Homogenization of Swedish mean monthly temperature series 1860–2021

Joelsson, Magnus; Engström, Erik; Kjellström, Erik

doi:10.1002/joc.7881

Cited by 8 publications

(9 citation statements)

References 22 publications

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“…"stitching" together nearby stations (e.g. concatenating observations from a older station with a newer one close by, meant to replace it, in order to obtain longer time-series) (Joelsson et al, 2022(Joelsson et al, , 2023.…”

Section: Methodsmentioning

confidence: 99%

“…The present study shows the relevance of the EOF method to better capture the spatial of climate variability, as a first step towards delivering region-specific climate indicators. The homogenisation of climatological observations is an ongoing task at SMHI (Joelsson et al, 2022(Joelsson et al, , 2023.…”

Section: Vosementioning

confidence: 99%

“…An a posteriori correction can be performed using homogenisation, such as BaRT/Homer (Joelsson et al, 2023) or Climatol (Guijarro et al, 2023), both homogenisation tools being used at SMHI. Homogenisation is particularly recommend when working with coupled records, since the 'stitching' is likely to introduce variations that are not related to climate, hence considered inhomogeneities.…”

Section: Added Value Of Station Coupling and Homogenisationmentioning

confidence: 99%

“…Further developments of the present method will investigate the impact of coupling and homogenisation on instrumental observations on the estimation of the climate indicators. An automated method for station coupling (Joelsson et al (2022(Joelsson et al ( , 2023 enables an optimal compromise between the maximum number of different time-series and the longest times-series. The coupling procedure will increase the number of time-series retained in the calibration data-set EOFcal that extend to the early parts of the record.…”

Section: Added Value Of Station Coupling and Homogenisationmentioning

confidence: 99%

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Spatially aggregated climate indicators over Sweden (1860–2020), Part 1: Temperature

Sturm

2024

Preprint

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Abstract. Climate indicators are useful tools to synthesise climate information from multiple station time-series into a single national indicator. The method applied should be spatially representative and robust over time. We introduce a new method, based on Empirical Orthogonal Functions (EOF) during the calibration period 1961–2018, in order to reconstruct the climate indicator for temperature in Sweden for the full 1860–2020 period of available observations. The new method delivers results in good overall agreement with the reference method (i.e. arithmetic mean from selected stations in the reference network). Discrepancies are found prior to 1900, primarily related to the reduced number of active stations: the robustness of the indicator estimation is assessed by an ensemble computation with added random noise, which confirms that the ensemble spread increases significantly prior to 1880. The present study establishes that the 10-year running averaged temperature indicator rose from −1.03 °C in 1903 to +1.19 °C in 2010 (with respect to a mean value of 4.64 °C over the 1961–2018 calibration period), i.e. an increase by +2.22 °C in a century. The temperature difference between 1860 and 2020 was largest for winter (DJF) averages (+3 °C) and minimal for summer (+2 °C). The leading EOF patterns illustrate the spatial modes of variability for climate variability, with a predominantly homogeneous, mono-modal distribution for temperature. For precipitation, the first EOF pattern displays more pronounced regional features (maximum over the West coast), which is completed by a north-south seesaw pattern for the second EOF. We illustrate that EOF patterns calculated from observation data reproduce the major features of EOF calculated from GridClim, a gridded data-set over Sweden, for annual and seasonal averages. The leading EOF patterns vary significantly for seasonal averages (DJF, MAM, JJA, SON) for temperature. Finally, future developments of the EOF-method are discussed for calculating regional aggregated climate indicators, their relationship to synoptic circulation patterns and the benefits of homogenisation of observation series. The EOF-based method to compute a spatially aggregated indicator for precipitation is presented in a companion article (Sturm, 2024a). The code and data for this study is available on Zenodo (Sturm, 2024b).

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Section: Methodsmentioning

confidence: 99%

Section: Vosementioning

confidence: 99%

Section: Added Value Of Station Coupling and Homogenisationmentioning

confidence: 99%

Section: Added Value Of Station Coupling and Homogenisationmentioning

confidence: 99%

See 2 more Smart Citations

Spatially aggregated climate indicators over Sweden (1860–2020), Part 1: Temperature

Sturm

2024

Preprint

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“…There have been a number of articles published and presentations given on the question of homogenization, temperature and precipitation (Izsák et al, 2022; Izsák & Szentimrey, 2020; Joelsson et al, 2023; Kocsis et al, 2022; Piccarreta et al, 2004; Woldesenbet et al, 2017). In addition to data on temperature, precipitation, global radiation and wind, measurements of relative humidity have also played an important role in the research (Cséplő et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Homogenization and interpolation of relative humidity hourly values with MASH and MISH software

Izsák

2023

Intl Journal of Climatology

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To understand and study the climate, there is a need to create high‐quality climatological databases. At the Climate Department of the OMSZ (Hungarian Meteorological Service), we use MASH (Multiple Analysis of Series of Homogenization) and MISH (Meteorological Interpolation based on Surface Homogenized Data Basis) software to produce a spatially and temporally representative database. However, these systems were developed for daily and monthly data series, so some improvements were required when applying them to hourly relative humidity data. The problem relates to the issue of the daily cycle. The aim of this article is to show how the data set of hourly values of the relative humidity measurements was prepared. To construct the grid point database, a homogenized data set of 41 stations for the period 1961–2020 was used; this was then interpolated into to a regular grid with a resolution of 0.1°. At the end of the study, the hourly and daily data gridded series are compared.

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ANOVA (Benova) correction in relative homogenization: Why it is indispensable

Domonkos,

Joelsson

2024

Intl Journal of Climatology

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This paper reviews the role of ANOVA correction model in the homogenization of climatic time series. In the present context ANOVA has only weak connection to its original meaning (analysis of variance), so we propose the new name “Benova” to replace the confusing old name. In the linear model of Benova corrections (hereafter Benova) the information of statistically detected inhomogeneities and metadata are jointly considered for all time series of a given climatic region. Benova has indisputable advantages on the accuracy of homogenization results, and this has both theoretical and practical evidence. The study presents two principal versions of Benova: in simple Benova the climate signal is presumed to be spatially invariant, while in weighted Benova the spatial variation of climate is considered. In Benova models usually only breaks (i.e., sudden shifts of section mean) are considered, but this restriction has practical reasons, rather than theoretical limits, and the study shows an extended version of the method with which trend‐like inhomogeneity biases can also be removed. Benova can be used for a group of time series covering varied time periods, the operations can be performed in any time resolution, and statistical characteristics others than climatic means can also be homogenized by the method. Benova can be used together with any break detection method. The study discusses the likely reasons of the relatively slow spread in practical application. PRODIGE was the first homogenization method which used Benova corrections. Until now, all homogenization methods including Benova corrections include also the same kind break detection method, i.e., penalized maximum likelihood method with step function fitting. The brief descriptions of two modern methods of this method family, that is, ACMANT and Bart methods, are also provided.

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Homogenization of Swedish mean monthly temperature series 1860–2021

Cited by 8 publications

References 22 publications

Spatially aggregated climate indicators over Sweden (1860–2020), Part 1: Temperature

Spatially aggregated climate indicators over Sweden (1860–2020), Part 1: Temperature

Homogenization and interpolation of relative humidity hourly values with MASH and MISH software

ANOVA (Benova) correction in relative homogenization: Why it is indispensable

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