Knerr, I.; Dienst, M.; Lindén, J.; Dobrovolný, P.; Geletic, J.; Büntgen, U. & Esper, J. (2019): <b>Addressing the relocation bias in a long temperature record by means of land cover assessment</b>. <i>Theoretical and Applied Climatology </i> <b> </b>, 1-11.
Resource Description
Title:
Addressing the relocation bias in a long temperature record by means of land cover assessment
FOR816dw ID:
328
Publication Date:
2019-01-25
License and Usage Rights:
Resource Owner(s):
Individual:
Isabel Knerr
Contact:
email:
isabel.knerr <at> geo.uni-marburg.de
Germany
Individual:
Manuel Dienst
Contact:
email:
webmaster <at> lcrs.de
Individual:
Jenny Lindén
Contact:
email:
webmaster <at> lcrs.de
Individual:
Petr Dobrovolný
Contact:
email:
webmaster <at> lcrs.de
Individual:
Jan Geletic
Contact:
email:
webmaster <at> lcrs.de
Individual:
Ulf Büntgen
Contact:
email:
webmaster <at> lcrs.de
Individual:
Jan Esper
Contact:
email:
webmaster <at> lcrs.de
Abstract:
The meteorological measurements in Brno, Czech Republic, is among the world's oldest measurements, operating since 1799. Like many others, station was initially installed in the city center, relocated several times, and currently operates at an airport outside the city. These geographical changes potentially bias the temperature record due to different station surroundings and varying degrees of urban heat island effects. Here, we assess the influence of land cover on spatial temperature variations in Brno, capitol of Moravia and the second largest city of the Czech Republic. We therefore use a unique dataset of half-hourly resolved measurements from 11 stations spanning a period of more than 3.5 years and apply this information to reduce relocation biases in the long-term temperature record from 1799 to the present. Regression analysis reveals a significant warming influence from nearby buildings and a cooling influence from vegetation, explaining up to 80{\%} of the spatial variability within our network. The influence is strongest during the warm season and for land cover changes between 300 and 500 m around stations. Relying on historical maps and recent satellite data, it was possible to capture the building densities surrounding the past locations of the meteorological station. Using the previously established land cover--temperature relation, the anthropogenic warming for each measurement site could be quantified and hence eliminated from the temperature record accordingly, thereby increasing the long-term warming trend.
