Development of Precipitation Outlooks for the Global Tropics Keyed to the MJO Cycle

1. Development of Precipitation Outlooks for the Global Tropics Keyed to the MJO Cycle Jon Gottschalck1, Qin Zhang1, Michelle L’Heureux1, Peitao Peng1, Kyong-Hwan Seo2, Huug van den Dool1, Wanqui Wang1,Wayne Higgins1, Arun Kumar1 1 NOAA / NWS / NCEP Climate Prediction Center 2 Pusan National University, Busan, Korea Climate Diagnostics and Prediction Workshop Tallahassee, Florida October 22-26, 2007

2. Outline • Motivation, Background, and Goals • Methodology 1. Basis of the outlooks -- MJO • MJO filtering • MJO Forecast Method Descriptions • Consolidation Specifics • Initial Findings and Impressions 2. Procedure for Precipitation Outlooks • Potential Interactions and Upcoming Plans

3. Motivation and Background • MJO substantially modulates tropical rainfall when active • Objective forecast input for CPC weekly MJO and international benefits/hazards assessments • Companion to CPC empirical temperature/precipitation outlooks keyed to the ENSO cycle (Higgins et al. 2004) • Consolidation of MJO forecast methods is first step • Several tools are available for MJO prediction and include both statistical and dynamical approaches

4. MJO Identification • Wheeler and Hendon (2004) • Multivariate EOF analysis using OLR, 850 hPa / 200 hPa zonal wind • Data pre-filtering: 1. Seasonal cycle removed 2. ENSO associated variability removed 3. Latest 120 day mean removed • Index is first two PCs (RMM1, RMM2) taken together Counterclockwise movement indicates eastward propagation Farther from circle the greater the MJO strength

5. MJO Forecast Method Framework • Data record available extends from 1979-2004 • Forecasts are based on pentad averaged data • Forecasts for leads 1 – 6 pentads • Forecasts are of RMM1 and RMM2 (WH2004 PCs 1 and 2) • Idea is to use methods of varying complexity, statistical and dynamical

6. MJO Forecast Method Descriptions • 5 MJO forecast methods currently used: 1. Autoregressive model (ARM) – statistical, (Jones et al. 2004) --Training period 1979-1989, order = 4 pentads, uses information from one PC only • PC(t+1)=∑ CjPC(t–j+1) + εt+1 2. Lagged linear regression (PCL) – statistical, (Jones et al. 2004) --Training period 1979-1989, 5 pentad lags, uses information from both PCs • PC(t+h) = ∑∑ Cij(h)PCi(t–j+1) 3. Empirical Phase Propagation (EPP) – statistical (Seo et al. 2007) --Fixed amplitude, constant 30° per pentad propagation speed 4. Constructed Analogue (ANL) – statistical (Peng and van den Dool, 2005) --Training period 1980-2006 CV 5. Climate Forecast System (CFS) – dynamical (Saha et al. 2006) --Lead dependent climatology, observed EOFs

7. Consolidation Specifics • Forecasts Utilized: 1990-2004, standardized anomalies • Consolidation Methods: 1. Equal Weights (CEQ): • Weights sum to unity • Each method is assigned a weight of 0.20 2. Ridge Regression (CRR): • Weights account for co-linearity between methods • Weights are a function of method, time of year, and lead • Pooled pentads (3,5,7 pentad tests) • Weights based on combining RMM1 and RMM2

8. Results – Ridge Regression Weights PCL Generally small weights at longer leads during the entire year. Largest weights at early leads during periods in the boreal spring and late fall.

9. Results – Ridge Regression Weights Greatest weights at all leads during late summer and at time at longer leads Little or no weight given at early leads during much of the year ARM

10. Results – Ridge Regression Weights EPP High weights during September and October at most leads.

11. Results – Ridge Regression Weights ANL Largest weights of all the methods mainly during the boreal winter and early summer.

12. Results – Ridge Regression Weights CFS Largest weights mainly during late summer and early fall.

13. Results – Sum of Individual Method Weights ALL Periods of little predictability Periods during February, May, June, August, and October offer the greatest predictability

14. Results – Cross-Validated Correlation – RMM1

15. Results – Cross-Validated Correlation – RMM2

16. Precipitation Outlooks – Background • Methodology is similar to Higgins et al. (2004)  empirical prediction of seasonal temperature and precipitation keyed to the ENSO cycle

17. Precipitation Outlooks – Methodology • Empirical prediction of MJO associated pentad precipitation  Consolidated MJO index to determine MJO phase so precipitation keyed to the MJO cycle 11.8 Contour intervals are differences from 33%

18. Precipitation Probabilities Keyed to the MJO Cycle • Pentad CPC Merged Analysis of Precipitation (CMAP) --1979-2006, 2.5x2.5 • Determined threshold limits for upper, middle, and lower terciles --Gamma distribution --Each grid point --Extended winter/summer seasons, 3-month running window • Identified MJO events (WH2004) in the historical record • Combining CMAP data and historical MJO information we can calculate probabilities of precipitation by MJO phase for upper, lower, and middle categories

19. Results – Consolidation Example in Phase Space 11.8 9.6 Contour intervals are differences from 33% 11.9 10.7

20. Closing Comments • Further investigate and improve the stability of weights • --Stratifying by season, additional “pooling” tests, etc. • Procedure can leverage work being conducted as part of the US CLIVAR MJO working group  Applying WH2004 methodology to operational models  Current participating centers: NCEP, ECMWF, UKMET, CMC, BMRC  Other dynamical model input may aid the consolidated MJO index forecast • Proceed with the development of precipitation outlooks if warranted • Objective input into international hazard assessments

21. Thank You. Comments/Suggestions/Questions?