Climate Dynamics

, Volume 50, Issue 3–4, pp 1321–1334 | Cite as

Dynamically-downscaled temperature and precipitation changes over Saskatchewan using the PRECIS model

  • Xiong Zhou
  • Guohe Huang
  • Xiuquan Wang
  • Guanhui Cheng


In this study, dynamically-downscaled temperature and precipitation changes over Saskatchewan are developed through the Providing Regional Climates for Impacts Studies (PRECIS) model. It can resolve detailed features within GCM grids such as topography, clouds, and land use in Saskatchewan. The PRECIS model is employed to carry out ensemble simulations for projections of temperature and precipitation changes over Saskatchewan. Temperature and precipitation variables at 14 weather stations for the baseline period are first extracted from each model run. Ranges of simulated temperature and precipitation variables are then obtained through combination of maximum and minimum values calculated from the five ensemble runs. The performance of PRECIS ensemble simulations can be evaluated through checking if observations of current temperature at each weather station are within the simulated range. Future climate projections are analyzed over three time slices (i.e., the 2030s, 2050s, and 2080s) to help understand the plausible changes in temperature and precipitation over Saskatchewan in response to global warming. The evaluation results show that the PRECIS ensemble simulations perform very well in terms of capturing the spatial patterns of temperature and precipitation variables. The results of future climate projections over three time slices indicate that there will be an obvious warming trend from the 2030s, to the 2050s, and the 2080s over Saskatchewan. The projected changes of mean temperature over the whole Saskatchewan area is [0, 2] °C in the 2030s at 10th percentile, [2, 5.5] °C in the 2050s at 50th percentile, and [3, 10] °C in the 2090s at 90th percentile. There are no significant changes in the spatial patterns of the projected total precipitation from the 2030s to the end of this century. The minimum change of the projected total precipitation over the whole Province of Saskatchewan is most likely to be −1.3% in the 2030s, and −0.2% in the 2050s, while the minimum value would be −2.1% to the end of this century at 50th percentile.


Global warming Regional climate modeling Climate change Saskatchewan 



This research was supported by the Natural Sciences Foundation (51190095, 51225904), the Program for Innovative Research Team in University (IRT1127), the 111 Project (B14008), and the Natural Science and Engineering Research Council of Canada.


  1. Bellprat O, Kotlarski S, Lüthi D, Schär C (2012) Exploring perturbed physics ensembles in a regional climate model. J Climate 25:4582–4599CrossRefGoogle Scholar
  2. Centella-Artola A, Taylor MA, Bezanilla-Morlot A, Martinez-Castro D, Campbell JD, Stephenson TS, Vichot A (2015) Assessing the effect of domain size over the Caribbean region using the PRECIS regional climate model. Clim Dyn 44:1901–1918CrossRefGoogle Scholar
  3. Chen J, Brissette FP, Leconte R (2014) Assessing regression-based statistical approaches for downscaling precipitation over North America. Hydrol Process 28:3482–3504CrossRefGoogle Scholar
  4. Cox P, Betts R, Bunton C, Essery R, Rowntree P, Smith J (1999) The impact of new land surface physics on the GCM simulation of climate and climate sensitivity. Clim Dyn 15:183–203CrossRefGoogle Scholar
  5. deJong A, McBean E, Gharabaghi B (2010) Projected climate conditions to 2100 for Regina, Saskatchewan. Can J Civ Eng 37:1247–1260CrossRefGoogle Scholar
  6. Denis B, Laprise R, Caya D, Côté J (2002) Downscaling ability of one-way nested regional climate models: the big-brother experiment. Climate Dynamics 18:627–646CrossRefGoogle Scholar
  7. Environment and Climate Change Canada (2013) Adjusted and Homogenized Canadian Climate Data (AHCCD). Accessed 15 August 2016
  8. Feser F, Rockel B, von Storch H, Winterfeldt J, Zahn M (2011) Regional climate models add value to global model data: a review and selected examples. Bull Am Meteorol Soc 92:1181–1192. doi: 10.1175/2011bams3061.1
  9. Gong W, Duan QY, Li DJ, Wang C, Di ZH, Ye AZ, Miao CY, Dai YJ (2015) An Intercomparison of Sampling Methods for Uncertainty Quantification of Environmental Dynamic Models. J Environ Inform.  doi: 10.3808/jei.201500310
  10. Gregory D, Rowntree P (1990) A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon Weather Rev 118:1483–1506CrossRefGoogle Scholar
  11. He J (2016) Probabilistic Evaluation of Causal Relationship between Variables for Water Quality Management. J Environ Inform 28(2):110–119. doi: 10.3808/jei.201600353
  12. 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, p 1535. doi: 10.1017/CBO9781107415324
  13. Jacobeit J, Hertig E, Seubert S, Lutz K (2014) Statistical downscaling for climate change projections in the Mediterranean region: methods and results. Reg Environ Change 14:1891–1906CrossRefGoogle Scholar
  14. Jones RG, Noguer M, Hassell DC, Hudson D, Wilson SS, Jenkins GJ, Mitchell JFB (2004) Generating high resolution climate change scenarios using PRECIS. Met Office Hadley Centre, Exeter, UKGoogle Scholar
  15. Jones R, Murphy J, Noguer M (1995) Simulation of climate change over europe using a nested regional-climate model. I: assessment of control climate, including sensitivity to location of lateral boundaries. Q J R Meteorol Soc 121:1413–1449Google Scholar
  16. Lavender SL, Walsh KJE (2011) Dynamically downscaled simulations of Australian region tropical cyclones in current and future climates. Geophys Res Lett 38:L10705. doi: 10.1029/2011GL047499
  17. Maraun D et al (2010) Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48Google Scholar
  18. Maraun D et al (2015) VALUE: A framework to validate downscaling approaches for climate change studies. Earths Future 3:1–14CrossRefGoogle Scholar
  19. Maurer EP, Brekke L, Pruitt T, Duffy PB (2007) Fine-resolution climate projections enhance regional climate change impact studies. Eos Trans Am Geophys Union 88:504–504CrossRefGoogle Scholar
  20. McSweeney C, Jones R (2010) Selecting members of the ‘QUMP’perturbed-physics ensemble for use with PRECIS, vol 9. Met Office Hadley Centre, ExeterGoogle Scholar
  21. Murphy JM, Sexton DMH, Jenkins GJ, Boorman PM, Booth BBB, Brown CC, Clark RT, Collins M, Harris GR, Kendon EJ, Betts RA, Brown SJ, Howard TP, Humphrey KA, McCarthy MP, McDonald RE, Stephens A, Wallace C, Warren R, Wilby R, Wood RA (2009) UK climate projections science report: climate change projections. Met Office Hadley Centre, Exeter, UKGoogle Scholar
  22. Nakicenovic N et al (2000) Special report on emissions scenarios: a special report of Working Group III of the Intergovernmental Panel on Climate ChangeGoogle Scholar
  23. Natural Resources Canada (2015) Overview of Climate Change in Canada. Accessed 15 August 2016
  24. Quintana-Seguí P, Peral C, Turco M, Llasat MC, Martin E (2016) Meteorological Analysis Systems in North-East Spain: Validation of SAFRAN and SPAN. J Enviro Inf 27(2):116-130. doi: 10.3808/jei.201600335 Google Scholar
  25. Sachindra DA, Huang F, Barton A, Perera BJC (2014) Statistical downscaling of general circulation model outputs to catchment scale hydroclimatic variables: issues, challenges and possible solutions. J Water Clim Change 5:496–525CrossRefGoogle Scholar
  26. Saskatchewan Eco Network (2009) Climate Change in Saskatchewan. Accessed 15 August 2016
  27. Saskatchewan Environmental Society (2016) Climate Change. Accessed 15 August 2016
  28. Saskatchewan Ministry of the Environment (2013) Climate Change. Accessed 15 August 2016
  29. Schaefer K, Zhang T, Bruhwiler L, Barrett AP (2011) Amount and timing of permafrost carbon release in response to climate warming. Tellus B 63:165–180CrossRefGoogle Scholar
  30. Schindler DW, Donahue WF (2006) An impending water crisis in Canada’s western prairie provinces. Proc Natl Acad Sci 103:7210–7216CrossRefGoogle Scholar
  31. Statistics Canada (2005) Land and freshwater area, by province and territory. Accessed 15 August 2016
  32. Statistics Canada (2014) Population by year, by province and territory (Number). Accessed 15 August 2016
  33. Tong LI, Saminathan R, Chang CW (2015) Uncertainty Assessment of Non-normal Emission Estimates Using Non-parametric Bootstrap Confidence Intervals. J Environ Inform. doi: 10.3808/jei.201500322
  34. Van Vuuren DP et al (2011) The representative concentration pathways: an overview. Clim change 109:5–31CrossRefGoogle Scholar
  35. Vincent LA, Gullett D (1999) Canadian historical and homogeneous temperature datasets for climate change analyses. Int J Climatol 19:1375–1388CrossRefGoogle Scholar
  36. Vincent LA, Zhang X, Bonsal B, Hogg W (2002) Homogenization of daily temperatures over Canada. J Climate 15:1322–1334CrossRefGoogle Scholar
  37. Wang X, Huang G, Lin Q, Liu J (2014) High-resolution probabilistic projections of temperature changes over Ontario, Canada. J Clim 27:5259–5284CrossRefGoogle Scholar
  38. Wang X, Huang G, Liu J (2015a) Projected increases in near-surface air temperature over Ontario, Canada: a regional climate modeling approach. Clim Dyn 45:1381–1393CrossRefGoogle Scholar
  39. Wang X, Huang G, Lin Q, Nie X, Liu J (2015b) High-resolution temperature and precipitation projections over Ontario, Canada: a coupled dynamical-statistical approach. Q J R Meteorol Soc 141:1137–1146CrossRefGoogle Scholar
  40. Wang X, Huang G, Liu J, Li Z, Zhao S (2015c) Ensemble projections of regional climatic changes over Ontario, Canada. J Clim 28:7327–7346CrossRefGoogle Scholar
  41. White CJ et al (2013) On regional dynamical downscaling for the assessment and projection of temperature and precipitation extremes across Tasmania, Australia. Clim Dyn 41:3145–3165CrossRefGoogle Scholar
  42. Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns OL (2004) Guidelines for the use of Climate scenarios developed from Statistical downscaling methods. Accessed 15 August 2016
  43. Wilson S, Hassell D, Hein D, Jones R, Taylor R (2005) Installing and using the Hadley Centre regional climate modelling system, PRECIS. Version 1:157Google Scholar
  44. Zhou X, Huang G, Zhu H, Cheng J, Xu JL (2015a) Chance-constrained two-stage fractional optimization for planning regional energy systems in British Columbia, Canada. Appl Energy 154:663–677. doi: 10.1016/j.apenergy.2015.05.013 CrossRefGoogle Scholar
  45. Zhou X, Huang G, Zhu H, Yan B (2015b) Two-Stage Chance-Constrained Fractional Programming for Sustainable Water Quality Management under Uncertainty. J Water Resou Plann Manage 141(5):04014074. doi: 10.1061/(ASCE)WR.1943-5452.0000470 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Xiong Zhou
    • 1
  • Guohe Huang
    • 1
  • Xiuquan Wang
    • 1
    • 2
  • Guanhui Cheng
    • 1
  1. 1.Institute for Energy, Environment and Sustainable CommunitiesUniversity of ReginaReginaCanada
  2. 2.Department of Civil and Resource EngineeringDalhousie UniversityHalifaxCanada

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