Skip to main content

Advertisement

Log in

Comparison of multiple downscaling techniques for climate change projections given the different climatic zones in China

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

General circulation models (GCMs) are important tools for the study of climate change, but their resolutions are too coarse for station-scale impact assessments. Statistical and dynamical downscaling methods are widely used to translate the predictions of GCMs to the finer spatial scale and it is important to understand the difference between statistical and dynamical downscaling methods in different climatic zones and time periods. Moreover, statistical downscaling can be used on both GCM and regional climate model (RCM) outputs. In this study, two sets of GCM precipitations were dynamically and statistically downscaled and their performances were evaluated against the observed precipitation from 308 stations distributed throughout the Yellow, Yangtze, and Pearl River basins. These stations have distinct climatic characteristics from the historical period (1961–2000) and future period (2031–2050). Results suggest dynamically downscaled GCM precipitation does not present lower biases when comparing observed site-specific precipitation to GCM outputs, and biases of the initial dynamically downscaled GCM outputs decreased in areas with higher humidity. This demonstrates that statistical downscaling can improve GCM and RCM outputs, and the statistical downscaling method can reproduce local-scale precipitation satisfactorily without dynamical downscaling. However, statistical downscaling reduced spatial regularity of the biases that exist in GCM and RCM outputs between the observations and simulation. Additionally, the spatial discrepancy between statistically downscaled GCM and RCM precipitations was very small. In the future period, discrepancies between statistically downscaled RCM and GCM precipitations in the two climate scenarios were larger than the historical period for all climate zones.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Ahmed KF, Wang G, Silander J, Wilson AM, Allen JM, Horton R, Anyah R (2013) Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast. Glob Planet Chang 100(1):320–332

    Google Scholar 

  • Bennett JC, Grose MR, Post DA, Ling FL, Corney SP, and Bindoff NL (2011) Performance of quantile-quantile bias-correction for use in hydroclimatological projections. MODSIM2011 International Congress on Modelling and Simulation, Perth (vol 26, pp 2668-2675)

  • Casanueva A, Herrera S, Fernández J, Gutiérrez JM (2016) Towards a fair comparison of statistical and dynamical downscaling in the framework of the EURO-CORDEX initiative. Clim Chang 137:411–426. https://doi.org/10.1007/s10584-016-1683-4

    Article  Google Scholar 

  • Chen J, Brissette FP, Leconte R (2011) Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. J Hydrol 401(3):190–202

    Google Scholar 

  • Chen H, Xu CY, Guo S (2012a) Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. J Hydrol s 434–435(s 434–435):36–45

    Google Scholar 

  • Chen J, Brissette FP, Leconte R (2012b) Coupling statistical and dynamical methods for spatial downscaling of precipitation. Clim Chang 114(3–4):509–526

    Google Scholar 

  • Chen J, Brissette FP, Diane C, Marco B (2013) Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resour Res 49(7):4187–4205

    Google Scholar 

  • Chen J, Zhang JX, Brissette FP (2014) Assessing scale effects for statistically downscaling precipitation with gpcc model. Int J Climatol 34(3):708–727

    Google Scholar 

  • Corte-Real J, Zhang X, Wang X (1995) Downscaling GCM information to regional scales: a non-parametric multivariate regression approach. Clim Dyn 11(7):413–424

    Google Scholar 

  • Di Luca A, de Elia R, Laprise R (2012) Potential for added value in precipitation simulated by high-resolution nested regional climate models and observations. Clim Dyn 38(5–6):1229–1247

    Google Scholar 

  • Diaconescu EP, Laprise R (2013) Can added value be expected in RCM-simulated large scales? Clim Dyn 41(7–8):1769–1800

    Google Scholar 

  • Ding Y (1994) Monsoons over China. Springer, Netherlands

    Google Scholar 

  • Eden JM, Widmann M, Maraun D, Vrac M (2014) Comparison of GCM- and RCM-simulated precipitation following stochastic postprocessing. J Geophys Res Atmos 119(19):11,040–11,053

    Google Scholar 

  • Flaounas E, Drobinski P, Vrac M, Bastin S, Lebeaupin-Brossier C, Stefanon M, Borga M, Calvet J-C (2013) Precipitation and temperature space-time variability and extremes in the Mediterranean region: evaluation of dynamical and statistical downscaling methods. Clim Dyn 40(11–12):2687–2705. https://doi.org/10.1007/s00382-012-1558-y

    Article  Google Scholar 

  • Gao H, Yang S (2009) A severe drought event in northern China in winter 2008–2009 and the possible influences of La Niña and Tibetan plateau. J Geophys Res 114(114):144–153

    Google Scholar 

  • Gao XJ, Zhao ZC, Ding YH (2001) Climate change due to greenhouse effects in China as simulated by a regional climate model. Adv Atmos Sci 18:1224–1230

    Google Scholar 

  • Gao X, Shi Y, Song R, Giorgi F, Wang Y, Zhang D (2008) Reduction of future monsoon precipitation over China: comparison between a high resolution RCM simulation and the driving GCM. Meteorol Atmos Phys 100(1):73–86

    Google Scholar 

  • Ghosh S, Mujumdar PP (2006) Future rainfall scenario over Orissa with GCM projections by statistical downscaling. Currentence 90(3):396–404

    Google Scholar 

  • Goodess C (2005) Statistical and regional dynamical downscaling of extremes for European regions. STARDEX final management report Available at http://www.cru.uea.ac.uk/cru/research/stardex(Last updated: November 2005)

  • Gutmann ED, Rasmussen RM, Liu C, Ikeda K, Gochis DJ, Clark MP, Dudhia J, Thompson G (2012) A comparison of statistical and dynamical downscaling of winter precipitation over complex terrain. J Clim 25(1):262–281. https://doi.org/10.1175/2011jcli4109.1

    Article  Google Scholar 

  • Haerter (2010) Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J Hydrol 395:199–215

    Google Scholar 

  • Hanafin JA, Mcgrath R, Semmler T, Wang S, Lynch P, Steele-Dunne S et al (2011) Air flow and stability indices in GCM future and control runs. Int J Climatol 31(8):1240–1247

    Google Scholar 

  • Hoomehr S, Schwartz JS, Yoder DC (2015) Potential changes in rainfall erosivity under GCM climate change scenarios for the southern appalachian region, USA. Catena

  • Hu XM, Nielsengammon JW, Zhang F (2010). Evaluation of Three Planetary Boundary Layer Schemes in the WRF Model. J Appl Meteorol Clim, 2010, 49(9):1831-1844

    Google Scholar 

  • Huth R, Mikšovský J, Štěpánek P, Belda M, Farda A, Chládová Z, Pišoft P (2015) Comparative validation of statistical and dynamical downscaling models on a dense grid in central Europe: temperature. Theor Appl Climatol 120(3):533–553

    Google Scholar 

  • Ines AVM, Hansen JW (2006) Bias correction of daily GCM rainfall for crop simulation studies. Agric For Meteorol 138:44–53

    Google Scholar 

  • Jones RG, Noguer M, Hassell DC, Hudson D, Wilson SS, Jenkins GJ, Mitchell JFB (2004) Generating high resolution climate change scenarios using PRECIS. Met Office Hadley Centre, Exeter 40pp

    Google Scholar 

  • Kerr RA (2013) Forecasting regional climate change flunks its first test. Science 339:638–638

    Google Scholar 

  • Kidson J, Thompson C (1998) A comparison of statistical and model-based downscaling techniques for estimating local climate variations. J Clim 11(4):735–753

    Google Scholar 

  • Laflamme EM, Linder E, Pan Y (2015) Statistical downscaling of regional climate model output to achieve projections of precipitation extremes. Weather and Climate Extremes 12:15–23

    Google Scholar 

  • Lafon T, Dadson S, Buys G, Prudhomme C (2013) Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods. Int J Climatol 33:1367–1381. https://doi.org/10.1002/joc.3518

    Article  Google Scholar 

  • Laprise R, de Elía R, Caya D, Biner S, Lucas-Picher P, Diaconescu E, Leduc M, Alexandru A, Separovic L, Canadian Network for Regional Climate Modelling and Diagnostics (2008) Challenging some tenets of regional climate modeling. Meteorog Atmos Phys 100:3–22

    Google Scholar 

  • Laprise R, Hernández-Díaz L, Tete K, Sushama L, Šeparović L, Martynov A, Winger K, Valin M (2013) Climate projections over CORDEX Africa domain using the fifth-generation Canadian Regional Climate Model (CRCM5). Clim Dyn 41:3219–3246

    Google Scholar 

  • Li R, Wang SY, Gillies RR (2016) A combined dynamical and statistical downscaling technique to reduce biases in climate projections: an example for winter precipitation and snowpack in the western United States. Theor Appl Climatol 124:281–289. https://doi.org/10.1007/s00704-015-1415-0

    Article  Google Scholar 

  • Liu Y, Lam JC, Tsang CL (2008) Energy performance of building envelopes in different climate zones in China. Appl Energy 85(9):800–817

    Google Scholar 

  • Lorant V, Mcfarlane NA, Scinocca JF (2006) Variability of precipitation intensity: sensitivity to treatment of moist convection in an RCM and a GCM. Clim Dyn 26(2):183–200

    Google Scholar 

  • Maraun D (2012) Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums. Geophys Res Lett 39:L06706. https://doi.org/10.1029/2012GL051210

    Article  Google Scholar 

  • Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M et al (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48(3):633–650

    Google Scholar 

  • Michalakes J, Hacker J, and Loft R (2010). WRF nature run. IEEE Conference on Supercomputing. 2010

  • Mujumdar PP, Ghosh S (2008) Modeling GCM and scenario uncertainty using a possibilistic approach: application to the mahanadi river, India. Water Resour Res 44(44):663–671

    Google Scholar 

  • Murphy J (1999) An evaluation of statistical and dynamical techniques for downscaling local climate. J Clim 12(8):2256–2284

    Google Scholar 

  • Piani C, Weedon GP, Best M, Gomes SM, Viterbo P, Hagemann S, O J (2010) Temperature for the application of hydrological models. J Hydrol 395:199–215

    Google Scholar 

  • Pizzigalli C, Palatella L, Zampieri M, Lionello P, Miglietta M, Paradisi P (2012) Dynamical and statistical downscaling of precipitation and temperature in a Mediterranean area. Ital J Agron 7(1):2. https://doi.org/10.4081/ija.2012.e2

    Article  Google Scholar 

  • Rashid MM, Beecham S, Chowdhury RK (2015) Statistical downscaling of rainfall: a non-stationary and multi-resolution approach. Theor Appl Climatol:1–15

  • Ribeiro AFS, Pires CAL (2015) Seasonal drought predictability in Portugal using statistical–dynamical techniques. Phys Chem Earth Parts A/B/C 94:155–166

    Google Scholar 

  • Schmidli J, Frei C, Vidale PL (2006) Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods. Int J Climatol 26(5):679–689

    Google Scholar 

  • Shen MX, Chen J, Zhuan MJ, Chen H, Xu CY, Xiong LH, (2018) Estimating uncertainty and its temporal variation related to global climate models in quantifying climate change impacts on hydrology. J Hydrol 556:10-24

    Google Scholar 

  • Spain P (2004) The Gamma function. Elements of mathematics functions of a real variable. Springer, Berlin Heidelberg

    Google Scholar 

  • Soares PMM. , Cardoso RM, Miranda PMA. , Medeiros J, Margarida Belo-Pereira, Fátima Espirito-Santo, (2012) WRF high resolution dynamical downscaling of ERA-Interim for Portugal. Clim Dynam 39 (9-10):2497-2522

    Google Scholar 

  • Teng J, Vaze J, Chiew FHS, Wang B, Perraud JM (2012) Estimating the relative uncertainties sourced from GCMs and hydrological models in modeling climate change impact on runoff. J Hydrometeorol 13(1):122–139

    Google Scholar 

  • Themeßl MJ, Gobiet A, Leuprecht A (2011) Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. Int J Climatol 31(10):1530–1544

    Google Scholar 

  • Töyrä J, Pietroniro A, Bonsal B (2013) Evaluation of GCM simulated climate over the Canadian prairie provinces. Can Water Resour J 30(3):245–262

    Google Scholar 

  • Troin M, Caya D, Velázquez JA, Brissette F (2015) Hydrological response to dynamical downscaling of climate model outputs: a case study for western and eastern snowmelt-dominated Canada catchments. J HYDROL: Regional Studies 4:595–610

    Google Scholar 

  • Vuuren DPV, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K et al (2011) The representative concentration pathways: an overview. Clim Chang 109(1–2):5–31. https://doi.org/10.1007/s10584-011-0148-z

    Article  Google Scholar 

  • Wilby RL, Wigley TML, Conway D, Jones PD, Hewitson BC, Main J, Wilks DS (1998) Statistical downscaling of general circulation model output: a comparison of methods. Water Resour Res 34(11):2995–3008

    Google Scholar 

  • Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Chang 62(1):189–216

    Google Scholar 

  • Xu Z, Yang Z (2015) A new dynamical downscaling approach with GCM bias corrections and spectral nudging. J Geophys Res-Atmos 120(8):3063–3084

    Google Scholar 

  • Yhang YB, Sohn SJ, Jung IW (2017) Application of dynamical and statistical downscaling to East Asian summer precipitation for finely resolved datasets. Adv Meteorol 2017(2017-02-8)

  • Yin JB, Guo SL, He SK, Guo JL, Hong XJ, Liu ZJ, (2018) A copula-based analysis of projected climate changes to bivariate flood quantiles. J Hydrol 566:23-42

    Google Scholar 

Download references

Acknowledgements

The authors are grateful to the National Climate Centre for providing the site-specific daily precipitation data in China. The authors also thank the Earth System Research Laboratory for the NCEP data and World Climate Research Program for the GCMs data. We would like to acknowledge the CORDEX-East Asia Databank for the HadGEM RCM data, which is responsible for the CORDEX dataset, and we would also like to thank the National Institute of Meteorological Research (NIMR), three universities in the Republic of Korea (Seoul National Univ., Yonsei Univ., and Kongju National Univ.), as well as other cooperative research institutes in East Asia, for producing and making their model outputs available. We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

Funding

The study was funded by the National Natural Science Foundation of China (No. 51339004, No. 51539009, and No. 51279138), National Basic Research Program of China (No. 2013CB430205), and the Fundamental Research Funds for the Central Universities (No.2015206020201).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Yan-feng He or Hua Chen.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hou, Yk., He, Yf., Chen, H. et al. Comparison of multiple downscaling techniques for climate change projections given the different climatic zones in China. Theor Appl Climatol 138, 27–45 (2019). https://doi.org/10.1007/s00704-019-02794-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-019-02794-z

Navigation