Advertisement

On the use of observations in assessment of multi-model climate ensemble

  • Donghui Xu
  • Valeriy Y. IvanovEmail author
  • Jongho Kim
  • Simone Fatichi
Original Paper

Abstract

The Bayesian weighted averaging (BWA) method is commonly used to integrate over multi-model ensembles of climate series. This method relies on two criteria to assign weights to individual outputs: model skill in reproducing historical observations, and inter-model agreement in simulating future period. Observations are generally thought to be relevant for correcting biases in model outputs in the BWA framework. However, they concurrently may introduce unpredictable impacts in the context of the downscaling process, in particular, when model output on precipitation is of interest. Specifically, the posterior distribution may excessively depend on few ‘outlier models’ being close to the observation, when all other models fail to capture observation of the historical period—a common situation for precipitation metrics. Another issue emerges for climates with very dry months: the inclusion of observation in BWA may result in a significant spread of the posterior distribution into the negative region. To address these problems, a modified version of the BWA method that removes observations in the initial phase of downscaling (computation of Factors of Change) and adds them in the estimation of posterior distributions is explored in this work. Comparisons of simulation results for the locations of Miami (FL), Fresno (CA), and Flint (MI) between the modified BWA and the traditional BWA demonstrate consistent outcomes with regards to the effect of observation in the Bayesian framework. Further, the modified BWA approach generally reduces uncertainty, as compared to ‘simple averaging’ in the Bayesian context, which assigns equal weights to all model outputs.

Keywords

Bayesian weighted averaging Multi-model ensemble Weighting skill Model bias Observations Factor of change 

Notes

Acknowledgements

This study was supported by the NSF Grant EAR 1151443. Jongho Kim was supported by a Grant (18AWMP-B127554-02) from the Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government. We acknowledge the modeling groups listed in Table 1, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for making the CMIP5 multi-model dataset available. We also thank the Office of Support, U.S. Department of Energy for providing the support of this dataset.

Supplementary material

477_2018_1621_MOESM1_ESM.docx (10.6 mb)
Supplementary material 1 (DOCX 10823 kb)

References

  1. Abramowitz G (2010) Model independence in multi-model ensemble prediction. Aust Meteorol Oceanogr J 59:3–6Google Scholar
  2. Akhtar M, Ahmad N, Booij MJ (2008) The impact of climate change on the water resources of Hindukush-Karakorum-Himalaya region under different glacier coverage scenarios. J Hydrol 355(1–4):148–163Google Scholar
  3. Allan RP, Liu CL, Zahn M, Lavers DA, Koukouvagias E, Bodas-Salcedo A (2014) Physically consistent responses of the global atmospheric hydrological cycle in models and observations. Surv Geophys 35(3):533–552Google Scholar
  4. Anandhi A, Frei A, Pierson DC, Schneiderman EM, Zion MS, Lounsbury D, Matonse AH (2011) Examination of change factor methodologies for climate change impact assessment. Water Resour Res.  https://doi.org/10.1029/2010WR009104 CrossRefGoogle Scholar
  5. Bentsen M, Bethke I, Debernard JB, Iversen T, Kirkevag A, Seland O, Drange H, Roelandt C, Seierstad IA, Hoose C, Kristjansson JE (2013) The Norwegian Earth System Model, NorESM1-M-Part 1: description and basic evaluation of the physical climate. Geosci Model Dev 6(3):687–720Google Scholar
  6. Bi DH, Dix M, Marsland SJ, O’Farrell S, Rashid HA, Uotila P, Hirst AC, Kowalczyk E, Golebiewski M, Sullivan A, Yan HL, Hannah N, Franklin C, Sun ZA, Vohralik P, Watterson I, Zhou XB, Fiedler R, Collier M, Ma YM, Noonan J, Stevens L, Uhe P, Zhu HY, Griffies SM, Hill R, Harris C, Puri K (2013) The ACCESS coupled model: description, control climate and evaluation. Aust Meteorol Oceanogr J 63(1):41–64Google Scholar
  7. Bishop CH, Abramowitz G (2013) Climate model dependence and the replicate Earth paradigm. Clim Dyn 41(3–4):885–900Google Scholar
  8. Burlando P, Rosso R (2002) Effects of transient climate change on basin hydrology. 1. Precipitation scenarios for the Arno River, central Italy. Hydrol Process 16(6):1151–1175Google Scholar
  9. Castro CL, Pielke RA, Leoncini G (2005) Dynamical downscaling: assessment of value retained and added using the regional atmospheric modeling system (RAMS). J Geophys Res Atmos.  https://doi.org/10.1029/2004JD004721 CrossRefGoogle Scholar
  10. Chandler RE (2013) Exploiting strength, discounting weakness: combining information from multiple climate simulators. Philos Trans R Soc A Math Phys Eng Sci 371(1991):20120388Google Scholar
  11. Chen J, Brissette FP, Leconte R (2011) Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. J Hydrol 401(3–4):190–202Google Scholar
  12. Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, Held R, Jones R, Kolli RK, Kwon WK, Laprise R, Magana Rueda V, Mearns L, Menendez CG, Räisänen J, Rinke A, Sarr A, Whetton P, Arritt R, Benestad R, Beniston M, Bromwich D, Caya D, Comiso J, de Elia R, Dethloff K (2007) Regional climate projections , climate change, 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate Change, Chapter 11. University Press, Cambridge. ISBN: 978-0-521-88009-1Google Scholar
  13. Christensen JH, Kjellstrom E, Giorgi F, Lenderink G, Rummukainen M (2010) Weight assignment in regional climate models. Clim Res 44(2–3):179–194Google Scholar
  14. Chylek P, Li J, Dubey MK, Wang M, Lesins G (2011) Observed and model simulated 20th century Arctic temperature variability: canadian Earth System Model CanESM2. Atmos Chem Phys Discuss 2011:22893–22907Google Scholar
  15. Collins M, Chandler RE, Cox PM, Huthnance JM, Rougier J, Stephenson DB (2012) Quantifying future climate change. Nat Clim Change 2:403Google Scholar
  16. Diaz-Nieto J, Wilby RL (2005) A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames, United Kingdom. Clim Change 69(2–3):245–268Google Scholar
  17. Dufresne JL, Foujols MA, Denvil S, Caubel A, Marti O, Aumont O, Balkanski Y, Bekki S, Bellenger H, Benshila R, Bony S, Bopp L, Braconnot P, Brockmann P, Cadule P, Cheruy F, Codron F, Cozic A, Cugnet D, de Noblet N, Duvel JP, Ethe C, Fairhead L, Fichefet T, Flavoni S, Friedlingstein P, Grandpeix JY, Guez L, Guilyardi E, Hauglustaine D, Hourdin F, Idelkadi A, Ghattas J, Joussaume S, Kageyama M, Krinner G, Labetoulle S, Lahellec A, Lefebvre MP, Lefevre F, Levy C, Li ZX, Lloyd J, Lott F, Madec G, Mancip M, Marchand M, Masson S, Meurdesoif Y, Mignot J, Musat I, Parouty S, Polcher J, Rio C, Schulz M, Swingedouw D, Szopa S, Talandier C, Terray P, Viovy N, Vuichard N (2013) Climate change projections using the IPSL-CM5 earth system model: from CMIP3 to CMIP5. Clim Dyn 40(9–10):2123–2165Google Scholar
  18. Dunne JP, John JG, Adcroft AJ, Griffies SM, Hallberg RW, Shevliakova E, Stouffer RJ, Cooke W, Dunne KA, Harrison MJ, Krasting JP, Malyshev SL, Milly PCD, Phillipps PJ, Sentman LT, Samuels BL, Spelman MJ, Winton M, Wittenberg AT, Zadeh N (2012) GFDL’s ESM2 global coupled climate-carbon earth system models. Part I: physical formulation and baseline simulation characteristics. J Clim 25(19):6646–6665Google Scholar
  19. Dunne JP, John JG, Shevliakova E, Stouffer RJ, Krasting JP, Malyshev SL, Milly PCD, Sentman LT, Adcroft AJ, Cooke W, Dunne KA, Griffies SM, Hallberg RW, Harrison MJ, Levy H, Wittenberg AT, Phillips PJ, Zadeh N (2013) GFDL’s ESM2 global coupled climate-carbon earth system models Part II: carbon system formulation and baseline simulation characteristics. J Clim 26(7):2247–2267Google Scholar
  20. Fatichi S, Ivanov VY, Caporali E (2011) Simulation of future climate scenarios with a weather generator. Adv Water Resour 34(4):448–467Google Scholar
  21. Fatichi S, Ivanov VY, Caporali E (2013) Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series. Clim Dyn 40(7–8):1841–1861Google Scholar
  22. Fatichi S, Ivanov VY, Paschalis A, Peleg N, Molnar P, Rimkus S, Kim J, Burlando P, Caporali E (2016) Uncertainty partition challenges the predictability of vital details of climate change. Earths Future 4(5):240–251Google Scholar
  23. Fowler HJ, Ekstrom M (2009) Multi-model ensemble estimates of climate change impacts on UK seasonal precipitation extremes. Int J Climatol 29(3):385–416Google Scholar
  24. Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27(12):1547–1578Google Scholar
  25. Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR, Lawrence DM, Neale RB, Rasch PJ, Vertenstein M, Worley PH, Yang ZL, Zhang MH (2011) The community climate system model version 4. J Clim 24(19):4973–4991Google Scholar
  26. Giorgetta MA, Jungclaus J, Reick CH, Legutke S, Bader J, Bottinger M, Brovkin V, Crueger T, Esch M, Fieg K, Glushak K, Gayler V, Haak H, Hollweg HD, Ilyina T, Kinne S, Kornblueh L, Matei D, Mauritsen T, Mikolajewicz U, Mueller W, Notz D, Pithan F, Raddatz T, Rast S, Redler R, Roeckner E, Schmidt H, Schnur R, Segschneider J, Six KD, Stockhause M, Timmreck C, Wegner J, Widmann H, Wieners KH, Claussen M, Marotzke J, Stevens B (2013) Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the coupled model intercomparison project phase 5. J Adv Model Earth Syst 5(3):572–597Google Scholar
  27. Giorgi F, Mearns LO (2002) Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the “reliability ensemble averaging’’ (REA) method. J Clim 15(10):1141–1158Google Scholar
  28. Gneiting T, Raftery AE, Westveld AH III, Goldman T (2005) Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon Weather Rev 133(5):1098–1118Google Scholar
  29. Hanson RT, Flint LE, Flint AL, Dettinger MD, Faunt CC, Cayan D, Schmid W (2012) A method for physically based model analysis of conjunctive use in response to potential climate changes. Water Resour Res.  https://doi.org/10.1029/2011WR010774 CrossRefGoogle Scholar
  30. Haughton N, Abramowitz G, Pitman A, Phipps SJ (2014) On the generation of climate model ensembles. Clim Dyn 43(7–8):2297–2308Google Scholar
  31. Haughton N, Abramowitz G, Pitman A, Phipps SJ (2015) Weighting climate model ensembles for mean and variance estimates. Clim Dyn 45(11–12):3169–3181Google Scholar
  32. Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90(8):1095–1108Google Scholar
  33. Hay LE, Wilby RJL, Leavesley GH (2000) A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. J Am Water Resour Assoc 36(2):387–397Google Scholar
  34. Ivanov VY, Bras RL, Curtis DC (2007) A weather generator for hydrological, ecological, and agricultural applications. Water Resour Res.  https://doi.org/10.1029/2006WR005364 CrossRefGoogle Scholar
  35. Jacob D, Petersen J, Eggert B, Alias A, Christensen OB, Bouwer LM, Braun A, Colette A, Déqué M, Georgievski G, Georgopoulou E, Gobiet A, Menut L, Nikulin G, Haensler A, Hempelmann N, Jones C, Keuler K, Kovats S, Kröner N, Kotlarski S, Kriegsmann A, Martin E, van Meijgaard E, Moseley C, Pfeifer S, Preuschmann S, Radermacher C, Radtke K, Rechid D, Rounsevell M, Samuelsson P, Somot S, Soussana J-F, Teichmann C, Valentini R, Vautard R, Weber B, Yiou P (2014) EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Change 14(2):563–578Google Scholar
  36. Jeffrey S, Rotstayn L, Collier M, Dravitzki S, Hamalainen C, Moeseneder C, Wong K, Syktus J (2013) Australia’s CMIP5 submission using the CSIRO-Mk3.6 model. Aust Meteorol Oceanogr J 63(1):1–13Google Scholar
  37. Jones CD, Hughes JK, Bellouin N, Hardiman SC, Jones GS, Knight J, Liddicoat S, O’Connor FM, Andres RJ, Bell C, Boo KO, Bozzo A, Butchart N, Cadule P, Corbin KD, Doutriaux-Boucher M, Friedlingstein P, Gornall J, Gray L, Halloran PR, Hurtt G, Ingram WJ, Lamarque JF, Law RM, Meinshausen M, Osprey S, Palin EJ, Chini LP, Raddatz T, Sanderson MG, Sellar AA, Schurer A, Valdes P, Wood N, Woodward S, Yoshioka M, Zerroukat M (2011) The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci Model Dev 4(3):543–570Google Scholar
  38. Kang EL, Cressie N, Sain SR (2012) Combining outputs from the North American Regional Climate Change Assessment Program by using a Bayesian hierarchical model. J R Stat Soc Ser C Appl Stat 61:291–313Google Scholar
  39. Kilsby CG, Jones PD, Burton A, Ford AC, Fowler HJ, Harpham C, James P, Smith A, Wilby RL (2007) A daily weather generator for use in climate change studies. Environ Model Softw 22(12):1705–1719Google Scholar
  40. Kim J, Ivanov VY (2015) A holistic, multi-scale dynamic downscaling framework for climate impact assessments and challenges of addressing finer-scale watershed dynamics. J Hydrol 522:645–660Google Scholar
  41. Kim J, Ivanov VY, Fatichi S (2016) Climate change and uncertainty assessment over a hydroclimatic transect of Michigan. Stoch Environ Res Risk Assess 30(3):923–944Google Scholar
  42. Knutti R (2010) The end of model democracy? Clim Change 102(3–4):395–404Google Scholar
  43. Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Clim 23(10):2739–2758Google Scholar
  44. Knutti R, Sedlacek J, Sanderson BM, Lorenz R, Fischer EM, Eyring V (2017) A climate model projection weighting scheme accounting for performance and interdependence. Geophys Res Lett 44(4):1909–1918Google Scholar
  45. Leith NA, Chandler RE (2010) A framework for interpreting climate model outputs. J R Stat Soc Ser C Appl Stat 59:279–296Google Scholar
  46. Li LJ, Lin PF, Yu YQ, Wang B, Zhou TJ, Liu L, Liu JP, Bao Q, Xu SM, Huang WY, Xia K, Pu Y, Dong L, Shen S, Liu YM, Hu N, Liu MM, Sun WQ, Shi XJ, Zheng WP, Wu B, Song MR, Liu HL, Zhang XH, Wu GX, Xue W, Huang XM, Yang GW, Song ZY, Qiao FL (2013) The flexible global ocean-atmosphere-land system model, grid-point version 2: FGOALS-g2. Adv Atmos Sci 30(3):543–560Google Scholar
  47. Mahlstein I, Portmann RW, Daniel JS, Solomon S, Knutti R (2012) Perceptible changes in regional precipitation in a future climate. Geophys Res Lett.  https://doi.org/10.1029/2011GL050738 CrossRefGoogle Scholar
  48. Mamalakis A, Langousis A, Deidda R, Marrocu M (2017) A parametric approach for simultaneous bias correction and high-resolution downscaling of climate model rainfall. Water Resour Res 53(3):2149–2170Google Scholar
  49. Manning LJ, Hall JW, Fowler HJ, Kilsby CG, Tebaldi C (2009) Using probabilistic climate change information from a multimodel ensemble for water resources assessment. Water Resour Res.  https://doi.org/10.1029/2007WR006674 CrossRefGoogle Scholar
  50. Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themessl M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys.  https://doi.org/10.1029/2009RG000314 CrossRefGoogle Scholar
  51. Mearns LO, Sain S, Leung LR, Bukovsky MS, McGinnis S, Biner S, Caya D, Arritt RW, Gutowski W, Takle E, Snyder M, Jones RG, Nunes AMB, Tucker S, Herzmann D, McDaniel L, Sloan L (2013) Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP). Clim Change 120(4):965–975Google Scholar
  52. Meehl GA, Covey C, McAvaney B, Latif M, Stouffer RJ (2005) Overview of the coupled model intercomparison project. Bull Am Meteorol Soc 86(1):89–93Google Scholar
  53. Meehl GA, Washington WM, Arblaster JM, Hu AX, Teng HY, Kay JE, Gettelman A, Lawrence DM, Sanderson BM, Strand WG (2013) Climate change projections in CESM1(CAM5) compared to CCSM4. J Clim 26(17):6287–6308Google Scholar
  54. Nunes JP, Seixas J, Keizer JJ (2013) Modeling the response of within-storm runoff and erosion dynamics to climate change in two Mediterranean watersheds: a multi-model, multi-scale approach to scenario design and analysis. catena 102:27–39Google Scholar
  55. Olson R, Fan YA, Evans JP (2016) A simple method for Bayesian model averaging of regional climate model projections: application to southeast Australian temperatures. Geophys Res Lett 43(14):7661–7669Google Scholar
  56. Onyutha C, Tabari H, Rutkowska A, Nyeko-Ogiramoi P, Willems P (2016) Comparison of different statistical downscaling methods for climate change rainfall projections over the Lake Victoria basin considering CMIP3 and CMIP5. J Hydro Environ Res 12:31–45Google Scholar
  57. Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen–Geiger climate classification. Hydrol Earth Syst Sci 11(5):1633–1644Google Scholar
  58. Peleg N, Fatichi S, Paschalis A, Molnar P, Burlando P (2017) An advanced stochastic weather generator for simulating 2-D high-resolution climate variables. J Adv Model Earth Syst 9(3):1595–1627Google Scholar
  59. Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99(1–2):187–192Google Scholar
  60. Raftery AE, Gneiting T, Balabdaoui F, Polakowski M (2005) Using Bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev 133(5):1155–1174Google Scholar
  61. Raisanen J (2007) How reliable are climate models? Tellus Ser A Dyn Meteorol Oceanogr 59(1):2–29Google Scholar
  62. Safeeq M, Fares A (2012) Hydrologic response of a Hawaiian watershed to future climate change scenarios. Hydrol Process 26(18):2745–2764Google Scholar
  63. Schmidli J, Frei C, Vidale PL (2006) Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods. Int J Climatol 26(5):679–689Google Scholar
  64. Schoof JT, Pryor SC (2001) Downscaling temperature and precipitation: a comparison of regression-based methods and artificial neural networks. Int J Climatol 21(7):773–790Google Scholar
  65. Scoccimarro E, Gualdi S, Bellucci A, Sanna A, Fogli PG, Manzini E, Vichi M, Oddo P, Navarra A (2011) Effects of tropical cyclones on ocean heat transport in a high-resolution coupled general circulation model. J Clim 24(16):4368–4384Google Scholar
  66. Semenov MA, Stratonovitch P (2010) Use of multi-model ensembles from global climate models for assessment of climate change impacts. Clim Res 41(1):1–14Google Scholar
  67. Smith RL, Tebaldi C, Nychka D, Mearns LO (2009) Bayesian modeling of uncertainty in ensembles of climate models. J Am Stat Assoc 104(485):97–116Google Scholar
  68. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of Cmip5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498Google Scholar
  69. Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc A Math Phys Eng Sci 365(1857):2053–2075Google Scholar
  70. Tebaldi C, Mearns LO, Nychka D, Smith RL (2004) Regional probabilities of precipitation change: a Bayesian analysis of multimodel simulations. Geophys Res Lett.  https://doi.org/10.1029/2004GL021276 CrossRefGoogle Scholar
  71. Tebaldi C, Smith RL, Nychka D, Mearns LO (2005) Quantifying uncertainty in projections of regional climate change: a Bayesian approach to the analysis of multimodel ensembles. J Clim 18(10):1524–1540Google Scholar
  72. 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):1530–1544Google Scholar
  73. Tyralis H, Koutsoyiannis D (2017) On the prediction of persistent processes using the output of deterministic models. Hydrol Sci J 62(13):2083–2102Google Scholar
  74. Voldoire A, Sanchez-Gomez E, Melia DSY, Decharme B, Cassou C, Senesi S, Valcke S, Beau I, Alias A, Chevallier M, Deque M, Deshayes J, Douville H, Fernandez E, Madec G, Maisonnave E, Moine MP, Planton S, Saint-Martin D, Szopa S, Tyteca S, Alkama R, Belamari S, Braun A, Coquart L, Chauvin F (2013) The CNRM-CM5,1 global climate model: description and basic evaluation. Clim Dyn 40(9–10):2091–2121Google Scholar
  75. Volodin EM, Dianskii NA, Gusev AV (2010) Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations. Izv Atmos Ocean Phys 46(4):414–431Google Scholar
  76. von Storch H, Hewitson B, Mearns L (2000) Review of empirical downscaling techniques. Regional climate development under global warming. General technical report 4Google Scholar
  77. Wang B, Yang HW (2008) Hydrological issues in lateral boundary conditions for regional climate modeling: simulation of east asian summer monsoon in 1998. Clim Dyn 31(4):477–490Google Scholar
  78. Watanabe M, Suzuki T, O’ishi R, Komuro Y, Watanabe S, Emori S, Takemura T, Chikira M, Ogura T, Sekiguchi M, Takata K, Yamazaki D, Yokohata T, Nozawa T, Hasumi H, Tatebe H, Kimoto M (2010) Improved climate simulation by MIROC5. Mean states, variability, and climate sensitivity. J Clim 23(23):6312–6335Google Scholar
  79. Wei T, Yang S, Moore JC, Shi P, Cui X, Duan Q, Xu B, Dai Y, Yuan W, Wei X, Yang Z, Wen T, Teng F, Gao Y, Chou J, Yan X, Wei Z, Guo Y, Jiang Y, Gao X, Wang K, Zheng X, Ren F, Lv S, Yu Y, Liu B, Luo Y, Li W, Ji D, Feng J, Wu Q, Cheng H, He J, Fu C, Ye D, Xu G, Dong W (2012) Developed and developing world responsibilities for historical climate change and CO2 mitigation. Proc Natl Acad Sci 109(32):12911–12915Google Scholar
  80. Weigel AP, Knutti R, Liniger MA, Appenzeller C (2010) Risks of model weighting in multimodel climate projections. J Clim 23(15):4175–4191Google Scholar
  81. Widmann M, Bretherton CS, Salathe EP (2003) Statistical precipitation downscaling over the Northwestern United States using numerically simulated precipitation as a predictor. J Clim 16(5):799–816Google Scholar
  82. 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):2995–3008Google Scholar
  83. Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62(1–3):189–216Google Scholar
  84. Wu TW (2012) A mass-flux cumulus parameterization scheme for large-scale models: description and test with observations. Clim Dyn 38(3–4):725–744Google Scholar
  85. Yang HW, Wang B, Wang B (2012a) Reducing biases in regional climate downscaling by applying Bayesian model averaging on large-scale forcing. Clim Dyn 39(9–10):2523–2532Google Scholar
  86. Yang HW, Wang B, Wang B (2012b) Reduction of systematic biases in regional climate downscaling through ensemble forcing. Clim Dyn 38(3–4):655–665Google Scholar
  87. Yukimoto S, Adachi Y, Hosaka M, Sakami T, Yoshimura H, Hirabara M, Tanaka TY, Shindo E, Tsujino H, Deushi M, Mizuta R, Yabu S, Obata A, Nakano H, Koshiro T, Ose T, Kitoh A (2012) A new global climate model of the Meteorological Research Institute: MRI-CGCM3-model description and basic performance. J Meteorol Soc Jpn 90a:23–64Google Scholar
  88. Zorita E, von Storch H (1999) The analog method as a simple statistical downscaling technique: comparison with more complicated methods. J Clim 12(8):2474–2489Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringUniversity of MichiganAnn ArborUSA
  2. 2.School of Civil and Environmental EngineeringUniversity of UlsanUlsanSouth Korea
  3. 3.Department of Civil and Environmental EngineeringSejong UniversitySeoulSouth Korea
  4. 4.Institute of Environmental Engineering, ETH ZurichZurichSwitzerland

Personalised recommendations