Predictability of Weather and Climate (Seamless Prediction of Weather and Climate)

1. Predictability of Weather and Climate(Seamless Prediction of Weather and Climate) Jagadish Shukla Department of Atmospheric, Oceanic and Earth Sciences George Mason University CLIM 751 Fall 2012 Lecture on Aug 29, 2012

2. Outline • Weather and Climate for Poets • Mechanisms of Variability of Weather and Climate • Predictability and Prediction of Weather and Climate • Weather • Climate (Seasonal, ENSO, Decadal) • Climate Change • Model Fidelity, Predictability and Sensitivity • Factors Limiting Predictability: Future Challenges • Observational and Theoretical (Physics & Dynamics of the Coupled Climate System) • Computational and Numerical • Summary and Conclusions

3. ECMWF: Steady improvement of weather forecast skill, 1980-2010 NH SH

4. ECMWF: Skill of deterministic forecasting systems, 1980-2010

5. Nino3.4 SST anomaly predictions from March 2011

6. Global-mean Surface Temperature On the Time-Varying Trend in Global-Mean Surface Temperature by Huang, Wu, Wallace, Smoliak, Chen, Tucker EEMD: Ensemble Empirical Mode Decomposition; MDV: Multi Decadal Variability

7. 1.0º C Courtesy of UCAR

8. Physics of Weather and Climate for Poets Climate is what you expect, weather is what you get. (quoted by E. N. Lorenz)

9. S, , a, g, Ω O3 H2O CO2 Ω CLIMATE DYNAMICS OF THE PLANET EARTH g  (albedo) Gases: H2O, CO2, O3 S a T4 h*: mountains, oceans (SST) w*: forest, desert (soil wetness) CLIMATE . stationary waves (Q, h*), monsoons WEATHER hydrodynamic instabilities of shear flows; stratification & rotation; moist thermodynamics day-to-day weather fluctuations; wavelike motions: wavelength, period, amplitude

11. Examples of Weather and Climate Variability • Annual Cycle • Daily Weather • Seasonal Climate • Interannual (ENSO) • Decadal • Centennial (Climate Change)

12. Daily, Intraseasonal, Seasonal, Interannual, and Decadal Variations

13. Mechanisms of Variability

14. 2008

15. MJO Bivariate Correlation between ECMWF Ensemble Mean Forecast and Observations Based on 80 Dates (1st Feb, May, Aug, Nov, 1989-2008)

16. Growth of Random Errors in the simple model of Tropics and midlatitudes Model 1: (Tropics) a = 1.98 Model 2: (Mid-latitude) b = 1.60 An ensemble of 10000 initial random errors was allowed to evolve for each model. Empirical fit forError growth

17. ERA Forecast Verification Anomaly Correlation of 500 hPa GPH, 20-90N 1980-2006

18. ERA Forecast Verification Anomaly Correlation of 500 hPa GPH, 20-90N Schematic Error Growth for the Winter (Red) & Summer (Blue)

19. RMS Error and Differences between Successive Forecasts Northern Hemisphere 500 hPa Height in Winter Current Limits of Predictability, A. Hollingsworth, Savannah, Feb 2003

20. Evolution of 1-Day Forecast Error, Lorenz Error Growth, and Forecast Skill for ECMWF Model (500 hPa NH Winter)

21. The “Knife’s Edge” – The Observed Spectrum Nastrom & Gage 1985 -3 spectrum -5/3 spectrum synoptic scales mesoscales

22. Interim Summary (NWP) • In spite of the k -5/3 spectrum, • NWP history (~40 years) suggests: Higher resolution models, improved physical parameterizations, and data assimilation techniques reduced initial errors; Increased the range of predictability (even though initial error growth increased). • Despite 40 years of research, we still cannot definitively state whether the range of predictability cannot be increased by reducing the initial error.

23. From Numerical Weather Prediction (NWP) To Dynamical Seasonal Prediction (DSP) (1975-2004) • Operational Short-Range NWP:was already in place • 15-day & 30-day Mean Forecasts: demonstrated by Miyakoda (basis for creating ECMWF-10 days) • Dynamical Predictability of Monthly Means: demonstrated by analysis of variance • Boundary Forcing: predictability of monthly & seasonal means (Charney & Shukla) • AGCM Experiments: prescribed SST, soil wetness, & snow to explain observed atmospheric circulation anomalies • OGCM Experiments: prescribed observed surface wind to simulate tropical Pacific sealevel & SST (Busalacchi & O’Brien; Philander & Seigel) • Prediction of ENSO: simple coupled ocean-atmosphere model (Cane, Zebiak) • Coupled Ocean-Land-Atmosphere Models: predict short-term climate fluctuations

24. Effects of SST Anomaly

25. Observed 5-month running mean SOI IC: Dec. 1988 IC: Dec. 1982

27. JFM Mean Rainfall Anomalies Model IC: Dec. 1988 “Predictability in the Midst Of Chaos” Model IC: Dec. 1982

30. Zonal Wind (m/s) at 200 Mb (10°S to 10°N, 120°W to 160°W) The atmosphere is so strongly forced by the underlying ocean that integrations with very large differences in the atmospheric initial conditions converge, when forced by the same SST. 1982-83 SST IC: Dec. 1988 IC: Dec. 1982

31. JFM Mean Rainfall Anomalies Model IC: Dec. 1988 “Predictability in the Midst Of Chaos” Model IC: Dec. 1982 Observations B.C.(SST): 1982 -83

32. When tropical forcing is very strong, it can enhance even the predictability of extratropical seasonal mean circulation, which, in the absence of anomalous SST, has no predictability beyond weather. Observed SST JFM83 IC: Dec. 1988 Observed SST JFM83 IC: Dec. 1982

33. When tropical forcing is very strong, it can enhance even the predictability of extratropical seasonal mean circulation, which, in the absence of anomalous SST, has no predictability beyond weather. Observed SST JFM83 IC: Dec. 1988 Observed SST JFM83 IC: Dec. 1982 Observed ϕ’ (meters)

34. When tropical forcing is very strong, it can enhance even the predictability of extratropical seasonal mean circulation, which, in the absence of anomalous SST, has no predictability beyond weather. Observed SST JFM83 Observed SST JFM89 IC: Dec. 1988 Observed SST JFM89 Observed SST JFM83 IC: Dec. 1982 Observed ϕ’ (meters)

35. When tropical forcing is very strong, it can enhance even the predictability of extratropical seasonal mean circulation, which, in the absence of anomalous SST, has no predictability beyond weather. Observed SST JFM83 Observed SST JFM89 IC: Dec. 1988 Observed SST JFM89 Observed SST JFM83 IC: Dec. 1982 Observed ϕ’ (meters)

36. When tropical forcing is very strong, it can enhance even the predictability of extratropical seasonal mean circulation, which, in the absence of anomalous SST, has no predictability beyond weather. Observed SST JFM83 Observed SST JFM89 IC: Dec. 1988 IC: Dec. 1988 Observed SST JFM89 Observed SST JFM83 IC: Dec. 1982 IC: Dec. 1982 Observed ϕ’ (meters)

37. Rainfall 1982-83 Zonal Wind 1988-89 1982-83 The atmosphere is so strongly forced by the underlying ocean that integrations with fairly large differences in the atmospheric initial conditions converge, when forced by the same SST (Shukla, 1982). 1988-89

38. Tropical Convection Tropical Convection Northward Propagating Rossby-Wave Train (Trenberth, et al. 1998)

39. Model Simulation of ENSO Effects 500 hPa Height Anomalies (ACC = 0.98) Vintage 2000 AGCM

40. Predictability Limited Due to Initial Condition Uncertainty: Two Time Scales in the Error Growth? Goswami and Shukla (1991, J. Clim.)

41. Skill in SST Anomaly Prediction for Nino3.4 DJF 1981/82 to AMJ 2004 15-member CFS reforecasts

42. Selected Dynamical Models, 5-month lead . . GMAO GMAO OBS . CFS LDEO . CFS . LDEO . 2002 2003 2004 2005 2006 2007 2008 2009 Tony Barnston and Mike Tippett

43. Current Limit of Predictability of ENSO (Nino3.4) Potential Limit of Predictability of ENSO 20 Years: 1980-1999 4 Times per Year: Jan., Apr., Jul., Oct. 6 Member Ensembles Kirtman, 2003

44. Factors Limiting Predictability Fundamental barriers to advancing weather and climate diagnosis and prediction on timescales from days to years are (partly)(almost entirely?)attributable to gaps in knowledge and the limited capability of contemporary operational and research numerical prediction systems to represent precipitating convection and its multi-scale organization, particularly in the tropics. (Moncrieff, Shapiro, Slingo, Molteni, 2007)

45. Percent Variance of PNA region explained by Tropical SST Probability Distribution

46. Percent Variance of PNA region explained by Tropical SST Probability Distribution

47. Boreal Winter (DJF) Rainfall Variance in AGCMs (mm2)

48. Observed and Simulated Surface Temperature (°C) Observed Simulated

49. Scientific Basis for Decadal Predictability • Slowly varying climate components • Atmosphere-ocean interactions (Pohlmann et al., 2006; Stouffer et al., 2006, 2007; Latif and Barnett, 1996; Held et al., 2005; Knight et al., 2006; Zhang and Delworth, 2006). •Decadal predictability in oceans (Griffes and Bryan, 1997; Collins and Sinha, 2003; Collins et al., 2006, Msadek et al., 2010, DelSole et al., 2010). •Potential predictability of temperature, precipitation, sea level pressure (Collins, 2002; Boer, 2004; Boer and Lambert2008; Pohlmann et al., 2004, 2006, Smith et al., 2007; Keenlyside et al., 2008). • Predictable external forcing(Hegerl et al., 2007).

50. Example of Unforced Predictability Study Percent of potential predictable variance of 5-yr mean Boer &Lambert, 2008, Geophys.Res. Lett. Little to no predictability over land !