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Ixodes scapularis is the vector of at least seven human pathogens in Minnesota, two of which are known to cause Lyme disease (Borrelia burgdorferi sensu stricto and Borrelia mayonii). In Minnesota, the statewide incidence of Lyme disease and other I. scapularis-borne diseases and the geographic extent over which cases have been reported have both increased substantially over the last two decades. These changes correspond with an expanding distribution of I. scapularis over a similar time frame. Because the risk of exposure to I. scapularis-borne pathogens is likely related to the number of ticks encountered, we developed an acarological risk model predicting the density of host-seeking I. scapularis nymphs (DON) in Minnesota. The model was informed by sampling 81 sites located in 42 counties in Minnesota. Two main foci were predicted by the model to support elevated densities of host-seeking I. scapularis nymphs, which included the seven-county Minneapolis-St. Paul metropolitan area and counties in northern Minnesota, including Lake of the Woods and Koochiching counties. There was substantial heterogeneity observed in predicted DON across the state at the county scale; however, counties classified as high risk for I. scapularis-borne diseases and counties with known established populations of I. scapularis had the highest proportion of the county predicted as suitable for host-seeking nymphs (≥ 0.13 nymphs/100 m2). The model provides insight into areas of potential I. scapularis population expansion and identifies focal areas of predicted suitable habitat within counties where the incidence of I. scapularis-borne diseases has been historically low.

Weather and climate variability strongly influence the people, infrastructure, and economy of Alaska. However, the sparse observational network in Alaska limits our understanding of meteorological variability, particularly of precipitation processes that influence the hydrologic cycle. Here, a new 14-yr (September 2002–August 2016) dataset for Alaska with 4-km grid spacing is described and evaluated. The dataset, generated with the Weather Research and Forecasting (WRF) Model, is useful for gaining insight into meteorological and hydrologic processes, and provides a baseline against which to measure future environmental change. The WRF fields are evaluated at annual, seasonal, and daily time scales against observation-based gridded and station records of 2-m air temperature, precipitation, and snowfall. Pattern correlations between annual mean WRF and observation-based gridded fields are r = 0.89 for 2-m temperature, r = 0.75 for precipitation, r = 0.82 for snow-day fraction, r = 0.55 for first snow day of the season, and r = 0.71 for last snow day of the season. A shortcoming of the WRF dataset is that spring snowmelt occurs too early over a majority of the state, due partly to positive 2-m temperature biases in winter and spring. Strengths include an improved representation of the interannual variability of 2-m temperature and precipitation and accurately simulated (relative to regional station observations) winter and summer precipitation maxima. This initial evaluation suggests that the 4-km WRF climate dataset robustly simulates meteorological processes and recent climatic variability in Alaska. The dataset may be particularly useful for applications that require high-temporal-frequency weather fields, such as driving hydrologic or glacier models. Future studies will provide further insight on its ability to represent other aspects of Alaska’s climate.

Understanding how impacts may differ across alternative levels of future climate change is necessary to inform mitigation and adaptation measures. The Benefits of Reduced Anthropogenic Climate changE (BRACE) project assesses the differences in impacts between two specific climate futures: a higher emissions future with global average temperature increasing about 3.7 °C above pre-industrial levels toward the end of the century and a moderate emissions future with global average warming of about 2.5 °C. BRACE studies in this special issue quantify avoided impacts on physical, managed, and societal systems in terms of extreme events, health, agriculture, and tropical cyclones. Here we describe the conceptual framework and design of BRACE and synthesize its results. Methodologically, the project combines climate modeling, statistical analysis, and impact assessment and draws heavily on large ensembles using the Community Earth System Model. It addresses uncertainty in future societal change by employing two pathways for future socioeconomic development. Results show that the benefits of reduced climate change within this framework vary substantially across types of impacts. In many cases, especially related to extreme heat events, there are substantial benefits to mitigation. The benefits for some heat extremes are statistically significant in some regions as early as the 2020s and are widespread by mid-century. Benefits are more modest for agriculture and exposure to some health risks. Benefits are negative for agriculture when CO2 fertilization is incorporated. For several societal impacts, the effect on outcomes of alternative future societal development pathways is substantially larger than the effect of the two climate scenarios.

Background The movement behavior of an animal is determined by extrinsic and intrinsic factors that operate at multiple spatio-temporal scales, yet much of our knowledge of animal movement comes from studies that examine only one or two scales concurrently. Understanding the drivers of animal movement across multiple scales is crucial for understanding the fundamentals of movement ecology, predicting changes in distribution, describing disease dynamics, and identifying efficient methods of wildlife conservation and management. Methods We obtained over 400,000 GPS locations of wild pigs from 13 different studies spanning six states in southern U.S.A., and quantified movement rates and home range size within a single analytical framework. We used a generalized additive mixed model framework to quantify the effects of five broad predictor categories on movement: individual-level attributes, geographic factors, landscape attributes, meteorological conditions, and temporal variables. We examined effects of predictors across three temporal scales: daily, monthly, and using all data during the study period. We considered both local environmental factors such as daily weather data and distance to various resources on the landscape, as well as factors acting at a broader spatial scale such as ecoregion and season. Results We found meteorological variables (temperature and pressure), landscape features (distance to water sources), a broad-scale geographic factor (ecoregion), and individual-level characteristics (sex-age class), drove wild pig movement across all scales, but both the magnitude and shape of covariate relationships to movement differed across temporal scales. Conclusions The analytical framework we present can be used to assess movement patterns arising from multiple data sources for a range of species while accounting for spatio-temporal correlations. Our analyses show the magnitude by which reaction norms can change based on the temporal scale of response data, illustrating the importance of appropriately defining temporal scales of both the movement response and covariates depending on the intended implications of research (e.g., predicting effects of movement due to climate change versus planning local-scale management). We argue that consideration of multiple spatial scales within the same framework (rather than comparing across separate studies post-hoc) gives a more accurate quantification of cross-scale spatial effects by appropriately accounting for error correlation. Electronic supplementary material The online version of this article (doi:10.1186/s40462-017-0105-1) contains supplementary material, which is available to authorized users.

Local perspectives on changing weather and climate and analyses of meteorological data represent two different but potentially complementary ways of knowing about the local-scale impacts of global climate change. This paper uses quantitative social survey data from the Kassena and Nankana Districts of Northern Ghana and the best available meteorological records to examine recent changes in weather patterns for this region. The most commonly mentioned changes perceived by respondents include changes in the timing or predictability of rains, and overall drier conditions. Both of these changes are corroborated by precipitation datasets: The onset of the peak rainy season has shifted progressively later over the past decade, by up to a month, and the rainy season has been drier over the past 3–5 years compared to the past 10–35 years, mainly due to lower rainfall during peak months (June and July). Many respondents also said that conditions had become windier, and we find that this perception varies spatially within the districts, but no meteorological data are available for this climate parameter in this region. The common perception that deforestation is responsible for observed changes in weather patterns is partly supported by Landsat imagery indicating a reduction in dense vegetation in recent decades. This comparison highlights some of the potential benefits and challenges involved in giving more voice to community perspectives in the co-production of knowledge on global climate change and its regional impacts.

The mosquito virus vector Aedes (Ae.) aegypti exploits a wide range of containers as sites for egg laying and development of the immature life stages, yet the approaches for modeling meteorologically sensitive container water dynamics have been limited. This study introduces the Water Height and Temperature in Container Habitats Energy Model (WHATCH’EM), a state-of-the-science, physically based energy balance model of water height and temperature in containers that may serve as development sites for mosquitoes. The authors employ WHATCH’EM to model container water dynamics in three cities along a climatic gradient in México ranging from sea level, where Ae. aegypti is highly abundant, to ~2100 m, where Ae. aegypti is rarely found. When compared with measurements from a 1-month field experiment in two of these cities during summer 2013, WHATCH’EM realistically simulates the daily mean and range of water temperature for a variety of containers. To examine container dynamics for an entire season, WHATCH’EM is also driven with field-derived meteorological data from May to September 2011 and evaluated for three commonly encountered container types. WHATCH’EM simulates the highly nonlinear manner in which air temperature, humidity, rainfall, clouds, and container characteristics (shape, size, and color) determine water temperature and height. Sunlight exposure, modulated by clouds and shading from nearby objects, plays a first-order role. In general, simulated water temperatures are higher for containers that are larger, darker, and receive more sunlight. WHATCH’EM simulations will be helpful in understanding the limiting meteorological and container-related factors for proliferation of Ae. aegypti and may be useful for informing weather-driven early warning systems for viruses transmitted by Ae. aegypti.

Climate change poses serious energy, water, and health challenges for the United Arab Emirates (UAE). While closely interconnected, the development of sustainable energy, water, and health policies has typically been viewed as independent, sector-specific planning challenges. However, changing demographics, a rapidly growing economy, dependence on desalination, and worsening air quality – all taking place as climate change unfolds – suggest a need for a more integrated approach to risk management. Accounting for the interactions between “energy, water, and health nexus” is one way to ensure that development strategies are considered within a framework that addresses the range of potential trade-offs, risks, and synergies. To address the energy-water-health nexus under a changing climate, research activities were undertaken as part of the Local, National, and Regional Climate Change Programme (LNRCCP) of the Abu Dhabi Global Environmental Data Initiative (AGEDI). Climate change modeling at the regional spatial scale (Arabian Peninsula; Arabian Gulf) was first carried out to establish the atmospheric and marine physical conditions that will underlie energy, water, and health challenges in the future. The results of this modeling were then used as inputs to an analysis of policies that account for linkages across the energy-water nexus on the one hand and the energy-health nexus on the other. The modeling results show that climate change will render an extreme hyper-arid climate even more so, while the waters of the Arabian Gulf will experience heightened salinity, changing circulation patterns, and higher temperatures as desalination activities intensify. The analysis of the energy-water-health nexus shows that energy efficiency and renewable energy can lead to significant reductions in annual greenhouse gas (GHG) emissions at negative to modest societal cost while leading to substantial decreases in premature mortality and health-care facility visits in the urban environment.

The mosquito Aedes (Stegomyia) aegypti (L.) is the primary vector of dengue, chikungunya, and Zika viruses in the United States. Surveillance for adult Ae. aegypti is limited, hindering understanding of the mosquito’s seasonal patterns and predictions of areas at elevated risk for autochthonous virus transmission. We developed a simple, intuitive empirical model that uses readily available temperature and humidity variables to predict environmental suitability for low, medium, or high potential abundance of adult Ae. aegypti in a given city 1 month in advance. Potential abundance was correctly predicted in 73% of months in arid Phoenix, AZ (over a 10-year period), and 63% of months in humid Miami, FL (over a 2-year period). The monthly model predictions can be updated daily, weekly, or monthly and thus may be applied to forecast suitable conditions for Ae. aegypti to inform vector-control activities and guide household-level actions to reduce mosquito habitat and human exposure.

Extreme heat is a significant public health challenge in urban environments that disproportionally impacts vulnerable members of society. In this research, demographic, economic and climate projections are brought together with a statistical approach linking extreme heat and mortality in Houston, Texas. The sensitivity of heat-related non-accidental mortality to future changes of demographics, income and climate is explored. We compare climate change outcomes associated with two different Representative Concentration Pathways (RCPs), RCP4.5 and RCP8.5, which describe alternate future scenarios for greenhouse gas emissions and concentrations. For each RCP, we explore demographic and economic scenarios for two plausible Shared Socioeconomic Pathways (SSPs), SSP3 and SSP5. Our findings suggest that non-accidental mortality in 2061–2080 may increase for all combinations of RCP and SSP scenarios compared to a historical reference period spanning 1991–2010. Notably, increased heat-related non-accidental mortality is associated with changes in the size and age of the population, but the degree of sensitivity is highly uncertain given the breadth of plausible socioeconomic scenarios. Beyond socioeconomic changes, climate change is also important. For each socioeconomic scenario, non-accidental mortality associated with the lower emissions RCP4.5 scenario is projected to be 50 % less than mortality projected under the higher emissions RCP8.5 scenario.

Complex urban environments with diverse urban canyon structures and building materials mixed with human-modified green space substantially influence the directional variation in upwelling thermal radiance. The thermal anisotropy at a satellite pixel scale (e.g. 1 km) is hard to quantify with current techniques through airborne observations and modeling. The various anisotropic features over urban and rural surfaces lead to an uncertainty regarding how this anisotropy of land surface temperature (LST) influences estimates of the surface urban heat island (SUHI). We quantify LST anisotropy over Chicago and New York using MODIS LST products during the warm months (May-September) for a 10-year period. The impacts of atmospheric attenuation and daily weather variability on LST are removed in order to isolate the anisotropic signal. We then calculate the anisotropy for different levels of urban land use intensity. The daytime maximum anisotropic effects can be up to 9 K for the most urbanized areas, while the nighttime anisotropic effects are weaker but still discernible. Two anisotropic “hot spot” features are observed during daytime: one corresponds to the sensor-sun geometric effect with a higher proportion of sunlit surface components been seen a given view configuration, and the other occurs when sensor zenith angles are near nadir in the afternoon. Similar anisotropic features between the two cities confirm the broader applicability of our approach, while some unique features specific to each city emphasize that diverse urban surface properties and geographic settings can affect anisotropy. This study is the first of its kind to directly evaluate the influence of directional anisotropy on satellite-retrieved SUHI measurements, demonstrating that anisotropic effects can possibly modify the SUHI as measured with MODIS LST by 2.3 K, which is about 25 – 50% of the total magnitude of the SUHI over Chicago and New York. Our findings provide a statistical basis for the quantification of satellite-retrieved LST anisotropy over heterogeneous urban environments globally.

In addition to serving as vectors of several other human pathogens, the black-legged tick, Ixodes scapularis Say, and western black-legged tick, Ixodes pacificus Cooley and Kohls, are the primary vectors of the spirochete (Borrelia burgdorferi) that causes Lyme disease, the most common vector-borne disease in the United States. Over the past two decades, the geographic range of I. pacificus has changed modestly while, in contrast, the I. scapularis range has expanded substantially, which likely contributes to the concurrent expansion in the distribution of human Lyme disease cases in the Northeastern, North-Central and Mid-Atlantic states. Identifying counties that contain suitable habitat for these ticks that have not yet reported established vector populations can aid in targeting limited vector surveillance resources to areas where tick invasion and potential human risk are likely to occur. We used county-level vector distribution information and ensemble modeling to map the potential distribution of I. scapularis and I. pacificus in the contiguous United States as a function of climate, elevation, and forest cover. Results show that I. pacificus is currently present within much of the range classified by our model as suitable for establishment. In contrast, environmental conditions are suitable for I. scapularis to continue expanding its range into northwestern Minnesota, central and northern Michigan, within the Ohio River Valley, and inland from the southeastern and Gulf coasts. Overall, our ensemble models show suitable habitat for I. scapularis in 441 eastern counties and for I. pacificus in 11 western counties where surveillance records have not yet supported classification of the counties as established.

The mosquito Aedes (Ae). aegypti transmits the viruses that cause dengue and chikungunya, two globally-important vector-borne diseases. We investigate how choosing alternate emissions and/or socioeconomic pathways may modulate future human exposure to Ae. aegypti. Occurrence patterns for Ae. aegypti for 2061-2080 are mapped globally using empirically downscaled air temperature and precipitation projections from the Community Earth System Model, for the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Population growth is quantified using gridded global population projections consistent with two Shared Socioeconomic Pathways (SSPs), SSP3 and SSP5. Change scenarios are compared to a 1950-2000 reference period. A global land area of 56.9 M km2 is climatically suitable for Ae. aegypti during the reference period, and is projected to increase by 8% (RCP4.5) to 13% (RCP8.5) by 2061-2080. The annual average number of people exposed globally to Ae. aegypti for the reference period is 3794 M, a value projected to statistically significantly increase by 298-460 M (8-12%) by 2061-2080 if only climate change is considered, and by 4805-5084 M (127-134%) for SSP3 and 2232-2483 M (59-65%) for SSP5 considering both climate and population change (lower and upper values of each range represent RCP4.5 and RCP8.5 respectively). Thus, taking the lower-emissions RCP4.5 pathway instead of RCP8.5 may mitigate future human exposure to Ae. aegypti globally, but the effect of population growth on exposure will likely be larger. Regionally, Australia, Europe and North America are projected to have the largest percentage increases in human exposure to Ae. aegypti considering only climate change.

Ixodes scapularis is the vector of at least seven human pathogens in Minnesota, two of which are known to cause Lyme disease (Borrelia burgdorferi sensu stricto and Borrelia mayonii). In Minnesota, the statewide incidence of Lyme disease and other I. scapularis–borne diseases and the geographic extent over which cases have been reported have both increased substantially over the last two decades. These changes correspond with an expanding distribution of I. scapularis over a similar time frame. Because the risk of exposure to I. scapularis–borne pathogens is likely related to the number of ticks encountered, we developed an acarological risk model predicting the density of host-seeking I. scapularis nymphs (DON) in Minnesota. The model was informed by sampling 81 sites located in 42 counties in Minnesota. Two main foci were predicted by the model to support elevated densities of host-seeking I. scapularis nymphs, which included the seven-county Minneapolis-St. Paul metropolitan area and counties in northern Minnesota, including Lake of the Woods and Koochiching counties. There was substantial heterogeneity observed in predicted DON across the state at the county scale; however, counties classified as high risk for I. scapularis–borne diseases and counties with known established populations of I. scapularis had the highest proportion of the county predicted as suitable for host-seeking nymphs (≥ 0.13 nymphs/100 m2). The model provides insight into areas of potential I. scapularis population expansion and identifies focal areas of predicted suitable habitat within counties where the incidence of I. scapularis–borne diseases has been historically low.

Mitigation of adverse effects of air pollution requires understanding underlying exposures, such as ambient ozone concentrations. Geostatistical approaches were employed to analyze temporal trends and estimate spatial patterns of summertime ozone concentrations for Houston, Texas, based on hourly ozone observations obtained from the Texas Commission on Environmental Quality. We systematically assess the accuracy of several spatial interpolation methods, comparing inverse distance weighting, simple kriging, ordinary kriging, and universal kriging methods utilizing the hourly ozone observations and meteorological measurements from monitoring sites. Model uncertainty was assessed by leave-one-out cross-validation. Kriging methods performed better, showing greater consistency in the generated surfaces, fewer interpolation errors, and lower biases. Universal kriging did not significantly improve the interpolation results compared to ordinary kriging, and thus ordinary kriging was determined to be the optimal method, striking a balance between accuracy and simplicity. The resulting spatial patterns indicate that the more industrialized areas east and northeast of Houston exhibit the highest summertime ozone concentrations. Estimated daily maximum 8 h ozone concentration fields generated will be used to inform research on population health risks from exposure to surface ozone in Houston.

Extreme heat is a leading cause of weather-related human mortality. The urban heat island (UHI) can magnify heat exposure in metropolitan areas. This study investigates the ability of a new MODIS-retrieved near-surface air temperature and humidity dataset to depict urban heat patterns over metropolitan Chicago, Illinois, during June–August 2003–13 under clear-sky conditions. A self-organizing mapping (SOM) technique is used to cluster air temperature data into six predominant patterns. The hottest heat patterns from the SOM analysis are compared with the 11-summer median conditions using the urban heat island curve (UHIC). The UHIC shows the relationship between air temperature (and dewpoint temperature) and urban land-use fraction. It is found that during these hottest events 1) the air temperature and dewpoint temperature over the study area increase most during nighttime, by at least 4 K relative to the median conditions; 2) the urban–rural temperature/humidity gradient is decreased as a result of larger temperature and humidity increases over the areas with greater vegetation fraction than over those with greater urban fraction; and 3) heat patterns grow more rapidly leading up to the events, followed by a slower return to normal conditions afterward. This research provides an alternate way to investigate the spatiotemporal characteristics of the UHI, using a satellite remote sensing perspective on air temperature and humidity. The technique has potential to be applied to cities globally and provides a climatological perspective on extreme heat that complements the many case studies of individual events.

The Lone star tick (Amblyomma americanum L.) is the primary vector for pathogens of significant public health importance in North America, yet relatively little is known about its current and potential future distribution. Building on a published summary of tick collection records, we used an ensemble modeling approach to predict the present-day and future distribution of climatically suitable habitat for establishment of the Lone star tick within the continental United States. Of the nine climatic predictor variables included in our five present-day models, average vapor pressure in July was by far the most important determinant of suitable habitat. The present-day ensemble model predicted an essentially contiguous distribution of suitable habitat extending to the Atlantic coast east of the 100th western meridian and south of the 40th northern parallel, but excluding a high elevation region associated with the Appalachian Mountains. Future ensemble predictions for 2061-2080 forecasted a stable western range limit, northward expansion of suitable habitat into the Upper Midwest and western Pennsylvania, and range contraction along portions of the Gulf coast and the lower Mississippi river valley. These findings are informative for raising awareness of A. americanum-transmitted pathogens in areas where the Lone Star tick has recently or may become established.

Dengue is the most prevalent mosquito-borne viral infection. Recent outbreaks in the southern United States illustrate the risk of reemergence. The first autochthonous cases since 1934 in Key West, FL, occurred in 2009–2010. We conducted a survey in 2012 with decision makers instrumental to the control of the outbreak to 1) determine their awareness of the multiple strategies used to control the outbreak and 2) assess their perceptions of the relative effectiveness of these strategies. An online survey was delivered to a predefined list of decision makers from multiple sectors to better understand dengue preparedness and response. Thirty-six out of 45 surveys were returned for an 80% response rate. Results indicate the need to focus prevention strategies on educational campaigns designed to increase population awareness of transmission risk. Respondents remain concerned about future dengue transmission risk in Key West and lack of resources to respond.

The wind resource and energy assessment is key to a wind farm development project. It allows for establishing the feasibility and economic viability of the project over the typical 10- to 30-year lifetime of a wind farm. Recent studies show that the accuracy of assessments has substantial room for improvement. Estimating and reducing uncertainty is important to secure financing and ensure the confidence of investors. A new method is proposed and demonstrated for the long-term estimation of the wind speeds at a target site, a key step in assessments. The method is based on ensembles made of analogs between a short-term observational record from the target site and a long-term historical record from a nearby site or an atmospheric model. It provides a high-quality long-term wind resource estimate, characterized by an accurate wind speed time series and frequency distribution. It also provides a reliable estimate of the uncertainty based on the actual physical processes determining the current atmospheric flow rather than the climatological wind distribution.

After a dengue outbreak in Key West, Florida, during 2009–2010, authorities, considered conducting the first US release of male Aedes aegypti mosquitoes genetically modified to prevent reproduction. Despite outreach and media attention, only half of the community was aware of the proposal; half of those were supportive. Novel public health strategies require community engagement.

The mosquitoes Aedes (Stegomyia) aegypti (L.)(Diptera:Culicidae) and Ae. (Stegomyia) albopictus (Skuse) (Diptera:Culicidae) transmit dengue, chikungunya, and Zika viruses and represent a growing public health threat in parts of the United States where they are established. To complement existing mosquito presence records based on discontinuous, non-systematic surveillance efforts, we developed county-scale environmental suitability maps for both species using maximum entropy modeling to fit climatic variables to county presence records from 1960–2016 in the contiguous United States. The predictive models for Ae. aegypti and Ae. albopictus had an overall accuracy of 0.84 and 0.85, respectively. Cumulative growing degree days (GDDs) during the winter months, an indicator of overall warmth, was the most important predictive variable for both species and was positively associated with environmental suitability. The number (percentage) of counties classified as environmentally suitable, based on models with 90 or 99% sensitivity, ranged from 1,443 (46%) to 2,209 (71%) for Ae. aegypti and from 1,726 (55%) to 2,329 (75%) for Ae. albopictus. Increasing model sensitivity results in more counties classified as suitable, at least for summer survival, from which there are no mosquito records. We anticipate that Ae. aegypti and Ae. albopictus will be found more commonly in counties classified as suitable based on the lower 90% sensitivity threshold compared with the higher 99% threshold. Counties predicted suitable with 90% sensitivity should therefore be a top priority for expanded mosquito surveillance efforts while still keeping in mind that Ae. aegypti and Ae. albopictus may be introduced, via accidental transport of eggs or immatures, and potentially proliferate during the warmest part of the year anywhere within the geographic areas delineated by the 99% sensitivity model.

Numerous Zika virus vaccines are being developed. However, identifying sites to evaluate the efficacy of a Zika virus vaccine is challenging due to the general decrease in Zika virus activity. We compare results from three different modeling approaches to estimate areas that may have increased relative risk of Zika virus transmission during 2017. The analysis focused on eight priority countries (i.e., Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, Mexico, Panama, and Peru). The models projected low incidence rates during 2017 for all locations in the priority countries but identified several subnational areas that may have increased relative risk of Zika virus transmission in 2017. Given the projected low incidence of disease, the total number of participants, number of study sites, or duration of study follow-up may need to be increased to meet the efficacy study endpoints.

High-resolution environmental observations from satellite remote sensing are useful for gaining insight into climate variability over large regions of the earth. However, data products derived from infrared remote sensing can have large amounts of missing data due to periodic cloud cover obscuring the earth's surface. In addition, these products are commonly derived from instruments aboard polar-orbiting satellites, which make relatively infrequent passes over each region of the planet, leaving large blocks of data that are missing in time. The goal of this research is to overcome these issues by spatially and temporally infilling temperature data using satellite observations. To accomplish this, we propose novel statistical modeling techniques to account for dissonance between the areal spatial support of the data and the continuous temporal domain. In addition, we develop a novel multiresolution approach that accommodates the large spatial dimensionality of high-resolution satellite images. The result of our work is a computationally efficient methodology that provides the means to infill satellite data products both spatially and temporally. The methodology is demonstrated using land surface temperature fields from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard NASA's Terra and Aqua satellites, over Harris County, Texas, USA.

In addition to serving as vectors of several other human pathogens, the black-legged tick, Ixodes scapularis Say, and western black-legged tick, Ixodes pacificus Cooley and Kohls, are the primary vectors of the spirochete (Borrelia burgdorferi) that causes Lyme disease, the most common vector-borne disease in the United States. Over the past two decades, the geographic range of I. pacificus has changed modestly while, in contrast, the I. scapularis range has expanded substantially, which likely contributes to the concurrent expansion in the distribution of human Lyme disease cases in the Northeastern, North-Central and Mid-Atlantic states. Identifying counties that contain suitable habitat for these ticks that have not yet reported established vector populations can aid in targeting limited vector surveillance resources to areas where tick invasion and potential human risk are likely to occur. We used county-level vector distribution information and ensemble modeling to map the potential distribution of I. scapularis and I. pacificus in the contiguous United States as a function of climate, elevation, and forest cover. Results show that I. pacificus is currently present within much of the range classified by our model as suitable for establishment. In contrast, environmental conditions are suitable for I. scapularis to continue expanding its range into northwestern Minnesota, central and northern Michigan, within the Ohio River Valley, and inland from the southeastern and Gulf coasts. Overall, our ensemble models show suitable habitat for I. scapularis in 441 eastern counties and for I. pacificus in 11 western counties where surveillance records have not yet supported classification of the counties as established.

Meningitis remains a major health burden throughout Sahelian Africa, especially in heavily populated northwest Nigeria with an annual incidence rate ranging from 18 to 200 per 100 000 people for 2000–11. Several studies have established that cases exhibit sensitivity to intra- and interannual climate variability, peaking during the hot and dry boreal spring months, raising concern that future climate change may increase the incidence of meningitis in the region. The impact of future climate change on meningitis risk in northwest Nigeria is assessed by forcing an empirical model of meningitis with monthly simulations of seven meteorological variables from an ensemble of 13 statistically downscaled global climate model projections from phase 5 of the Coupled Model Intercomparison Experiment (CMIP5) for representative concentration pathway (RCP) 2.6, 6.0, and 8.5 scenarios, with the numbers representing the globally averaged top-of-the-atmosphere radiative imbalance (in W m−2) in 2100. The results suggest future temperature increases due to climate change have the potential to significantly increase meningitis cases in both the early (2020–35) and late (2060–75) twenty-first century, and for the seasonal onset of meningitis to begin about a month earlier on average by late century, in October rather than November. Annual incidence may increase by 47% ± 8%, 64% ± 9%, and 99% ± 12% for the RCP 2.6, 6.0, and 8.5 scenarios, respectively, in 2060–75 with respect to 1990–2005. It is noteworthy that these results represent the climatological potential for increased cases due to climate change, as it is assumed that current prevention and treatment strategies will remain similar in the future.

We examined temporal changes in the abundance of the mosquitoes Aedes (Stegomyia) aegypti (L.) and Aedes (Ochlerotatus) epactius Dyar & Knab from June to October 2012 in one reference community at lower elevation (Rio Blanco; approximately 1,270 m) and three high-elevation communities (Acultzingo, Maltrata, and Puebla City; 1,670-2,150 m) in Veracruz and Puebla States, Mexico. The combination of surveys for pupae in water-filled containers and trapping of adults, using BG-Sentinel traps baited with the BG-Lure, corroborated previous data from 2011 showing that Ae. aegypti is present at low abundance up to 2,150 m in this part of Mexico. Data for Ae. aegypti adults captured through repeated trapping in fixed sites in Acultzingo--the highest elevation community (approximately 1,670 m) from which the temporal intra-annual abundance pattern for Ae. aegypti has been described--showed a gradual increase from low numbers in June to a peak occurrence in late August, and thereafter declining numbers in September. Ae. epactius adults were collected repeatedly in BG-Sentinel traps in all four study communities; this is the first recorded collection of this species with a trap aiming specifically to collect human-biting mosquitoes. We also present the first description of the temporal abundance pattern for Ae. epactius across an elevation gradient: peak abundance was reached in mid-July in the lowest elevation community (Rio Blanco) but not until mid-September in the highest elevation one (Puebla City). Finally, we present data for meteorological conditions (mean temperature and rainfall) in the examined communities during the study period, and for a cumulative measure of the abundance of adults over the full sampling period.

This is the author accepted manuscript. The published version can be found here: http://dx.doi.org/10.1002/2013JD021227.

Due to the importance that the El Nino-Southern Oscillation (ENSO) has on rainfall over the tropical Americas, future changes in ENSO characteristics and teleconnections are important for regional hydroclimate. Projected changes to the ENSO mean state and characteristics, and the resulting impacts on rainfall anomalies over Central America, Colombia, and Ecuador during the twenty-first century are explored for several forcing scenarios using a suite of coupled atmosphere–ocean global climate models (AOGCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Mean-state warming of eastern tropical Pacific sea surface temperatures, drying of Central America and northern Colombia, and wetting of southwest Colombia and Ecuador are consistent with previous studies that used earlier versions of the AOGCMs. Current and projected future characteristics of ENSO (frequency, duration, amplitude) show a wide range of values across the various AOGCMs. The magnitude of ENSO-related rainfall anomalies are currently underestimated by most of the models, but the model ensembles generally simulate the correct sign of the anomalies across the seasons around the peak ENSO effects. While the models capture the broad present-day ENSO-related rainfall anomalies, there is not a clear sense of projected future changes in the precipitation anomalies.

Using a geographic transect in Central Mexico, with an elevation/climate gradient, but uniformity in socio-economic conditions among study sites, this study evaluates the applicability of three widely-used remote sensing (RS) products to link weather conditions with the local abundance of the dengue virus mosquito vector, Aedes aegypti (Ae. aegypti). Field-derived entomological measures included estimates for the percentage of premises with the presence of Ae. aegypti pupae and the abundance of Ae. aegypti pupae per premises. Data on mosquito abundance from field surveys were matched with RS data and analyzed for correlation. Daily daytime and nighttime land surface temperature (LST) values were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua cloud-free images within the four weeks preceding the field survey. Tropical Rainfall Measuring Mission (TRMM)-estimated rainfall accumulation was calculated for the four weeks preceding the field survey. Elevation was estimated through a digital elevation model (DEM). Strong correlations were found between mosquito abundance and RS-derived night LST, elevation and rainfall along the elevation/climate gradient. These findings show that RS data can be used to predict Ae. aegypti abundance, but further studies are needed to define the climatic and socio-economic conditions under which the correlations observed herein can be assumed to apply.

High spatial resolution urban climate modeling is essential for understanding urban climatology and predicting the human health impacts under climate change. Satellite thermal remote-sensing data are potential observational sources for urban climate model validation with comparable spatial scales, temporal consistency, broad coverage, and long-term archives. However, sensor view angle, cloud distribution, and cloud-contaminated pixels can confound comparisons between satellite land surface temperature (LST) and modeled surface radiometric temperature. The impacts of sensor view angles on urban LST values are investigated and addressed. Three methods to minimize the confounding factors of clouds are proposed and evaluated using 10 years of Moderate Resolution Imaging Spectroradiometer (MODIS) data and simulations from the High-Resolution Land Data Assimilation System (HRLDAS) over Greater Houston, Texas, U.S. For the satellite cloud mask (SCM) method, prior to comparison, the cloud mask for each MODIS scene is applied to its concurrent HRLDAS simulation. For the max/min temperature (MMT) method, the 50 warmest days and coolest nights for each data set are selected and compared to avoid cloud impacts. For the high clear-sky fraction (HCF) method, only those MODIS scenes that have a high percentage of clear-sky pixels are compared. The SCM method is recommended for validation of long-term simulations because it provides the largest sample size as well as temporal consistency with the simulations. The MMT method is best for comparison at the extremes. And the HCF method gives the best absolute temperature comparison due to the spatial and temporal consistency between simulations and observations.

Lyme disease (Borrelia burgdorferi infection) is the most common vector-transmitted disease in the United States. The majority of human Lyme disease (LD) cases occur in the summer months, but the timing of the peak occurrence varies geographically and from year to year. We calculated the beginning, peak, end, and duration of the main LD season in 12 highly endemic states from 1992 to 2007 and then examined the association between the timing of these seasonal variables and several meteorological variables. An earlier beginning to the LD season was positively associated with higher cumulative growing degree days through Week 20, lower cumulative precipitation, a lower saturation deficit, and proximity to the Atlantic coast. The timing of the peak and duration of the LD season were also associated with cumulative growing degree days, saturation deficit, and cumulative precipitation, but no meteorological predictors adequately explained the timing of the end of the LD season.

Dynamical downscaling is a computationally expensive method whereby finescale details of the atmosphere may be portrayed by running a limited area numerical weather prediction model (often called a regional climate model) nested within a coarse-resolution global reanalysis or global climate model output. The goal of this study is to assess using sampling techniques to dynamically downscale a small subset of days to approximate the statistical properties of the entire period of interest. Two sampling techniques are explored: one where days are randomly selected and another where representative days are chosen (or targeted) based on a set of selection criteria. The relative merit of using random sampling versus targeted random sampling is demonstrated using daily mean 2-m air temperature (T2M). The first two moments of dynamically downscaled T2M can be approximated within 0.3 K using just 5% of the population of available days during a 20-yr period. Targeted random sampling can reduce the mean absolute error of these estimates by as much as 30% locally. Estimation of the more extreme values of T2M is more uncertain and requires a larger sample size. The potential reduction in computational cost afforded by these sampling techniques could greatly benefit applications requiring high-resolution dynamically downscaled depictions of regional climate, including situations in which an ensemble of regional climate simulations is required to properly characterize uncertainty in the model physics assumptions, scenarios, and so on.

Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.

There is growing use of limited-area models (LAMs) for high-resolution (A variety of terrestrial (topography and land use) input dataset configurations is employed to explore the impact of Earth model assumptions on a series of 1-km LAM simulations over Colorado. For the same terrestrial datasets, the ~20-km geolocation discrepancy between spheroidal-versus-spherical Earth models over the domain leads to simulated differences in near-surface and midtropospheric air temperature, humidity, and wind speed that are larger and more widespread than those due to using different topography and land use datasets altogether but not changing the Earth model. Simulated differences are caused by the shift of static fields with respect to boundary conditions, and altered Coriolis forcing and topographic gradients.The sensitivity of high-resolution LAM simulations to Earth model assumptions emphasizes the importance for users to ensure terrestrial and meteorological input data are consistently mapped during preprocessing (i.e., datasets share a common geographic coordinate system before remapping to the LAM domain). Concurrently, the modeling community should update preprocessing systems to make sure input data are correctly mapped for all global and limited-area simulation domains.

This paper describes a new computationally efficient and statistically robust sampling method for generating dynamically downscaled climatologies. It is based on a Monte Carlo method coupled with stratified sampling. A small yet representative set of “case days” is selected with guidance from a large-scale reanalysis. When downscaled, the sample closely approximates the long-term meteorological record at a location, in terms of the probability density function. The method is demonstrated for the creation of wind maps to help determine the suitability of potential sites for wind energy farms. Turbine hub-height measurements at five U.S. and European tall tower sites are used as a proxy for regional climate model (RCM) downscaled winds to validate the technique. The tower-measured winds provide an independent test of the technique, since RCM-based downscaled winds exhibit an inherent dependence upon the large-scale reanalysis fields from which the case days are sampled; these same reanalysis fields would provide the boundary conditions to the RCM. The new sampling method is compared with the current approach widely used within the wind energy industry for creating wind resource maps, which is to randomly select 365 case days for downscaling, with each day in the calendar year being represented. The new method provides a more accurate and repeatable estimate of the long-term record of winds at each tower location. Additionally, the new method can closely approximate the accuracy of the current (365 day) industry approach using only a 180-day sample, which may render climate downscaling more tractable for those with limited computing resources.

The West Antarctic Ice Sheet is contributing to sea-level rise, but temperature trends in the region have remained uncertain. A complete temperature record for Byrd Station in central West Antarctica, spanning from 1958 to 2010, establishes West Antarctica as one of the fastest-warming regions globally.

Local knowledge of when humans are at elevated risk for exposure to tick vectors of human disease agents is required both for the effective use of personal protection measures to avoid tick bites and for implementation of control measures to suppress host-seeking ticks. Here, we used previously published data on the seasonal density of host-seeking Ixodes pacificus Cooley and Kohls nymphs, the primary vectors of Lyme disease spirochetes in the far western USA, collected across a broad habitat and climate gradient in northwestern California to identify predictors of periods of time within the year when questing nymphal density is elevated. Models based on calendar week alone performed similarly to models based on calendar week and woodland type, or meteorological variables. The most suitable model for a given application will depend on user objectives, timescale of interest, and the geographic extent of predictions. Our models sought not only to identify when seasonal host-seeking activity commences, but also when it diminishes to low levels. Overall, we report a roughly 5–7 month period in Mendocino County during which host-seeking nymphal densities exceed a low threshold value.

Dengue virus, primarily transmitted by the Aedes aegypti (L.) mosquito, has rapidly expanded in geographic extent over the past several decades. In some areas, however, dengue fever has not emerged despite established Ae. aegypti populations. The reasons for this are unclear and have sometimes been attributed to socio-economic differences. In 2013 we compared Ae. aegypti adult density and population age structure between two cities in Sonora, Mexico: Hermosillo, which has regular seasonal dengue virus transmission, and Nogales, which has minimal transmission. Larval and pupal abundance was greater in Nogales, and adult density was only higher in Hermosillo during September. Population age structure, however, was consistently older in Hermosillo. This difference in longevity may have been one factor that limited dengue virus transmission in Nogales in 2013, as a smaller proportion of Ae. aegypti females survived past the extrinsic incubation period.

Introduction: An ongoing Zika virus pandemic in Latin America and the Caribbean has raised concerns that travel-related introduction of Zika virus could initiate local transmission in the United States (U.S.) by its primary vector, the mosquito Aedes aegypti. Methods: We employed meteorologically driven models for 2006-2015 to simulate the potential seasonal abundance of adult Aedes aegypti for fifty cities within or near the margins of its known U.S. range. Mosquito abundance results were analyzed alongside travel and socioeconomic factors that are proxies of viral introduction and vulnerability to human-vector contact. Results: Meteorological conditions are largely unsuitable for Aedes aegypti over the U.S. during winter months (December-March), except in southern Florida and south Texas where comparatively warm conditions can sustain low-to-moderate potential mosquito abundance. Meteorological conditions are suitable for Aedes aegypti across all fifty cities during peak summer months (July-September), though the mosquito has not been documented in all cities. Simulations indicate the highest mosquito abundance occurs in the Southeast and south Texas where locally acquired cases of Aedes-transmitted viruses have been reported previously. Cities in southern Florida and south Texas are at the nexus of high seasonal suitability for Aedes aegypti and strong potential for travel-related virus introduction. Higher poverty rates in cities along the U.S.-Mexico border may correlate with factors that increase human exposure to Aedes aegypti. Discussion: Our results can inform baseline risk for local Zika virus transmission in the U.S. and the optimal timing of vector control activities, and underscore the need for enhanced surveillance for Aedes mosquitoes and Aedes-transmitted viruses.

Foehn events over the McMurdo Dry Valleys (MDVs), the largest ice-free region of Antarctica, promote glacial melt that supports biological activity in the lakes, streams, rocks and soils. Although MDVs foehn events are known to depend upon the synoptic-scale circulation, the physical processes responsible for foehn events are unknown. A polar-optimized version of the Weather Research and Forecasting model (Polar WRF) is used for a case study of a representative summer foehn event from 29 December 2006 to 1 January 2007 in order to identify and explain the MDVs foehn mechanism. Pressure differences across an elevated mountain gap upstream of the MDVs provide forcing for southerly flow into the western, upvalley entrance of the MDVs. Complex terrain over the elevated gap and the MDVs leads to mountain wave effects such as leeside acceleration, hydraulic jumps, wave breaking and critical layers. These mountain wave effects depend on the ambient (geostrophic) wind direction. Pressure-driven channelling then brings the warm, dry foehn air downvalley to eastern MDV sites. Brief easterly intrusions of maritime air into the eastern MDVs during foehn events previously have been attributed to either a sea-breeze effect in summer or local cold-pooling effects in winter. In this particular case, the easterly intrusions result from blocking effects of nearby Ross Island and the adjacent Antarctic coast. Temperature variability during the summer foehn event, which is important for meltwater production and biological activity when it exceeds 0°C, primarily depends on the source airmass rather than differences in foehn dynamics.