Quality control and homogenization of the Belgian historical temperature data

Abstract

This paper presents a new dataset of quality controlled and homogenized monthly mean air temperature for Belgium. After in-depth data quality control, monthly maximum and minimum temperature time series have been homogenized with the HOMER software on the basis of available metadata. Homogenization results are provided for 61 series over the period 1954-2015 and for 16 series starting before 1931 including eight covering the full time period 1880-2015. The new dataset of monthly homogenized series allows to make a robust analysis of the temperature evolution in Belgium at a local scale from 1880 to nowadays. Trends in maximum and minimum temperatures indicate on average a similar and statistically significant warming of about 0.3 C/decade from 1954 and of about 0.15 C/decade from 1880 with moderate spatial variations. An abrupt warming is observed at the end of the 1980s, especially in summer and spring seasons.air temperature, Belgium, HOMER, monthly homogenization, temperature series, temperature trends

Building Long Homogeneous Temperature Series across Europe: A New Approach for the Blending of Neighboring Series

“…Several studies on singular cases or on small datasets have produced tailored homogeneity adjustments for composite series (Maugeri et al 2002;Böhm et al 2010;Yang et al 2013;Dienst et al 2017;Kruger and Nxumalo 2017;Delvaux et al 2019;Nemec et al 2013), and were able to account for the specific characteristics of the considered series and for metadata. In the case of large datasets, it is not possible to generalize a tailored approach for an individual case to the whole dataset due to the number of series involved (Dienst et al 2017;Delvaux et al 2019). For such datasets a fully automatic procedure is required that has to be able to handle the large spectrum of sometimes unknown circumstances (different surroundings, measurement techniques, protocols, and elevation) that each specific case presents.…”

Pan-European homogenization of daily multi-decadaltemperature series from station-based observations

“…The reduction of standard deviation (due to the removal of step-like changes) and the increase of mutual correlation that is observed Mamara et al [2014] have confirmed the trustworthy of the homogenized dataset. The improved features of the networks have also been seen in the better spatial consistency of the trends [Syrakova and Stefanova, 2009;Hannart et al, 2014;Delvaux et al, 2018;Osadchyi et al, 2018;Yosef et al, 2018;Fioravanti et al, 2019] and in the removal of the anomalous ones [Vincent et al, 2002;Mamara et al, 2014;Pérez-Zanón et al, 2015; as can be seen in Figure 1.8. [Lawrimore et al, 2011], calculated with the method by [Menne and Williams Jr, 2009] Figure 1.8: Trends on TX90p over Austria after (left) and before (right) the homogenization.…”

“…Since the end of 19 th century the atmosphere of Planet Earth has experienced a marked warming process attributed to the human activity. The average temperature has raised with a rate of about 0.085 • C per decade in the period from 1880 to 2019 and has shown an accelerated rate in the second half of the 20 th century [Klein Tank and Können, 2003;Hansen et al, 2010;Simolo et al, 2010;Lawrimore et al, 2011;Jones et al, 2012;Rohde et al, 2013;Delvaux et al, 2018;World Meteorological Organization, 2019]. The trend is confirmed by the fact that each of the four last decades (1980-1989, 1990-1999, 2000-2009 and 2010-2019) has been warmer than all the previous ones [Hartmann et al, 2013;World Meteorological Organization, 2019].…”

“…Several studies on singular cases or on small datasets have produced tailored homogeneity adjustments for composite series [Maugeri et al, 2002;Böhm et al, 2010;Yang et al, 2013;Dienst et al, 2017;Kruger and Nxumalo, 2017;Delvaux et al, 2018;Nemec et al, 2013], and were able to account for the specific characteristics of the considered series and for metadata. In the case of large datasets, it is not possible to generalize a tailored approach for an individual case to the whole dataset due to the number of series involved [Dienst et al, 2017;Delvaux et al, 2018]. For such datasets a fully automatic procedure is required which has to be able to handle the large spectrum of, sometimes unknown, circumstances (different surroundings, measurement techniques, protocols and elevation) that each specific case presents.…”

“…The latter is suitable to be adapted to a daily resolution and has been subject of a vivid debate. Several studies have stated that the use of metadata and the eventual subjective assessments and decisions are preferable for a thorough homogenization [Venema et al, 2013;Pérez-Zanón et al, 2015;Gubler et al, 2017;Delvaux et al, 2018]. Nevertheless the size of certain datasets and the need for objectivity and reproducibility require the use of completely automated processes [Venema et al, 2013].…”