Skip to main content
SpringerLink
Log in

A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

A new dataset of integrated and homogenized monthly surface air temperature over global land for the period since 1900 [China Meteorological Administration global Land Surface Air Temperature (CMA-LSAT)] is developed. In total, 14 sources have been collected and integrated into the newly developed dataset, including three global (CRUTEM4, GHCN, and BEST), three regional and eight national sources. Duplicate stations are identified, and those with the higher priority are chosen or spliced. Then, a consistency test and a climate outlier test are conducted to ensure that each station series is quality controlled. Next, two steps are adopted to assure the homogeneity of the station series: (1) homogenized station series in existing national datasets (by National Meteorological Services) are directly integrated into the dataset without any changes (50% of all stations), and (2) the inhomogeneities are detected and adjusted for in the remaining data series using a penalized maximal t test (50% of all stations). Based on the dataset, we re-assess the temperature changes in global and regional areas compared with GHCN-V3 and CRUTEM4, as well as the temperature changes during the three periods of 1900–2014, 1979–2014 and 1998–2014. The best estimates of warming trends and there 95% confidence ranges for 1900–2014 are approximately 0.102 ± 0.006 °C/decade for the whole year, and 0.104 ± 0.009, 0.112 ± 0.007, 0.090 ± 0.006, and 0.092 ± 0.007 °C/decade for the DJF (December, January, February), MAM, JJA, and SON seasons, respectively. MAM saw the most significant warming trend in both 1900–2014 and 1979–2014. For an even shorter and more recent period (1998–2014), MAM, JJA and SON show similar warming trends, while DJF shows opposite trends. The results show that the ability of CMA-LAST for describing the global temperature changes is similar with other existing products, while there are some differences when describing regional temperature changes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Alexander LV et al (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res 111:D18116. doi:10.1029/2005JD006290

    Google Scholar 

  • Auer I et al (2007) HISTALP—Historical instrumental climatological surface time series of the greater Alpine region 1760–2003. Int J Climatol 27:17–46

    Article  Google Scholar 

  • Brohan P, Kennedy J, Harris I, Tett SFB, Jones PD (2006) Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850. J Geophys Res 111:D12106. doi:10.1029/2005JD006548

    Article  Google Scholar 

  • Cao LJ, Zhao P, Yan ZW et al (2013) Instrumental temperature series in eastern and central China back to the 19th century. J Geophys Res Atmos 118(15):8197–8207

    Article  Google Scholar 

  • Cowtan K, Way RG (2014) Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Q J R Meteorol Soc 140(683):1935–1944. doi:10.1002/qj.2297

    Article  Google Scholar 

  • Dai A, Wang J, Thorne PW, Parker DE, Haimberger L, Wang XL (2011) A new approach to homogenize daily radiosonde humidity data. J Clim 24:965–991. doi:10.1175/2010JCLI3816.1

    Article  Google Scholar 

  • Dai A, Fyfe J, Xie S, Dai X (2015) Decadal modulation of global surface temperature by internal climate variability. Nat Clim Change 5:555–559. doi:10.1038/nclimate2605

    Article  Google Scholar 

  • Ding Y, Dai X (1994) Temperature variation in China during the Last 100 years. Meteorology 20:19–26

    Google Scholar 

  • Domonkos P, Venema V, Mestre O (2013) Efficiencies of homogenisation methods: our present knowledge and its limitation. In: Proceedings of the Seventh seminar for homogenization and quality control in climatological databases. http://www.wmo.int/pages/prog/wcp/wcdmp/documents/finalWCDMP78.pdf

  • Duan A, Xiao Z (2015) Does the climate warming hiatus exist over the Tibetan Plateau? Sci Rep 5:13711. doi:10.1038/srep13711

    Article  Google Scholar 

  • Durre I, Menne MJ, Vose R (2007) Strategies for evaluation quality assurance procedures. J Appl Meteorol Climatol. doi:10.1175/2007JAMC1706.1

    Google Scholar 

  • Feng S, Hu Q, Qian W (2004) Quality control of daily meteorological data in China, 1951–2000: a new dataset. Int J Climatol 24:853–870

    Article  Google Scholar 

  • Folland CK, Karl TR (2001) Recent rates of warming in marine environment meet controversy. Eos Trans Am Geophys Union 82(40):453–461

    Article  Google Scholar 

  • Hansen J, Ruedy R, Glascoe J et al (1999) GISS analysis of surface temperature change. J Geophys Res 104:30997–31022

    Article  Google Scholar 

  • Hansen J, Sato M, Ruedy R, Lo K, Lea DW, Medina-Elizade M (2006) Global temperature change. Proc Nat Acad Sci 103(39):14288–14293

    Article  Google Scholar 

  • Hansen J, Ruedy R, Sato M, Lo K (2010) Global surface temperature change. Rev Geophys 48:RG4004. doi:10.1029/2010RG000345

    Article  Google Scholar 

  • Harris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution monthly grids of monthly climatic observations: the CRU TS 3.10 dataset. Int J Climatol 34:623–642. doi:10.1002/joc.3711

    Article  Google Scholar 

  • Hartmann DL, AMG Klein Tank, Rusticucci M, Alexander LV, Brönnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM (2013) Observations: atmosphere and surface. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

    Google Scholar 

  • Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (eds) (2001) Climate change 2001, in the scientific basis. Cambridge University Press, Cambridge

    Google Scholar 

  • IPCC (2007) Climate Change (2007) The Physical Science Basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds.) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

  • Jaccard P (1901) Etude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull de la Soc Vaud des Sci Nat 37:547–579

    Google Scholar 

  • Jones PD (1994) Hemispheric surface air temperature variations: a reanalysis and an update to 1993. J Clim 7(11):1794–1802

    Article  Google Scholar 

  • Jones PD (2016) The reliability of global and hemispheric surface temperature records. Adv Atmos Sci 33(3):269–282. doi:10.1007/s00376-015-5194-4

    Article  Google Scholar 

  • Jones PD, Moberg A (2003) Hemispheric and large-scale surface air temperature variations: an extensive revision and an update to 2001, J Clim 16:206–223

    Article  Google Scholar 

  • Jones PD, Raper SCB, Santer B, Cherry BSB, Goodess C, Kelly PM, Wigley TML, Bradley RS, Diaz HF (1985) A grid point surface air temperature data set for the northern hemisphere, TR022. Department of Energy, Washington

    Google Scholar 

  • Jones PD, New M, Parker DE, Martin S, Rigor IG (1999) Surface air temperature and its changes over the past 150 years. Rev Geophys 37(2):173–199

    Article  Google Scholar 

  • Jones PD, Lister DH, Li Q (2008) Urbanization effects in large-scale temperature records, with an emphasis on China. J Geophys Res 113:D16122. doi:10.1029/2008/JD009916

    Article  Google Scholar 

  • Jones PD, Lister DH, Osborn TJ, Harpham C, Salmon M, Morice CP (2012) Hemispheric and large-scale land-surface air temperature variations: an extensive revision and an update to 2010. J Geophys Res 117:D05127. doi:10.1029/2011JD017139

    Google Scholar 

  • Karl TR, Arguez A, Huang B, Lawrimore JH, McMahon JR, Menne MJ, Peterson TC, Vose RS, Zhang H-M (2015) Possible artifacts of data biases in the recent global surface warming hiatus. Science 348:1469–1472. doi:10.1126/science.aaa5632

    Article  Google Scholar 

  • Klein Tank AMG, Wijngaard JB, Konnen GP et al (2002) Daily dataset of 20th-century surface air temperature and precipitation series for the European climate assessment. Int J Climatol 22:1441–1453

    Article  Google Scholar 

  • Kuglitsch FG, Auchmann R, Bleisch R, Broennigmann S, Martius O, Stewart M (2012) Break detection of annual Swiss temperature series. J Geophys Res 117:D13105. doi:10.1029/2012JD017729

    Article  Google Scholar 

  • Lawrimore JH, Menne MJ, Gleason BE, Williams CN, Wuertz DB, Vose RS, Rennie J (2011) An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3. J Geophys Res 116:D19121. doi:10.1029/2011jd016187

    Article  Google Scholar 

  • Legates DR, McCabe GJ (1999) Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model evaluation. Water Resour Res 35:233–241. doi:10.1029/1998WR900018

    Article  Google Scholar 

  • Lewandowsky S, Risbey J, Oreskes N (2015) The “Pause” in Global Warming: turning a routine fluctuation into a problem for science. Bull Am Meteorol Soc 97(5):723–733. doi:10.1175/BAMS-D-14-00106.1

    Article  Google Scholar 

  • Li Q (2013) Development of homogenized data sets in China and the possible contributions to global/regional products, International Workshop on Climate Data Requirements and Applications, March 4–8, Nanjing

  • Li Q, Dong WJ (2009) Detection and adjustment of undocumented discontinuities in Chinese temperature series using a composite approach. Adv Atmos Sci 26(1):143–153

    Article  Google Scholar 

  • Li Z, Yan ZW (2010) Application of multiple analysis of series for homogenization (MASH) to Beijing daily temperature series 1960–2006. Adv Atmos Sci 27(4):777–787

    Article  Google Scholar 

  • Li Q, Liu X, Zhang H et al (2004a) Detecting and adjusting on temporal inhomogeneity in Chinese mean surface air temperature datasets. Adv Atmos Sci 21:260–268

    Article  Google Scholar 

  • Li Q, Zhang HZ, Liu XN, Huang J-Y (2004b) Urban heat island effect on annual mean temperature during the last 50 years in China. Theor Appl Climatol 79:165–174

    Article  Google Scholar 

  • Li Q, Zhang H, Chen J, Li W, Liu X, Jones P (2009) A mainland China homogenized historical temperature dataset of 1951–2004. Bull Am Meteorol Soc 90:1062–1065. doi:10.1175/2009BAMS2736.1

    Article  Google Scholar 

  • Li Q, Dong W, Li W, Gao X, Jones P, Kennedy J, Parker D (2010a) Assessment of the uncertainties in temperature change in China during the last century. Chin Sci Bull 55:1974–1982. doi:10.1007/s11434-010-3209-1

    Article  Google Scholar 

  • Li Q, Li W, Si P et al (2010b) Assessment of surface air warming in northeast China, with emphasis on the impacts of urbanization. Theor Appl Climatol. doi:10.1007/s00704-009-0155-4

    Google Scholar 

  • Li Q, Yang S, Xu WH et al (2015) China experiences recent warming hiatus. Geophys Res Lett. doi:10.1002/2014GL062773

    Google Scholar 

  • Li Q, Zhang L, Xu W et al (2016) Comparisons of time series of annual mean surface air temperature for China since 1900: observations, model simulations and extended reanalysis. Bull Am Meteorol Soc. doi:10.1175/BAMS-D-16-0092.1 (in press)

    Google Scholar 

  • Mann ME et al (2016) The likelihood of recent record warmth. Sci Rep 6:19831. doi:10.1038/srep19831

    Article  Google Scholar 

  • Menne MJ, Williams JR (2009) Homogenization of temperature series via pairwise comparisons. J Clim 22:1700–1717

    Article  Google Scholar 

  • Oke TR (2004) Initial Guidance to Obtain Representative Meteorological Observations at Urban Sites, Instruments and Methods of Observation Program, IOM Report No. 81, WMO/TD 1250, World Meteorological Organization, Geneva

  • Parker DE (1994) Effects of changing exposure of thermometers at land stations. Int J Climatol 14:1–31

    Article  Google Scholar 

  • Parker DE (2004) Large-scale warming is not urban. Nature 432:290–290

    Article  Google Scholar 

  • Parker DE (2006) A demonstration that large-scale warming is not urban. J Clim 19:2882–2895

    Article  Google Scholar 

  • Peterson TC (2003) Assessment of urban versus rural in situ surface temperatures in the contiguous United States: no difference found. J Clim 16:2941–2959

    Article  Google Scholar 

  • Peterson TC, Easterling DR (1994) Creation of homogeneous composite climatological reference series. Int J Climatol 14:671–679

    Article  Google Scholar 

  • Peterson TC, Owen TW (2005) Urban heat island assessment: metadata are important. J Clim 18:2637–2646

    Article  Google Scholar 

  • Peterson TC, Vose RS (1997) An overview of the global historical climatology network temperature data base. Bull Am Meteorol Soc 78:2837–2849

    Article  Google Scholar 

  • Peterson TC, Easterling DR, Karl TR, Groisman P, Nicholls N et al (1998) Homogeneity adjustments of in situ atmospheric climate data: a review. Int J Climatol 18:1493–1517

    Article  Google Scholar 

  • Ren G, Chu Z, Zhou Y et al (2005) Recent progresses in studies of regional temperature changes in China. Clim Environ Res 10(4):701–716

    Google Scholar 

  • Rennie JJ, Lawrimore JH, Gleason BE, Thorne PW, Morice CP, Menne MJ, Williams CN, Gambi de Almeida W, Christy JR, Flannery M, Ishihara M, Kamiguchi K, Klein-Tank AMG, Mhanda A, Lister DH, Razuvaev V, Renom M, Rusticucci M, Tandy J, Worley SJ, Venema V, Angel W, Brunet M, Dattore B, Diamond H, Lazzara MA, Le Blancq F, Luterbacher J, Mächel H, Revadekar J, Vose RS, Yin X (2014) The international surface temperature initiative global land surface databank: monthly temperature data release description and methods. Geosci Data J 1(2):75–102

  • Rohde R, Muller RA, Jacobsen R et al (2013) A new estimate of the average earth surface land temperature spanning 1753 to 2011. Geoinform Geostat. doi:10.4172/gigs.1000101.

    Google Scholar 

  • Simmons AJ, Berrisford P, Dee DP, Hersbach H, Hirahara S, Thépaut J-N (2017) A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets. Q J R Meteorol Soc 143:101–119. doi:10.1002/qj.2949

    Article  Google Scholar 

  • Slingo J et al (2013) The recent pause in global warming parts 1–3 Rep., The met office, FitzRoy Road, Exeter, UK

  • Smith TM, Reynolds RW, Lawrimore J (2008) Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006). J Clim 21:2283–2296

    Article  Google Scholar 

  • Sun Q, Miao C, Duan Q et al (2014) Would the ‘real’ observed dataset stand up? A critical examination of eight observed gridded climate datasets for China. Environ Res Lett 9(1):015001. doi:10.1088/1748-9326/9/1/015001 (1–16)

    Article  Google Scholar 

  • Trewin BC (2004) Effects of changes in algorithms used for the calculation of Australian mean temperature. Aust Meteorol Mag 53:1–11

    Google Scholar 

  • Trewin BC (2010) Exposure, instrumentation and observing practice effects on land temperature measurements. Wiley Interdisciplinary reviews. Clim Change 1:409–506. doi:10.1002/wcc.46

    Google Scholar 

  • Trewin BC (2013) A daily homogenized temperature data set for Australia. Int J Climatol 33:1510–1529

    Article  Google Scholar 

  • Venema VKC, Mestre O, Aguilar E, Auer I, Guijarro JA, Domonkos P, Vertacnik G, Szentimrey T, Stepanek P, Zahradnicek P, Viarre J, Müller-Westermeier G, Lakatos M, Williams CN, Menne MJ, Lindau R, Rasol D, Rustemeier E, Kolokythas K, Marinova T, Andresen L, Acquaotta F, Fratianni S, Cheval S, Klancar M, Brunetti M, Gruber C, Prohom Duran M, Likso T, Esteban P, Brandsma T (2012) Benchmarking homogenization algorithms for monthly data. Clim Past 8:89–115. doi:10.5194/cp-8-89-2012

    Article  Google Scholar 

  • Vincent LA, Wang XL, Milewska EJ et al (2012) A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis. J Geophys Res 117:D18110. doi:10.1029/2012JD017859

    Google Scholar 

  • Vincent LA, Zhang X, Brown RD, Feng Y, Mekis E, Milewska EJ, Wan H, Wang XL (2015) Observed trends in Canada’s climate and influence of low-frequency variability modes. J Clim 28:4545–4560 (00697.1)

    Article  Google Scholar 

  • Vose RS, Schmoyer RL, Steurer PM, Peterson TC, Heim R, Karl TR, Eischeid J (1992) The Global Historical Climatology Network: Long-term monthly temperature, precipitation, sea level pressure, and station pressure data. ORNL/CDIAC-53, NDP-041, 325 pp. [Available from Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831]

  • Wan H, Wang XL, Swail VR (2010) Homogenization and trend analysis of Canadian near-surface wind speeds. J Clim 23:1209–1225

    Article  Google Scholar 

  • Wang XL (2008a) Accounting for autocorrelation in detecting mean-shifts in climate data series using the penalized maximal t or F test. J App Meteor Climatol 47:2423–2444. doi:10.1175/2008JAMC1741.1

    Article  Google Scholar 

  • Wang XL (2008b) Penalized maximal F test for detecting undocumented mean shift without trend change. J Atmos Ocean Technol 19:368–384

    Article  Google Scholar 

  • Wang XL, Feng Y (2010) RHtestsV4 user manual, climate research division, science and technology branch, environment Canada, Toronto, Ontario, Canada. http://etccdi.pacificclimate.org/software.shtml

  • Wang KC, Zhou CL (2015) Regional contrasts of the warming rate over land significantly depend on the calculation methods of mean air temperature. Sci Rep 5:12324. doi:10.1038/srep12324

    Article  Google Scholar 

  • Wang XL, Wen QH, Wu Y (2007) Penalized maximal t test for detecting undocumented mean change in climate data series. J Appl Meteorol Climatol 46:916–931

    Article  Google Scholar 

  • Wang XL, Chen H, Wu Y, Feng Y, Pu Q (2010) New techniques for detection and adjustment of shifts in daily precipitation data series. J Appl Meteor Climatol 49(12):2416–2436. doi:10.1175/2010JAMC2376.1.

    Article  Google Scholar 

  • Wang XL, Feng Y, Vincent LA (2013) Observed changes in one-in-20 year extremes of Canadian surface air temperatures. Atmos Ocean 52(3):222–231. doi:10.1080/07055900.2013.818526

    Article  Google Scholar 

  • Wang J, Xu C, Hu M et al (2014) A new estimate of the China temperature anomaly series and uncertainty assessment in 1900–2006. J Geophys Res Atmos. doi:10.1002/2013JD020542.

    Google Scholar 

  • Wang F, Ge Q, Wang S, Li Q, Jones P (2015) A new estimation of urbanization contribution to the warming trend in China. J Clim 28:8923–8938. doi:10.1175/JCLI-D-14-00427.1

    Article  Google Scholar 

  • Wickham C et al (2013) Influence of urban heating on the global temperature land average using rural sites identified from MODIS classifications. Geoinfor Geostat. doi:10.4172/2327-4581.1000104.

    Google Scholar 

  • Wijngaard JB, Klein Tank AMG, Konnen GP et al (2003) Homogeneity of 20th century European daily temperature and precipitation series. Int J Climatol 23:679

    Article  Google Scholar 

  • Williams CN, Menne MJ, Thorne PW (2012) Benchmarking the performance of pairwise homogenization of surface temperatures in the United States. J Geophys Res 117:D05116. doi:10.1029/2011JD016761

    Article  Google Scholar 

  • Willmott CJ (1981) On the validation of models. Phys Geogr 2:184–194

    Google Scholar 

  • Willmott CJ, Ackleson SG, Davis RE, Feddema JJ, Klink KM, Legates DR, O’Donnell J, Rowe CM (1985) Statistics for the evaluation and comparisons of models. J Geophys Res 90:8995–9005. doi:10.1029/JC090iC05p08995

    Article  Google Scholar 

  • Xu W, Li Q, Wang X et al (2013) Homogenization of Chinese daily surface air temperatures and analysis of trends in the extreme temperature indices. J Geophys Res Atmos 118(17):9708–9720

    Article  Google Scholar 

  • Yan Z, Li Z, Li Q et al (2009) Effects of site-change and urbanisation in the Beijing temperature series 1977–2006. Int J Climat. doi:10.1002/joc.1971

    Google Scholar 

  • Yin H, Donat MG, Alexander LV et al (2015) Multi-dataset comparison of gridded observed temperature and precipitation extremes over China. Int J Climatol 35(10):2809–2827. doi:10.1002/joc.4174

    Article  Google Scholar 

  • You Q, Kang S, Anguilar E et al (2011) Changes in daily climate extremes in China and its connection to the large scale atmospheric circulation during 1961–2003. Clim Dyn 36:2399–2417. doi:10.1007/s00382-009-0735-0

    Article  Google Scholar 

  • Zhai P, Chao Q, Zou X (2004) Progress in China’s climate change study in the 20th century. J Geogr Sci 14:3–11. doi:10.1007/BF02841101

    Google Scholar 

  • Zhai P, Yu R, Guo Y, Li Q, Ren X, Wang Y, Xu W, Liu Y, Ding Y (2016) The strong El Niño in 2015/2016 and its dominant impacts on global and China’s climate. J Meteorol Res 74(3):309–321. doi:10.11676/qxxb2016.049

    Google Scholar 

  • Zhang X, Hegerl G, Zwiers FW, Kenyon J (2005) Avoiding inhomogeneity in percentile-based indices of temperature extremes. J Clim 18:1641–1651. doi:10.1175/JCLI3366.1

    Article  Google Scholar 

  • Zhou L, Dickinson RE, Tian Y, Fang J, Li Q, Kaufmann RK (2004) Evidence for a significant urbanization effect on climate in China. Proc Natl Acad Sci USA 101:9540–9544

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Special Public Welfare Research Fund (Nos. GYHY201406016 and GYHY201206012), China Meteorological Administration Special Foundation for Climate Change (CCSF201438), and the Natural Science Foundation of China (Nos. 91546117 and 71373131). We thank the many contributors of data, which made establishment of the dataset possible.

Author information

Authors and Affiliations

  1. China Meteorological Administration, National Meteorological Information Center, 46 Zhongguancun South Avenue, Haidian District, Beijing, 100081, China

    Wenhui Xu, Su Yang & Chen Zhu

  2. School of Atmospheric Sciences, Sun Yat-sen University, 135 Xingangxi Road, Guangzhou, 510275, China

    Qingxiang Li

  3. Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK

    Phil Jones

  4. Center of Excellence for Climate Change Research, Department of Meteorology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Phil Jones

  5. Climate Research Division, Science and Technology Branch, Environment and Climate Change Canada, Toronto, ON, Canada

    Xiaolan L. Wang & Lucie Vincent

  6. Australian Bureau of Meteorology, Melbourne, VIC, Australia

    Blair Trewin

  7. China Meteorological Administration, China Academy of Meteorological Sciences, 46 Zhongguancun South Avenue, Haidian District, Beijing, 100081, China

    Panmao Zhai

  8. LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Jinfeng Wang

  9. Department of Atmospheric and Environmental Sciences, University at Albany, SUNY, Albany, NY, 12222, USA

    Aiguo Dai

  10. China Meteorological Administration, 46 Zhongguancun South Avenue, Haidian District, Beijing, 100081, China

    Yun Gao

  11. China Meteorological Administration, National Climate Center, 46 Zhongguancun South Avenue, Haidian District, Beijing, 100081, China

    Yihui Ding

Authors
  1. Wenhui Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qingxiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Phil Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaolan L. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Blair Trewin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Su Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chen Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Panmao Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jinfeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lucie Vincent
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Aiguo Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yun Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yihui Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingxiang Li.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 2019 KB)

Appendix

Appendix

All the datasets have been made available through the national meteorological services website (http://10.1.64.154/portal/web-home.index).

Monthly updates will be based on a three-step procedure (Fig. 13), as follows. (1) The first round updates are based on hourly real-time data from the WMO Global Telecommunications System (GTS) and Integrated Surface Database (ISD) from NCEI. The daily values are calculated every day from the merged (GTS and ISD) and quality-controlled hourly datasets, and then the monthly temperature is obtained and updated in the dataset at the beginning of the following month. (2) Monthly mean values from CLIMAT message through GTS are provided to update the dataset on about the 20th day of the following month; this is the second round of dataset update. In the two steps, only the WMO stations are matched to be updated. The identification of the same station is performed by the WMO station number. (3) Other monthly data from existing homogenized datasets of the data sources listed in Table 1 are accessed and updated to the dataset. This round of updates is not carried out regularly and must be based on the update of each homogenization data source. Generally speaking, the last round of data update replaces the previous two rounds if the stations are found to be the same in previous rounds of data update. In the step, the identification of the same station is performed by the data source symbol and the station number. It is worth noting that, there is no new merging algorithm to be used during the process of updating.

Fig. 13
figure 13

Near-real-time integration and updating procedures for CMA-LSAT

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, W., Li, Q., Jones, P. et al. A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900. Clim Dyn 50, 2513–2536 (2018). https://doi.org/10.1007/s00382-017-3755-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-017-3755-1

Keywords

Search

Navigation