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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]

The article presents updates related to climatology and weather studies in the U.S. Research reveals the significance of using the cloud-resolving model simulations in forecasting the dangers of lightning. Meanwhile, several climate scientists are searching for the best method in estimating the peak surface winds of a hurricane. Moreover, it also looks on the effectiveness of the latest multiple-Doppler tornado detection in determining the upcoming tornadoes.

The article presents a study which examines the effect of synoptic-scale forcing on the stratospheric sudden warnings (SSWs) in 2006 in the U.S. The researchers used the meteorological fields from Goddard Earth Observing System (GEOS)-4 analyses in determining such effects. The study found out that stratospheric polar displaced off the pole due to earlier minor warming events. The researchers also suggest that there is a need for further investigation to determine the kind of fraction of major SSWs are initiated.

James C. Knox’s 1977 paper “Human Impacts on Wisconsin Stream Channels,” published in the Annals of the Association of American Geographers, was a key component of a suite of three papers by him defining the response of rivers to the introduction and management of agriculture and to climate change. In this paper he used the Driftless Area of southwest Wisconsin as a laboratory where he could define fluvial responses by their sedimentary signatures in floodplain deposits. Land-use records dating back to the early 19th century along with shorter climate records provided his understanding of the drivers of change. He found that floods increased as an outcome of land-cover change. Upstream tributaries became wider and shallower as coarse deposits limited their adjustments, while main stem channels became narrower and deeper. His paper reflected the influence of his graduate advisor and especially of prominent faculty colleagues at the University of Wisconsin from fields ranging from soils and climatology to geomorphology and history. The paper was the subject of considerable debate in the professional community, but it remains a much-cited example of Knox’s work in unraveling the Quaternary and Holocene history of rivers of the Driftless Area and by extension the upper Mississippi River system. [ABSTRACT FROM PUBLISHER]

Climate regulates fire activity through the buildup and drying of fuels and the conditions for fire ignition and spread. Understanding the dynamics of contemporary climate-fire relationships at national and sub-national scales is critical to assess the likelihood of changes in future fire activity and the potential options for mitigation and adaptation. Here, we conducted the first national assessment of climate controls on US fire activity using two satellite-based estimates of monthly burned area (BA), the Global Fire Emissions Database (GFED, 1997-2010) and Monitoring Trends in Burn Severity (MTBS, 1984-2009) BA products. For each US National Climate Assessment (NCA) region, we analyzed the relationships between monthly BA and potential evaporation (PE) derived from reanalysis climate data at 0.5° resolution. US fire activity increased over the past 25 yr, with statistically significant increases in MTBS BA for entire US and the Southeast and Southwest NCA regions. Monthly PE was strongly correlated with US fire activity, yet the climate driver of PE varied regionally. Fire season temperature and shortwave radiation were the primary controls on PE and fire activity in the Alaska, while water deficit (precipitation - PE) was strongly correlated with fire activity in the Plains regions and Northwest US. BA and precipitation anomalies were negatively correlated in all regions, although fuel-limited ecosystems in the Southern Plains and Southwest exhibited positive correlations with longer lead times (6-12 months). Fire season PE in- creased from the 1980s-2000s, enhancing climate-driven fire risk in the southern and western US where PE-BA correlations were strongest. Spatial and temporal patterns of increasing fire season PE and BA during the 1990s-2000s highlight the potential sensitivity of US fire activity to climate change in coming decades. However, climate-fire relationships at the national scale are complex, based on the diversity of fire types, ecosystems, and ignition sources within each NCA region. Changes in the seasonality or magnitude of climate anomalies are therefore unlikely to result in uniform changes in US fire activity. [ABSTRACT FROM AUTHOR]

The article presents abstracts of the papers presented at the 63rd annual meeting of the Association of American Geographers, Saint. Louis, Missouri. The meeting took place in April 1967. The paper "Defense and the Morphology of Certain Settlements in the Western Mediterranean," discusses economic growth. Another paper "Approach to a Generating Model for Climatic Statistics," discusses a set of probability models for generating climatic statistics. After a brief review of previous work on this subject, a model based on moving waves superimposed, on standing waves in the zonal winds is outlined.

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]

In recent years, the US and the Netherlands have been on opposing sides of the spectrum regarding climate litigation. Dutch courts, in several revolutionary climate cases, have been an arena of societal change, whilst climate claims in the US have been largely unsuccessful. In a way this difference seems strange, because the US judiciary has the power of constitutional review, whilst the Dutch judiciary does not. Against that background, this paper extensively compares the doctrines of judicial competence regarding political questions in both jurisdictions. As a comparative framework, this paper uses three judicial phases, namely: the institutional phase, the substantial phase, and the remedial phase. Climate litigation reveals that the Dutch doctrine of judicial competence is focused on the substantial and remedial phases, which has allowed it more freedom in reviewing climate litigation. On the other hand, climate litigation reveals that the US doctrine of judicial competence is focused on a strict institutional phase, dominated by the Political Question Doctrine (PQD). The main contribution of this paper to the constitutional debate is that climate litigation reveals fundamental differences in doctrines of judicial competence. This is not only an important takeaway for future climate litigation, but also, in terms of the Radbruch formula, for other potential gaps between the executive and justice. [ABSTRACT FROM AUTHOR]

In this essay, I attempt to chronicle and assess the position of climatology in American geography during the past one hundred years with emphasis on the role of papers published in the Annals. The approach I take is influenced by my interest in the decline of physical geography, including climatology, beginning in the last half of the 1800s ( ) to its resurgence in the 1950s and 1960s, and reviews of the climatology literature that have appeared recently (e.g., ). I discuss climatology in American geography in three loosely defined periods. The first is the formative era in which physical geography, including climatology, was a dominant part of our discipline, and physical geographical conditions such as climate were considered determinants of human affairs. In the middle (regional) era, the physical geographic environment, including climatology, was largely taken to be a static physical milieu or “stage” on which human activity occurred, simply to be described by geographers as part of regional analyses. The modern era, which begins just after the Second World War, is a period of increasing diversity of subject matter and method in climatology within geography and in American geography generally. Today climatology enjoys a “growth industry” status that hardly could be imagined forty years ago. These changes did not occur in isolation but in the context of the scientific, technological, social, economic, and political conditions, and changes in which they were embedded, and the status of climatology as a field of study was an important part of the context. I also comment on the number of climatology papers published in the Annals, including a curious anomaly in the last decade. [ABSTRACT FROM AUTHOR]

In the past two decades, major breakthroughs that improve our understanding of the pathophysiology and therapy of kidney stones (KS) have been lacking. The disease continues to be challenging for patients, physicians, and healthcare systems alike. In this context, epidemiological studies are striving to elucidate the worldwide changes in the patterns and the burden of the disease and identify modifiable risk factors that contribute to the development of kidney stones. Our expanding knowledge of the epidemiology of kidney stones is of paramount importance and largely upgrades the modern management of the disease. In this paper, we review the variables affecting prevalence and incidence, including age, gender, race, ethnicity, occupation, climate, geography, systemic diseases, diabetes, vascular disease, chronic kidney disease, and dietary risk factors relevant to kidney stones. [ABSTRACT FROM AUTHOR]

In this paper, a new set of daily gridded fields and area averages of temperature and precipitation is introduced that covers the contiguous United States (CONUS) from 1951 to present. With daily updates and a grid resolution of approximately 0.0417° (nominally 5 km), the product, named nClimGrid-Daily, is designed to be used particularly in climate monitoring and other applications that rely on placing event-specific meteorological patterns into a long-term historical context. The gridded fields were generated by interpolating morning and midnight observations from the Global Historical Climatology Network–Daily dataset using thin-plate smoothing splines. Additional processing steps limit the adverse effects of spatial and temporal variations in station density, observation time, and other factors on the quality and homogeneity of the fields. The resulting gridded data provide smoothed representations of the point observations, although the accuracy of estimates for individual grid points and days can be sensitive to local spatial variability and the ability of the available observations and interpolation technique to capture that variability. The nClimGrid-Daily dataset is therefore recommended for applications that require the aggregation of estimates in space and/or time, such as climate monitoring analyses at regional to national scales. Significance Statement: Many applications that use historical weather observations require data on a high-resolution grid that are updated daily. Here, a new dataset of daily temperature and precipitation for 1951–present is introduced that was created by interpolating irregularly spaced observations to a regular grid with a spacing of 0.0417° across the contiguous United States. Compared to other such datasets, this product is particularly suitable for monitoring climate and drought on a daily basis because it was processed so as to limit artificial variations in space and time that may result from changes in the types and distribution of observations used. [ABSTRACT FROM AUTHOR]

The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992–2000 and for a study area at 55°–65°N, 160°–110°W in northwestern Canada and Alaska. Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model–data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate. [ABSTRACT FROM AUTHOR]

Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations because these products provide a spatially and temporally continuous and complete source of data. However, when the goal is to characterize climatological features of extreme precipitation over a spatial domain (e.g., a map of return values) at the native spatial scales of these phenomena, then gridded products may lead to incorrect conclusions because daily precipitation is a fractal field and hence any smoothing technique will dampen local extremes. To address this issue, we create a new "probabilistic" gridded product specifically designed to characterize the climatological properties of extreme precipitation by applying spatial statistical analysis to daily measurements of precipitation from the Global Historical Climatology Network over the contiguous United States. The essence of our method is to first estimate the climatology of extreme precipitation based on station data and then use a data-driven statistical approach to interpolate these estimates to a fine grid. We argue that our method yields an improved characterization of the climatology within a grid cell because the probabilistic behavior of extreme precipitation is much better behaved (i.e., smoother) than daily weather. Furthermore, the spatial smoothing innate to our approach significantly increases the signal-to-noise ratio in the estimated extreme statistics relative to an analysis without smoothing. Finally, by deriving a data-driven approach for translating extreme statistics to a spatially complete grid, the methodology outlined in this paper resolves the issue of how to properly compare station data with output from earth system models. We conclude the paper by comparing our probabilistic gridded product with a standard extreme value analysis of the Livneh gridded daily precipitation product. Our new data product is freely available on the Harvard Dataverse (https://bit.ly/2CXdnuj). [ABSTRACT FROM AUTHOR]

Abstract: This paper estimates the effects of humidity and temperature on mortality rates in the United States (c. 1973–2002) in order to provide an insight into the potential health impacts of climate change. I find that humidity, like temperature, is an important determinant of mortality. Coupled with Hadley CM3 climate-change predictions, I project that mortality rates are likely to change little on the aggregate for the United States. However, distributional impacts matter: mortality rates are likely to decline in cold and dry areas, but increase in hot and humid areas. Further, accounting for humidity has important implications for evaluating these distributional effects. [Copyright &y& Elsevier]

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]

Drawing from theory on the "co-production" of science and society, this paper provides an account of trajectories in US climatology, roughly from the 1850s to 1920, the period during which climatology emerged as an organized branch of meteorology and government administration. The historical narrative traces the development of climatology both as a professional/institutional project and as a component of a larger governmental logic. Historical analysis of climatologists' scientific texts, maps, and social organization within government provides a sociological explanation for the emergent "stabilization" of climate as a geographic-statistical category. Climatic stability, defined by the view that climate is unchanging, was advanced over this period in a way that linked the interests and practices of climatologists to actors invested in facilitating and administrating commercial agriculture and trade. I position the logic of climatology and the discourse of climatic stability historically, with reference to prior concern with climate change and, in recent decades, efforts to govern global warming through geoengineering climatic stability. [ABSTRACT FROM AUTHOR]

Estimates of the marginal value of carbon dioxide-the social cost of carbon (SCC)-were recently adopted by both the U.S. and U.K. Governments in order to satisfy requirements to value estimated greenhouse gas (GHG) changes of new federal regulations. However, the development and use of SCC estimates comes with significant challenges and controversial decisions. This paper summarizes the key issues and then derives guidance for developing, selecting, and applying the estimates that follows from the fundamental scientific nature and economic principles associated with GHGs and climate change, as well as an understanding of the state of the art for estimating the SCC. The paper evaluates recent estimates in light of the guidance and finds the estimates inconsistent with the guidance in a number of significant areas. The paper also differentiates SCC policy applications in terms of policies with incremental versus nonincremental implications for global GHGs, and identifies serious issues for application of the SCC in setting and evaluating climate policy targets. WIREs Clim Change 2012, 3:163-195. doi: 10.1002/wcc.163 For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]

Coastal communities are among the most vulnerable to climate risks. Despite the negligence of climate policy at the federal level in the United States, subnational governments have taken action to adapt to climate change impacts. Using national-level data on subnational government climate activities, this research examines three aspects of coastal community adaptation actions. First, it examines whether there are differences in climate adaptation activity between coastal and non-coastal communities. It then tests the relative importance of political partisanship, population, perception of climate risk, and awareness of state-level climate planning effort. Lastly, it examines the impact of participation within climate policy networks on the likelihood of a subnational government taking a climate adaptation action. Three types of network partners are examined: those focused on sea-level rise and coastal communities, climate change mitigation, and general environmental organizations. Concern about severe storms, awareness of state plans, and having a network partner focused either on climate mitigation or the environment were found to be statistically significant indicators of climate adaptation action. The paper discusses the importance of understanding the localized context of adaptation within nested governance structures, and how informal policy networks can facilitate learning and innovation across coastal communities. [ABSTRACT FROM AUTHOR]

Scientific evidence accumulating over the past decade documents that climate change impacts are already being experienced in the US Northeast. Policy-makers and resource managers must now prepare for the impacts from climate change and support implementing such plans on the ground. In this paper we argue that climate change challenges the region to maintain its economic viability, but also holds some opportunities that may enhance economic development, human well-being, and social justice. To face these challenges and seize these opportunities effectively we must better understand adaptation capacities, opportunities and constraints, the social processes of adaptation, approaches for engaging critical players and the broader public in informed debate, decision-making, and conscious interventions in the adaptation process. This paper offers a preliminary qualitative assessment, in which we emphasize the need for (1) assessing the feasibility and side effects of technological adaptation options, (2) increasing available resources and improving equitable access to them, (3) increasing institutional flexibility, fit, cooperation and decision-making authority, (4) using and enhancing human and social capital, (5) improving access to insurance and other risk-spreading mechanisms, and (6) linking scientific information more effectively to decision-makers while engaging the public. Throughout, we explore these issues through illustrative sectoral examples. We conclude with a number of principles that may guide the preparation of future adaptation plans for the Northeast. [ABSTRACT FROM AUTHOR]

Climate models provide information that resource managers, policy makers, and researchers can use when planning for the future. While this information is valuable in the broad sense, the low spatial-resolution often lacks local details that resource managers and decision makers need to plan for their communities. Therefore, statistically downscaled climate projections provide a high-resolution output and offer local information that is more beneficial than coarse-resolution global climate model output. In the Red River Basin, located in the south-central U.S., this detailed information is used to develop long-term water plans. This area is prone to drought conditions and heavy precipitation events, and studies have consistently estimated that this will continue in the future. This paper introduces a dataset of statistically downscaled climate projections of daily minimum and maximum temperature and daily precipitation that is a useful tool for studies regarding climatological and hydrological aspects in the region. The dataset was created using two quantile mapping techniques to downscale the CCSM4, MPI-ESM-LR, and MIROC5 model outputs to a 0.1-degree spatial resolution. Furthermore, we describe the added value of coproduction of knowledge between climate scientists and end users, or in this case impacts modelers and decision makers, for creating climate projections that can be used for climate risk assessments. A case study of the data's development and application is provided, detecting the mean daily changes in temperature and precipitation through the end of the century in the Red River Basin for two representative concentration pathways. After applying the users' inputs to develop the datasets, results for this example estimate an increase in mean daily precipitation in the eastern portion of the basin and as much as a 15% decline in the west by the end of the century. Furthermore, mean daily temperature is expected to rise across the entire basin in all scenarios by up to 6–7°C. [ABSTRACT FROM AUTHOR]

This paper utilizes the best available quality data from multiple sources to develop consistent surface boundary conditions (SBCs) for mesoscale regional climate model (RCM) applications. The primary SBCs include 1) fields of soil characteristic (bedrock depth, and sand and clay fraction profiles), which for the first time have been consistently introduced to define 3D soil properties; 2) fields of vegetation characteristic fields (landcover category, and static fractional vegetation cover and varying leaf-plusstem- area indices) to represent spatial and temporal variations of vegetation with improved data coherence and physical realism; and 3) daily sea surface temperature variations based on the most appropriate data currently available or other value-added alternatives. For each field, multiple data sources are compared to quantify uncertainties for selecting the best one or merged to create a consistent and complete spatial and temporal coverage. The SBCs so developed can be readily incorporated into any RCM suitable for U.S. climate and hydrology modeling studies, while the data processing and validation procedures can be more generally applied to construct SBCs for any specific domain over the globe. [ABSTRACT FROM AUTHOR]

Freezing rain can cause severe impacts, particularly when it persists for many hours. In this paper, we present the climatology of long-duration (6 or more hours) freezing rain events in the United States and Canada from 1979 to 2016. We identify three focus regions from this climatology and examine the archetypal thermodynamic evolution of events in each region using surface and radiosonde observations. Long-duration events occur most frequently in the northeastern United States and southeastern Canada, where freezing rain typically begins as lower-tropospheric warm-air advection develops the warm layer aloft. This warm-air advection and the latent heat of fusion released when rain freezes at the surface erode the cold layer, and freezing rain transitions to rain once the surface temperature reaches 0°C. In the southeastern United States, a larger percentage of events are of long duration than elsewhere in North America. Weak surface cold-air advection and evaporative cooling in the particularly dry onset cold layers there prevent surface temperatures from rising substantially during events. Finally, the south-central United States has a regional maximum in the occurrence of the top 1% of events by duration (18 or more hours), despite the relative rarity of freezing rain there. These events are associated with particularly warm/deep onset warm layers, with persistent low-level cold-air advection maintaining the cold layer. The thermodynamic evolutions we have identified highlight characteristics that are key to supporting persistent freezing rain in each region and may warrant particular attention from forecasters tasked with predicting these events. [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]

This paper recounts the surprising and disastrous results of attempting to do historical photographic research at a private archive. I had travelled to the Lee Miller Archives, in Sussex, UK, in order to conduct some preliminary research on Miller’s war work between the years 1944 and 1945. My intention was to begin comparing the drafts, original manuscripts, negatives, and captions to what Vogue published in both the US and the UK. The Archives are located in the former house of Lee Miller and husband Roland Penrose and they are managed by Miller’s son Antony Penrose. While my preparatory correspondence with the employees was straightforward and I had submitted lists of what I wanted to look at, once I arrived things immediately changed. This paper details the paternalistic climate of the Archives, as it reads some of Miller’s photography in relation to questions of history and representation. [ABSTRACT FROM PUBLISHER]

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]

In this study, we investigated the translation speed and intensity change characteristics for landfalling North American tropical cyclones (TCs) from 1971 to 2020. We calculated three variables—intensity change, mean translation speed, and translation speed change—prior to each TC landfall and investigated the climatology of these variables for seven coastal segments. We found that lower-latitude segments generally had greater positive intensity changes prior to landfall, and higher-latitude segments had greater translation speeds. Longitude primarily influenced translation speed changes, with landfalling TCs along the Atlantic coast of the United States notably accelerating prior to landfall. Temporal trends in each of these variables were inconsistent geographically, but most segments showed an increase in positive intensity changes over time, demonstrating the increasing likelihood of intensifying TCs before landfall in recent years. We defined extreme intensification and extreme weakening as the 90th and 10th percentile of all landfalling TC intensity changes, respectively. We found that extreme intensification and weakening have been increasing and decreasing in frequency, respectively. Results from this study can be used in a variety of future applications, including in operational forecasting and model production, and provide a baseline for climate attribution studies investigating extreme intensity change events. Significance Statement: Landfalling tropical cyclones pose significant hazards to life and property in coastal regions across North America. This study develops a comprehensive climatology of intensity change and translation speed of tropical cyclones during the final 36 h prior to landfall. We found that incidences of extreme intensification and weakening of landfalling North American tropical cyclones have increased and decreased, respectively, since 1971. We also identify lower-latitude coastal segments tend to average faster translation speeds that higher-latitude segments, while segments on the east coast of the United States tended to average a greater acceleration. These results show the importance of looking at tropical cyclone characteristics regionally and provide a useful baseline to assess how tropical cyclone risk is changing in a warming climate. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA), which is designed to look at internal modes of variability, has often been applied beyond its intended design to study coupled modes of variability in combined datasets, also referred to as combined PCA. There are statistical techniques better suited for this purpose such as singular value decomposition (SVD) and canonical correlation analysis (CCA). In this paper, a different technique is examined that has not often been applied in climate science, that is, redundancy analysis (RA). Similar to multivariate regression, RA seeks to maximize the variance accounted for in one random vector that is linearly regressed against another random vector. RA can be used for forecasting and prediction studies of the climate system. This technique has the added advantage that the time-lagged redundancy index offers a robust method of identifying lead-lag relations among climate variables. In this study, combined PCA and RA of global sea surface temperatures (SSTs) and sea level pressures (SLPs) are carried out for the National Centers for Environmental Prediction (NCEP) reanalysis data and a simulation of the Canadian Centre for Climate Modeling and Analysis (CCCma) climate model. A simplified state-space model is also constructed to aid in the diagnosis and interpretation of the results. The relative advantages and disadvantages of combined PCA and RA are discussed. Overall, RA tends to provide a clearer and more consistent picture of the underlying physical processes than combined PCA. [ABSTRACT FROM AUTHOR]

The author discusses the clarifications on the impact of subglobal climate policies and the impact of international trade in the climate policy in the U.S. Topics include the implications of a study by Christoph Boehringer, Carolyn Fischer, and Knut Einar Rosendahl on climate policy, the use of computable general equilibrium (CGE) model in tracking markets' complex interactions, and the reduction of energy demand in the U.S. due to carbon cap.

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]

This is the second of two papers that examine the organization of the precipitation field during central U.S. cold-season cyclones involving inverted troughs (ITs). The first paper (Part I) used a climatology and composites to find synoptic-scale differences between storms with precipitation located ahead of the IT (ahead cases) and those with precipitation located behind the IT (behind cases). This paper expands the conclusions in Part I through the use of a comparative case study between two cyclones. The first cyclone, on 29 October 1996, was an ahead case that produced heavy rainfall and was associated with a potential vorticity (PV) anomaly moving across the central plains. The IT formed in the lee of the Rockies prior to 0600 UTC 29 October and moved east into the northern plains over the next 18 h. The trough itself was coincident with the limiting streamline, which separated moist air rising over the warm front from dry air subsiding behind the cyclone. The second cyclone, on 17–18 January 1996, had precipitation on both sides of the IT and was associated with heavy snow and blizzard conditions in the northern plains and significant ice accumulation in the western Great Lakes. The IT was associated with large frontogenesis over the snow area. The ascent was further enhanced by a jet streak moving across southern Canada. Dynamically, the IT resembled a warm front, with veering winds with height and a strong frontal inversion. The mechanism that appeared to control the different precipitation organization between the two systems was the orientation of the PV anomalies and the airstreams associated with their secondary circulations. This resulted in a differing orientation of the baroclinicity north and east of the cyclone. In the ahead case, the rising branches of the secondary circulations forced by the northern and southern anomalies remained separate. This allowed the baroclinicity to develop along the traditional warm front, while the IT never developed a thermal gradient as it moved east. In the both sides case, the southern stream anomaly helped to fix the northern anomaly-forced jet streak in place, so that a strong temperature gradient developed along the IT with strong frontogenesis and warm-air advection observed behind the IT. As the frontal circulation developed, the direct circulation associated with the right entrance region of a jet streak enhanced the ascent to the west of the IT. A conceptual model is proposed based upon the case studies and the results of Part I. This model can be used by forecasters to differentiate between the precipitation regimes in cyclones associated with ITs. [ABSTRACT FROM AUTHOR]

Water managers in the western United States and throughout the world are facing the increasing challenge of supplying a variety of water demands under the growing stresses of climate variability and of population and economic growth. Accurate streamflow forecasts are key to the water resources planning and decision-making process. There is growing evidence that variability in western streamflow is modulated by large-scale ocean-atmospheric features. Traditional forecasting techniques, however, do not systematically utilize large-scale climate information. In this study we present a framework for incorporating climate information into water resources decision-making and demonstrate the method on the semiarid Truckee-Carson Basin in Nevada where environmental, agricultural, and municipal demands compete for a limited supply of water. In this basin, water managers must plan carefully to meet the demands of timing and required flowrates. The framework presented in this paper consists of a streamflow forecast system that is based on large-scale climate information. The forecasts are then incorporated in a decision-support tool to aid in water resources planning and decision making. Previous work on the Truckee-Carson Basin has demonstrated that incorporating climate information into the forecast can increase the accuracy (or skill) and lead time of forecasts. This paper focuses on the decision-support system that is used to evaluate decision strategies and demonstrates possible water management improvements. The performance of the framework and the improved forecasts are evaluated on the Truckee-Carson system by using a suite of years from the historical record. [ABSTRACT FROM AUTHOR]

In March 2001, President G.W. Bush announced that the United States had abandoned the ratification of the Kyoto Protocol. This paper describes the historical background as well as the political reasons and mechanisms behind the Bush administration's decision. Moreover, it deals with two alternative policy proposals, which have emerged at the US federal and the state level since then. The paper then highlights some important differences between EU and US climate change policies. The most important difference is that the EU wants global warming mitigation and the US, if necessary at all, is emphasizing the need to adapt to climate change. The paper concludes by suggesting that if a post-Kyoto treaty wants to bring the US back on board it will need to address this difference. Copyright © 2003 John Wiley & Sons, Ltd and ERP Environment. [ABSTRACT FROM AUTHOR]

Inhomogeneities in U.S. radiosonde data that used the VIZ and Vaisala RS80 cannot be explained by radiation errors, which can be removed by the heat balance models. WMO intercomparision data, modeling results, temperature time series, and 1200 minus 0000 UTC temperature differences are examined to show that there appears to be an error in the U.S. RS80/RSN93 temperature correction software. Radiosonde soundings taken at U.S. stations that launch Vaisala RS80 radiosondes, which are integrated within the National Weather Service (NWS) Microcomputer Automatic Radio-Theodolite (Micro-ART) system, should not be used in climate studies since there is a large systematic error of unknown origin in the temperature data. This paper is the first of two and is primarily concerned with the midtroposphere. The second paper discusses the large unexplained 0000 and 1200 UTC differences in the stratosphere. [ABSTRACT FROM AUTHOR]

This paper is the first of two papers that examines the organization of the precipitation field in central U.S. cold-season cyclones involving inverted troughs. The first portion of the study examines the varying precipitation distribution that occurred during a 6-yr synoptic climatology of inverted trough cases. The definition of inverted trough cases has been expanded from the groundbreaking work by Keshishian et al. by 1) not requiring a closed cyclonic isobar along the frontal wave along the conventional surface front and 2) not requiring a surface thermal gradient to be present along the inverted trough. Only 8.5% of the expanded dataset produced the precipitation primarily occurring to the west of the inverted trough ("behind" cases) as seen in Keshishian et al. The largest group of cases, comprising about 40% of the cases, produced precipitation that primarily occurred between the inverted trough and the conventional warm front ("ahead" cases). A composite study compared a subset of the ahead cases with a subset of the behind cases. The ahead cases tended to be more progressive with a stronger jet stream located over the center of the parent low. Broad warm-air advection and frontogenesis in the lower troposphere were observed between the inverted trough and the surface warm front. Cold-air advection to the west of the inverted trough precluded the development of "wraparound precipitation." In contrast, the behind cases had a stronger low-latitude wave couplet with a trough upstream of the surface low and a ridge downstream. The region of warm-air advection and frontogenesis were displaced to the west of the inverted trough and surface cyclone. In addition, the entrance region of a southwest-northeast-oriented jet streak aided the development of ascent to the west of the inverted trough while precluding the development of precipitation to the north of the conventional warm front. Thus, the inverted trough tended to act like a warm front in behind cases,... [ABSTRACT FROM AUTHOR]

This paper describes a new climate model and its potential application to the study of ENSO impacts. The model is a regional spectral model embedded within a global coupled ocean–atmosphere model. The atmospheric part of the model consists of a global spectral model with triangular truncation T63 and a nested regional spectral model. The regional model is a relocatable spectral perturbation model that can be run at any horizontal resolution. In this paper the regional model was run with a resolution of 40 km. The global atmosphere model is coupled to the Max Planck global ocean model. No flux correction or anomaly coupling is used. An ensemble of 120-day integrations was conducted using the coupled nested system for the boreal winters of 1987 and 1988. A control integration was also performed in which observed SSTs were used in both the global and regional models. Two domains were chosen for the regional model: the southeast United States and western North America. Results from the global models show that the models reproduce many of the large-scale ENSO climate variations including the shifts in the Pacific ITCZ and SPCZ along with a Pacific–North America response in the 500-hPa height field. These results are compared against the corresponding ECMWF and Global Precipitation Climatology Centre analysis. Over the southeast United States both the global and regional models captured the precipitation variations between the two years as compared with the monthly mean cooperative station data. It is shown that the regional model solution is consistent with the global model solution, but with more realistic detail. Finally prospects for using this coupled nested ocean–atmosphere regional spectral model for downscaling are discussed. [ABSTRACT FROM AUTHOR]

A new, high-resolution (4 km), gridded land surface dataset produced with the Land Information System (LIS) is introduced, and the first set of synthesis of key hydroclimatic variables is reported. The dataset is produced over a 33-yr time period (1980-2012) for the U.S. Midwest with the intent to aid the agricultural community in understanding hydroclimatic impacts on crop production and decision-making in operational practices. While approximately 20 hydroclimatic variables are available through the LIS dataset, the focus here is on soil water content, soil temperature, and evapotranspiration. To assess the performance of the model, the LIS dataset is compared with in situ hydrometeorological observations across the study domain and with coarse-resolution reanalysis products [NARR, MERRA, and NLDAS-2 (phase 2 of the North American Land Data Assimilation System)]. In agricultural regions such as the U.S. Midwest, finescale hydroclimatic mapping that links the regional scale to the field scale is necessary. The new dataset provides this link as an intermediate-scale product that links point observations and coarse gridded datasets. In general, the LIS dataset compares well with in situ observations and coarser gridded products in terms of both temporal and spatial patterns, but cases of strong disagreement exist particularly in areas with sandy soils. The dataset is made available to the broader research community as an effort to fill the gap in spatial hydroclimatic data availability. [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]

This study uses the convective adjustment time scale to identify the climatological frequency of equilibrium and nonequilibrium convection in different parts of the contiguous United States (CONUS) as modeled by the operational convection-allowing High-Resolution Rapid Refresh (HRRR) forecast system. We find a qualitatively different climatology in the northern and southern domains separated by the 40°N parallel. The convective adjustment time scale picks up the fact that convection over the northern domains is governed by synoptic flow (leading to equilibrium), while locally forced, nonequilibrium convection dominates over the southern domains. Using a machine learning algorithm, we demonstrate that the convective adjustment time-scale diagnostic provides a sensible classification that agrees with the underlying dynamics of equilibrium and nonequilibrium convection. Furthermore, the convective adjustment time scale can indicate the model quantitative precipitation forecast (QPF) quality, as it correctly reflects the higher QPF skill for precipitation under strong synoptic forcing. This diagnostic based on the strength of forcing for convection will be employed in future studies across different parts of CONUS to objectively distinguish different weather situations and explore the potential connection to warm-season precipitation predictability. Significance Statement: An objective classification metric that can delineate a wide range of forecasts into distinct scenarios can serve as a valuable tool. This study represents a pioneering effort in utilizing the convective adjustment time scale to identify the climatological frequency of warm-season precipitation under varying levels of synoptic forcing in different parts of the contiguous United States (CONUS). The results demonstrate that the convective adjustment time scale is a robust metric for categorizing precipitation events and establishing a direct link to their predictability. Overall, this study provides a valuable framework for future studies focused on the CONUS domain, offering guidance on how to employ the convective adjustment time scale to classify weather regimes and explore the influence of environmental conditions on predictability of convection. [ABSTRACT FROM AUTHOR]

Prior research evaluating snowfall conditions and temporal trends in the United States often acknowledges the role of various synoptic-scale weather systems in governing snowfall variability. While synoptic classifications have been performed in other regions of North America in applications to snowfall, there remains a need for enhanced understanding of the atmospheric mechanisms of snowfall in the central United States. Here we conduct a novel synoptic climatological investigation of the weather systems responsible for snowfall in the central United States from 1948 to 2021 focused on their identification and the quantification of associated snowfall totals and events. Ten unique synoptic weather types (SWTs) were identified, each resulting in distinct regions of enhanced snowfall across the study domain aligning with regions of sufficiently cold air temperatures and forcing mechanisms. While a substantial proportion of seasonal snowfall is attributed to SWTs associated with surface troughs and/or midlatitude cyclones, in portions of the southeastern and western study domain, as much as 70% of seasonal snowfall occurs during systems with high pressure centers as the domain's synoptic-scale forcing. Easterly flow, potentially resulting in topographic uplift from high pressure to east of the domain, was associated with between 15% and 25%of seasonal snowfall in Nebraska and South Dakota. On average, 64.8% of the SWT occurrences resulted in snowfall within the study region, ranging between 40.1%and 93.5%by SWT. Synoptic climatological investigations provide valuable insights into the unique weather systems that generate hydroclimatic variability. SIGNIFICANCE STATEMENT: By evaluating the weather patterns that are responsible for snowfall in the central United States, key insights can be gained into how and why snowfall varies and potentially changes over space and time. Using an approach that categorizes weather patterns based on their similarities, here 10 unique snowfall-producing weather patterns are identified and analyzed from 1948 to 2021. Each pattern resulted in different snowfall amounts across the central United States, varying substantially spatially and within the calendar year. Approximately 65% of the time that these weather patterns occur, snowfall is observed in the region. The majority of snowfall-producing weather patterns are associated with low pressure systems, but in some regions up to 70% of snowfall is associated with instances of high pressure in which winds can cause upward motions associated with topography. [ABSTRACT FROM AUTHOR]

The eastern United States is vulnerable to flooding from tropical cyclone rainfall. Understanding how both the frequency and intensity of this rainfall will change in the future climate is a major challenge. One promising approach is the dynamical downscaling of relatively coarse general circulation model results using higher-resolution regional climate models (RCMs). In this paper, we examine the frequency of landfalling tropical cyclones and associated rainfall properties over the eastern United States using Zetac, an 18-km resolution RCM designed for modeling Atlantic tropical cyclone activity. Simulations of 1980-2006 tropical cyclone frequency and rainfall intensity for the months of August-October are compared against results from previous studies and observation-based datasets. The 1980-2006 control simulations are then compared against results from three future climate scenarios: CMIP3/A1B (late twenty-first century) and CMIP5/RCP4.5 (early and late twenty-first century). In CMIP5 early and late twenty-first century projections, the frequency of occurrence of post-landfall tropical cyclones shows little net change over much of the eastern U.S. despite a decrease in frequency over the ocean. This reflects a greater landfalling fraction in CMIP5 projections, which is not seen in CMIP3-based projections. Average tropical cyclone rain rates over land within 500 km of the storm center increase by 8-17 % in the future climate projections relative to control. This is at least as much as expected from the Clausius-Clapeyron relation, which links a warmer atmosphere to greater atmospheric water vapor content. Over land, the percent enhancement of area-averaged rain rates from a given tropical cyclone in the warmer climate is greater for larger averaging radius (300-500 km) than near the storm, particularly for the CMIP3 projections. Although this study does not focus on attribution, the findings are broadly consistent with historical tropical cyclone rainfall changes documented in a recent observational study. The results may have important implications for future flood risks from tropical cyclones. [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]

This paper describes a case study applying multi-criteria decision analysis (MCDA) to weight indicators for assessing the exposure and sensitivity of seaports to climate and extreme weather impacts. Researchers employed the analytic hierarchy method (AHP) of MCDA to generate weights for a subset of expert-selected indicators of seaport exposure and sensitivity to climate and extreme weather. The indicators were selected from the results of a survey of port-experts who ranked candidate indicators by magnitude of perceived correlation with the three components of vulnerability; exposure, sensitivity, and adaptive capacity. As those port-expert respondents found significantly stronger correlation between candidate indicators and the exposure and sensitivity of a port than with a port's adaptive capacity, this AHP exercise did not include indicators of adaptive capacity. The weighted indicators were aggregated to generate composite indices of seaport exposure and sensitivity to climate and extreme weather for 22 major ports in the North East United States. Rank order generated by AHP-weighted aggregation was compared to a subjective expert-ranking of ports by expert-perceived vulnerability to climate and extreme weather. For the sample of 22 ports, the AHP-generated ranking matched three of the top four most vulnerable ports as assessed subjectively by port-experts. These results suggest that a composite index based on open data weighted via MCDA may eventually prove useful as a data-driven tool for identifying outliers in terms of relative seaport vulnerabilities, however, improvements in the standardized reporting and sharing of port data will be required before such an indicator-based assessment method can prove decision-relevant. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue including the evaluation of the climate of southeastern U.S., its dynamic downscaling, and its atmospheric reanalysis.