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Evaluating the response of vegetation to climate change is relevant to improving the management of both human and natural systems. Here, we quantify the response of the MODIS-based enhanced vegetation index (EVI) to temperature, precipitation, and large-scale natural variability across the South-Central U.S. for summer (JJA) from 2000 to 2013. We find statistically significant relationships between climate and EVI that vary across the region and are distinct for each land cover type: the mean coefficient of determination (R 2 ) between EVI and climate is greatest for pasture (0.61 ± 0.13) and lowest for forest (0.55 ± 0.14). Among the climate variables, three-month cumulative precipitation has the strongest influence on summer vegetation, particularly in semi-arid west Texas and eastern New Mexico. Summer monthly maximum temperature plays an important role in the eastern half of Texas and Oklahoma, moderated by the influence of both Atlantic and Pacific teleconnection indices over inter-annual time scales. Based on these relationships, we train, cross-validate, and, where statistically significant relationships exist, combine this multivariate predictive model with projected changes in teleconnection indices and statistically-downscaled temperature and precipitation from 16 CMIP5 global climate models to quantify future changes in EVI. As global mean temperature increases, projected EVI decreases, indicative of stressed and dry vegetation, particularly for grasslands as compared to other land types, and in Oklahoma and western, central and Gulf Coast Texas for mid- and end-of-century. These trends have potentially important implications for agriculture and the regional economy, as well as for ecosystems and endemic species that depend on vegetation. [ABSTRACT FROM AUTHOR]

Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate downscaling method and training observations used at the montane landscape of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three downscaling methods: the delta method (or the change factor method), monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD), and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models ( AOGCMs) ( CCSM3, Had CM3, PCM, and GFDL- CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8º resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model Pn ET- BGC (eight ensembles of six runs).The choice of downscaling approach and spatial resolution of the observations used to train the downscaling model impacted modeled soil moisture and streamflow, which in turn affected forest growth, net N mineralization, net soil nitrification, and stream chemistry. All three downscaling methods were highly sensitive to the observations used, resulting in projections that were significantly different between station-based and grid-based observations. The choice of downscaling method also slightly affected the results, however not as much as the choice of observations. Using spatially smoothed gridded observations and/or methods that do not resolve sub-monthly shifts in the distribution of temperature and/or precipitation can produce biased results in model applications run at greater temporal and/or spatial resolutions. These results underscore the importance of carefully considering field observations used for training, as well as the downscaling method used to generate climate change projections, for smaller-scale modeling studies. Different sources of variability including selection of AOGCM, emissions scenario, downscaling technique, and data used for training downscaling models, result in a wide range of projected forest ecosystem responses to future climate change. [ABSTRACT FROM AUTHOR]

Conceptual change theory holds that pre-existing misconceptions persist under regular instruction and interfere with student acquisition of correct concepts. This implies that effective instruction should address both concepts and common misconceptions. Here, we investigate the efficacy of incorporating corrective information regarding well-documented misconceptions on the greenhouse effect and how the climate is affected by human activities into instruction on the greenhouse effect. Specifically, we compare student acquisition of fundamental concepts related to the greenhouse effect and climate change via traditional reading-based instruction (TRI) to misconceptions reading-based instruction (MRI). Lab sections from a first year Atmospheric Sciences course at a large public university were assigned to either a TRI or MRI treatment group, yielding a total of 197 subjects who successfully completed a pretest, the treatment, an immediate post-test, and a 2-week delayed post-test. Repeated-measures analysis of variance (ANOVA) with contrasts reveals that while both treatments produced significant gains in the post-test and delayed post-test overall, only the MRI treatment produced longterm gains on misconception-related questions. These results support the conceptual change model's claims that misconceptions may persist through concept-based instruction, but may be uprooted by instruction that addresses them directly. Based on these findings, it appears that a relatively brief reading passage focused on the basic science underlying a complex phenomenon, such as the greenhouse effect and human impact on climate, can be effective in improving student understanding. In addition, specifically addressing and instructing against misconceptions may improve learner acquisition of key concepts on which misconceptions exist. [ABSTRACT FROM AUTHOR]

Habitat connectivity is a landscape attribute critical to the long-term viability of many wildlife species, includ-ing migratory birds. Climate change has the potential to affect habitat connectivity within and across the three main wetland complexes in the Great Plains of North America: the prairie potholes of the northern plains, the Rainwater Basin of Nebraska, and the playas of the southern plains. Here, we use these wetlands as model sys-tems in a graph-theory-based approach to establish links between climatic drivers and habitat connectivity for wildlife in current and projected wetland landscapes and to discern how that capacity can vary as a function of climatic forcing. We also provide a case study of macrosystems ecology to examine how the patterns and processes that determine habitat connectivity fluctuate across landscapes, regions, and continents. [ABSTRACT FROM AUTHOR]

Abstract: We use a quantitative modeling framework capable of translating increasing stress on energy demand and costs, infrastructure maintenance, and capital investments into economic impacts to estimate future climate change effects on urban infrastructure and economy. This framework enables quantitative estimates of the economic impacts of climate change based on observed relationships between key climate thresholds and their impacts on energy and infrastructure. Although the version presented here is based on information specific to city departments, the generalized modeling framework can be applied across entire urban and metro areas. For the City of Chicago, energy and infrastructure impacts, including both costs and savings, are driven primarily by increases in mean annual temperature and secondarily by increases in the frequency of extreme-heat events and decreases in cold days. With more frequent, severe, and longer periods of extreme-heat, annual average and peak electricity demands will increase. Aggregated costs for Chicago''s maintenance, labor, and capital investments could be as much as 3.5 times greater under a higher (A1FI) emissions scenario as compared to the lower (B1) scenario. These differences highlight how even partial success at reducing emissions could produce a disproportionately large reduction in economic costs for the City, the Great Lakes Region, and the nation at large. At the same time, since a single city''s mitigation efforts represent only a small proportion of what is required at the global scale, adaptation to anticipated changes is also essential. [Copyright &y& Elsevier]

Abstract: Range distributions for many animal species across North America are shifting, many in directions consistent with anthropogenic climate change over the last century. Assuming climate change continues to act as a driver for geographic range shifts, theoretical northern range shift movements can be calculated using a bioclimatic envelope approach that assume the need to maintain current temperatures and determine whether these would be attainable given projected temperature changes over the coming century. We focus on historically present small mammals in northern Indiana whose range shifts from their 1930s capture locations are attributed to be a hypothetical “response” to changes in recorded and projected average January temperature from 1914–1944 to 1961–1990, 2020–2049, and 2070–2099. Over the mid-20th century, the theoretical distance a mammal must move to remain at the same temperature (temperature-maintaining distance, TMD) ranged from 18.6 to 97.3 km (0.40–2.07 km/year) and appears to have been attainable. However, based on future temperature changes projected under the SRES higher (A1FI) and lower (B1) emissions scenarios, we found significantly larger increases in TMDs by both mid- and end-of-century relative to the historical period. Given recognized barriers to northern range extension, future small mammal TMDs greater than 4 km/year in some scenarios appear less viable than those experienced in the past in this region. As differences between higher and lower emissions scenarios may ultimately influence the ability of mammals to move TMDs, particularly by the end of the century, future emissions have the potential to markedly affect the resulting range shifts. [Copyright &y& Elsevier]

Abstract: Over the coming century, climate change is projected to increase both mean and extreme temperatures as heat waves become more frequent, intense, and long-lived. The city of Chicago has already experienced a number of severe heat waves, with a 1995 event estimated to be responsible for nearly 800 deaths. Here, future projections under SRES higher (A1FI) and lower (B1) emission scenarios are used to estimate the frequency of 1995-like heat wave events in terms of both meteorological characteristics and impacts on heat-related mortality. Before end of century, 1995-like heat waves could occur every other year on average under lower emissions and as frequently as three times per year under higher. Annual average mortality rates are projected to equal those of 1995 under lower emissions and reach twice 1995 levels under higher. An “analog city” analysis, transposing the weather conditions from the European Heat Wave of 2003 (responsible for 70,000 deaths across Europe) to the city of Chicago, estimates that if a similar heat wave were to occur over Chicago, more than ten times the annual average number of heat-related deaths could occur in just a few weeks. Climate projections indicate that an EHW-type heat wave could occur in Chicago by mid-century. Between mid- and end-of-century, there could be as many as five such events under lower, and twenty-five under higher emissions. These results highlight the importance of both preventive mitigation and responsive adaptation strategies in reducing the vulnerability of Chicago''s population to climate change-induced increases in extreme heat. [Copyright &y& Elsevier]

Abstract: Assessing regional impacts of climate change begins with development of climate projections at relevant temporal and spatial scales. Here, proven statistical downscaling methods are applied to relatively coarse-scale atmosphere–ocean general circulation model (AOGCM) output to improve the simulation and resolution of spatial and temporal variability in temperature and precipitation across the US Great Lakes region. The absolute magnitude of change expected over the coming century depends on the sensitivity of the climate system to human forcing and on the trajectory of anthropogenic greenhouse gas emissions. Annual temperatures in the region are projected to increase 1.4±0.6°C over the near-term (2010–2039), by 2.0±0.7°C under lower and 3±1°C under higher emissions by midcentury (2040–2069), and by 3±1°C under lower and 5.0±1.2°C under higher emissions by end-of-century (2070–2099), relative to the historical reference period 1961–1990. Simulations also highlight seasonal and geographical differences in warming, consistent with recent trends. Increases in winter and spring precipitation of up to 20% under lower and 30% under higher emissions are projected by end-of-century, while projections for summer and fall remain inconsistent. Competing effects of shifting precipitation and warmer temperatures suggest little change in Great Lake levels over much of the century until the end of the century, when net decreases are expected under higher emissions. Overall, these projections suggest the potential for considerable changes to climate in the US Great Lakes region; changes that could be mitigated by reducing global emissions to follow a lower as opposed to a higher emissions trajectory over the coming century. [Copyright &y& Elsevier]

Abstract: This paper introduces the analyses of the potential impacts of climate change on the city of Chicago and the Great Lakes region and potential response options that provide the basis for this special issue. Covering projected changes in climate and hydrology, this collection of studies first estimates the potential impacts of climate change on human health, natural ecosystems, water resources, energy, and infrastructure in the city of Chicago and the surrounding Great Lakes region. A consistent set of future climate projections have been used as the basis for each analysis, which together provide a vivid impression of the consequences likely to result under the SRES higher (A1FI) as compared to the lower (B1) emission scenarios. These findings highlight the importance of the next set of analyses, which focus on quantifying Chicago''s greenhouse gas emissions and prioritizing emissions reduction and adaptation options in light of the projected impacts. Together, these analyses form the basis for the City of Chicago''s Climate Action Plan, the first such plan to be based on a thorough climate change impact assessment exploring the implications of a range of future climate scenarios. Although focused on Chicago and the Great Lakes region, the methods described here are generally applicable across North America and many other parts of the world, serving as a pattern for future regional assessments to directly inform adaptation and mitigation policy at the local to regional scale. [Copyright &y& Elsevier]

• Some types of weather affect Americans' belief that they have experienced global warming. • Americans associate hot, dry days and extreme drought with global warming. • People often fail to interpret extreme rainfall or flooding as due to global warming. Public perceptions of climate change in the United States are deeply rooted in cultural values and political identities. Yet, as the public experiences extreme weather and other climate change-related impacts, their perceptions of the issue may shift. Here, we explore whether, when, and where local climate trends have already influenced perceived experiences of global warming in the United States. Using a large national survey dataset (n = 13,607), we compare Americans' experiences of climate with corresponding trends in seven different high-resolution climate indicators for the period 2008 to 2015. We find that increases in hot dry day exposure significantly increases individuals' perceptions that they have personally experienced global warming. We do not find robust evidence that other precipitation and temperature anomalies have had a similar effect. We also use multilevel modeling to explore county-level patterns of perceived experiences with climate change. Whereas the individual-level analysis describes a likely causal relationship between a changing climate and individuals' perceived experience, the multilevel model depicts county-level changes in perceived experience resulting from particular climate trends. Overall, we find that exposure to hot dry days, has a modest influence on perceived experience, independent of the political and socio-demographic factors that dominate U.S. climate opinions today. [ABSTRACT FROM AUTHOR]