1 / 82

DECADAL

DECADAL. PREDICTION. Forecast. George Boer CCCma. What are the prospects for decadal prediction?. Excellent prospects that decadal predictions will be made Modest prospects that skillful decadal predictions will be made Improving prospects for the use of decadal predictions.

althea
Télécharger la présentation

DECADAL

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. DECADAL PREDICTION Forecast George Boer CCCma

  2. What are the prospects for decadal prediction? • Excellent prospects that decadal predictions will be made • Modest prospects that skillful decadal predictions will be made • Improving prospects for the use of decadal predictions

  3. Decadal prediction motivations • Existence of “long timescale” processes • Climate models and climate simulations • Results of predictability studies • Scientific interest • Demonstrations of forecast skill • Societal importance of modestly skillful decadal prediction

  4. Long timescales and prediction • The butterfly effect, weather and decadal prediction • Externally forced and internally generated components of climate variation • Predictability and prediction

  5. Weather predictability limit Growth of initially small error Predictability limit of ~ 2 weeks

  6. time scale What’s special about decadal timescales? • Not much according to spectra (e.g. Pelletier,1997)

  7. How can we predict climate in the face of the predictability limit? • We do not predict the weather but rather the climate • the climate prediction “object” is a statistic such as next year’s (next decade’s) average temperature for some region • larger space and longer timescales • filter out less predictable scales • but what “processes” are involved in these long timescales we wish to predict?

  8. Sources of climate variation • Conceptually for a climate variable X = m + Q + X’ where • m is the mean • Q the externally forced component from • anthropogenic GHGs, aerosols, volcanoes, solar …. • X’ the internally generated component • arises spontaneously from the internal workings of the system • should be predictable from initial conditions at least for some period

  9. Observed global annual mean temperature • broad “trend” is considered to be forced by GHGs and aerosols • variations about the trend largely internally generated • behaviour similar locally but with larger variability vs trend

  10. Decadal predictability and prediction • Appeals to “long timescale” processes • externally forced (GHG+A, volcanoes, solar, ….) • internally generated • oceanic mechanisms (AMO, …) • coupled atmosphere/ocean processes • PDO, AMO, … • modulation of atmospheric modes (PNA, NAO, NAM, SAM, ….) • atmospheric processes (QBO …)

  11. Pacific Decadal Oscillation

  12. Atlantic Multidecadal Oscillation (AMO) S N Modelled Atlantic meridional overturning circulation (AMOC) Knight et al. 2005

  13. NAM and PNA JISAO website

  14. Modes of Variability • Modes of climate variability cover a range of timescales • part of the internally generated component • can have decadal variation in strength and/or envelope • Area of active research but we don’t fully understand the physics behind most “modes” • No governing equations for modes themselves but they can serve to “characterize” variability

  15. Long timescales • clearly exist in the system • forced component becoming more important • internally generated component • generally lives in the coupled system • predictable decadal effects may depend on “modish” and “non-modish” behaviour, e.g. advection of heat/salt anomalies

  16. Decadal prediction motivations • Existence of “long timescale” processes • Climate models and climate simulations • Results of predictability studies • Scientific interest • Demonstrations of forecast skill • Societal importance of modestly skillful decadal prediction

  17. Climate models Forced with GHG and aerosol precursors etc.

  18. Aspects of climate modelling • climate modelling and climate change: • initially with AGCMs (specified SSTs) • then coupled to “slab” oceans • equilibrium climate change for 2xCO2 • fully coupled to ocean • allows oceanic long timescales and dynamics • evolution of forced component • driven by emissions of GHGs and aerosol precursors • carbon and sulphur cycles including land and ocean “biology” • documented and analyzed in MIPs and IPCC reports

  19. Climate simulations and projections • specify forcings (GHG, aerosols…) and integrate the model forward in time • interest is in the forced component • simulation: when forcing is “known” (i.e. for 20th century) • projection: when forcings are specified from “scenarios” simulation projection

  20. X = m + Q + X' X’ m Q X For observed and modelled annual global mean temperature • black curve is the observed evolution; due to the sum of forced and internally generated long timescale processes • yellow curves are different “realizations” of climate evolution for the same forcing begun from control simulation in the past • small differences in initial conditions/model formulation give different evolutions • red curve is the average over the realizations => the simulation of the commonforced component • a climate prediction would be initialized from observations and would attempt to forecast the actual evolution of Q + X’

  21. Example of a decadal prediction of global annual mean temperature from CCCma model

  22. Climate “prediction” X(t) = m + Q(t) + X’(t) • climate simulation/projection studies: • simulate/project the forced component Q(t) • t spanning many years/centuries • seasonal/interannual prediction: • Q(t) basically constant • initialized (including at least upper ocean) • predict X’(t) for t from a month/season to a year • decadal prediction: • connects the timescales • initialized (including deep ocean) • prediction of Q + X’ for t from months to decades • all very well but is there any real hope for decadal prediction?

  23. Decadal prediction motivations • Existence of “long timescale” processes • Climate models and climate simulations • Results of predictability studies • Scientific interest • Demonstrations of forecast skill • Societal importance of modestly skillful decadal prediction

  24. How do we determine the predictability of the system on decadal timescales? • Prognostic perfect model predictability studies • Griffies and Bryan (1997) • Boer (2000) • Collins (2002) • Collins et al. (2006) • Latif et al., (2006) • and others • Diagnostic potential predictability studies • Boer (2000, 2004) • Pohlmann et al. (2004) • Predicate (2004…) • Boer and Lambert (2008) • Boer (2010) • and others • Investigations of forecast skill • Smith et al. (2007) • Keenlyside et al. (2008) • Pohlmann et al. (2009) • Merryfield et al., Fyfe et al. (2011) • CMIP5 (2011+ ….)

  25. Prognostic predictability studies: • Climate prediction of long timescale • internally generated variability • “early days” of coupled climate • change simulations • forcing is weak early in the • simulation • gives information on predictability • of internally generatedcomponent (1994)

  26. Long-timescale prognostic predictability study • Not able to overcome the technical “culture shock” at MPI to use their utilities etc. • Later, CCC managed to produce several runs • Initial “perfect model” predictability estimates from CCCma model • also “potential predictability” estimates Decadal perfect model predictability of T Boer,2000: Clim Dyn

  27. Atlantic Meridional Overturning Circulation Perfect model(s) prognostic predictability study of the internally generated component: AMV Collins et al., 2006

  28. Prognostic predictability of the internally generated component North Atlantic perfect model predictability measure Pohlmann et al. 2004

  29. Diagnosticpredictability of the internally generated component • Multi-model decadalpotential predictability of the internally generated component • Model control runs (CMIP3) - no external forcing • Annual mean of variable X is expressed as X = m + c + x • m is the mean • c is the long timescale internally generated component • x is the short timescale “noise” component • Associated variances are X s2X = s2c +s2x • Potential predictability variance fraction (ppvf) is p = s2c / s2X = s2c / (s2c +s2x)

  30. Connection of p with skill Observations: X = m + c+ x Forecasts: Y = n + y+ y Statistics: - variances: s2X = s2c +s2x s2Y = s2y +s2y - potential predictability variance fractions (ppvf) pc = s2c / s2X for observations py = s2y / s2Y for model - covariances: cov(XY) = scsyRcy(t), R is correlation for t the forecast range Correlation skill – limited above by pc1/2 rXY(t)= cov(XY)/sX sY = (pcpy)1/2Rcy(t) epc1/2 for a “perfect” model with (py ,Rcy = 1)

  31. Internally generated long timescale potential predictability Deviation from average M-year average s2 = s2c + s2x

  32. Temperature: potential predictability of internally generated variability pc = s2c/s2(%) for decadal means (CMIP3 multi-model control runs) • - Ratio of long timescale to • total variance • MME provides stability of • statistics: ppvf in white • areas <2% and/or not • significant at 98% level • Long timescale predictability • found mainly over oceans • Some incursion into land • areas but modest ppvf (denominator is large) Control simulations Boer and Lambert, 2008

  33. Precipitation: potential predictability of internally generated variability pc = s2c/s2 (%) for decadal means • MME provides “some” significant areas of precipitation • Much less potentially predictable than temperature • Little incursion into land • areas • Precipitation predictability a weakened version of temperature predictability at these timescales Control simulations

  34. 21st Century decadal potential predictability • Now includes a forced component X = m + Q + c+ x and variances become s2 = s2Q + s2c +s2x • Q is long timescale externally forced variability • c is long timescale internally generated variability • x is short timescale “noise” variability • statistics pooled across models • Potential predictability variance fraction now has two components p = (s2Q + s2c )/ (s2Q + s2c + sx2)= pQ + pc (denominator now includes both forced and internally generated variance)

  35. 21st century temperature at a point - forced component from 1st decade X = m + Q + c+x forced component (fitted quadratic) from 1st decade Q-Q1 }DQ c x variation about forcedcomponent contribution of forced and internally generated components to the variability over a decade QQ

  36. MME (CMIP) next decade potential predictability of Temperature for the 21st century • pQ = s2Q/s2 • forced component large over land • but discounted because noise is large • pc = s2c/s2 • internally generated component • largest over high latitude oceans • p = pQ + pc • low, mid-latitude land • oceans excluding equatorial Pacific Boer, 2010

  37. externally forced initial condition Prognostic predictability of forced and internally generated components upper ocean annual mean temperature (nominal) • Branstator and colleagues (NCAR model): • predictability from initial conditions declines • predictability of forced component increases • result is “U-shaped” predictability curve

  38. Prognostic predictability of forced and internally generated components forced different models initial condition NCAR model range for loss of predictability of internally generated component predictability differs for: - different basins - different models

  39. Long timescale predictability • internally generated component • largest over extratropical oceans • for T but little for P • externally forced component • more wide spread spatially • dominant over oceans/tropic and becomes more important with time • precipitation: only the forced component and then only for long times

  40. Decadal prediction motivations • Existence of “long timescale” processes • Climate models and climate simulations • Results of predictability studies • Scientific interest • Demonstrations of forecast skill • Societal importance of modestly skillful decadal prediction

  41. Scientific Interest

  42. International scientific interest

More Related