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This work evaluates the daily precipitation and mean temperature of eight CORDEX‐EUR11 ERA‐Interim‐driven simulations of EURO‐CORDEX over the Iberian Peninsula (IP) for the period 1989–2008. To this aim, three observational data sets (Iberia01, E‐OBS‐v19e, and MESAN‐0.11) were considered as reference and compared with the models by means of several indices reflecting the mean and extreme regimes over the IP. For precipitation the Lamb weather types were considered to identify synoptic conditions related with higher observational uncertainty. RCMs are able to reproduce the spatial pattern and the variability observed in the IP. However, there is a higher agreement between models and observations for mean temperature than for precipitation, decreasing when extremes are analyzed. For the observational uncertainty analysis, also extreme daily temperatures were considered to obtain a wider picture of this topic. A higher dependence on the observational data set has been found for precipitation than for temperature. This uncertainty is particularly significant when the 50‐year return value is considered for which the observational uncertainty doubles the model uncertainty. Only the wet‐day frequency presents values lower than 0.5 for all seasons, with most of the rest of values reflecting a similar contribution of both components to the uncertainty. In the case of temperatures, the main contribution of the observations has been found when the lower (MAE01) and upper (MAE99) extremes are considered, with values lower than 0.5. For precipitation the observational uncertainty increases when synoptic patterns affecting the Mediterranean Basin are considered, reflecting the difficulty to properly capture the Mediterranean precipitation regimes. Key Points: EURO‐CORDEX verication over Iberian PeninsulaObservational uncertainty analysis of the evaluation of the EURO‐CORDEX RCMsSynoptic patterns and observational uncertainty of precipitation regimes [ABSTRACT FROM AUTHOR]

We present an unprecedented ensemble of 196 future climate projections arising from different global and regional model intercomparison projects (MIPs): CMIP3, CMIP5, ENSEMBLES, ESCENA, EURO- and Med-CORDEX. This multi-MIP ensemble includes all regional climate model (RCM) projections publicly available to date, along with their driving global climate models (GCMs). We illustrate consistent and conflicting messages using continental Spain and the Balearic Islands as target region. The study considers near future (2021-2050) changes and their dependence on several uncertainty sources sampled in the multi-MIP ensemble: GCM, future scenario, internal variability, RCM, and spatial resolution. This initial work focuses on mean seasonal precipitation and temperature changes. The results show that the potential GCM-RCM combinations have been explored very unevenly, with favoured GCMs and large ensembles of a few RCMs that do not respond to any ensemble design. Therefore, the grand-ensemble is weighted towards a few models. The selection of a balanced, credible sub-ensemble is challenged in this study by illustrating several conflicting responses between the RCM and its driving GCM and among different RCMs. Sub-ensembles from different initiatives are dominated by different uncertainty sources, being the driving GCM the main contributor to uncertainty in the grand-ensemble. For this analysis of the near future changes, the emission scenario does not lead to a strong uncertainty. Despite the extra computational effort, for mean seasonal changes, the increase in resolution does not lead to important changes. [ABSTRACT FROM AUTHOR]

Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season. [ABSTRACT FROM AUTHOR]

Daily precipitation statistics as simulated by the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble are evaluated over two distinct regions of the European continent, namely the European Alps and Spain. The potential added value of the high-resolution 12 km experiments with respect to their 50 km resolution counterparts is investigated. The statistics considered consist of wet-day intensity and precipitation frequency as a measure of mean precipitation, and three precipitation-derived indicators (90th percentile on wet days-90pWET, contribution of the very wet days to total precipitation-R95pTOT and number of consecutive dry days-CDD). As reference for model evaluation high resolution gridded observational data over continental Spain (Spain011/044) and the Alpine region (EURO4M-APGD) are used. The assessment and comparison of the two resolutions is accomplished not only on their original horizontal grids (approximately 12 and 50 km), but the high-resolution RCMs are additionally regridded onto the coarse 50 km grid by grid cell aggregation for the direct comparison with the low resolution simulations. The direct application of RCMs e.g. in many impact modelling studies is hampered by model biases. Therefore bias correction (BC) techniques are needed at both resolutions to ensure a better agreement between models and observations. In this work, the added value of the high resolution (before and after the bias correction) is assessed and the suitability of these BC methods is also discussed. Three basic BC methods are applied to isolate the effect of biases in mean precipitation, wet-day intensity and wet-day frequency on the derived indicators. Daily precipitation percentiles are strongly affected by biases in the wet-day intensity, whereas the dry spells are better represented when the simulated precipitation frequency is adjusted to the observed one. This confirms that there is no single optimal way to correct for RCM biases, since correcting some distributional features typically leads to an improvement of some aspects but to a deterioration of others. Regarding mean seasonal biases before the BC, we find only limited evidence for an added value of the higher resolution in the precipitation intensity and frequency or in the derived indicators. Thereby, evaluation results considerably depend on the RCM, season and indicator considered. High resolution simulations better reproduce the indicators' spatial patterns, especially in terms of spatial correlation. However, this improvement is not statistically significant after applying specific BC methods. [ABSTRACT FROM AUTHOR]

ABSTRACT Observational gridded products are commonly used to evaluate the performance of regional climate models. To this aim, gridded datasets should be comparable to the output of these models and, thus, should represent grid-cell area-averaged values and, whenever possible, they should be defined on the same spatial domains as the models, in order to avoid re-gridding or re-projection. In this study, we present an update of the Spain02 gridded observational dataset for daily precipitation and mean temperature building on the grids defined for the EURO-CORDEX initiative. In order to assess and intercompare different interpolation approaches, we analysed (1) two standard methodologies (ordinary kriging and thin plate splines), (2) three horizontal resolutions: 0.11°, 0.22° and 0.44° (matching the rotated EURO-CORDEX and ENSEMBLES grids), (3) two different approaches to guarantee either area-averaged or point representativity of the resulting grid values and (4) including/excluding orography as a covariable in the interpolation procedure. Besides introducing the new gridded datasets, in this work we also present some preliminary results on the sensitivity of temperature and precipitation (both mean and extreme regimes) to all these factors. [ABSTRACT FROM AUTHOR]

This work shows that local-scale climate projections obtained by means of statistical downscaling are sensitive to the choice of reanalysis used for calibration. To this aim, a generalized linear model (GLM) approach is applied to downscale daily precipitation in the Philippines. First, the GLMs are trained and tested separately with two distinct reanalyses (ERA-Interim and JRA-25) using a cross-validation scheme over the period 1981-2000. When the observed and downscaled time series are compared, the attained performance is found to be sensitive to the reanalysis considered if climate change signal-bearing variables (temperature and/or specific humidity) are included in the predictor field. Moreover, performance differences are shown to be in correspondence with the disagreement found between the raw predictors from the two reanalyses. Second, the regression coefficients calibrated either with ERA-Interim or JRA-25 are subsequently applied to the output of a global climate model (MPI-ECHAM5) in order to assess the sensitivity of local-scale climate change projections (up to 2100) to reanalysis choice. In this case, the differences detected in present climate conditions are considerably amplified, leading to 'delta-change' estimates differing by up to 35% (on average for the entire country) depending on the reanalysis used for calibration. Therefore, reanalysis choice is an important contributor to the uncertainty of local-scale climate change projections and, consequently, should be treated with as much care as other better-known sources of uncertainty (e.g., the choice of the GCM and/or downscaling method). Implications of the results for the entire tropics, as well as for the model output statistics downscaling approach are also briefly discussed. [ABSTRACT FROM AUTHOR]

Wildfires are a major concern on the Iberian Peninsula, and the establishment of effective prevention and early warning systems are crucial to reduce impacts and losses. Fire weather indices are daily indicators of fire danger based upon meteorological information. However, their application in many studies is conditioned to the availability of sufficiently large climatological time series over extensive geographical areas and of sufficient quality. Furthermore, wind and relative humidity, important for the calculation of fire spread and fuel flammability parameters, are relatively scarce data. For these reasons, different reanalysis products are often used for the calculation of surrogate fire danger indices, although the agreement with those derived from observations remains as an open question to be addressed. In this study, we analyze this problem focusing on the Canadian Fire Weather Index (FWI) -- and the associated Seasonal Severity Rating (SSR) -- and considering three different reanalysis products of varying resolutions on the Iberian Peninsula: NCEP, ERA-40 and ERA-Interim. Besides the inter-comparison of the resulting FWI/SSR values, we also study their correspondence with observational data from 7 weather stations in Spain and their sensitivity to the input parameters (precipitation, temperature, relative humidity and wind velocity). As a general result, ERA-Interim reproduces the observed FWI magnitudes with better accuracy than NCEP, with lower/higher correlations in the coast/inland locations. For instance, ERA-Interim summer correlations are above 0.5 in inland locations -- where higher FWI magnitudes are attained -- whereas the corresponding values for NCEP are below this threshold. Nevertheless, departures from the observed distributions are generally found in all reanalysis, with a general tendency to underestimation, more pronounced in the case of NCEP. In spite of these limitations, ERA-Interim may still be useful for the identification of extreme fire danger events. (e.g. those above the 90th percentile value) and for the definition of danger levels/classes (with level thresholds adapted to the observed/reanalysis distributions). [ABSTRACT FROM AUTHOR]

In this paper, we present a new publicly available high-resolution daily precipitation gridded dataset developed for peninsular Spain and the Balearic islands using 2756 quality-controlled stations (this dataset is referred to as Spain02). The grid has a regular 0.2° (approx. 20 km) horizontal resolution and spans the period from 1950 to 2003. Different interpolation methods were tested using a cross-validation approach to compare the resulting interpolated values against station data: kriging, angular distance weighting, and thin plane splines. Finally, the grid was produced applying the kriging method in a two-step process. First, the occurrence was interpolated using a binary kriging and, in a second step, the amounts were interpolated by applying ordinary kriging to the occurrence outcomes. This procedure is similar to the interpolation method used to generate the E-OBS gridded data-the state-of-the-art publicly available high-resolution daily dataset for Europe-which was used in this study for comparison purposes. Climatological statistics and extreme value indicators from the resulting grid were compared to those from the 25 km E-OBS dataset using the observed station records as a reference. Spain02 faithfully reproduces climatological features such as annual precipitation occurrence, accumulated amounts and variability, whereas E-OBS has some deficiencies in the southern region. When focusing on upper percentiles and other indicators of extreme precipitation regimes, Spain02 accurately reproduces the amount and spatial distribution of the observed extreme indicators, whereas E-OBS data present serious limitations over Spain due to the sparse data used in this region. As extreme values are more sensitive to interpolation, the dense station coverage of this new data set was crucial to get an accurate reproduction of the extremes. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

BSTRACT The operational performance and usefulness of regional climate models at seasonal time scales are assessed by downscaling an ensemble of global seasonal forecasts. The Rossby Centre RCA regional model was applied to downscale a five-member ensemble from the ECMWF System3 global model in the European Atlantic domain for the period 1981-2001. One month lead time global and regional precipitation predictions were compared over Europe-and particularly over Spain-focusing the study in SON (autumn) dry events. A robust tercile-based probabilistic validation approach was applied to compare the forecasts from global and regional models, obtaining significant skill in both cases, but over a wider area for the later. Finally, we also analyse the performance of a mixed ensemble combining both forecasts. [ABSTRACT FROM AUTHOR]

The article presents an analysis and simulation with statistical downscaling methods of snow trends in Northern Spain. It examines various topics such as the annual snow frequency measure as the annual number of snow days and precipitation occurrence and the simulation of the observed trends utilizing the connection of daily snow occurrence (DSO). Result shows that the downscaling method with typical values of hit and false alarm rates has predicted that the DSO is around 60% and 2%.

The skill of state-of-the-art operational seasonal forecast models in extratropical latitudes is assessed using a multimodel ensemble from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) project. In particular, probabilistic forecasts of surface precipitation and maximum temperature in Spain are analyzed using a high-resolution observation gridded dataset (Spain02). To this aim, a simple statistical test based on the observed and predicted tercile anomalies is used. First, the whole period 1960–2000 is considered and it is shown that the only significant skill is found for dry events in autumn. Then, the influence of ENSO events as a potential source of conditional predictability is studied and the validation to strong La Niña or El Niño periods is restricted. Skillful seasonal predictions are found in partial agreement with the observed teleconnections derived from the historical records. On the one hand, predictability is found in spring related to El Niño events for dry events over the south and the Mediterranean coast and for hot events in the southeast areas. In contrast, La Niña drives predictability in winter for dry events over the western part and for hot events in summer over the south and the Mediterranean coast. This study considers both the direct model outputs and the postprocessed predictions obtained using a statistical downscaling method based on analogs. In general, the use of the downscaling method outperforms the direct output for precipitation, whereas in the case of the temperature no improvement is obtained. [ABSTRACT FROM AUTHOR]

We present an application of the fractal "chaos game representation" method in climatology for characterizing temporal precipitation aggregation patterns. To this aim, we establish an analogy with linguistic analysis considering precipitation as a discrete variable (e.g. rain, no rain). Each weekly, or monthly, symbolic sequence of observed precipitation is then considered a "word" and the climatological time series observed at a particular gauge defines a "language." The distribution of different words within the language characterizes the particular precipitation aggregation scheme. In this paper we show that the chaos game representation method provides a graphical representation (a fractal pattern, or fingerprint) of the distribution of words and also gives a quantitative characterization in terms of parameters such as the box-counting dimension and the entropy. We show that different climates exhibit characteristic patterns with different fractal exponents and entropies. As an illustrative application, the method is used for automatic regionalization of a set of gauges in the Iberian peninsula, showing that these new indices outperform standard averaged statistics (monthly means, etc.). [ABSTRACT FROM AUTHOR]