NAME SUITE OF STARDEX AND ETCCDI CLIMATE INDICES DATASETS

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Name:

Suite of STARDEX and ETCCDI Climate Indices Datasets based on E-OBS and CARPATCLIM Gridded Data

Acronym:

ClimData


The oncoming climate changes are the biggest challenge the mankind is faced with. The impacts of climate change are manifold and vary regionally, even locally, in their severity. For decades, most analyses of long-term global climate change using observational temperature and precipitation data have focused on changes in mean values. However, immediate damages to humans and their properties as well as the ecosystems, are not obviously caused by gradual changes in these variables but mainly by so-called extreme climate events. The relative rare occurrence of extremes makes it necessary to investigate long data records to determine significant changes in the frequency and intensity of extreme events. There are various methods to investigate extreme events, but the computation and analysis of climate indices (Cis) derived from daily data is probably the most widely used non-parametric approach. In order to detect changes in climate extremes, it is important to develop a set of indices that are statistically robust, cover a wide range of climates, and have a high signal-to-noise ratio. The Cis are numerical indicators, which are carefully designed to encompass magnitude (e.g., hot-day threshold), frequency (e.g., heavy rainfall days) and persistence (e.g., longest dry period) of climate extremes. They include absolute-thresholds indices, percentile-based indices, and indices based on the duration of an event. They are used in several projects on climate change with focus on at different spatial scales, from planetary to continental, regional, national or local scale, as prevailing indicators of changes of the extreme events. As far as many of these studies uses partially pre-existing datasets of CIs, the availability of such databases could facilitate any future work, which relies more or less on Cis-based analysis of the present climate. The objective of the present project is to construct and present to the expert community for barrier-free use a 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. Hence the importance of assessing trends in climate extremes is often emphasized (see literature), estimations of the magnitude of the trend as well as its statistical significance, are accepted as 'natural' supplement to the Cis-time series. Thus, such information for all indices on seasonal and annual basis, is included in ClimData also.


M1:

Main 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.


M2

Alexander, L. V., et al. (2006), Global observed changes in daily climate extremes of temperature and precipitation, J. Geophys. Res., 111, D05109, https://doi.org/10.1029/2005JD006290

Chervenkov, H., Tsonevsky, I., Slavov, K. (2016) Drought Events Assessment and Trend Estimation - Results from the Analysis of Long-term Time Series of the Standardized Precipitation Index, C. R. Acad. Bulg. Sci. Vol. 69, No 8, pp.983-994

Chervenkov H., Spiridonov V. (2018) Precipitation Pattern Estimation with the Standardized Precipitation Index in Projected Future Climate over Bulgaria. In: Lirkov I., Margenov S. (eds) Large-Scale Scientific Computing. LSSC 2017. Lecture Notes in Computer Science, vol 10665. Springer, Cham, https://doi.org/10.1007/978-3-319-73441-5 48

Haylock, M.R., N. Hofstra, A.M.G. Klein Tank, E.J. Klok, P.D. Jones, M. New. (2008) A European daily high-resolution gridded dataset of surface temperature and precipitation. J. Geophys. Res (Atmospheres), 113, D20119, https://doi.org/10.1029/2008JD10201

Hofstra, N., M. New, and C. McSweeney (2009) The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data, Clim. Dyn., 35(5), 841-858, https://doi.org/10.1007/s00382-009-0698-1

Klein Tank AM (2004) Changing temperature and precipitation extremes in Europe’s climate of the 20th Century. Dissertation, University Utrecht, The Netherlands

Malcheva, K., Chervenkov, H., Marinova, T. (2016) Winter Severity Assessment on the basis of Measured and Reanalysis Data, 16th International Multidisciplinary Scientific GeoConference SGEM 2016 Conference Proceedings, June 28-July 6, 2016, Book 4 Vol. 2, 719-726 https://doi.org/10.5593/SGEM2016/B42/S19.092

Moberg, A., et al. (2006), Indices for daily temperature and precipitation extremes in Europe analyzed for the period 1901-2000, J. Geophys. Res., 111, D22106, https://doi.org/10.1029/2006JD007103

Sillmann, J. Roeckner, E. (2008) Indices for extreme events in projections of anthropogenic climate change, Climatic Change 86:83 https://doi.org/10.1007/s10584-007-9308-6

Szalai S, Auer I, Hiebl J, Milkovich J, Radim T, Stepanek P, Zahradnicek P, Bihari Z, Lakatos M, Szentimrey T, Limanowka D, Kilar P, Cheval S, Deak, Gy, Mihic D, Antolovic I, Mihajlovic V, Nejedlik P, Stastny P, Mikulova K, Nabyvanets I, Skyryk O, Krakovskaya S, Vogt J, Antofie T, Spinoni J (2013) Climate of the Greater Carpathian Region. Final Technical Report. www.carpatclim-eu.org

van der Schrier, G., E. J. M. van den Besselaar, A. M. G. Klein Tank, and G. Verver (2013), Monitoring European average temperature based on the EOBS gridded data set, J. Geophys. Res. Atmos., 118, 5120-5135, https://doi.org/10.1002/jgrd.50444

Zhang, X., Alexander, L., Hegerl, G.C., Jones, P., Klein Tank, AM. Peterson, T.C., Trewin, B., Zwiers, F.W. (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data, WIREs Clim Change 2011. https://doi.org/10.1002/wcc.147



M3

The implementation of standard, own for the each of the corresponding projects, software for calculation of the CIs, ensures the reliability of the computational results. STARDEX provides FORTRAN 90 source code and ETCCDI-written in R script RClimDex as well as FORTRAN 90 source code FClimDex. The FclimDex is console application and thus is better than RClimDex situated for embedding in bigger projects. Such method is applied in some earlier studies, but in this case, we have not altered even the input/output interface. The pre-build FClimDex as well as the STARDEX code are invoked as external procedures and the CIs and additional quantities are calculated for each CARPATCLIM and E-OBS gridcell individually. The World Meteorological Organization standard period 1961-1990 is set as reference for calculation of the normals. Hence FClimDex do not performs calculation of CIs of S-basis, as well as linear trend and significance measure by means of the MK test, these quantities are obtained a posteriori the CIs with purposely written by the authors procedures. All output datasets, together with the corresponding descriptors, are stored as binary direct-access files, directly readable with Grid Analysis and Display System (GrADS). GrADS is very popular software and widely used in the meteorological community, including the Vi-SEEM project.


M4

This project is entirely based on free-available data and software. Thus, first and foremost, the investigator and the collaborator would like to express their deep gratitude to the organizations and institutes (CARPATCLIM, ECA&D, STARDEX, ETCCDI, Unidata), which provides free of charge software and data. Without their innovative data services and tools this project would be not possible. The free exchange of data and software is prerequisite for successful team working in the meteorology and boosts the international cooperation.

Primary goal of the Vi-SEEM project is to create unique Virtual Research Environment with special focus on the scientific communities of Climatology. The VI-SEEM repository service is very effective way for data exchange and redistribution among the partners and experts outside. The reliability and appropriateness of the service guarantees the proper storage from one side and the further, barrier-free, dissemination of scientific knowledge from other.


M6

Up to now the authors have experience with similar resources and services, provided mainly in frame of intergovernmental agreements-as specific facilities and structures of the World Meteorological Organisation – RAVI, Regional Climate Centres Implementation in Europe (see

http://www.wmo.int/pages/prog/wcp/wcasp/RCC-Europe.html), European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT, www.eumetsat.int) or regional initiatives. The services consists mainly in exchange of data and software.


M7

All 26 datasets, which are binary direct access files, together with their descriptors (ASCII files) would be placed in the repository for barrier-free download and use for scientific purposes. Some of the files could be converted by the investigator in standard (netCDF, GRIB1/2) file format upon request. There are not any confidentiality issues with the files in ClimData. The datasets would be available also for redistribution for non-commercial purposes. If the user acquire these data, we ask that she/he/they acknowledge us in your use of the data. This may be done by including text such the ClimData are prepared from Chervenkov at al. from the Bulgarian National Institute of Meteorology and Hydrology in any documents or publications using these data. We would also appreciate receiving a copy of the relevant publications.


M8.

If requested, the investigator could provide FORTRAN90/95 source code for data access as well as various GrADS scripts for analysis/visualisation of the 3D fields.


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Tags: climate indices, regional climate, climate, suite, stardex, etccdi, indices, datasets