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The climate change policy debate and ensuing discussions about industrial energy use and carbon emissions have highlighted the need to: (a) aggregate engineering information to a level relevant for economic policy analysis while maintaining sufficient detail so that results are meaningful for industry decision makers, (b) properly represent an industry’s capital vintage structure to better understand inertia associated with changes in aggregate industrial emissions profiles, and (c) identify policy instruments that leverage an industry’s potential for technological change such that carbon emissions can be noticeably reduced. This paper presents an econometric analysis of energy use and emissions profiles of the US Pulp and Paper Industry and uses the resulting set of equations to specify a dynamic model for the analysis of select climate change policies. Scenarios of cost of carbon, energy tax, and investment-led policies indicate that a combination of cost of carbon and investment-led policies can achieve the desired result of rapidly improving overall efficiency of the industry and promoting changes in fuel mix, which together can result in drastic reductions of carbon emissions. [Copyright &y& Elsevier]

While the majority of adults across the United States report that they believe that global warming is happening, far fewer report discussing global warming (Howe et al., 2015, https://doi.org/10.1038/nclimate2583; Marlon et al., 2022, https://climatecommunication.yale.edu/visualizations‐data/ycom‐us/). One way to inspire further climate action and engagement is to increase individuals' capacity to confidently and effectively discuss climate change. Climate communication science highlights that such communication is most effective when it is anecdotal, narrative, tailored to the audience, and place‐based. To generate climate conversations and inspire action in a variety of communities, partners at the University of Minnesota Extension piloted a program to train community members from across the state of Minnesota in effective climate communication through a series of instructional workshops, coaching, and participant‐led communication activities. Following the training portion of the program, participants identified and hosted their own climate‐related communication activities in their communities. These "climate conversations" took place across Minnesota and included community events, dialogue with elected leaders, and conversations stimulated through literature, among other activities. In their communities, program participants sparked conversations, initiated long‐term climate action efforts, and improved their sense of efficacy in response to climate change. Participants also reported that they improved their climate conversation skills, increased their local climate knowledge, established a support network with fellow participants, had reduced anxiety around communicating, and increased their confidence in being able to communicate about climate change in their communities. This pilot program provides a framework for future cohort‐ and community‐based climate communication programs in the state and beyond. Plain Language Summary: More frequent, effective climate conversations initiated by a diversity of trusted voices can help to increase climate concern and desire for action at the community level. However, in the United States, there is a disconnect between the level of concern individuals have about climate change and the extent to which individuals talk about the issue. To help bridge this gap, the University of Minnesota Climate Adaptation Partnership developed a training program aimed at inspiring and equipping local community members across Greater Minnesota with the skills and confidence to have effective conversations about climate change in their communities. This paper summarizes the programmatic activities we used to support our goals, and some reflections on the program's results. This pilot program provides a framework for future efforts that can be facilitated by Extension programs, community‐based organizations, universities, and others to inspire and accelerate similar community‐centered climate conversations. Key Points: We piloted a training program to teach Minnesotans to effectively communicate about climate change and its impacts on their communitiesParticipants improved behavioral and conversation skills to communicate about climate change more effectivelyThis program provides a framework for future programs aimed at increasing the diversity of people engaging in local climate conversations [ABSTRACT FROM AUTHOR]

Although substantial attention has focused on efforts by the new Administration to block environmental policies, climate politics have been contentious in the US since well before the election of Donald Trump. In this paper, we extend previous work on empirical examinations of echo chambers in US climate politics using new data collected on the federal climate policy network in summer 2016. We test for the similarity and differences at two points in time in homophily and echo chambers using Exponential Random Graph Models (ERGM) to compare new findings from 2016 to previous work on data from 2010. We show that echo chambers continue to play a significant role in the network of information exchange among policy elites working on the issue of climate change. In contrast to previous findings where echo chambers centered on a binding international commitment to emission reductions, we find that the pre-existing echo chambers have almost completely disappeared and new structures have formed around one of the main components of the Obama Administration’s national climate policy: the Clean Power Plan. These results provide empirical evidence that science communication and policymaking at the elite level shift in relation to the policy instruments under consideration. [ABSTRACT FROM AUTHOR]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]

Traditionally, the daily average air temperature at a weather station is computed by taking the mean of two values, the maximum temperature (Tmax) and the minimum temperature (Tmin), over a 24-h period. These values form the basis for numerous studies of long-term climatologies (e.g., 30-yr normals) and recent temperature trends and changes. However, many first-order weather stations-such as those at airports-also record hourly temperature data. Using an average of the 24 hourly temperature readings to compute daily average temperature has been shown to provide a more precise and representative estimate of a given day's temperature. This study assesses the spatial variability of the differences in these two methods of daily temperature averaging [i.e., (Tmax 1 Tmin)/2; average of 24 hourly temperature values] for 215 first-order weather stations across the conterminous United States (CONUS) over the 30-yr period 1981-2010. A statistically significant difference is shown between the two methods, as well as consistent overestimation of temperature by the traditional method [(Tmax 1 Tmin)/2], particularly in southern and coastal portions of the CONUS. The explanation for the long-term difference between the two methods is the underlying assumption for the twice-daily method that the diurnal curve of temperature is symmetrical. Moreover, this paper demonstrates a spatially coherent pattern in the difference compared to the most recent part of the temperature record (2001-15). The spatial and temporal differences shown have implications for assessments of the physical factors influencing the diurnal temperature curve, as well as the exact magnitude of contemporary climate change. [ABSTRACT FROM AUTHOR]

In this paper, we assess the challenges of water, waste and climate change in six cities across the U.S.: New York City, Boston, Milwaukee, Phoenix, Portland and Los Angeles. We apply the City Blueprint Approach which consists of three indicator assessments: (1) the Trends and Pressures Framework (TPF), (2) the City Blueprint Framework (CBF) and (3) the water Governance Capacity Framework (GCF). The TPF summarizes the main social, environmental and financial pressures that may impede water management. The CBF provides an integrated overview of the management performances within the urban watercycle. Finally, the GCF provides a framework to identify key barriers and opportunities to develop governance capacity. The GCF has only been applied in NYC. Results show that all cities face pressures from heat risk. The management performances regarding resource efficiency and resource recovery from wastewater and solid waste show considerable room for improvement. Moreover, stormwater separation, infrastructure maintenance and green space require improvement in order to achieve a resilient urban watercycle. Finally, in New York City, the GCF results show that learning through smart monitoring, evaluation and cross-stakeholder learning is a limiting condition that needs to be addressed. We conclude that the City Blueprint Approach has large potential to assist cities in their strategic planning and exchange of knowledge, experiences and lessons. Because the methodology is well-structured, easy to understand, and concise, it may bridge the gap between science, policy and practice. It could therefore enable other cities to address their challenges of water, waste and climate change. [ABSTRACT FROM AUTHOR]

This paper tries to identify one of the reasons for the poor land skin temperature simulated by a climate model over Greenland. It first compares ICEsat surface height measurements over Greenland with those used by the model and reveals that the surface height of Greenland prescribed in the National Center for Atmospheric Research (NCAR) Community Climate System Model/Community Land Model version 3 (CCSM/CLM3) differs greatly from the satellite measurements from National Aeronautics and Space Administration (NASA) ICEsat at edges and central glacier regions. This deficiency, in part, leads to underestimated skin temperatures at coastal regions-the areas where significant ice sheet melt is observed. Furthermore, sensitivity studies reveal that surface skin temperature simulations of Greenland would be significantly improved if the more accurate surface height is used. The problem of the height used in current global climate model is mainly due to the fact that the model has to use coarse grid size, and within one grid, land surface height has high heterogeneity. How to assign a proper surface height for each model grid and meanwhile adequately present the high heterogeneity of land surface is a great challenge in current model development. [ABSTRACT FROM AUTHOR]

To better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time‐evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station‐based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science. Plain Language Summary: While temperatures around the world continue to warm due to human‐caused climate change, some areas have observed smaller temperature trends than others. Understanding this regional variability in the rate of warming is important when assessing future projections. One location that has observed less warming is across the United States during their summer season. To evaluate temperature variability in this region using real‐world observations and climate model simulations, we use a statistical method from artificial intelligence called neural networks. The goal of the neural network setup is to learn temperature patterns across the United States and then identify whether climate change effects have exceeded the range of natural variability that has occurred in the past. This is called the timing of emergence (ToE), which is the first year that the effect has clearly appeared. We find that the average United States minimum temperature increase has already emerged in historical records. However, we find no ToE for the average maximum temperature, other than in the Western United States. Another important finding of this study is that by using higher resolution climate model data (i.e., more latitude and longitude points), we find better accuracy in the neural network predictions. Key Points: Forced temperature changes have emerged in observations during summer in the United States as detected by an artificial neural networkIncreasing spatial resolution improves neural network skill for predicting the year of a given summer temperature mapWestern United States land surface climate properties contribute to earlier timing of emergence predictions for the SPEAR climate model [ABSTRACT FROM AUTHOR]

Opposition against greenhouse gas emissions reductions is strong among some conservative Christian groups, especially in the United States. In this paper, we identify five scripture-based arguments against greenhouse gas mitigation put forward by a core group of Christian conservatives (‘the Cornwallists’): the anti-paganism argument, the enrichment argument, the omnipotence argument, the lack of moral relevance argument and the cost-benefit argument. We evaluate to what extent the arguments express positions that can be characterised as climate science denialist and to what degree they are consistent with support for climate adaptation. Using Stefan Rahmstorf’s (2004) taxonomy of climate science denial, we conclude that the Cornwallists could be labelled climate change deniers. However, their opposition is not only based on denial of climate science but often rests on premises that render the science irrelevant, a position we term ‘relevance denialism’. [ABSTRACT FROM AUTHOR]

Abstract: In the past several decades, decision makers in the United States have increasingly called upon publicly funded science to provide “usable” information for policy making, whether in the case of acid rain, famine prevention or climate change policy. As demands for usability become more prevalent for publicly accountable scientific programs, there is a need to better understand opportunities and constraints to science use in order to inform policy design and implementation. Motivated by recent critique of the decision support function of the US Global Change Research Program, this paper seeks to address this issue by specifically examining the production and use of climate science. It reviews empirical evidence from the rich scholarship focused on climate science use, particularly seasonal climate forecasts, to identify factors that constrain or foster usability. It finds, first, that climate science usability is a function both of the context of potential use and of the process of scientific knowledge production itself. Second, nearly every case of successful use of climate knowledge involved some kind of iteration between knowledge producers and users. The paper argues that, rather than an automatic outcome of the call for the production of usable science, iterativity is the result of the action of specific actors and organizations who ‘own’ the task of building the conditions and mechanisms fostering its creation. Several different types of institutional arrangements can accomplish this task, depending on the needs and resources available. While not all of the factors that enhance usability of science for decision making are within the realm of the scientific enterprise itself, many do offer opportunities for improvement. Science policy mechanisms such as the level of flexibility afforded to research projects and the metrics used to evaluate the outcomes of research investment can be critical to providing the necessary foundation for iterativity and production of usable science to occur. [Copyright &y& Elsevier]

Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement (''downscaling''), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median −−11%%) caused by the hydrologic model''s apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen--Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors'' findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climate-change impacts on water. [ABSTRACT FROM AUTHOR]

While most projections of climate change and its regional impacts focus on overall changes in the state of the climate system, useful information can also be found in the evolution of the climate system from one state to another. Here the authors introduce one method for identifying regions in which significant and systematic long-term nonlinear evolutions may be present, even given quasi-linear anthropogenic forcing. Using climate change projections taken from simulations of NCAR’s Community Climate System Model, version 3 (CCSM3), the authors then employ the technique to isolate systematic nonlinear behavior of soil moisture variations over the United States. While the projections presented here only represent the results from one model system, it is argued that such nonlinear behavior is an important characteristic of future climate change that should be considered when discussing both short-term and long-term impacts of anthropogenic climate forcing. [ABSTRACT FROM AUTHOR]

Uncertainties in observed records of atmospheric temperature aloft remain poorly quantified. This has resulted in considerable controversy regarding signals of climate change over recent decades from temperature records of radiosondes and satellites. This work revisits the problems associated with the removal of inhomogeneities from the historical radiosonde temperature records, and provides a method for quantifying uncertainty in an adjusted radiosonde climate record due to the subjective choices made during the data homogenization. This paper presents an automated homogenization method designed to replicate the decisions made by manual judgment in the generation of an earlier radiosonde dataset [i.e., the Hadley Centre radiosonde temperature dataset (HadAT)]. A number of validation experiments have been conducted to test the system performance and impact on linear trends. Using climate model data to simulate biased radiosonde data, the authors show that limitations in the homogenization method are sufficiently large to explain much of the tropical trend discrepancy between HadAT and estimates from satellite platforms and climate models. This situation arises from the combination of systematic (unknown magnitude) and random uncertainties (of order 0.05 K decade-1) in the radiosonde data. Previous assessment of trends and uncertainty in HadAT is likely to have underestimated the systematic bias in tropical mean temperature trends. This objective assessment of radiosonde homogenization supports the conclusions of the synthesis report of the U.S. Climate Change Science Program (CCSP), and associated research, regarding potential bias in tropospheric temperature records from radiosondes. [ABSTRACT FROM AUTHOR]

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mark 2 global coupled climatic model has been used to generate a 10 000-yr simulation of “present” climate. The resultant dataset has been used to investigate a number of aspects of extremes associated with annual mean rainfall. Multimillennial time series of normalized rainfall amounts for selected points are used to highlight secular variability, spatial variations, and the differences between pluvial and drought conditions. Global distributions are also presented for selected rainfall characteristics, including the frequency of occurrence of specified rainfall anomalies with annual durations, the frequency of occurrence of 5-yr sequences of specified rainfall anomalies, and the maximum and minimum normalized rainfall amounts attained in the simulation. Such features cannot be obtained from observations because of their limited duration. A case study is also made of a megadrought over the southwestern United States, together with an analysis of the associated causal mechanisms. Given the exclusion of all external forcing from the model, it is concluded that the extreme annual mean rainfall extremes presented in the paper are attributable to stochastic events. [ABSTRACT FROM AUTHOR]

In this paper, the authors analyze simulations of present and future climates in the western United States performed with four regional climate models (RCMs) nested within two global ocean–atmosphere climate models. The primary goal here is to assess the range of regional climate responses to increased greenhouse gases in available RCM simulations. The four RCMs used different geographical domains, different increased greenhouse gas scenarios for future-climate simulations, and (in some cases) different lateral boundary conditions. For simulations of the present climate, RCM results are compared to observations and to results of the GCM that provided lateral boundary conditions to the RCM. For future-climate (increased greenhouse gas) simulations, RCM results are compared to each other and to results of the driving GCMs. When results are spatially averaged over the western United States, it is found that the results of each RCM closely follow those of the driving GCM in the same region in both present and future climates. This is true even though the study area is in some cases a small fraction of the RCM domain. Precipitation responses predicted by the RCMs in many regions are not statistically significant compared to interannual variability. Where the predicted precipitation responses are statistically significant, they are positive. The models agree that near-surface temperatures will increase, but do not agree on the spatial pattern of this increase. The four RCMs produce very different estimates of water content of snow in the present climate, and of the change in this water content in response to increased greenhouse gases. [ABSTRACT FROM AUTHOR]

Aim Climate change has the potential to have an impact on the distribution of ticks and tick-borne diseases. This paper identifies the changes in climate suitability for the tick Rhipicephalus (Boophilus) microplus in the Americas by analysing climate data for the period 1950–99. Location The model was applied to the American continent. Methods A model based on Environmental Niche Modelling was used on a gridded (0.5°) long-term (1950–99) climate data set. A map of the core range of the species was constructed, and areas where habitat suitability (HS) changes suddenly over short time periods were identified as regions of high sensitivity. Tendency of climate in the continent was evaluated and scenarios constructed for 2025 and 2050. Results Regions of high sensitivity included the southern USA, Mexico and western and central Argentina. Analysis of climate variables in these regions identified water vapour pressure deficit and evaporation as underlying the high sensitivity of habitat suitability in the USA and Mexico, and showed that episodes of high variability are linked to the El Niño Southern Oscillation. Projections of the tendency of HS as observed for the 1950–99 period point to an increase in this value in parts of the southern USA and in central Argentina, a finding that can be attributed to the progressive increase in minimum and yearly averaged temperatures. Conclusions Short-term changes in climate may drive the system into unstable situations with sudden changes in habitat suitability for the target tick in specific zones of the Americas. Results suggest an increased abiotic (climate) suitability for R. microplus in areas whose habitat is currently unsuitable for this species. [ABSTRACT FROM AUTHOR]

In 2004 a new aspirated surface air temperature system was officially deployed nationally in the U.S. Climate Reference Network (USCRN) commissioned by the National Oceanic and Atmospheric Administration. The primary goal of the USCRN is to provide future long-term and high-quality homogeneous observations of surface air temperature and precipitation that can be coupled to past long-term observations for the detection and attribution of present and future climate change. In this paper two precision air temperature systems are included for evaluating the new USCRN air temperature system based on a 1-yr side-by-side field comparison. The measurement errors of the USCRN temperature sensor are systematically analyzed, and the components of error attributable to the datalogger, lead wires, fixed resistors, and the temperature coefficient of the resistors are presented. Although the current configuration is adequate, a more desirable configuration of USCRN temperature sensor coupled with the datalogger is proposed as a means of further reducing the uncertainty for the USCRN temperature measurement. [ABSTRACT FROM AUTHOR]

The “Ricardian” technique for projecting climate change impacts on agriculture has generated an unusual amount of critical attention. Ricardian climate sensitivities are typically viewed as the necessary and static result of exclusively local economic and biophysical conditions. In this paper, six spatial econometric models are estimated to explore how human-environment relationships associated with climate sensitivities have varied over space and time in the Great Plains, 1969–1992. Results indicate that spatial effects, such as extra-local communication processes and proximity to and regulation of Ogallala irrigation water, are important influences on climate sensitivities. Projected climate change impacts also vary significantly with the scale, location, and time of analysis. Under a hypothetical climate change, at the county scale, land values would decline (by up to one-third) in the western counties, but increase (by up to one-half) in the eastern counties. In some cases, the projected impacts for a given county change algebraic sign or order of magnitude during the study period. At the regional scale, impacts are significantly higher in the early years (a projected increase of about 5 percent of regional land values, ∼$7 billion [1992$]) than in the later years (an increase of about one-half of one percent, ∼$0.7 billion). These results suggest that, suitably modified, the Ricardian framework can be used constructively to explore subtle yet important social dimensions of dynamic climate risk, and that on balance the Great Plains system of agricultural production, despite a heterogeneous picture of projected impacts, appears to be increasingly vulnerable to the effects of climate change. [ABSTRACT FROM AUTHOR]

Past studies have suggested that the drought of the summer of 1988 over the midwestern United States may have been caused by sea surface temperature (SST) anomalies, an evolving stationary circulation, a soil-moisture feedback on circulation and rainfall, or even by remote forcings. The relative importance of various contributing factors is investigated in this paper through the use of Goddard Earth Observing System (GEOS) GCM simulations. Seven different experiments, each containing an ensemble of four simulations, were conducted with the GCM. For each experiment, the GCM was integrated through the summers of 1987 and 1988 starting from an analyzed atmosphere in early January of each yeah In the baseline case, only the SST anomalies and climatological vegetation parameters were prescribed, while everything else (such as soil moisture, snow cover, and clouds) was interactive. The precipitation differences (1988 minus 1987) show that the GCM was successful in simulating reduced precipitation in 1988, hut the accompanying low-level circulation anomalies in the Midwest were not well simulated. To isolate the influence of the model's climate drift, analyzed winds and analyzed soil moisture were prescribed globally as continuous updates (in isolation or jointly). The results show that remotely advected wind biases (emanating from potential errors in the model's dynamics and physics) are the primary cause of circulation biases over North America. Inclusion of soil moisture helps to improve the simulation as well as to reaffirm the strong feedback between soil moisture and precipitation. In a case with both updated winds and soil moisture, the model produces more realistic evapotranspiration and precipitation differences. An additional case also used soil moisture and winds updates, but only outside North America. Its simulation is very similar to that of the case with globally updated winds and soil moisture, which suggests that North American simulation errors originate largely outside the region. Two additional cases examining the influence of vegetation were built on this case using correct and opposite-year vegetation. The model did not produce a discernible improvement in response to vegetation for the drought year. One may conclude that the soil moisture governs the outcome of the land-atmosphere feedback interaction far more than the vegetation parameters. A primary inference of this study is that even though SSTs have some influence on the drought, model biases strongly influence the prediction errors. It must be emphasized that the results from this study are dependent upon the GEOS model's identified errors and biases, and that the conclusions do not necessarily apply to results from other models. [ABSTRACT FROM AUTHOR]

The regional climate of the western United States shows clear footprints of interaction between atmospheric circulation and orography. The unique features of this diverse climate regime challenges climate modeling. This paper provides detailed analyses of observations and regional climate simulations to improve our understanding and modeling of the climate of this region. The primary data used in this study are the 1/88 gridded temperature and precipitation based on station observations and the NCEP-NCAR global reanalyses. These data were used to evaluate a 20-yr regional climate simulation performed using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research (Penn State-NCAR) Mesoscale Model (MM5) driven by large-scale conditions of the NCEP-NCAR reanalyses. Regional climate features examined include seasonal mean and extreme precipitation; distribution of precipitation rates; and precipitation intensity, frequency, and seasonality. The relationships between precipitation and surface temperature are also analyzed as a means to evaluate how well regional climate simulations can be used to simulate surface hydrology, and relationships between precipitation and elevation are analyzed as diagnostics of the impacts of surface topography and spatial resolution. The latter was performed at five east-west transects that cut across various topographic features in the western United States. These analyses suggest that the regional simulation realistically captures many regional climate features. The simulated seasonal mean and extreme precipitation are comparable to observations. The regional simulation produces precipitation over a wide range of precipitation rates comparable to observations. Obvious biases in the simulation include the oversimulation of precipitation in the basins and intermountain West during the cold season, and the undersimulation in the Southwest in the warm season. There is a tendency of reduced precipitation... [ABSTRACT FROM AUTHOR]

From heat waves to hurricanes, tangible weather experiences have been shown to strengthen personal belief in climate change. We ask whether a high temperature day that breaks local heat records—which is a mathematical construct not directly accessible to the senses—has additional impacts on perceptions of worsening heat, above and beyond that of the absolute temperatures. Matching historical heat records to survey data from the United States, we find that each record heat day in a county in 2022 increases perceptions that excessive heat is getting worse, even when controlling for average temperatures, the number of extreme heat days, and demographic factors. Our estimates suggest that exposure to sixteen record heat days predicts roughly the same difference in excessive heat perceptions as between the average Democrat respondent and a political independent. This effect is stronger for populations that are more skeptical of climate science, including Republicans, as well as respondents with weaker beliefs in climate change and more frequent consumption of conservative media. We close with recommendations for media framing of local record-breaking heat events and call for more research on how media outlets cover record-breaking heat. [ABSTRACT FROM AUTHOR]

The standardized precipitation index (SPI) measures meteorological drought relative to historical climatology by normalizing accumulated precipitation. Longer record lengths improve parameter estimates, but these longer records may include signals of anthropogenic climate change and multidecadal natural climate fluctuations. Historically, climate nonstationarity has either been ignored or incorporated into the SPI using a quasi-stationary reference period, such as the WMO 30-yr period. This study introduces and evaluates a novel nonstationary SPI model based on Bayesian splines, designed to both improve parameter estimates for stationary climates and to explicitly incorporate nonstationarity. Using synthetically generated precipitation, this study directly compares the proposed Bayesian SPI model with existing SPI approaches based on maximum likelihood estimation for stationary and nonstationary climates. The proposed model not only reproduced the performance of existing SPI models but improved upon them in several key areas: reducing parameter uncertainty and noise, simultaneously modeling the likelihood of zero and positive precipitation, and capturing nonlinear trends and seasonal shifts across all parameters. Further, the fully Bayesian approach ensures all parameters have uncertainty estimates, including zero precipitation likelihood. The study notes that the zero precipitation parameter is too sensitive and could be improved in future iterations. The study concludes with an application of the proposed Bayesian nonstationary SPI model for nine gauges across a range of hydroclimate zones in the United States. Results of this experiment show that the model is stable and reproduces nonstationary patterns identified in prior studies, while also indicating new findings, particularly for the shape and zero precipitation parameters. SIGNIFICANCE STATEMENT: We typically measure how bad a drought is by comparing it with the historical record. With long-term changes in climate or other factors, however, a typical drought today may not have been typical in the recent past. The purpose of this study is to build a model that measures drought relative to a changing climate. Our results confirm that the model is accurate and captures previously noted climate change patterns}a drier western United States, a wetter eastern United States, earlier summer weather, and more extreme wet seasons. This is significant because this model can improve drought measurement and identify recent changes in drought. [ABSTRACT FROM AUTHOR]

Although experiments show that exposure to factual information can increase factual accuracy, the public remains stubbornly misinformed about many issues. Why do misperceptions persist even when factual interventions generally succeed at increasing the accuracy of people's beliefs? We seek to answer this question by testing the role of information exposure and decay effects in a four-wave panel experiment (n = 2,898 at wave 4) in which we randomize the media content that people in the United States see about climate change. Our results indicate that science coverage of climate change increases belief accuracy and support for government action immediately after exposure, including among Republicans and people who reject anthropogenic climate change. However, both effects decay over time and can be attenuated by exposure to skeptical opinion content (but not issue coverage featuring partisan conflict). These findings demonstrate that the increases in belief accuracy generated by science coverage are short lived and can be neutralized by skeptical opinion content. [ABSTRACT FROM AUTHOR]

Studies from the United States (U.S.) show that opposition to climate policy is strong among some Christian groups, especially White evangelical Protestants. Much of this opposition is channelled through organisations such as the Cornwall Alliance, which argue against climate measures on religious, economic and what they claim to be science-based grounds. In the present study, we investigated to what extent these convictions were present among Swedish evangelical denominations. Representatives from the Evangelical Free Church, the Pentecostal Alliance, the Swedish Alliance Mission, and the Seventh-day Adventist Church were interviewed to identify the denominations' views on the scientific underpinnings of climate change and the moral implications of climate policy. Our data show that the denominations' views differ markedly from those expressed by climate-oppositional evangelical groups in the U.S. The denominations held homogenous views on the legitimacy of climate science, expressed a clear biblical mandate for climate policy based on the notion of human stewardship, and believed that climate change was inextricably linked to poverty and, thus, had to be addressed. Our results point to the need for further studies on the factors behind acceptance and denial of climate science within and between faith-based and other communities in different countries. [ABSTRACT FROM AUTHOR]

As the effects of climate change accumulate and intensify, resource managers juggle existing goals and new mandates to operationalize adaptation. Fire managers contend with the direct effects of climate change on resources in addition to climate-induced disruptions to fire regimes and subsequent ecosystem effects. In systems stressed by warming and drying, increased fire activity amplifies the pace of change and scale of severe disturbance events, heightening the urgency for management action. Fire managers are asked to integrate information on climate impacts with their professional expertise to determine how to achieve management objectives in a changing climate with altered fire regimes. This is a difficult task, and managers need support as they incorporate climate adaptation into planning and operations. We present a list of adaptation strategies and approaches specific to fire and climate based on co-produced knowledge from a science–management partnership and pilot-tested in a two-day workshop with natural resource managers and regional stakeholders. This "menu" is a flexible and useful tool for fire managers who need to connect the dots between fire ecology, climate science, adaptation intent, and management implementation. It was created and tested as part of an adaptation framework used widely across the United States and should be applicable and useful in many fire-prone forest ecosystems. [ABSTRACT FROM AUTHOR]

Attribution of extreme events is a challenging science and one that is currently undergoing considerable evolution. In this paper are 19 analyses by 18 different research groups, often using quite different methodologies, of 12 extreme events that occurred in 2012. In addition to investigating the causes of these extreme events, the multiple analyses of four of the events, the high temperatures in the United States, the record low levels of Arctic sea ice, and the heavy rain in northern Europe and eastern Australia, provide an opportunity to compare and contrast the strengths and weaknesses of the various methodologies. The differences also provide insights into the structural uncertainty of event attribution, that is, the uncertainty that arises directly from the differences in analysis methodology. In these cases, there was considerable agreement between the different assessments of the same event. However, different events had very different causes. Approximately half the analyses found some evidence that anthropogenically caused climate change was a contributing factor to the extreme event examined, though the effects of natural fluctuations of weather and climate on the evolution of many of the extreme events played key roles as well. [ABSTRACT FROM AUTHOR]

The article presents an explanation on the econometric climate impact assessments on agriculture in the U.S. It states various agronomic crop models that convey the science of crop production and the farmers' abilities to adapt to new climatic constraints in counter-factual conditions. It mentions the structural approach to potential impacts of climate change on agriculture.

The study of climate change has deep roots in the history of North American ecology. At the time of the Wall Street crash and the Depression of the 1930s, America's Great Plains were struck by the Dust Bowl, a phenomenon of catastrophic soil erosion that resulted from the combined effects of intensive farming practices and a particularly harsh drought. Contemporaneously, the ecologist Frederic Clements proposed a theory of plant succession that itself took the history of the Great Plains as its model, and drew on the notion of climatic cycles. This theory became established as the model for ecological expertise in the politics of conservation adopted by the Roosevelt administration. In this paper, I will show how climatology became inscribed in plant ecology not only for epistemological reasons, but also due to an ideology that promoted the ecologist as an expert in the optimization of resources, in an illustration of the tripartite relationship between ecology, politics, and climate change. [ABSTRACT FROM AUTHOR]

NW flow snowfall (NWFS) events are common occurrences at higher elevations (1500 to >2000 m a.s.l.) and on windward slopes in the Southern Appalachian Mountain region of the south-eastern USA. The spatial patterns of NWFS are strongly controlled by topography, resulting in pronounced spatial variability. The strong topographic and low-level convective forcing, coupled with low temperatures and strong winds, increases societal impacts. This paper analyzes the relationships between NWFS and various topographic and geographic (TOPO/GEOG) variables in the Southern Appalachians. We identified NWFS events on the basis of low-level wind direction, extracted values for the TOPO/GEOG variables from Digital Elevation Models (DEMs), and developed statistical relationships between NWFS and the TOPO/GEOG variables. Using a multivariate regression model and GIS techniques, we also mapped mean annual NWFS across the region. Results indicate that elevation and NW exposure are most strongly correlated with NWFS; however, the strength of these relationships is mediated by temperature. In the colder NWFS events, the relative importance of elevation is diminished, while NW exposure and distance to a NW slope are relatively more important. Additionally, we demonstrate that multivariate regression modeling in conjunction with GIS techniques can be an effective way to map snowfall patterns associated with specific wind directions, particularly when a strong topographic control is evident. [ABSTRACT FROM AUTHOR]

A reliable flood event inventory that reflects the occurrence and evolution of past floods is important for studies of flood hazards and risks, hydroclimatic extremes, and future flood projections. However, currently available flood inventories are based on single-sourced data and often neglect underreported or less impactful flood events. Furthermore, traditional archives store flood events only at sparse geographic points, which significantly limits their further applicability. Also, few publicly available archives contain all-inclusive records of potential natural flooded area over time. To tackle these challenges, we construct two types of multisourced flood event inventories (MFI) for all river basins across the contiguous United States covering the period 1998–2013 on daily and subcatchment scales, which is publicly available at http://flood.umd.edu/download/CONUS/. These archives integrate flood information from in situ observations, remote sensing observations, hydrological model simulations, and five high-quality precipitation products. The first inventory (MFI-Actual) includes all actual floods that occurred in the presence of flood protection infrastructures, while the second, "natural (undefended)" inventory (MFI-Natural) reconstructs the possible "historical" floods without flood protection, which could be more directly influenced by climate variation. In the proposed two inventories, 2,755 and 4,661 flood events were estimated, respectively. MFI-Natural reconstructed 1,597 floods in ungauged basins, and recovered 608 extreme streamflow events in gauged subcatchments where floods would have happened if there were no flood protection. There is an average of four upstream dams located in these flood-recovered subcatchments, which indicates that modern flood defenses efficiently prevent significant flooding from extreme precipitation in many catchments over the country. [ABSTRACT FROM AUTHOR]

The U.S. appears to be misdefining the policy problem posed by the threat of global climate change, and is therefore not likely to find satisfactory policy solutions. The dominant definitions of the policy problem - alleging certain scientific, economic, and political barriers to effective policy - circumscribe the search for policy solutions. But those 'solutions' meet neither practical criteria of rationality nor the test of practical experience. We seem to be trapped within problem definitions that reflect and reinforce the convergence of powerful interests with elements of the culture. The problem may be a culture that fails to integrate science-based technologies for mastering nature with ethical or political constraints on their use and therefore jeopardizes its own sustain ability. If so, then reasoned action begins with political leadership that challenges selected elements of the dominant culture and directs attention to alternatives - thereby opening up the search for solutions. [ABSTRACT FROM AUTHOR]

Impacts modelers and stakeholders use publicly available data sets of downscaled climate projections to assess and design infrastructure for changes in future rainfall extremes. If differences across data sets exist, infrastructure resilience decisions could change depending on which single data set is used. We assess changes in U.S. rainfall extremes from 2044–2099 compared with 1951–2005 based on 227 projections under RCP4.5 and RCP8.5 from five widely used data sets. We show there are large differences in the change magnitude and its spatial structure between data sets. At the continental scale, the data sets show different increases, with high‐end extremes (e.g. 100‐year event) generally increasing more (between 10% and 50%) than low‐end extremes (e.g. 5‐year). These differences largely contribute to the overall uncertainty for small average recurrence intervals (ARIs) extremes (2‐ to 10‐year), while uncertainties due to short record length dominate large ARIs (25‐ to 100‐year). The results indicate that robust infrastructure planning should consider these uncertainties to enable resilient infrastructure under climate change. Plain Language Summary: Observed extreme rainfall magnitudes have increased since 1950, and climate model projections indicate that these increases will continue throughout the 21st century in many areas in the US. Adapting and designing infrastructure for climate change requires future extreme rainfall projections at high resolutions. Global climate model (GCM) output resolution is generally much coarser, and methods have been developed to create relevant climate information at regional scales. Multiple open data sets exist that provide downscaled projections of future rainfall extremes. We analyze changes in future daily extremes using five widely used data sets in climate change impacts assessments and decision making. We found large differences between data sets in how much and where extreme events will intensify by the end of the 21st century. This and other sources of uncertainty need to be considered when designing resilient infrastructure. Key Points: Extreme daily rainfall changes for the United States from five widely used public downscaled climate projection data sets are comparedLarge differences in magnitude and spatial patterns of projected extreme precipitation changes exist between data setsHigh‐end extremes (e.g., 100‐year event) increase 10–50 % faster than low‐end extremes (e.g., 2‐year event) at the continental scale [ABSTRACT FROM AUTHOR]

Although the link between droughts and heat waves is widely recognized, how climate change affects this link remains uncertain. Here we assess how, and by how much, human-induced climate change affects summertime hot drought compound events over the contiguous United States. Results are derived by comparing hot drought statistics in long simulations of a coupled climate model (CESM1) subjected to year-1850 and year-2000 radiative forcings. Within each climate state, a strong and nonlinear dependency of heat-wave intensity on drought severity is found in water-limited regions of the southern Great Plains and southwestern United States whereas heat-wave intensity is found to be insensitive to drought severity in energy-limited regions of the northern and/or northeastern United States. Applying a statistical model that is based on pair-copula constructions, we find that anthropogenic warming leads to enhanced soil moisture–temperature coupling in water-limited areas of the southern Great Plains and/or southwestern United States and consequently amplifies the intensity of extreme heat waves during severe droughts. This strengthened coupling accounts for a substantial fraction of rising temperature extremes related to the long-term climate change in CESM1, highlighting the importance of changes in land–atmosphere feedback in a warmer climate. In contrast, coupling effects remain weak and largely unchanged in energy-limited regions, thereby yielding no appreciable contribution to heat-wave intensification over the northern and/or northeastern United States apart from the long-term warming effects. [ABSTRACT FROM AUTHOR]

Climate normals are traditionally calculated every decade as the average values over a period of time, often 30 years. Such an approach assumes a stationary climate, with several alternatives recently introduced to account for monotonic climate change. However, these methods fail to account for interannual climate variability [e.g., El Niño–Southern Oscillation (ENSO)] that systematically alters the background state of the climate similar to climate change. These effects and their uncertainties are well established, but they are not reflected in any readily available climate normals datasets. A new high-resolution set of normals is derived for the contiguous United States that accounts for ENSO and uses the optimal climate normal (OCN)—a 10-yr (15 yr) running average for temperature (precipitation)—to account for climate change. Anomalies are calculated by subtracting the running means and then compositing into 5 ENSO phase and intensity categories: Strong La Niña, Weak La Niña, Neutral, Weak El Niño, and Strong El Niño. Seasonal composites are produced for each of the five phases. The ENSO normals are the sum of these composites with the OCN for a given month. The result is five sets of normals, one for each phase, which users may consult with respect to anticipated ENSO outcomes. While well-established ENSO patterns are found in most cases, a distinct east–west temperature anomaly pattern emerges for Weak El Niño events. This new product can assist stakeholders in planning for a broad array of possible ENSO impacts in a changing climate. [ABSTRACT FROM AUTHOR]

Cost-effective heat mitigation strategies are imperative for maintaining milk production and dairy farm profitability in the U.S. with projected climate change. This study investigated the cost-effectiveness of four heat abatement strategies, including Minimal (open barn or shading), Moderate (forced ventilation), High (fans and misting), and Intense (air conditioning). Heat stress and subsequent impacts on milk production per cow were predicted across nine climatic regions in the U.S. for early (2015 to 2034), mid (2045 to 2064) and late (2081 to 2100) 21st century, using downscaled climate projections. Heat abatements were used to adjust predicted milk production losses and illustrate the potential to reduce milk production losses due to heat stress. Economic analysis included a cost-benefit ratio calculation associated with the implementation of each heat abatement. Results showed that milk production losses were expected to accelerate across the U.S. at a mean rate of 174±7 kg/cow/decade, with the fastest rate in the Southeast region. Relative to Minimal heat abatement, Moderate, High, and Intense heat abatements increased annual milk production per cow by 3%, 4%, and 6% during early-21st century, 3%, 6%, and 11% during mid-21st century, and 3%, 8%, and 21% during late-21st century, respectively. The cost effectiveness of different heat abatement strategies generally increased with subsequently stronger heat abatements. In mid- and late-21st century, mean annual net values of High and Intense heat stress abatement implementation approached -$30 to $190 /cow and -$20 to $590 /cow, respectively, with the largest net annual benefit in late-21st century under Intense abatement. Findings from the study demonstrate the value of using downscaled climate projections to shed light on local and regional strategies to abate heat stress on cattle and mitigate potential milk production losses due to climate change. [ABSTRACT FROM AUTHOR]

We estimate the effects of climate anomalies (heat stress and drought) on annual maize production, variability, and trend from the country level to the global scale using a statistical model. Moderate climate anomalies and extremes are diagnosed with two indicators of heat stress and drought computed over maize growing regions during the most relevant period of maize growth. The calibrated model linearly combines these two indicators into a single Combined Stress Index. The Combined Stress Index explains 50% of the observed global production variability in the period 1980–2010. We apply the model on an ensemble of high‐resolution global climate model simulations. Global maize losses, due to extreme climate events with 10‐year return times during the period 1980–2010, will become the new normal already at 1.5 °C global warming levels (approximately 2020s). At 2 °C warming (late 2030s), maize areas will be affected by heat stress and drought never experienced before, affecting many major and minor production regions. Plain Language Summary: Global warming is negatively affecting several aspects of ecosystems and societies. This study analyzes the impact of climate on global and regional maize production in the past and in the future. The effects of warmer temperatures and lack of rain on maize production is first analyzed from reported national maize production time series, and these relationships are then projected into the future, using state‐of‐the‐art global climate model simulations. The worse climate events causing the larger maize production losses observed in the past will become normal already at the global warming level of 1.5 °C compared to the preindustrial period. Our findings highlight the importance of Paris Agreement mitigation goals to hold the increase of global temperature well below 2 °C and pursuing efforts to limit increase to 1.5 °C, as well as the need of identifying and adopting efficient adaptation measures. Key Points: Since the 1980s, climate has become more favorable for maize production in the United States, balancing increased heat and drought impacts elsewhereHeat and drought impacts will become common in several regions that rely on maize production, already at the 1.5 degrees Celsius global warming levelUnprecedented losses are projected to become common in most maize producing regions (including the United States) at 2 degrees Celsius global warming [ABSTRACT FROM AUTHOR]

Climate change is affecting the growth, phenology, and distribution of species across northeastern United States. In response to these changes, some species have been adversely impacted while others have benefited. One species that has benefited from climate change, historically and in response to experimental treatments, is common ragweed (Ambrosia artemisiifolia), a widely distributed annual weed and a leading cause of hay fever in North America. To better understand how climate change may affect the distribution of common ragweed, we built a maximum entropy (Maxent) predictive model using climate and bioclimatic data and over 700 observations across the eastern U.S. Our model performed well with an AUC score of 0.765 using four uncorrelated variables, including precipitation seasonality, mean diurnal temperature range, August precipitation, and January maximum temperature. After building and testing our model, we then projected potential future common ragweed distribution using a suite of 13 global climate models (GCMs) under two future greenhouse gas scenarios for mid and late-century. In addition to providing georeferenced hot spots of potential future expansion, we also provide a metric of confidence by evaluating the number of GCMs that agree. We show a substantial contraction of common ragweed in central Florida, southern Appalachian Mountains, and northeastern Virginia and areas of potential expansion at the northern margins of its current distribution, notably in northeastern U.S. However, the vast majority of this increase is projected to occur by mid-century and may be moderated somewhat by the 2070s, implying that common ragweed may be sensitive to climatic variability. Although other factors and modeling approaches should be explored, we offer preliminary insight into where common ragweed might be a new concern in the future. Due to the health impacts of ragweed, local weed control boards may be well advised to monitor areas of expansion and potentially increase eradication efforts. [ABSTRACT FROM AUTHOR]

Coastal risks are increasing from both development and climate change. Interest is growing in the protective role that coastal nature-based measures (or green infrastructure), such as reefs and wetlands, can play in adapting to these risks. However, a lack of quantitative information on their relative costs and benefits is one principal factor limiting their use more broadly. Here, we apply a quantitative risk assessment framework to assess coastal flood risk (from climate change and economic exposure growth) across the United States Gulf of Mexico coast to compare the cost effectiveness of different adaptation measures. These include nature-based (e.g. oyster reef restoration), structural or grey (e.g., seawalls) and policy measures (e.g. home elevation). We first find that coastal development will be a critical driver of risk, particularly for major disasters, but climate change will cause more recurrent losses through changes in storms and relative sea level rise. By 2030, flooding will cost $134–176.6 billion (for different economic growth scenarios), but as the effects of climate change, land subsidence and concentration of assets in the coastal zone increase, annualized risk will more than double by 2050 with respect to 2030. However, from the portfolio we studied, the set of cost-effective adaptation measures (with benefit to cost ratios above 1) could prevent up to $57–101 billion in losses, which represents 42.8–57.2% of the total risk. Nature-based adaptation options could avert more than $50 billion of these costs, and do so cost effectively with average benefit to cost ratios above 3.5. Wetland and oyster reef restoration are found to be particularly cost-effective. This study demonstrates that the cost effectiveness of nature-based, grey and policy measures can be compared quantitatively with one another, and that the cost effectiveness of adaptation becomes more attractive as climate change and coastal development intensifies in the future. It also shows that investments in nature-based adaptation could meet multiple objectives for environmental restoration, adaptation and flood risk reduction. [ABSTRACT FROM AUTHOR]

Factors responsible for extreme monthly rainfall over Texas and Oklahoma during May 2015 are assessed. The event had a return period of at least 400 years, in contrast to the prior record, which was roughly a 100-yr event. The event challenges attribution science to disentangle factors because it occurred during a strong El Niño, a natural pattern of variability that affects the region's springtime rains, and during the warmest global mean temperatures since 1880. Effects of each factor are diagnosed, as is the interplay between El Niño dynamics and human-induced climate change. Analysis of historical climate simulations reveals that El Niño was a necessary condition for monthly rains to occur having the severity of May 2015. The model results herein further reveal that a 2015 magnitude event, whether conditioned on El Niño or not, was made neither more intense nor more likely to be due to humaninduced climate change over the past century. The intensity of extreme May rainfall over Texas and Oklahoma, analogous to the 2015 event, increases by roughly 5% by the latter half of the twenty-first century. No material changes occur in either El Niño-related teleconnections or in overall atmospheric dynamics during extreme May rainfall over the twenty-first century. The increased severity of Texas/Oklahoma May rainfall events in the future is principally due to thermodynamic driving, although much less than implied by simple Clausius-Clapeyron scaling arguments given a projected 23% increase in atmospheric precipitable water vapor. Other thermodynamic factors are identified that act in opposition to the increase in atmospheric water vapor, thereby reducing the effectiveness of overall thermodynamic driving of extreme May rainfall changes over Texas and Oklahoma. [ABSTRACT FROM AUTHOR]

Climate and weather have been linked to criminal activity. The connection between climatological conditions and crime is of growing importance as we seek to understand the societal implications of climate change. This study describes the mechanisms theorized to link annual variations in climate to crime in California and examines the effect of drought on statewide crime rates from 2011–2015. California has suffered severe drought since 2011, resulting in intensely dry winters and several of the hottest days on record. It is likely that the drought increased economic stress and shifted routine activities of the population, potentially increasing the likelihood of crime. We used a synthetic control method to estimate the impact of California’s drought on both property and violent crimes. We found a significant increase in property crimes during the drought, but no effect on violent crimes. This result was robust to several sensitivity analyses, including a negative control. [ABSTRACT FROM AUTHOR]

Recent research in ecology suggests that generic indicators, referred to as early warning signals (EWS), may occur before significant transformations, both critical and non-critical, in complex systems. Up to this point, research on EWS has largely focused on simple models and controlled experiments in ecology and climate science. When humans are considered in these arenas they are invariably seen as external sources of disturbance or management. In this article we explore ways to include societal components of socio-ecological systems directly in EWS analysis. Given the growing archaeological literature on ‘collapses,’ or transformations, in social systems, we investigate whether any early warning signals are apparent in the archaeological records of the build-up to two contemporaneous cases of social transformation in the prehistoric US Southwest, Mesa Verde and Zuni. The social transformations in these two cases differ in scope and severity, thus allowing us to explore the contexts under which warning signals may (or may not) emerge. In both cases our results show increasing variance in settlement size before the transformation, but increasing variance in social institutions only before the critical transformation in Mesa Verde. In the Zuni case, social institutions appear to have managed the process of significant social change. We conclude that variance is of broad relevance in anticipating social change, and the capacity of social institutions to mitigate transformation is critical to consider in EWS research on socio-ecological systems. [ABSTRACT FROM AUTHOR]

Reducing carbon dioxide (CO2) emissions from power plants can have important “co-benefits” for public health by reducing emissions of air pollutants. Here, we examine the costs and health co-benefits, in monetary terms, for a policy that resembles the U.S. Environmental Protection Agency’s Clean Power Plan. We then examine the spatial distribution of the co-benefits and costs, and the implications of a range of cost assumptions in the implementation year of 2020. Nationwide, the total health co-benefits were $29 billion 2010 USD (95% CI: $2.3 to $68 billion), and net co-benefits under our central cost case were $12 billion (95% CI: -$15 billion to $51 billion). Net co-benefits for this case in the implementation year were positive in 10 of the 14 regions studied. The results for our central case suggest that all but one region should experience positive net benefits within 5 years after implementation. [ABSTRACT FROM AUTHOR]

Changes in extreme temperatures, heat waves, and heavy rainfall events have adverse effects on human health, air quality, and water resources. With aerosol-only (AER) and greenhouse gas-only (GHG) simulations from 1860 to 2005 in the GFDL CM3 chemistry-climate model, aerosol-induced versus greenhouse gas-induced changes in temperature (summer) and precipitation (all seasons) extremes over the United States are investigated. Small changes in these extremes in the all forcing simulations reflect cancellations between the effects of increasing anthropogenic aerosols and greenhouse gases. In AER, extreme high temperatures and the number of days with temperatures above the 90th percentile decline over most of the United States. The strongest response occurs in the western United States (−2.0°C and −14 days, regionally averaged) and the weakest response occurs in the southeastern United States (−0.6°C and −4.8 days). An opposite-signed response pattern occurs in GHG (+2.3°C and +11.5 days over the western United States and +1.6°C and +7.2 days over the southeastern United States). The similar spatial response patterns in AER versus GHG suggest a preferred regional mode of response that is largely independent of the type of forcing. Extreme precipitation over the eastern United States decreases in AER, particularly in winter, and increases over the eastern and central United States in GHG, particularly in spring. Over the twenty-first century under the representative concentration pathway 8.5 (RCP8.5) emissions scenario, the patterns of extreme temperature and precipitation associated with greenhouse gas forcing dominate. [ABSTRACT FROM AUTHOR]

Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate. [ABSTRACT FROM AUTHOR]

In the 1960s and early 1970s, sea surface temperatures in the North Atlantic Ocean cooled rapidly. There is still considerable uncertainty about the causes of this event, although various mechanisms have been proposed. In this observational study, it is demonstrated that the cooling proceeded in several distinct stages. Cool anomalies initially appeared in the mid-1960s in the Nordic Seas and Gulf Stream extension, before spreading to cover most of the subpolar gyre. Subsequently, cool anomalies spread into the tropical North Atlantic before retreating, in the late 1970s, back to the subpolar gyre. There is strong evidence that changes in atmospheric circulation, linked to a southward shift of the Atlantic ITCZ, played an important role in the event, particularly in the period 1972-76. Theories for the cooling event must account for its distinctive space-time evolution. The authors' analysis suggests that the most likely drivers were 1) the 'Great Salinity Anomaly' of the late 1960s; 2) an earlier warming of the subpolar North Atlantic, which may have led to a slowdown in the Atlantic meridional overturning circulation; and 3) an increase in anthropogenic sulfur dioxide emissions. Determining the relative importance of these factors is a key area for future work. [ABSTRACT FROM AUTHOR]

A new sea surface temperature (SST) climatological mean was constructed using the first 30 years (1982-2011) of the NOAA daily optimum interpolation (OI) SST. The daily analysis blends in situ and satellite data on a ¼° (~25 km) spatial grid. Use of an analysis allows computation of a climatological value for all ocean grid points, even those without observations. Comparisons were made with a monthly, 1°-spatial-resolution climatology produced by the National Centers for Environmental Prediction, computed primarily from the NOAA weekly OISST. Both climatologies were found to provide a good representation of major oceanic features and the annual temperature cycle. However, the daily climatology showed tighter gradients along western boundary currents and better resolution along coastlines. The two climatologies differed by over 0.6°C in high-SST-gradient regions because of resolution differences. The two climatologies also differed at very high latitudes, where the sea ice processing differed between the OISST products. In persistently cloudy areas, the new climatology was generally cooler by approximately 0.4°C, probably reflecting differences between the input satellite SSTs to the two analyses. Since the new climatology represents mean conditions at scales that match the daily analysis, it would be more appropriate for computing the corresponding daily anomalies. [ABSTRACT FROM AUTHOR]

The effects on early and late twenty-first-century North Atlantic tropical cyclone statistics resulting from imposing the patterns of maximum/minimum phases of the observed Atlantic multidecadal oscillation (AMO) onto projected sea surface temperatures (SSTs) from two climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are examined using a 100-km resolution global atmospheric model. By imposing the observed maximum positive and negative phases of the AMO onto two CMIP5 SST projections from the representative concentration pathway (RCP) 4.5 scenario, this study places bounds on future North Atlantic tropical cyclone activity during the early (2020-39) and late (2080-99) twenty-first century. Averaging over both time periods and both AMO phases, the mean named tropical cyclones (NTCs) count increases by 35% when compared to simulations using observed SSTs from 1982 to 2009. The positive AMO simulations produce approximately a 68% increase in mean NTC count, while the negative AMO simulations are statistically indistinguishable from the mean NTC count determined from the 1995-2009 simulations-a period of observed positive AMO phase. Examination of the tropical cyclone track densities shows a statistically significant increase in the tracks along the East Coast of the United States in the future simulations compared to the models' 1982-2009 climate simulations. The increase occurs regardless of AMO phase, although the negative phase produces higher track densities. The maximum wind speeds increase by 6%, in agreement with other climate change studies. Finally, the NTC-related precipitation is found to increase (approximately by 13%) compared to the 1982-2009 simulations. [ABSTRACT FROM AUTHOR]

Heat-related mortality in US cities is expected to more than double by the mid-to-late 21st century. Rising heat exposure in cities is projected to result from: 1) climate forcings from changing global atmospheric composition; and 2) local land surface characteristics responsible for the urban heat island effect. The extent to which heat management strategies designed to lessen the urban heat island effect could offset future heat-related mortality remains unexplored in the literature. Using coupled global and regional climate models with a human health effects model, we estimate changes in the number of heat-related deaths in 2050 resulting from modifications to vegetative cover and surface albedo across three climatically and demographically diverse US metropolitan areas: Atlanta, Georgia, Philadelphia, Pennsylvania, and Phoenix, Arizona. Employing separate health impact functions for average warm season and heat wave conditions in 2050, we find combinations of vegetation and albedo enhancement to offset projected increases in heat-related mortality by 40 to 99% across the three metropolitan regions. These results demonstrate the potential for extensive land surface changes in cities to provide adaptive benefits to urban populations at risk for rising heat exposure with climate change. [ABSTRACT FROM AUTHOR]