IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v27y2011i4p968-995.html
   My bibliography  Save this article

Validation and forecasting accuracy in models of climate change

Author

Listed:
  • Fildes, Robert
  • Kourentzes, Nikolaos

Abstract

Forecasting researchers, with few exceptions, have ignored the current major forecasting controversy: global warming and the role of climate modelling in resolving this challenging topic. In this paper, we take a forecaster's perspective in reviewing established principles for validating the atmospheric-ocean general circulation models (AOGCMs) used in most climate forecasting, and in particular by the Intergovernmental Panel on Climate Change (IPCC). Such models should reproduce the behaviours characterising key model outputs, such as global and regional temperature changes. We develop various time series models and compare them with forecasts based on one well-established AOGCM from the UK Hadley Centre. Time series models perform strongly, and structural deficiencies in the AOGCM forecasts are identified using encompassing tests. Regional forecasts from various GCMs had even more deficiencies. We conclude that combining standard time series methods with the structure of AOGCMs may result in a higher forecasting accuracy. The methodology described here has implications for improving AOGCMs and for the effectiveness of environmental control policies which are focussed on carbon dioxide emissions alone. Critically, the forecast accuracy in decadal prediction has important consequences for environmental planning, so its improvement through this multiple modelling approach should be a priority.

Suggested Citation

  • Fildes, Robert & Kourentzes, Nikolaos, 2011. "Validation and forecasting accuracy in models of climate change," International Journal of Forecasting, Elsevier, vol. 27(4), pages 968-995, October.
  • Handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:968-995
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207011000604
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Green, Kesten C & Armstrong, J Scott & Soon, Willie, 2008. "Benchmark forecasts for climate change," MPRA Paper 12163, University Library of Munich, Germany.
    2. Stern,Nicholas, 2007. "The Economics of Climate Change," Cambridge Books, Cambridge University Press, number 9780521700801, September.
    3. N. S. Keenlyside & M. Latif & J. Jungclaus & L. Kornblueh & E. Roeckner, 2008. "Advancing decadal-scale climate prediction in the North Atlantic sector," Nature, Nature, vol. 453(7191), pages 84-88, May.
    4. Suraje Dessai & Mike Hulme, 2004. "Does climate adaptation policy need probabilities?," Climate Policy, Taylor & Francis Journals, vol. 4(2), pages 107-128, June.
    5. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    6. D. A. Stainforth & T. Aina & C. Christensen & M. Collins & N. Faull & D. J. Frame & J. A. Kettleborough & S. Knight & A. Martin & J. M. Murphy & C. Piani & D. Sexton & L. A. Smith & R. A. Spicer & A. , 2005. "Uncertainty in predictions of the climate response to rising levels of greenhouse gases," Nature, Nature, vol. 433(7024), pages 403-406, January.
    7. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    8. Armstrong, J. Scott & Fildes, Robert, 2006. "Making progress in forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 433-441.
    9. Dana L. Royer & Robert A. Berner & Jeffrey Park, 2007. "Climate sensitivity constrained by CO2 concentrations over the past 420 million years," Nature, Nature, vol. 446(7135), pages 530-532, March.
    10. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    11. Green, Kesten C. & Armstrong, J. Scott & Soon, Willie, 2009. "Validity of climate change forecasting for public policy decision making," International Journal of Forecasting, Elsevier, vol. 25(4), pages 826-832, October.
    12. Fang, Yue, 2003. "Forecasting combination and encompassing tests," International Journal of Forecasting, Elsevier, vol. 19(1), pages 87-94.
    13. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    14. Clive W.J. Granger & Yongil Jeon, 2003. "Interactions between large macro models and time series analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 8(1), pages 1-10.
    15. George B. Kleindorfer & Liam O'Neill & Ram Ganeshan, 1998. "Validation in Simulation: Various Positions in the Philosophy of Science," Management Science, INFORMS, vol. 44(8), pages 1087-1099, August.
    16. Kesten C. Green & J. Scott Armstrong, 2007. "Global Warming: Forecasts by Scientists Versus Scientific Forecasts," Energy & Environment, , vol. 18(7), pages 997-1021, December.
    17. J. R. Petit & J. Jouzel & D. Raynaud & N. I. Barkov & J.-M. Barnola & I. Basile & M. Bender & J. Chappellaz & M. Davis & G. Delaygue & M. Delmotte & V. M. Kotlyakov & M. Legrand & V. Y. Lipenkov & C. , 1999. "Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica," Nature, Nature, vol. 399(6735), pages 429-436, June.
    18. Jose, Victor Richmond R. & Winkler, Robert L., 2008. "Simple robust averages of forecasts: Some empirical results," International Journal of Forecasting, Elsevier, vol. 24(1), pages 163-169.
    19. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Meng, Xiaochun & Taylor, James W., 2022. "Comparing probabilistic forecasts of the daily minimum and maximum temperature," International Journal of Forecasting, Elsevier, vol. 38(1), pages 267-281.
    2. Beenstock, Michael & Reingewertz, Yaniv & Paldor, Nathan, 2016. "Testing the historic tracking of climate models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1234-1246.
    3. Young, Peter C., 2018. "Data-based mechanistic modelling and forecasting globally averaged surface temperature," International Journal of Forecasting, Elsevier, vol. 34(2), pages 314-335.
    4. Jenny Cifuentes & Geovanny Marulanda & Antonio Bello & Javier Reneses, 2020. "Air Temperature Forecasting Using Machine Learning Techniques: A Review," Energies, MDPI, vol. 13(16), pages 1-28, August.
    5. Hassani, Hossein & Silva, Emmanuel Sirimal & Gupta, Rangan & Das, Sonali, 2018. "Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 121-139.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    2. Fildes, Robert, 2006. "The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion," International Journal of Forecasting, Elsevier, vol. 22(3), pages 415-432.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    5. Trapero, Juan R. & Kourentzes, N. & Fildes, R., 2012. "Impact of information exchange on supplier forecasting performance," Omega, Elsevier, vol. 40(6), pages 738-747.
    6. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    7. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    8. Barrow, Devon K. & Crone, Sven F., 2016. "A comparison of AdaBoost algorithms for time series forecast combination," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1103-1119.
    9. Gary Madden & Joachim Tan, 2008. "Forecasting international bandwidth capacity using linear and ANN methods," Applied Economics, Taylor & Francis Journals, vol. 40(14), pages 1775-1787.
    10. Hassani, Hossein & Silva, Emmanuel Sirimal & Gupta, Rangan & Das, Sonali, 2018. "Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 121-139.
    11. Kourentzes, Nikolaos & Barrow, Devon & Petropoulos, Fotios, 2019. "Another look at forecast selection and combination: Evidence from forecast pooling," International Journal of Production Economics, Elsevier, vol. 209(C), pages 226-235.
    12. Evangelos Spiliotis & Spyros Makridakis & Artemios-Anargyros Semenoglou & Vassilios Assimakopoulos, 2022. "Comparison of statistical and machine learning methods for daily SKU demand forecasting," Operational Research, Springer, vol. 22(3), pages 3037-3061, July.
    13. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    14. Gardner Jr., Everette S. & Diaz-Saiz, Joaquin, 2008. "Exponential smoothing in the telecommunications data," International Journal of Forecasting, Elsevier, vol. 24(1), pages 170-174.
    15. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251.
    16. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    17. Mirakyan, Atom & Meyer-Renschhausen, Martin & Koch, Andreas, 2017. "Composite forecasting approach, application for next-day electricity price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 228-237.
    18. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    19. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    20. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:968-995. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.