Skip to main content

Addressing the relocation bias in a long temperature record by means of land cover assessment

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Aguilar E, Auer I, Brunet M, Peterson TC, Wieringa J (2003) Guidelines on climate metadata and homogenization WMO/TD 1186

  2. Allen L, Lindberg F, Grimmond CSB (2011) Global to city scale urban anthropogenic heat flux: model and variability. Int J Climatol 31:1990–2005

    Article  Google Scholar 

  3. Arnfield AJ (2003) Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int J Clim 23:1–26. https://doi.org/10.1002/joc.859

    Article  Google Scholar 

  4. Auer I et al (2005) A new instrumental precipitation dataset for the greater alpine region for the period 1800-2002. Int J Clim 25:139–166. https://doi.org/10.1002/joc.1135

    Article  Google Scholar 

  5. Auer I et al (2007) HISTALP—historical instrumental climatological surface time series of the Greater Alpine Region. Int J Clim 27:17–46. https://doi.org/10.1002/joc.1377

    Article  Google Scholar 

  6. Begert M, Schlegel T, Kirchhofer W (2005) Homogeneous temperature and precipitation series of Switzerland from 1864 to 2000. Int J Climatol 25:65–80

    Article  Google Scholar 

  7. Böhm R, Jones PD, Hiebl J, Frank D, Brunetti M, Maugeri M (2009) The early instrumental warm-bias: a solution for long central European temperature series 1760–2007. Clim Chang 101:41–67. https://doi.org/10.1007/s10584-009-9649-4

    Article  Google Scholar 

  8. Bozsaky D (2010) The historical development of thermal insulation materials. Periodica Polytechnica Architecture 41(2):49

  9. Brázdil R, Řezníčková L, Valášek H (2006) Early instrumental meteorological observations in the Czech lands I: Ferdinand Knittelmayer, Brno, 1799-1812. Meteorologický časopis 9:59–71

    Google Scholar 

  10. Brunet M et al (2006a) A case-study/guidance on the development of long-term daily adjusted temperature datasets WMO/TD 1425

  11. Brunet M et al (2006b) The development of a new dataset of Spanish Daily Adjusted Temperature Series (SDATS) (1850–2003). Int J Climatol 26:1777–1802. https://doi.org/10.1002/joc.1338

    Article  Google Scholar 

  12. Chen X-L, Zhao H-M, Li P-X, Yin Z-Y (2006) Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens Environ 104:133–146

    Article  Google Scholar 

  13. Chow W, Roth M (2006) Temporal dynamics of the urban heat island of Singapore. Int J Clim 26:2243–2260. https://doi.org/10.1002/joc.1364

    Article  Google Scholar 

  14. Cox PM, Betts RA, Bunton CB, Essery RLH, Rowntree PR, Smith J (1999) The impact of new land surface physics on the GCM simulation of climate and climate sensitivity. Clim Dyn 15:183–203. https://doi.org/10.1007/s003820050276

    Article  Google Scholar 

  15. Dienst M, Lindén J, Engström E, Esper J (2017) Removing the relocation bias from the 155-year Haparanda temperature record in northern Europe. Int J Climatol 37:4015–4026. https://doi.org/10.1002/joc.4981

    Article  Google Scholar 

  16. Dimoudi A, Nikolopoulou M (2003) Vegetation in the urban environment: microclimatic analysis and benefits. Energ Buildings 35:69–76

    Article  Google Scholar 

  17. Dobrovolný P et al (2012) Klima Brna. Víceúrovňová analýza městského klimatu. Brno

  18. Erk F (1883) Die Bestimmung wahrer Tagesmittel der Temperatur unter besonderer Berücksichtigung langjähriger Beobachtungen von München. Verl. d. Akad. München 1883. Abhandlungen: Bd. 14, Abth. 2 = [9]

  19. Esper J, Frank D, Büntgen U (2007) On selected issues and challenges in dendroclimatology. In: Kienast F, Wildi O, Ghosh S (eds) A changing world: challenges for landscape research. Springer, Berlin, pp 113–132

    Chapter  Google Scholar 

  20. Gaffin SR et al (2008) Variations in New York city’s urban heat island strength over time and space. Theor Appl Climatol 94:1–11. https://doi.org/10.1007/s00704-007-0368-3

    Article  Google Scholar 

  21. Giridharan R, Kolokotroni M (2009) Urban heat island characteristics in London during winter. Sol Energy 83:1668–1682. https://doi.org/10.1016/j.solener.2009.06.007

    Article  Google Scholar 

  22. Giridharan R, Lau SSY, Ganesan S, Givoni B (2007) Urban design factors influencing heat island intensity in high-rise high-density environments of Hong Kong. Build Environ 42:3669–3684. https://doi.org/10.1016/j.buildenv.2006.09.011

    Article  Google Scholar 

  23. Goodridge JD, (1992) Urban bias influences on long-term California air temperature trends. Atmospheric Environment. Part B. Urban Atmosphere 26 (1):1–7. https://doi.org/10.1016/0957-1272(92)90032-n

  24. Hart MA, Sailor DJ (2009) Quantifying the influence of land-use and surface characteristics on spatial variability in the urban heat island. Theor Appl Climatol 95:397–406. https://doi.org/10.1007/s00704-008-0017-5

    Article  Google Scholar 

  25. Ho HC et al (2014) Mapping maximum urban air temperature on hot summer days. Remote Sens Environ 154:38–45

  26. Huang L, Li J, Zhao D, Zhu J (2008) A fieldwork study on the diurnal changes of urban microclimate in four types of ground cover and urban heat island of Nanjing, China. Build Environ 43:7–17. https://doi.org/10.1016/j.buildenv.2006.11.025

    Article  Google Scholar 

  27. Jones PD, Lister D, Li Q (2008) Urbanization effects in large-scale temperature records, with an emphasis on China. J Geophys Res 113:1–12

    Article  Google Scholar 

  28. Kalnay E, Cai M (2003) Impact of urbanization and land-use change on climate. Nature 423:528–531

    Article  Google Scholar 

  29. Kolokotsa D, Psomas A, Karapidakis E, (2009) Urban heat island in southern Europe: the case study of Hania, Crete. Solar Energy 83(10):1871–1883. https://doi.org/10.1016/j.solener.2009.06.018

  30. Leach AJ (2007) The climate change learning curve. J Econ Dyn Control 31:1728–1752. https://doi.org/10.1016/j.jedc.2006.06.001

    Article  Google Scholar 

  31. Li RM, Roth M (2009) Spatial variation of the canopy-level urban heat island in Singapore. Paper presented at the seventh International Conference on Urban Climate, Yokohama, Japan

  32. Lindén J (2011) Nocturnal cool island in the Sahelian city of Ouagadougou, Burkina Faso. Int J Climatol 31:605–620

    Article  Google Scholar 

  33. Lindén J, Esper J, Holmer B (2015) Using land cover, population, and night light data for assessing local temperature differences in Mainz, Germany. J Appl Meteorol Climatol 54:658–670

    Article  Google Scholar 

  34. Lo CP, Quattrochi DA (2003) Land-use and land-cover change, urban heat island phenomenon, and health implications: a remote sensing approach. Photogramm Eng Remote Sens 69:1053–1063

    Article  Google Scholar 

  35. Moberg A, Bergström H (1997) Homogenization of Swedish temperature data - part III - the long temperature records from Uppsala and Stockholm. Int J Climatol 17:667–699

    Article  Google Scholar 

  36. Morris CJG, Simmonds I, Plummer N (2001) Qualification of the influences of wind and cloud on the nocturnal urban heat island of a large city. J Appl Meteorol 40:169–182

    Article  Google Scholar 

  37. Oke TR (1982) The energetic basis of the urban heat island. Q J R Meteorol Soc 108:1–24

    Google Scholar 

  38. Parker DE (2010) Urban heat island effects on estimates of observed climate change. Wiley Interdiscip Rev Clim Chang 1(1):123–133

  39. Peel MC, Finlayson BL, Mcmahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci Discuss EGU 11:1633–1644

    Article  Google Scholar 

  40. Pérez-Zanón N, Sigró J, Domonkos P, Ashcroft L (2015) Comparison of HOMER and ACMANT homogenization methods using a central Pyrenees temperature dataset. Adv Sci Res 12:111–119. https://doi.org/10.5194/asr-12-111-2015

    Article  Google Scholar 

  41. Portman DA (1993) Identifying and correcting urban bias in regional time series: surface temperature in China’s Northern Plains. J Climate 6:2298–2308. https://doi.org/10.1175/1520-0442(1993)006<2298:iacubi>2.0.co;2

    Article  Google Scholar 

  42. Rahimzadeh F, Zavareh MN (2014) Effects of adjustment for non-climatic discontinuities on determination of temperature trends and variability over Iran. Int J Climatol 34:2079–2096

    Article  Google Scholar 

  43. Štěpánek P, Zahradníček P, Skalák P (2009) Data quality control and homogenization of air temperature and precipitation series in the area of the Czech Republic in the period 1961–2007. Adv Sci Res 3:23–26. https://doi.org/10.5194/asr-3-23-2009

    Article  Google Scholar 

  44. Stewart ID, Oke TR (2012) Local climate zones for urban temperature studies. Bull Am Meteorol Soc 93:1879–1900. https://doi.org/10.1175/bams-d-11-00019.1

    Article  Google Scholar 

  45. Syrakova M, Stefanova M (2008) Homogenization of Bulgarian temperature series. Int J Climatol 29:1835–1849

    Article  Google Scholar 

  46. Taha H (1997) Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat. Energ Buildings 25:99–103

    Article  Google Scholar 

  47. Tuomenvirta H (2001) Homogeneity adjustments of temperature and precipitation series - Finnish and Nordic data. Int J Climatol 21:495–506

    Article  Google Scholar 

  48. Unwin DJ (1980) The synoptic climatology of Birmingham’s urban heat island, 1965–74. Weather 35:43–50

    Article  Google Scholar 

  49. Valentin J (1901) Der tägliche Gang der Lufttemperatur in Österreich Veröffentlichungen des Preußischen Meteorologischen Instituts 254

  50. Venema V et al (2012a) Benchmarking homogenization algorithms for monthly data. Clim Past 8:89–115

    Article  Google Scholar 

  51. Venema V et al (2012b) Detecting and repairing inhomogeneities in datasets, assessing current capabilities. Bull Am Meteorol Soc 93:951–954

    Google Scholar 

  52. Vincent LA (1998) A technique for the identification of inhomogeneities in Canadian temperature series. J Climate 11:1094–1104. https://doi.org/10.1175/1520-0442(1998)011<1094:atftio>2.0.co;2

    Article  Google Scholar 

  53. Voldoire A et al (2012) The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dyn 40:2091–2121. https://doi.org/10.1007/s00382-011-1259-y

    Article  Google Scholar 

  54. Wang ZH, Zhao X, Yang J, Song J (2016) Cooling and energy saving potentials of shade trees and urban lawns in a desert city. Appl Energy 161:437–444. https://doi.org/10.1016/j.apenergy.2015.10.047

    Article  Google Scholar 

  55. Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89:467–483. https://doi.org/10.1016/j.rse.2003.11.005

    Article  Google Scholar 

  56. Wicki A, Parlow E, Feigenwinter C (2018) Evaluation and modeling of urban heat island intensity in Basel, Switzerland. Climate 6(3):55

  57. Wigley TML, Santer BD (2013) A probabilistic quantification of the anthropogenic component of twentieth century global warming. Clim Dyn 40:1087–1102. https://doi.org/10.1007/s00382-012-1585-8

    Article  Google Scholar 

  58. Yang P, Ren G, Liu W (2013) spatial and temporal characteristics of Bejing urban heat island instensity. J Appl Meteorol Climatol 52:1803–1816

    Article  Google Scholar 

  59. Yokobori T, Ohta S (2009) Effect of land cover on air temperatures involved in the development of an intra-urban heat island. Clim Res 39:61–73. https://doi.org/10.3354/cr00800

    Article  Google Scholar 

  60. Zhang L, Ren G, Ren Y, Zhang A, Chu Z, Zhou Y (2014) Effect of data homogenization on estimate of temperature trend: a case of Huairou station in Bejing municipality. Theor Appl Climatol 115:365–373

    Article  Google Scholar 

  61. Zhen L and Zhong-Wei Y (2015) Homogenized Daily Mean/Maximum/Minimum Temperature Series for China from 1960-2008. Atmospheric and Oceanic Science Letters 2(4):237–243

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Isabel Knerr.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Knerr, I., Dienst, M., Lindén, J. et al. Addressing the relocation bias in a long temperature record by means of land cover assessment. Theor Appl Climatol 137, 2853–2863 (2019). https://doi.org/10.1007/s00704-019-02783-2

Download citation