Towards the probabilistic Earth-system simulator: A vision for the future of climate and weather prediction

Quarterly Journal of the Royal Meteorological Society

Volumn 138, Issue 665, 2012, Pages 841-861

  Palmer, T N ^a,b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b EUROPEAN CENTRE FOR MEDIUM RANGE WEATHER FORECASTS   (United Kingdom)

Author keywords

Earth system simulation; Ensemble prediction; Stochastic parametrization

Indexed keywords

AIRBUS CONSORTIUMS; CLIMATE MODELLING; CLIMATE PREDICTION; CLIMATIC PROCESS; COMPUTATIONAL RESOURCES; COMPUTATIONAL SCIENCE; DYNAMICAL CORE; EARTH'S CLIMATE; EARTH-SYSTEM SIMULATION; ECONOMIES OF SCALE; ENSEMBLE PREDICTION; ENSEMBLE PREDICTION SYSTEMS; FORECAST ERRORS; GENE POOLS; HIGH RESOLUTION; HIGHER RESOLUTION; NUMERICAL WEATHER PREDICTION; SCIENCE AND TECHNOLOGY; STOCHASTIC PARAMETRIZATION; STOCHASTIC PROCESSING; SYSTEMIC FAILURE; TIME-SCALES; VERIFICATION DATA; WEATHER FORECASTS; WEATHER PREDICTION;

CLIMATE CHANGE; EQUATIONS OF MOTION; ESTIMATION; GENES; LAKES; METEOROLOGY; RANDOM PROCESSES; SIMULATORS; STOCHASTIC SYSTEMS; UNCERTAINTY ANALYSIS; WEATHER FORECASTING;

EARTH (PLANET);

ACCURACY ASSESSMENT; CLIMATE MODELING; ENSEMBLE FORECASTING; MATHEMATICAL ANALYSIS; NUMERICAL MODEL; PARAMETERIZATION; PROBABILITY; QUANTITATIVE ANALYSIS; RELIABILITY ANALYSIS; SCIENCE AND TECHNOLOGY; STOCHASTICITY; UNCERTAINTY ANALYSIS; WEATHER FORECASTING;

EID: 84862160611     PISSN: 00359009     EISSN: 1477870X     Source Type: Journal    
DOI: 10.1002/qj.1923     Document Type: Article

References (79)

1. Arnold H, Moroz I, Palmer TN. 2012. Stochastic parametrisations and model uncertainty in the Lorenz '96 system. Phil. Trans. R. Soc. (submitted).
2. Arrhenius S. 1896. On the influence of carbonic acid in the air on the temperature of the ground. The London, Edinburgh and Dublin Philosophical Magazine and Journal of Science Series 41: (reprinted in The Warming Papers, 2011, Archer D, Pierrehumbert R (eds). Wiley-Blackwell: Chichester).
3. Bengtsson-Sedlar L, Körnich H, Källén E, Svensson G. 2011. Large-scale dynamical response to sub-grid scale organization provided by cellular automata. J. Atmos. Sci. 68: 3132-3144.
4. Berner J, Shutts GJ, Leutbecher M, Palmer TN. 2009. A spectral stochastic kinetic energy backscatter scheme and its impact on flow dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci. 66: 603-626.
5. Berner J, Jung T, Palmer TN. 2012. Systematic error model error: the impact of increased horizontal resolution versus improved stochastic and deterministic parameterizations. J. Climate (in press).
6. Bjerknes V. 1904. Das Problem der Wettervorhersage betrachter von Standpunkte der Mechanik und der Physik. Meteorol. Z. 21: 1-7.
7. Bonavita M. 2011. Ensemble of data assimilations and estimation of uncertainty. In ECMWF 2011 Seminar Proceedings.
8. Buizza R, Palmer TN. 1995. The singular vector structure of the atmospheric global circulation. J. Atmos. Sci. 52: 1434-1456.
9. Buizza R, Miller M, Palmer TN. 1999. Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Q. J. R. Meteorol. Soc. 125: 2887-2908.
10. Doblas-Reyes FJ, Balmaseda MA, Weisheimer A, Palmer TN. 2011. Decadal climate prediction with the ECMWF coupled forecast system: impact of ocean observations. J. Geophys. Res. 116: D19111, DOI: 10.1029/2010JD015394.
11. Frederiksen JS. 1999. Subgrid-scale parameterizations of eddy-topographic force, eddy viscosity, and stochastic backscatter for flow over topography. J. Atmos. Sci. 56: 1481-1494.
12. Frederiksen JS, Kepert SM. 2006. Dynamical subgrid-scale parameterizations from direct numerical simulations. J. Atmos. Sci. 63: 3006-3019.
13. Frederiksen JS, Davies AG. 1997. Eddy viscosity and stochastic backscatter parameterizations on the sphere for atmospheric circulation models. J. Atmos. Sci. 54: 2475-2492.
14. Frenkel Y, Majda AJ, Khouider B. 2012. Using the stochastic multicloud model to improve tropical convective parametrization: a paradigm example. J. Atmos. Sci. 69: 1080-1105.
15. Goldstein M, Rougier JC. 2004. Probabilistic formulations for transferring inferences from mathematical models to physical systems. SIAM J. Sci. Comput. 26: 467-487.
16. Hansen J, Russell G, Rind D, Stone P, Lacis A, Lebedeff S, Ruedy R, Travis L. 1983. Efficient three-dimensional global models for climate studies. Mon. Wea. Rev. 111: 609-662.
17. Hasselmann K. 1976. Stochastic climate models, Part I: Theory. Tellus 28: 473-485.
18. Hazeleger W, Severijns C, Semmler T, Ştefǎnescu S, Yang S, Wang X, Wyser K, Dutra E, Baldasano JM, Bintanja R, Bougeault P, Caballero R, Ekman AML, Christensen JH, van den Hurk B, Jimenez P, Jones C, Kållberg P, Koenigk T, McGrath R, Miranda P, Van Noije T, Palmer T, Parodi JA, Schmith T, Selten F, Storelvmo T, Sterl A, Tapamo H, Vancoppenolle M, Viterbo P, Willén U. 2010. EC-Earth: a seamless Earth-system prediction approach in action. Bull. Am. Meteorol. Soc. 91: 1357-1363.
19. Isaksen L, Bonavita M, Buizza R, Fisher M, Haseler J, Leutbecher M, Raynaud L. 2010. 'Ensemble of data assimilations at ECMWF'. ECMWF Technical Memorandum 636, Reading, UK.
20. Jakob C. 2010. Accelerating progress in global atmospheric model development through improved parametrizations. Bull. Am. Meteorol. Soc. 91: 869-875.
21. Jung T, Palmer TN, Shutts GJ. 2005. Influence of stochastic parametrization on the frequency of occurrence of North Pacific weather regimes in the ECMWF model. Geophys. Res. Lett. 32: L23811, DOI: 10.1029/2005GL024248.
22. Jung T, Miller MJ, Palmer TN, Towers P, Wedi N, Achuthavarier D, Adams JM, Altshuler EL, Cash BA, Kinter JL III, Marx L, Stan C, Hodges KI. 2012. High-resolution global climate simulations with the ECMWF model in Project Athena: experimental design, model climate and seasonal forecast skill. J. Climate (in press).
23. Khouider B, Majda AJ, Katsoulakis MA. 2003. Coarse-grained stochastic models for tropical convection and climate. Proc. Natl Acad. Sci. USA 100: 11941-11946.
24. Kinter JL III, Cash B, Achuthavarier D, Adams J, Altshuler E, Dirmeyer P, Doty B, Huang B, Marx L, Manganello J, Stan C, Wakefield T, Jin E, Palmer T, Hamrud M, Jung T, Miller M, Towers P, Wedi N, Satoh M, Tomita H, Kodama C, Nasuno T, Oouchi K, Taniguchi H, Andrews P, Baer T, Ezell M, Halloy C, John D, Loftis B, Mohr R, Wong K. 2012. Revolutionizing climate modeling-Project Athena: a multi-institutional international collaboration. Bull. Am. Meteorol. Soc. (in press).
25. Lander J, Hoskins BJ. 1997. Believable scales and parametrizations in a spectral transform model. Mon. Weather Rev. 125: 292-303.
26. Lewis JM. 2005. Roots of ensemble forecasting. Mon. Weather Rev. 133: 1865-1885.
27. Lilly DK. 1973. Lectures in sub-synoptic scales of motions and two-dimensional turbulence. In Dynamic Meteorology, Morel P (ed.). Reidel: Boston, MA; pp. 353-418.
28. Lin JW-B, Neelin JD. 2003. Toward stochastic deep convective parameterization in general circulation models. Geophys. Res. Lett. 30: 1162, DOI: 10:1029/2002GL016203.
29. Lindborg E. 2007. Horizontal wavenumber spectra of vertical vorticity and horizontal divergence in the upper troposphere and lower stratosphere. J. Atmos. Sci. 64: 1017-1025.
30. Lorenz EN. 1969. The predictability of a flow which possesses many scales of motion. Tellus 21: 289-307.
31. Lorenz EN. 1975. Climatic predictability. In The Physical Basis of Climate and Climate Modelling. WMO GARP Publication Series No 16, World Meteorological Organisation: Geneva.
32. Lorenz EN. 1996. Predictability: a problem partly solved. In Proceedings: Seminar on Predictability. ECMWF. Reprinted in Palmer TN, Hagedorn R. 2006. Predictability of Weather and Climate. Cambridge University Press: Cambridge, UK; 40-48.
33. Majda AJ, Bertozzi AL. 2001. Vorticity and Incompressible Flow. Cambridge Texts in Applied Mathematics, Cambridge University Press: Cambridge, UK.
34. 2 concentration on the climate of a general circulation model. J. Atmos. Sci. 32: 3-15.
35. Masson D, Knutti R. 2011. Climate model genealogy. Geophys. Res. Lett. 38: L08703, DOI: 10.1029/2011GL046864.
36. Matsueda M, Palmer TN. 2011. Accuracy of climate change predictions using high resolution simulations as surrogates of truth. Geophys. Res. Lett. 38: L05803, DOI:10.1029/2010GL046618.
37. Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE. 2007. The WCRP CMIP3 multimodel dataset. Bull. Am. Meteorol. Soc. 42: 950-960.
38. Murphy AH. 1969. Measures of the utility of probabilistic predictions in cost-loss ratio decision situations in which knowledge of the cost-loss ratios is incomplete. J. Appl. Meteorol. 8: 863-873.
39. Murphy JM, Palmer TN. 1986. Experimental monthly forecasts for the United Kingdom. Part II: A real-time long-range forecast by an ensemble of numerical integrations. Meteorol. Mag. 115: 337-344.
40. Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M, Stainforth DA. 2004. Quantification of modelling uncertainties in a large ensemble of 265 climate change simulations. Nature 430: 768-772.
41. Nastrom GD, Gage KS. 1985. A climatology of atmosphere wavenumber spectra of wind and temperature observed by commercial aircraft. J. Atmos. Sci. 43: 857-870.
42. Palem KV. 2005. Energy aware computing through probabilistic switching: a study of limits. IEEE Trans. Comput. 54: 1123-1137.
43. Palmer TN. 1997. 'On parametrising scales that are only somewhat smaller than the smallest resolved scale, with application to convection and orography'. In Proceedings of the ECMWF Workshop on New Insights and Approaches to Convective Parametrisation. ECMWF: Reading, UK; 328-337.
44. Palmer TN. 1998. Climate change from a nonlinear dynamical perspective. J. Climate 12: 575-591.
45. Palmer TN. 2001. A nonlocal dynamical perspective on model error: a proposal for nonlocal stochastic-dynamic parametrization in weather and climate prediction models. Q. J. R. Meteorol. Soc. 127: 279-304.
46. Palmer TN. 2011. A CERN for climate change. Physics World 24: 14-15.
47. Palmer TN, Webster PJ. 1993. 'Towards a unified approach to climate and weather prediction'. In Proceedings of 1st Demetra Conference on Climate Change. European Community Press: Brussels.
48. Palmer TN, Weisheimer A. 2011. Diagnosing the causes of bias in climate models: why is it so hard? Geophys. Astro. Fluid 105: 351-365.
49. Palmer TN, Doblas-Reyes FJ, Weisheimer A, Rodwell MJ. 2008Towards seamless prediction: calibration of climate change projections using seasonal forecasts. Bull. Am. Meteorol. Soc. 89: 459-470.
50. Palmer TN, Buizza R, Doblas-Reyes F, Jung T, Leutbecher M, Shutts GJ, Steinheimer M, Weisheimer A. 2009. 'Stochastic parametrization and model uncertainty'. Technical Report 598, European Centre for Medium-Range Weather Forecasts: Reading, UK.
51. Pennell C, Reichler T. 2011. On the effective number of climate models. J. Climate 24: 2358-2367.
52. Phillips NA. 1956. The general circulation of the atmosphere: a numerical experiment. Q. J. R. Meteorol. Soc. 82: 123-164.
53. Plant RS, Craig CG. 2008. A stochastic parameterization for deep convection based on equilibrium statistics. J. Atmos. Sci. 65: 87-105.
54. Pollard D. 2002. A User's Guide to Measure Theoretic Probability. Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press: Cambridge, UK.
55. Rodwell MJ, Palmer TN. 2007. Using numerical weather prediction to assess climate models. Q. J. R. Meteorol. Soc. 133: 129-146.
56. Rossow WL, Cairns B. 1995. Monitoring changes of clouds. Climate Change 31: 305-347.
57. Rotunno R, Snyder C. 2008. A generalization of Lorenz's model for the predictability of flows with many scales of motion. J. Atmos. Sci. 65: 1063-1076.
58. Scaife AA, Spangehl T, Fereday D, Cubasch U, Langematz U, Akiyoshi H, Bekki S, Braesicke P, Butchart N, Chipperfield M, Gettelman A, Hardiman S, Michou M, Rozanov E, Shepherd TG. 2011. Climate change and stratosphere-troposphere interaction. Clim. Dynam. DOI 10.1007/s00382-011-1080-7.
59. Schertzer D, Lovejoy S. 1993. 'Lecture notes: nonlinear variability in geophysics 3: Scaling and mulitfractal processes in geophysics. Institut d'Etudes Scientifique de Cargèse, Cargèse, France.
60. Schertzer D, Lovejoy S. 2004. Uncertainty and unpredictability in geophysics: chaos and multifractal insights. State of the planet: frontiers and challenges in geophysics. Geophys. Monogr. Series 150: 317-334.
61. Shukla J, Palmer TN, Hagedorn R, Hoskins B, Kinter J, Marotzke J, Miller M, Slingo J. 2010. Toward a new generation of world climate research and computing facilities. Bull. Am. Meteorol. Soc. 91: 1407-1412.
62. Shutts G. 2005. A kinetic energy backscatter algorithm for use in ensemble prediction systems. Q. J. R. Meteorol. Soc. 131: 3079-3102.
63. Shutts GJ, Palmer TN. 2007. Convective forcing fluctuations in a cloud-resolving model: relevance to the stochastic parametrzation problem. J. Climate 20: 187-202.
64. Simmons AJ, Hollingsworth A. 2002. Some aspects in the improvement in skill in numerical weather prediction. Q. J. R. Meteorol. Soc. 128: 647-677.
65. Slingo J, Palmer TN. 2011. Uncertainty in weather and climate prediction. Phil. Trans. R. Soc. A 369: 4751-4767.
66. Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds). 2007. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007. Cambridge University Press: Cambridge, UK.
67. Stainforth D, Aina T, Christensen C, Collins M, Faull N, Frame DJ, Kettleborough JA, Knight S, Martin A, Murphy JM, Piani C, Sexton D, Smith LA, Spicer RA, Thorpe AJ, Allen MR. 2005. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433: 403-406.
68. Stephens GL. 2005. Cloud feedbacks in the climate system: a critical review. J. Climate 18: 237-273.
69. Straus DM, Corti S, Molteni F. 2007. Circulation regimes: chaotic variability versus SST-forced predictability. J. Climate 20: 2251-2272.
70. Thuburn J, Kent J, Wood N. 2011. 'Energy and enstrophy cascades in numerical models'. In Proceedings of ECMWF Workshop on Representing Model Error in Weather and Climate prediction.
71. Tompkins AM, Berner J. 2008. A stochastic convective approach to account for model uncertainty due to unresolved humidity variability. J. Geophys. Res. 113: D18101.
72. WCRP. 2005. 'The World Climate Research Programme Strategic Framework, 2005-2015 Coordinated Observation and Prediction of the Earth System (COPES)'. Technical Document No. 1291 (WCRP-123), World Meteorological Organization: Geneva.
73. Webster PJ, Jian J, Hopson TM, Hoyos CD, Agudelo PA, Chang H-R, Curry JA, Grossman RL, Palmer TN, Subbiah AR. 2010. Extended-range probabilistic forecasts of Ganges and Brahmaputra floods. Bull. Am. Meteorol. Soc. 91: 1493-1514.
74. Weisheimer A, Doblas-Reyes FJ, Palmer TN, Alessandri A, Arribas A, Deque M, Keenlyside N, MacVean M, Navarra A, Rogel P. 2009. ENSEMBLES: a new multi-model ensemble for seasonal-to-annual predictions: skill and progress beyond DEMETER in forecasting tropical Pacific SSTs. Geophys. Res. Lett. 36: L21711, DOI: 10.1029/2009GL040896.
75. Weisheimer A, Palmer TN, Doblas-Reyes FJ. 2011. Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles. Geophys. Res. Lett. 38: L16703, DOI: 10.1029/2011GL048123.
76. Williams K, Webb M. 2009. A quantitative performance assessment of cloud regimes in climate models. Clim. Dynam. 33: 141-157.
77. Wilks DS. 2005. Effects of stochastic parametrizations in the Lorenz '96 system. Q. J. R. Meteorol. Soc. 131: 389-407.
78. Wilks DS. 2006. Statistical Methods in the Atmospheric Sciences. Vol. 1 of International Geophysics Series, Elsevier: Amsterdam.
79. Woollings T, Hoskins BJ, Blackburn M, Berrisford P. 2008. A new Rossby wave-breaking interpretation of the North Atlantic Oscillation. J. Atmos. Sci. 65: 609-626.