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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↗
NASA NTRS2017

Future Global Mortality from Changes in Air Pollution Attributable to Climate Change

Ground-level ozone and fine particulate matter (PM (sub 2.5)) are associated with premature human mortality; their future concentrations depend on changes in emissions, which dominate the near-term, and on climate change. Previous global studies of the air-quality-related health effects of future climate change used single atmospheric models. However, in related studies, mortality results differ among models. Here we use an ensemble of global chemistry-climate models to show that premature mortality from changes in air pollution attributable to climate change, under the high greenhouse gas scenario RCP (Representative Concentration Pathway) 8.5, 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 percent of the increase in global ozone-related mortality). For PM (sub 2.5), 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 percent the global decrease in PM (sub 2.5)-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. 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.

Atmohspheric chemistry↗
NASA NTRS2012

Tree Density and Species Decline in the African Sahel Attributable to Climate

Increased aridity and human population have reduced tree cover in parts of the African Sahel and degraded resources for local people. Yet, tree cover trends and the relative importance of climate and population remain unresolved. From field measurements, aerial photos, and Ikonos satellite images, we detected significant 1954-2002 tree density declines in the western Sahel of 18 +/- 14% (P = 0.014, n = 204) and 17 +/- 13% (P = 0.0009, n = 187). From field observations, we detected a significant 1960-2000 species richness decline of 21 +/- 11% (P = 0.0028, n = 14) across the Sahel and a southward shift of the Sahel, Sudan, and Guinea zones. Multivariate analyses of climate, soil, and population showed that temperature most significantly (P < 0.001) explained tree cover changes. Multivariate and bivariate tests and field observations indicated the dominance of temperature and precipitation, supporting attribution of tree cover changes to climate variability. Climate change forcing of Sahel climate variability, particularly the significant (P < 0.05) 1901-2002 temperature increases and precipitation decreases in the research areas, connects Sahel tree cover changes to global climate change. This suggests roles for global action and local adaptation to address ecological change in the Sahel.

tree density declines↗
NASA NTRS2012

Climate Change Detection and Attribution of Infrared Spectrum Measurements

Climate change occurs when the Earth's energy budget changes due to natural or possibly anthropogenic forcings. These forcings cause the climate system to adjust resulting in a new climate state that is warmer or cooler than the original. The key question is how to detect and attribute climate change. The inference of infrared spectral signatures of climate change has been discussed in the literature for nearly 30 years. Pioneering work in the 1980s noted that distinct spectral signatures would be evident in changes in the infrared radiance emitted by the Earth and its atmosphere, and that these could be observed from orbiting satellites. Since then, a number of other studies have advanced the concepts of spectral signatures of climate change. Today the concept of using spectral signatures to identify and attribute atmospheric composition change is firmly accepted and is the foundation of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) satellite mission being developed at NASA. In this work, we will present an overview of the current climate change detection concept using climate model calculations as surrogates for climate change. Any future research work improving the methodology to achieve this concept will be valuable to our society.

Phojanamongkolkij, Nipa↗
NASA NTRS2015

Climate Absolute Radiance and Refractivity Observatory (CLARREO)

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a mission, led and developed by NASA, that will measure a variety of climate variables with an unprecedented accuracy to quantify and attribute climate change. CLARREO consists of three separate instruments: an infrared (IR) spectrometer, a reflected solar (RS) spectrometer, and a radio occultation (RO) instrument. The mission will contain orbiting radiometers with sufficient accuracy, including on orbit verification, to calibrate other space-based instrumentation, increasing their respective accuracy by as much as an order of magnitude. The IR spectrometer is a Fourier Transform spectrometer (FTS) working in the 5 to 50 microns wavelength region with a goal of 0.1 K (k = 3) accuracy. The FTS will achieve this accuracy using phase change cells to verify thermistor accuracy and heated halos to verify blackbody emissivity, both on orbit. The RS spectrometer will measure the reflectance of the atmosphere in the 0.32 to 2.3 microns wavelength region with an accuracy of 0.3% (k = 2). The status of the instrumentation packages and potential mission options will be presented.

Leckey, John P.↗
NASA NTRS2013

Achieving Climate Change Absolute Accuracy in Orbit

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREO's inherently high absolute accuracy will be verified and traceable on orbit to Système Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earth's thermal infrared spectrum (5-50 micron), the spectrum of solar radiation reflected by the Earth and its atmosphere (320-2300 nm), and radio occultation refractivity from which accurate temperature profiles are derived. The mission has the ability to provide new spectral fingerprints of climate change, as well as to provide the first orbiting radiometer with accuracy sufficient to serve as the reference transfer standard for other space sensors, in essence serving as a "NIST [National Institute of Standards and Technology] in orbit." CLARREO will greatly improve the accuracy and relevance of a wide range of space-borne instruments for decadal climate change. Finally, CLARREO has developed new metrics and methods for determining the accuracy requirements of climate observations for a wide range of climate variables and uncertainty sources. These methods should be useful for improving our understanding of observing requirements for most climate change observations.

Wielicki, Bruce A.↗
DOE OSTI2026

Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)

Anthropogenic climate change is unfolding rapidly, yet its regional manifestation can be obscured by internal variability. A primary goal of climate science is to identify the externally forced climate response from among the noise of internal variability. Separating the forced response from internal variability can be addressed in climate models by using a large ensemble to average over different possible realizations of internal variability. However, with only one realization of the real world, it is a major challenge to isolate the forced response directly in observations. In the Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP), contributors used existing and newly developed statistical and machine learning methods to estimate the forced response over 1950–2022 within individual realizations of the climate system. Participants used neural networks, linear inverse models, fingerprinting methods, and low-frequency component analysis, among other approaches. These methods were trained using large ensembles from multiple climate models and then applied to observations. Here, we evaluate method performance within large ensembles and investigate the estimates of the forced response in observations. Our results show that many different types of methods are skillful for estimating the forced response in climate models, though the relative skill of individual methods varies depending on the variable and evaluation metric. Methods with comparable skill in models can give a wide range of estimates of the forced response pattern in observations, illustrating the epistemic uncertainty in forced response estimates. ForceSMIP gives new insights into the forced response in observations, its uncertainty, and methods for its estimation.

Climate attribution↗
NASA NTRS2003

Tides and Decadal Variability

This paper reviews the mechanisms by which oceanic tides and decadal variability in the oceans are connected. We distinguish between variability caused by tides and variability observed in the tides themselves. Both effects have been detected at some level. The most obvious connection with decadal timescales is through the 18.6-year precession of the moon's orbit plane. This precession gives rise to a small tide of the same period and to 18.6-year modulations in the phase and amplitudes of short-period tides. The 18.6-year "node tide" is very small, no more than 2 cm anywhere, and in sea level data it is dominated by the ocean's natural Variability. Some authors have naively attributed climate variations with periods near 19 years directly to the node tide, but the amplitude of the tide is too small for this mechanism to be operative. The more likely explanation (Loder and Garrett, JGR, 83, 1967-70, 1978) is that the 18.6-y modulations in short-period tides, especially h e principal tide M2, cause variations in ocean mixing, which is then observed in temperature and other climatic indicators. Tidally forced variability has also been proposed by some authors, either in response to occasional (and highly predictable) tidal extremes or as a nonlinear low-frequency oscillation caused by interactions between short-period tides. The former mechanism can produce only short-duration events hardly more significant than normal tidal ranges, but the latter mechanism can in principle induce low-frequency oscillations. The most recent proposal of this type is by Keeling and Whorf, who highlight the 1800-year spectral peak discovered by Bond et al. (1997). But the proposal appears contrived and should be considered, in the words of Munk et al. (2002), "as the most likely among unlikely candidates."

Ray, Richard D.↗
NASA NTRS2013

Detection and Attribution of Anthropogenic Climate Change Impacts

Human-influenced climate change is an observed phenomenon affecting physical and biological systems across the globe. The majority of observed impacts are related to temperature changes and are located in the northern high- and midlatitudes. However, new evidence is emerging that demonstrates that impacts are related to precipitation changes as well as temperature, and that climate change is impacting systems and sectors beyond the Northern Hemisphere. In this paper, we highlight some of this new evidence-focusing on regions and sectors that the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) noted as under-represented-in the context of observed climate change impacts, direct and indirect drivers of change (including carbon dioxide itself), and methods of detection. We also present methods and studies attributing observed impacts to anthropogenic forcing. We argue that the expansion of methods of detection (in terms of a broader array of climate variables and data sources, inclusion of the major modes of climate variability, and incorporation of other drivers of change) is key to discerning the climate sensitivities of sectors and systems in regions where the impacts of climate change currently remain elusive. Attributing such changes to human forcing of the climate system, where possible, is important for development of effective mitigation and adaptation. Current challenges in documenting adaptation and the role of indigenous knowledge in detection and attribution are described.

detection↗
DOE OSTI2025

Climate change and federal aid disbursements after Hurricane Harvey: an extreme event attribution analysis

The role climate change plays in increasing the burden placed on governments and insurers to pay for recovery has not been extensively explored and is the focus of this study. This study examines the impacts of climate change attributed flooding on federal disaster aid disbursement in Harris County, Texas following Hurricane Harvey in 2017. Our approach uses flood models to estimate the amount of flood damages attributable and not attributable to climate change under two climate change attribution scenarios from peer reviewed studies: 20% and 38% increases in rainfall associated with the hurricane due to climate change. These estimates are combined with census tract-level disbursement data for FEMA’s National Flood Insurance Program (NFIP) and the Individual Assistance (IA) part of the Individuals and Households Program. We employ spatial lag regression models with direct and spatial spillover effects to analyze the relationship between a tract’s flood damages—both attributed and not attributed to climate change—and federal disaster aid. We find that both types of flood damage shape federal aid disbursements, but that climate change attributed damages tend to have larger effect sizes (elasticities) especially for IA. Specifically, for a 1% increase in additional climate change attributed damages per household in a census tract (under the 20% scenario), expected NFIP levels in that census tract are 0.26% higher and IA levels are 0.3% higher. Implications center on federal funding in an era of climate change.

FEMA↗
NASA NTRSDate not supplied

NO2 Anomalies - Economy Attribution and Rapid Climate Response

Using principal component (PC) analysis of 16 years of monthly series of NO2 from OMI/Aura, we show that it is the third PC (PC3) from the full hierarchy of principal modes that is best coupled with the economic indicators. This coupling is positive, i.e. PC3 and economic indicators manifest positive covariance. However, the economic variability can explain only 40% of the information in PC3. Furthermore, this mode by itself explains only 3% of the total deseasonalized NO2 variability. We thus conclude that, while having an unambiguous impact, the economy can be awarded at best third order of importance in driving the deseasonalized NO2 series, i.e. after seasonal variability is removed from the series. Once we identified PC3 as the NO2 mode that is coupled with economic variability, we use this mode as an indicator and look for rapid climate adjustments to that part of NO2 variability that we are confident is coupled with the economic variability. We focus on observational data from the Atmospheric Infrared Sounder (AIRS) on board of NASA Aqua satellite, decompose series of surface skin temperature and clear-sky outgoing longwave radiances (OLR) into principal components, and identify potential impacts of NO2 PC3 on these climate variables.

NO2↗
NASA NTRSDate not supplied

NO2 Anomalies - Economy Attribution and Rapid Climate Response

Using principal component (PC) analysis of 16 years of monthly series of NO2 from OMI/Aura, we show that it is the third PC (PC3) from the full hierarchy of principal modes that is best coupled with the economic indicators. This coupling is positive, i.e. PC3 and economic indicators manifest positive covariance. However, the economic variability can explain only 40%of the information in PC3. Furthermore, this mode by itself explains only 3% of the total deseasonalized NO2 variability. We thus conclude that, while having an unambiguous impact, the economy can be awarded at best third order of importance in driving the deseasonalized NO2 series, i.e. after seasonal variability is removed from the series. Once we identified PC3 as the NO2 mode that is coupled with economic variability, we use this mode as an indicator and look for rapid climate adjustments to that part of NO2 variability that we are confident is coupled with the economic variability. We focus on observational data from the Atmospheric Infrared Sounder (AIRS) on board of NASA Aqua satellite, decompose series of surface skin temperature and clear-sky outgoing longwave radiances (OLR) into principal components, and identify potential impacts of NO2 PC3 on these climate variables.

NO2↗
DOE OSTI2024

Large Divergence of Projected High Latitude Vegetation Composition and Productivity Due To Functional Trait Uncertainty

Abstract Vegetation distribution and composition are expected to change in northern high latitudes under rapid warming, which regulates ecosystem functions but remains challenging to predict. Vegetation change arises from the interplay of chronic climate trends such as warming and transient demographic processes of recruitment, growth, competition, and mortality. Most predictive models overlooked the role of demographic dynamics controlled by plant traits. Here, we simulate vegetation dynamics at the Kougarok Hillslope site in Alaska under historical and future climates using the E3SM Land Model coupled to the Functionally Assembled Terrestrial Simulator (ELM‐FATES). To evaluate the roles of plant traits, we parameterize the model with 5,265 trait configurations representing diverse physiological and demographic strategies. Results show current modeled biomass, composition, and productivity are most sensitive to traits controlling photosynthetic capacity, carbon allocation, allometry, and phenology. Among all trait configurations, ∼5% reproduce in situ biomass and plant functional type (PFT) composition measured in 2016, that are indistinguishable from these two observed ecosystem states. Notably, these same trait configurations produce diverging biomass, composition, and productivity under future climate, where the uncertainty attributable to traits is twice the change attributable to climate change. The variation of projected productivity arises from emerging PFT composition under novel climate regimes, primarily explained by traits controlling cold‐induced mortality, recruitment, and allometry. Our findings highlight the importance and uncertainty of demographic dynamics and its interaction with climate change in shaping Arctic vegetation change. Improved model predictions will likely benefit from explicit consideration of vegetation demography and better constraints of critical traits.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Lapse-Rate Feeback: The Key to Reconciling TOA and Surface Attributions of Surface Warming

The impact of climate feedbacks on surface warming is conventionally evaluated using a decomposition of the top-of-atmosphere (TOA) energy budget. Alternatively, the climate feedback analysis can also be carried out using the surface energy budget. However, the two perspectives do not provide the same interpretation of process contributions to surface warming, particularly when executing a spatial analysis. The TOA energy budget is equal to the sum of the surface and atmospheric energy budgets. Using the CMIP5 RCP 8.5 model projections, we show that the major discrepancies between the surface and TOA climate feedback attributions of surface warming are due to non-negligible changes in the atmospheric energy budget that differ from their counterparts at the surface. Individual radiative and non-radiative processes cause vertically non-uniform energy flux perturbations, in response to an external forcing, that naturally lead to a vertically non-uniform temperature change. The TOA lapse-rate feedback is the manifestation of these multiple processes that produce a vertically non-uniform warming response such that it accounts for the asymmetry between the changes in the atmospheric and surface energy budgets. Using the climate feedback-response analysis method, we can decompose the lapse-rate feedback into contributions by individual processes. The negative lapse-rate feedback in the tropics is due to greater moist convection and condensational heating along with ocean heat storage. On the other hand, the positive lapse-rate feedback in polar regions is primarily due to the surface albedo and water vapor feedbacks. Combining the process contributions that are hidden within the lapse-rate feedback with their respective direct impacts on the TOA energy budget allows for a very consistent picture of process contributions to surface warming and its inter-model spread as that given by the surface energy budget approach. With the modified approach, both perspectives indicate water vapor feedback is the largest contributor in the tropics, while surface albedo feedback is the greatest contributor in polar regions. Dynamics and ocean heat storage are the main suppressors of surface warming, except over Antarctica. Both perspectives show there is large inter-model uncertainty in the contributions of water vapor, clouds, albedo, and dynamics plus ocean heat storage.

Sergio A Sejas↗
NASA NTRS2012

The Effects of Solar Variability on Earth's Climate: A Workshop Report

Solar irradiance, the flux of the Sun s output directed toward Earth, is Earth s main energy source.1 The Sun itself varies on several timescales over billions of years its luminosity increases as it evolves on the main sequence toward becoming a red giant; about every 11 years its sunspot activity cycles; and within just minutes flares can erupt and release massive amounts of energy. Most of the fluctuations from tens to thousands of years are associated with changes in the solar magnetic field. The focus of the National Research Council's September 2011 workshop on solar variability and Earth's climate, and of this summary report, is mainly magnetically driven variability and its possible connection with Earth's climate variations in the past 10,000 years. Even small variations in the amount or distribution of energy received at Earth can have a major influence on Earth's climate when they persist for decades. However, no satellite measurements have indicated that solar output and variability have contributed in a significant way to the increase in global mean temperature in the last 50 years. Locally, however, correlations between solar activity and variations in average weather may stand out beyond the global trend; such has been argued to be the case for the El Nino-Southern Oscillation, even in the present day. A key area of inquiry deals with establishing a unified record of the solar output and solar-modified particles that extends from the present to the prescientific past. The workshop focused attention on the need for a better understanding of the links between indices of solar activity such as cosmogenic isotopes and solar irradiance. A number of presentations focused on the timescale of the solar cycle and of the satellite record, and on the problem of extending this record back in time. Highlights included a report of progress on pyroheliometer calibration, leading to greater confidence in the time history and future stability of total solar irradiance (TSI), and surprising results on changes in spectral irradiance over the last solar cycle, which elicited spirited discussion. New perspectives on connections between features of the quiet and active areas of the photosphere and variations in TSI were also presented, emphasizing the importance of developing better understanding in order to extrapolate back in time using activity indices. Workshop participants reviews highlighted difficulties as well as causes for optimism in current understanding of the cosmogenic isotope record and the use of observed variability in Sun-like stars in reconstructing variations in TSI occurring on lower frequencies than the sunspot cycle. The workshop succeeded in bringing together informed, focused presentations on major drivers of the Sun-climate connection. The importance of the solar cycle as a unique quasi-periodic probe of climate responses on a timescale between the seasonal and Milankovitch cycles was recognized in several presentations. The signal need only be detectable, not dominant, for it to play this role of a useful probe. Some workshop participants also found encouraging progress in the top-down perspective, according to which solar variability affects surface climate by first perturbing the stratosphere, which then forces the troposphere and surface. This work is now informing and being informed by research on tropospheric responses to the Antarctic ozone hole and volcanic aerosols. In contrast to the top-down perspective is the bottom-up view that the interaction of solar energy with the ocean and surface leads to changes in dynamics and temperature. During the discussion of how dynamical air-sea coupling in the tropical Pacific and solar variability interact from a bottom-up perspective, several participants remarked on the wealth of open research questions in the dynamics of the climatic response to TSI and spectral variability. The discussion of the paleoclimate record emphasized that the link between solar varbility and Earth s climate is multifaceted and that some components are understood better than others. According to two presenters on paleoclimate, there is a need to study the idiosyncrasies of each key proxy record. Yet they also emphasized that there may be an emerging pattern of paleoclimate change coincident with periods of solar activity and inactivity, but only on long timescales of multiple decades to millennia. Several speakers discussed the effects of particle events and cosmic-ray variability. These are all areas of exciting fundamental research; however, they have not yet led to conclusive evidence for significant related climate effects. The key problem of attribution of climate variability on the timescales of the Little Ice Age and the Maunder Minimum were directly addressed in several presentations. Several workshop participants remarked that the combination of solar, paleoclimatic, and climate modeling research has the potential to dramatically improve the credibility of these attribution studies.

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