Advertisement

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

  • Isabel KnerrEmail author
  • Manuel Dienst
  • Jenny Lindén
  • Petr Dobrovolný
  • Jan Geletic
  • Ulf Büntgen
  • Jan Esper
Original Paper

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.

Notes

References

  1. Aguilar E, Auer I, Brunet M, Peterson TC, Wieringa J (2003) Guidelines on climate metadata and homogenization WMO/TD 1186Google Scholar
  2. Allen L, Lindberg F, Grimmond CSB (2011) Global to city scale urban anthropogenic heat flux: model and variability. Int J Climatol 31:1990–2005CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle Scholar
  6. Begert M, Schlegel T, Kirchhofer W (2005) Homogeneous temperature and precipitation series of Switzerland from 1864 to 2000. Int J Climatol 25:65–80CrossRefGoogle 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 CrossRefGoogle Scholar
  8. Bozsaky D (2010) The historical development of thermal insulation materials. Periodica Polytechnica Architecture 41(2):49Google Scholar
  9. Brázdil R, Řezníčková L, Valášek H (2006) Early instrumental meteorological observations in the Czech lands I: Ferdinand Knittelmayer, Brno, 1799-1812. Meteorologický časopis 9:59–71Google Scholar
  10. Brunet M et al (2006a) A case-study/guidance on the development of long-term daily adjusted temperature datasets WMO/TD 1425Google Scholar
  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 CrossRefGoogle 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 104:133–146CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle Scholar
  16. Dimoudi A, Nikolopoulou M (2003) Vegetation in the urban environment: microclimatic analysis and benefits. Energ Buildings 35:69–76CrossRefGoogle Scholar
  17. Dobrovolný P et al (2012) Klima Brna. Víceúrovňová analýza městského klimatu. BrnoGoogle Scholar
  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]Google Scholar
  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 113–132CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle Scholar
  25. Ho HC et al (2014) Mapping maximum urban air temperature on hot summer days. Remote Sens Environ 154:38–45Google Scholar
  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 CrossRefGoogle Scholar
  27. Jones PD, Lister D, Li Q (2008) Urbanization effects in large-scale temperature records, with an emphasis on China. J Geophys Res 113:1–12CrossRefGoogle Scholar
  28. Kalnay E, Cai M (2003) Impact of urbanization and land-use change on climate. Nature 423:528–531CrossRefGoogle 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 CrossRefGoogle 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, JapanGoogle Scholar
  32. Lindén J (2011) Nocturnal cool island in the Sahelian city of Ouagadougou, Burkina Faso. Int J Climatol 31:605–620CrossRefGoogle 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 54:658–670CrossRefGoogle 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 69:1053–1063CrossRefGoogle 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 17:667–699CrossRefGoogle 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 40:169–182CrossRefGoogle Scholar
  37. Oke TR (1982) The energetic basis of the urban heat island. Q J R Meteorol Soc 108:1–24Google Scholar
  38. Parker DE (2010) Urban heat island effects on estimates of observed climate change. Wiley Interdiscip Rev Clim Chang 1(1):123–133Google Scholar
  39. Peel MC, Finlayson BL, Mcmahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci Discuss EGU 11:1633–1644CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 34:2079–2096CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle Scholar
  45. Syrakova M, Stefanova M (2008) Homogenization of Bulgarian temperature series. Int J Climatol 29:1835–1849CrossRefGoogle Scholar
  46. Taha H (1997) Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat. Energ Buildings 25:99–103CrossRefGoogle Scholar
  47. Tuomenvirta H (2001) Homogeneity adjustments of temperature and precipitation series - Finnish and Nordic data. Int J Climatol 21:495–506CrossRefGoogle Scholar
  48. Unwin DJ (1980) The synoptic climatology of Birmingham’s urban heat island, 1965–74. Weather 35:43–50CrossRefGoogle Scholar
  49. Valentin J (1901) Der tägliche Gang der Lufttemperatur in Österreich Veröffentlichungen des Preußischen Meteorologischen Instituts 254Google Scholar
  50. Venema V et al (2012a) Benchmarking homogenization algorithms for monthly data. Clim Past 8:89–115CrossRefGoogle Scholar
  51. Venema V et al (2012b) Detecting and repairing inhomogeneities in datasets, assessing current capabilities. Bull Am Meteorol Soc 93:951–954Google 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle Scholar
  56. Wicki A, Parlow E, Feigenwinter C (2018) Evaluation and modeling of urban heat island intensity in Basel, Switzerland. Climate 6(3):55Google Scholar
  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 CrossRefGoogle Scholar
  58. Yang P, Ren G, Liu W (2013) spatial and temporal characteristics of Bejing urban heat island instensity. J Appl Meteorol Climatol 52:1803–1816CrossRefGoogle 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 CrossRefGoogle 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 115:365–373CrossRefGoogle 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):237–243Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Laboratory for Climatology and Remote Sensing, Department of GeographyPhilipps-University MarburgMarburgGermany
  2. 2.Department of GeographyJohannes Gutenberg-UniversityMainzGermany
  3. 3.IVL – Swedish Environmental Research InstituteGothenburgSweden
  4. 4.Department of GeographyMasaryk UniversityBrnoCzech Republic
  5. 5.Institute of Computer ScienceCzech Academy of SciencesBrnoCzech Republic
  6. 6.Department of GeographyUniversity of CambridgeCambridgeUK

Personalised recommendations