Skip to main content

2018 | OriginalPaper | Buchkapitel

Harnessing Butterflies: Theory and Practice of the Stochastic Seasonal to Interannual Prediction System (StocSIPS)

verfasst von : S. Lovejoy, L. Del Rio Amador, R. Hébert

Erschienen in: Advances in Nonlinear Geosciences

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The atmosphere is governed by continuum mechanics and thermodynamics yet simultaneously obeys statistical turbulence laws. Up until its deterministic predictability limit (τ w ≈ 10 days), only general circulation models (GCMs) have been used for prediction; the turbulent laws being still too difficult to exploit. However, beyond τ w —in macroweather—the GCMs effectively become stochastic with internal variability fluctuating about the model—not the real world—climate and their predictions are poor. In contrast, the turbulent macroweather laws become advantageously notable due to (a) low macroweather intermittency that allows for a Gaussian approximation, and (b) thanks to a statistical space-time factorization symmetry that (for predictions) allows much decoupling of the strongly correlated spatial degrees of freedom. The laws imply new stochastic predictability limits. We show that pure macroweather—such as in GCMs without external forcings (control runs)—can be forecast nearly to these limits by the ScaLIng Macroweather Model (SLIMM) that exploits huge system memory that forces the forecasts to converge to the real world climate.
To apply SLIMM to the real world requires pre-processing to take into account anthropogenic and other low frequency external forcings. We compare the overall Stochastic Seasonal to Interannual Prediction System (StocSIPS, operational since April 2016) with a classical GCM (CanSIPS) showing that StocSIPS is superior for forecasts 2 months and further in the future, particularly over land. In addition, the relative advantage of StocSIPS increases with forecast lead time.
In this chapter we review the science behind StocSIPS and give some details of its implementation and we evaluate its skill both absolute and relative to CanSIPS.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
Zurück zum Zitat Baillie, R.T., and S.-K. Chung. 2002. Modeling and forecasting from trend-stationary long memory models with applications to climatology. International Journal of Forecasting 18: 215–226.CrossRef Baillie, R.T., and S.-K. Chung. 2002. Modeling and forecasting from trend-stationary long memory models with applications to climatology. International Journal of Forecasting 18: 215–226.CrossRef
Zurück zum Zitat Biagini, F., Y. Hu, B. Øksendal, and T. Zhang. 2008. Stochastic calculus for fractional Brownian motion and applications. London: Springer-Verlag.CrossRef Biagini, F., Y. Hu, B. Øksendal, and T. Zhang. 2008. Stochastic calculus for fractional Brownian motion and applications. London: Springer-Verlag.CrossRef
Zurück zum Zitat Chen, W., S. Lovejoy, and J.P. Muller. 2016. Mars’ atmosphere: The sister planet, our statistical twin. Journal of Geophysical Research—Atmospheres 121: 11968–11988. doi:10.1002/2016JD025211.CrossRef Chen, W., S. Lovejoy, and J.P. Muller. 2016. Mars’ atmosphere: The sister planet, our statistical twin. Journal of Geophysical Research—Atmospheres 121: 11968–11988. doi:10.​1002/​2016JD025211.CrossRef
Zurück zum Zitat Del Rio Amador, L. 2017. The stochastic seasonal to interannual prediction system. Montreal: McGill University. Del Rio Amador, L. 2017. The stochastic seasonal to interannual prediction system. Montreal: McGill University.
Zurück zum Zitat Garcıa-Serrano, J., and F. J. Doblas-Reyes (2012), On the assessment of near-surface global temperature and North Atlantic multi-decadal variability in the ENSEMBLES decadal hindcast, Climate Dynamics, 39, 2025–2040 doi: 10.1007/s00382-012-1413-1. Garcıa-Serrano, J., and F. J. Doblas-Reyes (2012), On the assessment of near-surface global temperature and North Atlantic multi-decadal variability in the ENSEMBLES decadal hindcast, Climate Dynamics, 39, 2025–2040 doi: 10.​1007/​s00382-012-1413-1.
Zurück zum Zitat Gripenberg, G., and I. Norros. 1996. On the Prediction of Fractional Brownian Motion. Journal of Applied Probability 33: 400–410.CrossRef Gripenberg, G., and I. Norros. 1996. On the Prediction of Fractional Brownian Motion. Journal of Applied Probability 33: 400–410.CrossRef
Zurück zum Zitat Guemas, V., F.J. Doblas-Reyes, I. Andreu-Burillo, and M. Asif. 2013. Retrospective prediction of the global warming slowdown in the past decade. Nature Climate Change 3: 649–653.CrossRef Guemas, V., F.J. Doblas-Reyes, I. Andreu-Burillo, and M. Asif. 2013. Retrospective prediction of the global warming slowdown in the past decade. Nature Climate Change 3: 649–653.CrossRef
Zurück zum Zitat Hasselmann, K. 1976. Stochastic climate models, part I: Theory. Tellus 28: 473–485.CrossRef Hasselmann, K. 1976. Stochastic climate models, part I: Theory. Tellus 28: 473–485.CrossRef
Zurück zum Zitat Hébert, R., and S. Lovejoy. 2015. The runaway Green’s function effect: Interactive comment on “Global warming projections derived from an observation-based minimal model” by K. Rypdal. Earth System Dynamics Discovery 6: C944–C953. Hébert, R., and S. Lovejoy. 2015. The runaway Green’s function effect: Interactive comment on “Global warming projections derived from an observation-based minimal model” by K. Rypdal. Earth System Dynamics Discovery 6: C944–C953.
Zurück zum Zitat Hebert, R., S. Lovejoy, and A. de Vernal. 2017. A scaling model for the forced climate variability in the anthropocene. Climate Dynamics. (in preparation). Hebert, R., S. Lovejoy, and A. de Vernal. 2017. A scaling model for the forced climate variability in the anthropocene. Climate Dynamics. (in preparation).
Zurück zum Zitat Hirchoren, G.A., and D.S. Arantes. 1998. Predictors for the discrete time fractional Gaussian processes. In Telecommunications symposium. ITS '98 proceedings, SBT/IEEE international, 49–53. Sao Paulo: IEEE.CrossRef Hirchoren, G.A., and D.S. Arantes. 1998. Predictors for the discrete time fractional Gaussian processes. In Telecommunications symposium. ITS '98 proceedings, SBT/IEEE international, 49–53. Sao Paulo: IEEE.CrossRef
Zurück zum Zitat Hirchoren, G.A., and C.E. D’attellis. 1998. Estimation of fractal signals, using wavelets and filter banks. IEEE Transactions on Signal Processing 46 (6): 1624–1630.CrossRef Hirchoren, G.A., and C.E. D’attellis. 1998. Estimation of fractal signals, using wavelets and filter banks. IEEE Transactions on Signal Processing 46 (6): 1624–1630.CrossRef
Zurück zum Zitat Kolesnikov, V.N., and A.S. Monin. 1965. Spectra of meteorological field fluctuations. Izvestiya, Atmospheric and Oceanic Physics 1: 653–669. Kolesnikov, V.N., and A.S. Monin. 1965. Spectra of meteorological field fluctuations. Izvestiya, Atmospheric and Oceanic Physics 1: 653–669.
Zurück zum Zitat Lean, J.L., and D.H. Rind. 2008. How natural and anthropogenic influences alter global and regional surface temperatures: 1889 to 2006. Geophysical Research Letters 35: L18701. doi:10.1029/2008GL034864.CrossRef Lean, J.L., and D.H. Rind. 2008. How natural and anthropogenic influences alter global and regional surface temperatures: 1889 to 2006. Geophysical Research Letters 35: L18701. doi:10.​1029/​2008GL034864.CrossRef
Zurück zum Zitat Lilley, M., S. Lovejoy, D. Schertzer, K.B. Strawbridge, and A. Radkevitch. 2008. Scaling turbulent atmospheric stratification. Part II: Empirical study of the the stratification of the intermittency. Quarterly Journal of the Royal Meteorological Society 134: 301–315. doi:10.1002/qj.1202.CrossRef Lilley, M., S. Lovejoy, D. Schertzer, K.B. Strawbridge, and A. Radkevitch. 2008. Scaling turbulent atmospheric stratification. Part II: Empirical study of the the stratification of the intermittency. Quarterly Journal of the Royal Meteorological Society 134: 301–315. doi:10.​1002/​qj.​1202.CrossRef
Zurück zum Zitat ———. 2017. How accurately do we know the temperature of the surface of the earth? Climate Dynamics. (in press). ———. 2017. How accurately do we know the temperature of the surface of the earth? Climate Dynamics. (in press).
Zurück zum Zitat Lovejoy, S., and M.I.P. de Lima. 2015. The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models. Chaos 25: 075410. doi:10.1063/1.4927223.CrossRef Lovejoy, S., and M.I.P. de Lima. 2015. The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models. Chaos 25: 075410. doi:10.​1063/​1.​4927223.CrossRef
Zurück zum Zitat Lovejoy, S., and D. Schertzer. 1986. Scale invariance in climatological temperatures and the local spectral plateau. Annales Geophysicae 4B: 401–410. Lovejoy, S., and D. Schertzer. 1986. Scale invariance in climatological temperatures and the local spectral plateau. Annales Geophysicae 4B: 401–410.
Zurück zum Zitat ———. 2007. Scale, scaling and multifractals in geophysics: Twenty years on. In Nonlinear dynamics in geophysics, ed. J.E.A.A. Tsonis. New York, NY: Elsevier. ———. 2007. Scale, scaling and multifractals in geophysics: Twenty years on. In Nonlinear dynamics in geophysics, ed. J.E.A.A. Tsonis. New York, NY: Elsevier.
Zurück zum Zitat ———. 2013. The weather and climate: Emergent laws and multifractal cascades., 496 pp. Cambridge: Cambridge University Press.CrossRef ———. 2013. The weather and climate: Emergent laws and multifractal cascades., 496 pp. Cambridge: Cambridge University Press.CrossRef
Zurück zum Zitat Lovejoy, S., D. Schertzer, M. Lilley, K.B. Strawbridge, and A. Radkevitch. 2008. Scaling turbulent atmospheric stratification. Part I: Turbulence and waves. Quarterly Journal of the Royal Meteorological Society 134: 277–300. doi:10.1002/qj.201.CrossRef Lovejoy, S., D. Schertzer, M. Lilley, K.B. Strawbridge, and A. Radkevitch. 2008. Scaling turbulent atmospheric stratification. Part I: Turbulence and waves. Quarterly Journal of the Royal Meteorological Society 134: 277–300. doi:10.​1002/​qj.​201.CrossRef
Zurück zum Zitat Mandelbrot, B.B., and J.W. Van Ness. 1968. Fractional Brownian motions, fractional noises and applications. SIAM Review 10: 422–450.CrossRef Mandelbrot, B.B., and J.W. Van Ness. 1968. Fractional Brownian motions, fractional noises and applications. SIAM Review 10: 422–450.CrossRef
Zurück zum Zitat Merryfield, W.J., B. Denis, J.-S. Fontecilla, W.-S. Lee, S. Kharin, J. Hodgson, and B. Archambault. 2011. The Canadian Seasonal to Interannual Prediction System (CanSIPS): An overview of its design and operational implementationRep., 51pp. Environment Canada. Merryfield, W.J., B. Denis, J.-S. Fontecilla, W.-S. Lee, S. Kharin, J. Hodgson, and B. Archambault. 2011. The Canadian Seasonal to Interannual Prediction System (CanSIPS): An overview of its design and operational implementationRep., 51pp. Environment Canada.
Zurück zum Zitat Panofsky, H.A., and I. Van der Hoven. 1955. Spectra and cross-spectra of velocity components in the mesometeorlogical range. Quarterly Journal of the Royal Meteorological Society 81: 603–606.CrossRef Panofsky, H.A., and I. Van der Hoven. 1955. Spectra and cross-spectra of velocity components in the mesometeorlogical range. Quarterly Journal of the Royal Meteorological Society 81: 603–606.CrossRef
Zurück zum Zitat Papoulis, A. 1965. Probability, random variables and stochastic processes. New York, NY: Mc Graw Hill. Papoulis, A. 1965. Probability, random variables and stochastic processes. New York, NY: Mc Graw Hill.
Zurück zum Zitat Penland, C. 1996. A stochastic model of IndoPacific sea surface temperature anomalies. Physica D 98: 534–558.CrossRef Penland, C. 1996. A stochastic model of IndoPacific sea surface temperature anomalies. Physica D 98: 534–558.CrossRef
Zurück zum Zitat Penland, C., and P.D. Sardeshmuhk. 1995. The optimal growth of tropical sea surface temperature anomalies. Journal of Climate 8: 1999–2024.CrossRef Penland, C., and P.D. Sardeshmuhk. 1995. The optimal growth of tropical sea surface temperature anomalies. Journal of Climate 8: 1999–2024.CrossRef
Zurück zum Zitat Radkevitch, A., S. Lovejoy, K.B. Strawbridge, D. Schertzer, and M. Lilley. 2008. Scaling turbulent atmospheric stratification. Part III: Empirical study of space-time stratification of passive scalars using lidar data. Quarterly Journal of the Royal Meteorological Society 134: 317–335. doi:10.1002/qj.1203.CrossRef Radkevitch, A., S. Lovejoy, K.B. Strawbridge, D. Schertzer, and M. Lilley. 2008. Scaling turbulent atmospheric stratification. Part III: Empirical study of space-time stratification of passive scalars using lidar data. Quarterly Journal of the Royal Meteorological Society 134: 317–335. doi:10.​1002/​qj.​1203.CrossRef
Zurück zum Zitat Ragone, F., V. Lucarini, and F. Lunkeit. 2015. A new framework for climate sensitivity and prediction: A modelling perspective. Climate Dynamics 46: 1459–1471. doi:10.1007/s00382-015-2657-3.CrossRef Ragone, F., V. Lucarini, and F. Lunkeit. 2015. A new framework for climate sensitivity and prediction: A modelling perspective. Climate Dynamics 46: 1459–1471. doi:10.1007/s00382-015-2657-3.CrossRef
Zurück zum Zitat Richardson, L.F. 1926. Atmospheric diffusion shown on a distance-neighbour graph. Proceedings of the Royal Society A110: 709–737.CrossRef Richardson, L.F. 1926. Atmospheric diffusion shown on a distance-neighbour graph. Proceedings of the Royal Society A110: 709–737.CrossRef
Zurück zum Zitat Rypdal, M., and K. Rypdal. 2014. Long-memory effects in linear response models of Earth's temperature and implications for future global warming. Journal of Climate 27 (14): 5240–5258. doi:10.1175/JCLI-D-13-00296.1.CrossRef Rypdal, M., and K. Rypdal. 2014. Long-memory effects in linear response models of Earth's temperature and implications for future global warming. Journal of Climate 27 (14): 5240–5258. doi:10.​1175/​JCLI-D-13-00296.​1.CrossRef
Zurück zum Zitat Sardeshmukh, P., G.P. Compo, and C. Penland. 2000. Changes in probability assoicated with El Nino. Journal of Climate 13: 4268–4286.CrossRef Sardeshmukh, P., G.P. Compo, and C. Penland. 2000. Changes in probability assoicated with El Nino. Journal of Climate 13: 4268–4286.CrossRef
Zurück zum Zitat Schertzer, D., and S. Lovejoy. 1985. The dimension and intermittency of atmospheric dynamics. In Turbulent shear flow, ed. L.J.S. Bradbury et al., 7–33. Berlin: Springer-Verlag.CrossRef Schertzer, D., and S. Lovejoy. 1985. The dimension and intermittency of atmospheric dynamics. In Turbulent shear flow, ed. L.J.S. Bradbury et al., 7–33. Berlin: Springer-Verlag.CrossRef
Zurück zum Zitat ———. 1995. From scalar cascades to Lie cascades: Joint multifractal analysis of rain and cloud processes. In Space/time variability and interdependance for various hydrological processes, ed. R.A. Feddes, 153–173. New York, NY: Cambridge University Press.CrossRef ———. 1995. From scalar cascades to Lie cascades: Joint multifractal analysis of rain and cloud processes. In Space/time variability and interdependance for various hydrological processes, ed. R.A. Feddes, 153–173. New York, NY: Cambridge University Press.CrossRef
Zurück zum Zitat ———. 2004. Uncertainty and predictability in geophysics: Chaos and multifractal insights. In State of the planet, frontiers and challenges in geophysics, ed. R.S.J. Sparks and C.J. Hawkesworth, 317–334. Washington, DC: American Geophysical Union.CrossRef ———. 2004. Uncertainty and predictability in geophysics: Chaos and multifractal insights. In State of the planet, frontiers and challenges in geophysics, ed. R.S.J. Sparks and C.J. Hawkesworth, 317–334. Washington, DC: American Geophysical Union.CrossRef
Zurück zum Zitat Schertzer, D., S. Lovejoy, F. Schmitt, Y. Chigirinskaya, and D. Marsan. 1997. Multifractal cascade dynamics and turbulent intermittency. Fractals 5: 427–471.CrossRef Schertzer, D., S. Lovejoy, F. Schmitt, Y. Chigirinskaya, and D. Marsan. 1997. Multifractal cascade dynamics and turbulent intermittency. Fractals 5: 427–471.CrossRef
Zurück zum Zitat Schertzer, D., I. Tchiguirinskaia, S. Lovejoy, and A.F. Tuck. 2012. Quasi-geostrophic turbulence and generalized scale invariance, a theoretical reply. Atmospheric Chemistry and Physics 12: 327–336. doi:10.5194/acp-12-327-2012.CrossRef Schertzer, D., I. Tchiguirinskaia, S. Lovejoy, and A.F. Tuck. 2012. Quasi-geostrophic turbulence and generalized scale invariance, a theoretical reply. Atmospheric Chemistry and Physics 12: 327–336. doi:10.​5194/​acp-12-327-2012.CrossRef
Zurück zum Zitat Schmidt, G.A., D.T. Shindell, and K. Tsigaridis. 2014. Reconciling warming trends. Nature Geoscience 7: 158–160.CrossRef Schmidt, G.A., D.T. Shindell, and K. Tsigaridis. 2014. Reconciling warming trends. Nature Geoscience 7: 158–160.CrossRef
Zurück zum Zitat Schwartz, S.E. 2012. Determination of Earth’s transient and equilibrium climate sensitivities from observations over the twentieth century: Strong dependence on assumed forcing. Surveys in Geophysics 33: 745–777.CrossRef Schwartz, S.E. 2012. Determination of Earth’s transient and equilibrium climate sensitivities from observations over the twentieth century: Strong dependence on assumed forcing. Surveys in Geophysics 33: 745–777.CrossRef
Zurück zum Zitat Suckling, E.B., E. Hawkins, G. Jan van Oldenborgh, and J.M. Eden. 2016. An empirical model for probabilistic decadal prediction: A global analysis. Climate Dynamics (submitted). Suckling, E.B., E. Hawkins, G. Jan van Oldenborgh, and J.M. Eden. 2016. An empirical model for probabilistic decadal prediction: A global analysis. Climate Dynamics (submitted).
Zurück zum Zitat Tennekes, H. 1975. Eulerian and Lagrangian time microscales in isotropic turbulence. Journal of Fluid Mechanics 67: 561–567.CrossRef Tennekes, H. 1975. Eulerian and Lagrangian time microscales in isotropic turbulence. Journal of Fluid Mechanics 67: 561–567.CrossRef
Zurück zum Zitat Vallis, G. 2010. Mechanisms of climate variaiblity from years to decades. In Stochastic physics and climate modelliing, ed. P.W.T. Palmer, 1–34. Cambridge: Cambridge University Press. Vallis, G. 2010. Mechanisms of climate variaiblity from years to decades. In Stochastic physics and climate modelliing, ed. P.W.T. Palmer, 1–34. Cambridge: Cambridge University Press.
Zurück zum Zitat Van der Hoven, I. 1957. Power spectrum of horizontal wind speed in the frequency range from 0.0007 to 900 cycles per hour. Journal of Meteorology 14: 160–164.CrossRef Van der Hoven, I. 1957. Power spectrum of horizontal wind speed in the frequency range from 0.0007 to 900 cycles per hour. Journal of Meteorology 14: 160–164.CrossRef
Zurück zum Zitat Zeng, X., and K. Geil. 2017. Global warming projection in the 21st Century based on an observational data driven model. Geophysical Research Letters. (in press). Zeng, X., and K. Geil. 2017. Global warming projection in the 21st Century based on an observational data driven model. Geophysical Research Letters. (in press).
Metadaten
Titel
Harnessing Butterflies: Theory and Practice of the Stochastic Seasonal to Interannual Prediction System (StocSIPS)
verfasst von
S. Lovejoy
L. Del Rio Amador
R. Hébert
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-58895-7_17