Temperature trends in Libya over the second half of the 20th century

This paper investigates spatial variability of temperature trends over Libya in the second half of the 20th century. The study is based on complete and homogeneous time series of minimum, maximum, and mean temperature for ten observatories. During the investigated period (1951–1999), temperature trend analyses have experienced a downward trend in the maximum surface temperature (about –0.06°C decade^–1) and an upward trend in the minimum surface temperature (about 0.23°C decade^–1). Cooling tendency in maximum temperature is spatially more pronounced in inland stations compared to coastal stations. At the seasonal scale, maximum temperature cooling is more obvious in winter and spring, meanwhile minimum temperature warming is more pronounced in summer and fall. In accordance with global trends, the surface mean temperature has moderately risen at an average rate of 0.09°C decade^–1. However, this trend has shown considerable temporal variability considering a more pronounced upward trend in summer and fall. In conjunction with other regional and global investigations, clear trends towards smaller diurnal range are presented (–0.28°C decade^–1).     

Increasing trend of northeast monsoon rainfall over the equatorial Indian Ocean and peninsular India

Peninsular India and Sri Lanka receive major part of their annual rainfall during the northeast monsoon season (October–December). The long-term trend in the northeast monsoon rainfall over the Indian Ocean and peninsular India is examined in the vicinity of global warming scenario using the Global Precipitation Climatology Project (GPCP) dataset available for the period 1979–2010. The result shows a significant increasing trend in rainfall rate of about 0.5 mm day^?1 decade^?1 over a large region bounded by 10 °S–10 °N and 55 °E–100 °E. The interannual variability of seasonal rainfall rate over peninsular India using conventional rain gauge data is also investigated in conjunction to the Indian Ocean dipole. The homogeneous rain gauge data developed by Indian Institute of Tropical Meteorology over peninsular India also exhibit the considerable upward rainfall trend of about 0.4 mm day^?1 decade^?1 during this period. The associated outgoing longwave radiation shows coherent decrease in the order of 2 W?m^?2 decade^?1 over the rainfall increase region.     

Assessment of surface air warming in northeast China, with emphasis on the impacts of urbanization

Based on homogenized land surface air temperature (SAT) data (derived from China Homogenized Historical Temperature (CHHT) 1.0), the warming trends over Northeast China are detected in this paper, and the impacts of urban heat islands (UHIs) evaluated. Results show that this region is undergoing rapid warming: the trends of annual mean minimum temperature (MMIT), mean temperature (MT), and mean maximum temperature (MMAT) are 0.40 C decade^?1, 0.32 C decade^?1, and 0.23 C decade^?1, respectively. Regional average temperature series built with these networks including and excluding “typical urban stations” are compared for the periods of 1954–2005. Although impacts of UHIs on the absolute annual and seasonal temperature are identified, UHI contributions to the long-term trends are less than 10% of the regional total warming during the period. The large warming trend during the period is due to a regime shift in around 1988, which accounted for about 51% of the regional warming.     

Possible Impact of Spatial and Temporal Non-Uniformity in Land Surface Temperature Data on Trend Estimation

The present work investigates possible impact of the non-uniformity in observed land surface temperature on trend estimation, based on Climatic Research Unit (CRU) Temperature Version 4 (CRUTEM4) monthly temperature datasets from 1900 to 2012. The CRU land temperature data exhibit remarkable non-uniformity in spatial and temporal features. The data are characterized by an uneven spatial distribution of missing records and station density, and display a significant increase of available sites around 1950. Considering the impact of missing data, the trends seem to be more stable and reliable when estimated based on data with < 40% missing percent, compared to the data with above 40% missing percent. Mean absolute error (MAE) between data with < 40% missing percent and global data is only 0.011°C (0.014°C) for 1900–50 (1951–2012). The associated trend estimated by reliable data is 0.087°C decade^–1 (0.186°C decade^–1) for 1900–50 (1951–2012), almost the same as the trend of the global data. However, due to non-uniform spatial distribution of missing data, the global signal seems mainly coming from the regions with good data coverage, especially for the period 1900–50. This is also confirmed by an extreme test conducted with the records in the United States and Africa. In addition, the influences of spatial and temporal non-uniform features in observation data on trend estimation are significant for the areas with poor data coverage, such as Africa, while insignificant for the countries with good data coverage, such as the United States.     

An overview of mainland China temperature change research

There has been significant effort devoted to investigating long-term trends in land surface air temperature over mainland China by Chinese scientists over the past 50 years, and much progress has been made in understanding dynamics of the changes. This review highlights research conducted by early Chinese climatologists, and particularly Professor Shaowu Wang from Peking University, with special focus on systematic work that has been conducted since the mid to late 1970s. We also discuss major issues that remain unresolved in past and current studies. The most recent analyses indicate that the country-average annual mean surface air temperature rose by 1.12°C over the past 115 years (1901–2015), with a rate of increase of about 0.10°C decade^–1. Temperatures have risen more rapidly since the 1950s, with the rate of increase of more than 0.25°C decade^-1. However, the recent increase in temperatures is in large part due to contamination by systematically biased data. These data are influenced by unprecedented urbanization in China, with a contribution of urbanization to the overall increase of annual mean temperatures in mainland China of about one third over the past half a century. If the bias is corrected, the rate of increase for the country-average annual mean surface air temperature is 0.17°C decade–1 over the last 50–60 years, which is approximately the same as global and Northern Hemispheric averages in recent decades. Future efforts should be focused towards the recovery and digitization of early-year observational records, the homogenization of observational data, the evaluation and adjustment of urbanization bias in temperature data series from urban stations, the analysis of extreme temperatures over longer periods including the first half of the 20th century, and the investigation of the observed surface air temperature change mechanisms in mainland China.     

Observed and simulated sensitivities of summertime urban surface air temperatures to anthropogenic heat in downtown areas of two Japanese Major Cities,Tokyo and Osaka

In this study, the sensitivities of surface air temperatures to anthropogenic heat (AH) were investigated in downtowns of the two Japanese major cities, Tokyo and Osaka. First, meteorological measurements were made with the simultaneous monitoring of electricity demand in a contrastive couple of a downtown commercial area (C-area) and a residential area (R-area) within each city in summer 2007. From the measurements, the areal-mean surface air temperatures were obtained as \( {\overline{T}}_{\mathrm{C}} \) and \( {\overline{T}}_{\mathrm{R}} \) for each of the C-area and R-area, respectively. Using the actual electricity demand and the estimated motor fuels consumption, their areal total was evaluated as the energy-consumption-basis AH. The estimated C-areas' AH indicated greater values up to 220 W/m² on weekdays and remarkable decrease about by half on weekends, whereas that in the R-areas showed less values of 10–20 W/m² stably. Then, \( {\overline{T}}_{\mathrm{C}}-{\overline{T}}_{\mathrm{R}} \) on calm and fine days were found to be systematically decreased from weekdays to weekends in both cities roughly indicating a proportional relationship with the reductions in the C-areas' AH on weekends. The result suggested a common afternoon sensitivity for both C-areas of around 1.0°C/100 W/m², which indicated an intensity of the AH impact on surface air temperature there. Next, to simulate the observed AH impact, the authors' CM-BEM (a multilayer urban canopy model coupled with a building energy model) was newly implemented in the mesoscale Weather Research and Forecasting (WMF) model. This new system, WRF-CM-BEM, was applied to Tokyo and almost reasonably validated from the aspects of the reproducibility of urban surface air temperature and electricity demand in the observation areas. The simulations also suggested that WRF-CM-BEM underestimated the observed air temperature sensitivity to AH in the Tokyo C-area roughly by half but still in the same order of magnitude.     

Seasonal variation of lightning activity over the Indian subcontinent

The seasonal variation of lightning flash activity over the Indian subcontinent (0°N–35°N and 60°E–100°E) is studied using the quality checked monthly lightning flash data obtained from lightning imaging sensor on board the Tropical Rainfall Measuring Mission satellite. This paper presents results of spatio-temporal variability of lightning activity over the Indian subcontinent. The study of seasonal total lightning flashes indicates that the lightning flash density values are in qualitative agreement with the convective activity observed over this region. Maximum seasonal total flash counts are observed during the monsoon season. The propagation of the inter-tropical convergence zone over this region is also confirmed. Synoptic conditions responsible for variation of lightning activity are also investigated with the help of an observed dataset. The mean monthly flash counts show a peak in the month of May, which is the month of maximum temperatures over this region. Maximum flash density (40.2 km^?2 season^?1) is observed during the pre-monsoon season at 25.2°N/91.6°E and the annual maximum flash density of 28.2 km^?2 year^?1 is observed at 33.2°N/74.6°E. The study of the inter-annual variability of flash counts exhibits bimodal nature with the first maximum in April/May and second maximum in August/September.     

Climate change impacts on forest fire potential in boreal conditions in Finland

The aim of this work was to study the forest fire potential and frequency of forest fires under the projected climate change in Finland (N 60°–N 70°). Forest fire index, generally utilized in Finland, was used as an indicator for forest fire potential due to climatological parameters. Climatic scenarios were based on the A2 emission scenario. According to the results, the forest fire potential will have increased by the end of this century; as a result of increased evaporative demand, which will increase more than the rise in precipitation and especially in southern Finland. The annual number of forest fire alarm days is expected to increase in southern Finland to 96–160 days by the end of this century, compared to the current 60–100 days. In the north, the corresponding increase was from 30 to 36 days. The expected increase in the annual frequency of forest fires over the whole country was about 20% by the end of this century compared to the present day. The greatest increase in the frequency of fires, per 1,000 km², was in the southernmost part of the country, with six to nine fires expected annually per 1,000 km² at the end of this century, meaning a 24–29% increase compared to the present day frequencies.     

Changes in Extreme Maximum Temperature Events and Population Exposure in China under Global Warming Scenarios of 1.5 and 2.0°C: Analysis Using the Regional Climate Model COSMO-CLM

We used daily maximum temperature data (1986–2100) from the COSMO-CLM (COnsortium for Small-scale MOdeling in CLimate Mode) regional climate model and the population statistics for China in 2010 to determine the frequency, intensity, coverage, and population exposure of extreme maximum temperature events (EMTEs) with the intensity–area–duration method. Between 1986 and 2005 (reference period), the frequency, intensity, and coverage of EMTEs are 1330–1680 times yr^–1, 31.4–33.3°C, and 1.76–3.88 million km², respectively. The center of the most severe EMTEs is located in central China and 179.5–392.8 million people are exposed to EMTEs annually. Relative to 1986–2005, the frequency, intensity, and coverage of EMTEs increase by 1.13–6.84, 0.32–1.50, and 15.98%–30.68%, respectively, under 1.5°C warming; under 2.0°C warming, the increases are 1.73–12.48, 0.64–2.76, and 31.96%–50.00%, respectively. It is possible that both the intensity and coverage of future EMTEs could exceed the most severe EMTEs currently observed. Two new centers of EMTEs are projected to develop under 1.5°C warming, one in North China and the other in Southwest China. Under 2.0°C warming, a fourth EMTE center is projected to develop in Northwest China. Under 1.5 and 2.0°C warming, population exposure is projected to increase by 23.2%–39.2% and 26.6%–48%, respectively. From a regional perspective, population exposure is expected to increase most rapidly in Southwest China. A greater proportion of the population in North, Northeast, and Northwest China will be exposed to EMTEs under 2.0°C warming. The results show that a warming world will lead to increases in the intensity, frequency, and coverage of EMTEs. Warming of 2.0°C will lead to both more severe EMTEs and the exposure of more people to EMTEs. Given the probability of the increased occurrence of more severe EMTEs than in the past, it is vitally important to China that the global temperature increase is limited within 1.5°C.     

Observed trends in various aspects of compound heat waves across China from 1961 to 2015

Based on combined thresholds of daily maximum and minimum temperatures, a compound heat wave is defined, and then changes in multiple aspects of such heat waves in China are estimated between 1961 and 2015. Our results intriguingly indicate that severe compound heat waves in northern China are characterized by excessively high intensity within short duration, while long duration determines great disaster-causing potential of severe events in the south. In the past few decades, large areas of China have experienced longer, stronger, and more frequent compound heat waves. Northern China has witnessed dramatic intensity increases, with a maximum amplification over 5°C decade^–1; while remarkable lengthening in duration has been mostly recorded in the south, with a maximum trend over 1 day decade^–1. The spatial extent affected by compound heat waves has significantly expanded since the 1960s, with the largest expanding rate over 6% decade^–1 detected in North China and Northeast China. These systematic assessments serve to deepen our understanding of observed changes in compound heat waves across China, and may further shed some light on future adaptations and mitigations against an increasingly warming climate.     

Evaluating observed and projected future climate changes for the Arctic using the K?ppen-Trewartha climate classification

The ecosystems in the Arctic region are known to be very sensitive to climate changes. The accelerated warming for the past several decades has profoundly influenced the lives of the native populations and ecosystems in the Arctic. Given that the K?ppen-Trewartha (K-T) climate classification is based on reliable variations of land-surface types (especially vegetation), this study used the K-T scheme to evaluate climate changes and their impact on vegetation for the Arctic (north of 50°N) by analyzing observations as well as model simulations for the period 1900–2099. The models include 16 fully coupled global climate models from the Intergovernmental Panel on Climate Change Fourth Assessment. By the end of this century, the annual-mean surface temperature averaged over Arctic land regions is projected to increase by 3.1, 4.6 and 5.3°C under the Special Report on Emissions Scenario (SRES) B1, A1b, and A2 emission scenarios, respectively. Increasing temperature favors a northward expansion of temperate climate (i.e., Dc and Do in the K-T classification) and boreal oceanic climate (i.e., Eo) types into areas previously covered by boreal continental climate (i.e., Ec) and tundra; and tundra into areas occupied by permanent ice. The tundra region is projected to shrink by ?1.86?×?10⁶?km² (?33.0%) in B1, ?2.4?×?10⁶?km² (?42.6%) in A1b, and ?2.5?×?10⁶?km² (?44.2%) in A2 scenarios by the end of this century. The Ec climate type retreats at least 5° poleward of its present location, resulting in ?18.9, ?30.2, and ?37.1% declines in areal coverage under the B1, A1b and A2 scenarios, respectively. The temperate climate types (Dc and Do) advance and take over the area previously covered by Ec. The area covered by Dc climate expands by 4.61?×?10⁶?km² (84.6%) in B1, 6.88?×?10⁶?km² (126.4%) in A1b, and 8.16?×?10⁶?km² (149.6%) in A2 scenarios. The projected redistributions of K-T climate types also differ regionally. In northern Europe and Alaska, the warming may cause more rapid expansion of temperate climate types. Overall, the climate types in 25, 39.1, and 45% of the entire Arctic region are projected to change by the end of this century under the B1, A1b, and A2 scenarios, respectively. Because the K-T climate classification was constructed on the basis of vegetation types, and each K-T climate type is closely associated with certain prevalent vegetation species, the projected large shift in climate types suggests extensive broad-scale redistribution of prevalent ecoregions in the Arctic.     

Drought history inferred from tree ring δ 13C and δ 18O in the central Tianshan Mountains of China and linkage with the North Atlantic Oscillation

Annual tree ring δ ¹⁸O and δ ¹³C chronologies from 1790 to 2008 were established using Tianshan spruce (Picea schrenkiana) in the central Tianshan Mountains of northwestern China. Temperature has a positive effect on tree ring δ ¹⁸O and δ ¹³C in the study area, while precipitation and relative humidity have negative effects. The standardized precipitation–evapotranspiration index (SPEI) considered all of these effects and was significantly negatively correlated with tree ring δ ¹⁸O and δ ¹³C. We combined the tree ring δ ¹⁸O and δ ¹³C series to reconstruct the past 192 years of SPEI, which accounted for about 46 % of the total variance of SPEI from 1950 to 2006. The reconstruction showed good spatial agreement with gridded data in Palmer Drought Severity Index and precipitation and an inverse relationship with temperature. Our SPEI reconstruction reveals several wet and dry periods over the past 192 years and has good agreement with other drought records. Wavelet analysis showed quasi-periodic 10-, 20-, 30-, and 70-year fluctuations in the reconstruction. The 10-, 20-, and 30-year periodicities may reflect the potential influence of North Atlantic Oscillation (NAO). Overall, this study indicates that the SPEI is a potential drought index, and the winter NAO affects regional moisture conditions in the long term.     

Indirect determination of broadband turbidity coefficients over Egypt

Long-term data from diffuse and global irradiances were used to calculate direct beam irradiance which was used to determine three atmospheric turbidity coefficients (Linke T _L , Ångström β and Unsworth–Monteith δ _a ) at seven sites in Egypt in the period from 1981 to 2000. Seven study sites (Barrani, Matruh, Arish, Cairo, Asyut, Aswan and Kharga) have been divided into three categories: Mediterranean climate (MC), desert Nile climate (DNC) and urban climate (UC, Cairo). The indirect method (i.e., global irradiance minus diffuse irradiance) used here allows to estimate the turbidity coefficients with an RMSE% ≤20 % (for β, δ _a and T _L ) and ~30 % (for β) if compared with those estimated by direct beam irradiance and sunphotometeric data, respectively. Monthly averages of T _L , β and δ _a show seasonal variations with mainly maxima in spring at all stations, due to Khamsin depressions coming from Sahara. Secondary maxima is observed in summer and autumn at DNC and MC (Barrani and Arish) stations in summer due to dust haze which prevails during that season and at UC (Cairo) in autumn, due to the northern extension of the Sudan monsoon trough, which is accompanied by small-scale depressions with dust particles. The mean annual values of β, δ _a , and T _L (0.216, 0.314, and 4.6, respectively) are larger in Cairo than at MC stations (0.146, 0.216, and 3.8, respectively) and DNC stations (0.153, 0.227, and 3.8, respectively). Both El-Chichon and Mt. Pinatubo eruptions were examined for all records data at MC, UC and DNC stations. The overburden caused by Mt. Pinatubo’s eruption was larger than El-Chichon’s eruption and overburden for β, and T _L at DNC stations (0.06, and 0.58 units, respectively) was more pronounced than that at MC (0.02, and 0.26, respectively) and UC (0.05 and 0.52 units, respectively) stations. The annual variations in wind speed and turbidity parameters show high values for both low and high wind speed at all stations. The wind directions have a clear effect on atmospheric turbidity, and consequently, largest turbidities occur when the wind carries aerosols from the main particle sources, such as industrial particle sources around Cairo or to some extent from the Sahara surrounding all study stations.     

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.     

Thermal features of the Mackenzie basin from NOAA AVHRR observations for summer 1994

Abstract

A series of mid‐afternoon Advanced Very High Resolution Radiometer (AVHRR) thermal radiance scenes were assembled in order to develop a better understanding of the complex energy and water processes leading to variations in surface temperature. An in‐depth knowledge of the temperature variability is of interest to land surface process modelling and its application to the Mackenzie Global Energy and Water Cycle Experiment (GEWEX) Study (MAGS).

Clear‐sky land surface temperatures are estimated by applying a split window technique to remove atmospheric effects. A maximum land surface temperature map of the Mackenzie basin at 1‐km scale for summer 1994 is produced. The patterns are related to land surface features and elevation. The basin's maximum land surface temperature patterns can be subdivided into three land zones (≥ 35°C, 33–34°C and 27–32°C) and a water dominated zone (20.5°C on average). The highest maximum temperature zone (≥35°C) corresponds to a combination of minimal vegetation, drier soils and low terrain. This zone is not in the southern part of the basin as might be speculated in the absence of these data, but in a wide low elevation corridor from west of Great Bear Lake along the Mackenzie River down to 50°N, 120°W. The maximum land surface temperatures tend to decrease with increasing vegetation density and surface moisture; they also decrease with elevation at a rate of –4.5°C km^–1. This is confirmed by weather station data. The AVHRR data extend this relationship to the 1200 – 2200 m altitude ranges, where there are no station data. The data suggest that elevation and land cover should be taken into account in the objective analysis (spatial interpolation) of station data.     

Analysis of changes in extreme temperatures using quantile regression

This study examines the changes in regional extreme temperature in South Korea using quantile regression, which is applied to analyze trends, not only in the mean but in all parts of the data distribution. The results show considerable diversity across space and quantile level in South Korea. In winter, the slopes in lower quantiles generally have a more distinct increase trend compared to the upper quantiles. The time series for daily minimum temperature during the winter season only shows a significant increasing trend in the lower quantile. In case of summer, most sites show an increase trend in both lower and upper quantiles for daily minimum temperature, while there are a number of sites with a decrease trend for daily maximum temperature. It was also found that the increase trend of extreme low temperature in large urban areas (0.80°C decade^?1) is much larger than in rural areas (0.54°C decade^?1) due to the effects of urbanization.     

Simulation of rice brown planthopper, Nilaparvata lugens (Stal.) population and crop-pest interactions to assess climate change impact

Brown planthopper (BPH), Nilaparvata lugens (Stal.) development studied at six constant temperatures, 19, 22, 25, 28, 31 and 33 ±1 °C on rice plants revealed that developmental period from egg hatching to adult longevity decreased from 46.8 to 18.4 days as temperature increased from 19 to 31 °C. Through regression of development rate on temperature, thermal constant of small nymph (1st-2nd instar), large nymph (3rd–5th instar) and adult were determined to be 126.6, 140.8 and 161.3 degree days (DD), respectively with corresponding development threshold being 8.8, 9.5 and 9.6 °C. A thermal constant-based mechanistic-hemimetabolous-population model was adapted for BPH and linked with InfoCrop, a crop simulation model to simulate climate change impact on both the pest population and crop-pest interactions. The model was validated with field data at New Delhi and Aduthurai (Tamil Nadu, India), (R ²?=?0.96, RMSE?=?1.87 %). Climate-change-impact assessment through coupled BPH-InfoCrop model, in the light of the projected climate-change scenario for Indian subcontinent, showed a decline of 3.5 and 9.3–14 % in the BPH population by 2020 and 2050, respectively, during the rainy season at New Delhi, while the pest population exhibited only a small decline of 2.1–3.5 % during the winter at Aduthurai by 2050. BPH population decline is attributed to reduction in fecundity and survival by simulation model, which otherwise was not possible to account for with an empirical model. Concomitant to its population decline, BPH-induced yield loss also indicated a declining trend with temperature rise. However, the study considered the effect of only CO₂ and temperature rise on the BPH population and crop yield, and not that of probable changes in feeding rate and adaptive capacity of the pest.     

Geo-spatial analysis of temporal trends of temperature and its extremes over India using daily gridded (1°×1°) temperature data of 1969–2005

Daily gridded (1°×1°) temperature data (1969–2005) were used to detect spatial patterns of temporal trends of maximum and minimum temperature (monthly and seasonal), growing degree days (GDDs) over the crop-growing season (kharif, rabi, and zaid) and annual frequencies of temperature extremes over India. The direction and magnitude of trends, at each grid level, were estimated using the Mann–Kendall statistics (α = 0.05) and further assessed at the homogeneous temperature regions using a field significance test (α=0.05). General warming trends were observed over India with considerable variations in direction and magnitude over space and time. The spatial extent and the magnitude of the increasing trends of minimum temperature (0.02–0.04 °C year^?1) were found to be higher than that of maximum temperature (0.01–0.02 °C year^?1) during winter and pre-monsoon seasons. Significant negative trends of minimum temperature were found over eastern India during the monsoon months. Such trends were also observed for the maximum temperature over northern and eastern parts, particularly in the winter month of January. The general warming patterns also changed the thermal environment of the crop-growing season causing significant increase in GDDs during kharif and rabi seasons across India. The warming climate has also caused significant increase in occurrences of hot extremes such as hot days and hot nights, and significant decrease in cold extremes such as cold days and cold nights.     

Spatiotemporal variability of reference evapotranspiration and its contributing climatic factors in Yunnan Province, SW China, 1961–2004

Evapotranspiration is an important flux term in the water cycle that integrates atmospheric demand and surface conditions. Using the FAO Penman–Monteith method, we calculated monthly reference evapotranspiration (ET0) for 119 stations during 1961–2004 over Yunnan Province (YP), southwest China. Linear trend analysis shows that area-averaged annual and seasonal ET0 rates declined, with most remarkable decreases during pre-monsoon (?1.5 mm decade^?1, Mar–May) and monsoon (?0.6 mm decade^?1, Jun–Aug) seasons. Most of the stations with negative trends were concentrated in the eastern and northern parts of YP. Over the 44–year period, wind speed (WS), relative sunshine duration (SD) and relative humidity (RH) all showed decreasing trends, whereas maximum temperature (TMX) increased slightly. Multivariate regression analysis indicated that the variability of ET0 rates is most sensitive to the variations of SD, followed by RH, TMX and WS. The temporal evolution of these contributing factors was not stable during the study period, with an increasing contribution of SD and a decreasing contribution of TMX after the 1970s. Temporally changing contributions of climatic variables to ET0 should be taken into account when evapotranspiration rates are calculated with equations that rely on parameterization of climatic variables. Linking the changing contributions of climatic variables to ET0 rates to circulation features may help to better understand how ET0 responds to regional climatic change.     

Trends of urban surface temperature and heat island characteristics in the Mediterranean

Urban air temperature studies usually focus on the urban canopy heat island phenomenon, whereby the city center experiences higher near surface air temperatures compared to its surrounding non-urban areas. The Land Surface Temperature (LST) is used instead of urban air temperature to identify the Surface Urban Heat Island (SUHI). In this study, the nighttime LST and SUHI characteristics and trends in the seventeen largest Mediterranean cities were investigated, by analyzing satellite observations for the period 2001–2012. SUHI averages and trends were based on an innovative approach of comparing urban pixels to randomly selected non-urban pixels, which carries the potential to better standardize satellite-derived SUHI estimations. A positive trend for both LST and SUHI for the majority of the examined cities was documented. Furthermore, a 0.1 °C decade^?1 increase in urban LST corresponded to an increase in SUHI by about 0.04 °C decade^?1. A longitudinal differentiation was found in the urban LST trends, with higher positive values appearing in the eastern Mediterranean. Examination of urban infrastructure and development factors during the same period revealed correlations with SUHI trends, which can be used to explain differences among cities. However, the majority of the cities examined show considerably increased trends in terms of the enhancement of SUHI. These findings are considered important so as to promote sustainable urbanization, as well as to support the development of heat island adaptation and mitigation plans in the Mediterranean.