# Presentation: 190567

## 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

**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”

## Initial Conditions

- 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

## Example Event Control Simulation Unfiltered Anomalies

**Example Event Control Simulation Unfiltered Anomalies**
**Averaged 10S-10N**

- Precipitation (mm/day)

- U200 (m/s)

- SST (degrees C)

## Example Event

**Example Event**
**Unfiltered, Ensemble Mean Precipitation Anomalies Averaged 10S-10N**

**Clim**

**Persisted Anomaly**

**FCST SST**

**Perfect SST**

** ****Forecast Day**

- mm/day

## A good SST forecast is important to the predictability of the TISO.

**CLIM**

**Coupled**

**Perfect SST**

**Persistence**

**FCST SST**

**Persisted Anoms**

**Example Event Predictability Estimates **
**Correlation Ensemble Mean with Control**

**Filtered (30-day) Precipitation Indo-Pacific Region**

## SST Sensitivity Experiments (All 10 Events)

**SST Sensitivity Experiments (All 10 Events)**
**Unfiltered, Ensemble Mean Precipitation Anomalies Averaged 10S-10N**

**Coupled**

**Perfect**

** ****Forecast Day**

**Control**

- mm/day

## SST Sensitivity Experiments (All 10 Events)

**Persist Anom**

**Forecast**

**Clim**

** ****Forecast Day**

**Monthly**

- mm/day

**SST Sensitivity Experiments (All 10 Events)**
**Unfiltered, Ensemble Mean Precipitation Anomalies Averaged 10S-10N**

## Predictability Estimates (Ten Events)

**CLIM**

**Coupled**

**Perfect SST**

**Monthly**

**FCST SST**

**Persisted 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**

**Correlation Coefficient**

## Predictability Estimates (Ten Events) 

**CLIM**

**Coupled**

**Perfect SST**

**Monthly**

**FCST SST**

**Persisted 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**

**Correlation Coefficient**

## Week 2

**Point Correlation of Unfiltered Precipitation Anomalies**
**Ensemble Members with Control**

**Week 2**

**Coupled**

**Perfect**

**Persist Anom**

**Forecast**

**Monthly**

**Precipitation Anoms**

## Week 3

**Point Correlation of Unfiltered Precipitation Anomalies**
**Ensemble Members with Control**

**Week 3**

**Coupled**

**Perfect**

**Persist Anom**

**Forecast**

**Monthly**

**Precipitation Anoms**

## Week 4

**Point Correlation of Unfiltered Precipitation Anomalies**
**Ensemble Members with Control**

**Week 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.
- 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.

## 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

## Slide 18

## Week 1

**Point Correlation of Unfiltered Precipitation Anomalies**
**Ensemble Members with Control**

**Week 1**

**Coupled**

**Perfect**

**Persist Anom**

**Forecast**

**Monthly**