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Optimal selection of representative climate models and statistical downscaling for climate change impact studies: a case study of Rhode Island, USA

Theoretical and Applied Climatology volume 149, pages 695–708 (2022)Cite this article

Abstract

The future climate impact studies rely on future projections obtained from downscaling of Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. The main challenge is large, yet growing pool of CMIP5 models, posing a high computational cost for analyzing the climate model ensemble. The optimal model selection from the large pool is hence necessary. It is critical that the selected climate models represent the uncertainty range of possible future and have high skill in past performance. In this study, we use a multi-criteria decision process based on the coupling of the envelope and past performance approach to identify potential future climate stresses for RI, USA. The selection is based on projected changes in annual climatic means (precipitation and temperature) followed by a range of projected changes in climatic extremes and past performance among these models. From a pool of 109 models from RCP4.5 and 79 models from RCP8.5, a final subset of 4 models was selected for RCP4.5 and RCP8.5 respectively. The change in annual climatic means for temperature varied + 1.7 to + 3.0 °C in RCP4.5 and + 2.4 to + 5.8 °C in RCP8.5, and the range in climate mean of annual precipitation varied from 0.2 to 12.7% in RCP4.5 and − 4.1 to 15.9% in RCP8.5. The selected climate models are statistically downscaled to produce reliable local-scale climate estimates. Various variants of quantile mapping are studied, and quantile delta mapping is applied to systematically reduce biases and preserve raw GCM signals.

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Data availability

All the output data used in this work are available at the University of Rhode Island and are free of charge.

Code availability

Software used for the work is open source R: a Language and Environment for Statistical Computing.

References

  • Agel L, Barlow M, Polonia J, Coe D (2020) Simulation of northeast US extreme precipitation and its associated circulation by CMIP5 models. J Clim 33:9817–9834

    Article  Google Scholar 

  • Anscombe FJ, 1973: Graphs in statistical analysis. The american statistician, 27, 17–21 %@ 0003–1305.

  • Brekke LD, Dettinger MD, Maurer EP, Anderson M (2008) Significance of model credibility in estimating climate projection distributions for regional hydroclimatological risk assessments. Clim Change 89:371–394

    Article  Google Scholar 

  • Bürger G, Murdock T, Werner A, Sobie S, Cannon A (2012) Downscaling extremes—an intercomparison of multiple statistical methods for present climate. J Clim 25:4366–4388

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Change IC, 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 2013. There is no corresponding record for this reference.[Google Scholar], 33–118.

  • Chen J, Brissette FP, Chaumont D, Braun M (2013) Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North American river basins. J Hydrol 479:200–214

    Article  Google Scholar 

  • Daly C, Bryant K (2013) The PRISM climate and weather system—an introduction. PRISM climate group, Corvallis

  • Eden JM, Widmann M, Maraun D, Vrac M (2014) Comparison of GCM- and RCM-simulated precipitation following stochastic postprocessing. J Geophys Res Atmos 119(11):040–011, 053

    Google Scholar 

  • Evans JP, Ji F, Abramowitz G, Ekström M (2013) Optimally choosing small ensemble members to produce robust climate simulations. Environ Res Lett 8:044050

    Article  Google Scholar 

  • Flato, G., and Coauthors, 2014: Evaluation of climate models. Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 741–866.

  • Friedlingstein P, Meinshausen M, Arora VK, Jones CD, Anav A, Liddicoat SK, Knutti R (2014) Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J Clim 27:511–526

    Article  Google Scholar 

  • Gudmundsson L, Bremnes JB, Haugen JE, Engen-Skaugen T (2012) Downscaling RCM precipitation to the station scale using statistical transformations–a comparison of methods. Hydrol Earth Syst Sci 16:3383–3390

    Article  Google Scholar 

  • Gulizia C, Camilloni I (2015) Comparative analysis of the ability of a set of CMIP3 and CMIP5 global climate models to represent precipitation in South America. Int J Climatol 35:583–595

    Article  Google Scholar 

  • Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteor Soc 90:1095–1108

    Article  Google Scholar 

  • Hayhoe K, Coauthors (2006) Past and future changes in climate and hydrological indicators in the US Northeast. Clim Dyn 28:381–407

    Article  Google Scholar 

  • Hempel S, Frieler K, Warszawski L, Schewe J, Piontek F, 2013: A trend-preserving bias correction–the ISI-MIP approach.

  • Hidalgo HG, Alfaro EJ (2012) Global model selection for evaluation of climate change projections in the Eastern Tropical Pacific Seascape. Rev Biol Trop 60:67–81

    Google Scholar 

  • Hidalgo HG, Alfaro EJ (2015) Skill of CMIP5 climate models in reproducing 20th century basic climate features in Central America. Int J Climatol 35:3397–3421

    Article  Google Scholar 

  • Hodgkins GA, Dudley RW (2006) Changes in late-winter snowpack depth, water equivalent, and density in Maine, 1926–2004. Hydrol Process 20:741–751

    Article  Google Scholar 

  • Hodgkins GA, Dudley RW, 2006b: Changes in the timing of winter–spring streamflows in eastern North America, 1913–2002. Geophys Res Lett 33.

  • Hodgkins GA, James IC, Huntington TG (2002) Historical changes in lake ice-out dates as indicators of climate change in New England, 1850–2000. Int J Climatol 22:1819–1827

    Article  Google Scholar 

  • Hodgkins GA, Dudley RW, Huntington TG (2003) Changes in the timing of high river flows in New England over the 20th century. J Hydrol 278:244–252

    Article  Google Scholar 

  • Immerzeel W, Pellicciotti F, Bierkens M (2013) Rising river flows throughout the twenty-first century in two Himalayan glacierized watersheds. Nat Geosci 6:742–745

    Article  Google Scholar 

  • Karmalkar AV, Horton RM (2021) Drivers of exceptional coastal warming in the northeastern United States. Nat Clim Chang 11:854–860

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Knutti R, Sedláček J (2012) Robustness and uncertainties in the new CMIP5 climate model projections. Nat Clim Chang 3:369–373

    Article  Google Scholar 

  • Knutti R, Masson D, Gettelman A (2013) Climate model genealogy: generation CMIP5 and how we got there. Geophys Res Lett 40:1194–1199

    Article  Google Scholar 

  • Kunkel K, Coauthors, 2022: State climate summaries for the United States 2022. NOAA Technical Report NESDIS 150.

  • Liu J, Song M, Horton RM, Hu Y (2013) Reducing spread in climate model projections of a September ice-free Arctic. Proc Natl Acad Sci U S A 110:12571–12576

    Article  Google Scholar 

  • Lutz AF, ter Maat HW, Biemans H, Shrestha AB, Wester P, Immerzeel WW (2016) Selecting representative climate models for climate change impact studies: an advanced envelope-based selection approach. Int J Climatol 36:3988–4005

    Article  Google Scholar 

  • Maraun D (2013) Bias correction, quantile mapping, and downscaling: revisiting the inflation issue. J Clim 26:2137–2143

    Article  Google Scholar 

  • Maurer EP, Pierce DW (2014) Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean. Hydrol Earth Syst Sci 18:915–925

  • McSweeney CF, Jones RG (2016) How representative is the spread of climate projections from the 5 CMIP5 GCMs used in ISI-MIP? Clim Serv 1:24–29

    Article  Google Scholar 

  • Pennell C, Reichler T (2011) On the effective number of climate models. J Clim 24:2358–2367

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Peterson T (2005) Climate Change Indices. WMO Bull 54:83–86

    Google Scholar 

  • 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 106:8441–8446

    Article  Google Scholar 

  • Pincus R, Batstone CP, Hofmann RJP, Taylor KE, Glecker PJ, 2008: Evaluating the present‐day simulation of clouds, precipitation, and radiation in climate models. J Geophys Re Atmos 113.

  • Rosen RA, Guenther E (2015) The economics of mitigating climate change: what can we know? Technol Forecast Soc Chang 91:93–106

    Article  Google Scholar 

  • Ruane AC, SP McDermid, 2017: Selection of a representative subset of global climate models that captures the profile of regional changes for integrated climate impacts assessment. Earth Perspect 4.

  • 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(10):884–810, 906

    Google Scholar 

  • San José R, Pérez JL, González RM, Pecci J, Garzón A, Palacios M (2016) Impacts of the 4.5 and 8.5 RCP global climate scenarios on urban meteorology and air quality: application to Madrid, Antwerp, Milan, Helsinki and London. J Comput Appl Math 293:192–207

    Article  Google Scholar 

  • Sánchez 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 10:241–248

  • Sillmann J, Kharin V, Zhang X, Zwiers F, Bronaugh D (2013a) Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. J Geophys Res Atmos 118:1716–1733

    Article  Google Scholar 

  • Sillmann J, Kharin VV, Zwiers F, Zhang X, Bronaugh D (2013b) Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. J Geophys Res Atmos 118:2473–2493

    Article  Google Scholar 

  • 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 9:104018

    Article  Google Scholar 

  • Sunyer MA, Coauthors (2014) Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe. Hydrol Earth Syst Sci Discuss 11:6167–6214

    Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106:7183–7192

    Article  Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteor Soc 93:485–498

    Article  Google Scholar 

  • Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J Hydrol 456:12–29

    Article  Google Scholar 

  • Teutschbein C, Seibert J (2013) Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions? Hydrol Earth Syst Sci 17:5061–5077

    Article  Google Scholar 

  • Themeßl MJ, Gobiet A, Heinrich G (2012) Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Clim Change 112:449–468

    Article  Google Scholar 

  • Tu J (2009) Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts, USA. J Hydrol 379:268–283

    Article  Google Scholar 

  • Van Vuuren DP, Coauthors, 2011: The representative concentration pathways: an overview. Clim Change, 109(5)

  • 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 111:3228–3232

    Article  Google Scholar 

  • Willems P, Vrac M (2011) Statistical precipitation downscaling for small-scale hydrological impact investigations of climate change. J Hydrol 402:193–205

    Article  Google Scholar 

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Acknowledgements

We acknowledge the World Climate Research Program’s Working Group on Coupled Modeling for CMIP5 model output availability. We acknowledge Royal Netherlands Meteorological Institute for the KNMI Climate Explorer tool. In addition to that, we also acknowledge J. Sillmann for their output data on changes in climate change indices of CMIP5 models.

Funding

This work was supported by USDA RI0014- S1063, RI0021-S1089, McIntire Stennis RI0020-MS984, and Rhode Island HUD, Sandy grant for funding the research work.

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  1. Department of Geosciences, University of Rhode Island, 9 E. Alumni Ave, Kingston, RI, 02881, USA

    Shiva Gopal Shrestha & Soni M. Pradhanang

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  1. Shiva Gopal Shrestha
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SMP and SGS both conceived the presented idea. SGS performed the computations and analyzed the results and wrote the manuscript. SMP and SGS reviewed and edited the manuscript. SMP acquired funding to do this research.

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Correspondence to Soni M. Pradhanang.

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Shrestha, S.G., Pradhanang, S.M. Optimal selection of representative climate models and statistical downscaling for climate change impact studies: a case study of Rhode Island, USA. Theor Appl Climatol 149, 695–708 (2022). https://doi.org/10.1007/s00704-022-04073-w

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