Recent trends of mean maximum and minimum air temperatures in the western half of Iran

In this study, the trends of the annual, seasonal and monthly maximum (T _max) and minimum (T _min) air temperatures time series were investigated for 20 stations in the western half of Iran during 1966?C2005. Three statistical tests including Mann?CKendall, Sen??s slope estimator and linear regression were used for the analysis. The annual T _max and T _min series showed a positive trend in 85% of the stations and a negative trend in 15% of the stations in the study region. The highest increase of T _max and T _min values were obtained over Kermanshah and Ahwaz at the rates of (+)0.597°C/decade and (+)0.911°C/decade, respectively. On the seasonal scale, the strongest increasing trends were identified in T _max and T _min data in summer. The highest numbers of stations with positive significant trends occurred in the monthly T _max and T _min series in August. In contrast, the lowest numbers of stations with significant positive trends were observed between November and March. Overall, the results showed similar increasing trends for the study variables, although T _min generally increased at a higher rate than T _max in the study period.     

Watershed-wide trend analysis of temperature characteristics in Karun-Dez watershed, southwestern Iran

Trend estimation of climatic characteristics for a watershed is required to determine developing compatible strategies related to design, development, and management of water resources. In this study, the trends of the annual maximum (T _max), minimum (T _min), and mean (T _mean) air temperature; temperature anomaly (T _anomaly); and diurnal temperature range (DTR) time series at 13 meteorological stations located in the Karun-Dez watershed were analyzed using the Mann–Kendall and linear regression trend tests. The pre-whitening method was used to eliminate the influence of serial correlation on the Mann–Kendall test. The result showed increasing trends in the T _min, T _mean, and T _anomaly series at the majority of stations and decreasing trend in the T _max and DTR series. A geographical analysis of the trends revealed a broad warming trend in most of the watershed, and the cooling trends were observed only in the southern parts. Furthermore, the geographical pattern of the trends in the T _mean and T _anomaly series was similar, and the T _max data did not show any dominant trend for the whole watershed. This study provides temperature change scenarios that may be used for the design of future water resource projects in the watershed.     

Shift changes and monotonic trends in autocorrelated temperature series over Iran

Temperature data from 29 synoptic stations in Iran for a period of 40?years (1966–2005) were analyzed to test for the existence of monotonic trends and shift changes in the annual, seasonal, and monthly mean air temperature series using the Mann–Kendall and Mann–Whitney tests. The influences of significant lag-1 serial correlation were eliminated from data by the trend-free pre-whitening method prior to the trend analysis. The magnitude of the temperature trends was derived from the Theil–Sen’s slope estimator. It was found that annual mean air temperature increased at 25 out of the 29 stations, of which 17 stations showed significant monotonic trends. The magnitude of the annual mean air temperature trends averagely was (+)0.224°C per decade. Most of the stations with the significant positive monotonic trends had a significant upward shift change. The analysis indicated that the change point year of the significant upward shift changes was 1972 for the whole stations except the coastal ones. Moreover, the strongest monotonic increasing trends and upward shift changes were observed in summer especially in August and September. The spatial analysis of the mean air temperature trends revealed the highest numbers of significant monotonic trends in the big cities of Iran. These findings provide more insights for better understanding of regional temperature behavior in the study area.     

Characterization and estimation of urban heat island at Toronto: impact of the choice of rural sites

In this study, the urban heat island of Toronto was characterized and estimated in order to examine the impact of the selection of rural sites on the estimation of urban heat island (UHI) intensity (?T _u-r). Three rural stations, King Smoke Tree (KST), Albion Hill, and Millgrove, were used for the analysis of UHI intensity for two urban stations, Toronto downtown (Toronto) and Toronto Pearson (Pearson) using data from 1970 to 2000. The UHI intensity was characterized as winter dominating and summer dominating, depending on the choice of the rural station. The analyses of annual and seasonal trends of ?T _u-r suggested that urban heat island clearly appears in winter at both Toronto and Pearson. However, due to the mitigating effect on temperature from Lake Ontario, the estimated trend of UHI intensity was found to be less at Toronto compared to that at Pearson which has no direct lake effect. In terms of the impacts of the rural stations, for both KST and Millgrove, the trends in UHI intensity were found to be statistically significant and also were in good agreement with the estimates of UHI intensities reported for other large cities in the USA. Depending on the choice of the rural station, the estimated trend for the UHI intensity at Toronto ranges from 0.01°C/decade to 0.02°C/decade, and that at Pearson ranges from 0.03°C/decade to 0.035°C/decade during 1970–2000. From the analysis of the seasonal distribution of ?T _u-r, the UHI intensity was found to be higher at Toronto in winter than that at Pearson for all three rural stations. This was likely accounted for by the lower amount of anthropogenic heat flux at Pearson. Considering the results from the statistical analysis with respect to the geographic and surface features for each rural station, KST was suggested to be a better choice to estimate UHI intensity at Toronto compared to the other rural stations. The analysis from the current study suggests that the selection of a unique urban–rural pair to estimate UHI intensity for a city like Toronto is a critical task, as it will be for any city, and it is imperative to consider some key features such as the physiography, surface characteristics of the urban and rural stations, the climatology such as the trends in annual and seasonal variation of UHI with respect to the physical characteristics of the stations, and also more importantly the objectives of a particular study in the context of UHI effect.     

A survey of temporal and spatial reference crop evapotranspiration trends in Iran from 1960 to 2005

Reference crop evapotranspiration (ET₀) is one of the most important climatic parameters which plays a key role in estimating crop water demand and scheduling irrigation. Under global warming and climate change conditions, it is needed to survey the trend of ET₀ in Iran. In this study, ET₀ values were determined based on FAO-56 Penman-Monteith equation over 32 synoptic meteorological stations during 1960–2005; and analyzed spatially and temporally in monthly, seasonal and annual time scales. After removing the significant lag-1 serial correlation effect by pre-whitening, non-parametric statistical Mann–Kendall (MK) test was used to detect the trends. The slope of the changes was determined by Sen’s slope estimator. In order to facilitate in trend analysis, the 10 moving average low pass filter were also applied on the normalized annual ET₀ time series. Annual ET₀ time series and filtered ones were then classified by hierarchical clustering in three clusters and then mapped in order to show the patterns of different clusters. Results showed that the significant decreasing trends were more considerable than increasing ones. Among surveyed stations, and on an annual time scale, the highest and lowest annual values of Sen’s slope estimator were observed in Tabas with (+) 72.14 mm per decade and Shahrud with (?) 62.22 mm per decade, respectively. Results also indicated that the clustered map based on normalized and filtered annual ET₀ time series is in accordance with another map which showed spatial distribution of increasing, decreasing and non-significant trends of ET₀ on annually time scale. Exploratory and visual analysis of smoothed time series showed increasing trend in recent years especially after 1980 and 1995. In brief, the upward trend of ET₀ in recent years is a crucial issue with regard to the high cost of dam construction for agricultural aims in arid and semi-arid regions e.g. Iran.     

Ground-based GPS measurements: time behavior from half-hour to years

Ground-based GPS and weather stations time series for the period 2010–2012 of precipitable water vapor (PWV), relative humidity (RH), and surface temperature (T) of half-hourly resolution are analyzed to demonstrate their value for dynamical analyses and weather forecasting. Three sample stations in the USA from the SoumiNet network are considered, which have rather continuous data for the last 3 years and a few missing values. Results for the three stations reveal the following features: (1) PWV time behavior is dominated by the annual cycle superimposed on high-frequency fluctuations with missing daily cycle, indicating a prevailing large-scale transport source of precipitable water at these sites; (2) RH is characterized by the daily cycle and high-frequency variability, while the annual cycle is missing; (3) T mainly varies following the annual and diurnal cycles; and (4) all variables show similar scaling properties of their variance spectra, S(f)?～?f ^–β , with a high-frequency regime of red noise type scaling (β?～?2) up to a day and long-term persistence beyond a week (β?～?0.5), with a week-long frequency interval of transition. Detrended fluctuation analysis of relative humidity indicates a clear long-term persistence scaling covering more than three decades. Implications of these findings on weather forecasting and climate modeling are discussed.     

Spatial dependence of diurnal temperature range trends on precipitation from 1950 to 2004

This paper analyzes the spatial dependence of annual diurnal temperature range (DTR) trends from 1950–2004 on the annual climatology of three variables: precipitation, cloud cover, and leaf area index (LAI), by classifying the global land into various climatic regions based on the climatological annual precipitation. The regional average trends for annual minimum temperature (T _min) and DTR exhibit significant spatial correlations with the climatological values of these three variables, while such correlation for annual maximum temperature (T _max) is very weak. In general, the magnitude of the downward trend of DTR and the warming trend of T _min decreases with increasing precipitation amount, cloud cover, and LAI, i.e., with stronger DTR decreasing trends over drier regions. Such spatial dependence of T _min and DTR trends on the climatological precipitation possibly reflects large-scale effects of increased global greenhouse gases and aerosols (and associated changes in cloudiness, soil moisture, and water vapor) during the later half of the twentieth century.     

Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region

Soil temperature (T _S) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of T _S from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily T _S at six depths (5, 10, 20, 30, 50 and 100?cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (T _a), solar radiation (R _S), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted T _S values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that T _a, RH, R _S and P were reasonably correlated with T _S at various depths, but the most effective parameters influencing T _S at different depths were T _a and RH.     

Streamflow drought severity analysis by percent of normal index (PNI) in northwest Iran

The streamflow drought is the most important type of drought due to the high dependence of many activities on surface water resources. The streamflow drought severity was identified by the percent of normal index (PNI) in the western basins of the Lake Urmia located in northwest Iran. The streamflow records were obtained from 14 hydrometric stations for the period October 1975–September 2009. The temporal trends of the streamflow drought severity were detected by the parametric Student’s t test and the nonparametric Mann–Kendall and Sen’s tests. The worst streamflow droughts at almost all the stations occurred in 1999–2000 and 2000–2001. The streamflow drought severity based on the PNI increased during the last 34 years. The results also indicated that the temporal dependency of time series had a dominant role in detecting trend by the parametric Student’s t test.     

Multivariate drought frequency estimation using copula method in Southwest China

This study reveals the impacts of climatic variable trends on drought severity in Xinjiang, China. Four drought indices, including the self-calibrating Palmer drought severity index (sc-PDSI), Erinç’s index (I _m), Sahin’s index (I _sh), and UNEP aridity index (AI), were used to compare drought severity. The ensemble empirical mode decomposition and the modified Mann-Kendall trend test were applied to analyze the nonlinear components and trends of the climatic variable and drought indices. Four and six climatic scenarios were generated in sc-PDSI, I _m, I _sh, and AI with different combinations of the observed and detrended climatic variables, respectively. In Xinjiang, generally increasing trends in minimal, average, and maximal air temperature (T _min, T _ave, T _max) and precipitation (P) were found, whereas a decreasing trend in wind speed at 2 m height (U ₂) was observed. There were significantly increasing trends in all of the four studied drought indices. Drought relief was more obvious in northern Xinjiang than in southern Xinjiang. The strong influences of increased P on drought relief and the weak influences of increased T _min, T _ave, and T _max on drought aggravation were shown by comparing four drought indices under different climate scenarios. Decreased U ₂ had a weak influence on drought, as shown by the AI in different climate scenarios. The weak influences of T and U ₂ were considered to be masked by the strong influences of P on droughts. Droughts were expected to be more severe if P did not increase, but were likely milder without an increase in air temperature and with a decrease in U ₂.     

Analysis of the change in temperature trends in Subansiri River basin for RCP scenarios using CMIP5 datasets

This study focuses on changes in the maximum and minimum temperature over the Subansiri River basin for different climate change scenarios. For the study, dataset from Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5) (i.e., coupled model intercomparison project phase five (CMIP5) dataset with representative concentration pathway (RCP) scenarios) were utilized. Long-term (2011–2100) maximum temperature (T _max) and minimum temperature (T_min) time series were generated using the statistical downscaling technique for low emission scenario (RCP2.6), moderate emission scenario (RCP6.0), and extreme emission scenario (RCP8.5). Trends and change of magnitude in T _max, T _min, and diurnal temperature range (DTR) were analyzed for different interdecadal time scales (2011–2100, 2011–2040, 2041–2070, 2070–2100) using Mann-Kendall non-parametric test and Sen’s slope estimator, respectively. The temperature data series for the observed duration (1981–2000) has been found to show increasing trends in T _max and T _min at both annual and monthly scale. Trend analysis of downscaled temperature for the period 2011–2100 shows increase in annual maximum temperature and annual minimum temperature for all the selected RCP scenarios; however, on the monthly scale, T _max and T _min have been seen to have decreasing trends in some months.

     

Time trend estimation with breaks in temperature time series

This paper deals with the modelling of the global and northern and southern hemispheric anomaly temperature time series using a novel technique based on segmented trends and fractional integration. We use a procedure that permits us to estimate linear time trends and orders of integration at various subsamples, where the periods for the changing trends are endogenously determined by the model. Moreover, we use a non-parametric approach (Bloomfield P, Biometrika, 60:217–226, 1973) for modelling the I(0) deviation term. The results show that the three series (global, northern and southern temperatures) can be well described in terms of fractional integration with the orders of integration around 0.5 in the three cases. The coefficients associated to the time trends are statistically significant in all subsamples for the three series, especially during the final part of the sample, giving then some support to the global warming theories.     

A Study of Surface Humidity Changes in China During the Recent 50 Years

This paper analyzes in detail the characteristics of surface humidity changes in China under the background of global warming in the recent 50 years by using the observation data compiled by the National Meteorological Information Center of China. The results reveal that (1) with the increase of temperature,surface saturation specific humidity (qs) shows basically an exponential growth, according to the Clausius-Clapeyron equation. The nationwide average moistening rate in winter is obviously less than the annual average rate and the summer rate. There are some regional differences in qstrends in different regions of China. For example, qsin central and eastern parts of China exhibits a reducing trend in summer, consistent with the weakening trend of temperature in these areas; (2) except parts of South China and Jianghuai Region in eastern China, unanimously increasing trends of annual and winter specific humidity (q) are found in most of China, especially in western China. In summer, except parts of Northeast China, Northwest China, and some areas over the Qinghai-Tibetan Plateau, the decreasing and drying trends are significant in most of China, which is not consistent with the global mean situation; (3) the surface relative humidity(RH) in most of China shows a reducing trend. One of the major reasons for the reduction of RH is that the increasing rates of q are smaller than those of qs. Nonetheless, upward trends of RH in central and eastern China mainly due to the cooling temperature and rising q in these regions are observed in summer, leading to more precipitation. From about 2003 or so, qshas remarkably increased while q has sharply decreased in most parts of China; therefore, RH has reduced to a great extent. This may be closely related to the persistent growth of drought areas in China in the recent 10 years.     

Trend analysis of Climatic Research Unit temperature dataset for Gangotri glacier,India

The absence of continuous long term meteorological dataset has led to limited knowledge of glaciers’ response to climate change over Himalayas. This study presents an open source long term temperature dataset Climatic Research Unit (CRU) available since 1901 to study trend analysis of temperature (T_max, T_min and T_mean) for Gangotri basin in Himalayas. The study first establishes close agreement between CRU time series data and observed temperature dataset available from National Institute of Hydrology (NIH), Roorkee for a period of 11 years from 2005 to 2015 using standard anomaly, Wilcoxon Signed-Rank (WSR) and correlation tests. The close agreement of CRU with NIH data validate the use of CRU time series to study variation in meteorological parameter for hilly terrain of Himalayas. The second part includes application of different statistical tests such as Mann-Kendall (MK), Sen’s slope and CUSUM technique on CRU data to detect existence of any possible trends and identification of change points in T_max, T_min and T_mean on long term scale. On annual scale, significant increasing trends for T_mean and T_min were observed with no significant trend for T_max. On seasonal and monthly scale, T_max showed significant decreasing trend for monsoon season and increasing trend for winters while T_min show significant increasing trend for all months (except May) and seasons. CUSUM technique identified 8 change points from 3 annual time series with 2 for T_mean (1974 and 1999), 3 each for T_max (1941, 1975 and 1999) and T_min (1941, 1965 and 1999) respectively. Overall, significant increase in T_min with no significant trend for T_max has been identified over the study area.     

Trends of seasonal maximum and minimum temperatures and precipitation in Southern Brazil for the 1913–2006 period

Long-term variations of monthly average maximum and minimum temperature (TMAX and TMIN) and precipitation records in southern Brazil are investigated for the 1913–2006 period. These variations are carefully analyzed for seasonal and annual indices, taken as regional averages. For this purpose, the serial correlation and trend of the indices are investigated using the run and Mann–Kendall tests. The significant trends are obtained from linear least-square fits. The annual and seasonal TMIN indices show significant warming trends with magnitudes (1.7°C per 100 years for annual index) comparable to those reported by the Intergovernmental Panel on Climate Change, but lower than those found for the southern Brazil in another previous work. Regarding the two other variables, the indices show significant trends only for summer, being a cooling trend of 0.6°C per 100 years for the TMAX and an increasing trend of 93 mm per 100 years over an average summer precipitation of 367 mm. Concerning the decadal analysis, the 1920s present the lowest annual, autumn, and spring TMIN and the 1990s, the highest ones. The 1970s is the decade with the lowest summer TMAX, and the 1940s the decade with the highest one. The driest decade is the 1940s and the wettest, the 1980s.     

Assessment of the changes in extreme vulnerability over East Asia due to global warming

A number of indices have been employed to describe weather extremes on the basis of climate regimes and public concerns. In this study, we combined these traditional indices into four groups according to whether they relate to warm (T_warm), cold (T_cold), wet (P_wet), or dry (P_dry) extremes. Analysis of the combined indices calculated for the daily temperatures and precipitation at 750 meteorological stations in Korea, China, and Japan for 1960s?C2000s shows increasing trends in T_warm and P_dry events and decreasing trends in T_cold events in recent decades, particularly in the northern part of East Asia. A notable regional variation is an increase in the P_wet events in the Korean Peninsula. We applied the same analysis to a 200-year global climate model simulation for 1900?C2099 using the National Center for Atmospheric Research-Community Climate System Model 3. During the 20th century, the changes in T_warm and T_cold calculated from the model data are largely consistent with those calculated from the observations, especially in northern East Asia. The model projections for the 21st century indicate statistically significant increasing T_warm and decreasing T_cold trends in extreme events over the region. Results obtained from historical archives and model simulations using our combined weather extreme indices suggest that northern East Asia will be subject to increased warm and dry extremes and the Korea Peninsula will experience more wet extremes.     

Evaluating persistence and identifying trends and abrupt changes in monthly and annual rainfalls of a semi-arid region in Western India

In this study, 43-year (1965–2007) monthly and annual rainfall time series of ten rainfall stations in a semi-arid region of western India are analyzed by adopting three tests for testing normality and by applying autoregressive technique for exploring persistence. Gradual trends are identified by three tests, and their magnitudes are assessed by the Sen’s slope estimator. Also, abrupt changes are detected by using four tests and they are further confirmed by two tests. Box-whisker plots revealed that the rainfalls of June and September are right skewed for all the stations. The annual rainfalls of Bhinder, Dhariawad, and Gogunda stations are found considerably right skewed. The normality tests indicated that the rainfall of July does not deviate from the normal distribution at all the stations. However, the annual rainfall is found non-normal at five stations. The monthly rainfalls of June, July, and August have persistence respectively at three (Mavli, Salumber, and Sarada), two (Kherwara and Sarada), and one (Mavli) stations, whereas the annual rainfall has persistence at Girwa and Mavli stations. Significantly increasing trend is detected at Mavli in the rainfall of July and in the annual rainfall (p value?>?0.05), while the negative trend in August rainfall at Dhariawad is found significant (p value?>?0.10). This study revealed that the presence of serial correlation does not affect the performance of the Mann-Kendall test. Mean values of trend magnitudes for the rainfalls of June, July, August, and September are 0.3, 0.8, ?0.4, and 0.4 mm year^?1, respectively, and the overall mean value for the annual rainfall is 0.9 mm year^?1. It is found that the standard normal homogeneity test and the Pettitt test are biased towards the end of the series to locate a change point. Conversely, the Bayesian test has a tendency to look for a change point in the beginning of time series. Confirmed abrupt changes in the rainfall time series are found in the year 2003 (Bhinder) in June; years 1974 (Mavli) and 1989 (Dhariawad and Salumber) in July; years 1972 (Sarada), 1990 (Dhariawad), and 2003 (Mavli) in August; years 1977 (Dhariawad), 1991 (Sarada), and 2004 (Kotra) in September; and in the year 1972 (Mavli and Sarada stations) in the annual series. It is emphasized that the significantly increasing trend of rainfall may have linkages with climate change and/or variability. Finally, this study recommends use of multiple statistical tests for analyzing hydrologic time series in order to ensure reliable decisions.     

Change point detection of the Persian Gulf sea surface temperature

In this study, the Student’s t parametric and Mann-Whitney nonparametric change point models (CPMs) were applied to detect change point in the annual Persian Gulf sea surface temperature anomalies (PGSSTA) time series for the period 1951–2013. The PGSSTA time series, which were serially correlated, were transformed to produce an uncorrelated pre-whitened time series. The pre-whitened PGSSTA time series were utilized as the input file of change point models. Both the applied parametric and nonparametric CPMs estimated the change point in the PGSSTA in 1992. The PGSSTA follow the normal distribution up to 1992 and thereafter, but with a different mean value after year 1992. The estimated slope of linear trend in PGSSTA time series for the period 1951–1992 was negative; however, that was positive after the detected change point. Unlike the PGSSTA, the applied CPMs suggested no change point in the Niño3.4SSTA time series.     

Recent Climate Change at the Upper Danube—A temporal and spatial analysis of temperature and precipitation time series

The following study investigates temperature and precipitation trends in instrumental time series between 1960 and 2006 at 88 meteorological stations located in the Upper Danube Basin. Time series were tested for inhomogeneities with several common homogeneity tests, trend magnitudes of annual and seasonal time series were calculated by least square fitting and the significance of trend values was checked and quantified by the Mann-Kendall test. The results confirm a particularly strong recent Climate Change in the investigation area. Increasing temperature trends show remarkably high trend values up to 0.8°C/decade in the summer season. The trends are highly significant for all investigated summer, spring and annual time series. Winter and spring temperature trends show consistently positive trend values as well even though some time series show no significance at all and the calculated trend values are smaller. Autumn temperature trends are mostly non-significant with low values (up to 0.3°C/decade) and several negative trends. Most of the highest trend values can be found in lower altitudes whereas stations situated in alpine regions tend to show low trend magnitudes and often exhibit non-significant results. Precipitation time series show positive as well as negative trends in the annual and seasonal analysis. At most stations a precipitation decrease in summer and autumn and an increase in winter was observed during the last 47?years whereas the spring and mean annual precipitation exhibits no change at all. But most time series are not conclusive since they show predominantly no significance and they often exhibit only low trend values.     

Spatiotemporal trends of aridity index in arid and semi-arid regions of Iran

The spatiotemporal trends of aridity index in the arid and semi-arid regions of Iran in 1966–2005 were investigated using the Mann–Kendall test and Theil–Sen’s slope estimator. The results of the analysis showed negative trends in annual aridity index at 55 % of the stations, while just one site had a statistically significant (α?=?0.1) negative trend. Furthermore, the positive trends in the annual aridity index series were significant at the 95 % confidence level at Bushehr and Isfahan stations. The significant negative trend in the annual aridity index was obtained over Mashhad at the rate of ?0.004. In the seasonal series, the negative trends in the spring and winter aridity index were larger compared with those in the other seasonal series. A noticeable decrease in the winter aridity index series was observed mostly in the southeast of the study area. In the summer and autumn aridity index, two significant positive trends were found.