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The STatistical Analogue Resampling Scheme (STARS) statistical approach was recently used to project changes of climate variables in Germany corresponding to a supposed degree of warming. We show by theoretical and empirical analysis that STARS simply transforms interannual gradients between warmer and cooler seasons into climate trends. According to STARS projections, summers in Germany will inevitably become dryer and winters wetter under global warming. Due to the dominance of negative interannual correlations between precipitation and temperature during the year, STARS has a tendency to generate a net annual decrease in precipitation under mean German conditions. Furthermore, according to STARS, the annual level of global radiation would increase in Germany. STARS can be still used, e.g., for generating scenarios in vulnerability and uncertainty studies. However, it is not suitable as a climate downscaling tool to access risks following from changing climate for a finer than general circulation model (GCM) spatial scale. [ABSTRACT FROM AUTHOR]

Among papers stating a position on anthropogenic global warming (AGW), 97 % endorse AGW. What is happening with the 2 % of papers that reject AGW? We examine a selection of papers rejecting AGW. An analytical tool has been developed to replicate and test the results and methods used in these studies; our replication reveals a number of methodological flaws, and a pattern of common mistakes emerges that is not visible when looking at single isolated cases. Thus, real-life scientific disputes in some cases can be resolved, and we can learn from mistakes. A common denominator seems to be missing contextual information or ignoring information that does not fit the conclusions, be it other relevant work or related geophysical data. In many cases, shortcomings are due to insufficient model evaluation, leading to results that are not universally valid but rather are an artifact of a particular experimental setup. Other typical weaknesses include false dichotomies, inappropriate statistical methods, or basing conclusions on misconceived or incomplete physics. We also argue that science is never settled and that both mainstream and contrarian papers must be subject to sustained scrutiny. The merit of replication is highlighted and we discuss how the quality of the scientific literature may benefit from replication. [ABSTRACT FROM AUTHOR]

Melting layer usually exists in precipitation where ice particles gradually melt into liquid particles. Utilizing the detection from the Dual-frequency Precipitation Radar onboard Global Precipitation Measurement Mission Core Observatory during 2018-2022, this paper investigates the quasi-global climatological features of Melting layer over 65°S ~65°N and analyzes the relationships with elevation in the golden case involving the section along 32.5°N across China. The distribution of Melting layer is latitude-dependent: Melting layer is higher in 30°S~30°N and decrease toward the mid-and high-latitude, which are generally lower in 30°S~65°S throughout the year. The height and thickness of Melting layer change more dramatically in mid-and high latitudes than in low latitudes for diurnal variations. The higher elevation terrain has the lower Melting layer top height than other terrains, yet the geometric thickness of Melting layer is also thinner. A golden case is selected to represent the analysis of Melting layer play role in rainfall. When the Melting layer grow thinner with steeply rising elevation due to the thermal change and orographic uplift, there is weak rain with a greater number of smaller raindrops in the golden case. However, Melting layer can get widen and raindrops can get sufficient growth as the elevation declines. [ABSTRACT FROM AUTHOR]

Rainfall in the Moist Tropical Caribbean Region (MTCR) in Costa Rica occurs practically throughout the year, with the quarters June–August (JJA) and December–February (DJF) concentrating over 70% of annual rainfall. Contrarily, in March–April and September–October, it rains below 100 mm per month. This seasonal rainfall behavior makes the region ideal for producing bananas (Musa spp.) and pineapple (Ananas comosus) for export (10% and 8% of total exports in 2021, respectively). A national-scale study determined that agriculture in the MTCR is one of the most vulnerable sectors to climate changes. However, the climate in this region has been poorly studied so far. This research analyzed the spatial and temporal variability of annual, monthly, and seasonal (DJF, JJA, SO) rainfall in the MTCR and how they change in the study period based on quality-checked series of daily rainfall from 28 weather stations in two periods: 1985–2009 and 1997–2019. The results show that rainfall regimes in the region are variable in space and throughout the year, with peaks occurring close to the mountain range and minimum values close to the coast. Trends were statistically significant in the period 1985–2009 with a predominance of significant positive trends in DJF and significant negative trends in SO. No significant trends (positive or negative) were observed in the period 1997–2019. JJA rainfall has uneven regional distribution and presents a positive and significant trend in the mountain region. This paper contributes to filling the knowledge gap in rainfall seasonality, variability, and trends in a region where banana and pineapple commercial plantations are fundamental to the country's economy thus providing information to decision-making in the agri-food sector to reduce the negative impacts of changing rainfall regimes. [ABSTRACT FROM AUTHOR]

This paper introduces three quantitative indicators to conduct research for characterizing Northeast China cold vortex persistence activity: cold vortex persistence, generalized "cold vortex," and cold vortex precipitation. As discussed in the first part of paper, a hindcast is performed by multiple regressions using Northeast China precipitation from 2012 to 2014 combination with the previous winter 144 air-sea system factors. The results show that the mentioned three cold vortex index series can reflect the spatial and temporal distributions of observational precipitation in 2012–2014 and obtain results. The cold vortex factors are then added to the Forecast System on Dynamical and Analogy Skills (FODAS) to carry out dynamic statistical hindcast of precipitation in Northeast China from 2003 to 2012. Based on the characteristics and significance of each index, precipitation hindcast is carried out for Northeast China in May, June, July, August, May–June, and July–August. It turns out that the Northeast Cold Vortex Index Series, as defined in this paper, can make positive corrections to the FODAS forecast system, and most of the index correction results are higher than the system's own correction value. This study provides quantitative index products and supplies a solid technical foundation and support for monthly precipitation forecast in Northeast China. [ABSTRACT FROM AUTHOR]

The applicability of the four-parameter (4P) kappa distribution to fit the annual maximum (AM) and the peak over threshold (POT) extreme precipitation series in the Amman Zarqa Basin (AZB), Jordan, is assessed. This paper presents a practical, easy-to-implement, yet accurate method in light of the method of L-moments for parameter estimation of 4P kappa. Based on the results from the data analysis of daily precipitation records during a period that exceeded 50 years at 23 weather stations, the 4P kappa distribution can fit the precipitation extremes for both the AM and POT series for each weather station well. The 4P kappa distribution can be employed in place of its three-parameter distributions typically selected for simulating precipitation extremes (i.e., generalized extreme value (GEV), generalized logistic (GLO) distribution for AM series, and generalized Pareto (GP) distribution for POT series). The bootstrap resampling for performance evaluation beyond the dataset length shows that the 4P kappa distribution outperforms its three special cases (GLO, GEV, and GP distributions) in the context of modeling the AM and POT series for all sampling sizes. Additionally, in representing the distribution's upper right tail quantile estimations, the 4P kappa distribution performed better and, in some circumstances, was comparable to its three special cases (GLO, GEV, and GP distributions). The outcomes of this study and the method employed should lay the groundwork for wider implementation of the 4P kappa distribution in hydrological and climate science applications, as well as in other disciplines. [ABSTRACT FROM AUTHOR]

This paper reports analysis of the ability of 24 coupled global climate models that were used in the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change to simulate the current monthly seasonal cycle of sea level pressure, surface air temperature and precipitation over the Iberian Peninsula in the last two decades of the twentieth century. The period investigated runs from 1979 to 1998. In order to assess the performance of the models, averaged seasonal cycles and probability density functions (PDFs) calculated from model simulations are compared with the corresponding seasonal cycles, whilst PDFs are also obtained using the data from the ERA40 reanalysis and the Global Precipitation Climatology Project. We found that simulated PDFs generally provided a better fit to actual PDFs than seasonal cycles do. This conclusion indicates that when evaluating model performance, the climate variability as measured by means of PDFs is not the only climatic element that should be tested. Regarding the comparison based on the seasonal cycle, results also show that the root mean square skill score is more useful than the r skill score. To rank the AR4 models, sea level pressure, surface air temperature and precipitation variables were selected and a group of five AR4 models were identified as the models which best reproduce current climate in the area: MIROC3.2-HIRES, MPI-ECHAM5, GFDL-CM2.1, BCCR-BCM2.0 and UKMO-HADGEM1. The rank obtained should not be understood in a hierarchical manner because there is a certain degree of internal variability in the model ensembles. Finally, it should be noted that these results are in good agreement with other classifications found in the scientific literature. [ABSTRACT FROM AUTHOR]

The present paper aims to compute climatological zones apt for the cultivation of pitaya based on trends in the occurrence of climate change events from the IPCC (Intergovernmental Panels on Climate Change) in Brazil. We used temperature and precipitation data from 4942 cities collected on the NASA/POWER platform (National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources) from 1990 to 2020 to elaborate on the current scenario. The climate change scenarios were obtained using the CHELSA platform (Climatologies at high resolution for the earth's land surface areas) and corresponded to the periods 2041–2060 and 2061–2080 associated with four IPCC climate change scenarios. The spatialization of the data occurred according to the bioclimatic classes designed to meet the thermal and water needs of the crop. In the current scenario, class B5 has a predominance of 37.07% of the country, characterizing the Midwest, Southeast, and Northeast regions, as well as the state of Paraná, as suitable for the cultivation of pitaya. Projections of temperature increase and reduction of accumulated rainfall were found throughout Brazil, but with greater impact in the North and Northeast regions, which had the greatest reduction of areas suitable for the cultivation of pitaya with a greater predominance of classes B8 and B9. In the South and Southeast regions, class B5 makes up a large part of the areas that remained suitable for the production of this fruit until 2080. The results suggest that climate change does not benefit the cultivation of pitaya in some regions of Brazil because the dimensions of the areas suitable for economic production are restricted. [ABSTRACT FROM AUTHOR]

The Ångström–Prescott equation defines generically the relationship between solar energy available at ground level and sunshine duration. From the very beginning in the history of the solar energy converters, the equation was extensively used to estimate the amount of collectable solar energy. In the present paper, we propose a different way of looking to the Ångström–Prescott equation, namely, to use it for the estimation of cloud and clear sky atmospheric transmittances. The key tools are the mathematical expressions which give the clear sky and cloud transmittances in terms of the Ångström–Prescott empirical coefficients. The theory is verified by comparing the mean all-sky atmospheric transmittance (innovatively inferred from the cloud and clear sky atmospheric transmittances) with the measured clearness index. Reasonably good concordance between the two quantities has been found. It was shown that the best performance is obtained with the linear Ångström–Prescott equation. Higher-order polynomial equations tend to overfit the measurements. Examples are also shown about how to use the linear Ångström–Prescott equation to obtain information about the time series stationarity and the seasonal sensitivity of the cloud and clear-sky transmittances. The present point of view gives the perspective to estimate past cloud and clear-sky climatology in places where the Ångström–Prescott equation has been fitted to the sunshine data. [ABSTRACT FROM AUTHOR]

This paper evaluates the simulation performance of the 37 coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) with respect to the East Asian summer monsoon (EASM) and the Indo-Pacific warm pool and North Pacific Ocean dipole (IPOD) and also the interrelationships between them. The results show that the majority of the models are unable to accurately simulate the interannual variability and long-term trends of the EASM, and their simulations of the temporal and spatial variations of the IPOD are also limited. Further analysis showed that the correlation coefficients between the simulated and observed EASM index (EASMI) is proportional to those between the simulated and observed IPOD index (IPODI); that is, if the models have skills to simulate one of them then they will likely generate good simulations of another. Based on the above relationship, this paper proposes a conditional multi-model ensemble method (CMME) that eliminates those models without capability to simulate the IPOD and EASM when calculating the multi-model ensemble (MME). The analysis shows that, compared with the MME, this CMME method can significantly improve the simulations of the spatial and temporal variations of both the IPOD and EASM as well as their interrelationship, suggesting the potential for the CMME approach to be used in place of the MME method. [ABSTRACT FROM AUTHOR]

This paper is a contribution towards the link between urban microclimate and building energy modelling using the Town Energy Balance model TEB as a new component embedded into the non-stationary building energy model TRNSYS. A number of comparative features between TEB and TRNSYS motivated this work, which includes commensurate time processing speed, similarity in describing the building facets, comparable inputs and simulation time scope, and the versatile modular architecture of TRNSYS. The paper describes the parameter tabs, inputs and outputs of TEB-Type 201, which offers (i) a user-friendly graphical interface, (ii) short time for data pre-processing with consistency check of inputs, (iii) versatility in selecting and storing outputs in small-sized files and (iv) easy installation. Besides illustrating the capabilities and practicality of this new component, an extensive sensitivity analysis highlights in a hierarchical form the main decisive urban and building parameters responsible in the formation of urban canopy heat or cool islands. The anthropogenic heat, the canyon geometry (aspect ratio, roof plan density) as well as the thermo-physical and radiative properties of the building envelope (thermal insulation, thermal inertia, albedo, emissivity) considered individually or in combination with each other appear to have clear effects on the formation of a microclimate in-canyon on the one hand, and in the magnitude and frequency of canopy heat or cool island episodes at daily and monthly basis on the other hand. The warming of canyon air is heavily influenced by the combination of high urban density (deep canyons and high plan density), high level of anthropogenic heat and weak thermal insulation. Low emissivity, no or low anthropogenic heat, better thermal insulation and low thermal mass favour the cooling of the canyon. These findings reveal the decisiveness of urban and building design choices on the outdoor thermal environment and hence on the energy demand for heating and cooling indoors. As outlook, the paper also notes the necessity of a synchronised coupling of urban canopy and building energy models. [ABSTRACT FROM AUTHOR]

As global warming intensifies, efforts to understand the changes in extreme climate events have increased in recent years. A combined analysis of the changes in extreme temperature and precipitation events is presented in this paper. Using observational data from 1961 to 2015, a set of hotter and wetter (HW) events is defined, and we examine the changes in these events across China. The results show that more HW events occur in Central and Eastern China than in other subregions, especially in South China (SC). The rate of increase in HW events is 2.7 and 1.9 per decade in SC and East China (EC), respectively. In China, most HW events occurred in the last 20 years of the study period, indicating that China entered a period of high-frequency HW events. Indeed, the range in anomalies in the torrential rain days is greater than that of the high-temperature days in Northwest China (NWC), Central China (CC), and EC after the mid- to late 1990s. The opposite pattern is found in Northeast China (NEC), Southwest China-region 1 (SWC1), Southwest China-region 2 (SWC2), and SC. Finally, the increase in HW events in most regions of China is closely associated with warming. [ABSTRACT FROM AUTHOR]

This paper investigates patterns of daily evolutions of CO in the lower atmosphere at a rural site over 2 years. The first part is devoted to observation analysis using a clustering procedure. However, direct application of the average-linkage method yielded undesirable results. In order to improve this procedure, data were previously processed using three smoothing procedures: the kernel smoothing method, the elliptical procedure, and the second-order cylindrical model. These procedures successfully revealed that clusters were based on daily concentration and range. However, the unequal distribution of frequencies in the clusters proved to be a noticeable disadvantage. Four alternative and simpler schemes for grouping observations were proposed in the second part of this paper. The first, comprising groups following fixed values of daily range and mean concentration, provided a sharp contrast between spring, with a marked daily cycle linked to the biological peak, and summer with a smooth daily cycle and low concentration when the biological minimum was reached. The second scheme is based on isopleth analysis and considers observation groups of similar frequencies following an increasing order of mean concentration and daily range. As a result, seasonal evolution was less marked. Straight lines were the borders for groups in the third scheme, which was similar and simpler than the second. The final scheme divided observations by means of equations of daily range as a quadratic function of daily concentration. The groups formed may be linked to seasons, with the group prevailing in summer presenting a noticeable daily range. [ABSTRACT FROM AUTHOR]

With the aim to achieve quantitative monitoring of sand-dust storms in real time, wind-profiling radar is applied to monitor and study the process of four sand-dust storms in the Tazhong area of the Taklimakan Desert. Through evaluation and analysis of the spatial-temporal distribution of reflectivity factor, it is found that reflectivity factor ranges from 2 to 18 dBz under sand-dust storm weather. Using echo power spectrum of radar vertical beams, sand-dust particle spectrum and sand-dust mass concentration at the altitude of 600 ∼ 1500 m are retrieved. This study shows that sand-dust mass concentration reaches 700 μg/m3 under blowing sand weather, 2000 μg/m3 under sand-dust storm weather, and 400 μg/m3 under floating dust weather. The following equations are established to represent the relationship between the reflectivity factor and sand-dust mass concentration: Z = 20713.5 M0.995 under floating dust weather, Z = 22988.3 M1.006 under blowing sand weather, and Z = 24584.2 M1.013 under sand-dust storm weather. The retrieval results from this paper are almost consistent with previous monitoring results achieved by former researchers; thus, it is implied that wind-profiling radar can be used as a new reference device to quantitatively monitor sand-dust storms. [ABSTRACT FROM AUTHOR]

Low-level temperature inversions play crucial roles in inhibiting vertical exchange of energy and mass, and may lead to air pollutants accumulation. The climatological study of them provides a fundamental overview of the static stability of atmosphere and is indispensable for air pollution controls. Based on high-vertical-resolution radiosondes at 00 and 12 UTC during 2011–2018, this work comprehensively examined the spatial and temporal variability of three characteristics (occurrences, strengths, and depths) of low-level inversions in China. Results revealed that inversions are prevalent (more than 80% of the observation records) over Eastern China. The northern part of Eastern China is dominated by the thick strong surface-based inversions with median thickness and strength of 200 m and 3 K, while the southern part by thin weak elevated ones of 80 m and 0.8 K. Tibetan Plateau experiences rare inversions (less than 50% of the time) which tend to be strong thick surface-based inversions with median depth and intensity of 170 m and 4 K. Inversion depths and strengths are positively correlated. The three properties of inversions present remarkable seasonal variations. Generally, inversions are strongest, thickest, and most frequent in winter and least so in summer, with median intensity and depth ranging from 0.3 K and 80 m to 5 K and 220 m. This paper also analyzed the monthly variations of inversions of four representative stations (Harbin, Urumqi, Beijing, and Chongqing) in details. These four cities are known for frequent severe air pollution events. The quantitative investigations of inversions may play a significant role in atmospheric environmental management. [ABSTRACT FROM AUTHOR]

The snow cover extent in mid-high latitude areas of the Northern Hemisphere has significantly declined corresponding to the global warming, especially since the 1970s. Snow-climate feedbacks play a critical role in regulating the global radiation balance and influencing surface heat flux exchange. However, the degree to which snow cover changes affect the radiation budget and energy balance on a regional scale and the difference between snow-climate and land use/cover change (LUCC)-climate feedbacks have been rarely studied. In this paper, we selected Heilongjiang Basin, where the snow cover has changed obviously, as our study area and used the WRF model to simulate the influences of snow cover changes on the surface radiation budget and heat balance. In the scenario simulation, the localized surface parameter data improved the accuracy by 10 % compared with the control group. The spatial and temporal analysis of the surface variables showed that the net surface radiation, sensible heat flux, Bowen ratio, temperature and percentage of snow cover were negatively correlated and that the ground heat flux and latent heat flux were positively correlated with the percentage of snow cover. The spatial analysis also showed that a significant relationship existed between the surface variables and land cover types, which was not obviously as that for snow cover changes. Finally, six typical study areas were selected to quantitatively analyse the influence of land cover types beneath the snow cover on heat absorption and transfer, which showed that when the land was snow covered, the conversion of forest to farmland can dramatically influence the net radiation and other surface variables, whereas the snow-free land showed significantly reduced influence. Furthermore, compared with typical land cover changes, e.g., the conversion of forest into farmland, the influence of snow cover changes on net radiation and sensible heat flux were 60 % higher than that of land cover changes, indicating the importance of snow cover changes in the surface-atmospheric feedback system. [ABSTRACT FROM AUTHOR]

Various meteorological studies require probabilistic modeling such as the flood, drought, and wet and dry spell occurrences. The most commonly employed approaches are based on the binomial probability distribution function (pdf), which is valid for homogeneous temporal and spatial probabilities. However, in practice, heterogeneous probabilities are very common, and in this case, the classical binomial pdf cannot be employed. This paper presents a rigorous probabilistic methodology for heterogeneous probabilities by considerations from the random field concept. The general form of the binomial pdf is derived and it is shown that for homogeneous probabilities it yields the classical binomial pdf exactly. The necessary analytical formulations are derived with the assumption that the meteorological occurrences at a set of sites or time periods (day, month, and year) are independent from each other. The probability statements derived in this paper are helpful in predicting the regional and temporal probable potential future meteorological occurrences. The application of the proposed methodology is presented for annual precipitation occurrences at six meteorology stations around Istanbul, Turkey. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to highlight the relevance of cutoff low systems (CoLs) to large-scale heavy precipitation events within the Alpine region which often lead to catastrophic flooding. The main results of this study are (1) a detailed climatology (1971-1999) of CoLs for the European region, (2) contribution of CoLs to extreme precipitation events in the European Alpine region, (3) identification of regions within the European Alps most affected by extreme precipitation caused by CoLs, and (4) identification of regions where presence of CoLs is related to extreme precipitation in the Alpine region. The findings of this paper suggest that CoLs have a significant correlation with extreme precipitation events and strongly influence the climate of the Alpine region. The total contribution of CoLs to large-scale heavy precipitation events ranges between 20 and 95 % and is most pronounced in the northern and eastern parts of the Alps. More than 80 % of the events occur in the summer season. The area around the Alps and West of Spain (over the Atlantic Ocean) is the most affected region. The location of the center of CoLs that affect the Alpine region most occur on the northern and southern sides of the Alpine ridge. [ABSTRACT FROM AUTHOR]

A new method for calculating evaporation is proposed, using the Penman-Monteith (P-M) model with remote sensing. This paper achieved the effective estimation to daily evapotranspiration in the Ziya river catchment by using the P-M model based on MODIS remote sensing leaf area index and respectively estimated plant transpiration and soil evaporation by using coefficient of soil evaporation. This model divided catchment into seven different sub-regions which are prairie, meadow, grass, shrub, broad-leaved forest, cultivated vegetation, and coniferous forest through thoroughly considering the vegetation diversity. Furthermore, optimizing and calibrating parameters based on each sub-region and analyzing spatio-temporal variation rules of the model main parameters which are coefficient of soil evaporation f and maximum stomatal conductance g . The results indicate that f and g calibrated by model are basically consistent with measured data and have obvious spatio-temporal distribution characteristics. The monthly average evapotranspiration value of simulation is 37.96 mm/mon which is close to the measured value with 33.66 mm/mon and the relative error of simulation results in each subregion are within 11 %, which illustrates that simulated values and measured values fit well and the precision of model is high. In addition, plant transpiration and soil evaporation account for about 84.64 and 15.36 % respectively in total evapotranspiration, which means the difference between values of them is large. What is more, this model can effectively estimate the green water resources in basin and provide effective technological support for water resources estimation. [ABSTRACT FROM AUTHOR]

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

The aim of this paper is to investigate the ability of artificial neural network (ANN) models for post-processing the monthly precipitation forecasts under North American multi-model ensemble (NMME) project and proposing a new multi-model ensemble neural network (MME-NN) model. Monthly precipitation hindcasts of eight models from NMME project are considered in this study. Multi-layer perceptron neural networks are employed for post-processing the output of the models in comparison with PERSIANN-CDR climatology data. Also, utilizing a multi-criteria decision-making approach, NMME models are ranked for each month. The study is implemented over Iran and detailed discussions are provided. The results indicate that the skill of NMME models is different for each month and for each region of the country. Also, it is shown that the neural network outperforms all NMME models for all months. By using the ranking of the models, for each month, the NMME models are ordered based on their skill and a monthly rank is devoted for each model. [ABSTRACT FROM AUTHOR]

Research has shown that the climate in northwest China has turned to warm-wet in recent decades. Does this mean that the entire northwest of China has become wet in the last few decades? In the context of the climate transition in northwest China, this paper investigates whether the Bosten Lake Basin (BLB) located in southern Xinjiang has been dry or wet in the last 30 years. We used the Standardized Precipitation Evapotranspiration Index (SPEI) and Temperature Vegetation Drought Index (TVDI) to investigate these changes. The results show that although the dry-wet changes indicated by the SPEI and TVDI exhibited slight spatiotemporal differences, the entire BLB reflected a tendency to be dry. That is, the BLB has not become wet during the climate transition in northwest China, but is developing a trend toward aridification. The main conclusions of this study are as follows: (1) The SPEI indicated a drying trend in seasonal and interannual variations in plains; in mountains, except in spring and summer it has a drying trend, too. (2) The TVDI showed that although the drying trend in summer and winter was weaker than that in spring and autumn in the BLB, where some regions represented a significant tendency toward wetness owing to seasonal or annual changes, the entire study area has still become more dry. [ABSTRACT FROM AUTHOR]

In this paper, 16 cities representing four major architectural climate zones of China were selected, and the heating and cooling loads of office building during the period of 1961–2017 were simulated by using TRNSYS software. Daily extreme heating and cooling loads were determined by percentile methods, and the climate change impact on extreme loads was analyzed. The results showed that the days of extreme heating load of office building significantly decreased in almost all the selected cities, whereas there were no significant changes in the days of extreme cooling load. The daily extreme heating load was dominantly affected by the temperature (R2 = 0.43–0.79), but the daily extreme cooling load was related to multiple climate factors, including temperature, relative humidity, and solar radiation. Single temperature can only explain 3–37% of the changes of daily extreme cooling load, and the combination of multiple climate factors can explain 58–85%. Importantly, the effect of temperature on extreme cooling load showed decrease from the cold to hot climate zones, but the impacts of relative humidity and solar radiation gradually increased. These results suggest that the maximum design capacity of heating systems can properly decrease, and the design capacity of cooling systems may not largely increase with the climate warming. In addition, the combined effect of multiple climate factors rather than the single temperature, as well as the different responses of extreme cooling energy consumption to climate change in different climate zones, should be considered when making measures for improving operating efficiency of air-conditioning system. [ABSTRACT FROM AUTHOR]

This paper addresses two fundamental questions on climate change and variability: to what extent has climate changed and/or varied over years in two districts of different agro-ecological regions or zones and how do any changes differ between the zones or districts? Given the rural-rural migration pattern observed between the districts, understanding climate change risk to rural livelihoods cannot be overemphasised. To assess change and variability, we utilise rainfall data-records over a 36-year period from 1980 to 2016. Results show that there are wide variations and differences within and between the districts. Evidence suggests a general reduction in both annual rainfall and wet days. There is also ground to suggest that the rainy season duration is becoming shorter, given that rainfall onset is increasingly starting late, while cessation is increasingly coming early. Dry spells frequency and duration trends within rainy season show an increase over the period examined. We conclude that local climate in both areas has changed over the period investigated. However, while Livingstone seems to have experienced more droughts and unreliable rainfall, Kabwe experienced a bigger change in both rainfall and rainy season duration. We further conclude that migrants into Kabwe and other inhabitants are not any safer from climate change risk. [ABSTRACT FROM AUTHOR]

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

Summary This paper promotes a GIS-based method that allows the visualization, the identification and the qualitative assessment of relationships existing among a limitless number of climatological variables. We use a variety of precipitation variables from fourteen Romanian weather stations to demonstrate the techniques and discuss its benefits and limitations. [ABSTRACT FROM AUTHOR]

The principal objectives of this paper are to develop and validate an optimum interpolation method for the spatial analysis of monthly precipitation in Turkey. A two-dimensional optimum interpolation objective analysis scheme has been developed for the spatial analysis of precipitation. The model is developed for generating statistically optimum interpolation based on the irregular distribution of meteorological stations. One question that affects the optimum interpolation method and, indeed, all such techniques, is how many observations should be allowed to influence a given grid point? The method developed in this paper addresses this question. For the implementation of the method, 52 stations are considered for Turkey, with 30 years of monthly data at each point. It is observed that each monthly average spatial correlation function shows a monotonically decreasing pattern based on 15 km interval averages. The method provides high estimation accuracy in dense station locations such as in northwestern Turkey. Precipitation contour maps obtained by the optimum interpolation method indicate two spatial trends over Turkey which have not been identified in any previous study. [ABSTRACT FROM AUTHOR]

Climate is an important tourism resource and a vital component of the attractiveness of a destination. Climate has a strong impact on supply and demand in the tourism industry. Most studies concerning the distribution of tourism climate resources are based on point measurements from meteorological stations. Tourism climate distribution maps with high resolution are required. In this paper, the tourism climate index (TCI) and spatial interpolation based on polynomial regression are used to analyze the distribution of summer tourism climate resources in China. The results indicate that there exists an obvious positive linear relationship between the TCI and latitude and a quadratic relationship between the TCI and elevation. The tourism climate is unfavorable in 8% of the study area, acceptable in 34% of the study area, good and very good in 36% of the study area, and excellent in 22% of the study area. From the perspective of climate, the places comfortable for summer tourism are mainly concentrated to the north of 35°N, on the second elevation step, and in the temperate continental climate zone. The tourism climate information provided is accurate at the 90% level for most areas. [ABSTRACT FROM AUTHOR]

The study of climatic variables in large scales with surface meteorological stations is limited due to the low density of these stations in many regions, possible sources of errors related to missing data, and uncertainties about the calibration sensors. Global gridded data (GD) systems can minimize these problems. Thus, studies that validate GDs with "ground truth" are important for several applications such as climate change. The objective of this study was to compare long series of surface data with 10-day estimates of average air temperature (T) and precipitation (P) using data from the European Center for Medium-Range Weather Forecast (ECMWF) and the National Aeronautics and Space Administration (NASA) for important agricultural locations in the states of Minas Gerais and São Paulo in Brazil. Despite the different spatial resolutions between ECMWF and NASA, the purpose of this paper was to evaluate the two data sources as they are readily available. The GD performance was evaluated by linear regression analysis. Analyses were performed for each meteorological variable for entire years and separated by seasons. The estimates of T from both ECMWF and NASA systems were accurate with the minimum Willmott concordance index (d) and RMSEp of 0.86, 0.37 °C, respectively, and precision with R2 0.61. The estimates of P had a minimum R2, d, and RMSEp of 0.48, 0.79, 2.15 °C respectively. The decreasing orders of (R2) were autumn > winter > spring > summer for T and winter > autumn > spring > summer for P, varying from 0.93 to 0.61 for T and from 0.77 to 0.48 for P. [ABSTRACT FROM AUTHOR]

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

In this study, region-of-influence (ROI) approach was used for modeling extreme values of 24-h maximum rainfall using seven groups of defined attributes including climatic, geographical, and statistical attributes and their hybrids. According to the degree of importance and the role of each defined attribute, different weighting scenarios were defined for each of the attributes relating to the corresponding group. The goal of this paper is to select the best weighting scenario for attributes in the ROI model for estimating more accurate and reliable quantiles of 24-h maximum rainfall over Urmia Lake Basin. The investigation of 140 pooling groups built with the ROI approach showed that each of the seven groups of defined attributes has a different performance in the estimation of quantiles. Moreover, results showed that, in most cases, performance of the models with weighted attributes was better than that of the non-weighted ones. Among the seven categories, the statistical attributes and their combination with climatic and geographical attributes had the best estimation of quantiles in terms of low relative error. In addition, results showed that skewness plays an effective role in the estimation of quantiles. In general, it can be concluded that by applying appropriate weights for available attributes in regions without data, the acceptable and reliable results of extreme values can be earned at ungauged stations. Furthermore, results showed that the use of median of metric distance as one of the threshold options can obtain better results in the estimation of quantiles. [ABSTRACT FROM AUTHOR]

Simulated sea surface temperature (SSTs) constitutes error source in tropical climate, simulated by coupled general circulation models (GCMs). Therefore, atmospheric GCMs that use observed SSTs are expected to simulate tropical climate with reduced bias. This paper evaluates the performance of the atmosphere only GCM of the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP-AGCM4.1) in simulating the climate of West Africa. Monthly simulated climatology of precipitation and temperature in five precipitation regions are compared with observed CRU climatology. Inter-annual variability in simulated precipitation and temperature are also compared with observation. In addition, the 10th, 50th and 90th percentiles of simulated precipitation and temperature are compared with the observed precipitation and temperature using normalised deviation from mean and normalised root-mean-square difference. Finally, observed and simulated links between multivariate ENSO index (MEI) and precipitation (temperature) are compared. IAP-AGCM4.1 simulates the mean climatology well over Western part of West Africa (WWA), Central Guinea Coast (GC), and Eastern GC. At the Eastern Sahel, precipitation is over estimated at the 10th and 50th percentile. Generally, there is overestimation at the 10th percentile and underestimation at the 90th percentile. The model also simulates the observed annual variability reasonably well. As expected positive MEI leads to reduction in precipitation in substantial part of West Africa while negative MEI leads to wetness. Furthermore, temperature increases with positive MEI while it decreases with negative MEI over West Africa. However, the model reproduces the dynamics of the influence of MEI on the climate of West Africa with weaker signal. [ABSTRACT FROM AUTHOR]

Drought is a major natural hazard with significant effects in the agricultural sector, especially in arid and semi-arid regions. The accurate and timely characterisation of agricultural drought is crucial for devising contingency plans, including the necessary mitigation measures. Many drought indices have been developed during the last decades for drought characterisation and analysis. One of the most widely used indices worldwide is the Standardised Precipitation Index (SPI). Although other comprehensive indices have been introduced over the years, SPI remains the most broadly accepted index due to a number of reasons, the most important of which are its simple structure and the fact that it uses only precipitation data. In this paper, a modified version of SPI is proposed, namely the Agricultural Standardised Precipitation Index (aSPI), based on the substitution of the total precipitation by the effective precipitation, which describes more accurately the amount of water that can be used productively by the plants. Further, the selection of the most suitable reference periods and time steps for agricultural drought identification using aSPI is discussed. This conceptual enhancement of SPI aims at improving the suitability of the index for agricultural drought characterisation, while retaining the advantages of the original index, including its dependence only on precipitation data. The evaluation of the performance of both SPI and aSPI in terms of correlating drought magnitude with crop yield response in four regions of Greece under Mediterranean conditions indicated that aSPI is more robust than the original index in identifying agricultural drought. [ABSTRACT FROM AUTHOR]

Wind erosion climatic erosivity is an important parameter to assess the possible effects of climatic conditions on wind erosion. In this paper, the wind erosion climatic factor (C-factor), which was used to quantify the wind erosion climatic erosivity, was calculated for the period 1960–2014 based on monthly meteorological data collected from 101 stations in the farming-pastoral zone of Northern China. The Mann-Kendall (M-K) test, trend analysis, and geostatistical analysis methods were used to explore the spatial and temporal characteristics of the wind erosion climatic erosivity in this region. The result suggests that the annual C-factor, with a maximum of 76.05 in 1969 and a minimum of 26.57 in 2007, has a significant decreasing trend over the past 55 years. Strong seasonality in the C-factor was found, with the highest value in spring, which accounts for a significant proportion of the annual C-factor (41.46%). However, the coefficient of variation of the seasonal C-factor reaches a maximum in winter and a minimum in spring. The mean annual C-factor varies substantially across the region. Areas with high values of the mean annual C-factor (C ≥ 100) are located in Ulanqab and Dingxi, while areas with low values (C ≤ 10) lie in Lanzhou, Linxia, Dingxi, Xining, and Chengde. Spatial analysis on the trend of the C-factor reveals that 81% of the stations show statistically significant decreases at a 90% confidence level. An examination of the concentration ratio of the C-factor shows that the wind erosion climatic erosivity is concentrated in spring, especially in April, which makes this period particularly important for implementing soil conservation measures. [ABSTRACT FROM AUTHOR]

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

This study analyses 371 yearly series of 1-day precipitation maxima extracted from a data set of high-quality homogenized rain gauge records covering an area of about 25,000 km2 in the western part of northern-central Italy that includes eastern Liguria and northern Tuscany and is very prone to severe floods and shallow landslides. Return levels of 1-day extreme precipitation (corresponding to 10-, 50-, and 100-year return periods as predicted by a generalized extreme value distribution (GEV)) are estimated both for station sites and for the nodes of a high-resolution grid (30 arcsec). GEV parameters are estimated by regional frequency analysis using the station-year approach. Grid-point index floods are estimated from yearly cumulative precipitation normals exploiting the very high correlation existing at local scale between station index flood and the corresponding yearly precipitation normals. Results clearly show the high vulnerability of this region to extreme precipitation events and highlight the different role played by total annual precipitation normals and by both scale and shape parameters in regulating the longer period return levels. The results presented in this paper allow better estimating the climatology of extreme events in the study area, contributing to a more effective hazard assessment at different spatial and temporal scales. [ABSTRACT FROM AUTHOR]

Reliable information of land precipitation along with other atmospheric variables is crucial for monsoon studies, ecosystem modelling, crop modelling and numerous other applications. In this paper, three multisatellite and three reanalysis precipitation products, namely Global Precipitation Climatology Project (GPCP), Climate Prediction Center Mapping of Precipitation (CMAP1 and CMAP2), European Center for Medium Range Weather Forecasts Reanalysis-Interim (ERA-I) and National Center for Environmental Prediction (NCEP1 and NCEP2), are compared with the recent version of gauge-based gridded Global Precipitation Climatology Centre (GPCC) data sets over the global land region. The analysis is done at monthly scale and at 2.5° latitude × 2.5° longitude resolution for a 25-year (1986-2010) period. Large-scale prominent features of precipitation and its variability are qualitatively represented by all the precipitation products. However, the magnitudes considerably differ among themselves. Among the six precipitation products, GPCP performs better than the others when compared to the gridded GPCC data sets. Among the three reanalysis precipitation products, ERA-I is better than NCEP1 and NCEP2 in general. Even though NCEP2 is improved over NCEP1 over the mid-latitudes, NCEP2 has more serious problem over the orographic regions than that of NCEP1. Moreover, all the precipitation estimates exhibit similar kind of interannual variability over the global and tropical land regions. Additionally, the comparison is done for the six global monsoon regions for the regional analysis which shows that all the precipitation estimates exhibit similar kind of interannual variability in the seasonal monsoon precipitation. However, there are some regional differences among these precipitation products in the representation of monsoon variability. [ABSTRACT FROM AUTHOR]

Using NASA’s TRMM Lightning Imaging Sensor (LIS) data from 1998 to 2014, this paper presents a 17-year lightning climatology of Bangladesh, at 0.5° × 0.5° spatial resolution. Diurnal, seasonal, monthly and annual variations in the occurrence of lightning flashes were explored. The diurnal regime of lightning is dominated by afternoon/evening events. Overall, peak lightning activity occurs in the early morning (0200 LST) and evening (1900 LST). The distribution of lightning flash counts by season over Bangladesh landmass is as follows: pre-monsoon (69.2%), monsoon (24.1%), post-monsoon (4.6%) and winter (2.1%). Flash rate density (FRD) hotspots were primarily located in the north and north-eastern parts of Bangladesh, with a maximum of 72 fl km−2 year−1. Spatially, the distribution of FRD increases from the Bay of Bengal in the south to relatively higher elevations (of the Himalayan foothills) in the north. A spatial shift in FRD hotspots occurs with change in season. For example, in monsoon season, hotspots of lightning activity move in a south-westerly direction from their pre-monsoon location (i.e. north-eastern Bangladesh) towards West Bengal in India. South and south-eastern parts of Bangladesh experience high lightning activity during post-monsoon season due to regional orographic lifting and low-pressure systems (i.e. cyclone) in the Bay of Bengal. To the best of our knowledge, this is the first study focused on LIS-based lightning climatology over Bangladesh. This baseline study, therefore, is an essential first step towards effective management of lightning-related hazards in Bangladesh. [ABSTRACT FROM AUTHOR]

The paper, 'Study on the reliable computation time of the numerical model using the sliding temporal correlation method,' was published in Theoretical and Applied Climatology. In that work, the sliding temporal correlation analysis is employed to investigate the predictable time of two typical chaotic numerical models, namely the Lorenz system and the Chen chaotic system. However, it has been recently shown by us when using a linear scaling in time and state variables that generically, the Chen system is only a particular case of the Lorenz system, with time reversion if the parameter c is positive. Consequently, to study the Chen chaotic system is simply to consider the Lorenz system integrated backwards. [ABSTRACT FROM AUTHOR]

Since Global Positioning System dropwindsonde wind measurements are taken in neither a conventional Eulerian framework nor a perfect Lagrangian framework, proper post-processing is necessary to derive conventional Eulerian wind statistics from dropwindsonde measurements. In this study, the equations governing the dropwindsonde motions were first linearized and then analytically solved to explore the effects of the wind-finding equations introduced by Hock and Franklin (Bull Amer Meteor Soc 80:407-420, 1999) when retrieving horizontal winds from dropwindsonde measurements. It is found that the wind-finding equations are essential for calculating turbulence statistics from dropwindsonde measurements. To confirm, numerical simulations of dropwindsonde motions in a pseudo-stochastic wind field were further conducted based on the full nonlinear dropwindsonde motion-governing equations. Based on the simulation results, the present paper showed the effects of the wind-finding equations and the companion paper discussed the impacts of various post-processing/composition schemes on both the mean and turbulent wind statistics composited from pseudo-dropwindsonde measurements. [ABSTRACT FROM AUTHOR]

The paper presents a comprehensive analysis of drought phenomena in the Region of Abruzzo (Central Italy) using the Standardized Precipitation Index (SPI) computed at different time scales (3, 6, 12, 24 months). The study is based on monthly precipitation data collected from 1951 to 2009 at 69 climatic stations uniformly distributed over the region. According to the trend analysis, most stations are characterized by increments in both drought severity and variability, particularly at the longer time scales. A principal component analysis applied to SPI time series enabled to identify two main patterns: the first more correlated to the coastal areas, the second more correlated to the inland, mountainous areas. However, the spatial patterns become less defined as the time scale increases, making more uncertain the definition of homogenous areas to be used in drought management plans. In most cases, the identified drought patterns have similar negative overall tendencies, but different trend directions in some sub-periods. In particular, the first drought pattern is clearly characterized by a trend reversal (from decreasing to increasing) during the last decade. This temporal evolution, consistent with that observed by large-scale analyses in the corresponding (or near) grid points, was not detected for the second pattern, which is probably influenced by local topographic and/or orographic factors. The results confirm the complexity of drought phenomenon in a typical Mediterranean region and the necessity of high-resolution datasets to capture its temporal and spatial variability. [ABSTRACT FROM AUTHOR]

This paper evaluates the monthly mean air temperature over complex terrain in China determined using two methods: Australian National University spline and GridMet_Climate_Temperature (grid meteorological and climatic temperature (GMCT)) models. It is found that the macroscopic patterns of the temperature distributions by the two methods are very similar. The comparison of modelled temperatures with observations from 40 verification stations shows that both methods have the same value of total mean absolute bias errors (MABE) of 0.43 °C. Since the observational data used are from meteorological observational stations which are located on horizontal flat and open areas, these evaluation results only represent those on the flat open areas. In order to evaluate the effects of topographical factors on the temperature, a concept of relief amplitude is defined. The temperatures are grouped according to range of the relief amplitudes, and variation of temperature with relief amplitudes is worked out. It is found that the temperature difference between the two models varies significantly with relief amplitudes. The averaged maximum difference can be as large as 12 °C in January when the relief amplitude is greater than 700 m. The reasons for the temperature difference are investigated, and they are due to the effects of topographical slopes and aspects. One model (GMCT) includes these effects while the other does not. The evaluation results demonstrate that it is necessary to include the effects of topographical factors in the model simulation in order to produce realistic temperature distributions in the complex terrain areas. The distributions of temperature on the northern and southern slopes are determined using the GMCT model, and the results show that the temperatures on the southern slopes are clearly greater than those of the northern slopes, especially in winter. These results can be used as a guide for the reasonable and sustainable utilization of heat resources in the mountainous area. [ABSTRACT FROM AUTHOR]

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

Based on climatic data from 18 stations on the southern slopes of the eastern Himalayas in Bhutan for the period from 1996 to 2009, this paper investigates monthly characteristics of the near-surface air temperature lapse rate (TLR). The station elevations used in this study range from 300 to 2760 m a. s. l. TLRs were evaluated using a linear regression model. The monthly values of maximum TLRs were always smaller than those of the minimum TLRs, which is in contrast to results from the surrounding mountainous regions. In this study, annual patterns of TLRs were somewhat consistent, particularly in the summer; during the other seasons, patterns contrasted to results from the southeastern Tibetan Plateau (China) and were almost comparable to results from Nepal. The shallowest observed values for TLRs in summer are due to intense latent heating at the higher elevation, associated with water vapor condensation from moist convection and evapotranspiration, and decreasing sensible heating at lower elevation, due to heavier rainfall, cloud, and forest cover. When compared to summer, the steeper TLRs in the non-monsoon season are due to sensible heating at the lower elevations, corresponding to dry and clear weather seasons, as well as increasing cooling at higher elevations, particularly in winter due to snow and cloud cover. Owing to lower albedo and higher aerodynamic roughness of forested areas, the TLRs were considerably reduced in daytime because of the dissipation of sensible heat to the atmospheric boundary layer. The distinct variation in diurnal TLR range is due to the diurnal variation in net radiation associated with reduced turbulent heating in the day and increased turbulent heating in the night, in addition to the effect of moisture and cloud cover. The shallower values of TLRs in this study when compared with the surrounding mountainous regions are due to high humidity, as well as the differing elevations and local climates. [ABSTRACT FROM AUTHOR]

Most often than not, incomplete datasets or short-term recorded data in vast regions impedes reliable climate and water studies. Various methods, such as simple correlation with stations having long-term time series, are practiced to infill or extend the period of observation at stations with missing or short-term data. In the current paper and for the first time, the hypothesis on the feasibility of extending the downscaling concept to backcast local observation records using large-scale atmospheric predictors is examined. Backcasting is coined here to contrast forecasting/projection; the former is implied to reconstruct in the past, while the latter represents projection in the future. To assess our hypotheses, daily and monthly statistical downscaling models were employed to reconstruct past precipitation data and lengthen the data period. Urmia and Tabriz synoptic stations, located in northwestern Iran, constituted two case study stations. SDSM and data-mining downscaling model (DMDM) daily as well as the group method of data handling (GMDH) and model tree (Mp5) monthly downscaling models were trained with National Center for Environmental Prediction (NCEP) data. After training, reconstructed precipitation data of the past was validated against observed data. Then, the data was fully extended to the 1948 to 2009 period corresponding to available NCEP data period. The results showed that DMDM performed superior in generation of monthly average precipitation compared with the SDSM, Mp5, and GMDH models, although none of the models could preserve the monthly variance. This overall confirms practical value of the proposed approach in extension of the past historic data, particularly for long-term climatological and water budget studies. [ABSTRACT FROM AUTHOR]

This paper presents the impact of two snow cover schemes (NY07 and SL12) in the Community Land Model version 4.5 (CLM4.5) on the snow distribution and surface energy budget over the Tibetan Plateau. The simulated snow cover fraction (SCF), snow depth, and snow cover days were evaluated against in situ snow depth observations and a satellite-based snow cover product and snow depth dataset. The results show that the SL12 scheme, which considers snow accumulation and snowmelt processes separately, has a higher overall accuracy (81.8%) than the NY07 (75.8%). The newer scheme performs better in the prediction of overall accuracy compared with the NY07; however, SL12 yields a 15.1% underestimation rate while NY07 overestimated the SCF with a 15.2% overestimation rate. Both two schemes capture the distribution of the maximum snow depth well but show large positive biases in the average value through all periods (3.37, 3.15, and 1.48 cm for NY07; 3.91, 3.52, and 1.17 cm for SL12) and overestimate snow cover days compared with the satellite-based product and in situ observations. Higher altitudes show larger root-mean-square errors (RMSEs) in the simulations of snow depth and snow cover days during the snow-free period. Moreover, the surface energy flux estimations from the SL12 scheme are generally superior to the simulation from NY07 when evaluated against ground-based observations, in particular for net radiation and sensible heat flux. This study has great implications for further improvement of the subgrid-scale snow variations over the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

There is still considerable uncertainty about precipitation at high elevation in mountain terrain due to the relatively few in situ measurements available and to the particular variability of the parameter. In this study, several spatialization techniques were tested, some for climatological time scale and others for daily fields, for precipitation over the western Alps for the period of 1990-2012. The study domain and period were chosen for the quality of available in situ observations and density of the network. First, a weather-type classification was established with a technique based on canonical correlation analysis combining large- and regional-scale data. The spatialization techniques applied for the climatological time scale were adapted from the Aurelhy method which uses elevation and principal components of the topography as predictors. The spatialization techniques applied to daily fields were based on kriging of daily rain gauges and used the climatological fields as predictors. This study aims to validate the advantage of using the climatology of the weather type of the day as predictor for daily fields over a monthly climatology. The climatology of the weather type of the day seems to demonstrate some small improvement. Finally, annual means over the period of 1990-2012 were produced using several methods, including some from accumulation of daily fields and others from the spatialization of in situ station means. Precipitation at high elevations and vertical climatological gradients were particularly scrutinized. Annual means based on sums of daily fields seem to have better performances. This paper only presents results for precipitation but temperature was also analysed. [ABSTRACT FROM AUTHOR]

Quality assurance of meteorological data is crucial for ensuring the reliability of applications and models that use such data as input variables, especially in the field of environmental sciences. Spatial validation of meteorological data is based on the application of quality control procedures using data from neighbouring stations to assess the validity of data from a candidate station (the station of interest). These kinds of tests, which are referred to in the literature as spatial consistency tests, take data from neighbouring stations in order to estimate the corresponding measurement at the candidate station. These estimations can be made by weighting values according to the distance between the stations or to the coefficient of correlation, among other methods. The test applied in this study relies on statistical decision-making and uses a weighting based on the standard error of the estimate. This paper summarizes the results of the application of this test to maximum, minimum and mean temperature data from the Agroclimatic Information Network of Andalusia (southern Spain). This quality control procedure includes a decision based on a factor f, the fraction of potential outliers for each station across the region. Using GIS techniques, the geographic distribution of the errors detected has been also analysed. Finally, the performance of the test was assessed by evaluating its effectiveness in detecting known errors. [ABSTRACT FROM AUTHOR]

This paper describes different ways of reducing urban air temperature and their results in two cities: Campinas, Brazil-a warm temperate climate with a dry winter and hot summer (Cwa), and Mendoza, Argentina-a desert climate with cold steppe (BWk). A high-resolution microclimate modeling system-ENVI-met 3.1-was used to evaluate the thermal performance of an urban canyon in each city. A total of 18 scenarios were simulated including changes in the surface albedo, vegetation percentage, and the H/W aspect ratio of the urban canyons. These results revealed the same trend in behavior for each of the combinations of strategies evaluated in both cities. Nevertheless, these strategies produce a greater temperature reduction in the warm temperate climate (Cwa). Increasing the vegetation percentage reduces air temperatures and mean radiant temperatures in all scenarios. In addition, there is a greater decrease of urban temperature with the vegetation increase when the H/W aspect ratio is lower. Also, applying low albedo on vertical surfaces and high albedo on horizontal surfaces is successful in reducing air temperatures without raising the mean radiant temperature. The best combination of strategies-60 % of vegetation, low albedos on walls and high albedos on pavements and roofs, and 1.5 H/W-could reduce air temperatures up to 6.4 °C in Campinas and 3.5 °C in Mendoza. [ABSTRACT FROM AUTHOR]

The present paper aims of computing climatology and trend analysis of occurrence and intensity of extreme events of precipitation in subregions of Northeast Brazil (NEB). We used daily rainfall data of 148 rain gauges collected from the hydrometeorological network managed by the National Water Agency during 1972 to 2002 and used quantiles technique in order to select rainfall events. Defining heavy rainfall events as those when at least one rain gauge recorded rainfall above the 95th percentile, normal rainfall was between the 45th and 55th percentiles, and weak rainfall events were under the 5th percentile. The Mann-Kendall nonparametric test was used to calculate the linear trend of the quantity and intensity of rainfall events. The NEB was divided in five subregions using the cluster analysis based on Euclidean distance and Ward's method: Northern coast, Northern semiarid, Northwest, Southern semiarid, and Southern coast. The results suggest that the subregions are less influenced by El Niño and La Niña, and dry areas have higher variability, with the greatest number of intense events. [ABSTRACT FROM AUTHOR]