Climate Dynamics

, Volume 50, Issue 7–8, pp 2513–2536 | Cite as

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

  • Wenhui Xu
  • Qingxiang Li
  • Phil Jones
  • Xiaolan L. Wang
  • Blair Trewin
  • Su Yang
  • Chen Zhu
  • Panmao Zhai
  • Jinfeng Wang
  • Lucie Vincent
  • Aiguo Dai
  • Yun Gao
  • Yihui Ding
Article

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.

Keywords

CMA-LSAT dataset Surface air temperature Homogenized Climate change 

Notes

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.

Supplementary material

382_2017_3755_MOESM1_ESM.doc (2 mb)
Supplementary material 1 (DOC 2019 KB)

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Wenhui Xu
    • 1
  • Qingxiang Li
    • 2
  • Phil Jones
    • 3
    • 4
  • Xiaolan L. Wang
    • 5
  • Blair Trewin
    • 6
  • Su Yang
    • 1
  • Chen Zhu
    • 1
  • Panmao Zhai
    • 7
  • Jinfeng Wang
    • 8
  • Lucie Vincent
    • 5
  • Aiguo Dai
    • 9
  • Yun Gao
    • 10
  • Yihui Ding
    • 11
  1. 1.China Meteorological AdministrationNational Meteorological Information CenterBeijingChina
  2. 2.School of Atmospheric SciencesSun Yat-sen UniversityGuangzhouChina
  3. 3.Climatic Research Unit, School of Environmental SciencesUniversity of East AngliaNorwichUK
  4. 4.Center of Excellence for Climate Change Research, Department of MeteorologyKing Abdulaziz UniversityJeddahSaudi Arabia
  5. 5.Climate Research Division, Science and Technology BranchEnvironment and Climate Change CanadaTorontoCanada
  6. 6.Australian Bureau of MeteorologyMelbourneAustralia
  7. 7.China Meteorological AdministrationChina Academy of Meteorological SciencesBeijingChina
  8. 8.LREIS, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  9. 9.Department of Atmospheric and Environmental SciencesUniversity at Albany, SUNYAlbanyUSA
  10. 10.China Meteorological AdministrationBeijingChina
  11. 11.China Meteorological AdministrationNational Climate CenterBeijingChina

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