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Climate of the Past An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 1 | Copyright
Clim. Past, 8, 89-115, 2012
https://doi.org/10.5194/cp-8-89-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 10 Jan 2012

Research article | 10 Jan 2012

Benchmarking homogenization algorithms for monthly data

V. K. C. Venema1, O. Mestre2, E. Aguilar3, I. Auer4, J. A. Guijarro5, P. Domonkos3, G. Vertacnik6, T. Szentimrey7, P. Stepanek8,9, P. Zahradnicek8,9, J. Viarre3, G. Müller-Westermeier10, M. Lakatos7, C. N. Williams11, M. J. Menne11, R. Lindau1, D. Rasol12, E. Rustemeier1, K. Kolokythas13, T. Marinova14, L. Andresen15, F. Acquaotta16, S. Fratianni16, S. Cheval17,18, M. Klancar6, M. Brunetti19, C. Gruber4, M. Prohom Duran20,21, T. Likso12, P. Esteban20,22, and T. Brandsma23 V. K. C. Venema et al.
  • 1Meteorological institute of the University of Bonn, Germany
  • 2Meteo France, Ecole Nationale de la Meteorologie, Toulouse, France
  • 3Center on Climate Change (C3), Universitat Rovira i Virgili, Tarragona, Spain
  • 4Zentralanstalt für Meteorologie und Geodynamik, Wien, Austria
  • 5Agencia Estatal de Meteorologia, Palma de Mallorca, Spain
  • 6Slovenian Environment Agency, Ljubljana, Slovenia
  • 7Hungarian Meteorological Service, Budapest, Hungary
  • 8Czech Hydrometeorological Institute, Brno, Czech Republic
  • 9Czechglobe-Global Change Research Centre AS CR, v.v.i., Brno, Czech Republic
  • 10Deutscher Wetterdienst, Offenbach, Germany
  • 11NOAA/National Climatic Data Center, USA
  • 12Meteorological and hydrological service, Zagreb, Croatia
  • 13Laboratory of Atmospheric Physics, University of Patras, Greece
  • 14National Institute of Meteorology and Hydrology – BAS, Sofia, Bulgaria
  • 15Norwegian Meteorological Institute, Oslo, Norway
  • 16Department of Earth Science, University of Turin, Italy
  • 17National Meteorological Administration, Bucharest, Romania
  • 18National Institute for R&D in Environmental Protection, Bucharest, Romania
  • 19Institute of Atmospheric Sciences and Climate (ISAC-CNR), Bologna, Italy
  • 20Grup de Climatologia, Universitat de Barcelona, Spain
  • 21Meteorological Service of Catalonia, Area of Climatology, Barcelona, Catalonia, Spain
  • 22Centre d'Estudis de la Neu i de la Muntanya d'Andorra (CENMA-IEA), Andorra
  • 23Royal Netherlands Meteorological Institute, De Bilt, The Netherlands

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.

Participants 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, state-of-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.

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