Sensitivity of MJO Predictability to SST

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