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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]

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]

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]

Long instrumental records of meteorological variables such as temperature and precipitation are very useful for studying regional climate in the past, present, and future. They can also be useful for understanding the influence of large-scale atmospheric circulation processes on the regional climate. This paper investigates the monthly, winter, and annual temperature time series obtained from the instrumental records in Zagreb, Croatia, for the period 1864-2010. Using wavelet analysis, the dominant modes of variability in these temperature series are identified, and the time intervals over which these modes may persist are delineated. The results reveal that all three temperature records exhibit low-frequency variability with a dominant periodicity at around 7.7 years. The 7.7-year cycle has also been observed in the temperature data recorded at several other stations in Europe, especially in Northern and Western Europe, and may be linked to the North Atlantic Oscillation (NAO) and/or solar/geomagnetic activity. [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]

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]

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]

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]

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]

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]

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]

In mountain regions, important differences in the time trends of climate series can be detected even within relatively small areas, leading to uncertainty when assessing climate change. The paper deals with a structured algorithm for high-resolution downscaling of climate characterisation in a region (precipitation and temperature), leading to a twofold application: increasing spatial resolution of past climate definition for the area and attaining high-resolution downscaling for climate projections. In the first stage, multi-variate analysis ('partial least squares' regression) was applied to a number of time series (10) in order to obtain climate averages for a larger number of sites. Predictions made with single-site values (such as seasonal means) can in some cases be improved by applying 'random perturbation' of the value and averaging single predictions in the ensemble. This analysis laid the foundation for implementing the same technique to the output of statistical downscaling of multi-model climate projections. Climate shift in the study area (Trentino), located in the north-eastern Italian Alps, was simulated for two 30-year time windows: 2021-2050 and 2071-2099. Progressive warming is predicted, being stronger in the summer, along with a mixed, seasonally differentiated trend for precipitation. [ABSTRACT FROM AUTHOR]

This paper analyses the temporal and spatial changes in the amount and variability of rainfall in Scotland. The sequential Mann-Kendall test reveals that total annual precipitation has increased across Scotland since the 1970s with increasing trends in variability beginning between the mid-1960s and the mid-1970s. Whilst temporally consistent increasing trends in precipitation totals prevail in the West, many weather stations in the East have experienced subsequent trend turning points in the following two decades, explaining the larger magnitude of the trends in western Scotland in recent decades. Trend analyses on six measures of rainfall variability indicate an increase in rainfall variability during the period 1961-2000, as measured by the intra-annual variance, the winter to summer precipitation ratio and the annual cumulative sum range, with decreasing trends observed in the number of dry days. Periodicities associated with the North Atlantic Oscillation and the Atlantic Multidecadal Oscillation could explain the observed temporal variability of rainfall. [ABSTRACT FROM AUTHOR]

The paper aims to study the variability of spring barley and winter wheat yields, the most important crops in the Czech Republic, with respect to the variability of weather and climatic factors. Yields of both crops have been studied for 13 districts in Southern Moravia for the 1961-2007 period. From detrended series of spring barley and winter wheat yields, years with very low (lower than the mean minus a 2.5-multiple of the standard deviation) and extremely low (interval given by the mean minus a 1.5- and 2.5-multiple of the standard deviation) yields were selected. Years in which at least one of the districts had extremely low/very low yields were further analyzed. From 10 such years selected separately for spring barley and winter wheat, six of them agreed for both crops. Extreme years were studied using NUTS4-level yield data with respect to temperature, precipitation, the self-calibrated Palmer Drought Severity Index (scPDSI), snow cover, frost patterns, and the onset and duration of select phenophases. Extremely/very low barley yields in 1993, 2000, and 2007 were related to high April-June (AMJ) temperatures, low AMJ precipitation totals, and negative AMJ scPDSI (indicating drought) with an earlier onset of flowering and full ripeness and shorter intervals from tillering to flowering and from flowering to full ripeness compared to the entire 1961-2007 mean. As for extremely/very low winter wheat yields, in addition to the previously mentioned factors, winter patterns also played an important role, particularly the occurrence of severe frosts with a coinciding lack of snow cover and a long-lasting snow cover (in highlands), indicating that low yields are the result of not only one unfavorable factor but a combination of several of them. [ABSTRACT FROM AUTHOR]

We present an analysis of a present-day climate simulation (1990-2009) for the Southern Africa region with the RegCM4 regional climate model (RCM, dx = 25 km ~0.22°) driven by the Community Atmospheric Model version 4 (CAM4) global climate model (GCM, dx = 1°). We assess the capability of the models to simulate the observed climate of the region with emphasis on precipitation, 2-m mean, minimum, and maximum temperature and large-scale circulation. The analysis focuses on seasonal climatologies, annual cycles, interannual variability, and extreme events. In addition to evaluating the performance of the models, we also attempt to assess the added value of the regional model compared to the driving one. With a few exceptions, we find that the models reproduce reasonably well the mean spatial patterns of 2-m temperature and precipitation, along with the associated seasonal cycle and interannual variability over selected sub-regions. Extreme indices of temperature and precipitation are also reasonably well reproduced. However, the RegCM4 substantially improves the simulation of daily precipitation frequency and duration of dry and wet events compared to CAM4 due to its higher resolution. The global and regional models exhibit quite different patterns of bias, an indication of the importance of internal variability and process representation for the simulation of surface climate. Given the good performance shown by the nested CAM4-RegCM4 system, we plan to use these models to generate an ensemble of projections for use in impact assessment studies for the region. [ABSTRACT FROM AUTHOR]

Changes in temperature and precipitation can have direct effects on a country's economic and sustainable development. This study presents the historical trends in daily extreme temperature and precipitation indices for Barbados, a Caribbean island, for the period 1969–2017. Linear trends were determined on monthly, seasonal, and annual indices derived from daily rainfall, and daily maximum and minimum temperatures collected at Husbands using the Mann-Kendall trend test and the Sen slope estimator. Annual precipitation total, the simple daily rainfall intensity index (SDII), and the total precipitation when daily rainfall was greater than the 95th percentile (R95P) changed significantly by 3.9% per decade, 0.38 mm day−1 per decade, and 2.9% per decade, respectively. Annual trends in mean maximum (TXmean) and mean minimum temperatures (TNmean), 0.06 °C decade−1 and 0.34 °C decade−1, respectively, mask stronger monthly warming trends: TXmean for September and October both warmed at a rate of approximately 0.17 °C decade−1 while TNmean for April warmed at 0.40 °C decade−1. In addition, warming manifests in the frequency of warm nights and cool days at rates of 4.23% per decade and − 2.34% per decade, respectively. Of the extreme temperature indices, the percentage of warm nights (TN90P), cool nights (TN10P), the maximum of the daily minimum temperature (TNX), and the minimum of the daily minimum temperature (TNn) show trends in their monthly indices. These trends indicate warming at Husbands, Barbados, to a greater degree in increasing nighttime temperatures than daytime temperatures. [ABSTRACT FROM AUTHOR]

The present study analyzed precipitation and temperature trends of 45 years (1971–2015) over two protected areas (Kanha Tiger Reserve representing Sal forest and Satpura Tiger Reserve representing Teak forest) in Central India. The specific trends (decadal, annual, and seasonal) are calculated from various available data sources (IMD, REMO, and Station) which are at different horizontal resolutions. The non-parametric Mann–Kendall test statistic and Sen's estimate methods are used to detect the presence and quantum of a trend. The results indicate significant decrease in post-monsoon and winter precipitation and an increasing significant temperature trend (Tmean, Tmax, Tmin) at seasonal, annual, and decadal scale, with a maximum increase during post-monsoon. Also, the last decade (2001–2010) was warmer than the previous three decades. The presence of break points and structural changes in climatic variables are analyzed using OLS-CUSUM and MOSUM tests available in "strucchange" package in R. A structural change in precipitation was observed only in REMO dataset over Satpura Tiger Reserve, whereas significant structural change in temperature was observed in REMO dataset over both the regions. These findings are useful for assessing precipitation and temperature trends in protected areas. In particular, it can aid in understanding the impact of climate change on forest/species. On comparative analysis among different data sources at varying resolutions, the study recommends the use of finer resolution datasets for regional/micro-scale studies for formulating and planning effective adaptation strategies. [ABSTRACT FROM AUTHOR]

The vast areal extent of Texas, USA, observes acute variations in extreme precipitation, which result in devastating floods in various regions. Hydrometeorological literature suggests that these precipitation extremes are driven by the changes in global-scale climatic cycles. This study analyzed potential statistical links between Atlantic- and Pacific Ocean-based climatic cycles and annual precipitation extremes in Köppen–Geiger climate regions of Texas. Sensitivity analysis was done for annual precipitation extremes and differing phases of the most correlated climatic cycles. The study also analyzed the spatial variation of climatic indices and determined the projected increments in annual precipitation extremes as per the highest historically recorded increments or decrements in the most correlated climatic cycles in consecutive months. Weather stations with lower elevation and higher average precipitation are likely to observe higher extreme precipitation events in the warmer AMO state. Sensitivity analysis showed that a 20% decrement in empirical probabilities of projected precipitation extreme events corresponded to a decline of 35% in the same for humid subtropical climate region. The humid subtropical climate region is highly prone to devastating floods after heavy precipitation events. Results of the study will help prepare regional water boards for extreme precipitation events with reliable long-term forecasts of climatic cycles. [ABSTRACT FROM AUTHOR]

Global warming has substantial impacts on the hydrologic cycle and precipitation characteristics. This study aimed to explore variability in the form of precipitation by analysis of trends in the snowfall-day (SD)/total precipitation-day (PD) ratio across Iran. To this end, the PD and SD records were acquired from 33 synoptic stations during 1965–2014. The Mann-Kendall (MK) test was the main method used for trend detection. Analyzing data exposed that the annual SD/PD ratio was less than 25% in a plethora of the stations. The MK test findings indicated downward tendencies in the SD/PD ratio over most regions of the country; although in many cases, the observed trends were not statistically significant. The changes in the annual SD/PD ratio were estimated down to − 2% per decade over ~ 70% of Iran's total area. Further studies are required to ascertain the linkages between the large-scale climate oscillations and the form of precipitation across this region. Also, using remotely sensed data (e.g., obtained from the MODIS) and fine-resolution gridded data (e.g., global and regional reanalysis data) in future research could improve the findings of such studies and dwindle the uncertainties caused by the scanty site-based observations. [ABSTRACT FROM AUTHOR]

Stochastic weather simulation models are used to generate data for hydrologic and agricultural applications. In recent years, stochastic downscaling has been employed in downscaling scenarios-based GCM or RCM output data, especially highly unpredictable hydrometeorological variables (e.g., precipitation). However, downscaling produces a number of traces to apply. In practice, multiple scenarios are difficult to handle. To overcome this difficulty, trace selection methods (TSMs) were proposed and tested in the current study, based on the mean and standard deviation (MS), empirical cumulative distribution function (Ecdf), and density estimate (Dens) methods. Simulation results indicated that key statistics were well preserved by the selected trace with the MS and Ecdf methods, while the Dens method did not show a reasonable performance in selecting a representative trace. The MS and Ecdf methods preserved well the mean and standard deviation of the average of these statistics from all simulated series and the Ecdf method was superior in preserving extrema, such as maximum and minimum. In a real application involving over 62 weather stations from South Korea, results showed that the MS and Ecdf methods were feasible ways to select the best trace to represent all temporally downscaled hourly precipitation from daily data. Overall, the MS and Ecdf can be both good alternatives as a trace selection method (TSM). The MS method can be used in general cases and the Ecdf can be employed in extreme analysis, such as estimating design rainfall with selected traces. [ABSTRACT FROM AUTHOR]

Gridded climatic datasets with fine spatial resolution can potentially be used to depict the climatic characteristics across the complex topography of China. In this study, we collected records of monthly temperature at 1153 stations and precipitation at 1202 stations in China and neighboring countries to construct a monthly climate dataset in China with a 0.025° resolution (~ 2.5 km). The dataset, named LZU0025, was designed by Lanzhou University and used a partial thin plate smoothing method embedded in the ANUSPLIN software. The accuracy of LZU0025 was evaluated based on three aspects: (1) Diagnostic statistics from the surface fitting model during 1951–2011. The results indicate a low mean square root of generalized cross validation (RTGCV) for the monthly air temperature surface (1.06 °C) and monthly precipitation surface (1.97 mm1/2). The method used variable square root transformation for the spline surface fitting to reduce positive skewness in the measured precipitation values and no variable transformation in air temperature case. This indicates that the surface fitting models are accurate. (2) Error statistics of comparisons between interpolated monthly LZU0025 with the withholding of climatic data from 265 stations during 1951–2011. The results show that the predicted values closely tracked the real true values with values of mean absolute error (MAE) of 0.59 °C and 70.5 mm and standard deviation of the mean error (STD) of 1.27 °C and 122.6 mm. In addition, the monthly STDs exhibited a consistent pattern of variation with RTGCV. (3) Comparison with other datasets. This was done in two ways. The first was via comparison of standard deviation, mean, and time trend derived from all datasets to a reference dataset released by the China Meteorological Administration (CMA), using Taylor diagrams. The second was to compare LZU0025 with the station dataset in the Tibetan Plateau. Taylor diagrams show that the standard deviation derived from LZU had a higher correlation with that produced by the CMA (R = 0.76 for air temperature, and R = 0.96 for precipitation) compared to those from other datasets. The standard deviation for the index derived from LZU was more close to that induced from CMA, and the centered normalized root-mean-square difference for this index derived from LZU and CMA was lower. A similar superior performance of LZU was found in the comparison of mean and time trend derived from LZU and those from other datasets. LZU0025 had high correlation with the Coordinated Energy and Water Cycle Observation Project (CEOP)—Asian Monsoon Project (CAMP) Tibet surface meteorology station dataset for air temperature, despite a non-significant correlation for precipitation at a few stations. Based on this comprehensive analysis, we conclude that LZU0025 is a reliable dataset. LZU0025, which has a fine resolution, can be used to identify a greater number of climate types, such as tundra and subpolar continental, along the Himalayan Mountain. We anticipate that LZU0025 can be used for the monitoring of regional climate change and precision agriculture modulation under global climate change. [ABSTRACT FROM AUTHOR]

Shifts in precipitation regimes have been given increased attention along with climate trends in the arid and semiarid Mongolian Plateau (MP), because precipitation is the dominant factor that controls vegetation growth and land-use practices in the region. However, the temporal variability and spatial heterogeneity of decadal trends in summer precipitation fraction have not been well understood. In this study, the temporal variability and spatial heterogeneity of annual and summer precipitation change were investigated with the major focus on the summer precipitation fraction across the entire MP, using monthly precipitation data from 141 meteorological stations and the Tropical Rainfall Measuring Mission (TRMM) precipitation data. The Mann–Kendall trend test, Sen's regression, and Spatial Analyst methods were used for time series analysis. Results show that the regional summer precipitation fraction declined significantly at a rate of − 1.0%/decade (p < 0.05) during 1961–2010, which was supported by TRMM satellite data with a rate of − 2.86%/decade over 1998–2016. Interestingly, rates of decrease in the summer precipitation fraction varied by ecoregion. Spatially, trend in summer precipitation fraction was very heterogeneous, with significant decreasing trends in the eastern typical steppe and southeastern agro-pastoral ecotone, but increasing trends in the southwestern desert and Gobi desert region. In conclusion, the summer precipitation fraction declined because of the asymmetric decreasing or increasing rates in summer and non-summer precipitation during both 1961–2010 and 1998–2010. This implies a major shift of precipitation regime in the region. This is likely driven by decadal changes of the East Asia monsoon system. [ABSTRACT FROM AUTHOR]

Future changes projected in surface temperature and precipitation behave differently in different regions or watersheds and vary greatly in space and time, even within the same region under climate change. This study aims to detect and compare differences in the climatological characteristics in response to climate change in arid and humid areas. Based on the CN05.1 reanalysis gridded dataset (high-resolution climate model validation launched by China Meteorological Administration) and output from the Coupled Model Inter-comparison Project Phase 5 (CMIP5), the projected responses of temperature and precipitation to climate change under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) in the arid basin (the Tarim River Basin, the TRB) and the humid basin (the Yangtze River Basin, the YRB) were characterized. Our results show that the swings of annual temperature and precipitation will extend with the rising emission scenarios in the future, while those are more vulnerable in the TRB. The topography-related changes of temperature and precipitation were further evaluated to explore the uncertainty. The elevation-dependent warming (EDW) may be weakened but still hold in the future at the watershed scale. Change in precipitation will decrease with increase in elevation in the TRB, while it will increase in the YRB. Compared with the general growth in seasonal temperature, precipitation shows more striking changes during spring and winter under climate change. Further researches to improve our understanding of the projected changes in temperature and precipitation are needed for better prediction of extreme weather conditions, such as drought and floods. [ABSTRACT FROM AUTHOR]

The existence of significant trend and change point in the aridity index (AI), precipitation, and reference evapotranspiration (ET0) was evaluated using the Mann-Kendall and Pettitt tests, respectively, over 22 semi-arid, humid, and sub-humid sites of Iran during 1966–2012. Detrending method was also employed to quantify each factor's contribution to the AI and ET0 trends. The ET0 and AI sensitivity to their input perturbations was analyzed using a derivative sensitivity coefficient. The results indicated a downward trend in precipitation and AI and an upward trend in ET0 on both seasonal and annual scales for majority of sites. Compared with AI and precipitation, more significant change points were detected, mainly during the 1990s, in the ET0 series. Except for wintertime, solar radiation (SR) was found as the most sensitive factor on the AI and ET0 dynamics in the semi-arid regions. However, with an exception for summertime, AI and ET0 exhibited greater sensitivity to the RH changes followed by the SR changes in the humid/sub-humid areas. For more than 70% of semi-arid stations and Gorgan (a sub-humid location), wind speed (U) was the most important variable contributing to seasonal and annual ET0 trends. However, the ET0 trend was primarily caused by the mean temperature (Tmean) changes for the humid environments. Further, the precipitation changes made the largest contribution to seasonal and annual AI trends for most cases followed by the U changes. An integrated water resource management is required to be implemented to reduce negative impacts of decreased AI and ET0 increment over Iran. [ABSTRACT FROM AUTHOR]

The Hengduan Mountains and Tibetan Plateau possess unique topographical characteristics that serve as an effective blocking of the movement of the westerly wind in the middle and lower troposphere towards East China. This study examines results from a regional climate model (REMO) at the resolutions of 25 and 50 km for the period 1980–2012. The model is run using lateral boundary conditions from ERA-Interim (European Centre for Medium-Range Weather Forecasts interim reanalysis). There are only a few differences between 25 and 50 km in land surface/vegetation characteristics, but the major differences in this region are due to the orography. Results show that the high-resolution simulation performance is poor in winter, when southwesterly wind prevails, whereas it performs well in summer, when the westerly wind is substantially weakened in southern China. In comparison to the ERA-Interim wind field, the high-resolution simulation overestimates the air flow over the Hengduan Mountains near the ground surface, which influences the transport of atmospheric water vapor in the downstream region, i.e., over southern China. Specifically, with the help of the overestimated southwesterly wind, the amount of atmospheric water vapor transported increases considerably perennially by up to 20% in southern China, while it decreases remarkably by more than 5% throughout the year in a large area of Central and North China. These features lead to excessive precipitation and underestimated cloud cover in southern China, which probably causes the overestimated 2-m temperature in southern China. Our study emphasizes that, in such high-resolution-model studies for East Asia, special attention should be paid to the near-surface winds over the Hengduan Mountains. [ABSTRACT FROM AUTHOR]

The climatological means and surface air temperature (SAT) trends of the Indochina Peninsula (ICP) are being analyzed on a yearly and seasonal basis using a newly published observation dataset (SA-OBS). The SAT for the period 1981 to 2010 shows a north-south gradient over the ICP, with the highest mean annual temperature in the central plain and the lowest in the northern mountain region. In addition, over the past 30 years, the ICP has been undergoing a significant warming trend of 0.37 °C/decade. The seasonal mean SAT fluctuations are significant in the dry seasons compared to the wet seasons, with a rapid increase in JFM (January to March) and OND (October to December). Further, comparisons are made using SA-OBS and the other observation (CRU, GHCN_CAMS, DEL) or reanalysis (ERA-20C, CERA-20C, ERA-Interim, JRA-55) datasets. The result shows (i) that the SA-OBS dataset can capture the spatial distributions and temporal patterns reasonably well throughout the ICP on annual and seasonal scales. (ii) CERA-20C is very similar to SA-OBS in replicating the annual mean SAT over the ICP, suggesting that the ECMWF's coupled data assimilation system (CERA) could obviously improve the temperature estimates in that region. (iii) That significant differences, however, still exist between observations and reanalyses in annual and seasonal trends. These discrepancies need to be taken into account to study climate change and variability or to assess regional climate models with focus on the ICP. [ABSTRACT FROM AUTHOR]

General Circulation Models (GCMs) are advanced tools for impact assessment and climate change studies. Previous studies show that the performance of the GCMs in simulating climate variables varies significantly over different regions. This study intends to evaluate the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) GCMs in simulating temperature and precipitation over Iran. Simulations from 37 GCMs and observations from the Climatic Research Unit (CRU) were obtained for the period of 1901–2005. Six measures of performance including mean bias, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), linear correlation coefficient (r), Kolmogorov-Smirnov statistic (KS), Sen's slope estimator, and the Taylor diagram are used for the evaluation. GCMs are ranked based on each statistic at seasonal and annual time scales. Results show that most GCMs perform reasonably well in simulating the annual and seasonal temperature over Iran. The majority of the GCMs have a poor skill to simulate precipitation, particularly at seasonal scale. Based on the results, the best GCMs to represent temperature and precipitation simulations over Iran are the CMCC-CMS (Euro-Mediterranean Center on Climate Change) and the MRI-CGCM3 (Meteorological Research Institute), respectively. The results are valuable for climate and hydrometeorological studies and can help water resources planners and managers to choose the proper GCM based on their criteria. [ABSTRACT FROM AUTHOR]

Marine occupations are plagued by some of the highest accident and mortality rates of any occupation, due in part to the variety and severity of environmental hazards presented by the ocean environment. In order to better study and communicate the potential impacts of these hazards on occupational health and safety, a semi-objective, hazard-focused climatology of a particularly dangerous marine environment (Northwestern Atlantic) has been developed. Specifically, climate has been summarized as the frequency with which responsible government agencies are expected to issue relevant warnings or watches, couching results in language relevant to marine stakeholders. Applying cluster analysis to warning/watch frequencies identified seven distinct 'hazard climatologies', ranging from near-Arctic conditions to areas dominated by calm seas and warm waters. Spatial and temporal variability in these clusters reflects relevant annual cycles, such as the advance/retreat of sea ice and shifts in the Atlantic storm track; the clusters also highlight regions and seasons with comparable operational risks. Our approach is proposed as an effective means to summarize and communicate marine risk with stakeholders, and a potential framework for describing climate change impacts. [ABSTRACT FROM AUTHOR]

Vegetation cover is a commonly used indicator for evaluating terrestrial environmental conditions, and for revealing environmental evolution and transitions. Spatiotemporal variations in the vegetation cover of the Hexi Corridor and surrounding areas from 2000 to 2010 were investigated using MODIS NDVI data, and the causes of vegetation cover changes were analyzed, considering both climatic variability and human activities. The vegetation cover of the study area increased during 2000–2010. The greenness of the vegetation showed a significant increase from the northwest to the southeast, which was similar to the spatial distribution of the annual precipitation. Variations in vegetation have a close relationship with those in precipitation within the Qilian Mountains region, but the NDVI is negatively correlated with precipitation in oasis areas. Increasing temperatures led to drought, inhibiting vegetation growth in summer; however, increasing temperatures may have also advanced and prolonged the growing periods in spring and autumn. The NDVI showed a slight degradation in March and July, primarily in the Qilian Mountains, and especially the Wushao Mountains. In March, due to low temperatures, the metabolism rate of vegetation was too slow to enable strong plant growth in high elevations of the Qilian Mountains. In July, increasing temperatures enhanced the intensity of transpiration and decreasing precipitation reduced the moisture available to plants, producing a slight degradation of vegetation in the Qilian Mountains. In May and August, the NDVI showed a significant improvement, primarily in the artificial oases and the Qilian Mountains. Abundant precipitation provided the necessary water for plant growth, and suitable temperatures increased the efficiency of photosynthesis, resulting in a significant improvement of vegetation in the Qilian Mountains. The improvement of production technologies, especially in irrigation, has been beneficial to the growth of vegetation in oasis areas. The implementation of large-scale vegetation management has led to several beneficial effects in the artificial oases and grasslands of the Qilian Mountains. Graphical abstract [ABSTRACT FROM AUTHOR]

As the recent global warming hiatus and the warming on high elevations are attracting worldwide attention, this study examined the robustness of the warming slowdown over the Tibetan plateau (TP) and its related driving forces. By integrating multiple-source data from 1982 to 2015 and using trend analysis, we found that the mean temperature (Tmean), maximum temperature (Tmax) and minimum temperature (Tmin) showed a slowdown of the warming trend around 1998, during the period of the global warming hiatus. This was found over both the growing season (GS) and non-growing season (NGS) and suggested a robust warming hiatus over the TP. Due to the differences in trends of Tmax and Tmin, the trend of diurnal temperature range (DTR) also shifted after 1998, especially during the GS temperature. The warming rate was spatially heterogeneous. The northern TP (NTP) experienced more warming than the southern TP (STP) in all seasons from 1982 to 1998, while the pattern was reversed in the period from 1998 to 2015. Water vapour was found to be the main driving force for the trend in Tmean and Tmin by influencing downward long wave radiation. Sunshine duration was the main driving force behind the trend in Tmax and DTR through a change in downward shortwave radiation that altered the energy source of daytime temperature. Water vapour was the major driving force for temperature change over the NTP, while over the STP, sunshine duration dominated the temperature trend. [ABSTRACT FROM AUTHOR]

This study analyses spatial and temporal trends of precipitation and temperatures over Abruzzo Region (central Italy), using historical climatic data from a dense observation network. The results show a general, although not significant, negative trend in the regionally averaged annual precipitation (− 1.8% of the yearly mean rainfall per decade). This reduction is particularly evident in winter, especially at mountain stations (average − 3% change/decade). Despite this general decreasing trend, a partial rainfall recovery is observed after the 1980s. Furthermore, the majority of meteorological stations register a significant warming over the last 60 years, (mean annual temperature increase of + 0.15 °C/decade), which reflects a rise in both minimum and maximum temperatures, with the latter generally increasing at a faster rate. Spring and summer are the seasons which contribute most to the general temperature increase, in particular at high elevation sites, which exhibit a more pronounced warming (+ 0.24 °C/decade). However, this tendency has not been uniform over 1951–2012, but it has been characterised by a cooling phenomenon in the first 30 years (1951–1981), followed by an even stronger warming during the last three decades (1982–2012). Finally, correlations between the climatic variables and the dominant teleconnection patterns in the Mediterranean basin are analysed to identify the potential influence of large-scale atmospheric dynamics on observed trends in Abruzzo. The results highlight the dominant role of the East-Atlantic pattern on seasonal temperatures, while more spatially heterogeneous associations, depending on the complex topography of the region, are identified between winter precipitation and the North Atlantic Oscillation, East-Atlantic and East-Atlantic/Western Russian patterns. [ABSTRACT FROM AUTHOR]

The surface warming has been widespread over the entire globe. Central Europe, including Poland, is not an exception. Global temperature increases are accompanied by changes in other climatic variables. Climate change in Poland manifests itself also as change in annual sums of precipitation. They have been slightly growing but, what is more important, seasonal and monthly distributions of precipitation have been also changing. The most visible increases have been observed during colder half-year, especially in March. A decreasing contribution of summer precipitation total (June–August) to the annual total is observed. Climate projections for Poland predict further warming and continuation of already observed changes in the quantity of precipitation as well as its spatial and seasonal distribution. [ABSTRACT FROM AUTHOR]

The study aims at regional and probabilistic evaluation of bivariate drought characteristics to assess both the past and future drought duration and severity in Bangladesh. The procedures involve applying (1) standardized precipitation index to identify drought duration and severity, (2) regional frequency analysis to determine the appropriate marginal distributions for both duration and severity, (3) copula model to estimate the joint probability distribution of drought duration and severity, and (4) precipitation projections from multiple climate models to assess future drought trends. Since drought duration and severity in Bangladesh are often strongly correlated and do not follow same marginal distributions, the joint and conditional return periods of droughts are characterized using the copula-based joint distribution. The country is divided into three homogeneous regions using Fuzzy clustering and multivariate discordancy and homogeneity measures. For given severity and duration values, the joint return periods for a drought to exceed both values are on average 45% larger, while to exceed either value are 40% less than the return periods from the univariate frequency analysis, which treats drought duration and severity independently. These suggest that compared to the bivariate drought frequency analysis, the standard univariate frequency analysis under/overestimate the frequency and severity of droughts depending on how their duration and severity are related. Overall, more frequent and severe droughts are observed in the west side of the country. Future drought trend based on four climate models and two scenarios showed the possibility of less frequent drought in the future (2020–2100) than in the past (1961–2010). [ABSTRACT FROM AUTHOR]

Strong logical relationships exist between temperature difference and hydro-meteorological variables such as precipitation, dry days, and dry spells. These relationships can be used for knowing how the differences of total and top 10% heavy precipitations, dry days, and dry spells will change with respect to temperature differences? Here, daily precipitation and temperature records over Turkey are considered from 1971 to 2010, inclusive. First 30-year (1971-2000) data are used for training for temperature difference function (ΔT) and the remaining 10-year (2001-2010) is reserved for validation. The temperature difference function is validated by mean relative error, and the results are calculated for total and top 10% heavy precipitation, dry days, and spells as 14.60%, 6.14%, 2.81%, and 11.89%, respectively. The strong relationship exists between temperature difference function and standard deviation of corresponding climatic variables. Results show that standard deviation and mean relative error (MRE) have linear correlation. Also, ΔT model predictions are compared with CMIP5 RCPs projections from 2020 to 2100. The suggested model predictions fall within the range of various atmosphere-ocean global circulation (climate) model (AOGCM) scenario RCP 4.5 and RCP 8.5 projections, in addition to model predictions cross-correlation for RCP 2.6 and RCP 6.0. [ABSTRACT FROM AUTHOR]

Quantile mapping (QM) represents a common post-processing technique used to connect climate simulations to impact studies at different spatial scales. Depending on the simulation-observation spatial scale mismatch, QM can be used for two different applications. The first application uses only the bias correction component, establishing transfer functions between observations and simulations at similar spatial scales. The second application includes a statistical downscaling component when point-scale observations are considered. However, knowledge of alterations to climate change signal (CCS) resulting from these two applications is limited. This study investigates QM impacts on the original temperature and precipitation CCSs when applied according to a bias correction only (BC-only) and a bias correction plus downscaling (BC + DS) application over reference stations in Central Italy. BC-only application is used to adjust regional climate model (RCM) simulations having the same resolution as the observation grid. QM BC + DS application adjusts the same simulations to point-wise observations. QM applications alter CCS mainly for temperature. BC-only application produces a CCS of the median ~ 1 °C lower than the original (~ 4.5 °C). BC + DS application produces CCS closer to the original, except over the summer 95th percentile, where substantial amplification of the original CCS resulted. The impacts of the two applications are connected to the ratio between the observed and the simulated standard deviation (STD) of the calibration period. For the precipitation, original CCS is essentially preserved in both applications. Yet, calibration period STD ratio cannot predict QM impact on the precipitation CCS when simulated STD and mean are similarly misrepresented. [ABSTRACT FROM AUTHOR]

The Wetness Index was calculated using gridded monthly precipitation and potential evapotranspiration in China during 1924-2013. Variations of dry and wet periods in different areas and the impact of meteorological factors were analyzed. The results show the following: (1) Drought areas (W ≤ 0.5) and wet areas (W > 0.65) in China constituted 33-56% and 35-55%, respectively, of the total land area and there were no secular trends during 1924-2013. During this period, the areas of drought and wet were inversely proportional, but had different changes among seven regions. (2) Since 1954, the overall trend in China has changed from wet to dry. Drought areas increased significantly (p < 0.05) during 1954-1983. Drought areas increased (not significantly) during 1984-2013, but the rate of drought/wetness clearly decreased (tendency rates were between − 0.05 and 0.05/30 year, and accounted for 54.79% of China). (3) Dry/wet variation in North, Northeast, Central, and Southwest China played major roles. (4) Except in the North region, drought/wet conditions were affected by surface warming. [ABSTRACT FROM AUTHOR]

As the global climate changes, different degrees of variation in climatic factors will have various impacts on agricultural production, leading to appropriate or stressed conditions. The impact threshold of climate change (ITCC) for agriculture refers to the value of a climatic factor at which the agricultural status mutates. Due to the urgency of addressing the negative impacts of climate change on agriculture, research on the ITCC of agriculture is crucial. Here, we built models to reflect the relationship between agricultural climate yield and climatic factors based on climate change characteristics and objective agricultural status. Using the models, we obtained the appropriate and stressed thresholds of different climatic factors. We calculated the ITCC of spring wheat in Wuchuan County as an example and identified warming and drying trends in Wuchuan County from 1961 to 2013, especially during the period from 1991 to 2013 (i.e., the period after mutation or the change period). Over the past 53 years, spring wheat yield increased with an average rate of 81.3 kg ha−1 dec−1. Over the change period, however, yield decreased with an average rate of 13.8 kg ha−1 dec−1 and the fluctuation range increased. The appropriate threshold for average temperature during the growth period of spring wheat was 11.4 °C, and the stressed thresholds were 8.2 and 14.6 °C. The appropriate threshold for precipitation during the growth period was 391.1 mm, and the stressed thresholds were 247.4 and 534.9 mm. During the period before mutation (i.e., the basic period), the average temperature was below the upper temperature threshold, and precipitation was 26.9 mm above the lower precipitation threshold. During the change period, the average temperature was 0.3 °C above the upper temperature threshold, and precipitation was 9.8 mm above the lower precipitation threshold. [ABSTRACT FROM AUTHOR]

During recent years, various studies have focused on investigating the direct and indirect impacts of climate changes in Iran while the noteworthy fact is the achievement gained by these researches. Furthermore, what should be taken into consideration is whether these studies have been able to provide appropriate opportunities for improving further studies in this particular field or not. To address these questions, this study systematically reviewed and summarized the current available literature (n = 150) regarding the impacts of climate change on temperature and precipitation in Iran to assess our current state of knowledge. The results revealed that while all studies discuss the probable changes in temperature and precipitation over the next decades, serious contradictions could be seen in their results; also, the general pattern of changes was different in most of the cases. This matter may have a significant effect on public beliefs in climate change, which can be a serious warning for the activists in this realm. [ABSTRACT FROM AUTHOR]

Over the past decades, drought has become a major concern in Morocco due to the importance of agriculture in the economy of the country. In the present work, the standardized precipitation index (SPI) is used to monitor the evolution, frequency, and severity of droughts in the High Atlas basins (N'Fis, Ourika, Rhéraya, Zat, and R'dat), located south of Marrakech city. The spatiotemporal characterization of drought in these basins is performed by computing the SPI with precipitation spatially interpolated over the catchments. The Haouz plain, located downstream of these basins, is strongly dependent on water provided by the mountain ranges, as shown by the positive correlations between the normalized difference vegetation index (NDVI) in the plain and the 3, 6, and 12-month SPI in the High Atlas catchments. On the opposite, no significant correlations are found with piezometric levels of the Haouz groundwater due to intensified pumping for irrigation in the recent decades. A relative SPI index was computed to evaluate the climate change impacts on drought occurrence, based on the projected precipitation (2006-2100) from five high-resolution CORDEX regional climate simulations, under two emission scenarios (RCP 4.5 and RCP 8.5). These models show a decrease in precipitation towards the future up to − 65% compared to the historical period. In terms of drought events, the future projections indicate a strong increase in the frequency of SPI events below − 2, considered as severe drought condition. [ABSTRACT FROM AUTHOR]

Homogeneous time series of meteorological variables are necessary for hydrologic and climate studies. Dependability of historical precipitation data is subjected to keen evaluation prior to every study in water resources, hydrology, and climate change fields. This study aims to characterize the homogeneity of long-term Turkish precipitation data in order to ensure that they can be reliably used. The homogeneity of monthly precipitation data set was tested using the standard normal homogeneity test, Buishand test, Von Neumann ratio test, and Pettitt test at the 5% significance level across Turkey. Our precipitation records including the most updated observations, extracted from 160 meteorological stations, for the periods 1974-2014 were analyzed by all the four homogeneity tests. According to the results of all tests, five out of 160 stations have an inhomogeneity. With regard to our strict confirmation rule, 44 out of 160 stations are said to be inhomogeneous since they failed from at least one of the four tests. The breaks captured by the Buishand and Pettitt tests usually tend to appear in the middle of the precipitation series, whereas the ability of standard normal homogeneity test is in favor of identifying inhomogeneities mostly at the beginning or at the end of the records. Our results showed that 42 out of 44 inhomogeneous stations passed all the four tests after applying a correction procedure based on the double mass curve analysis. Available metadata was used to interpret the detected inhomogeneity. [ABSTRACT FROM AUTHOR]

Feddema’s generic climate classification method is applied to study climate and climate change in the Austrian-Swiss region of the European Alps during the course of the twentieth century. A fine-tuned version of it is also tested in addition to the original scheme. Monthly precipitation and air temperature data at a spatial resolution of 10′ × 10′ are taken from the Climatic Research Unit TS 1.2 database to construct 30- and 50-year period averages. It is shown that the alpine climate is sufficiently heterogeneous to make it unnecessary to perform fine-tuning of the original scheme for its characterization on the meso-β scale (20-200 km). It is also demonstrated that data organizational effects are much less intense than the effects caused by the fine-tuning. The area heterogeneity of climate and climate change types is the highest in the vicinity of lakes (Austria: Lake Constance; Switzerland: Lakes Geneva, Neuchâtel, Biel, Zurich, and Constance) and along river valleys (Austria: the Danube, Drava, and Mur; Switzerland: the Aare and Ticino). The dominant climate change process was drying in Austria and warming in Switzerland. Large areas characterized by cold and saturated climate in the Central Eastern Alps did not experience climate change during the twentieth century. [ABSTRACT FROM AUTHOR]

Indian summer monsoon rainfall is characterized by large intra-seasonal fluctuations in the form of active and break spells in rainfall. This study investigates the role of soil moisture and vegetation on 30-h precipitation forecasts during the break monsoon period using Weather Research and Forecast (WRF) model. The working hypothesis is that reduced rainfall, clear skies, and wet soil condition during the break monsoon period enhance land-atmosphere coupling over central India. Sensitivity experiments are conducted with modified initial soil moisture and vegetation. The results suggest that an increase in antecedent soil moisture would lead to an increase in precipitation, in general. The precipitation over the core monsoon region has increased by enhancing forest cover in the model simulations. Parameters such as Lifting Condensation Level, Level of Free Convection, and Convective Available Potential Energy indicate favorable atmospheric conditions for convection over forests, when wet soil conditions prevail. On spatial scales, the precipitation is more sensitive to soil moisture conditions over northeastern parts of India. Strong horizontal gradient in soil moisture and orographic uplift along the upslopes of Himalaya enhanced rainfall over the east of Indian subcontinent. [ABSTRACT FROM AUTHOR]

In this study, two statistical approaches were adopted in the analysis of observed maximum temperature data collected from fifteen stations over Saudi Arabia during the period 1985-2014. In the first step, the behavior of extreme temperatures was analyzed and their changes were quantified with respect to the Expert Team on Climate Change Detection Monitoring indices. The results showed a general warming trend over most stations, in maximum temperature-related indices, during the period of analysis. In the second step, stationary and non-stationary extreme-value analyses were conducted for the temperature data. The results revealed that the non-stationary model with increasing linear trend in its location parameter outperforms the other models for two-thirds of the stations. Additionally, the 10-, 50-, and 100-year return levels were found to change with time considerably and that the maximum temperature could start to reappear in the different T-year return period for most stations. This analysis shows the importance of taking account the change over time in the estimation of return levels and therefore justifies the use of the non-stationary generalized extreme value distribution model to describe most of the data. Furthermore, these last findings are in line with the result of significant warming trends found in climate indices analyses. [ABSTRACT FROM AUTHOR]

Much uncertainty exists in reproducing Arctic temperature using different general circulation models (GCMs). Therefore, evaluating the performance of GCMs in reproducing Arctic temperature is critically important. In our study, 32 GCMs in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) during the period 1900-2005 are used, and several metrics, i.e., bias, correlation coefficient (R), and root mean square error (RMSE), are applied. The Cowtan data set is adopted as the reference data. The results suggest that the GCMs used can reasonably reproduce the Arctic warming trend during the period 1900-2005, as observed in the observational data, whereas a large variation of inter-model differences exists in modeling the Arctic warming magnitude. With respect to the reference data, most GCMs have large cold biases, whereas others have weak warm biases. Additionally, based on statistical thresholds, the models MIROC-ESM, CSIRO-Mk3-6-0, HadGEM2-AO, and MIROC-ESM-CHEM (bias ≤ ±0.10 °C, R ≥ 0.50, and RMSE ≤ 0.60 °C) are identified as well-performing GCMs. The ensemble of the four best-performing GCMs (ES4), with bias, R, and RMSE values of −0.03 °C, 0.72, and 0.39 °C, respectively, performs better than the ensemble with all 32 members, with bias, R, and RMSE values of −0.04 °C, 0.64, and 0.43 °C, respectively. Finally, ES4 is used to produce projections for the next century under the scenarios of RCP2.6, RCP4.5, and RCP8.0. The uncertainty in the projected temperature is greater in the higher emissions scenarios. Additionally, the projected temperature in the cold half year has larger variations than that in the warm half year. [ABSTRACT FROM AUTHOR]

Climate variability has become a hot topic worldwide in recent years. Although large numbers of climate data series have been reconstructed based on hundreds to thousands, or even tens of thousands, of years, our understanding of this problem is still controversial. We use the precipitation index (PI) series to study the periodicity of the precipitation in northern China from 1870 to 2002 and explore the climate variability on a large timescale. The analysis shows that the precipitation has periods of 2.27-3.03, 7.14, 10.00, 11.11, 12.50, 14.29, 16.67, 20.00, and 25.00 a. Based on complete ensemble empirical mode decomposition (CEEMD), the Pacific sea surface temperature (SST) and Pacific interdecadal oscillation (PDO) are decomposed into different frequencies. The results show that the SST and PDO have interannual to interdecadal periodicity. To determine the impact of the Pacific SST and PDO on the PI, we make use of the cross wavelet power spectrum and wavelet coherency spectrum to analyze their period relation and reveal their periodic change characteristics; it is found that different bands of the Pacific SST, PDO, and PI have high power. [ABSTRACT FROM AUTHOR]

A two-step statistical downscaling method has been reviewed and adapted to simulate twenty-first-century climate projections for the Gulf of Fonseca (Central America, Pacific Coast) using Coupled Model Intercomparison Project (CMIP5) climate models. The downscaling methodology is adjusted after looking for good predictor fields for this area (where the geostrophic approximation fails and the real wind fields are the most applicable). The method’s performance for daily precipitation and maximum and minimum temperature is analysed and revealed suitable results for all variables. For instance, the method is able to simulate the characteristic cycle of the wet season for this area, which includes a mid-summer drought between two peaks. Future projections show a gradual temperature increase throughout the twenty-first century and a change in the features of the wet season (the first peak and mid-summer rainfall being reduced relative to the second peak, earlier onset of the wet season and a broader second peak). [ABSTRACT FROM AUTHOR]

This study was conducted using daily precipitation records gathered at 37 meteorological stations in northern Xinjiang, China, from 1961 to 2010. We used the extreme value theory model, generalized extreme value (GEV) and generalized Pareto distribution (GPD), statistical distribution function to fit outputs of precipitation extremes with different return periods to estimate risks of precipitation extremes and diagnose aridity-humidity environmental variation and corresponding spatial patterns in northern Xinjiang. Spatiotemporal patterns of daily maximum precipitation showed that aridity-humidity conditions of northern Xinjiang could be well represented by the return periods of the precipitation data. Indices of daily maximum precipitation were effective in the prediction of floods in the study area. By analyzing future projections of daily maximum precipitation (2, 5, 10, 30, 50, and 100 years), we conclude that the flood risk will gradually increase in northern Xinjiang. GEV extreme value modeling yielded the best results, proving to be extremely valuable. Through example analysis for extreme precipitation models, the GEV statistical model was superior in terms of favorable analog extreme precipitation. The GPD model calculation results reflect annual precipitation. For most of the estimated sites’ 2 and 5-year T for precipitation levels, GPD results were slightly greater than GEV results. The study found that extreme precipitation reaching a certain limit value level will cause a flood disaster. Therefore, predicting future extreme precipitation may aid warnings of flood disaster. A suitable policy concerning effective water resource management is thus urgently required. [ABSTRACT FROM AUTHOR]