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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 307 records · Page 17
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NASA NTRS2020

Use of Climate Change Projections for Resilience Planning in Rio de Janeiro, Brazil

In this study we use three different methodologies to document and compare temperature and precipitation projections for the city of Rio de Janeiro (RJ) over the 21st century. It aims to explore in what way the differences and similarities of those methodologies and their outcomes support the incorporation of climate risks in urban planning and improve effective urban climate change governance. We compared the projections for RJ from the Eta Regional Climate Model from the Brazilian National Institute for Space Research nested in two Hadley Center Global Climate Models (GCMs) (Eta-HadCM3 method and Eta-HadGEM2-ES method) and 33 GCMs from the Coupled Model Intercomparison Project Phase 5 multi-model dataset (Urban Climate Change Research Network - UCCRN method). The three methods showed increasing temperatures for RJ at the end of the century. Precipitation projections span a 13% decrease to a 12% increase when using the UCCRN method or are reduced between 0.4 and 0.5%, when using the Eta-HadGEM2-ES method. However, the middle range of the projections from UCCRN and Eta-HadGEM2-ES is similar. The three methods project an increase of warm days and nights and a decrease of cold days and nights. Nevertheless, although the directions of change are the same applying the three methods, the magnitude differs when considering warm and cold nights. Hence, city stakeholders are better informed when we apply different projection methods as it gives them the opportunity to consider the level of risk they are willing to bear in the future. We observed that defining climate change projections on the city scale based on clear communication and an interactive process between scientists and stakeholders can be used to inform citywide adaptation strategies and sector-specific uses, as well as promote urban climate risk governance.

urban governance and management↗
NASA NTRS2021

Methodology to Assess the Changing Risk of Yield Failure Due to Heat and Drought Stress Under Climate Change

While the understanding of average impacts of climate change on crop yields is improving, few assessments have quantified expected impacts on yield distributions and the risk of yield failures. Here we present the relative distribution as a method to assess how the risk of yield failure due to heat and drought stress (measured in terms of return period between yields falling 15% below previous five year Olympic average yield) responds to changes of the underlying yield distributions under climate change. Relative distributions are used to capture differences in the entire yield distribution between baseline and climate change scenarios, and to further decompose them into changes in the location and shape of the distribution. The methodology is applied here for the case of rainfed wheat and grain maize across Europe using an ensemble of crop models under three climate change scenarios with simulations conducted at 25 km resolution. Under climate change, maize generally displayed shorter return periods of yield failures (with changes under RCP 4.5 between −0.3 and 0 years compared to the baseline scenario) associated with a shift of the yield distribution towards lower values and changes in shape of the distribution that further reduced the frequency of high yields. This response was prominent in the areas characterized in the baseline scenario by high yields and relatively long return periods of failure. Conversely, for wheat, yield failures were projected to become less frequent under future scenarios (with changes in the return period of −0.1 to +0.4 years under RCP 4.5) and were associated with a shift of the distribution towards higher values and a change in shape increasing the frequency of extreme yields at both ends. Our study offers an approach to quantify the changes in yield distributions that drive crop yield failures. Actual risk assessments additionally require models that capture the variety of drivers determining crop yield variability and scenario climate input data that samples the range of probable climate variation.

Climate risk assessment↗
NASA NTRS2022

Integrated Assessment of Air Pollution and Climate Change for Sustainable Development in Africa - Summary for Decision Makers

Air pollution and climate change are a deadly duo for Africa, and must be tackled together. Air pollutants and greenhouse gases often share the same sources and can be even more dangerous when combined. Africa is particularly vulnerable to climate change, and currently, an estimated 1 million people per year die prematurely from air pollution on the continent. But there is a way to improve the situation: preventing emissions from short-lived climate pollutants, like methane and black carbon, is crucial for the world to stay below 1.5°C. Reducing SLCPs will help both save lives and protect the environment. Africa has a huge opportunity to continue developing sustainably, improve human well-being, and protect nature by investing in solutions to fight climate change and air pollution together. A new Integrated Assessment of Air Pollution and Climate Change for Sustainable Development in Africa from the African Union Commission, the Climate and Clean Air Coalition, and the UN Environment Programme, developed by African scientists in a process led by the Stockholm Environment Institute, shows how African leaders can act quickly across 5 key areas—transport, residential, energy, agriculture, and waste—to fight climate change, prevent air pollution, and protect human health.

Africa↗
NASA NTRSDate not supplied

Forecasted Climate Penalties to Surface Ozone Across the 21st Century

Future climate change may bring local benefits or penalties to surface ozone pollution, resulting from changing temperature, precipitation, and transport patterns, as well as climate-sensitive natural precursor emissions. Here, we estimate climate penalty and benefits at the end of this century using version 2.0 of the Global Change and Air Pollution (GCAP 2.0) model framework, a one-way offline coupling between version E2.1 of the NASA Goddard Institute for Space Studies (GISS) general circulation model (GCM) and the GEOS-Chem global 3-D chemical-transport model (CTM). We archive meteorology for driving GEOS-Chem from the E2.1 contributions to Phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the present day (2005-2014 C.E.) and end-of-the 21st century (2090-2099 C.E.). Emissions and boundary conditions used for input to GEOS-Chem are consistent with the CMIP6 experimental design. We apply the model to isolate the impact of forecasted anthropogenic precursor emission changes versus climate-only driven changes on surface ozone for warming limited to 1.9 W m-2 versus 8.5 W m-2 above preindustrial levels. We find a global climate penalty of 47 (16-77) thousand additional deaths due to surface ozone at the end of the century in the extreme warming scenario, but only 13 (4-21) thousand additional deaths in the extreme mitigation scenario. Similarly, the extreme warming scenario is associated with a 40 % increase in economic losses due to crop damage from climate-only changes in surface ozone, but only an 8 % increase in the extreme mitigation scenario. The simulated climate penalties to surface ozone are most severe in South and East Asia.

Climate penalties↗
DOE OSTI2025

Assessing the impact of climate change on rainfall-triggered landslides: a case study in California

Landslides are widespread natural hazards which take a heavy toll on lives, property, and infrastructure each year. In January 2023 and 2024, widespread landslides occurred in California as a result of extreme winter precipitation. Estimating the regional evolution of landslide hazard in a changing climate is essential for adaptation planning and risk mitigation efforts. The complex effects of climate change on landslide hazard, however, are poorly understood. Here, we use climate and landslide observations to develop a novel data-driven approach for landslide susceptibility assessment under historical and future climate conditions. We apply this framework to investigate the evolution of landslide susceptibility in California and its impacts on the roadway system based on downscaled climate projections under moderate and high greenhouse gas emission scenarios. On average, the results indicate an increase in landslide susceptibility through 2100 in regions where historical susceptibility is moderate to very high. Although the spatio-temporal variations in landslide susceptibility are sensitive to climate model uncertainties in predicting extreme rainfall, the direst impacts are largely circumvented in lower emission scenarios.

Climate Change↗
DOE OSTI2026

Systematic Benchmarking of Climate Models: Methodologies, Applications, and New Directions

As climate models become increasingly complex, there is a growing need to comprehensively and systematically assess model performance with respect to observations. Given the increasing number and diversity of climate model simulations in use, the community has moved beyond simple model intercomparison and toward developing methods capable of benchmarking a large number of simulations against a suite of climate metrics. Here, we present a detailed review of evaluation and benchmarking methods and approaches developed in the last decade, focusing primarily on scientific implications for Coupled Model Intercomparison Project (CMIP) simulations and CMIP6 results that contributed to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Based on this review, we explain the resulting contemporary philosophy of model benchmarking, and provide clear distinctions and definitions of the terms model verification, process validation, evaluation, and benchmarking. While significant progress has been made in model development based on systematic evaluation and benchmarking efforts, some climate system biases still remain. The development of open‐source community software packages has played a fundamental role in identifying areas of significant model improvement and bias reduction. We review the key features of several software packages that have been commonly used over the past decade to evaluate and benchmark global and regional climate models. Additionally, we discuss best practices for the selection of evaluation and benchmarking metrics and for interpreting the obtained results, the importance of selecting suitable sources of reference data and accurate uncertainty quantification.

Environmental sciences↗
NASA NTRS2024

CLIMATE CHANGE 2023 Synthesis Report Summary for Policymakers

This Synthesis Report (SYR) of the IPCC Sixth Assessment Report (AR6) summarises the state of knowledge of climate change, its widespread impacts and risks, and climate change mitigation and adaptation. It integrates the main findings of the Sixth Assessment Report (AR6) based on contributions from the three Working Groups, and the three Special Reports. The summary for Policymakers (SPM) is structured in three parts: SPM.A Current Status and Trends, SPM.B Future Climate Change, Risks, and Long-Term Responses, and SPM.C Responses in the Near Term. This report recognizes the interdependence of climate, ecosystems and biodiversity, and human societies; the value of diverse forms of knowledge; and the close linkages between climate change adaptation, mitigation, ecosystem health, human well-being and sustainable development, and reflects the increasing diversity of actors involved in climate action. Based on scientific understanding, key findings can be formulated as statements of fact or associated with an assessed level of confidence using the IPCC calibrated languages.

climate impacts↗
DOE OSTI2025

Harvest legacies and climate change interact to shape forest structure and biomass through time

Forest structural change driven by climate trends has been observed worldwide and is expected to increase in the future. Management of forest structure has been an important tool for mitigating the impacts of climate change but forest structure may shift independently of management goals as it interacts with climate change. Here, we investigated the long-term impacts of harvest-based management strategies on structure and resistance to climate-induced biomass loss using a process-based ecosystem model for a midwestern USA hardwood forest. We identified aboveground biomass loss events and compared the cumulative number of these events following a five-year period of active management under four management strategies and two climate change scenarios. Management legacy had the clearest impact on climate-driven biomass loss over the mid-term (∼25 years) with the shelterwood scenario experiencing no loss events during this period. However, by the end of the century legacy effects faded and climate change severity became the driver of differences as greater warming scenarios experienced twice the loss events, and end-of-century loss events were 3–10 times more frequent than mid-century events. We found that while structure was distinct among harvest scenarios through the mid-century, differences were negligible by the end-of-century; identical to when management correlated with loss event frequency. Here, we found that loss events were preceded by a drop in precipitation two years prior, while no specific forest structure preceded a loss event. However, the structures preceding a biomass loss event were distinct across different legacies, implying additional influences of past management.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Ground Water and Climate Change

As the world's largest distributed store of fresh water, ground water plays a central part in sustaining ecosystems and enabling human adaptation to climate variability and change. The strategic importance of ground water for global water and food security will probably intensify under climate change as more frequent and intense climate extremes (droughts and floods) increase variability in precipitation, soil moisture and surface water. Here we critically review recent research assessing the impacts of climate on ground water through natural and human-induced processes as well as through groundwater-driven feedbacks on the climate system. Furthermore, we examine the possible opportunities and challenges of using and sustaining groundwater resources in climate adaptation strategies, and highlight the lack of groundwater observations, which, at present, limits our understanding of the dynamic relationship between ground water and climate.

climate change↗
NASA NTRS2014

Assessing the Impact of Laurentide Ice-sheet Topography on Glacial Climate

Simulations of past climates require altered boundary conditions to account for known shifts in the Earth system. For the Last Glacial Maximum (LGM) and subsequent deglaciation, the existence of large Northern Hemisphere ice sheets caused profound changes in surface topography and albedo. While ice-sheet extent is fairly well known, numerous conflicting reconstructions of ice-sheet topography suggest that precision in this boundary condition is lacking. Here we use a high-resolution and oxygen-isotopeenabled fully coupled global circulation model (GCM) (GISS ModelE2-R), along with two different reconstructions of the Laurentide Ice Sheet (LIS) that provide maximum and minimum estimates of LIS elevation, to assess the range of climate variability in response to uncertainty in this boundary condition.We present this comparison at two equilibrium time slices: the LGM, when differences in ice-sheet topography are maximized, and 14 ka, when differences in maximum ice-sheet height are smaller but still exist. Overall, we find significant differences in the climate response to LIS topography, with the larger LIS resulting in enhanced Atlantic Meridional Overturning Circulation and warmer surface air temperatures, particularly over northeastern Asia and the North Pacific. These up- and downstream effects are associated with differences in the development of planetary waves in the upper atmosphere, with the larger LIS resulting in a weaker trough over northeastern Asia that leads to the warmer temperatures and decreased albedo from snow and sea-ice cover. Differences between the 14 ka simulations are similar in spatial extent but smaller in magnitude, suggesting that climate is responding primarily to the larger difference in maximum LIS elevation in the LGM simulations. These results suggest that such uncertainty in ice-sheet boundary conditions alone may significantly impact the results of paleoclimate simulations and their ability to successfully simulate past climates, with implications for estimating climate sensitivity to greenhouse gas forcing utilizing past climate states.

glaciology↗
NASA NTRS2014

Reply to Comment by Cole-dai Et Al. on "Climatic Impact of the Long-lasting Laki Eruption: Inapplicability of Mass-independent Sulfur Isotope Composition Measurements"

Here we respond to the comments by Cole-Dai et al. [2014] on our article Schmidt et al. [2012]. Specifically, in response to section 2 of their reply, we argued in Schmidt et al. [2012] that based on previously published estimates of the volatile release height during the 1783-1784 C.E. Laki eruption, the lack of a sulfur massindependent fractionation (MIF) anomaly is expected. In other words, no previous study on Laki ever argued that this eruption emitted SO2 into altitudes >13-15 km. In section 2.3, Cole-Dai et al. [2014] argue that the nonzero Δ33S value of their Laki sample 1 may be explained by a short-lived explosive phase at Laki during which volatiles reached the stratosphere. In Schmidt et al. [2012] in section 2, we argued in agreement with Cole-Dai et al. [2014] (section 3.1) that for a MIF anomaly to be preserved, the Laki volatiles would have had to be emitted in >20 km altitude. Our main point is that eruption column heights >20 km are unlikely based on the historical accounts and plume-rise modeling for the Laki eruption [Stothers et al., 1986; Woods, 1993; Thordarson and Self, 2003]. In Schmidt et al. [2012], we argued that to deduce a short-lived climatic impact of the Laki eruption based on the lack of a MIF anomaly and the length of the sulfate deposition in Greenland ice cores may be misleading because the climatic impact will outlast the radiative forcing of the Laki aerosol cloud. Cole-Dai et al. [2014] acknowledge the latter in their reply in section 4.2. We agreewith Cole-Dai et al. [2014] in that themagnitude and length of the climatic impact during the winter of 1783-1784 depends on the altitude of the volatile release during the eruption (sections 2.3 and 4.2). However, even if we assumed that during Laki all sulfur dioxide (SO2) would have been released in the troposphere, then the aerosol cloud would still be present in the upper troposphere during March 1784, as is evident from independent model simulations of this "tropospheric-only" scenario [Stevenson et al., 2003]. We acknowledge that there is uncertainty on the volatile release height for Laki; however, it is worth considering that those climate model simulations that used an injection altitude between 9 km and 13 km for the Laki SO2 [Highwood and Stevenson, 2003; Oman et al., 2006a, 2006b; Schmidt et al., 2012] best match the observed temperature changes during summer of 1783 [Angell and Korshover, 1985; Brázdil et al., 2010; Briffa et al., 1998; D'Arrigo and Jacoby, 1999; Jacoby et al., 1999; Kington, 1988; Manley, 1974; Parker et al., 1992; Thordarson and Self, 2003]. Based on these model simulations, a climatic impact during the winter of 1783-1784, albeit weaker than during the climactic phases of Laki, is expected (and our argument here does not exclude the role of natural variability in contributing to the cold winter of 1783-1784 as discussed in Schmidt et al. [2012]). Therefore, we continue to argue that for high-latitude eruptions such as Laki, the applicability of sulfur isotopic measurements to interpret the climatic relevance has yet to be demonstrated. Itmay transpire that the interpretation of MIF signals for the climate-relevance of an eruption is valid and unambiguous only for short-lived explosive eruptions in the tropics. In terms of the processes producing a MIF anomaly (section 3.3 in Cole-Dai et al. [2014]), the works by Hattori et al. [2013] and Ono et al. [2013] suggest that there are remaining issues not discussed by Cole-Dai et al. [2014], for instance, self-shielding of SO2 due to high column densities typical for eruptions of Pinatubo-scale and greater, and the preservation of the MIF signature in general.

general circulation model↗
NASA NTRS2014

Heat-Related Mortality in a Warming Climate: Projections for 12 U.S. Cities

Heat is among the deadliest weather-related phenomena in the United States, and the number of heat-related deaths may increase under a changing climate, particularly in urban areas. Regional adaptation planning is unfortunately often limited by the lack of quantitative information on potential future health responses. This study presents an assessment of the future impacts of climate change on heat-related mortality in 12 cities using 16 global climate models, driven by two scenarios of greenhouse gas emissions. Although the magnitude of the projected heat effects was found to differ across time, cities, climate models and greenhouse pollution emissions scenarios, climate change was projected to result in increases in heat-related fatalities over time throughout the 21st century in all of the 12 cities included in this study. The increase was more substantial under the high emission pathway, highlighting the potential benefits to public health of reducing greenhouse gas emissions. Nearly 200,000 heat-related deaths are projected to occur in the 12 cities by the end of the century due to climate warming, over 22,000 of which could be avoided if we follow a low GHG emission pathway. The presented estimates can be of value to local decision makers and stakeholders interested in developing strategies to reduce these impacts and building climate change resilience.

heat-related mortality↗
NASA NTRS2011

Climate Model Diagnostic and Evaluation: With a Focus on Satellite Observations

Each year, we host a summer school that brings together the next generation of climate scientists - about 30 graduate students and postdocs from around the world - to engage with premier climate scientists from the Jet Propulsion Laboratory and elsewhere. Our yearly summer school focuses on topics on the leading edge of climate science research. Our inaugural summer school, held in 2011, was on the topic of "Using Satellite Observations to Advance Climate Models," and enabled students to explore how satellite observations can be used to evaluate and improve climate models. Speakers included climate experts from both NASA and the National Oceanic and Atmospheric Administration (NOAA), who provided updates on climate model diagnostics and evaluation and remote sensing of the planet. Details of the next summer school will be posted here in due course.

climate↗
NASA NTRS2015

AgMIP Climate Data and Scenarios for Integrated Assessment

Climate change presents a great challenge to the agricultural sector as changes in precipitation, temperature, humidity, and circulation patterns alter the climatic conditions upon which many agricultural systems rely. Projections of future climate conditions are inherently uncertain owing to a lack of clarity on how society will develop, policies that may be implemented to reduce greenhouse-gas (GHG) emissions, and complexities in modeling the atmosphere, ocean, land, cryosphere, and biosphere components of the climate system. Global climate models (GCMs) are based on well-established physics of each climate component that enable the models to project climate responses to changing GHG concentration scenarios (Stocker et al., 2013).The most recent iteration of the Coupled Model Intercomparison Project (CMIP5; Taylor et al., 2012) utilized representative concentration pathways (RCPs) to cover the range of plausible GHG concentrations out past the year 2100, with RCP8.5 representing an extreme scenario and RCP4.5 representing a lower concentrations scenario (Moss et al., 2010).

agriculture↗
NASA NTRS2015

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

simulation↗
NASA NTRS2015

Implications for Climate Sensitivity from the Response to Individual Forcings

Climate sensitivity to doubled CO2 is a widely-used metric of the large-scale response to external forcing. Climate models predict a wide range for two commonly used definitions: the transient climate response (TCR: the warming after 70 years of CO2 concentrations that riseat 1 per year), and the equilibrium climate sensitivity (ECS: the equilibrium temperature change following a doubling of CO2 concentrations). Many observational datasets have been used to constrain these values, including temperature trends over the recent past 16, inferences from paleo-climate and process-based constraints from the modern satellite eras. However, as the IPCC recently reported different classes of observational constraints produce somewhat incongruent ranges. Here we show that climate sensitivity estimates derived from recent observations must account for the efficacy of each forcing active during the historical period. When we use single forcing experiments to estimate these efficacies and calculate climate sensitivity from the observed twentieth-century warming, our estimates of both TCR and ECS are revised upward compared to previous studies, improving the consistency with independent constraints.

Sensitivity↗
NASA NTRS2016

Ocean-Atmosphere Interactions Modulate Irrigation's Climate Impacts

Numerous studies have focused on the local and regional climate effects of irrigated agriculture and other land cover and land use change (LCLUC) phenomena, but there are few studies on the role of ocean- atmosphere interaction in modulating irrigation climate impacts. Here, we compare simulations with and without interactive sea surface temperatures of the equilibrium effect on climate of contemporary (year 2000) irrigation geographic extent and intensity. We find that ocean-atmosphere interaction does impact the magnitude of global-mean and spatially varying climate impacts, greatly increasing their global reach. Local climate effects in the irrigated regions remain broadly similar, while non-local effects, particularly over the oceans, tend to be larger. The interaction amplifies irrigation-driven standing wave patterns in the tropics and mid-latitudes in our simulations, approximately doubling the global-mean amplitude of surface temperature changes due to irrigation. The fractions of global area experiencing significant annual-mean surface air temperature and precipitation change also approximately double with ocean-atmosphere interaction. Subject to confirmation with other models, these findings imply that LCLUC is an important contributor to climate change even in remote areas such as the Southern Ocean, and that attribution studies should include interactive oceans and need to consider LCLUC, including irrigation, as a truly global forcing that affects climate and the water cycle over ocean as well as land areas.

Air water interactioins↗
NASA NTRS2016

IN11B-1621: Quantifying How Climate Affects Vegetation in the Amazon Rainforest

Amazon droughts in 2005 and 2010 have raised serious concern about the future of the rainforest. Amazon forests are crucial because of their role as the largest carbon sink in the world which would effect the global warming phenomena with decreased photosynthesis activity. Especially, after a decline in plant growth in 1.68 million km2 forest area during the once-in-a-century severe drought in 2010, it is of primary importance to understand the relationship between different climatic variables and vegetation. In an earlier study, we have shown that non-linear models are better at capturing the relation dynamics of vegetation and climate variables such as temperature and precipitation, compared to linear models. In this research, we learn precise models between vegetation and climatic variables (temperature, precipitation) for normal conditions in the Amazon region using genetic programming based symbolic regression. This is done by removing high elevation and drought affected areas and also considering the slope of the region as one of the important factors while building the model. The model learned reveals new and interesting ways historical and current climate variables affect the vegetation at any location. MAIAC data has been used as a vegetation surrogate in our study. For temperature and precipitation, we have used TRMM and MODIS Land Surface Temperature data sets while learning the non-linear regression model. However, to generalize the model to make it independent of the data source, we perform transfer learning where we regress a regularized least squares to learn the parameters of the non-linear model using other data sources such as the precipitation and temperature from the Climatic Research Center (CRU). This new model is very similar in structure and performance compared to the original learned model and verifies the same claims about the nature of dependency between these climate variables and the vegetation in the Amazon region. As a result of this study, we are able to learn, for the very first time how exactly different climate factors influence vegetation at any location in the Amazon rainforests, independent of the specific sources from which the data has been obtained.

global warming↗