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Evaluating the long-term changes in temperature over the low-latitude plateau in China using a statistical downscaling method

  • Jian Wu
  • Pengwei Zhang
  • Jinlin Zha
  • Deming Zhao
  • Wenxi Lu
Article

Abstract

A statistical downscaling method (SDM) has been established through multiple stepwise regressions of predictor principal components using the ERA-Interim reanalysis data and the meteorological data collected from 115 stations in the low-latitude plateau in China from 1981 to 2015. The skill of the SDM was checked by comparing the results of the different predictor combinations and the different time lengths used to construct the SDM. In addition, compared to the historical simulation of the coupled Max Planck Institute Earth System Model (MPI-ESM-LR), better performance can be achieved by using the ERA-Interim data to construct the SDM in the low-latitude plateau. The long-term changes in temperature from 1981 to 2015 in the ERA-Interim reanalysis data are calibrated by the SDM over the low-latitude plateau of China. Furthermore, the SDM is projected into the simulation results of the MPI-ESM-LR model to construct a suitable SDM (ERA-SDM), and then the ERA-SDM is implemented to evaluate the future temperature changes in the low-latitude plateau during the period of 2018–2100 using the simulation results of the MPI-ESM-LR model under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. The results showed that an increase in temperature of 0.3 °C decade−1 was found from 1981 to 2015, in which the fastest increase of 0.4 °C decade−1 occurred in winter and the slowest increase of 0.2 °C decade−1 occurred in autumn. Most models in CMIP5 failed to simulate the long-term changes in temperature over the last 30 years in the low-latitude plateau region, and the temperature in the low-latitude plateau was underestimated by 2.4 °C using the 22 models. The SDM improved the annual and seasonal temperature characteristics and inter-annual and seasonal changes simulated by the MPI-ESM-LR. The future temperature predictions by the ERA-SDM indicated that the fastest temperature increase of 0.271 °C decade−1 was found in spring under the RCP8.5 scenario. A faster rate of temperature increase was found in the northern part of the low-latitude plateau than in the southern part under the RCP8.5 scenario.

Keywords

Low-latitude plateau Temperature Statistical downscaling model CMIP5 

Notes

Acknowledgements

We cordially thank the reviewers for their thorough comments and constructive suggestions, which improve the paper quality significantly. The Daily air temperature data is provided by Yunnan Meteorological Information Center and the ERA-Interim dataset comes from the ECMWF. We thank all the data providers. The work is supported by Chinese National Science Foundation (Grant number 41675149, 41775087), the National Key Research and Development Program of China (Grant number 2016YFA0600403), and Yunnan Province Education Department Project (Grant number 2017YJS106). This work is also supported by the Chinese Jiangsu Collaborative Innovation Center for Climate Change, the Program for Key Laboratory in University of Yunnan Province, and Young Scholar of Distinction for Doctoral Candidate of Yunnan Province in 2016.

References

  1. Aizen VB, Aizen EM, Melack JM, Dozier J (1997) Climatic and hydrologic changes in the Tien Shan, central Asia. J Clim 10(6):1393–1404CrossRefGoogle Scholar
  2. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723CrossRefGoogle Scholar
  3. Baker BB, Moseley RK (2007) Advancing treeline and retreating glaciers: implications for conservation in Yunnan, P. R. China. Arct Antarct Alpine Res 39(2):200–209CrossRefGoogle Scholar
  4. Beniston M (2003) Climatic change in mountain regions: a review of possible impacts. Clim Change 59(1):5–31CrossRefGoogle Scholar
  5. Beniston M, Rebetez M (1996) Regional behavior of minimum temperatures in Switzerland for the period 1979–1993. Theor Appl Climatol 53(4):231–243CrossRefGoogle Scholar
  6. Bergant K, Kajfež-Bogataj L, Črepinšek Z (2002) Statistical downscaling of general-circulation-model- simulated average monthly air temperature to the beginning of flowering of the dandelion (taraxacum officinale) in Slovenia. Int J Biometeorol 46(1):22–32CrossRefGoogle Scholar
  7. Bittner M, Timmreck C, Schmidt H (2013) The influence of different El Nino types on the northern hemisphere stratosphere simulated by the MPI-ESM. EGU General Assembly Conference (vol 15). EGU General Assembly Conference AbstractsGoogle Scholar
  8. Block K, Mauritsen T (2013) Forcing and feedback in the Mpi-esm-lr coupled model under abruptly quadrupled CO2. J Adv Model Earth Syst 5(4):676–691CrossRefGoogle Scholar
  9. Bretherton CS, Smith C, Wallace JM (1992) An intercomparison of methods for finding coupled patterns in climate data. J Clim 5(6):541–560CrossRefGoogle Scholar
  10. Brohan P, Kennedy JJ, Harris I, Tett SFB, Jones PD (2006) Uncertainty estimates in regional and global observed temperature changes: a new data set from 1850. J Geophys Res Atmos 111(D12):12106CrossRefGoogle Scholar
  11. Brovkin V, Boysen L, Arora VK, Boisier JP, Cadule P, Chini L et al (2013) Effect of anthropogenic land-use and land-cover changes on climate and land carbon storage in cmip5 projections for the twenty-first century. J Clim 26(18):6859–6881CrossRefGoogle Scholar
  12. Busuioc A, Chen D, Hellström C (2001) Performance of statistical downscaling models in GCM validation and regional climate change estimates: application for Swedish precipitation. Int J Climatol 21(5):557–578CrossRefGoogle Scholar
  13. Chen L, Frauenfeld OW (2014) Surface air temperature changes over the twentieth and twenty-first centuries in china simulated by 20 cmip5 models. J Clim 27(11):3920–3937CrossRefGoogle Scholar
  14. Chen JH, Lin SJ (2013) Seasonal predictions of tropical cyclones using a 25-km-resolution general circulation model. J Clim 26(2):380–398CrossRefGoogle Scholar
  15. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597CrossRefGoogle Scholar
  16. Diaz HF, Bradley RS (1997) Temperature variations during the last century at high elevation sites. Clim Change 36(3):253–279CrossRefGoogle Scholar
  17. Elshamy ME, Wheater HS, Gedney N, Huntingford C (2006) Evaluation of the rainfall component of a weather generator for climate impact studies. J Hydrol 326(1):1–24CrossRefGoogle Scholar
  18. Fan L, Fu C, Chen D (2007) Estimation of local temperature change scenarios in north China using statistical downscaling method. Chin J Atmos Sci 31(5):887–897 (in Chinese) Google Scholar
  19. Fan L, Fu C, Chen D (2011a) Long-term trend of temperature derived by statistical downscaling based on EOF analysis. Acta Meteorol Sin 25(3):327–339 (in Chinese) CrossRefGoogle Scholar
  20. Fan Z, Achim B, Axel T, Li J, Cao K (2011b) Spatial and temporal temperature trends on the Yunnan plateau (southwest china) during 1961–2004. Int J Climatol 31(14):2078–2090CrossRefGoogle Scholar
  21. Fan L, Chen D, Fu CB, Yan Z (2013) Statistical downscaling of summer temperature extremes in northern china. Adv Atmos Sci 30(4):1085–1095CrossRefGoogle Scholar
  22. Feng L, Li T, Yu W (2014) Cause of severe droughts in southwest china during 1951–2010. Clim Dyn 43(7–8):2033–2042CrossRefGoogle Scholar
  23. Feser F, Rockel B, Von Storch H, Winterfeldt J, Zahn M (2011) Regional climate models add value to global model data: a review and selected examples. Bull Am Meteorol Soc 92(9):1181–1192CrossRefGoogle Scholar
  24. Frauenfeld OW, Zhang T, Serreze MC (2005) Climate change and variability using European centre for medium-range weather forecasts reanalysis (ERA-40) temperatures on the Tibetan plateau. J Geophys Res Atmos.  https://doi.org/10.1029/2004JD005230 Google Scholar
  25. Gall JS, Ginis I, Lin SJ, Marchok TP, Chen JH (2011) Experimental tropical cyclone prediction using the GFDL 25-km-resolution global atmospheric model. Weather Forecast 26(6):1008–1019CrossRefGoogle Scholar
  26. García-Bustamante E, González-Rouco JF, Navarro J, Xoplaki E, Jiménez PA, Montávez JP (2012) North Atlantic atmospheric circulation and surface wind in the northeast of the Iberian peninsula: uncertainty and long term downscaled variability. Clim Dyn 38(1):141–160CrossRefGoogle Scholar
  27. Gay-Garcia C, Estrada F, Sánchez A (2009) Global and hemispheric temperatures revisited. Clim Change 94(3):333–349CrossRefGoogle Scholar
  28. Giorgi F, Jones C, Asrar GR (2009) Addressing climate information needs at the regional level: the cordex framework. Bull World Meteorol Org 3:175–183Google Scholar
  29. Guo J, Chen H, Xu CY, Guo S, Guo J (2012) Prediction of variability of precipitation in the Yangtze river basin under the climate change conditions based on automated statistical downscaling. Stoch Env Res Risk Assess 26(2):157–176CrossRefGoogle Scholar
  30. Gutiérrez JM, Sanmartin D, Brands S, Manzanas R, Herrera S (2013) Reassessing statistical downscaling techniques for their robust application under climate change conditions. J Clim 26(1):171–188CrossRefGoogle Scholar
  31. Huang J (2004) Meteorological statistic analysis and forecast technique. China Meteorological, Peking (in Chinese) Google Scholar
  32. Hundecha Y, Bárdossy A (2010) Statistical downscaling of extremes of daily precipitation and temperature and construction of their future scenarios. Int J Climatol 28(28):589–610Google Scholar
  33. Huth R (2004) Sensitivity of local daily temperature change estimates to the selection of downscaling models and predictors. J Clim 17(3):640–652CrossRefGoogle Scholar
  34. Huth R (2010) Statistical downscaling of daily temperature in central Europe. J Clim 15(13):1731–1742CrossRefGoogle Scholar
  35. Huth R, Kliegrová S, Metelka L (2008) Non-linearity in statistical downscaling: does it bring an improvement for daily temperature in Europe? Int J Climatol 28(4):465–477CrossRefGoogle Scholar
  36. IPCC (2013) Synthesis report, working group I contribution to the IPCC fifth assessment report climate change 2013: the physical science bases. Cambridge University Press, CambridgeGoogle Scholar
  37. 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 Atmos 117(D5):5127Google Scholar
  38. Kamp DVD, Curry CL, Monahan AH (2012) Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. II. Predicting wind components. Clim Dyn 38(7–8):1301–1311CrossRefGoogle Scholar
  39. Kazmi DH, Li J, Rasul G, Tong J, Ali G, Cheema SB et al (2015) Statistical downscaling and future scenario generation of temperatures for Pakistan region. Theor Appl Climatol 120(1–2):341–350CrossRefGoogle Scholar
  40. Ke W, Wen C, Wen Z (2011) Changes in the east Asian cold season since 2000. Adv Atmos Sci 28(1):69–79CrossRefGoogle Scholar
  41. Liu X, Yin Z, Shao X, Qin N (2006) Temporal trends and variability of daily maximum and minimum, extreme temperature events, and growing season length over the eastern and central Tibetan plateau during 1961–2003. J Geophys Res Atmos 111(D19):4617–4632CrossRefGoogle Scholar
  42. Liu XD, Cheng ZG, Yan LB, Yin ZY (2009) Elevation dependency of recent and future minimum surface air temperature trends in the Tibetan Plateau and its surroundings. Glob Planet Change 68(3):164–174CrossRefGoogle Scholar
  43. Meehl GA, Covey C, Karl ET, Thomas D, Ronald JS, Mojib L et al (2007) The WCRP CMIP3 multi-model dataset: a new era in climate change research. Bull Amer Meteorol Soc 88(9):1383–1394CrossRefGoogle Scholar
  44. Mo R, Straus DM (2002) Statistical-dynamical seasonal prediction based on principal component regression of GCM ensemble integrations. Mon Weather Rev 130(9):2167–2187CrossRefGoogle Scholar
  45. Murray RJ (1996) Explicit generation of orthogonal grids for ocean models. J Comput Phys 126(2):251–273CrossRefGoogle Scholar
  46. Randall D, Branson M, Wang M, Ghan S, Craig C, Edwards AGA (2013) A community atmosphere model with super-parameterized clouds. Eos Trans Am Geophys Union 94(25):221–222CrossRefGoogle Scholar
  47. Rasmussen J, Sonnenborg TO, Stisen S, Seaby LP, Christensen BSB, Hinsby K (2012) Elevation correction of era-interim temperature data in complex terrain. Hydrol Earth Syst Sci 16(12):4661–4673CrossRefGoogle Scholar
  48. Satoh M, Matsuno T, Tomita H, Miura H, Nasuno T, Iga S (2008) Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations. J Comput Phys 227(7):3486–3514CrossRefGoogle Scholar
  49. Schubert S (2015) Downscaling local extreme temperature changes in southeastern Australia from the CSIRO MARK2 GCM. Int J Climatol 18(13):1419–1438CrossRefGoogle Scholar
  50. Sigdel M, Ma Y (2016) Evaluation of future precipitation scenario using statistical downscaling model over humid, subhumid, and arid region of Nepala case study. Theor Appl Climatol 123(3):453–460CrossRefGoogle Scholar
  51. Simmons AJ, Willett KM, Jones PD, Thorne PW, Dee DP (2010) Low frequency variations in surface atmospheric humidity, temperature, and precipitation: inferences from reanalyses and monthly gridded observational data sets. J Geophys Res Atmos 115(D1):1–21CrossRefGoogle Scholar
  52. Simmons AJ, Poli P, Dee DP, Berrisford P, Hersbach H, Kobayashi S et al (2014) Estimating low-frequency variability and trends in atmospheric temperature using ERA-Interim. Q J R Meteorol Soc 140(679):329–353CrossRefGoogle Scholar
  53. Soares PMM, Cardoso RM, Miranda PMA, Medeiros JD, Belo-Pereira M, Espirito-Santo F (2012) WRF high resolution dynamical downscaling of ERA-Interim for Portugal. Clim Dyn 39(39):2497–2522CrossRefGoogle Scholar
  54. Stone R (2010) Severe drought puts spotlight on Chinese Dams. Science 327(5971):1311CrossRefGoogle Scholar
  55. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106(D7):7183–7192CrossRefGoogle Scholar
  56. Thomas A (1993) The onset of the rainy season in Yunnan province, PR china and its significance for agricultural operations. Int J Biometeorol 37(3):170–176CrossRefGoogle Scholar
  57. Von Storch H (1999) On the use of “inflation” in statistical downscaling. J Clim 12(12):3505–3506CrossRefGoogle Scholar
  58. Wang S, Zhang M, Sun M, Wang B, Huang X, Wang Q et al (2015) Comparison of surface air temperature derived from NECP/DOE R2, ERA-Interim, and observations in the arid northwestern china: a consideration of altitude errors. Theor Appl Climatol 119(1):99–111CrossRefGoogle Scholar
  59. Wilks DS (1995) Statistical methods in the atmospheric science. Academic, New York, p 467Google Scholar
  60. Winkler JA, Palutikof JP, Andresen JA, Goodess CM (1997) The simulation of daily temperature time series from GCM output. Part II: sensitivity analysis of an empirical transfer function methodology. J Clim 10(10):2514–2532CrossRefGoogle Scholar
  61. Wu J, Zha JL, Zhao D (2016) Estimating the impact of the changes in land use and cover on the surface wind speed over the east china plain during the period 1980–2011. Clim Dyn 46(3–4):1–17Google Scholar
  62. Wu J, Zha JL, Zhao D (2017) Evaluating the effects of land use and cover change on the decrease of surface wind speed over china in recent 30 years using a statistical downscaling method. Clim Dyn 48(1–2):131–149CrossRefGoogle Scholar
  63. Xing N, Li J, Wang L (2016) Effect of the early and late onset of summer monsoon over the Bay of Bengal on Asian precipitation in May. In: EGU General Assembly Conference (vol. 18). EGU General Assembly Conference AbstractsGoogle Scholar
  64. Xu Y, Xu CH (2012) Preliminary assessment of simulations of climate changes over china by CMIP5 multi-models. Atmos Ocean Sci Lett 5(6):489–494 (in Chinese) CrossRefGoogle Scholar
  65. Zha JL, Wu J, Zhao DM (2016) Changes of probabilities in different wind grades induced by land use and cover change in eastern china plain during 1980–2011. Atmos Sci Lett 17(4):264–269CrossRefGoogle Scholar
  66. Zha JL, Wu J, Zhao DM (2017) Effects of land use and cover change on the near-surface wind speed over China in the last 30 years. Prog Phys Geogr 41(1):46–67CrossRefGoogle Scholar
  67. Zha JL, Wu J, Zhao DM, Tang JP (2018) A possible recovery of the near-surface wind speed in Eastern China during winter after 2000 and the potential causes. Theor Appl Climatol.  https://doi.org/10.1007/s00704-018-2471-z Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Atmospheric Environment and Processes in the Boundary Layer over the Low-Latitude Plateau Region, Department of Atmospheric ScienceYunnan UniversityKunmingChina
  2. 2.CAS Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  3. 3.Yu Xi Meteorological BureauYuxiChina

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