The Mediterranean region is highly vulnerable to the impacts of climate change including Heatwave Events (HEs). Greece in particular, has experienced the effects of such events across various sectors, including public health, the environment, energy, and the socio-economic domain. This study presents a climatology of HE over Greece from 1960 to 2022 utilising data from 67 weather stations operated by the Hellenic National Meteorological Service (HNMS). The frequency, duration, and severity of HE are analysed using statistical methods including trend and changepoint analysis. To better understand the intensity of HEs, they are classified into five intensity categories (ICs) based on percentile-based thresholds of the maximum daily temperature. In addition, the interannual, seasonal and monthly occurrence of HEs is examined and compared across seven distinct climate regions of Greece. The findings reveal positive trends in heatwave frequency (HF) in almost 80% of the stations across Greece and across all ICs particularly during spring and summer. Furthermore, heatwave duration (HD), mostly during summer and autumn, and heatwave severity (HS), mostly during spring, exhibited the same behaviour, although with lower percentages compared to HF. Negative trends in HS are observed at a small number of stations. Spatial analysis indicates that the most affected regions by HEs are northern and western Greece, although changes are observed throughout the country. Finally, changepoint analysis using Pettitt's test reveals significant changes in HF and HD between the 1970s and 2010s. Additionally, the number of stations showing significant changes decreases as the intensity of ICs increases.

This study investigates the long-term trends and variability of heatwaves in Greece, analyzing their frequency, duration, and intensity from 1960 to 2022 using high-quality meteorological data from the Hellenic National Meteorological Service. The research utilizes robust statistical methods, including Theil–Sen regression and the Mann–Kendall trend test, to assess long-term trends across different timescales. The findings reveal a significant increase in heatwave frequency and intensity, particularly in recent decades, with notable seasonal differences. While summer remains the most affected period, an upward trend in spring and autumn heatwaves suggests an extension of the heatwave season. The intensity of heatwaves has also increased, indicating a growing risk to vulnerable populations and critical infrastructure.

The stable hydrogen and oxygen isotope compositions of leaf water reflect the water exchange between the atmosphere, pedosphere and biosphere. Although leaf water isotopes can be experimentally analyzed at a specific location, it is still challenging to accurately map the leaf water isotopes on a large spatial scale. In this work, we modelled the spatial distribution of the seasonal-scale stable hydrogen and oxygen isotopes in leaf water across China using climate datasets, including the ECMWF European Reanalysis 5-Land (ERA) and the China Meteorological Forcing Dataset (CMFD), and examined Standard Model (STD) with equilibrium assumption and Non-Equilibrium Empirical Temperature Model (NEET) with non-equilibrium assumption. Annually, the NEET model generated numerically higher leaf water delta H-2 and delta O-18 values than the STD model. Compared to STD, the NEET model exhibited delta H-2 enrichments of 14 parts per thousand (ERA) and 7 parts per thousand (CMFD), with delta O-18 enrichments reaching 2 parts per thousand (ERA) and 1 parts per thousand (CMFD). ERA-derived delta H-2 values are lower than CMFD-derived values by 10 parts per thousand under STD, but this gap narrowed to 3 parts per thousand with NEET. Similarly, ERA and CMFD differences in delta O-18 decreased from 4 parts per thousand (STD) to 3 parts per thousand (NEET). This phenomenon persists on seasonal scales, but with numerical differences. Validation using measured data indicates that the model shows better performance in southern China, the Tibetan Plateau, and northeastern China (r > 0.7, p < 0.01), while the simulation results in the arid region need to be improved (r > 0.3, p < 0.05). Based on a comprehensive assessment of various data sources and fractionation assumptions, we recommend combining ERA data under equilibrium fractionation conditions, although the spatial dependence of their performance still exists. Leaf water isotope values highly correlate with relative humidity, which may explain regional incoherence. This new leaf water isotope map provides a high-resolution reference for large-scale plant water isotope studies and is also valuable for isotope ecology, agriculture and food science.

Stable water isotopes are natural tracers quantifying the contribution of moisture recycling to local precipitation,i.e.,the moisture recycling ratio,but various isotope-based models usually lead to different results,which affects the accuracy of local moisture recycling.In this study,a total of 18 stations from four typical areas in China were selected to compare the performance of isotope-based linear and Bayesian mixing models and to determine local moisture recycling ratio.Among the three vapor sources including advection,transpiration,and surface evaporation,the advection vapor usually played a dominant role,and the contribution of surface evaporation was less than that of transpiration.When the abnormal values were ignored,the arithmetic averages of differences between isotope-based linear and the Bayesian mixing models were 0.9%for transpiration,0.2%for surface evaporation,and-1.1%for advection,respectively,and the medians were 0.5%,0.2%,and-0.8%,respectively.The importance of transpiration was slightly less for most cases when the Bayesian mixing model was applied,and the contribution of advection was relatively larger.The Bayesian mixing model was found to perform better in determining an efficient solution since linear model sometimes resulted in negative contribution ratios.Sensitivity test with two isotope scenarios indicated that the Bayesian model had a relatively low sensitivity to the changes in isotope input,and it was important to accurately estimate the isotopes in precipitation vapor.Generally,the Bayesian mixing model should be recommended instead of a linear model.The findings are useful for understanding the performance of isotope-based linear and Bayesian mixing models under various climate backgrounds.

The stable water isotopes of precipitation provide important information about the hydrological circulation. In the arid mountain-basin system in central Asia, the altitude effect of precipitation isotopes has been a controversial topic in recent years, but the sample availability in extreme environments constrains the accurate understanding of the relationship between altitude and stable isotopes in precipitation. Based on the observation of precipitation isotopes around the Tarim Basin covered by the world's second-largest shifting desert, we examined the relationship between altitude and isotope composition. There is an altitude effect of precipitation isotopes between the basin and the surrounding mountains, with the modelled gradient for annual mean 618O being approximately 1.96 %o per 1000 m, which is weaker than the observed gradient focusing on the oases (2.75 %o per 1000 m). The largest modelled difference in 618O between 1000-2000 m and 2000-3000 m above sea level occurs in August and September. The periods with a larger gradient of altitude effect usually have higher temperature and more precipitation. Across the westerlies-dominated central Asia, the below-cloud evaporation enhances the altitude gradient of precipitation isotopes for most areas. The findings are useful to understand the local and remote drivers of precipitation isotopes and the paleoaltimetry of stable isotopes in climate proxies.

Caragana korshinskii and Tamarix ramosissima are pioneer shrubs for water and soil conservation and windbreak and sand fixation in arid and semi-arid areas. Understanding the water use patterns of C. korshinskii and T. ramosissima and their response to rainfall is of great importance for their survival in regions where drought occurs. In this work we present the monitoring results of stable isotopes in soil water (depths from 0 to 200 cm), twig xylem water of juvenile, intermediate, and adult C. korshinskii and T. ramosissima. The monitoring campaign took place in western Chinese Loess Plateau from July to October 2020. During the same period, we also measured relevant environmental and meteorological variables and soil water content. The results showed that juvenile and of intermediate age C. korshinskii both mainly absorb water from the surface (0–10 cm) and shallow (10–40 cm) soil layers, but adult C. korshinskii use mainly water from the deep soil layers. Juvenile and of intermediate age T. ramosissima extract water from deep soil layers, while adult T. ramosissima use mainly water from middle (40–100 cm) soil depths. Both plant species increase the proportion of surface and shallow soil layer water after precipitation. This increase is more pronounced and faster for the C. korshinskii of intermediate age rather than for juvenile and adult plants. On the contrary, it is the absorption of surface and shallow soil water from juvenile and of intermediate T. ramosissima plants that fluctuates more after precipitation than from adult plants. Our findings provide a reference for vegetation restoration and ecological management of the western Chinese Loess Plateau.

This paper explores the potential contribution of quantum computing, specifically the Variational Quantum Eigensolver (VQE), into atmospheric physics research and application problems using as an example the Lorenz system, a paradigm of chaotic behavior in atmospheric dynamics. Traditionally, the complexity and non-linearity of atmospheric systems have presented significant computational challenges. However, the advent of quantum computing, and in particular the VQE algorithm, offers a novel approach to these problems. The VQE, known for its efficiency in quantum chemistry for determining ground state energies, is adapted in our study to analyze the non-Hermitian Jacobian matrix of the Lorenz system. We employ a method of Hermitianization and dimensionality augmentation to make the Jacobian amenable to quantum computational techniques. This study demonstrates the application of VQE in calculating the eigenvalues of the Lorenz system's Jacobian, thus providing insights into the system's stability at various equilibrium points. Our results reveal the VQE's potential in addressing complex systems in atmospheric physics. Furthermore, we discuss the broader implications of VQE in handling non-Hermitian matrices, extending its utility to operations like diagonalization and Singular Value Decomposition (SVD), thereby highlighting its versatility across various scientific fields. This research extends beyond the realm of chaotic systems in atmospheric physics, underscoring the significant potential of quantum computing to tackle complex, real-world challenges.

This study investigates the variability and forecasting ability of time-trend in mean annual surface air temperatures in Greece. Using Gaussian time-trend models, we first investigate some basic statistical characteristics associated with time-trends, such the mean and variance. This can reveal whether temperatures’ mean and volatility changes are associated with time. To do so, we have used mean measures of the minimum and maximum air temperatures observed at several meteorological stations of the Hellenic Meteorological Service located in Greece for the 1960-2010 period. As a second experiment, we investigate whether temperature trends are forecastable or not using various Gaussian time-trend and no-time-trend models. The results are highly significant since they reveal the seasons, the periods and the type of models for which the inter-annual trends out-perform the no-trend ones. Moreover, they also show the statistical characteristics, such as the mean and variability of the time-trend under various seasons and sub-periods.

This study investigates the trend dynamics of some extreme air temperature bounds estimated via quantile models. In practice, we investigate the dynamics of the 5% lower, the median and the 95% upper quantile measures of the minimum and maximum temperature in various regions of Greece. For this purpose, we propose some semi-parametric quantile trend models. Our estimation is based on a Bayesian early-rejection Markov chain Monte Carlo algorithm. The climatic data used, are the mean monthly homogenized minimum and maximum air temperatures observed at several meteorological stations of the Hellenic Meteorological Service located in Greece for the 1960-2010 period. Results based on several margins of datas' distribution, reveal the strongest and the weakest inter-annual quantile trends for both temperature extremes. The results are very significant since they show the existence of time-trend heterogeneities in temperature extremes under alternative types of quantiles, time periods, geographical zones and seasons.

Integration of photovoltaic modules into greenhouse roofs is a novel and intriguing method. The cost of products grown in greenhouses is particularly high because of their high energy consumption for heating and cooling, and at the same time the increase in demand for available land, increasing its cost and creating spatial issues, the integration of photovoltaics on the roof of greenhouses is a highly viable solution. Simultaneously, the use of solar radiation is critical to maintain optimal crop development, while also being a renewable energy source. However, photovoltaics reduce the incoming solar radiation in the greenhouse, due to their shade. Shading can be either beneficial for the crops or not, depending on the crop type, thus it is vital to find the shading caused by photovoltaics both temporally and spatially. In this study, a model calculating the shading in a greenhouse due to roof-integrated photovoltaics is developed, based on the Sun position, the geometry of both the greenhouse and of the roof-integrated photovoltaics and their position on the greenhouse roof. Calculating the coefficient of variation of radiation data, for the shaded and unshaded areas using the proposed algorithm, it was found the coefficient of variation for the shaded areas is lower than that for the unshaded areas for a least 76% of the time. Also, the radiation values under the shaded area are more uniform. The proposed model is a tool for PV designers, operators, and owners, in order to optimize the potential of their solar panel installations.

We are investigating the possible origin of small-scale anomalies, like the annual stratospheric temperature anomalies. Unexpectedly within known physics, their observed planetary "dependency", does not match concurrent solar activity, whose impact on the atmosphere is unequivocal; this points at an additional energy source of exo-solar origin. A viable concept behind such observations is based on possible gravitational focusing by the Sun and its planets towards the Earth of low-speed invisible streaming matter; its influx towards the Earth gets temporally enhanced. Only a somehow "strongly" interacting invisible streaming matter with the small upper atmospheric screening can be behind the observed temperature excursions. Ordinary dark matter (DM) candidates like axions or WIMPs, cannot have any noticeable impact. The associated energy deposition is $\mathcal{O}(\sim 1000\, \mathrm{GeV}/{{\mathrm{cm}}^2}/\mathrm{sec})$. The atmosphere has been uninterruptedly monitored for decades. Therefore, the upper atmosphere can serve as a novel (low-threshold) detector for the dark Universe, with built-in spatiotemporal resolution while the solar system gravity acts temporally as a signal amplifier. Interestingly, the anomalous ionosphere shows a relationship with the inner earth activity like earthquakes. Similarly investigating the transient sudden stratospheric warmings within the same reasoning, the nature of the assumed "invisible streams" could be deciphered.

Diurnal Temperature Range (DTR), defined as the difference between the daily maximum (Tmax) and daily minimum (Tmin) air temperature, has received considerable attention as an important indicator of climate change. In the present study, we analyse long-term highly homogenized DTR data from 51 Greek stations and investigate their spatiotemporal changes. The long-term temporal changes of DTR revealed mixed patterns with both increasing and decreasing trends over the study period and distinct seasonal differentiations. DTR pattern in Athens has fluctuated since the beginning of the twentieth century, generally following warming and cooling air temperature trends. After the mid-1980s, DTR showed a pronounced decreasing trend at a rate of 0.47 °C/decade in summer (p < 0.01) due to higher warming rates of Tmin, suggesting the combined effects of regional warming and urbanization levels.

In this paper, we describe and analyze two datasets entitled “Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021” and “Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010”. These datasets provide the homogenised monthly and daily mean (TG), minimum (TN), and maximum (TX) temperature and precipitation (RR) records since 1960 at 366 stations in China and 56 stations in Greece. The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720 . For China, the regional mean annual TG, TX, TN, and RR series during 1960–2021 showed significant warming or increasing trends of 0.27°C (10 yr) −1 , 0.22°C (10 yr) −1 , 0.35°C (10 yr) −1 , and 6.81 mm (10 yr) −1 , respectively. Most of the seasonal series revealed trends significant at the 0.05 level, except for the spring, summer, and autumn RR series. For Greece, there were increasing trends of 0.09°C (10 yr) −1 , 0.08°C (10 yr) −1 , and 0.11°C (10 yr) −1 for the annual TG, TX, and TN series, respectively, while a decreasing trend of −23.35 mm (10 yr) −1 was present for RR. The seasonal trends showed a significant warming rate for summer, but no significant changes were noted for spring (except for TN), autumn, and winter. For RR, only the winter time series displayed a statistically significant and robust trend [−15.82 mm (10 yr) −1 ]. The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses.

Altitude is one of the important factors influencing the spatial distribution of precipitation, especially in a complex topography, and simulations of isotope-enabled climate models can be improved by altitude correlation. Here we compiled isotope observations at 12 sites in Lanzhou, and examined the relationship between isotope error and altitude in this valley in the Chinese Loess Plateau using isoGSM2 isotope simulations. Before altitude correction, the performance using the nearest four grid boxes to the target site is better than that using the nearest box; the root mean square error in δ18O using the nearest four grid boxes averagely decreases by 0.37‰ compared to that using the nearest grid boxes, and correlation coefficient increases by 0.05. The influences of altitude on precipitation isotope errors were examined, and the linear relationship between altitude error and isotope simulations was calculated. The strongest altitude isotopic gradient between δ18O mean bias error and altitude error is in summer, and the weakest is in winter. The regression relationships were used to correct the simulated isotope composition. After altitude correction, the root mean square error decreases by 1.21‰ or 0.86‰ using the nearest one or four grid boxes, respectively, and the correlation coefficient increases by 0.13 or 0.08, respectively. The differences between methods using the nearest one or four grids are also weakened, and the differences are 0.02‰ for root mean square error and −0.01 for the correlation coefficient. The altitude correction of precipitation isotopes should be considered to downscale the simulations of climate models, especially in complex topography.

Wind energy power plants are vulnerable, among others, to abrupt weather changes caused especially by thunderstorms associated with lightning activity and the accompanying severe wind gusts and rapid wind direction changes. Due to a range of damages such phenomena may cause, the knowledge of the relationship between the storm systems and the produced wind field is essential to establish a wind power plant during the construction and operation phase as well. In first part of this study, the relationship of severe wind gusts in regard to lightning activity in a wind farm in a hilly region of western Greece is investigated. Wind data come from wind turbines covering a period of three years (2012-2014), while the corresponding lightning data from the ZEUS lighting detection network. The analysis shows that wind gusts are well correlated to lightning strikes. Furthermore, correlation maximizes during winter when well organized weather systems affect the area and minimum in summer as a result of local storms due to thermal instability. In the second part the study focuses on the development of an ANN model in order to forecast these two parameters in a horizon of 1-h ahead by using except for the wind data, four variables namely CAPE, TTI, wind speed at the 500 hPa isobaric level and the 0-6 km vertical wind shear. The results revealed that proposed model could be considered as a promising tool in simulating the occurrence both of wind gusts and lightning flashes providing a relatively good evidence of the possibility of occurrence of such events.

Atmospheric water vapor is an important greenhouse gas, mainly distributed in the lower tropospheric levels where its concentration varies significantly in space and time; consequently, so does precipitable water. This work uses information from thermal infrared images to model precipitable water (PW) under clear skies. PW is measured using a portable sun-photometer and thermal images obtained through a high-cost thermal infrared camera. PW depends on the zenith-point temperature (Tb) exhibiting a non-linear positive exponential relationship, with systematic and dispersion errors of 0.04 mm and 1.9 mm.

Buildings have a significant energy and environmental footprint. In Greece, there are about 4.1 million buildings, a quarter of which are exclusive-use nonresidential buildings. The aim of this work is to define the average construction and technical characteristics of the existing Hellenic nonresidential buildings, and to assess their energy performance, responding to the need for more information and insight on this sector. The work exploits data from about 2400 building energy audits that include general information as well as specific characteristics on the envelope construction and the electromechanical installations. The information were first screened by a data quality control and were then organized into 239 classes that correspond to the Hellenic nonresidential building typology, defined in terms of building use, vintage, and location. The analysis revealed that there is a great potential for upgrading the existing building stock, since only 4% of the audited buildings meet the current minimum thermal envelope code requirements and 15% are in compliance with code recommendations for the technical installations. Overall, only 1% of the buildings have fully compliant envelope and technical installations. The calculated total final energy use averages 217.5kWh/m2 for the entire NR sector, ranging between 89.0kWh/m2 and 530.0kWh/m2 among the different building uses.

Caragana korshinskii Kom. and Tamarix ramosissima Ledeb. are pioneer shrubs for water and soil conservation, and for windbreak and sand fixation in arid and semi-arid areas. Understanding the water use characteristics of different pioneer shrubs at different ages is of great importance for their survival when extreme rainfall occurs. In recent years, the stable isotope tracing technique has been used in exploring the water use strategies of plants. However, the widespread δ2H offsets of stem water from its potential sources result in conflicting interpretations of water utilization of plants in arid and semi-arid areas. In this study, we used three sets of hydrogen and oxygen stable isotope data (δ2H and δ18O, corrected δ2H_c1 based on SW-excess and δ18O, and corrected δ2H_c2 based on −8.1‰ and δ18O) as inputs for the MixSIAR model to explore the water use characteristics of C. korshinskii and T. ramosissima at different ages and in response to rainfall. The results showed that δ2H_c1 and δ18O have the best performance, and the contribution rate of deep soil water was underestimated because of δ2H offset. During the dry periods, C. korshinskii and T. ramosissima at different ages both obtained mostly water from deeper soil layers. After rainfall, the proportions of surface (0–10 cm) and shallow (10–40 cm) soil water for C. korshinskii and T. ramosissima at different ages both increased. Nevertheless, there were different response mechanisms of these two plants for rainfall. In addition, C. korshinskii absorbed various potential water sources, while T. ramosissima only used deep water. These flexible water use characteristics of C. korshinskii and T. ramosissima might facilitate the coexistence of plants once extreme rainfall occurs. Thus, reasonable allocation of different plants may be a good vegetation restoration program in western Chinese Loess Plateau.

Satellite and reanalysis-derived solar products have gained great attention due to the inadequate number of radiometric stations worldwide, however, they are associated with considerable uncertainties. This study deals with the ground-based validation of Global Horizontal Irradiance from CAMS radiation service (GHICAMS) and the application of supervised machine learning algorithms (MLAs) to site-adapt GHICAMS. The validation of GHICAMS against measurements shows significant systematic and dispersion errors for all-sky (nMBE = 4.9% and nRMSE = 15.7%) and cloudy conditions (nMBE = 17.6% and nRMSE = 38.8%). Under clear skies, CAMS performs adequately (nMBE < 1% and nRMSE < 5%). All MLAs lead to reduced errors for the site-adapted irradiances. MBE is improved by more than 50%, accompanied by significant reductions in RMSE for various solar zenith angles and cloud fractions. The best results are revealed for the tree-based MLAs and especially for Random Forests. (C) 2022 Elsevier Ltd. All rights reserved.