Abstract
When selecting a subset of climate change scenarios (GCM models), the priority is to ensure that the subset reflects the comprehensive range of possible model results for all variables concerned. Though many studies have attempted to improve the scenario selection, there is a lack of studies that discuss methods to ensure that the results from a subset of climate models contain the same range of uncertainty in hydrologic variables as when all models are considered. We applied the Katsavounidis–Kuo–Zhang (KKZ) algorithm to select a subset of climate change scenarios and demonstrated its ability to reduce the number of GCM models in an ensemble, while the ranges of multiple climate extremes indices were preserved. First, we analyzed the role of 27 ETCCDI climate extremes indices for scenario selection and selected the representative climate extreme indices. Before the selection of a subset, we excluded a few deficient GCM models that could not represent the observed climate regime. Subsequently, we discovered that a subset of GCM models selected by the KKZ algorithm with the representative climate extreme indices could not capture the full potential range of changes in hydrologic extremes (e.g., 3-day peak flow and 7-day low flow) in some regional case studies. However, the application of the KKZ algorithm with a different set of climate indices, which are correlated to the hydrologic extremes, enabled the overcoming of this limitation. Key climate indices, dependent on the hydrologic extremes to be projected, must therefore be determined prior to the selection of a subset of GCM models.
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References
Bao Q, Lin P, Zhou T et al (2013) The flexible global ocean–atmosphere–land system model, spectral version 2: FGOALS-s2. Adv Atmos Sci 30:561–576. https://doi.org/10.1007/s00376-012-2113-9
Bentsen M, Bethke I, Debernard JB et al (2013) The Norwegian Earth System Model, NorESM1-M—part 1: description and basic evaluation of the physical climate. Geosci Model Dev 6:687–720. https://doi.org/10.5194/gmd-6-687-2013
Cannon AJ (2015) Selecting GCM scenarios that span the range of changes in a multimodel ensemble: application to CMIP5 climate extremes indices. J Clim 28:1260–1267. https://doi.org/10.1175/JCLI-D-14-00636.1
Cannon AJ, Sobie SR, Murdock TQ (2015) Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes? J Clim 28:6938–6959. https://doi.org/10.1175/JCLI-D-14-00754.1
Chen J, Brissette FP, Lucas-Picher P (2016) Transferability of optimally-selected climate models in the quantification of climate change impacts on hydrology. Clim Dyn 47:3359–3372. https://doi.org/10.1007/s00382-016-3030-x
Chylek P, Li J, Dubey MK et al (2011) Observed and model simulated 20th century Arctic temperature variability: Canadian Earth System Model CanESM2. Atmos Chem Phys Discuss 2011:22893–22907. https://doi.org/10.5194/acpd-11-22893-2011
Clark MP, Wilby RL, Gutmann ED et al (2016) Characterizing uncertainty of the hydrologic impacts of climate change. Curr Clim Change Rep 2:55–64
Collins WJ, Bellouin N, Doutriaux-Boucher M et al (2011) Development and evaluation of an earth-system model—HadGEM2. Geosci Model Dev 4:1051–1075. https://doi.org/10.5194/gmd-4-1051-2011
Davini P, Cagnazzo C, Fogli PG et al (2014) European blocking and Atlantic jet stream variability in the NCEP/NCAR reanalysis and the CMCC-CMS climate model. Clim Dyn 43:71–85. https://doi.org/10.1007/s00382-013-1873-y
Dessai S, Hulme M, Lempert R et al (2009) Adapting to climate change: thresholds, values, governance. In: Adger WN, Lorenzoni I, O'Brien KL (eds) Climate prediction: a limit to adaptation. Cambridge University Press, Cambridge, UK, pp 64–78
Dubrovsky M, Trnka M, Holman IP et al (2015) Developing a reduced-form ensemble of climate change scenarios for Europe and its application to selected impact indicators. Clim Change 128:169–186. https://doi.org/10.1007/s10584-014-1297-7
Dufresne J-L, Foujols M-A, Denvil S et al (2013) Climate change projections using the IPSL-CM5 earth system model: from CMIP3 to CMIP5. Clim Dyn 40:2123–2165. https://doi.org/10.1007/s00382-012-1636-1
Dunne JP, John JG, Adcroft AJ et al (2012) GFDL’s ESM2 global coupled climate—carbon earth system models. Part I: physical formulation and baseline simulation characteristics. J Clim 25:6646–6665. https://doi.org/10.1175/JCLI-D-11-00560.1
Eum H-I, Cannon AJ (2017) Intercomparison of projected changes in climate extremes for South Korea: application of trend preserving statistical downscaling methods to the CMIP5 ensemble. Int J Climatol 37:3381–3397. https://doi.org/10.1002/joc.4924
Gent PR, Danabasoglu G, Donner LJ et al (2011) The community climate system model version 4. J Clim 24:4973–4991. https://doi.org/10.1175/2011JCLI4083.1
Giorgetta MA, Jungclaus J, Reick CH et al (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 5:572–597. https://doi.org/10.1002/jame.20038
Hargreaves GH, Samani ZA (1982) Estimating potential evapotranspiration. J Irrig Drain Div 108:225–230
Houle D, Bouffard A, Duchesne L et al (2012) Projections of future soil temperature and water content for three southern Quebec forested sites. J Clim 25:7690–7701. https://doi.org/10.1175/JCLI-D-11-00440.1
Katsavounidis I, Kuo CCJ, Zhang Z (1994) A new initialization technique for generalized Lloyd iteration. IEEE Signal Process Lett 1:144–146. https://doi.org/10.1109/97.329844
Kim Y-O, Chung ES (2017) Adaptation to climate change: decision-making. In: Kolokytha E et al (eds) Sustainable water resources planning and management under climate change. Springer, Singapore, pp 189–221
Knutti R, Masson D, Gettelman A (2013) Climate model genealogy: generation CMIP5 and how we got there. Geophys Res Lett 40:1194–1199. https://doi.org/10.1002/grl.50256
Lee J-K, Kim Y-O (2012) Selecting climate change scenarios reflecting uncertainties. Atmosphere 22:149–161
Lee J-K, Kim Y-O (2017) Selection of representative GCM scenarios preserving uncertainties. J Water Clim Change 8(4):641–651. https://doi.org/10.2166/wcc.2017.101
Masson D, Knutti R (2011) Climate model genealogy. Geophys Res Lett 38:L08703. https://doi.org/10.1029/2011GL046864
McSweeney CF, Jones RG, Booth BBB (2012) Selecting ensemble members to provide regional climate change information. J Clim 25:7100–7121. https://doi.org/10.1175/JCLI-D-11-00526.1
Meehl GA, Washington WM, Arblaster JM et al (2013) Climate change projections in CESM1 (CAM5) compared to CCSM4. J Climate 26:6287–6308. https://doi.org/10.1175/JCLI-D-12-00572.1
Mendlik T, Gobiet A (2016) Selecting climate simulations for impact studies based on multivariate patterns of climate change. Clim Change 135:381–393. https://doi.org/10.1007/s10584-015-1582-0
Moore JK, Lindsay K, Doney SC et al (2013) Marine ecosystem dynamics and biogeochemical cycling in the Community Earth System Model [CESM1(BGC)]: comparison of the 1990s with the 2090s under the RCP4.5 and RCP8.5 scenarios. J Clim 26:9291–9312. https://doi.org/10.1175/JCLI-D-12-00566.1
Mote PW, Salathé EP (2010) Future climate in the Pacific Northwest. Clim Change 102:29–50. https://doi.org/10.1007/s10584-010-9848-z
Perrin C, Michel C, Andréassian V (2003) Improvement of a parsimonious model for streamflow simulation. J Hydrol 279:275–289. https://doi.org/10.1016/S0022-1694(03)00225-7
Rupp DE, Abatzoglou JT, Hegewisch KC, Mote PW (2013) Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA. J Geophys Res Atmos 118:2013JD020085. https://doi.org/10.1002/jgrd.50843
Schaefli B, Gupta HV (2007) Do Nash values have value? Hydrol. Process 21:2075–2080. https://doi.org/10.1002/hyp.6825
Schmidt GA, Kelley M, Nazarenko L et al (2014) Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J Adv Model Earth Syst 6:141–184. https://doi.org/10.1002/2013MS000265
Scoccimarro E, Gualdi S, Bellucci A et al (2011) Effects of tropical cyclones on ocean heat transport in a high-resolution coupled general circulation model. J Clim 24:4368–4384. https://doi.org/10.1175/2011JCLI4104.1
Seo SB, Sinha T, Mahinthakumar G et al (2016) Identification of dominant source of errors in developing streamflow and groundwater projections under near-term climate change. J Geophys Res Atmos 121:7652–7672. https://doi.org/10.1002/2016JD025138
Sillmann J, Kharin VV, Zhang X et al (2013) Climate extremes indices in the CMIP5 multimodel ensemble: part 1. Model evaluation in the present climate. J Geophys Res Atmos 118:1716–1733. https://doi.org/10.1002/jgrd.50203
Tatebe H, Ishii M, Mochizuki T et al (2012) The initialization of the MIROC climate models with hydrographic data assimilation for decadal prediction. J Meteorol Soc Jpn Ser II 90A:275–294. https://doi.org/10.2151/jmsj.2012-A14
Thiessen A (1911) Precipitations averages for large areas. Mon Weather Rev 39:1082–1089
Tian Y, Xu Y-P, Zhang X-J (2013) Assessment of climate change impacts on river high flows through comparative use of GR4J, HBV and Xinanjiang models. Water Resour Manag 27:2871–2888. https://doi.org/10.1007/s11269-013-0321-4
Vano JA, Kim JB, Rupp DE, Mote PW (2015) Selecting climate change scenarios using impact-relevant sensitivities. Geophys Res Lett 42:2015GL063208. https://doi.org/10.1002/2015GL063208
Voldoire A, Sanchez-Gomez E, Mélia DS y, et al (2013) The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dyn 40:2091–2121. https://doi.org/10.1007/s00382-011-1259-y
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 46:414–431. https://doi.org/10.1134/S000143381004002X
Watanabe S, Hajima T, Sudo K et al (2011) MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci Model Dev 4:845–872. https://doi.org/10.5194/gmd-4-845-2011
Wilcke RAI, Bärring L (2016) Selecting regional climate scenarios for impact modelling studies. Environ Model Softw 78:191–201. https://doi.org/10.1016/j.envsoft.2016.01.002
Wu T (2012) A mass-flux cumulus parameterization scheme for large-scale models: description and test with observations. Clim Dyn 38:725–744. https://doi.org/10.1007/s00382-011-0995-3
Yukimoto S, Adachi Y, Hosaka M et al (2012) A new global climate model of the Meteorological Research Institute: MRI-CGCM3—model description and basic performance. J Meteorol Soc Jpn 90A:23–64. https://doi.org/10.2151/jmsj.2012-A02
Zhang X, Alexander L, Hegerl GC et al (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Clim Change 2:851–870. https://doi.org/10.1002/wcc.147
Acknowledgements
This research was supported by a Grant (NRF-2017R1A6A3A11031800) through the Young Researchers program funded by the National Research Foundation of Korea. This research was also supported by a Grant (2014001310007) from the Climate Change Correspondence Program funded by the Ministry of Environment in Korea.
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Seo, S.B., Kim, YO., Kim, Y. et al. Selecting climate change scenarios for regional hydrologic impact studies based on climate extremes indices. Clim Dyn 52, 1595–1611 (2019). https://doi.org/10.1007/s00382-018-4210-7
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DOI: https://doi.org/10.1007/s00382-018-4210-7