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A Novel Method for the Homogenization of Daily Temperature Series and Its Relevance for Climate Change Analysis

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  • 1 Institute of Geography, Climatology and Meteorology, and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland, and Istituto Superiore per la Protezione e la Ricerca Ambientale, Rome, Italy
  • | 2 Institute of Geography, Climatology and Meteorology, and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
  • | 3 Institute of Geography, Climatology and Meteorology, University of Bern, Bern, Switzerland, and The Cyprus Institute, EEWRC, Nicosia, Cyprus
  • | 4 Department of Geography, Climatology, Climate Dynamics and Climate Change, Justus-Liebig University of Giessen, Giessen, Germany
  • | 5 Institute of Geography, Climatology and Meteorology, and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
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Abstract

Instrumental daily series of temperature are often affected by inhomogeneities. Several methods are available for their correction at monthly and annual scales, whereas few exist for daily data. Here, an improved version of the higher-order moments (HOM) method, the higher-order moments for autocorrelated data (HOMAD), is proposed. HOMAD addresses the main weaknesses of HOM, namely, data autocorrelation and the subjective choice of regression parameters. Simulated series are used for the comparison of both methodologies. The results highlight and reveal that HOMAD outperforms HOM for small samples. Additionally, three daily temperature time series from stations in the eastern Mediterranean are used to show the impact of homogenization procedures on trend estimation and the assessment of extremes. HOMAD provides an improved correction of daily temperature time series and further supports the use of corrected daily temperature time series prior to climate change assessment.

Corresponding author address: Andrea Toreti, ISPRA, 48 Via Vitaliano Brancati, Rome 00144, Italy. Email: toreti@giub.unibe.ch

Abstract

Instrumental daily series of temperature are often affected by inhomogeneities. Several methods are available for their correction at monthly and annual scales, whereas few exist for daily data. Here, an improved version of the higher-order moments (HOM) method, the higher-order moments for autocorrelated data (HOMAD), is proposed. HOMAD addresses the main weaknesses of HOM, namely, data autocorrelation and the subjective choice of regression parameters. Simulated series are used for the comparison of both methodologies. The results highlight and reveal that HOMAD outperforms HOM for small samples. Additionally, three daily temperature time series from stations in the eastern Mediterranean are used to show the impact of homogenization procedures on trend estimation and the assessment of extremes. HOMAD provides an improved correction of daily temperature time series and further supports the use of corrected daily temperature time series prior to climate change assessment.

Corresponding author address: Andrea Toreti, ISPRA, 48 Via Vitaliano Brancati, Rome 00144, Italy. Email: toreti@giub.unibe.ch

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