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Climate change represents one of the most pressing societal and scientific challenges of our time. While much of the current research on climate change focuses on future prediction, some of the strongest signals of warming can already be seen in Arctic and alpine areas, where temperatures are rising faster than the global average, and in the oceans, where the combination of rising temperatures and acidification due to increased CO2 concentrations has had catastrophic consequences for sensitive marine organisms inhabiting coral reefs. The scientific papers highlighted as part of this anniversary issue represent some of the most impactful advances in our understanding of the consequences of anthropogenic climate change. Here, we reflect on the legacy of these papers from the biotic perspective. [ABSTRACT FROM AUTHOR]

Tick-borne diseases are a public health issue. To predict vector tick abundance and activity, it is necessary to understand the driving factors for these variables. In this study, the activity of Ixodes ricinus was investigated in forest and meadow habitats in Germany with a focus on abiotic factors. Ixodes ricinus adults, nymphs and larvae were caught by flagging over a period of 2 years. Microclimatic and weather conditions were recorded at the collection sites. Statistical models were applied to describe correlations between abiotic factors and tick activity in univariable and multivariable analyses. Tick activity was observed in a broad range of air temperature between 3 and 28 °C, and air humidity varied between 35 and 95%. In general, tick activity of nymphs and larvae was higher in forest habitats than that in meadows. With the exception of a single specimen of Dermacentor reticulatus, all ticks were Ixodes ricinus, most of them nymphs (63.2% in 2009 and 75.2% in 2010). For the latter, a negative binomial mixed-effects model fitted best to the observed parameters. The modelling results showed an activity optimum between 20 and 23 °C for air temperature and between 13 and 15 °C for ground temperature. In univariable analyses, the collection site, month, season, ground and air temperature were significant factors for the number of ticks caught and for all life stages. In the multivariable analysis, temperature, season and habitat turned out to be key drivers. Ixodes ricinus positive for RNA of tick-borne encephalitis virus was only found at a single sampling site. The results of this study can be used in risk assessments and to parameterise predictive models. [ABSTRACT FROM AUTHOR]

Using data from 32 European countries for nearly 244 million live births between 1969 and 2021, this paper examines the effects of temperatures on birth rates. The results show that exposure to hot days slightly reduces birth rates five to eight months later, while much stronger negative effects are observed nine to ten months after exposure to hot temperatures. Thereafter, a partial recovery is observed, with slightly increased birth rates. This study also shows that the effect of high-humidity hot days is much stronger than that of hot days with low humidity. Besides, the effect of heatwave days has been found to be more severe than that of hot days that are not preceded by other hot days. This study finds that some adaptation to heat might be expected only in the long run. [ABSTRACT FROM AUTHOR]

Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate change, compound dry and hot events become more frequent on a global scale. This paper proposes a mathematical method to quantitatively attribute changes of CDHEs to changes of precipitation, change in temperature and change in the dependence between precipitation and temperature. The attribution is achieved by formulating the total differential equation of the return period of CDHEs among Meta-gaussian model. A case study of China is devised based on monthly precipitation and temperature data during the period from 1921 to 2020 for 80 major river basins. It is found that temperature is the main driving factor of increases in CDHEs for 49 major river basins in China, except for the upper and middle reaches of the Yangtze River. In West China, precipitation changes drove the increase in CDHEs in 18 river basins (23%), particularly in parts of North Xinjiang, Qinghai and Gansu. On the other hand, dependence between precipitation and temperature dominated changes of CDHEs in 13 river basins (16%) of China with other factors, including parts of South China, East China and Northwestern China. Furthermore, changes in both the mean and spread of precipitation and temperature can also contribute to changes in CDHEs. [ABSTRACT FROM AUTHOR]

Results of calculations of temperature trends in the free atmosphere (troposphere and lower stratosphere) using the quantile regression apparatus are considered and analyzed. In traditional techniques used in climatology, trends are estimated by use of regression based on the least squares method. Quantile regression, in contrast to these techniques, makes it possible to estimate regression parameters for each quantile of predictand values in the quantile range from zero to one. Using quantile regression to estimate climate changes results in a detailed picture of the dependence of the climate trend on the variation range of meteorological parameters in the quantile range of these parameters from zero to one. In particular, climate trends can be estimated for meteorological parameter values close to extreme. This paper uses the global radiosonde data array from which the stations are selected if the completeness of their data meets the requirements stated. Using the radiosonde data from the selected stations, the dependences of climatic trends of temperature on isobaric surfaces on values of quantiles (so-called process diagrams), as well as vertical quantile cross sections of climate trend values, are calculated, plotted, and analyzed. For thirteen high-latitude stations in the Northern Hemisphere among the selected ones, temperature trends are estimated both using radiosonde data and based on the ERA 5/ERA 5.1 reanalyses. An analysis of the results allows one to note the nonuniform character of tropospheric warming trends in the range of quantile variation, which is more apparent in the winter season. The nonuniform (for the range of quantile variation) character of tropospheric temperature trends is due to the fact that the tropospheric warming rate in the "cold" part of the quantile range is higher than that in its "warm" part. This agrees with the results obtained previously by analysis of surface temperature trends using the quantile regression method (QRM). The nonuniform character of cooling trends in the lower stratosphere is noted for the range of quantile variations. In winter and, to a lesser extent, in spring, the rate of stratospheric cooling decreases in absolute magnitude with an increase in quantile values at some stations in northern latitudes. Moreover, for the quantiles close to 1.0, negative trends can change sign. This can be both due to incomplete data on lower stratospheric temperature, which is particularly inherent in the high-latitude regions of the Northern Hemisphere, and due to the influence of more frequently occurring sudden stratospheric warmings (SSWs) on the temperature trend structure that is detailed within the range of quantile values. In is noted that the detailed structures of climate temperature trends that are obtained on the basis of radiosonde data proved to be very similar to those obtained based on arrays of ERA 5/ERA 5.1 reanalysis. [ABSTRACT FROM AUTHOR]

Changing climate has altered the trends and variability of precipitation and temperature globally and thereby increasing the risk of natural and social disasters, especially in coastal climatic transitional zones such as the Huaihe river basin (HRB). This paper applies the Empirical Quantile Mapping (EQM) method for bias correction and systematically evaluates the performance of the 30 Global climate models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating precipitation and temperature over the Huaihe river basin (HRB) for 1979–2014. A suitable multi-model ensemble subset (BMME) is selected based on the Euclidean Distance (ED) synthetic metric. Results show that in comparison to the baseline period 1995–2014, precipitation (temperature) over HRB is projected to increase at the rate of around 15 mm/decade (0.1 °C/decade), 16 mm/decade (0.3 °C/decade), 20 mm/decade (0.5 °C/decade), and 15 mm/decade (0.6 °C/decade) for the period 2015–2100, under Shared Socioeconomic Pathways SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. In the long-term (2081–2100), the annual precipitation is projected to increase by 32%, 27%, 35%, and 26%, under the four scenarios, respectively. The temperature is projected to remain stable or slightly decrease under SSP1-2.6 (2 °C) and SSP2-4.5 (3 °C), while will keep rising and increasing by 6 °C under SSP5-8.5 and by 4 °C under SSP3-7.0 by 2100. The isotherm and isohyet are projected to shift northwest from southeastern coastal China to inland in the future, which is likely associated with the northwestward shift of the western Pacific anticyclone in summer. [ABSTRACT FROM AUTHOR]

Cellular signaling generates calcium (Ca2+) ions, which are ubiquitous secondary messengers decoded by calcium-dependent protein kinases, calcineurins, calreticulin, calmodulins (CAMs), and CAM-binding proteins. Previous studies in the model plant Arabidopsis thaliana have shown the critical roles of the CAM-BINDING PROTEIN 60 (CBP60) protein family in plant growth, stress responses, and immunity. Certain CBP60 factors can regulate plant immune responses, like pattern-triggered immunity, effector-triggered immunity, and synthesis of major plant immune-activating metabolites salicylic acid (SA) and N-hydroxypipecolic acid (NHP). Although homologous CBP60 sequences have been identified in the plant kingdom, their function and regulation in most species remain unclear. In this paper, we specifically characterized 11 members of the CBP60 family in the agriculturally important crop tomato (Solanum lycopersicum). Protein sequence analyses revealed that three CBP60 homologs have the closest amino acid identity to Arabidopsis CBP60g and SARD1, master transcription factors involved in plant immunity. Strikingly, AlphaFold deep learning–assisted prediction of protein structures highlighted close structural similarity between these tomato and Arabidopsis CBP60 homologs. Conserved domain analyses revealed that they possess CAM-binding domains and DNA-binding domains, reflecting their potential involvement in linking Ca2+ signaling and transcriptional regulation in tomato plants. In terms of their gene expression profiles under biotic (Pseudomonas syringae pv. tomato DC3000 pathogen infection) and/or abiotic stress (warming temperatures), five tomato CBP60 genes were pathogen-responsive and temperature-sensitive, reminiscent of Arabidopsis CBP60g and SARD1. Overall, we present a genome-wide identification of the CBP60 gene/protein family in tomato plants, and we provide evidence on their regulation and potential function as Ca2+-sensing transcriptional regulators. [ABSTRACT FROM AUTHOR]

This paper deals with the study of stationarity and mean reversion in the temperature anomalies series in the southwestern American cone. In particular, monthly temperatures in 12 Chilean meteorological stations were studied (from the 1960's to nowadays), examining if temperature shocks are expected to remain in the long term or if they are reversible. The results clearly show a significant relationship between the latitude, climate, and the order of integration of the temperatures. The orders of integration tend to be smaller in colder southern parts, therefore impacts of climate change are expected to be more reversible. However, in northern desert areas the orders of integration are larger than 0.5, thus impacts are expected to be maintained for a longer time. [ABSTRACT FROM AUTHOR]

Context: Documenting trends in the health impacts of ambient temperature is key to supporting adaptation strategies to climate change. This paper explores changes in the temperature-related mortality in 18 French urban centers between 1970 and 2015. Method: A multicentric time-series design with time-varying distributed lag nonlinear models was adopted to model the shape of the relationship and assess temporal changes in risks and impacts. Results: The general shape of the temperature-mortality relationship did not change over time, except for an increasing risk at very low percentiles and a decreasing risk at very high percentiles. The relative risk at the 99.9th percentile compared to the 50th percentile of the 1970–2015 temperature distribution decreased from 2.33 [95% confidence interval (CI): 1.95:2.79] in 1975 to 1.33 [95% CI: 1.14:1.55] in 2015. Between 1970 and 2015, 302,456 [95% CI: 292,723:311,392] deaths were attributable to non-optimal temperatures, corresponding to 5.5% [95% CI: 5.3:5.6] of total mortality. This burden decreased progressively, representing 7.2% [95% CI: 6.7:7.7] of total mortality in the 1970s to 3.4% [95% CI: 3.2:3.6] in the 2000s. However, the contribution of hot temperatures to this burden (higher than the 90th percentile) increased. Discussion: Despite the decreasing relative risk, the fraction of mortality attributable to extreme heat increased between 1970 and 2015, thus highlighting the need for proactive adaptation. [ABSTRACT FROM AUTHOR]

In this paper, we examine the relationship between temperature and human conception rates and project the impacts of climate change by the mid-twenty-first century. Using complete administrative data on 6.8 million pregnancies between 1980 and 2015 in Hungary, we show that exposure to hot temperatures reduces the conception rate in the first few weeks following exposure, but a partial rebound is observed after that. We project that with absent adaptation, climate change will increase seasonal differences in conception rates and annual conception rates will decline. A change in the number of induced abortions and spontaneous fetal losses drives the decline in conception rates. The number of live births is unaffected. However, some newborns will experience a shift in the timing of conception that leads to changes in in utero temperature exposure and therefore might have further consequences. [ABSTRACT FROM AUTHOR]

This paper deals with the earliest meteorological observations in Florence after the Medici Network (1654–1670), i.e., from mid-seventeenth to mid-eighteenth century, and puts them in the context of the history of meteorology in Florence. After the gap caused by the Inquisition, observations started again in the eighteenth century, made by Cipriano Antonino Targioni (1728–1748), Giovanni Targioni-Tozzetti (1737–1740), Pietro Gaetano Grifoni (1751–1766), Leonardo Ximenes (1752), and Luca Martini (1756–1772). The first two records were lost, and this paper considers those by Grifoni and Ximenes. The latter is affected by severe bias; the former is of good quality and has been recovered and analyzed. Both the observers made only one reading a day and the metadata are scarce. The paper discusses several issues: the conversion from the apparent solar time to the Central Europe Time; the transformation from a single reading to a daily average; the identification of the thermometric liquid and the scale; the test made with the snow benchmark; the comparison with the contemporary series in Bologna. The comparison of the reconstructed series with other periods, i.e., 1654–1670, 1881–1910, 1961–1990, and 1991–2017, reveals that in mid-eighteenth century the temperature reached the lowest levels, especially in summer, and showed a sudden warming in the most recent decades. [ABSTRACT FROM AUTHOR]

The assessment of the impacts of climate change at different levels of global warming helps inform national and international policy discussion around mitigation targets. This paper provides consistent estimates of global and regional impacts and risks at increases in global mean temperature up to 5 °C above pre-industrial levels, for over 30 indicators representing temperature extremes and heatwaves, hydrological change, floods and droughts and proxies for impacts on crop yields. At the global scale, all the impacts that could plausibly be either adverse or beneficial are adverse, and impacts and risks increase with temperature change. For example, the global average chance of a major heatwave increases from 5% in 1981–2010 to 28% at 1.5 °C and 92% at 4 °C, of an agricultural drought increases from 9 to 24% at 1.5 °C and 61% at 4 °C, and of the 50-year return period river flood increases from 2 to 2.4% at 1.5 °C and 5.4% at 4 °C. The chance of a damaging hot spell for maize increases from 5 to 50% at 4 °C, whilst the chance for rice rises from 27 to 46%. There is considerable uncertainty around these central estimates, and impacts and risks vary between regions. Some impacts—for example heatwaves—increase rapidly as temperature increases, whilst others show more linear responses. The paper presents estimates of the risk of impacts exceeding specific targets and demonstrates that these estimates are sensitive to the thresholds used. [ABSTRACT FROM AUTHOR]

Dynamical downscaling (DD) consists in using archives of Coupled Global Climate Models (CGCM) simulations as the atmospheric and sea-surface boundary conditions (BC) to drive nested, Regional Climate Model (RCM) simulations. Biases in the CGCM-generated driving BC, however, can have detrimental impacts on RCM performance. It is well documented for the historical period that CGCM-simulated sea-surface temperatures (SST) suffer substantial biases, especially important near coastal regions. Assuming that these SST biases are time-invariant, they could in principle be subtracted from century-long CGCM projections before being used to drive RCMs. This paper investigates the performance of a 3-step DD approach as follows. The CGCM-simulated sea-surface temperatures (SST) are first empirically corrected by subtracting their systematic biases; the corrected SST are then used as ocean surface BC for an atmosphere-only GCM (AGCM) simulation; finally this AGCM simulation provides the atmospheric lateral BC to drive an RCM simulation. This is what we refer to as the 3-step approach CGCM–AGCM–RCM of DD, which can be compared to the traditional 2-step approach CGCM–RCM consisting of driving an RCM simulation directly by CGCM-generated BC. In this paper we compare the results obtained with the two approaches, for present and future climates under RCP8.5, using the fifth-generation Canadian Regional Climate Model (CRCM5) with a grid mesh of 0.22° over the North American CORDEX domain, driven by two CMIP5 models: the Canadian Earth System Model of the Canadian Centre for Climate modelling and analysis (CanESM2) and the Earth System Model of the Max-Planck-Institut für Meteorologie (MPI-ESM-MR). The results show that, in current climate, the seasonal-mean 2-m temperature fields simulated with the 3-step DD have generally smaller biases with respect to the observations than those simulated with the 2-step DD; in fact the performance of the 3-step DD simulations often approaches that of the reanalyses-driven simulation. For the seasonal-mean precipitation field, however, the differences between the two DD methods are not conclusive. Differences between the projected climate changes with the two DD methods vary substantially depending upon the variable being considered. Differences are particularly important for temperature: over the bulk of the North American continent, the 3-step DD projects more warming in winter and less in summer. This result highlights the nonlinearities of the climate system, and constitutes an additional measure of uncertainty with DD. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of prenatal exposure to extreme temperatures on birth outcomes—specifically, the log of birth weight and an indicator for low birth weight—using a nationally representative dataset on rural China. During the time period we examine (1991–2000), indoor air conditioning was not widely available and migration was limited, allowing us to address identification issues endemic in the climate change literature related to adaptation and location sorting. We find substantial heterogeneity in the effects of extreme temperature exposure on birth outcomes. In particular, prenatal exposure to heat waves has stronger negative effects than exposure to cold spells on surviving births. [ABSTRACT FROM AUTHOR]

In this paper, we argue that important questions of the intergenerational distribution of the costs of climate change remain even if a temperature target 2 °C is assumed because the target can be pursued in ways that assign costs differently across generations. Moreover, the discounted utilitarian approach that is standard in the economics literature is suspect in light of an a priori argument about its fairness. We also compare the results of modeling Nordhaus's version of discounted utilitarianism, constrained by a 2-degree warming parameter, with a similarly constrained version of a principle that requires minimizing the accumalted differences in the burdens of climate change costs across generations. The model comparison demonstrates that the a priori worry about discounted utilitarianism is largely born out. Nordhaus's version of discounted utilitarianism assigns poorer generations a heavier burden because it is optimific to do so. These arguments call into question the tremendous credibility that discounted utilitarianism enjoys in climate policy. The most important policy implication of the paper is that in order to pursue a fair path towards limiting warming to 2 °C, policy should be directed to increasing the price of carbon over the short term significantly more than what is called for by Nordhaus's model. [ABSTRACT FROM AUTHOR]

Xinjiang is located in the core China's 'Belt and Road' development, and northern Xinjiang is an important region for economic development. In recent years, due to the strong influence of global climate change and human disturbance, regional climate instability and ecological-economic-social system sensitivity have grown. In this paper, seasonal, interannual, interdecadal, spatial, abrupt, and periodic variations of temperature and precipitation in northern Xinjiang were analyzed using daily surface air temperature and precipitation data from 49 meteorological stations during 1961–2017. At the same time, the driving factors of climate change are discussed. Methods included linear regression, cumulative anomaly, the Mann-Kendall test, and Morlet wavelet analysis. The results indicated that during the study period, annual mean temperature and annual precipitation increased significantly at rates of 0.35°C/10 yr and 13.25 mm/10 yr, respectively, with abrupt changes occurring in 1994 and 1986. Annual mean temperature and annual precipitation in all four seasons showed increasing trends, with the maximum increases in winter of 0.42°C/10 yr and 3.95 mm/10 yr, respectively. The general climate in northern Xinjiang showed a trend towards increasingly warm and humid. In terms of spatial distribution, the temperature and precipitation in high mountainous areas increased the most, while basins areas increased only slightly. Periodic change analysis showed that annual mean temperature and annual precipitation experienced two climatic shifts from cold to warm and dry to wet, respectively. Population change, economic development and land use change are important factors affecting climate change, and more research should be done in this field. [ABSTRACT FROM AUTHOR]

Phenological models are widely used to estimate the influence of weather and climate on plant development. The goodness of fit of phenological models often is assessed by considering the root-mean-square error (RMSE) between observed and predicted dates. However, the spatial patterns and temporal trends derived from models with similar RMSE may vary considerably. In this paper, we analyse and compare patterns and trends from a suite of temperature-based phenological models, namely extended spring indices, thermal time and photothermal time models. These models were first calibrated using lilac leaf onset observations for the period 1961–1994. Next, volunteered phenological observations and daily gridded temperature data were used to validate the models. After that, the two most accurate models were used to evaluate the patterns and trends of leaf onset for the conterminous US over the period 2000–2014. Our results show that the RMSEs of extended spring indices and thermal time models are similar and about 2 days lower than those produced by the other models. Yet the dates of leaf out produced by each of the models differ by up to 11 days, and the trends differ by up to a week per decade. The results from the histograms and difference maps show that the statistical significance of these trends strongly depends on the type of model applied. Therefore, further work should focus on the development of metrics that can quantify the difference between patterns and trends derived from spatially explicit phenological models. Such metrics could subsequently be used to validate phenological models in both space and time. Also, such metrics could be used to validate phenological models in both space and time. [ABSTRACT FROM AUTHOR]

Climatic change is exhibiting significant effects on the ecosystem of the Tibetan Plateau (TP), a climate-sensitive area. In particularly, winter frost, freezing events and snow avalanche frequently causing severe effects on ecosystem and social economy, however, few long-term winter temperature records or reconstructions hinder a better understanding on variations in winter temperature in the vast area of the TP. In this paper, we present a minimum winter (November–February) temperature reconstruction for the past 668 years based on a tree-ring network (12 new tree-ring chronologies) on the southeastern TP. The reconstruction exhibits decadal to inter-decadal temperature variability, with cold periods occurring in 1423–1508, 1592–1651, 1729–1768, 1798–1847, 1892–1927, and 1958–1981, and warm periods in 1340–1422, 1509–1570, 1652–1728, 1769–1797, 1848–1891, 1928–1957, and 1982–2007. As suggested by the comparisons with existing winter temperature series and spatial correlations with Climatic Research Unit gridded data, our reconstruction is reliable and indicative, and it can represent large-scale winter temperature variability on the southeastern TP. Furthermore, it shows an overall agreement with winter temperature from the northeastern TP on decadal to inter-decadal timescales. It also shows the possible effects of volcanic eruption and reducing solar activity on the winter temperature variability for the past six centuries on the southeastern TP. [ABSTRACT FROM AUTHOR]

The availability of reliable meteorological records is crucial for the development of a number of environmental studies. Unfortunately, these records are not always complete, usually show errors and/or have an insufficient length. This paper presents a gap filling and data record extension methodology for minimum temperature, maximum temperature, and precipitation. It uses climatic information from the NCEP-NCAR Reanalysis project, identifying pixels (grid cells) within a Reanalysis domain that have the highest Pearson's correlation coefficient with the variable of interest. Nine stations in the Maipo River basin (Santiago, Chile) were selected for a reconstruction experiment (from 1950 to 1970) and a subsequent gap filling experiment (from 1970 to 2012). A generalized linear mixed model with a bidirectional stepwise fit procedure was used to model temperature, whereas precipitation occurrence was represented using a generalized linear mixed model with binomial distribution, and precipitation amount used an exponential generalized linear model. The performance of the algorithm was compared with inverse distance weighting and spline interpolation methods and further evaluated using the Standardized Precipitation Evapotranspiration Index, contrasting real versus modeled data. Values of the coefficient of determination averaged 0.76 (0.74–0.84) minimum temperature, 0.73 (0.73–0.81) for maximum temperature, and 0.68 (0.51–0.78) for precipitation. Root-mean-squared error was around 1.5 °C and 5 mm for temperature and precipitation, respectively. The model explains local variation of climatic variables and indicators and can be replicated anywhere, as the Reanalysis data are easily accessible and have a worldwide coverage. [ABSTRACT FROM AUTHOR]

Climate variability has been a threat to the socio-economic development of Ethiopia. This paper examined the changes in rainfall, minimum, and maximum temperature extremes of Jemma Sub-Basin of the Upper Blue Nile Basin for the period of 1981 to 2014. The nonparametric Mann-Kendall, seasonal Mann-Kendall, and Sen's slope estimator were used to estimate annual trends. Ten rainfall and 12 temperature indices were used to study changes in rainfall and temperature extremes. The results showed an increasing trend of annual and summer rainfall in more than 78% of the stations and a decreasing trend of spring rainfall in most of the stations. An increase in rainfall extreme events was detected in the majority of the stations. Several rainfall extreme indices showed wetting trends in the sub-basin, whereas limited indices indicated dryness in most of the stations. Annual maximum and minimum temperature and extreme temperature indices showed warming trend in the sub-basin. Presence of extreme rainfall and a warming trend of extreme temperature indices may suggest signs of climate change in the Jemma Sub-Basin. This study, therefore, recommended the need for exploring climate induced risks and implementing appropriate climate change adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

This paper examines an asymmetric spatiotemporal connection and climatic impact between the winter atmospheric blocking activity in the Euro-Atlantic sector and the life cycle of the North Atlantic Oscillation (NAO) during the period 1950-2012. Results show that, for positive NAO (NAO+) events, the instantaneous blocking (IB) frequency exhibits an enhancement along the southwest-northeast (SW-NE) direction from the eastern Atlantic to northeastern Europe (SW-NE pattern, hereafter), which is particularly evident during the NAO+ decaying stage. By contrast, for negative NAO (NAO−) events, the IB frequency exhibits a spatially asymmetric southeast-northwest (SE-NW) distribution from central Europe to the North Atlantic and Greenland (SE-NW pattern, hereafter). Moreover, for NAO− (NAO+) events, the most marked decrease (increase) in the surface air temperature (SAT) in winter over northern Europe is in the decaying stage. For NAO+ events, the dominant positive temperature and precipitation anomalies exhibit the SW-NE-oriented distribution from western to northeastern Europe, which is parallel to the NAO+-related blocking frequency distribution. For NAO- events, the dominant negative temperature anomaly is in northern and central Europe, whereas the dominant positive precipitation anomaly is distributed over southern Europe along the SW-NE direction. In addition, the downward infrared radiation controlled by the NAO’s circulation plays a crucial role in the SAT anomaly distribution. It is further shown that the NAO’s phase can act as an asymmetric impact on the European climate through producing this asymmetric spatiotemporal connection with the Euro-Atlantic IB frequency. [ABSTRACT FROM AUTHOR]

Adaption for temperature should be suitable to local conditions for regional differences in temperature change features. This paper proposed to utilize nine temperature modes that joint the trend (increasing/decreasing/unchanged) with variability (intensifying/weakening/unchanged) to investigate features of temperature change in mainland China. Monthly temperature data over the period 1960-2013 were obtained from 522 national basic and reference meteorological stations. Here, temperature trend (TT) was reflected by the trend of mean annual temperature (MAT) and the uptrend (downtrend) of inter-monthly sliding standard deviation (SSD) series with a sliding length of 29 years (348 months) was used for representing the intensification (weakening) of temperature variability (TV). The Mann-Kendall method and the least squares method were applied to assess the significance and quantify the magnitude of trend in MAT and SSD time series, respectively. The results show that there is a consistent warming trend throughout the country except for only three stations in which a cooling trend is identified. Moreover, the overall increasing rate in the north of 35° N is the highest, over 0.4 °C/decade for most stations. TV is weakened for almost 98% of the stations, indicating the low instability of temperature at a national scale. Finally, temperature mode (TM), for more than 90% of the stations, is the combination of an increasing TT with a weakened TV (mode 8). So, it is more important for people to adapt to the increasing temperature in these regions. Compared to using annual temperature data to calculate SSD, monthly data can accurately reflect the inter-monthly change of temperature and reserve more initial characteristics of temperature. [ABSTRACT FROM AUTHOR]

Reliable knowledge of the near-future climate change signal of extremes is important for adaptation and mitigation strategies. Especially compound extremes, like heat and drought occurring simultaneously, may have a greater impact on society than their univariate counterparts and have recently become an active field of study. In this paper, we use a 12-member ensemble of high-resolution (7 km) regional climate simulations with the regional climate model COSMO-CLM over central Europe to analyze the climate change signal and its uncertainty for compound heat and drought extremes in summer by two different measures: one describing absolute (i.e., number of exceedances of absolute thresholds like hot days), the other relative (i.e., number of exceedances of time series intrinsic thresholds) compound extreme events. Changes are assessed between a reference period (1971–2000) and a projection period (2021–2050). Our findings show an increase in the number of absolute compound events for the whole investigation area. The change signal of relative extremes is more region-dependent, but there is a strong signal change in the southern and eastern parts of Germany and the neighboring countries. Especially the Czech Republic shows strong change in absolute and relative extreme events. [ABSTRACT FROM AUTHOR]

In this paper, temperature and rainfall data series were analysed from 34 meteorological stations distributed throughout Bangladesh over a 40-year period (1971 to 2010) in order to evaluate the magnitude of these changes statistically and spatially. Linear regression, coefficient of variation, inverse distance weighted interpolation techniques and geographical information systems were performed to analyse the trends, variability and spatial patterns of temperature and rainfall. Autoregressive integrated moving average time series model was used to simulate the temperature and rainfall data. The results confirm a particularly strong and recent climate change in Bangladesh with a 0.20 °C per decade upward trend of mean temperature. The highest upward trend in minimum temperature (range of 0.80-2.4 °C) was observed in the northern, northwestern, northeastern, central and central southern parts while greatest warming in the maximum temperature (range of 1.20-2.48 °C) was found in the southern, southeastern and northeastern parts during 1971-2010. An upward trend of annual rainfall (+7.13 mm per year) and downward pre-monsoon (−0.75 mm per year) and post-monsoon rainfall (−0.55 mm per year) trends were observed during this period. Rainfall was erratic in pre-monsoon season and even more so during the post-monsoon season (variability of 44.84 and 85.25 % per year, respectively). The mean forecasted temperature exhibited an increase of 0.018 °C per year in 2011-2020, and if this trend continues, this would lead to approximately 1.0 °C warmer temperatures in Bangladesh by 2020, compared to that of 1971. A greater rise is projected for the mean minimum (0.20 °C) than the mean maximum (0.16 °C) temperature. Annual rainfall is projected to decline 153 mm from 2011 to 2020, and a drying condition will persist in the northwestern, western and southwestern parts of the country during the pre- and post-monsoonal seasons. [ABSTRACT FROM AUTHOR]

The present paper examines variability of characteristics of snow cover (snow cover depth, number of days with snow cover and dates of beginning and end of snow cover) in Poland. The study makes use of a set of 43 long time series of observation records from the stations in Poland, from 1952 to 2013. To describe temporal changes in snow cover characteristics, the intervals of 1952-1990 and of 1991-2013 are compared and trends in analysed data are sought (e.g., using the Mann-Kendall test). Observed behaviour of time series of snow-related variables is complex and not easy to interpret, for instance because of the location of the research area in the zone of transitional moderate climate, where strong variability of climate events is one of the main attributes. A statistical link between the North Atlantic Oscillation (NAO) index and the snow cover depth, as well as the number of snow cover days is found. [ABSTRACT FROM AUTHOR]

Statistical downscaling techniques are required to refine atmosphere-ocean global climate data and provide reliable meteorological information such as a realistic temporal variability and relationships between sites and variables in a changing climate. To this end, the present paper introduces a modular structure combining two statistical tools of increasing interest during the last years: (1) Gaussian copula and (2) quantile regression. The quantile regression tool is employed to specify the entire conditional distribution of downscaled variables and to address the limitations of traditional regression-based approaches whereas the Gaussian copula is performed to describe and preserve the dependence between both variables and sites. A case study based on precipitation and maximum and minimum temperatures from the province of Quebec, Canada, is used to evaluate the performance of the proposed model. Obtained results suggest that this approach is capable of generating series with realistic correlation structures and temporal variability. Furthermore, the proposed model performed better than a classical multisite multivariate statistical downscaling model for most evaluation criteria. [ABSTRACT FROM AUTHOR]

Projections of changes in temperature extremes are critical to assess the potential impacts of climate change on agricultural and ecological systems. Statistical downscaling can be used to efficiently downscale output from a large number of general circulation models (GCMs) to a fine temporal and spatial scale, providing the opportunity for future projections of extreme temperature events. This paper presents an analysis of extreme temperature data downscaled from 7 GCMs selected from the Coupled Model Intercomparison Project phase 5 (CMIP5) using a skill score based on spatial patterns of climatological means of daily maximum and minimum temperature. Data for scenarios RCP4.5 and RCP8.5 for the New South Wales (NSW) wheat belt, south eastern Australia, have been analysed. The results show that downscaled data from most of the GCMs reproduces the correct sign of recent trends in all the extreme temperature indices (except the index for cold days) for 1961-2000. An independence weighted mean method is used to calculate uncertainty estimates, which shows that multi-model ensemble projections produce a consistent trend compared to the observations in most extreme indices. Great warming occurs in the east and northeast of the NSW wheat belt by 2061-2100 and increases the risk of exposure to hot days around wheat flowering date, which might result in farmers needing to reconsider wheat varieties suited to maintain yield. This analysis provides a first overview of projected changes in climate extremes from an ensemble of 7 CMIP5 GCM models with statistical downscaling data in the NSW wheat belt. [ABSTRACT FROM AUTHOR]

Climate change is a fundamental challenge for which agriculture is sensitive and vulnerable. The Intergovernmental Panel on Climate Change has identified relevant information as key to enabling appropriate climate adaptation and mitigation action. Information specifically directed to farmers can be found, for example, in specialized farming magazines. While recent studies examine how national news media frame climate change, less-if any-studies have addressed climate framings and coverage in specialized media. Media framings are storylines that provide meaning by communicating how and why an issue should be seen as a problem, how it should be handled, and who is responsible for it. This paper analyses the framings and coverage of climate change in two Swedish specialized farming magazines from 2000 to 2009. It examines the extent of the climate change coverage, the content of the media items, and the dominant framings underlying their climate change coverage. The study identifies: increased coverage of climate change starting in 2007; frequent coverage of agriculture's contribution to climate change, climate change impacts on agriculture, and consequences of climate politics for agriculture; and four prominent frames: conflict, scientific certainty, economic burden, and action. The paper concludes that climate change communicators addressing farmers and agricultural extension officers should pay attention to how these frames may be interpreted by different target audiences. Research is needed on how specialized media reports on climate-related issues and how science-based climate information is understood by different groups of farmers and which other factors influence farmers' engagement in climate mitigation and adaptation. [ABSTRACT FROM AUTHOR]

This paper presents the earliest temperature observations, scheduled every 3-4 h in the 1654-1670 period, which have been recovered and analysed for the first time. The observations belong to the Medici Network, the first international network of meteorological observations, based on eleven stations, the two main ones being Florence and Vallombrosa, Italy. All observations were made with identical thermometers and operational methodology, including outdoor exposure in the shade and in the sunshine to evaluate solar heating, state of the sky, wind direction and precipitation frequency. This paper will consider only the regular temperature series taken in the shade. The observations were made with the newly invented spirit-in-glass thermometer, also known as Little Florentine Thermometer (LFT). The readings have been transformed into modern units of temperature (°C) and time (TMEC). The LFT has been analysed in detail: how it was made, its linearity, calibration and performances. Since the middle of the LIA, the climate in Florence has shown less than 0.18°C warming. However, although the yearly average showed little change, the seasonal departures are greater, i.e. warmer summers, colder winters and unstable mid seasons. The temperature in the Vallombrosa mountain station, 1,000 m a.m.s.l, apparently rose more, i.e. 1.41°C. A discussion is made on the interpretation of this finding: how much it is affected by climate change or bias. A continuous swinging of the temperature was observed in the Mediterranean area, as documented by the long instrumental observations over the 1654-2009 period. However, changes in vegetation, or exposure bias might have contributed to reduce the homogeneity of the series over the centuries. [ABSTRACT FROM AUTHOR]

August 2006 was an exceptionally wet month in the Netherlands, in particular near the coast where rainfall amounts exceeded 300% of the climatological mean. August 2006 was preceded by an extremely warm July with a monthly mean temperature of almost 1°C higher than recorded in any other summer month in the period 1901–2006. This had resulted in very high sea surface temperatures (SSTs) in the North Sea at the end of July. In this paper the contribution of high SSTs to the high rainfall amounts is investigated. In the first part of this study, this is done by analyzing short-term integrations with a regional climate model (RACMO2) operated at 6 km resolution, which are different in the prescribed values of the SSTs. In the second part of the paper the influence of SSTs on rainfall is analyzed statistically on the basis of daily observations in the Netherlands during the period 1958–2006. The results from both the statistical analysis as well as the model integrations show a significant influence of SSTs on precipitation. This influence is particularly strong in the coastal area, that is, less than 30–50 km from the coastline. With favorable atmospheric flow conditions, the analyzed dependency is about +15% increase per degree temperature rise, thereby exceeding the Clausius–Clapeyron relation—which is often used as a temperature related constraint on changes in extreme precipitation—by approximately a factor of two. It is shown that the coastal area has consistently become wetter compared to the inland area since the 1950s. This finding is in agreement with the rather strong observed trend in SST over the same period and the dependencies of rainfall on SST reported in this study. [ABSTRACT FROM AUTHOR]

Combining GIS measures and statistic methods, this paper selects the physiographic factors and climatic factors to study the influencing strength and its functional forms of all factors on population density in the LRGR. In addition, this paper simulates the status of population density distribution in the LRGR with the factors influencing the population density distribution. The findings indicate the following circumstances: (1) All natural factors have influence on the spatial pattern of population density in the LRGR, but the influencing extent and forms are quite different. Physiographic factors and traffic network density have great influence on population density, while such factors as temperature and precipitation have little influence on population distribution. The relationship between the population density and the altitude and traffic network density assumes the negative and positive linear correlation trends, while the influence of water system density and slope appears to be the exponential function and logarithmic function separately. (2) The population is scattered throughout LRGR and takes on such features as integral scattering and local concentration because of the influence of natural conditions. Thus, a high population density belt has been formed in the middle of LRGR, and a high population density patch comes into being in the south of LRGR. (3) There is a good linear regression relationship between population density and human activity index (HAI) in the LRGR, which reflects that population density can present the artificial disturbance on natural ecosystem in the LRGR. (4) The increase of population density leads to the decrease of NDVI index. [ABSTRACT FROM AUTHOR]

In this paper, we present regional climate change projections for the Tamil Nadu state of India, simulated by the Met Office Hadley Centre regional climate model. The model is run at 25 km horizontal resolution driven by lateral boundary conditions generated by a perturbed physical ensemble of 17 simulations produced by a version of Hadley Centre coupled climate model, known as HadCM3Q under A1B scenario. The large scale features of these 17 simulations were evaluated for the target region to choose lateral boundary conditions from six members that represent a range of climate variations over the study region. The regional climate, known as PRECIS, was then run 130 years from 1970. The analyses primarily focus on maximum and minimum temperatures and rainfall over the region. For the Tamil Nadu as a whole, the projections of maximum temperature show an increase of 1.0, 2.2 and 3.1 °C for the periods 2020s (2005-2035), 2050s (2035-2065) and 2080s (2065-2095), respectively, with respect to baseline period (1970-2000). Similarly, the projections of minimum temperature show an increase of 1.1, 2.4 and 3.5 °C, respectively. This increasing trend is statistically significant (Mann-Kendall trend test). The annual rainfall projections for the same periods indicate a general decrease in rainfall of about 2-7, 1-4 and 4-9 %, respectively. However, significant exceptions are noticed over some pockets of western hilly areas and high rainfall areas where increases in rainfall are seen. There are also indications of increasing heavy rainfall events during the northeast monsoon season and a slight decrease during the southwest monsoon season. Such an approach of using climate models may maximize the utility of high-resolution climate change information for impact-adaptation-vulnerability assessments. [ABSTRACT FROM AUTHOR]

The foremost role of a building is to assure the comfort of its occupants. The thermal comfort of a building depends on the outdoor climate and requires a demand in energy for heating and cooling. In this paper, demand of energy (heating/cooling) in the buildings is discussed in Douala, Cameroon. Daily data of the last 40 years coming from five weather stations of Cameroon have been studied. Some forecasts have been carried out with 14 GCM models, associated to three future climate scenarios B1, A2, and A1B. However, only INCM3 of General Circulation Model (GCM) and A2 scenario was used. Energy demand in buildings is valued by HDD (heating degree day) and CDD (cooling degree day) indices. Obtained results show that the temperature evolves more quickly in dry season than in rainy season in Douala. Climate rise indicates an increasing demand of energy in the buildings for cooling. Global Douala heating shows a definite effect on outdoor comfort. From 2045 to 2075, the demand of energy for cooling will be superior to 50%. The total demand in energy for heating in the buildings is estimated to be 67.882 kcal from 1970 to 2000 and will be around 67.774 kcal from 2013 to 2043. [ABSTRACT FROM AUTHOR]

Any attempt to describe the spatial ecology of sharks and rays should consider the drivers responsible for movement. Research has shown fluctuations in the environment (abiotic factors) can trigger movement and changes in behaviour and habitat use for many elasmobranch species. Most studies to date have selectively focused on a small number of abiotic factors (i.e. temperature, salinity); however, other factors such as dissolved oxygen, tide, photoperiod, barometric pressure and pH have also been documented to act as drivers of movement in shark and ray species. Although usually examined individually, abiotic factors rarely act in isolation and often differ in their level of influence between species, sex, ontogenetic stage, season and geographic location. This paper reviews the role of abiotic factors as a driver of movement and changes in behaviour and habitat use in elasmobranchs. In the context of a changing climate, insight into how sharks and rays may respond to fluctuating environmental conditions projected under future scenarios is required. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of meeting a predetermined temperature target cost-effectively under uncertainty and gradual learning on climate sensitivity. The firstorder optimality conditions to a stochastic cost-minimization problem with a temperature constraint are first provided, portraying how marginal costs evolve with an optimal hedging strategy. Then, numerical stochastic scenarios with cost curves fitted to recent climate changemitigation scenarios are presented, illustrating both the range of possible future pathways and the effect of uncertainty to the solution. Last, the effect of several different sets of assumptions on the optimal hedging strategy are analyzed. The results highlight that the hedging of climate sensitivity risk calls for deeper early reductions, although the possibility of different assumptions prevents providing accurate policy guidance. [ABSTRACT FROM AUTHOR]

This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme value analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels. [ABSTRACT FROM AUTHOR]

Since climatic condition is the important foundation for human subsistence and development and the key factor in sustainable development of economy and society, climate change has been a global issue attracting great attentions of politicians, scientists, governments, and the public alike throughout the world. Existing climate regionalization in China aims to characterize the regional differences in climate based on years of the mean value of different climate indexes. However, with the accelerating climate change nowadays, existing climate regionalization cannot represent the regional difference of climate change, nor can it reflect the disasters and environmental risks incurred from climate changes. This paper utilizes the tendency value and fluctuation value of temperature and precipitation from 1961 to 2010 to identify the climate change quantitatively, and completes the climate change regionalization in China (1961-2010) with county administrative regionalization as the unit in combination with China's terrain feature. Level-I regionalization divides China's climate change (1961-2010) into five tendency zones based on the tendency of temperature and precipitation, which are respectively Northeast China-North China warm-dry trend zone, East China-Central China wet-warm trend zone, Southwest China-South China dry-warm trend zone, Southeast Tibet-Southwest China wet-warm trend zone, and Northwest China-Qinghai-Tibet Plateau warm-wet trend zone; level-II regionalization refers to fourteen fluctuation regions based on level-I regionalization according to the fluctuation of temperature and precipitation. [ABSTRACT FROM AUTHOR]

Increasing epidemiological studies have shown that a rapid temperature change within 1 day is an independent risk factor for human health. This paper aimed to systematically review the epidemiological evidence on the relationship between diurnal temperature range (DTR) and human health and to propose future research directions. A literature search was conducted in October 2013 using the databases including PubMed, ScienceDirect, and EBSCO. Empirical studies regarding the relationship between DTR and mortality and morbidity were included. Twenty-five relevant studies were identified, among which, 11 investigated the relationship between DTR and mortality and 14 examined the impact of DTR on morbidity. The majority of existing studies reported that DTR was significantly associated with mortality and morbidity, particularly for cardiovascular and respiratory diseases. Notably, compared with adults, the elderly and children were more vulnerable to DTR effects. However, there were some inconsistencies regarding the susceptible groups, lag time, and threshold of DTR. The impact of DTR on human health may be confounded or modified by season, socioeconomic, and educational status. Further research is needed to further confirm the adverse effects of DTR in different geographical locations; examine the effects of DTR on the health of children aged one or under; explore extreme DTR effects on human health; analyze the difference of DTR effects on human health in different locations and the modified effects of potential confounding factors; and develop detailed preventive measures against large DTR, particularly for susceptible groups. [ABSTRACT FROM AUTHOR]

This paper presents results of trend analysis and change point detection of annual and seasonal precipitation, and mean temperature (TM), maximum temperature (TMAX) and minimum temperature (TMIN) time series of the period 1950-2007. Investigations were carried out for 50 precipitation stations and 39 temperature stations located in southwest Iran. Three statistical tests including Pettitt's test, Sequential Mann-Kendall test (SQ-MK test) and Mann-Kendall rank test (MK-test) were used for the analysis. The results obtained for precipitation series indicated that most stations showed insignificant trends in annual and seasonal series. Out of the stations which showed significant trends, highest numbers were observed during winter season while no significant trends were detected in summer precipitation. Moreover, no decreasing significant trends were detected by statistical tests in annual and seasonal precipitation series. The analysis of temperature trends revealed a significant increase during summer and spring seasons. TMAX was more stable than TMIN and TM, and winter was stable compared to summer, spring and autumn seasons. The results of change point detection indicated that most of the positive significant mutation points in TM, TMAX and TMIN began in the 1990s. [ABSTRACT FROM AUTHOR]

It is an undisputed fact that weather risk increases over time due to climate change. However, qualification of this statement with regard to the type of weather risk and geographical location is needed. In this paper we compare alternative tests for trend detection and discuss their sensitivity. We use local t tests, change point tests and Mann-Kendall tests to analyze the trends of weather risk indices that are relevant from an agricultural viewpoint. Local test procedures offer more information about the timing and the kind of change in weather risk than global tests do. We also use quantile regression to analyze changes in the tails of weather index distributions. These methods are applied to temperature and rainfall based weather indices in three different climatic zones. Our results show that weather risk follows different patterns depending on the type of risk and the location. We also find differences in the sensitivity of the statistical test procedures. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of climate change on destination choice decisions in a context of domestic coastal tourism in Spain. Destinations are characterized in terms of travel cost and coastal 'attractors', such as temperature and beach-related attributes. By means of a discrete choice model based on the random utility theory, these variables are used to explain the observed pattern of interprovincial domestic trips, showing trade-offs between temperature and attractiveness in the probability of a particular destination being chosen. The model is used to investigate the impact of two climate change scenarios on the allocation of domestic tourism within Spain. The findings show that while Spain's northern colder provinces would benefit from rising temperatures, provinces in the south would experience a decrease in the frequency of trips. [ABSTRACT FROM AUTHOR]

This paper presents an empirical study of the relationship between residential energy demand and temperature. Unlike previous studies in this field, the data sample has a global coverage and special emphasis is given to the heterogeneous response of different regions and to the contrasting effects on energy demand for cooling and heating purposes. To account for this we distinguish between different regions, seasons, and energy sources. Short- and long-run temperature demand elasticities are estimated. These features make the model results especially valuable in the analysis of climate change impacts as they provide an empirical basis for the study of the impact of climate change on energy demand. To illustrate the potential of the results as a basis for the study of climate change impacts, the estimates are used in a simple exercise that projects changes in energy demand due to temperatures increase in 2085. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to estimate how the observed and predicted climate changes may affect the development rates and emergence of the codling moth in the southern part of the Wielkopolska region in Poland. In order to simulate the future climate conditions one of the most frequently used A1B SRES scenarios and two different IPCC climate models (HadCM3 and GISS modelE) are considered. A daily weather generator (WGENK) was used to generate temperature values for present and future climate conditions (time horizons 2020-2040 and 2040-2060). Based on the generated data set, the degree-days values were then calculated and the emergence dates of the codling moth at key stages were estimated basing on the defined thresholds. Our analyses showed that the average air surface temperature in the Wielkopolska region may increase from 2.8°C (according to GISS modelE) even up to 3.3°C (HadCM3) in the period of 2040-2060. With the warming climate conditions the cumulated degree-days values may increase at a rate of about 142 DD per decade when the low temperature threshold ( T) of 0°C is considered and 91 DD per decade when T = 10°C. The key developmental stages of the codling moth may occur much earlier in the future climate conditions than currently, at a rate of about 3.8-6.8 days per decade, depending on the considered GCM model and the pest developmental stage. The fastest changes may be observed in the emergence dates of 95% of larvae of the second codling moth generation. This could increase the emergence probability of the pest third generation that has not currently occurred in Poland. [ABSTRACT FROM AUTHOR]

The relationship between temperature and drought was investigated using the temperature-sensitive growth of Larix chinensis Beissn in the Qinling Mountains, central China. Extremely high tree-ring width index values (TRWI) agreed well with dry conditions defined by the dryness-wetness index (DWI) obtained from data in Chinese historical documents and climate-related papers between 1814 and 1956 (before the short of instrumental measurements); the reverse applied to extremely low TRWI values. The main severe drought epochs occurred from the late 1850s to the 1870s, the 1920s to 1930s and in the 2000s, whereas wet spells occurred from 1817-1827 and 1881-1886. The droughts in the 2000s exhibited a similar pattern as the ones from the 1920s to 1930s, with obviously an increasing temperature. The variation of tree growth agreed well with other reconstructed temperature series from nearby and remote regions, suggesting that Larix chinensis could respond to broad-scale climate variability. The longest cold interval, 1817-1827, could be associated with the influence of the Tambora eruption in 1815. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to analyze the trends and variability in extreme temperature indices and its impact on rice-wheat productivity over two districts of Bihar, India, which is part of the middle Indo-Gangetic Basin. Mann-Kendall non-parametric test was employed for detection of trend and Sen slope was determined to quantify the magnitude of such trends. We have analyzed 10 extreme temperature indices for monthly and seasonally. The influence of extreme temperature indices on rice-wheat productivity was determined using correlation analysis. As far as Patna is concerned, if the number of cool days during September ≥10, the rice productivity will increase due to the availability of sufficient duration to fill up the grain. However, higher warm days during all the months except June will affect the productivity. A significant negative correlation was noticed between maximum value of minimum temperature during September and rice productivity. Highly significant positive correlation was noticed between number of cool days during September with rice productivity while it was highly significant negative correlation in the case of number of warm days during the same month. As far as Samastipur is concerned, a negative correlation was noticed between wheat productivity and maximum value of maximum temperature (TXx) during February, but not statistically significant. The higher temperature may affect the kernel weight and thereby yield. It is seen that a critical value of TXx ≥29.2 °C will be harmful to wheat crop during February. A significant positive correlation of number of cool nights with wheat productivity also supports the above relationship. The critical values of extreme temperature indices during rice and wheat growing months provide an indicator to assess the vulnerability of rice-wheat productivity to temperature for Patna and Samastipur districts and there is a need to prepare an adaptive strategy and also develop thermo-insensitive rice-wheat high yielding varieties suitable for this region to sustain rice-wheat productivity under projected climate change situation. [ABSTRACT FROM AUTHOR]

This paper analyzes climate variability and change in the Urmia Lake Basin, northwest of Iran. Annual average of the following data time series has been analyzed by statistical methods: dry bulb temperature, maximum and minimum temperature, precipitation, and number of rainy and snowy days. We have also used mean monthly temperature and precipitation data for analysis of drought spells for the period 1964-2005 to find out whether fluctuations in the lake level are attributable to natural drought. Our results indicate that mean precipitation has decreased by 9.2 % and the average maximum temperature has increased by 0.8°C over these four decades. The seasonal changes are particularly visible in winter and spring. Results of the Palmer Drought Severity Index show that on average, drought episodes have hit the Urmia Lake Basin every 5 years and most of them reached severe levels, but recent droughts have become more intense and last longer. [ABSTRACT FROM AUTHOR]

The primary factor controlling C/C relative abundance in terrestrial ecosystem since the Last Glacial has been widely debated. Now more and more researchers recognize that climate, rather than atmospheric CO concentration, is the dominant factor. However, for a specific area, conflicting viewpoints regarding the more influential one between temperature and precipitation still exist. As temperature and precipitation in a specific area usually not only vary within limited ranges, but also covary with each other, it is difficult to get a clear understanding of the mechanism driving C/C relative abundance. Therefore, systematic analysis on greater spatial scales may promote our understanding of the driving force. In this paper, records of C/C relative abundance since the Last Glacial on a global scale have been reviewed, and we conclude that: except the Mediterranean climate zone, C plants predominated the high latitudes during both the Last Glacial and the Holocene; from the Last Glacial to the Holocene, C relative abundances increased in the middle latitudes, but decreased in the low latitudes. Combining with studies of modern process, we propose a simplified model to explain the variations of C/C relative abundance in global ecosystem since the Last Glacial. On the background of atmospheric CO concentration since the Last Glacial, temperature is the primary factor controlling C/C relative abundance; when temperature is high enough, precipitation then exerts more influence. In detail, in low latitudes, temperature was high enough for the growth of C plants during both the Last Glacial and the Holocene; but increased precipitation in the Holocene inhibited the growth of C plants. In middle latitudes, rising temperature in the Holocene promoted the C expansion. In high latitudes, temperature was too low to favor the growth of C plants and the biomass was predominated by C plants since the Last Glacial. Our review would benefit interpretation of newly gained records of C/C relative abundance from different areas and different periods, and has its significance in the understanding of the driving mechanisms of C/C variations on longer timescales (e.g., since the late Miocene) with reliable records of temperature and atmospheric CO concentration. [ABSTRACT FROM AUTHOR]

Atmospheric variables play a fundamental role in driving man-managed ecosystems and more specifically in agro-ecosystems, determining the quantity and quality of crop production. On the other hand, climate variability can be seen as the superimposition of gradual and abrupt changes. This paper is focused on European surface air temperature in the period 1951-2010. Analysis of this dataset identified breakpoints that define two homogeneous sub-periods: 1951-1987 and 1988-2010. Thermal resources for crops were analyzed adopting a 'normal heat hours' approach. Computation highlighted a general increase in thermal resources in the European continent for crop groups II and III (C3 and C4 plants adapted to high or moderate temperatures), while a decline of thermal resources for crop group I (cold adapted C3) was highlighted in the Mediterranean area. The climate variability justifies a change in the potential latitudinal limits of different groups of crops, representing a fundamental step for crop adaptation to climate change. [ABSTRACT FROM AUTHOR]

In spring, Arctic coastal fast ice is inhabited by high densities of sea ice algae and, among other fauna, juveniles of benthic polychaetes. This paper investigates the hypothesis that growth rates of juveniles of the common sympagic polychaete, Scolelepis squamata (Polychaeta: Spionidae), are significantly faster at sea ice algal bloom concentrations compared to concurrent phytoplankton concentrations. Juvenile S. squamata from fast ice off Barrow, Alaska, were fed with different algal concentrations at 0 and 5 °C, simulating ambient high sea ice algal concentrations, concurrent low phytoplankton concentrations, and an intermediate concentration. Growth rates, calculated using a simple linear regression equation, were significantly higher (up to 115 times) at the highest algal concentration compared to the lowest. At the highest algal concentration, juveniles grew faster at 5 °C compared to those feeding at 0 °C with a Q of 2.0. We conclude that highly concentrated sea ice algae can sustain faster growth rates of polychaete juveniles compared to the less dense spring phytoplankton concentrations. The earlier melt of Arctic sea ice predicted with climate change might cause a mismatch between occurrence of polychaete juveniles and food availability in the near future. Our data indicate that this reduction in food availability might counteract any faster growth of a pelagic juvenile stage based on forecasted increased water temperatures. [ABSTRACT FROM AUTHOR]

This paper studies some impacts of climate change on electricity markets, focusing on three climate effects. First, demand for electricity is affected because of changes in the temperature. Second, changes in precipitation and temperature have impact on supply of hydro electric production through a shift in the inflow of water. Third, plant efficiency for thermal generation will decrease because the temperature of water used to cool equipment increases. To find the magnitude of these partial effects, as well as the overall effects, on Western European energy markets, we use the multi-market equilibrium model LIBEMOD. We find that each of the three partial effects changes the average electricity producer price by less than 2%, while the net effect is an increase of only 1%. The partial effects on total electricity supply are small, and the net effect is a decrease of 4%. The greatest effects are found for Nordic countries with a large market share for reservoir hydro. In these countries, annual production of electricity increases by 8%, reflecting more inflow of water, while net exports doubles. In addition, because of lower inflow in summer and higher in winter, the reservoir filling needed to transfer water from summer to winter is drastically reduced in the Nordic countries. [ABSTRACT FROM AUTHOR]