breeding with the nsipp global coupled model: applications to enso prediction and data assimilation
DESCRIPTION
Breeding with the NSIPP global coupled model: applications to ENSO prediction and data assimilation . Shu-Chih Yang Advisors: Profs. Eugenia Kalnay and Ming Cai. Outline . Introduction Objectives NASA/NSIPP CGCM Breeding method Results from a 10-year perfect model experiment - PowerPoint PPT PresentationTRANSCRIPT
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Breeding with the NSIPP global Breeding with the NSIPP global coupled model: applications to ENSO coupled model: applications to ENSO
predictionprediction andand data assimilation data assimilation
Shu-Chih YangShu-Chih Yang
Advisors: Profs. Eugenia Kalnay Advisors: Profs. Eugenia Kalnay and Ming Caiand Ming Cai
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Outline Outline – Introduction– Objectives– NASA/NSIPP CGCM– Breeding method– Results from a 10-year perfect model
experiment– Comparison with breeding in NCEP CGCM
– Summary– NSIPP operational system: preliminary results
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IntroductionIntroduction ENSO simulation
Because the coupled nature of ENSO phenomenon, the key factor to simulate and predict ENSO lies in the correct depiction of SST.
ENSO prediction skill The prediction skill of a coupled model can be significantly improved
through more refined initialization procedures (ex: Chen et al.,1995 and Rosati et al, 1997)
Initialization of operational ensemble forecast for CGCMs Two-tier (Bengtsson et al., 1993)
An ensemble of atmospheric forecast generated by a forecasted SST One-tier (Stockdale et al., 1998, adopted in ECMWF)
Generate all the ensemble members via CGCM Initial perturbations are introduced in atmosphere components only
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How to construct effective ensemble members?
2 methods have been considered to construct initial perturbations:
Singular vectors have been used for ENSO prediction with the Cane and Zebiak model
Limitations Strong dependence on the choice of norm and
optimization time High computational cost makes it impractical for CGCMs
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Breeding method Breeding method Toth and Kalnay (1996)Toth and Kalnay (1996)Cai et al. (2002) with CZ modelCai et al. (2002) with CZ model
Bred vectors are sensitive to the background ENSO, showing that the growth rate is weakest at the peak time of the ENSO states and strongest between the events.
Bred vectors can be applied to improve the forecast skill and reduce the impact of the “spring-barrier”.
The results show the potential impact for ensemble forecast and data assimilation
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“Spring Barrier”: The “dip” in the error growth chart indicates a large error growth for forecasts that begin in the spring and pass through the summer. Removing the BV from the initial errors reduces the spring barrier
Monthly Amplification Factor of Bred Vector
Background ENSO
El Niño
La Niña La Niña
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Improvement on ensemble forecastsImprovement on ensemble forecastsFCT error with BV FCT error with RDM
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Objectives of this researchObjectives of this research Implement the breeding method with the NASA/NSIPP
CGCM Construct effective perturbations for initial conditions of
ENSO ensemble forecasts Test methods first with a “perfect model” simulation to
develop understanding
Apply methodology to NSIPP operational system, which is more complex (e.g. model errors)
The ultimate goals is to improve seasonal and interannual forecasts through ensemble forecasting and data assimilation using coupled breeding
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NASA Seasonal-to Interannual NASA Seasonal-to Interannual Prediction (NSIPP) coupled GCMPrediction (NSIPP) coupled GCM
AGCM AGCM (Suarez, 1996)(Suarez, 1996)
Model features
Primitive equations Empirical cloud diagnostic model 4th-order version of the enstrophy
conserving scheme 4th-order horizontal advection schemes for
potential temperature, moisture Penetrative convection parameterized with
Relaxed Arakawa-Schubert scheme
Coordinates Finite-difference C grid in horizontal Generalized sigma coordinate
Resolution 2 2.534 levels
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OGCM OGCM Poseidon V4, (Schopf and Loughe,1995)Poseidon V4, (Schopf and Loughe,1995)
Model features
Quasi-isopycnal model Reduced-gravity formulation Turbulent well-mixed layer with entrainment parameterized according to a Kraus-Turner bulk mixed layer model
Vertical mixing and diffusion are parameterized using a Richardson number dependent scheme
Horizontal mixing is implemented with high order Shapiro filtering
Coordinates generalized horizontal and vertical coordinates Resolution 13 58 27 layers
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Current prediction skill from NSIPP CGCM hindcasts
ObservationsEnsemble memberEnsemble mean
Niño-3 Forecast SST anomalies up to 9-month leadApril 1 starts
September 1 starts
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Breeding methodBreeding method
Bred vectors : The differences between the control forecast and perturbed runs Tuning parameters
Size of perturbation Rescaling period (important for coupled system)
Advantages Low computational cost Easy to apply to CGCM
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10 years breeding “perfect model” experiment10 years breeding “perfect model” experiment
BreedingSize of perturbation: 10% of the RMS of the SSTA
(0.085C)Rescaling period: one month
CGCMAGCM NSIPP-1: 3° X 3.75° X 34 (global)
OGCM Poseidon V4: 1/2° X 1.25° X 27 (90S - 72N)
NINO3 INDEX(ºC)
SOI INDEX
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Snapshot of background SST (color) and bred vector SST (contour)
Instabilities associated with the equatorial waves in the NSIPP coupled model are naturally captured by the breeding method
model year JUN2024
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BV grows before the background event
Peak of the background event
Lead/lag correlation between BV growth rate and
absolute value of background NINO3 index
nt
nt
NINO
NINO
t
t
tt
tG
2
2
3
3
)]1(
)]([
)1()(
)(
SST
SST
SST
SST
[BV
BV
BVBV
:rate growth BV
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EOF analysis of SSTEOF analysis of SSTBackground SST anomaly EOF1 (46%)
BV SST EOF1 (11%)
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EOF analysis of thermocline (Z20)EOF analysis of thermocline (Z20)Background Z20 EOF1 (22%)
Background Z20 EOF2 (16%)
BV Z20 EOF1 (10%)
BV Z20 EOF2 (7%)
Z20 EOF2, SST EOF1 represent the mature phase of ENSO
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Oceanic maps regressed with PCsOceanic maps regressed with PCsBackground:
regressed with SST PC1BV:
regressed with Z20 PC1
SST
Thermocline(Z20)
Surface zonal current
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Atmospheric maps regressed with PCsAtmospheric maps regressed with PCsTropical Pacific domain
Wind at 850mb
Surface pressure
Geopotentialat 500mb
OLR
Background BV
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Atmospheric maps regressed with PCsAtmospheric maps regressed with PCsNorthern Hemisphere
BVBackground
Sea-level pressure
Geopotential at 200mb
Even though the breeding rescaling is in the Nino3 region, the atmospheric response is global
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Southern Hemisphere
BVBackground
Sea-level pressure
Geopotential at 200mb
Atmospheric maps regressed with PCsAtmospheric maps regressed with PCs
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Lead/lag regression mapsLead/lag regression mapsBV SST vs. | CNT NINO3 |
BV zonal wind stress vs. | CNT NINO3 |
BV surface height vs. | CNT NINO3 |
Bred vector leads ENSO episode in the Eastern Pacific
Bred vector lags ENSO episode in the Central Pacific
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NASA/NSIPP BV vs. NCEP/CFS BVNASA/NSIPP BV vs. NCEP/CFS BV
Z20 EOF2
Z20 EOF1
SST EOF1
NCEPNSIPP
Results obtained with a 4-year NCEP run are extremely similar to oursResults obtained with a 4-year NCEP run are extremely similar to ours
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NASA/NSIPP BV vs. NCEP/CFS BVNASA/NSIPP BV vs. NCEP/CFS BVNorthern Hemisphere
NSIPP geopotential height at 500mb
NCEP geopotential height at 500mb
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Summary of “perfect model” resultsSummary of “perfect model” results
Larger BV growth rate leads the warm/cold events by about 3 months.
The amplitude of BV in the eastern tropical Pacific increases before the The amplitude of BV in the eastern tropical Pacific increases before the development of the warm/cold events. development of the warm/cold events.
The ENSO related coupled instability exhibits large amplitude in the eastern The ENSO related coupled instability exhibits large amplitude in the eastern tropical Pacific.tropical Pacific.
In N.H, BV teleconnection pattern reflect their sensitivity associated with In N.H, BV teleconnection pattern reflect their sensitivity associated with background ENSO. Rossby wave-train atmospheric anomalies over both background ENSO. Rossby wave-train atmospheric anomalies over both Hemispheres.Hemispheres.
Breeding method is able to isolate the slowly growing coupled ENSO instability from weather noise
Bred vectors can capture the tropical instability waves
Results of a “perfect model” experiment with the NCEP CGCM are very similar
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Develop breeding strategy for the NASA/NSIPP coupled Develop breeding strategy for the NASA/NSIPP coupled operational forecasting systemoperational forecasting systemPerform breeding runs with different rescaling norms
Perform experiments with a modified breeding cycle to reduce Perform experiments with a modified breeding cycle to reduce spin-up:spin-up:
Replace the restart file from an AMIP run to NCEP atmospheric re-analysis data
Current workCurrent work
t=1 t=2 t=3 t=4 t=5
A
F1month
B2month
B’ B’
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Relationship between bred vectors and background errors
This case was chosen because the BV growth rate was large. The excellent agreement suggests that the operational OI could be improved by augmenting the background error covariance with the BV as in Corazza et al, 2002
BV Temp (contour) vs. analysis increment (color) at OCT1996
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SST: Analysis - Control forecast
Analysis – BV ensemble ave fcst
For this case, we performed the first ensemble forecast: [(+BV fcst)+(-BV fcst)]/2
OCT1996
OCT1996
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Summary of plans for application to Summary of plans for application to the operational NSIPP systemthe operational NSIPP system
Develop a strategy to include the coupled growing modes extracted from coupled bred vectors in the initial condition of the ensemble system: For example, use perturbations +BV and –BV with an appropriate amplitude in the ensemble forecast system
Develop a methodology for using advantage of the ENSO BVs within the operational NSIPP ocean ensemble data assimilation: For example, augment the OI background error covariance with BVs.
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BV Geopotential at 500mb
NCEP
NSIPP
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From 10 year perfect model simulation
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Joint EOF map of BV SST Joint EOF map of BV SST
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BV1 Z20PC1 vs. BV1 growth rate
BV2 Z20PC1 vs. BV2 growth rate
Growth rateZ20 PC1
Growth rateZ20 PC1
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CNT
Background Z20 EOF1 Background Z20 PC1
Background Z20 EOF2 Background Z20 PC2
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Background ENSO vs. ENSO embryoBackground ENSO vs. ENSO embryo
CNT EOF1 BV1 EOF1 BV2 EOF1
CNT EOF2 BV1 EOF2 BV2 EOF2
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BV growth rate
BV SST vs. (SSTfcst-SSTa) MAR1996
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BV regression maps constructed with BV regression maps constructed with ZZ20 PC120 PC1
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Color: Tfcst-TaContour: BV (SST norm)
Vertical cross-section along the Equator
Color: Tfcst-TaContour: BV (Z20 norm)
JAN2000
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Color: Tfcst-TaContour: BV (SST norm)
Color: Tfcst-TaContour: BV (Z20 norm)
MAR1996
Vertical cross-section along the Equator
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