Vol. 124, No. 1 * Pages 1–141 * January - March 2020

Numerous studies on the effects of the El Niño-Southern Oscillation (ENSO) on the stratosphere have been conducted in recent years. However, few of these have examined whether the use of an adequate representation of the stratosphere might affect simulations of the ENSO. In the present work, sea surface temperature data from two numerical model configurations, namely one with a well-resolved stratosphere, the “high top configuration” (Hadley Centre Global Environmental Model, HadGEM2-CCS), and the other without a well-resolved stratosphere, the “low top configuration” (HadGEM2-CC), are employed to study the impact of the stratosphere on the surface climate, especially on the ENSO. A pre-industrial control run is performed to eliminate interference from other factors, such as greenhouse gas warming and volcanic eruptions. Based on the present research, both model configurations function reasonably well and have shown little difference from each other when analyzng the global annual and seasonal mean sea surface temperatures, except for the Northern Atlantic Ocean region. A statistical analysis performed using the t-test method shows that the significant differences in the annual and seasonal mean sea surface temperatures in the Northern Atlantic region result from real signals rather than random noises. Furthermore, the configuration with a better representation of the stratosphere simulates the quasi-period of the ENSO and the seasonal phase-locking characteristics of El Niño more precisely. Therefore, it is probably advantageous to adopt climate models that resolved stratosphere for a more realistic representation of ENSO climatology and its possible variations under certain conditions.

This study aims to give a brief overview of an ensemble of regional climate model (RCM) simulations with and without bias correction for daily precipitation for the Carpathian Region located in Central/Eastern Europe. Within the international initiative called the Coordinated Regional Downscaling Experiment (CORDEX), EURO-CORDEX and Med-CORDEX provide RCM simulations targeting Europe as a whole or in a part at the grid resolutions of 0.44° (~50 km) and 0.11° (~12 km). The ensemble of RCMs provides a huge amount of data, which are, however, prone to biases compared to high-resolution observations. First, the bias correction of the daily precipitation output of EURO-CORDEX and Med‑CORDEX RCM ensemble at a common 0.11° × 0.11° horizontal grid resolution is performed based on the high-resolution, high-quality observational dataset CARPATCLIM. The region covered by the CARPATCLIM dataset can be considered as the Carpathian Region, for which the RCM ensemble (consisting of six members in total at 0.11° resolution) of a historical period (1976−2005) and under the Representative Concentration Pathway 8.5 (RCP8.5) over two future time slices (2021−2050 and 2070−2099) are assessed. Percentile-based bias correction method was used in order to adjust systematic biases in all simulated precipitation fields. The present study focuses on different precipitation climate indices derived from high-resolution RCM outputs over the entire Carpathian Region and specifically two sub-regions representing high- and lowlands within the target region. The analyzed indices are as follows: the frequency of rainy days (RR1, days with a total rainfall of at least 1 mm), heavy precipitation days (RR10, days with a total rainfall of at least 10 mm), highest daily precipitation (RX1), maximum consecutive dry periods (CDD, the duration of the longest period with < 1 mm total daily precipitation), maximum consecutive wet periods (CWD, the duration of the longest period with > 1 mm total daily precipitation). Our results indicate that both the spatial distribution and magnitude of mean changes are similar to those found in previous works based on ENSEMBLES project simulations using a different greenhouse gas emission scenario. Furthermore, the present study also aims to introduce a high-resolution bias-corrected precipitation database, which can serve as input for climate change impact and adaptation studies to be carried out for the Carpathian Region and to provide important information to stakeholders and decision makers at local/regional/national levels.

The main objective of this study is to review and test the applicability of well-established models collected from the literature for estimating the monthly average daily diffuse solar radiation on a horizontal surface in Egypt. The different meteorological data measured at eight stations during the period 1987–2016 were used to calculate the monthly mean values of diffuse solar radiation over these stations using the collected models. The selected eight stations measure diffuse solar radiation component and have been chosen to cover the whole of Egypt. The collected models (fourteen models) were compared on the basis of many statistical error tests such as the relative percentage error, (e%), mean percentage error (MPE), mean bias error (MBD), root mean square error (RMSE), t-test, and Nash-Sutcliffe equation (NSE). According to the results, the Tarhan and Sarı model (Model 12) showed the best estimation of the diffuse solar radiation on a horizontal surface for all of the eight stations, and therefore, it is recommended for predicting diffuse solar radiation at any location in Egypt.

Investigations of the long-term observations of climate variables, as a practical approach to monitor climate changes, have attracted the interest of many researchers around the world. One of the important variables in this context is precipitation. The investigation of precipitation, one of the most important meteorological factors directly affecting accessibility to water resources, is of special importance. In every region, studies of precipitation on daily, monthly, or annual scales provide important information on the distribution, concentration, and dispersion of precipitation, as well as some conclusions about the associated hydrological problems. In this study, the precipitation concentration was calculated and zoned by means of the precipitation concentration index (PCI) in the basin of Lake Urmia, using monthly and annual rainfall data of 42 selected rain gauge stations, from which 24 stations located in the West Azerbaijan province (in the west of Lake Urmia) and 18 stations located in the East Azerbaijan province (in the east of Lake Urmia) during 1984–2013. The results of the studies of the precipitation concentration index over the basin of Lake Urmia showed that the dominant concentrations of spring, autumn, and winter precipitation were moderate, indicating a moderate distribution for the precipitation of the months in these seasons. In addition, in the period under study, uniform and regular precipitation concentrations (PCI<10) were observed only in winter and in the borders of the basin. In summer, almost the entire surface of the basin (excluding its northeastern part) faced a strongly irregular distribution of precipitation, indicating irregular rainfall in July, August, and September. Most of the basin of Lake Urmia is covered by an irregular distribution of precipitation on an annual scale. By investigating the precipitation distribution in the first and the last 10 years of the statistical period considered and by comparing them, it was revealed that the greatest increase in the precipitation concentration index was in autumn, it rose by approximately 20.55 percent. According to the results, on the basin scale, the concentration index showed that the daily rainfall of the Lake Urmia basin was neither in regular nor in strongly irregular conditions at any of the stations studied. All the stations studied were in fairly regular, moderate concentration and fairly irregular conditions of daily precipitation distribution. The results also showed that the moderate concentration includes most of the daily precipitation distributions throughout the basin.

The paper discusses the circulation and thermal conditions over Poland and their dependence on the sign and values of the North Atlantic Oscillation (NAO) index (hereinafter NAO+ and NAO-). The input data used in the research consisted of average monthly values ​​of air temperature in Warsaw, NAO index values, and a calendar of atmospheric circulation types according to Lityński (1969). The study comprised the three winter months (December, January,February) from the period 1951–2015. The dependence between the circulatory and thermal conditions was investigated on the basis of 10 months representing each of the winter months with the highest NAO+ and NAO- values and 10 months with the greatest positive and negative temperature deviations from the long-term average (Dt+, Dt-), based on the assumption, that they should largely be the same months. In general, the analysis confirmed these assumptions, but it also showed that there are deviations from previously known regularities as regards the effect of the positive or negative phases of the NAO on the thermal conditions in Poland.

Air pollution occurs when harmful or excessive quantities of substances including gases, solid particulates, and biological molecules are introduced into the atmosphere. The analysis of the relationship between air pollutants and meteorological factors can provide important information about air pollution. The aim of this study is to examine and explore the relationship between the different monitored air pollutant concentrations such as carbon-monoxide (CO), nitrogen-oxides (NO_x), ozone (O₃), particulate matter (PM₁₀), and sulphur-dioxide (SO₂) and the selected meteorological factors such as wind speed, temperature, precipitation, and atmospheric pressure. The investigation is based on data observed during a 10-year-long measurement period (2004–2014) in the city of Veszprem located in the western part of Hungary, in the Transdanubia region. In the present research, regression analysis was the chosen statistical tool for the investigation. The analysis found that there is a moderate or a weak relation between the air pollutant concentrations and the meteorological factors.

This work presents the variation in the spatial distribution of atmospheric precipitation determined by means of multidimensional analyses. Precipitation data observed at nine stations of the Institute of Meteorology and Water Management (IMGW) located in central-eastern Poland in the period 1971–2005 are analyzed. Precipitation periodicity index was calculated for each station (measurement point). The index was subjected to descriptive analysis by calculating the average value for the long-term period and the average rate of change. Multidimensional analyses were used to examine the spatial differentiation of precipitation variation. Periodicity indexes in months associated with the first and second principal component were found to account for over 70% variation between the measurement points. The months were as follows: April, May, July, and October. Cluster analysis was performed based on principal components, and it yielded three groups of measurement points with different distribution of precipitation periodicity indexes. The first group consisted of localities characterized by a low precipitation periodicity index in July and October. The second cluster included measurement points which had the lowest precipitation periodicity in May, June, August, and September. The third cluster was formed by only one locality (Białowieża), whose precipitation periodicity index was the highest in every month of the growing season. Both principal component analysis and cluster analysis may be used for an assessment of spatial variation of precipitation periodicity. Their results agree with findings based on the method of isoline interpolation.