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This paper describes five sets of regions intended for use in summarizing extreme weather over Earth’s land areas from a climate perspective. The sets differ in terms of their target size: ∼10 Mm2, ∼5 Mm2, ∼2 Mm2, ∼0.5 Mm2, and ∼0.1 Mm2 (where 1 Mm2= 1 million km2). The regions are based on political/economic divisions, and hence are intended to be primarily aligned with geographical domains of decision-making and disaster response rather than other factors such as climatological homogeneity. This paper describes the method for defining these sets of regions; provides the final definitions of the regions; and performs some comparisons across the five sets and other available regional definitions with global land coverage, according to climatological and non-climatological properties.

This paper describes five sets of regions intended for use in summarizing extreme weather over Earth’s land areas from a climate perspective. The sets differ in terms of their target size: ∼10 Mm2, ∼5 Mm2, ∼2 Mm2, ∼0.5 Mm2, and ∼0.1 Mm2 (where 1 Mm2= 1 million km2). The regions are based on political/economic divisions, and hence are intended to be primarily aligned with geographical domains of decision-making and disaster response rather than other factors such as climatological homogeneity. This paper describes the method for defining these sets of regions; provides the final definitions of the regions; and performs some comparisons across the five sets and other available regional definitions with global land coverage, according to climatological and non-climatological properties.

Anthropogenic climate change has triggered impacts on natural and human systems world-wide, yet the formal scientific method of detection and attribution has been only insufficiently described. Detection and attribution of impacts of climate change is a fundamentally cross-disciplinary issue, involving concepts, terms, and standards spanning the varied requirements of the various disciplines. Key problems for current assessments include the limited availability of long-term observations, the limited knowledge on processes and mechanisms involved in changing environmental systems, and the widely different concepts applied in the scientific literature. In order to facilitate current and future assessments, this paper describes the current conceptual framework of the field and outlines a number of conceptual challenges. Based on this, it proposes workable cross-disciplinary definitions, concepts, and standards. The paper is specifically intended to serve as a baseline for continued development of a consistent cross-disciplinary framework that will facilitate integrated assessment of the detection and attribution of climate change impacts. © 2013 The Author(s).

Anthropogenic climate change has triggered impacts on natural and human systems world-wide, yet the formal scientific method of detection and attribution has been only insufficiently described. Detection and attribution of impacts of climate change is a fundamentally cross-disciplinary issue, involving concepts, terms, and standards spanning the varied requirements of the various disciplines. Key problems for current assessments include the limited availability of long-term observations, the limited knowledge on processes and mechanisms involved in changing environmental systems, and the widely different concepts applied in the scientific literature. In order to facilitate current and future assessments, this paper describes the current conceptual framework of the field and outlines a number of conceptual challenges. Based on this, it proposes workable cross-disciplinary definitions, concepts, and standards. The paper is specifically intended to serve as a baseline for continued development of a consistent cross-disciplinary framework that will facilitate integrated assessment of the detection and attribution of climate change impacts. © 2013 The Author(s).

Hurricane Harvey is one of the costliest tropical cyclones in history. In this paper, we use a probabilistic event attribution framework to estimate the costs associated with Hurricane Harvey that are attributable to anthropogenic influence on the climate system. Results indicate that the “fraction of attributable risk” for the rainfall from Harvey was likely about at least a third with a preferable/best estimate of three quarters. With an average estimate of damages from Harvey assessed at about US$90bn, applying this fraction gives a best estimate of US$67bn, with a likely lower bound of at least US$30bn, of these damages that are attributable to the human influence on climate. This “bottom-up” event-based estimate of climate change damages contrasts sharply with the more “top-down” approach using integrated assessment models (IAMs) or global macroeconometric estimates: one IAM estimates annual climate change damages in the USA to be in the region of US$21.3bn. While the two approaches are not easily comparable, it is noteworthy that our “bottom-up” results estimate that one single extreme weather event contributes more to climate change damages in the USA than an entire year by the “top-down” method. Given that the “top-down” approach, at best, parameterizes but does not resolve the effects of extreme weather events, our findings suggest that the “bottom-up” approach is a useful avenue to pursue in future attempts to refine estimates of climate change damages.

Hurricane Harvey is one of the costliest tropical cyclones in history. In this paper, we use a probabilistic event attribution framework to estimate the costs associated with Hurricane Harvey that are attributable to anthropogenic influence on the climate system. Results indicate that the “fraction of attributable risk” for the rainfall from Harvey was likely about at least a third with a preferable/best estimate of three quarters. With an average estimate of damages from Harvey assessed at about US$90bn, applying this fraction gives a best estimate of US$67bn, with a likely lower bound of at least US$30bn, of these damages that are attributable to the human influence on climate. This “bottom-up” event-based estimate of climate change damages contrasts sharply with the more “top-down” approach using integrated assessment models (IAMs) or global macroeconometric estimates: one IAM estimates annual climate change damages in the USA to be in the region of US$21.3bn. While the two approaches are not easily comparable, it is noteworthy that our “bottom-up” results estimate that one single extreme weather event contributes more to climate change damages in the USA than an entire year by the “top-down” method. Given that the “top-down” approach, at best, parameterizes but does not resolve the effects of extreme weather events, our findings suggest that the “bottom-up” approach is a useful avenue to pursue in future attempts to refine estimates of climate change damages.

We describe a new approach that allows for systematic causal attribution of weather and climate-related events, in near-real time. The method is designed so as to facilitate its implementation at meteorological centers by relying on data and methods that are routinely available when numerically forecasting the weather. We thus show that causal attribution can be obtained as a by-product of data assimilation procedures run on a daily basis to update numerical weather prediction (NWP) models with new atmospheric observations; hence, the proposed methodology can take advantage of the powerful computational and observational capacity of weather forecasting centers. We explain the theoretical rationale of this approach and sketch the most prominent features of a “data assimilation–based detection and attribution” (DADA) procedure. The proposal is illustrated in the context of the classical three-variable Lorenz model with additional forcing. The paper concludes by raising several theoretical and practical questions that need to be addressed to make the proposal operational within NWP centers.

Climate change impacts on water resources in the United States are likely to be far-reaching and substantial because the water is integral to climate, and the water sector spans many parts of the economy. This paper estimates impacts and damages from five water resource-related models addressing runoff, drought risk, economics of water supply/demand, water stress, and flooding damages. The models differ in the water system assessed, spatial scale, and unit of assessment, but together provide a quantitative and descriptive richness in characterizing water sector effects that no single model can capture. The results, driven by a consistent set of greenhouse gas (GHG) emission and climate scenarios, examine uncertainty from emissions, climate sensitivity, and climate model selection. While calculating the net impact of climate change on the water sector as a whole may be impractical, broad conclusions can be drawn regarding patterns of change and benefits of GHG mitigation. Four key findings emerge: 1) GHG mitigation substantially reduces hydro-climatic impacts on the water sector; 2) GHG mitigation provides substantial national economic benefits in water resources related sectors; 3) the models show a strong signal of wetting for the Eastern US and a strong signal of drying in the Southwest; and 4) unmanaged hydrologic systems impacts show strong correlation with the change in magnitude and direction of precipitation and temperature from climate models, but managed water resource systems and regional economic systems show lower correlation with changes in climate variables due to non-linearities created by water infrastructure and the socio-economic changes in non-climate driven water demand.

This paper is among the first to report on the full integration of basin-scale models that include projections of the demand and supply of water and energy for residential, commercial, industrial, and agricultural sector users. We link two widely used regional planning models that allow one to study the impact of rising climate variability on water and electricity use in Sacramento, California. Historic data combined with the current energy and water system configuration was used to assess the implications of changes in temperature and precipitation. Climate simulations suggest that electricity imports to the region would increase during hot dry spells, when regional power production is most constrained. In particular, regional imports of electricity would increase over 35 % in hot dry years, assuming a 4 °C increase in average temperature and a 25 % decrease in average precipitation.

This paper is among the first to report on the full integration of basin-scale models that include projections of the demand and supply of water and energy for residential, commercial, industrial, and agricultural sector users. We link two widely used regional planning models that allow one to study the impact of rising climate variability on water and electricity use in Sacramento, California. Historic data combined with the current energy and water system configuration was used to assess the implications of changes in temperature and precipitation. Climate simulations suggest that electricity imports to the region would increase during hot dry spells, when regional power production is most constrained. In particular, regional imports of electricity would increase over 35 % in hot dry years, assuming a 4 °C increase in average temperature and a 25 % decrease in average precipitation.

This paper develops and applies methods to quantify and monetize projected impacts on terrestrial ecosystem carbon storage and areas burned by wildfires in the contiguous United States under scenarios with and without global greenhouse gas mitigation. The MC1 dynamic global vegetation model is used to develop physical impact projections using three climate models that project a range of future conditions. We also investigate the sensitivity of future climates to different initial conditions of the climate model. Our analysis reveals that mitigation, where global radiative forcing is stabilized at 3.7 W/m 2 in 2100, would consistently reduce areas burned from 2001 to 2100 by tens of millions of hectares. Monetized, these impacts are equivalent to potentially avoiding billions of dollars (discounted) in wildfire response costs. Impacts to terrestrial ecosystem carbon storage are less uniform, but changes are on the order of billions of tons over this time period. The equivalent social value of these changes in carbon storage ranges from hundreds of billions to trillions of dollars (discounted). The magnitude of these results highlights their importance when evaluating climate policy options. However, our results also show national outcomes are driven by a few regions and results are not uniform across regions, time periods, or models. Differences in the results based on the modeling approach and across initializing conditions also raise important questions about how variability in projected climates is accounted for, especially when considering impacts where extreme or threshold conditions are important.

This paper develops and applies methods to quantify and monetize projected impacts on terrestrial ecosystem carbon storage and areas burned by wildfires in the contiguous United States under scenarios with and without global greenhouse gas mitigation. The MC1 dynamic global vegetation model is used to develop physical impact projections using three climate models that project a range of future conditions. We also investigate the sensitivity of future climates to different initial conditions of the climate model. Our analysis reveals that mitigation, where global radiative forcing is stabilized at 3.7 W/m 2 in 2100, would consistently reduce areas burned from 2001 to 2100 by tens of millions of hectares. Monetized, these impacts are equivalent to potentially avoiding billions of dollars (discounted) in wildfire response costs. Impacts to terrestrial ecosystem carbon storage are less uniform, but changes are on the order of billions of tons over this time period. The equivalent social value of these changes in carbon storage ranges from hundreds of billions to trillions of dollars (discounted). The magnitude of these results highlights their importance when evaluating climate policy options. However, our results also show national outcomes are driven by a few regions and results are not uniform across regions, time periods, or models. Differences in the results based on the modeling approach and across initializing conditions also raise important questions about how variability in projected climates is accounted for, especially when considering impacts where extreme or threshold conditions are important.

Climate change impacts on water resources in the United States are likely to be far-reaching and substantial because the water is integral to climate, and the water sector spans many parts of the economy. This paper estimates impacts and damages from five water resource-related models addressing runoff, drought risk, economics of water supply/demand, water stress, and flooding damages. The models differ in the water system assessed, spatial scale, and unit of assessment, but together provide a quantitative and descriptive richness in characterizing water sector effects that no single model can capture. The results, driven by a consistent set of greenhouse gas (GHG) emission and climate scenarios, examine uncertainty from emissions, climate sensitivity, and climate model selection. While calculating the net impact of climate change on the water sector as a whole may be impractical, broad conclusions can be drawn regarding patterns of change and benefits of GHG mitigation. Four key findings emerge: 1) GHG mitigation substantially reduces hydro-climatic impacts on the water sector; 2) GHG mitigation provides substantial national economic benefits in water resources related sectors; 3) the models show a strong signal of wetting for the Eastern US and a strong signal of drying in the Southwest; and 4) unmanaged hydrologic systems impacts show strong correlation with the change in magnitude and direction of precipitation and temperature from climate models, but managed water resource systems and regional economic systems show lower correlation with changes in climate variables due to non-linearities created by water infrastructure and the socio-economic changes in non-climate driven water demand.

As managers of agricultural and natural resources are confronted with uncertainties in global change impacts, the complexities associated with the interconnected cycling of nitrogen, carbon, and water present daunting management challenges. Existing models provide detailed information on specific sub-systems (e.g., land, air, water, and economics). An increasing awareness of the unintended consequences of management decisions resulting from interconnectedness of these sub-systems, however, necessitates coupled regional earth system models (EaSMs). Decision makers’ needs and priorities can be integrated into the model design and development processes to enhance decision-making relevance and “usability” of EaSMs. BioEarth is a research initiative currently under development with a focus on the U.S. Pacific Northwest region that explores the coupling of multiple stand-alone EaSMs to generate usable information for resource decision-making. Direct engagement between model developers and non-academic stakeholders involved in resource and environmental management decisions throughout the model development process is a critical component of this effort. BioEarth utilizes a bottom-up approach for its land surface model that preserves fine spatial-scale sensitivities and lateral hydrologic connectivity, which makes it unique among many regional EaSMs. This paper describes the BioEarth initiative and highlights opportunities and challenges associated with coupling multiple stand-alone models to generate usable information for agricultural and natural resource decision-making.

As managers of agricultural and natural resources are confronted with uncertainties in global change impacts, the complexities associated with the interconnected cycling of nitrogen, carbon, and water present daunting management challenges. Existing models provide detailed information on specific sub-systems (e.g., land, air, water, and economics). An increasing awareness of the unintended consequences of management decisions resulting from interconnectedness of these sub-systems, however, necessitates coupled regional earth system models (EaSMs). Decision makers’ needs and priorities can be integrated into the model design and development processes to enhance decision-making relevance and “usability” of EaSMs. BioEarth is a research initiative currently under development with a focus on the U.S. Pacific Northwest region that explores the coupling of multiple stand-alone EaSMs to generate usable information for resource decision-making. Direct engagement between model developers and non-academic stakeholders involved in resource and environmental management decisions throughout the model development process is a critical component of this effort. BioEarth utilizes a bottom-up approach for its land surface model that preserves fine spatial-scale sensitivities and lateral hydrologic connectivity, which makes it unique among many regional EaSMs. This paper describes the BioEarth initiative and highlights opportunities and challenges associated with coupling multiple stand-alone models to generate usable information for agricultural and natural resource decision-making.

A recent paper in this journal argues that the choice of statistical model is responsible for the divergence in damage estimates of climate change on US agriculture. We provide five arguments why we believe this assertion is misguided. © 2013 Springer Science+Business Media Dordrecht.

A recent paper in this journal argues that the choice of statistical model is responsible for the divergence in damage estimates of climate change on US agriculture. We provide five arguments why we believe this assertion is misguided. © 2013 Springer Science+Business Media Dordrecht.