sensitivity of mjo predictability to sst
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
Sensitivity of MJO Predictability to SST
Kathy PegionCenter 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
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?
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”
0 + 1 + 2 + 3 + 4 + 5- 1- 2- 3- 4- 24
TIME (Hours)
9 Atm Perturbations
Generated by running the model in 1 hour increments & resetting the calendar
Coupled
Ocn ICs from Control
Uncoupled
Prescribed SSTs
Initial Conditions
Predictability Experiments
Experiment Description
Coupled Fully coupled
Perfect SSTs Uncoupled w/Perfect SSTs from control
Forecast SSTsUncoupled w/Forecast SSTs from coupled
predictability experiments
Persisted SSTAnomalies
Uncoupled w/Persisted SST anomalies
Monthly SSTs Uncoupled w/ monthly SSTs from control
Climatological SSTs Uncoupled w/Climatological SSTs from control
Example Event Control Simulation Unfiltered Anomalies
Averaged 10S-10N
Precipitation (mm/day)U200 (m/s) SST (degrees C)
Example Event
Unfiltered, Ensemble Mean Precipitation Anomalies Averaged 10S-10N
Clim Persisted Anomaly FCST SSTPerfect SST
Fo
rec
as
t D
ay
mm/day
CLIM
CoupledPerfect SST
Persistence
FCST SSTPersisted Anoms
A good SST forecast is important to the predictability of the TISO.
Example Event Predictability Estimates
Correlation Ensemble Mean with Control
Filtered (30-day) Precipitation Indo-Pacific Region
SST Sensitivity Experiments (All 10 Events)
Unfiltered, Ensemble Mean Precipitation Anomalies Averaged 10S-10N
Coupled Perfect
Fo
rec
as
t D
ay
Control
mm/day
Persist Anom ForecastClim
Fo
rec
as
t D
ay
Monthly
mm/day
SST Sensitivity Experiments (All 10 Events)
Unfiltered, Ensemble Mean Precipitation Anomalies Averaged 10S-10N
CLIM
CoupledPerfect SST
Monthly
FCST SSTPersisted Anoms
Predictability Estimates (Ten Events)
Correlation Ensemble Members with Control
Filtered (30-day) Precipitation Indo-Pacific Region
Coupled 18
Perfect 17
Fcst 16
Persist 16
Monthly 14
Clim 9
Predictability (Days)
Forecast Day
Co
rrel
atio
n C
oe
ffic
ien
t
CLIM
CoupledPerfect SST
Monthly
FCST SSTPersisted Anoms
Predictability Estimates (Ten Events)
Correlation Ensemble Mean with Control
Filtered (30-day) Precipitation Indo-Pacific Region
Coupled 36
Perfect 25
Fcst 23
Persist 20
Monthly 17
Clim 10
Predictability (Days)
Forecast Day
Co
rrel
atio
n C
oe
ffic
ien
t
Week 1 Week 2 Week 3 Week 4
Point Correlation of Unfiltered Precipitation Anomalies
Ensemble Members with ControlWeek 2
Coupled Perfect
Persist Anom
Forecast
Monthly Precipitation Anoms
Point Correlation of Unfiltered Precipitation Anomalies
Ensemble Members with ControlWeek 3
Coupled Perfect
Persist Anom
Forecast
Monthly Precipitation Anoms
Point Correlation of Unfiltered Precipitation Anomalies
Ensemble Members with ControlWeek 4
Coupled Perfect
Persist Anom
Forecast
Monthly Precipitation Anoms
Conclusions
1. Degrading the quality of the SST degrades the skill of the precipitation forecast beyond week-1.
2. 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.
3. 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.
4. Forecasting MJO-related precipitation beyond week-2 is a challenge even under a “perfect” model assumption.
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