Selecting representative climate models for climate change impact studies: an advanced envelope-based selection approach

International Journal of Climatology

Volumn 36, Issue 12, 2016, Pages 3988-4005

  Lutz, Arthur F ^a,b   ter Maat, Herbert W ^c   Biemans, Hester ^c   Shrestha, Arun B ^d   Wester, Philippus ^d   Immerzeel, Walter W ^a,b  

^a FutureWater Wageningen   (Netherlands)

^b UTRECHT UNIVERSITY   (Netherlands)

^c WAGENINGEN UNIVERSITY   (Netherlands)

^d INTERNATIONAL CENTRE FOR INTEGRATED MOUNTAIN DEVELOPMENT   (Nepal)

Author keywords

Brahmaputra; climate change impact studies; climate model ensemble; climate model selection; Ganges; GCM; Indus

Indexed keywords

CLIMATE MODELS;

BRAHMAPUTRA; CLIMATE CHANGE IMPACT; GANGES; INDUS; MODEL ENSEMBLES; MODEL SELECTION;

CLIMATE CHANGE;

CLIMATE CHANGE; CLIMATE EFFECT; CLIMATE MODELING; CLIMATE VARIATION; ENSEMBLE FORECASTING; GENERAL CIRCULATION MODEL;

BRAHMAPUTRA RIVER; GANGES RIVER; INDUS RIVER;

EID: 84955068989     PISSN: 08998418     EISSN: 10970088     Source Type: Journal    
DOI: 10.1002/joc.4608     Document Type: Article

References (51)

1. Bakker AMR. 2015. The Robustness of the Climate Modelling Paradigm. PhD thesis, Vrije Universiteit, Amsterdam.
2. Biemans H, Speelman LH, Ludwig F, Moors EJ, Wiltshire AJ, Kumar P, Gerten D, Kabat P. 2013. Future water resources for food production in five South Asian river basins and potential for adaptation – a modeling study. Sci. Total Environ. 468–469: S117–S131, doi: 10.1016/j.scitotenv.2013.05.092.
3. Blázquez J, Nuñez MN. 2013. Analysis of uncertainties in future climate projections for South America: comparison of WCRP-CMIP3 and WCRP-CMIP5 models. Clim. Dyn. 41(3–4): 1039–1056, doi: 10.1007/s00382-012-1489-7.
4. Bookhagen B, Burbank DW. 2010. Toward a complete Himalayan hydrological budget: spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge. J. Geophys. Res. 115(F3): 1–25, doi: 10.1029/2009JF001426.
5. Cannon AJ. 2014. Selecting GCM scenarios that span the range of changes in a multimodel ensemble: application to CMIP5 climate extremes indices. J. Clim. 28: 1260–1267, doi: 10.1175/JCLI-D-14-00636.1.
6. De Souza K, Kituyi E, Harvey B, Leone M, Murali KS, Ford JD. 2015. Vulnerability to climate change in three hot spots in Africa and Asia: key issues for policy-relevant adaptation and resilience-building research. Reg. Environ. Change 15: 747–753, doi: 10.1007/s10113-015-0755-8.
7. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette J-J, Park B-K, Peubey C, de Rosnay P, Tavolato C, Thépaut J-N, Vitart F. 2011. The ERA-interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137(656): 553–597, doi: 10.1002/qj.828.
8. Evans JP, Ji F, Abramowitz G, Ekström M. 2013. Optimally choosing small ensemble members to produce robust climate simulations. Environ. Res. Lett. 8(4): 1–4, doi: 10.1088/1748-9326/8/4/044050.
9. Finger D, Heinrich G, Gobiet A, Bauder A. 2012. Projections of future water resources and their uncertainty in a glacierized catchment in the Swiss Alps and the subsequent effects on hydropower production during the 21st century. Water Resour. Res. 48(2): 1–20, doi: 10.1029/2011WR010733.
10. Houle D, Bouffard A, Duchesne L, Logan T, Harvey R. 2012. Projections of future soil temperature and water content for three Southern Quebec forested sites. J. Clim. 25(21): 7690–7701, doi: 10.1175/JCLI-D-11-00440.1.
11. Immerzeel WW, Pellicciotti F, Bierkens MFP. 2013. Rising river flows throughout the twenty-first century in two Himalayan glacierized watersheds. Nat. Geosci. 6(8): 1–4, doi: 10.1038/ngeo1896.
12. IPCC. 2007. Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Mitigation of Climate Change. IPCC: Geneva, Switzerland.
13. IPCC. 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC: Cambridge, UK.
14. Joetzjer E, Douville H, Delire C, Ciais P. 2013. Present-day and future Amazonian precipitation in global climate models: CMIP5 versus CMIP3. Clim. Dyn. 41(11–12): 2921–2936, doi: 10.1007/s00382-012-1644-1.
15. Jun M, Knutti R, Nychka DW. 2008. Spatial analysis to quantify numerical model bias and dependence. J. Am. Stat. Assoc. 103(483): 934–947, doi: 10.1198/016214507000001265.
16. Kjellström E, Boberg F, Castro M, Christensen JH, Nikulin G, Sánchez E. 2010. Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models. Clim. Res. 44: 135–150, doi: 10.3354/cr00932.
17. Knutti R, Sedláček J. 2012. Robustness and uncertainties in the new CMIP5 climate model projections. Nat. Clim. Change 3: 369–373, doi: 10.1038/nclimate1716.
18. Knutti R, Masson D, Gettelman A. 2013. Climate model genealogy: generation CMIP5 and how we got there. Geophys. Res. Lett. 40(6): 1194–1199, doi: 10.1002/grl.50256.
19. Kundzewicz ZW, Stakhiv EZ. 2010. Are climate models “ready for prime time” in water resources management applications, or is more research needed? Hydrol. Sci. J. 55(7): 1085–1089, doi: 10.1080/02626667.2010.513211.
20. Lutz AF, Immerzeel WW, Gobiet A, Pellicciotti F, Bierkens MFP. 2013. Comparison of climate change signals in CMIP3 and CMIP5 multi-model ensembles and implications for Central Asian glaciers. Hydrol. Earth Syst. Sci. 17(9): 3661–3677, doi: 10.5194/hess-17-3661-2013.
21. Lutz AF, Immerzeel WW, Shrestha AB, Bierkens MFP. 2014. Consistent increase in High Asia's runoff due to increasing glacier melt and precipitation. Nat. Clim. Change 4: 587–592, doi: 10.1038/NCLIMATE2237.
22. Masson D, Knutti R. 2011. Climate model genealogy. Geophys. Res. Lett. 38(8): 1–4, doi: 10.1029/2011GL046864.
23. Maussion F, Scherer D, Mölg T, Collier E, Curio J, Finkelnburg R. 2014. Precipitation seasonality and variability over the Tibetan Plateau as resolved by the High Asia reanalysis. J. Clim. 27(5): 1910–1927, doi: 10.1175/JCLI-D-13-00282.1.
24. McSweeney CF, Jones RG, Booth BBB. 2012. Selecting ensemble members to provide regional climate change information. J. Clim. 25(20): 7100–7121, doi: 10.1175/JCLI-D-11-00526.1.
25. Meehl GA, Covey C, Delworth T, Latif M, Mcavaney B, Mitchell JFB, Stouffer RJ, Taylor KE. 2007. The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull. Am. Meteorol. Soc. 88(9): 1382–1394.
26. Minville M, Brissette F, Leconte R. 2008. Uncertainty of the impact of climate change on the hydrology of a Nordic watershed. J. Hydrol. 358(1–2): 70–83, doi: 10.1016/j.jhydrol.2008.05.033.
27. Nepal S, Shrestha AB. 2015. Impact of climate change on the hydrological regime of the Indus, Ganges and Brahmaputra river basins: a review of the literature. Int. J. Water Resour. Dev. 31(2): 201–218, doi: 10.1080/07900627.2015.1030494.
28. Palazzi E, von Hardenberg J, Terzago S, Provenzale A. 2014. Precipitation in the Karakoram-Himalaya: a CMIP5 view. Clim. Dyn. 45: 21–45, doi: 10.1007/s00382-014-2341-z.
29. Pepin N, Bradley RS, Diaz HF, Baraer M, Caceres EB, Forsythe N, Fowler H, Greenwood G, Hashmi MZ, Liu XD, Miller JR, Ning L, Ohmura A, Palazzi E, Rangwala I, Schöner W, Severskiy I, Shahgedanova M, Wang MB, Williamson SN, Yang DQ. 2015. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Change 5: 424–430, doi: 10.1038/nclimate2563.
30. Perkins SE, Pitman AJ, Holbrook NJ, McAneney J. 2007. Evaluation of the AR4 climate models' simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. J. Clim. 20: 4356–4376, doi: 10.1175/JCLI4253.1.
31. Peterson TC. 2005. Climate change indices. WMO Bull. 54(2): 83–86.
32. Pielke RA, Wilby RL. 2012. Regional climate downscaling: what's the point? Eos 93(5): 52–53, doi: 10.1002/wea543.Wilby.
33. Pierce DW, Barnett TP, Santer BD, Gleckler PJ. 2009. Selecting global climate models for regional climate change studies. Proc. Natl. Acad. Sci. U. S. A. 106(21): 8441–8446, doi: 10.1073/pnas.0900094106.
34. Rangwala I, Miller JR. 2012. Climate change in mountains: a review of elevation-dependent warming and its possible causes. Clim. Change 114: 527–547, doi: 10.1007/s10584-012-0419-3.
35. Sanchez E, Romera R, Gaertner MA, Gallardo C, Castro M. 2009. A weighting proposal for an ensemble of regional climate models over Europe driven by 1961–2000 ERA40 based on monthly precipitation probability density functions. Atmos. Sci. Lett. 10: 249–254, doi: 10.1002/asl.
36. Sanderson BM, Knutti R, Caldwell P. 2015a. A representative democracy to reduce interdependency in a multimodel ensemble. J. Clim. 28(13): 5171–5194, doi: 10.1175/JCLI-D-14-00362.1.
37. Sanderson BM, Knutti R, Caldwell P. 2015b. Addressing interdependency in a multimodel ensemble by interpolation of model properties. J. Clim. 28(13): 5150–5170, doi: 10.1175/JCLI-D-14-00361.1.
38. Schaner N, Voisin N, Nijssen B, Lettenmaier DP. 2012. The contribution of glacier melt to streamflow. Environ. Res. Lett. 7: 034029, doi: 10.1088/1748-9326/7/3/034029.
39. Schneider U, Becker A, Finger P, Meyer-Christoffer A, Ziese M, Rudolf B. 2013. GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol. 115: 15–40, doi: 10.1007/s00704-013-0860-x.
40. Sillmann J, Kharin VV, Zhang X, Zwiers FW, Bronaugh D. 2013a. Climate extremes indices in the CMIP5 multimodel ensemble: part 1. Model evaluation in the present climate. J. Geophys. Res.-Atmos. 118(4): 1716–1733, doi: 10.1002/jgrd.50203.
41. Sillmann J, Kharin VV, Zwiers FW, Zhang X, Bronaugh D. 2013b. Climate extremes indices in the CMIP5 multimodel ensemble: part 2. Future climate projections. J. Geophys. Res.-Atmos. 118(6): 2473–2493, doi: 10.1002/jgrd.50188.
42. Sorg A, Huss M, Rohrer M, Stoffel M. 2014. The days of plenty might soon be over in glacierized Central Asian catchments. Environ. Res. Lett. 9(10), doi: 10.1088/1748-9326/9/10/104018.
43. Sperber KR, Annamalai H. 2014. The use of fractional accumulated precipitation for the evaluation of the annual cycle of monsoons. Clim. Dyn. 43(12): 3219–3244, doi: 10.1007/s00382-014-2099-3.
44. Sperber KR, Annamalai H, Kang IS, Kitoh A, Moise A, Turner A, Wang B, Zhou T. 2013. The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Clim. Dyn. 41(9–10): 2711–2744, doi: 10.1007/s00382-012-1607-6.
45. Taylor KE, Stouffer RJ, Meehl GA. 2012. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93(4): 485–498, doi: 10.1175/BAMS-D-11-00094.1.
46. 2 in 2100. Energy Econ. 32(5): 1105–1120, doi: 10.1016/j.eneco.2010.03.001.
47. van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque J-F, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK. 2011a. The representative concentration pathways: an overview. Clim. Change 109(1–2): 5–31, doi: 10.1007/s10584-011-0148-z.
48. van Vuuren DP, Stehfest E, den Elzen MGJ, Kram T, van Vliet J, Deetman S, Isaac M, Klein Goldewijk K, Hof A, Mendoza Beltran A, Oostenrijk R, van Ruijven B. 2011b. RCP2.6: exploring the possibility to keep global mean temperature increase below 2°C. Clim. Change 109: 95–116, doi: 10.1007/s10584-011-0152-3.
49. Warszawski L, Frieler K, Huber V, Piontek F, Serdeczny O, Schewe J. 2014. The inter-sectoral impact model intercomparison project (ISI-MIP): project framework. Proc. Natl. Acad. Sci. U. S. A. 111(9): 3228–3232, doi: 10.1073/pnas.1312330110.
50. Weedon GP, Gomes S, Viterbo P, Shuttleworth WJ, Blyth E, Österle H, Adam JC, Bellouin N, Boucher O, Best M. 2011. Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J. Hydrometeorol. 12(5): 823–848, doi: 10.1175/2011JHM1369.1.
51. Weedon GP, Balsamo G, Bellouin N, Gomes S, Best MJ, Viterbo P. 2014. The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-interim reanalysis data. Water Resour. Res. 50(9): 7505–7514, doi: 10.1002/2014WR015638.