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ERA5 is widely considered as a valid proxy of observation at region scales. Surface air temperature from the E-OBS database and 196 meteorological stations across Europe are being applied for evaluation of the fifth-generation ECMWF reanalysis ERA5 temperature data in the period of 1981–2010. In general, ERA5 captures the mean and extreme temperatures very well and ERA5 is reliable for climate investigation over Europe. High correlations ranging from 0.995 to 1.000 indicate that ERA5 could capture the annual cycle very well. However, the high mean biases and high Root Mean Square Error (RMSE) for some European sub-regions (e.g., the Alps, the Mediterranean) reveal that ERA5 underestimates temperatures. The biases can be mainly attributed to the altitude differences between ERA5 grid points and stations. Comparing ERA5 with the other two datasets, ERA5 temperature presents more extreme temperature and small outliers for regions southern of 40° latitude and less extreme temperatures in areas over the Black Sea. In Scandinavia, ERA5 temperatures are more frequently extreme than the observational ones.

The Mediterranean region has long been identified as a climate change hotspot. However, within the Mediterranean, there are smaller sub-areas that exhibit a higher risk of climate change and extremes. Previous research has often focused on indices based on mean climate values, yet extremes are typically more impactful on humans and ecosystems. This study aims to identify the most vulnerable sub-areas of the Mediterranean as climate change hotspots using two indices: the newly introduced Mediterranean Hotspot Index (MED-HOT) and the well-defined Regional Climate Change Index (RCCI). The MED-HOT focuses on extreme high maximum and minimum temperatures, rainfall, and drought, while RCCI assesses changes in mean climate conditions. By combining these indices, we provide an identification of Mediterranean hotspots, capturing both mean climate shifts and extremes. The spatiotemporal variation of both indices across the Mediterranean region is presented and the 20 subregions are categorized into distinct groups. The results reveal that the southeastern Mediterranean is at high risk according to both indices. Additionally, southern Italy is identified as high risk due to changes in mean climate (RCCI), while the northern part is at risk due to extreme events (MED-HOT). The Iberian Peninsula and Greece are also highlighted as vulnerable areas requiring extra attention.

Cyprus is a European island state in the eastern Mediterranean climate change hotspot. Despite being a relatively small island, it has diverse climatic zones, ranging from semi-arid to subhumid in the mountains and humid on Mount Olympos. Given the accelerated rate of environmental change in the region, the present study aims to identify, and update observed trends of critical climate parameters, highlighting vulnerable climatic areas within the island. Moreover, since nationwide multi-model assessments of future climate conditions are limited or outdated, we aim to investigate the range of future climate projections using a 21-member EURO-CORDEX ensemble under pathways RCP2.6 and RCP8.5. Besides mean conditions, we analyze various extreme climate indicators relevant to socio-economic activities such as agriculture, biodiversity, tourism, energy and water resources. Our historical analysis revealed a statistically significant increasing temperature trend (0.4 °C–0.6 °C per decade), which is more pronounced during the summer and spring. Concerning precipitation, the observed trends are not as robust, nevertheless, the southeastern coast and the central regions near the capital city of Nicosia are substantially drier and more prone to further changes in precipitation regimes. The projections for the end of the 21st century, according to the high radiative forcing scenario (RCP8.5), indicate that Cyprus is likely to experience an annual temperature increase of over 4 °C and an approximate 20%–30% reduction in annual rainfall, relative to 1981–2000. These projections highlight an alarming trend that requires urgent attention and proactive measures to mitigate the potential impacts of climate change on the island.

An exceptionally cold episode occurred in January 2017 over the Balkan Peninsula. Analysis of historical records showed that it was one of the coldest extreme episodes. Even though the low temperatures of January 2017 did not break previous low records for all stations, the long duration was quite extreme, resulting in strong socioeconomic impacts in the region of interest. The 10-year to 100-year return values of minimum temperatures were calculated based on block maxima method and the maximum likelihood estimates. The estimated return periods of the absolute minimum temperature are approximately 15 or 20 years for almost all stations. For only one station, the absolute minimum temperature of January 2017 might happen once in every 300 years according to the return level results. Moreover, the extreme cold episode over the Balkans during the period of 5 January 2017 to 12 January 2017 was associated with a significant outbreak of arctic air masses into eastern–central Europe and the Balkans and a cutoff low at the level of 500 hPa over the region.

Meteorological station measurements are an important source of information for understanding the weather and its association with risk, and are vital in quantifying climate change. However, such data tend to lack spatial coverage and are often plagued with flaws such as erroneous outliers and missing values. Alternative meteorological data exist in the form of climate model output that have better spatial coverage, at the expense of bias. We propose a probabilistic framework to integrate temperature measurements with climate model (reanalysis) data, in a way that allows for biases and erroneous outliers, while enabling prediction at any spatial resolution. The approach is Bayesian which facilitates uncertainty quantification and simulation based inference, as illustrated by application to two countries from the Middle East and North Africa region, an important climate change hotspot. We demonstrate the use of the model in: identifying outliers, imputing missing values, non-linear bias correction, downscaling and aggregation to any given spatial configuration.

Triggering hydrological simulations with climate change gridded datasets is one of the prevailing approaches in climate change impact assessment at a river basin scale, with bias correction and spatio-temporal interpolation being functions routinely used on the datasets preprocessing. The research object is to investigate the dilemma arisen when climate datasets are used, and shed light on which process—i.e., bias correction or spatio-temporal interpolation—should go first in order to achieve the maximum hydrological simulation accuracy. In doing so, the fifth generation of the European Centre for Medium Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) temperature and precipitation products of 9 × 9 km spatial resolution, which are considered as the reference data, are initially compared with the same hindcast variables of a regional climate model of 12.5 × 12.5 km spatial resolution over a specific case study basin and for a 10-year period (1991–2000). Thereafter, the climate model’s variables are (a) bias corrected followed by their spatial interpolation at the reference resolution of 9 × 9 km with the use of empirical quantile mapping and spatio-temporal kriging methods respectively, and (b) spatially downscaled and then bias corrected by using the same methods as before. The derived outputs from each of the produced dataset are not only statistically analyzed at a climate variables level, but they are also used as forcings for the hydrological simulation of the river runoff. The simulated runoffs are compared through statistical performance measures, and it is established that the discharges attributed to the bias corrected climate data followed by the spatio-temporal interpolation present a high degree of correlation with the reference ones. The research is considered a useful roadmap for the preparation of gridded climate change data before being used in hydrological modeling.

Terrestrial vegetation emits vast amounts of monoterpenes into the atmosphere, influencing ecological interactions and atmospheric chemistry. Global emissions are simulated as a function of temperature with a fixed exponential relationship (β coefficient) across forest ecosystems and environmental conditions. We applied meta-analysis algorithms on 40 years of published monoterpene emission data and show that relationship between emissions and temperature is more sensitive and intricate than previously thought. Considering the entire dataset, a higher temperature sensitivity (β = 0.13 ± 0.01 °C −1 ) is derived but with a linear increase with the reported coefficients of determination (R 2 ), indicating that co-occurring environmental factors modify the temperature sensitivity of the emissions that is primarily related to the specific plant functional type (PFT). Implementing a PFT-dependent β in a biogenic emission model, coupled with a chemistry – climate model, demonstrated that atmospheric processes are exceptionally dependent on monoterpene emissions which are subject to amplified variations under rising temperatures.

During the last few decades, the utilization of the data from climate models in hydrological studies has increased as they can provide data in the regions that lack raw meteorological information. The data from climate models data often present biases compared to the observed data and consequently, several methods have been developed for correcting statistical biases. The present study uses the copula for modeling the dependence between the daily mean and total monthly precipitation using E-OBS data in the Mesta/Nestos river basin in order to use this relationship for the bias correction of the MPI climate model monthly precipitation. Additionally, both the non-corrected and bias corrected data are tested as they are used as the inputs to a spatial distributed hydrological model for simulating the basin runoff. The results showed that the MPI model significantly overestimates the E-OBS data while the differences are reduced sufficiently after the bias correction. The outputs from the hydrological models were proven to coincide with the precipitation analysis results and hence, the simulated discharges in the case of copula corrected data present an increased correlation with the observed flows.

This study analyses the temperature and precipitation dependence among stations in the Mediterranean. The first station group is located in the eastern Mediterranean (EM) and includes two stations, Athens and Thessaloniki, while the western (WM) one includes Malaga and Barcelona. The data was organized in two time periods, the hot-dry period and the cold-wet one, composed of 5 months, respectively. The analysis is based on a new statistical technique in climatology: the Copula method. Firstly, the calculation of the Kendall tau correlation index showed that temperatures among stations are dependant during both time periods whereas precipitation presents dependency only between the stations located in EM or WM and only during the cold-wet period. Accordingly, the marginal distributions were calculated for each studied station, as they are further used by the copula method. Finally, several copula families, both Archimedean and Elliptical, were tested in order to choose the most appropriate one to model the relation of the studied data sets. Consequently, this study achieves to model the dependence of the main climate parameters (temperature and precipitation) with the Copula method. The Frank copula was identified as the best family to describe the joint distribution of temperature, for the majority of station groups. For precipitation, the best copula families are BB1 and Survival Gumbel. Using the probability distribution diagrams, the probability of a combination of temperature and precipitation values between stations is estimated.

Extreme heat events in the Middle East have become increasingly frequent and intense due to human-driven climate change. During the Hajj pilgrimage in Mecca, Saudi Arabia, in June 2024, temperatures soared to a record-breaking 51.8 °C, resulting in the tragic deaths of at least 1300 pilgrims and over 2700 non-fatal injuries. Our analysis of future projections, tailored for the region, indicates that in a warmer climate, such hazards may become a regular occurrence. Addressing these challenges through effective climate mitigation and adaptation is essential to building resilience against future extreme heat risks.