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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Grand challenges in understanding the interplay of climate and land changes

Half of the Earth s land surface has been altered by human activities, creating various consequences on the climate and weather systems at local to global scales, which in turn affects a myriad of land surface processes and our adaptation behaviors. We here review the status and major knowledge gaps of studying the interactions of land and atmospheric changes and present eleven grand challenge areas for scientific research and adaptation communities in the coming decade: (1) collective and separate impacts of major land changes and the interactions with non-land-change factors such as atmospheric CO2 increase, (2) carbon and other biogeochemical cycles, (3) climatically relevant biospheric emissions such as aerosols, (4) water cycle, (5) agriculture, (6) urbanization, (7) gradual acclimation of plants, communities, and ecosystems to climate and environmental changes, (8) plant migration, (9) land use projections, (10) reduction of uncertainties in models and data, and finally (11) adaptation strategies. We conclude that we need to create and maintain a close cross-disciplinary coordination between measurements and process representation in models to analyze complex multi-facet interrelated perturbations and feedbacks between land and climate changes. Along with major scientific research thrusts, land-use and land cover change mitigation and adaptation assessments should be strengthened to identify barriers that need to be overcome, evaluate and prioritize opportunities, and examine how decision making processes work in specific contexts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Climate Dynamics Preceding Summer Forest Fires in California and the Extreme Case of 2018

Recent record-breaking wildfire seasons in California prompt an investigation into the climate patterns that typically precede anomalous summer burned forest area. Using burned-area data from the U.S. Forest Service’s Monitoring Trends in Burn Severity (MTBS) product and climate data from the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) over 1984–2018, relationships between the interannual variability of antecedent climate anomalies and July California burned area are spatially and temporally characterized. Lag correlations show that antecedent high vapor pressure deficit (VPD), high temperatures, frequent extreme high temperature days, low precipitation, high subsidence, high geopotential height, low soil moisture, and low snowpack and snowmelt anomalies all correlate significantly with July California burned area as far back as the January before the fire season. Seasonal regression maps indicate that a global midlatitude atmospheric wave train in late winter is associated with anomalous July California burned area. July 2018, a year with especially high burned area, was to some extent consistent with the general patterns revealed by the regressions: low winter precipitation and high spring VPD preceded the extreme burned area. However, geopotential height anomaly patterns were distinct from those in the regressions. Extreme July heat likely contributed to the extent of the fires ignited that month, even though extreme July temperatures do not historically significantly correlate with July burned area. While the 2018 antecedent climate conditions were typical of a high-burned-area year, they were not extreme, demonstrating the likely limits of statistical prediction of extreme fire seasons and the need for individual case studies of extreme years.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Separating Dynamic and Thermodynamic Impacts of Climate Change on Daytime Convective Development over Land

Climate change affects the dynamics and thermodynamics of moist convection. Changes in the dynamics concern, for instance, an increase of convection strength due to increases of convective available potential energy (CAPE). Thermodynamics involve increases in water vapor that the warmer atmosphere can hold and convection can work with. Small-scale simulations are conducted to separate these two components for daytime development of unorganized convection over land. The simulations apply a novel modeling technique referred to as the piggybacking (or master–slave) approach and consider the global climate model (GCM)-predicted change of atmospheric temperature and moisture profiles in the Amazon region at the end of the century under a business-as-usual scenario. The simulations show that the dynamic impact dominates because changes in cloudiness and rainfall come from cloud dynamics considerations, such as the change in CAPE and convective inhibition (CIN) combined with the impact of environmental relative humidity (RH) on deep convection. The small RH reduction between the current and future climate significantly affects the mean surface rain accumulation as it changes from a small reduction to a small increase when the RH decrease is eliminated. The thermodynamic impact on cloudiness and precipitation is generally small, with the extreme rainfall intensifying much less than expected from an atmospheric moisture increase. These results are discussed in the context of previous studies concerning climate change–induced modifications of moist convection. Future research directions applying the piggybacking method are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

What Drives Upper-Ocean Temperature Variability in Coupled Climate Models and Observations?

Abstract A key question in climate modeling is to what extent sea surface temperature and upper-ocean heat content are driven passively by air–sea heat fluxes, as opposed to forcing by ocean dynamics. This paper investigates the question using a climate model at different resolutions, and observations, for monthly variability. At the grid scale in a high-resolution climate model with resolved mesoscale ocean eddies, ocean dynamics (i.e., ocean heat flux convergence) dominates upper 50 m heat content variability over most of the globe. For deeper depths of integration to 400 m, the heat content variability at the grid scale is almost totally controlled by ocean heat flux convergence. However, a strong dependence on spatial scale is found—for the upper 50 m of ocean, after smoothing the data to around 7°, air–sea heat fluxes, augmented by Ekman heat transports, dominate. For deeper depths of integration to 400 m, the transition scale becomes larger and is above 10° in western boundary currents. Comparison of climate model results with observations show that the small-scale influence of ocean intrinsic variability is well captured by the high-resolution model but is missing from a comparable model with parameterized ocean-eddy effects. In the deep tropics, ocean dynamics dominates in all cases and all scales. In the subtropical gyres at large scales, air–sea heat fluxes play the biggest role. In the midlatitudes, at large scales >10°, atmosphere-driven air–sea heat fluxes and Ekman heat transport variability are the dominant processes except in the western boundary currents for the 400 m heat content.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Responses in the Subpolar North Atlantic in Two Climate Model Sensitivity Experiments with Increased Stratospheric Aerosols

Abstract The subpolar North Atlantic (SPNA) shows contrasting responses in two sensitivity experiments with increased stratospheric aerosols, offering insight into the physical processes that may impact the Atlantic meridional overturning circulation (AMOC) in a warmer climate. In one, the upper ocean becomes warm and salty, but in the other it becomes cold and fresh. The changes are accompanied by diverging AMOC responses. The first experiment strengthens the AMOC, opposing the weakening trend in the reference simulation. The second experiment shows a much smaller impact. Both simulations use the Community Earth System Model with the Whole Atmosphere Community Climate Model component (CESM-WACCM) but differ in model versions and stratospheric aerosol specifications. Despite both experiments using similar approaches to increase stratospheric aerosols to counteract the rising global temperature, the contrasting SPNA and AMOC responses indicate a considerable dependency on model physics, climate states, and model responses to forcings. This study focuses on examining the physical processes involved with the impact of stratospheric aerosols on the SPNA salinity changes and their potential connections with the AMOC and the Arctic. We find that in both cases, increased stratospheric aerosols act to enhance the SPNA upper-ocean salinity by reducing freshwater export from the Arctic, which is closely tied to the Arctic sea ice changes. The impact on AMOC is primarily through the thermal component of the surface buoyancy fluxes, with negligible contributions from the freshwater component. These experiments shed light on the physical processes that dictate the important connections between the SPNA, the Arctic, the AMOC, and their subsequent feedbacks on the climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantifying the Uncertainty of the Future Hydrological Impacts of Climate Change: Comparative Analysis of an Advanced Hierarchical Sensitivity in Humid and Semiarid Basins

Comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty at different spatiotemporal scales. Here, a hierarchical sensitivity analysis framework (HSAF) incorporated with a variance-based global sensitivity analysis is developed to quantify the spatiotemporal contributions of different uncertainties in hydrological impacts of climate change in two different climatic (humid and semiarid) basins in China. The uncertainty sources include three emission scenarios (ESs), 20 global climate models (GCs), three hydrological models (HMs), and the associated sensitive hydrological parameters (PAs) screened and sampled by the Morris and Latin hypercube sampling methods, respectively. Further, the results indicate that the overall trend of uncertainty is PA > HM > GC > ES, but their uncertainties have discrepancies in projections of different hydrological variables. The HM uncertainty in annual and monthly discharge projections is generally larger than the PA uncertainty in the humid basin than semiarid basin. The PA has greater uncertainty in extreme hydrological event (annual peak discharge) projections than in annual discharge projections for both basins (particularly for the humid basin), but contributes larger uncertainty to annual and monthly discharge projections in the semiarid basin than humid basin. The GC contributes larger uncertainty in all the hydrological variables projections in the humid basin than semiarid basin, while the ES uncertainty is rather limited in both basins. Overall, our results suggest there is greater spatiotemporal variability of hydrological uncertainty in more arid regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Economic Valuation of Coccidioidomycosis (Valley Fever) Projections in the United States in Response to Climate Change

Coccidioidomycosis, or valley fever, is an infectious fungal disease currently endemic to the southwestern United States. Symptoms of valley fever range in severity from flu-like illness to severe morbidity and mortality. Warming temperatures and changes in precipitation patterns may cause the area of endemicity to expand northward throughout the western United States, putting more people at risk for contracting valley fever. This may increase the health and economic burdens from this disease. We developed an approach to describe the relationship between climate conditions and valley fever incidence using historical data and generated projections of future incidence in response to both climate change and population trends using the Climate Change Impacts and Risk Analysis (CIRA) framework developed by the U.S. Environmental Protection Agency. We also developed a method to estimate economic impacts of valley fever that is based on case counts. For our 2000–15 baseline time period, we estimated annual medical costs, lost income, and economic welfare losses for valley fever in the United States were 400,000 USD per case, and the annual average total cost was 3.9 billion USD per year. For a high greenhouse gas emission scenario and accounting for population growth, we found that total annual costs for valley fever may increase up to 164% by year 2050 and up to 380% by 2090. By the end of the twenty-first century, valley fever may cost 620,000 USD per case and the annual average total cost may reach $18.5 billion per year. This work contributes to the broader effort to monetize climate change–attributable damages in the United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Unprecedented cloud resolution in a GPU-enabled full-physics atmospheric climate simulation on OLCF’s summit supercomputer

Clouds represent a key uncertainty in future climate projection. While explicit cloud resolution remains beyond our computational grasp for global climate, we can incorporate important cloud effects through a computational middle ground called the Multi-scale Modeling Framework (MMF), also known as Super Parameterization. This algorithmic approach embeds high-resolution Cloud Resolving Models (CRMs) to represent moist convective processes within each grid column in a Global Climate Model (GCM). The MMF code requires no parallel data transfers and provides a self-contained target for acceleration. This study investigates the performance of the Energy Exascale Earth System Model-MMF (E3SM-MMF) code on the OLCF Summit supercomputer at an unprecedented scale of simulation. Hundreds of kernels in the roughly 10K lines of code in the E3SM-MMF CRM were ported to GPUs with OpenACC directives. A high-resolution benchmark using 4600 nodes on Summit demonstrates the computational capability of the GPU-enabled E3SM-MMF code in a full physics climate simulation.

58 GEOSCIENCES↗
DOE OSTI2020

Climate smart agriculture and global food-crop production

Most business-as-usual scenarios for farming under changing climate regimes project that the agriculture sector will be significantly impacted from increased temperatures and shifting precipitation patterns. Perhaps ironically, agricultural production contributes substantially to the problem with yearly greenhouse gas (GHG) emissions of about 11% of total anthropogenic GHG emissions, not including land use change. It is partly because of this tension that Climate Smart Agriculture (CSA) has attracted interest given its promise to increase agricultural productivity under a changing climate while reducing emissions. Considerable resources have been mobilized to promote CSA globally even though the potential effects of its widespread adoption have not yet been studied. Here we show that a subset of agronomic practices that are often included under the rubric of CSA can contribute to increasing agricultural production under unfavorable climate regimes while contributing to the reduction of GHG. However, for CSA to make a significant impact important investments and coordination are required and its principles must be implemented widely across the entire sector.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Agriculture, bioenergy, and water implications of constrained cereal trade and climate change impacts

International trade increases connections and dependencies between countries, weaving a network of global supply chains. Agricultural commodity trade has implications for crop producers, consumers, crop prices, water and land uses, and other human systems. Interconnections among these systems are not always easy to observe when external impacts penetrate across multiple sectors. To better understand the interactions of non-linear and globally coupled agricultural-bioenergy-water systems under the broader economy, we introduce systematic perturbations in two dimensions, one human (restrictions on agricultural trade) and the other physical (climate impacts on crop yields). We explore these independently and in combination to distinguish the consequences of individual perturbation and interactive effects in long-term projections. We show that most regions experience larger changes in cereal consumption due to cereal import dependency constraints than due to the impacts of climate change on agricultural yields. In the scenario where all regions ensure an import dependency ratio of zero, the global trade of cereals decreases ~50% in 2050 compared to the baseline, with smaller decreases in cereal production and consumption (4%). The changes in trade also impact water and bioenergy: global irrigation water consumption increases 3% and corn ethanol production decreases 7% in 2050. Climate change results in rising domestic prices and declining consumption of cereal crops in general, while the import dependency constraint exacerbates the situation in regions which import more cereals in the baseline. The individual and interactive effects of trade perturbations and climate change vary greatly across regions, which are also affected by the regional ability to increase agricultural production through intensification or extensification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

AMOC decadal variability in Earth system models: Mechanisms and climate impacts

This is the final report for the project titled "AMOC decadal variability in Earth system models: Mechanisms and climate impacts". The central goal of this one-year research project was to understand the mechanisms of decadal and multi-decadal variability of the Atlantic Meridional Overturning Circulation (AMOC) within a hierarchy of climate models ranging from realistic ocean GCMs to Earth system models. The AMOC is a key element of ocean circulation responsible for oceanic transport of heat from low to high latitudes and controlling, to a large extent, climate variations in the North Atlantic. The questions of the AMOC stability, variability and predictability, directly relevant to the questions of climate predictability, were at the center of the research work.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Field Validation of Air-Source Heat Pumps for Cold Climates

Heating energy is the largest end-use for U.S. residential buildings accounting for approximately one-third of residential building energy consumption (EIA 2021). Historically, air-source heat pumps have been limited to temperate climates because of subpar performance at extremely cold outdoor air temperatures. However, recent advances to cold-climate air-source heat pump technology, which typically rely on inverter-driven, variable-speed compressors and variable-speed fans, have significantly improved low-temperature heat pump performance enabling the technology to save energy for many homes in cold climates. The primary objective of this project was to measure in-field performance of centrally ducted, variable-capacity air-source heat pumps in cold climates to validate performance and develop field-based performance maps. The project focused on quantifying heat pump performance at cold temperatures. The sites identified for the study were primarily located in the Northwest United States since homes in the region tend to have all-electric space heating systems and high-efficiency heat pumps have been incentivized in the region for several years. NREL partnered with Ecotope, Inc., a small energy consulting firm located in Seattle, WA, for site recruitment, monitoring equipment installation, data quality management. All the sites included in the study had previously installed a high-efficiency, central heat pump system. One site was in a Denver, CO suburb, which was the only dual fuel heat pump in the study. We used airside and power measurements, collected at 5-second intervals, to quantify heat pump capacity, coefficient of performance (COP), and auxiliary heater energy consumption. We developed algorithms to automatically determine the heat pump operating mode including defrost and auxiliary heating operation. A whole-house thermal and duct audit was completed during the initial site visit to estimate winter heating loads and assess heat pump sizing. Whole-home heating design loads were calculated at ASHRAE 99% design temperatures and compared to manufacturer-reported maximum capacities to assess the heat pump sizing at each site.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Soil Microbes Trade-Off Biogeochemical Cycling for Stress Tolerance Traits in Response to Year-Round Climate Change

Winter air temperatures are rising faster than summer air temperatures in high-latitude forests, increasing the frequency of soil freeze/thaw events in winter. To determine how climate warming and soil freeze/thaw cycles affect soil microbial communities and the ecosystem processes they drive, we leveraged the Climate Change across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest in the northeastern United States, where replicate field plots receive one of three climate treatments: warming (+5°C above ambient in the growing season), warming in the growing season + winter freeze/thaw cycles (+5°C above ambient +4 freeze/thaw cycles during winter), and no treatment. Soil samples were taken from plots at six time points throughout the growing season and subjected to amplicon (rDNA) and metagenome sequencing. We found that soil fungal and bacterial community composition were affected by changes in soil temperature, where the taxonomic composition of microbial communities shifted more with the combination of growing-season warming and increased frequency of soil freeze/thaw cycles in winter than with warming alone. Warming increased the relative abundance of brown rot fungi and plant pathogens but decreased that of arbuscular mycorrhizal fungi, all of which recovered under combined growing-season warming and soil freeze/thaw cycles in winter. The abundance of animal parasites increased significantly under combined warming and freeze/thaw cycles. We also found that warming and soil freeze/thaw cycles suppressed bacterial taxa with the genetic potential for carbon (i.e., cellulose) decomposition and soil nitrogen cycling, such as N fixation and the final steps of denitrification. These new soil communities had higher genetic capacity for stress tolerance and lower genetic capacity to grow or reproduce, relative to the communities exposed to warming in the growing season alone. Our observations suggest that initial suppression of biogeochemical cycling with year-round climate change may be linked to the emergence of taxa that trade-off growth for stress tolerance traits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Implementing and Improving CBMZ-MAM3 Chemistry and Aerosol Modules in the Regional Climate Model WRF-CAM5: An Evaluation over the Western US and Eastern North Pacific

The representation of aerosols in climate-chemistry models is important for air quality and climate change research, but it can require significant computational resources. The objective of this study was to improve the representation of aerosols in climate–chemistry models, specifically in the carbon bond mechanism, version Z (CBMZ), and modal aerosol modules with three lognormal modes (MAM3) in the WRF-CAM5 model. The study aimed to enhance the model’s chemistry capabilities by incorporating biomass burning emissions, establishing a conversion mechanism between volatile organic compounds (VOCs) and secondary organic carbons (SOCs), and evaluating its performance against observational benchmarks. The results of the study demonstrated the effectiveness of the enhanced chemistry capabilities in the WRF-CAM5 model. Six simulations were conducted over the western U.S. and northeastern Pacific region, comparing the model’s performance with observational benchmarks such as reanalysis, ground-based, and satellite data. The findings revealed a significant reduction in root-mean-square errors (RMSE) for surface concentrations of black carbon (BC) and organic carbon (OC). Specifically, the model exhibited a 31% reduction in RMSE for BC concentrations and a 58% reduction in RMSE for OC concentrations. These outcomes underscored the importance of accurate aerosol representation in climate-chemistry models and emphasized the potential for improving simulation accuracy and reducing errors through the incorporation of enhanced chemistry modules in such models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Hourly Electricity Demand Projections for Eight Combined Climate and Socioeconomic Scenarios

This dataset contains 40 years (1980-2019) of simulated historical hourly electricity demand (i.e., loads) and 80 years (2020-2099) of projected hourly loads for 54 Balancing Authorities (BAs) and 48 states plus the District of Columbia. Details about the scenarios and variables included in this dataset are in the readme.pdf file. The two primary models that created the dataset are a version of the Global Change Analysis Model with detailed sectoral resolution over the United States (GCAM-USA) and the Total ELectricity Loads (TELL) model. Links to the model source code and workflow for deriving the dataset are provided in an accompanying meta-repository: https://github.com/IMMM-SFA/burleyson-etal_2023_applied_energy. Projections are for four future climate scenarios that represent combinations of Representative Concentration Pathways (RCPs) 4.5 and 8.5 combined with two levels of climate model sensitivities: rcp45cooler, rcp45hotter, rcp85cooler, and rcp85hotter. The four climate scenarios are crossed with Shared Socioeconomic Pathways (SSPs) 3 and 5 to yield eight different future load projections: rcp45cooler_ssp3, rcp45cooler_ssp5, rcp45hotter_ssp3, rcp45hotter_ssp5, rcp85cooler_ssp3, rcp85cooler_ssp5, rcp85hotter_ssp3, and rcp85hotter_ssp5. The climate scenarios are from the IM3 Thermodynamic Global Warming (TGW) dataset which is linked below in the related metadata. The related metadata also contains links to a repository containing the raw GCAM-USA output files.

Climate Change↗
DOE OSTI2024

Hourly Electricity Demand Projections for Eight Combined Climate and Socioeconomic Scenarios

This dataset contains 40 years (1980-2019) of simulated historical hourly electricity demand (i.e., loads) and 80 years (2020-2099) of projected hourly loads for 54 Balancing Authorities (BAs) and 48 states plus the District of Columbia. Details about the scenarios and variables included in this dataset are in the readme.pdf file. The two primary models that created the dataset are a version of the Global Change Analysis Model with detailed sectoral resolution over the United States (GCAM-USA) and the Total ELectricity Loads (TELL) model. Links to the model source code and workflow for deriving the dataset are provided in an accompanying meta-repository: https://github.com/IMMM-SFA/burleyson-etal_2024_applied_energy. Projections are for four future climate scenarios that represent combinations of Representative Concentration Pathways (RCPs) 4.5 and 8.5 combined with two levels of climate model sensitivities: rcp45cooler, rcp45hotter, rcp85cooler, and rcp85hotter. The four climate scenarios are crossed with Shared Socioeconomic Pathways (SSPs) 3 and 5 to yield eight different future load projections: rcp45cooler_ssp3, rcp45cooler_ssp5, rcp45hotter_ssp3, rcp45hotter_ssp5, rcp85cooler_ssp3, rcp85cooler_ssp5, rcp85hotter_ssp3, and rcp85hotter_ssp5. The climate scenarios are from the IM3 Thermodynamic Global Warming (TGW) dataset which is linked below in the related metadata. The related metadata also contains links to a repository containing the raw GCAM-USA output files.

Climate Change↗
DOE OSTI2024

Global Corn Heat Stress: Mean and SD of Degree Days Above 29°C based on NEX-GDDP-CMIP6 Climate Projections

Description This global dataset provides the estimated mean and standard deviation (SD) of corn heat stress (degree days above 29°C) for a set of climate models in NEX-GDDP-CMIP6 at 0.25-degree resolution. The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6). The current dataset includes: Long-Term Average Degree Days Above 29°C- Historical Long-Term Average Degree Days Above 29°C- SSP245 Long-Term Standard Deviation of Degree Days Above 29°C- Historical Long-Term Standard Deviation of Degree Days Above 29°C- SSP245 The mean and SD are calculated over 1985-2014 for the historical period and over 2035-2064 for future projections. A full description of methods, including growing season, daily temperature distribution, and statistical coefficients, can be found in Haqiqi (2024). The source climate data are obtained from https://ds.nccs.nasa.gov/thredds2/catalog/catalog.html and are described in Thrasher et al (2022). The codes used to create this dataset are available at https://github.com/ihaqiqi/dd29c_nex_cmip6. Acknowledgments This work was supported by the US Department of Energy, Office of Science, Biological and Environmental Research Program, Earth and Environmental Systems Modeling, MultiSector Dynamics under Cooperative Agreement DE-SC0022141. The data processing, computation, and storage were completed on Purdue Anvil supercomputer and cyberinfrastructure supported by the National Science Foundation HDR award # 2118329: "NSF Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE)". References Haqiqi. I. (2024). Trade can buffer climate-induced risks and volatilities in crop supply. Environmental Research: Food Systems. https://doi.org/10.1088/2976-601X/ad7d12 Thrasher, B., Wang, W., Michaelis, A., Melton, F., Lee, T., & Nemani, R. (2022). NASA global daily downscaled projections, CMIP6. Scientific Data, 9(1), 262. https://doi.org/10.1038/s41597-022-01393-4

Climate Change↗