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Changes in the frequency of temperature extremes are often attributed to global warming. The recent availability of near‐surface temperature data records from reference networks, such as the U.S. Climate Reference Network (USCRN), enables the quantification of measurement uncertainties. Within an activity of the Copernicus Climate Change Service, the estimation of the measurement uncertainty has been provided for USCRN temperature data, using metadata made available by the National Oceanic and Atmospheric Administration (NOAA). In this paper, four climate extreme indices (Frost Days, Summer Days, Ice Days, Tropical Nights) and the related uncertainties are calculated for the period 2006–2020 from the USCRN data set and compared with traditional indices. Moreover, the asymmetric USCRN measurement uncertainties are propagated to estimate the uncertainties of climate indices. The comparison shows expanded uncertainties homogeneously distributed with the latitude and typically within 15 days per year for Frost Days and within 10 days for Ice Days, while smaller uncertainties are estimated for Summer Days and Tropical Nights, with values typically within six to seven days per year. Positive uncertainties are typically larger than negative ones for all the indices. The values of Frost and Ice Days with the related uncertainties for USCRN have also been compared with the corresponding values calculated from reanalyses data, showing differences typically within 60 days for median values, quite often smaller than USCRN and inconsistent within the related uncertainties, Overall, the results show that USCRN measurement uncertainties increase confidence in the estimation of climate extreme indices and decisions for adaptation. Plain Language Summary: The relationship between the intensity and frequency of extremes and climate change as well as their attribution to human activities is fundamental for improving the assessment of risk and the elaboration of adaptation strategies. Temperature extremes are often reported and estimated using observations or model data using indices, which are widely adopted in the research community and by decision‐makers. However, the number of temperature extremes is quantified assuming input observations as perfect, whereas these are always affected by uncertainties due to instrumental noise and systematic effects that cannot be always properly accounted for. This also implies that climate extreme indices may under or over‐represent the number of temperature extremes. The advent of reference measurement networks, as well as the overall increase in observational data quality due to recent technological improvements, allows us to quantify measurement uncertainties in detail. In this paper, temperature extremes over the US are estimated from near‐surface temperature measurements provided by the USCRN network in the period 2006–2020 with related uncertainties. The use of uncertainty illustrates the range of values that climate extreme indices may assume. Possible sources of uncertainties and comparisons with data from atmospheric reanalysis are also discussed. Key Points: An extensive assessment of uncertainties for four climate extreme indices is provided using reference near‐surface temperaturesEstimate uncertainties of climate indices for reanalysis validation and quantification of extremes by propagating measurement uncertaintiesUSCRN traceable measurements with quantified uncertainties increase confidence in estimating extreme indices and decisions for adaptation [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Economics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding how crop phenology responds to climate change is critical for enabling agricultural adaptation measures. Pre‐season temperature alone leads to well‐understood changes in crop phenology. Nevertheless, the modulation effect of concurrent precipitation extremes on the response to temperature extremes has been largely under‐addressed. Here, we investigate the response of rice transplanting dates to pre‐season temperature extremes and reveal the modulation effects of concurrent precipitation extremes by using station‐observed rice transplanting dates from 1981 to 2018 across mainland China. We also evaluate the performance of a remotely sensed phenology product, ChinaCropPhen1km, in reproducing the above temperature responses and modulation effects. Our results showed that transplanting dates tended to advance in response to an extremely hot pre‐season, while concurrent extreme drought offset the advance by up to 2.6 days. Transplanting dates tended to be delayed in response to an extremely cold pre‐season, while concurrent extreme precipitation exacerbated the delay by up to 1 day. Responses to temperature extremes and modulation effects were divergent across different regions. Under extremely hot conditions, transplanting dates advanced further in hotter and wetter regions, while under extremely cold pre‐seasons, transplanting dates delayed less in colder and drier regions. Transplanting dates from the ChinaCropPhen1km product underestimated the responses to temperature extremes by up to 4.7 days and detected the opposite modulation effect compared to those obtained from observations. Our results highlight that the need to improve our understanding and modeling of modulation effects of precipitation extremes on temperature–phenology relationship, which benefits the field of agriculture risk analysis and climate change adaptation. Plain Language Summary: Frequent climate extremes are projected with ongoing climate change. This paper reveals the effect of concurrent precipitation extremes in modulating rice transplanting date response to pre‐season temperature extremes in China and assesses remotely sensed transplanting dates from the ChinaCropPhen1km product in reproducing the above temperature responses and modulation effects. Results show that concurrent precipitation extremes substantially influence the temperature–transplanting date relationship. Two kinds of modulation effects are detected: (a) concurrent extreme precipitation tends to exacerbate the extremely cold‐induced delay by up to 1 day, and (b) concurrent extreme drought conditions tend to offset the extremely hot‐induced advance by up to 2.6 days. From a spatial perspective, responses to temperature extremes and modulation effects are divergent across regions. Additionally, remotely sensed transplanting dates from the ChinaCropPhen1km product underestimate the temperature responses and detect the opposite modulation effects compared to observations. This study highlights the critical role of concurrent precipitation extremes in modulating temperature impact on crop phenology, which requires more understanding and modeling in future analysis. Key Points: Concurrent extreme drought offset the extreme hot induced‐advance, and extreme precipitation exacerbate the extreme cold induced‐delayResponses to temperature extremes and modulation effects of precipitation extremes are divergent across regionsRemotely sensed transplanting dates from ChinaCropPhen1km underestimate the responses to temperature extremes and modulation effects [ABSTRACT FROM AUTHOR]

Corrosion of steel in concrete is a complex process with many factors influencing its kinetics. Especially under on‐site condition, the prediction of corrosion rates is a challenge since many influencing factors such as the temperature change constantly. The whole corrosion process is the concert of three sub‐processes: (i) the anodic reaction rate, (ii) the cathodic reaction rate and (iii) the electrolytic conductivity of the concrete. Each of the processes show temperature dependency and existing corrosion monitoring systems record either data to evaluate part of the processes or the resulting corrosion rate. For service life estimates, prediction of corrosion rates is necessary. Consequently, the knowledge on the temperature effect on the corrosion rate is crucial, especially in view of rising temperatures due to climate change. This paper presents an overview on the existing knowledge on the temperature dependency on the corrosion rates; highlights the analysis of temperature data and provides guidance for service life prediction. [ABSTRACT FROM AUTHOR]

The events of recent years indicate climate change in nature. Climate change could affect the lifetime of transport infrastructure. Changes in the temperature, humidity, and use of de‐icing material may stimulate the development of damages in road and bridge structures. According to Latvian Environment, Geology, and Meteorology Centre data, average air temperature values have increased by 0.7 °C (1981‐2010 compared to 1961‐1990 g). Road maintenance organizations need information on the actual weather conditions on each road: above the road pavement, on the surface, and below the surface. Sixtyfive meteorological stations on roads throughout Latvia's territory provide this information. This paper analyzes data from six road metrological stations obtained from 2001 to 2021. The results show that over the 20 years period, the average temperature determined nearby asphalt pavement of the road has increased by 1.4 C. This is because the road asphalt pavement heats more, and the temperature measured in its vicinity is higher and should be considered when modeling the development of damage to the transport infrastructure. [ABSTRACT FROM AUTHOR]

Compound climate extremes, such as events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. This paper aims to analyze temporal and spatial characteristics of compound extremes of monthly temperature and precipitation, evaluate the performance of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models in simulating compound extremes, and investigate their future changes under Shared Socioeconomic Pathways (SSPs). The results show a significant increase in the frequency of compound warm extremes (warm/dry and warm/wet) but a decrease in compound cold extremes (cold/dry and cold/wet) during 1985–2014 relative to 1955–1984. The observed upward trends of compound warm extremes over China are much higher than those worldwide during the period of interest. A multi‐model ensemble (MME) of CMIP6 models performs well in simulating temporal changes of warm/wet extremes, and temporal correlation coefficients between MME and observations are above 0.86. Under future scenarios, CMIP6 simulations show substantial rises in compound warm extremes and declines in compound cold extremes. Globally, the average frequency of warm/wet extremes over a 30‐yr period is projected to increase for 2070–2099 relative to 1985–2014 by 18.53, 34.15, 48.79, and 59.60 under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, respectively. Inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremes. The projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 and especially high for the Amazon and the Tibetan Plateau. Plain Language Summary: Compound climate extremes, such as the events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. Can climate model simulate the historical compound extremes? If yes, how the global compound extremes will change in the future? In this study, we found that the global climate model performs well in simulating temporal changes of warm/wet and warm/dry extremes during the period 1955–2014. With greenhouse gas emissions continuing to increase in the future, compound warm/dry and warm/wet extremes show a continuous increase in frequency in the next few decades, while compound cold/dry and cold/wet extremes are projected to occur less frequently. Key Points: A multi‐model ensemble of CMIP6 models performs well in simulating temporal changes of warm/wet extremesThe inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremesThe projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 [ABSTRACT FROM AUTHOR]

Riparian tree planting is increasingly being used as a strategy to shade river corridors and offset the impact of climate change on river temperature. Because the circumstances under which tree planting generates the greatest impact are still largely unknown, researchers are increasingly using process‐based models to simulate the impacts of tree planting (or felling) on river temperature. However, the high‐resolution data on existing riparian tree cover needed to parameterise these models can be difficult to obtain, especially in data‐sparse areas. In this paper, we compare the performance of a river temperature model parameterised with a range of different tree cover datasets, to assess whether tree cover data extracted from readily available GIS databases or coarser (i.e., 2–5 m) digital elevation products are able to generate river temperature simulations approaching the accuracy of higher resolution structure from motion (SfM) or LiDAR. Our results show that model performance for simulations incorporating these data is generally degraded in relation to LiDAR/SfM inputs and that tree cover data from "alternative" sources can lead to unexpected temperature model outcomes. We subsequently use our model to simulate the addition/removal of riparian tree cover from alongside the river channel. Simulations indicate that the vast majority of the "shading effect" is generated by tree cover within the 5‐m zone immediately adjacent to the river channel, a key finding with regards to developing efficient riparian tree planting strategies. These results further emphasise the importance of incorporating the highest possible resolution tree cover data when running tree planting/clearcutting scenario simulations. [ABSTRACT FROM AUTHOR]

Aim: The latitudinal diversity gradient (LDG) is a consequence of evolutionary and ecological mechanisms acting over long history, and thus is best investigated with organisms that have rich fossil records. However, combined neontological‐palaeontological investigations are mostly limited to large, shelled invertebrates, which keeps our mechanistic understanding of LDGs in its infancy. This paper aims to describe the modern meiobenthic ostracod LDG and to explore the possible controlling factors and the evolutionary mechanisms of this large‐scale biodiversity pattern. Location: Present‐day Western North Atlantic. Taxon: Ostracoda. Methods: We compiled ostracod census data from shallow‐marine environments of the western North Atlantic Ocean. Using these data, we documented the marine LDG with multiple metrics of alpha, beta (nestedness and turnover) and gamma diversity, and we tested whether macroecological patterns could be governed by different environmental factors, including temperature, salinity, dissolved oxygen, pH and primary productivity. We also explored the geologic age distribution of ostracod genera to investigate the evolutionary mechanisms underpinning the LDG. Results: Our results show that temperature and climatic niche conservatism are important in setting LDGs of these small, poorly dispersing organisms. We also found evidence for some dispersal‐driven spatial dynamics in the ostracod LDG. Compared to patterns observed in marine bivalves, however, dispersal dynamics were weaker and they were bi‐directional, rather than following the 'out‐of‐the‐tropics' model. Main conclusions: Our detailed analyses revealed that meiobenthic organisms, which comprise two‐thirds of marine diversity, do not always follow the same rules as larger, better‐studied organisms. Our findings suggest that the understudied majority of biodiversity may be more sensitive to climate than well‐studied, large organisms. This implies that the impacts of ongoing Anthropocene climatic change on marine ecosystems may be much more serious than presently thought. [ABSTRACT FROM AUTHOR]

From 1988 to July 2019 more than 100 review articles were published, including opinion papers and book chapters, that focused on potential climate change effects on plant pathogens and the future crop disease risks. Therefore, an overview of them is presented herein, particularly helpful for beginners and non‐experts in climate change biology research. Specifically, this overview contributes to a faster and more convenient identification of appropriate review articles, for example, related to a certain crop, pathogen, plant disease or country of interest. However, not all important crops, pathogens, diseases and countries are considered specifically and in‐depth in any of these review articles, suggesting that there are still research gaps prevalent, which are also highlighted herein. Nevertheless, the overview suggests that researchers are increasingly busy and successful in summarizing the fragmented information spread throughout the international literature. Consequently, they are providing 'step‐by‐step' a comprehensive, in‐depth, and continuously updated knowledge platform on potential climate change effects on plant pathogens and the respective crop disease risks in the future, although some aspects will, by nature, be repeated. [ABSTRACT FROM AUTHOR]

This paper investigates the time trend coefficients in the temperatures in 48 US states using monthly data from January 1895 to December 2017, as well as in their anomalies with respect to the base period 1901–2000. For this purpose, we use techniques based on fractional integration, which is a more general approach than the standard methods used in the literature based on integer differentiation. The results indicate that with the exception of 10 states, in the remaining 38, the temperature anomalies have increased across time, with the increase being higher than the one expected under the other more standard approaches. The highest increases correspond to New Jersey and Rhode Island, with an increase of approximately 2.9°C over the last 100 years. [ABSTRACT FROM AUTHOR]

Climate change is the result of complex interactions between a wide array of climatic variables. Over a long period of time, climatic patterns can shift, possibly multiple times. Abrupt shifts that occur over a relatively short period of time are known as change points. During these intervals, different climatic variables may undergo dramatic shifts posing serious consequences for many biological and physical systems. In this paper, we discuss a Bayesian algorithm for detecting the location of change points in time series data. In particular, we utilize our method to analyse five different global surface temperature anomaly data sets, as well as temperature records associated with land, ocean, and different zonal bands in an attempt to identify common features of these data sets. Change points in the five global records were detected between 1902–1917, 1936–1945, and 1963–1976, but do these change points also manifest themselves across other parts of the globe? Our analysis indicates that the timing of change points is consistent from one record to the next, but not all change points appear in all records. In particular, there were more change points in the ocean than on land, and also more change points in southern latitudes than in northern latitudes. [ABSTRACT FROM AUTHOR]

The Fixed Anvil Temperature (FAT) hypothesis proposes that upper tropospheric cloud fraction peaks at a special isotherm that is independent of surface temperature. It has been argued that a FAT should result from simple ingredients: Clausius‐Clapeyron, longwave emission from water vapor, and tropospheric energy and mass balance. Here the first cloud‐resolving simulations of radiative‐convective equilibrium designed to contain only these basic ingredients are presented. This setup does not produce a FAT: the anvil temperature varies by about 40% of the surface temperature range. However, the tropopause temperature varies by only 4% of the surface temperature range, which supports the existence of a Fixed Tropopause Temperature (FiTT). In full‐complexity radiative‐convective equilibrium simulations, the spread in anvil temperature is smaller by about a factor of 2, but the tropopause temperature remains more invariant than the anvil temperature by an order of magnitude. In other words, our simulations have a FiTT, not a FAT. Plain Language Summary: Tropical anvil clouds play a large role in Earth's radiation balance, and their effect on anthropogenic global warming has been a point of contention. One popular school of thought is the Fixed Anvil Temperature (FAT) hypothesis, which argues that no matter how much the Earth warms, anvil clouds would continue to radiate to space at the same temperature. This behavior would amplify the warming caused by the addition of greenhouse gases to the atmosphere. In this paper, we show that the chain of logic underlying the FAT hypothesis—which attributes anvil cloud formation to a rapid decline of radiative cooling at the top of the troposphere—contains many weak and unsupported links. These weak links undermine the FAT hypothesis, which does not hold empirically in our simulations. However, the radiative tropopause does remain at a fixed temperature as the surface warms in our simulations. Therefore, we find evidence for a Fixed Tropopause Temperature (FiTT) rather than a FAT. There is currently no accepted theory for the tropopause temperature or why it appears to be independent of the surface temperature. Key Points: A Fixed Anvil Temperature (FAT) does not result from temperature‐dependent longwave radiative emission from water vaporSimulations of radiative‐convective equilibrium without a FAT still have a Fixed Tropopause Temperature (FiTT)A FiTT does not imply a FAT because anvil clouds are not produced by enhanced detrainment below the tropopause [ABSTRACT FROM AUTHOR]

ABSTRACT This paper proposes a method for describing the distribution of observed temperatures on any day of the year such that the distribution and summary statistics of interest derived from the distribution vary smoothly through the year. The method removes the noise inherent in calculating summary statistics directly from the data thus easing comparisons of distributions and summary statistics between different periods. The method is demonstrated using daily effective temperatures (DET) derived from observations of temperature and wind speed at De Bilt, Holland. Distributions and summary statistics are obtained from 1985 to 2009 and compared to the period 1904-1984. A two-stage process first obtains parameters of a theoretical probability distribution, in this case the generalized extreme value (GEV) distribution, which describes the distribution of DET on any day of the year. Second, linear models describe seasonal variation in the parameters. Model predictions provide parameters of the GEV distribution, and therefore summary statistics, that vary smoothly through the year. There is evidence of an increasing mean temperature, a decrease in the variability in temperatures mainly in the winter and more positive skew, more warm days, in the summer. In the winter, the 2% point, the value below which 2% of observations are expected to fall, has risen by 1.2 °C, in the summer the 98% point has risen by 0.8 °C. Medians have risen by 1.1 and 0.9 °C in winter and summer, respectively. The method can be used to describe distributions of future climate projections and other climate variables. Further extensions to the methodology are suggested. [ABSTRACT FROM AUTHOR]

Climatology is one of the areas that rely on collecting huge quantities of data. The longer a time period is observed, the better; the more parameters are included, the better. But the human mind cannot easily extract useful information from the abundance of data; thus, many valuable facts may be overlooked. Having that in mind, the authors of this paper have focused on the data condensation with the goal of gathering more information about the underlying trends of the main climatologic parameters change to show climatic variability. The data from the Belgrade Meteorological Observatory are analysed using a number of different methods of multivariate statistical analysis. Separation of the years and time periods with similar weather pattern characteristics was successful and indicates that there is a trend of temperature increase, as well as a trend of the temperature range decrease. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Abstract: At the 21st session of the United Nations Framework Convention on Climate Change Conference of the Parties (COP21) in Paris, an agreement to strengthen the effort to limit the global temperature increase well below 2°C was decided. However, even if global warming is limited, some regions might still be substantially affected by climate change, especially for continents like Africa where the socio‐economic conditions are strongly linked to the climatic conditions. In the paper we will discuss the analysis of indices assigned to the sectors health, agriculture, and infrastructure in a 1.5, 2, and 3°C global warming world for the African continent. For this analysis an ensemble of 10 different general circulation model‐regional climate model simulations conducted in the framework of the COordinated Downscaling EXperiment for Africa was investigated. The results show that the African continent, in particular the regions between 15°S and 15°N, has to expect an increase in hot nights and longer and more frequent heat waves even if the global temperature will be kept below 2°C. These effects intensify if the global mean temperature will exceed the 2°C threshold. Moreover, the daily rainfall intensity is expected to increase toward higher global warming scenarios and will affect especially the African Sub‐Saharan coastal regions. [ABSTRACT FROM AUTHOR]

ABSTRACT The biases in the Global Circulation Models ( GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed residual-based bagging tree ( RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modelling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared with the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios ( RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44, 2.59, and 4.71 °C by the end of the century, respectively, when compared with the average temperature during 1970-1999. [ABSTRACT FROM AUTHOR]

Many problems in current climate research deal with extreme events. Since by definition there are few observations of really extreme events, it is a statistical challenge to assess whether observed trends are significant. In this paper we illustrate one method to look for climate signals in extreme temperature data, and how to compare the data to a climate simulation based on a regional climate model. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A homogenized 76-year daily-mean temperature series is presented for Moor House, an upland peatland meteorological station in northern England, which provides the longest instrumental temperature record in the UK uplands. This paper assesses trends in the temperature record over time compared to the nearby lowland temperature record at Durham. Overall warming for 1991-2006 compared with 1931-1960 and 1961-1990 was approximately the same at the upland site (0.53 °C and 0.73 °C for the two time periods, respectively) as at Durham (0.61 °C and 0.71 °C), maintaining a similar lapse rate. However, winter warming is dominant at Moor House and more pronounced and concentrated over a shorter period than at Durham; summer warming is more prominent at Durham. Therefore, seasonal lapse rates have changed and become more divergent between winter and summer half-years. Rather than changes in the dominance of particular air-masses over the study area accounting for seasonal lapse rate changes, there had been significant steepening of lapse rates over time for some air flow types. Minimum temperatures have increased more than maximum temperatures at Moor House with a consequent significant reduction in diurnal range. At Durham minimum and maximum temperatures have changed in approximate tandem so that there is no significant change in diurnal temperature range. The frequency of air frosts reduced by approximately 23% (from 129 days per year to 99) at Moor House from 1991-2006 compared to 1953-1980, and this may have major implications for the functioning of terrestrial and aquatic systems in the region. These results demonstrate that while overall annual warming in the uplands and lowlands within particular regions may be similar, even where annual lapse rates have been maintained, the seasonal distribution of that warming may be quite different even over short distances and so lowland records are not a reliable indicator of regional upland temperature change. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The recent quantification of changes in time series of phenology data with Bayesian methods has provided compelling evidence for changes during the last 20 years. In this paper we correlate the phenological observations with spring temperature time series. We provide quantitative answers to the question whether changes in temperature and phenological time series should be regarded as coherent or independent. For the three considered species snowdrops, cherry and lime tree we find factors of 1.05, 2.19 and 3.26, respectively, in favor of coherence. The functional behavior and the trend in the temperature time series are presented. They amount to 0.15°C yr−1 for the January–March average, 0.09°C yr−1 for February–April and 0.1°C yr−1 for March–May in 2002. In addition, we compare blossom trends for the coherent and independent hypotheses and find that the transition from trend values slightly positive before 1970 to strongly negative at present becomes sharper as the temperature data are included in the analysis. [ABSTRACT FROM AUTHOR]

Changes to forest production drivers (light, water, temperature, and site nutrient) over the last 55 years have been documented in peer-reviewed literature. The main objective of this paper is to review documented evidence of the impacts of climate change trends on forest productivity since the middle of the 20th century. We first present a concise overview of the climate controls of forest production, provide evidence of how the main controls have changed in the last 55 years, followed by a core section outlining our findings of observed and documented impacts on forest productivity and a brief discussion of the complications of interpreting trends in net primary production (NPP). At finer spatial scales, a trend is difficult to decipher, but globally, based on both satellite and ground-based data, climatic changes seemed to have a generally positive impact on forest productivity when water was not limiting. Of the 49 papers reporting forest production levels we reviewed, 37 showed a positive growth trend, five a negative trend, three reported both a positive and a negative trend for different time periods, one reported a positive and no trend for different geographic areas, and two reported no trend. Forests occupy ≈52% of the Earth's land surface and tend to occupy more temperature and radiation-limited environments. Less than 7% of forests are in strongly water-limited systems. The combined and interacting effects of temperature, radiation, and precipitation changes with the positive effect of CO2, the negative effects of O3 and other pollutants, and the presently positive effects of N will not be elucidated with experimental manipulation of one or a few factors at a time. Assessments of the greening of the biosphere depend on both accurate measurements of rates (net ecosystem exchange, NPP), how much is stored at the ecosystem level (net ecosystem production) and quantification of disturbances rates on final net biome production. [ABSTRACT FROM AUTHOR]

This paper examines a large number of agricultural and other phenological records kept by a farmer in Sussex, UK from 1980 to 2000. Twenty five of the 29 events were earlier in 1990-2000 than in 1980-1989. The average advancement of all 29 events was 5.5 days at a time when January-March mean temperature increased by 1.4°C. In comparing the events with monthly mean temperatures, 18 of the events were significantly negatively related to temperatures of the three calendar months preceding the mean event date. Response rates to temperature varied between 4 and 12 days earlier for each °C warmer. A comparison with historical farming records reveals that many of the current farming events appear as responsive to temperature now as they were 200 years ago. [ABSTRACT FROM AUTHOR]

Subarctic ecosystems are exposed to elevated temperatures and increased cloudiness in a changing climate with potentially important effects on vegetation structure, composition, and ecosystem functioning. We investigated the individual and combined effects of warming and increased cloudiness on vegetation greenness and cover in mesocosms from two tundra and one palsa mire ecosystems kept under strict environmental control in climate chambers. We also investigated leaf anatomical and biochemical traits of four dominant vascular plant species (Empetrum hermaphroditum, Vaccinium myrtillus, Vaccinium vitis‐idaea, and Rubus chamaemorus). Vegetation greenness increased in response to warming in all sites and in response to increased cloudiness in the tundra sites but without associated increases in vegetation cover or biomass, except that E. hermaphroditum biomass increased under warming. The combined warming and increased cloudiness treatment had an additive effect on vegetation greenness in all sites. It also increased the cover of graminoids and forbs in one of the tundra sites. Warming increased leaf dry mass per area of V. myrtillus and R. chamaemorus, and glandular trichome density of V. myrtillus and decreased spongy intercellular space of E. hermaphroditum and V. vitis‐idaea. Increased cloudiness decreased leaf dry mass per area of V. myrtillus, palisade thickness of E. hermaphroditum, and stomata density of E. hermaphroditum and V. vitis‐idaea, and increased leaf area and epidermis thickness of V. myrtillus, leaf shape index and nitrogen of E. hermaphroditum, and palisade intercellular space of V. vitis‐idaea. The combined treatment caused thinner leaves and decreased leaf carbon for V. myrtillus, and increased leaf chlorophyll of E. hermaphroditum. We show that under future warmer increased cloudiness conditions in the Subarctic (as simulated in our experiment), vegetation composition and distribution will change, mostly dominated by graminoids and forbs. These changes will depend on the responses of leaf anatomical and biochemical traits and will likely impact carbon gain and primary productivity and abiotic and biotic stress tolerance. [ABSTRACT FROM AUTHOR]

This study introduces a novel approach to leverage high‐resolution historical climate data and opportunistically collected historical species occurrence data for detecting adaptive responses to global change. We applied this procedure to the temperature data and the most comprehensive Iberian dataset of dung beetle occurrences as an illustrative example. To understand how populations of different species are responding, we devised a procedure that compares the temporal trend of spatial and temperature variables at the locations and times of all the occurrence data collection (overall trend) with the specific temporal trends among the occurrences of each species. The prevalence of various species responses is linked to life history or taxonomic characteristics, enabling the identification of key factors influencing the propensity to experience different effects from climate change. Our findings suggest that nearly half of the Iberian dung beetle species may be adversely affected by temperature increases, with a geographic shift being the most common response. The results generated through the proposed methodology should be regarded as preliminary information, serving to formulate hypotheses about the diverse responses of species to climate change and aiding in the selection of candidate species capable of coping with challenges posed by changing temperatures. [ABSTRACT FROM AUTHOR]

The connections of climatic variables to zoonotic and wildlife diseases remain uncertain. Here, we compiled a systematic database for the prevalence of 121 pathogenic microbial taxa in birds (ca 376 species) and bats (ca 39 species), including 11 939 observations from over 450 000 individuals across Europe and surrounding regions. We modelled the potential connection of climatic variables with the prevalence of 75 pathogenic microbial taxa at a multi‐pathogenic‐taxa level and of 17 most‐studied pathogenic taxa at a single‐pathogenic‐taxon level. According to the multi‐taxa model, the prevalence of bacterial taxa was positively associated with temperature, while this association was significantly weaker for eukaryotes and viruses. The prevalence of bacterial taxa was negatively associated with rainfall, while viruses showed a positive association with rainfall. These associations between climatic variables and prevalence of pathogenic taxa were not different between bird and bat hosts. According to the single‐taxon models, the prevalence of influenza A viruses, Plasmodium, and several bacterial taxa in birds and bats was positively associated with temperature. Rainfall showed positive associations with the prevalence of Usutu, Sindbis and Influenza A viruses but the directions of significant associations varied among bacterial taxa. Strikingly, this was evidenced also between bacterial taxa that share hosts and transmission mechanism hinting towards hitherto unknown features on pathogen ecology, e.g. Salmonella versus Campylobacter and Anaplasma versus Borrelia. Our results suggest that rising temperature and increasing precipitation will accelerate the threat of bird‐ and bat‐associated bacterial and viral pathogens to wildlife, domesticated animals and humans, respectively. However, the idiosyncratic relationships with climatic conditions among pathogenic taxa highlight the need for pathogen‐specific predictive models to understand future pathogen distributions. [ABSTRACT FROM AUTHOR]

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The phase of precipitation at the land surface is critical to determine the timing and amount of water available for hydrological and ecological systems. However, there are few techniques to directly observe the precipitation phase and many prediction tools apply a single temperature threshold (e.g., 0°C) to determine phase. In this paper, we asked two questions: (1) what is the accuracy of default and station optimized daily temperature thresholds for predicting precipitation phase and (2) what are the regions and conditions in which typical temperature-based precipitation phase predictions are most suited. We developed a ground truth dataset of rain vs. snow using an expert decision-making system based on precipitation, snow depth, and snow water equivalent observations. This dataset was used to evaluate the accuracy of three temperature-threshold-based techniques of phase classification. Optimizing the temperature threshold improved the prediction of precipitation phase by 34% compared to using 0°C threshold. Developing a temperature threshold based on station elevation improved the error by 12% compared with using the 0°C temperature threshold. We also found the probability of snow as a function of temperature differed among ecoregions, which suggests a varied response to future climate change. These results highlight a current weakness in our ability to predict the effects of regional warming that could have uneven impacts on water and ecological resources. [ABSTRACT FROM AUTHOR]

The availability of freshwater from glaciers and snowmelt is of vital importance for people and ecosystems in the context of global climate change. Here, we focus on 25 glaciers located in different climates and latitudes and investigate their recent (1958–2020) and future projected trends (2020–2050 and 2070–2100) in monthly precipitation (Pr), maximum and minimum temperatures, ice mass loss, and their relationships with cloud properties. The study sites are located in Temperate Europe (France), the Inner (Ecuador, Venezuela, and Colombia) and Outer Tropics (Bolivia and Peru), Central America (Mexico), tropical Southeast Asia (Indonesia), Equatorial Africa (Uganda), and the Southern dry and Patagonian Andes (Chile and Argentina). The climate analyses are based on TerraClimate data (Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces) and 28 CORDEX (Coordinated Regional Climate Downscaling Experiment) climate simulations. Our findings reveal that, extrapolating current glacier volume change trends, almost half of the studied glaciers are likely to vanish (95%–100% volume loss) by 2050, with widespread warming and drying trends since 1958. A shift toward wetter conditions at Pico Humboldt (Venezuela) and Martial Este (Argentina) identifiable in the CORDEX simulation will very likely not have a limiting impact on glacier mass loss owing to increasing temperatures, which will raise the elevation of the rain/snow limit. Our results provide useful new information to better understand glacier‐climate relationships and future scenarios dominated by ice mass loss trends across the globe. These findings suggest serious consequences for future water availability, which exacerbate the vulnerability of local populations and ecosystems. Plain Language Summary: In this study, we employ the monthly TerraClimate data, Coordinated Regional Climate Downscaling Experiment climate projections, and glaciological data to investigate potential associations between climate variables (precipitation and temperatures), cloud conditions, and changes in glacier volume across 25 glaciers worldwide. Over the historical period (1958–2020) and in the future decades (2020–2050 and 2070–2100), none of the glacierized sites exhibit both cooling and wetting trends. In contrast, widespread warmer and drier conditions in recent and future decades are identified for the studied glaciers located in Temperate Europe, Central America, the Southern dry Andes, and Northern Patagonia, where almost half of the glaciers are likely to vanish by 2050 if current or recent trends persist. Relationships between the temperature range and changes in cloud cover are significant at the Outer Tropics and Northern Patagonian sites. These findings could be used by the scientific community to improve climate change projections and by decision‐makers to develop adaptation and mitigation strategies to cope with the consequences of climate change and support regional socioeconomic development. Key Points: TerraClimate and Coordinated Regional Climate Downscaling Experiment data reveal widespread warming and drying trends since 1958 over 25 glaciers located in different climatesConsidering current trends, almost half of the studied glaciers are likely to vanish by 2050. Additional warming would increase this numberTemperatures are more influential predictors than precipitation for glaciers volume [ABSTRACT FROM AUTHOR]

Global and regional climate models are large computer models that simulate the Earth's climate system and are used extensively in climate change research. Climate models are based on natural laws of physics, chemistry, and fluid dynamics. However, different inputs and parameterizations lead to different climate projections. To help account for inherent uncertainties in the process, collections of climate models, called ensembles, are often used. This paper proposes a method for combining climate ensemble output using a spatial confirmatory factor analysis model to characterize modes of similarity among ensemble members. The proposed model uses both Bayesian and spatial methods to estimate a common climate factor as well as unique factor loadings for each ensemble member. These spatial factor loadings indicate the degree of agreement for each member with the common climate factor. The model is applied to the North American Regional Climate Change Assessment Program ensemble, using both winter surface temperature and winter precipitation. In both cases, the spatial confirmatory factor analysis model finds areas of disagreement among ensemble members where no suitable consensus can be obtained. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Aim Little is known about how marine biodiversity responds to oceanographic and climatic changes over the decadal to centennial time-scales which are most relevant for predicted climate changes due to greenhouse gas forcing. This paper aims to reveal decadal-centennial scale deep-sea biodiversity dynamics for the last 20,000 years and then explore potential environmental drivers. Location The North Atlantic Ocean. Methods We investigated deep-sea benthic microfossil records to reveal biodiversity dynamics and subsequently applied comprehensive ecological modelling to test possible environmental factors (i.e. surface productivity, seasonality of productivity or deepwater circulation related to bottom-water temperature) that may have influenced deep-sea biodiversity over these time-scales. Results Deep-sea biodiversity changed synchronously with stadial-interstadial climate changes over the last 20,000 years across a large area of the North Atlantic in both ostracod crustaceans and foraminiferan protozoa (in spite of their different dispersal abilities). Species diversity rapidly increased during abrupt stadial events during the last deglacial and the Holocene interglacial periods. These include the well-known Heinrich 1, the Younger Dryas and the 8.2 ka events when the strength of Atlantic Meridional Overturning Circulation ( AMOC) decreased. There is also evidence for quasi-cyclic changes in biodiversity at a c. 1500-year periodicity, consistent with the well-known '1500-year climatic cycle'. Statistical analyses revealed that AMOC variability (probably specifically the variability in AMOC-driven bottom-water temperature) is correlated with deep-sea biodiversity. Main conclusions Our finding of a significant AMOC-diversity relationship may indicate pervasive control of the diversity of deep-sea benthic species by rapidly changing climate, specifically bottom-water temperature, over decadal to centennial time-scales. Our results, based on highly resolved fossil records, may portend pervasive, synchronous and sudden ecosystem responses to human-induced changes to climate and ocean circulation in this century. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents an analysis of winter climate variability based on daily mean temperature and precipitation data since 1970 in the Chic-Chocs Mountain range (located in the Gaspé Peninsula, Eastern Quebec, Canada). Mountain environments are particularly sensitive to rapid climate change and are therefore good indicators of recent global warming. The main goal of this study is to demonstrate how joint probability temperature/precipitation distributions can be used to estimate winter condition changes (trends) for six meteorological stations in the study area (the altitudinal range for the stations is from 5 to 574 m). The presence and persistence of snow cover was also estimated. Previous studies have shown a lack of evidence of significant trends in snow-cover characteristics (density, depth and snow water equivalent (SWE)) from the early 1980s to the present, despite an increase in temperature over the same period. A reanalysis of these data sets in addition to the use of a combination of temperature and precipitation data categorized into four modes (warm/wet, warm/dry, cold/wet and cold/dry) was also performed. Despite this new analysis, no clear evidence of climate change could be found in the study area over the last four decades. The results revealed that patterns and trends are quite different from one station to another, but when the environment is taken into account (valley or plateau, coastal versus inland) some apparent patterns emerge. [ABSTRACT FROM AUTHOR]

Because of its low topographic relief, unique hydrology, and the large interannual variability of precipitation, Florida is especially vulnerable to climate change. In this paper, we investigate a comprehensive collection of climate metrics to study historical trends in both averages and extremes of precipitation and temperature in the state. The data investigated consist of long-term records (1892-2008) of precipitation and raw (unadjusted) temperature at 32 stations distributed throughout the state. To evaluate trends in climate metrics, we use an iterative pre-whitening method, which aims to separate positive autocorrelation from trend present in time series. Results show a general decrease in wet season precipitation, most evident for the month of May and possibly tied to a delayed onset of the wet season. In contrast, there seems to be an increase in the number of wet days during the dry season, especially during November through January. We found that the number of dog days (above 26.7 °C) during the year and during the wet season has increased at many locations. For the post-1950 period, a widespread decrease in the daily temperature range (DTR) is observed mainly because of increased daily minimum temperature (Tmin). Although we did not attempt to formally attribute these trends to natural versus anthropogenic causes, we find that the urban heat island effect is at least partially responsible for the increase in Tmin and its corresponding decrease in DTR at urbanized stations compared with nearby rural stations. In the future, a formal trend attribution study should be conducted for the region. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Satellite product validation is a key to ensure the delivery of quality products for climate and weather applications. To do this, a fundamental step is the comparison with other instruments, such as radiosonde. This is especially true for essential climate variables such as temperature and humidity. Thanks to a functional data representation, this paper uses a likelihood‐based approach that exploits the measurement uncertainties in a natural way. In particular, the comparison of temperature and humidity radiosonde measurements collected within the network of the Universal Rawinsonde Observation Program (RAOB) and the corresponding atmospheric profiles derived from the infrared atmospheric sounding interferometer aboard MetOp‐A and MetOp‐B satellites is developed with the aim of understanding the vertical smoothing mismatch uncertainty. Moreover, conventional RAOB functional data representation is assessed by means of a comparison with radiosonde reference measurements given by the Global Climate Observing System (GCOS) Reference Upper‐Air Network (GRUAN), which provides high‐resolution fully traceable radio‐sounding profiles. In this way, the uncertainty related to coarse vertical resolution, or sparseness, of the conventional RAOB is assessed. It has been found that the uncertainty of vertical smoothing mismatch averaged along the profile is 0.50 K for temperature and 0.16 g/kg for water‐vapor mixing ratio. Moreover, the uncertainty related to RAOB sparseness, averaged along the profile, is 0.29 K for temperature and 0.13 g/kg for water‐vapor mixing ratio. [ABSTRACT FROM AUTHOR]

Rising ocean temperatures are threatening marine species and populations worldwide, and ectothermic taxa are particularly vulnerable. Echinoderms are an ecologically important phylum of marine ectotherms and shifts in their population dynamics can have profound impacts on the marine environment. The effects of warming on echinoderms are highly variable across controlled laboratory‐based studies. Accordingly, synthesis of these studies will facilitate the better understanding of broad patterns in responses of echinoderms to ocean warming. Herein, a meta‐analysis incorporating the results of 85 studies (710 individual responses) is presented, exploring the effects of warming on various performance predictors. The mean responses of echinoderms to all magnitudes of warming were compared across multiple biological responses, ontogenetic life stages, taxonomic classes, and regions, facilitated by multivariate linear mixed effects models. Further models were conducted, which only incorporated responses to warming greater than the projected end‐of‐century mean annual temperatures at the collection sites. This meta‐analysis provides evidence that ocean warming will generally accelerate metabolic rate (+32%) and reduce survival (−35%) in echinoderms, and echinoderms from subtropical (−9%) and tropical (−8%) regions will be the most vulnerable. The relatively high vulnerability of echinoderm larvae to warming (−20%) indicates that this life stage may be a significant developmental bottleneck in the near‐future, likely reducing successful recruitment into populations. Furthermore, asteroids appear to be the class of echinoderms that are most negatively affected by elevated temperature (−30%). When considering only responses to magnitudes of warming representative of end‐of‐century climate change projections, the negative impacts on asteroids, tropical species and juveniles were exacerbated (−51%, −34% and −40% respectively). The results of these analyses enable better predictions of how keystone and invasive echinoderm species may perform in a warmer ocean, and the possible consequences for populations, communities and ecosystems. [ABSTRACT FROM AUTHOR]

The sensitivity of atmospheric river (AR)‐induced precipitation to climate change is primarily driven by increases in atmospheric water vapor with warming. However, the rate at which AR‐based precipitation intensifies with warming and whether this rate differs from non‐AR events remains uncertain. This work uses multiple statistical models to estimate regional, extreme precipitation‐temperature scaling rates in California for AR and non‐AR events. Scaling rates are determined using cold‐season daily and hourly precipitation, along with multiple temperature variables to assess robustness of the results. We find that regional scaling rates for ARs are consistently larger than non‐ARs, especially for hourly event maxima (posterior median scale rates of 5.7% and 2.4% per °C for ARs and non‐ARs, respectively). ARs remain near saturated (i.e., high relative humidity) and exhibit more lift and a stronger increase in specific humidity aloft with warming as compared to non‐ARs, helping to explain the difference in precipitation‐temperature scaling rates. Plain Language Summary: Atmospheric rivers are long and narrow conveyor belts of water vapor in the sky that transport moisture in the lower atmosphere. They often cause extreme precipitation that can cause flooding along the west coast of the United States. The rate of intensification of atmospheric river‐based precipitation with warming is a fundamental scientific question with important implications for adapting civil infrastructure to climate change. This study aims to quantify the relationship between atmospheric river‐based extreme precipitation and temperature, and to determine how it differs from other precipitation events. To do so, we utilize multiple statistical methods to quantify cold‐season precipitation‐temperature scaling rates at weather stations across California. In our analysis, we consider different temperature variables, timescales of data, and ways of accumulating precipitation during events to determine the robustness of our results. Overall, we find that extreme precipitation increases faster with warming during atmospheric rivers compared to other types of events. This difference is linked to the fact that atmospheric rivers exhibit a more direct increase in moisture content and lift with warming. These results suggest that extreme precipitation during atmospheric rivers will increase faster than other events as temperatures rise in the future, which could accelerate damage linked to future flooding events. Key Points: Precipitation‐temperature (P‐T) scaling rates during atmospheric rivers (ARs) are larger than other precipitation events in CaliforniaP‐T scaling rates for AR‐based events approach the Clausius‐Clapeyron rate (7% per °C) when using dew point temperatureThe differences in P‐T scaling rates for ARs versus non‐ARs are linked to the sensitivity of moisture content and lifting mechanism to warming [ABSTRACT FROM AUTHOR]

The distribution and abundance of plant species are intimately related to their reproductive success, which in turn is affected by a large number of environmental variables. Yet, reproductive success is rarely taken into account in species distribution models ( SDMs). In this paper we examine the extent to which consideration of the reproduction niche and its relationship with temperature could improve SDMs. We review the literature on plant reproductive responses to temperature and the influence of these relationships on species range delimitation. We define the reproduction niche and discuss how temperature influences several stages of the reproductive process. Furthermore, we review examples that illustrate how the reproduction niche influences species distributions and discuss how aspects of the reproduction niche could be considered in SDMs. We show that the reproduction niche fundamentally influences species distributions and that in principle it is easy to include aspects of the reproduction niche in SDMs, although sufficient data are only available for a restricted number of species. Bayesian methods and inverse parameterization may be the most efficient way to use existing data. [ABSTRACT FROM AUTHOR]

ABSTRACT Robust relationships among soil, water, atmosphere and plants are needed to reliably forecast the plant species composition. In this paper, we show the need for, and the application of, a process-based relationship between soil moisture conditions and vegetation characteristics. We considered 366 groundwater-dependent sites, where oxygen stress, caused by a surplus of soil moisture, codetermines plant performance. We compared two existing indirect proxies for the soil oxygen status - namely mean spring groundwater level ( MSL) and sum exceedence value ( SEV) - with our newly developed process-based proxy, viz. root respiration stress ( RS). The two indirect proxies and the process-based proxy for oxygen stress performed equally well in describing vegetation characteristics for the Netherlands under the current climate. However, relationships based on MSL and SEV appeared to produce systematic prediction errors when applied outside their calibration range, in contrast to the relationship based on RS. Hence, the two indirect proxies cannot be used in projections, such as in predicting effects of climate change on vegetation composition, all the more because they - unlike RS - do not account for essential parameters that determine oxygen stress (e.g. temperature and extreme rainfall events in the growing season). We advocate using RS for estimating vegetation impacts in climate projections to increase the reliability and effectiveness of adaptive strategies. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Climate change and habitat loss are both key threatening processes driving the global loss in biodiversity. Yet little is known about their synergistic effects on biological populations due to the complexity underlying both processes. If the combined effects of habitat loss and climate change are greater than the effects of each threat individually, current conservation management strategies may be inefficient and at worst ineffective. Therefore, there is a pressing need to identify whether interacting effects between climate change and habitat loss exist and, if so, quantify the magnitude of their impact. In this article, we present a meta-analysis of studies that quantify the effect of habitat loss on biological populations and examine whether the magnitude of these effects depends on current climatic conditions and historical rates of climate change. We examined 1319 papers on habitat loss and fragmentation, identified from the past 20 years, representing a range of taxa, landscapes, land-uses, geographic locations and climatic conditions. We find that current climate and climate change are important factors determining the negative effects of habitat loss on species density and/or diversity. The most important determinant of habitat loss and fragmentation effects, averaged across species and geographic regions, was current maximum temperature, with mean precipitation change over the last 100 years of secondary importance. Habitat loss and fragmentation effects were greatest in areas with high maximum temperatures. Conversely, they were lowest in areas where average rainfall has increased over time. To our knowledge, this is the first study to conduct a global terrestrial analysis of existing data to quantify and test for interacting effects between current climate, climatic change and habitat loss on biological populations. Understanding the synergistic effects between climate change and other threatening processes has critical implications for our ability to support and incorporate climate change adaptation measures into policy development and management response. [ABSTRACT FROM AUTHOR]

Recently, published a paper proposing a mechanism by which temperature-dependent sex determination (TSD) may play a key role at facilitating species with this strategy to track their climatic niches across space under climate change. Kallimanis hypothesized that TSD species currently inhabiting stable climatic conditions show reduced population growth rates at the edges of their distributional ranges; under warming conditions, these populations will experience faster growth rates and thus are able to colonize new suitable sites. These ideas are based on the assumption that populations of TSD species have balanced sex ratios at the core of their geographic ranges and biased proportions at the edges. However, Kallimanis' model overlooks complex processes that may produce a more broadly and less predictable aftermath of climate change on TSD species, so we discuss some of his postulates and underlying assumptions. Kallimanis' model is based only on one of three known TSD strategies in reptiles, thus it lacks generality; and it does not consider the phenological, behavioral, and physiological strategies that TSD species exhibit across their geographic ranges to buffer the potential impacts of climatic variation over the whole reproductive process. We conclude that simple models such as the one proposed by Kallimanis are not broadly applicable; hence, forecasts of TSD species' responses to climate change will need to be more specific to groups with similar ecologies and modes of TSD. [ABSTRACT FROM AUTHOR]

In this paper we reconstructed river runoff to the Baltic Sea since 1500 using temperature and atmospheric circulation indices, showing the important atmospheric processes for river runoff in different regions. Runoff appears to be strongly linked to temperature, wind and rotational circulation components in the northern region and Gulf of Finland, but more associated with rotational and deformation circulation components in the south. No significant long-term change has been detected in total river runoff to the Baltic Sea for 500 years, although decadal and regional variability is large. Analysis of runoff sensitivity to temperature shows that the south region may become drier with rising air temperatures. This is in contrast to the north region and Gulf of Finland where warmer temperatures are associated with more river runoff. Over the past 500 years the total river runoff to the Baltic Sea has decreased by 3% (450 m/s) per degree Celsius increase. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This paper examines the available information on the potential for climate-change impacts on mycotoxigenic fungi and mycotoxin contamination of food crops pre- and postharvest. It considers the effect of changes in temperature/water availability on mycotoxin contamination, especially incidences where aflatoxin B and ochratoxin A production has been influenced. The potential of using preharvest models to predict risk from deoxynivalenol (DON) in wheat, fumonisin B in maize and aflatoxins in maize and peanuts in different continents are considered in the context of potential for adaptation to include climate-change scenarios. Available information suggests that slightly elevated CO concentrations and interactions with temperature and water availability may stimulate growth of some mycotoxigenic species, especially under water stress. The accumulated knowledge on interacting conditions of water/temperature effects on optimum and boundary conditions for growth and mycotoxin production has been used to predict the effects that +3 and +5°C increases under water stress would have on growth/mycotoxin production by mycotoxigenic species. Various spatial scales, from toxin gene expression to regional approaches using geostatistics, are examined for their use in understanding the impact that climate change may have on food contamination in developing and developed countries. The potential for using an integrated systems approach to link gene expression data, phenotypic toxin production under different interacting abiotic conditions is discussed using Fusarium species and DON as examples. Such approaches may be beneficial for more accurate predictions of risk from mycotoxins on a regional basis and also the potential for new emerging toxin threats. [ABSTRACT FROM AUTHOR]

This paper addresses some of the conceptual issues involved in the analysis of the age and origin of mediterranean-climate plant taxa, paying particular attention to three topics: (1) the importance of an explicit time frame in the definition of biogeographical origins, (2) the distinction between the age of traits and the age of taxa, and (3) the idea of mediterranean-type ecosystems as environmental islands. (1) In California, recent analyses demonstrate that the diversity of species derived from different biogeographical origins is significantly correlated with temperature and precipitation gradients. These patterns support the hypothesis that niche conservatism is an important factor structuring modern diversity gradients. However, depending on how far back in time one looks, a species may be assigned to different origins; future discussions of biogeographical origins need to address the appropriate time frame for analysis. (2) Past research has demonstrated distinctive trait syndromes among woody plants of the Mediterranean, Chile, California and Mexico, and proposed that the syndromes are associated with lineages of different age in these floras. Reanalysis of individual traits demonstrates greater variability among regions than previously reported. The classification of plants into ‘old’ and ‘new’ genera is re-evaluated, and it is suggested that greater attention be paid to the age of traits, rather than to the age of taxa, especially at an arbitrary rank such as genus. (3) The idea of mediterranean-climate regions as ‘climatic islands’ is examined. Space–time diagrams of climate enable one to view the emergence of distinctive climatic regions in a continental context. The terms ‘synclimatic’ and ‘anticlimatic’ are proposed, referring to migration routes that parallel climate contours in space and time versus those that cross contours (including the case of geographic stasis in the face of climate change), respectively. Mediterranean-climate regions have served as important case studies in plant ecology and evolution, and merit continued close examination in the light of continued advances in phylogenetics and palaeoecology. [ABSTRACT FROM AUTHOR]

Global warming is expected to affect the ecosystem in the Northeast Atlantic, and substantial changes will also affect the aquaculture industry. Farming of salmon and trout is the biggest aquaculture industry in Norway, with an export value of about 3 billion US dollars in 2007. The objective of the paper is to analyze the potential economic effect a general increase in sea temperature can have on the Norwegian salmon aquaculture industry. The assessment of the economic impact of global warming is made possible by estimating a growth function, which explicitly includes sea water temperature. The analysis compares the economic effect of a climate change on fish farming plants in the south and the north of Norway. The scenarios are based on a model with monthly seasonal variation in temperature. [ABSTRACT FROM AUTHOR]

This experiment was designed to study three determinant factors in decomposition patterns of soil organic matter (SOM): temperature, water and carbon (C) inputs. The study combined field measurements with soil lab incubations and ends with a modelling framework based on the results obtained. Soil respiration was periodically measured at an oak savanna woodland and a ponderosa pine plantation. Intact soils cores were collected at both ecosystems, including soils with most labile C burnt off, soils with some labile C gone and soils with fresh inputs of labile C. Two treatments, dry-field condition and field capacity, were applied to an incubation that lasted 111 days. Short-term temperature changes were applied to the soils periodically to quantify temperature responses. This was done to prevent confounding results associated with different pools of C that would result by exposing treatments chronically to different temperature regimes. This paper discusses the role of the above-defined environmental factors on the variability of soil C dynamics. At the seasonal scale, temperature and water were, respectively, the main limiting factors controlling soil CO2 efflux for the ponderosa pine and the oak savanna ecosystems. Spatial and seasonal variations in plant activity (root respiration and exudates production) exerted a strong influence over the seasonal and spatial variation of soil metabolic activity. Mean residence times of bulk SOM were significantly lower at the Nitrogen (N)-rich deciduous savanna than at the N-limited evergreen dominated pine ecosystem. At shorter time scales (daily), SOM decomposition was controlled primarily by temperature during wet periods and by the combined effect of water and temperature during dry periods. Secondary control was provided by the presence/absence of plant derived C inputs (exudation). Further analyses of SOM decomposition suggest that factors such as changes in the decomposer community, stress-induced changes in the metabolic activity of decomposers or SOM stabilization patterns remain unresolved, but should also be considered in future SOM decomposition studies. Observations and confounding factors associated with SOM decomposition patterns and its temperature sensitivity are summarized in the modeling framework. [ABSTRACT FROM AUTHOR]

Some previous studies have shown that drying-up of the lower Yellow River resulted from decreasing precipitation and excessive industrial and agricultural consumption of water from the middle and downstream regions of the Yellow River. On the basis of average air temperature, precipitation, and pan evaporation data from nearly 80 gauging stations in the Yellow River basin, the monotonic trends of major climate variables over the past several decades are analysed. The analysis was mainly made for 12 months and the annual means. The isograms for annual and typical months are given in the paper. The result shows that the average temperature in the study area exhibits an increasing trend, mainly because of the increase of temperature in December, January and February. The largest trend is shown in December and the smallest is in August. There are 65 of 77 stations exhibiting a downward trend for annual precipitation. In all seasons except summer, there is a similar trend in the upstream region of the Yellow River, south of latitude 35°N. It is interesting to note that the pan evaporation has decreased in most areas of the Yellow River basin during the past several decades. April and July showed the greatest magnitude of slope, and the area from Sanmenxia to Huayuankou as well as the Yiluo River basin exhibited the strongest declining trend. The conclusion is that the decreasing pan evaporation results from complex changes of air temperature, relative humidity, solar radiation, and wind speed, and both climate change and human activities have affected the flow regime of the Yellow River during the past several decades. [ABSTRACT FROM AUTHOR]

A strong relationship between dissolved organic carbon (DOC) and sulphate (SO42−) dynamics under drought conditions has been revealed from analysis of a 10-year time series (1993–2002). Soil solution from a blanket peat at 10 cm depth and stream water were collected at biweekly and weekly intervals, respectively, by the Environmental Change Network at Moor House-Upper Teesdale National Nature Reserve in the North Pennine uplands of Britain. DOC concentrations in soil solution and stream water were closely coupled, displaying a strong seasonal cycle with lowest concentrations in early spring and highest in late summer/early autumn. Soil solution DOC correlated strongly with seasonal variations in soil temperature at the same depth 4-weeks prior to sampling. Deviation from this relationship was seen, however, in years with significant water table drawdown (>−25 cm), such that DOC concentrations were up to 60% lower than expected. Periods of drought also resulted in the release of SO42−, because of the oxidation of inorganic/organic sulphur stored in the peat, which was accompanied by a decrease in pH and increase in ionic strength. As both pH and ionic strength are known to control the solubility of DOC, inclusion of a function to account for DOC suppression because of drought-induced acidification accounted for more of the variability of DOC in soil solution ( R2=0.81) than temperature alone ( R2=0.58). This statistical model of peat soil solution DOC at 10 cm depth was extended to reproduce 74% of the variation in stream DOC over this period. Analysis of annual budgets showed that the soil was the main source of SO42− during droughts, while atmospheric deposition was the main source in other years. Mass balance calculations also showed that most of the DOC originated from the peat. The DOC flux was also lower in the drought years of 1994 and 1995, reflecting low DOC concentrations in soil and stream water. The analysis presented in this paper suggests that lower concentrations of DOC in both soil and stream waters during drought years can be explained in terms of drought-induced acidification. As future climate change scenarios suggest an increase in the magnitude and frequency of drought events, these results imply potential for a related increase in DOC suppression by episodic acidification. [ABSTRACT FROM AUTHOR]

Changes to the environment surrounding a temperature measuring station can cause local changes to the recorded temperature that deviate from regional temperature behavior. This phenomenon—often caused by construction or urbanization—occurs at a local level. If these local changes are assumed to represent regional or global processes it can have significant impacts on historical data analyses. These changes or deviations are generally gradual, but can be abrupt, and arise as construction or other environmental changes occur near a recording station. We propose a methodology to examine if changes in temperature behavior at a point in time exist at a local level at various locations in a region assuming that regional or global processes are correlated among nearby stations. Specifically, we propose a Bayesian change point model for spatio‐temporally dependent data where we select the number of change points at each location using a "forward" selection process using deviance information criterion. We then fit the selected version of the model and examine the linear slopes across time to quantify the local changes in long‐term temperature behavior. We show the utility of this model and method using both synthetic data and observed temperature measurements from eight stations in Utah consisting of daily temperature data for 60 years. [ABSTRACT FROM AUTHOR]

Critical thermal maxima methodology (CTM) has been used to infer acute upper thermal tolerance in fishes since the 1950s, yet its ecological relevance remains debated. In this study, the authors synthesize evidence to identify methodological concerns and common misconceptions that have limited the interpretation of critical thermal maximum (CTmax; value for an individual fish during one trial) in ecological and evolutionary studies of fishes. They identified limitations of, and opportunities for, using CTmax as a metric in experiments, focusing on rates of thermal ramping, acclimation regimes, thermal safety margins, methodological endpoints, links to performance traits and repeatability. Care must be taken when interpreting CTM in ecological contexts, because the protocol was originally designed for ecotoxicological research with standardized methods to facilitate comparisons within study individuals, across species and contexts. CTM can, however, be used in ecological contexts to predict impacts of environmental warming, but only if parameters influencing thermal limits, such as acclimation temperature or rate of thermal ramping, are taken into account. Applications can include mitigating the effects of climate change, informing infrastructure planning or modelling species distribution, adaptation and/or performance in response to climate‐related temperature change. The authors' synthesis points to several key directions for future research that will further aid the application and interpretation of CTM data in ecological contexts. [ABSTRACT FROM AUTHOR]

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Various climatic factors and their interactions affect China's snow depth alterations. However, a measure of the relative importance of climatic factors regarding the variance in snow depth is lacking. Here, we use a heretofore largest dataset, quality‐controlled daily observations from 1,665 meteorological stations, and statistical analyses to identify proxy drivers and their contributions regarding regional variability and trends in China's snow depth and snow depth‐climate relationships from 1951 to 2019. Results show that seasonal snow depth alterations are substantial in China and caused by various climatic drivers. In winter, observed positive trends in the northwest and northeast of China mainly respond to augmentation in solid precipitation and snow accumulation in fall. For the Tibetan Plateau, the negative snow depth anomaly seems to respond to the rising air temperature below the freezing point along with some secular changes in solid precipitation and shallower fall snowpacks. All these trends in winter snow depth over the northwest, northeast, and Tibetan Plateau of China account approximately for 30% of the total interannual variance in spring snow depth in these regions. In the milder central north and south of China, snow accumulation is not evident. Besides solid precipitation and rising air temperature below the freezing point, atmospheric humidity is also notable for its effect on the interannual variance in winter snow depth in these regions. In spring and fall, however, the negative effect of warming climate on snow depth becomes remarkably more important. It seems that about 13–33% (12–50%) of the total interannual variance in the widespread decrease (insignificant perturbation), except for some weak increases in the northeast region (decreases in the Tibetan Plateau), of China's spring (fall) snow depth are caused by the rising air temperature below the freezing point. [ABSTRACT FROM AUTHOR]

Drought and aridity are good measures to study climate change that are measured using several indices. In this study, the performance of Standardized Copula‐based Drought Index (SCDI) is compared with Standardized Precipitation Temperature Index (SPTI) and Reconnaissance Drought Index (RDI) for measuring drought in Balochistan province of Pakistan. These standardized drought indices combine the outcomes of De‐Martone Aridity Index (DAI) and UNEP Aridity Index (UAI). SCDI is calculated using probability distributions and copula models that combine the outcomes of UAI and DAI utilizing data of precipitation, potential evapotranspiration (PE), and temperature. Climate data from nine including arid and humid metrological stations are used to study drought conditions in Balochistan province. SCDI outcomes are graphically and numerically compared with SPTI and RDI. Selected drought indices are compared based on frequency of drought and wet events occurred in moderate, severe, and extreme drought categories, which indicate that SCDI has maximum number of events in these categories. It gives better results due to the utilization of more climate data as compared to RDI and SPTI. Severity–duration frequency curves are used to find future projections which shows that drought severity and drought duration have increasing trend with the passage of time. The indices have more differences in results of arid stations. The results indicate that there are no significant chances of drought at lower time scales, but the chances increase with the increase in time scale. The SCDI may also be more useful for agricultural and hydrological droughts due to temperature and PE variables. This study's results will be helpful for climate planning in Balochistan province. [ABSTRACT FROM AUTHOR]