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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Simulated Radar Characteristics of LBA Convective Systems: Easterly and Westerly Regimes

The 3D Goddard Cumulus Ensemble (GCE) model was used to simulate convection that occurred during the TRMM LBA field experiment in Brazil. Convection in this region can be categorized into two different regimes. Low-level easterly flow results in moderate to high CAPE and a drier environment. Convection is more intense like that seen over continents. Low-level westerly flow results in low CAPE and a moist environment. Convection is weaker and more widespread characteristic of oceanic or monsoon-like systems. The GCE model has been used to study both regimes n order to provide cloud datasets that are representative of both environments in support of TRMM rainfall and heating algorithm development. Two different cases are analyzed: Jan 26, 1999, an eastely regime case, and Feb 23, 1999, a westerly regime case. The Jan 26 case is an organized squall line, while the Feb 23 case is less organized with only transient lines. Radar signatures, including CFADs, from the two simulated cases are compared to each other and with observations. The microphysical processes simulated in the model are also compared between the two cases.

Lang, Stephen E.↗
NASA NTRSDate not supplied

Improving the Representation of Land Surface Processes using the Data Assimilation Research Testbed (DART)

The land surface is a critical part of the earth system as processes related to water, carbon, energy and nitrogen cycling have important implications for climate forcing, air quality, water availability and seasonal atmospheric forecasting. Despite advances in land surface modeling, land surface model performance is often limited because of errors related to initial and boundary conditions, model structure, and parameters. Data assimilation (DA) techniques combined with an expanding network of earth system observations present an opportunity to reduce these errors and improve simulations. Here we apply an Ensemble Kalman Filter DA system as part of the Data Assimilation Research Testbed to a variety of land surface simulations. First, we describe the use of remotely sensed biomass observations to provide improved simulations of plant phenology, carbon and water cycling for regions highly sensitive to climate change (Western US, China, and Arctic). We discuss approaches to account for systemic biases between models and observations, including the use of spatially-varying adaptive ensemble inflation as an alternative approach to re-scaling soil moisture observations. Finally, we discuss a strategy to incorporate complementary observations (snow water equivalent, solar-induced fluorescence) to better constrain the representation of carbon and water cycling across complex terrain.

DART↗
NASA NTRS2016

The Effect of Future Ambient Air Pollution on Human Premature Mortality to 2100 Using Output from the ACCMIP Model Ensemble

Ambient air pollution from ground-level ozone and fine particulate matter (PM(sub 2.5)) is associated with premature mortality. Future concentrations of these air pollutants will be driven by natural and anthropogenic emissions and by climate change. Using anthropogenic and biomass burning emissions projected in the four Representative Concentration Pathway scenarios (RCPs), the ACCMIP ensemble of chemistry climate models simulated future concentrations of ozone and PM(sub 2.5) at selected decades between 2000 and 2100. We use output from the ACCMIP ensemble, together with projections of future population and baseline mortality rates, to quantify the human premature mortality impacts of future ambient air pollution. Future air-pollution-related premature mortality in 2030, 2050 and 2100 is estimated for each scenario and for each model using a health impact function based on changes in concentrations of ozone and PM(sub 2.5) relative to 2000 and projected future population and baseline mortality rates. Additionally, the global mortality burden of ozone and PM(sub 2.5) in 2000 and each future period is estimated relative to 1850 concentrations, using present-day and future population and baseline mortality rates. The change in future ozone concentrations relative to 2000 is associated with excess global premature mortality in some scenarios/periods, particularly in RCP8.5 in 2100 (316 thousand deaths per year), likely driven by the large increase in methane emissions and by the net effect of climate change projected in this scenario, but it leads to considerable avoided premature mortality for the three other RCPs. However, the global mortality burden of ozone markedly increases from 382000 (121000 to 728000) deaths per year in 2000 to between 1.09 and 2.36 million deaths per year in 2100, across RCPs, mostly due to the effect of increases in population and baseline mortality rates. PM(sub 2.5) concentrations decrease relative to 2000 in all scenarios, due to projected reductions in emissions, and are associated with avoided premature mortality, particularly in 2100: between 2.39 and 1.31 million deaths per year for the four RCPs. The global mortality burden of PM(sub 2.5) is estimated to decrease from 1.70 (1.30 to 2.10) million deaths per year in 2000 to between 0.95 and 1.55 million deaths per year in 2100 for the four RCPs due to the combined effect of decreases in PM(sub 2.5) concentrations and changes in population and baseline mortality rates. Trends in future air-pollution-related mortality vary regionally across scenarios, reflecting assumptions for economic growth and air pollution control specific to each RCP and region. Mortality estimates differ among chemistry climate models due to differences in simulated pollutant concentrations, which is the greatest contributor to overall mortality uncertainty for most cases assessed here, supporting the use of model ensembles to characterize uncertainty. Increases in exposed population and baseline mortality rates of respiratory diseases magnify the impact on premature mortality of changes in future air pollutant concentrations and explain why the future global mortality burden of air pollution can exceed the current burden, even where air pollutant concentrations decrease.

Silva, Raquel A.↗
NASA NTRS2022

Model Evaluation of Short-Lived Climate Forcers for the Arctic Monitoring and Assessment Programme: A Multi-Species, Multi-Model Study

While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over 4 years (2008-2009 and 2014-2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean (mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance. For the SLCFs with the greatest radiative impact (CH4, 03, BC, and SO(sup 2-)(sub 4)), the mmm was within ±25 % of the measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs. Of the SLCFs in our study, model biases were smallest for C"4 and greatest for OA. For most SLCFs, model biases skewed from positive to negative with increasing latitude. Our analysis suggests that vertical mixing, long-range transport, deposition, and wildfires remain highly uncertain processes. These processes need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. As model development proceeds in these areas, we highly recommend that the vertical and 3-D distribution of SLCFs be evaluated, as that information is critical to improving the uncertain processes in models.

Arctic Monitoring and Assessment Programme↗
NASA NTRS2012

The Influence of Internal Model Variability in GEOS-5 on Interhemispheric CO2 Exchange

An ensemble of eight atmospheric CO2 simulations was completed employing the National Aeronautics and Space Administration (NASA) Goddard Earth Observation System, Version 5 (GEOS-5) for the years 2000-2001, each with initial meteorological conditions corresponding to different days in January 2000 to examine internal model variability. Globally, the model runs show similar concentrations of CO2 for the two years, but in regions of high CO2 concentrations due to fossil fuel emissions, large differences among different model simulations appear. The phasing and amplitude of the CO2 cycle at Northern Hemisphere locations in all of the ensemble members is similar to that of surface observations. In several southern hemisphere locations, however, some of the GEOS-5 model CO2 cycles are out of phase by as much as four months, and large variations occur between the ensemble members. This result indicates that there is large sensitivity to transport in these regions. The differences vary by latitude-the most extreme differences in the Tropics and the least at the South Pole. Examples of these differences among the ensemble members with regard to CO2 uptake and respiration of the terrestrial biosphere and CO2 emissions due to fossil fuel emissions are shown at Cape Grim, Tasmania. Integration-based flow analysis of the atmospheric circulation in the model runs shows widely varying paths of flow into the Tasmania region among the models including sources from North America, South America, South Africa, South Asia and Indonesia. These results suggest that interhemispheric transport can be strongly influenced by internal model variability.

Allen, Melissa↗
NASA NTRS2021

The Climate Response to Emissions Reductions due to COVID-19: Initial Results from CovidMIP

Many nations responded to the COVID-19 pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO2, other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. Twelve models performed multiple initial-condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020-2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID- 19-related emission reductions on near-term climate.

Chris D. Jones↗
DOE OSTI2024

Reducing uncertainty of polar to midlatitude linkages using DOE’s E3SM in a coordinated model-experiment setting

This project brought DOE’s climate modeling effort with the Energy Exascale Earth System Model into the Polar Amplification Model Intercomparison Project (PAMIP), which is part of the sixth and latest Coupled Model Intercomparison Project, CMIP6. PAMIP examines the causes and consequences of polar amplification, when external forcing results in a larger temperature increase in high latitudes than the global average, in a coordinated set of model experiments with a common modeling protocol. Our teams from UC Irvine and the University of Toronto have designed, carried out, analyzed, and disseminated PAMIP output from the Energy Exascale Earth System Model (E3SM) and the Community Earth System Model (CESM). PAMIP’s ongoing stream of significant new results have advanced progress in the community’s understanding and led to new outstanding research questions that have motivated further work. PAMIP has led to improved consensus on the atmospheric response to sea ice loss. The important finding is that for a similar sea ice anomaly forcing, the simulated atmospheric response in the troposphere is remarkably consistent among the 16 models’ runs analyzed. The zonal-mean tropospheric response consists of a very robust equatorward shift of the westerly flow in mid-latitudes. However, while the multi-model mean response is robust, it has a weak amplitude relative to internal variability. We identified a weakness in the models (including E3SM) in terms of their response to sea-ice forcing that is related to eddy forcing (or nonlinear dynamical effects) at mid- to high latitudes. In fact, E3SM is an outlier in terms of the models participating, and in that sense, it turned out to be a vital participant model. We found that reductions in energy transport due sea-ice loss and involving dry air only are compensated by increases in moist energy transport from warmer sea surface temperature in midlatitudes. This leads to a large spread in energy transport into the Arctic and is a potential source of spread in Arctic amplification. We identified an important role that climate modes, including tropical modes of variability (El Nino and the Southern Oscillation (ENSO); the Quasi-biennial Oscillation (QBO)) play in the response to sea-ice anomalies, including in ocean coupled experiments. Similarly, we identified and quantified the contribution of sea-ice thickness to the atmospheric response compared to the response to sea-ice extent only. We found that it is important to run large ensembles and even with an ensemble size of 100 simulations the response is largely influenced by internal variability. We demonstrated convincingly that Ural blocking, not sea-ice loss, provides the weakening of the stratospheric polar vortex in fall/early winter and a negative phase of the North Atlantic Oscillation that can last for up to two months. However, sea-ice anomalies can influence the background flow so that the response to Ural blocking is more persistent under low sea-ice conditions in the Barents/Kara Sea than high sea ice Atmospheric model hierarchies that progressively add individual processes have a long history in providing dynamical insight for modeling the atmosphere. Similarly, coupled model hierarchies that progressively add individual ocean processes can provide insights into the workings of the coupled climate system, however such hierarchies have not been available except for a non-dynamical slab ocean model. Because of the missing processes, surface flux corrections must be added to produce a target climate. In this project, we managed to overcome this problem and develop a globally coupled ocean model hierarchy in CESM that can turn on and off the processes of mixed-layer entrainment and Ekman flow. We used the hierarchy to study the impact of Arctic sea-ice loss on the climate system. We find that the effect of mixed-layer entrainment on ocean heat uptake influences the atmospheric circulation by shifting the latitudinal positions of the mid-latitude westerly jet and the Intertropical Convergence Zone (ITCZ). In quadrupled CO 2 experiments, we studied how air-sea coupling affects the response of tropical rainfall under global warming. In order to identify the importance of individual ocean processes, we used the hierarchy of ocean models to separate the effects of seasonal mixed-layer entrainment, wind-driven Ekman flows, and frictional flows. We showed that including Ekman and frictional flows allows our simulation to produce the Pacific Ocean's enhanced equatorial warming pattern and equatorward ITCZ contraction noted in previous climate simulations. We also showed that the frictional flow, which has yet to receive much attention, is as important as the Ekman flow in generating equatorial heat convergence.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1997

An Application of the "Virtual Spacecraft" Concept in Evaluation of the Mars Pathfinder Lander Low Gain Antenna

The virtual spacecraft concept is embodied in a set of subsystems, either in the form of hardware or computational models, which together represent all, or a portion of, a spacecraft. For example, the telecommunications transponder may be a hardware prototype while the propulsion system may exist only as a simulation. As the various subsystems are realized in hardware, the spacecraft becomes progressively less virtual. This concept is enabled by JPL's Mission System Testbed which is a set of networked workstations running a message passing operating system called "TRAMEL" which stands for Task Remote Asynchronous Message Exchange Layer. Each simulation on the workstations, which may in fact be hardware controlled by the workstation, "publishes" its operating parameters on TRAMEL and other simulations requiring those parameters as input may "subscribe" to them. In this manner, the whole simulation operates as a single virtual system. This paper describes a simulation designed to evaluate a communications link between the earth and the Mars Pathfinder Lander module as it descends under a parachute through the Martian atmosphere toward the planet's surface. This link includes a transmitter and a low gain antenna on the spacecraft and a receiving antenna and receiver on the earth as well as a simulation of the dynamics of the spacecraft. The transmitter, the ground station antenna, the receiver and the dynamics are all simulated computationally while the spacecraft antenna is implemented in hardware on a very simple spacecraft mockup. The dynamics simulation is a record of one output of the ensemble of outputs of a Monte Carlo simulation of the descent. Additionally, the antenna/spacecraft mock-up system was simulated using APATCH, a shooting and bouncing ray code developed by Demaco, Inc. The antenna simulation, the antenna hardware, and the link simulation are all physically located in different facilities at JPL separated by several hundred meters and are linked via the local area network (LAN).

Pogorzelski, R. J.↗
DOE OSTI2024

Regional climate change: consensus, discrepancies, and ways forward

Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the “signal-to-noise paradox” and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Sensitivity Analysis of Drivers Water Shortage in the Los Angeles Region During Drought

The code and detailed step-by-step instructions for generating the model output data, processing results, and analysis and plotting are provided at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. The PyArtes model is a python adaptation of the Artes model. PyArtes uses many of the same input data and optimization model architecture as Artes. Documentation for the PyArtes model is provided in the Supplement to the paper. The primary data product are simulated monthly water shortages for indoor and outdoor demand under a large ensemble of drought scenarios (>13,000). The droughts are hypothetical and are not based on historical time series data of supply sources - though historical data did help inform ranges explored for supply parameters. Demands are informed by recent 2017-2021 water supply data. Demands used for the model can be accessed at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. Simulations resolve demand for over 90 water providers in the study region. The results report 36 months of water shortage data for each indoor and outdoor demand node. The study also developed a multilayer perceptron (MLP) neural network trained on a subset of the simulated shortage ensemble to emulate worst annual water shortage for a given set of parameter multipliers -- provided the parameter values fall within the ranges sampled in the ensemble. Emulated water shortages for synthetic ensembles are in the MLP-generated shortages folder. The MLP model was used to generate larger ensembles to support Sobol analysis that would have been extremely computationally expensive to simulate. Datasets provided in this repository*: Simulated shortages. These results are used for the analysis for Figures 5, 8, and 9 in the paper, and also to train the MLP emulator. .zip file containing outputs for the 13,312 scenario ensemble. Separate .csv files for indoor and outdoor shortage for each scenario. Rows = demand ids (~100), Columns = months (36) Units = acre-feet/month of shortage (shortage = monthly demand - supply). 1 acft = 1233.48 m^3 .csv files of aggregated shortages derived from the 13,312 ensemble Rows = scenarios (13,312), Columns = demand ids (~100) Units = acre-feet/year (either worst annual shortage or total shortage over the 3-year drought) .csv file of the parameter multipliers scenarios for the ensemble .csv file of the parameter ranges and baseline values the multipliers were applied to MLP-generated shortages. These results are used for Figures 4, 6, and 7 in the paper. mwd higher folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results Emulated shortages. Rows = scenarios, columns = demand ids, units acft Sobol results. Rows = demand ids, columns Sobol (S1, ST, or 95% confidence interval) value for each parameter mwd lower folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results same organization as mwd higher MLP performance: performance metrics (R^2, RMSE, BIAS, MAPE) for the testing subset (20% or 2,662 scenarios) and simulated vs emulated worst year shortage (acre-feet/year) for every demand node, MWD wholesale regions, and the entire study region (LAC). Supporting data for figures. Figure plotting scripts in the associated GitHub repo. These files support analysis and visualization. Geospatial Data used for plotting simulated water shortages and Sobol results. Dictionary of full names for demand nodes in the model and estimates of water supply by source type informed by Artes input files and California Urban Water Management Planning data: https://water.ca.gov/Programs/Water-Use-And-Efficiency/Urban-Water-Use-Efficiency/Urban-Water-Management-Plans *Readme files provided for each folder.

drought↗
DOE OSTI2025

Structure–Function Relationships in Sequence-Controlled Copolymers for Rare Earth Element Chelation

The ability to tune material function through primary sequence is a defining feature of biological macromolecules, allowing precise control over structure and target interactions in complex aqueous environments. However, translating sequence–structure–function relationships to synthetic macromolecules is challenging due to their dispersity in sequence, conformation, and composition. Here, we report systematic studies of amphiphilic polymer chelators designed to probe how composition and patterning influence binding affinity and selectivity for rare earth elements (REEs), a series of technologically relevant metals with challenging separation profiles. A library of copolymers varying hydrophobic monomer composition and patterning was synthesized via reversible addition–fragmentation chain transfer (RAFT) polymerization, spanning statistical, gradient, and block architectures. REE binding was quantified using a high-throughput colorimetric assay, and reconstruction of polymer ensembles using kinetic stochastic simulations enabled quantitative comparisons of sequence heterogeneity, linking local monomer colocalization to emergent REE binding. Further, we investigated the role of different hydrophobic comonomers in tuning metal coordination, with binding trends linked to structural features that influence binding site desolvation. Complementary dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS) measurements showed that both polymer and monomer architecture modulate metal-induced conformational changes, and that multichain assembly behavior emerges beyond critical hydrophobic thresholds. Sequence control also altered REE selectivity, with nonmonotonic differences observed across compositionally identical polymers with different sequence architectures. Together, these findings establish design principles that connect polymer sequence and structure to binding performance, guiding the design of macromolecular chelators with enhanced affinity and selectivity for applications in separations, sensing, and catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Future Changes in Midwest Extreme Precipitation Depend on Storm Type

Midwestern U.S. extreme precipitation is associated with multiple storm types including mesoscale convective systems (MCSs) and/or training thunderstorms, tropical cyclone (TC) remnants, and winter storms. Anthropogenic warming is expected to increase climatological precipitation globally, however, there may be little correspondence with regional storm-based changes. Furthermore, uncertainty remains in precipitation-temperature scaling due to use of convective parameterization in most global models. In this study, we investigated historically impactful extreme precipitation events from multiple types of Midwest storms using the Weather Research and Forecasting model at convection-permitting resolution. We simulated five-member ensembles of historical hindcasts and experiments representing the storms in the future using the pseudo-global warming method. We found that future precipitation changes depend on storm type, with increases near Clausius-Clapeyron (CC) for winter storms, no consensus for MCSs and/or training thunderstorms, and sub-CC increases for TC remnants. This research highlights the importance of considering storm type in future extreme precipitation projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Summer Convective Precipitation Changes Over the Great Lakes Region Under a Warming Scenario

To understand future summer precipitation changes over the Great Lakes Region (GLR), we performed an ensemble of regional climate simulations through the Pseudo-Global Warming (PGW) approach. We found that different types of convective precipitation respond differently to the PGW signal. Isolated deep convection (IDC), usually concentrated in the southern domain, shows an increase in precipitation to the north of the GLR. Mesoscale convective systems (MCSs), usually concentrated upwind of the GLR, shift to the downwind region with increased precipitation. Thermodynamic variables such as convective available potential energy (CAPE) and convective inhibition energy (CIN) are found to increase across almost the entire studied domain, creating a potential environment more favorable for stronger convection systems and less favorable for weaker ones. Meanwhile, changes in the lifting condensation level (LCL) and level of free convection (LFC) show a strong correlation with variations in convective precipitation, highlighting the significance of these thermodynamic factors in controlling precipitation over the domain. Our results indicate that the decrease in LCL and LCF in areas with increased convective precipitation is mainly due to increased atmospheric moisture. In response to the prescribed warming perturbation, MCSs occur more frequently downwind, while localized IDCs exhibit more intense rain rates, longer durations, and larger rainfall area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Projected runoff declines from plant physiological effects on precipitation

Abstract The impact of plants on runoff under high atmospheric CO 2 is a major uncertainty for future water resources. Theory and Earth system models (ESMs) suggest that stricter plant stomatal regulation under high CO 2 will reduce transpiration, potentially boosting runoff. Yet, across a 12-member ensemble of idealized ESM simulations that isolate plant responses to CO 2 , we show that lower transpiration robustly enhances runoff over only 5% of modelled global land area. Precipitation changes are five times more important than transpiration changes in driving runoff responses and are a significant signal of CO 2 physiological forcing over 31–57% of land areas across models. Crucially, ESMs largely disagree on where physiologically forced precipitation changes occur but agree that plant responses in most locations are as likely to reduce runoff as increase it. These results imply that large model uncertainties in precipitation responses, rather than transpiration responses, explain why ESMs disagree on plant physiologically driven runoff changes.

Lesk, Corey S. (ORCID:0000000308684040)↗
DOE OSTI2024

Scalable Risk Assessment of Rare Events in Power Systems With Uncertain Wind Generation and Loads

Risk assessment of rare events has become increasingly important in power system planning and operation with the increasing integration of renewable energy and the presence of system uncertainties. However, quantifying the risk posed by rare events via the traditional method, i.e., Monte Carlo sampling (MCS), incurs substantial computational expense stemming from the vast ensemble of power flow simulations. To accelerate the assessment, this paper proposes a Deep Neural Network (DNN)-kernelized vector-valued Gaussian Process (VVGP) approach with excellent computational efficiency while maintaining high accuracy. Consequently, serving as a surrogate model for the power flow solver, the DNN-kernelized VVGP enables significantly faster but accurate risk assessment compared to the power flow solver. The developed surrogate model evaluates low-order N - k events that contain more than 90% instances by adeptly capturing the topological features while the high-order N - k events are assessed via a power flow solver, thereby striking a balance between computational efficiency and uncertainty quantification accuracy. Moreover, the model incorporates a Support Vector Machine (SVM) classifier to resample concerning low-probability tail events to counteract the biases potentially introduced during the DNN-kernelized VVGP evaluations. Simulations conducted on the modified IEEE 24-bus, 118-bus, and European 1354-bus systems demonstrate that the proposed method maintains the accuracy benchmark set by MCS while significantly reducing computational demands in large-scale power systems as compared to other state-of-the-art methods.

17 WIND ENERGY↗
NASA NTRS1974

Solar wind three; Proceedings of the Third Conference, Pacific Grove, Calif., March 25-29, 1974

Papers are presented relating to the study of solar abundances, the evolution of the solar wind, solar corona dynamics, solar wind interactions, and stellar winds. Some of the topics covered include the nuclear composition of cosmic rays, the solar wind as deduced from lunar samples, spectral characteristics of flares, numerical simulation of interplanetary shock ensembles, radial gradient of solar wind velocity from 1 to 5 AU, the enhancement of solar wind fluctuations with scale size near the proton gyroradius, solar wind-Venus interaction observed from magnetic field experiment on Mariner 10, and binary stellar winds. Individual items are announced in this issue.

Russell, C. T.↗
NASA NTRS1992

Semiprognostic tests of the Arakawa-Schubert cumulus parameterization using simulated data

Semiprognostic tests are performed against data simulated by a cumulus ensemble model to evaluate the Arakawa-Schubert (A-S) cumulus parametrization. It is found that the A-S cumulus parametrization is generally valid despite the existence of mesoscale organization in cumulus convection. The nondiagnostic and nondeterministic aspects of the A-S cumulus parametrization are examined by testing the sensitivity of the parametrization to the horizontal grid resolution. It is also shown that the inclusion of convective-scale downdrafts improves the results of semiprognostic tests.

Xu, Kuan-Man↗
NASA NTRS1991

Comparison of numerically-simulated microphysical characteristics of convective storms with multiparameter radar observations during COHMEX

The microphysical/dynamical nature and evolution of numerically simulated convective storms observed on July 11, 1986 during the Cooperative Huntsville Meteorological Experiments are investigated with the Goddard Cumulus Ensemble model. The model-simulated hydrometeor and radar reflectivity structure is verified with results derived from the multiparameter radar observations, which includes a description of the reflectivity, differential reflectivity, linear depolarization ratio, and hail signal for two thunderstorm cases analyzed by Fulton and Heymsfield (1990). Radar analysis shows the storm to split with the newer more vigorous cell in the SE quadrant. Echo tops are near 15 km while peak reflectivities exceed 50 dBZ. Preliminary model simulations likewise show a splitting storm with regeneration occurring on the SSE flank. Echo tops are around 14 km with peak reflectivities over 45 dBZ.

Tao, W.-K.↗