Transcript Unprecedented Perspective of Monsoon Intraseasonal

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)
Definition of Predictability
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.
Two Methods to Measure the Predictability
Ratio of Signal- to- Forecast Error
Lead Time
Anomaly Correlation Coefficient (ACC)
1.0
0.5
Lead Time
Ratio of Signal-to-Forecast Error
Control run
Perturbed Forecasts
(Signal) L=25 days
(Forecast Error)
Waliser et al. (2003)
Estimate of TISO Predictability from Observations
Wet
Dry
X
X
X
X
(70-90E,15-25E)
Dry-to-Wet
Error
Signal vs. Error
Signals
X
X
Wet-to-Dry
Error
Goswami and Xavier (2003)
(Days)
The Dry phase
Is more
predictable
than
the Wet phase
Two Different Error-Growth Regimes
Large-scale
Subsidence
Strong
Convective
Instability
Dry
Slow Error Growth
Wet
Fast Error Growth
Dry
Potential Predictability of TISO Rainfall
in NASA GLA AGCM
Signal
Forecast error variance
Domain: (12oN-16oN, 117.5oE-122.5oE): SCS
Waliser et al. (2003)
Potential Predictability of TISO VP200 and
Rainfall in NCEP Seasonal Forecasting Model
Perfect Initial/Boundary Conditions
Perfect Boundary Conditions
(ACC)
Perfect Initial 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 3
Phase 2
Phase 4
Phase 1
Spatial-temporal Evolutions of Signal vs. Forecast Error
Predictability of TISO Rainfall in the Eastern Indian Ocean
Air-Sea Coupling Extends the Predictability
of Tropical Intraseasonal Oscillation
ATM Forecast Error
Signal
Fu et al. (2007)
CPL Forecast Error
[ATM: 17 days; CPL: 24 days]
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
TISO Predictability is Phase-dependent
Coupled
Forecasts
Atmosphere-only
Forecasts
Break phase
Active phase
Summary I
 The predictability of TISO-related rainfall in UH hybrid
coupled GCM reaches about 24 days averaged over the Asianwestern 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., tierone 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 3
Phase 2
Phase 4
Phase 1
Event-I
Event-II
Control cases
Coupled forecasts
(CPL)
Ten-ensemble-mean
Atmosphere-only
forecasts (ATM)
Rainfall averaged over (65oE-120oE)
Ensemble Rainfall Evolutions of CPL
and ATM Forecasts for Event-II
SSTs in Five Experiments
Control
Mixed-layer
“Smoothed”
Coupled/Daily
Damped persistent
TISO predictability measured by signal-to-error ratio
ATM Forecast Error
CPL Forecast Error
Signal
Individual ensembles
ATM/ATMp: 24 days
CPL/ATMd: 34 days
TISO predictability measured by ACC
ACC
ATM/ATMp:21 days
Individual ensembles
CPL/ATMd: 30 days
TISO predictability measured by ACC
ACC
Ensemble means
ATM/ATMp: 30 days
CPL/ATMd: 42 days
Coupling also extends the predictability of weather
ATM Forecast Error
Signal
CPL Forecast Error
ATM/(Negative): 8 days
(During break-to-active transition)
CPL/(Positive): 16 days
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
Thanks
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
Reconcile with Previous Findings
Phase relationship
between SST and rainfall
in three different forecasts
(Coupled; Daily-forced;
and Daily-forced with
different initial conditions)
Mean Vertical Shear in First-month
Forecasts of CPL and ATM
Event-I
Event-II
Control (Solid), CPL (Long-dash), ATM (Dotted)