Breeding in NSIPP global coupled model and its implication on data assimilation system and ENSO prediction

Breeding with the NSIPP globalcoupled model: applications to ENSOprediction and data assimilationBreeding with the NSIPP globalcoupled model: applications to ENSOprediction and data assimilation

Shu-Chih YangShu-Chih Yang

Advisors: Profs. Eugenia Kalnayand Ming Cai Advisors: Profs. Eugenia Kalnayand Ming Cai

OutlineOutline

––Introduction

––Objectives

––NASA/NSIPP CGCM

––Breeding method

––Results from a 10-year perfect modelexperiment

––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 tosimulate and predict ENSO lies in the correct depiction of SST.

ENSO prediction skill

The prediction skill of a coupled model can be significantly improvedthrough more refined initialization procedures (ex: Chen et al.,1995 andRosati 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 ensemblemembers?

2 methods have been considered to constructinitial perturbations:

Singular vectors have been used for ENSOprediction with the Cane and Zebiak model

Limitations

Strong dependence on the choice of norm andoptimization 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 model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 timeof the ENSO states and strongest between the events.

Bred vectors can be applied to improve the forecast skilland reduce the impact of the “spring-barrier”.

The results show the potential impact for ensembleforecast and data assimilation

“Spring Barrier”: The “dip” in the error growth chart indicates a large errorgrowth 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

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/NSIPPCGCM

Construct effective perturbations for initial conditions ofENSO ensemble forecasts

Test methods first with a “perfect model” simulation todevelop understanding

Apply methodology to NSIPP operational system,which is more complex (e.g. model errors)

The ultimate goals is to improve seasonal andinterannual forecasts through ensemble forecastingand data assimilation using coupled breeding

NASA Seasonal-to InterannualPrediction (NSIPP) coupled GCMNASA Seasonal-to InterannualPrediction (NSIPP) coupled GCM

AGCM (Suarez, 1996)AGCM (Suarez, 1996)

Model features

Primitive equations

Empirical cloud diagnostic model

4th-order version of the enstrophyconserving scheme

4th-order horizontal advection schemes forpotential temperature, moisture

Penetrative convection parameterized withRelaxed Arakawa-Schubert scheme

Coordinates

Finite-difference C grid in horizontal

Generalized sigma coordinate

Resolution

2 2.534 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 entrainmentparameterized according to a Kraus-Turner bulkmixed layer model

Vertical mixing and diffusion are parameterizedusing a Richardson number dependent scheme

Horizontal mixing is implemented with high orderShapiro filtering

Coordinates

generalized horizontal and vertical coordinates

Resolution

13 58 27 layers

Current prediction skill from NSIPP CGCM hindcasts

Observations

Ensemble member

Ensemble mean

Niño-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

Breeding

Size of perturbation: 10% of the RMS of the SSTA (0.085C)

Rescaling period: one month

CGCM

AGCM

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 inthe NSIPP coupled model are naturally captured bythe breeding method

model year JUN2024

BV grows before thebackground event

Peak of thebackground event

Lead/lag correlation between

BV growth rate and

absolute value of background NINO3 index



Lead/lag correlation betweenBV growth rate andabsolute value of BV NINO3 index

EOF analysis of SSTEOF analysis of SST

Background 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 PCs

Background:

regressed with SST PC1

BV:

regressed with Z20 PC1

SST

Thermocline

(Z20)

Surface zonalcurrent

Atmospheric maps regressed with PCsAtmospheric maps regressed with PCs

Tropical Pacific domain

Wind

at 850mb

Surfacepressure

Geopotential

at 500mb

OLR

Background

Atmospheric maps regressed with PCsAtmospheric maps regressed with PCs

Northern Hemisphere

Background

Sea-levelpressure

Geopotential

at 200mb

Even though the breeding rescaling is in the Nino3region, the atmospheric response is global

Southern Hemisphere

Background

Sea-levelpressure

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 episodein the Eastern Pacific

Bred vector lags ENSO episodein the Central Pacific



NASA/NSIPP BV vs. NCEP/CFS BVNASA/NSIPP BV vs. NCEP/CFS BV

NSIPP

NCEP

SST

Z20

x

Tropical Pacific domain

NASA/NSIPP BV vs. NCEP/CFS BVNASA/NSIPP BV vs. NCEP/CFS BV

Z20EOF2

Z20EOF1

SSTEOF1

NCEP

NSIPP

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 BV

Northern 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 thedevelopment of the warm/cold events.The amplitude of BV in the eastern tropical Pacific increases before thedevelopment of the warm/cold events.

The ENSO related coupled instability exhibits large amplitude in the easterntropical Pacific.The ENSO related coupled instability exhibits large amplitude in the easterntropical Pacific.

In N.H, BV teleconnection pattern reflect their sensitivity associated withbackground ENSO. Rossby wave-train atmospheric anomalies over bothHemispheres.In N.H, BV teleconnection pattern reflect their sensitivity associated withbackground ENSO. Rossby wave-train atmospheric anomalies over bothHemispheres.

Breeding method is able to isolate the slowly growing coupled ENSO instabilityfrom weather noise

Bred vectors can capture the tropical instability waves

Results of a “perfect model” experiment with the NCEP CGCM are verysimilar

Develop breeding strategy for the NASA/NSIPP coupledoperational forecasting systemDevelop breeding strategy for the NASA/NSIPP coupledoperational forecasting system

Perform breeding runs with different rescaling norms

Perform experiments with a modified breeding cycle to reducespin-up:Perform experiments with a modified breeding cycle to reducespin-up:



Replace the restart file from an AMIP run to NCEP atmosphericre-analysis data

Current workCurrent work

t=1

t=2

t=3

t=4

t=5



F1month

B2month



B’





Relationship between bred vectorsand background errors

This case was chosen because the BV growth rate was large. Theexcellent agreement suggests that the operational OI could beimproved by augmenting the background error covariance with theBV 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

Summary of plans for application tothe operational NSIPP systemSummary of plans for application tothe operational NSIPP system

Develop a strategy to include the coupledgrowing modes extracted from coupled bredvectors in the initial condition of the ensemblesystem: For example, use perturbations +BVand –BV with an appropriate amplitude in theensemble forecast system

Develop a methodology for using advantage of theENSO BVs within the operational NSIPP oceanensemble data assimilation: For example, augmentthe OI background error covariance with BVs.