DECADAL

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

10. 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

11. 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 …)

12. Pacific Decadal Oscillation

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

14. NAM and PNA JISAO website

15. 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

16. 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

17. 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

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

19. 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

20. 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

21. 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’

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

23. 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?

24. 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

26. 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+ ….)

27. 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)

28. 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

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

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

31. 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)

32. 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)

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

34. 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

35. 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

36. 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)

37. 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

38. 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

39. 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

40. 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

41. 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

42. 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

43. Scientific Interest

44. International scientific interest