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In the context of the CORDEX project, an ensemble of regional climate simulations (RCMs) of high resolution on a 0.11° grid has been generated for Europe with the objective of improving the representation of regional to local‐scale atmospheric phenomena. However, such simulations are computationally expensive and do not always reveal added value. Here, a recently proposed metric (the distribution added value [DAV]) is used to determine the added value of all available EURO‐CORDEX high‐resolution simulations at 0.11° for daily mean wind speed compared to their respective coarser‐gridded 0.44° counterparts and their driving fields, hindcast and historical experiments. The analysis consists in comparing the degree of similarity between normalized wind probability density function (PDF) of simulations and observations. In addition, the use of a normalized PDF allows for a direct spatial comparison among the different regions and time periods. Results show that RCMs add value to their reanalysis or forcing global model, but the nature and magnitude of the improvement on the representation of wind speed may vary depending on the model, region and season. We found most RCMS at 0.11° to outperform models at the 0.44° resolution in terms of their quality in capturing the measured wind speed PDF. When looking at the upper tail of the wind speed PDF, the benefits of downscaling are generally larger. At the regional scale, added value is obtained for 0.11° with respect to 0.44° resolution for all subdomains studied, particularly over the Mediterranean, the Iberian Peninsula and the Alps. When analysing the added value dependence on altitude, runs at 0.11° models represent better the locations below 50 m and above 350 m of altitude, and the 0.44° increasingly under‐perform for higher altitudes. Overall, DAV are larger at 0.11° than at 0.44° resolution, due to a better performance of local‐scale feedbacks at high resolution. [ABSTRACT FROM AUTHOR]

The recent ECMWF (European Centre of Medium‐Range Weather Forecasts) reanalysis product (ERA5) is a 3D‐atmospheric dataset of unparalleled value to advance the characterization and understanding of the main global coastal low‐level jet (CLLJ) systems, due to its finer resolution (horizontal and vertical) and hourly output. Regional climate studies have revealed the added value of higher spatial and temporal resolutions of models and reanalyses for the representation of CLLJs, which play an important role in the definition of the regional climate along the Eastern Boundary Current Systems. ERA5 reveals an improvement in the representation of the wind speed and temperature vertical profiles within the boundary layer in CLLJ regions. This improvement enables a better representation of CLLJs and therefore a new view on their features. ERA5 show larger CLLJ frequencies of occurrence and larger wind speed intensities, when compared with previous reanalysis. The maximum frequency of occurrence ranges from 20% in the West Australian and 73% in the Oman CLLJs, in the summer season, which constitutes an increase of 10% compared to ERA‐Interim. Moreover, a shift for higher wind speeds is also found, where the maximum wind speed approach 30 m·s−1. Typically, CLLJs are found below 500 m above sea level, where the finer vertical discretization results clearly in a better description of the CLLJ peak height. Regarding the diurnal cycles of CLLJs frequency of occurrence, ERA5 exposes higher peaks and larger amplitudes, where the maximum hourly frequency is about 38% in the Oman CLLJ and the diurnal amplitude can vary between 7 and 14%. The diurnal maximum and minimum frequency of CLLJ occurrence can be found between 1900 and 2200 UTC, and between 1000 and 1400 LST, respectively. The momentum and temperature budget profiles reveal significant differences between CLLJ regions and emphasize the relevance of local processes in its occurrences. [ABSTRACT FROM AUTHOR]

In a recent study, Coppola et al. assessed the ability of an ensemble of convection‐permitting models (CPM) to simulate deep convection using three case studies. The ensemble exhibited strong discrepancies between models, which were attributed to various factors. In order to shed some light on the issue, we quantify in this article the uncertainty associated to different physical parameterizations from that of using different initial conditions, often referred to as the internal variability. For this purpose, we establish a framework to quantify both signals and we compare them for upper atmospheric circulation and near‐surface variables. The analysis is carried out in the context of the CORDEX Flagship Pilot Study on Convective phenomena at high resolution over Europe and the Mediterranean, in which the intermediate RCM WRF simulations that serve to drive the CPM are run several times with different parameterizations. For atmospheric circulation (geopotential height), the sensitivity induced by multi‐physics and the internal variability show comparable magnitudes and a similar spatial distribution pattern. For 2‐m temperature and 10‐m wind, the simulations with different parameterizations show larger differences than those launched with different initial conditions. The systematic effect over 1 year shows distinct patterns for the multi‐physics and the internal variability. Therefore, the general lesson of this study is that internal variability should be analysed in order to properly distinguish the impact of other sources of uncertainty, especially for short‐term sensitivity simulations. [ABSTRACT FROM AUTHOR]

This work analyses three uncertainty sources affecting the observation‐based gridded data sets: station density, interpolation methodology and spatial resolution. For this purpose, we consider precipitation in two countries, Poland and Spain, three resolutions (0.11, 0.22 and 0.44°), three interpolation methods, both areal‐ and point‐representative implementations, and three different densities of the underlying station network (high/medium/low density). As a result, for each resolution and interpolation approach, nine different grids have been obtained for each country and inter‐compared using a variance decomposition methodology. Results indicate larger differences among the data sets for Spain than for Poland, mainly due to the larger spatial variability and complex orography of the former region. The variance decomposition points out to station density as the most influential factor, independent of the season, the areal‐ or point‐representative implementation and the country considered, and slightly increasing with the spatial resolution. In contrast, the decomposition is stable when extreme precipitation indices are considered, in particular for the 50‐year return value. Finally, the uncertainty due to station sub‐sampling inside a particular grid box decreases with the number of stations used in the averaging/interpolation. In the case of spatially homogeneous grid boxes, the interpolation approach obtains similar results for all the parameters, excepting the wet day frequency, independently of the number of stations. When there is a more significant internal variability in the grid box, the interpolation is more sensitive to the number of stations, pointing out to a minimum stations' density for the target resolution (six to seven stations). [ABSTRACT FROM AUTHOR]

Credible information about the properties and changes of extreme events on the regional and local scales is of prime importance in the context of future climate change. Within the EU‐COST Action VALUE a comprehensive validation framework for downscaling methods has been developed. Here we present validation results for extremes of temperature and precipitation from the perfect predictor experiment that uses reanalysis‐based predictors to isolate downscaling skill. The raw reanalysis output reveals that there is mostly a large bias with respect to the extreme index values at the considered stations across Europe, clearly pointing to the necessity of downscaling. The performance of the downscaling methods is closely linked to their specific structure and setup. All methods using parametric distributions require non‐standard distributions to correctly represent marginal aspects of extremes. Also, the performance is much improved by explicitly including a seasonal component, particularly in case of precipitation. With respect to the marginal aspects of extremes the best performance is found for model output statistics (MOS), weather generators (WGs) as well as perfect prognosis (PP) methods using analogues. Spell‐length‐related extremes of temperature are best assessed by MOS and WGs, spell‐length‐related extremes of precipitation by MOS and PP methods using analogues. The skill of PP methods with transfer functions varies strongly across the methods and depends on the extreme index, region and season considered. [ABSTRACT FROM AUTHOR]

The influence of uncertainties in gridded observational reference data on regional climate model (RCM) evaluation is quantified on a pan‐European scale. Three different reference data sets are considered: the coarse‐resolved E‐OBS data set, a compilation of regional high‐resolution gridded products (HR) and the European‐scale MESAN reanalysis. Five high‐resolution ERA‐Interim‐driven RCM experiments of the EURO‐CORDEX initiative are evaluated against each of these references over eight European sub‐regions and considering a range of performance metrics for mean daily temperature and daily precipitation. The spatial scale of the evaluation is 0.22°, that is, the grid spacing of the coarsest data set in the exercise (E‐OBS). While the three reference grids agree on the overall mean climatology, differences can be pronounced over individual regions. These differences partly translate into RCM evaluation uncertainty. For most cases observational uncertainty is smaller than RCM uncertainty. Nevertheless, for individual sub‐regions and performance metrics observational uncertainty can dominate. This is especially true for precipitation and for metrics targeting the wet‐day frequency, the pattern correlation and the distributional similarity. In some cases the spatially averaged mean bias can also be considerably affected. An illustrative ranking exercise highlights the overall effect of observational uncertainty on RCM ranking. Over individual sub‐domains, the choice of a specific reference can modify RCM ranks by up to four levels (out of five RCMs). For most cases, however, RCM ranks are stable irrespective of the reference. These results provide a twofold picture: model uncertainty dominates for most regions and for most performance metrics considered, and observational uncertainty plays a minor role. For individual cases, however, observational uncertainty can be pronounced and needs to be definitely taken into account. Results can, to some extent, also depend on the treatment of precipitation undercatch in the observational reference. [ABSTRACT FROM AUTHOR]

Temporal variability is an important feature of climate, comprising systematic variations such as the annual cycle, as well as residual temporal variations such as short‐term variations, spells and variability from interannual to long‐term trends. The EU‐COST Action VALUE developed a comprehensive framework to evaluate downscaling methods. Here we present the evaluation of the perfect predictor experiment for temporal variability. Overall, the behaviour of the different approaches turned out to be as expected from their structure and implementation. The chosen regional climate model adds value to reanalysis data for most considered aspects, for all seasons and for both temperature and precipitation. Bias correction methods do not directly modify temporal variability apart from the annual cycle. However, wet day corrections substantially improve transition probabilities and spell length distributions, whereas interannual variability is in some cases deteriorated by quantile mapping. The performance of perfect prognosis (PP) statistical downscaling methods varies strongly from aspect to aspect and method to method, and depends strongly on the predictor choice. Unconditional weather generators tend to perform well for the aspects they have been calibrated for, but underrepresent long spells and interannual variability. Long‐term temperature trends of the driving model are essentially unchanged by bias correction methods. If precipitation trends are not well simulated by the driving model, bias correction further deteriorates these trends. The performance of PP methods to simulate trends depends strongly on the chosen predictors. [ABSTRACT FROM AUTHOR]

The spatial variability and the climatic characteristics of the coastal low-level jet along the west coast of the Iberian Peninsula are presented in this study, based on a 9-km resolution downscaling data set, produced using the weather research and forecasting model, forced by the ERA-Interim reanalysis. The west coast of Iberia is divided into two (north and south) subareas, where the Iberian Peninsula coastal jet (IPCJ) characteristics are studied separately, and compared to the whole western Iberia. It is shown that while IPCJ occurrences are more frequent in the southern area, during summer (June–August), the jet mean wind speed maxima is highest in the northernmost area. The along- and cross-coast sea surface temperature (SST) structure is studied and linked to the coastal wind speed. It is also shown that the local cross-coast marine atmospheric boundary layer gradient at the coast is considerably higher than the synoptic one, and that this difference is responsible for the local intensification of the wind speed. The flow interaction with the three major capes along the west coast of Iberia (Finisterre, Roca, and Saint Vincent) is responsible for local higher occurrence of the IPCJ, as well as the downwind intensification of the wind speed. The along-coast west Iberia wind speed structure due to IPCJ occurrences is shown. The diurnal cycle of the IPCJ is studied in the extended boreal summer (May–September), with higher mean IPCJ occurrences and jet wind speeds in late the afternoon. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) are used with increasing resolutions seeking to represent in an improved way regional to local-scale atmospheric phenomena. The EURO-CORDEX simulations at 0.11° and simulations exploiting finer grid spacing approaching the convective-permitting regimes are representative examples. These climate runs are computationally very demanding and do not always show improvements, which depend on the region, variable and object of study. The gains or losses associated with the use of higher resolution in relation to the forcing model (global climate model or reanalysis), or to different resolution RCM simulations, are widely known as added value. Its characterization is a long-standing issue, and many different added-value measures have been proposed. In the current study, a new method is proposed to assess the added value of finer-resolution simulations, in comparison to its forcing data or coarser-resolution counterparts. This approach builds on a probability density function (PDF) matching score, giving a normalized measure of the difference between diverse resolution PDFs, mediated by the observational ones. The distribution added value (DAV) is an objective added-value measure that can be applied to any variable, region or temporal scale, from hindcast or historical (non-synchronous) simulations. The DAVs metric and its application to the EURO-CORDEX hindcast daily precipitation data are presented here. Generally, the EURO-CORDEX simulations at both resolutions (0.44° and 0.11°) display a clear added value in relation to ERA-Interim, with maximum values around 30% in summer and 20% in the intermediate seasons. When both RCM resolutions are directly compared, only three of five models (0.11°) show added value, with a maximum of ∼10%. The regions with the larger DAVs are areas where convection is relevant, e.g. Alps and Iberia. When looking at the extreme PDF tail, the higher-resolution improvement is generally greater than the low resolution for seasons and regions. [ABSTRACT FROM AUTHOR]

ABSTRACT The summer time cloud diurnal cycle over western Iberia is analysed here using a satellite climate data record of fractional cloud cover based on 9 years of Meteosat Second Generation observations which is distributed by the EUMETSAT's Climate Monitoring Satellite Applications Facility. These observations were complemented with a corresponding mean cloud diurnal cycle using SYNOP reports on six locations over the studied domain. It is shown that the main coastal mountain range separates regions that are characterized by two very different cloud regimes: stratocumulus-topped boundary layer convection dominates the region towards the coast and continental cumulus convection dominates the region to the east of these mountains. To explain the observed variability, a long-term regional climate model [Weather Research and Forecasting model ( WRF)] simulation over Iberia was used. A comparison of the observations against model output for the common period between observations and simulation shows that although the model generally underestimates cloudiness, it is able to represent the diurnal cycle in a realistic manner. It is shown that the observed cloud diurnal evolution is linked to the thermal circulations generated by the land-sea contrast and orography. The extent to which the cloud deck penetrates inland is closely related to the coastal orography: although smaller hills tend to enhance cloudiness, larger mountains block the progression of the marine boundary layer further inland, as it behaves as a density current. Larger mountains also produce katabatic flow and a rather strong subsidence aloft during the night. The warming due to this subsidence helps the blocking of the cloud deck as it is partially responsible for evaporating clouds, as shown by a potential temperature budget analysis. [ABSTRACT FROM AUTHOR]