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Performance of multi-model ensembles for the simulation of temperature variability over Ontario, Canada

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Abstract

Climate ensembles utilize outputs from multiple climate models to estimate future climate patterns. These multi-model ensembles generally outperform individual climate models. In this paper, the performance of seven global climate model and regional climate model combinations were evaluated for Ontario, Canada. Two multi-model ensembles were developed and tested, one based on the mean of the seven combinations and the other based on the median of the same seven models. The performance of the multi-model ensembles were evaluated on 12 meteorological stations, as well as for the entire domain of Ontario, using three temperature variables (average surface temperature, maximum surface temperature, and minimum surface temperature). Climate data for developing and validating the multi-model ensembles were collected from three major sources: the North American Coordinated Regional Downscaling Experiment, the Digital Archive of Canadian Climatological Data, and the Climactic Research Unit’s TS v4.00 dataset. The results showed that the climate ensemble based on the mean generally outperformed the one based on the median, as well as each of the individual models. Future predictions under the Representative Concentration Pathway 4.5 (RCP4.5) scenario were generated using the multi-model ensemble based on the mean. This study provides credible and useful information for climate change mitigation and adaption in Ontario.

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References

  • Barfus K, Bernhofer C (2014) Assessment of GCM performances for the Arabian Peninsula, Brazil, and Ukraine and indications of regional climate change. Environ Earth Sci 72:4689–4703. https://doi.org/10.1007/s12665-014-3147-3

    Article  Google Scholar 

  • Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?—arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250. https://doi.org/10.5194/gmd-7-1247-2014

    Article  Google Scholar 

  • Dasari HP, Salgado R, Perdigao J, Challa VS (2014) A regional climate simulation study using WRF-ARW model over Europe and evaluation for extreme temperature weather events International. J Atmos Sci 704079:1–22

    Google Scholar 

  • Demerse C (2016) Ignoring climate change will cost us too—big time. Clean Energy Canada. http://cleanenergycanada.org/ignoring-climate-change-will-cost-us-too-big-time/. Accessed 22 Sep 2017

  • Devineni N, Sankarasubramanian A, Ghosh S (2008) Multimodel ensembles of streamflow forecasts: role of predictor state in developing optimal combinations. Water Resour Res 44:W09404. https://doi.org/10.1029/2006WR005855

    Article  Google Scholar 

  • Giorgi F, Jones C, Asrar GR (2009) Addressing climate information needs at the regional level: the CORDEX framework. World Meteorol Organ (WMO) Bull 58:175

    Google Scholar 

  • Hagedorn R, Doblas-Reyes FJ, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting—I. Basic concept. Tellus A 57:219–233. https://doi.org/10.1111/j.1600-0870.2005.00103.x

    Google Scholar 

  • Harris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int J Climatol 34:623–642. https://doi.org/10.1002/joc.3711

    Article  Google Scholar 

  • Herrmann F, Kunkel R, Ostermann U, Vereecken H, Wendland F (2016) Projected impact of climate change on irrigation needs and groundwater resources in the metropolitan area of Hamburg (Germany) Environ Earth Sci 75 https://doi.org/10.1007/s12665-016-5904-y

  • Huo AD, Li H (2013) Assessment of climate change impact on the stream-flow in a typical debris flow watershed of Jianzhuangcuan catchment in Shaanxi Province. China Environ Earth Sci 69:1931–1938. https://doi.org/10.1007/s12665-012-2025-0

    Article  Google Scholar 

  • IPCC (2013) Climate change 2013: The physical science basis. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 1535

    Google Scholar 

  • Jarsjo J, Tornqvist R, Su Y (2017) Climate-driven change of nitrogen retention-attenuation near irrigated fields: multi-model projections for Central Asia. Environ Earth Sci 76 https://doi.org/10.1007/s12665-017-6418-y

  • Katz RW (1992) Role of statistics in the validation of general circulation models. Clim Res 2:35–45

    Article  Google Scholar 

  • Kirtman BP, Min D (2009) Multimodel ensemble ENSO prediction with CCSM and CFS. Mon Weather Rev 137:2908–2930

    Article  Google Scholar 

  • Krishnamurti TN et al (2000) Multimodel ensemble forecasts for weather and seasonal climate. J Clim 13:4196–4216

    Article  Google Scholar 

  • Lambert SJ, Boer GJ (2001) CMIP1 evaluation and intercomparison of coupled. climate models. Clim Dyn 17:83–106

    Article  Google Scholar 

  • Laprise R et al (2013) Climate projections over CORDEX Africa domain using the fifth-generation Canadian Regional Climate Model (CRCM5). Clim Dyn 41:3219–3246. https://doi.org/10.1007/s00382-012-1651-2

    Article  Google Scholar 

  • Lee JY, Wang B (2014) Future change of global monsoon in the CMIP5. Climate Dynamics 42:101–119. https://doi.org/10.1007/s00382-012-1564-0

    Article  Google Scholar 

  • Li Z, Huang G, Wang X, Han J, Fan Y (2016) Impacts of future climate change on river discharge based on hydrological inference: a case study of the Grand River Watershed in Ontario. CanSci Tot Environ 548:198–210. https://doi.org/10.1016/j.scitotenv.2016.01.002

    Article  Google Scholar 

  • Lucas-Picher P, Somot S, Deque M, Decharme B, Alias A (2013) Evaluation of the regional climate model ALADIN to simulate the climate over North America in the CORDEX framework. Clim Dyn 41:1117–1137. https://doi.org/10.1007/s00382-012-1613-8

    Article  Google Scholar 

  • Mezghani A et al (2017) CHASE-PL Climate Projection dataset over Poland—Bias adjustment of EURO-CORDEX simulations. Earth Syst Sci Data Discuss 2017:1–29. https://doi.org/10.5194/essd-2017-51

    Article  Google Scholar 

  • MOECC (2011) Climate Ready: Ontario’s Adaptation Strategy and Action Plan 2011–2014. Ontario Ministry of the Environment and Climate Change, Canada

    Google Scholar 

  • Nagelkerke NJD (1991) A note on a general definition of the coefficient of determination. Biometrika 78:691–692. https://doi.org/10.1093/biomet/78.3.691

    Article  Google Scholar 

  • Palmer TN, Doblas-Reyes FJ, Hagedorn R, Weisheimer A (2005) Probabilistic prediction of climate using multi-model ensembles: from basics to applications. Philos Trans R Soc B 360:1991–1998

    Article  Google Scholar 

  • Perera AH, Euler D, Thompson ID (2000) Ecology of a managed terrestrial landscape: patterns and processes of forest landscapes in Ontario. UBC Press in cooperation with the Ontario Ministry of Natural Resources, Vancouver

    Google Scholar 

  • Ragone F, Lucarini V, Lunkeit F (2016) A new framework for climate sensitivity and prediction: a modelling perspective. Clim Dyn 46:1459–1471. https://doi.org/10.1007/s00382-015-2657-3

    Article  Google Scholar 

  • Rotstayn LD, Jeffrey SJ, Collier MA, Dravitzki SM, Hirst AC, Syktus JI, Wong KK (2012) Aerosol- and greenhouse gas-induced changes in summer rainfall and circulation in the Australasian region: a study using single-forcing climate simulations. Atmos Chem Phys 12:6377–6404. https://doi.org/10.5194/acp-12-6377-2012

    Article  Google Scholar 

  • Rozante J, Moreira D, Godoy R, Fernandes A (2014) Multi-model ensemble: technique and validation. Geosci Model Dev Discuss 7:2933–2959

    Article  Google Scholar 

  • Suklitsch M, Gobiet A, Truhetz H, Awan NK, Göttel H, Jacob D (2011) Error characteristics of high resolution regional climate models over the Alpine area. Clim Dyn 37:377–390

    Article  Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106:7183–7192. https://doi.org/10.1029/2000jd900719

    Article  Google Scholar 

  • Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc A 365:2053–2075. https://doi.org/10.1098/rsta.2007.2076

    Article  Google Scholar 

  • Thomson AM et al (2011) RCP4. 5: a pathway for stabilization of radiative forcing by 2100. Clim Change 109:77

    Article  Google Scholar 

  • Wagner T, Themessl M, Schuppel A, Gobiet A, Stigler H, Birk S (2017) Impacts of climate change on stream flow and hydro power generation in the Alpine region Environ Earth Sci. https://doi.org/10.1007/s12665-016-6318-6

    Google Scholar 

  • Wallach D, Mearns L, Ruane A, Rotter R, Asseng S (2016) Lessons from climate modeling on the design and use of ensembles for crop modeling. Clim Change 139:551–564. https://doi.org/10.1007/s10584-016-1803-1

    Article  Google Scholar 

  • Wang X et al (2013) A stepwise cluster analysis approach for downscaled climate projection—a Canadian case study. Environ Model Softw 49:141–151

    Article  Google Scholar 

  • Wang XQ, Huang GH, Lin QG, Nie XH, Liu JL (2015) High-resolution temperature and precipitation projections over Ontario, Canada: a coupled dynamical-statistical approach. Quart J R Meteorol Soc 141:1137–1146

    Article  Google Scholar 

  • Weigel AP, Knutti R, Liniger MA, Appenzeller C (2010) Risks of model weighting in multimodel climate projections. J Clim 23:4175–4191. https://doi.org/10.1175/2010jcli3594.1

    Article  Google Scholar 

  • Wotton B, Martell D, Logan K (2003) Climate change and people-caused forest fire occurrence in Ontario. Clim Change 60:275–295

    Article  Google Scholar 

  • Xue PF, Pal JS, Ye XY, Lenters JD, Huang CF, Chu PY (2017) Improving the simulation of large lakes in regional climate modeling: two-way lake–atmosphere coupling with a 3D hydrodynamic model of the great lakes. J Clim 30:1605–1627. https://doi.org/10.1175/Jcli-D-16-0225.1

    Article  Google Scholar 

  • Yan RH, Gao JF, Li LL (2016) Streamflow response to future climate and land use changes in Xinjiang basin, China. Environ Earth Sci 75 https://doi.org/10.1007/s12665-016-5805-0

  • Zhai Y, Huang G, Wang X, Zhou X, Lu C, Li Z (2018) Future projections of temperature changes in Ottawa, Canada through stepwise clustered downscaling of multiple GCMs under RCPs. Clim Dyn. https://doi.org/10.1007/s00382-018-4340-y

    Google Scholar 

  • Zhang Q, Dool H, Saha S, Mendez M, Becker E, Peng P, Huang J (2011) Preliminary evaluation of multi-model ensemble system for monthly and seasonal prediction. In: 36th NOAA annual climate diagnostics and prediction workshop, Fort Worth, USA, 3–6 October 2011. Science and Technology Infusion Climate Bulletin, pp 124–131

  • Zhao N, Chen CF, Zhou X, Yue TX (2015) A comparison of two downscaling methods for precipitation in China. Environ Earth Sci 74:6563–6569. https://doi.org/10.1007/s12665-015-4750-7

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the Natural Science and Engineering Research Council of Canada. We acknowledge the World Climate Research Programme’s Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modelling groups (listed in Table 2 of this paper) for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure an international effort led by the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison, the European Network for Earth System Modelling and other partners in the Global Organisation for Earth System Science Portals (GO-ESSP). We would like to express our very great appreciation to Dr. Alessandro Selvitella for his valuable advice and guidance for the statistical techniques used in this research paper.

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Authors and Affiliations

  1. Department of Civil Engineering, McMaster University, Hamilton, ON, L8S 4L7, Canada

    Aly Al Samouly, Chanh Nien Luong, Zhong Li, Brian Baetz & Maysara Ghaith

  2. Department of Computing and Software Engineering, McMaster University, Hamilton, ON, L8S 4L7, Canada

    Spencer Smith

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  1. Aly Al Samouly
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  2. Chanh Nien Luong
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  3. Zhong Li
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  4. Spencer Smith
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  5. Brian Baetz
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  6. Maysara Ghaith
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Correspondence to Zhong Li.

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Aly Al Samouly and Chanh Nien Luong are joint first authors.

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Samouly, A.A., Luong, C.N., Li, Z. et al. Performance of multi-model ensembles for the simulation of temperature variability over Ontario, Canada. Environ Earth Sci 77, 524 (2018). https://doi.org/10.1007/s12665-018-7701-2

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