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dc.contributor.authorChervenkov, Hristo
dc.contributor.authorSlavov, Kiril
dc.date.accessioned2018-05-22T07:33:16Z
dc.date.available2018-05-22T07:33:16Z
dc.date.issued2018-05-02
dc.identifier.citationN/Aen_US
dc.identifier.urihttp://hdl.handle.net/21.15102/VISEEM-344
dc.descriptionMain objective of the present project is to construct comprehensive suite of climate indices datasets (called ClimData), computed from reliable and up-to-date input data from one side and well elaborated and internationally accepted methodology from other. The gridded time series of the necessary parameters from the CARPATCLIM and E-OBS projects are used as input and the procedures from the STARDEX and ETCCDI initiatives are applied for computation of the Cis. Although similar sets are already available, partially from the row data vendors, they completeness are not full and/or are based on outdated data. In contrast, our intend is to provide consolidated database, based on the most recent source. Daily maximum, minimum and mean temperatures as well as the daily precipitation sum (prec.) are core climatic parameters particularly involved in determining climate change impacts on society and ecosystems. The fact, however, that the relative sparseness of long digitally available records of daily temperature and precipitation measurements hampers analyses of observed changes in climate extremes, is often emphasized. In the presented project we use two data sets: both of them are surface measurements-based, are in form of gridded database and, not at least, are free available. First of the used data sets is CARPATCLIM (http://www.carpatclim-eu.org), which is a high-resolution homogeneous gridded database covering 1961-2010 for the Carpathian region (44ºN-50ºN and 17ºE-27ºE) with 0.1º horizontal resolution, containing all the major surface meteorological variables. The commonly used methods and software were the method MASH (Multiple Analysis of Series for Homogenization) for homogenization, quality control, completion of the observed daily data series; and the method MISH (Meteorological Interpolation based on Surface Homogenized Data Basis) for gridding of homogenized daily data series. Besides the common software, the harmonization of the results across country borders was promoted also by near border data exchange. CARPATCLIM is the most advanced validation database in the region at the moment. The second data set is the well known and widely used in the meteorological community E-OBS of the European Climate Assessment & Dataset (ECA&D) project (http://www.ecad.eu). Unlike the CARPATCLIM, E-OBS is updated periodically and version 16.0, spanning from 1950 til the end of 2016, for domain, covering entirely Europe (30.125ºN-71.875ºN and 11.875ºW-59.875ºE) with 0.25º horizontal resolution, is selected. The E-OBS production procedure includes two step spatial interpolation of station observations (thin-plate spline interpolation of monthly means/totals; kriging of daily anomalies) after the quality control. In the last decades was recognized that it is important to document the exact formulation of an internationally agreed suite of indices of climate extremes from daily precipitation and temperature data. The use of agreed indices allows comparison of analyses conducted in any part of the world and seamless merging of index data to produce a global picture as well. Such are the European Commission funded CIRCE (Climate change and impact research: the Mediterranean environment, https://www.cmcc.it/projects/circe-climate-change-and-impact-research-the-mediterranean-environment) and STARDEX (STAtistical and Regional dynamical Downscaling of EXtremes for European regions, http://www.cru.uea.ac.uk/projects/stardex). STARDEX is focused on relatively moderate extremes rather than the most extreme events. The project uses in total 57 CIs calulated on annual and seasonal basis, 24 are temperature- and 33 precipitation-based. The STARDEX core subset consist of 10 indices. Additionally are calculated the slope of the linear trend by means of Least Squares Estimation (LSE) and, second, the statistical significance of trends with the Mann-Kendall (MK) test for each CI is analyzed. The MK test is a nonparametric and rank-based procedure, especially suitable for non-normally distributed data, data containing outliers and nonlinear trends. Consequently, this test is widely used in the geosciences as standard tool for trend significance estimation. The Commission for Climatology (CCl)/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) (previously known as the Expert Team on Climate Change, Detection, Monitoring and Indices (ETCCDMI), (http://www.clivar.org/organization/etccdi/etccdi.php) defined a suite of indices that have subsequently become known as the ’ETCCDI’ indices. These indices were chosen to sample a wide variety of climates and included indicators such as the total number of days annually with frost and the maximum number of consecutive dry days in a year. However, the definitions and usefulness of some of these indices, although meant to be globally valid, became the subject of discussion. As a result, definitions of some of them as well as their calculations were reconsidered. The ETCCDI-indices are obtained on Y-basis and monthly basis (M-basis) and the threshold-based ones that have to be calculated relative to a base period are calculated according to the bootstrap method. ClimData encompasses the full suite of the STARDEX and ETCCDI Cis for all time scales, as well as the linear trend slope estimation and statistical significance, calculated in the case of the ETCCDI with external, purposely-build procedures. ClimData is intended to be convenient versatile for broad range of experts – meteorologists, climatologists, hydrologist which scientific interest includes Cis-based analysis.en_US
dc.description.abstractThe same as the abstract of this collection.en_US
dc.description.sponsorshipVI-SEEM projecten_US
dc.language.isoenen_US
dc.publisherVI-SEEM projecten_US
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectclimateen_US
dc.subjectclimate indicesen_US
dc.subjectCARPATCLIMen_US
dc.subjectE-OBSen_US
dc.subjectSTARDEXen_US
dc.subjectETCCDIen_US
dc.titleClimate Indices Datasetsen_US
dc.typeDataseten_US


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