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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Nature-based climate solutions can help mitigate the radiative forcing that follows deforestation

Widespread expansion of agriculture and forestry has altered the surface of the Earth, the composition of the atmosphere, and as a result, the climate. Here we quantify the radiative forcing caused by the historical deforestation of an ecoregion in the U.S. Upper Midwest and the adoption of eight nature-based climate solutions. We combined regional forest inventory data with over three decades of remote sensing and in situ data from a replicated land use change experiment. Deforestation of the region caused net global warming (1626 ± 44 µW m -2 ), mainly from the 76 % reduction of ecosystem carbon stocks, but also from the 84 % reduction of the soil methane sink and the 115 % increase in soil nitrous oxide emissions. The associated albedo increase offset 24 % of this greenhouse gas induced warming. For the adoption of nature-based climate solutions, we found that conservation agriculture can provide -39 to -76 ± 31 µW m -2 of climate mitigation over a 100-year time period while short/medium length forestry rotations can provide more at -296 to -881 ± 44 µW m -2 and natural forest regeneration can provide the most at -1555 ± 44 µW m -2 . As the impacts of climate change on nature and society intensify, consideration should be given to the climate mitigation, habitat, and ecosystem services that nature-based climate solutions can provide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Changes in high-latitude surface energy balance driven by snowpack and vegetation dynamics under warmer climate

With rapid climate warming, expected changes in snowpack and vegetation will alter the seasonal surface albedo of high-latitude ecosystems. The extent to which these albedo changes may affect surface energy balances and thus soil temperatures is uncertain, but represents a potentially important component of ecosystem feedbacks to climate change. Here, we apply a well-tested process-rich ecosystem model, ecosys , to examine changes in seasonal surface albedo and soil temperature driven by climate-induced snowpack and vegetation changes across Alaska under a warmer twenty-first century climate. Under the Representative Concentration Pathway 8.5 climate change scenario, the modeled changes in surface albedo exhibited large seasonal and spatial variations. We found spring albedo decreases driven by increases in snow-free periods (>20 d) and an extended growing season length that resulted in greater gains in leaf area index (LAI) in most parts of Alaska. In contrast, we modeled increases in summer and winter albedo (despite modeled increases in LAI) across much of the boreal forest due to an increased proportion of aspen, which has a higher leaf albedo than the currently dominant black spruce. Modeled latent heat fluxes generally increase across the twenty-first century, particularly during the spring and summer. Overall, climate warming and changes in surface energy fluxes resulted in a 3.5 ± 0.50 °C increase in spatial- and annual-averaged top 10 cm surface soil temperatures across Alaskan ecosystems by the year 2100, with larger increases in tundra than boreal forest regions. We conclude that under warmer climates, seasonal variations in albedo and surface energy fluxes are particularly pronounced during the spring and summer, driven by changes in snowpack and vegetation dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Water isotopes, climate variability, and the hydrological cycle: recent advances and new frontiers

Abstract The hydrologic cycle is a fundamental component of the climate system with critical societal and ecological relevance. Yet gaps persist in our understanding of water fluxes and their response to increased greenhouse gas forcing. The stable isotope ratios of oxygen and hydrogen in water provide a unique opportunity to evaluate hydrological processes and investigate their role in the variability of the climate system and its sensitivity to change. Water isotopes also form the basis of many paleoclimate proxies in a variety of archives, including ice cores, lake and marine sediments, corals, and speleothems. These records hold most of the available information about past hydrologic variability prior to instrumental observations. Water isotopes thus provide a ‘common currency’ that links paleoclimate archives to modern observations, allowing us to evaluate hydrologic processes and their effects on climate variability on a wide range of time and length scales. Building on previous literature summarizing advancements in water isotopic measurements and modeling and describe water isotopic applications for understanding hydrological processes, this topical review reflects on new insights about climate variability from isotopic studies. We highlight new work and opportunities to enhance our understanding and predictive skill and offer a set of recommendations to advance observational and model-based tools for climate research. Finally, we highlight opportunities to better constrain climate sensitivity and identify anthropogenically-driven hydrologic changes within the inherently noisy background of natural climate variability.

Dee, Sylvia (ORCID:000000022140785X)↗
DOE OSTI2025

Climate Nowcasting

The climate is changing so rapidly that climatologies based on historical statistics cannot reliably capture the current risk of extreme weather events hazardous to society. Decision relevant projections of weather extreme probability over the next 10–15 years are needed to enable adaptation and resilience in the face of this evolving risk. Current weather forecasts/predictions and long-term climate projections for decades into the future are inadequate for providing this information to stakeholders that need it, targeting forecast horizons either too short or too far into the future. We argue that a new approach is needed: climate nowcasting. Climate nowcasting would focus on user-inspired extreme metrics over the next 10–15 year time frame, targeting specific impacts and locations down to a local scale, by engaging with stakeholders to understand their needs and provide information in a format relevant for decision making. Importantly, climate nowcasting will not consist of a single approach or data set, involving rather data fusion from different sources of information: simulations, observations and data driven methods, likely through different weights depending on the metric of interest. Predictions must be accompanied by serious engagement with stakeholders facing climate risks, and clearly present uncertainties and limitations of any prediction. Such a vision is very different from how typical climate or weather forecasts are applied today.

Gettelman, Andrew↗
DOE OSTI2022

Climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics

Predicted growth in world population will put unparalleled stress on the need for sustainable energy and global food production, as well as increase the likelihood of future pandemics. In this work, we identify high-resolution environmental zones in the context of a changing climate and predict longitudinal processes relevant to these challenges. We do this using exhaustive vector comparison methods that measure the climatic similarity between all locations on earth at high geospatial resolution relative to global-scale analyses. The results are captured as networks, in which edges between geolocations are defined if their historical climate similarities exceed a threshold. We apply Markov clustering and our novel Correlation of Correlations method to the resulting climatic networks, which provides unprecedented agglomerative and longitudinal views of climatic relationships across the globe. The methods performed here resulted in the fastest (9.37x10 18 operations/sec) and one of the largest (168.7x10 21 operations) scientific computations ever performed, with more than 100 quadrillion edges considered for a single climatic network. Our climatic analysis reveals areas of the world experiencing rapid environmental changes, which can have important implications for global carbon fluxes and zoonotic spillover events. Correlation and network analyses of this kind are widely applicable across computational and predictive biology domains, including systems biology, ecology, carbon cycles, biogeochemistry, and zoonosis research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Predicting soil carbon changes in switchgrass grown on marginal lands under climate change and adaptation strategies

Abstract The United States Great Lakes Region (USGLR) is a critical geographic area for future bioenergy production. Switchgrass ( Panicum virgatum ) is widely considered a carbon (C)‐neutral or C‐negative bioenergy production system, but projected increases in air temperature and precipitation due to climate change might substantially alter soil organic C (SOC) dynamics and storage in soils. This study examined long‐term SOC changes in switchgrass grown on marginal land in the USGLR under current and projected climate, predicted using a process‐based model (Systems Approach to Land‐Use Sustainability) extensively calibrated with a wealth of plant and soil measurements at nine experimental sites. Simulations indicate that these soils are likely a net C sink under switchgrass (average gain 0.87 Mg C ha −1 year −1 ), although substantial variation in the rate of SOC accumulation was predicted (range: 0.2–1.3 Mg C ha −1 year −1 ). Principal component analysis revealed that the predicted intersite variability in SOC sequestration was related in part to differences in climatic characteristics, and to a lesser extent, to heterogeneous soils. Although climate change impacts on switchgrass plant growth were predicted to be small (4%–6% decrease on average), the increased soil respiration was predicted to partially negate SOC accumulations down to 70% below historical rates in the most extreme scenarios. Increasing N fertilizer rate and decreasing harvest intensity both had modest SOC sequestration benefits under projected climate, whereas introducing genotypes better adapted to the longer growing seasons was a much more effective strategy. Best‐performing adaptation scenarios were able to offset >60% of the climate change impacts, leading to SOC sequestration 0.7 Mg C ha −1 year −1 under projected climate. On average, this was 0.3 Mg C ha −1 year −1 more C sequestered than the no adaptation baseline. These findings provide crucial knowledge needed to guide policy and operational management for maximizing SOC sequestration of future bioenergy production on marginal lands in the USGLR.

09 BIOMASS FUELS↗
DOE OSTI2024

Beyond PCA: Additional Dimension Reduction Techniques to Consider in the Development of Climate Fingerprints

Abstract Dimension reduction techniques are an essential part of the climate analyst’s toolkit. Due to the enormous scale of climate data, dimension reduction methods are used to identify major patterns of variability within climate dynamics, to create compelling and informative visualizations, and to quantify major named modes such as El Niño–Southern Oscillation. Principal components analysis (PCA), also known as the method of empirical orthogonal functions (EOFs), is the most commonly used form of dimension reduction, characterized by a remarkable confluence of attractive mathematical, statistical, and computational properties. Despite its ubiquity, PCA suffers from several difficulties relevant to climate science: high computational burden with large datasets, decreased statistical accuracy in high dimensions, and difficulties comparing across multiple datasets. In this paper, we introduce several variants of PCA that are likely to be of use in climate sciences and address these problems. Specifically, we introduce non-negative , sparse , and tensor PCA and demonstrate how each approach provides superior pattern recognition in climate data. We also discuss approaches to comparing PCA-family results within and across datasets in a domain-relevant manner. We demonstrate these approaches through an analysis of several runs of the E3SM climate model from 1991 to 1995, focusing on the simulated response to the Mt. Pinatubo eruption; our findings are consistent with a recently identified stratospheric warming fingerprint associated with this type of stratospheric aerosol injection.

Weylandt, Michael↗
DOE OSTI2025

Tracing the impacts of Mount Pinatubo eruption on regional climate using spatially-varying changepoint detection

Significant events, such as volcanic eruptions, can have global and long-lasting impacts on climate. These global impacts, however, are not uniform across space and time. Understanding how the Mt. Pinatubo eruption affects global and regional climate is of great interest for predicting the impact on climate due to similar events as well as understanding the possible effect of the stratospheric aerosol injections proposed to combat climate change. While many studies illustrated the impact of the Pinatubo eruption on a global scale, studies at a fine regional scale are scarce. Here, we propose a Bayesian spatially-varying changepoint detection and estimation method to trace the impact of Mt. Pinatubo eruption on regional climate. Our approach takes into account the diffusing nature and spatial correlation of the climate changes attributed to the volcanic eruption. We illustrate our method and demonstrate its advantages over an existing changepoint detection method through simulations. Finally, we apply our method to monthly stratospheric aerosol optical depth and surface temperature data from 1985 to 1995 to detect and estimate changepoints following the 1991 Mt. Pinatubo eruption. Our results quantitatively characterize the spatial pattern of the eruption’s impact on regional climate, complementing the previous studies on the global impact of the Pinatubo eruption.

Aerosol optical depth↗
DOE OSTI2023

Assessing the Influence of Climate on the Spatial Pattern of West Nile Virus Incidence in the United States

West Nile virus (WNV) is the leading cause of mosquito-borne disease in humans in the United States. Since the introduction of the disease in 1999, incidence levels have stabilized in many regions, allowing for analysis of climate conditions that shape the spatial structure of disease incidence. Our goal was to identify the seasonal climate variables that influence the spatial extent and magnitude of WNV incidence in humans. We developed a predictive model of contemporary mean annual WNV incidence using U.S. county-level case reports from 2005 to 2019 and seasonally averaged climate variables. We used a random forest model that had an out-of-sample model performance of R 2 =0.61. Our model accurately captured the V-shaped area of higher WNV incidence that extends from states on the Canadian border south through the middle of the Great Plains. It also captured a region of moderate WNV incidence in the southern Mississippi Valley. The highest levels of WNV incidence were in regions with dry and cold winters and wet and mild summers. The random forest model classified counties with average winter precipitation levels <23.3 mm/month as having incidence levels over 11 times greater than those of counties that are wetter. Among the climate predictors, winter precipitation, fall precipitation, and winter temperature were the three most important predictive variables. We consider which aspects of the WNV transmission cycle climate conditions may benefit the most and argued that dry and cold winters are climate conditions optimal for the mosquito species key to amplifying WNV transmission. Our statistical model may be useful in projecting shifts in WNV risk in response to climate change.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Data for Climate vs Energy Security: Quantifying the Trade-offs of BECCS Deployment and Overcoming Opportunity Costs on Set-Aside Land

Bioenergy with carbon capture and storage (BECCS) sits at the nexus of the climate and energy security. We evaluated trade-offs between scenarios that support climate stabilization (negative emissions and net climate benefit) or energy security (ethanol production). Our spatially explicit model indicates that the foregone climate benefit from abandoned cropland (opportunity cost) increased carbon emissions per unit of energy produced by 14–36%, making geologic carbon capture and storage necessary to achieve negative emissions from any given energy crop. The toll of opportunity costs on the climate benefit of BECCS from set-aside land was offset through the spatial allocation of crops based on their individual biophysical constraints. Dedicated energy crops consistently outperformed mixed grasslands. We estimate that BECCS allocation to land enrolled in the Conservation Reserve Program (CRP) could capture up to 9 Tg C year–1 from the atmosphere, deliver up to 16 Tg CE year–1 in emissions savings, and meet up to 10% of the US energy statutory targets, but contributions varied substantially as the priority shifted from climate stabilization to energy provision. Our results indicate a significant potential to integrate energy security targets into sustainable pathways to climate stabilization but underpin the trade-offs of divergent policy-driven agendas.

Field Data↗
DOE OSTI2020

Impacts of climate and biophysical variability on global agriculture markets

Agricultural production is highly sensitive to changes in climate and weather patterns. The focus of the great majority of studies assessing climate impacts on agriculture has been on mean changes in agricultural responses. However, the manner in which global agricultural markets respond to the interannual variability of the climate and biophysical shocks is poorly understood. Here we show a strong transmission of interannual variations in climate-induced biophysical yield shocks to agriculture markets, which is magnified further by endogenous market fluctuations. We demonstrate the importance of imperfect versus perfect expectations of market and weather in generating the market fluctuations that play a key role in transferring the interannual variations to markets. We find that the volatility of market prices and consumption could be potentially reduced on average by 55% and 41%, respectively, with improved expectations, where agricultural producers make better decisions to adapt to climate and biophysical variability. We also find much heterogeneity in interannual variability across crops and regions, which is considerably mediated by trade as part of the economic response. Our study provides new insights on climate impacts on agricultural market variability and lays a foundation for further investigating the full range of climate impacts on biophysical and human systems.

Zhao, Xin↗
DOE OSTI2021

A Modular System for Increasing Predictiveness for Extreme Climate Predictions

We know that climate change is poised to reshape our world, but we lack clear enough predictions about precisely how. The preponderance of these changes is associated with human activity, specifically the emission of CO 2 and other greenhouse gases. Problematically, projections of climate change continue to be marred by unacceptably large uncertainties which hamper informed decision-making and cost society a chance to adapt proactively and effectively. These uncertainties stem from deficiencies in predictions of future greenhouse gas emissions, but also from inaccuracies in the representation of the physical models used to predict the climate response to such emissions. The uncertainties in projections associated with the inaccurate representation of climate physics, chemistry and biology are similar to those that plagued the first global climate models developed fifty years ago, despite more than a factor 10 8 increase in computer performance. Our transformational question is then, how can the accuracy of climate projections be dramatically improved by applying recent advances in the computational and data sciences to train the models with the wealth of data being constantly collected about the ongoing changes in the climate system?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Guide to Determining Climate Zone by County: Building America and IECC 2021 Updates

This report describes the climate zone designations used by the U.S. Department of Energy (DOE) Building America Program. The report aims to help residential building stakeholders identify the appropriate climate zone designation for each county in the United States, including Hawaii and Alaska. Identifying the correct climate zone is important for many activities including residential construction projects, code compliance, energy analysis and modeling, and other analytical activities where climate zones impact the energy and moisture performance of residential buildings. This report supersedes the previous Building America publication: Guide to Determining Climate Regions by County, published in 2010. This report reflects climate designations used by the International Code Council (ICC) in the 2021 versions of the International Energy Conservation Code (IECC), the International Residential Code (IRC), and other codes produced by ICC. The information provided here and associated data should be used for the most up-to-date information regarding climate zone designations in the United States.

2021 IECC↗
DOE OSTI2023

Field Validation of Innovative Air-Source Heat Pumps for Cold-Climate Heating Applications

Air-source heat pumps (ASHPs) have historically found application in milder climates in the United States (US). Their application in cold climates has been hindered by reduced performance as outdoor temperatures fall below freezing and the need for backup or auxiliary electric resistance heaters to meet peak heating loads. Recent advances in cold-climate air-source heat pumps (ccASHPs) with features such as variable-speed compressors, multistage systems, and highly optimized thermal design have improved performance by increasing the coefficient of performance (COP), low-ambient-temperature capacity, and heating seasonal performance factor (HSPF) dramatically. Despite their benefits, ccASHPs are still not widely prescribed for cold climate conditions. This is due in part to a lack of verified, demonstrated performance and analysis in cold climates. Ultimately, high-efficiency ccASHPs play a key role in emerging energy/grid renovation efforts in addition to their potential energy savings and avoided carbon emissions, but their actual performance in various cold climate field settings must be better understood. The primary objective of this project was to measure the in-field performance of variable-capacity air-source heat pumps in cold climates with the goal of enabling the development of field-based performance maps. Specifically, the study looked at how the heat pumps operated in the field, the frequency of cycling, the frequency of defrost events, and the time spent in each mode of operation. The results are intended to be used by DOE to inform research and development of energy-efficient equipment and to develop guidelines for optimizing primary energy savings when using air-source heat pumps in heating-dominated regions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Are long-term climate projections useful for on-farm adaptation decisions?

The current literature on climate services for farmers predominantly focuses on seasonal forecasts, with an assumption that longer-term climate projections may not be suitable for informing farming decisions. In this paper, we explore whether certain types of long-term climate projections may be useful for some specific types of farming decisions. Through interviews with almond tree crop farmers and farm advisors in California, we examine how farmers perceive the utility and accuracy levels of long-term climate projections and identify the types of projections that they may find useful. The interviews revealed that farmers often perceive long-term climate projections as an extension of weather forecasts, which can lead to their initial skepticism of the utility of such information. However, we also found that when farmers were presented with long-term trends or shifts in crop-specific agroclimatic metrics (such as chill hours or summer heat), they immediately perceived these as valuable for their decision-making. Hence, the manner in which long-term projections are framed, presented, and discussed with farmers can heavily influence their perception of the potential utility of such projections. The iterative conversations as part of the exploratory interview questions, served as a tool for “ joint construction of meaning” of complex and ambiguous terms such as “long-term climate projections,” “long-term decisions” and “uncertainty.” This in-turn supported a joint identification (and understanding) of the types of information that can potentially be useful for on-farm adaptive decisions, where the farmer and the interviewer both improvise and iterate to find the best types of projections that fit specific decision-contexts. Overall, this research identifies both the types of long-term climate information that farmers may consider useful, and the engagement processes that are able to effectively elicit farmers' long-term information needs.

Jagannathan, Kripa↗
DOE OSTI2021

Sensitivity of Riparian Buffer Designs to Climate Change—Nutrient and Sediment Loading to Streams: A Case Study in the Albemarle-Pamlico River Basins (USA) Using HAWQS

Riparian buffer zones (RBZs) provide multiple benefits to watershed ecosystems. We aimed to conduct an extensive sensitivity analysis of the RBZ designs to climate change nutrient and sediment loadings to streams. We designed 135 simulation scenarios starting with the six baselines RBZs (grass, urban, two-zone forest, three-zone forest, wildlife, and naturalized) in three 12-digit Hydrologic Unit Code watersheds within the Albemarle-Pamlico river basin (USA). Using the hydrologic and water quality system (HAWQS), we assessed the sensitivity of the designs to five water quality indicator (WQI) parameters: dissolved oxygen (DO), total phosphorous (TP), total nitrogen (TN), sediment (SD), and biochemical oxygen demand (BD). To understand the climate mitigation potential of RBZs, we identified a subset of future climate change projection models of air temperature and precipitation using EPA’s Locating and Selecting Scenarios Online tool. Analyses revealed optimal RBZ designs for the three watersheds. In terms of watershed ecosystem services sustainability, the optimal Urban RBZ in contemporary climate (1983–2018) reduced SD from 61–96%, TN from 34–55%, TP from 9–48%, and BD from 53–99%, and raised DO from 4–10% with respect to No-RBZ in the three watersheds. The late century’s (2070–2099) extreme mean annual climate changes significantly increased the projected SD and BD; however, the addition of urban RBZs was projected to offset the climate change reducing SD from 28–94% and BD from 69–93% in the watersheds. All other types of RBZs are also projected to fully mitigate the climate change impacts on WQI parameters except three-zone RBZ.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Climate change as an incentive for future human migration

Human migration is both motivated and constrained by a multitude of socioeconomic and environmental factors, including climate-related factors. Climatic factors exert an influence on local and regional population density. Here, we examine the implications of future motivation for humans to migrate by analyzing today's relationships between climatic factors and population density, with all other factors held constant. Such “all other factors held constant” analyses are unlikely to make quantitatively accurate predictions, but the order of magnitude and spatial pattern that come out of such an analysis can be useful when considering the influence of climate change on the possible scale and pattern of future incentives to migrate. Our results indicate that, within decades, climate change may provide hundreds of millions of people with additional incentive to migrate, largely from warm tropical and subtropical countries to cooler temperate countries, with India being the country with the greatest number of people with additional incentive to migrate. These climate-driven incentives would be among the broader constellation of incentives that influence migration decisions. Areas with the highest projected population growth rates tend to be areas that are likely to be most adversely affected by climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Indices of extremes: geographic patterns of change in extremes and associated vegetation impacts under climate intervention

Extreme weather events have been demonstrated to be increasing in frequency and intensity across the globe and are anticipated to increase further with projected changes in climate. Solar climate intervention strategies, specifically stratospheric aerosol injection (SAI), have the potential to minimize some of the impacts of a changing climate while more robust reductions in greenhouse gas emissions take effect. However, to date little attention has been paid to the possible responses of extreme weather and climate events under climate intervention scenarios. We present an analysis of 16 extreme surface temperature and precipitation indices, as well as associated vegetation responses, applied to the Geoengineering Large Ensemble (GLENS). GLENS is an ensemble of simulations performed with the Community Earth System Model (CESM1) wherein SAI is simulated to offset the warming produced by a high-emission scenario throughout the 21st century, maintaining surface temperatures at 2020 levels. GLENS is generally successful at maintaining global mean temperature near 2020 levels; however, it does not completely offset some of the projected warming in northern latitudes. Some regions are also projected to cool substantially in comparison to the present day, with the greatest decreases in daytime temperatures. The differential warming–cooling also translates to fewer very hot days but more very hot nights during the summer and fewer very cold days or nights compared to the current day. Extreme precipitation patterns, for the most part, are projected to reduce in intensity in areas that are wet in the current climate and increase in intensity in dry areas. We also find that the distribution of daily precipitation becomes more consistent with more days with light rain and fewer very intense events than currently occur. In many regions there is a reduction in the persistence of long dry and wet spells compared to present day. However, asymmetry in the night and day temperatures, together with changes in cloud cover and vegetative responses, could exacerbate drying in regions that are already sensitive to drought. Overall, our results suggest that while SAI may ameliorate some of the extreme weather hazards produced by global warming, it would also present some significant differences in the distribution of climate extremes compared to the present day.

54 ENVIRONMENTAL SCIENCES↗