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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evaluating How Climate Adaptation Measures Affect the Interconnected Water‐Energy Resource Systems of the Western United States

Abstract The Western US faces increasing water stress from the impacts of climate change, making it difficult to meet water demands for the region's cities, agriculture, and hydropower generators. Existing literature suggests that climate adaptation measures such as water conservation, cropland retirement, wastewater recycling, and managed aquifer recharge can alleviate some of these challenges. Few analyses, however, compare the relative efficacy and system‐wide effects of these adaptations under different climate projections across the entire Western United States. Here we use a Western US‐wide water systems model to evaluate, by sector and sub‐region, how the widespread implementation of these adaptive measures impacts water demands, water deliveries, and electricity use related to the water system for three different climate projections. We find that wastewater recycling has greater potential to lower unmet indoor water demands than urban indoor water conservation measures. However, when implemented at scale, indoor water conservation reduces electricity use by an average of 683 Terawatt hours while wastewater recycling increases energy use by an average of 721 Terawatt hours, cumulatively from 2020 to 2070. Cropland retirement and aquifer recharge adaptations increase the ability to meet agricultural water demand, increase groundwater storage, and reduce summertime electricity use. While most of these findings are consistent across different climate projections, the benefits of aquifer recharge are sensitive to spatial variation of precipitation. Given the limitations and tradeoffs of each individually, the results suggest that a portfolio of adaptation measures will be needed for a climate‐resilient water and energy future in the Western US. Plain Language Summary The Western US faces increased water stress from the impacts of climate change, making it difficult to meet demands for cities, agriculture, and hydropower facilities. Adaptation measures like water conservation, retiring agricultural lands, recycling wastewater, and storing water underground can address these challenges. However, there is little modeling to understand the impact of these adaptations if they were implemented across the entire Western US while also considering climate change. We use a Western US‐wide water systems model to evaluate how implementing adaptation measures impacts groundwater levels, the ability to meet water demands, and electricity use related to water in three different possible climate futures. We find that recycling wastewater to drinking water standards does a better job of meeting urban water demands than water conservation measures. However, indoor water conservation reduces energy use for water while recycling wastewater increases energy use for water. Retiring agricultural lands and storing water underground both increase the ability to meet agricultural water demands, increase underground water storage, and reduce summertime energy use. No adaptation measures provides benefits across every metric we track. Therefore, multiple adaptation measures will likely be needed to achieve a climate‐resilient future for energy and water in the Western US. Key Points Water‐related climate change adaptations have different benefits and tradeoffs for interconnected water‐energy systems in the Western U.S. Wastewater recycling most increases urban water coverage, but has a significant tradeoff in the form of greater electricity use Cropland retirement and aquifer recharge benefit water and electricity systems most in the summer when those systems are most stressed

Singhal, A↗
DOE OSTI2025

A Decadal Hybrid GCM Simulation Using Deep‐Learning‐Based Cloud and Convection Parameterization Generalized to a Warm Climate

A critical challenge for machine‐learning (ML) parameterization in global climate models (GCMs) is to achieve stable, accurate simulations under climates not seen during training. Previous studies have demonstrated promising offline performance and year‐long online stability in aquaplanet simulations but have encountered difficulties in real geography and under climate warming. Here we report that a GCM with real geography configuration using neural‐network‐based cloud and convection parameterization, trained exclusively with present‐day climate data, successfully performs a stable, decade‐long simulation of a warm climate with +4 K sea surface temperature (SST). The neural network (NN) is based on Han et al. (2023, https://doi.org/10.1029/2022ms003508 ) with additional inputs. The simulation captures the global precipitation distribution, surface temperatures, vertical atmospheric structures, and extreme precipitation very well, closely matching simulations from both the superparameterized CAM (SPCAM) and the conventional CAM5 in the warm climate without accuracy degradation compared to those in the baseline climate. Moreover, it produces a climate response to +4 K SST in atmospheric thermodynamic states and circulations similar to those from SPCAM and CAM5. Prognostic ablation tests on NN input variables show that the NN without convective memory as input suffers from numerical instability, and the NN without considering radiative variables and land fraction as input, or with reduced training samples produce less accurate results. To our knowledge, this is the first time an ML parameterization successfully achieves online extrapolation to a warm climate without using additional warm‐climate data for training. It demonstrates the potential of ML‐driven parameterizations for credible long‐term climate projections.

Atmosphere model↗
DOE OSTI2018

Quantifying climate–growth relationships at the stand level in a mature mixed‐species conifer forest

Abstract A range of environmental factors regulate tree growth; however, climate is generally thought to most strongly influence year‐to‐year variability in growth. Numerous dendrochronological (tree‐ring) studies have identified climate factors that influence year‐to‐year variability in growth for given tree species and location. However, traditional dendrochronology methods have limitations that prevent them from adequately assessing stand‐level (as opposed to species‐level) growth. We argue that stand‐level growth analyses provide a more meaningful assessment of forest response to climate fluctuations, as well as the management options that may be employed to sustain forest productivity. Working in a mature, mixed‐species stand at the Howland Research Forest of central Maine, USA , we used two alternatives to traditional dendrochronological analyses by (1) selecting trees for coring using a stratified (by size and species), random sampling method that ensures a representative sample of the stand, and (2) converting ring widths to biomass increments, which once summed, produced a representation of stand‐level growth, while maintaining species identities or canopy position if needed. We then tested the relative influence of seasonal climate variables on year‐to‐year variability in the biomass increment using generalized least squares regression, while accounting for temporal autocorrelation. Our results indicate that stand‐level growth responded most strongly to previous summer and current spring climate variables, resulting from a combination of individualistic climate responses occurring at the species‐ and canopy‐position level. Our climate models were better fit to stand‐level biomass increment than to species‐level or canopy‐position summaries. The relative growth responses (i.e., percent change) predicted from the most influential climate variables indicate stand‐level growth varies less from to year‐to‐year than species‐level or canopy‐position growth responses. By assessing stand‐level growth response to climate, we provide an alternative perspective on climate–growth relationships of forests, improving our understanding of forest growth dynamics under a fluctuating climate.

Teets, Aaron↗
DOE OSTI2024

gcm_eval (Global Climate Model Evaluation) [SWR-24-37]

The interplay between energy, climate, and weather is becoming more complex as our changing climate continues to affect the weather we experience which in turn drives changes in the ever increasing share of renewable energy generation and energy demand. Energy system analyses commonly rely on meteorological inputs to estimate renewable energy generation and energy demand; however, these inputs rarely represent the estimated impacts of future climate change. Climate models and publicly available climate change datasets can be used for this purpose, but the selection of inputs from the myriad of available models and datasets is a nuanced and often subjective process. There is no single perfect climate model or dataset for all applications. In this repository, we include software and the corresponding assessments of various global climate models (GCMs) from the Coupled Model Intercomparison Project (CMIP6), evaluating their skills with respect to the historical climate and comparing of their future projections of climate change. We focus on variables that directly affect the energy system including the representation of extreme values that can drive grid resilience events. The objective of this repository is not to recommend the best climate model and dataset for a given analysis, but instead to provide a reference to facilitate the selection of climate models and datasets in subsequent work.

Buster, Grant↗
DOE OSTI2023

Evolution of the Internal Climate Modes under Future Warming

Abstract Climate modes play an important role in weather and climate variability over multiple spatial and temporal scales. This research assesses Earth system model (ESM) projections of the spatiotemporal characteristics of key internal climate modes (NAM, SAM, PNA, ENSO, PDO, and AMO) under high (SSP585) and low (SSP126) radiative forcing scenarios and contextualizes those projections using historical fidelity. Time series analyses are used to assess trends and mode phase characteristics are summarized for the historical period and for the end of the twenty-first century. Spatial patterns are compared to infer morphological changes. Shifts in the power spectra are used to examine changes in variability at subannual, interannual, and interdecadal scales. Changes in time-lagged correlations are used to capture the evolution of first-order interactions. While differences in historical skill are predominantly ESM dependent, changing mode characteristics in a warmer climate also exhibit variability between individual ensemble realizations. NAM, SAM, and ENSO tend to evolve toward increased prevalence of the positive phase up to 2100 across the multimodel ensemble while the PNA and PDO exhibit little trend but increasing phase intensity. AMO characteristics are shown to depend on the method used to remove the external signal. ESMs that show higher historical fidelity tend to show more modest changes in those modes under global nonstationarity. Changes in mode interactions are found to be highly ESM dependent but exhibit broadly similar behavior to historical relationships. These findings have implications for our understanding of internal variability and make clear that the choice of ESM, and even the ESM realization, matters for applications of climate projections. Significance Statement Internal modes of variability are important to understand due to their impact on local, regional, and global weather and climate patterns. Future climate changes will not only be affected by the variability arising from these modes, but the modes will themselves change in response to the changing climate. Spatial and temporal aspects of the modes are assessed from projections of future climate and related to how well they are captured in the historical climate. This yields some measure of confidence in the changes exhibited by the models. In most cases, when historically skillful models exhibit changes that are different from those produced by less skillful models, they tend to produce more modest changes. These results, as well as the variability between model outcomes, mean decisions on which ESM to use for projections of the future climate matter significantly.

Meteorology & Atmospheric Sciences↗
DOE OSTI2025

The impact of climate change on Korea’s agricultural sector under the national self-sufficiency policy

Evolving environmental conditions due to climate change have brought about changes in agriculture, which is required for human life as both a source of food and income. International trade can act as a buffer against potential negative impacts of climate change on crop yields, but recent years have seen breakdowns in global trade, including export bans to improve domestic food security. For countries that rely heavily on imported food, governments may institute policies to protect their agricultural industry from changes in climate-induced crop yield changes and other countries’ potential trade restrictions. This study assesses the individual and combined effects of climate impacts and food self-sufficiency policies in Korea, which is highly dependent on imports. We use the Global Change Analysis Model (GCAM), a global integrated assessment model, to explore (1) the direct impact of climate change on Korea’s agricultural yields, (2) the full impacts of global climate change on agricultural production, including trade-induced changes due to yield changes in other regions, (3) the impacts of food self-sufficiency policy, and (4) the interactive impact of climate change and self-sufficiency policies. We find that, in Korea, the direct impact of climate change on agricultural yields would be overshadowed by the impact of global climate change due to changing trade patterns. Second, global climate change leads to a rise (rice and wheat) or a decline (soybeans) in Korean producer revenues, while simultaneously raising consumer expenditures on both staples and non-staples. Third, implementing self-sufficiency policies for wheat and soybeans in Korea boosts the nation’s producer revenues, in conjunction with the effects of climate change, at the cost of additional increases in consumer expenditures for both staples and non-staples.

Science & Technology - Other Topics↗
DOE OSTI2019

Potential Impacts of Accelerated Climate Change (Second Annual Report of Work)

This study is part of the U.S. Nuclear Regulatory Commission’s (NRC) Probabilistic Flood Hazard Assessment (PFHA) research plan that aims to develop regulatory tools and guidance to support and enhance the NRC’s capacity to perform thorough and efficient reviews of license applications and license amendment requests. In Year 1, Pacific Northwest National Laboratory staff prepared an annual report that summarized recent scientific findings on climate change, with a particular focus on climatic elements that are relevant to NRC concerns on a regional level (i.e., increasing air and water temperatures, decreasing water availability, increasing frequency and intensity of storms and flooding, and sea-level rise). This report summarizes Year 2 activities, which focused on reviewing scientific findings regarding region-specific climatic extremes for the southeastern United States. According to the U.S. Global Change Research Program Third National Climate Assessment, the U.S. Southeast Region consists of 11 southeastern states, Puerto Rico, and the Virgin Islands. Except for Kentucky, Puerto Rico, and the Virgin Islands, all included states have operating nuclear power plants. Further, new nuclear power reactor permit and license applications have been submitted to the NRC in the recent past for sites located in several southeastern states. Therefore, having an improved understanding of potential climate changes and their hydrologic impacts in the Southeast Region is important to inform the PFHA research plan. Climatic features relevant to the NRC for the Southeast Region include high temperature extremes, precipitation extremes, flooding, high winds associated with tropical cyclones and tornadoes, sea-level rise, and storm surge and associated inland inundation. Drawing primarily from the climate assessment reports and peer-reviewed literature, this Year 2 annual report summarizes the observed climate, its past changes, and its projected changes, as well as 21st century hydrologic impacts in the Southeast Region. Despite the relatively mild changes observed in the past and projected in the future for the mean climate of the Southeast Region, many aspects of climatic extremes such as extreme precipitation have exhibited changes in the last century. In addition, climate and hydrologic models project further changes in the future. Pacific Northwest National Laboratory staff presented updates summarizing these findings at the Second Annual PFHA Research Workshop, which took place from January 23 to 25, 2017, at NRC Headquarters in Rockville, Maryland.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Data-Driven Exploration of Climate Attractor Manifolds For Long-Term Predictability

Focal Area: This white paper responds to Focal Area 3. We seek to gain insight into decadal-scale climate predictability by applying novel manifold-finding probabilistic AI techniques to the complex data produced by Earth System models (ESMs) such as E3SM. The associated portfolio of research activities leverages DOE’s asset mix of HPC platforms, climate expertise, climate simulation codes, and AI expertise. Science Challenge: Climate and climate models are dynamical systems exhibiting properties that are interpretable through chaos theory. The theory contains an important concept that is relevant to multi-decade-scale climate prediction: a chaotic attractor. While the space containing all the possible states of the Earth’s atmosphere and ocean, the possible weather, is large, the realized states tend to stay near the smaller-dimensioned attractor. This behavior is responsible for the “order behind the irregularity” [1] of climate phenomena. Climate change can be thought of as a change in the properties of the attractor, and predicting the climate over years to decades is equivalent to predicting how those properties will change. To date, the attractor has been a useful conceptual tool, but has not been amenable to direct characterization. A new development is the advent of efficient high-dimensional manifold-finding probabilistic AI techniques, which permit a data-driven characterization of the ESM attractor and its probability distribution over weather states. Such a characterization would result in a natural dimensional reduction — a “non-linear Principal Components Analysis (PCA) adapted to climate simulation data” — leading to important advances in scenario-based long-term climate prediction, long-term prediction of water cycle extremes, ESM verification, inter-model comparison, and process model development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Assumptions and advantages of diagnosing climate change at global warming levels

Scientists increasingly assess climate changes at specific levels of global warming rather than at specific times in the future. This has numerous advantages over other methods of diagnosing future climate changes and allows for a more direct connection with various climate mitigation targets like the 1.5° or 2°C Paris agreement goals. A key assumption inherent to global warming levels (GWLs) is that all climate models and all future experiments that cross a given global temperature change threshold can be averaged together – that the response of the climate at a given GWL is largely independent of the emissions scenario or the sensitivity to carbon dioxide in the models comprising the average. This assumption has not been systematically vetted in the literature. Here we mine the entire CMIP5 and CMIP6 archives to determine the extent to which the climate response of several extreme climate indices at various GWLs is sensitive to the future emissions scenario and to the models’ climate sensitivities. We quantify this sensitivity to see whether using GWLs as a control in climate projections is justified. This information will be valuable for establishing the extent to which constraints on climate sensitivity can have tangible benefits for regional climate resiliency planning.

58 GEOSCIENCES↗
DOE OSTI2023

Soil organic matter diagenetic state informs boreal forest ecosystem feedbacks to climate change

The fate of soil organic carbon (SOC) in boreal forests is dependent on the integrative ecosystem response to climate change. For example, boreal forest productivity is often nitrogen (N) limited, and climate warming can enhance N cycling and primary productivity. However, the net effect of this feedback on the SOC reservoir and its longevity with climate change remain unclear due to difficulty in detecting small differences between large and variable carbon (C) fluxes needed to determine net changes in soil reservoirs. The diagenetic state of SOC – resulting from the physicochemical and biological transformations that alter the original biomolecular composition of detrital inputs to soil over time – is useful for tracing the net response of SOC at the timescales relevant to climate change not usually discernible from fluxes and stocks alone. Here, we test two hypotheses using a mesic boreal forest climate transect: (1) the SOC diagenetic state is maintained across this climosequence, and (2) the maintenance of the SOC diagenetic state is a consequence of coupled soil C and N cycling, signifying the role of enhanced N cycling supporting SOC inputs that maintain SOC stocks within the warmer-climate forests. Shifts in nonvascular to vascular plant inputs with climate observed in these and other boreal forests highlighted the need to carefully separate biogeochemical indicators of SOC source from those signifying diagenetic alteration. We thus evaluated and applied lignin biomarkers to assess the diagenetic alteration of SOC in these boreal forest organic soils and directly compared the lignin diagenetic state with that of soil organic nitrogen (SON) assessed through amino acid composition. The lignin diagenetic state remained constant across the climate transect, indicating a balance between the input and removal of lignin in these mesic boreal forests. When combined with previous knowledge of these forest ecosystems, including the diagenetic state of SON and direct measures of C fluxes and stocks, the results indicate a coupled increase in C and N cycling with climate warming that supports forest productivity and maintains SOC stocks. This balance could markedly shift as other factors begin to limit forest productivity (e.g., trace nutrients, water) with further climate change or affect forest nutrient allocation (e.g., forest age or compositional change). Further application of the approach presented here could be used to detect the limits of this and other ecosystem–climate feedbacks, by providing a tractable and parameterizable index of the lignin state across large spatial scales, necessary for ecosystem-scale parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Synergy between land use and climate change increases future fire risk in Amazon forests

Tropical forests have been a permanent feature of the Amazon basin for at least 55 million years, yet climate change and land use threaten the forest's future over the next century. Understory forest fires, which are common under the current climate in frontier forests, may accelerate Amazon forest losses from climate-driven dieback and deforestation. Far from land use frontiers, scarce fire ignitions and high moisture levels preclude significant burning, yet projected climate and land use changes may increase fire activity in these remote regions. Here, we used a fire model specifically parameterized for Amazon understory fires to examine the interactions between anthropogenic activities and climate under current and projected conditions. In a scenario of low mitigation efforts with substantial land use expansion and climate change – Representative Concentration Pathway (RCP) 8.5 – projected understory fires increase in frequency and duration, burning 4–28 times more forest in 2080–2100 than during 1990–2010. In contrast, active climate mitigation and land use contraction in RCP4.5 constrain the projected increase in fire activity to 0.9–5.4 times contemporary burned area. Importantly, if climate mitigation is not successful, land use contraction alone is very effective under low to moderate climate change, but does little to reduce fire activity under the most severe climate projections. These results underscore the potential for a fire-driven transformation of Amazon forests if recent regional policies for forest conservation are not paired with global efforts to mitigate climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

Abrupt climate change and extinction events

There is a growing body of theoretical and empirical support for the concept of instabilities in the climate system, and indications that abrupt climate change may in some cases contribute to abrupt extinctions. Theoretical indications of instabilities can be found in a broad spectrum of climate models (energy balance models, a thermohaline model of deep-water circulation, atmospheric general circulation models, and coupled ocean-atmosphere models). Abrupt transitions can be of several types and affect the environment in different ways. There is increasing evidence for abrupt climate change in the geologic record and involves both interglacial-glacial scale transitions and the longer-term evolution of climate over the last 100 million years. Records from the Cenozoic clearly show that the long-term trend is characterized by numerous abrupt steps where the system appears to be rapidly moving to a new equilibrium state. The long-term trend probably is due to changes associated with plate tectonic processes, but the abrupt steps most likely reflect instabilities in the climate system as the slowly changing boundary conditions caused the climate to reach some threshold critical point. A more detailed analysis of abrupt steps comes from high-resolution studies of glacial-interglacial fluctuations in the Pleistocene. Comparison of climate transitions with the extinction record indicates that many climate and biotic transitions coincide. The Cretaceous-Tertiary extinction is not a candidate for an extinction event due to instabilities in the climate system. It is quite possible that more detailed comparisons and analysis will indicate some flaws in the climate instability-extinction hypothesis, but at present it appears to be a viable candidate as an alternate mechanism for causing abrupt environmental changes and extinctions.

Crowley, Thomas J.↗
NASA NTRS2002

A National Program for Analysis of the Climate System

Perhaps the single greatest roadblock to fundamental advances in our understanding of climate variability and climate change is the lack of robust and unbiased long-term global observations of the climate system. Such observations are critical for the identification and diagnosis of climate variations, and provide the constraints necessary for developing and validating climate models. The first generation of reanalysis efforts, by using fixed analysis systems, eliminated the artificial climate signals that occurred in analyses generated at the operational numerical weather prediction centers. These datasets are now widely used by the scientific community in a variety of applications including atmosphere-ocean interactions, seasonal prediction, climate monitoring, the hydrological cycle, and a host of regional and other diagnostic studies. These reanalyses, however, had problems that made them sub-optimal or even unusable for some applications. Perhaps the most serious problem for climate applications was that, while the assimilation system remained fixed, changes in the observing systems did produce spurious changes in the perceived climate. The first generation reanalysis products also exposed problems with physical consistency of the products and the accurate representation of physical processes in the climate system. Examples are bias in the estimates of ocean surface fluxes, and inadequate representation of polar hydrology. In this talk, I will describe some initial plans for a national program on reananlysis. The program is envisioned to be part of an on-going activity to maintain, improve, and reprocess our record of climate observations. I will discuss various issues affecting the quality of reanalyses, with a special focus on those relevant to the ocean.

Schubert, Siegfried↗
NASA NTRS2008

Climate Model Evaluation using New Datasets from the Clouds and the Earth's Radiant Energy System (CERES)

There are some in the science community who believe that the response of the climate system to anthropogenic radiative forcing is unpredictable and we should therefore call off the quest . The key limitation in climate predictability is associated with cloud feedback. Narrowing the uncertainty in cloud feedback (and therefore climate sensitivity) requires optimal use of the best available observations to evaluate and improve climate model processes and constrain climate model simulations over longer time scales. The Clouds and the Earth s Radiant Energy System (CERES) is a satellite-based program that provides global cloud, aerosol and radiative flux observations for improving our understanding of cloud-aerosol-radiation feedbacks in the Earth s climate system. CERES is the successor to the Earth Radiation Budget Experiment (ERBE), which has widely been used to evaluate climate models both at short time scales (e.g., process studies) and at decadal time scales. A CERES instrument flew on the TRMM satellite and captured the dramatic 1998 El Nino, and four other CERES instruments are currently flying aboard the Terra and Aqua platforms. Plans are underway to fly the remaining copy of CERES on the upcoming NPP spacecraft (mid-2010 launch date). Every aspect of CERES represents a significant improvement over ERBE. While both CERES and ERBE measure broadband radiation, CERES calibration is a factor of 2 better than ERBE. In order to improve the characterization of clouds and aerosols within a CERES footprint, we use coincident higher-resolution imager observations (VIRS, MODIS or VIIRS) to provide a consistent cloud-aerosol-radiation dataset at climate accuracy. Improved radiative fluxes are obtained by using new CERES-derived Angular Distribution Models (ADMs) for converting measured radiances to fluxes. CERES radiative fluxes are a factor of 2 more accurate than ERBE overall, but the improvement by cloud type and at high latitudes can be as high as a factor of 5. Diurnal cycles are explicitly resolved by merging geostationary satellite observations with CERES and MODIS. Atmospheric state data are provided from a frozen version of the Global Modeling and Assimilation Office- Data Assimilation System at the NASA Goddard Space Flight Center. In addition to improving the accuracy of top-of-atmosphere (TOA) radiative fluxes, CERES also produces radiative fluxes at the surface and at several levels in the atmosphere using radiative transfer modeling, constrained at the TOA by CERES (ERBE was limited to the TOA). In all, CERES uses 11 instruments on 7 spacecraft all integrated to obtain climate accuracy in TOA to surface fluxes. This presentation will provide an overview of several new CERES datasets of interest to the climate community (including a new adjusted TOA flux dataset constrained by estimates of heat storage in the Earth system), show direct comparisons between CERES ad ERBE, and provide a detailed error analysis of CERES fluxes at various time and space scales. We discuss how observations can be used to reduce uncertainties in cloud feedback and climate sensitivity and strongly argue why we should NOT "call off the quest".

Loeb, Norman G.↗
NASA NTRS2006

Role of Climate Change in Global Predictions of Future Tropospheric Ozone and Aerosols

A unified tropospheric chemistry-aerosol model within the Goddard Institute for Space Studies general circulation model II is applied to simulate an equilibrium CO2-forced climate in the year 2100 to examine the effects of climate change on global distributions of tropospheric ozone and sulfate, nitrate, ammonium, black carbon, primary organic carbon, secondary organic carbon, sea salt, and mineral dust aerosols. The year 2100 CO2 concentration as well as the anthropogenic emissions of ozone precursors and aerosols/aerosol precursors are based on the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) A2. Year 2100 global O3 and aerosol burdens predicted with changes in both climate and emissions are generally 5-20% lower than those simulated with changes in emissions alone; as exceptions, the nitrate burden is 38% lower, and the secondary organic aerosol burden is 17% higher. Although the CO2-driven climate change alone is predicted to reduce the global O3 concentrations over or near populated and biomass burning areas because of slower transport, enhanced biogenic hydrocarbon emissions, decomposition of peroxyacetyl nitrate at higher temperatures, and the increase of O3 production by increased water vapor at high NOx levels. The warmer climate influences aerosol burdens by increasing aerosol wet deposition, altering climate-sensitive emissions, and shifting aerosol thermodynamic equilibrium. Climate change affects the estimates of the year 2100 direct radiative forcing as a result of the climate-induced changes in burdens and different climatological conditions; with full gas-aerosol coupling and accounting for ozone and direct radiative forcings by the O2, sulfate, nitrate, black carbon, and organic carbon are predicted to be +0.93, -0.72, -1.0, +1.26, and -0.56 W m(exp -2), respectively, using present-day climate and year 2100 emissions, while they are predicted to be +0.76, -0.72, 0.74, +0.97, and -0.58 W m(exp -2), respectively, with year 2100 climate and emissions.

Liao, Hong↗
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.

Source record↗
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.↗
NASA NTRS2015

"Intelligent Ensemble" Projections of Precipitation and Surface Radiation in Support of Agricultural Climate Change Adaptation

Earth's climate is changing and will continue to change into the foreseeable future. Expected changes in the climatological distribution of precipitation, surface temperature, and surface solar radiation will significantly impact agriculture. Adaptation strategies are, therefore, required to reduce the agricultural impacts of climate change. Climate change projections of precipitation, surface temperature, and surface solar radiation distributions are necessary input for adaption planning studies. These projections are conventionally constructed from an ensemble of climate model simulations (e.g., the Coupled Model Intercomparison Project 5 (CMIP5)) as an equal weighted average, one model one vote. Each climate model, however, represents the array of climate-relevant physical processes with varying degrees of fidelity influencing the projection of individual climate variables differently. Presented here is a new approach, termed the "Intelligent Ensemble, that constructs climate variable projections by weighting each model according to its ability to represent key physical processes, e.g., precipitation probability distribution. This approach provides added value over the equal weighted average method. Physical process metrics applied in the "Intelligent Ensemble" method are created using a combination of NASA and NOAA satellite and surface-based cloud, radiation, temperature, and precipitation data sets. The "Intelligent Ensemble" method is applied to the RCP4.5 and RCP8.5 anthropogenic climate forcing simulations within the CMIP5 archive to develop a set of climate change scenarios for precipitation, temperature, and surface solar radiation in each USDA Farm Resource Region for use in climate change adaptation studies.

Taylor, Patrick C.↗