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Sensitivity of MJO Predictability to SST

Sensitivity of MJO Predictability to SST. Kathy Pegion Center for Ocean-Land-Atmosphere Studies Ben Kirtman University of Miami Center for Ocean-Land-Atmosphere Studies. NOAA 32nd Annual Climate Diagnostics and Prediction Workshop Tallahassee, FL. Motivation.

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Sensitivity of MJO Predictability to SST

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  1. Sensitivity of MJO Predictability to SST Kathy Pegion Center for Ocean-Land-Atmosphere Studies Ben Kirtman University of Miami Center for Ocean-Land-Atmosphere Studies NOAA 32nd Annual Climate Diagnostics and Prediction Workshop Tallahassee, FL

  2. Motivation Prediction Skill Studies from DERF experiments (Chen & Alpert 1990, Lau & Chang 1992, Hendon et al. 1999, Jones et al. 2000, Seo et al. 2005) Use atmosphere-only with initial SST values damped to climatology with a 90-day e-folding time Predictability Studies Climatological SST (Waliser et al. 2003, 2004, Liess et al. 2004) Coupled and uncoupled with daily SST w/intraseasonal variability removed (Fu et al. 2006) Coupled and uncoupled w/“perfect” SST (Pegion and Kirtman 2007) How sensitive is the predictability of the MJO to SST?

  3. Predictability Experiments • Ten model intraseasonal events (>2) selected from a 52-year CFS03 (T62L64) control simulation • Initialized when MJO-related precip is in Indian Ocean • Perturb atm ICs to generate 9 member ensembles • 60-day forecast • “Perfect” Model - Forecast skill calculated with control as “truth”

  4. Initial Conditions 9 Atm Perturbations Generated by running the model in 1 hour increments & resetting the calendar TIME (Hours) - 24 - 4 - 3 - 2 - 1 0 + 1 + 2 + 3 + 4 + 5 Coupled Ocn ICs from Control Uncoupled Prescribed SSTs

  5. Predictability Experiments

  6. Example Event Control Simulation Unfiltered Anomalies Averaged 10S-10N U200 (m/s) Precipitation (mm/day) SST (degrees C)

  7. Example Event Unfiltered, Ensemble Mean Precipitation Anomalies Averaged 10S-10N Perfect SST Clim Persisted Anomaly FCST SST Forecast Day mm/day

  8. Example Event Predictability Estimates Correlation Ensemble Mean with Control Filtered (30-day) Precipitation Indo-Pacific Region Coupled Perfect SST FCST SST Persisted Anoms CLIM Persistence A good SST forecast is important to the predictability of the TISO.

  9. SST Sensitivity Experiments (All 10 Events) Unfiltered, Ensemble Mean Precipitation Anomalies Averaged 10S-10N Control Coupled Perfect Forecast Day mm/day

  10. SST Sensitivity Experiments (All 10 Events) Unfiltered, Ensemble Mean Precipitation Anomalies Averaged 10S-10N Clim Persist Anom Monthly Forecast Forecast Day mm/day

  11. Predictability Estimates (Ten Events) Correlation Ensemble Members with Control Filtered (30-day) Precipitation Indo-Pacific Region Coupled Perfect SST FCST SST Persisted Anoms Monthly CLIM Predictability (Days) Correlation Coefficient Forecast Day

  12. Predictability Estimates (Ten Events) Correlation Ensemble Mean with Control Week 1 Week 2 Week 3 Week 4 Filtered (30-day) Precipitation Indo-Pacific Region Coupled Perfect SST FCST SST Persisted Anoms Monthly CLIM Predictability (Days) Correlation Coefficient Forecast Day

  13. Point Correlation of Unfiltered Precipitation Anomalies Ensemble Members with Control Week 2 Coupled Perfect Forecast Monthly Persist Anom Precipitation Anoms

  14. Point Correlation of Unfiltered Precipitation Anomalies Ensemble Members with Control Week 3 Coupled Perfect Forecast Monthly Persist Anom Precipitation Anoms

  15. Point Correlation of Unfiltered Precipitation Anomalies Ensemble Members with Control Week 4 Coupled Perfect Forecast Monthly Persist Anom Precipitation Anoms

  16. Conclusions 1. Degrading the quality of the SST degrades the skill of the precipitation forecast beyond week-1. If we hope to make better forecasts of the MJO, forecasts for week-2 and beyond should be made using ensembles and a coupled model. Most of the model skill on intraseasonal timescales at lead times beyond week-2 comes from regions outside the active/supressed precipitation of the MJO and in regions where precipitation is small. Forecasting MJO-related precipitation beyond week-2 is a challenge even under a “perfect” model assumption.

  17. Caveats & Future Work 1. Time filtering - not realistic for operational forecasting and not particularly satisfying • Plan to apply the Wheeler and Hendon real-time multivariate MJO index as is being used by the Clivar MJO working group 2. Model Error - these are perfect model predictability experiments. What happens for observed MJO events using observed SST? • Plan to perform hindcast SST sensitivity experiments using observed SST

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