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

experiment– Comparison with breeding in NCEP CGCM

– Summary– NSIPP operational system: preliminary results

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

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 forecasts

FCT 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

Coordinates generalized horizontal and vertical coordinates Resolution 13 58 27 layers

Current prediction skill from NSIPP CGCM hindcasts

Observations

Ensemble member

Ensemble mean

Niño-3 Forecast SST anomalies up to 9-month leadNiño-3 Forecast SST anomalies up to 9-month lead

April 1 starts

September 1 starts

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

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

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

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

t

ttG

2

2

3

3

)]1(

)]([

)1(

)()(

SST

SST

SST

SST

[BV

BV

BV

BV

:rate growth BV

EOF analysis of SSTEOF analysis of SSTBackground SST anomaly EOF1 (46%)

BV SST EOF1 (11%)

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

Oceanic maps regressed with PCsOceanic maps regressed with PCsBackground:

regressed with SST PC1BV:

regressed with Z20 PC1

SST

Thermocline(Z20)

Surface zonal current

Atmospheric maps regressed with PCsAtmospheric maps regressed with PCsTropical Pacific domain

Wind at 850mb

Surface pressure

Geopotentialat 500mb

OLR

Background BV

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

Southern Hemisphere

BVBackground

Sea-level pressure

Geopotential at 200mb

Atmospheric maps regressed with PCsAtmospheric maps regressed with PCs

Lead/lag regression mapsLead/lag regression maps

BV 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

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

Color: Tfcst-Ta

Contour: BV (SST norm)

Vertical cross-section along the Equator

Color: Tfcst-Ta

Contour: BV (Z20 norm)

JAN2000

Color: Tfcst-Ta

Contour: BV (SST norm)

Color: Tfcst-Ta

Contour: BV (Z20 norm)

MAR1996

Vertical cross-section along the Equator