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VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process‐based, etc.). Here we describe the participating methods and first results from the first experiment, using "perfect" reanalysis (and reanalysis‐driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques. Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method‐to‐method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO‐CORDEX initiative (where VALUE activities have merged and follow on). Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value‐cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value‐cost.eu/validationportal. [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]

A new atmospheric-river detection and tracking scheme based on the magnitude and direction of integrated water vapour transport is presented and applied separately over 13 regions located along the west coasts of Europe (including North Africa) and North America. Four distinct reanalyses are considered, two of which cover the entire twentieth-century: NOAA-CIRES Twentieth Century Reanalysis v2 (NOAA-20C) and ECMWF ERA-20C. Calculations are done separately for the OND and JFM-season and, for comparison with previous studies, for the ONDJFM-season as a whole. Comparing the AR-counts from NOAA-20C and ERA-20C with a running 31-year window looping through 1900-2010 reveals differences in the climatological mean and inter-annual variability which, at the start of the twentieth-century, are much more pronounced in western North America than in Europe. Correlating European AR-counts with the North Atlantic Oscillation (NAO) reveals a pattern reminiscent of the well-know precipitation dipole which is stable throughout the entire century. A similar analysis linking western North American AR-counts to the North Pacific index (NPI) is hampered by the aforementioned poor reanalysis agreement at the start of the century. During the second half of the twentieth-century, the strength of the NPI-link considerably varies with time in British Columbia and the Gulf of Alaska. Considering the period 1950-2010, AR-counts are then associated with other relevant large-scale circulation indices such as the East Atlantic, Scandinavian, Pacific-North American and West Pacific patterns (EA, SCAND, PNA and WP). Along the Atlantic coastline of the Iberian Peninsula and France, the EA-link is stronger than the NAO-link if the OND season is considered and the SCAND-link found in northern Europe is significant during both seasons. Along the west coast of North America, teleconnections are generally stronger during JFM in which case the NPI-link is significant in any of the five considered subregions, the PNA-link is significant in British Columbia and the Gulf of Alaska and the WP-link is so along the U.S. West Coast. During OND, these links are significant in the Gulf of Alaska only. If AR-counts are calculated upon persistent (instead of instantaneous) ARs, the link to the NAO weakens over the British Isles and western Iberia. For the experimental set-ups most closely mirroring those applied in Lavers et al. (J Geophys Res Atmos 117, 2012. doi:) and Ramos et al. (J Hydrometeorol 16(2):579-597, 2015. doi:), the NAO-links are completely or partly insignificant indicating that the inclusion of the persistence criterion notably alters the results. Visual support for the present study is provided by an exhaustive historical atmospheric river archive built at . [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]

This study tests the applicability of Eurasian snow cover increase in October, as described by the recently published snow advance index (SAI), for forecasting December-February precipitation totals in Europe. On the basis of a classical correlation analysis, global significance was obtained and locally significant correlation coefficients of up to 0.89 and −0.78 were found for the Iberian Peninsula and southern Norway, respectively. For a more robust assessment of these results, a linear regression approach is followed to hindcast the precipitation sums in a 1-yr-out cross-validation framework, using the SAI as the only predictor variable. With this simple empirical approach, local-scale precipitation could be reproduced with a correlation of up to 0.84 and 0.71 for the Iberian Peninsula and southern Norway, respectively, while catchment aggregations on the Iberian Peninsula could be hindcast with a correlation of up to 0.73. These findings are confirmed when repeating the hindcast approach to a degraded but much longer version of the SAI. With the recommendation to monitor the robustness of these results as the sample size of the SAI increases, the authors encourage its use for the purpose of seasonal forecasting in southern Norway and the Iberian Peninsula, where general circulation models are known to perform poorly for the variable in question. [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]