ALBERT

Description: 〈h3〉Abstract〈/h3〉 〈p〉The scarcity of rainfall data is one of the main problems affecting the use of hydrological models. Several model satellite-based rainfall estimates (SREs) have been developed to provide an alternative to poorly or ungauged basins. The aim of this work was to evaluate the suitability of SREs for hydrological modeling using a semi-distributed model in the transboundary basin of Medjerda, shared by Tunisia and Algeria. Two satellite-based rainfall products (PERSIANN-CDR and CHIRPSv2) were first compared to rain gauge observations based on sub-basin and point-to-pixel analysis. The selected SREs products were then used as inputs to simulate discharge at a daily time-step over the 1996–2016 period. The simulated streamflows were compared to data measured at four runoff gauging stations and at the outlet of two dams. It was first shown that both SRE products perform weakly at daily scale but that the CHIRPSv2 product performs better at monthly scale. Second, comparison at sub-basin scale led to a better correlation with rain gauge observations than point-to-pixel analysis. Third, direct sampling can be reliably used to fill gaps in discharge time series by using auxiliary stations highly correlated with the target station. Finally, the CHIPRSv2 daily satellite rainfall product is more efficient and more suitable than the PERSIANN-CDR product for hydrological modeling. Thus, CHIRPSv2 can be used as an alternative or as a complementary source of information to simulate hydrological models in arid and semi-arid regions and can successfully solve the issue of missing rainfall data in transboundary catchments.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This paper presents an evaluation of the Weather Research and Forecasting (WRF) model in simulating wet and dry West African monsoon (WAM) rainfall seasons. Three model experiments with varying selected microphysics (MP), cumulus convection (CU), and planetary boundary layer (PBL) schemes based on previous study were performed. Each of the model combinations is used to run four WAM seasons that consist of two wet (2008 and 2010) and two dry years (2001 and 2011). To investigate the behavior of WAM in the context of wet and dry years, the four seasons were used to compute composites of wet and dry WAM seasons in terms of rainfall amount. The analyses majorly focus on the rainfall composites relative to rainfall from Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Measurement Mission (TRMM) as well as temperature, moisture, and atmospheric circulation fields with respect to NCEP reanalyses. This study documents significant sensitivity in simulation of the West African monsoon to the choices of the MP, CU, and PBL schemes. The simulation with the combination of WRF single moment 5 (WSM5) MP, Yonsei University (YSU) PBL, and new Simplified Arakawa-Schubert CU (WSM5-YSU-nSAS) shows good spatial distribution pattern of rainfall and the dynamics associated with the monsoon. Quantitatively, the combination shows less agreement in distinguishing the selected WAM seasons compared with the Goddard MP, Mellor-Yamada-Janjic PBL, and Betts-Miller-Janjić CU (GD-MYJ-BMJ) and the WSM5, Mellor-Yamada-Nakanishi-Niino 2.5 level and new Tiedtke CU (WSM5-MYNN-nTDK). Also, the dynamical structures of the wet and dry WAM circulation composites are reasonably reproduced in GD-MYJ-BMJ and WSM5-YSU-nSAS. The GD-MYJ-BMJ was able to distinguish between wet and dry years and thus underscores its potential to reproduce climate change signals in future work.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The freeze/thaw process and the surface energy budget of the seasonally frozen ground in the source region of the Yellow River were investigated by using observed soil temperature and moisture and the energy flux from May 12, 2014, to May 11, 2015. Compared with the Maduo site, the starting date of the freezing process was later and the freezing depth was shallower at Maqu site. The maximum frozen depth was about 320 cm at Maduo site and 90 cm at Maqu site. The soil temperature of Maqu site was higher than of Maduo site due to lower latitude and altitude. The soil was the driest under the depth of 40 cm and 80 cm at Maduo and Maqu sites, respectively. The diurnal amplitudes of soil temperature of Maduo site were larger than of Maqu site at four freeze/thaw stages; the amplitudes were the largest in the completely thawed stage (9.19 °C and 4.35 °C) and minimal in the freezing stage (1.23 °C and 0.47 °C). The diurnal amplitudes of soil moisture of Maqu site were greater than of Maduo site at all stages. The net radiation Rn’s seasonal change was hardly influenced by the freeze/thaw process. The mean ground heat flux (G〈sub〉0〈/sub〉) was negative during the freezing and completely frozen stage and positive during the thawing and completely thawed stage. During the completely thawed and frozen stages, the latent heat flux (LE) and sensible heat flux (H) predominated in the surface energy distribution, respectively. Overall, the variations of fluxes were affected by both the monsoon and freeze/thaw process of the soil layer in seasonally frozen region. The freeze/thaw process had a significant effect on the diurnal change of G〈sub〉0〈/sub〉 during the freezing stage. The annual energy closure status of Maduo and Maqu sites was 0.77 and 0.58, respectively. The energy closure status was the highest during the completely thawed stage at Maduo site and during the thawing stage at Maqu site and lowest during the freezing stage among the four stages, due to the snow cover’s impact. Overall, the freeze/thaw process and the high albedo caused by snow cover had effects on the energy closure status.〈/p〉

Description: 〈p〉The presentation of Fig. 4 was incorrect.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This study evaluates the monthly, seasonal, and annual characteristics of near-surface air temperature as a function of both elevation and geographical coordinates over Pakistan, using a climatic data set from 53 observation stations ranging between 5 and 2317 m asl and stepwise multiple regression. Forcing processes for temperature gradient (TG) variation were determined by analyzing its relationship with moisture flux (〈em〉qv〈/em〉) and gradients of rainfall and saturation vapor pressure (〈em〉e〈/em〉〈sub〉s〈/sub〉). The bi-model pattern of TG〈sub〉E〈/sub〉 (temperature gradient with topographic elevation), which earlier studies (TAAC 113(3):671-682, TAAC 132:1129-1141) from the southern slopes of the Himalayas have observed, is also consistent in this study region. However, the forcing strengths, mechanisms, and processes for the monthly variations in TGs magnitude and their diurnal range are somewhat different. The annual cycle of TG〈sub〉E〈/sub〉 in this study is inversely (positively) associated with the rainfall (〈em〉e〈/em〉〈sub〉s〈/sub〉) gradient. Monsoon and orographic controls for TG〈sub〉E〈/sub〉 are stronger in summer, especially in the day, while the influences of inversions and mountainous topoclimate are higher during the non-monsoon period, in particular in winter and at night. Mountain barrier effects are higher in summer, which considerably hamper the relationship between temperature and geographical coordinates and rainfall and elevations. Steeper values of mean TG〈sub〉E〈/sub〉s in the non-monsoon period are the result of strong dry convectional cooling at higher elevations due to high thermal forcing effects at lower elevations. Sensible heating reduction due to westerly-driven precipitation and cloud cover in daytime in the northern mountainous region (or higher latitudes) further strengthens the TG〈sub〉E〈/sub〉 value in this period. This effect is weaker in the surrounding regions; therefore, the TG〈sub〉E〈/sub〉 magnitude in this study is expected to be higher, especially in the pre-monsoon season. Gradients are more shallow in winter (excluding latitude), but values are greater than those in summer, due to distinct variations in moisture and cloud amount, i.e., lower in winter and higher in summer, particularly at lower elevations. Observed distinct differences in TG diurnal range between elevation (high) and latitude (low) in this study are due to a wide variation in temperature, topography, and elevations within the same latitudinal belts. In this study, the lowest gradient values for elevation are observed in August and the highest in May, 1 month later than observed in adjacent mountain regions due to the late arrival of monsoon moisture in summer and intense thermal forcing effects following the wet early pre-monsoon months. Distinct variability of gradient magnitudes between the northern mountainous region and the southern flat terrain is due to differences in topoclimates, moisture amounts, elevations, and geographical coordinates.〈/p〉

Description: 〈p〉The original version of this article unfortunately contained a mistake.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Land surface temperature (LST) plays a key role in the surface energy budget computation and land surface process studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Aqua and Terra satellites provide comprehensive global LST estimates at a fine spatial resolution. The MODIS products were recently upgraded to Collection 6, and shown to be more accurate than its predecessor Collection 5 products. In this study, LST and its variability have been examined across India from Collection 6 of the Aqua MODIS data at 0.05° spatial resolution for the period of 2003 to 2017. All-India mean LST characteristics show distinctive features as compared to the well-documented mean characteristics of near-surface air temperature. All land cover types except permanent snow and ice, and cold desert areas exhibit bimodal peaks in seasonal variations of daytime LST. The daytime LST over the coldest and high-altitude regions of northern India shows anomalous positive linear relationship with NDVI at a monthly scale. However, monthly domain-mean daytime LST of cropland regions is largely negatively correlated with NDVI as compared to other land cover types. Results reveal that about 17% of the Indian landmass received its hottest LST during 2010 followed by 2016. Linear trend analysis for the 15-year period of mean annual LST shows a decrease in diurnal temperature range over most parts of the country due to rather rapid increase in nighttime LST than daytime LST, similar as changes in near-surface air temperature across the country.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This study presents the first comprehensive evaluation of the performance of the monthly TRMM rainfall product (3B43-v7) in capturing the rainfall patterns, on the geographic area of the central Mediterranean during the period 1998–2017. Monthly rainfall data from a network of ground-based stations were used to compute a series of spatiotemporal evaluation statistics for the satellite measurement accuracy. These statistics showed a satisfactory accuracy of the examined satellite products in capturing seasonal and annual rainfall patterns, having rather high correlation coefficients (0.75 to 0.96). Nevertheless, a slight 〈em〉overestimation〈/em〉 of the rainfall values (with a mean value lower than 15 mm) implied by the TRMM was found at seasonal basis with the exception of 〈em〉winter〈/em〉 when a small 〈em〉underestimation〈/em〉 (with a mean value amounting to 8 mm) was detected. The 〈em〉overestimation〈/em〉 tendency was mainly detected over the northern and the western parts of the studied domain, where the effect of the earth surface altitude seems to be important. An 〈em〉underestimation〈/em〉 tendency prevails over the marine regions and seems to be associated with the rain rate (in the sense that heavy rainfall episodes are underestimated more). The overall biases of the TRMM rainfall product in respect to the control ground-based station sample found to be within ± 0.2 of the local rainfall standard deviation and nonstatistically significant almost in half of the cases in all seasons except winter. The possible causes of the detected diversity of the seasonal error statistics behavior were further explored and discussed.〈/p〉

Description: 〈p〉Figures 3 and 4 were the same.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉A suite of Regional Climate Model (RegCM) experiments are performed over south Asia to examine the skill of RegCM to simulate the seasonal and sub-seasonal mean features of Indian summer monsoon (ISM). Because of coupled nature of ISM, three model experiments with RegCM are conducted to examine the skill of coupled and uncoupled configurations of RegCM by forcing it with observed SST (Exp1), coupling it to a simple slab ocean model (SOM) with constant mixed layer depth (MLD; Exp2) and with climatology of MLD (Exp3). The coupled experiments show an overall improvement in several aspects of ISM variability at seasonal and intraseasonal time-scales, despite bias in simulated SSTs. Between coupled experiments; Exp3 reduces biases in SST distribution over the region to the north of Arabian Peninsula, eastern Arabian Sea (AS), and broadly over north Indian Ocean (NIO). Noteworthy is the improved precipitation over central India (CI), head Bay of Bengal (BoB), as well as the representation of easterly wind shear in coupled experiments. At intraseasonal time scales, Exp3 produces spectral peaks above red noise at 25–50-day and 15–20-day periods closely representing the northward propagating intraseasonal mode and quasi-biweekly oscillating mode as in observed precipitation. The improved representation of spatial distribution of intraseasonal activity over NIO as well as the SST and precipitation relationships over head BoB and eastern AS is attributed to better representation of air-sea interaction in Exp3. In brief, the coupling improves the model skill for the true representation of mean ISM variability during boreal summer, and the thorough evaluation of model for longer periods is required to employ it as a downscaling tool for regional climate change studies.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Spatiotemporal changes of temperature and precipitation extremes from 1956 to 2015 were analyzed at 200 representative weather stations evenly distributed in the temperate continental zone (TCZ), temperate monsoon zone (TMZ), mountain plateau zone (MPZ), and (sub) tropical monsoon zone (SMZ) of China, using 16 extreme temperature and 11 extreme precipitation indexes. The results showed that warm days (TX90p) and warm nights (TN90p) increased significantly, while cool days (TX10p) and cool nights (TN10p) decreased significantly in the whole China. Overall increasing trends were found for maximum and minimum daily maximum temperature (TXx and TXn) and maximum and minimum daily minimum temperature (TNx and TNn). Warm indexes, including summer days (SU25), tropical nights (TR20), warm spell duration indicator (WSDI), and growing season length (GSL), showed increasing trends, whereas cold indexes such as frost days (FD0), ice days (ID0), cold spell duration indicator (CSDI), and diurnal temperature range (DTR) showed decreasing trends. These extreme temperature indexes exhibited high correlations with mean air temperature. MPZ exhibited the most remarkable change magnitudes among the four zones, while the smallest changes occurred in SMZ. An accelerating warming trend was particularly observed since 1986. Nationally, only daily rainfall intensity (SDII) showed significantly increasing trends, while the increasing trends of other precipitation indexes were not significant. Apart from consecutive wet days (CWD), changes of precipitation extremes presented increasing trends. PRCPTOT and R10mm exhibited the highest correlation coefficient across contrasting climatic zones. Regionally averaged precipitation totals were decreasing in TMZ during 1956–1985, but increasing trends were identified after 1985. The upward tendency of precipitation totals in MPZ and the west part of TCZ may alleviate the pressure of water shortage in arid and semi-arid regions of China, but the upward trend in SMZ, especially in the coastal areas of southeastern China, may aggravate the risk of flood-induced disasters in these regions.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The objective of this work is to investigate the interaction between the climate indices precipitation concentration degree (PCD), precipitation concentration period (PCP), and oceanic patterns associated with drought disasters in the Northeast of Brazil (NEB). The average values of drought disasters were situated at the semi-arid zone, while the hotspots between 95 and 99% of significance were more centralized, particularly in the states of Ceará, Rio Grande do Norte, Paraíba, and Pernambuco, where significant areas were located. The PCD patterns showed that precipitation was concentrated in the north of this region and the wettest quarter happened from February to April, in accordance with PCP patterns. The analysis of variance (ANOVA) A showed evidence of the variability of PDC and PCP in relation to the well-known variability modes of the oceans. However, only two areas of NEB presented statistical significance.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Based on daily mean temperature data from 32 meteorological stations in the Qinling Mountains (QMs) of China, we analyzed the characteristics and differences of the spatiotemporal changes in the climatic growing season (CGS) in the QMs from 1964 to 2015. Our results are as follows. First, over the past 52 years, the temperature of the QMs significantly increased at a mean rate of 0.22 °C/decade (〈em〉P〈/em〉 〈 0.01) in over 98.04% of the area. Significant north–south spatial differences were observed in temperature changes; also, significant differences in the temperature changing trends were observed before and after the abrupt change in temperature. Second, the spatial distributions of the mean growing season start (GSS), end (GSE), and length (GSL) in the QMs varied based on regional differences in latitude and topography. Notably, the GSS, GSE, and GSL were gradually delayed, advanced, and shortened, respectively, as latitude and elevation increased. After the abrupt change in temperature, whether it is in the NSQM (northern slopes of the QMs) or the SSQM (southern slopes of the QMs), the GSS, GSE, and GSL expanded into high-elevation areas. Third, over the past 52 years, the GSS in the QMs exhibited a significant advancing trend of 2.7 days/decade, the GSE was delayed at a rate of 0.66 days/decade, and the GSL displayed a significant extension of 3.36 days/decade. Before the abrupt change in temperature, the GSS, GSE, and GSL exhibited non-significant changing trends; however, the trends in the GSS, GSE, and GSL were more significant after the abrupt change than before. Fourth, the GSS, GSE, and GSL trends in the QMs were significantly different in the NSQM and SSQM regions. After the abrupt change, the GSS, GSE, and GSL trends along the NSQM were more significant than those along the SSQM.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Monsoon rainfall exhibits the prominent diurnal variability. It is essential to capture the diurnal behaviour of the rainfall on the spatiotemporal scale by mesoscale models for its accurate prediction. The present study appraises the diurnal variability of simulated rainfall by Weather Research and Forecasting (WRF) model over the South Asian region during Indian summer monsoon seasons of the years 2008–2012. For examining the amplitude, phase, and variance of the simulated diurnal cycle of rainfall, harmonic analysis of model outputs are carried out and compared it with that of TRMM rainfall estimates to quantify errors. The model is overall successful in simulating the pattern of diurnal variation of rainfall, but underestimates its amplitude compared to the observed one especially over the western Himalayas, northeast India, central India, and the north Bay of Bengal (BoB). However, the amplitudes of the diurnal variation match well over south BoB. The simulated phases of the diurnal cycle either leads (over central India and NW India by 3 h) or lags (over the sub-Himalayan region by 12 h) than the observed phase angle. The simulated time of maximum rainfall matches over south Peninsula, while over the west coast, neither observation nor forecast shows pronounced diurnal cycle. The north BoB shows morning maxima in rainfall in both observation and forecast; however, over south BoB, the time of maximum simulated rainfall leads by 3 h. Even though the model is overall successful in representing the diurnal variation of rainfall, it requires improvement for proper representation of phase and sub-diurnal variation appropriately.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The bulk transfer method is commonly used to calculate the surface sensible heat (SSH) flux in climatology and in the numerical model, but in the calculation, the value of the bulk transfer coefficient for heat (〈em〉C〈/em〉〈sub〉〈em〉h〈/em〉〈/sub〉) is generally regarded as a fixed value (e.g., 0.004 in the Tibetan Plateau), without considering its seasonal variations and regional differences. In this study, a new method for upscaling data from the local to regional scale is proposed and tested to estimate 〈em〉C〈/em〉〈sub〉〈em〉h〈/em〉〈/sub〉 over the east-central Tibetan Plateau (TP) using the latest version Global Inventory Modeling and Mapping Studies (GIMMS), Normalized Difference Vegetation Index (NDVI) dataset, and micrometeorological observations collected at four field sites. Results suggest that the proposed method is capable of reflecting the variations in 〈em〉C〈/em〉〈sub〉〈em〉h〈/em〉〈/sub〉 at the regional scale in a climatic context. It is a relatively reliable way of estimating the value of 〈em〉C〈/em〉〈sub〉〈em〉h〈/em〉〈/sub〉 using satellite remote sensing data and field observations. Then, the monthly values of 〈em〉C〈/em〉〈sub〉〈em〉h〈/em〉〈/sub〉 and the SSH flux at 70 China Meteorological Administration (CMA) stations on the east-central TP during the period 1982–2012 are estimated. Results show that the value of 〈em〉C〈/em〉〈sub〉〈em〉h〈/em〉〈/sub〉 ranges from 0.0025–0.0050, with obvious seasonal variations and spatial differences. Because the surface vegetation coverage in the southeastern part of the TP is better than that in the north-central part, the values of 〈em〉C〈/em〉〈sub〉〈em〉h〈/em〉〈/sub〉 are higher than that in the north-central part of the TP throughout the year. The SSH flux also shows obvious seasonal variations and spatial differences, which is strongest in spring, followed by summer and autumn, and smallest in winter. Furthermore, the seasonal mean SSH has undergone a significant interdecadal transition in the early 2000s, from the previous weakening trend to the strengthening trend. Based on the results of this study, the values of 〈em〉C〈/em〉〈sub〉〈em〉h〈/em〉〈/sub〉 obtained from a limited number of local field observations on the plateau can be applied to more CMA stations at the regional scale. Such a method can improve the calculation accuracy of the SSH flux over the TP and aid studies of plateau meteorology and climatology.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This study investigates the potential impacts of climate change on wind power over West Africa under various global warming levels. For the study, we analysed eleven multi-model multi-ensemble simulation datasets from the Coordinated Regional Climate Downscaling Experiment (CORDEX) project. The model simulations for the present-day climate were compared with available station observation data and two examples of reanalysis data (ERA-INTERIM and ERA-20C). The results show that model ensemble mean gives a realistic simulation of wind speed and wind power density (WPD) over West Africa, although it overestimates them. In agreement with the reanalysis, the models indicate that the strongest winds and largest WPD are in the Sahel zone, especially around Dakar. However, while the regional climate models (RCMs) show thirteen cities are viable for potential wind power generation in the historical climate, the reanalysis indicates only four Sahelian cities are suitable for it. The RCMs project an increase in monsoon wind speed and WPD over West African cities and the magnitude of the increase grows with the global warming levels. Nevertheless, the increase is not sufficient to make the cities in the Guinean and Savannah zones viable for wind power generation in the warmer climate. The results of the study may guide policymakers on harnessing wind power potential to meet the electricity demands of West Africa in the future.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Land surface air temperature (SAT) change is one of the core issues in monitoring and assessing regional climate change. In this study, the characteristics of SAT change over DPR Korea for the period 1918–2015 were investigated using a high-quality historical dataset. Results show that the region-averaged annual mean SAT increased 0.21 °C/decade for the period 1918–2015 on the basis of data from four stations and 0.19 °C/decade for the period 1941–2015 as estimated based on data from nine stations. Before the 1970s, Pyongyang station in the central region experienced the largest warming trend. Linear trends of seasonal mean SAT during 1941–1970 were negative for all seasons in eastern coast and for summer and autumn in western coast and northern inland areas. Since 1971, however, the annual and seasonal mean SAT trends have shifted to positive values in all regions, with winter experiencing the most rapid warming. During the period of global warming slowdown since 1998 or 2000, no significant seasonal warming trend of wintertime was detectable, and this caused the smallest winter warming for the last 45 years. Other seasons also witnessed a generally weakened warming during 1971–2015 compared to that of 1971–2000. The results of the study will help in understanding regional climate change and in assessing the impacts of climate change on economic and natural ecosystems in the country.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This study attempts to make comparison between different parametric regressive models for the bivariate binary data with a machine learning technique. The data on sequential occurrence of rainfall in consecutive days is considered. The outcomes are classified as rainfall in both days, rainfall in one of the consecutive days, and no rainfall in both days. The occurrence of rainfall in consecutive days is analyzed by using statistical models with covariate dependence and classification tree for the period from 1980 to 2014. We have used relative humidity, minimum temperature, maximum temperature, sea level pressure, sunshine hour, and cloud cover in the model as covariates. The binary outcome variable is defined as the occurrence or non-occurrence of rainfall. Five regions of Bangladesh are considered in this study and one station from each region is selected on the basis of two criteria: (i) contains fewer missing values and (ii) representative of the regional characteristics geographically. Several measures are used to compare the models based on Markov chain and classification tree. It is found that for yearly data, both the Markov model and classification tree performed satisfactorily. However, the seasonal data show variation of rainfall. In some seasons, both models perform equally good such as monsoon, pre-monsoon, and post-monsoon, but in the winter season, the Markov model works poorly whereas classification tree fails to work. Additionally, we also observe that the Markov model performed consistently for each season and performs better compared with the classification tree. It has been demonstrated that the covariate-dependent Markov models can be used as classifiers alternative to the classification tree. It is revealed that the predictive ability of the covariate-dependent Markov model based on Markovian assumption performs either better or equally good compared with the classification tree. The joint models also consistently showed better predictive performance compared with regressive model for whole year data as well as for several seasonal data.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The rapid development of cities has led to the urban sprawl to the suburbs. Studies have shown that urban sprawl leads to the increasing intensity of heat island effect. This paper, based on the DMSP/OLS nighttime light data and the MODIS land surface temperature data during the years 2000, 2005, and 2013, discussed the spatial and temporal characteristics of urban sprawl, urban heat island intensity in China, and their relationship, respectively. The results show that (1) the national urban expansion index showed an upward trend. The urban area was 104,000 km〈sup〉2〈/sup〉 in 2000, 169,000 km〈sup〉2〈/sup〉 in 2005, and 312,000 km〈sup〉2〈/sup〉 in 2013.The average annual growth rate was 10.2% in 2000–2005, 8% in 2000–2013, and 8.8% in 2000–2013. (2) The national heat island index also showed an upward trend; total heat island intensity was 716.73 °C in 2000, 812.84 °C in 2005, and 995.46 °C in 2013. The annual average growth rate was 2.55% in 2000–2005, 2.57% in 2005–2013, and 2.56% in 2000–2013. (3) There is a positive correlation between urban spatial expansion and heat island intensity in China. The urban and suburban range of China can be objectively extracted from the DMSP/OLS nighttime light data every year and superimposed with the MODIS land surface temperature data of the same period, which ensures the correctness of the urban heat island intensity based on the actual changes of suburban areas and main urban areas in each city, and overcomes the calculation error of the traditional method, which neglects that some suburbs have become urban areas and are still regarded as suburbs. These results provide spatiotemporal changes characterization of urban area in China during recent decades, which are helpful in decision-making of land management and planning for sustainable development.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Storm is a convective cell much smaller than a mesoscale convective system (MCS) but typically larger than a cumulonimbus cloud and heavy precipitation and lightning are often associated with it. Storms contribute major fraction of the convective precipitation in MCSs. Storm characteristics and precipitation estimates around New Delhi (28.6°N, 77.2°E; an Indian land location) during June–September period of the year 2013 are reported here using data of C-band polarimetric Doppler weather radar. Storms, defined based on radar reflectivity thresholds (30 dBZ for simple storms and 40 dBZ for intense storms), are tracked and their properties are extracted. Our results show that about 80% of storms exist for 1 h or less. The areas of 90% of simple storms are less than 100 km〈sup〉2〈/sup〉 and the largest area averaged over storm lifespan does not exceed 400 km〈sup〉2〈/sup〉. The majority of storms (〉 80%) move with speeds less than 30 km h〈sup〉−1〈/sup〉. About 60–65% of simple/intense storms have echo top heights between 6 and 10 km, while only few of them exceed 17 km. The values of average thickness of simple and intense storms lie between ~ 2–10 and ~ 1–7 km, respectively. It is not the vertical extent of a storm but its area-time integral that correlates better with the total precipitation amount. Around the New Delhi area, daily accumulated precipitation derived from relations incorporating polarimetric variables is in good agreement with the rain gauge measurements while that obtained from relations based on radar reflectivity factor (Z〈sub〉〈em〉h〈/em〉〈/sub〉) alone highly underestimates precipitation. This suggests that polarimetric capability is needed in Doppler weather radars to get the realistic precipitation estimates. The mean precipitation water content derived from Z〈sub〉〈em〉h〈/em〉〈/sub〉 (~ 0.96 g m〈sup〉−3〈/sup〉) is about 30–40% less compared to that derived from polarimetric relations. Our findings on storm properties have implications for cloud parameterizations and in short-term weather forecasting.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The evolution of mean annual minimum (TMIN) and maximum temperature (TMAX) on the Swiss Plateau shows distinct differences over the last 150 years. TMIN increased relatively steadily by about 3 °C. TMAX increased by only 1.5 °C with substantial decadal variability and hardly any increase until about 1940. However, in the most recent decades, TMAX trends are somewhat larger than TMIN trends. While most aspects of the TMIN evolution can be well explained by the global forcing and the modifying effects of the large-scale atmospheric flow alone, local sunshine duration (SD) information is crucial to explain major features of the TMAX series and the differences between TMIN and TMAX since about 1950. SD shows no clear trend until 1950, a decline from 1950 to 1980 and an increase since 1980 resembling the global dimming and brightening signal. TMAX is strongly influenced by SD and the TMAX evolution can be well reconstructed with local TMIN and SD. Strong TMAX declines are found from 1950 to the 1970s. TMIN shows no trend in this period. Between 1980 and about 2005, both TMIN and TMAX show strong increases caused by the greenhouse gas forcing, decreasing aerosols and probably also decreasing cloud cover. Since about 2005, the increases are weaker. The brightening has weakened and the warming effect of the continuously growing greenhouse gas forcing has additionally been reduced by cooling effects caused by large-scale atmospheric flow anomalies. The reasons for the considerable differences in the TMIN and TMAX evolution prior to 1950 remain unknown and further investigations are needed to shed more light on this disparity.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉In the present study, the temperature data of 125 stations (1941 to 2012) over parts of India have been analysed to evaluate trends and homogeneity using various statistical tests. The annual average, maximum, and minimum temperature data showed a positive trend of 0.44 °C/100 years, 0.51 °C/100 years, and 0.19 °C/100 years, respectively. All the seasonal temperature variables exhibited a positive trend with exception of the minimum temperature of the monsoon season which showed the negative trend at the rate of 0.05 °C/100 years. The maximum temperature of monsoon season depicted a maximum increase at the rate of 0.80 °C/100 years. The results indicated that the process of urbanisation impacted over the change in temperatures. The homogeneity analysis showed the break years at 1972, 1974, and 1977 for annual average, maximum, and minimum temperatures, respectively, for entire India. The seasonal analysis exhibited that the minimum temperatures of both winter and monsoon seasons and the maximum temperature of the post-monsoon season were homogeneous in nature. The rate of change in magnitude for all annual and seasonal temperature variables showed a considerable increase after the break years.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Recent advances in variable-resolution (VR) global models provide the tools necessary to investigate local and global impacts of land cover by embedding a high-resolution grid over areas of interest in a seamless and computationally efficient manner. We used two eddy covariance tower clusters in the Eastern USA to evaluate surface energy fluxes (latent heat, λ〈em〉E〈/em〉; sensible heat, 〈em〉H〈/em〉; net radiation, 〈em〉R〈/em〉〈sub〉n〈/sub〉; and ground heat, 〈em〉G〈/em〉) and surface properties (aerodynamic resistance to heat transfer, 〈em〉r〈/em〉〈sub〉aero〈/sub〉; Bowen ratio, 〈em〉β〈/em〉; and albedo, 〈em〉α〈/em〉) by uncoupled point simulations of the land-only Community Land Model (PTCLM4.5) and two coupled land–atmosphere Community Earth System Model (CESM1.3) simulations. The CESM simulations included a 1° uniform grid global simulation and global 1° simulation with a 0.25° refined VR grid over the Eastern USA. Tower clusters included the following plant functional types—broadleaf deciduous temperate (hardwood) forest, C3 non-Arctic grass (grass), a cropland, and needleleaf evergreen temperate (pine) forest. During the growing season, diurnal cycles of λ〈em〉E〈/em〉 and 〈em〉H〈/em〉 for grass and the cropland were simulated well by PTCLM4.5 and VR-CESM1.3; however, λ〈em〉E〈/em〉 (〈em〉H〈/em〉) was biased low (high) at the hardwood and pine forested sites, contributing to biases in 〈em〉β.〈/em〉 Growing season 〈em〉R〈/em〉〈sub〉n〈/sub〉 was generally well simulated by CLM4.5 and VR-CESM1.3; however, modeled elevated albedo (indicative of snow cover) persisted longer in winter and spring leading to large biases in 〈em〉R〈/em〉〈sub〉n〈/sub〉 and 〈em〉α〈/em〉. The introduction of a VR grid does not adversely impact surface energy fluxes compared to 1° uniform grids and highlights the usefulness of this approach for future efforts to predict land–atmosphere fluxes across heterogeneous landscapes.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉When long time series are analyzed, two nearby periods may show significantly different trends, which is known as trend turning. Trend turning is common in climate time series and crucial when climate change is investigated. However, the available detection methods for climate trend turnings are relatively few, especially for the methods which have the ability of detecting multiple trend turnings. In this article, we propose a new methodology named as the running slope difference (RSD) 〈em〉t〈/em〉 test to detect multiple trend turnings. This method employs a 〈em〉t〈/em〉-distributed statistic of slope difference to test the sub-series trend difference of the time series, thereby identifying the turning points. We compare the RSD 〈em〉t〈/em〉 test method with some other existing trend turning detection methods in an idealized time series case and several climate time series cases. The results indicate that the RSD 〈em〉t〈/em〉 test method is an effective tool for detecting climate trend turnings.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Researchers have calibrated satellite signals successfully using novel artificial intelligence (AI) methods to estimate discharge at ungauged river sites accurately. However, common AI methods including neural networks have a recognized defect in time series forecasting known as input imitation error. The present study addresses this significant source of error by combining evolutionary polynomial regression (EPR) with the Nondominated Sorting Genetic Algorithm (NSGA-II) for multiobjective optimization. This new method of forecasting signal time series is called the evolutionary polynomial regression-time series predictor (EPR-T). EPR-T can simultaneously minimize the model prediction error based on traditional performance indices as well as a new index, peak similarity (PS), to prevent the model from imitating its input variables when forecasting. The prediction accuracy of the new EPR-T and traditional AI methods is compared for six case studies, namely the Connecticut, Missouri, Pee Dee, Red, White, and Willamette rivers. The results demonstrate the considerably superior accuracy of EPR-T over the regular EPR method.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Understanding the changing characteristics and related mechanisms behind precipitation extremes is crucial for developing adaptive measures of water resource management as well as flood risk management. Based on daily precipitation measurement taken at 57 meteorological stations from 1960 to 2016, the variability of precipitation extremes over the Yangtze River Delta (YRD) is analyzed utilizing a Mann-Kendall trend test as well as a Hurst exponent analysis. Moreover, the climatic teleconnection that occurs from large-scale atmospheric circulation and such precipitation extremes is also investigated with the help of a wavelet coherence analysis. Results indicate that most extreme precipitation indices predominantly exhibit significant positive trends, indicating a wetting trend in the YRD over the past 61 years. Meanwhile, the contribution of precipitation of very wet days to annual total wet-day precipitation increased significantly. Furthermore, the increasing trend of precipitation extremes in the YRD is found to be attributable to the frequency and intensity, rather than to the duration of extreme precipitation events. The patterns of variation in these precipitation extremes reveal dramatic spatial heterogeneity, with extreme events concentrated primarily along the coastal plains. Overall, the YRD will likely face more severe flood risks in the foreseeable future. This is especially the case for the southern and central-western regions of the YRD. In contrast, the northern region of the YRD is forecast to become drier over time. The increasing trends in precipitation extremes for the YRD observed in this study are found to be linked closely with the positive anomalies of the El Niño-Southern Oscillation as well as the negative anomalies of the East Asian summer monsoon.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉One of the solutions for resolving the problem of energy production deficit in Central Africa is to promote renewable energy sources. The knowledge of the solar variability represents a determining factor for design, dimensioning, performance assessment, and energetic management of renewable energy conversion systems. In this work, we analyze the behaviour of solar radiation from diurnal to seasonal time scales for the region of Douala, the largest industrial city of Cameroon. Observed data of temperature, sunshine duration and precipitation, and satellite estimates of solar radiation (from Soda Solar Project) and cloudiness (acquired from Meteosat Second Generation) were used. The results show that the solar radiation annual cycle at Douala can be decomposed into four seasons: the main dry season in December–January–February (DJF) which corresponds to the most illuminated season, the main rainy season in June–July–August–September (JJAS) which is the least illuminated, and two intermediate periods, March–April–May (MAM) and October–November (ON) which correspond to semi-illuminated periods. Using a hierarchical clustering analysis (HCA), we found that Douala usually experiences five main types of solar radiation diurnal cycles depicting very bright to obscure days. A characterization of sky conditions during these five diurnal cycles shows a predominance of low and high opaque clouds during obscure days.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The relationship between blocking events and mean precipitation frequency (MPF) was investigated in this work for Turkey during 1977–2016. The overall MPF for the examined stations during blocked days (nonblocked days) fluctuated between 0.15 and 0.43 (0.12 and 0.38). The ratio of MPF during the blocked days to nonblocked days ranges between 12 and 38%. During the winter season, the country has higher MPF values during blocked days. The greatest ratio of MPF values during blocked days to nonblocked days is observed in summer due to smaller values of MPF occurring during this season overall. Higher MPF occurs when the event center was located between 0〈sup〉o〈/sup〉 and 30〈sup〉o〈/sup〉 E (mean 0.27, range 0.17–0.51). There is no relationship between blocking duration and MPF for all seasons. There is a positive relationship between blocking intensity and MPF during summer (CC = 0.35, significant at the 95% level) and fall (CC = 0.43, significant at the 95% level). The relationship between blocking longitudinal extent and MPF is significant at the 95% confidence level during the summer and fall seasons with correlation coefficients of 0.29 and 0.25, respectively. A composite of the 10 blocking events associated with the largest MPF demonstrated that there is moist advection via westerly flow into Turkey in all seasons. During winter, a greater proportion of these events is observed during the La Niña phase of El Niño Southern Oscillation, but during El Niño in spring and summer. For the blocking case study (31 October to 5 November 2009) associated with the largest MPF, the mean value across the country was 0.73. The mean total precipitation during this period was 63.4 mm.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉In this paper, trends of minimum and maximum temperatures in Iran were studied using time series of daily minimum and maximum temperatures of 45 meteorological stations from 1976 to 2005 (as the baseline period). Mann-Kendall test, for maximum and minimum temperature, was obtained 1.85 and 3.56, respectively, which was positive and significant. The slope of the trend line for maximum and minimum temperature was obtained 0.23 and 0.39 °C decade〈sup〉−1〈/sup〉, respectively. In this study, the trend of extreme temperature indicators was also evaluated. According to the obtained results, in annual terms, TX10, FDO, TN10, and IDO indices have had a negative trend at most stations, but TX90, TR20, TNx, TNx, TXn, TN90, SDI, and SU25 indices showed a positive trend. In the seasonal scale, the indices of cold days (TX10) and cold nights (TN10) showed significant negative trends in most seasons. Warm days (TX90) and warm nights (TN90) showed significant positive trends at most stations. The results of future simulations using several general circulation models in different time periods showed that the highest increase in maximum and minimum temperature related to the RCP8.5 scenario in periods of 2071 to 2099. The results also showed that northwest of Iran would have the highest temperature rise. The results also showed that the probability density function of the minimum and maximum temperatures will shift to warmer temperatures. This could be an indication of climate change in the future decades in Iran.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This paper analyses a recently created continuous 305-year (1711–2016) monthly rainfall series for the island of Ireland. The findings are as follows. The excess skewness in the monthly series may be eradicated by using a Box-Cox transformation with parameter equal to 0.6: a value very similar to that found for the U.K. and its regions. There is no evidence of either an overall stochastic trend or of evolving monthly seasonal patterns, but positive linear trends are found for January, March, and December and a negative linear trend is found for July. Analysis of the seasonal and annual series (which require no transformation) confirms the implication from the monthly data that winters have become progressively wetter and summers progressively drier, with the positive linear trend for winter being twice the size of the negative summer trend. Since there is no trend in either spring or autumn rainfall, annual rainfall shows a positive linear trend. Given that the rainfall series exists for over three centuries, breaks and structural shifts in the model were investigated. Five breaks were identified, three of which occurred in the early portion of the series during the eighteenth century. However, trends were found to be much more stable from the middle of the nineteenth century. For the seasonal series, only a single break, at 1790 for the winter series, was found: it was only after this break that winters became wetter; before then, winter rainfall had a negative trend. In terms of predictability, predictions from the model were found to be more volatile during the second half of the eighteenth century and again from 1976 onwards.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Soil moisture (SM) feedback on climate variables especially temperature is an important aspect in land-atmosphere coupling. Based on the Global Land Data Assimilation System (GLDAS) V2.0 SM data and the gridded observational temperature data, we statistically analyze the thermal feedback of SM over North China (NC). The results show that SM exerts a decreasing trend under the background of evident warming over NC, inducing a decadal enhancement of SM feedbacks on the local temperature and extreme hot events. The SM feedback contributes 6% of the total air temperature variation during 1961–2012, while it reaches 36% after the regional warming during 1994–2012. Such SM affecting temperature is mainly reflected in its feedback on daily maximum temperature, which is also intensified during the warm period. The decadal intensification is also found in the feedback of SM on hot extremes. Further analyses show that the abnormal changes of the latent and sensible heat fluxes caused by the SM anomaly are the main reasons that affect the thermal conditions. Besides, the rising Bowen ratio indicates that upward thermal transfer on the land surface is enhanced in recent years, which suggests that the atmosphere is more sensible to the abnormal heating on the ground. This consequently translates into the decadal intensification of the local thermal feedback of SM in summer over NC.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Investigation of precipitation characteristics on daily, monthly, and annual time scales can contribute to gaining important information related to temporal and spatial distribution of precipitation or even flow rate challenges (e.g., hydrological droughts). The low levels of long-term precipitation and high variability in different time scales are considered the main inherent characteristics of climate in Iran. Due to the direct effects of precipitation on water resources, especially on the river flow rate, it is necessary to assess the efficient indices to visualize the variations in the components of water resources. One of the main indices is the precipitation concentration index (PCI) which is known as a strong indicator of the precipitation distribution generally used on annual and seasonal scales. In this study, drought analysis in the Lake Urmia Basin (LUB) located in northwest of Iran was performed with the daily river flow rate and monthly precipitation values within the period of 1984–2013. The results of changes in precipitation indicated that the irregularity of precipitation distribution had grown in spring months. Also, due to the diminishing precipitation trend on the annual time scale, PCI index also increased. It is concluded that LUB detected a significant descending trend on the annual, spring, and winter time scales in the last 30 years. The PCI values were proved high irregularity in summer with PCI amount of 20.1 and most regularity in winter with PCI amount of 10.4. This paper also aims to assess the effects of PCI on the river flow rate along with the flow shortness volume values using hydrometric and rain gauge stations within LUB. The results obtained from the changes in river flow rate and flow shortness volume revealed that the river flow rate has mostly a falling trend. Finally, it was observed that the time when the river flow rate data changed happened after beginning of changes in the precipitation data. A decrease in inflow from 900 million cubic meters up to 14 billion cubic meters with high flow shortness volume may happen in worst conditions. These results highlighted the importance of applying water resources management in LUB.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Cluster extreme event (CEE), which is characterized by large affected area and long duration of extreme weather, has been arousing the worldwide serious concerns due to its severe impact on society. The winter cluster extreme events of low temperature (LT_CEEs) in the northern hemisphere from 1979 to 2013 are identified with a simplified objective method based on ERA-Interim and JRA-25 daily minimum surface air temperature. The probability density function (PDF) distributions of most indices in the winter LT_CEEs derived from the two datasets are well consistent with each other, especially in the occurrence frequency, duration, and affected area. As expected, the downward trend of all indices in recent 35 years under global warming is congruously revealed from both reanalysis. Nevertheless, the various indices of the winter LT_CEEs after 1998 are generally stable accompanied with slight upward trend, which might be closely related to the speed slowdown of global warming but require further investigation. Similar analysis was carried out with the simulated results of BCC_CSM1.1 model under four scenarios of RCP2.6, RCP4.5, RCP6.0, and RCP8.5. The occurrence frequency of the winter LT_CEEs under RCP2.6 remains stable after 2050, but significantly decreases under RCP4.5 and RCP6.0, and disappears under RCP8.5 scenario. Overall, the descent rate of the winter LT_CEEs accelerates with the emission rise.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Surface sensible heat flux (〈em〉SH〈/em〉) and latent heat flux (〈em〉LH〈/em〉) are important variables linking the energy and hydrological cycles over the Tibetan Plateau (TP). Through comparison with eddy covariance observations of turbulent fluxes at two sites from 2013 to 2015 and at eight sites in August 2014 over the TP, five reanalysis flux products were evaluated for monthly mean biases and daily variations after bilinear interpolation. The results showed the JRA-55 and ERA-Interim reanalyses had optimal performances regarding 〈em〉SH〈/em〉, while NCEP2 had reasonable performance regarding 〈em〉LH〈/em〉. The 〈em〉SH〈/em〉 in the reanalysis data generally reached maximum negative biases in winter and minimum positive biases in summer, whereas the seasonal bias of 〈em〉LH〈/em〉 was found opposite to that of 〈em〉SH〈/em〉. The diurnal variation of reanalysis fluxes was found consistent with observed data at most sites, and the diurnal 〈em〉SH〈/em〉 and 〈em〉LH〈/em〉 maxima were all overestimated except 〈em〉SH〈/em〉 in the JRA-55 at Nagri. The 〈em〉SH〈/em〉 bias and the bias of the surface wind speed in NCEP2 were highly significantly correlated, suggesting the bias in the surface wind dominates the 〈em〉SH〈/em〉 bias over the TP in NCEP2. Large spreads of averaged biases and standard deviation errors were found among the five reanalysis bulk variables. In particular, the maximum-minimum ranges of the biases of surface wind speed and ground-air–specific humidity contrast were 〉 5 m s〈sup〉−1〈/sup〉 and 〉 15 g kg〈sup〉−1〈/sup〉, respectively.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Autumn rain of West China (ARWC), a typical climate phenomenon characterized by continuous rainfall, is prone to flooding and secondary disasters. The ARWC in 2017, the greatest one since the last 30 years, resulted in an economic loss of 1.9 billion dollars and serious social impacts. It is thus urgent to understand the cause for this anomaly. In this article, the atmospheric circulations affecting the 2017’s ARWC are identified. They are (1) a dipole pattern with the blocking over Europe and the trough over Lake Balkhash, which favors the southward outbreak of cold airs into West China, (2) increased water vapor transportation toward West China from the Pacific and Indian Oceans, and (3) a strengthening and northward displacement of the East Asian jet stream. The cold sea surface temperature anomaly (SSTA) in the equatorial central Pacific provides a superimposed effect. Further analysis reveals a synergic role in the interannual variability of the ARWC from the anomalies of the atmospheric circulation over Lake Balkhash and the SST in the equatorial central Pacific. The combination of anomalous trough over Lake Balkhash with cold SSTA in the equatorial central Pacific is most favorable for the increase of ARWC, while that of anomalous ridge with warm SSTA generally results in a decrease of ARWC. The combination of anomalous ridge with cold SSTA or that of anomalous trough with warm SSTA has no specific indication for ARWC anomalies.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The statistical characteristics of precipitation play an extremely important role in the risk assessment of drought and flood disasters and water resource management. In this paper, the precipitation concentration degree (PCD), precipitation concentration period (PCP) and precipitation concentration index (PCI) were used to analyse the spatiotemporal variation in precipitation concentration characteristics in China from 1960 to 2015. In addition, the cross-wavelet transform was used to analyse the possible dependencies and spatial characteristics between these three precipitation concentration indices (PCD, PCP and PCI) and monsoon indices (East Asian summer monsoon index, EASMI; South Asian summer monsoon index, SASMI; and South China Sea summer monsoon index, SCSSMI) of nine river basins in China. The results show that the spatial distribution of precipitation concentration indices in China has obvious north-south and east-west variability. China’s precipitation concentration indices are generally declining, with weak volatility. Among these indices, the PCD (〈em〉P〈/em〉 〈 0.05) and PCI (〈em〉P〈/em〉 〈 0.01) have decreased significantly at rates of − 0.005/10 year and − 0.006/10 year, respectively, while there has been no obvious decrease in the PCP. Compared with the PCD before 1978, the PCD in the rainy season after 1978 has decreased. In most areas, the rainy season is concentrated in July every year; however, the rainy season has been delayed in the middle and lower reaches of the Yangtze River and has advanced mainly in the other basins. The PCI has declined since 1978, indicating a decreasing contribution of heavy precipitation to the total annual precipitation, but this change in the PCI has occurred mainly at high-value stations (PCI 〉 0.7), and the PCI at low-value stations has increased. Most of China’s precipitation concentration indices and monsoon indices, as well as the NWRB, have small-scale (2–4 years) or medium-scale (12–15 years) oscillation periods. The oscillation periods between the precipitation concentration indices and monsoon indices in the Yangtze and Pearl River basins are more significant than those in other basins. The results of this study can help to understand the differences among the precipitation concentration characteristics in different basins in China and the intrinsic relationship between these characteristics and the summer monsoon and provide a reference for further research.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Water requirement is sensitive to the impacts of climate change, especially in Bangladesh because of limited freshwater availability in the dry season, despite the fact that the country’s agriculture sector requires large quantities of water for the crop production. Hence, gaining a better understanding of changes in water requirements in Bangladesh during dry periods is important in the management of agricultural water resources. This study assesses the recent impacts of climate change on the design water requirement (DWR) of the Boro rice–growing season in Bangladesh using a frequency analysis over a 5-year period. The reference evapotranspiration (ET〈sub〉ref〈/sub〉), crop evapotranspiration (ET〈sub〉p〈/sub〉), effective rainfall (ERF), and gross irrigation water requirements (GIWR) of Boro rice were estimated based on daily weather data for the period of 1984–2013 using the CROPWAT8.0 model. The results showed the significant decreasing trends of ET〈sub〉ref〈/sub〉 in most of these Boro rice growth stages in all districts. The GIWR of Boro rice and its trends demonstrated significant spatial heterogeneity in the last three decades due to significant changes in the ERF and ET〈sub〉p〈/sub〉. The DWR of Boro rice–growing season also supported the results of the GIWR, and the Weibull probability distribution function (PDF) is found to be an optimal PDF among eight PDFs for the estimation of DWR. Overall, the results indicate that a recent climate change does not only contribute to high water demands for the crop but also result in decrease water requirements due to variations in wind speed, sunshine hours, and relative humidity.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This study assesses the high-resolution, 0.11° (12 km), Canadian Regional Climate Model Version 5 (CRCM5), interactively coupled to the one-dimensional Freshwater Lake model (FLake), to predict wintertime precipitation along the Canadian snowbelts of Lake Superior and Lake Huron. CRCM5–FLake was compared against various datasets to evaluate the 20-year (1995–2014) SWE and wintertime precipitation, seven lake-induced precipitation events and lake effect snowfall (LES) predictor variables during the months of December and January. The findings of SWE along both snowbelts in December and January show MBD ≤− 10 mm and ≤− 30 mm, respectively. Similarly, precipitation results along both snowbelts in December and January show MBD ≤− 5 mm and ≤− 10 mm, respectively. The negative biases in simulated SWE and precipitation, predominantly along the snowbelts, suggest that the model may un-realistically represent lake effect processes. Comparison of lake-induced precipitation events also indicates that the model mostly under-predicts the daily accumulated precipitation associated with each event but tends to accurately capture the timing and the general location of the squalls along the snowbelts, though not for highly localised snow bands. Furthermore, lake-wide results of LES predictor variables indicate that the model over-estimates lake surface temperature (LST) for both lakes during December and January and under-estimates ice cover concentrations for both lakes in December. The resultant biases could be attributed to limitations within the coupled RCM because the quality of reproducing lake-induced precipitation in this region is highly dependent on the performance of FLake.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This study discusses the role of natural factors and related teleconnections for Indian summer monsoon (ISM) with a special emphasis on later two decades of the last century. The combined influence of the sun and volcanos on ISM is examined using observational data as well as CMIP5 model outputs. Possible mechanisms relating to a disruption of the usual ENSO-ISM teleconnection for those decades are explored. Observation suggested that the regional Hadley circulation, via the NAO in the northern hemisphere and Indian Ocean Dipole in the southern hemisphere, may have a role in the change in ISM behaviour. Such features though captured well in the observation are shown missing in models. Additionally, it indicates that differences among models mainly originate in a regional level, which could be due to inconsistency in representing regional teleconnection features. Interestingly, all models perform reasonably well in terms of global thermodynamic scaling arguments. The overall study underpins important areas, where natural factors influence regional climate, but models miss out and suggest discrepancies among each other. Such knowledge has major implications in regional as well as global scale. The modelling community will also greatly benefit by an improved representation of ENSO and ISM in models.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉In the context of global warming, the future spatiotemporal distribution of aerosols in China is a common concern of the government and the scientific community. In this study, the regional climate model RegCM4 is used to simulate the spatiotemporal distribution of anthropogenic aerosols including sulfate, black carbon, and organic carbon in China around 2030 under the RCP4.5 and RCP8.5 scenarios and estimate the contributions of climate difference, emission difference, and extra-regional transport difference to the change of anthropogenic aerosol concentration in the study area. The results show that the annual average concentrations of anthropogenic aerosols around 2030 decreased significantly with respect to those around 2010, and the decrease amplitude of black carbon surface concentration is the smallest, especially in the RCP8.5 scenario. The annual averages for sulfate, black carbon, and organic carbon surface concentrations in the central and eastern parts of China will be 8.5, 1.7, and 3.7 μg m〈sup〉−3〈/sup〉, respectively, under the RCP4.5 scenario, whereas 10.0, 2.2, and 4.4 μg m〈sup〉−3〈/sup〉, respectively, under the RCP8.5 scenario. The surface concentration of sulfate is higher in summer and spring, while lower in winter and autumn. The surface concentrations of black carbon and organic carbon are higher in winter and lower in other seasons. The results of sensitivity experiments demonstrate that the future difference in local emissions between RCP8.5 and RCP4.5 scenarios has the greatest impact on the anthropogenic aerosol concentrations throughout China, while the effects of future climate difference and extra-regional transport difference are much smaller around 2030. For the aerosol column burdens, the effect of future local emission difference between the two scenarios is still dominant, and the effect of extra-regional transport difference becomes very significant during spring and winter for organic carbon and black carbon. The results of this paper suggest that the impacts of future climate difference and extra-regional transport difference between RCP8.5 and RCP4.5 scenarios on anthropogenic aerosols are non-negligible in certain regions and seasons besides the impact of future local emission difference in China around 2030.〈/p〉

Description: 〈p〉Equations 1–4 have been typed wrongly during the steps of corrections and some figures and tables are placed way too far from the citations.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉It is undeniable that winter weather is the most dangerous for all road users. Temperate climate creates winter weather conditions that are definitely challenging for road maintenance specialists: one year can be excessively snowy and relatively warm (high daily accident risk and maintenance costs), while the other would be cold and dry (lack of preparedness, lower maintenance, but higher standby costs). Therefore, with ongoing climate change, it is uncertain what weather can be expected in the future, especially with current predictions claiming climate change will bring higher variability and more frequent weather extremes. In order to estimate future impacts of climate change to road conditions (and traffic safety) during temperate winter, an analysis of climate model predictions was performed, using Lithuania as an example of such climate conditions. The focus of this analysis is on meteorological variables that are unfavourable for roads: number of days when air temperature fluctuates around 0 °C, number of days with snow and number of days with adverse driving conditions. Several time periods were chosen: reference (1986–2005), near-term (2016–2035) and long-term (2081–2100). The projections were made using Coupled Model Intercomparison Project Phase 5 (CMIP5) outputs of 4 Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0 and RCP8.5). The data outputs of 3 global circulation models (GFDL-CM3, NorESM1-M and HadGEM2-ES) were statistically downscaled for meteorological stations in different regions of Lithuania in order to create distribution maps of climatic variables in the twenty-first century. Results showed that winter driving conditions should improve, and maintenance levels should decrease by the end of the twenty-first century. Nevertheless, a possibility remains that road maintenance and traffic safety might become less effective due to lack of awareness and preparedness, resulting in sudden and unexpected worsening of driving conditions on a day to day basis.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The climatic characteristics of 260 East Asian tropical monsoon depressions (EAMDs) are investigated using the ERA-Interim reanalysis dataset and a tracking dataset of global monsoon low-pressure systems. Most EAMDs form over the South China Sea (SCS) and the western tropical Pacific Ocean in July–October and have an average lifetime of 10 days. The vertical structures of EAMDs are usually upright or tilt slightly westward with height. The warm-over-cold thermal structure is a distinctive characteristic of EAMDs and two potential vorticity (PV) centers are related to the warm core in the upper level and the specific humidity center in the lower level, respectively. We divided the EAMDs into four groups: eastward-moving, westward-moving, turning, and northwestward-moving EAMDs. Most of the eastward-moving EAMDs form over the SCS in May and June, whereas the westward-moving EAMDs form over both the SCS and the western Pacific Ocean in July–October. The turning and northwestward-moving EAMDs are mainly generated over the western Pacific Ocean and have longer lifetimes. The structures of the eastward-moving and turning EAMDs show common characteristics in each stage. Their vertical structures change from upright in the developing and peak stages to northeast tilting with height in the attenuating stage, especially for the specific humidity. By contrast, the structures of westward- and northwestward-moving EAMDs show little change during their lifetime. They are symmetrical relative to the vertical axis of the EAMDs over their whole lifetime and only vary in strength.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉For any given time series, how to optimize its forecast strategies and what prediction model is adopted are of great importance. In order to reach this goal, insight from analyzing predictability of series with known structure information is necessary. Time series generated by theoretical models with four kinds of known predictive structures, i.e., short-term correlation, long-term correlation, and multifractal and chaotic patterns, are applied to demonstrate that there is a well-defined relation between series’ intrinsic predictability and prediction accuracy of any specific prediction model. And results show that both intrinsic predictability and prediction accuracy are enhanced by these well-defined structures. There are different regimes in the relation between intrinsic predictability and prediction accuracy for series with different known deterministic or stochastic predictive structures. These regimes in the relation between intrinsic predictability and prediction accuracy can guide us to preselect a suitable prediction model and forecast strategies for any underlying series by only analyzing the permutation entropy of a given series. Results from three pieces of climate series further confirm that insights from theoretical series with known structure information indeed work well.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉In this study, we investigate spatial differences in thermal comfort conditions using the net effective temperature (〈em〉NET〈/em〉) considering the synthetic effects of air temperature, relative humidity, and wind speed. Using a daily-scale dataset of maximum air temperature (〈em〉T〈/em〉〈sub〉max〈/sub〉), relative humidity, and wind speed from 518 stations during 1960–2016 across China, we analyze the influence of different climate conditions on 〈em〉NET〈/em〉 or 〈em〉T〈/em〉〈sub〉max〈/sub〉 at three different levels of hot conditions (35 °C 〈 〈em〉T〈/em〉〈sub〉max〈/sub〉 〈em〉〈〈/em〉 37 °C, 〈em〉NET〈/em〉〈sub〉〈em〉T〈/em〉max35〈/sub〉 for 〈em〉HT〈/em〉〈sub〉35〈/sub〉 cases; 37 °C 〈 〈em〉T〈/em〉〈sub〉max〈/sub〉 〈em〉〈〈/em〉 40 °C, 〈em〉NET〈/em〉〈sub〉〈em〉T〈/em〉max37〈/sub〉 for 〈em〉HT〈/em〉〈sub〉37〈/sub〉 cases; 〈em〉NET =〈/em〉 27 °C, 〈em〉NET〈/em〉〈sub〉27〈/sub〉 cases). In 〈em〉HT〈/em〉〈sub〉35〈/sub〉 (〈em〉HT〈/em〉〈sub〉37〈/sub〉) cases, 〈em〉NET〈/em〉〈sub〉〈em〉T〈/em〉max35〈/sub〉 (〈em〉NET〈/em〉〈sub〉〈em〉T〈/em〉max37〈/sub〉) can reach up to 32 °C (34 °C) in southern China and also can be less than 29 °C (31 °C) in western Northwest China. In 〈em〉NET〈/em〉〈sub〉27〈/sub〉 cases as the threshold for the thermal sensation of very hot, 〈em〉T〈/em〉〈sub〉max〈/sub〉 should be over 33 °C in western Northwest China and was less than 30.5 °C in southern China, by contrast. With global warming, there is an increasing trend in the number of extreme hot days in most part of China, but a decreasing trend is detected in the part of Jianghuai region, partly due to the decreasing trend in 〈em〉T〈/em〉〈sub〉max〈/sub〉〈em〉.〈/em〉〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Iran is a part of the arid and semi-arid regions of the world and many of its areas suffer from repeated droughts. Due to the fact that recent droughts have put a lot of pressure on water supply systems, drought management have a particular importance. For this purpose, monitoring and forecasting systems can be considered as appropriate tools. In this research, monitoring and prediction of drought in the Urmia synoptic station in short-, medium-, and long-term timescales were investigated using the SPEI (standardized precipitation and evapotranspiration drought index) and gene expression programming model. For this purpose, the SPEI was calculated for timescales 1, 3, 6, 12, 24, and 48 months with using monthly rainfall and temperature data from 1951 to 2009, and its performance was compared with standard precipitation index (SPI). The results showed that the correlation coefficient of SPEI and SPI in the 3-, 12-, and 48-month timescales were 0.84, 0.845, and 0.776, respectively. The monitoring results showed two long periods of drought-related years 1959–1967 and 1998–2009 during the statistical period. After drought monitoring based on SPEI time series, drought was predicted using gene expression programming models with delays of 1 to 5 months. According to the results, the prediction accuracy was increased by increasing the scale of SPEI so that the correlation coefficient in the test stage in the one-month scale (SPEI1) increased from 0.215 to 0.984 in 48-month scale (SPEI48) and the overall accuracy of the model increased from 60.1 in SPEI1 to 92.3% in SPEI48.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Since the extra-tropical eddies carry a huge amount of moisture, heat, and momentum from their source regions to remote places, the long-term changes in their properties can lead to substantial changes in the Earth’s major climatic zones with huge socio-economic consequences. We present the long-term variability and trends in the spatial properties of the Rossby wave packets (RWPs) in the Northern Hemisphere in all seasons for the period of 1980–2013 using NCEP reanalysis for the three major storm track regions: Eurasian, North Pacific, and North Atlantic. A region of high-amplitude meandering flow in the upper troposphere that in the mid-latitudes travel in coherent wave train structures or wave packets is often referred to as a RWP. We found that the activity volume of the RWPs is largest during winter in all storm track basins. Our results suggest that the highest variability of the activity volume is in the North Atlantic region, while the minimum variability of this quantity is found during summer in the Eurasian basin. The largest relative frequency of significant wave packets is found in winter in the North Atlantic basin; however, the largest variability of this quantity is found in the North Pacific region during spring. The largest variability of the long-term daily maximum of WPA is found in the Eurasian region in winter and the minimum variability of this quantity is found in summer in the North Pacific region. The long-term northernmost point of the Rossby wave packets (which is an indicator of the meridional extent of the RWPs) shows that during autumn in the North Atlantic basin, the Rossby wave packets meander more than other seasons and regions. The minimum value of this quantity is found in the summer season in the Eurasian region. The largest variability of the northernmost edge of the RWPs is found in the autumn season in the Eurasian basin, while the minimum variability of this quantity is found in the autumn season in the North Atlantic region. We employ the non-parametric Mann-Kendall test to identify the statistically significant trends in the spatial properties of the Rossby wave packets. No significant trend is found for any diagnostic tools in any region and any season. This suggests that the global mean temperature rise or other climatic changes (over the 34-year period of this study) do not have a detectable impact on the Rossby wave packets.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Examining the physical mechanisms through which large-scale climate indicators, e.g., El Niño–Southern Oscillation and Indian Ocean Dipole, affect hydroclimatic variables in the tropics and extratropics is a forefront scientific challenge. We examined climatic teleconnections between large-scale climate indices and temperature variability over South Korea. To do this, we calculated not only leading patterns of observed monthly mean and extreme temperature through an empirical orthogonal teleconnection (EOT) decomposition technique but also statistical correlations on a monthly basis using cross-correlation and lag regression analyses for the leading modes and global atmospheric circulation dataset. As a result, the spatial pattern of the leading EOT modes for mean (extreme high) temperature represents an eastern (southern) coastal mode for boreal summer and a northern (middle) inland mode in boreal winter, while extreme low EOTs exhibit a northern inland mode in summer and a western coastal mode in winter. The temporal evolution of the leading EOT modes exhibits a mostly increasing trend and an interdecadal oscillation. The leading EOT modes of mean temperature explain more variance than those of extreme temperature during warm and cold seasons. The findings from this study illustrate that tropical ENSO forcing has a coherent association with August and December temperature patterns, while the Indian Ocean Dipole is identified as a driver for temperature variability during fall season. The monsoon circulation over the western North Pacific also exhibits a significant negative correlation with the December temperature EOTs. The leading EOTs for October temperature exhibit the positive correlation with the tropical cyclone variability, while the leading EOTs for mean and extreme high temperature exhibit significant negative correlations with the snow depth over northeastern Eurasia in November. The leading patterns of the August and December mean temperature time series are predictable at up to 5-month lead time from the tropical Pacific sea surface temperatures (SSTs), while a predictable response from Indian Ocean SSTs was detected at up to 4-month lead time.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The characteristics of eolian sand activity are greatly influenced by the wind regime, and wind regimes have been changing around the world in response to climate change. This has also been true in the desert area of northwestern China since 1965, and these changes have changed the region’s landforms, sandstorm frequency, and desertification. In this study, we analyzed the temporal and spatial variation of the region’s near-surface wind field since 1965. We found an average annual wind speed during this period of 1.7 m s〈sup〉−1〈/sup〉, with a decreasing trend from 1965 to 2000 and an increasing trend from 2000 to 2015. The maximum rate of decrease occurred in the spring and in the eastern Taklimakan Desert. The variation of the average wind speed depended on the frequency of winds strong enough to entrain sand (with a wind speed 〉 6 m s〈sup〉−1〈/sup〉). We also found that variations of the drift potential were primarily controlled by three prevailing wind groups (winds from the northwest, north, and northeast), but showed complex changes between seasons and regions. The wind direction in the Taklimakan Desert is characterized by two characteristics of branch and steering, the branch line is swinging in the direction of the east and the west (81.5° E~84° E). The changes in wind speed were mainly caused by a decreased frequency of strong winds, precipitation, and urban development. However, the variation of wind speed had less impact on the desert environment than the variation of wind direction.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The paper focuses on investigation of ‘twin’ subtropical oceanic highs of the Northern Hemisphere, i.e. the Azores High (AH) and the Hawaiian High (HH) in January and July based on gridded 2.5° × 2.5° data of Reanalysis Project of the National Center for Atmospheric Research for the period 1948–2018. The aim is to answer three questions: (1) Are there any connections between AH and HH (both within and between the systems)? (2) What is the long-term variability and trends of the basic characteristics of AH and HH? (3) Do the AH and HH move, and if so, in what directions? The most important results are as follows: (1) the long-term trend of sea level air pressure in the AH centre in January is positive, statistically significant with the increase of 0.63 hPa/10 years, (2) pressure in both centres significantly relates with the latitude of each system; variables characterising the HH in January explain 11% of variation of the variables of the AH in July, (3) the NE-SW/SW-NE index proves the shifting of the AH in January from the south-west to the north-east from the 1990s of the twentieth century and again to the south-west in the twenty-first century, (4) the HH in January and July moved generally from the north-east to the south-west until the end of the twentieth century and shifted again to north-east during the twenty-first century, (5) the AH in July was characterised by complicated displacement system with the prevalence of the shifting from the north-east to the south-west with the exception for the period 1980–1990. In winter, the AH moves towards the land area of Europe in the second half of the twentieth century, while the HH moves towards the open Pacific. The statistically significant increase of pressure in the centre of the AH in January is closely related to the shifting of the system to the north-east. The positive pressure trend in the centre of the AH in January combined with the zero trend in July is the cause of diminishing difference between summer and winter air pressure value of the high. Due to increased sea surface temperature of the Atlantic, the AH does not lose its strength in winter as it used to a few decades ago.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉In this study, the long-range relationships between North Atlantic Oscillation (NAO) and precipitation data obtained from Climate Prediction Centre (CPC) Merged Analysis of Precipitation (CMAP) from 1979 to 2016 are investigated using Detrended Fluctuation Moving Average Cross-Correlation Analysis (DMCA). In the atmosphere, teleconnections through strong convective processes sporadically affect various climatic regimes in Europe, Mediterranean basin, North Africa, Middle East, and Caucasus. The NAO is one of the teleconnection processes and results in heavy rainfall in the Mediterranean basin during its negative phase while it gives rise to rain in Europe during its positive phase. The DMCA technique shows that the NAO fluctuation series exhibit different long-range cross-correlation coefficients, ρDMCA(〈em〉s〈/em〉) with “s” being the moving average time window length, between the precipitation values and NAO. Large ρDMCA(〈em〉s〈/em〉) coefficients with time window(s) larger than 12 months were obtained particularly over the Mediterranean basin, near the North Pole including northern Europe. Furthermore, the ρDMCA(〈em〉s〈/em〉) coefficients were grouped into clusters using K-mean method to distinguish the similar patterns. The 1st cluster refers to the negative phase of the NAO indicating warm-rainy conditions and dry spells, which is especially evident in the Mediterranean basin. The 2nd cluster represents the long-range cross-correlation with respect to the positive phase of NAO and precipitation values, particularly for the Western and Northern Europe. Conversely, the 3rd cluster is evaluated as power law of long-range cross-correlations between the precipitation and NAO with respect to the different time scale processes.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Many hydrometeorological studies have evaluated the impact of climate variability on hydrologic extremes. Recent studies have shown that the varying state of climatic cycles has intensified the regional hydrologic cycle within a wide range of geographical regions in the state of Texas. These climatic cycles define numerous sea surface temperature and pressure anomalies which lead to heavy precipitation in a region. The objective of this paper is to quantify the impact of five major Atlantic and Pacific Ocean related climatic cycles, including (i) Atlantic Multidecadal Oscillation (AMO), (ii) North Atlantic Oscillation (NAO), (iii) Pacific Decadal Oscillation (PDO), (iv) Pacific North American Pattern (PNA), and (v) Southern Oscillation Index (SOI), on maximum daily precipitation within a year in various climate regions of Texas, using a weighted correlation approach incorporating Leave-One-Out Test (LOOT). The uncertainty in the estimated correlation coefficient is factored in by determining the sample correlation coefficient at the 95% confidence interval. The influence of these global scale climatic cycles on the regional hydrologic cycle is found to be governed by the integrated hydrometeorological properties of weather stations, including (i) station elevation, (ii) average temperature, and (iii) average total precipitation, in the months of extremes. Results of this study will help regional water boards prepare for extreme hydrometeorological events in a changing climate.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Updating the urban land cover information has been proved a necessary method for the numerical studies of urban climate and urban atmospheric environment in China, a fast urbanizing country. However, there are uncertainties in the urban land use/cover (ULUC) information in different datasets due to the uncertainties in raw data sources and produce methods. In this study, the Weather Research and Forecasting model is used to simulate the summer climate over the Yangtze River Delta in July and August 2013, when a heatwave episode occurred. Five numerical experiments are designed; one is the baseline experiment (non-urban land cover (NOURB)) in which all urban land covers are removed, and different urban land use/cover (ULUC) experiments from four different global land cover datasets are used in other four experiments. The differences between the ULUC experiments and the NOURB experiment are used to observe the urbanization effects. The results show a remarkable urbanization effect in all the ULUC experiments, and the urbanization influences are most different over the 〈em〉union-urban〈/em〉 area, which defined as the area marked as urban area by at least one land cover dataset, because the uncertainties in urban land cover information are greatest in this area. All the ULUC experiments show the regional influence of urbanization as well, which means urbanization effects are not limited in cities. The Climate Change Initiative (CCI) experiment using the European Space Agency CCI 2013 dataset showed the greatest urbanization influence and the highest effect index in all analyzed variables, because of the largest urban land cover area in the analysis domain. The urban land cover area in the Global Land Cover experiment is the smallest among the ULUC experiments, but it distributes in a more concentrated way than in other experiments; it indicates that both city size and city shape affect the regional influence.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Over the past century, extreme precipitation events have posed many problems on socio-economic status of India, a country that spans over a wide variety of climate regimes. Assessment of past changes in precipitation associated climate change indicators is very essential for effective management of the hydroclimatic risks. Present study evaluates spatio-temporal variation of eleven precipitation-based climate change indices over three 35-year epochs, i.e., 1906–1940, 1941–1975, and 1976–2010. The analysis is undertaken with the annual and monsoon season (June–September) daily precipitation data separately. Regions of significant changes are identified across the country, reflecting different characteristics (magnitude, frequency, intensity, and duration) of precipitation-based climate change indices. The results indicate a clear temporal evolution in the spatial distribution of trends over the years. During the recent epoch (1976–2010), a split is noticed with segregated increasing trends in southern region and patches of decreasing trends in northern region of the country. In contrast to the indices derived with monsoonal daily precipitation, significant trends over the country were more prominent for the indices derived with annual daily precipitation. Duration of annual maximum dry spell (wet spell) is found to significantly increase (decrease) over most of the regions. However, there is no change in total precipitation indicating an increase in short spell heavy rainfall events. The analysis and the results offer an opportunity to identify the regions of interest and to adopt revised water management policies in the future through revised water allocations, alteration of cropping pattern, etc.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉In this study, an attempt has been made to analyze long-term annual and seasonal rainfall trends along with change point of annual rainfall in West Bengal, India for 102 years (1901 to 2002) using monthly rainfall data of 18 rainfall stations. The Mann-Kendall test is used to identify trend in rainfall time series and Theil-Sen’s slope estimator to assess the magnitude of this trend. Trend-free pre-whitening method is used to eliminate the influence of significant lag-1 correlation from the series. Change in magnitude is derived in terms of percentage change over mean rainfall. Pettitt-Mann-Whitney and standard normal homogeneity test have been used to identify change point of annual rainfall. The results show that significant trend is found at five stations in annual rainfall, six stations in monsoon rainfall, and eight stations in postmonsoon rainfall. Maldah station has recorded highest negative change in magnitude in annual (− 14%) as well as monsoon rainfall (− 20.48%). South 24 Parganas rainfall station exhibits highest positive change in magnitude in annual (+ 13.98%) and monsoon (+ 13.27%) rainfall. Postmonsoon rainfall portrays positive change in magnitude at 16 rainfall stations with highest change in Birbhum station (+ 40.07%). Three most probable change point years of annual rainfall, viz. 1956, 1967, and 1952 have been observed for the rainfall stations situated in northern, southern, and western part in West Bengal. In the post change point period, the number of rainfall stations with decreasing trend has risen in northern and western part whereas it has lessened in southern part.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The article deals with annual and multiannual courses of the highest monthly precipitation in the Polish and the Ukrainian Carpathians on the basis of the data from 27 measuring stations in the period from 1984 to 2013. The monthly precipitation variability in terms of the highest values in the annual, decadal and the 30-year dimension, taking into consideration the spatial coverage, is the key to the understanding of the precipitation climates of the Carpathian mountain regions with distinctive local features of climate change. The highest monthly amounts of precipitation are regularly observed in the period from May to September and only occasionally in the rest of the months. In winter months, the frequency of occurrence of the highest monthly precipitation totals does not exceed 1%. The highest monthly total precipitation in the whole period out of all the stations was recorded in the most western part of the Polish Carpathians and in the central part of the Ukrainian Carpathians. July 2008 was the only month when the highest monthly precipitation in a year was observed at all stations. The range of the highest monthly precipitation values is broader in the Ukrainian part. Cyclonic circulation patterns are the predominant cause of the highest monthly precipitation in both the Polish and the Ukrainian Carpathians. The findings suggest no significant trends, no apparent patterns in the temporal and spatial distribution of the highest monthly precipitation cases with the biggest number of the highest monthly precipitations cases in the last decade and the prevalence of the cases in the warm period.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Historical temperature records are often partially biased by the urban heat island (UHI) effect. However, the exact magnitude of these biases is an ongoing, controversial scientific question, especially in regions like China where urbanization has greatly increased in recent decades. Previous studies have mainly used statistical information and selected static population targets, or urban areas in a particular year, to classify urban-rural stations and estimate the influence of urbanization on observed warming trends. However, there is a lack of consideration for the dynamic processes of urbanization. The Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are three major urban agglomerations in China which were selected to investigate the spatiotemporal heterogeneity of urban expansion effects on observed warming trends in this study. Based on remote sensing (RS) data, urban area expansion processes were taken into consideration and the relationship between urban expansion rates and warming trends was investigated using data from 975 meteorological stations throughout China. Although urban areas constitute less than 1% of land in China, more than 90% of the meteorological stations experienced urban land use change and the average urban expansion rate was 0.33%/a. There was also a significant positive relationship between observed warming trends and urban expansion rates. Background warming, without the influence of urbanization and extra warming induced by urbanization processes, was estimated using a linear regression model based on observed warming trends and urban expansion rates. On average, urbanization led to an additional annual warming of 0.034 ± 0.005 °C/10a. This urbanization warming effect was 0.050 ± 0.007 °C/10a for minimum temperatures and 0.008 ± 0.004 °C/10a for maximum temperatures. Moreover, it appeared that urbanization induced greater warming on the minimum temperature during the cold season and maximum temperature during the warm season.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Due to increasing heat-related health risks, as well as the human requirements on energy and side effects, this study attempts to figure out a methodology of quantifying the potential requirements on human thermal comfort and air humidity towards the achievements of the Sustainable Development Goals (SDGs). Based on the Köppen-Geiger climate classification and the selected 11 climate types with sample cities, the methodology has been detailed. Meanwhile, the quantified thermal and humid conditions in each city, as the primary representation of each climate type, have been presented. According to the potential application of the quantified information and based on the qualitative analysis of SDGs text, the correlation between the information of human thermal comfort and air humidity, and 12 SDGs has been summarised. Furthermore, the difference of the qualitative description between the original Köppen-Geiger climate classification and the quantified information has been discussed. For instance, obvious difference appeared in the same climate zone (e.g. climate zone C); comparing with the original climate classification, there is a different order of hot/cold conditions or wet/dry conditions. The identified differences are regarded as the enlightenments for further studies.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This study uses a multi-model ensemble (MME) for the assessment of the spatial and temporal variations of rainfall in peninsular Malaysia under climate change scenarios. The past performance approach was used for the selection of GCM ensemble from a pool of Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs. The performances of four bias correction methods, namely, scaling, gamma quantile mapping, generalized quantile mapping, and power transformation were assessed to select the most suitable method for the downscaling of daily rainfall of selected GCMs based on APHRODITE rainfall at a spatial resolution of 0.25° × 0.25°. The downscaling model was used for the projections of daily rainfall for the period 2010–2099 for four representative concentration pathways (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP6.0, and RCP8.5. Random forest regression algorithm was used to develop the multi-model ensemble (MME) mean of GCM-projected rainfall for different RCPs in order to show the changes in rainfall for three future periods, 2010–2039, 2040–2069, and 2070–2099. The results revealed four GCMs, BCC-CSM1.1(M), CCSM4, CSIRO-Mk3.6.0, and HadGEM2-ES as the most suitable GCMs for the projection of daily rainfall of peninsular Malaysia. The power transformation was found as the most suitable method for the correction of biases in GCM daily rainfall. The MME mean of projected rainfall showed the increase in rainfall in peninsular Malaysia for all the scenarios and future periods. The maximum increase in annual rainfall was projected by 15.72% during 2070–2099 for RCP8.5. The variability of future rainfall was also found to increase along with mean rainfall. The increase in rainfall variability was projected by 26.15% for RCP8.5 during 2070–2099. The spatial pattern of rainfall changes revealed more variability in future rainfall in the northeast where frequency of hydro-climatic disasters is higher compared to other regions. The results indicate the possible increase in hydro-climatic disaster in peninsular Malaysia due to climate change.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The Taiwan government has been following the green energy policy for decades to cope with drastic climate change due to global warming and tremendous urbanization. Forming a database to assist Taiwanese policy makers in using the concept of energy efficiency and environmental comfort in urban planning and architectural design is required. This paper depicts how the field and CFD studies for an industrial region located in rural and monsoon wind prevailed district of the mid-western coast Taiwan were conducted, and how the data are used to construct building layout principles for regions having similar climate conditions. For the purpose of analyzing field data and forming database, measured atmospheric and microclimate parameters, such as the physiological equivalent temperature (PET), were first converted into empirical relations. Then, these empirical equations were used as the input and validating values for the CFD simulation model based on the FLUNENT software. Five field-observed wind profiles were used as the inlet conditions, and the computed results of the five-street canyon aspect ratio, and of four-building arrangements, are analyzed to construct the building layout principles. The study results show that the principles for a monsoon wind prevailed region should better be the following: (1) the street canyon aspect ratio is 0.1 and not over 0.3; (2) the building layout is a single open arrangement, avoiding a staggered setting.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The present study examines the prominent patterns of Indian Ocean dipole (IOD) associated with the recent changes in the Pacific Ocean variability centered around 1998. EOF analysis of boreal fall (September through November (SON)) Indian Ocean sea surface temperature anomaly (SSTA) reveals that EOF1 (32% variability) is dominated by East Pacific El Niño-IOD co-occurring pattern during 1980–1998 (period 1). During period 2 (1999–2016), central Pacific El Niño also influence EOF1 (26% variability) and the IOD co-occurrence is reduced. EOF2 with 22% variability has central Indian Ocean dipole pattern co-occurred with El Niño Modoki during period 1, and it became basin-wide south-north dipole pattern in period 2 with reduced (20%) variability. At the same time, local Indian Ocean variability induced by SST anomalies centered on western side of Australia (Ningaloo Niño/Niña) is found to be related to EOF2 in both the periods and its influence is stronger after 1998, when its association with El Niño weakens. Third mode is ENSO independent IOD pattern in both the epochs and has more variability in period 2. Thus, during period 1, both East Pacific and central Pacific El Niño events are well separated as major two modes of tropical Pacific Ocean. These two flavors are associated with two different IOD patterns. But in the second period, first mode of Pacific Ocean is strongly related to both the El Niño flavors and reduced the co-occurrence of IOD with the El Niño modes and the effect of local Indian Ocean processes in the IO variability is intensified.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The land-ocean convective contrasts observed by the satellite-driven data from the Tropical Rainfall Measuring Mission for the year 1998 to 2014 (17 years) are analysed to examine the role of static land surface relative to the dynamic ocean surface for the lightning flash counts (FC). We present the relationship between the lightning flash counts per convective available potential energy (CAPE) (FC/CAPE), the product of CAPE and rainfall (CAPE × RF) and Bowen ratio, FC (land/ocean) and AOD (land/ocean) and FC (land/ocean) and maximum updraft speed (land/ocean) to examine the land-ocean contrasts over the Indian region. The results show that FC/CAPE over Indian land increases by up to 520% with respect to FC/CAPE over Indian oceanic regions. The land-ocean contrast seems to be a result of increase of Bowen ratio and FC over land by up to 800% as compared with Bowen ratio and FC over ocean surface. The increase of CAPE over land is by up to 47% relative to CAPE over ocean with corresponding increase of maximum updraft speed over land by up to 117% with respect to maximum updraft speed over ocean. The temperature profiles over the land and ocean show the same temperature in the lower troposphere, followed by a temperature difference of 8 °C between land and ocean at about 16000 m during pre-monsoon and post-monsoon periods. The product of CAPE with RF over land is higher by 66% to that of oceanic region with aerosol load (AOD) of about 49% more over land as compared with that of ocean. The results demonstrate the warming contrasts of land-ocean in a comparative analysis of FC, CAPE, RF and AOD in the tropics.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Tropical cyclones (TCs) formed over the western North Pacific (WNP) often make landfalls over East Asia and Southeast Asia, causing strong winds and torrential rain in the coastal countries. However, they also bring cool weather on hot summer days and mitigate drought impacts. The present study demonstrates that TC activities over the WNP can strongly modulate extreme summer weather events in eastern mainland China, i.e., frequent TC activities would indirectly lead to more hot days in central-eastern China along the lower and middle reaches of Yangtze River, besides compensating the decreasing of hot days induced by the direct impact of TCs. Such indirect impact is largely determined by the feedback effect of TCs on pressure system, especially on the western Pacific subtropical high, resulting in an abnormal anticyclonic circulation band that dominates a large area from central-eastern China to the Pacific Ocean to the east of Japan in the middle and lower troposphere.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Tamil Nadu, an agriculturally important state in India, is recurrently exposed to floods, cyclones, and droughts that have devastating effects on human, agriculture, and economy. This persuades the study to focus on future climate extremities of the state. The future climate extremes are examined using daily temperature and rainfall simulations developed by Hadley Center’s Regional Climate Model termed PRECIS (Providing REgional Climates for Impacts Studies) at a horizontal resolution of 25 km. The study uses the simulations that are driven by the lateral boundary condition of HadCM3Q0 (Hadley Centre Coupled Model-Q0) generated by a Perturbed Physical Ensemble of 17 ensembles under A1B scenario for the period of 2005 to 2095. Extreme indices have been acquired from the simulations using RClimDex. The trends of extreme indices are computed and verified for statistical significance using Mann–Kendall and Sen’s slope test. The changes in extreme indices with respect to baseline (1970–2000) reveal that almost all temperature indices denote a highly significant trend. The minimum temperature indices have shown prominent increase compared with maximum temperature indices, which is also upheld by the significant decrease in the diurnal temperature range trend. The summer days above 40 °C have indicated a substantial increase with a stronger slope of 0.77. However, the rainfall indices depict the insignificant trend. The changes in extreme wet days (R99p) and very wet days (R95p) exhibit a positive shift, and the increase of maximum 1-day rainfall is projected to be higher than maximum 5-day rainfall. Furthermore, the probability of rainfall indices exemplifies the increase of intense rainfall by the end of the century. The overall results of indices intimate that Tamil Nadu will be shifted to the extreme warmer and wetter condition by 2080s (2065–2095). Such information will act as a baseline to study the future impact assessment on different sectors thereby supporting policymakers and stakeholders to formulate suitable adaptation and mitigation strategies.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The Qinghai Lake is the largest saltwater lake in the Tibetan Plateau and in mainland China. Effects of the lake on surface climate of the coastal areas, however, are not well understood. This article utilizes hourly observation data of warm season from 17 automatic weather stations to analyze the diurnal and seasonal variations in air temperature in different coastal zones of the Qinghai Lake. The EOS/MODIS data was also used to analyze the surface temperature differences between the lake and its surrounding areas. The results show a few unique characteristics of the plateau lake: (1) based on the satellite data, lake surface temperature is lower than its surrounding areas (4.9~25.1 °C and 12.2~35.6 °C respectively) in the daytime, while it is obviously higher than that in the surrounding areas (1.9~10.5 °C and − 13.1~6.3 °C respectively) at the nighttime. Daily mean water surface temperature is higher than that in the surrounding areas, which is 7.8~17.9 °C and − 2.6~17.9 °C respectively; (2) based on data of meteorological stations, coastal zones closer to the shoreline have a higher air temperature during the warm season largely due to the much warmer nighttime near the shoreline than those in deep inland zones. For the on-shore, near-shore, and far-shore zones, mean daytime air temperature is 8.7~9.6 °C, 9.5~11.1 °C, and 10.0~10.7 °C, respectively, and the mean nighttime air temperature is 2.9~4.1 °C, 1.3~12.3 °C, and − 0.2~0.4 °C respectively. The hourly mean diurnal temperature range (DTR) increases with distance to the shoreline, which is 8.6 °C, 10.9 °C, and 12.3 °C in the on-shore, near-shore, and far-shore zones respectively; (3) differences between maximum and minimum 5-day mean daily maximum (minimum) air temperature during the warm season are 20.3 °C (23.3 °C), 21.7 °C (25.6 °C), and 22.6 °C (26.7 °C) for the on-shore, near-shore, and far-shore zones, respectively, indicating an asymmetrical effect on daytime and nighttime air temperature in the on-shore zone; (4) daily maximum air temperature of the on-shore zone is lower before early October, but it is slightly higher afterward and occurs later; (5) daily minimum air temperature of the on-shore zone is on average higher than that of the far-shore zone, especially after the mid-September, and it also appears later in autumn than in summer.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The impacts of climate change on future drought properties in various regions across China are accessed using multiple statistical approaches, based on 20 downscaled global climate models provided by NASA (NEX-GDDP) under Representative Concentration Pathways (RCP) 4.5 and RCP8.5 emission scenarios. Results show that temperature plays a crucial role on the variability of drought conditions in China by comparing the discrepancies between Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Unobvious variability of drought extent is projected for SPI, while the drought extent for SPEI is remarkable. Based on SPEI, a considerable aggravation in spatial extent and severity of future drought events are found in the majority of regions, particularly in northwest and northeast China, except for winter over northeast region. The drought extent increases more significantly after late 2070s under RCP8.5 scenario, and the differences of drought extent are not significant between RCP4.5 and RCP8.5 scenarios in the early and mid-twenty-first century. More than 85% of the regions show a decreasing trend for SPEI in spring, summer, and autumn, suggesting drought tendency in most of China, and drought frequency also increases significantly in north and northwest China except for winter. The dramatic aggravation of drought attribution is mainly projected to the increases in potential evapotranspiration (PET) in northwestern and northern regions of China, whereas in northwestern region, the exacerbating drought conditions are expected to the attribution of deficiencies of rainfall. At national scale, PET plays a more dominant role to the future severe and widespread droughts across China in the context of climate change.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Meteorological observations in Tibet are poor in quality with a severe amount of missing data; this is mostly caused by extreme climatological conditions and higher maintenance costs. This paper focuses on the imputation of missing data and the reconstruction of the regional temperature field. Due to insufficient observation stations and complicated topography, we employ the weather research and forecasting (WRF) model to produce the proper orthogonal decomposition (POD) basis for the study region. We then develop the gappy POD method for the imputation of missing data. Both methods are compared and tested for various missing data cases, and the results show that the gappy POD method dramatically outperforms the regularized EM algorithm when the amount of missing spatial data is not severe. Furthermore, between the two methods, only the gappy POD method is capable of reconstructing the temperature field at locations where the data are absent. The gappy POD method can also be generalized for data assimilation with the assumption that the data across all model grids have missing values.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉ERA-Interim six-hourly reanalysis data supplied by the European Centre for Medium-Range Weather Forecasts were used to examine the applicability of Hewson’s 〈span〉1998〈/span〉 objective front-plotting method to the recognition of the Kunming quasi-stationary front. The optimum locating variable and masking variables, together with their optimum thresholds, were determined for three typical Kunming quasi-stationary fronts that occurred in January and February 2008. Hewson’s method was more suitable for objectively recognizing the Kunming quasi-stationary front than a subjective method. Thirty Kunming quasi-stationary front events during January–February 2008 were then identified using Hewson’s method. A composite analysis clearly showed the horizontal and vertical structure of the Kunming quasi-stationary front, consistent with the theory. This objective method of recognizing the Kunming quasi-stationary front has advantages in terms of objectivity, practicality, and reliability, and therefore has important potential applications.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉In recent years, satellite data has been used to estimate precipitation with the aim of increasing the accuracy of rainfall spatial distribution especially at ungauged locations. In this research, the satellite data, including visible and infrared reflection data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and observation data, consists of rainfall records (10 years 2005–2015) from three synoptic stations in Semnan province, were used to simulate rainfall using an artificial neural network (ANN) method. The network performance is evaluated through three performance criteria, i.e., correlation coefficient (〈em〉R〈/em〉), root mean square error (RMSE), and Nash–Sutcliffe (NS). Findings show that using a combination of visible reflection data of band 3 and infrared reelection data of bands 5, 18, and 19 as input data results in better performance compared with other possible combinations. In this model, the values of 〈em〉R〈/em〉, NS, and RMSE for test period data were 0.93, 0.81, and 1.49, respectively.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This paper presents a comparative sensitivity analysis of six of the widely used human thermal comfort indices. The analysis consists of the evaluation of the effect of indices ‘input parameters’ variation and change rate on the output of human energy balance and simple thermohygrometric indices. For the implementation of the sensitivity analysis, the generalized additive model’s methodology is applied on a long period and high temporal resolution dataset from Athens, Greece. The results indicate that the proposed methodology of generalized additive models is adequate for such an analysis. Moreover, this research revealed the differences in index behaviour. The thermohygrometric indices (i.e. Thermohygrometric Index and HUMIDEX) exhibit a clearly deferent sensitivity pattern in comparison to the human energy balance indices (i.e. physiologically equivalent temperature (PET), perceived temperature (PT), modified physiologically equivalent temperature (mPET) and Universal Thermal Comfort Index (UTCI)), and they are incapable to handle the complexity of the atmospheric stimuli on human thermal perception. On the other hand, human energy balance indices can follow the input parameters fluctuations but with different grades of sensitivity. PET and mPET present a moderate and gradual sensitivity both in terms of the input variation and input change rate. PT is the less sensitive index among the human energy balance investigated, but it is able to follow efficiently the input parameters variation during the measurements period. Moreover, UTCI is the most sensitive among all the selected indices for low values (and low change rate) of the input parameters but for high input parameter values (except the wind speed), UTCI exhibits a low sensitivity in comparison to the other human energy balance indices. In terms of sensitivity, the most influential input parameter is global radiation, and the less influential is vapour pressure.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Evapotranspiration is one of the crucial components of hydrological cycle. The Penman-Monteith method (PM) is recommended as the sole standard method for estimating reference evapotranspiration (ET〈sub〉0〈/sub〉). The usage of the PM method is limited in many regions due to the lack of required weather data. In such circumstances, simple Thornthwaite equation is often used to estimate ET〈sub〉0〈/sub〉. The main objectives of the present study are (i) to estimate reference evapotranspiration using different Thornthwaite approaches, (ii) to develop optimal adjusted equation, and (iii) to consider the spatial variability of the empirical coefficient(s) of adjusted equation for the study area. In this study, six Thornthwaite approaches were compared to the full set PM equation using weather data from Vojvodina region, Serbia. The original Thornthwaite equation was very poor in estimating ET〈sub〉0〈/sub〉 and greatly underestimated PM values at all locations. It can be concluded that an adjustment of the Thornthwaite equation is necessary. The obtained results indicate that ET〈sub〉0〈/sub〉 could be estimated from the new Th65 approach (effective temperature, 〈em〉k〈/em〉 = 0.65), which reproduced statistical characteristics better compared to other Thornthwaite approaches. The spatial variability of the empirical 〈em〉k〈/em〉 coefficient showed that 〈em〉k〈/em〉 values varied from 0.62 to 0.69 across the study area with deviations of − 5% to 6% compared to a unique 〈em〉k〈/em〉 value of 0.65. These results suggested that single regional 〈em〉k〈/em〉 value can be successfully used for estimating ET〈sub〉0〈/sub〉.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Experiments are conducted with a regional climate model (RegCM4) to understand the role of the vegetation-runoff system in simulating African climate. In some tests, the original leaf area index (LAI) formula is replaced with a new one derived from the BIOME-BGC model and then three simulations were conducted: SP-TOP (SP, satellite phenology with TOP as the default runoff scheme in the community land model, CLM), CN-TOP (CN, carbon-nitrogen module), and finally CN-VIC, the CN module with the variable infiltration capacity (VIC) runoff scheme turned on. Results show that the different vegetation-runoff configurations have a significant effect on the energy balance and regional climate. For example, the CN-VIC configuration leads to decreased evapotranspiration and increased sensible heat flux. Consequent changes to the surface energy balance also affect the regional climate. The CN-TOP configuration shows a cold bias over evergreen forest, while the CN-VIC configuration replaces this with a large warm bias. Regarding total precipitation, both CN-TOP and CN-VIC configurations do not affect the overall magnitude relative to the SP-TOP configuration; however, they simulate the timing of onset and offset in comparison with the observational dataset. Despite the substantial biases in the CN-VIC configuration, it can be recommended for future climate studies over Africa, as long as the four VIC parameters are calibrated over the African domain to obtain good results for the surface climate.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉It is of importance to perform hydrological forecast using a finite hydrological time series. Most time series analysis approaches presume a data series to be ergodic without justifying this assumption. To our knowledge, there are no methods available for test of ergodicity to date. This paper presents a practical approach to analyze the mean ergodic property of hydrological processes by means of augmented Dickey Fuller test, Mann-Kendall trend test, a radial basis function neural network, and the assessment methods derived from the definition of ergodicity. The mean ergodicity of precipitation processes at Newberry, MI, USA, is analyzed using the proposed approach. The results indicate that the precipitations of January, May, and July in Newberry are highly likely to have ergodic property, the precipitations of February, and October through December have tendency toward mean ergodicity, and the precipitations of all the other months are non-ergodic.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The frequency and severity of high temperature and drought extremes are expected to increase under future climate change (CC) and considerably affect the agricultural sector particularly in water-limited ecosystems. This study was conducted to assess future CC impacts on rainfed wheat yield, water requirement (CWR), water use efficiency (WUE), precipitation use efficiency (PUE), reference crop evapotranspiration (ET〈sub〉0〈/sub〉), and agricultural rainfall index (ARI) in northeast of Iran. The outputs of five global climate models (GCMs) under RCP-4.5 and RCP-8.5 during three time periods (i.e., the 2025s, 2055s, and 2085s) downscaled by MarkSimGCM model were applied. CWR was estimated using the CROPWAT 8.0 model. Further, the CSM-CERES-Wheat model was employed to simulate rainfed wheat yield, WUE, PUE, and ET〈sub〉0〈/sub〉 responses to CC. The results showed that the mean monthly ET〈sub〉0〈/sub〉 and CWR would likely increase under both emission pathways over the studied sites. The mean monthly ARI is also anticipated to decline in the future indicating a drier climatic condition over northeastern Iran by 2100. Furthermore, CC is highly likely to decrease rainfed grain yield, WUE, and PUE during the current century. The largest changes in ET〈sub〉0〈/sub〉, ARI, CWR, yield, WUE, and PUE were projected in the late twenty-first century (the 2085s) under RCP-8.5. The CC-induced wheat yield loss will likely endanger food security in the country. Yield reduction can be partially offset by adopting appropriate adaptation measures.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Using minute-based precipitation data for the period of 1961–2016 from the National Basic Weather Station of Shapingba, the main urban region of Chongqing, China, and by combining the concept of moving sampling with the natural rainstorm process, a novel sampling technique called natural rainstorm moving sampling was designed. A novel design hyetograph method, the natural rainstorm moving regularity (NRMR) method, was derived from this sampling technique for hyetograph design. The samples obtained through natural rainstorm moving sampling were compared with those obtained from the annual maximum method (a commonly used sampling method) and with actual waterlogging records. Fuzzy identification verified the rationality of the NRMR method in determining the peak shape. In addition, the NRMR method, the Pilgrim and Cordery (PC) method, the Chicago method, and the common-frequency method were compared; the results showed that natural rainstorm moving sampling is a reasonable sampling approach, and it is simpler and more applicable than the other methods. The results of this study provide a scientific basis and reference for urban drainage design to reduce urban waterlogging disasters in Chongqing. At present, the results have been applied in the formulation of the rainstorm intensity and the design of a rainstorm hyetograph in Chongqing.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉High resolution and global coverage of the satellite-based precipitation data have been found useful in many climate and hydrological studies, particularly in limited and nongauged areas. Due to systematic and nonsystematic factors, there are always deviations between ground-based and satellite-based precipitation. Among many satellite-based precipitation products, the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) family products attracted more attention. In this paper, we evaluated the PERSIANN family of monthly, seasonal, and annual precipitation data over Fars Province, Iran, at four different spatial scales, namely point (station), pixel, regional, and provincial during the period of 2003–2015 using 132 rain gauge data as baseline. The performance of the products was first evaluated by calculating some statistical metrics. The result shows that in all spatial and temporal scales, the three products mirror the precipitation pattern and underestimate the precipitation in the province. The PERSIANN-CDR outperforms the other two products and is the superior product. The performance of PERSIANNN products is the best at the provincial scale followed by regional, pixel, and point scales. The performance of PERSIANN family products is also evaluated by the quantile–quantile plot from which a set of equations is proposed to accurately predict precipitation in Fars Province from the PERSIANN-CDR precipitation data. The proposed equations are verified by 2-year precipitation data that were not used in the assessment of the products. These equations provide highly accurate precipitation data from PERSIANN-CDR data particularly in nongauged sites at various spatiotemporal scales for application such as in statistical hydrology and hydroclimate-related projects.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Projections of seasonal extreme precipitation changes in eight Mediterranean subregions between the end of the twentieth and the end of the twenty-first century are analyzed using weighted multi-model ensembles. Weights are based on the performance of predictor variables in the scope of statistical downscaling. Two indices of precipitation scarcity as well as two indices of heavy precipitation are downscaled from global climate model data of the Coupled Model Intercomparison Project phase 3 and 5 (CMIP3, CMIP5) multi-model ensembles, considering two emission scenarios each. Based on the performance with regard to observations of extreme precipitation as well as inter-model consistency, three weighting metrics are calculated and subsequently applied to each ensemble. While meteorological droughts are projected to increase in most cases, the tendency is less pronounced for heavy precipitation events and mostly points towards reduction. The weighting does not affect the multi-model mean changes, but induces a decrease of ensemble spread (although mostly not significant), implying a decrease of model uncertainty. As the ensemble and scenario considered have minor effect on the findings and also the differences between seasons and subregions are not marked, there is strong evidence for enhanced droughts in the Mediterranean region, implying major socio-economic and ecological consequences.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The relationship between the Indian Ocean High (IOH) and austral autumn (March–May) precipitation variability over Tasmania during 1951–2016 is described. We present evidence that changes in autumn rainfall are influenced primarily by a strength of the Indian Ocean high pressure (IOH_P) and its longitudinal position (IOH_LN). The variation of the IOH shows a clear influence on the seasonal variability of precipitation over Tasmania. The most important factor of autumn precipitation variability is found to be IOH_P system, particularly in the regions neighboring the west coast. Positive anomalies of IOH_P are associated with less precipitation over the island. In addition, the overall eastward shift of the center of the IOH, i.e., IOH_LN, also plays a significant role in rainfall reduction. These two effects are found to be effectively statistically independent with each other. A multi-linear statistical model of autumn precipitation in Tasmania using the IOH_P and IOH_P accounts for 43% of the precipitation variability during 1951–2016. A series of well-defined atmospheric circulation composite analyses are identified during dry (wet) Tasmanian autumn mean sea level pressure, 850 hPa vector wind, and 850 hPa vertical wind velocity over the Indian Ocean. These circulations generate abnormally strong (weak) westerly winds along much of the western Tasmania which is responsible to provide more (less) moisture over Tasmania.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Prediction of the Indian summer monsoon rainfall is essential for the country because the food production depends on the rainfall during the summer monsoon season. India being an agricultural country, its economy also depends on the monsoon rainfall. Various institutes across the world give global prediction. An experimental prediction based on the numerical simulations produced by North American Multi-Model Ensemble (NMME) has been attempted for the initial conditions of May to understand the strength, variability, predictability, and associated changes in the Indian summer monsoon. This study is particularly aimed to find out the efficacy of NMME models over the Indian region during the southwest monsoon season. The analysis shows that all the nine models underestimate the climatology. A mismatch exists between the spatial patterns of model climatology as compared to the observed climatology. It is found that models underestimate the inter-annual variability of the precipitation as compared to the observed ones. This can be attributed to the overestimation of sea surface temperature-Indian summer monsoon rainfall (SST-ISMR) response. This might lead to poor performance of the model in terms of precipitation prediction. The spatial correlation shows varying correlation pattern as compared to the observed one. However, almost all models have positive correlation over the peninsular India. The basic idea of MME approach is to generate a single prediction from the predictions from different models. The MME approach shows positive correlation over the peninsular and central India.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The target of the current paper was to examine the performance of three Markovian and seasonal based artificial neural network (ANN) models for one-step ahead and three-step ahead prediction of monthly precipitation which is the most important parameter of any hydrological study. The models proposed here are feed forward neural network (FFNN, as a classic ANN-based models), Wavelet-ANN (WANN, as a hybrid model), and Emotional-ANN (EANN, as a modern generation of ANN-based models). The models were used to precipitation prediction of seven stations located in the Northern Cyprus. Two scenarios were examined each having specific inputs set. The scenario 1 was developed for predicting each station’s precipitation through its own data at previous time steps, while in scenario 2, the central station’s data were also imposed into the models in addition to each station’s data, as exogenous inputs. The obtained results showed the better performance of the EANN model in comparison with other models (FFNN and WANN) especially in three-step ahead prediction. The superiorities of the EANN model over other models are due to its ability in dealing with error magnification in multi-step ahead prediction. Also, the results indicated that the performance of the scenario 2 was better than scenario 1, showing improvement of modeling efficiency up to 17% and 26% in calibration and verification steps, respectively.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉In this study, downward (LW) and outgoing longwave radiation measurements, air temperature (T), aerosol optical depth (AOD) at seven wavelengths, Ångstrom exponent (α), and precipitable water vapor (PWV) data from Riyadh, an arid site in central Saudi Arabia, for the period between 2014 and 2016 were used to study their variations and to investigate the influence of the meteorological variables on the measured downward LW radiation under clear sky conditions. Downward LW radiation and the air temperature have the same distributions. While the outgoing LW radiation and the Ångstrom exponent presented more than one peak in their distributions, the PWV was normally distributed with a mean value of about 11.9 ± 3.9 mm. Distribution of the AOD for all wavelengths has a log-normal shape. Theoretical simulations using SBDART code were conducted and showed that the downward LW radiative forcing increases by about 8% for every 1 mm increases in the water vapor, while it increases by about 4% in every 1 increase in the AOD at 500 nm value. Two variable models containing the PWV and T were developed to model the downward LW radiation. This model has a correlation coefficient of 0.91, MBE = − 0.004 W m〈sup〉−2〈/sup〉, RMSE = 20.4 W m〈sup〉−2〈/sup〉, and MPE = − 0.30%. Likewise, correlation analyses between the downward LW radiation and three independent variables (T, PWV, and AOD at 500 nm) were carried out. This model slightly improves the prediction of the LW radiation and has correlation coefficient of 0.93, MBE = 0.1 W m〈sup〉−2〈/sup〉, RMSE = 17.3 W m〈sup〉−2〈/sup〉, and MPE = − 0.20%.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This study investigates the daily, monthly, and seasonal characteristics of precipitation and associated forcing processes for their spatial-temporal variations over two geographically distinct river basins (Kaligandaki and Koshi) in Nepal, located on the southern slopes of the central Himalayas. A 34-year (1981–2015) daily precipitation data set of 49 stations between the elevation ranges of 143 to 3870 m asl was used for this investigation. The Gini coefficient, and index, degree and period of precipitation concentrations, and rainfall gradients were derived to examine precipitation distribution characteristics and causes for their variation. A rapid decrease of rainfall with elevation was observed in the Kaligandaki basin throughout the period of record, while this pattern was reversed in the Koshi basin, due to the rain shadow and orographic effects, which is pronounced in the monsoon season. Mountain effects are likely responsible for the distinct differences in daily and monthly precipitation distribution, with irregular patterns observed in the northern region of Kaligandaki basin and southern region in Koshi basin, with uniform patterns in the southern region of Kaligandaki basin and the northern region of Koshi basin. There is also a distinct variation in seasonality, which is higher (concentrated over several months) in the central northern region in Kaligandaki basin and less concentrated in the northern region of Koshi basin. But monthly precipitation characteristics in the western portion of Koshi and the southern region of Kaligandaki basins show high variability and short concentration duration. However, the months when precipitation is concentrated differed between the basins: from late July to August in Kaligandaki and June to September in Koshi basin. This is because the monsoon arrives later in the west than in the eastern part of the country. Variation in number of rainy days is higher in Koshi, but rainfall amount is greatest in Kaligandaki basin due to its proximity to the ocean and the intense effect of the monsoon. In addition to its value for rainfall-induced disaster mitigation strategies and management planning, this study will be useful for hydrological modeling in these areas in the future.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Northeast Brazil’s semiarid region is characterized by water deficit. However, little is known about the trends in joint water loss by evaporation and transpiration (evapotranspiration) in the region. Thus, this study aimed to analyze indications on the linear trend of reference evapotranspiration (ET〈sub〉0〈/sub〉) in the monthly, annual, and interdecadal scales in irrigated agricultural areas in the microregions of Juazeiro, Bahia State (9° 24’ S; 40° 26’ W and 375.5 m), and Petrolina, Pernambuco State (09° 09’ S, 40° 22’ W, and 376 m). For this purpose, we used a set of daily meteorological data, collected from January 1, 1976, to December 31, 2014. Daily ET〈sub〉0〈/sub〉 values were estimated by the FAO’s Penman-Monteith equation (ET〈sub〉0〈/sub〉PM). The main statistical tests used were the nonparametric Mann-Kendall test, the Sen test to detect the magnitude of the trend, and the Pettitt test to detect the start of the trend. For all tests, statistical significance was 5% and/or 1%. Analysis of variance (ANOVA) was also applied to identify significant differences between the mean interdecadal values for each season. The results showed a significant decreasing trend for ET〈sub〉0〈/sub〉 in both Juazeiro (〈em〉α〈/em〉 = 5%) and Petrolina (〈em〉α〈/em〉 = 1%), with declines of 14.5 mm/year (Juazeiro) and 7.7 mm/year (Petrolina) and that this trend started in 1996. Probably, the ET〈sub〉0〈/sub〉 decrease was associated with the increase in the irrigated agricultural areas, and the lake formed upstream from the areas by the construction of Sobradinho Dam.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The use of regional climate models (RCMs) to localise results from coarse-resolution global climate models has recently attracted more interest in agricultural impact studies. Theoretically, it has advantages over global climate models in terms of realisation of future climate projections. However, there are few studies that have used dynamical downscaling results to assess climate change impacts on Australian cropping systems. In this study, we used post-processing bias-corrected climate data from the NSW/ACT Regional Climate Modelling (NARCliM) project to drive the Agricultural Production Systems SIMulator (APSIM) for modelling and evaluating the response of three major crops (wheat, canola and lupin) to projected climate conditions under various farm management practices in south-eastern Australia. Our results showed that historical crop yields from APSIM simulations forced by RCM output tended to underestimate yields from simulations forced by observations due to large biases in the NARCliM simulations. Therefore, bias correction was used to correct the APSIM outputs before conducting future impact analysis. The bias-corrected results showed that ensemble-mean yields based on 12 RCMs were projected to increase over the study area under the A2 emission scenario. However, the magnitude of yield increase depended on the time periods, crop type and location. Multiple linear regression models showed that the changes of radiation, rainfall and temperature and elevated CO〈sub〉2〈/sub〉 concentration could explain 60–78% of crop yield changes. It is interesting to note that residue incorporation and nitrogen application had a large effect on percentage yield increases for wheat due to future climate change, but had limited effects on the response of lupin and canola yields. Our study suggests that sufficient bias-correction method is needed when using NARCliM outputs in crop models. Although uncertainties (e.g. the choice of emission scenarios and RCMs) still exist in our study, the results are of central importance in the development of high-yield adaptive strategies for local farmers and policy makers in south-eastern Australia.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉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 mm〈sup〉1/2〈/sup〉). 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 〈em〉standard deviation〈/em〉, 〈em〉mean〈/em〉, and 〈em〉time trend〈/em〉 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 〈em〉standard deviation〈/em〉 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 〈em〉mean〈/em〉 and 〈em〉time trend〈/em〉 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.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉During the simulation of the urban heat island phenomenon, the accurate representation of urban geometry in numerical models is crucial. In this study, the local climate zone (LCZ) system was incorporated into the Weather Research and Forecasting (WRF) model in order to facilitate proper land surface information for the model integrations. After the calculation of necessary input canopy parameters, based on local static datasets, simulations were performed to test the model’s performance in predicting near-surface air temperature (〈em〉T〈/em〉〈sub〉a〈/sub〉) and urban heat island intensity (Δ〈em〉T〈/em〉) under a heatwave period in July 2017. The modelled values were evaluated against the observations of the local urban climate monitoring system. The results suggest that WRF with a single-layer canopy scheme and the LCZ-based static database was able to capture the spatiotemporal variation of the aforementioned variables reasonably well. The daytime 〈em〉T〈/em〉〈sub〉a〈/sub〉 was generally overestimated in each LCZ. At nights, slight overestimations (underestimations) occurred in LCZ 6, LCZ 9, and LCZ D (LCZ 2 and LCZ 5). The mean Δ〈em〉T〈/em〉 was underestimated in the night-time; however, the daytime Δ〈em〉T〈/em〉 was estimated accurately. The mean maxima (minima) of Δ〈em〉T〈/em〉 were underestimated (overestimated) with around 1.5–2 °C, particularly in LCZ 2 and LCZ 5. Some components of the surface energy budget were also computed to shed light on the inter-LCZ differences of 〈em〉T〈/em〉〈sub〉a〈/sub〉. It was concluded that the nocturnal ground heat flux was about five times higher in urban LCZs than in the rural LCZ D, which resulted in a reduced cooling potential over the urbanized areas.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉We analyzed the anomalous characteristics of autumn rain in western China (ARWC) (26.2–36° N, 101.9–111.8° E) in 2017 and the related large-scale atmospheric circulations based on new monitoring indicators published by the China Meteorological Administration in 2015 and the National Center for Environmental Prediction reanalysis data. We found that the characteristics of autumn rain in the southern (33–36° N, 101.9–111.8° E) and northern (26.2–33° N, 101.9–111.8° E) regions of the monitoring area were different. The onset date of autumn rain in the north (south) of the monitoring area in 2017 was 14 (16) days earlier than in normal years, which is in the 4th (4th) place since 1961. The end of the autumn rain was 5 days later (earlier) than in normal years. The autumn rain period was 54 (63) days long, 19 (11) days longer than in normal years. The autumn rainfall in the north (south) of the monitoring area was 234.1 (322.2) mm, 72.6% (74.4%) higher than that in normal years. The amount of autumn rain in the southern region is in the second place since 1965. The cause of this significantly early onset of the ARWC in 2017 was the advanced transition of the East Asian atmospheric circulation system from a summer pattern to a winter pattern. The westerly jet in the upper troposphere appeared about 10 days earlier than in normal years. The meridional circulation of a west-high and east-low pattern was observed in the mid- and lower troposphere in the mid- and high latitudes of Eurasia. This circulation configuration resulted in an early onset of the ARWC. As a result of the influence of the persistent warming of the Indian Ocean basin-wide mode from spring to summer, the west Pacific subtropical high was extremely strong (the second strongest since 1981) and its westernmost point was greatly extended to the west (the second greatest extension since 1981) during the autumn rain period. The intensity of the moisture flux from the southwest of the western Pacific subtropical high was stronger than usual and the low trough of Lake Baikal was also strong. Cold air from the north and the abundant warm, humid water vapor from the south converged in western China, resulting in more precipitation during the autumn rain period.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Hydro-meteorological variations are fundamental environmental factors affecting site-level ecosystem carbon balance variations; however, how hydro-meteorological variations influence continuous CO〈sub〉2〈/sub〉 exchange over alpine meadows during the growing season in permafrost regions on the Tibetan Plateau is poorly understood. Net ecosystem CO〈sub〉2〈/sub〉 exchange (NEE) measured using the eddy covariance method was partitioned into its components of ecosystem respiration (ER) and gross ecosystem CO〈sub〉2〈/sub〉 exchange (GPP). ER and GPP had a significant relationship with air temperature, vapor pressure deficit, and net radiation, during the growing season of an alpine meadow. There was a positive correlation between GPP and active layer soil temperatures at 5–40 cm except soil temperature at 80 cm, located in the vicinity of the bottom of the active layer. The microbial activity of underground rhizospheric microorganism, mainly distributed in in shallow soil layers at 0–20 cm in alpine permafrost regions on the Tibetan Plateau, can significantly promote plant photosynthesis. Thus, the correlation between GPP and soil temperatures at 5–20 cm was stronger than that between GPP and air temperature. The correlation between ER (GPP) and active layer soil moisture at 5–80 cm was complicated, especially for soils at 5–20 cm. With the increase of active layer soil moisture, ER (GPP) increased first and then decreased. This was related to the thawing process of the active layer, increase of rainfalls, the frozen layer acting as a weak permeable layer, and the replenishment of super-permafrost water. Our results suggested that long-term observations on how hydro-meteorological variations affect the continuous CO〈sub〉2〈/sub〉 exchange of alpine meadows with different active layer thicknesses should be carried out in alpine permafrost regions.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This study investigates the linkage between agricultural drought and meteorological drought, using normalization different vegetation index (NDVI) and land surface temperature (LST) for the whole of Iran in four periods of 16 days of late March, April, May, and June between 2000 and 2017. LST and NDVI were obtained from the production of MODIS MOD13A2 and MOD11A2. In the next step, the basic time synchronization of LST and NDVI was performed by the mean of two 8-day LST images and their conversion to 16-day LST for each year. The vegetation health index (VHI) was calculated from the combination of temperature condition index (TCI) and vegetation condition index (VCI). Then, Iran’s map of standardized precipitation-evapotranspiration index (SPEI) was calculated on a 12-month timescale for 68 meteorological stations with the inverse distance weighted (IDW) method. By using ArcGIS 10.5, the Pearson correlation coefficient and the slope of linear regression were calculated between 12-month SPEI and VHI for all four periods in different climates: hyper dry, dry, semi-dry, semi-humid, and humid. Results showed that the correlation increased when the temperature increased. This increase occurred in dry and hyper-dry climates. As the temperature rose, the slope of the linear regression for 12-month standardized precipitation-evapotranspiration index (SPEI) on vegetation health index (VHI) increased. The highest and lowest average effects of the slope were observed in dry and climatic class, respectively. With increasing temperature, the need for water in plants will increase. Hence, there is a direct relation or a positive correlation between temperature and correlation strength and slope effect. Therefore, plants more seriously will face drought stress during the drought period, and this drought stress has a positive correlation with drought intensity. From this study, it was concluded that the highest correlation and the highest slope effect between the 12-month SPEI and VHI happened in the dry climatic class in June.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Monsoon has been puzzling that the monsoon occurrence is sudden with the rapid onset observed while the evolution of the differentiating heating is gradual with time of year. Here, focusing on the Asian summer monsoon, we use catastrophe theory to show how gradual changes in the atmospheric heating source with time will cause an abrupt variation of the relevant South Asian High (SAH) to atmospheric circulations and thus lead to the monsoon onset. This argumentation illustrates that the Asian summer monsoon is essentially caused by temporal changes in the differentiating heating owing to surface inhomogeneites such as land-sea contrasts, and the monsoon onset results, in nature, from the nonlinearity of the complex atmospheric system itself. Employing the cusp catastrophe (one of the elementary catastrophes) model, it is found that a sudden change like the rapid monsoon onset is an inevitable outcome when the control parameters such as the differentiating heating exceed a threshold though the parameters evolves gradually.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Pan evaporation plays a critical role in estimating water budget and modeling crop water requirements. However, it has been measured at a very limited number of meteorological stations. Estimation of pan evaporation from measured meteorological variables offers an important alternative and drawn increasing attention in the recent years. This paper investigated the performance of support vector machine (SVM) in the estimation of monthly pan evaporation using commonly measured meteorological variables in Three Gorges Reservoir Area in China. Evaluation suggested that SVM models showed remarkable performances and significantly outperformed the empirical model. The SVM model with polynomial as kernel function outperformed that with radial basis function. In the case of unavailable measurements of pan evaporation and meteorological variables to construct the SVM model, pan evaporation can be well-estimated by SVM model developed using data at other sites. The results indicated that the SVM method would be a promising alternative over the traditional approaches for estimating pan evaporation from measured meteorological variables.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Climate change assessment is usually based upon air temperature and precipitation changes on an annual and seasonal basis, but there are more levels to their significance as presented by parameters derived from these two basic parameters. In order to define their relevance for climate changes, the principal component analysis (PCA) was performed. In this case, ten meteorological parameters and climate change indicators were defined for two meteorological stations located in geographically completely opposite parts of the country; station Osijek is in continental region of Croatia, and Dubrovnik station is located in the Mediterranean region. Analyses were done for the period 1985–2016 on an annual and seasonal basis. All defined indicators present basic climate change characteristics on annual and seasonal basis as follows: precipitation sum, mean air temperature, air temperature sum, standard deviation of daily air temperature, maximum daily air temperature, maximum daily precipitation, number of days with precipitation 〉 30 mm, number of days with no precipitation, 1-month standardized precipitation index, and aridity index. In the first step, it was applied on the set of linear regression coefficients defined for 10 climate change indicators. During the second step, PCA was applied on the computed Mann–Kendall test statistic, 〈em〉Z〈/em〉〈sub〉MK〈/sub〉.in order to determine the existence of significant temporal tendencies in the indicator values. The provided research proves PCA is a very useful tool for implementing this approach, particularly in the Mediterranean region which shows high sensitivity to many variables important for climate characterization.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉Extreme event magnitude and frequency are prerequisites for accurately designing and managing various water infrastructure systems. This paper studies precipitation extremes in the Amman Zara Basin (AZB) using daily precipitation records from 24 weather stations during a period that exceeds 50 years. Two extreme precipitation series (the annual maximum (AM) and the peak over threshold (POT)), four generalized probability distributions (generalized extreme value (GEV), generalized Pareto (GP), generalized lognormal (GLN), and generalized logistic (GLO)) and the 〈em〉L〈/em〉 moment method for distribution parameter estimation are used. A mix of increasing and decreasing trends is observed at different stations for both the AM and POT series over the study period. Since the POT series considers up to the fifth largest precipitation in some years, in contrast to the AM series, and skips the largest precipitation in the AM series in other years, the trend analysis results for the POT series differ slightly from those for the AM series. According to the goodness-of-fit (Kolmogorov-Smirnov test and 〈em〉L〈/em〉 moment diagram), the probability distributions GEV, GLN, and GLO can better fit the AM series, with no unique distribution among them consistently ranking the best for all stations, while the POT series is better fitted by the GP distribution. The calculated extreme precipitation amounts of the AM series are up to 18% greater than those of the POT series for the same return period. Additionally, the AM series can describe extreme precipitation events better than the POT series based on relative error calculations. The 50- and 100-year extreme precipitation events occurred more frequently in recent years.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The change of water resources resulting from climate warming in the Yangtze River Source Region (YRSR) has aroused wide concern in recent years. This research used the soil and water assessment tool (SWAT) to estimate the blue water (BW), green water flow (GWF), and green water storage (GWS). Then, the spatial and temporal change of BW, GWF, and GWS over the recent decades and next several decades was investigated. Results show that (1) the BW and GWF have increased by 2.3 mm/10a and 34.2 mm/10a from 1960 to 2012, while the GWG has decreased slightly by 0.19 mm/10a during the same period. (2) The spatial patterns of BW, GWF, and GWS have been changed obviously from 1980s to 2000s, especially for BW and GWF. The BW and GWF in the west and north of the YRSR where the water resource is relatively less are more sensitive to climate change; (3) the annual average temperature and precipitation in the YRSR are projected to increase by 2.2 °C and 9.8% during 2021 to 2050 compared to that during the 1961 to 1990. There is higher uncertainty in BW and GWS projection and the model-averaged BW and GWS would change little. The projected percent changes of GWF and GWS would be 15% and − 11.1% with less uncertainty. The obvious change of BW, GWF, and GWS in the future would occur in the west and north of the YRSR, which is similar with the spatial change in the historical period.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉This study utilized 34-years hourly data of wind speed and direction, precipitation, and air temperature over land to investigate the climatology of land and sea breezes (LSB) at seven coastline stations along the Guinean Coast of West Africa. Daily sea surface temperature from the adjacent Atlantic Ocean and Tropical Rainfall Measurement Mission daily precipitation data was used. Ten years reanalysis data of ERA-Interim, Climate Forecast System Reanalysis version 2 (CFSRv2), and National Centers for Environmental Prediction (NCEP2) were also used for comparison with the observations. Adopted criteria were based on both surface and upper air information to identify LSB days. The results show that LSB is a seasonal phenomenon but most frequent in winter. All reanalyses underestimated the monthly occurrence and seasonal cycle of LSB, although CFSRv2 is better in reproducing the observations than ERA-Interim and NCEP2. The wind roses revealed the existence of night/early morning offshore winds. Except in summer when the southwesterly monsoon winds prevail all day, onshore winds are observed from about 1100 local solar time (LST), with a mean cessation time at 0100 LST, giving a mean duration of about 14 h. However, in Abidjan, they occurred at all hours. The vertical plots confirmed the wind direction reversals for LSB days and give an indication of its depth and speed in different months. It has been found that LSB height is influenced by the West African monsoon as intertropical discontinuity advances northwards from January. This was also seen in LSB increased speed from December to August.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The Mediterranean Sea is subject to significant and variable climatic changes. In this work, an analysis of a 39-year-long wave time series was performed in order to identify ongoing trends of two synthetic parameters, significant wave height and energy period, along the coasts of the Calabria region (southern Italy). First, wave data were acquired from the global atmospheric reanalysis data set European Centre for Medium-Range Weather Forecasts Re-Analysis Interim (ERA-Interim) and were processed in order to check their quality. Then, the data were subject to a geographical transposition method in order to transfer the wave parameters at a representative water depth of 100 m. Finally, trend analyses have been performed using the nonparametric Mann–Kendall (MK) test and a graphical technique, the Innovative Trend Analysis (ITA), which allows to identify trends in the several values of a series. At annual and seasonal scale, the results deduced from MK test mainly evidenced a slight or null increase of the significant wave height and a relevant increase of the energy period, influencing the magnitude of the wave power by the occurrence of longer waves. Moreover, the ITA method highlights a general increase in the highest values of the involved wave parameters. For the studied area, these aspects have a significant impact on the retreating of the beaches and on the future field installations of wave energy converters (WECs) for electricity purposes.〈/p〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉The Norwegian University of Life Sciences (NMBU) started global radiation (〈em〉G〈/em〉) measurements in 1949. PAR measurements started in 1978 as part of a Nordic project. The project terminated in 1981, but the measurements continued at NMBU. We have used the data from the Nordic project’s seven stations and the long time series from NMBU to investigate the limitations of using the relative flux density method for the estimation of PAR from 〈em〉G〈/em〉. The yearly mean value of the relative flux density PAR/〈em〉G〈/em〉 at NMBU is 0.48 with a standard deviation of 0.02. The monthly mean values of PAR/〈em〉G〈/em〉 show a seasonal variation, with its maximum (0.50) in late summer and minimum in the winter months (0.45). The seasonal variation corresponds to 15% difference in relative cloud cover. Data from the original Nordic project gave the opportunity to investigate the usability of the relative flux density method in the Nordic area. The flux ratio for July was generally larger than the ratio for September or October, even though the differences are small and not statistically significant. In trying to explain this, one obvious candidate is the cloud cover. In the lack of cloud data, time records of global radiation were used to select fair weather and overcast days. For all stations, fair days show higher flux ratios and overcast days show lower flux ratios. Neglect of cloud cover may lead to an error in the calculation of PAR from the simple formula PAR = constant 〈em〉G〈/em〉 of the order of 10%.〈/p〉