Download - Role of Air-Sea Interaction on the Predictability of Tropical Intraseasonal Oscillation (TISO)
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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
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OUTLINE
Motivation Review of Previous Studies Air-Sea Coupling on TISO Predictability Best Lower Boundary Condition for TISO Predictability Summary
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Intra-Seasonal Oscillation
WCRP-COPES (2005-2015)
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Review of Previous Studies on the Predictability of Tropical Intraseasonal Oscillation (TISO)
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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
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Two Methods to Measure the Predictability Ratio of Signal- to- Forecast Error
Anomaly Correlation Coefficient (ACC)
Lead Time
Lead Time
0.5
1.0
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(Signal) L=25 days
(Forecast Error)
Control run Perturbed Forecasts
Ratio of Signal-to-Forecast Error
Waliser et al. (2003)
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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
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DryDry Wet
StrongConvective Instability
Large-scale Subsidence
Slow Error Growth Fast Error Growth
Two Different Error-Growth Regimes
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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
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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)
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Practical Predictability of TISO U200 in NCEP Seasonal Forecasting Model
Winter
Summer
( 7 days) Seo et al. (2005)
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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
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Role of Air-Sea Coupling on TISO Predictability
Fu et al. 2007, JAS
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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
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Filtered Rainfall over (5oS-5oN, 80oE–100oE)
Phase 1
Phase 2
Phase 3
Phase 4
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Spatial-temporal Evolutions of Signal vs. Forecast Error
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Predictability of TISO Rainfall in the Eastern Indian Ocean
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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)
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ACC between Target Fields and Forecasts Target Forecast
0.91
0.86
0.84
0.73
0.43
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ACC over (10oS-30oN, 60oE-160oE)
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Predictability of TISO Rainfall in Days
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Coupled Forecasts
Atmosphere-only Forecasts
Break phase Active phase
TISO Predictability is Phase-dependent
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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.
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Best Lower Boundary Condition for TISO Predictability
Fu et al. 2007 MWR, in press
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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?
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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
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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)
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Filtered rainfall over (80oE–100oE, 5oS-5oN)
Phase 1
Phase 2
Phase 3
Phase 4
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Rainfall averaged over (65oE-120oE)
Control cases
Coupled forecasts (CPL)
Atmosphere-only forecasts (ATM)
Ten-ensemble-mean
Event-I Event-II
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Ensemble Rainfall Evolutions of CPL and ATM Forecasts for Event-II
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SSTs in Five Experiments
Control
Coupled/Daily
Mixed-layer
Damped persistent
“Smoothed”
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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
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ATM/ATMp:21 days CPL/ATMd: 30 days
Individual ensembles
ACC
TISO predictability measured by ACC
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Ensemble means
ATM/ATMp: 30 days CPL/ATMd: 42 days
ACC
TISO predictability measured by ACC
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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)
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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.
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An Example of MJO Forecast
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An Example of Boreal-Summer TISO Forecast
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Why does the daily SST-forced atmospheric forecasts (ATMd, tier-two) have similar predictability with the coupled forecasts (CPL, tier-one)?
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Air-sea coupling maintains correct phase relationship between ISO rainfall and underlying SST
Fu et al. (2003), Fu and Wang (2004)
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Evolutions of SST and Rainfall Anomalies in the CPL and ATM Forecasts
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Phase relationship between SST and rainfallin three different forecasts (Coupled; Daily-forced; and Daily-forced with different initial conditions)
Reconcile with Previous Findings
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Event-I Event-II
Mean Vertical Shear in First-month Forecasts of CPL and ATM
Control (Solid), CPL (Long-dash), ATM (Dotted)