Role of Air-Sea Interaction on the Predictability of Tropical Intraseasonal

Oscillation (TISO)

Xiouhua Fu

International Pacific Research Center (IPRC) SOEST, University of Hawaii (UH) at Manoa Honolulu, Hawaii 96822

http://www.soest.hawaii.edu/~xfu

OUTLINE

Motivation Review of Previous Studies Air-Sea Coupling on TISO Predictability Best Lower Boundary Condition for TISO Predictability Summary

Intra-Seasonal Oscillation

WCRP-COPES (2005-2015)

Review of Previous Studies on the Predictability of Tropical Intraseasonal Oscillation (TISO)

Potential Predictability: The extent to which prediction is possible if “an optimum procedure” is used.

Perfect model assumption and subject to initial condition errors

Practical Predictability: The extent to which we ourselves are able to predict by the “best-known procedures”.

Subject to both model errors and initial condition errors

Adopted from E. N. Lorenz, 2006: Predictability - a problem partly solved. Chapter 3 in “Predictability of Weather and Climate”, Cambridge University Press, 702pp.

Definition of Predictability

Two Methods to Measure the Predictability Ratio of Signal- to- Forecast Error

Anomaly Correlation Coefficient (ACC)

Lead Time

Lead Time

0.5

1.0

(Signal) L=25 days

(Forecast Error)

Control run Perturbed Forecasts

Ratio of Signal-to-Forecast Error

Waliser et al. (2003)

Goswami and Xavier (2003)

Estimate of TISO Predictability from Observations

Signal vs. Error

Wet

Dry

Signals

Wet-to-Dry Error

Dry-to-Wet Error

(Days)

(70-90E,15-25E)

XX X X

The Dry phase Is more predictable than the Wet phase

X X

DryDry Wet

StrongConvective Instability

Large-scale Subsidence

Slow Error Growth Fast Error Growth

Two Different Error-Growth Regimes

Waliser et al. (2003)

Domain: (12oN-16oN, 117.5oE-122.5oE): SCS

Potential Predictability of TISO Rainfall in NASA GLA AGCM

Signal Forecast error variance

Potential Predictability of TISO VP200 and Rainfall in NCEP Seasonal Forecasting Model

(ACC)

Perfect Initial/Boundary Conditions

Perfect Initial Conditions

Perfect Boundary Conditions

Reichler and Roads (2005)

Practical Predictability of TISO U200 in NCEP Seasonal Forecasting Model

Winter

Summer

( 7 days) Seo et al. (2005)

UH Hybrid coupled GCM (UH_HcGCM)

Atmospheric component: ECHAM-4 T30L19 AGCM (Roeckner et al. 1996) Ocean component: Wang-Li-Fu intermediate upper ocean model (0.5ox0.5o) (Wang et al. 1995; Fu and Wang 2001)

Wang, Li, and Chang (1995): upper-ocean thermodynamics McCreary and Yu (1992): upper-ocean dynamics Jin (1997) : mean and ENSO (intermediate fully coupled model) Zebiak and Cane (1987): ENSO (intermediate anomaly coupled model)

Fully coupling without heat flux correction Coupling region: Tropical Indian and Pacific Oceans (30oS-30oN) Coupling interval: Once per day

Role of Air-Sea Coupling on TISO Predictability

Fu et al. 2007, JAS

Experimental Design 20 TISO events in 15-year coupled control run 4 phases for each TISO event “Twin” perturbed experiments starting from each phase (Lorenz 1963; Waliser et al. 2003) For both the atmosphere-ocean coupled model and atmosphere-only model, each with 160 forecasts

Methods to Measure ISO Predictability Signal-to-forecast error ratio ACC

Filtered Rainfall over (5oS-5oN, 80oE–100oE)

Phase 1

Phase 2

Phase 3

Phase 4

Spatial-temporal Evolutions of Signal vs. Forecast Error

Predictability of TISO Rainfall in the Eastern Indian Ocean

Signal CPL Forecast Error

ATM Forecast Error

Air-Sea Coupling Extends the Predictability of Tropical Intraseasonal Oscillation

[ATM: 17 days; CPL: 24 days]Fu et al. (2007)

ACC between Target Fields and Forecasts Target Forecast

0.91

0.86

0.84

0.73

0.43

ACC over (10oS-30oN, 60oE-160oE)

Predictability of TISO Rainfall in Days

Coupled Forecasts

Atmosphere-only Forecasts

Break phase Active phase

TISO Predictability is Phase-dependent

Summary I

The predictability of TISO-related rainfall in UH hybrid coupled GCM reaches about 24 days averaged over the Asian-western Pacific region (10oS-30oN, 60oE-160oE) when measured with the signal-to-error ratio. The averaged predictability in the atmosphere-only model is about 17 days. This result suggests that air-sea coupling is able to extend the predictability of the TISO by about a week.

The break phase of TISO is more predictable than the active phase.

Best Lower Boundary Condition for TISO Predictability

Fu et al. 2007 MWR, in press

What are the best SST configurations (e.g., tier- one vs. tier-two) for TISO hindcasts and forecasts? Could air-sea coupling extend the weather predictability?

Experimental Design 2 TISO events in a coupled control run 4 phases for each TISO event 10 ensemble forecasts starting from each phase of selected events under 5 different SST settings

Data Processing TISO: 20-90-day filtered daily rainfall Weather: unfiltered daily rainfall

Method to Measure TISO Predictability Signal-to-forecast error ratio ACC

Ensemble Experiments With Five Different SST Configurations

Experiment Name

SSTs Used in 90-day Forecasts

CPL Forecasted directly by interactive air-sea coupling (tier-one)

ATM Daily SST from the coupled control run after removing 20-90-day variability ( “smoothed” SST)

ATMp Daily SST from the coupled control run is linearly interpolated to the “smoothed” SST within first 10-day forecast (damped persistent SST)

ATMf Daily SST anomaly from a coupled slab mixed-layer ocean (ML depth = 30 m) is added to the “smoothed” SST

ATMd Ensemble-mean daily SST from the CPL forecasts (tier-two)

Filtered rainfall over (80oE–100oE, 5oS-5oN)

Phase 1

Phase 2

Phase 3

Phase 4

Rainfall averaged over (65oE-120oE)

Control cases

Coupled forecasts (CPL)

Atmosphere-only forecasts (ATM)

Ten-ensemble-mean

Event-I Event-II

Ensemble Rainfall Evolutions of CPL and ATM Forecasts for Event-II

SSTs in Five Experiments

Control

Coupled/Daily

Mixed-layer

Damped persistent

“Smoothed”

TISO predictability measured by signal-to-error ratio

ATM/ATMp: 24 days CPL/ATMd: 34 days

Signal

ATM Forecast Error

CPL Forecast Error

Individual ensembles

ATM/ATMp:21 days CPL/ATMd: 30 days

Individual ensembles

ACC

TISO predictability measured by ACC

Ensemble means

ATM/ATMp: 30 days CPL/ATMd: 42 days

ACC

TISO predictability measured by ACC

Coupling also extends the predictability of weather

ATM/(Negative): 8 days CPL/(Positive): 16 days

ATM Forecast Error

CPL Forecast Error

Signal

(During break-to-active transition)

Summary II The TISO predictability in UH_HcGCM reaches about 30 days averaged over the Southeast Asia. The predictability in the stand-alone atmospheric model is about 20 days. Interactive air-sea coupling extends the TISO predictability by about 10 days. During break-to -active transition, coupling also significantly extends weather predictability.

Tier-two system could reach similar TISO predictability as tier-one system, suggesting that using observed high-frequency SST for TISO hindcasts and using interactive air-sea coupling and forecasted daily SST for real-time forecasts are good options.

An Example of MJO Forecast

An Example of Boreal-Summer TISO Forecast

ThanksThanks

Why does the daily SST-forced atmospheric forecasts (ATMd, tier-two) have similar predictability with the coupled forecasts (CPL, tier-one)?

Air-sea coupling maintains correct phase relationship between ISO rainfall and underlying SST

Fu et al. (2003), Fu and Wang (2004)

Evolutions of SST and Rainfall Anomalies in the CPL and ATM Forecasts

Phase relationship between SST and rainfallin three different forecasts (Coupled; Daily-forced; and Daily-forced with different initial conditions)

Reconcile with Previous Findings

Event-I Event-II

Mean Vertical Shear in First-month Forecasts of CPL and ATM

Control (Solid), CPL (Long-dash), ATM (Dotted)