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@article{1570476,
author = {Knerr, Isabel and Dienst, Manuel and Linden, Jenny and Dobrovolný, Petr and Geletič, Jan and Büntgen, Ulf and Esper, Jan},
article_location = {Wien},
article_number = {3-4},
doi = {http://dx.doi.org/10.1007/s00704-019-02783-2},
keywords = {urban heat island; precipitation; air temperature; land cover; homogenization; vegetation; impact},
language = {eng},
issn = {0177-798X},
journal = {Theoretical and Applied Climatology},
title = {Addressing the relocation bias in a long temperature record by means of land cover assessment},
url = {https://link.springer.com/article/10.1007/s00704-019-02783-2},
volume = {137},
year = {2019}
}
TY - JOUR
ID - 1570476
AU - Knerr, Isabel - Dienst, Manuel - Linden, Jenny - Dobrovolný, Petr - Geletič, Jan - Büntgen, Ulf - Esper, Jan
PY - 2019
TI - Addressing the relocation bias in a long temperature record by means of land cover assessment
JF - Theoretical and Applied Climatology
VL - 137
IS - 3-4
SP - 2853-2863
EP - 2853-2863
PB - Springer Wien
SN - 0177798X
KW - urban heat island
KW - precipitation
KW - air temperature
KW - land cover
KW - homogenization
KW - vegetation
KW - impact
UR - https://link.springer.com/article/10.1007/s00704-019-02783-2
L2 - https://link.springer.com/article/10.1007/s00704-019-02783-2
N2 - 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 3.5years 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 500m 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.
ER -
KNERR, Isabel, Manuel DIENST, Jenny LINDEN, Petr DOBROVOLNÝ, Jan GELETIČ, Ulf BÜNTGEN and Jan ESPER. Addressing the relocation bias in a long temperature record by means of land cover assessment. \textit{Theoretical and Applied Climatology}. Wien: Springer Wien, 2019, vol.~137, 3-4, p.~2853-2863. ISSN~0177-798X. Available from: https://dx.doi.org/10.1007/s00704-019-02783-2.
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