Breeding With The Nsipp Global

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
Comparison with breeding in NCEP CGCM
Summary
NSIPP operational system: preliminary results

Introduction
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

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
Toth and Kalnay (1996)
Cai 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 Ni ño La Ni ña La Ni ña

Improvement on ensemble forecasts FCT error with  BV FCT error with  RDM

Objectives 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 Prediction (NSIPP) coupled GCM
AGCM (Suarez, 1996)
Finite-difference C grid in horizontal
Generalized sigma coordinate
Coordinates 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
2  2.5  34 levels Resolution

OGCM
Poseidon V4, (Schopf and Loughe,1995)
generalized horizontal and vertical coordinates Coordinates 1  3  5  8  27 layers Resolution
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
Model features

Current prediction skill from NSIPP CGCM h indcasts Niño-3 Forecast SST anomalies up to 9-month lead April 1 starts September 1 starts Observations Ensemble member Ensemble mean

Breeding 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


Size of perturbation: 10% of the RMS of the SSTA (0.085  C) Rescaling period: one month Breeding OGCM AGCM Poseidon V4: 1/2° X 1.25° X 27 (90S - 72N) 10 years breeding “perfect model” experiment NSIPP-1: 3° X 3.75° X 34 (global) CGCM NINO3 INDEX(ºC) SOI INDEX

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

Lead/lag correlation between BV growth rate and absolute value of background NINO3 index  BV grows before the background event Peak of the background event

Lead/lag correlation between BV growth rate and absolute value of BV NINO3 index

EOF analysis of SST Background SST anomaly EOF1 (46%) BV SST EOF1 (11%)

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

Oceanic maps regressed with PCs Background: regressed with SST PC1 BV: regressed with Z20 PC1 SST Thermocline (Z20) Surface zonal current

Atmospheric maps regressed with PCs Tropical Pacific domain Wind at 850mb Surface pressure Geopotential at 500mb OLR Background BV

Atmospheric maps regressed with PCs Northern Hemisphere BV Background S ea-level pressure G eopotential at 200mb Even though the breeding rescaling is in the Nino3 region, the atmospheric response is global

Southern Hemisphere BV Background S ea-level pressure G eopotential at 200mb Atmospheric maps regressed with PCs

NASA/NSIPP BV vs. NCEP/CFS BV NSIPP NCEP SST Z20  x Tropical Pacific domain

Results obtained with a 4-year NCEP run are extremely similar to ours
NASA/NSIPP BV vs. NCEP/CFS BV Z20 EOF2 Z20 EOF1 SST EOF1 NCEP NSIPP

NASA/NSIPP BV vs. NCEP/CFS BV Northern Hemisphere NSIPP geopotential height at 500mb NCEP geopotential height at 500mb

Summary 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 development of the warm/cold events.
The ENSO related coupled instability exhibits large amplitude in the eastern tropical Pacific.
In N.H, BV teleconnection pattern reflect their sensitivity associated with background ENSO. Rossby wave-train atmospheric anomalies over both 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 operational forecasting system
Perform breeding runs with different rescaling norms
Perform experiments with a modified breeding cycle to reduce spin-up:

Replace the restart file from an AMIP run to NCEP atmospheric re-analysis data
Current work t=1 t=2 t=3 t=4 t=5   A F 1month B 2month  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 the 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

BV1 Z20PC1 vs. BV1 growth rate BV2 Z20PC1 vs. BV2 growth rate Growth rate Z20 PC1 Growth rate Z20 PC1

CNT Background Z20 EOF1 Background Z20 PC1 Background Z20 EOF2 Background Z20 PC2

Background ENSO vs. ENSO embryo CNT EOF1 BV1 EOF1 BV2 EOF1 CNT EOF2 BV1 EOF2 BV2 EOF2

BV growth rate BV SST vs. (SST fcst -SST a ) MAR1996

BV regression maps constructed with Z 20 PC1

Color: T fcst -T a Contour: BV (SST norm) Vertical cross-section along the Equator Color: T fcst -T a Contour: BV (Z20 norm) JAN2000

Color: T fcst -T a Contour: BV (SST norm) Color: T fcst -T a Contour: BV (Z20 norm) MAR1996 Vertical cross-section along the Equator

Breeding With The Nsipp Global

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