Strategies for Development of Dynamical Seasonal Prediction (DSP) System at Operational Centers

1. Strategies for Development of Dynamical Seasonal Prediction (DSP) System at Operational Centers By Masao Kanamitsu Climate Research Division SIO/UCSD

2. Preface Operational vs. Research Statistical vs. Dynamical

3. Operational vs. Research • Operational • Produce useful results now! • whatever the method is. • Research • Great future potential • Does not produce useful results now • Take time (and money)

4. Operational vs. Research • Requires balanced thinking. Do not go too much towards one direction. • Think science • Promote understanding between managers and developers

5. Statistical/Empirical vs. Dynamical • Statistical • Relatively easy and fast • Produces results now • Skill statistically assured • Not much future (statistical limitation) • Dynamical • Difficult • May take time to produce useful results • Skill not assured • Great potential for future

6. Current Status of Seasonal Prediction

7. Current Status of Statistical/Empirical Methods • Statistical method based on EOF is now well established. Currently, most reliable method. • CCA* • Composites* • Trend (persistence)* • Extrapolation of phase, amplitude* • Identification of “modes”. *) applies to forecast over Taiwan

8. Current DSP status (1) • Current products are NOT as useful as we hope for operational seasonal forecasts. • Still the method is not matured • It is an investment for future. • 5-years? 10-years? • Need to be up-to-date to be competitive with other centers

9. Current DSP status (2) • May be useful in particular situations • This is still not demonstrated • Progress in modeling and research may make the DSP as useful tool in a short time.

10. Statistical: ANALOG NEURAL CCA SSA/MEM (LIM) Dynamical (simple): OXFORD LDEO SCR/MPI BMRC Dynamical (2-tier) NCEP Dynamical (1-tier?) COLA After Landsea amd Knaff (2000) Skill measured against persistency/climatology

11. Basic Approach to Seasonal Prediction

12. Basic approaches to seasonal forecasting • Phenomenological approach • Atmospheric “modes” approach * • Specific external forcing approach * • Pure empirical approach • Dynamical Modeling *

13. Phenomenological Approach

14. Phenomenological consideration(what phenomena to predict) • Summer monsoon (Meiyu) • Winter monsoon (cold surges) • Typhoon • Subtropical highs Be aware that our interest is the prediction of “anomaly”

15. Application to Prediction- phenomenological - • Normally does not help making predictions • Help targeting what to predict • Physical understanding of the mechanism helps what to look and how to model.

16. Atmospheric Modes

17. Atmospheric modes Decompose/extract typical signal from complex atmospheric patterns. Convenient tool*

18. Consideration from “Modes” • Madden Julian Oscillation (MJO)* • Pacific North America (PNA) pattern* • North Atlantic Oscillation (NAO) • Arctic Oscillation (AO), Annular mode.* • Pacific Decadal Oscillation (PDO)* • Pacific Japan (PJ) pattern* • Many other patterns*

19. Examples of various “modes” NAO* PNA*

20. Examples of various modes AO Thompson and Wallace (2000)

21. Examples of various modes PJ Nitta (1987)

22. Examples of various modes

23. Examples of various modes • PDO

24. Examples of various modes MJO Waliser et al. (2000)

25. Kawamura et al., 1996

26. Application to Prediction- Modes - • Use as input/output for statistical method • Composite maps based on modes • Extrapolate phase and amplitude • Physical understanding of the mechanism helps what to look and how to model

27. External Forcing

28. External Forcing (1) • Sea Surface Temperature • This is what started DSP • Use as input for statistical method (CCA) • Direct impact in tropics* • Nordeste, Indonesia precipitation,Taiwan(??) • Remote response • ENSO =>PNA • Western Pacific (Phillipines) => PJ • Indian Ocean dipole

29. ENSO Impacts Halpert and Ropelewski (1992)

31. External Forcing (2) • Soil moisture • Normally local • Summer season • Delayed SST impact via soil moisture • Snow • Similar to Soil moisture • Delayed effect • Sea-ice • Vegetation, urbanization • CO2, ozone and other greenhouse gasses

32. Application to prediction - External forcing - • Use as input in statistical methods • Composite • Key factor for dynamical seasonal forecasts • How to predict external forcing?

33. Pure Empirical Approach

34. Pure empirical approach • Analog method • Pattern recognition • Constructed analog • Using the behaviors of animals, plants and other natural phenomena. Lacks scientific basis Not recommended !!!

35. Dynamical Approach

36. Application to prediction - Dynamical - • Based on physical principles • Most promising • Should be able to predict rare events (in principle, even events not occurred in history)

37. DSP Approach • Identify sources of predictability • Dynamical consistency • Signal to noise • Systematic error

38. Source of seasonal predictability • SST • Soil wetness • Snow • Sea ice • CO2, ozone and other trace gasses for trend • Initial conditions, particularly ocean, land Need for incorporating as many predictability sources as possible into the system. Discourages simplification. Need for coupled modeling

39. Dynamical Consistency • Two-tier (one-way coupling) • One-tier (two-way coupling)  should be one-tier MJO example Extra tropical sst example

40. Signal and noise (1) • In seasonal forecasts, transient disturbances with the frequency less than about 30 days are considered to be “noise”. • The targets of prediction for short-range and medium-range are noise in seasonal forecast. • Lorenz’s chaos theory • Infinitesimally small difference in initial condition results in very large differences after 2 weeks. • There is no way to avoid this “noise”. • You need to consider that the highs and lows in the middle latitudes in real atmosphere is also a “noise”. We describe nature as “One of many realizations”.

41. Daily forecast score decay curve

42. Ensemble spread of precipitation simulation

43. Example of signal to noise ratio Sugi et al (1997)

44. Signal and noise (2) • If there is no external forcing, atmospheric mean state is not predictable. • Signal is the pattern that shows up on all the predictions regardless of the “noise”.

45. Signal and noise (3) • Why predict “noise” • Middle latitude disturbances are essential component of general circulation. Transport heat and momentum. • Low frequency model and difficulties of parameterizing time mean effect of transient disturbances. • Prediction of individual disturbances need not be accurate, but time mean property of the transient disturbances need to be accurate. • This is in good analogy to cumulus parameterization.

46. Signal and noise (4) • Thus, ensemble forecasting is an essential part of the seasonal prediction. • One integration provides only on “realization” • All the seasonal forecasts need to be probabilistic.

47. Signal and Noise (5) • Theory on Seasonal Ensemble forecasting • Sardeshumukh (2000) • Number of ensemble members required • Simple mean (order of 20-40) • Second moment (order of a few hundred) • Skewness (even more)

48. Systematic Error • Average of many forecasts minus corresponding observation. • Model mean error • Model climatology • Fairly large amplitude • Cannot use the model forecast “as is” • Systematic error correction • Assuming there is no interaction between systematic error and model dynamics. This is a big assumption! • Built-in statistical correction to dynamical model

49. Example of the importance of systematic error correction Kanamitsu et al (2002)

50. 1st recommendation • Use statistical method as one of the leading methods for operational forecast. • CCA • Composite • Extrapolation of “modes” • Persistency (or trend) • Other statistical method with some physical basis. • Do not go into “pure empirical” without any physical basis => my personal opinion.