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We present the first evaluation of the wind field from the ensemble of kilometer-scale simulations from the CORDEX-Flagship Pilot Study on convection, with focus on the Adriatic region. Kilometer-scale climate models, also known as convection-permitting models (CPMs), produce a good representation of small-scale topographic features and consequently a more detailed depiction of dynamical and thermal circulations. These enable a reliable view of climate characteristics of the wind field, especially in coastal regions and over complex terrain, such as the Adriatic region. We investigate the (potential) added value introduced by CPMs compared to classical "cumulus-parametrized" regional climate models (RCMs), reanalysis and station observations. For this purpose, wind components at 10 m level are used at 3-hourly frequency. All simulations cover a 10-year period, extending from 2000 to 2009. In terms of the standard statistical parameters such as correlation coefficient and temporal standard deviation, CPMs are very dependent on their parent RCM performance. However, the orographic forcing emphasizes the potential added value and CPMs contain some fine spatial scale variability (i.e., stronger extremes by 25% and more accurate wind direction) that is absent in coarser RCMs and reanalysis. The potential added value is higher in the cold season compared to the warm season due to the proportion of severe wind events. CPMs reproduce well the typical wind regimes along the Adriatic coast, namely Bora and Sirocco. The benefit of using CPMs is especially pronounced in simulating Bora maximum wind speeds in northern Adriatic and Sirocco frequencies in southern Adriatic. Based on our overall analysis, we conclude that CPMs provide added value compared to coarser models, especially in the complex coastal terrain. [ABSTRACT FROM AUTHOR]

The main goal of this study is to present a recently developed classification method for weather types based on the vorticity and the location of the synoptic centers relative to the Adriatic region. The basis of the present objective classification, applied to the Adriatic region, is the subjective classification developed by Poje. Our algorithm considered daily mean sea-level pressure and 500 hPa geopotential height to define one out of 17 possible weather types. We applied the algorithm to identify which weather type was relevant in the generation of the two typical near-surface winds over the Adriatic region, namely Bora and Sirocco. Two high-resolution (0.11°) EURO-CORDEX regional climate models were used, SMHI-RCA4 and DHMZ-RegCM4, forced by several CMIP5 global climate models and analyzed for two 30-year periods: near-present day and mid-21st century climate conditions under the high-end Representative Concentration Pathway (RCP8.5) scenario. Bora and Sirocco days were extracted for each weather type and a distribution over the 30-year period was presented. Our results suggest that in the winter season, climate model projections indicate a reduction in the main cyclonic types relevant in the formation of Bora over the entire Adriatic region and an increase in the number of anticyclonic types relevant in Sirocco events. In contrast, for the summer season, an increase in the main anticyclonic Bora-related weather types is found in the ensemble over the northern Adriatic region. [ABSTRACT FROM AUTHOR]

Since changes in temperature and precipitation have different effects on (a) all developmental stages of grapevines in most of the wine regions worldwide (i.e., on their phenological characteristics) and (b) different varieties, a comprehensive database of bioclimatic indices has been calculated and analysed for Croatian wine producing regions. The database consists of the average growing season temperature, growing degree‐days, Huglin index, dryness index and cool night index that are based on all available meteorological measurements as well as the outputs of regional climate models (RCMs) from the EURO‐CORDEX database. The horizontal grid spacing of 0.11° from the RCM ensembles enabled a fine‐scale determination of bioclimatic indices for the present and future climate in Croatia. In addition, statistical analyses (standard statistical parameters and Bayesian method) were carried out to examine trends in sugar content, total acidity and date of harvest. Calculations were performed for the present and future climate on the basis of data from seven selected vineyards/wineries and four varieties ('Graševina', 'Plavac mali', 'Chardonnay' and 'Merlot'). The results show whether the part of Croatia that is suitable for grape cultivation in the present climate will continue to be favourable in the future within the Mediterranean area. In general, projections suggest further warming and drying of the climate in Croatia and an earlier harvest, with some variations among varieties that show latitude dependence. Projections for the future climate also suggest that the existing viticultural zoning will be much less adequate for the Croatian territory because it reduces the economically sustainable production of wine in certain areas. [ABSTRACT FROM AUTHOR]

Statistical downscaling methods (SDMs) are techniques used to downscale and/or bias‐correct climate model results to regional or local scales. The European network VALUE developed a framework to evaluate and inter‐compare SDMs. One of VALUE's experiments is the perfect predictor experiment that uses reanalysis predictors to isolate downscaling skill. Most evaluation papers for SDMs employ simple statistical diagnostics and do not follow a process‐based rationale. Thus, in this paper, a process‐based evaluation has been conducted for the more than 40 participating model output statistics (MOS, mostly bias correction) and perfect prognosis (PP) methods, for temperature and precipitation at 86 weather stations across Europe. The SDMs are analysed following the so‐called "regime‐oriented" technique, focussing on relevant features of the atmospheric circulation at large to local scales. These features comprise the North Atlantic Oscillation, blocking and selected Lamb weather types and at local scales the bora wind and the western Iberian coastal‐low level jet. The representation of the local weather response to the selected features depends strongly on the method class. As expected, MOS is unable to generate process sensitivity when it is not simulated by the predictors (ERA‐Interim). Moreover, MOS often suffers from an inflation effect when a predictor is used for more than one station. The PP performance is very diverse and depends strongly on the implementation. Although conditioned on predictors that typically describe the large‐scale circulation, PP often fails in capturing the process sensitivity correctly. Stochastic generalized linear models supported by well‐chosen predictors show improved skill to represent the sensitivities. [ABSTRACT FROM AUTHOR]

In this study, an investigation of a new thunderstorm intensity index (TSII) derived from lightning data is performed, along with its relationship to rain, wind, hail and waterspouts as well as instability indices (CAPE, LI, KI, and DLS). The study area is located in the northeastern Adriatic and includes various terrain types in a relatively small area (coastal, flatlands, hills and valleys, and mountain regions). The investigated period covers 11 years (2008–2018). The mathematical algorithm standing behind the TSII is based on the well-established methodology of lightning jump, allowing us to recognize areas where intensification in thunderstorms occurred. Our results suggest that these areas (with a positive TSII) experience significantly higher rain intensities and have higher total amounts of precipitation compared with areas where thunderstorms did not generate a TSII. Moreover, 76% of thunderstorm hail cases were associated with the presence of a TSII within a 15 km distance. The maximum reported wind speed also has higher values on a day with a TSII. Out of 27 waterspout events associated with lightning, 77% were related to a TSII. Due to the good spatial (3 km × 3 km) and high temporal (2 min) resolution of lightning data, the TSII can recognize even a local and short-lived intense system that is often misread by radars and satellites due to their inferior temporal resolution. The TSII is designed to be used as a climatological and diagnostic variable that could serve in lieu of more established data sources (e.g., station measurements and observations, radar imagery, etc.) if they are unavailable. [ABSTRACT FROM AUTHOR]