Urban-rural fog differences in Belgrade area,Serbia

Urban/rural fog appearance during the last 27 years in the Belgrade region is analysed using hourly meteorological records from two meteorological stations: an urban station at Belgrade-Vra?ar (BV) and a rural station at Belgrade-Airport (BA). The effects of urban development on fog formation are discussed through analysis of fog frequency trends and comparison with a number of meteorological parameters. The mean annual and the mean annual minimum temperatures were greater at the urban BV station than at the rural BA station. The mean monthly relative humidity and the mean monthly water vapour pressure were greater at the rural than urban station. During the period of research (1988–2014), BA experiences 425 more days with fog than BV, which means that BV experiences fog for 62.68% of foggy days at BA. Trends in the number of days with fog were statistically non-significant. We analysed the fog occurrence during different types of weather. Fog in urban BV occurred more frequently during cyclonal circulation (in 52.75% of cases). In rural BA, the trend was the opposite and fog appeared more frequently during anticyclonic circulation (in 53.58% of cases). Fog at BV occurred most frequently in stable anticyclonic weather with light wind, when a temperature inversion existed (21.86% of cases). Most frequently, fog at BA occurred in the morning and only lasted a short time, followed by clearer skies during the anticyclonic warm and dry weather (22.55% of cases).     

Drivers of long-term precipitation and runoff variability in the southeastern USA

The hydroclimatology of the southeastern USA (AL, GA, NC, SC, and TN) is analyzed from a holistic perspective, including multiple climate drivers. Monthly precipitation modeled by the PRISM group and runoff data (1952–2011) from 18 basins are analyzed using a single-field based principal component’s analysis. Results indicate that the Atlantic Multidecadal Oscillation and El Niño-Southern Oscillation are the main atmospheric drivers of hydroclimate variability in the region, sometimes operating at several months’ lag. Their influence is the strongest in the fall through spring, which corresponds with the dry season in the southern parts of the study area thereby increasing pressure on already limited water resources. The Arctic Oscillation, North Atlantic Oscillation, and Pacific-North American patterns vary on shorter-term bases, and also show a significant, but temporally more sporadic influence. Insight is also brought to the ongoing discussion, confirming the disassociation of the Arctic and North Atlantic Oscillation. Findings can be used in water resources forecasting, giving an indication of expected water volumes several months ahead.     

Drought analysis in the Tons River Basin,India during 1969-2008

The primary focus of this study is the analysis of droughts in the Tons River Basin during the period 1969–2008. Precipitation data observed at four gauging stations are used to identify drought over the study area. The event of drought is derived from the standardized precipitation index (SPI) on a 3-month scale. Our results indicated that severe drought occurred in the Allahabad, Rewa, and Satna stations in the years 1973 and 1979. The droughts in this region had occurred mainly due to erratic behavior in monsoons, especially due to long breaks between monsoons. During the drought years, the deficiency of the annual rainfall in the analysis of annual rainfall departure had varied from ?26% in 1976 to ?60% in 1973 at Allahabad station in the basin. The maximum deficiency of annual and seasonal rainfall recorded in the basin is 60%. The maximum seasonal rainfall departure observed in the basin is in the order of ?60% at Allahabad station in 1973, while maximum annual rainfall departure had been recorded as ?60% during 1979 at the Satna station. Extreme dry events (z score <?2) were detected during July, August, and September. Moreover, severe dry events were observed in August, September, and October. The drought conditions in the Tons River Basin are dominantly driven by total rainfall throughout the period between June and November.     

Impacts of climate change on building energy demands in the intra-Americas region

It was recently reported a regional warming in the intra-Americas region where sea surface temperature exhibited increases exceeding 0.15 °C/decade and an accelerated air temperature rise that could impact building energy demands per capita (EDC). Reanalysis data is used herein to quantify the impacts of these warming trends on EDC. Results of the analysis depict a Southern Greater Antilles and inland South America with a positive annual EDC rate of 1–5 kWh per year. The Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCP) 2.6 and 4.5 scenarios were selected to analyze energy demand changes in the twenty-first century. A multi-model ensemble forecasts an EDC increase of 9.6 and 23 kWh/month in the RCP2.6 and RCP4.5 at the end of the twenty-first century, which may increase average building cooling loads in the region by 7.57 GW (RCP2.6) and 8.15 GW (RCP4.5), respectively. Furthermore, 4 of 9 (RCP2.6) and 7 of 9 (RCP4.5) of the major countries in this region have EDCs ranging between 1887 and 2252 kWh/year at the end of this century. Therefore, increased energy production and improved energy infrastructure will be required to maintain ideal indoor building conditions at the end of the twenty-first century in these tropical coastal regions as consequence of a warmer climate.     

Evaluation of global climate model on performances of precipitation simulation and prediction in the Huaihe River basin

Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960–2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001–2010) and the predicting period (2011–2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans.     

Effects of the low-frequency zonal wind variation on the high frequency atmospheric variability over the tropics

Recently, there is increasing evidence on the interaction of atmospheric high-frequency (HF) variability with climatic low-frequency (LF) variability. In this study, we examine this relationship of HF variability with large scale circulation using idealized experiments with an aqua-planet Atmospheric GCM (with zonally uniform SST), run in different zonal momentum forcing scenarios. The effect of large scale circulation changes to the HF variability is demonstrated here. The HF atmospheric variability is enhanced over the westerly forced region, through easterly vertical shear. Our study also manifests that apart from the vertical wind shear, strong low-level convergence and horizontal zonal wind shear are also important for enhancing the HF variance. This is clearly seen in the eastern part of the forcing, where the HF activity shows relatively maximum increase, in spite of similar vertical shear over the forced regions. The possible implications for multi-scale interaction (e.g. MJO–ENSO interaction) are also discussed.     

Nitrous oxide emissions from gasohol, ethanol and CNG light duty vehicles

A sample of 21 light duty vehicles powered by Otto cycle engines were tested on a chassis dynamometer to measure the exhaust emissions of nitrous oxide (N₂O). The tests were performed at the Vehicle Emission Laboratory of CETESB (Environmental Company of the State of Sao Paulo) using the US-FTP-75 (Federal Test Procedure) driving cycle. The sample tested included passenger cars running on three types of fuels used in Brazil: gasohol, ethanol and CNG. The measurement of N₂O was made using two methods: Non Dispersive InfraRed (NDIR) analyzer and Fourier Transform InfraRed spectroscopy (FTIR). Measurements of regulated pollutants were also made in order to establish correlations between N₂O and NO_x. The average N₂O emission factors obtained by the NDIR method was 78?±?41?mg.km^?1 for vehicles running with gasohol, 73?±?45?mg.km^?1 for ethanol vehicles and 171?±?69?mg.km^?1 for CNG vehicles. Seventeen results using the FTIR method were also obtained. For gasohol vehicles the results showed a good agreement between the two methods, with an average emission factor of 68?±?41?mg.km^?1. The FTIR measurement results of N₂O for ethanol and CNG vehicles were much lower than those obtained by the NDIR method. The emission factors were 17?±?10?mg.km^?1 and 33?±?17?mg.km^?1, respectively, possibly because of the interference of water vapor (present at a higher concentration in the exhaust gases of these vehicles) on measurements by the NDIR method.     

Estimation of relative humidity based on artificial neural network approach in the Aegean Region of Turkey

The aim of this study is to estimate the monthly mean relative humidity (MRH) values in the Aegean Region of Turkey with the help of the topographical and meteorological parameters based on artificial neural network (ANN) approach. The monthly MRH values were calculated from the measurement in the meteorological observing stations established in Izmir, Mugla, Aydin, Denizli, Usak, Manisa, Kutahya and Afyonkarahisar provinces between 2000 and 2006. Latitude, longitude, altitude, precipitation and months of the year were used in the input layer of the ANN network, while the MRH was used in output layer of the network. The ANN model was developed using MATLAB software, and then actual values were compared with those obtained by ANN and multi-linear regression methods. It seemed that the obtained values were in the acceptable error limits. It is concluded that the determination of relative humidity values is possible at any target point of the region where the measurement cannot be performed.     

Some evidence on European monsoon existence

Large-scale European atmospheric circulation induced by temperature differences between the continent and the North Atlantic Ocean causes thermodynamic and climatic conditions that initiate a European monsoon. In Eastern Europe, the rainy season occurs in early summer, and the dry season occurs in winter. In Western Europe, the rainy season is in the early winter and the dry season is in the spring. This precipitation trend, as well as other climatic features, suggests the existence of a European monsoon.     

Impacts of extreme weather on wheat and maize in France: evaluating regional crop simulations against observed data

Extreme weather conditions can strongly affect agricultural production, with negative impacts that can at times be detected at regional scales. In France, crop yields were greatly influenced by drought and heat stress in 2003 and by extremely wet conditions in 2007. Reported regional maize and wheat yields where historically low in 2003; in 2007 wheat yields were lower and maize yields higher than long-term averages. An analysis with a spatial version (10?×?10?km) of the EPIC crop model was tested with regards to regional crop yield anomalies of wheat and maize resulting from extreme weather events in France in 2003 and 2007, by comparing simulated results against reported regional crops statistics, as well as using remotely sensed soil moisture data. Causal relations between soil moisture and crop yields were specifically analyzed. Remotely sensed (AMSR-E) JJA soil moisture correlated significantly with reported regional crop yield for 2002–2007. The spatial correlation between JJA soil moisture and wheat yield anomalies was positive in dry 2003 and negative in wet 2007. Biweekly soil moisture data correlated positively with wheat yield anomalies from the first half of June until the second half of July in 2003. In 2007, the relation was negative the first half of June until the second half of August. EPIC reproduced observed soil dynamics well, and it reproduced the negative wheat and maize yield anomalies of the 2003 heat wave and drought, as well as the positive maize yield anomalies in wet 2007. However, it did not reproduce the negative wheat yield anomalies due to excessive rains and wetness in 2007. Results indicated that EPIC, in line with other crop models widely used at regional level in climate change studies, is capable of capturing the negative impacts of droughts on crop yields, while it fails to reproduce negative impacts of heavy rain and excessively wet conditions on wheat yield, due to poor representations of critical factors affecting plant growth and management. Given that extreme weather events are expected to increase in frequency and perhaps severity in coming decades, improved model representation of crop damage due to extreme events is warranted in order to better quantify future climate change impacts and inform appropriate adaptation responses.