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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A Global Methane Observation System to Reduce Uncertainty for Anthropogenic and Natural Sources and Sinks for Detecting and Attributing Climate Feedbacks

Atmospheric methane (CH4) concentrations are accelerating global warming as net emissions increase. Observing systems that quantify sources remain too sparse and fragmented to detect trends—especially in remote regions where climate‐driven natural emissions may be rising. We provide a framework for quantifying uncertainty reductions through the implementation of a global ecosystem‐methane observing system designed to: (i) substantially lower uncertainty in sectoral and regional emissions, (ii) separate co‐occurring anthropogenic and natural fluxes, and (iii) trend detection at regional scales to verify mitigation progress and provide early warning of natural feedbacks. Using bottom‐up inventories and process‐model ensembles for 2014–2023, we show that anthropogenic emissions remain uncertain by ∼32% globally, while natural sources—tropical and boreal‐arctic wetlands, fires, and inland waters—carry far larger uncertainties (+ 70%) and trend uncertainties reaching ∼200%. Additional observations must match spatial emission structure to increase observability of emissions: high‐resolution satellite constellations for point sources combined with expanded flux networks and wetland mapping for diffuse sources, and denser ground‐based atmospheric column measurements to restore observability in under‐sampled tropics and high latitudes. Notional analyses indicate that targeted additions of flux towers and ∼20 in situ atmospheric column concentration instruments per key tropical region could reduce continental‐scale uncertainties at modest cost. Conceptual illustration of a Global Ecosystem Methane Observing System (GEM‐OS) integrating satellites, aircraft, atmospheric networks, and ecosystem measurements to quantify methane emissions from anthropogenic and natural sources. The multi‐scale observing framework improves source attribution, reduces uncertainty in regional methane budgets, and enables early detection of climate‐driven feedbacks from wetlands, fires, permafrost, agriculture, and fossil‐fuel emissions.

Ciais, P↗
DOE OSTI2025

Granger causal inference for climate change attribution

Abstract Climate change detection and attribution (D&A) is concerned with determining the extent to which anthropogenic activities have influenced specific aspects of the global climate system. D&A fits within the broader field of causal inference, the collection of statistical methods that identify cause and effect relationships. There are a wide variety of methods for making attribution statements, each of which require different types of input data and focus on different types of weather and climate events and each of which are conditional to varying extents. Some methods are based on Pearl causality (direct experimental interference) while others leverage Granger (predictive) causality, and the causal framing provides important context for how the resulting attribution conclusion should be interpreted. However, while Granger-causal attribution analyses have become more common, there is no clear statement of their strengths and weaknesses relative to Pearl-causal attribution and no clear consensus on where and when Granger-causal perspectives are appropriate. In this prospective paper, we provide a formal definition for Granger-based approaches to trend and event attribution and a clear comparison with more traditional methods for assessing the human influence on extreme weather and climate events. Broadly speaking, Granger-causal attribution statements can be constructed quickly from observations and do not require computationally-intesive dynamical experiments. These analyses also enable rapid attribution, which is useful in the aftermath of a severe weather event, and provide multiple lines of evidence for anthropogenic climate change when paired with Pearl-causal attribution. Confidence in attribution statements is increased when different methodologies arrive at similar conclusions. Moving forward, we encourage the D&A community to embrace hybrid approaches to climate change attribution that leverage the strengths of both Granger and Pearl causality.

Risser, Mark D. (ORCID:0000000319561783)↗
DOE OSTI2024

Conditional multi-step attribution for climate forcings

SAND2025-11608O Conditional multi-step attribution for climate forcings and its associated dataset present a framework for attributing a climate impacts, such as stratospheric warming or surface cooling, to a specific magnitude of climate forcing, such as stratospheric aerosol injection. The objective is to improve on existing methods that struggle to attribute highly noisy climate signals. This has potential broader use in climate security applications to identify and understand geoengineering and climate tipping point effects. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Swiler, Laura↗
DOE OSTI2017

Future global mortality from changes in air pollution attributable to climate change

Ground-level ozone and fine particulate matter (PM2.5) are associated with premature human mortality(1-4); their future concentrations depend on changes in emissions, which dominate the near-term(5), and on climate change(6,7). Previous global studies of the air-quality-related health effects of future climate change(8,9) used single atmospheric models. But, in related studies, mortality results differ among models(10-12). Here we use an ensemble of global chemistry-climate models(13) to show that premature mortality from changes in air pollution attributable to climate change, under the high greenhouse gas scenario RCP8.5 (ref. 14), is probably positive. We estimate 3,340 (-30,300 to 47,100) ozone-related deaths in 2030, relative to 2000 climate, and 43,600 (-195,000 to 237,000) in 2100 (14% of the increase in global ozone-related mortality). For PM2.5, we estimate 55,600 (-34,300 to 164,000) deaths in 2030 and 215,000 (-76,100 to 595,000) in 2100 (countering by 16% the global decrease in PM2.5-related mortality). Premature mortality attributable to climate change is estimated to be positive in all regions except Africa, and is greatest in India and East Asia. Finally, most individual models yield increased mortality from climate change, but some yield decreases, suggesting caution in interpreting results from a single model. Climate change mitigation is likely to reduce air-pollution-related mortality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Detection and attribution of climate extremes in the observed record

We present an overview of practices and challenges related to the detection and attribution of observed changes in climate extremes. Detection is the identification of a statistically significant change in the extreme values of a climate variable over some period of time. Issues in detection discussed include data quality, coverage, and completeness. Attribution takes that detection of a change and uses climate model simulations to evaluate whether a cause can be assigned to that change. Additionally, we discuss a newer field of attribution, event attribution, where individual extreme events are analyzed for the express purpose of assigning some measure of whether that event was directly influenced by anthropogenic forcing of the climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Extreme Weather Events and Climate Change Attribution

A report from the National Academies of Sciences, Engineering, and Medicine concludes it is now possible to estimate the influence of climate change on some types of extreme events. The science of extreme event attribution has advanced rapidly in recent years, giving new insight to the ways that human-caused climate change can influence the magnitude or frequency of some extreme weather events. This report examines the current state of science of extreme weather attribution, and identifies ways to move the science forward to improve attribution capabilities. Confidence is strongest in attributing types of extreme events that are influenced by climate change through a well-understood physical mechanism, such as, the more frequent heat waves that are closely connected to human-caused global temperature increases, the report finds. Confidence is lower for other types of events, such as hurricanes, whose relationship to climate change is more complex and less understood at present. For any extreme event, the results of attribution studies hinge on how questions about the event's causes are posed, and on the data, modeling approaches, and statistical tools chosen for the analysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Social inequalities in climate change-attributed impacts of Hurricane Harvey

Climate change is already increasing the severity of extreme weather events such as with rainfall during hurricanes. But little research to date investigates if, and to what extent, there are social inequalities in climate change-attributed extreme weather event impacts. Here, we use climate change attribution science paired with hydrological flood models to estimate climate change-attributed flood depths and damages during Hurricane Harvey in Harris County, Texas. Using detailed land-parcel and census tract socio-economic data, we then describe the socio-spatial characteristics associated with these climate change-induced impacts. We show that 30 to 50% of the flooded properties would not have flooded without climate change. Climate change-attributed impacts were particularly felt in Latina/x/o neighborhoods, and especially so in Latina/x/o neighborhoods that were low-income and among those located outside of FEMA’s 100-year floodplain. Our focus is thus on climate justice challenges that not only concern future climate change-induced risks, but are already affecting vulnerable populations disproportionately now.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Climate Intervention Assessment and Attribution

Focal Area(s): To ensure the US is positioned to scientifically attribute noncooperative climate interventions, we propose developing a foundational analytical framework for increased confidence in the assessment and attribution of observed changes in the climate. This framework will enable probabilistic attribution of prospective climate interventions. The target is to glean insight from complex Earth system data (Focal Area 3) by using AI to develop a prediction system comprised of a hierarchy of models (Focal Area 2).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Attributing runoff changes to climate variability and human activities: Uncertainty analysis using four monthly water balance models

Hydrological simulations to delineate the impacts of climate variability and human activities are subjected to uncertainties related to both parameter and structure of the hydrological models. To analyze the impact of these uncertainties on the model performance and to yield more reliable simulation results, a global calibration and multimodel combination method that integrates the Shuffled Complex Evolution Metropolis (SCEM) and Bayesian Model Averaging (BMA) of four monthly water balance models was proposed. The method was applied to the Weihe River Basin (WRB), the largest tributary of the Yellow River, to determine the contribution of climate variability and human activities to runoff changes. The change point, which was used to determine the baseline period (1956-1990) and human-impacted period (1991-2009), was derived using both cumulative curve and Pettitt’s test. Results show that the combination method from SCEM provides more skillful deterministic predictions than the best calibrated individual model, resulting in the smallest uncertainty interval of runoff changes attributed to climate variability and human activities. This combination methodology provides a practical and flexible tool for attribution of runoff changes to climate variability and human activities by hydrological models.

runoff change, uncertainty assessment, monthly wat↗
DOE OSTI2022

Trends in Downwelling Longwave Radiance Over the Southern Great Plains

Downwelling longwave radiation is an important part of the surface energy budget. Spectral trends in the downwelling longwave radiance (DLR) provide insight into the radiative drivers of climate change. In this research, we process and analyze a 23-year DLR record measured by the Atmospheric Emitted Radiance Interferometer (AERI) at the U.S. Department of Energy Atmospheric Radiation Program Southern Great Plains (SGP) site. Two AERIs were deployed at SGP with an overlapping observation period of about 10 years, which allows us to examine the consistency and accuracy of the measurements and to account for discrepancies between them due to errors associated with the instruments themselves. We then analyzed the all-sky radiance trends in DLR, which are associated with the surface warming trend at SGP during this same period and also the complex changes in meteorological conditions. For instance, the observed radiance in the CO2 absorption band follows closely the near-surface air temperature variations. The significant positive radiance trends in weak absorption channels, such as in the wings of the CO 2 band and in the weak absorption channels in the H 2 O vibration-rotational band, show earlier detectability of climate change. The magnitude of the radiance trend uncertainty in the DLR record mainly results from internal climate variability rather than from measurement error, which highlights the importance of continuing the DLR spectral measurements to unambiguously detect and attribute climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Attributing observed Greenland responses to natural and anthropogenic climate forcings

We attribute climate variability in four independent reconstructions of Greenland-average temperature and precipitation over the twentieth century. The reconstructions exhibit substantial differences in the timing and amplitudes of climate variations. Linear, empirical models of Greenland-average temperature and precipitation variations on multi-decadal timescales are established from a suite of Community Climate System Model 3 simulations of the preindustrial millennium. They are compared against observational reconstructions after being tested against simulations of the industrial and future periods. Empirical estimates of variations over the industrial and future periods are correlated at greater than 0.95 with simulated values. Greenhouse gas increases account for the majority of the temperature and precipitation increases after the mid-1900s. In contrast to the simulations, observed temperatures and precipitation do not increase until the mid-1990s. Thus, the empirical models over-predict the response to greenhouse gases over the twentieth century. We conclude that CCSM3 is not capturing processes that are proving important to Greenland surface conditions. Furthermore, modes of North Atlantic variability exhibit opposite relationships with some observations compared with the simulations. In those cases, reversing the sign of this component of variability yields significant correlations between the estimated and observed accumulation values.

Meteorology & Atmospheric Sciences↗
DOE OSTI2016

Detecting sulphate aerosol geoengineering with different methods

Sulphate aerosol injection has been widely discussed as a possible way to engineer future climate. Monitoring it would require detecting its effects amidst internal variability and in the presence of other external forcings. Here, we investigate how the use of different detection methods and filtering techniques affects the detectability of sulphate aerosol geoengineering in annual-mean global-mean near-surface air temperature. This is done by assuming a future scenario that injects 5 Tg yr -1 of sulphur dioxide into the stratosphere and cross-comparing simulations from 5 climate models. 64% of the studied comparisons would require 25 years or more for detection when no filter and the multi-variate method that has been extensively used for attributing climate change are used, while 66% of the same comparisons would require fewer than 10 years for detection using a trend-based filter. This then highlights the high sensitivity of sulphate aerosol geoengineering detectability to the choice of filter. With the same trend-based filter but a non-stationary method, 80% of the comparisons would require fewer than 10 years for detection. This does not imply sulphate aerosol geoengineering should be deployed, but suggests that both detection methods could be used for monitoring geoengineering in global, annual mean temperature should it be needed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Extreme drought impacts have been underestimated in grasslands and shrublands globally

Climate change is increasing the frequency and severity of short-term (~1 y) drought events—the most common duration of drought—globally. Yet the impact of this intensification of drought on ecosystem functioning remains poorly resolved. This is due in part to the widely disparate approaches ecologists have employed to study drought, variation in the severity and duration of drought studied, and differences among ecosystems in vegetation, edaphic and climatic attributes that can mediate drought impacts. To overcome these problems and better identify the factors that modulate drought responses, we used a coordinated distributed experiment to quantify the impact of short-term drought on grassland and shrubland ecosystems. With a standardized approach, we imposed ~a single year of drought at 100 sites on six continents. Here we show that loss of a foundational ecosystem function—aboveground net primary production (ANPP)—was 60% greater at sites that experienced statistically extreme drought (1-in-100-y event) vs. those sites where drought was nominal (historically more common) in magnitude (35% vs. 21%, respectively). This reduction in a key carbon cycle process with a single year of extreme drought greatly exceeds previously reported losses for grasslands and shrublands. Our global experiment also revealed high variability in drought response but that relative reductions in ANPP were greater in drier ecosystems and those with fewer plant species. Overall, our results demonstrate with unprecedented rigor that the global impacts of projected increases in drought severity have been significantly underestimated and that drier and less diverse sites are likely to be most vulnerable to extreme drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Ecohydrological drivers of Neotropical vegetation in montane ecosystems

Montane ecosystems are known for their high numbers of endemic species, unique climate conditions, and wide variety of ecosystem services such as water supply and carbon storage. Although many ecohydrological and climatic studies of montane environments have been carried out in temperate and boreal regions, few have been done in Neotropical regions. Hence, the objective of this review is to synthesize the existing literature on the main factors (biotic and abiotic) that influence vegetation distribution, functional traits, and ecohydrological processes and feedbacks in tropical montane ecosystems and to identify key knowledge gaps. Most of the literature used includes work conducted in Neotropical montane rainforests, cloud forests, and grass/scrublands (e.g., páramos, punas, and campos de altitude/rupestres). Fog is a major climatic attribute in tropical montane habitats. We found that fog regimes (frequency and intensity of fog events) influence both water inputs (i.e., canopy interception and foliar water uptake) and outputs (evapotranspiration) and represent an important driver of local species composition, dominance of plant functional types, and ecological functioning. The stability and conservation of tropical montane ecosystems depends on such ecohydrological fluxes, which are sensitive to increases in air temperature and changing precipitation and fog regimes. Furthermore, to better inform effective conservation and restoration strategies, more work is needed to elucidate how key ecohydrological processes are affected by land use conversion to agriculture and pasture lands, as human activities influence the water budgets in Neotropical montane watersheds not only at regional-scales but also globally.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Environmental limitation mapping of potential biomass resources across the conterminous U nited S tates

Abstract Several crops have recently been identified as potential dedicated bioenergy feedstocks for the production of power, fuels, and bioproducts. Despite being identified as early as the 1980s, no systematic work has been undertaken to characterize the spatial distribution of their long‐term production potentials in the United states. Such information is a starting point for planners and economic modelers, and there is a need for this spatial information to be developed in a consistent manner for a variety of crops, so that their production potentials can be intercompared to support crop selection decisions. As part of the Sun Grant Regional Feedstock Partnership (RFP), an approach to mapping these potential biomass resources was developed to take advantage of the informational synergy realized when bringing together coordinated field trials, close interaction with expert agronomists, and spatial modeling into a single, collaborative effort. A modeling and mapping system called PRISM‐ELM was designed to answer a basic question: How do climate and soil characteristics affect the spatial distribution and long‐term production patterns of a given crop? This empirical/mechanistic/biogeographical hybrid model employs a limiting factor approach, where productivity is determined by the most limiting of the factors addressed in submodels that simulate water balance, winter low‐temperature response, summer high‐temperature response, and soil pH , salinity, and drainage. Yield maps are developed through linear regressions relating soil and climate attributes to reported yield data. The model was parameterized and validated using grain yield data for winter wheat and maize, which served as benchmarks for parameterizing the model for upland and lowland switchgrass, CRP grasses, Miscanthus, biomass sorghum, energycane, willow, and poplar. The resulting maps served as potential production inputs to analyses comparing the viability of biomass crops under various economic scenarios. The modeling and parameterization framework can be expanded to include other biomass crops.

09 BIOMASS FUELS↗