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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau

With the highest albedo of the land surface, snow plays a vital role in Earth's surface energy budget and water cycle. Snow albedo is primarily controlled by snow grain properties (e.g., size and shape) and light-absorbing particles (LAPs) such as black carbon (BC) and dust. The mixing state of LAPs in snow also has impacts on LAP-induced snow albedo reduction and surface radiative forcing (RF). However, most land surface models assume that snow grain shape is spherical and LAPs are externally mixed with the snow grains. This study improves the snow radiative transfer model in the Energy Exascale Earth System Model version 2.0 (E3SM v2.0) Land Model (ELM v2.0) by considering non-spherical snow grain shapes (i.e., spheroid, hexagonal plate, and Koch snowflake) and internal mixing of dust–snow, and it systematically evaluates the impacts on the surface energy budget and water cycle over the Tibetan Plateau (TP). A series of ELM simulations with different treatments of snow grain shape, mixing state of BC–snow and dust–snow, and sub-grid topographic effects (TOP) on solar radiation are performed. Compared with two remote sensing snow products derived from the Moderate Resolution Imaging Spectroradiometer, the control ELM simulation (ELM_Control) with the default configurations of spherical snow grain shape, internal mixing of BC–snow, external mixing of dust–snow, and without TOP as well as the ELM simulation with new model features (ELM_New) can both capture the overall snow distribution reasonably. Additionally, ELM_New overall shows smaller biases in snow cover fraction than ELM_Control in spring when snowmelt is important for water management. The estimated LAP-induced RF in ELM_New ranges from 0 to 19.3 W m -2 with the area-weighted average value of 1.5 W m -2 that is comparable to the reported values in existing studies. The Koch snowflake shape, among other non-spherical shapes, shows the largest difference from the spherical shape in spring when snow processes related to the surface energy budget and water cycle have high importance. The impacts of the mixing state of LAP in snow are smaller than the shape effects and depend on snow grain shape. Compared to external mixing, internal mixing of LAP–snow can lead to larger snow albedo reduction and snowmelt, which further affect the surface energy budget and water cycle. The individual contributions of non-spherical snow shape, mixing state of LAP–snow, and local topography impacts on the snow and surface fluxes have different signs and magnitudes, and their combined effects may be negative or positive due to complex and nonlinear interactions among the factors. Overall, the changes in net solar radiation in spring due to individual and combined effects range from -28.6 to 16.9 W m -2 and -29.7 to 12.2 W m -2 , respectively. This study advances understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offers guidance for improving snow simulations and RF estimates in Earth system models under climate change.

58 GEOSCIENCES↗
DOE OSTI2025

Vibrational sum frequency generation spectroscopy reveals the inertness of chromium oxide (001) surfaces

Nanoengineered metal oxides such as Cr(III)-oxide (chromia) films have diverse potential applications in corrosion inhibition, remediation, energy generation, catalysis, data storage, and biological and environmental systems. Concerns about material degradation or oxidation to toxic chromate necessitate an understanding of chromia/aqueous interfaces, beginning with their hydroxylation and hydration behavior. Vibrational sum-frequency generation spectroscopy (vSFG) provides specific molecular-level information about water at the oxide/aqueous junction with high surface selectivity. To overcome the strong absorber problem typical of certain metal oxides in the UV–visible range during nonlinear optical studies, we employed molecular beam epitaxy to deposit transparent, epitaxial nanofilms of Cr 2 O 3 with (001) crystalline orientation on sapphire (Al 2 O 3 (001)) substrates, as confirmed by atomic force microscopy and X-ray diffraction. vSFG spectra of the air and water interfaces of the Cr 2 O 3 (001) films reveal hydroxyl features corresponding to both dissociated and molecular water on the surface. In contrast to the dangling hydroxyls found on the bare Al 2 O 3 (001) substrate, the hydroxyl groups on the deposited Cr 2 O 3 (001) nanofilm do not readily undergo isotopic H/D exchange when exposed to varying forms of D 2 O under ambient conditions. When considering the chemistries of the corresponding trivalent cations in aqueous solution, the finding is at variance with their similar acidities and proton exchange dynamics but consistent with markedly slower inner-sphere water ligand exchange of hexaquo Cr 3+ . This finding challenges the idea that proton and water exchange at the water/Cr₂O₃(001) interface are solely correlated, driven by the strength of metal-bridging oxygen bonds and surface hydroxyl distribution, without direct causation.

Boamah-Agyemang, Mavis D. [Pacific Northwest Natio↗
DOE OSTI2022

Equatorial Origin of the Observed Tropical Pacific Quasi-Decadal Variability From ENSO Nonlinearity

Quasi-decadal (QD) climate variability is detected in the tropical Pacific based on the recent 70 years of observations. This QD variability is identified in equatorial sea surface temperatures (SSTs), the pattern of which resembles the Central Pacific (CP) El Niño-Southern Oscillation (ENSO) but extends further meridionally to the northeastern subtropical Pacific. Whereas equatorial upper-ocean heat content and SSTs are in quadrature for ENSO, these two quantities are almost in phase on the QD timescale. Further analysis shows that nonlinear dynamical heating, primarily originating from strong El Niño events, tends to lead QD SSTs by a quarter of its dominant period (approximately 30 months) and shapes the dominant QD periodicity in observations. Here, our results suggest that the observed QD variability largely originates from ENSO nonlinearity and thus is fundamentally different from ENSO's oscillatory nature.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning

Knowledge of the physical properties of ionic liquids (ILs), such as the surface tension and speed of sound, is important for both industrial and research applications. Unfortunately, technical challenges and costs limit exhaustive experimental screening efforts of ILs for these critical properties. Previous work has demonstrated that the use of quantum-mechanics-based thermochemical property prediction tools, such as the conductor-like screening model for real solvents, when combined with machine learning (ML) approaches, may provide an alternative pathway to guide the rapid screening and design of ILs for desired physiochemical properties. However, the question of which machine-learning approaches are most appropriate remains. In the present study, we examine how different ML architectures, ranging from tree-based approaches to feed-forward artificial neural networks, perform in generating nonlinear multivariate quantitative structure–property relationship models for the prediction of the temperature- and pressure-dependent surface tension of and speed of sound in ILs over a wide range of surface tensions (16.9–76.2 mN/m) and speeds of sound (1009.7–1992 m/s). The ML models are further interrogated using the powerful interpretation method, shapley additive explanations. We find that several different ML models provide high accuracy, according to traditional statistical metrics. The decision tree-based approaches appear to be the most accurate and precise, with extreme gradient-boosting trees and gradient-boosting trees being the best performers. However, our results also indicate that the promise of using machine-learning to gain deep insights into the underlying physics driving structure–property relationships in ILs may still be somewhat premature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Hierarchical Testing of a Hybrid Machine Learning‐Physics Global Atmosphere Model

Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predictions. While prior studies have assessed their fidelity in capturing synoptic-scale atmospheric dynamics, their performance across timescales and under out-of-distribution forcing, such as +3K or +4K uniform-warming forcings, and the sources of biases remain elusive, to establish the model's reliability for Earth science. Here, we design three sets of experiments targeting synoptic-scale phenomena, interannual variability, and out-of-distribution uniform-warming forcings. We evaluate the Neural General Circulation Model (NeuralGCM), a hybrid model integrating a dynamical core with ML-based component, against observations and physics-based Earth system models (ESMs). At the synoptic scale, NeuralGCM captures the evolution and propagation of extratropical cyclones with performance comparable to ESMs. At the interannual scale, when forced by El Niño-Southern Oscillation sea surface temperature (SST) anomalies, NeuralGCM successfully reproduces associated teleconnection patterns but exhibits deficiencies in capturing nonlinear response. Under out-of-distribution uniform-warming forcings, NeuralGCM simulates similar responses in global-average temperature and precipitation and reproduces large-scale tropospheric circulation features similar to those in ESMs. Notable weaknesses include overestimating the tracks and spatial extent of extratropical cyclones, biases in the teleconnected wave train triggered by tropical SST anomalies, and differences in upper-level warming and stratospheric circulation responses to SST warming compared to physics-based ESMs. The causes of these weaknesses were explored. Despite the noted weaknesses, NeuralGCM reproduces responses across experiments reasonably and performs comparably to ESMs. By integrating a dynamical core with ML, NeuralGCM shows potential for developing ML-based ESMs.

global warming↗
DOE OSTI2021

ENSO Dynamics in the E3SM-1-0, CESM2, and GFDL-CM4 Climate Models

We report that this study examines historical simulations of ENSO in the E3SM-1-0, CESM2, and GFDL-CM4 climate models, provided by three leading U.S. modeling centers as part of the Coupled Model Intercomparison Project phase 6 (CMIP6). These new models have made substantial progress in simulating ENSO’s key features, including: amplitude; timescale; spatial patterns; phase-locking; spring persistence barrier; and recharge oscillator dynamics. However, some important features of ENSO are still a challenge to simulate. In the central and eastern equatorial Pacific, the models’ weaker-than-observed subsurface zonal current anomalies and zonal temperature gradient anomalies serve to weaken the nonlinear zonal advection of subsurface temperatures, leading to insufficient warm/cold asymmetry of ENSO’s sea surface temperature anomalies (SSTA). In the western equatorial Pacific, the models’ excessive simulated zonal SST gradients amplify their zonal temperature advection, causing their SSTA to extend farther west than observed. The models underestimate both ENSO’s positive dynamic feedbacks (due to insufficient zonal wind stress responses to SSTA) and its thermodynamic damping (due to insufficient convective cloud shading of eastern Pacific SSTA during warm events); compensation between these biases leads to realistic linear growth rates for ENSO, but for somewhat unrealistic reasons. The models also exhibit stronger-than-observed feedbacks onto eastern equatorial Pacific SSTAs from thermocline depth anomalies, which accelerates the transitions between events and shortens the simulated ENSO period relative to observations. Implications for diagnosing and simulating ENSO in climate models are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Spectral interferometry-based microwave-frequency vibrometry for integrated acoustic wave devices

Microwave phononics is a promising platform for sensing, computing, and quantum information science; thus, sensitive and high-throughput characterization tools are needed not only for device verification and optimization but also for revealing transient and nonlinear dynamics. Existing interferometric optical vibrometers for 2D mapping are challenged by operating point stabilization, surface reflectivity contrast, and long acquisition time. Here, we use spectral interferometry, which is insensitive to these factors and utilizes a continuous raster scanning scheme for vibration mapping with high throughput. We intensity-modulate our broadband light source with an electro-optic modulator to resolve vibrations at microwave frequencies. Our system requires no fast photodetector or digitizer operating in the microwave frequency range. We image the 1 GHz vibration field of a 300 × 150 µm 2 area of an entire surface acoustic wave device in 10 min with simultaneous surface profilometry. Our system has a vibration sensitivity of 120 fm/sqrt(Hz) and a linear throughput of 0.77 mm/s on the chip surface. The technique offers capabilities for characterizing a wide range of acoustic wave and micromechanical devices to better understand their behavior and performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

The future of the El Niño–Southern Oscillation: using large ensembles to illuminate time-varying responses and inter-model differences

Future changes in the El Niño–Southern Oscillation (ENSO) are uncertain, both because future projections differ between climate models and because the large internal variability of ENSO clouds the diagnosis of forced changes in observations and individual climate model simulations. By leveraging 14 single model initial-condition large ensembles (SMILEs), we robustly isolate the time-evolving response of ENSO sea surface temperature (SST) variability to anthropogenic forcing from internal variability in each SMILE. We find nonlinear changes in time in many models and considerable inter-model differences in projected changes in ENSO and the mean-state tropical Pacific zonal SST gradient. We demonstrate a linear relationship between the change in ENSO SST variability and the tropical Pacific zonal SST gradient, although forced changes in the tropical Pacific SST gradient often occur later in the 21st century than changes in ENSO SST variability, which can lead to departures from the linear relationship. Single-forcing SMILEs show a potential contribution of anthropogenic forcing (aerosols and greenhouse gases) to historical changes in ENSO SST variability, while the observed historical strengthening of the tropical Pacific SST gradient sits on the edge of the model spread for those models for which single-forcing SMILEs are available. Our results highlight the value of SMILEs for investigating time-dependent forced responses and inter-model differences in ENSO projections. The nonlinear changes in ENSO SST variability found in many models demonstrate the importance of characterizing this time-dependent behavior, as it implies that ENSO impacts may vary dramatically throughout the 21st century.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Ion Pairing Mediates Molecular Organization Across Liquid/Liquid Interfaces

Liquid/liquid interfaces play a central role in scientific fields ranging from nanomaterial synthesis and soft matter electronics to nuclear waste remediation and chemical separations. This diversity of functions arises from an interface’s ability to respond to changing conditions in its neighboring bulk phases. Understanding what drives this interfacial flexibility can provide novel avenues for designing new functional interfaces. However, limiting this progress is an inadequate understanding of the subtle intermolecular and interphase interactions taking place at the molecular level. Here, we use surface-specific vibrational sum frequency generation spectroscopy combined with atomistic molecular dynamics simulations to investigate the self-assembly and structure of model ionic oligomers consisting of an oligodimethylsiloxane (ODMS) tail covalently attached to a positively charged methyl imidazolium (MIM + ) head group at buried oil/aqueous interfaces. We show how the presence of seemingly innocuous salts can impart dramatic changes to the ODMS tail conformations in the oil phase via specific ion effects and ion-pairing interactions taking place in the aqueous phase. These specific ion interactions are shown to drive enhanced amphiphile adsorption, induce morphological changes, and disrupt emergent hydrogen-bonding structures at the interface. Tuning these interactions allows for independent control over the oligomer structure in the oil phase versus interfacial population changes and represents key mechanistic insight that is needed to control chemical reactions at liquid/liquid interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Spatially co-registered wide-field nonlinear optical imaging of living and complex biosystems in a total internal reflection geometry

Nonlinear optical microscopy that leverages an objective based total internal reflection (TIR) excitation scheme is an attractive means for rapid, wide-field imaging with enhanced surface sensitivity. Through select combinations of distinct modalities, one can, in principle, access complementary chemical and structural information for various chemical species near interfaces. Here, we report a successful implementation of such a wide-field nonlinear optical microscope system, which combines coherent anti-Stokes Raman scattering (CARS), two-photon fluorescence (TPF), second harmonic generation (SHG), and sum frequency generation (SFG) modalities on the same platform. The intense optical fields needed to drive these high order nonlinear optical processes are achieved through the use of femtosecond pulsed light in combination with the intrinsic field confinement induced by TIR over a large field of view. The performance of our multimodal microscope was first assessed through the experimental determination of its chemical fidelity, intensity and polarization dependences, and spatial resolution using a set of well-defined model systems. Subsequently, its unique capabilities were validated through imaging complex biological systems, including Hydrangea quercifolia pollen grains and Pantoea sp. YR343 bacterial cells. Specifically, the spatial distribution of different molecular groups in the former was visualized via vibrational contrast mechanisms of CARS, whereas co-registered TPF imaging allowed the identification of spatially localized intrinsic fluorophores. We further demonstrate the feasibility of our microscope for wide-field CARS imaging on live cells through independent characterization of cell viability using spatially co-registered TPF imaging. Overall, this approach to TIR enabled wide-field imaging is expected to provide new insights into bacterial strains and their interactions with other species in the rhizosphere in a time-resolved and chemically selective manner.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Dynamically tunable second-harmonic generation using hybrid nanostructures incorporating phase-change chalcogenides

Nonlinear metasurfaces with high conversion efficiencies have been vastly investigated. However, strong dynamic tunability of such devices is limited in conventional passive plasmonic and dielectric material platforms. Germanium antimony telluride (GST) is a promising phase-change chalcogenide for the reconfiguration of metamaterials due to strong nonvolatile changes of the real and imaginary parts of the refraction index through amorphous-crystalline phase change. The orderly structured GST has an even higher potential in tunable second-harmonic generation (SHG) with a non-centrosymmetric crystal structure at the crystalline phase, while the amorphous phase of GST does not exhibit bulk second-order nonlinearity. Here, we experimentally demonstrate SHG switches by actively controlling the crystalline phase of GST for a GST-based hybrid metasurface featuring a gap-surface plasmon resonance, and a quarter-wave asymmetric Fabry–Perot (F–P) cavity incorporating GST. We obtain SHG switches with modulation depths as high as ~ 20 dB for the wavelengths at the on-state resonance. We also demonstrate the feasibility of multi-level SHG modulation by leveraging three controlled GST phases, i.e., amorphous, semi-crystalline, and crystalline, for the gap-surface plasmon hybrid device, which features stronger light–matter interaction and has higher resonant SHG efficiencies than the asymmetric F–P cavity device at respective GST phases. This research reveals that GST-based dynamic SHG switches can be potentially employed in practical applications, such as microscopy, optical communication, and photonic computing in the nonlinear regime.

42 ENGINEERING↗
DOE OSTI2021

The Spiral Galaxies Flat Rotational Velocity Curve Explained by the Constant Group Velocity of a Nonlinear Density Wave

The rotation velocity curves of stars in galaxies, the motions of pairs of galaxies, and the behavior of galaxies in clusters and super-clusters all indicate that there is a lack of mass on different scales in the universe. In this paper, we derive the expression for rotational velocity using the nonlinear density wave theory considering only stellar components and we show that such theory can support the observed flat rotational velocity curve due to the main property of the soliton wave, which is a constant group velocity of the wave. The surface mass density (SMD) function, used to derive gravitational potential gradient and rotational velocity, is not assumed but rather derived as a solution of the nonlinear Srödinger equation, on the contrary to the widely used, in the literature, exponential disk approximation. Three parameters relevant to the curve shape are the intensities of equilibrium SMD, the amplitude of the wave, and total angular velocity or differential rotation, equivalently. Since the shape of the rotational velocity is highly sensitive to the mentioned parameters, this theory eventually provides a method for a very accurate estimation of galaxy mass and angular velocity as well.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Surface-sensitive photon avalanche behavior revealed by single-avalanching-nanoparticle imaging

Avalanching nanoparticles (ANPs) are a new class of lanthanide-based upconverting material demonstrating steep optical nonlinearities with the potential to advance applications ranging from subwavelength bioimaging to neuromorphic computing, nanothermometry, and pressure transduction. Here, we use single-nanocrystal imaging to uncover design-dependent heterogeneity in ANP threshold intensity (I th ). Quantitative comparisons between distributions of I th and ANP shell properties reveal correlations between mean I th values, histogram widths, and nanocrystal shell thickness. Evaluating avalanching behaviors using an established model of shell-dependent surface energy transfer shows that variations in shell thickness-and the resultant energy transfer through the shell to the surface and environment-are likely the primary contributor to ANP-to-ANP I th heterogeneity. Further, nanocrystals with an ~6 nm average shell thickness show I th heterogeneity beyond the extent expected from statistical measurements of shell size and variability using transmission electron microscopy (TEM). Here these results provide a principal guide for the design and application of ANPs to environmental sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology

Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlinear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)-based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial regression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.

42 ENGINEERING↗
DOE OSTI2020

Nonlinear spectroscopy in the condensed phase: The role of Duschinsky rotations and third order cumulant contributions

First-principles modeling of nonlinear optical spectra in the condensed phase is highly challenging because both environment and vibronic interactions can play a large role in determining spectral shapes and excited state dynamics. In this work, we compute two dimensional electronic spectroscopy (2DES) signals based on a cumulant expansion of the energy gap fluctuation operator, with specific focus on analyzing mode mixing effects introduced by the Duschinsky rotation and the role of the third order term in the cumulant expansion for both model and realistic condensed phase systems. We show that for a harmonic model system, the third order cumulant correction captures effects introduced by a mismatch in curvatures of ground and excited state potential energy surfaces, as well as effects of mode mixing. We also demonstrate that 2DES signals can be accurately reconstructed from purely classical correlation functions using quantum correction factors. We then compute nonlinear optical spectra for the Nile red and methylene blue chromophores in solution, assessing the third order cumulant contribution for realistic systems. We show that the third order cumulant correction is strongly dependent on the treatment of the solvent environment, revealing the interplay between environmental polarization and the electronic-vibrational coupling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A robust framework for frictional fault contact in geological formations using a stabilized augmented Lagrangian approach

Numerical simulations are essential to evaluate the performance and safety of engineered subsurface systems such as geological carbon storage sites, enhanced geothermal fields, and oil and gas reservoirs. A key challenge lies in accurately modeling the frictional contact behavior along fault surfaces. This problem involves inequality constraints that arise from the physics of frictional slip, requiring specialized numerical methods to handle the resulting highly nonlinear and path-dependent behavior. Here, in this work, we address this challenge using an Augmented Lagrangian Method (ALM) implemented via the Uzawa algorithm. The formulation employs mixed finite element spaces, combining low-order piecewise linear displacements within the 3D domain cells with piecewise constant tractions defined on the fault surfaces. Furthermore, to ensure stability and satisfy the inf-sup condition, the discrete displacement space is enriched with face bubble functions on both sides of the contact interfaces. This approach offers several advantages over other stabilization techniques that rely on additional terms, and it integrates naturally in the Uzawa framework.

58 GEOSCIENCES↗
DOE OSTI2025

Saturation and decay of an induced scattering instability driven by static field asymmetries in a non-neutral plasma

Experimental data on the saturation and decay of a previously described induced scattering instability are presented. The instability is driven by static background field asymmetries and initially produces a single growing mode (ω 1 , k 1 ). As the instability develops, several harmonically related (ω n =nω 1 , k n =nk 1 ) eigenmodes of the plasma column grow up, resulting in a spatially localized moving potential perturbation with δϕ/T ≈ 20%. After a period of rapid growth, the mode amplitudes saturate and vary slowly for roughly 10 5 ω 1 −1 . This quasi-equilibrium ends with an unusual change in the eigenmode phases, wherein δϕ changes from positive to negative. Plasma heating during this period increases the damping of all modes and rapidly quenches the instability. This instability is of particular interest because the coupling with the field asymmetry produces an enhanced level of radial transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Land surface dynamics and meteorological forcings modulate land surface temperature characteristics

This study examines the effect of land cover, vegetation health, climatic forcings, elevation heat loads, and terrain characteristics (LVCET) on land surface temperature (LST) distribution over West Africa (WA). We employ fourteen machine-learning models, which preserve nonlinear relationships, to downscale LST and other predictands while preserving the geographical variability of WA. Our results showed that the random forest model performs best in downscaling predictands. This is important for the sub-region since it has limited access to mainframes to power multiplex machine-learning algorithms. In contrast to the northern regions, the southern regions consistently exhibit healthy vegetation. Also, areas with unhealthy vegetation coincide with hot LST clusters. The positive Normalized Difference Vegetation Index (NDVI) trends in the Sahel underscore rainfall recovery and subsequent Sahelian greening. The southwesterly winds cause the upwelling of cold waters, lowering LST in southern WA and highlighting the cooling influence of water bodies on LST. Identifying regions with elevated LST is paramount for prioritizing greening initiatives, and our study underscores the importance of considering LVCET factors in urban planning. Topographic slope-facing angles, heat loads, and diurnal anisotropic heat all contribute to variations in LST, emphasizing the need for a holistic approach when designing resilient and sustainable landscapes.

54 ENVIRONMENTAL SCIENCES↗