Breeding with the NSIPP global Breeding with the NSIPP global coupled model: applications to coupled model: applications to ENSO prediction ENSO prediction and and data data assimilation assimilation Shu-Chih Yang Shu-Chih Yang Advisors: Profs. Advisors: Profs. Eugenia Kalnay and Ming Eugenia Kalnay and Ming Cai Cai
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Breeding with the NSIPP global coupled model: applications to ENSO prediction and data assimilation
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 Presentation
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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
Outline Outline – Introduction– Objectives– NASA/NSIPP CGCM– Breeding method– Results from a 10-year perfect model
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
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
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
“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
Improvement on ensemble forecastsImprovement on ensemble forecastsFCT error with BV FCT error with RDM
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
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
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
Bred vector leads ENSO episode in the Eastern Pacific
Bred vector lags ENSO episode in the Central Pacific
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
NASA/NSIPP BV vs. NCEP/CFS BVNASA/NSIPP BV vs. NCEP/CFS BVNorthern Hemisphere
NSIPP geopotential height at 500mb
NCEP geopotential height at 500mb
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
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’
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
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
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.
BV Geopotential at 500mb
NCEP
NSIPP
From 10 year perfect model simulation
Joint EOF map of BV SST Joint EOF map of BV SST
BV1 Z20PC1 vs. BV1 growth rate
BV2 Z20PC1 vs. BV2 growth rate
Growth rateZ20 PC1
Growth rateZ20 PC1
CNT
Background Z20 EOF1 Background Z20 PC1
Background Z20 EOF2 Background Z20 PC2
Background ENSO vs. ENSO embryoBackground ENSO vs. ENSO embryo
CNT EOF1 BV1 EOF1 BV2 EOF1
CNT EOF2 BV1 EOF2 BV2 EOF2
BV growth rate
BV SST vs. (SSTfcst-SSTa) MAR1996
BV regression maps constructed with BV regression maps constructed with ZZ20 PC120 PC1