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Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin

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Abstract

Climate change is expected to have severe impacts on natural systems as well as various socio-economic aspects of human life. This has urged scientific communities to improve the understanding of future climate and reduce the uncertainties associated with projections. In the present study, ten statistically downscaled CMIP5 GCMs at 1/16th deg. spatial resolution from two different downscaling procedures are utilized over the Columbia River Basin (CRB) to assess the changes in climate variables and characterize the associated uncertainties. Three climate variables, i.e. precipitation, maximum temperature, and minimum temperature, are studied for the historical period of 1970–2000 as well as future period of 2010–2099, simulated with representative concentration pathways of RCP4.5 and RCP8.5. Bayesian Model Averaging (BMA) is employed to reduce the model uncertainty and develop a probabilistic projection for each variable in each scenario. Historical comparison of long-term attributes of GCMs and observation suggests a more accurate representation for BMA than individual models. Furthermore, BMA projections are used to investigate future seasonal to annual changes of climate variables. Projections indicate significant increase in annual precipitation and temperature, with varied degree of change across different sub-basins of CRB. We then characterized uncertainty of future projections for each season over CRB. Results reveal that model uncertainty is the main source of uncertainty, among others. However, downscaling uncertainty considerably contributes to the total uncertainty of future projections, especially in summer. On the contrary, downscaling uncertainty appears to be higher than scenario uncertainty for precipitation.

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References

  • Abatzoglou JT, Brown TJ (2012) A comparison of statistical downscaling methods suited for wildfire applications. Int J Climatol 32:772–780. doi:10.1002/joc.2312

    Article  Google Scholar 

  • Abatzoglou JT, Rupp DE, Mote PW (2014) Seasonal climate variability and change in the Pacific Northwest of the US. J Clim 27:2125–2142. doi:10.1175/JCLI-D-13-00218.1

    Article  Google Scholar 

  • Ahmadalipour A, Rana A, Moradkhani H, Sharma A (2015) Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis. Theor Appl Climatol. doi:10.1007/s00704-015-1695-4

    Google Scholar 

  • Ahmadalipour A, Moradkhani H, Svoboda M (2016) Centennial drought outlook over the CONUS using NASA-NEX downscaled climate ensemble. Int J Climatol. doi:10.1002/joc.4859

    Google Scholar 

  • Berghuijs WR, Woods RA, Hrachowitz M (2014) A precipitation shift from snow towards rain leads to a decrease in streamflow. Nat Clim Change 4:583–586

    Article  Google Scholar 

  • 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 41:1039–1056

    Article  Google Scholar 

  • Boberg F, Christensen JH (2012) Overestimation of Mediterranean summer temperature projections due to model deficiencies. Nat Clim Change 2:433–436. doi:10.1038/nclimate1454

    Article  Google Scholar 

  • Cattiaux J, Douville H, Peings Y (2013) European temperatures in CMIP5: origins of present-day biases and future uncertainties. Clim Dyn 41:2889–2907. doi:10.1007/s00382-013-1731-y

    Article  Google Scholar 

  • Chen J, Brissette FP, Poulin A, Leconte R (2011) Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed. Water Resour Res 47. doi:10.1029/2011WR010602

  • 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 

  • Clark MP, Wilby RL, Gutmann ED, et al (2016) Characterizing uncertainty of the hydrologic impacts of climate change. Curr Clim Change Reports 2:55–64

    Article  Google Scholar 

  • Cooper MG, Nolin AW, Safeeq M (2016) Testing the recent snow drought as an analog for climate warming sensitivity of Cascades snowpacks. Environ Res Lett 11:84009

    Article  Google Scholar 

  • DeChant CM, Moradkhani H (2014) Toward a reliable prediction of seasonal forecast uncertainty: addressing model and initial condition uncertainty with ensemble data assimilation and sequential bayesian combination. J Hydrol 519:2967–2977

    Article  Google Scholar 

  • Demirel MC, Moradkhani H (2015) Assessing the impact of CMIP5 climate multi-modeling on estimating the precipitation seasonality and timing. Clim Change 135(2):357–372

  • Demirel MC, Booij M, Hoekstra A (2015) The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models. Hydrol Earth Syst Sci 19:275–291

  • Deser C, Phillips AS, Alexander M a., Smoliak BV (2014) Projecting North American climate over the next 50 years: uncertainty due to internal variability*. J Clim 27:2271–2296. doi:10.1175/JCLI-D-13-00451.1

    Article  Google Scholar 

  • Diffenbaugh NS, Scherer M, Ashfaq M (2013) Response of snow-dependent hydrologic extremes to continued global warming. Nat Clim Change 3:379–384

    Article  Google Scholar 

  • Duan Q, Phillips TJ (2010) Bayesian estimation of local signal and noise in multimodel simulations of climate change. J Geophys Res 115:D18123. doi:10.1029/2009JD013654

    Article  Google Scholar 

  • Etemadi H, Samadi S, Sharifikia M (2013) Uncertainty analysis of statistical downscaling models using general circulation model over an international wetland. Clim Dyn 42:2899–2920. doi:10.1007/s00382-013-1855-0

    Article  Google Scholar 

  • Eum H-I, Gachon P, Laprise R (2013) Developing a likely climate scenario from multiple regional climate model simulations with an optimal weighting factor. Clim Dyn 43:11–35. doi:10.1007/s00382-013-2021-4

    Article  Google Scholar 

  • Eum H-I, Cannon AJ, Murdock TQ (2016) Intercomparison of multiple statistical downscaling methods: multi-criteria model selection for South Korea. Stoch Environ Res Risk Assess 31:683. doi:10.1007/s00477-016-1312-9

  • Feng J, Lee D-K, Fu C, et al (2010) Comparison of four ensemble methods combining regional climate simulations over Asia. Meteorol Atmos Phys 111:41–53. doi:10.1007/s00703-010-0115-7

    Article  Google Scholar 

  • Ficklin DL, Barnhart BL, Knouft JH et al (2014) Climate change and stream temperature projections in the Columbia River basin: habitat implications of spatial variation in hydrologic drivers. Hydrol Earth Syst Sci 18:4897–4912

    Article  Google Scholar 

  • Ficklin DL, Abatzoglou JT, Robeson SM, Dufficy A (2016a) The influence of climate model biases on projections of aridity and drought. J Clim 29(4):1269–1285

  • Ficklin DL, Letsinger SL, Stewart IT, Maurer EP (2016b) Assessing differences in snowmelt-dependent hydrologic projections using CMIP3 and CMIP5 climate forcing data for the western US. Hydrol Res 47:483–500

    Google Scholar 

  • Gleckler PJ, Taylor KE, Doutriaux C (2008) Performance metrics for climate models. J Geophys Res 113:D06104. doi:10.1029/2007JD008972

    Article  Google Scholar 

  • Gutmann E, Pruitt T, Clark MP et al (2014) An intercomparison of statistical downscaling methods used for water resource assessments in the US. Water Resour Res 50:7167–7186

    Article  Google Scholar 

  • Hawkins E, Sutton R (2010) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn 37:407–418. doi:10.1007/s00382-010-0810-6

    Article  Google Scholar 

  • Hawkins E, Smith RS, Gregory JM, Stainforth DA (2015) Irreducible uncertainty in near-term climate projections. Clim Dyn. doi:10.1007/s00382-015-2806-8

    Google Scholar 

  • Huang D, Zhu J, Zhang Y, Huang A (2013) Uncertainties on the simulated summer precipitation over Eastern China from the CMIP5 models. J Geophys Res Atmos 118:9035–9047

    Article  Google Scholar 

  • Huang S, Huang Q, Chang J, Leng G (2015) Linkages between hydrological drought, climate indices and human activities: a case study in the Columbia River basin. Int J Climatol. doi:10.1002/joc.4344

    Google Scholar 

  • Isaak DJ, Wollrab S, Horan D, Chandler G (2012) Climate change effects on stream and river temperatures across the northwest US from 1980 to 2009 and implications for salmonid fishes. Clim Change 113:499–524

    Article  Google Scholar 

  • Jung I-W, Moradkhani H, Chang H (2012) Uncertainty assessment of climate change impacts for hydrologically distinct river basins. J Hydrol 466:73–87

    Article  Google Scholar 

  • Kapnick S, Hall A (2012) Causes of recent changes in western North American snowpack. Clim Dyn 38:1885–1899

    Article  Google Scholar 

  • Knutti R (2008) Should we believe model predictions of future climate change? Philos Trans A Math Phys Eng Sci 366:4647–4664. doi:10.1098/rsta.2008.0169

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Kwok R (2011) Observational assessment of Arctic Ocean sea ice motion, export, and thickness in CMIP3 climate simulations. J Geophys Res 116:C00D05

  • Li J, Zhang Q, Chen YD, Singh VP (2013) GCMs-based spatiotemporal evolution of climate extremes during the 21st century in China. J Geophys Res Atmos 118:11–17

    Google Scholar 

  • Livneh B, Rosenberg EA, Lin C et al (2013) A long-term hydrologically based dataset of land surface fluxes and states for the conterminous US: update and extensions*. J Clim 26:9384–9392. doi:10.1175/JCLI-D-12-00508.1

    Article  Google Scholar 

  • Madadgar S, Moradkhani H (2014) Improved Bayesian multimodeling; integration of Copulas and BMA. Water Resour Res:1–18. doi:10.1002/2014WR015965.Received

  • Mankin JS, Diffenbaugh NS (2015) Influence of temperature and precipitation variability on near-term snow trends. Clim Dyn 45:1099–1116

    Article  Google Scholar 

  • Matheussen B, Kirschbaum RL, Goodman IA et al (2000) Effects of land cover change on streamflow in the interior Columbia River Basin (USA and Canada). Hydrol Process 14:867–885

    Article  Google Scholar 

  • Miao C, Duan Q, Sun Q, et al (2014) Assessment of CMIP5 climate models and projected temperature changes over Northern Eurasia. Environ Res Lett 9:55007. doi:10.1088/1748-9326/9/5/055007

    Article  Google Scholar 

  • Miao C, Su L, Sun Q, Duan Q (2016) A nonstationary bias-correction technique to remove bias in GCM simulations. J Geophys Res Atmos 121:5718–5735

  • Mizukami N, Clark MP, Gutmann ED et al (2016) Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous US: statistically downscaled forcing data and hydrologic models. J Hydrometeorol 17:73–98

    Article  Google Scholar 

  • Mote PW, Salathé EP (2010) Future climate in the Pacific Northwest. Clim Change 102:29–50. doi:10.1007/s10584-010-9848-z

    Article  Google Scholar 

  • Najafi MR, Moradkhani H (2015) Multi-model ensemble analysis of runoff extremes for climate change impact assessments. J Hydrol 525:352–361. doi:10.1016/j.jhydrol.2015.03.045

    Article  Google Scholar 

  • Najafi MR, Moradkhani H, Jung IW (2011) Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrol Process 25:2814–2826. doi:10.1002/hyp.8043

    Article  Google Scholar 

  • Pascale S, Lucarini V, Feng X, et al (2016) Projected changes of rainfall seasonality and dry spells in a high greenhouse gas emissions scenario. Clim Dyn 46:1331–1350

    Article  Google Scholar 

  • Perez J, Menendez M, Mendez FJ, Losada IJ (2014) Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-east Atlantic region. Clim Dyn 43:2663–2680. doi:10.1007/s00382-014-2078-8

    Article  Google Scholar 

  • Raftery AE, Gneiting T, Balabdaoui F, Polakowski M (2005) Using Bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev 133:1155–1174. doi:10.1175/MWR2906.1

    Article  Google Scholar 

  • Rana A, Moradkhani H (2015) Spatial, temporal and frequency based climate change assessment in Columbia River Basin using multi downscaled-Scenarios. Clim Dyn 47:579. doi:10.1007/s00382-015-2857-x

  • Rana A, Moradkhani H, Qin Y (2016) Understanding the joint behavior of temperature and precipitation for climate change impact studies. Theor Appl Climatol 1–19. doi:10.1007/s00704-016-1774-1

  • Reintges A, Martin T, Latif M, Keenlyside NS (2016) Uncertainty in twenty-first century projections of the Atlantic Meridional Overturning Circulation in CMIP3 and CMIP5 models. Clim Dyn 1–17. doi:10.1007/s00382-016-3180-x

  • Robine J-M, Cheung SLK, Le Roy S et al (2008) Death toll exceeded 70,000 in Europe during the summer of 2003. C R Biol 331:171–178

    Article  Google Scholar 

  • 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 118:10,884–10,906. doi:10.1002/jgrd.50843

  • Rupp DE, Abatzoglou JT, Mote PW (2016) Projections of 21st century climate of the Columbia River Basin. Clim Dyn 1–17. doi:10.1007/s00382-016-3418-7

  • Shukla S, Safeeq M, Aghakouchak A, et al (2015) Temperature impacts on the water year 2014 drought in California, 1–10. doi:10.1002/2015GL063666.Received

  • 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. doi:10.1002/jgrd.50203

    Article  Google Scholar 

  • Sima S, Ahmadalipour A, Tajrishy M (2013) Mapping surface temperature in a hyper-saline lake and investigating the effect of temperature distribution on the lake evaporation. Remote Sens Environ 136:374–385

    Article  Google Scholar 

  • Sun Q, Miao C, Duan Q (2016) Extreme climate events and agricultural climate indices in China: CMIP5 model evaluation and projections. Int J Climatol 36:43–61

    Article  Google Scholar 

  • Taylor KE (2000) Summarizing multiple aspects of model performance in a single diagram. Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, University of California

  • Thibeault JM, Seth A (2014) A framework for evaluating model credibility for warm-season precipitation in Northeastern North America: a case study of CMIP5 simulations and projections. J Clim 27:493–510. doi:10.1175/JCLI-D-12-00846.1

    Article  Google Scholar 

  • Vano JA, Nijssen B, Lettenmaier DP (2015) Seasonal hydrologic responses to climate change in the Pacific Northwest. Water Resour Res 51:1959–1976

    Article  Google Scholar 

  • Wang D, Hagen SC, Alizad K (2013) Climate change impact and uncertainty analysis of extreme rainfall events in the Apalachicola River basin, Florida. J Hydrol 480:125–135. doi:10.1016/j.jhydrol.2012.12.015

    Article  Google Scholar 

  • Werner AT, Cannon AJ (2016) Hydrologic extremes-an intercomparison of multiple gridded statistical downscaling methods. Hydrol Earth Syst Sci 20:1483

    Article  Google Scholar 

  • Woldemeskel FM, Sharma A., Sivakumar B, Mehrotra R (2012) An error estimation method for precipitation and temperature projections for future climates. J Geophys Res Atmos 117. doi:10.1029/2012JD018062

  • Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62:189–216

    Article  Google Scholar 

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Acknowledgements

Partial financial support for this study was provided by the DOE, cooperative agreement 00063182.

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Authors and Affiliations

  1. Remote Sensing and Water Resources Laboratory, Department of Civil and Environmental Engineering, Portland State University, Portland, USA

    Ali Ahmadalipour & Hamid Moradkhani

  2. Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

    Arun Rana

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  1. Ali Ahmadalipour
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  2. Hamid Moradkhani
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Correspondence to Ali Ahmadalipour.

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Ahmadalipour, A., Moradkhani, H. & Rana, A. Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin. Clim Dyn 50, 717–733 (2018). https://doi.org/10.1007/s00382-017-3639-4

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