across the ABL top. The strong moisture and temperature gradients result in a sharp refractivity gradient that can be precisely detected by the Global Positioning System (GPS) radio occultation (RO) measurements. In this paper, the Constellation Observing System for Meteorology, Ionosphere & Climate (COSMIC) GPS RO soundings, radiosondes and the high-resolution ECMWF analysis over the SE Pacific are analyzed. COSMIC RO is able to detect a wide range of ABL height variations (1-2 kilometer) as observed from the radiosondes. However, the ECMWF analysis systematically underestimates the ABL heights. The sharp refractivity gradient at the ABL top frequently exceeds the critical refraction (e.g., −157 N-unit per kilometer) and becomes the so-called ducting condition, which results in a systematic RO refractivity bias (or called N-bias) inside the ABL. Simulation study based on radiosonde profiles reveals the magnitudes of the N-biases are vertical resolution dependent. The N-bias is also the primary cause of the systematically smaller refractivity gradient (rarely exceeding −110 N-unit per kilometer) at the ABL top from RO measurement. However, the N-bias seems not affect the ABL height detection. Instead, the very large RO bending angle and the sharp refractivity gradient due to ducting allow reliable detection of the ABL height from GPS RO. The seasonal mean climatology of ABL heights derived from a nine-month composite of COSMIC RO soundings over the SE Pacific reveals significant differences from the ECMWF analysis. Both show an increase of ABL height from the shallow stratocumulus near the coast to a much higher trade wind inversion further off the coast. However, COSMIC RO shows an overall deeper ABL and reveals different locations of the minimum and maximum ABL heights as compared to the ECMWF analysis. At low latitudes, despite the decreasing number of COSMIC RO soundings and the lower percentage of soundings that penetrate into the lowest 500-m above the mean-sea-level, there are small sampling errors in the mean ABL height climatology. The difference of ABL height climatology between COSMIC RO and ECMWF analysis over SE Pacific is significant and requires further studies.

COSMIC RO↗

NASA NTRS1993

The role of the geothermal gradient in the emplacement and replenishment of ground ice on Mars

Knowledge of the mechanisms by which ground ice is emplaced, removed, and potentially replenished, are critical to understanding the climatic and hydrologic behavior of water on Mars, as well as the morphologic evolution of its surface. Because of the strong temperature dependence of the saturated vapor pressure of H2O, the atmospheric emplacement or replenishment of ground ice is prohibited below the depth at which crustal temperatures begin to monotonically increase due to geothermal heating. In contrast, the emplacement and replenishment of ground ice from reservoirs of H2O residing deep within the crust can occur by at least three different thermally-driven processes, involving all three phases of water. In this regard, Clifford has discussed how the presence of a geothermal gradient as small as 15 K/km can give rise to a corresponding vapor pressure gradient sufficient to drive the vertical transport of 1 km of water from a reservoir of ground water at depth to the base of the cryosphere every 10(exp 6) - 10(exp 7) years. This abstract expands on this earlier treatment by considering the influence of thermal gradients on the transport of H2O at temperatures below the freezing point.

Clifford, Stephen M.↗

DOE OSTI2022

Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning

Stream temperature (Ts) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate Ts predictions at different spatial and temporal scales can inform water management decisions that account for the effects of changing climate and extreme events. In particular, widespread predictions of Ts in unmonitored stream reaches can enable decision makers to be responsive to changes caused by unforeseen disturbances. In this study, we demonstrate the use of classical machine learning (ML) models, support vector regression and gradient boosted trees (XGBoost), for monthly Ts predictions in 78 pristine and human-impacted catchments of the Mid-Atlantic and Pacific Northwest hydrologic regions spanning different geologies, climate, and land use. The ML models were trained using long-term monitoring data from 1980–2020 for three scenarios: (1) temporal predictions at a single site, (2) temporal predictions for multiple sites within a region, and (3) spatiotemporal predictions in unmonitored basins (PUB). In the first two scenarios, the ML models predicted Ts with median root mean squared errors (RMSE) of 0.69–0.84 °C and 0.92–1.02 °C across different model types for the temporal predictions at single and multiple sites respectively. For the PUB scenario, we used a bootstrap aggregation approach using models trained with different subsets of data, for which an ensemble XGBoost implementation outperformed all other modeling configurations (median RMSE 0.62 °C).The ML models improved median monthly Ts estimates compared to baseline statistical multi-linear regression models by 15–48% depending on the site and scenario. Air temperature was found to be the primary driver of monthly Ts for all sites, with secondary influence of month of the year (seasonality) and solar radiation, while discharge was a significant predictor at only 10 sites. The predictive performance of the ML models was robust to configuration changes in model setup and inputs, but was influenced by the distance to the nearest dam with RMSE <1 °C at sites situated greater than 16 and 44 km from a dam for the temporal single site and regional scenarios, and over 1.4 km from a dam for the PUB scenario. Our results show that classical ML models with solely meteorological inputs can be used for spatial and temporal predictions of monthly Ts in pristine and managed basins with reasonable (<1 °C) accuracy for most locations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2012

Critical Reflectance Derived from MODIS: Application for the Retrieval of Aerosol Absorption over Desert Regions

Aerosols are tiny suspended particles in the atmosphere that scatter and absorb sunlight. Smoke particles are aerosols, as are sea salt, particulate pollution and airborne dust. When you look down at the earth from space sometimes you can see vast palls of whitish smoke or brownish dust being transported by winds. The reason that you can see these aerosols is because they are reflecting incoming sunlight back to the view in space. The reason for the difference in color between the different types of aerosol is that the particles arc also absorbing sunlight at different wavelengths. Dust appears brownish or reddish because it absorbs light in the blue wavelengths and scatters more reddish light to space, Knowing how much light is scattered versus how much is absorbed, and knowin~ that as a function of wavelength is essential to being able to quantify the role aerosols play in the energy balance of the earth and in climate change. It is not easy measuring the absorption properties of aerosols when they are suspended in the atmosphere. People have been doing this one substance at a time in the laboratory, but substances mix when they are in the atmosphere and the net absorption effect of all the particles in a column of air is a goal of remote sensing that has not yet been completely successful. In this paper we use a technique based on observing the point at which aerosols change from brightening the surface beneath to darkening it. If aerosols brighten a surface. they must scatter more light to space. If they darken the surface. they must be absorbing more. That cross over point is called the critical reflectance and in this paper we show that critical reflectance is a monotonic function of the intrinsic absorption properties of the particles. This parameter we call the single scattering albedo. We apply the technique to MODIS imagery over the Sahara and Sahel regions to retrieve the single scattering albedo in seven wavelengths, compare these retrievals to ground-based retrievals from AERONET instruments and compute error bars on each retrieval. The results show that we can retrieve single scattering albedo for pure dust to within +/-0.02 and mixtures of dust and smoke to within +/-0.03. No other space based instrument has achieved a retrieval of single scattering albedo that spans the spectrum from 0.47 microns to 2.13 microns and produces regional maps of aerosol absorption showing gradients and changes. Applied in a more operational fashion, such information will narrow uncertainties in estimating aerosol forcing on climate.

Wells, Kelley C.↗

DOE OSTI2024

Detecting ecological signatures of long-term human activity across an elevational gradient in the Šumava Mountains, Central Europe

Central European mountains, including the Šumava Mountains located along the Czechia/Germany border, have a long and rich anthropogenic history. Yet, documenting prehistoric human impact in Central European mountain environments remains a challenge because of the need to disentangle climate and human-caused responses in terrestrial systems. Here, we present the first reconstructed water table depths (WTDs) from two sites, Pěkná and Blatenská slať, located in the Šumava Mountains. We compare these local WTD records with new and published pollen, non-pollen palynomorphs (NPPs), plant macrofossils, geochemistry and archeological records to investigate how changes in local hydrology and human activities impacted forest succession and fire activity throughout the Holocene across an elevational gradient. Using a generalized additive model, our results suggest that changes in forest succession and fire activity have been primarily caused by climate throughout the Holocene. However, humans have been utilizing mountain environments and their resources continuously since ∼4600 cal yr BP, thus playing a secondary role in modifying forest succession to increase resources beneficial to both humans and grazers. Over the last 1000 years, we provide evidence of directly observed human-caused modifications to the landscape. These results contribute to a growing body of literature illustrating human activities and landscape modifications in Central European mountains.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Impacts of climate change on estuarine stratification and implications for hypoxia within a shallow subtropical system

Vertical density stratification often plays an important role in the formation and expansion of coastal hypoxic zones through its effect on near-bed circulation and vertical oxygen flux. However, the impact of future climate change on estuarine circulation is widely unknown. Here, we developed and calibrated a three-dimensional hydrodynamic model for Pensacola Bay, a shallow subtropical estuary in the northeastern Gulf of Mexico. Model simulations based on years 2013–2017 were applied to examine changes in salinity, temperature, and density under future climate scenarios, including increased radiative forcing (IR) and temperature (T), increased freshwater discharge (D), sea level rise (SLR), and wind intensification (W). Simulations showed that the impacts of climate change on modeled state variables varied over time with external forcing conditions. The model demonstrated the potential for sea level rise and increased freshwater discharge to episodically increase vertical density gradients in the Bay. However, increased wind forcing destabilized vertical gradients, at times reducing the spatial extent and duration of stable stratification. For time periods with low freshwater discharge, moderate increases in wind speed (10%) can destabilize density gradients strengthened by increased discharge (10%) and sea level rise (0.48 m). In contrast, destruction of strong density gradients that form near the mid-Bay channel following flood events requires stronger wind forcing. Furthermore, these results highlight the importance of considering natural variability in freshwater and wind forcing, as well as local phenomena that are generally unresolved by global climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

In Situ Non-Destructive Temporal Measurements of the Rhizosphere Microbiome ‘Hot-Spots’ Using Metaproteomics

Rhizosphere arguably embodies the most diverse microbial ecosystem on the planet, yet it is largely a functional ‘black box’ of belowground plant-microbiome interactions. The rhizosphere is the primary site of entry for subsurface injection of fixed carbon (C) into soil with impacts on local to global scale C biogeochemistry and ultimately Earth’s climate. While spatial organization of rhizosphere is central to its function, small scale and steep microbial and geochemical gradients within this dynamic region make it easily disrupted by sampling. The significant challenge presented by sampling blocks elucidation of discreet functions, drivers, and interactions within rhizosphere ecosystems. Here, we describe a non-destructive sampling method linked to metaproteomic analysis in order to measure temporal shifts in the microbial composition and function of rhizosphere. A robust, non-destructive method of sampling microbial hotspots within rhizosphere provides an unperturbed window into the elusive functional interactome of this system over time and space.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2014

Quantification of Aerosol Type, and Sources of Aerosols Over the Indo- Gangetic Plain

Differences and similarities in aerosol characteristics, for the first time, over two environmentally distinct locations in Indo-Gangetic plain (IGP) e Kanpur (KPR) (urban location) and Gandhi College (GC) (rural site) are examined. Aerosol optical depths (AODs) exhibit pronounced seasonal variability with higher values during winter and premonsoon. Aerosol fine mode fraction (FMF) and Ångstrom exponent (a) are higher over GC than KPR indicating relatively higher fine mode aerosol concentration over GC. Higher FMF over GC is attributed to local biomass burning activities. Analysis of AOD spectra revealed that aerosol size distribution is dominated by wide range of fine mode fractions or mixture of modes during winter and postmonsoon, while during premonsoon and monsoon coarse mode aerosols are more abundant. Single scattering albedo (SSA) is lower over GC than KPR. SSA spectra reveals the abundance of fine mode (coarse mode) absorbing (scattering) aerosols during winter and postmonsoon (premonsoon and monsoon). Spectral SSA features reveal that OC contribution to enhanced absorption is negligible. Analysis shows that absorbing aerosols can be classified as Mostly Black Carbon (BC), and Mixed BC and Dust over IGP. Mixed BC and dust is always higher over KPR, while Mostly BC is higher over GC throughout the year. The amount of long range transported dust exhibits a gradient between KPR (higher) and GC (lower). Results on seasonally varying aerosol types, and absorbing aerosol types and their gradients over an aerosol hotspot are important to tune models and to reduce the uncertainty in radiative and climate impact of aerosols.

Organic Carbon↗

NASA NTRS2011

A Record-High Ocean Bottom Pressure in the South Pacific Observed by GRACE

In late 2009 to early 2010, the Gravity Recovery and Climate Experiment (GRACE) satellite pair observed a record increase in ocean bottom pressure (OBP) over a large mid-latitude region of the South East Pacific. Its magnitude is substantially larger than other oceanic events in the Southern Hemisphere found in the entire GRACE data records (2003-2010) on multi-month time scales. The OBP data help to understand the nature of a similar signal in sea surface height (SSH) anomaly observed by altimetry: the SSH increase is mainly due to mass convergence. Analysis of the barotropic vorticity equation using scatterometer data, atmospheric reanalysis product, and GRACE and altimeter an atmospheric reanalysis product observations suggests that the observed OBP/SSH signal was primarily caused by wind stress curl associated with a strong and persistent anticyclone in late 2009 in combination with effects of planetary vorticity gradient, bottom topography, and friction

Gravity Recovery and Climate Experiment (GRACE)↗

DOE OSTI2024

Soil microenvironmental variation drives below‐ground trait variation and interacts with macroclimate to structure above‐ground trait variation of arctic shrubs

Abstract Intraspecific trait variation can influence plant performance in different environments and may thereby determine the ability of individual plants to respond to climate change. However, our understanding of its patterns and environmental drivers across different spatial scales is incomplete, especially in understudied regions like the Arctic. To fill this knowledge gap, we examined above‐ground and below‐ground traits from three shrub taxa expanding across the tundra biome and evaluated their relationships with multiple microenvironmental and macroclimatic factors. The traits reflected plant size and structure (plant height, leaf area and root to shoot ratio), leaf economics (specific leaf area, nitrogen content), and root economics and collaboration with mycorrhizal fungi (specific root length, root tissue density, nitrogen content, and ectomycorrhizal colonisation intensity). We also measured leaf and root δ 15 N and leaf δ 13 C to characterise nitrogen source and acquisition pathways and plant water stress. Traits were measured in replicated plots ( N = 135) varying in soil microclimate, thaw depth and organic layer thickness established across five sites spanning a macroclimate gradient in northern Alaska. This hierarchical design allowed us to disentangle the independent and combined effects of fine‐scale and broad‐scale factors on intraspecific trait variation. We found substantial intraspecific variation at fine spatial scales for most traits and less variation along the macroclimate gradient and between shrub taxa. Consistent with these patterns, microenvironmental factors, mainly soil moisture and thaw depth, interacted with macroclimate, mainly climatic water deficit, to structure size‐structural and leaf trait variation. In contrast, most root traits responded additively to thaw depth and macroclimate. Synthesis . Our results demonstrate that above‐ground and below‐ground tundra shrub traits respond differently to microenvironmental and macroclimatic variation. These differing responses contribute to substantial trait variation at fine spatial scales and may decouple above‐ground and below‐ground trait responses to climate change.

Environmental Sciences & Ecology↗

DOE OSTI2025

Soil moisture gradients strengthen mesoscale convective systems by increasing wind shear

Mesoscale convective systems are a class of storm linked to extensive flooding and other destructive hazards in many regions globally. In West Africa, soil moisture impacts provide a valuable source of predictability for mature storm hazards, but little is known about mature storm sensitivity to soil moisture in other climatic regions. Here we use a storm track dataset, satellite observations and reanalysis fields to investigate the response of mature storms to soil moisture in seven global storm hotspots—West Africa, India, South America, South Africa, Australia and the United States Great Plains. We demonstrate that mesoscale soil moisture gradients (~500 km) can enhance storms by driving increased vertical wind shear conditions, a crucial ingredient for storm organization, through the strengthening of atmospheric temperature gradients. This is evidenced by a 10–30% increase in precipitation feature size and rainfall for the largest storms on days with favourable soil moisture gradients compared with unfavourable gradients. Global simulations confirm that soil moisture gradients influence wind shear. The results demonstrate the importance of soil moisture feedbacks for accurate forecasting of mesoscale convective systems and future projections of extreme events under climate change.

54 ENVIRONMENTAL SCIENCES↗