Is Weather and Climate Prediction Deterministic or Stochastic ?

1. Based on the talk to the U.S. National Academy of Sciences on August 4th, 2005, Washington, DC Is Weather and Climate Prediction Deterministic or Stochastic? Ensemble Forecasting: a new era for weather and climate study Dr. Jun Du NCEP/NOAA Jun.Du@noaa.gov

2. Using weather forecasting as example but the same principle should be applied to climate prediction since both weather and climate study use same model (only with different forcing such as doubling CO2 or deforestation for climate change study)

3. NWS National Digital Forecast Database (NDFD) Deterministic !!!!!!!!!!!!!!!!!!!!!!!!

4. Deterministic too!!!!!!!!!!!!!!!!!!!!!!!!

6. X = Xm( current deterministic practice and scientists’ dream too)

7. X = Xm + X0( actual realization, X0 is unknown)

8. Numerical Weather Prediction system • Observation • Data assimilation (prepare initial conditions to initiate model integration) • Prediction (model integration: model dynamics, physics) • Application to real world situation

9. EarthObservations

10. Models need to represent many irresolvable processes

11. One thing certain:Uncertainties in all steps! They are intrinsic, unavoidable and could be random

12. Lorenz • “… one flap of a sea-gull’s wing may forever change the future course of the weather” (Lorenz, 1963): butterfly effect

13. Two consecutive NCEP operational Global Forecasting System (GFS) 16-day 500mb HGT/VORT forecasts (with only 6hr-hour apart in initial conditions)! (A) 00z, Oct. 3 – 00z, Oct. 19, 2006 (B) 06z, Oct. 3 – 06z, Oct. 19, 2006

16. X=> {Xm + X’}(X’ is a kind of distribution)

17. How to estimate X’? • Traditionally, using statistical approach based on model’s historical performance over a period of time in the past to estimate either X’ (PoP) or X0 (MOS, Perfect Prog) • Problem 1: not flow-dependent but model’s systematic error in the past (not necessary to reflect “error of the day”) • Problem 2: not work well if a model changes frequently (a common practice)

18. Our Fundamental Problem:a dynamical approach: starting from some current states to be projected to some future states within the limits of predictability Chaos as a motivator for probabilistic forecasting

19. Ensemble Product type in general: 1. single outcome type: mean, median, mode, extremes, consensus, … 2. uncertainty: spread, confidence factor, predictability measure … 3. distribution type: probability, spaghetti, clustering, envelope …

21. (mode)

25. Four Major Tasks: How to capture uncertainty in a forecasting system? 2. How to convey uncertainty to users and public? 3. How to use uncertainty and probabilistic information in decision-making? How to possibly reduce uncertainty to better serve people and society?

26. Task 1: How to capture uncertainty in a forecasting system?

27. How NCEP Short-Range Ensemble Forecasting (SREF) system to capture uncertainties? *IC aspect: (1) perturb analysis (bred vector, ET/ETKF, singular vector, random), (2) multi-analysis (gdas, ndas) *Model aspect:(1) multi-model (Eta, RSM, NMM, ARW) (2) multi-physics (GFS, Eta, MM5/ BMJ, KF, SAS, RAS, LSM, cloud, PBL, radiation …), (3) stochastic physics *Residual Part: statistical post processing http://www.emc.ncep.noaa.gov/mmb/SREF/SREF.html

33. f12 f12 worst member best member f24 f24

35. ~10”

36. 2.5-5”

43. Task 2: How to convey uncertainty to users and public?

44. 100 balls in a jar: 10 red and 90 blue. Driving after Sept. 11, 2001 US terrorist attack. Riding bicycle after July 7, 2005 London Bombing. 10 balls in a jar: 1 red and 9 blue. Flying after Sept. 11, 2001 US terrorist attack. Taking bus/metro after July 7, 2005 London Bombing. Psychological Effects