• Acquaotta, F., and S. Fratianni, 2014: The importance of the quality and reliability of the historical time series for the study of climate change. Rev. Bras. Climatol., 14, 2038, https://doi.org/10.5380/abclima.v14i1.38168.

    • Search Google Scholar
    • Export Citation
  • Acquaotta, F., S. Fratianni, and V. Venema, 2016: Assessment of parallel precipitation measurements networks in Piedmont, Italy. Int. J. Climatol., 36, 39633974, https://doi.org/10.1002/joc.4606.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aguilar, E., I. Auer, M. Brunet, T. C. Peterson, and J. Wieringa, 2003: Guidelines on climate metadata and homogenization. WMO Tech. Doc. WCDMP-53, WMO/TD-1186, 51 pp.

  • Alexandersson, H., 1986: A homogeneity test applied to precipitation data. J. Climatol., 6, 661675, https://doi.org/10.1002/joc.3370060607.

  • Auer, I., and Coauthors, 2005: A new instrumental precipitation dataset for the greater Alpine region for the period 1800–2002. Int. J. Climatol., 25, 139166, https://doi.org/10.1002/joc.1135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beaulieu, C., O. Seidou, T. B. M. J. Ouarda, X. Zhang, G. Boulet, and A. Yagouti, 2008: Intercomparison of homogenization techniques for precipitation data. Water Resour. Res., 44, W02425, https://doi.org/10.1029/2006WR005615.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Böhm, R., P. D. Jones, J. Hiebl, D. Frank, M. Brunetti, and M. Maugeri, 2010: The early instrumental warm-bias: A solution for long central European temperature series 1760–2007. Climatic Change, 101, 4167, https://doi.org/10.1007/s10584-009-9649-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brunet, M., and Coauthors, 2011: The minimization of the screen bias from ancient western Mediterranean air temperature records: An exploratory statistical analysis. Int. J. Climatol., 31, 18791895, https://doi.org/10.1002/joc.2192.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caussinus, H., and F. Lyazrhi, 1997: Choosing a linear model with a random number of change-points and outliers. Ann. Inst. Stat. Math., 49, 761775, https://doi.org/10.1023/A:1003230713770.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caussinus, H., and O. Mestre, 2004: Detection and correction of artificial shifts in climate series. J. Roy. Stat. Soc., 53, 405425, https://doi.org/10.1111/j.1467-9876.2004.05155.x.

    • Search Google Scholar
    • Export Citation
  • Chimani, B., V. Venema, A. Lexer, K. Andre, I. Auer, and J. Nemec, 2018: Inter-comparison of methods to homogenize daily relative humidity. Int. J. Climatol., 38, 31063122, https://doi.org/10.1002/joc.5488.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dienst, M., J. Lindén, E. Engström, and J. Esper, 2017: Removing the relocation bias from the 155-year Haparanda temperature record in northern Europe. Int. J. Climatol., 37, 40154026, https://doi.org/10.1002/joc.4981.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Domonkos, P., 2011a: Efficiency evaluation for detecting inhomogeneities by objective homogenisation methods. Theor. Appl. Climatol., 105, 455467, https://doi.org/10.1007/s00704-011-0399-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Domonkos, P., 2011b: Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT). Int. J. Geosci., 2, 293309, https://doi.org/10.4236/ijg.2011.23032.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Domonkos, P., 2013a: Measuring performances of homogenization methods. Idöjárás, 117, 91112.

  • Domonkos, P., 2013b: Efficiencies of inhomogeneity-detection algorithms: Comparison of different detection methods and efficiency measures. J. Climatol., 2013, 390945, https://doi.org/10.1155/2013/390945.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Domonkos, P., 2017: Time series homogenisation with optimal segmentation and ANOVA correction: Past, present and future. Proc. Ninth Seminar for Homogenization and Quality Control in Climatological Databases and Fourth Conf. on Spatial Interpolation Techniques in Climatology and Meteorology, WMO WCDMP-85, Geneva, Switzerland, OMSZ, 29–45.

  • Domonkos, P., 2020: ACMANTv4: Scientific content and operation of the software. Tech. Doc., 71 pp., https://github.com/dpeterfree/ACMANT.

  • Domonkos, P., and J. Coll, 2017a: Time series homogenisation of large observational datasets: The impact of the number of partner series on the efficiency. Climate Res., 74, 3142, https://doi.org/10.3354/cr01488.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Domonkos, P., and J. Coll, 2017b: Homogenisation of temperature and precipitation time series with ACMANT3: Method description and efficiency tests. Int. J. Climatol., 37, 19101921, https://doi.org/10.1002/joc.4822.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Domonkos, P., and J. Coll, 2019: Impact of missing data on the efficiency of homogenization: Experiments with ACMANTv3. Theor. Appl. Climatol., 136, 287299, https://doi.org/10.1007/s00704-018-2488-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Domonkos, P., V. Venema, and O. Mestre, 2011: Efficiencies of homogenisation methods: Our present knowledge and its limitation. Proc. Seventh Seminar for Homogenisation and Quality Control in Climatological Databases, WMO-WCDMP-78, Geneva, Switzerland, OMSZ, 19–32.

  • Domonkos, P., V. Venema, I. Auer, O. Mestre, and M. Brunetti, 2012: The historical pathway towards more accurate homogenisation. Adv. Sci. Res., 8, 4552, https://doi.org/10.5194/asr-8-45-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Domonkos, P., J. Coll, J. Guijarro, M. Curley, E. Rustemeier, E. Aguilar, S. Walsh, J. Sweeney, 2020: Precipitation trends in the island of Ireland using a dense, homogenized, observational dataset. Int. J. Climatol., 40, 64586472, https://doi.org/10.1002/joc.6592.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Easterling, D. R., and T. C. Peterson, 1995: A new method for detecting undocumented discontinuities in climatological time series. Int. J. Climatol., 15, 369377, https://doi.org/10.1002/joc.3370150403.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gubler, S., and Coauthors, 2017: The influence of station density on climate data homogenization. Int. J. Climatol., 37, 46704683, https://doi.org/10.1002/joc.5114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guijarro, J. A., 2018: Homogenization of climatic series with Climatol. Tech. Doc., 22 pp., http://www.climatol.eu/homog_climatol-en.pdf.

  • Guijarro, J. A., J. A. López, E. Aguilar, P. Domonkos, V. Venema, J. Sigró, and M. Brunet, 2017: Comparison of homogenization packages applied to monthly series of temperature and precipitation: The MULTITEST project. Proc. Ninth Seminar for Homogenization and Quality Control in Climatological Databases and Fourth Conf. on Spatial Interpolation Techniques in Climatology and Meteorology, WMO WCDMP-85, 46–62.

  • Guijarro, J. A., E. Aguilar, P. Domonkos, J. Sigró, P. Štěpánek, V. Venema, and P. Zahradníček, 2019: Benchmarking results of the homogenization of daily Essential Climatic Variables within the INDECIS project. Proc. 21st EGU General Assembly, Vienna, Austria, EGU, 10896, https://meetingorganizer.copernicus.org/EGU2019/EGU2019-10896-1.pdf.

  • Hausfather, Z., M. J. Menne, C. N. Williams, T. Masters, R. Broberg, and D. Jones, 2013: Quantifying the effect of urbanization on U.S. Historical Climatology Network temperature records. J. Geophys. Res. Atmos., 118, 481494, https://doi.org/10.1029/2012JD018509.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hua, W., S. S. P. Shen, A. Weithmann, and H. Wang, 2017: Estimation of sampling error uncertainties in observed surface air temperature change in China. Theor. Appl. Climatol., 129, 11331144, https://doi.org/10.1007/s00704-016-1836-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Killick, R. E., 2016: Benchmarking the performance of homogenisation algorithms on daily temperature data. Ph.D. thesis, University of Exeter, 249 pp.

  • Lindau, R., and V. Venema, 2013: On the multiple breakpoint problem and the number of significant breaks in homogenization of climate records. Idöjárás, 117, 134.

    • Search Google Scholar
    • Export Citation
  • Lindau, R., and V. Venema, 2018: On the reduction of trend errors by the ANOVA joint correction scheme used in homogenization of climate station records. Int. J. Climatol., 38, 52555271, https://doi.org/10.1002/joc.5728.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindau, R., and V. Venema, 2019: A new method to study inhomogeneities in climate records: Brownian motion or random deviations? Int. J. Climatol., 39, 47694783, https://doi.org/10.1002/joc.6105.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindau, R., and V. Venema, 2020: Random trend errors in climate station data due to inhomogeneities. Int. J. Climatol., 40, 23932402, https://doi.org/10.1002/joc.6340.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mamara, A., A. A. Argiriou, and M. Anadranistakis, 2014: Detection and correction of inhomogeneities in Greek climate temperature series. Int. J. Climatol., 34, 30243043, https://doi.org/10.1002/joc.3888.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menne, M. J., and C. N. Williams Jr., 2005: Detection of undocumented changepoints using multiple test statistics and composite reference series. J. Climate, 18, 42714286, https://doi.org/10.1175/JCLI3524.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menne, M. J., and C. N. Williams Jr., 2009: Homogenization of temperature series via pairwise comparisons. J. Climate, 22, 17001717, https://doi.org/10.1175/2008JCLI2263.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menne, M. J., C. N. Williams Jr., and R. S. Vose, 2009: The U.S. Historical Climatology Network monthly temperature data, version 2. Bull. Amer. Meteor. Soc., 90, 9931008, https://doi.org/10.1175/2008BAMS2613.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mestre, O., and Coauthors, 2013: HOMER: Homogenization software in R—Methods and applications. Idöjárás, 117, 4767.

  • Moberg, A., and H. Alexandersson, 1997: Homogenization of Swedish temperature data. II: Homogenized gridded air temperature compared with a subset of global gridded air temperature since 1861. Int. J. Climatol., 17, 3554, https://doi.org/10.1002/(SICI)1097-0088(199701)17:1<35::AID-JOC104>3.0.CO;2-F.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parker, D. E., 1994: Effects of changing exposure of thermometers at land stations. Int. J. Climatol., 14 (1), 131, https://doi.org/10.1002/joc.3370140102.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peterson, T. C., and D. R. Easterling, 1994: Creation of homogeneous composite climatological reference series. Int. J. Climatol., 14, 671679, https://doi.org/10.1002/joc.3370140606.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reeves, J., J. Chen, X. L. Wang, R. Lund, and Q. Lu, 2007: A review and comparison of changepoint detection techniques for climate data. J. Appl. Meteor. Climatol., 46, 900915, https://doi.org/10.1175/JAM2493.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rennie, J. J., and Coauthors, 2014: The International Surface Temperature Initiative Global Land Surface Databank: Monthly temperature data release description and methods. Geosci. Data J., 1, 75102, https://doi.org/10.1002/gdj3.8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ribeiro, S., J. Caineta, and A. C. Costa, 2016: Review and discussion of homogenisation methods for climate data. Phys. Chem. Earth, 94, 167179, https://doi.org/10.1016/j.pce.2015.08.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rienzner, M., and C. Gandolfi, 2011: A composite statistical method for the detection of multiple undocumented abrupt changes in the mean value within a time series. Int. J. Climatol., 31, 742755, https://doi.org/10.1002/joc.2113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sanchez-Lorenzo, A., M. Wild, M. Brunetti, J. A. Guijarro, M. Z. Hakuba, J. S. Calbó, S. Mystakidis, and B. Bartok, 2015: Reassessment and update of long-term trends in downward surface shortwave radiation over Europe (1939–2012). J. Geophys. Res. Atmos., 120, 95559569, https://doi.org/10.1002/2015JD023321.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Squintu, A. A., G. van der Schrier, P. Štěpánek, P. Zahradníček, and A. Klein Tank, 2020: Comparison of homogenization methods for daily temperature series against an observation-based benchmark dataset. Theor. Appl. Climatol., 140, 285301, https://doi.org/10.1007/s00704-019-03018-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Szentimrey, T., 1999: Multiple Analysis of Series for Homogenization (MASH). Proc. Second Seminar for Homogenization of Surface Climatological Data, WMO WCDMP-41, Geneva, Switzerland, OMSZ, 27–46.

  • Szentimrey, T., 2010: Methodological questions of series comparison. Proc. Sixth Seminar for Homogenization and Quality Control in Climatological Databases, WMO-WCDMP-76, Geneva, Switzerland, OMSZ, 1–7.

  • Szentimrey, T., 2014: Manual of homogenization software MASHv3.03. Hungarian Meteorological Service Doc., 69 pp.

  • Szentimrey, T., M. Lakatos, and Z. Bihari, 2014: Mathematical questions of homogenization and quality control. Proc. Eighth Seminar for Homogenization and Quality Control in Climatological Databases and Third Conf. on Spatial Interpolation Techniques in Climatology and Meteorology, WMO WCDMP-84, Geneva, Switzerland, OMSZ, 5–22.

  • Thorne, P. W., and Coauthors, 2016: Reassessing changes in diurnal temperature range: A new data set and characterization of data biases. J. Geophys. Res. Atmos., 121, 51155137, https://doi.org/10.1002/2015JD024583.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Venema, V., S. Bachner, H. W. Rust, and C. Simmer, 2006: Statistical characteristics of surrogate data based on geophysical measurements. Nonlinear Processes Geophys., 13, 449466, https://doi.org/10.5194/npg-13-449-2006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Venema, V., and Coauthors, 2012: Benchmarking monthly homogenization algorithms. Climate Past, 8, 89115, https://doi.org/10.5194/cp-8-89-2012.

  • Vincent, L. A., X. L. Wang, E. J. Milewska, H. Wan, F. Yang, and V. Swail, 2012: A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis. J. Geophys. Res., 117, D18110, https://doi.org/10.1029/2012JD017859.

    • Search Google Scholar
    • Export Citation
  • Vose, R. S., C. N. Williams Jr., T. C. Peterson, T. R. Karl, and D. R. Easterling, 2003: An evaluation of the time of observation bias adjustment in the U.S. Historical Climatology Network. Geophys. Res. Lett., 30, 2046, https://doi.org/10.1029/2003GL018111.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X. L., 2003: Comments on “Detection of undocumented changepoints: A revision of the two-phase regression model.” J. Climate, 16, 33833385, https://doi.org/10.1175/1520-0442(2003)016<3383:CODOUC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X. L., 2008: Accounting for autocorrelation in detecting mean-shifts in climate data series using the penalized maximal t or F test. J. Appl. Meteor. Climatol., 47, 24232444, https://doi.org/10.1175/2008JAMC1741.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X. L., and Y. Feng, 2013: RHtestsV4 user manual. Tech. Doc., 29 pp., https://github.com/ECCC-CDAS/RHtests.

  • Wang, X. L., Q. H. Wen, and Y. Wu, 2007: Penalized maximal t test for detecting undocumented mean change in climate data series. J. Appl. Meteor. Climatol., 46, 916931, https://doi.org/10.1175/JAM2504.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X. L., H. Chen, Y. Wu, Y. Feng, and Q. Pu, 2010: New techniques for detection and adjustment of shifts in daily precipitation data series. J. Appl. Meteor. Climatol., 49, 24162436, https://doi.org/10.1175/2010JAMC2376.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Willett, K. M., and Coauthors, 2014: A framework for benchmarking of homogenisation algorithm performance on the global scale. Geosci. Instrum. Methods Data Syst., 3, 187200, https://doi.org/10.5194/gi-3-187-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, C. N., M. J. Menne, and P. Thorne, 2012: Benchmarking the performance of pairwise homogenization of surface temperatures in the United States. J. Geophys. Res., 117, D05116, https://doi.org/10.1029/2011JD016761.

    • Search Google Scholar
    • Export Citation
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Efficiency of Time Series Homogenization: Method Comparison with 12 Monthly Temperature Test Datasets

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  • 1 Tortosa, Spain
  • | 2 State Meteorological Agency (AEMET), Unit of Islas Baleares, Palma, Spain
  • | 3 Meteorological Institute, University of Bonn, Bonn, Germany
  • | 4 Centre for Climate Change, Universitat Rovira i Virgili, Vila-seca, Spain
  • | 5 Climatic Research Unit, University of East Anglia, Norwich, United Kingdom
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Abstract

The aim of time series homogenization is to remove nonclimatic effects, such as changes in station location, instrumentation, observation practices, and so on, from observed data. Statistical homogenization usually reduces the nonclimatic effects but does not remove them completely. In the Spanish “MULTITEST” project, the efficiencies of automatic homogenization methods were tested on large benchmark datasets of a wide range of statistical properties. In this study, test results for nine versions, based on five homogenization methods—the adapted Caussinus-Mestre algorithm for the homogenization of networks of climatic time series (ACMANT), “Climatol,” multiple analysis of series for homogenization (MASH), the pairwise homogenization algorithm (PHA), and “RHtests”—are presented and evaluated. The tests were executed with 12 synthetic/surrogate monthly temperature test datasets containing 100–500 networks with 5–40 time series in each. Residual centered root-mean-square errors and residual trend biases were calculated both for individual station series and for network mean series. The results show that a larger fraction of the nonclimatic biases can be removed from station series than from network-mean series. The largest error reduction is found for the long-term linear trends of individual time series in datasets with a high signal-to-noise ratio (SNR), where the mean residual error is only 14%–36% of the raw data error. When the SNR is low, most of the results still indicate error reductions, although with smaller ratios than for large SNR. In general, ACMANT gave the most accurate homogenization results. In the accuracy of individual time series ACMANT is closely followed by Climatol, and for the accurate calculation of mean climatic trends over large geographical regions both PHA and ACMANT are recommended.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-20-0611.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Peter Domonkos, dpeterfree@gmail.com

Abstract

The aim of time series homogenization is to remove nonclimatic effects, such as changes in station location, instrumentation, observation practices, and so on, from observed data. Statistical homogenization usually reduces the nonclimatic effects but does not remove them completely. In the Spanish “MULTITEST” project, the efficiencies of automatic homogenization methods were tested on large benchmark datasets of a wide range of statistical properties. In this study, test results for nine versions, based on five homogenization methods—the adapted Caussinus-Mestre algorithm for the homogenization of networks of climatic time series (ACMANT), “Climatol,” multiple analysis of series for homogenization (MASH), the pairwise homogenization algorithm (PHA), and “RHtests”—are presented and evaluated. The tests were executed with 12 synthetic/surrogate monthly temperature test datasets containing 100–500 networks with 5–40 time series in each. Residual centered root-mean-square errors and residual trend biases were calculated both for individual station series and for network mean series. The results show that a larger fraction of the nonclimatic biases can be removed from station series than from network-mean series. The largest error reduction is found for the long-term linear trends of individual time series in datasets with a high signal-to-noise ratio (SNR), where the mean residual error is only 14%–36% of the raw data error. When the SNR is low, most of the results still indicate error reductions, although with smaller ratios than for large SNR. In general, ACMANT gave the most accurate homogenization results. In the accuracy of individual time series ACMANT is closely followed by Climatol, and for the accurate calculation of mean climatic trends over large geographical regions both PHA and ACMANT are recommended.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-20-0611.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Peter Domonkos, dpeterfree@gmail.com

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