Climate Simulators and Climate Projections

Literature Cited

1. Ahrens CD. 2000. Meteorology Today: An Introduction to Weather, the Climate, and the Environment Pacific Grove, CA: Brooks/Cole, 6th ed..
2. Arakawa A. 1997. Adjustment mechanisms in atmospheric models. J. Meteorol. Soc. Jpn. 75:1B155–79 [Google Scholar]
3. Bastos LS, O'Hagan A. 2009. Diagnostics for Gaussian process emulators. Technometrics 51:4425–38 [Google Scholar]
4. Box GEP. 1980. Sampling and Bayes' inference in scientific modelling and robustness. J. R. Stat. Soc. A 143:4383–430 [Google Scholar]
5. Chatfield C. 2004. The Analysis of Time Series Boca Raton, FL: Chapman & Hall/CRC
6. Craig PS, Goldstein M, Rougier JC, Seheult AH. 2001. Bayesian forecasting for complex systems using computer simulators. J. Am. Stat. Assoc. 96:717–29 [Google Scholar]
7. Craig PS, Goldstein M, Seheult AH, Smith JA. 1997. Pressure matching for hydrocarbon reservoirs: a case study in the use of Bayes linear strategies for large computer experiments. Case Studies in Bayesian Statistics III C Gatsonis, JS Hodges, RE Kass, RE McCulloch, P Rossi, ND Singpurwalla 37–87 New York: Springer-Verlag [Google Scholar]
8. Cumming JA, Goldstein M. 2009. Small sample Bayesian designs for complex high-dimensional models based on information gained using fast approximations. Technometrics 51:4377–88 [Google Scholar]
9. Danabasoglu G, Ferrari R, McWilliams JC. 2008. Sensitivity of an ocean general circulation model to a parameterization of near-surface eddy fluxes. J. Clim. 21:1192–208 [Google Scholar]
10. Edwards N, Cameron D, Rougier JC. 2011. Precalibrating an intermediate complexity climate model. Clim. Dyn. 37:1469–82 [Google Scholar]
11. Forrester AIJ, Sóbester A, Keane AJ. 2008. Engineering Design via Surrogate Modelling: A Practical Guide Chichester, UK: John Wiley & Sons
12. Forster P, Ramaswamy V, Artaxo P, Berntsen T, Betts R. et al. 2007. Changes in atmospheric constituents and in radiative forcing. See Solomon et al. 2007 129–234
13. Gelman A, Carlin JB, Stern HS, Rubin DB. 2003. Bayesian Data Analysis Boca Raton, FL: Chapman & Hall/CRC, 2nd ed..
14. Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC. et al. 2011. The Community Climate System Model version 4. J. Clim. 24:4973–91 [Google Scholar]
15. Gladstone RM, Lee V, Rougier JC, Payne AJ, Hellmer H. et al. 2012. Calibrated prediction of Pine Island Glacier retreat during the 21st and 22nd centuries with a coupled flowline model. Earth Planet. Sci. Lett. 333–34:191–99 [Google Scholar]
16. Goldstein M, Rougier JC. 2004. Probabilistic formulations for transferring inferences from mathematical models to physical systems. SIAM J. Sci. Comput. 26:2467–87 [Google Scholar]
17. Goldstein M, Rougier JC. 2006. Bayes linear calibrated prediction for complex systems. J. Am. Stat. Assoc. 101:1132–43 [Google Scholar]
18. Goldstein M, Rougier JC. 2009. Reified Bayesian modelling and inference for physical systems (with discussion). J. Stat. Plan. Inf. 139:1221–56 [Google Scholar]
19. Goldstein M, Wooff DA. 2007. Bayes Linear Statistics: Theory & Methods Chichester, UK: John Wiley & Sons
20. Golub GH, Van Loan CF. 1996. Matrix Computations Baltimore, MD: Johns Hopkins Univ. Press, 3rd ed..
21. Guttorp P. 2014. Statistics and climate. Annu. Rev. Stat. Appl. 187–101
22. Hacking I. 2001. An Introduction to Probability and Inductive Logic Cambridge, UK: Cambridge Univ. Press
23. Hasselmann K. 1976. Stochastic climate models. Part I: theory. Tellus 28:473–85 [Google Scholar]
24. Hegerl GC, Zwiers FW. 2011. Use of models in detection and attribution of climate change. WIREs Clim. Change 2:570–91 [Google Scholar]
25. Irvine PJ, Ridgwell A, Lunt DJ. 2011. Climatic effects of surface albedo geoengineering. J. Geophys. Res. 116:D24112 [Google Scholar]
26. Jones PD, Briffa KR, Osborn TJ, Lough JM, van Ommen TD. et al. 2009. High-resolution palaeoclimatology of the last millennium: a review of current status and future prospects. Holocene 19:13–49 [Google Scholar]
27. Kennedy MC, O'Hagan A. 2001. Bayesian calibration of computer models (with discussion). J. R. Stat. Soc. B 63:425–64 [Google Scholar]
28. Knutti R, Masson D, Gettelman A. 2013. Climate model genealogy: generation CMIP5 and how we got there. Geophys. Res. Lett. 40:1194–99 [Google Scholar]
29. Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ. et al. 2010. Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev. Geophys. 48:RG3003 [Google Scholar]
30. Mardia KV, Kent JT, Bibby JM. 1979. Multivariate Analysis London: Academic
31. Masson D, Knutti R. 2011. Climate model genealogy. Geophys. Res. Lett. 38:L08703 [Google Scholar]
32. Mauritsen T, Stevens B, Roeckner E, Cruger T, Esch M. et al. 2012. Tuning the climate of a global model. J. Adv. Model. Earth Syst. 4:M00A01 [Google Scholar]
33. McGuffie K, Henderson-Sellers A. 2005. A Climate Modelling Primer Chichester, UK: John Wiley & Sons, 3rd ed..
34. McNeall DJ, Challenor PG, Gattiker JR, Stone EJ. 2013. The potential of an observational data set for calibration of a computationally expensive computer model. Geosci. Model Dev. Discuss. 6:2369–401 [Google Scholar]
35. McWilliams JC. 2007. Irreducible imprecision in atmospheric and oceanic simulations. Proc. Natl. Acad. Sci. USA 104:218709–13 [Google Scholar]
36. Murphy JM, Clark R, Collins M, Jackson C, Rodwell M. et al. 2011. Perturbed parameter ensembles as a tool for sampling model uncertainties and making climate projections. Proc. ECMWF Workshop Represent. Model Uncertain. Error Numer. Weather Clim. Predict. Models, 20–24 June 2011183–208 Reading, UK: Eur. Cent. Medium-Range Weather Forecast http://www.ecmwf.int/publications/library/ecpublications/_pdf/workshop/2011/Model_uncertainty/Murphy.pdf [Google Scholar]
37. Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ. et al. 2004. Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430:768–72 [Google Scholar]
38. Nicholls RJ. 2011. Planning for the impacts of sea-level rise. Oceanography 24:2144–57 [Google Scholar]
39. Oreskes N, Conway EM. 2010. Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming New York: Bloomsbury
40. Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE. 2007. Climate Change 2007: Impacts, Adaptation and Vulnerability. Cambridge, UK: Cambridge Univ. Press
41. Peixoto JP, Oort AH. 1992. Physics of Climate New York: Springer
42. Petoukhov V, Ganapolski A, Brovkin V, Claussen M, Eliseev A. et al. 2000. CLIMBER-2: a climate system model of intermediate complexity. Part I: model description and performance for present climate. Clim. Dyn. 16:1–17 [Google Scholar]
43. Randall DA, Wood RA, Bony S, Colman R, Fichefet T. et al. 2007. Climate models and their evaluation. See Solomon et al. 2007 589–662
44. Rockström J, Steffen W, Noone K, Persson Å, Chapin FS III. et al. 2009. A safe operating space for humanity. Nature 461:472–75 [Google Scholar]
45. Rougier JC. 2007. Probabilistic inference for future climate using an ensemble of climate model evaluations. Clim. Change 81:247–64 [Google Scholar]
46. Rougier JC. 2008a. Annotated bibliography: climate change detection and attribution. ISBA Bull. 15:43–6 http://bayesian.org/sites/default/files/fm/bulletins/0812.pdf [Google Scholar]
47. Rougier JC. 2008b. Efficient emulators for multivariate deterministic functions. J. Comput. Graph. Stat. 17:4827–43 [Google Scholar]
48. Rougier JC, Goldstein M, House L. 2013. Second-order exchangeability analysis for multi-model ensembles. J. Am. Stat. Assoc. 108852–63
49. Rougier JC, Guillas S, Maute A, Richmond A. 2009a. Expert knowledge and multivariate emulation: the thermosphere-ionosphere electrodynamics general circulation model (TIE-GCM). Technometrics 51:4414–24 [Google Scholar]
50. Rougier JC, Sexton DMH. 2007. Inference in ensemble experiments. Philos. Trans. R. Soc. A 365:2133–43 [Google Scholar]
51. Rougier JC, Sexton DMH, Murphy JM, Stainforth D. 2009b. Analysing the climate sensitivity of the HadSM3 climate model using ensembles from different but related experiments. J. Clim. 22:133540–57 [Google Scholar]
52. Rubin DB. 1984. Bayesianly justifiable and relevant frequency calculations for the applied statistician. Ann. Stat. 12:41151–72 [Google Scholar]
53. Salt JD. 2008. The seven habits of highly defective simulation projects. J. Simul. 2:155–61 [Google Scholar]
54. Sansó B, Forest C, Zantedeschi D. 2008. Inferring climate system properties using a computer model (with discussion). Bayesian Anal. 3:11–62 [Google Scholar]
55. Santner TJ, Williams BJ, Notz WI. 2003. The Design and Analysis of Computer Experiments New York: Springer
56. Sexton DMH, Murphy J, Collins M, Webb MJ. 2012. Multivariate probabilistic projections using imperfect climate models part I: outline of methodology. Clim. Dyn. 38:11–122513–42 [Google Scholar]
57. Solomon S, Qin D, Manning M, Chen Z, Marquis M et al. 2007. Climate Change 2007: The Physical Science Basis. Cambridge, UK: Cambridge Univ. Press
58. Stull RB. 2000. Meteorology for Scientists and Engineers Pacific Grove, CA: Brooks/Cole, 2nd ed..
59. Tokmakian R, Challenor P. 2013. Uncertainty in modeled upper ocean heat content change. Clim. Dyn. In press. doi:10.1007/s00382-013-1709-9
60. Valdes P. 2011. Built for stability. Nat. Geosci. 4:414–16 [Google Scholar]
61. Vernon I, Goldstein M, Bower RG. 2010. Galaxy formation: a Bayesian uncertainty analysis. Bayesian Anal. 5:4619–70 [Google Scholar]
62. von Storch H, Zwiers FW. 1999. Statistical Analysis in Climate Research Cambridge, UK: Cambridge Univ. Press
63. Watanabe M, Suzuki T, O'ishi R, Komuro Y, Watanabe S. et al. 2010. Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J. Clim. 23:6312–35 [Google Scholar]
64. Williamson D, Goldstein M. 2012. Bayesian policy support for adaptive strategies using computer models for complex physical systems. J. Oper. Res. Soc. 63:1021–33 [Google Scholar]
65. Williamson D, Goldstein M, Blaker A. 2012. Fast linked analyses for scenario-based hierarchies. Appl. Stat. 61:5665–91 [Google Scholar]