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.

No abstract

Homogenization methods are developed to reduce the impact of non-climatic factors on climate series. Martínex et al. (2009), (International Journal of Climatology, Doi 10.1002/joc.1884) applied a set of homogenization procedures to available Spanish temperature series. In this report, we address critical issues of that paper concerning a specific property of the standard normal homogeneity test and the application scheme of the homogenization tests. We conclude with some important recommendations on the application of homogenization methodologies. Copyright After a reliable quality control procedure, they carried out a set of four different homogeneity tests: (1) standard normal homogeneity test (SNHT; Alexandersson, 1986;Alexandersson and Moberg, 1997a), (2) Buishand range (Buishand, 1982), (3) Pettitt test (Pettitt, 1979) and (4) Von Neumann ratio test (Von Neumann, 1941). The authors did not correct series but decided to reject them in case of inhomogeneity detection. In the description of the homogenization procedure, there are a few misleading points that we would like to discuss in this short comment.The thorough understanding of the behaviour of homogeneity tests and their correct application to climatic time series preserve the climatic signal and eliminate or reduce the influence of non-climatic factors. The removal of false detected inhomogeneities and the acceptance of inhomogeneous series affect each subsequent analysis (e.g. trend assessments, extreme analysis). Therefore, it is of major In the following paragraphs, a brief description of SNHT behaviour is provided showing that SNHT performance decays for breaks located at the beginning and the end of series.There are several studies that have investigated the strengths and weakness of break detection algorithms. For instance, Alexandersson and Moberg (1997a), since because the exact distribution of the test statistic under the null hypothesis is unknown, reported on critical levels of the SNHT statistic for series with a number of values from 10 to 250. Khaliq and Ouarda (2007) extended these critical values from 10 to 50 000. Furthermore, Alexandersson and Moberg (1997b) avoided the application of SNHT to segments with a length less than ten values. Ducré-Robitaille et al. (2003) analysed the behaviour of eight techniques for break detection with simulated

ABSTRACT:The main objective of this study is to estimate the probable maximum precipitation (PMP) in Barcelona for durations ranging from 5 min to 30 h. To this end, rain records from the Jardí gauge of the Fabra Observatory located in Barcelona (1927Barcelona ( -1992) and the urban pluviometric network supported by Clavegueram de Barcelona, S.A. (CLABSA, 1994(CLABSA, -2007 were analysed. Two different techniques were used and compared: a physical method based on the maximization of actual storms, and the Hershfield' statistical method. The PMP values obtained using the two techniques are very similar. In both cases, the expected increasing behaviour of the PMP with duration was found, with the increase especially notable for the mesoscale durations 2-9 h, and not significant from 12 h on up. This result seems to be related to the scale of the meteorological situations producing high intense rainfall amounts over our territory.

Detection and reconstruction of early instrumental series is an interdisciplinary activity that allows us to extend climate data records to periods prior to the mid-19th century, extending the overlapping periods with climate proxies and characterizing extreme events. In this work, the collection of several data sources corresponding to different periods and locations, obtained with a wide range of methods and instruments by institutions or private observers, provides the following results: Barcelona has a continuous rainfall series with monthly resolution since 1786 and with daily resolution since 1850. It is worth mentioning that the records from Barcelona provide the longest continuous monthly series available on rainfall in the Iberian Peninsula. The monthly records have been homogenized by using a relative homogenization approach, HOMER. The results highlight the existence of five breaks, most of them due to relocations or instrumentation changes documented in the metadata, which have been adjusted to remove non-climatic factors. The homogenized annual and winter precipitation series in Barcelona show a statistically significant increase from 1786 to 2014, although this increase is mainly due to the concentration of negative anomalies during the first half of the 19th century, which is also clearly visible in the seasonal series. Specifically, an extreme mega-drought episode was observed from the 1810s to the 1830s, which is supported by different proxy data. For a better dissemination of the homogenized monthly series developed in this study, the data set is freely available to the research community.

No abstract

In a warmer climate, significant variations in the snow regime are expected. Thus, it is crucial to better understand present‐day snow cover regime, its duration and thickness, in order to anticipate future changes. This work presents the first characterization of snow patterns in the Catalan Pyrenees based on 11 snow stations located in high elevation areas (>2,000 m). Here, we examine spatio‐temporal evolution of the daily snow depth and new snow height (HN) since the earliest 2000s to 2020. In addition, we analyse the different synoptic patterns that cause HN events in the study area as well as the low frequency climate modes on the different stages of the snow season. Our results show evidence that the measured snow amount differs considerably between the western and the eastern Catalan Pyrenees independently of the considered elevation. While the eastern part has an average seasonal cumulative HN of 278 cm, the western sector gets almost twice (433 cm). Nonetheless, the onset of the snow melting does not show substantial differences, being primarily ruled by the elevation in both areas. The longest snow records (Núria, 1971 m) point to an increase of HN from 1985 to 2020, a trend which is also observed in most stations from 2000 to 2020. Nevertheless, some stations of the N western fringe record negative trends associated with the low frequency variability of the Western Mediterranean Oscillation (WeMO). Results also indicate that the NW Atlantic low‐pressure systems are the circulation weather types that provide more abundant HN in the majority of snow stations. The Atlantic advections are more frequent in autumn and winter, while the Mediterranean advections provide more intense and recurrent HN in spring. The atmospheric circulation is basically ruled by the East Atlantic/West Russia and the WeMO teleconnection patterns.

Abstract. In previous studies the Western Mediterranean Oscillation index (WeMOi) at daily resolution has proven to constitute an effective tool for analysing the occurrence of episodes of torrential precipitation over eastern Spain. The western Mediterranean region is a very sensitive area, since climate change can enhance these weather extremes. In the present study we created a catalogue of the extreme torrential episodes (≥200 mm in 24 h) that took place in Catalonia (NE Iberia) during the 1951–2016 study period (66 years). We computed daily WeMOi values and constructed WeMOi calendars. Our principal result reveals the occurrence of 50 episodes (0.8 cases per year), mainly concentrated in the autumn. We confirmed a threshold of WeMOi ≤ −2 to define an extreme negative WeMO phase at daily resolution. Most of the 50 episodes (60 %) in the study area occurred on days presenting an extreme negative WeMOi value. Specifically, the most negative WeMOi values are detected in autumn, from 11 to 20 October, coinciding with the highest frequency of extreme torrential events. On comparing the subperiods, we observed a statistically significant decrease in WeMOi values in all months, particularly in late October and in November and December. No changes in the frequency of these extreme torrential episodes were observed between both subperiods. In contrast, a displacement of the extreme torrential episodes is detected from early to late autumn; this can be related to a statistically significant warming of sea temperature.