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Plum Rains Season (PRS) has the typical characteristics of outdoor air temperature dramatic changes and high air humidity in the hot summer and cold winter region in China. When the outdoor temperature rises rapidly during PRS, the building envelope surface temperature is probably lower than the indoor air dew point temperature, resulting in moisture condensation. This paper evaluates the influence of geographical location and outdoor meteorological parameters on the indoor humidity environment. The effects of critical parameters such as altitude, average temperature, relative humidity, total precipitation, total precipitation days, atmospheric pressure, and wind speed on the building envelope moisture condensation in nine typical cities in the hot summer and cold winter region were simulated and analyzed. The results show that the Condensation Frequency (CFn) in the western, central, and eastern regions reached the highest in April, May, and June, respectively. Among the nine typical cities, Changsha has the highest Condensation Risk (CR). In addition, the altitude, total precipitation, and atmospheric pressure have little effect on the indoor humidity environment, and it is not directly related to CR; The average temperature and total precipitation days were not related to CR in the western and eastern regions and positively correlated with CR in the central region; The wind speed was positively correlated with CR in the western and central regions and negatively correlated in the eastern region; The relative humidity can affect the indoor humidity environment and moisture condensation on the inner surface of walls, when the relative humidity increases, the CR increases. [ABSTRACT FROM AUTHOR]

Black ice, a phenomenon that occurs abruptly owing to freezing rain, is difficult for drivers to identify because it mirrors the color of the road. Effectively managing the occurrence of unforeseen accidents caused by black ice requires predicting their probability using spatial, weather, and traffic factors and formulating appropriate countermeasures. Among these factors, weather and traffic exhibit the highest levels of uncertainty. To address these uncertainties, a study was conducted using a Monte Carlo simulation based on random values to predict the probability of black ice accidents at individual road points and analyze their trigger factors. We numerically modeled black ice accidents and visualized the simulation results in a geographical information system (GIS) by employing a sensitivity analysis, another feature of Monte Carlo simulations, to analyze the factors that trigger black ice accidents. The Monte Carlo simulation allowed us to map black ice accident occurrences at each road point on the GIS. The average black ice accident probability was found to be 0.0058, with a standard deviation of 0.001. Sensitivity analysis using Monte Carlo simulations identified wind speed, air temperature, and angle as significant triggers of black ice accidents, with sensitivities of 0.354, 0.270, and 0.203, respectively. We predicted the probability of black ice accidents per road section and analyzed the primary triggers of black ice accidents. The scientific contribution of this study lies in the development of a method beyond simple road temperature predictions for evaluating the risk of black ice occurrences and subsequent accidents. By employing Monte Carlo simulations, the probability of black ice accidents can be predicted more accurately through decoupling meteorological and traffic factors over time. The results can serve as a reference for government agencies, including road traffic authorities, to identify accident-prone spots and devise strategies focused on the primary triggers of black ice accidents. [ABSTRACT FROM AUTHOR]

Icy bridge deck in winter has tremendous consequences for expressway traffic safety, which is closely related to the bridge pavement temperature. In this paper, the critical meteorological conditions of icy bridge deck were predicted by multiple linear regression and BP neural network respectively. Firstly, the main parameters affecting the bridge pavement temperature were determined by Pearson partial correlation analysis based on the three-year winter meteorological data of the traffic meteorological monitoring station on the bridge in Shandong province. Secondly, the bridge pavement temperature is selected as the dependent variable, while air temperature, wind speed, relative humidity, dew point temperature, wet bulb temperature and wind cold temperature were selected as independent variables, and the bridge pavement temperature prediction models of linear regression and 5-layer hidden layer classical BP neural network regression were established respectively based on whether the variables are linear or not. Finally, the prediction accuracy of the above models was compared by using the measured data. The results show that the linear regression model could be established only with air temperature, relative humidity and wind speed, owing to collinearity problem. Compared with multiple linear regression model, the predicted value of the BP neural network has a higher degree of fitting with the measured data, and the coefficient of determination reaches 0.7929. Using multiple linear regression and BP neural network, the critical meteorological conditions of bridge deck icing in winter can be effectively predicted even when the sample size is insufficient. [ABSTRACT FROM AUTHOR]

Within the context of anthropogenic climate change, but also considering the Earth’s natural climate variability, this paper explores the speculative possibility of large-scale active control of the Earth’s radiative forcing. In particular, the paper revisits the concept of deploying a large sunshade or occulting disk at a static position near the Sun-Earth L1 Lagrange equilibrium point. Among the solar radiation management methods that have been proposed thus far, space-based concepts are generally seen as the least timely, albeit also as one of the most efficient. Large occulting structures could potentially offset all of the global mean temperature increase due to greenhouse gas emissions. This paper investigates optimal configurations of orbiting occulting disks that not only offset a global temperature increase, but also mitigate regional differences such as latitudinal and seasonal difference of monthly mean temperature. A globally resolved energy balance model is used to provide insights into the coupling between the motion of the occulting disks and the Earth’s climate. This allows us to revise previous studies, but also, for the first time, to search for families of orbits that improve the efficiency of occulting disks at offsetting climate change on both global and regional scales. Although natural orbits exist near the L1 equilibrium point, their period does not match that required for geoengineering purposes, thus forced orbits were designed that require small changes to the disk attitude in order to control its motion. Finally, configurations of two occulting disks are presented which provide the same shading area as previously published studies, but achieve reductions of residual latitudinal and seasonal temperature changes. [ABSTRACT FROM AUTHOR]

We have developed and applied a relatively simple disaggregation scheme that uses spatial patterns of Land Surface Temperature (LST) from MODIS warm-season composites to improve the spatial characterization of daily maximum and minimum air temperatures. This down-scaling model produces qualitatively reasonable 1 km daily maximum and minimum air temperature estimates that reflect urban and coastal features. In a 5-city validation, the model was shown to provide improved daily maximum air temperature estimates in the three coastal cities, compared to 12 km NLDAS-2 (North American Land Data Assimilation System). Down-scaled maximum temperature estimates for the other two (non-coastal) cities were marginally worse than the original NLDAS-2 temperatures. For daily minimum temperatures, the scheme produces spatial fields that qualitatively capture geographic features, but quantitative validation shows the down-scaling model performance to be very similar to the original NLDAS-2 minimum temperatures. Thus, we limit the discussion in this paper to daily maximum temperatures. Overall, errors in the down-scaled maximum air temperatures are comparable to errors in down-scaled LST obtained in previous studies. The advantage of this approach is that it produces estimates of daily maximum air temperatures, which is more relevant than LST in applications such as public health. The resulting 1 km daily maximum air temperatures have great potential utility for applications such as public health, energy demand, and surface energy balance analyses. The method may not perform as well in conditions of strong temperature advection. Application of the model also may be problematic in areas having extreme changes in elevation. [ABSTRACT FROM AUTHOR]

Serial correlations within temperature time series serve as indicators of the temporal consistency of climate events. This study delves into the serial correlations embedded in global surface air temperature (SAT) data. Initially, we preprocess the SAT time series to eradicate seasonal patterns and linear trends, resulting in the SAT anomaly time series, which encapsulates the inherent variability of Earth's climate system. Employing diverse statistical techniques, we identify three distinct types of serial correlations: short-term, long-term, and nonlinear. To identify short-term correlations, we utilize the first-order autoregressive model, AR(1), revealing a global pattern that can be partially attributed to atmospheric Rossby waves in extratropical regions and the Eastern Pacific warm pool. For long-term correlations, we adopt the standard detrended fluctuation analysis, finding that the global pattern aligns with long-term climate variability, such as the El Niño-Southern Oscillation (ENSO) over the Eastern Pacific. Furthermore, we apply the horizontal visibility graph (HVG) algorithm to transform the SAT anomaly time series into complex networks. The topological parameters of these networks aptly capture the long-term correlations present in the data. Additionally, we introduce a novel topological parameter, Δσ, to detect nonlinear correlations. The statistical significance of this parameter is rigorously tested using the Monte Carlo method, simulating fractional Brownian motion and fractional Gaussian noise processes with a predefined DFA exponent to estimate confidence intervals. In conclusion, serial correlations are universal in global SAT time series and the presence of these serial correlations should be considered carefully in climate sciences. [ABSTRACT FROM AUTHOR]

Mitigating extreme heat in urban areas is beneficial and sometimes critical to human health. Thriving plant communities in community parks play an important role in mitigating extreme heat through providing cooling effect, while inevitably affecting how people perceive the benefits of using community parks for recreation. Thus, the impacts of plant communities on the thermal environment should be quantified to determine the optimal structure of the plant community. The goal would be to harmonize the functions of improving the thermal environment with the preferences people have related to the recreational benefits of plant communities with various levels of vegetation density. In this paper, the correlations between the structural characteristics of plant communities and their function in mitigating the thermal environment were investigated on calm summer days in Xincheng Central Park, Minhang District, Shanghai, China. In addition to analyzing the plant communities present and their effects on the park microclimate, a questionnaire was employed to determine the plant community preferences of recreational park users. The results showed that plant communities could reduce the air temperature by 1.23–2.42 °C and increase the relative humidity by 2.4–4.2% during the daytime. The microclimate conditions in plant communities with varying vegetation densities were significantly different. The canopy density and leaf area index primarily controlled the temperature reduction, while the canopy density and total canopy cover ratio primarily controlled the increase in humidity; meanwhile, these correlations varied at different times of the day. Moreover, most of the park users preferred a moderately dense plant community which met their environmental perceptions for recreation in parks. Age or education level variables of park users would also predict preferences for different plant community densities. Ultimately, one plant community pattern with appropriate canopy density (60%), leaf area index (≥3) and canopy cover ratio (total 0.80–1.20, with 0.6–0.75 for trees and 0.20–0.45 for shrubs/woodland area) was recommended, which would harmonize the functions of the mitigation of the thermal environment with most people’s perception of a desirable vegetation density. [ABSTRACT FROM AUTHOR]

Climate models project an increase in mean annual air temperatures and a reduction in the depth and duration of winter snowpack for many mid and high latitude and high elevation seasonally snow-covered ecosystems over the next century. The combined effects of these changes in climate will lead to warmer soils in the growing season and increased frequency of soil freeze-thaw cycles (FTCs) in winter due to the loss of a continuous, insulating snowpack. Previous experiments have warmed soils or removed snow via shoveling or with shelters to mimic projected declines in the winter snowpack. To our knowledge, no experiment has examined the interactive effects of declining snowpack and increased frequency of soil FTCs, combined with soil warming in the snow-free season on terrestrial ecosystems. In addition, none have mimicked directly the projected increase in soil FTC frequency in tall statured forests that is expected as a result of a loss of insulating snow in winter. We established the Climate Change Across Seasons Experiment (CCASE) at Hubbard Brook Experimental Forest in the White Mountains of New Hampshire in 2012 to assess the combined effects of these changes in climate on a variety of pedoclimate conditions, biogeochemical processes, and ecology of northern hardwood forests. This paper demonstrates the feasibility of creating soil FTC events in a tall statured ecosystem in winter to simulate the projected increase in soil FTC frequency over the next century and combines this projected change in winter climate with ecosystem warming throughout the snow-free season. Together, this experiment provides a new and more comprehensive approach for climate change experiments that can be adopted in other seasonally snow-covered ecosystems to simulate expected changes resulting from global air temperature rise. [ABSTRACT FROM AUTHOR]

Understanding the topographic basis for microclimatic variation remains fundamental to predicting the site level effects of warming air temperatures. Quantifying diurnal fluctuation and seasonal extremes in relation to topography offers insight into the potential relationship between site level conditions and changes in regional climate. The present study investigated an annual understory temperature regime for 50 sites distributed across a topographically diverse area (>12 km2) comprised of mixed evergreen-deciduous woodland vegetation typical of California coastal ranges. We investigated the effect of topography and tree cover on site-to-site variation in near-surface temperatures using a combination of multiple linear regression and multivariate techniques. Sites in topographically depressed areas (e.g., valley bottoms) exhibited larger seasonal and diurnal variation. Elevation (at 10 m resolution) was found to be the primary driver of daily and seasonal variations, in addition to hillslope position, canopy cover and northness. The elevation effect on seasonal mean temperatures was inverted, reflecting large-scale cold-air pooling in the study region, with elevated minimum and mean temperature at higher elevations. Additionally, several of our sites showed considerable buffering (dampened diurnal and seasonal temperature fluctuations) compared to average regional conditions measured at an on-site weather station. Results from this study help inform efforts to extrapolate temperature records across large landscapes and have the potential to improve our ecological understanding of fine-scale seasonal climate variation in coastal range environments. [ABSTRACT FROM AUTHOR]

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m-2 d-1 and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m-2 d-1 and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution. [ABSTRACT FROM AUTHOR]

Changes in soil temperature (ST) play an important role in the main mechanisms within the soil, including biological and chemical activities. For instance, they affect the microbial community composition, the speed at which soil organic matter breaks down and becomes minerals. Moreover, the growth and physiological activity of plants are directly influenced by the ST. Additionally, ST indirectly affects plant growth by influencing the accessibility of nutrients in the soil. Therefore, designing an efficient tool for ST estimating at different depths is useful for soil studies by considering meteorological parameters as input parameters, maximal air temperature, minimal air temperature, maximal air relative humidity, minimal air relative humidity, precipitation, and wind speed. This investigation employed various statistical metrics to evaluate the efficacy of the implemented models. These metrics encompassed the correlation coefficient (r), root mean square error (RMSE), Nash-Sutcliffe (NS) efficiency, and mean absolute error (MAE). Hence, this study presented several artificial intelligence-based models, MLPANN, SVR, RFR, and GPR for building robust predictive tools for daily scale ST estimation at 05, 10, 20, 30, 50, and 100cm soil depths. The suggested models are evaluated at two meteorological stations (i.e., Sulaimani and Dukan) located in Kurdistan region, Iraq. Based on assessment of outcomes of this study, the suggested models exhibited exceptional predictive capabilities and comparison of the results showed that among the proposed frameworks, GPR yielded the best results for 05, 10, 20, and 100cm soil depths, with RMSE values of 1.814°C, 1.652°C, 1.773°C, and 2.891°C, respectively. Also, for 50cm soil depth, MLPANN performed the best with an RMSE of 2.289°C at Sulaimani station using the RMSE during the validation phase. Furthermore, GPR produced the most superior outcomes for 10cm, 30cm, and 50cm soil depths, with RMSE values of 1.753°C, 2.270°C, and 2.631°C, respectively. In addition, for 05cm soil depth, SVR achieved the highest level of performance with an RMSE of 1.950°C at Dukan station. The results obtained in this research confirmed that the suggested models have the potential to be effectively used as daily predictive tools at different stations and various depths. [ABSTRACT FROM AUTHOR]

Mitigating the impacts of global warming on wildlife entails four practical steps. First, we need to study how processes of interest vary with temperature. Second, we need to build good temperature scenarios. Third, processes can be forecast accordingly. Only then can we perform the fourth step, testing mitigating measures. While having good temperature data is essential, this is not straightforward for stream ecologists and managers. Water temperature (WT) data are often short and incomplete and future projections are currently not routinely available. There is a need for generic models which address this data gap with good resolution and current models are partly lacking. Here, we expand a previously published hierarchical Bayesian model that was driven by air temperature (AT) and flow (Q) as a second covariate. The new model can hindcast and forecast WT time series at a daily time step. It also allows a better appraisal of real uncertainties in the warming of water temperatures in rivers compared to the previous version, stemming from its hybrid structure between time series decomposition and regression. This model decomposes all-time series using seasonal sinusoidal periodic signals and time varying means and amplitudes. It then links the contrasted frequency signals of WT (daily and six month) through regressions to that of AT and optionally Q for better resolution. We apply this model to two contrasting case study rivers. For one case study, AT only is available as a covariate. This expanded model further improves the already good fitting and predictive capabilities of its earlier version while additionally highlighting warming uncertainties. The code is available online and can easily be run for other temperate rivers. [ABSTRACT FROM AUTHOR]

We have used bias-corrected data from CMIP6 models to drive a regional climate model and project climate on the Tibetan Plateau (TP) in the 21st century. Changes in two background fields, namely, 2-meter air temperature and total precipitation, were analyzed. The results show that the WRF simulations capture the terrain effect that cannot be represented in low-resolution models. The simulation of temperature is better in summer than in winter, while the simulated precipitation is the opposite. By the end of the 21st century, the entire TP region experiences significant warming, with an average warming of 3°C and 7°C in the SSP245 and SSP585 scenarios, respectively. The western region shows a greater warming amplitude, with a maximum of more than 10°C in the SSP585 scenario. Most regions of the TP had significant increases in precipitation by the end of the 21st century, with precipitation increasing by 90 mm and 200 mm in the two scenarios, respectively. However, in the low-altitude areas of southeastern TP, total winter precipitation is significantly reduced in the SSP585 scenario. The strengthening of the East Asian summer monsoon and westerly disturbances collectively leads to a significant increase in precipitation within the TP region. By the end of the 21st century, the average annual precipitation in the TP is projected to reach approximately 600 millimeters. [ABSTRACT FROM AUTHOR]

Near-surface air temperature (Ta) is an important parameter in agricultural production and climate change. Satellite remote sensing data provide an effective way to estimate regional-scale air temperature. Therefore, taking Gansu section of the upper Weihe River Basin as the study area, using the filtered reconstructed high-quality long-time series normalized difference vegetation index (NDVI), interpolated reconstructed land surface temperature (LST), surface albedo, and digital elevation model (DEM) as the input data, the back-propagation artificial neural network algorithm (BP-ANN) was combined with a multiple linear regression method to estimate regional air temperature, and the influencing factors of air temperature estimation were analyzed. This method effectively compensates for the fact that air temperature data provided by a single station cannot represent regional air temperature information. The result shows that the temperature estimation accuracy is high. In terms of interannual variation, the air temperature in the study area showed a slightly increasing trend, with an average annual increase of 0.047°C. The calculation results of the interannual variation rate of temperature showed that the area with increased air temperature accounted for 75.8% of the total area. In terms of seasonal variation, compared with that in summer and winter, the air temperature rising trend in autumn was obvious, and the air temperature in the middle of the study area decreased in spring, which is prone to frost disasters. LST and NDVI in the study area were positively correlated with air temperature, and their positive correlation distribution areas accounted for 93.62% and 94.34% of the total study area, respectively. NDVI, LST and DEM influence the temperature change in the study area. The results show that there is a significant positive correlation between NDVI and air temperature, and the change of NDVI has a positive effect on the spatiotemporal variation of air temperature. The correlation coefficient between LST and air temperature in the southeast of the study area is negative, and there is a difference. In addition, the correlation coefficient between LST and air temperature in other areas of the study area is positive. The air temperature decreased with elevation, air temperature decreases by 0.27°C every hundred meters. [ABSTRACT FROM AUTHOR]

Impulse-cyclone drying (ICD) is a new type of pretreatment method to remove the excess moisture of wood fibers (WFs) with high speed and low energy consumption. However, the process parameters are often determined by the experience of the process operators, thus the quality of WF drying lacks an objective basis and cannot be ensured. To address this issue, this study adopted the long short-term memory (LSTM) neural network, backpropagation neural network, and Central-Composite response surface method to establish a moisture content (MC) prediction model and a process parameter optimization model based on single-factor experiments. The initial MC, inlet air temperature, feed rate, and inlet air velocity were taken as the experimental factors, and the final MC was taken as the inspection index. The parameters of LSTM were optimized by particle swarm optimization (PSO) algorithm, and the predicted value of MC was fitted to the model. The PSO-optimized LSTM had higher prediction accuracy than did the typical prediction models. The optimal process for the targeted MC, which was obtained by PSO, was featured with an initial MC of 10.3%, inlet air temperature of 242°C, feed rate of 90 kg/h, and inlet air velocity of 8 m/s. PSO-LSTM could be a new approach for predicting the MC of WFs, which, in turn, could provide a theoretical basis for the application of ICD technology in the biomass composite industry. [ABSTRACT FROM AUTHOR]

Our objective was to quantify the similarity in the meteorological measurements of 17 stations under three weather networks in the Alberta oil sands region. The networks were for climate monitoring under the water quantity program (WQP) and air program, including Meteorological Towers (MT) and Edge Sites (ES). The meteorological parameters were air temperature (AT), relative humidity (RH), solar radiation (SR), barometric pressure (BP), precipitation (PR), and snow depth (SD). Among the various measures implemented for finding correlations in this study, we found that the use of Pearson's coefficient (r) and absolute average error (AAE) would be sufficient. Also, we applied the percent similarity method upon considering at least 75% of the value in finding the similarity between station pairs. Our results showed that we could optimize the networks by selecting the least number of stations (for each network) to describe the measure-variability in meteorological parameters. We identified that five stations are sufficient for the measurement of AT, one for RH, five for SR, three for BP, seven for PR, and two for SD in the WQP network. For the MT network, six for AT, two for RH, six for SR, and four for PR, and the ES network requires six for AT, three for RH, six for SR, and two for BP. This study could potentially be critical to rationalize/optimize weather networks in the study area. [ABSTRACT FROM AUTHOR]

For ectotherms, behaviour and associated energetic costs are directly related to thermal conditions. In the present context of global change, estimating time-budget for these species is relevant to assess and predict their capacity to adapt to near future. We tested the hypothesis that in ectotherms where reproduction is highly energy consuming, energy expenditure should vary throughout the breeding season with a maximum around nesting events. To test this hypothesis, we assessed the fine-scale behaviour, time-budget and estimated energetic costs in eight adult female European pond turtles Emys orbicularis equipped with data-loggers recording ambient temperature, pressure, light and the animals' 3-axis acceleration. Deployments occurred over four months throughout the nesting season 2017 in semi-natural captive conditions in Alsace, France. All study turtles showed a clear daily pattern over the 24h cycle, with four distinct phases (referred to as Night, Morning, Midday and Evening), associated with different behaviours and activity levels. Before oviposition, turtles were mostly active during Morning, and activity was positively driven by ambient temperature. Activity levels doubled during the nesting period, mostly due to the increased activity in the Evening, when nesting events occurred. Throughout the active season, basking occurrence at Midday was related to air temperature but cloud coverage was an even more important factor. Our results are a first step in predicting the seasonal time and energy budgets of the European pond turtle, and demonstrate the usefulness of animal-borne accelerometers to study free living freshwater turtles over extended periods of time. [ABSTRACT FROM AUTHOR]

Morphological and physiological characteristics of phytoplankton cells are highly sensitive to changes in environmental conditions and, in turn, influence the dynamics of phytoplankton populations and communities. To cope with environmental change, trait variability and phenotypic plasticity may play an important role. Since global change comprises simultaneous changes in abiotic parameters, we assessed the impact of multiple drivers on functional traits of the diatom Thalassiosira (Conticribra) weissflogii by manipulating concurrently temperature, pCO2, and dissolved nitrogen:phosphorus (N:P) ratio. We tested three scenarios: ambient (ambient temperature and atmospheric pCO2; 16 N:P ratio), moderate future scenario (+1.5°C and 800 ppm CO2; 25 N:P ratio), and more severe future scenario (+3°C and 1000 ppm CO2; 25 N:P ratio). We applied flow cytometry to measure on single-cell levels to investigate trait variability and phenotypic plasticity within one strain of diatoms. Growth rates differed significantly between the treatments and were strongly correlated with cell size and cellular chlorophyll a content. We observed a negative correlation of growth rate with chlorophyll a variability among single strain populations and a negative correlation with the phenotypic plasticity of cell size, i.e. when growth rates were higher, the cell size cell-to-cell variability within cultures was lower. Additionally, the phenotypic plasticity in cell size was lower under the global change scenarios. Overall, our study shows that multiple traits are interlinked and driven by growth rate and that this interconnection may partly be shaped by environmental factors. [ABSTRACT FROM AUTHOR]

We present an exploratory cross-sectional analysis of the effect of season and weather on Freesurfer-derived brain volumes from a sample of 3,279 healthy individuals collected on two MRI scanners in Hartford, CT, USA over a 15 year period. Weather and seasonal effects were analyzed using a single linear regression model with age, sex, motion, scan sequence, time-of-day, month of the year, and the deviation from average barometric pressure, air temperature, and humidity, as covariates. FDR correction for multiple comparisons was applied to groups of non-overlapping ROIs. Significant negative relationships were found between the left- and right- cerebellum cortex and pressure (t = -2.25, p = 0.049; t = -2.771, p = 0.017). Significant positive relationships were found between left- and right- cerebellum cortex and white matter between the comparisons of January/June and January/September. Significant negative relationships were found between several subcortical ROIs for the summer months compared to January. An opposing effect was observed between the supra- and infra-tentorium, with opposite effect directions in winter and summer. Cohen's d effect sizes from monthly comparisons were similar to those reported in recent psychiatric big-data publications, raising the possibility that seasonal changes and weather may be confounds in large cohort studies. Additionally, changes in brain volume due to natural environmental variation have not been reported before and may have implications for weather-related and seasonal ailments. [ABSTRACT FROM AUTHOR]

Arctic and boreal environments are changing rapidly, which could decouple behavioral and demographic traits of animals from the resource pulses that have shaped their evolution. Dall's sheep (Ovis dalli dalli) in northwestern regions of the USA and Canada, survive long, severe winters and reproduce during summers with short growing seasons. We sought to understand the vulnerability of Dall's sheep to a changing climate in Lake Clark National Park and Preserve, Alaska, USA. We developed ecological hypotheses about nutritional needs, security from predators, energetic costs of movement, and thermal shelter to describe habitat selection during winter, spring, and summer and evaluated habitat and climate variables that reflected these hypotheses. We used the synoptic model of animal space use to estimate parameters of habitat selection by individual females and calculated likelihoods for ecological hypotheses within seasonal models. Our results showed that seasonal habitat selection was influenced by multiple ecological requirements simultaneously. Across all seasons, sheep selected steep rugged areas near escape terrain for security from predators. During winter and spring, sheep selected habitats with increased forage and security, moderated thermal conditions, and lowered energetic costs of movement. During summer, nutritional needs and security influenced habitat selection. Climate directly influenced habitat selection during the spring lambing period when sheep selected areas with lower snow depths, less snow cover, and higher air temperatures. Indirectly, climate is linked to the expansion of shrub/scrub vegetation, which was significantly avoided in all seasons. Dall's sheep balance resource selection to meet multiple needs across seasons and such behaviors are finely tuned to patterns of phenology and climate. Direct and indirect effects of a changing climate may reduce their ability to balance their needs and lead to continued population declines. However, several management approaches could promote resiliency of alpine habitats that support Dall's sheep populations. [ABSTRACT FROM AUTHOR]

Winter activities on ice are culturally important for many countries, yet they constitute a high safety risk depending upon the stability of the ice. Because consistently cold periods are required to form stable and thick ice, warmer winters could degrade ice conditions and increase the likelihood of falling through the ice. This study provides the first large-scale assessment of winter drowning from 10 Northern Hemisphere countries. We documented over 4000 winter drowning events. Winter drownings increased exponentially in regions with warmer winters when air temperatures neared 0°C. The largest number of drownings occurred when winter air temperatures were between -5°C and 0°C, when ice is less stable, and also in regions where indigenous traditions and livelihood require extended time on ice. Rates of drowning were greatest late in the winter season when ice stability declines. Children and adults up to the age of 39 were at the highest risk of winter drownings. Beyond temperature, differences in cultures, regulations, and human behaviours can be important additional risk factors. Our findings indicate the potential for increased human mortality with warmer winter air temperatures. Incorporating drowning prevention plans would improve adaptation strategies to a changing climate. [ABSTRACT FROM AUTHOR]

The quality attributes of jujube fruit can be directly and indirectly affected by abiotic stresses associated with climate change. Increased temperature and drought are among the most important factors challenging sustainable jujube production in the temperate semi-arid region in northwest China. The main objective of the present study was to understand the effects of elevated air temperature and drought stress on sugar and acid accumulation and coloration of jujube fruits. The content of soluble sugar, organic acid and pigments of traditional jujube "Linwuchangzao" under different atmospheric temperatures and drought stresses were analyzed during three different fruit ripening stages. The elevated temperature (1.5–2.5° C than normal temperature) significantly increased the fruit sugar content, sugar-acid ratio, anthocyanins, flavonoids and carotenoids content. Under the drought stress where the soil moisture was 30% -50% of the field capacity, sugar content, anthocyanin, flavonoid and carotenoid content of the fruit were significantly reduced at the same temperature, but the chlorophyll and organic acid content increased. No significant interaction of Temperature x Drought was observed for all the analyzed quality parameters. The current results showed that the fruit quality of jujube variety "Lingwuchangzao" could be improved when the atmospheric temperature increases by 2° C in this region. However, drought stress had a negative impact on the fruit's sugar-acid ratio and pigment content. The present results also showed that the synthesis and accumulation of anthocyanins in jujube fruit were positively correlated with sugar content and related enzyme activities, especially Phenylalanine Ammonia-lyase (PAL) activity. This study, therefore, provides novel information for understanding the influence of growth environment on the quality properties of jujube fruits. This knowledge will help develop appropriate crop management practices for jujube production in arid and semi-arid areas in northwest China. [ABSTRACT FROM AUTHOR]

Organ temperature and variation therein plays a key role in plant functioning and its responses to e.g. climate change. There is a strong feedback between organ, especially leaf, temperature and the climate within the canopy (canopy climate), which in turn interacts with the climate outside the canopy (ambient climate). For greenhouses, the determinants of this interplay and how they drive differences between canopy and ambient climate are poorly understood. Yet, as many experiments on both regular greenhouse crops and field crops are done in greenhouses, this is crucial to know. Therefore, we designed an experiment to quantify the differences between ambient and canopy climate and leaf temperature. A path analysis was performed to quantify the interactions between components of the greenhouse canopy-climate system. We found that with high radiation the canopy climate can be up to 5°C cooler than the ambient climate, while for cloudy days this was only 2°C. Canopy relative humidity (RH) was up to 25% higher compared to ambient RH. We showed that radiation is very important for these climate differences, but that this effect could be partly counteracted by turning off supplementary light (i.e. due to its indirect effects e.g. changing light distribution). Leaf temperature was substantially different, both higher and lower, from the canopy air temperature. This difference was determined by leaf area index (LAI), temperature of the heating pipe and the use of supplementary light, which all strongly influence radiation, either shortwave or thermal radiation. The difference between leaf and ambient air temperature could be decreased by decreasing the LAI or increasing the temperature of the heating pipe. [ABSTRACT FROM AUTHOR]

Warmer winters may lead to changes in the hibernation behaviour of bats, such as the barbastelle Barbastella barbastellus, which prefers to hibernate at low temperatures. The species is also known for its large annual fluctuations in the number of wintering individuals, so inference about population trends should be based on long-term data. Prior to 2005, analyses indicated stable or even increasing barbastelle population in Poland. We analysed the results of 13 winter bat counts (2005–2017) of the species from 15 of the largest hibernacula, and additional site of 47 small bunkers, in Poland. The total number of wintering individuals remained stable during the study period, because the barbastelle is not a long-distance migrant, this likely reflects the national population trend. On the basis of mean winter air temperatures we divided the country into four thermal regions. Analyses of barbastelle abundance in hibernacula in the four regions revealed a 4.8% annual mean increase in numbers in the coldest region, where mean winter temperatures were below -2°C, annual mean declines of 3.3% and 3.1% in two warmer regions of western Poland, but no trend in the region of intermediate mean winter temperatures of between -1°C and -2°C. Overall, there was a significant, but weak, negative correlation between the abundance of hibernating individuals and the mean winter temperature. On the other hand, the number of individuals hibernating in small bunkers increased, even though the site was located in one of the warm regions. The results indicate a warming climate will likely reduce the use of large, well-insulated winter roosts by species that prefer colder conditions–and that this is already happening. For forest-dwelling bats, such as the barbastelle, for which monitoring schemes are primarily based on winter surveys of large hibernacula, estimations of population trends may consequently become less reliable. [ABSTRACT FROM AUTHOR]

Similarity in community composition declines as distance between locations increases, a phenomenon that has been observed in a wide variety of freshwater, marine and terrestrial ecosystems. One driver of the distance-similarity relationship is the presence of environmental gradients that alter the suitability of sites for particular species. Although some environmental gradients, such as geology, do not change on a year-to-year basis, others, such as temperature, vary annually and over longer time periods. Here, we used a 21-year dataset of fish communities in the upper Mississippi River to examine the effect of distance on variation in community composition and to assess whether the effect of distance is primarily due to its effect on thermal regime. Because the Mississippi River is aligned mostly north-to-south, larger distances along the river roughly correspond to larger differences in latitude and therefore thermal regime. As expected, there was a moderate distance-similarity relationship, suggesting greater distance leads to less similarity. The effect of distance appeared to increase slightly over time. Using a subset of data for which air temperature was available, we compared models that incorporated both difference among sites in degree days (a surrogate for thermal regime) and physical distance (river km). Although physical distance presumably incorporates more environmental gradients than just temperature (and other potential mechanisms), temperature alone appears to be more strongly associated with differences in the Mississippi River fish community than distance. [ABSTRACT FROM AUTHOR]

The objective was to evaluate vaginal temperature as a thermoregulatory response of Nellore heifers under different microclimatic conditions. The study was conducted during one year in an experimental area located at 54°37′W, 20°27′S, and 530 m of altitude in Brazil. Twenty-four Nellore females were reared in integrated crop-livestock-forestry systems with different shading patterns. The randomized complete block experimental design was utilized with a sub subdivided plots scheme, with plot corresponding to the production systems, the subplot to the season of the year, and the sub subplot, the hours of the day. To characterize the microclimate, data regarding air and black globe temperatures, and air relative humidity were collected and from them, temperature-humidity index was calculated. Vaginal temperature was obtained by using a bottom-type temperature logger (i-button) attached to an adapted intravaginal device. The black-globe temperature explained the variation on vaginal temperature. Increases on vaginal temperature were only observed, on average, two hours after the increase of black-globe temperature. The microclimatic conditions found in the systems, resulting from the various tree densities, modify the vaginal temperature in different degrees, demonstrating that the shading effect is not always accompanied by improvements in thermal comfort. The system with intermediate density showed a better microclimatic condition and, consequently, a lower increase in vaginal temperature. The interaction between air temperature, humidity and solar radiation resulted in adverse environmental conditions, however, Nellore heifers showed good adaptation to the environment. In conclusion, vaginal temperature is a good indicator to evaluate the thermoregulatory response in Nellore heifers. [ABSTRACT FROM AUTHOR]

Air temperature data retrieved from global atmospheric models may show a systematic bias with respect to measurements from weather stations. This is a common concern in local climate studies. The current study presents two methods based upon copulas and Conditional Probability (CP) to predict bias-corrected mean air temperature in a data-scarce environment: CP-I offers a single conditional probability as a predictor, CP-II is a pixel-wise version of CP-I and offers spatially varying predictors. The methods were applied on daily air temperature in the Qazvin Plain, Iran that were collected at 24 weather stations and 150 ECMWF ERA-interim grid cells from a single month (June) over 11 years. We compared the methods with the commonly applied conditional expectation and conditional median methods. Leave-k-out cross-validation and correlation scores show that the new methods reduced the bias with 44–68% for the full data set and with 34–74% on a homogeneous subarea. We conclude that the two methods are able to locally improve ECMWF air temperatures in a data-scarce area. [ABSTRACT FROM AUTHOR]

Surface incoming shortwave (solar) radiation data are an important component of many scientific analyses, but direct measurements are not commonly available. Estimates can be obtained from gridded meteorological analysis or reanalysis systems, such as the Global Data Assimilation Systems (GDAS) and Modern Era Retrospective Reanalysis System (MERRA-2), or calculated using empirical models dependent on meteorological variables such as air temperature. The purpose of this analysis was to compare multiple methods for estimating daily shortwave radiation in a tropical highland environment in Ethiopia. Direct solar radiation outputs of GDAS and MERRA-2, topographically corrected outputs of the two analysis systems, and empirically estimated solar radiation values calculated with the systems’ air temperature data were compared to see which produced the most reliable radiation values. GDAS appeared to underestimate the seasonal variability, resulting in low correlation (R2) with in situ data and large mean bias error (MBE). In comparison, MERRA-2 did not underestimate variability, but produced larger bias than the empirical model estimates. There was an improvement in correlation and reduction in MBE when using the GDAS air temperature predictions in the empirical model, but the opposite was true for MERRA-2. The empirical model using station air temperature data (stationT) produced the highest correlation across all four stations, with best performance at the lower elevation sites. The direct shortwave radiation outputs of MERRA-2 produced comparable correlation values, with larger R2 at stations at higher elevation. Topography possibly influenced these results, as MERRA-2 performed comparably to stationT at the stations in moderate terrain, but not in steeper terrain. This work can serve as a starting point for analyses in other tropical highland regions, where continuous in situ solar radiation data are rarely available. [ABSTRACT FROM AUTHOR]

In this study, experiments are conducted to gauge the relative importance of model, initial condition, and driving climate uncertainty for simulations of malaria transmission at a highland plantation in Kericho, Kenya. A genetic algorithm calibrates each of these three factors within their assessed prior uncertainty in turn to see which allows the best fit to a timeseries of confirmed cases. It is shown that for high altitude locations close to the threshold for transmission, the spatial representativeness uncertainty for climate, in particular temperature, dominates the uncertainty due to model parameter settings. Initial condition uncertainty plays little role after the first two years, and is thus important in the early warning system context, but negligible for decadal and climate change investigations. Thus, while reducing uncertainty in the model parameters would improve the quality of the simulations, the uncertainty in the temperature driving data is critical. It is emphasized that this result is a function of the mean climate of the location itself, and it is shown that model uncertainty would be relatively more important at warmer, lower altitude locations. [ABSTRACT FROM AUTHOR]

Warming temperatures associated with climate change can have indirect effects on migratory birds that rely on seasonally available food resources and habitats that vary across spatial and temporal scales. We used two heat-based indices of spring onset, the First Leaf Index (FLI) and the First Bloom Index (FBI), as proxies of habitat change for the period 1901 to 2012 at three spatial scales: the US National Wildlife Refuge System; the four major bird migratory flyways in North America; and the seasonal ranges (i.e., breeding and non-breeding grounds) of two migratory bird species, Blue-winged Warbler (Vermivora cyanoptera) and Whooping Crane (Grus americana). Our results show that relative to the historical range of variability, the onset of spring is now earlier in 76% of all wildlife refuges and extremely early (i.e., exceeding 95% of historical conditions) in 49% of refuges. In all flyways but the Pacific, the rate of spring advance is generally greater at higher latitudes than at lower latitudes. This differential rate of advance in spring onset is most pronounced in the Atlantic flyway, presumably because of a “warming hole” in the southeastern US. Both FLI and FBI have advanced markedly in the breeding ranges–but not the non-breeding ranges–of the two selected bird species, albeit with considerable intra-range variation. Differences among species in terms of migratory patterns and the location and extent of seasonal habitats, as well as shifts in habitat conditions over time, may complicate predictions of the vulnerability of migratory birds to climate change effects. This study provides insight into how differential shifts in the phenology of disparate but linked habitats could inform local- to landscape-scale management strategies for the conservation of migratory bird populations. [ABSTRACT FROM AUTHOR]

Climate change has caused shifts in the phenology and distributions of many species but comparing responses across species is challenged by inconsistencies in the methodology and taxonomic and temporal scope of individual studies. Natural history collections offer a rich source of data for examining phenological shifts for a large number of species. We paired specimen records from Pacific Northwest insect collections to climate data to analyze the responses of 215 moth species to interannual climate variation over a period of 119 years (1895–2013) during which average annual temperatures have increased in the region. We quantified the effects of late winter/early spring temperatures, averaged annually across the region, on dates of occurrence of adults, taking into account the effects of elevation, latitude, and longitude. We assessed whether species-specific phenological responses varied with adult flight season and larval diet breadth. Collection dates were significantly earlier in warmer years for 36.3% of moth species, and later for 3.7%. Species exhibited an average phenological advance of 1.9 days/°C, but species-specific shifts ranged from an advance of 10.3 days/°C to a delay of 10.6 days/°C. More spring-flying species shifted their phenology than summer- or fall-flying species. These responses did not vary among groups defined by larval diet breadth. The highly variable phenological responses to climate change in Pacific Northwest moths agree with other studies on Lepidoptera and suggest that it will remain difficult to accurately forecast which species and ecological interactions are most likely to be affected by climate change. Our results also underscore the value of natural history collections as windows into long-term ecological trends. [ABSTRACT FROM AUTHOR]

Climate change projections show that temperature and precipitation increases can alter the exchange of greenhouse gases between the atmosphere and high latitude landscapes, including their freshwaters. Dissolved organic carbon (DOC) plays an important role in greenhouse gas emissions, but the impact of catchment productivity on DOC release to subarctic waters remains poorly known, especially at regional scales. We test the hypothesis that increased terrestrial productivity, as indicated by the normalized difference vegetation index (NDVI), generates higher stream DOC concentrations in the Stordalen catchment in subarctic Sweden. Furthermore, we aimed to determine the degree to which other generic catchment properties (elevation, slope) explain DOC concentration, and whether or not land cover variables representing the local vegetation type (e.g., mire, forest) need to be included to obtain adequate predictive models for DOC delivered into rivers. We show that the land cover type, especially the proportion of mire, played a dominant role in the catchment’s release of DOC, while NDVI, slope, and elevation were supporting predictor variables. The NDVI as a single predictor showed weak and inconsistent relationships to DOC concentrations in recipient waters, yet NDVI was a significant positive regulator of DOC in multiple regression models that included land cover variables. Our study illustrates that vegetation type exerts primary control in DOC regulation in Stordalen, while productivity (NDVI) is of secondary importance. Thus, predictive multiple linear regression models for DOC can be utilized combining these different types of explanatory variables. [ABSTRACT FROM AUTHOR]

Understanding how climate change affects host-parasite systems and predicting the consequences for ecosystems, economies, and human health has emerged as an important task for science and society. Some basic insight into this complex problem can be gained by comparing the thermal physiology of interacting host and parasite species. In this study, we compared upper thermal tolerance among three component species in a natural host-parasitoid-hyperparasitoid system from Virginia, USA. To assess the ecological relevance of our results, we also examined a record of maximum daily air temperatures collected near the study site in the last 124 years. We found that the caterpillar host Manduca sexta had a critical thermal maximum (CTmax) about 4°C higher than the parasitic wasp, Cotesia congregata, and the hyperparasitic wasp, Conura sp., had a CTmax about 6°C higher than its host, C. congregata. We also found significant differences in CTmax among instars and between parasitized and non-parasitized M. sexta. The highest maximum daily air temperature recorded near the study in the last 124 years was 42°C, which equals the average CTmax of one species (C. congregata) but is several degrees lower than the average CTmax of the other two species (M. sexta, Conura sp.) in this study. Our results combined with other studies suggest that significant differences in thermal performance within and among interacting host and parasite species are common in nature and that climate change may be largely disruptive to these systems with responses that are highly variable and complex. [ABSTRACT FROM AUTHOR]

The Earth′s climate is warming, especially in the mid- and high latitudes of the Northern Hemisphere. The northern elephant seal (Mirounga angustirostris) breeds and haul-outs on islands and the mainland of Baja California, Mexico, and California, U.S.A. At the beginning of the 21st century, numbers of elephant seals in California are increasing, but the status of Baja California populations is unknown, and some data suggest they may be decreasing. We hypothesize that the elephant seal population of Baja California is experiencing a decline because the animals are not migrating as far south due to warming sea and air temperatures. Here we assessed population trends of the Baja California population, and climate change in the region. The numbers of northern elephant seals in Baja California colonies have been decreasing since the 1990s, and both the surface waters off Baja California and the local air temperatures have warmed during the last three decades. We propose that declining population sizes may be attributable to decreased migration towards the southern portions of the range in response to the observed temperature increases. Further research is needed to confirm our hypothesis; however, if true, it would imply that elephant seal colonies of Baja California and California are not demographically isolated which would pose challenges to environmental and management policies between Mexico and the United States. [ABSTRACT FROM AUTHOR]

From 2007 to 2013, the southeastern Bering Sea was dominated by extensive sea ice and below-average ocean temperatures. In 2014 there was a shift to reduced sea ice on the southern shelf and above-average ocean temperatures. These conditions continued in 2015 and 2016. During these three years, the spring bloom at mooring site M4 (57.9°N, 168.9°W) occurred primarily in May, which is typical of years without sea ice. At mooring site M2 (56.9°N, 164.1°W) the spring bloom occurred earlier especially in 2016. Higher chlorophyll fluorescence was observed at M4 than at M2. In addition, these three warm years continued the pattern near St. Matthew Island of high concentrations (>1 μM) of nitrite occurring during summer in warm years. Historically, the dominant parameters controlling sea-ice extent are winds and air temperature, with the persistence of frigid, northerly winds in winter and spring resulting in extensive ice. After mid-March 2014 and 2016 there were no cold northerly or northeasterly winds. Cold northerly winds persisted into mid-April in 2015, but did not result in extensive sea ice south of 58°N. The apparent mechanism that helped limit ice on the southeastern shelf was the strong advection of warm water from the Gulf of Alaska through Unimak Pass. This pattern has been uncommon, occurring in only one other year (2003) in a 37-year record of estimated transport through Unimak Pass. During years with no sea ice on the southern shelf (e.g. 2001–2005, 2014–2016), the depth-averaged temperature there was correlated to the previous summers ocean temperature. [ABSTRACT FROM AUTHOR]

The persistence of atmospheric circulation anomalies over East Asia shows a winter to winter recurrence (WTWR) phenomenon. Seasonal variations in sea level pressure anomalies and surface wind anomalies display significantly different characteristics between WTWR and non-WTWR years. The WTWR years are characterized by the recurrence of both a strong (weak) anomalous Siberian High and an East Asian winter monsoon over two successive winters without persistence through the intervening summer. However, anomalies during the non-WTWR years have the opposite sign between the current and ensuing winters. The WTWR of circulation anomalies contributes to that of surface air temperature anomalies (SATAs), which is useful information for improving seasonal and interannual climate predictions over East Asia and China. In the positive (negative) WTWR years, SATAs are cooler (warmer) over East Asia in two successive winters, but the signs of the SATAs are opposite in the preceding and subsequent winters during the non-WTWR years. [ABSTRACT FROM AUTHOR]

Several studies on global change over the next century predict increases in mean air temperatures of between 1°C to 5°C that would affect not only water temperature but also river flow. Climate is the predominant environmental driver of thermal and flow regimes of freshwater ecosystems, determining survival, growth, metabolism, phenology and behaviour as well as biotic interactions of aquatic fauna. Thus, these changes would also have consequences for species phenology, their distribution range, and the composition and dynamics of communities. These effects are expected to be especially severe in the Mediterranean basin due its particular climate conditions, seriously threatening Southern European ecosystems. In addition, species with restricted distributions and narrow ecological requirements, such as those living in the headwaters of rivers, will be severely affected. The study area corresponds to the Spanish Mediterranean and Balearic Islands, delimited by the Köppen climate boundary. With the application of the MEDPACS (MEDiterranean Prediction And Classification System) predictive approach, the macroinvertebrate community was predicted for current conditions and compared with three posible scenarios of watertemperature increase and its associated water flow reductions. The results indicate that the aquatic macroinvertebrate communities will undergo a drastic impact, with reductions in taxa richness for each scenario in relation to simulated current conditions, accompanied by changes in the taxa distribution pattern. Accordingly, the distribution area of most of the taxa (65.96%) inhabiting the mid-high elevations would contract and rise in altitude. Thus, families containing a great number of generalist species will move upstream to colonize new zones with lower water temperatures. By contrast, more vulnerable taxa will undergo reductions in their distribution area. [ABSTRACT FROM AUTHOR]

Understanding the warming trends at local level is critical; and, the development of relevant adaptation and mitigation policies at those levels are quite challenging. Here, our overall goal was to generate local warming trend map at 1 km spatial resolution by using: (i) Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composite surface temperature data; (ii) weather station-based yearly average air temperature data; and (iii) air temperature normal (i.e., 30 year average) data over the Canadian province of Alberta during the period 1961–2010. Thus, we analysed the station-based air temperature data in generating relationships between air temperature normal and yearly average air temperature in order to facilitate the selection of year-specific MODIS-based surface temperature data. These MODIS data in conjunction with weather station-based air temperature normal data were then used to model local warming trends. We observed that almost 88% areas of the province experienced warming trends (i.e., up to 1.5°C). The study concluded that remote sensing technology could be useful for delineating generic trends associated with local warming. [ABSTRACT FROM AUTHOR]

Using an extensive network of occurrence records for 293 plant species collected over the past 40 years across a climatically diverse geographic section of western North America, we find that plant species distributions were just as likely to shift upwards (i.e., towards higher elevations) as downward (i.e., towards lower elevations)–despite consistent warming across the study area. Although there was no clear directional response to climate warming across the entire study area, there was significant region- to region- variation in responses (i.e. from as many as 73% to as few as 32% of species shifting upward). To understand the factors that might be controlling region-specific distributional shifts of plant species, we explored the relationship between the direction of change in distribution limits and the nature of recent climate change. We found that the direction that distribution limits shifted was explained by an interaction between the rate of change in local summer temperatures and seasonal precipitation. Specifically, species were more likely to shift upward at their upper elevational limit when minimum temperatures increased and snowfall was unchanging or declined at slower rates (<0.5 mm/year). This suggests that both low temperature and water availability limit upward shifts at upper elevation limits. By contrast, species were more likely to shift upwards at their lower elevation limit when maximum temperatures increased, but also shifted upwards under conditions of cooling temperatures when precipitation decreased. This suggests increased water stress may drive upward shifts at lower elevation limits. Our results suggest that species’ elevational distribution shifts are not predictable by climate warming alone but depend on the interaction between seasonal temperature and precipitation change. [ABSTRACT FROM AUTHOR]

Deforestation is associated with increased atmospheric CO2 and alterations to the surface energy and mass balances that can lead to local and global climate changes. Previous modelling studies show that the global surface air temperature (SAT) response to deforestation depends on latitude, with most simulations showing that high latitude deforestation results in cooling, low latitude deforestation causes warming and that the mid latitude response is mixed. These earlier conclusions are based on simulated large scal land cover change, with complete removal of trees from whole latitude bands. Using a global climate model we examine the effects of removing fractions of 5% to 100% of forested areas in the high, mid and low latitudes. All high latitude deforestation scenarios reduce mean global SAT, the opposite occurring for low latitude deforestation, although a decrease in SAT is simulated over low latitude deforested areas. Mid latitude SAT response is mixed. In all simulations deforested areas tend to become drier and have lower SAT, although soil temperatures increase over deforested mid and low latitude grid cells. For high latitude deforestation fractions of 45% and above, larger net primary productivity, in conjunction with colder and drier conditions after deforestation cause an increase in soil carbon large enough to produce a net decrease of atmospheric CO2. Our results reveal the complex interactions between soil carbon dynamics and other climate subsystems in the energy partition responses to land cover change. [ABSTRACT FROM AUTHOR]

An inhibitor-tolerance strain, Bacillus coagulans GKN316, was developed through atmospheric and room temperature plasma (ARTP) mutation and evolution experiment in condensed dilute-acid hydrolysate (CDH) of corn stover. The fermentabilities of other hydrolysates with B. coagulans GKN316 and the parental strain B. coagulans NL01 were assessed. When using condensed acid-catalyzed steam-exploded hydrolysate (CASEH), condensed acid-catalyzed liquid hot water hydrolysate (CALH) and condensed acid-catalyzed sulfite hydrolysate (CASH) as substrates, the concentration of lactic acid reached 45.39, 16.83, and 18.71 g/L by B. coagulans GKN316, respectively. But for B. coagulans NL01, only CASEH could be directly fermented to produce 15.47 g/L lactic acid. The individual inhibitory effect of furfural, 5-hydroxymethylfurfural (HMF), vanillin, syringaldehyde and p-hydroxybenzaldehyde (pHBal) on xylose utilization by B. coagulans GKN316 was also studied. The strain B. coagulans GKN316 could effectively convert these toxic inhibitors to the less toxic corresponding alcohols in situ. These results suggested that B. coagulans GKN316 was well suited to production of lactic acid from undetoxified lignocellulosic hydrolysates. [ABSTRACT FROM AUTHOR]

An accident is an unwanted hazard to a person. However, accidents occur. In this work, we search for correlations between daily accident rates and environmental factors. To study daily hospital outpatients who were admitted for accidents during a 5-year period, 2007–2011, we analyzed data regarding 168,366 outpatients using univariate regression models; we also used multivariable regression models to account for confounding factors. Our analysis indicates that the number of male outpatients admitted for accidents was approximately 1.31 to 1.47 times the number of female outpatients (P < 0.0001). Of the 12 parameters (regarding air pollution and meteorology) considered, only daily temperature exhibited consistent and significant correlations with the daily number of hospital outpatient visits for accidents throughout the 5-year analysis period. The univariate regression models indicate that older people (greater than 66 years old) had the fewest accidents per 1-degree increase in temperature, followed by young people (0–15 years old). Middle-aged people (16–65 years old) were the group of outpatients that were more prone to accidents, with an increase in accident rates of 0.8–1.2 accidents per degree increase in temperature. The multivariable regression models also reveal that the temperature variation was the dominant factor in determining the daily number of outpatient visits for accidents. Our further multivariable model analysis of temperature with respect to air pollution variables show that, through the increases in emissions and concentrations of CO, photochemical O3 production and NO2 loss in the ambient air, increases in vehicular emissions are associated with increases in temperatures. As such, increases in hospital visits for accidents are related to vehicular emissions and usage. This finding is consistent with clinical experience which shows about 60% to 80% of accidents are related to traffic, followed by accidents occurred in work place. [ABSTRACT FROM AUTHOR]

Cooperative binding is one of the most interesting and not fully understood phenomena involved in control and regulation of biological processes. Here we analyze the simplest phenomenological model that can account for cooperativity (i.e. ligand binding to a macromolecule with two binding sites) by generating equilibrium binding isotherms from deterministically simulated binding time courses. We show that the Hill coefficients determined for cooperative binding, provide a good measure of the Gibbs free energy of interaction among binding sites, and that their values are independent of the free energy of association for empty sites. We also conclude that although negative cooperativity and different classes of binding sites cannot be distinguished at equilibrium, they can be kinetically differentiated. This feature highlights the usefulness of pre-equilibrium time-resolved strategies to explore binding models as a key complement of equilibrium experiments. Furthermore, our analysis shows that under conditions of strong negative cooperativity, the existence of some binding sites can be overlooked, and experiments at very high ligand concentrations can be a valuable tool to unmask such sites. [ABSTRACT FROM AUTHOR]

Determining the position of range edges is the first step in developing an understanding of the ecological and evolutionary dynamics in play as species’ ranges shift in response to climate change. Here, we study the leading (poleward) range edge of Ocypode cordimanus, a ghost crab that is common along the central to northern east coast of Australia. Our study establishes the poleward range edge of adults of this species to be at Merimbula (36.90°S, 149.93°E), 270 km (along the coast) south of the previous southernmost museum record. We also establish that dispersal of pelagic larvae results in recruitment to beaches 248 km (along the coast; 0.9° of latitude) beyond the adult range edge we have documented here. Although we cannot conclusively demonstrate that the leading range edge for this species has moved polewards in response to climate change, this range edge does fall within a “hotspot” of ocean warming, where surface isotherms are moving southwards along the coast at 20–50 km.decade-1; coastal air temperatures in the region are also warming. If these patterns persist, future range extensions could be anticipated. On the basis of their ecology, allied with their occupancy of ocean beaches, which are home to taxa that are particularly amenable to climate-change studies, we propose that ghost crabs like O. cordimanus represent ideal model organisms with which to study ecological and evolutionary processes associated with climate change. The fact that “hotspots” of ocean warming on four other continents correspond with poleward range edges of ghost crab species suggests that results of hypothesis tests could be generalized, yielding excellent opportunities to rapidly progress knowledge in this field. [ABSTRACT FROM AUTHOR]

Climate in part determines species’ distributions, and species’ distributions are shifting in response to climate change. Strong correlations between the magnitude of temperature changes and the extent of range shifts point to warming temperatures as the single most influential factor causing shifts in species’ distributions species. However, other abiotic and biotic factors may alter or even reverse these patterns. The importance of temperature relative to these other factors can be evaluated by examining range shifts of the same species in different geographic areas. When the same species experience warming in different geographic areas, the extent to which they show range shifts that are similar in direction and magnitude is a measure of temperature’s importance. We analyzed published studies to identify species that have documented range shifts in separate areas. For 273 species of plants, birds, mammals, and marine invertebrates with range shifts measured in multiple geographic areas, 42-50% show inconsistency in the direction of their range shifts, despite experiencing similar warming trends. Inconsistency of within-species range shifts highlights how biotic interactions and local, non-thermal abiotic conditions may often supersede the direct physiological effects of temperature. Assemblages show consistent responses to climate change, but this predictability does not appear to extend to species considered individually. [ABSTRACT FROM AUTHOR]

Climate change is known to affect ecosystems globally, but our knowledge of its impact on large and widespread mammals, and possibly population-specific responses is still sparse. We investigated large-scale and long-term effects of climate change on local population dynamics using the wild boar (Sus scrofa L.) as a model species. Our results show that population increases across Europe are strongly associated with increasingly mild winters, yet with region-specific threshold temperatures for the onset of exponential growth. Additionally, we found that abundant availability of critical food resources, e.g. beech nuts, can outweigh the negative effects of cold winters on population growth of wild boar. Availability of beech nuts is highly variable and highest in years of beech mast which increased in frequency since 1980, according to our data. We conclude that climate change drives population growth of wild boar directly by relaxing the negative effect of cold winters on survival and reproduction, and indirectly by increasing food availability. However, region-specific responses need to be considered in order to fully understand a species’ demographic response to climate change. [ABSTRACT FROM AUTHOR]

Atmospheric temperature time series for the nine climate regions of the contiguous U.S. are accurately reproduced by the superposition of oscillatory modes, representing the Atlantic multidecadal oscillation (AMO) and the Pacific decadal oscillation (PDO), on a monotonic mode representing, at least in part, the effect of radiant forcing due to increasing atmospheric CO2. The relative importance of the different modes varies among the nine climate regions, grouping them into three mega-regions: Southeastern comprising the South, Southeast and Ohio Valley; Central comprising the Southwest, Upper Midwest, and Northeast; and Northwestern comprising the West, Northwest, and Northern Rockies & Plains. The defining characteristics of the mega-regions are: Southeastern – dominated by the AMO, no PDO influence; Central – influenced by the AMO, no PDO influence, Northwestern – influenced by both the AMO and PDO. Temperature vs. time curves calculated by combining the separate monotonic and oscillatory modes agree well with the measured temperature time series, indicating that the 1938-1974 small decrease in contiguous U.S. temperature was caused by the superposition of the downward-trending oscillatory mode on the upward-trending monotonic mode while the 1980-2000 large increase in temperature was caused by the superposition of the upward-trending oscillatory mode on the upward-trending monotonic mode. The oscillatory mode, mostly representing the AMO, was responsible for about 72% of the entire contiguous U.S. temperature increase over that time span with the contribution varying from 86 to 42% for individual climate regions. [ABSTRACT FROM AUTHOR]

Based on observational data and Asian monsoon intensity datasets from China, the relationships between the East Asian winter monsoon index and winter temperature, the East Asian summer monsoon index and Meiyu precipitation over the middle and lower reaches of the Yangtze River, were analyzed. We found that the monsoon signals were reflected in the temperature and Meiyu precipitation variations. Thus, we used the reconstructed Meiyu precipitation and winter temperature series for the past 300 years and detected the summer/winter monsoon intensity signals using multi-taper spectral estimation method and wavelet analysis. The main periodicities of Meiyu precipitation and winter temperature, such as interannual cycle with 2–7-year, interdecadal-centennial cycles with 30–40-year and 50–100-year, were found. The good relationships between the East Asian summer and winter monsoons suggested that they were in phase at 31-year cycle, while out of phase at 100-year cycle, but with 20-year phase difference. In addition, the winter monsoon intensity may be regulated by the North Atlantic Oscillation, the Arctic Oscillation and the Atlantic Multidecadal Oscillation, and the summer monsoon is closely related to the signal intensities of the Pacific Decadal Oscillation. [ABSTRACT FROM AUTHOR]

Frankliniella occidentalis (Pergande) is a key pest of various crops worldwide. In this study, we analyse the dependence of the infestation of this pest on spatially distributed micro climatic factors in a rose greenhouse. Despite the importance of this subject, the few existing studies have been realized in laboratory rather than in greenhouse conditions. However, recent progress on greenhouse microclimate characterisation has highlighted the strong indoor climate heterogeneity that may influence the within-crop pest distribution. In this study, both microclimate (air temperature and humidity) and thrips distribution were simultaneously mapped in a rose greenhouse. The measurements were sensed in a horizontal plane situated at mid-height of the rose crop inside the greenhouse. Simultaneously, thrips population dynamics were assessed after an artificial and homogeneous infestation of the rose crop. The spatio-temporal distribution of climate and thrips within the greenhouse were compared, and links between thrips infestation and climatic conditions were investigated. A statistical model was used to define the favourable climate conditions for thrips adults and larvae. Our results showed that (i) the air temperature and air humidity were very heterogeneously distributed within the crop, (ii) pest populations aggregated in the most favourable climatic areas and (iii) the highest population density of thrips adults and larvae were recorded at 27°C and 22°C for temperature and 63% and 86% for humidity, respectively. These findings confirm, in real rose cropping conditions, previous laboratory studies on the F. occidentalis climatic optimum and provide a solid scientific support for climatic-based control methods against this pest. [ABSTRACT FROM AUTHOR]

Understanding the functional relationship between greenhouse gas fluxes and environmental variables is crucial for predicting the impacts of wetlands on future climate change in response to various perturbations. We examined the relationships between methane (CH4) emission and temperature in two marsh stands dominated by the Phragmites australis and Cyperus malaccensis, respectively, in a subtropical estuarine wetland in southeast China based on three years of measurement data (2007–2009). We found that the Q10 coefficient of CH4 emission to soil temperature (Qs10) from the two marsh stands varied slightly over the three years (P > 0.05), with a mean value of 3.38 ± 0.46 and 3.89 ± 0.41 for the P. australis and C. malaccensis stands, respectively. On the other hand, the three-year mean Qa10 values (Q10 coefficients of CH4 emission to air temperature) were 3.39 ± 0.59 and 4.68 ± 1.10 for the P. australis and C. malaccensis stands, respectively, with a significantly higher Qa10 value for the C. malaccensis stand in 2008 (P < 0.05). The seasonal variations of Q10 (Qs10 and Qa10) differed among years, with generally higher values in the cold months than those in the warm months in 2007 and 2009. We found that the Qs10 values of both stands were negatively correlated with soil conductivity, but did not obtain any conclusive results about the difference in Q10 of CH4 emission between the two tidal stages (before flooding and after ebbing). There were no significant differences in both Qs10 and Qa10 values of CH4 emission between the P. australis stand and the C. malaccensis stands (P > 0.05). Our results show that the Q10 values of CH4 emission in this estuarine marsh are highly variable across space and time. Given that the overall CH4 flux is governed by a suite of environmental factors, the Q10 values derived from field measurements should only be considered as a semi-empirical parameter for simulating CH4 emissions. [ABSTRACT FROM AUTHOR]