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DOE OSTI2024

Global burned area increasingly explained by climate change

Fire behaviour is changing in many regions worldwide. However, nonlinear interactions between fire weather, fuel, land use, management and ignitions have impeded formal attribution of global burned area changes. Here, in this work, we demonstrate that climate change increasingly explains regional burned area patterns, using an ensemble of global fire models. The simulations show that climate change increased global burned area by 15.8% (95% confidence interval (CI) [13.1–18.7]) for 2003–2019 and increased the probability of experiencing months with above-average global burned area by 22% (95% CI [18–26]). In contrast, other human forcings contributed to lowering burned area by 19.1% (95% CI [21.9–15.8]) over the same period. Moreover, the contribution of climate change to burned area increased by 0.22% (95% CI [0.22–0.24]) per year globally, with the largest increase in central Australia. Our results highlight the importance of immediate, drastic and sustained GHG emission reductions along with landscape and fire management strategies to stabilize fire impacts on lives, livelihoods and ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Decadal trends in the seasonal-cycle amplitude of terrestrial CO 2 exchange resulting from the ensemble of terrestrial biosphere models

The seasonal-cycle amplitude (SCA) of the atmosphere–ecosystem carbon dioxide (CO 2 ) exchange rate is a useful metric of the responsiveness of the terrestrial biosphere to environmental variations. It is unclear, however, what underlying mechanisms are responsible for the observed increasing trend of SCA in atmospheric CO 2 concentration. Using output data from the Multi-scale Terrestrial Model Intercomparison Project (MsTMIP), we investigated how well the SCA of atmosphere–ecosystem CO 2 exchange was simulated with 15 contemporary terrestrial ecosystem models during the period 1901–2010. Also, we made attempt to evaluate the contributions of potential mechanisms such as atmospheric CO 2 , climate, land-use, and nitrogen deposition, through factorial experiments using different combinations of forcing data. Under contemporary conditions, the simulated global-scale SCA of the cumulative net ecosystem carbon flux of most models was comparable in magnitude with the SCA of atmospheric CO 2 concentrations. Results from factorial simulation experiments showed that elevated atmospheric CO 2 exerted a strong influence on the seasonality amplification. When the model considered not only climate change but also land-use and atmospheric CO 2 changes, the majority of the models showed amplification trends of the SCAs of photosynthesis, respiration, and net ecosystem production (+0.19 % to +0.50 % yr -1 ). In the case of land-use change, it was difficult to separate the contribution of agricultural management to SCA because of inadequacies in both the data and models. The simulated amplification of SCA was approximately consistent with the observational evidence of the SCA in atmospheric CO 2 concentrations. Large inter-model differences remained, however, in the simulated global tendencies and spatial patterns of CO 2 exchanges. Further studies are required to identify a consistent explanation for the simulated and observed amplification trends, including their underlying mechanisms. Nevertheless, this study implied that monitoring of ecosystem seasonality would provide useful insights concerning ecosystem dynamics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Data Synthesis and Data Assimilation at Global Change Experiments and Fluxnet Toward Improving Land Process Models

The project was conducted during the period from 7/1/2012 to 6/30/2017 with three major tasks: (1) data synthesis and development of data assimilation (DA) techniques to constrain modeled ecosystem feedback to climate change; (2) applications of DA techniques to improve process models at different scales from ecosystem to regions and the globe; and 3) improvements of modeling soil carbon (C) dynamics by land surface models. During this period, we have synthesized published data from soil incubation experiments (e.g., Chen et al., 2016; Xu et al., 2016; Feng et al., 2016), global change experiments (e.g., Li et al., 2013; Shi et al., 2015, 2016; Liang et al., 2016) and fluxnet (e.g., Niu et al., 2012., Xia et al., 2015; Li et al., 2016). These data have been organized into multiple data products and have been used to identify general mechanisms and estimate parameters for model improvement. We used the data sets that we collected and the DA techniques to improve model performance of both ecosystem models and global land models. The objectives are: 1) to improve model simulations of litter and soil carbon storage (e.g., Schädel et al., 2013; Hararuk and Luo, 2014; Hararuk et al., 2014; Liang et al., 2015); 2) to explore the effects of CO 2 , warming and precipitation on ecosystem processes (e.g., van Groenigen et al., 2014; Shi et al., 2015, 2016; Feng et al., 2017); and 3) to estimate parameters variability in different ecosystems (e.g., Li et al., 2016). We developed a traceability framework, which was based on matrix approaches and decomposed the modeled steady-state terrestrial ecosystem carbon storage capacity into four can trace the difference in ecosystem carbon storage capacity among different biomes to four traceable components: net primary productivity (NPP), baseline C residence times, environmental scalars and climate forcing (Xia et al., 2013). With this framework, we can diagnose the differences in modeled carbon storage across ecosystems, biomes, and models. This framework has been successfully implemented by several global land models, such as CABLE (Xia et al., 2013), LPJ-GUESS (Ahlström et al., 2015), CLM (Hararuk et al., 2014; Huang et al., 2017, submitted; Shi et al., 2017, submitted), and ORCHIDEE (Huang et al., 2017, unpublished). Moreover, we have identified the theoretical foundation of the determinants of transient C storage dynamics by adding another term, C storage potential, to the steady-state traceability framework (Luo et al., 2017). The theoretical foundation of transient C storage dynamics has been applied to develop a transient traceability framework to explore the traceable components of transient C storage dynamics responded to the rising CO 2 and climate change in the two contrasting ecosystem types Duke needleleaved forest and Harvard deciduous broadleaved forest (Jiang et al., 2017, in revision). Overall, with the data synthesis, data assimilation techniques, and the steady-state and transient traceability frameworks, we have greatly improved land process models for predicting responses and feedback of terrestrial C dynamics to global change. The matrix approaches has the potential to be applied in theoretical research on nitrogen and phosphorus cycle, and therefore, the coupling of carbon-nitrogen-phosphorus.

58 GEOSCIENCES↗

DOE OSTI2026

Climate impacts in scenarios: time to close the loop?

Reaching a full understanding of the consequences of climate change for society and ecosystems, and the ensuing needs for adaptation, requires a consideration of the interactions between human and Earth systems. Currently, however, climate research largely separates the influence of society on climate from the influence of climate on society; that is, it doesn’t “close the loop.” A primary example of this approach is the generation and use of earth system model (ESM) simulations in the climate change research community. Large-scale socio-economic models, known as integrated assessment models (IAMs), are used to project emissions and land use change which serve as input to ESMs. ESM projections then serve as input to models of impacts on society and ecosystems. But, according to this modeling chain, those impacts do not affect the emissions and land use that drove the ESMs in the first place. Previous work has not drawn firm conclusions on whether this feedback would be large enough to warrant explicitly accounting for it. Two prominent possibilities, however, are that emissions and land use scenarios representing the high and low ends of the plausible range of future climate change are both too extreme. The high-end scenario may miss damaging impacts that would reduce economic activity, and therefore emissions, while the low-end scenario may ignore climate feedbacks that would make large-scale land-based carbon removal ineffective and therefore would hamper mitigation at the level assumed by the scenario. In this piece, we identify the opportunities and challenges that implementing such feedback loops would face. We argue that recent developments in climate impact research, human system modeling and ESM emulation make the time ripe to use IAMs in a structured model intercomparison exercise. Model intercomparison projects have benefitted the climate modeling community for decade snow, and more recently have also benefitted the impact modeling community. An IAM intercomparison focused on integrating impacts could make large strides in testing the implications of these feedbacks and assessing whether closing the loop would fundamentally change our outlook on future climate changes and their consequences.

Tebaldi, Claudia↗

DOE OSTI2026

Climate change drives convergent evolution of root traits on Sky Island climate relicts

Roots are essential to the strategies plants use to survive in variable environments, yet we know little of how they vary within species. Experimental conditions demonstrate that intraspecific plant root traits respond strongly to variation in the environment; however, it is unclear when these responses can be characterized as evolution in response to selective pressures of climate change over many generations. Sky Islands are model, natural climate relict ecosystems to examine climate-change driven evolution. Utilizing a common garden with replicate genotypes of Populus angustifolia (Narrowleaf cottonwood) from six Sky Island (SI) populations and nine adjacent Mountain Chain (MC) populations across three genetic provenances, we hypothesized that SI root traits have diverged due to historical isolation in warmer, drier climates. When grown in common conditions, populations originating on SI’s showed convergent evolution across three distinct genetic provenances, which was characterized by 44.16% decreased total root length, 42.64% decreased average root volume, 43.31% decreased root surface area, and significantly less root trait variation, relative to adjacent mountain chains. Convergent evolution of root traits from trees originating on SI’s is correlated with changes in mean annual precipitation and potential evapotranspiration in the field over the past ~ 125 years. These results demonstrate a consistent pattern in root trait evolution at the landscape scale and the role of climate on the evolution of root traits in a genetic and geographic context relevant to climate change.

Convergent evolution↗

DOE OSTI2022

NGEE Arctic LANL Overview [Slides]

The Next-Generation Ecosystem Experiments (NGEE Arctic) project has a goal to "deliver a process-rich ecosystem model, extending from bedrock to the top of the vegetative canopy/atmospheric interface, in which the evolution of Arctic ecosystems in a changing climate can be modeled at the scale of a high resolution Earth System Model (ESM) grid cell." LANL works across multiple NGEE Arctic science questions, such as: Q1. How does the structure and organization of the landscape control permafrost evolution and associated carbon and nutrient fluxes in a changing climate? Q5. Where, when, and why will the Arctic become wetter or drier, and what are the implications for climate forcing? and Q6. What controls the vulnerability of Arctic ecosystems to disturbance, and how do disturbances alter the structure and function of these ecosystems? This report features LANL's research summaries.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Multi-Model Assessment of Global Hydropower and Cooling Water Discharge Potential Under Climate Change

Worldwide, 98% of total electricity is currently produced by thermoelectric power and hydropower. Climate change is expected to directly impact electricity supply, in terms of both water availability for hydropower generation and cooling water usage for thermoelectric power. Improved understanding of how climate change may impact the availability and temperature of water resources is therefore of major importance. Here we use a multi-model ensemble to show the potential impacts of climate change on global hydropower and cooling water discharge potential. For the first time, combined projections of streamflow and water temperature were produced with three global hydrological models (GHMs) to account for uncertainties in the structure and parametrization of these GHMs in both water availability and water temperature. The GHMs were forced with bias-corrected output of five general circulation models (GCMs) for both the lowest and highest representative concentration pathways (RCP2.6 and RCP8.5). The ensemble projections of streamflow and water temperature were then used to quantify impacts on gross hydropower potential and cooling water discharge capacity of rivers worldwide. We show that global gross hydropower potential is expected to increase between +2.4% (GCM-GHM ensemble mean for RCP 2.6) and +6.3% (RCP 8.5) for the 2080s compared to 1971-2000. The strongest increases in hydropower potential are expected for Central Africa, India, central Asia and the northern high-latitudes, with 18-33% of the world population living in these areas by the 2080s. Global mean cooling water discharge capacity is projected to decrease by 4.5-15% (2080s). The largest reductions are found for the United States, Europe, eastern Asia, and southern parts of South America, Africa and Australia, where strong water temperature increases are projected combined with reductions in mean annual streamflow. These regions are expected to affect 11-14% (for RCP2.6 and the shared socioeconomic pathway (SSP)1, SSP2, SSP4) and 41-51% (RCP8.5-SSP3, SSP5) of the world population by the 2080s.

global hydrological models↗

NASA NTRS2013

Tropospheric Ozone Changes, Radiative Forcing and Attribution to Emissions in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)

Ozone (O3) from 17 atmospheric chemistry models taking part in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) has been used to calculate tropospheric ozone radiative forcings (RFs). All models applied a common set of anthropogenic emissions, which are better constrained for the present-day than the past. Future anthropogenic emissions follow the four Representative Concentration Pathway (RCP) scenarios, which define a relatively narrow range of possible air pollution emissions. We calculate a value for the pre-industrial (1750) to present-day (2010) tropospheric ozone RF of 410 mW m−2. The model range of pre-industrial to present-day changes in O3 produces a spread (+/-1 standard deviation) in RFs of +/-17%. Three different radiation schemes were used - we find differences in RFs between schemes (for the same ozone fields) of +/-10 percent. Applying two different tropopause definitions gives differences in RFs of +/-3 percent. Given additional (unquantified) uncertainties associated with emissions, climate-chemistry interactions and land-use change, we estimate an overall uncertainty of +/-30 percent for the tropospheric ozone RF. Experiments carried out by a subset of six models attribute tropospheric ozone RF to increased emissions of methane (44+/-12 percent), nitrogen oxides (31 +/- 9 percent), carbon monoxide (15 +/- 3 percent) and non-methane volatile organic compounds (9 +/- 2 percent); earlier studies attributed more of the tropospheric ozone RF to methane and less to nitrogen oxides. Normalising RFs to changes in tropospheric column ozone, we find a global mean normalised RF of 42 mW m(−2) DU(−1), a value similar to previous work. Using normalised RFs and future tropospheric column ozone projections we calculate future tropospheric ozone RFs (mW m(−2); relative to 1750) for the four future scenarios (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) of 350, 420, 370 and 460 (in 2030), and 200, 300, 280 and 600 (in 2100). Models show some coherent responses of ozone to climate change: decreases in the tropical lower troposphere, associated with increases in water vapour; and increases in the sub-tropical to mid-latitude upper troposphere, associated with increases in lightning and stratosphere-to-troposphere transport. Climate change has relatively small impacts on global mean tropospheric ozone RF.

climate change↗

DOE OSTI2024

Controls and relationships of soil organic carbon abundance and persistence vary across pedo–climatic regions

One of the largest uncertainties in the terrestrial carbon cycle is the timing and magnitude of soil organic carbon (SOC) response to climate and vegetation change. This uncertainty prevents models from adequately capturing SOC dynamics and challenges the assessment of management and climate change effects on soils. Reducing these uncertainties requires simultaneous investigation of factors controlling the amount (SOC abundance) and duration (SOC persistence) of stored C. We present a global synthesis of SOC and radiocarbon profiles (n Profile = 597) to assess the timescales of SOC storage. We use a combination of statistical and depth–resolved compartment models to explore key factors controlling the relationships between SOC abundance and persistence across pedo–climatic regions and with soil depth. This allows us to better understand (i) how SOC abundance and persistence covary across pedo–climatic regions and (ii) how the depth dependence of SOC dynamics relates to climatic and mineralogical controls on SOC abundance and persistence. We show that SOC abundance and persistence are differently related; the controls on these relationships differ substantially between major pedo–climatic regions and soil depth. For example, large amounts of persistent SOC can reflect climatic constraints on soils (e.g., in tundra/polar regions) or mineral absorption, reflected in slower decomposition and vertical transport rates. In contrast, lower SOC abundance can be found with lower SOC persistence (e.g., in highly weathered tropical soils) or higher SOC persistence (e.g., in drier and less productive regions). We relate variable patterns of SOC abundance and persistence to differences in the processes constraining plant C input, microbial decomposition, vertical C transport and mineral SOC stabilization potential. This process–oriented grouping of SOC abundance and persistence provides a valuable benchmark for global C models, highlighting that pedo–climatic boundary conditions are crucial for predicting the effects of climate change and soil management on future C abundance and persistence.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1992

Modeling Earth system changes of the past

This review outlines some of the challenging problems to be faced in understanding the causes and mechanisms of large climatic changes and gives examples of initial studies of these problems with climate models. The review covers climatic changes in three main periods of earth history: (1) the past several centuries; (2) the past several glacial-interglacial cycles; and (3) the past several million years. The review will concentrate on studies of climate but, where possible, will mention broader aspects of the earth system.

Kutzbach, John E.↗

NASA NTRS2022

Impacts of Climate Change on Global Food Trade Networks

Countries' reliance on global food trade networks implies that regionally different climate change impacts on crop yields will be transmitted across borders. This redistribution constitutes a significant challenge for climate adaptation planning and may affect how countries engage in cooperative action. This paper investigates the long-term (2070–2099) potential impacts of climate change on global food trade networks of three key crops: wheat, rice and maize. We propose a simple network model to project how climate change impacts on crop yields may be translated into changes in trade. Combining trade and climate impact data, our analysis proceeds in three steps. First, we use network community detection to analyse how the concentration of global production in present-day trade communities may become disrupted with climate change impacts. Second, we study how countries may change their network position following climate change impacts. Third, we study the total climate-induced change in production plus import within trade communities. Results indicate that the stability of food trade network structures compared to today differs between crops, and that countries' maize trade is least stable under climate change impacts. Results also project that threats to global food security may depend on production change in a few major global producers, and whether trade communities can balance production and import loss in some vulnerable countries. Overall, our model contributes a baseline analysis of cross-border climate impacts on food trade networks.

climate change↗

DOE OSTI2012

Effects of soil rewetting and thawing on soil gas fluxes: a review of current literature and suggestions for future research

Rewetting of dry soils and thawing of frozen soils are short-term, transitional phenomena in terms of hydrology and thermodynamics in soil systems. The impact of these short-term phenomena on larger-scale ecosystem fluxes has only recently been fully appreciated, and a growing number of studies show that these events affect various biogeochemical processes including fluxes of biogenic gases such as carbon dioxide (CO{sub 2}), methane (CH{sub 4}), nitrous oxide (N{sub 2}O), ammonia (NH{sub 3}) and nitric oxide (NO). Global climate models predict that future climatic change is likely to alter the frequency and intensity of drying-rewetting events and thawing of frozen soils, highlighting the importance of understanding how rewetting and thawing will influence biogenic gas fluxes. Here we summarize findings in an acquired database from 338 studies conducted from 1956-2010, and propose future research questions. Studies have reported conflicting results, ranging from large increases in gas fluxes to non-significant changes following rewetting and thawing in various terrestrial ecosystems. An analysis of published data revealed that CO{sub 2}, CH{sub 4}, N{sub 2}O, NO and NH{sub 3} fluxes increase 7.6 (standard error 1.1) times following rewetting and thawing with no significant difference between these events. We explore possible mechanisms and controls that regulate flux responses, and note that there is a lack of studies on variation of CH{sub 4}, NO and NH{sub 3} fluxes following rewetting and thawing events. High temporal resolution of flux measurements is critical to capture rapid changes in the gas fluxes after these soil perturbations. Finally, we propose that future studies should investigate the interactions between biological (i.e., microbial community) and physical (i.e., gas production, flux, and dissolution) changes in biogenic gas fluxes, and explore synergistic experimental and modelling approaches.

03 NATURAL GAS↗

NASA NTRS1988

A vertically integrated snow/ice model over land/sea for climate models. I - Development. II - Impact on orbital change experiments

A vertically integrated formulation (VIF) model for sea ice/snow and land snow is discussed which can simulate the nonlinear effects of heat storage and transfer through the layers of snow and ice. The VIF demonstates the accuracy of the multilayer formulation, while benefitting from the computational flexibility of linear formulations. In the second part, the model is implemented in a seasonal dynamic zonally averaged climate model. It is found that, in response to a change between extreme high and low summer insolation orbits, the winter orbital change dominates over the opposite summer change for sea ice. For snow over land the shorter but more pronounced summer orbital change is shown to dominate.

Neeman, Binyamin U.↗

DOE OSTI2020

The Earth has humans, so why don’t our climate models?

While climate models have rapidly advanced in sophistication over recent decades, they lack dynamic representation of human behavior and social systems despite strong feedbacks between social processes and climate. The impacts of climate change alter perceptions of risk and emissions behavior that, in turn, influence the rate and magnitude of climate change. Addressing this deficiency in climate models requires a substantial interdisciplinary effort to couple models of climate and human behavior. We suggest a multi-model approach that considers a range of theories and implementations of human behavior and social systems, similar to the multi-model approach that has been used to explore the physical climate system. In this work, we describe the importance of linking social factors with climate processes and identify four priorities essential to advancing the development of coupled social-climate models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2020

Adaptation in U.S. Corn Belt Increases Resistance to Soil Carbon Loss With Climate Change

Increasing the amount of soil organic carbon (SOC) has agronomic benefits and the potential to mitigate climate change. Previous regional predictions of SOC trends under climate change often ignore or do not explicitly consider the effect of crop adaptation (i.e., changing planting dates and varieties). We used the DayCent biogeochemical model to examine the effect of adaptation on SOC for corn and soybean production in the U.S. Corn Belt using climate data from three models. Without adaptation, yields of both corn and soybean tended to decrease and the decomposition of SOC tended to increase leading to a loss of SOC with climate change compared to a baseline scenario with no climate change. With adaptation, the model predicted a substantially higher crop yield. The increase in yields and associated carbon input to the SOC pool counteracted the increased decomposition in the adaptation scenarios, leading to similar SOC stocks under different climate change scenarios. Consequently, we found that crop management adaptation to changing climatic conditions strengthen agroecosystem resistance to SOC loss. However, there are differences spatially in SOC trends. The northern part of the region is likely to gain SOC while the southern part of the region is predicted to lose SOC.

Soil Organic Carbon (SOC)↗

NASA NTRS1990

On the limitations of General Circulation Climate Models

General Circulation Models (GCMs) by definition calculate large-scale dynamical and thermodynamical processes and their associated feedbacks from first principles. This aspect of GCMs is widely believed to give them an advantage in simulating global scale climate changes as compared to simpler models which do not calculate the large-scale processes from first principles. However, it is pointed out that the meridional transports of heat simulated GCMs used in climate change experiments differ from observational analyses and from other GCMs by as much as a factor of two. It is also demonstrated that GCM simulations of the large scale transports of heat are sensitive to the (uncertain) subgrid scale parameterizations. This leads to the question whether current GCMs are in fact superior to simpler models for simulating temperature changes associated with global scale climate change.

Stone, Peter H.↗

NASA NTRS2012

Aerosol Direct, Indirect, Semidirect, and Surface Albedo Effects from Sector Contributions Based on the IPCC AR5 Emissions for Preindustrial and Present-day Conditions

The anthropogenic increase in aerosol concentrations since preindustrial times and its net cooling effect on the atmosphere is thought to mask some of the greenhouse gas-induced warming. Although the overall effect of aerosols on solar radiation and clouds is most certainly negative, some individual forcing agents and feedbacks have positive forcing effects. Recent studies have tried to identify some of those positive forcing agents and their individual emission sectors, with the hope that mitigation policies could be developed to target those emitters. Understanding the net effect of multisource emitting sectors and the involved cloud feedbacks is very challenging, and this paper will clarify forcing and feedback effects by separating direct, indirect, semidirect and surface albedo effects due to aerosols. To this end, we apply the Goddard Institute for Space Studies climate model including detailed aerosol microphysics to examine aerosol impacts on climate by isolating single emission sector contributions as given by the Coupled Model Intercomparison Project Phase 5 (CMIP5) emission data sets developed for Intergovernmental Panel on Climate Change (IPCC) AR5. For the modeled past 150 years, using the climate model and emissions from preindustrial times to present-day, the total global annual mean aerosol radiative forcing is -0.6 W/m(exp 2), with the largest contribution from the direct effect (-0.5 W/m(exp 2)). Aerosol-induced changes on cloud cover often depends on cloud type and geographical region. The indirect (includes only the cloud albedo effect with -0.17 W/m(exp 2)) and semidirect effects (-0.10 W/m(exp 2)) can be isolated on a regional scale, and they often have opposing forcing effects, leading to overall small forcing effects on a global scale. Although the surface albedo effects from aerosols are small (0.016 W/m(exp 2)), triggered feedbacks on top of the atmosphere (TOA) radiative forcing can be 10 times larger. Our results point out that each emission sector has varying impacts by geographical region. For example, the single sector most responsible for a net positive radiative forcing is the transportation sector in the United States, agricultural burning and transportation in Europe, and the domestic emission sector in Asia. These sectors are attractive mitigation targets.

Bauer, Susanne E.↗