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The Taiwan government has been following the green energy policy for decades to cope with drastic climate change due to global warming and tremendous urbanization. Forming a database to assist Taiwanese policy makers in using the concept of energy efficiency and environmental comfort in urban planning and architectural design is required. This paper depicts how the field and CFD studies for an industrial region located in rural and monsoon wind prevailed district of the mid-western coast Taiwan were conducted, and how the data are used to construct building layout principles for regions having similar climate conditions. For the purpose of analyzing field data and forming database, measured atmospheric and microclimate parameters, such as the physiological equivalent temperature (PET), were first converted into empirical relations. Then, these empirical equations were used as the input and validating values for the CFD simulation model based on the FLUNENT software. Five field-observed wind profiles were used as the inlet conditions, and the computed results of the five-street canyon aspect ratio, and of four-building arrangements, are analyzed to construct the building layout principles. The study results show that the principles for a monsoon wind prevailed region should better be the following: (1) the street canyon aspect ratio is 0.1 and not over 0.3; (2) the building layout is a single open arrangement, avoiding a staggered setting. [ABSTRACT FROM AUTHOR]

Climate elements are important indicators of climate change in China's main grain-producing areas during the April–September growth season and affect the growth and yield of crops. This paper combines grain concentration and geographical detector to divide the North and South regions of China's main grain production. The linear trend and Morlet wavelet transform methods are used to analyse the characteristics of climate change based on observational climate data from April to September 1981–2015. The results show that the climate in the North region is warm and dry during the growth season, whereas the climate is warm and humid in the South region. The main periods of the change in temperature in the North and South regions are 3 years, and that in precipitation is 5 years, and that in sunshine hours is 3–4 years. Changes in the climate elements in various provinces show complex, varying and regional characteristics of cold-warm and dry-wet cycles. The changes in climate elements are significant and different climatic conditions and regions have various possible impacts on grain production in China during the growth season. For China's agricultural-economy sustainable development and grain security, the study suggests that governments should place more emphasis on climatic element changes during the growth season and invest more money in disaster prevention and mitigation, especially in the main grain-producing areas. [ABSTRACT FROM AUTHOR]

Climate change is now unequivocal; however, the type and extent of terrestrial impacts are still widely debated. Among these, the effects on slope stability are receiving a growing attention in recent years, both as terrestrial indicators of climate change and implications for hazard assessment. High-elevation areas are particularly suitable for these studies, because of the presence of the cryosphere, which is particularly sensitive to climate. In this paper, we analyze 358 slope failures which occurred in the Italian Alps in the period 2000–2016, at an elevation above 1500 m a.s.l. We use a statistical-based method to detect climate anomalies associated with the occurrence of slope failures, with the aim to catch an eventual climate signal in the preparation and/or triggering of the considered case studies. We first analyze the probability values assumed by 25 climate variables on the occasion of a slope-failure occurrence. We then perform a dimensionality reduction procedure and come out with a set of four most significant and representative climate variables, in particular heavy precipitation and short-term high temperature. Our study highlights that slope failures occur in association with one or more climate anomalies in almost 92% of our case studies. One or more temperature anomalies are detected in association with most case studies, in combination or not with precipitation (47% and 38%, respectively). Summer events prevail, and an increasing role of positive temperature anomalies from spring to winter, and with elevation and failure size, emerges. While not providing a final evidence of the role of climate warming on slope instability increase at high elevation in recent years, the results of our study strengthen this hypothesis, calling for more extensive and in-depth studies on the subject. [ABSTRACT FROM AUTHOR]

In this paper, we have analyzed the time series of daily values for three meteorological elements, two continuous and a discontinuous one, i.e., the maximum and minimum air temperature and the precipitation. The analysis was done based on the observations from seven stations in Serbia from the period 1951-2010. The main aim of this paper was to quantify the complexity of the annual values for the mentioned time series and to calculate the rate of its change. For that purpose, we have used the sample entropy and the Kolmogorov complexity as the measures which can indicate the variability and irregularity of a given time series. Results obtained show that the maximum temperature has increasing trends in the given period which points out a warming, ranged in the interval 1-2 °C. The increasing temperature indicates the higher internal energy of the atmosphere, changing the weather patterns, manifested in the time series. The Kolmogorov complexity of the maximum temperature time series has statistically significant increasing trends, while the sample entropy has increasing but statistically insignificant trend. The trends of complexity measures for the minimum temperature depend on the location. Both complexity measures for the precipitation time series have decreasing trends. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET) is considered a key parameter for evaluating the climatic changes as well as spatial and temporal patterns of parameters influencing the eco-hydrological processes. The analysis of trend variations of this index can be used to determine appropriate strategies in planning and management of water resources. In this paper, the trend variations of monthly and annual ET in Urmia Lake basin, located in the northwest of Iran, have been analyzed using data from 14 synoptic stations in the study area. Regarding the significant effect of autocorrelation coefficients with different lags on trend variations of ET, this paper has resorted to modified Mann-Kendall test via eliminating the significance effect of autocorrelation coefficients with different lags to analyze the trend variations. Furthermore, Theil-Sen estimator has been used to determine the slope of trend line of ET. The results indicated an increasing trend in ET values at all the studied stations. Having used the modified Mann-Kendall test, the values of significant increasing (positive) trend, which were estimated using common Mann-Kendall test, dramatically decreased. As such, the values of only 7 stations have been significant at 95 % level. The results confirmed the need for eliminating the significance effect of autocorrelation coefficients with different lags to determine and evaluate the trend of hydrological variables. [ABSTRACT FROM AUTHOR]

This paper studies the Upper Tigris River (UTR) drainage basin in Turkey for climate change impacted runoff estimations. Statistical downscaling method (SDM) is used by taking into consideration spatial dependence function (SDF) for the scenario precipitation projections at a set of available meteorology stations. Temporal adjustment between the climate scenarios and precipitation record time series is achieved by the white Markov (WM) stochastic process. Although various climate research center scenario data are considered, herein, only the general circulation model (GCM) A2 scenario data are adapted from the Hadley Center, England. The precipitation and runoff results are presented in decadal groups starting from 2001 to 2050 as cumulative monthly precipitation (CMP) and cumulative monthly runoff (CMR) graphs. It is observed that after 2021, precipitation decreases at about 12.5% and after 2030, it is 26%. Runoff projections indicate that they may decrease at about 30% especial after 2040. [ABSTRACT FROM AUTHOR]

Based on daily mean temperature data from 32 meteorological stations in the study area, this paper discusses the relationship between the changes in the climatic growing season and altitude and the differences in the northern (NSQM) and southern (SSQM) slopes of the Qinling Mountains (QMs) from 1985 to 2015. Our analyses generated three primary results. First, the spatial distribution patterns of the growing season start (GSS), growing season end (GSE), and growing season length (GSL) exhibited an altitudinal gradient and differences between the NSQM and SSQM. The occurrence time of the GSS, GSE, and the GSL of the NSQM was approximately 15 days later, 10 days earlier, and 25 days shorter respectively than that of the SSQM. Second, the three factors exhibited regular changes with an increasing elevation gradient: the GSS, GSE, and GSL were gradually delayed, advanced, and shortened, respectively. In the area below 1800 m above sea level, the GSS, GSE, and GSL of the NSQM was later, earlier, and shorter, respectively, than that of the SSQM; while the changing trends in the three were opposite in the areas above 2700 m; and the three in the areas between 1800 and 2700 m varied little in the NSQM and SSQM. The sensitivities of the GSS, GSE, and GSL of the NSQM and SSQM to elevation also reflected in the trends of inter-annual variability. Third, among the different altitudinal vegetation belts from low-altitude to high-altitude areas, the occurrence time of the GSS and GSE and the duration of the GSL in the alpine meadow areas were all exchanged in the NSQM and SSQM. The GSS, GSE, and GSL of the NSQM were earlier, later, and longer respectively than that of the SSQM. [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]

The Mediterranean region is an area potentially vulnerable to climatic changes. In fact, it is characterized by a significant precipitation variability resulting from synoptic dynamics of hazardous events moving and evolving along the Mediterranean basin. In this paper, the results of an investigation on rainfall trend in the Sardinia region are presented. The trend analysis has been performed by means of the Mann–Kendall test and of the Theil–Sen estimator applied on annual, seasonal, and monthly scales. To this aim, a monthly database of precipitation registered at 158 rain gauges, with an average observation of 84 years, was used for the analyses. The results mainly evidenced a rainfall amount reduction in the winter months and an increase during the summer months. The monthly rainfall distribution throughout the year was also investigated by means of the precipitation concentration index (PCI). A negative trend emerged and, more specifically, a tendency towards a more uniform rainfall distribution on the Sardinia region throughout the year. [ABSTRACT FROM AUTHOR]

In this paper, we analyzed the climate change impact in some wine-growing areas of the Abruzzi region through the estimated dates of bud burst and the study of the relationships between some bioclimatic indexes with the beginning of harvest date of cv. Montepulciano, during the period 1974–2013. The earlier harvest date would seem mainly linked to the increase of heat summation in the period March–June. The break point of the time series of harvest date showed good correlation with the heat summation in the period from March to June. Climate change, occurred in European continent begun around 1980, would determine in the wine-growing areas of Abruzzi, a significant reduction in the growth cycle of the cv. Montepulciano, mainly due to the earlier harvest date, considering that there were no substantial changes in the estimated dates of bud burst. [ABSTRACT FROM AUTHOR]

The generalized Pareto distribution (GPD) is often used in the statistical analysis of climate extremes. For a sample of independent and identically distributed observations, the parameters of the GPD can be estimated by the maximum likelihood (ML) method. In this paper, we drop the assumption of identically distributed random variables. We consider independent observations from GPD distributions having a common shape parameter but possibly an increasing trend in the scale parameter. Such a model, with increasing scale parameter, can be used to describe a trend in the observed extremes as time progresses. Estimating an increasing trend in a distribution parameter is common in the field of isotonic regression. We use ideas and tools from that area to compute ML estimates of the GPD parameters. In a simulation experiment, we show that the iterative convex minorant (ICM) algorithm is much faster than the projected gradient (PG) algorithm. We apply the approach to the daily maxima of the central England temperature (CET) data. A clear positive trend in the GPD scale parameter is found, leading to an increase in the 100-year return level from about 31º in the 1880s to 34º in 2015. [ABSTRACT FROM AUTHOR]

This paper aims to study what influence different meteorological parameters have on the radial tree growth of Scots pine (Pinus sylvestris L.) in peat and mineral soils, as well as to make predictions of radial tree growth responses to changing climate based on various future climate projections. Four Lithuanian peatland complexes representing different geographical settings and hydrological conditions were studied. From each study site, two tree-ring width (TRW) series were derived, one from trees growing on peat soil and one from trees on mineral soil at the periphery of the peatland. The annual growth rings from trees grown on mineral soils, in different geographical regions in Lithuania, show synchronicity, whereas the correlation between the TRW series from different peatland sites was weak to absent. The main factor that explains radial tree growth at the mineral-soil sites was air temperature during early spring (February–March), which influences the onset and duration of the growing season. However, variations in radial tree growth on the peatland sites were also attributed to lagged hydrological responses relating to precipitation and evaporation over several years. Our future projections show that growth conditions for pine trees on mineral soils will improve in the twenty-first century in Lithuania following an increase of air temperature in early spring. The predictions for the trees growing on peat soils, however, rely on the groundwater-level changes governed by a combination of precipitation and evaporation changes. Towards the end of the twenty-first century, the groundwater level in most Lithuanian peatlands is expected to increase, which most likely will result in harsher growth conditions for the peatland trees. This assumption is, however, open for debate as the peatland trees appear to favour the current warming conditions. It may therefore be too early to precisely predict future growth responses for the peatland trees, but this study is a next step to better understand future climate dynamics and vegetation responses in the Baltic region. [ABSTRACT FROM AUTHOR]

The paper discusses temporal changes in the configuration of vertical climatic belts in the Tatra Mountains as a result of current climate change. Meteorological stations are scarce in the Tatra Mountains; therefore, we modelled decadal air temperatures using existing data from 20 meteorological stations and the relationship between air temperature and altitude. Air temperature was modelled separately for northern and southern slopes and for convex and concave landforms. Decadal air temperatures were additionally used to delineate five climatic belts previously distinguished by Hess on the basis of threshold values of annual air temperature. The spatial extent and location of the borderline isotherms of 6, 4, 2, 0, and − 2 °C for four decades, including 1951–1960, 1981–1990, 1991–2000, and 2001–2010, were compared. Significant warming in the Tatra Mountains, uniform in the vertical profile, started at the beginning of the 1980s and led to clear changes in the extent and location of the vertical climatic belts delineated on the basis of annual air temperature. The uphill shift of the borderline isotherms was more prominent on southern than on northern slopes. The highest rate of changes in the extent of the climatic belts was found above the isotherm of 0 °C (moderately cold and cold belts). The cold belt dramatically diminished in extent over the research period. [ABSTRACT FROM AUTHOR]

For hundreds of years, human activity has modified the planet's surface through land-use practices. Policies and decisions on how land is managed and land-use changes due to replacement of forests by agricultural cropping and grazing lands affect greenhouse gas emissions. Agricultural management and agroforestry and the resulting changes to the land surface alter the global carbon cycle as well as the Earth's surface albedo, both of which in turn change the Earth's radiation balance. This makes land-use change the second anthropogenic source of climate change after fossil fuel burning. However, the scientific research community has so far not been able to identify the direction and magnitude of the global impact of land-use change. This paper examines the effects of net carbon flux from land-use change on temperature by applying Granger causality and error correction models. The results reveal a significant positive long-run equilibrium relationship between land-use change and the temperature series as well as an opposing short-term effect such that land-use change tends to lead to global warming; however, a rise in temperature causes a decline in land-use change. [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]

The North Atlantic Oscillation (NAO) is responsible for the climatic variability in the Northern Hemisphere, in particular, in Europe and is related to extreme events, such as droughts. The purpose of this paper is to study the correlation between precipitation and winter (December-January-February-March (DJFM)) NAO both for the historical period (1951-2000) and two future periods (2001-2050 and 2051-2100). NAO is calculated for these three periods by using sea level pressure, while precipitation data from seven climate models following the representative concentration pathway (RCP) 8.5 are also used in this study. An increasing trend in years with positive DJFM NAO values in the future is defined by this data, along with higher average DJFM NAO values. The correlation between precipitation and DJFM NAO is high, especially in the Northern (high positive) and Southern Europe (high negative). Therefore, higher precipitation in Northern Europe and lower precipitation in Southern Europe are expected in the future. Cross-spectral analysis between precipitation and DJFM NAO time series in three different locations in Europe revealed the best coherence in a dominant cycle between 3 and 4 years. Finally, the maximum drought period in terms of consecutive months with drought is examined in these three locations. The results can be used for strategic planning in a sustainable water resources management plan, since there is a link between drought events and NAO. [ABSTRACT FROM AUTHOR]

In this paper, we quantify the changes in precipitation distribution in south-western Poland between the periods 1891-1930 and 1981-2010. The average monthly precipitation totals available for 368 and 245 stations, respectively, are spatially interpolated and processed to calculate maps of differences and pluvial continentality indices for both periods. The maps are analysed against changes in atmospheric circulation and take into account the potential role of the mountain barrier of the Sudetes. The main findings are (1) winter totals generally increased in conjunction with a higher frequency of zonal circulation; it is more pronounced in the upper parts of the Sudetes due to orographic effects; (2) a decreased frequency of zonal circulation together with an increased frequency of southern flows has likely led to the reduction of precipitation in spring and autumn. These changes have led to a significant modification of the region's pluvial regime, mostly by decreasing continentality features (especially in the Sudetes and the western lowlands). In addition, the strong sheltering influence of the mountain barrier is observed, driving the preservation or even the enhancement of the continental pluvial characteristics in the north-eastern to eastern foregrounds of the Sudetes. This leads to the conclusion that in areas where long-term observed trends or future projections of precipitation are ambiguous, there may occur significant modifications in regional characteristics, especially as a result of changes in atmospheric circulation modified by local orography. [ABSTRACT FROM AUTHOR]

The objective of this paper is to quantify the various sources of uncertainty in the assessment of climate change impact on hydrology in the Tamakoshi River Basin, located in the north-eastern part of Nepal. Multiple climate and hydrological models were used to simulate future climate conditions and discharge in the basin. The simulated results of future climate and river discharge were analysed for the quantification of sources of uncertainty using two-way and three-way ANOVA. The results showed that temperature and precipitation in the study area are projected to change in near- (2010-2039), mid- (2040-2069) and far-future (2070-2099) periods. Maximum temperature is likely to rise by 1.75 °C under Representative Concentration Pathway (RCP) 4.5 and by 3.52 °C under RCP 8.5. Similarly, the minimum temperature is expected to rise by 2.10 °C under RCP 4.5 and by 3.73 °C under RCP 8.5 by the end of the twenty-first century. Similarly, the precipitation in the study area is expected to change by − 2.15% under RCP 4.5 and − 2.44% under RCP 8.5 scenarios. The future discharge in the study area was projected using two hydrological models, viz. Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center's Hydrologic Modelling System (HEC-HMS). The SWAT model projected discharge is expected to change by small amount, whereas HEC-HMS model projected considerably lower discharge in future compared to the baseline period. The results also show that future climate variables and river hydrology contain uncertainty due to the choice of climate models, RCP scenarios, bias correction methods and hydrological models. During wet days, more uncertainty is observed due to the use of different climate models, whereas during dry days, the use of different hydrological models has a greater effect on uncertainty. Inter-comparison of the impacts of different climate models reveals that the REMO climate model shows higher uncertainty in the prediction of precipitation and, consequently, in the prediction of future discharge and maximum probable flood. [ABSTRACT FROM AUTHOR]

Given the general notion that dynamical downscaling leads to added accuracy in both historical simulations as well as climate change projections, this paper investigates its validity over India using historical data (1975-2005) from the CORDEX models and their driving global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5), and comparing them against observed temperature and rainfall. We find that downscaling invariably leads to an improvement in the spatial pattern of surface air temperature, but compared to the driving GCMs, the errors in magnitude after downscaling are even worse in some cases. In regard to JJAS rainfall simulations, the CMIP5 driving GCMs are found to be superior to their dynamically downscaled counterparts both in terms of spatial patterns as well as magnitude of errors. Both CMIP5 driving GCMs as well as the CORDEX models underestimate rainfall during JJAS; however, negative bias in CORDEX models is worse. Unlike the driving CMIP5 GCMs, their dynamically downscaled counterparts simulate an early onset followed by a slow and late withdrawal of the Indian summer monsoon rainfall. The frequency of occurrence of rainfall intensities is simulated well by both sets of models in the lower intensity regime (0-20 mm/day); however, for higher intensities, the driving CMIP5 GCMs underestimate whereas the CORDEX models overestimate. [ABSTRACT FROM AUTHOR]

In this paper, robust statistical methods (including the climatic tendency ratio, inverse distance weight (IDW), and Mann-Kendall’s non-parametric statistical tests) were applied to analyze the annual sequences of meteorological data for the period 1960-2014. The results indicated that the mean annual air temperature in the Pan-Hexi and the surrounding regions has increased by 0.9 °C. Over the 55-year period of study, this represented a warming rate that was significantly higher than the contemporaneous warming rate in China and the rest of the world. The warming trend in northeastern Tibet was the most pronounced. Here, the mean warming rate exhibited an increasing trend from the mid-late 1980s, and the cold season-dominated warming changed to year-round warming. The rise in the amplitude of the mean minimum temperature was significant, and the variation in the amplitude of the extreme minimum temperature was greater than that of the extreme maximum temperature, exhibiting a variation period of approximately 10 years. Since 1960, the mean precipitation in the study region and the surrounding areas first increased, and then decreased, and then increased again rapidly from 2007. Precipitation and relative humidity trend showed a 13- and a 10-year variation period, respectively. Crop evapotranspiration exhibited a decreasing trend, but from 1993 onwards, there was an increasing trend and a 9-year variation period was also notable based on observed datasets. In the most recent 55 years (1960-2014), the sunshine duration and mean wind velocity in the study area first showed a decreasing trend followed by an increasing trend, and then it exhibited a decreasing trend from east to west of the study region. The temporal variations in the climatic elements were similar to those of China, suggesting that the climate change signal in the study region over the last 55 years appeared to be well correlated with the global climate change signal. [ABSTRACT FROM AUTHOR]

IPCC reports that a changing climate can affect the frequency and the intensity of extreme events. However, the extremes appear in the tail of the probability distribution. In order to know the relationship between extreme events in the tail of temperature and precipitation, an important but previously unobserved dependence structure is analyzed in this paper. Here, we examine the dependence structure by building a bivariate joint of Gumbel copula model for temperature and precipitation using monthly average temperature (T) and monthly precipitation (P) data from Beijing station in China covering a period of 1951-2015 and find the dependence structure can be divided into two sections, they are the middle part and the upper tail. We show that T and P have a strong positive correlation in the high tail section (T > 25.85 °C and P > 171.1 mm) (=0.66, p < 0.01) while they do not demonstrate the same relation in the other section, which suggests that the identification of a strong influence of T on extreme P needs help from the dependence structure analysis. We also find that in the high tail section, every 1 °C increase in T is associated with 73.45 mm increase in P. Our results suggested that extreme precipitation fluctuations by changes in temperature will allow the data dependence structure to be included in extreme affect for the disaster risk assessment under future climate change scenarios. Copula bivariate jointed probability distribution is useful to the dependence structure analysis. [ABSTRACT FROM AUTHOR]

Road authorities, freight, and logistic industries face a multitude of challenges in a world changing at an ever growing pace. While globalization, changes in technology, demography, and traffic, for instance, have received much attention over the bygone decades, climate change has not been treated with equal care until recently. However, since it has been recognized that climate change jeopardizes many business areas in transport, freight, and logistics, research programs investigating future threats have been initiated. One of these programs is the Conference of European Directors of Roads’ (CEDR) Transnational Research Programme (TRP), which emerged about a decade ago from a cooperation between European National Road Authorities and the EU. This paper presents findings of a CEDR project called CliPDaR, which has been designed to answer questions from road authorities concerning climate-driven future threats to transport infrastructure. Pertaining results are based on two potential future socio-economic pathways of mankind (one strongly economically oriented “A2” and one more balanced scenario “A1B”), which are used to drive global climate models (GCMs) producing global and continental scale climate change projections. In order to achieve climate change projections, which are valid on regional scales, GCM projections are downscaled by regional climate models. Results shown here originate from research questions raised by European Road Authorities. They refer to future occurrence frequencies of severely cold winter seasons in Fennoscandia, to particularly hot summer seasons in the Iberian Peninsula and to changes in extreme weather phenomena triggering landslides and rutting in Central Europe. Future occurrence frequencies of extreme winter and summer conditions are investigated by empirical orthogonal function analyses of GCM projections driven with by A2 and A1B pathways. The analysis of future weather phenomena triggering landslides and rutting events requires downscaled climate change projections. Hence, corresponding results are based on an ensemble of RCM projections, which was available for the A1B scenario. All analyzed risks to transport infrastructure are found to increase over the decades ahead with accelerating pace towards the end of this century. Mean Fennoscandian winter temperatures by the end of this century may match conditions of rather warm winter season experienced in the past and particularly warm future winter temperatures have not been observed so far. This applies in an even more pronounced manner to summer seasons in the Iberian Peninsula. Occurrence frequencies of extreme climate phenomena triggering landslides and rutting events in Central Europe are also projected to rise. Results show spatially differentiated patterns and indicate accelerated rates of increases. [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]

The global circulation models (GCMs) are useful tools for simulating climate change, projecting future temperature changes, and therefore, supporting the preparation of national climate adaptation plans. However, different GCMs are not always in agreement with each other over various regions. The reason is that GCMs’ configurations, module characteristics, and dynamic forcings vary from one to another. Model ensemble techniques are extensively used to post-process the outputs from GCMs and improve the variability of model outputs. Root-mean-square error (RMSE), correlation coefficient (CC, or R) and uncertainty are commonly used statistics for evaluating the performances of GCMs. However, the simultaneous achievements of all satisfactory statistics cannot be guaranteed in using many model ensemble techniques. In this paper, we propose a multi-model ensemble framework, using a state-of-art evolutionary multi-objective optimization algorithm (termed MOSPD), to evaluate different characteristics of ensemble candidates and to provide comprehensive trade-off information for different model ensemble solutions. A case study of optimizing the surface air temperature (SAT) ensemble solutions over different geographical regions of China is carried out. The data covers from the period of 1900 to 2100, and the projections of SAT are analyzed with regard to three different statistical indices (i.e., RMSE, CC, and uncertainty). Among the derived ensemble solutions, the trade-off information is further analyzed with a robust Pareto front with respect to different statistics. The comparison results over historical period (1900-2005) show that the optimized solutions are superior over that obtained simple model average, as well as any single GCM output. The improvements of statistics are varying for different climatic regions over China. Future projection (2006-2100) with the proposed ensemble method identifies that the largest (smallest) temperature changes will happen in the South Central China (the Inner Mongolia), the North Eastern China (the South Central China), and the North Western China (the South Central China), under RCP 2.6, RCP 4.5, and RCP 8.5 scenarios, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a statistical forecast model using the time-scale decomposition method is established to do the seasonal prediction of the rainfall during flood period (FPR) over the middle and lower reaches of the Yangtze River Valley (MLYRV). This method decomposites the rainfall over the MLYRV into three time-scale components, namely, the interannual component with the period less than 8 years, the interdecadal component with the period from 8 to 30 years, and the interdecadal component with the period larger than 30 years. Then, the predictors are selected for the three time-scale components of FPR through the correlation analysis. At last, a statistical forecast model is established using the multiple linear regression technique to predict the three time-scale components of the FPR, respectively. The results show that this forecast model can capture the interannual and interdecadal variation of FPR. The hindcast of FPR during 14 years from 2001 to 2014 shows that the FPR can be predicted successfully in 11 out of the 14 years. This forecast model performs better than the model using traditional scheme without time-scale decomposition. Therefore, the statistical forecast model using the time-scale decomposition technique has good skills and application value in the operational prediction of FPR over the MLYRV. [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]

This paper presents an evaluation of two simulations by regional climate model (RCM) ALADIN-Climate/CZ with different sizes of integration domain and their driving simulation of global climate model (GCM) ARPÉGE-Climat over central Europe during the period 1961-2010. After a brief evaluation of seasonal means of air temperature characteristics (daily mean, maximum and minimum temperature) and their variability, we focus on the ability of the simulations to represent observed changes in seasonal mean air temperature and its variability. We distinguish between intrinsic and trend-induced variability. Moreover, the dependence on the size of the integration domain and the improvement brought about by the nested RCM compared to the driving GCM are analysed. Both the evaluation of temperature means and variability and the evaluation of their changes confirm that RCM simulation on a larger domain often differs more considerably from driving GCM than simulation on a smaller domain. The larger domain simulation usually produces smaller biases for mean temperature but has worse agreement with observations in terms of variability. Further, the areas with the greatest discrepancies are often in the south-eastern region. In addition, noticeable differences between RCM and GCM simulations mostly emerge for the Alpine region and eastern regions. A possible explanation is the complex terrain and larger distance from domain boundaries. Moreover, it was analysed whether removing of the linear trend enhances the representation of changes in variability. This was not generally confirmed. Only in summer the simulations underestimated the observed increase in intrinsic variability less considerably than for total variability. However, biases were still considerable in many cases. [ABSTRACT FROM AUTHOR]

In this paper, expected changes in the spatial and altitudinal distribution patterns of Holdridge life zone (HLZ) types are analysed to assess the possible ecological impacts of future climate change for the Carpathian Region, by using 11 bias-corrected regional climate model simulations of temperature and precipitation. The distribution patterns of HLZ types are characterized by the relative extent, the mean centre and the altitudinal range. According to the applied projections, the following conclusions can be drawn: (a) the altitudinal ranges are likely to expand in the future, (b) the lower and upper altitudinal limits as well as the altitudinal midpoints may move to higher altitudes, (c) a northward shift is expected for most HLZ types and (d) the magnitudes of these shifts can even be multiples of those observed in the last century. Related to the northward shifts, the HLZ types warm temperate thorn steppe and subtropical dry forest can also appear in the southern segment of the target area. However, a large uncertainty in the estimated changes of precipitation patterns was indicated by the following: (a) the expected change in the coverage of the HLZ type cool temperate steppe is extremely uncertain because there is no consensus among the projections even in terms of the sign of the change (high inter-model variability) and (b) a significant trend in the westward/eastward shift is simulated just for some HLZ types (high temporal variability). Finally, it is important to emphasize that the uncertainty of our results is further enhanced by the fact that some important aspects (e.g. seasonality of climate variables, direct CO effect, etc.) cannot be considered in the estimating process. [ABSTRACT FROM AUTHOR]

One of the effects of global climate change is increase in the frequency and severity of drought, which strongly affects the Chinese agricultural production. In order to cope these changes more effectively, it is important to document and analyze the agricultural losses caused by drought. We collected and analyzed conventional meteorological data and agricultural statistics data, in order to outline trends in drought occurrence and decline in agricultural yield. Data were assembled for the period 1960-2010. The study pays particular attention to regional differences between northern and southern China. Our results show the drought-caused agricultural loss rates (DCALR) in China have increased by approximately 0.5% per decade in the past 50 years. The study area in this paper is for the whole of the People's Republic of China, minus the Qinghai-Tibetan Plateau; when we analyzed regional differences, we found that losses increased by approximately 0.6% per decade in northern China, close to twice the increase in southern China. Moreover, drought risks and agricultural losses are rising faster in northern China. Our results also indicate that the agriculture in northern China is more sensitive to changes in precipitation, whereas the agriculture in southern China is more sensitive to temperature changes. [ABSTRACT FROM AUTHOR]

Lake Urmia, in northwest Iran, is the second most saline lake in the world. During the past two decades, the level of water has markedly decreased. In this paper, climate of the lake region is investigated by using data from four meteorological stations near the lake. The data include climatic parameters such as temperature, precipitation, humidity, wind speed, sunshine hours, number of rain days, and evaporation. Climate around the lake is examined by way of climate classification in the periods before and after the reduction in water level. Rainfall in the lake catchment is also evaluated using both gauge and satellite data. The results show a significant decreasing trend in mean annual precipitation and wind speed and an increasing trend in annual average temperature and sunshine hours at the four stations. Precipitation and wind speed have decreased by 37 mm and 2.7 m/s, respectively, and the mean annual temperature and sunshine hours have increased by 1.4 °C and 41.6 days, respectively, over these six decades. Only the climate of the Tabriz region is seen to have significantly changed, going from semiarid to arid. Gauge records and satellite data show a large-scale decreasing trend in rainfall since 1995. The correlation between rainfall and year-to-year changes in lake level is 0.69 over the period 1965 to 2010. The relationship is particularly strong from the early 1990s to 2005. This suggests that precipitation has played an important role in the documented decline of the lake. [ABSTRACT FROM AUTHOR]

Large-scale atmospheric circulations significantly affect regional precipitation patterns. However, it is not well known whether and how these phenomena affect regional precipitation distribution in northern China. This paper reported the individual and coupled effects of El Niño-Southern Oscillation (ENSO), Indian summer monsoon (ISM), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) on annual precipitation for the Ordos Basin, an arid and semi-arid basin, currently with major industries of coal, fossil oil, natural gas, and halite in north central China. Our results showed that ENSO and ISM exerted substantial impact on annual precipitation while the impact of PDO and AMO was relatively limited. There were 24 and 15 out of 33 stations showing significant differences ( p < 0.1) in annual precipitation (from 1950 to 2013) for ENSO and ISM, respectively. The median precipitation across the basin during El Niño years was 21.49 % higher than that during La Niña years and 17.28 % higher during the positive phase of ISM years compared to the negative phase of the ISM years. The impacts of ENSO and ISM on precipitation were enhanced during a PDO cold phase but weakened in a PDO warm phase. The impact of ENSO was still enhanced by an AMO warm phase. The effects of climatic phenomena on precipitation showed a strong spatial difference in the Ordos Basin. The impact of ENSO was mostly evident around the edges of the basin while the impact of ISM decreases from south to north. The deserts (i.e., Hobq Desert and Mu Us Sandy Land) in the center of the basin were less affected by these large-scale climatic phenomena. An improved understanding of such relationships would be helpful in water resource planning and disaster management for the Ordos Basin. [ABSTRACT FROM AUTHOR]

This paper appraises the association of lightning flash count (FC) with rainfall using the satellite-borne Lightning Imaging Sensor's (LIS) data along with gridded rainfall data (0.5 × 0.5) for Indian summer monsoon seasons over 10 years (2001-2010). During strong El Nino years, 2002 and 2009, FCs were greater in magnitude by about 26.5 % and 37 %, than the long-term average, respectively, while during weak El Nino year (2004), it was more by 8 %. During the same years, the rainfall was deficient by about 10 % than the long-term average. Similarly, a rise in aerosol optical depth (AOD) over its average value (by about 15 % and 20 %) reduces the ratio of rainfall to FC (RLR) by 41 % and 44 % for strong El Nino years 2002 and 2009, respectively, and for weak El Nino year (2004), a 6.5 % rise in AOD lowers the RLR by 20 %. Bowen ratio more by 11 % and 17 % of its average value reduces the RLR by 41 % and 44 % for strong El Nino years 2002 and 2009, respectively, and, also, Bowen ratio higher by 8 % for 2004 declines RLR by 20 %. On the other hand, Bowen ratio less by 9 % and 6 % raises the RLR by 19 % and 56 % for moderate La Nina year (2007) and strong La Nina year (2010), respectively. Results for the daily rainfall, AOD and Bowen ratio over Indian regions, are discussed for strong El Nino and La Nina years. Correlations of FC with AOD and Bowen ratio of 0.66 and 0.71, respectively, while, that of FC with ONI of 0.56 indicates numerous (fewer) break days during El Nino (La Nina) years. [ABSTRACT FROM AUTHOR]

Analyses of the variation characteristics for aridity index (AI) can further enhance the understanding of climate change and have effect on hydrology and agriculture. In this paper, based on the data of 283 standard meteorological stations, the temporal-spatial variations and the influences of climate factors on AI were investigated and the relationship between AI and two climate indices (the Arctic Oscillation (AO); El Nino-Southern Oscillation (ENSO)) were also assessed in northern China (NC) during the period from 1961 to 2012. The results revealed that the annual mean AI decreased at the rate of −0.031 per decade in the past 52 years and the trend was statistically significant at the 0.01 level. The Mann-Kendall (M-K) test presented that the percentages of stations with positive trends and negative trends for AI were 10 and 81.9 % (22.6 % statistically significant), respectively. Spatially, in the western part of 100° E, the extremely dry area declined and the climate tended to become wet obviously. In the eastern part of 100° E, dry area moved toward the east and the south, which resulted in the enhancement of semiarid area and the shrinkage of subhumid area. The contributions of sunshine duration and precipitation to the decline of AI are more than those of other meteorological variables in NC. Moreover, the average temperature has risen significantly and AI decreased in NC, which indicated the existence of 'paradox.' Relationship between climate indices (AO and ENSO) and AI demonstrated that the influence of ENSO on AI overweight the AO on AI in NC. [ABSTRACT FROM AUTHOR]

This paper examines the likely perceived causes of climate change, adaptation strategies employed and technical inefficiency of arable crop farmers in Ondo State, Nigeria. Data were obtained from primary sources using a set of structured questionnaire assisted with interview schedule. Multistage sampling technique was used. Data were analyzed using the following: descriptive statistics and the stochastic frontier production function. The findings showed that majority of the respondents (59.1 %) still believed that climate change is a natural phenomenon that is beyond man's power to abate while industrial release, improper sewage disposal, fossil fuel use, deforestation and bush burning were perceived as the most human factors that influence climate change by the category that chose human activities (40.9 %) as the main causes of climate change. The main employed adaptation strategies by the farmers were mixed cropping, planting early matured crop, planting of resistant crops and use of agrochemicals. The arable crop farmers were relatively technically efficient with about 53 % of them having technical efficiency above the average of 0.784 for the study area. The study observed that education, adaptation, perception, climate information and farming experience were statistically significant in decreasing inefficiency of arable crop production. Therefore, advocacy on climate change and its adaptation strategies should be intensified in the study area. [ABSTRACT FROM AUTHOR]

The parameterization of physical processes is one of the critical elements to properly simulate the regional climate over eastern China. It is essential to conduct detailed analyses on the effect of physical parameterization schemes on regional climate simulation, to provide more reliable regional climate change information. In this paper, we evaluate the 25-year (1983-2007) summer monsoon climate characteristics of precipitation and surface air temperature by using the regional spectral model (RSM) with different physical schemes. The ensemble results using the reliability ensemble averaging (REA) method are also assessed. The result shows that the RSM model has the capacity to reproduce the spatial patterns, the variations, and the temporal tendency of surface air temperature and precipitation over eastern China. And it tends to predict better climatology characteristics over the Yangtze River basin and the South China. The impact of different physical schemes on RSM simulations is also investigated. Generally, the CLD3 cloud water prediction scheme tends to produce larger precipitation because of its overestimation of the low-level moisture. The systematic biases derived from the KF2 cumulus scheme are larger than those from the RAS scheme. The scale-selective bias correction (SSBC) method improves the simulation of the temporal and spatial characteristics of surface air temperature and precipitation and advances the circulation simulation capacity. The REA ensemble results show significant improvement in simulating temperature and precipitation distribution, which have much higher correlation coefficient and lower root mean square error. The REA result of selected experiments is better than that of nonselected experiments, indicating the necessity of choosing better ensemble samples for ensemble. [ABSTRACT FROM AUTHOR]

Satellite-based observations provide a unique data record to study the Earth system. Recent efforts of the space agencies to reprocess the archives of satellite observations aim to provide Essential Climate Variable (ECV) data records for manifold applications in climate sciences. Varying lengths of a data record or gaps in a data time series are likely to affect the analysis results obtained from long-term satellite data records. The present paper provides a systematic assessment of the impact of variations in the observational record of terrestrial ECVs for selected climate applications like trend detection and the analysis of relationships between different ECVs. As an example, the Sahelian drought and the subsequent recovery in precipitation and vegetation will be analyzed in detail using observations of precipitation, surface albedo, vegetation index, as well as ocean indices. The paper provides a different perspective on the robustness of long-term satellite observations than previous studies. It shows in particular that the long-term significant trends in precipitation and vegetation dynamics are rather sensitive to the investigation period chosen and that small data gaps can already have a considerable influence on the analysis results. It is therefore a plea for continuous climate observations from space. [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]

Assessing the regional impact of climate change on agriculture, hydrology, and forests is vital for sustainable management. Trustworthy projections of climate change are needed to support these assessments. In this paper, 18 global climate models (GCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated for their ability to simulate regional climate change in Zhejiang Province, Southeast China. Simple graphical approaches and three indices are used to evaluate the performance of six key climatic variables during simulations from 1971 to 2000. These variables include maximum and minimum air temperature, precipitation, wind speed, solar radiation, and relative humidity. These variables are of great importance to researchers and decision makers in climate change impact studies and developing adaptation strategies. This study found that most GCMs failed to reproduce the observed spatial patterns, due to insufficient resolution. However, the seasonal variations of the six variables are simulated well by most GCMs. Maximum and minimum air temperatures are simulated well on monthly, seasonal, and yearly scales. Solar radiation is reasonably simulated on monthly, seasonal, and yearly scales. Compared to air temperature and solar radiation, it was found that precipitation, wind speed, and relative humidity can only be simulated well at seasonal and yearly scales. Wind speed was the variable with the poorest simulation results across all GCMs. [ABSTRACT FROM AUTHOR]

This paper analyzes the climate change impact on water diversion plan of Melamchi Water Supply Project (MWSP) in Nepal. The MWSP is an interbasin water transfer project aimed at diverting water from the Melamchi River of the Indrawati River basin to Kathmandu Valley for drinking water purpose. Future temperature and precipitation of the basin were predicted using the outputs of two regional climate models (RCMs) and two general circulation models (GCMs) under two representative concentration pathway (RCP) scenarios which were then used as inputs to Soil and Water Assessment Tool (SWAT) to predict the water availability and evaluate the water diversion strategies in the future. The average temperature of the basin is projected to increase by 2.35 to 4.25 °C under RCP 4.5 and RCP 8.5, respectively, by 2085s. The average precipitation in the basin is projected to increase by 6-18 % in the future. The annual water availability is projected to increase in the future; however, the variability is observed in monthly water availability in the basin. The water supply and demand scenarios of Kathmandu Valley was also examined by considering the population increase, unaccounted for water and water diversion from MWSP in the future. It is observed that even with the additional supply of water from MWSP and reduction of unaccounted for water, the Kathmandu Valley will be still under water scarcity in the future. The findings of this study can be helpful to formulate water supply and demand management strategies in Kathmandu Valley in the context of climate change in the future. [ABSTRACT FROM AUTHOR]

Climate change impact assessment has become one of the most important subjects of the research community because of the recent increase in frequency of extreme events and changes in the spatiotemporal patterns of climate. This paper analyses the ability of 46 coupled climate models from Coupled Model Intercomparison Project phases 3 and 5 (CMIP5 and CMIP3). The performance of each climate model was assessed based on its skills in simulating the current seasonal cycles (monthly) of both maximum temperature and minimum temperature (T, T) over India. The performance measures such as coefficient of correlation ( Skill_r), root mean square error ( Skill_rmse), and the skill in simulating the observed probability density function ( Skill_s) are mainly employed for evaluation of the simulated monthly seasonal cycle. A new metric called Skill_All which is an intersection of the above three metrics has been defined for the first time. A notable enhancement of Skill_All for CMIP5 vis-a-vis CMIP3 is observed. Further, three best CMIP5 models each for T and T were selected. The methodology employed in this study for model assessment is implemented for the first time for India, which establishes a robust foundation for the climate impact assessment study. The seasonal trends in T and T were analyzed over all the temperature homogenous regions of India for different time slots during the 20th century. Significant trends in T can be seen during most of the seasons over the entire Indian region during last four decades. This establishes the signature of climate change over most parts of India. [ABSTRACT FROM AUTHOR]

Coupled atmosphere-ocean general circulation models (GCMs) simulate different realizations of possible future climates at global scale under contrasting scenarios of land-use and greenhouse gas emissions. Such data require several additional processing steps before it can be used to drive impact models. Spatial downscaling, typically by regional climate models (RCM), and bias-correction are two such steps that have already been addressed for Europe. Yet, the errors in resulting daily meteorological variables may be too large for specific model applications. Crop simulation models are particularly sensitive to these inconsistencies and thus require further processing of GCM-RCM outputs. Moreover, crop models are often run in a stochastic manner by using various plausible weather time series (often generated using stochastic weather generators) to represent climate time scale for a period of interest (e.g. 2000 ± 15 years), while GCM simulations typically provide a single time series for a given emission scenario. To inform agricultural policy-making, data on near- and medium-term decadal time scale is mostly requested, e.g. 2020 or 2030. Taking a sample of multiple years from these unique time series to represent time horizons in the near future is particularly problematic because selecting overlapping years may lead to spurious trends, creating artefacts in the results of the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data for Europe that addresses these problems. Input data consist of daily temperature and precipitation from three dynamically downscaled and bias-corrected regional climate simulations of the IPCC A1B emission scenario created within the ENSEMBLES project. Solar radiation is estimated from temperature based on an auto-calibration procedure. Wind speed and relative air humidity are collected from historical series. From these variables, reference evapotranspiration and vapour pressure deficit are estimated ensuring consistency within daily records. The weather generator ClimGen is then used to create 30 synthetic years of all variables to characterize the time horizons of 2000, 2020 and 2030, which can readily be used for crop modelling studies. [ABSTRACT FROM AUTHOR]

Changes in extreme precipitation should be one of the primary impacts of climate change (CC) in urban areas. To assess these impacts, rainfall data from climate models are commonly used. The main goal of this paper is to report on the state of knowledge and recent works on the study of CC impacts with a focus on urban areas, in order to produce an integrated review of various approaches to which future studies can then be compared or constructed. Model output statistics (MOS) methods are increasingly used in the literature to study the impacts of CC in urban settings. A review of previous works highlights the non-stationarity nature of future climate data, underscoring the need to revise urban drainage system design criteria. A comparison of these studies is made difficult, however, by the numerous sources of uncertainty arising from a plethora of assumptions, scenarios, and modeling options. All the methods used do, however, predict increased extreme precipitation in the future, suggesting potential risks of combined sewer overflow frequencies, flooding, and back-up in existing sewer systems in urban areas. Future studies must quantify more accurately the different sources of uncertainty by improving downscaling and correction methods. New research is necessary to improve the data validation process, an aspect that is seldom reported in the literature. Finally, the potential application of non-stationarity conditions into generalized extreme value (GEV) distribution should be assessed more closely, which will require close collaboration between engineers, hydrologists, statisticians, and climatologists, thus contributing to the ongoing reflection on this issue of social concern. [ABSTRACT FROM AUTHOR]

Deforestation is expanding and accelerating into the remaining areas of undisturbed forest, and the quality of the remaining forests is declining today. Assessing the climatic impacts of deforestation can help to rectify this alarming situation. In this paper, how historical deforestation may affect global climate through interactive ocean and surface albedo is examined using an Earth system model of intermediate complexity (EMIC). Control and anomaly integrations are performed for 1000 years. In the anomaly case, cropland is significantly expanded since AD 1700. The response of climate in deforested areas is not uniform between the regions. In the background of a global cooling of 0.08 °C occurring with cooler surface air above 0.4 °C across 30° N to 75° N from March to September, the surface albedo increase has a global cooling effect in response to global-scale replacement of forests by cropland, especially over northern mid-high latitudes. The northern mid-latitude (30° N-60° N) suffers a prominent cooling in June, suggesting that this area is most sensitive to cropland expansion through surface albedo. Most regions show a consistent trend between the overall cooling in response to historical deforestation and its resulting cooling due to surface albedo anomaly. Furthermore, the effect of the interactive ocean on shaping the climate response to deforestation is greater than that of prescribed SSTs in most years with a maximum spread of 0.05 °C. This difference is more prominent after year 1800 than that before due to the more marked deforestation. These findings show the importance of the land cover change and the land surface albedo, stressing the necessity to analyze other biogeophysical processes of deforestation using interactive ocean. [ABSTRACT FROM AUTHOR]

Hydro-climatological time series might embed characteristics of past changes concerning climate variability in terms of shifts, cyclic fluctuations, and more significantly in the form of trends. Identification of such features from the available records is one of the prime tasks of hydrologists, climatologists, applied statisticians, or experts in related topics. Although there are different trend identification and significance tests in the literature, they require restrictive assumptions, which may not be existent in the structure of hydro-climatological time series. In this paper, a method is suggested with statistical significance test for trend identification in an innovative manner. This method has non-parametric basis without any restrictive assumption, and its application is rather simple with the concept of sub-series comparisons that are extracted from the main time series. The method provides privilege for selection of sub-temporal half periods for the comparison and, finally, generates trend on objective and quantitative manners. The necessary statistical equations are derived for innovative trend identification and statistical significance test application. The application of the proposed methodology is suggested for three time series from different parts of the world including Southern New Jersey annual temperature, Danube River annual discharge, and Tigris River Diyarbakir meteorology station annual total rainfall records. Each record has significant trend with increasing type in the New Jersey case, whereas in other two cases, decreasing trends exist. [ABSTRACT FROM AUTHOR]

Soil temperature is an important factor within the climate system. Changes of trends in soil temperature and analysis of vulnerability due to heat stress can provide useful information on climate change. In this paper, the soil temperature regime was analyzed on seasonal and annual scales at depths of 2, 5, 10, 20, 30, and 50 cm at 26 sites in Croatia. Trends of maximal, mean, and minimal soil temperatures were analyzed in the periods 1961-2010 and 1981-2010. Duration of extreme soil temperatures and vulnerability due to high or low soil temperatures in the recent standard period 1981-2010 was compared with the reference climate period 1961-1990. The results show a general warming in all seasons and depths for maximal and mean temperatures in both observed periods, while only at some locations for minimal soil temperature. Warming is more pronounced in the eastern and coastal parts of Croatia in the surface layers, especially in the spring and summer season in the second period. Significant trends of maximal, minimal, and mean soil temperature in both observed periods range from 2.3 to 6.6 °C/decade, from −1.0 to 1.3 °C/decade, and from 0.1 to 2.5 °C/decade, respectively. The highest vulnerability due to heat stress at 35 °C is noted in the upper soil layers of the coastal area in both observed periods. The mountainous and northwestern parts of Croatia at surface soil layers are the most vulnerable due to low soil temperature below 0 °C. Vulnerability due to high or low soil temperature decreases with depth. [ABSTRACT FROM AUTHOR]

Warming of the climate system is unequivocal, and the change of climate variables will eventually have a great impact on vegetation cover and agricultural practices, especially in the arid area Xinjiang in China, whose agriculture and ecosystems are heavily vulnerable to climate change. In this paper, normalized difference vegetation index (NDVI) was used to study the vegetation growth and its response to climate change in Xinjiang. Firstly, two NDVI datasets (Global Inventory Modeling and Mapping Studies (GIMMS) and Moderate Resolution Imaging Spectroradiometer (MODIS)) were merged through a pixel-wise regression analysis to obtain a long time series of NDVI data, and then, relationships between yearly NDVI and yearly climate variables, and monthly NDVI and monthly climate variables were extensively investigated for grassland and cropland in northern and southern Xinjiang, respectively. Results show the following: (1) there was an increasing trend in NDVI for both grassland and cropland in both northern and southern Xinjiang over the past decades and trends were significant except that for grassland in northern Xinjiang; (2) precipitation and evaporation were more important than temperature for grassland in northern Xinjiang, while precipitation and temperature were more important than evaporation for grassland in southern Xinjiang and cropland in both northern and southern Xinjiang; (3) NDVI was highly correlated with accumulated monthly precipitation instead of monthly precipitation, and there was a lagged effect of precipitation, temperature, and evaporation on NDVI change. However, lagged effects were only significant in specific months. The results could be helpful to agricultural practices; e.g., based on lagged effect of precipitation, irrigation in July is very important for crop growth. [ABSTRACT FROM AUTHOR]

In this paper, changes in the long and short spells of different rain intensities are statistically analyzed using daily gridded rainfall data prepared by the India Meteorological Department for the period 1951-2008. In order to study regional changes, analyses have been conducted over nine selected agro-meteorological (agro-met) divisions, five homogeneous zones, and also over the whole of India. Rain events of different intensities with continuous rainfall of more than or equal to 4 days are classified here as long spells. Those with less than 4 days are termed as short spells. Those results which are statistically significant at 95% confidence level are discussed in this paper. Trend analysis shows that during the summer monsoon months of June to September, short spell rain events with heavy intensity have increased over India as a whole. On the other hand, long spell rain events with moderate and low intensities have decreased in numbers. Results further show that the contributions of long spell moderate and short spell low-intensity rain events to the total rainfall have decreased whereas the contributions of short spell heavy and moderate-intensity rain events to the total seasonal rainfall have increased. Percentage changes in various categories of long and short spells in the decade 1991-2000 compared with the earlier decade 1951-1960, highlight the maximum increase in heavy-intensity short spell category and decrease in moderate-intensity long spell category in India as a whole and in most of the homogeneous zones and agro-met divisions. The changes in different types of rain events differ in the six homogeneous zones and nine selected agro-met divisions. However, in three homogeneous zones and three agro-met divisions, the short spell heavy-intensity rain events dominate as in the entire country. There are also changes observed in the monthly occurrences of above categories of rain events during the 4 months of summer monsoon. Such results with details of changes in rain categories in different parts of India have important implications in agriculture sector in the country. [ABSTRACT FROM AUTHOR]

Several studies have been devoted to dynamic and statistical downscaling for both climate variability and climate change. This paper introduces an application of temporal neural networks for downscaling global climate model output and autocorrelation functions. This method is proposed for downscaling daily precipitation time series for a region in the Amazon Basin. The downscaling models were developed and validated using IPCC AR4 model output and observed daily precipitation. In this paper, five AOGCMs for the twentieth century (20C3M; 1970–1999) and three SRES scenarios (A2, A1B, and B1) were used. The performance in downscaling of the temporal neural network was compared to that of an autocorrelation statistical downscaling model with emphasis on its ability to reproduce the observed climate variability and tendency for the period 1970–1999. The model test results indicate that the neural network model significantly outperforms the statistical models for the downscaling of daily precipitation variability. [ABSTRACT FROM AUTHOR]

As a result of climatic change associated with global warming, aridity is an increasing problem in many parts of the world, including south-eastern and southern regions of Romania. This paper clarifies the concept of aridity, and discusses related concepts including indices of aridity, and their influence on some landscape and soil features including climatic water deficit (WD) and the depth to soil carbonates (DC). As used here, WD is calculated as the difference between precipitation sum (P) and the Penman-Monteith reference evapotranspiration sum (ETo-PM) over certain periods. Another three well-known aridity indices are also considered: De Martonne’s index (Iar-DM), Thornthwaite’s index (Iar-TH), the UNESCO (1979) P/ETo-PM ratio index (Iar-P/ETo-PM). WD is as high as −450 mm during the growing season in the most arid, south-eastern and southern regions of Romania, especially in the Dobrogea and Baragan areas. In other regions of Romania, including most of the plains and plateaus where agriculture is an important branch of the economy, WD reaches −100 to −300 mm during the growing season. The above aridity indices were spatially interpolated for specific periods by kriging, to generate relatively homogeneous areas. WD can also be seen as an aridity index which has the advantage of a more accurate quantification of the water supply needed for a reference crop, e.g. grass under standardised conditions, for various geographical regions. WD is significantly correlated with the other aridity indexes and with DC. This paper also examines the risk of aridity spreading, and suggests improvements to the water management system for agriculture in Romania. [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]