Abstract. The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added.Published by Copernicus Publications on behalf of the European Geosciences Union. V. K. C. Venema et al.: Benchmarking monthly homogenization algorithmsParticipants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, stateof-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.

Abstract. The COST (European Cooperation in Science and Technology) Action ES0601: Advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies. The algorithms were validated against a realistic benchmark dataset. Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including i) the centered root mean square error relative to the true homogeneous values at various averaging scales, ii) the error in linear trend estimates and iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Moreover, state-of-theart relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that currently automatic algorithms can perform as well as manual ones.

Given the inconsistencies of wind gust trends under the widespread decline in near-surface wind speed (stilling), our study aimed to assess trends of observed daily peak wind gusts (DPWG) across Spain and Portugal for 1961-2014 by analyzing trends of (i) the frequency (90th percentile) and (ii) the magnitude (wind speed maxima) of DPWG. Wind gust series were homogenized on a daily basis, using MM5-simulated series as reference, resulting in 80 suitable station-based data sets. The average DPWG 90th percentile frequency declined by À1.49 d decade À1 (p < 0.05) annually. This showed marked seasonal differences: decreasing in winter (À0.75 d decade À1 ; p < 0.05) and increasing in summer (+0.18 d decade À1 ; p > 0.10). A negligible trend was calculated for the annual magnitude of DPWG (À0.005 m s À1 decade À1 ; p > 0.10), with distinct seasonality: declining in winter (À0.168 m s À1 decade À1 ; p < 0.10) and increasing in summer (+0.130 m s À1 decade À1 ; p < 0.05). Combined, these results reveal less frequent and declining DPWG during the cold semester (November-April) and more frequent and increasing DPWG during the warm semester (May-October). Large-scale atmospheric changes such as the North Atlantic Oscillation Index (negative correlations~À0.4-À0.6; p < 0.05) and the Jenkinson and Collison scheme (positive correlations mainly with Westerly regime:~+0.5-0.6; p < 0.05) partly account for the decadal fluctuations of both frequency and magnitude of DPWG, particularly in winter. However, the North Atlantic Oscillation index-DPWG relationships are smaller in spring, summer, and autumn (~À0.1-À0.2; p > 0.10), especially for the frequency, suggesting the role of local-to-mesoscale drivers.In view of (i) the minimal number of studies reporting long-term changes from observed DPWG over land; (ii) the overall inconclusive nature DPWG trends observed from anemometers; and (iii) the substantial societal and environmental impact of this natural hazard (e.g., for human safety, maritime and aviation activities, engineering and insurance applications, and energy production), additional trend assessments, and studies that assess these trends from a large-scale atmospheric circulation perspective are needed to increase our understanding of wind extremes [Vose et al., 2014]. The principal objective of this study is to determine, for AZORIN-MOLINA ET AL.

The COST (European Cooperation in Science and Technology) Action ES0601: Advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random break-type inhomogeneities were added to the simulated datasets modeled as a Poisson process with normally distributed breakpoint sizes. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. <br><br> Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that currently automatic algorithms can perform as well as manual ones

The Mediterranean Sea is a region that possesses a high frequency of cyclones. Many of them are lee depressions and so, mesoscale and weak, but others are strong and cover a wide area. In this study some characteristics of surface cyclones in the Western Mediterranean are presented. First a database was built from hand analyses of sea‐level pressure from 1992 to 1995. Next, a similar database was obtained from the LAM‐INM (Limited Area Model of the Instituto Nacional de Meteorología of Spain) objective analyses of the 1000 hPa geopotential field. Results show an important increase in the number of cyclones when a mesoscale analysis is carried out. Most of the Western Mediterranean cyclones are mesoscale and weak, and they are not uniformly distributed in space and in time. On the one hand, there are some areas with a high concentration of cyclones. The location of some of them, close to the main mountain ranges, suggests their possible orographic origin. On the other hand, in some areas the cyclones are present during the whole year, but in other areas are seasonally distributed. Finally, a study of typologies of the cyclones was conducted by using the cluster analysis technique. The classification was performed from the intensity of the cyclones and the shape of the sea‐level pressure around the centre. Results are similar from both databases and so the existence of seven typologies have been identified. Copyright © 2000 Royal Meteorological Society