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NASA NTRS2023

Uncertainty in Land-Use Adaptation Persists Despite Crop Model Projections Showing Lower Impacts Under High Warming

Climate change is expected to impact crop yields and alter resource availability. However, the understanding of the potential of agricultural land-use adaptation and its costs under climate warming is limited. Here, we use a global land system model to assess land-use-based adaptation and its cost under a set of crop model projections, including CO2 fertilization, based on climate model outputs. In our simulations of a low-emissions scenario, the land system responds through slight changes in cropland area in 2100, with costs close to zero. For a high emissions scenario and impacts uncertainty, the response tends toward cropland area changes and investments in technology, with average adaptation costs between −1.5 and +19 US$05 per ton of dry matter per year. Land-use adaptation can reduce adverse climate effects and use favorable changes, like local gains in crop yields. However, variance among high-emissions impact projections creates challenges for effective adaptation planning.

Agriculture↗

NASA NTRS2005

NCAR Johns Hopkins/CDC Climate and Health Summer Institute

The interactions between climate and health are rife with complexity and present many conceptual and methodological challenges. Possible effects of climate change on health are considered some of the most sensitive impacts of climate change and are a high priority for policy-makers and the public. As a first step toward improving tlit: quality of research, we developed a Climate and Health Workshop (Institute), geared toward teaching students various aspects of how to conduct integrated climate and health research. At the workshop scientists presented selected case studies of climate and health (e.g., heat mortality, vector-borne diseases), thus demonstrating a subset of key analytical tools and databases most useful to researchers in this field. Key research gaps in this research area were discussed. In this six-day Institute (21-28 July 2004, Boulder, Colorado), health scientists and students benefited from lectures and hands-on tools taught by top NCAR scientists. The attendees learned about health databases and epidemiologic methods from leading health scientists from CDC, Johns Hopkins, and other institutions from around the globe.

Mearns, Linda O.↗

NASA NTRS2014

Ontology Engineering in Provenance Enablement for the National Climate Assessment

The National Climate Assessment of the U.S. Global Change Research Program (USGCRP) analyzes and presents the impacts of climate change on the United States. The provenance information in the assessment is important because the assessment findings are of great public and academic concern and are used in policy and decision-making. By applying a use case-driven iterative methodology, we developed information models and ontology to represent the content structure of the recent National Climate Assessment draft report and its associated provenance information. We tested the ontology by using it in pilot systems serving information about instances of chapters, scientific findings, figures, tables, images, datasets, references, people, and organizations, etc. in the draft report, as well as interrelationships among those instances. The results successfully help users trace provenance in the draft report, such as finding all the journal articles from which a figure in the report was derived. The provenance information in our work was maintained in the context of the "Web of Data". In addition to the pilot systems we developed, other tools and services are also able to retrieve and utilize the provenance information. Our work is part of a Global Change Information System coordinated by the USGCRP that will eventually cover provenance information for the entire scope of global change research. Such a system will greatly increase understanding, credibility and trust in the global change research and foster reproducibility of scientific results and conclusions.

Ontology engineering↗

NASA NTRS2023

Risks of Synchronized Low Yields Are Underestimated in Climate and Crop Model Projections

Simultaneous harvest failures across major crop-producing regions are a threat to global food security. Concurrent weather extremes driven by a strongly meandering jet stream could trigger such events, but so far this has not been quantified. Specifically, the ability of state-of-the art crop and climate models to adequately reproduce such high impact events is a crucial component for estimating risks to global food security. Here we find an increased likelihood of concurrent low yields during summers featuring meandering jets in observations and models. While climate models accurately simulate atmospheric patterns, associated surface weather anomalies and negative effects on crop responses are mostly underestimated in bias-adjusted simulations. Given the identified model biases, future assessments of regional and concurrent crop losses from meandering jet states remain highly uncertain. Our results suggest that model-blind spots for such high-impact but deeply-uncertain hazards have to be anticipated and accounted for in meaningful climate risk assessments.

Atmospheric dynamics↗

DOE OSTI2025

Spatio-temporal multivariate cluster evolution analysis for detecting and tracking climate impacts

Recent years have seen a growing concern about climate change and its impacts. While Earth System Models (ESMs) can be invaluable tools for studying the impacts of climate change, the complex coupling processes encoded in ESMs and the large amounts of data produced by these models, together with the high internal variability of the Earth system, can obscure important source-to-impact relationships. Here, this paper presents a novel and efficient unsupervised data-driven approach for detecting statistically-significant impacts and tracing spatio-temporal source-impact pathways in the climate through a unique combination of ideas from anomaly detection, clustering and Natural Language Processing (NLP). Using as an exemplar the 1991 eruption of Mount Pinatubo in the Philippines, we demonstrate that the proposed approach is capable of detecting known post-eruption impacts/events. We additionally describe a methodology for extracting meaningful sequences of post-eruption impacts/events by using NLP to efficiently mine frequent multivariate cluster evolutions, which can be used to confirm or discover the chain of physical processes between a climate source and its impact(s).

Anomaly detection↗

DOE OSTI2022

Incorporating climate change into risk-informed resilience planning

In response to the development of portfolio-wide Climate Action Plans by federal agencies, federal sites are working to incorporate the impacts of climate change into their resilience assessments. However, it can be challenging to incorporate climate change scenarios into resilience assessments given the uncertainty inherent in climate change modeling. Incorporating these factors into a resilience plan requires an understanding of what the different climate scenarios mean, as well as how to estimate potential impacts of climate change on hazard occurrence on a regional, or even local, scale under different scenarios. We discuss approaches to incorporating this data into risk-informed resilience assessment processes, such as those implemented in the Department of Energy’s (DOE) Federal Energy Management Program’s (FEMP) Technical Resilience Navigator (TRN) and the Sustainability Performance Division’s (SPD) Vulnerability Assessment and Resilience Plan (VARP) Risk Assessment Tool. We also describe the climate scenarios and the availability of hazard data for site resilience planning, based on modeling included in the International Panel on Climate Change (IPCC), the National Climate Assessment (NCA), and state-level reports. We present examples from the TRN risk assessment and the VARP Risk Assessment Tool to illustrate how sensitivity analysis can be used to incorporate climate change projections into the resilience planning process.

Rabinowitz, Hannah S.↗

DOE OSTI2021

A Review of Water and Climate Change Analysis in Electric Utility Integrated Resource Planning

The purpose of this paper is to examine the extent to which electric utility integrated resource plans (IRPs) evaluate the impact of water constraints on electric generation resource portfolios and the extent to which IRPs consider the impacts of climate change to generation and loads. This white paper reviewed 30 IRPs to determine best practices with respect to analyzing and reporting on potential water-based and climate change risks within the integrated resource planning process. Best practices from electric utility IRPs are identified and additional recommendations and considerations are put forth. In recent years, thermal generating facilities have experienced challenges with water availability: a shortage of cooling water, conditions in which incoming cooling water is too warm for optimal operation, and/or water discharge temperatures exceeding permit limits. S&P Global Market Intelligence recently reported on a study that identified for the year 2030, 98.2 gigawatts (GW) of coal capacity at risk due to water stress. Climate change effects on hydrological cycles may adjust the timing, temperature, and volume of water availability for thermal electric cooling and for hydropower generation, which could further exacerbate the frequency and duration of operational constraints. Climate change can also impact the timing and intensity of electric loads that utilities must serve, most notably for heating and cooling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2018

Distinct Influences of Land Cover and Land Management on Seasonal Climate

Anthropogenic land use and land cover change is primarily represented in climate model simulations through prescribed transitions from natural vegetation to cropland or pasture. However, recent studies have demonstrated that land management practices, especially irrigation, have distinct climate impacts. Here we disentangle the seasonal climate impacts of land cover change and irrigation across areas of high agricultural intensity using climate simulations with three different land surface scenarios: (1) natural vegetation cover/no irrigation, (2) year 2000 crop cover/no irrigation, and (3) year 2000 crop cover and irrigation rates. We find that irrigation substantially amplifies land cover‐induced climate impacts but has opposing effects across certain regions. Irrigation mostly causes surface cooling, which substantially amplifies land cover change‐induced cooling in most regions except over Central, West, and South Asia, where it reverses land cover change‐induced warming. Despite increases in net surface radiation in some regions, this cooling is associated with enhancement of latent relative to sensible heat fluxes by irrigation. Similarly, irrigation substantially enhances the wetting influence of land cover change over several regions including West Asia and the Mediterranean. The most notable contrasting impacts of these forcings on precipitation occur over South Asia, where irrigation offsets the wetting influence of land cover during the monsoon season. Differential changes in regional circulations and moist static energy induced by these forcings contribute to their precipitation impacts and are associated with differential changes in surface and tropospheric temperature gradients and moisture availability. These results emphasize the importance of including irrigation forcing to evaluate the combined impacts of land surface changes for attributing historical climatic changes and managing future impacts.

Singh, Deepti↗

DOE OSTI2020

Responses of tundra plant community carbon flux to experimental warming, dominant species removal and elevation

1. Rising temperatures can influence ecosystem processes both directly and indirectly, through effects on plant species and communities. An improved understanding of direct versus indirect effects of warming on ecosystem processes is needed for robust predictions of the impacts of climate change on terrestrial ecosystem carbon (C) dynamics. 2. To explore potential direct and indirect effects of warming on C dynamics in arctic tundra heath, we established a warming (open top chambers) and dominant plant species (Empetrum hermaphroditum Hagerup) removal experiment at a high and low elevation site. We measured the individual and interactive effects of warming, dominant species removal and elevation on plant species cover, the normalized difference vegetation index (NDVI), leaf area index (LAI), temperature, soil moisture and instantaneous net ecosystem CO 2 exchange. 3. We hypothesized that ecosystems would be stronger CO 2 sinks at the low elevation site, and that warming and species removal would weaken the CO 2 sink because warming should increase ecosystem respiration (ER) and species removal should reduce gross primary productivity (GPP). Furthermore, we hypothesized that warming and species removal would have the greatest impact on processes at the high elevation where site temperature should be most limiting and dominant species may buffer the overall community to environmental stress more compared to the low elevation site where plants are more likely to compete with the dominant species. 4. The instantaneous CO 2 flux, which reflected a weak CO 2 sink, was similar at both elevations. Neither experimental warming nor dominant species removal significantly changed GPP or instantaneous net ecosystem CO 2 exchange even though species removal significantly reduced ER, NDVI and LAI. 5. Our results show that even the loss of dominant plant species may not result in significant landscape-scale responses of net ecosystem CO 2 exchange to warming. They also show that NDVI and LAI may be limited in their ability to predict changes in GPP in these tundra heaths systems. Our study highlights the need for more detailed vegetation analyses and ground-truthed measurements in order to accurately predict direct and indirect impacts of climatic change on ecosystem C dynamics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

How will United States commercial building energy use be impacted by IPCC climate scenarios?

Climate change and anthropogenically-forced shift of weather in the future will impact energy use and resilience of both the built environment and the electric grid. The aim of this analysis is to understand how future climate scenarios will impact electricity and natural gas use of commercial buildings in the United States. Here, this study analyzes this impact for 2030, 2045, and 2100 using Representative Concentration Pathways (RCP) scenarios defined in Intergovernmental Panel on Climate Change (IPCC) Assessment Report 5. The large, gridded simulation of meteorological variables for RCPs 2.6, 4.5, 6.0, and 8.5 are selected and downscaled to make available hourly Future Meteorological Year (FMY) weather files for use and improvement in subsequent studies. High performance computing resources use these FMYs to simulate commercial prototype buildings in every American Society of Heating, Refrigeration, and Air Conditioning Engineers (ASHRAE) climate zone of the United States (US), and results are scaled to nation-wide energy use using conditioned floor area multipliers. The analysis is conducted without speculating the physical and performance traits of future buildings or the grid characteristics. This analysis quantifies the impact of climate change on source electrical and natural gas usage for commercial buildings in the United States over the next 80 years. If US commercial floorspace remained constant, total energy use by 2100 is predicted between an 1.75% decrease under the greatest emission scenario (8.5) and a 1.76% increase under the lowest emission scenario (2.6). When adjusted for anticipated urban growth by 2100, the predicted range is 65% increase (8.5) and 71% increase (2.6). Under a global temperature rise climate scenario, the warmest US climate zones will see a large increases in electricity use derived from space cooling while the coldest US climate zones will see significant decreases in natural gas use caused by the decrease in heating necessary. While climate change may ultimately require adaptations of the built environment to withstand its effects and because the United States is a country that requires more heating than cooling, from a building energy perspective, climate change (average temperature rise) is a net energy saver for the United States.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Deep learning techniques to disentangle water use efficiency, climate change, and carbon sequestration across ecosystem scales

Focal Areas: We plan to use machine learning (ML) to disentangle the impact of climate change on plant water use efficiency (WUE) across the leaf level, structured canopy, and community ecosystem scales. Due to the complex behavior of modeling plant WUE’s connection to carbon storage (and hidden co-varying interactions) we suggest that physics-based deep learning must be utilized. The second topic we propose is, based on a better understanding of WUE response to climate change, there is a need to predict how WUE will limit or enhance the recycling of water from transpiration by vegetation back to the land surface worldwide, and thus impact water storage capacity (i.e., natural and reservoirs), using machine learning optimization techniques such as genetic algorithms.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2007

Effects of Global Change on U.S. Urban Areas: Vulnerabilities, Impacts, and Adaptation

Human settlements, both large and small, are where the vast majority of people on the Earth live. Expansion of cities both in population and areal extent, is a relentless process that will accelerate in the 21st century. As a consequence of urban growth both in the United States and around the globe, it is important to develop an understanding of how urbanization will affect the local and regional environment. Of equal importance, however, is the assessment of how cities will be impacted by the looming prospects of global climate change and climate variability. The potential impacts of climate change and variability has recently been annunciated by the IPCC's "Climate Change 2007" report. Moreover, the U.S. Climate Change Science Program (CCSP) is preparing a series of "Synthesis and Assessment Products" (SAPs) reports to support informed discussion and decision making regarding climate change and variability by policy matters, resource managers, stakeholders, the media, and the general public. We are authors on a SAP describing the effects of global climate change on human settlements. This paper will present the elements of our SAP report that relate to what vulnerabilities and impacts will occur, what adaptation responses may take place, and what possible effects on settlement patterns and characteristics will potentially arise, on human settlements in the U.S. as a result of climate change and climate variability. We will also present some recommendations about what should be done to further research on how climate change and variability will impact human settlements in the U.S., as well as how to engage government officials, policy and decision makers, and the general public in understanding the implications of climate change and variability on the local and regional levels. Additionally, we wish to explore how technology such as remote sensing data coupled with modeling, can be employed as synthesis tools for deriving insight across a spectrum of impacts (e.g. public health, urban planning for mitigation strategies) on how cities can cope and adapt to climate change and variability. This latter point parallels the concepts and ideas presented in the U.S. National Academy of Sciences, Decadal Survey report on "Earth Science Applications from Space: National Imperatives for the Next Decade and Beyond" wherein the analysis of the impacts of climate change and variability, human health, and land use change are listed as key areas for development of future Earth observing remote sensing systems.

Quattrochi, Dale A.↗

DOE OSTI2021

10 New Insights in Climate Science 2020 - a Horizon Scan

We summarize some of the past year’s most important findings within climate change-related research. New research has improved our understanding of Earth’s sensitivity to carbon dioxide, finds that permafrost thaw could release more carbon emissions than expected and that the uptake of carbon in tropical ecosystems is weakening. Adverse impacts on human society include increasing water shortages and impacts on mental health. Options for solutions emerge from rethinking economic models, rights-based litigation, strengthened governance systems and a new social contract. The disruption caused by COVID-19 could be seized as an opportunity for positive change, directing economic stimulus towards sustainable investments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1994

An analysis of the impacts of global climate and emissions changes on regional tropospheric ozone

Many of the synergistic impacts resulting from future changes in emissions as well as changes in ambient temperature, moisture, and UV flux have not been quantified. A three-dimensional regional-scale photo-chemical model (STEM-2) is used in this study to evaluate these perturbations to trace gas cycles over the eastern half of the United States of America. The model was successfully used to simulate a regional-scale ozone episode (base case - June 1984) and four perturbations scenarios - viz., perturbed emissions, temperature, water vapor column, and incoming UV flux cases, and a future scenario (for the year 2034). The impact of these perturbation scenarios on the distribution of ozone and other major pollutants such as SO2 and sulfates were analyzed in detail. The spatial distribution and the concentration of ozone at the surface increased by about 5-15 percent for most cases except for the perturbed water vapor case. The regional scale surface ozone concentration distribution for the year 2034 (future scenario) showed an increase of non-attainment areas. The rural areas of Pennsylvania, West Virginia, and Georgia showed the largest change in the surface ozone field for the futuristic scenario when compared to the base case.

John, Kuruvilla↗

DOE OSTI2020

Distinct response of gross primary productivity in five terrestrial biomes to precipitation variability

Abstract Climate change will impact precipitation variability, potentially accelerating climate-terrestrial carbon feedbacks. However, the response of ecosystems to precipitation variability is difficult to constrain due to myriad physiological and abiotic variables that limit terrestrial productivity. Based on a combination of satellite imagery and a global network of daily precipitation data, we present here a statistical framework to isolate the impact of precipitation variability on the gross primary productivity of five biomes that collectively account for 50% of global land area. The productivity of mesic grasslands and forests decreases by ~28% and ~7% (respectively) in response to more irregular rain within the year, while the sensitivity is halved in response to higher year-to-year variability. Xeric grasslands are similarly impacted by intra-annual rainfall variance, but they show an increase in productivity with higher interannual rainfall variability. Conversely, the productivity of boreal forests increases under higher variability on both timescales. We conclude that projected changes in precipitation variability will have a measurable global impact on the terrestrial carbon sink.

Ritter, François↗

NASA NTRS1992

A model of the CO2 exchanges between biosphere and atmosphere in the tundra

A physical model of the soil thermal regime in a permafrost terrain has been developed and validated with soil temperature measurements at Barrow, Alaska. The model calculates daily soil temperatures as a function of depth and average moisture contents of the organic and mineral layers using a set of five climatic variables, i.e., air temperature, precipitation, cloudiness, wind speed, and relative humidity. The model is not only designed to study the impact of climate change on the soil temperature and moisture regime, but also to provide the input to a decomposition and net primary production model. In this context, it is well known that CO2 exchanges between the terrestrial biosphere and the atmosphere are driven by soil temperature through decomposition of soil organic matter and root respiration. However, in tundra ecosystems, net CO2 exchange is extremely sensitive to soil moisture content; therefore it is necessary to predict variations in soil moisture in order to assess the impact of climate change on carbon fluxes. To this end, the present model includes the representation of the soil moisture response to changes in climatic conditions. The results presented in the foregoing demonstrate that large errors in soil temperature and permafrost depth estimates arise from neglecting the dependence of the soil thermal regime on soil moisture contents. Permafrost terrain is an example of a situation where soil moisture and temperature are particularly interrelated: drainage conditions improve when the depth of the permafrost increases; a decrease in soil moisture content leads to a decrease in the latent heat required for the phase transition so that the heat penetrates faster and deeper, and the maximum depth of thaw increases; and as excepted, soil thermal coefficients increase with moisture.

Labgaa, Rachid R.↗

NASA NTRSDate not supplied

Impact of Anthropogenic Activity, Climate Change, and Urbanization on Wetland Habitat in the Platte River Basin

The Platte River Basin (PRB) is a dynamic ecosystem where wetlands play a pivotal role as essential habitats for various flora and fauna, including local and migratory birds. It provides many crucial ecosystem services that benefit humans directly and indirectly. However, anthropogenic activity, climate change, and urbanization have resulted in decline in wildlife habitat, elevated flood risk, and wetland loss. To address this issue, NASA DEVELOP partnered with Audubon Great Plains (AGP) to address the vital habitats within urban areas to protect bird species, reduce flood hazards, and analyze the potential impact of future development on wetlands. We utilized remotely sensed data from Landsat 8 Operational Land Imager (OLI), Sentinel-2 Multispectral Instrument (MSI), and Sentinel-1 Synthetic Aperture Radar (SAR) to assess land use and land cover (LULC) change. Nighttime lights data from Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) as well as NASA Socioeconomic Data and Applications Center (SEDAC) population data were also used as inputs to simulate urban growth potential up to 2050 using the open-source FUTure Urban-Regional Environment Simulation (FUTURES) model. A broad scale analysis across 13 focal cities showed varied changes in land use patterns across the PRB, with the most notable being a decrease in agricultural land coverage and an increase in vegetation and grassland coverage. We overlaid a flood extent map with the LULC classifications in Grand Island to identify possible restoration sites under AGP’s Urban Woods and Prairies Initiative. The results for two proposed scenarios showed that at least 51 counties out of 81 in the PRB would experience growth by year 2050. The first scenario (all wetlands are protected) showed that there will be no loss of wetlands by 2050. However, the second scenario (no wetlands are protected) showed a decrease in wetland area and loss of habitat for bird conservation. The results will help AGP to lead awareness workshops for communities about wetland protection and to form impactful conservation strategies in the future.

Nancee Uniyal↗

DOE OSTI2026

Community-Informed Urban Flood Modeling for Impact Mitigation

Climate change is intensifying the hydrologic cycle, leading to more frequent and severe rainfall-driven (pluvial) flooding in urban areas. In the mid-Atlantic US cities, aging and under-designed stormwater infrastructure is increasingly strained by these events, resulting in recurring damage to property and disruptions to transportation networks. In this study, we combine community engagement with hydrologic modeling to develop and evaluate potential urban flood adaptation strategies. Over a three-year period, local technical experts and community representatives met regularly to discuss flooding concerns, identify priorities, and co-develop adaptation strategies. These discussions informed the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed, the focus location of this study. The flooding model integrates complex surface and subsurface stormwater infrastructure data, local expert knowledge, and community insights. We simulate stakeholder-prioritized adaptations, such as green and gray infrastructure strategies. Model results demonstrate that enhanced infrastructure maintenance is the most effective adaptation for reducing flood depths, but has varied effects across the watershed, and can increase flooding in some locations. Spatially concentrated greening provides limited benefit to the watershed as a whole, but moderate benefit in community priority areas. Together, these adaptations have the potential to reduce flood depths by as much as 58% in some locations, greatly reducing property damage and mobility impacts, primary concerns of stakeholders. Future work will implement robust optimization tools to search for adaptations which meet stakeholder objectives and perform highly under varied future climate conditions. This work contributes to the expanding literature on collaborative modeling and demonstrates that community-engaged approaches can enhance model credibility and generate more actionable insights for communities seeking to strengthen climate resilience.

Baltimore MD↗