Philip E. Morefield

Urban adaptation can roll back warming of emerging megapolitan regions.

Significance Conversion to urban landforms has consequences for regional climate and the many inhabitants living within the built environment. The purpose of our investigation was to explore hydroclimatic impacts of 21st century urban expansion across the United States and examine the efficacy of commonly proposed urban adaptation strategies in context of long-term global climate change. We show that, in the absence of any adaptive urban design, urban expansion across the United States imparts warming over large regional swaths of the country that is a significant fraction of anticipated temperature increases resulting from greenhouse gas-induced warming. Adapting to urban-induced climate change is geographically dependent, and the robust analysis that we present offers insights into optimal approaches and anticipated tradeoffs associated with varying expansion pathways. Modeling results incorporating several distinct urban expansion futures for the United States in 2100 show that, in the absence of any adaptive urban design, megapolitan expansion, alone and separate from greenhouse gas-induced forcing, can be expected to raise near-surface temperatures 1–2 °C not just at the scale of individual cities but over large regional swaths of the country. This warming is a significant fraction of the 21st century greenhouse gas-induced climate change simulated by global climate models. Using a suite of regional climate simulations, we assessed the efficacy of commonly proposed urban adaptation strategies, such as green, cool roof, and hybrid approaches, to ameliorate the warming. Our results quantify how judicious choices in urban planning and design cannot only counteract the climatological impacts of the urban expansion itself but also, can, in fact, even offset a significant percentage of future greenhouse warming over large scales. Our results also reveal tradeoffs among different adaptation options for some regions, showing the need for geographically appropriate strategies rather than one size fits all solutions.

National housing and impervious surface scenarios for integrated climate impact assessments

Understanding the impacts of climate change on people and the environment requires an understanding of the dynamics of both climate and land use/land cover changes. A range of future climate scenarios is available for the conterminous United States that have been developed based on widely used international greenhouse gas emissions storylines. Climate scenarios derived from these emissions storylines have not been matched with logically consistent land use/cover maps for the United States. This gap is a critical barrier to conducting effective integrated assessments. This study develops novel national scenarios of housing density and impervious surface cover that are logically consistent with emissions storylines. Analysis of these scenarios suggests that combinations of climate and land use/cover can be important in determining environmental conditions regulated under the Clean Air and Clean Water Acts. We found significant differences in patterns of habitat loss and the distribution of potentially impaired watersheds among scenarios, indicating that compact development patterns can reduce habitat loss and the number of impaired watersheds. These scenarios are also associated with lower global greenhouse gas emissions and, consequently, the potential to reduce both the drivers of anthropogenic climate change and the impacts of changing conditions. The residential housing and impervious surface datasets provide a substantial first step toward comprehensive national land use/land cover scenarios, which have broad applicability for integrated assessments as these data and tools are publicly available.

Variation in Estimated Ozone-Related Health Impacts of Climate Change due to Modeling Choices and Assumptions

Background: Future climate change may cause air quality degradation via climate-induced changes in meteorology, atmospheric chemistry, and emissions into the air. Few studies have explicitly modeled the potential relationships between climate change, air quality, and human health, and fewer still have investigated the sensitivity of estimates to the underlying modeling choices. Objectives: Our goal was to assess the sensitivity of estimated ozone-related human health impacts of climate change to key modeling choices. Methods: Our analysis included seven modeling systems in which a climate change model is linked to an air quality model, five population projections, and multiple concentration–response functions. Using the U.S. Environmental Protection Agency’s (EPA’s) Environmental Benefits Mapping and Analysis Program (BenMAP), we estimated future ozone (O3)-related health effects in the United States attributable to simulated climate change between the years 2000 and approximately 2050, given each combination of modeling choices. Health effects and concentration–response functions were chosen to match those used in the U.S. EPA’s 2008 Regulatory Impact Analysis of the National Ambient Air Quality Standards for O3. Results: Different combinations of methodological choices produced a range of estimates of national O3-related mortality from roughly 600 deaths avoided as a result of climate change to 2,500 deaths attributable to climate change (although the large majority produced increases in mortality). The choice of the climate change and the air quality model reflected the greatest source of uncertainty, with the other modeling choices having lesser but still substantial effects. Conclusions: Our results highlight the need to use an ensemble approach, instead of relying on any one set of modeling choices, to assess the potential risks associated with O3-related human health effects resulting from climate change.

Climate change-related temperature impacts on warm season heat mortality: a proof-of-concept methodology using BenMAP.

Climate change is anticipated to raise overall temperatures and is likely to increase heat-related human health morbidity and mortality risks. The objective of this work was to develop a proof-of-concept approach for estimating excess heat-related premature deaths in the continental United States resulting from potential changes in future temperature using the BenMAP model. In this approach we adapt the methods and tools that the US Environmental Protection Agency uses to assess air pollution health impacts by incorporating temperature modeling and heat mortality health impact functions. This new method demonstrates the ability to apply the existing temperature-health literature to quantify prospective changes in climate-sensitive heat-related mortality. We compared estimates of future temperature with and without climate change and applied heat-mortality health functions to estimate relative changes in heat-related premature mortality. Using the A1B emissions scenario, we applied the GISS-II global circulation model downscaled to 36-km using MM5 and formatted using the Meteorology-Chemistry Interface Processor. For averaged temperatures derived from the 5 years 2048-2052 relative to 1999-2003 we estimated for the warm season May-September a national U.S. estimate of annual incidence of heat-related mortality to be 3700-3800 from all causes, 3500 from cardiovascular disease, and 21 000-27 000 from nonaccidental death, applying various health impact functions. Our estimates of mortality, produced to validate the application of a new methodology, suggest the importance of quantifying heat impacts in economic assessments of climate change.

Estimated losses of plant biodiversity in the United States from historical N deposition (1985–2010)

Although nitrogen (N) deposition is a significant threat to herbaceous plant biodiversity worldwide, it is not a new stressor for many developed regions. Only recently has it become possible to estimate historical impacts nationally for the United States. We used 26 years (1985-2010) of deposition data, with ecosystem-specific functional responses from local field experiments and a national critical loads (CL) database, to generate scenario-based estimates of herbaceous species loss. Here we show that, in scenarios using the low end of the CL range, N deposition exceeded critical loads over 0.38, 6.5, 13.1, 88.6, and 222.1 million ha for the Mediterranean California, North American Desert, Northwestern Forested Mountains, Great Plains, and Eastern Forest ecoregions, respectively, with corresponding species losses ranging from < 1% to 30%. When we ran scenarios assuming ecosystems were less sensitive (using a common CL of 10 kg x ha(-1) x yr(-1), and the high end of the CL range) minimal losses were estimated. The large range in projected impacts among scenarios implies uncertainty as to whether current critical loads provide protection to terrestrial plant biodiversity nationally and urge greater research in refining critical loads for U.S. ecosystems.

The Vulnverability Cube: A Multi-Dimensional Framework for Assessing Relative Vulnerability

The diversity and abundance of information available for vulnerability assessments can present a challenge to decision-makers. Here we propose a framework to aggregate and present socioeconomic and environmental data in a visual vulnerability assessment that will help prioritize management options for communities vulnerable to environmental change. Socioeconomic and environmental data are aggregated into distinct categorical indices across three dimensions and arranged in a cube, so that individual communities can be plotted in a three-dimensional space to assess the type and relative magnitude of the communities’ vulnerabilities based on their position in the cube. We present an example assessment using a subset of the USEPA National Estuary Program (NEP) estuaries: coastal communities vulnerable to the effects of environmental change on ecosystem health and water quality. Using three categorical indices created from a pool of publicly available data (socioeconomic index, land use index, estuary condition index), the estuaries were ranked based on their normalized averaged scores and then plotted along the three axes to form a vulnerability cube. The position of each community within the three-dimensional space communicates both the types of vulnerability endemic to each estuary and allows for the clustering of estuaries with like-vulnerabilities to be classified into typologies. The typologies highlight specific vulnerability descriptions that may be helpful in creating specific management strategies. The data used to create the categorical indices are flexible depending on the goals of the decision makers, as different data should be chosen based on availability or importance to the system. Therefore, the analysis can be tailored to specific types of communities, allowing a data rich process to inform decision-making.

Grasslands, wetlands, and agriculture: the fate of land expiring from the Conservation Reserve Program in the Midwestern United States

The Conservation Reserve Program (CRP) is the largest agricultural land-retirement program in the United States, providing many environmental benefits, including wildlife habitat and improved air, water, and soil quality. Since 2007, however, CRP area has declined by over 25% nationally with much of this land returning to agriculture. Despite this trend, it is unclear what types of CRP land are being converted, to what crops, and where. All of these specific factors greatly affect environmental impacts. To answer these questions, we quantified shifts in expiring CRP parcels to five major crop-types (corn, soy, winter and spring wheat, and sorghum) in a 12-state, Midwestern region of the United States using a US Department of Agriculture (USDA), field-level CRP database and USDAs Cropland Data Layer. For the years 2010 through 2013, we estimate almost 30%, or more than 530 000 ha, of expiring CRP land returned to the production of these five crops in our study area, with soy and corn accounting for the vast majority of these shifts. Grasslands were the largest type of CRP land converted (360 000 ha), followed by specifically designated wildlife habitat (76 000 ha), and wetland areas (53 000 ha). These wetland areas were not just wetlands themselves, but also a mix of land covers enhancing or protecting wetland ecosystem services (e.g., wetland buffers). Areas in the Dakotas, Nebraska, and southern Iowa were hotspots of change, with the highest areas of CRP land moving back to agriculture. By contrast, we estimate only a small amount (~3%) of the expiring land shifted into similar, non-CRP land-retirement or easement programs. Reconciling needs for food, feed, fuel, and healthy ecosystems is an immense challenge for farmers, conservationists, and state and federal agencies. Reduced enrollment and the turnover of CRP land from conservation to agriculture raises questions about sustaining ecosystem services in this region.

ESTIMATES OF CHANGES IN COUNTY-LEVEL HOUSING PRICES IN THE UNITED STATES UNDER SCENARIOS OF FUTURE CLIMATE CHANGE

Climate in a given location influences peoples housing decisions, and changes in climate may affect these decisions in ways that alter our understanding of desirable locations. This study examines the potential sensitivity of future housing prices in the United States to changes in temperature, precipitation, and humidity by developing a hedonic regression model of the relationship between climate variables and housing prices and exploring implications of different climate futures for the amenity value of climate in these prices. The model shows a significant relationship between housing prices in urban areas and certain climate variables. The study then examines the sensitivity of the amenity value of climate to future climate scenarios. Results suggest that, nationally, climate change represents a disamenity, particularly in central-to-southeastern states. However, detailed housing prices vary spatially and among scenarios. Seasonal variation in temperature, including the relative magnitudes of the change in January and July temperatures, is a key determinant of housing price change, contributing to variation across both climate scenarios and geographic location.