Climate Dynamics

, Volume 32, Issue 2–3, pp 429–440

Spatial dependence of diurnal temperature range trends on precipitation from 1950 to 2004

  • Liming Zhou
  • Aiguo Dai
  • Yongjiu Dai
  • Russell S. Vose
  • Cheng-Zhi Zou
  • Yuhong Tian
  • Haishan Chen
Article

Abstract

This paper analyzes the spatial dependence of annual diurnal temperature range (DTR) trends from 1950–2004 on the annual climatology of three variables: precipitation, cloud cover, and leaf area index (LAI), by classifying the global land into various climatic regions based on the climatological annual precipitation. The regional average trends for annual minimum temperature (Tmin) and DTR exhibit significant spatial correlations with the climatological values of these three variables, while such correlation for annual maximum temperature (Tmax) is very weak. In general, the magnitude of the downward trend of DTR and the warming trend of Tmin decreases with increasing precipitation amount, cloud cover, and LAI, i.e., with stronger DTR decreasing trends over drier regions. Such spatial dependence of Tmin and DTR trends on the climatological precipitation possibly reflects large-scale effects of increased global greenhouse gases and aerosols (and associated changes in cloudiness, soil moisture, and water vapor) during the later half of the twentieth century.

Keywords

Climate change and variability DTR Cloud cover Precipitation Land cover 

References

  1. Chen M, Xie P, Janowiak JE, Arkin PA (2001) Global land precipitation: a 50-year monthly analysis based on gauge observations. J Hydrom 3:249–266CrossRefGoogle Scholar
  2. Collatz GJ, Bounoua L, Los SO, Randall DA, Fung IY, Sellers PJ (2000) A mechanism for the influence of vegetation on the response of the diurnal temperature range to changing climate. Geophy Res Lett 27:3381–3384CrossRefGoogle Scholar
  3. Dai A, Del Genio AD, Fung IY (1997) Clouds, precipitation, and temperature range. Nature 386:665–666CrossRefGoogle Scholar
  4. Dai A, Trenberth KE, Karl TR (1999) Effects of clouds, soil moisture, precipitation, and water vapor on diurnal temperature range. J Clim 12:2451:2473CrossRefGoogle Scholar
  5. Dai A, Karl TR, Sun B, Trenberth KE (2006) Recent trends in cloudiness over the United States: a tale of monitoring inadequacies. Bull Am Met Soc 87(5):597–606CrossRefGoogle Scholar
  6. Easterling DR et al (1997) Maximum and minimum temperature trends for the globe. Science 277:364–367CrossRefGoogle Scholar
  7. Friedl MA et al (2002) Global land cover from MODIS: algorithms and early results. Remote Sens Environ 83:287–302CrossRefGoogle Scholar
  8. Granger CWJ, Newbold P (1974) Spurious regressions in econometrics. J Econom 2:111–120CrossRefGoogle Scholar
  9. Gujarati DN (1995) Basic econometrics, 3rd edn. McGraw-Hill, New York. ISBN 0-07-025214-9Google Scholar
  10. Hansen J, Sato M, Ruedy R (1995) Long-term changes of the diurnal temperature cycle: implications about mechanisms of global climate change. Atmos Res 37:175–209CrossRefGoogle Scholar
  11. Huang Y, Dickinson RE, Chameides WL (2006) Impact of aerosol indirect effect on surface temperature over East Asia. Proc Natl Acad Sci USA 103:4371–4376CrossRefGoogle Scholar
  12. IPCC (2001) Climate change 2001: the scientific basis. Cambridge University Press, CambridgeGoogle Scholar
  13. IPCC (2007) Climate change 2007: the physical science basis, contribution of working group I to the fourth assessment report of the IPCC (ISBN 978 0521 88009-1), Cambridge University Press, CambridgeGoogle Scholar
  14. Jones PD, Moberg A (2003) Hemispheric and land-scale surface air temperature variations: an extensive revision and an update to 2001. J Clim 16:206–223CrossRefGoogle Scholar
  15. Kaiser DP (1998) Analysis of total cloud amount over China. Geophys Res Lett 25(19):3599–3602CrossRefGoogle Scholar
  16. Karl T et al (1993) A new perspective on recent global warming: asymmetric trends of daily maximum and minimum temperature. Bull Am Meteorol Soc 74(6):1007–1023CrossRefGoogle Scholar
  17. Madden RA, Williams J (1978) The correlation between temperature and precipitation in the United States and Europe. Mon Weather Rev 106:142–147Google Scholar
  18. Menne MJ, Williams CW Jr (2005) Detection of undocumented change point: on the use of multiple test statistics and composite reference series. J Clim 18(20):4271–4286CrossRefGoogle Scholar
  19. Mitchell JFB, Davis RA, Ingram WJ, Senior CA (1995) On surface temperature, greenhouse gases, and aerosols: models and observations. J Clim 8:2364–2386CrossRefGoogle Scholar
  20. Mitchell KE et al (2004) The multi-institution North American Land Data Assimilation System (NLDAS): utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J Geophys Res 109:D07S90. doi:10.1029/2003JD003823 CrossRefGoogle Scholar
  21. Myneni RB et al (2002) Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens Environ 83:214–231CrossRefGoogle Scholar
  22. New M, Lister D, Hulme M, Makin I (2002) A high-resolution data set of surface climate for terrestrial land areas. Clim Res 21:1–25CrossRefGoogle Scholar
  23. Nicholls N (2004) The changing nature of Australian droughts. Clim Change 63:323–336CrossRefGoogle Scholar
  24. Peterson TC, Vose RS, Razuvaey VN, Schmoyer RL (1998) Global Historical Climatology Network (GHCN) quality control of monthly temperature data. Int J Climatol 18:1169–1179CrossRefGoogle Scholar
  25. Qian T, Dai A, Trenberth KE, Oleson KW (2006) Simulation of global land surface conditions from 1948–2004. Part I: forcing data and evaluation. J Hydrom 7:953–975CrossRefGoogle Scholar
  26. Stenchikov GL, Robock A (1995) Diurnal asymmetry of climatic response to increased CO2 and aerosols: forcings and feedbacks. J Geophys Res 100:26211–26227CrossRefGoogle Scholar
  27. Stone DA, Weaver AJ (2003) Factors contributing to diurnal temperature range trends in twentieth and twenty-first century simulations of the CCCma coupled model. Clim Dyn 20:435–445Google Scholar
  28. Trenberth KE, Shea DJ (2005) Relationships between precipitation and surface temperature. Geophys Res Lett 32:L14703. doi:10.1029/2005GL022760 CrossRefGoogle Scholar
  29. Trenberth KE et al (2007) Observations: surface and atmospheric climate change. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  30. Vose RS, Easterling DR, Gleason B (2005) Maximum and minimum temperature trends for the globe: an update through 2004. Geophys Res Lett 32:L23822. doi:10.1029/2005GL024379 CrossRefGoogle Scholar
  31. Zhou L et al (2003) A sensitivity study of climate and energy balance simulations with use of satellite derived emissivity data over the northern Africa and the Arabian peninsula. J Geophys Res 108(D24):4795. doi:10.1029/2003JD004083 CrossRefGoogle Scholar
  32. Zhou L et al (2004) Evidence for a significant urbanization effect on climate in China. Proc Natl Acad Sci USA 101(26):9540–9544CrossRefGoogle Scholar
  33. Zhou L, Dickinson RE, Tian Y, Vose R, Dai Y (2007) Impact of vegetation removal and soil aridation on diurnal temperature range in a semiarid region—application to the Sahel. Proc Natl Acad Sci USA 104(46):17937–17942CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Liming Zhou
    • 1
  • Aiguo Dai
    • 2
  • Yongjiu Dai
    • 3
  • Russell S. Vose
    • 4
  • Cheng-Zhi Zou
    • 5
  • Yuhong Tian
    • 6
  • Haishan Chen
    • 7
  1. 1.School of Earth and Atmospheric SciencesGeorgia Institute of TechnologyAtlantaUSA
  2. 2.National Center for Atmospheric ResearchBoulderUSA
  3. 3.School of GeographyBeijing Normal UniversityBeijingChina
  4. 4.Climate Analysis BranchNational Climatic Data CenterAshevilleUSA
  5. 5.Office of Research and ApplicationsNOAA/NESDISCamp SpringsUSA
  6. 6.IMSG at NOAA/NESDISCamp SpringsUSA
  7. 7.Jiangsu Key Laboratory of Meteorological DisasterNanjing University of Information Science and TechnologyNanjingChina

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