godae final symposium, 12 – 15 november 2008, nice, france ocean initialization for seasonal...

GODAE Final Symposium, 12 – 15 November 2008, Nice, France

Ocean Initialization for seasonal forecasts

ECMWF

CAWCR

Met Office

JMASTEC

NCEP

MERCATOR-Ocean

MRI

JPL

GMAO

NOAA/GFDL

University of Hamburg

Magdalena A. Balmaseda

Oscar Alves

Alberto Arribas

T. Awaji

David Behringer

Nicolas Ferry

Yosuke Fujii

Tony Tee

Michele Rienecker

Tony Rosati

Detlef Stammer


Outline

• Background The basis of seasonal forecasts Standard practice Different operational efforts around the world

• Ocean Model initialization Impact of assimilation on ocean estate Impact on seasonal forecast skill. Overview

• Towards “coupled” initialization: ongoing efforts

This talk only deals with prediction of SST. But seasonal forecasts products go beyond SST:

Temperature, Precipitation Tropical cyclones and hurricanes Applications such as hydropower, agriculture and health


The basis for seasonal forecasts

•Atmospheric point of view: Boundary condition problem– Forcing by lower boundary conditions changes the PDF of the

atmospheric attractor

“Loaded dice”– The lower boundary conditions (SST, land) have longer memory

o Higher heat capacity (Thermodynamic argument)

o Predictable dynamics

•Oceanic point of view: Initial value problem– Prediction of tropical SST: need to initialize the ocean subsurface.– Examples:

• A well established case is ENSO

• A more tantalizing case is the importance subsurface temperature in the North Subtropical Atlantic for seasonal forecasts of NAO and European Winters.

• Indian Ocean Dipole


Typical Seasonal Forecasting System: dealing with model error & forecast uncertanty

Ocean reanalysis

Coupled Hindcasts, needed to estimate climatological PDF, require a historical ocean reanalysis

Real time Probabilistic Coupled Forecast

time

Consistency between historical and real-time initial initial conditions is requiredQuality of reanalysis affects

the climatological PDF


Common features of the SF initialization systems

• Emphasis on upper ocean thermal structure and SST

• Climate configuration: Global domain, resolution ~1 deg with equatorial refinement. 30-50 vertical levels.

• Usually rely on previously analyzed SST field.

• Balance relationships (T and S, density and velocity)

• Assimilation cycle; 5-to-10 days

• Some control of the mean state: – Relaxation to climatology– Online bias correction (T, S, prssure gradient)– MDT: either prescribed (from free model, or T+S analysis) or

estimated (corrected) online

• Reanalysis period (15-20-50 years).

• Usually 2 products: – Delayed: 7-30 days– NRT : (0-7 days)

• Some have an ensemble of analyses (3-5)


Operational efforts: routine production of seasonal forecasts and ocean analysis

• MRI-JMA: MOVE/MRI-COM.G :

http://ds.data.jma.go.jp/tcc/tcc/products/elnino/index.html

• ECMWF: ORA-S3

http://www.ecmwf.int/ products/forecasts/d/charts/ocean

http://www.ecmwf.int/products/forecasts/d/charts/seasonal/

• CAWCR(Melbourne): POAMA-PEODAS

http://www.bom.gov.au/climate/coupled_model/poama.shtml

• NCEP (GODAS):

http://www.cpc.ncep.noaa.gov/products/GODAS/

• Mercator/Meteo-France:

http://bulletin.mercator-ocean.fr/html/welcome_en.jsp

• MetOffice GLOSEA3:

http://www.metoffice.gov.uk/research/seasonal/

• GMAO: ODAS-1

http://gmao.gsfc.nasa.gov/research/oceanassim/ODA_vis.php

http://gmao.gsfc.nasa.gov/cgi-bin/products/climateforecasts/index.cgi


Reducing Uncertainty

Equatorial Atlantic upper heat content anomalies. No assimilation

Equatorial Atlantic: Taux anomalies

Equatorial Atlantic upper heat content anomalies. Assimilation

A simple way of producing ocean initial conditions is to force and ocean model with atmospheric fluxes

But large uncertainty in wind products lead to large uncertainty in the ocean subsurface

The possibility is to use additional information from ocean data (temperature, others…)

Questions:

1. Does assimilation of ocean data constrain the ocean state?

2. Does the assimilation of ocean data improve the ocean estimate?

3. Does the assimilation of ocean data improve the seasonal forecasts


Ocean observations assimilated

XBT’s 60’s Satellite SST Moorings/Altimeter ARGO

1982 1993 2001

The ocean observing system has slowly been building up…

Its non-stationary nature is a challenge for the estimation of interannual variability


Example of potential problem:

Assim of mooring data

CTL=No data

Large impact of data in the mean state: Shallower thermocline

PIRATA

EQATL Depth of the 20 degrees isotherm

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002Time

-95

-90

-85

-80

-75

-70ega8 omona.assim_an0edp1 omona.assim_an0

From an Old DA system


Impact of assimilation on the ocean state

Alves et al

Fit to TAO observations (RMSE)

Temperature Salinity

Zonal velocityPOAMA: Only T, univariate (1st generation)

PEODAS: T+S, multivariate (2nd generation)

ORA-S3: T+S+, “ “ (2rd generation)

CONTROL : no data assimilation

Improvements was slow to achieve. But progress is evident

Alves et al 2008


Importance of Salinity

Results from MRIFujii et al 2008

T+S: both temperature and salinity corrections

NOS: No Salinity corrections, only temperature


barrier layer and warm water content

Fujii et al 2008

The WWC, function of the barrier layer thickness, plays an important role on ENSO

Barrier layer thkness T+S WWC: T+S -NOS


• Until very recently seasonal forecasts skill was considered a “blunt” tool to measure quality of ocean analysis: coupled models were not discerning enough.

• Examples of good impact were encouraging, but considered a strike of good luck.

• Improvements in the coupled ocean – atmosphere models also translate on the ability of using SF as evaluation of ocean initial conditions. In this presentation there are

several examples showing the positive impact of data assimilation on the skill of seasonal forecast.

• There are even results with observing system experiments, where the seasonal forecasts show significantly different behaviour

Need good coupled models to gauge the quality of initial conditions

The initialization problem is different from the state estimation problem .

– “Initialization shock” can be detrimental if non linearities matter.

Impact on Seasonal Forecasts Skill


Progress is not monotonic

0 1 2 3 4 5 6Forecast time (months)

0.4

0.5

0.6

0.7

0.8

0.9

1

Ano

mal

y co

rrel

atio

n

wrt NCEP adjusted OIv2 1971-2000 climatology

NINO3 SST anomaly correlation

0 1 2 3 4 5 6Forecast time (months)

0

0.2

0.4

0.6

0.8

1

1.2

Rm

s er

ror

(deg

C)

Ensemble sizes are 5 (0001) and 1 (0001) 60 start dates from 19870501 to 20010201

NINO3 SST rms errors

Fc S2 /m1 Fc S2 /m0 Persistence

MAGICS 6.11 verhandi - neh Fri Jun 1 16:49:20 2007

ERA15/OPS S2 NOdata S2 Assim

ERA40/OPS DEM NOdata DEM Assim

The quality of the initial conditions is not always the limiting factor on the skill


ALL

NO-OCOBS

SST-ONLY

Impact of Initialization strategy on SFECMWF S3

•Relation between drift and Amplitude of Interannual variability.

•Possible non linearity: is the warm drift interacting with the amplitude of ENSO?

•Drift and variability depend on initialization!!

•More information corrects for model error, and the information is retained during the fc.

•Need better (more balanced) initialization

•Relation between drift and Amplitude of Interannual variability.

•Upwelling area penetrating too far west leads to stronger IV than desired.


Impact on Initialiazation on SF SkillECMWF S3

NINO3.4 RMS ERRORALL NO-OCOBS SST-ONLY

Adding information about the real world improves ENSO forecasts


Impact of Different Ocean ObservationsJMA-MRI

Significance

NTT

NAF

６１％

７５％

９１％

９５％

８９％

８５％

７２％

７７％

４１％

７２％

６６％

７４％

３９％

５５％

Normarized RMSE (0-6month)

0.45

0.5

0.55

0.6

0.65

0.7

0.75

NINO12 NINO3 NINO34 NINO4 NINO-W STIO WTIO

RM

SE

ALLNTTNAF

Significance

NTT

NAF

６１％

７５％

９１％

９５％

８９％

８５％

７２％

７７％

４１％

７２％

６６％

７４％

３９％

５５％

Normarized RMSE (0-6month)

0.45

0.5

0.55

0.6

0.65

0.7

0.75

NINO12 NINO3 NINO34 NINO4 NINO-W STIO WTIO

RM

SE

ALLNTTNAF

Fujii etal 2008

NINO-W

EQATL

EQ3

STIO

WTIO

OSEs in JM-MRI confirm the complementary nature of the observing systems (moorings and floats) on the skill of SF.


Impact of initialization SF skill CAWCR POAMA

OLD POAMA initial conditions

New PEODAS initial conditions

No Data Assimilation

In the CAWCR system, an improved data assimilation system improves the seasonal forecast skill.


Improvements in SF: Mercator-MeteoFrance S3

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

North.Hem

South.Hem.

Global Nino34 Nino3 Nino4

system2

system3

The new Meteo-France SF system 3 shows improved skill. A large contribution to the improvement is likely due to better ocean initial conditions


The ECCO-JPL / UCLA example

• Improvement on SF by using ECCO-JPL. Baseline is a forecast from ocean initial conditions without data assimilation.

From Cazes-Boezio et al 2008.

RMS ERROR on SF of SST

Initialization and non linearities

Forecast lead time

pha

se s

pace

Model Attractor (MA)

non-linear interactions important

Real World (RW)

Initialization shock

a

b

c


More balance intialization

• Coupled Data Assimilation“Assimilation of ocean data with a coupled model”– Coupled 4D-var: JAMSTEC– EnKF: GMAO, GFDL

• Coupled Breeding Vectors: – generation of the ensemble by projecting the uncertainty of

the initial conditions on the fastest error-growth modes of the coupled system

• Anomaly Initialization:– Depresys (Met Office)– GECCO


Towards more balanced Initialization (I)Coupled 4D-var: JAMSTEC

Sugiura et al 2008

OBS

First guess

Analysis

Control: initial conditions (IC)

Control: Parameters (PRM)

Control: IC+PRM


Towards more balanced coupled initialization (II): Breeding Vectors in GMAO

Yang et al 2008

May starts

Nov starts

4 BV ens Control 4BV-Control

ACC of 9-month lead FC of SST

BV can also be used to formulate flow dependent covariances in the ocean data assimilation


Summary

•Ocean data assimilation is used operationally in several centres around the world to initialize seasonal forecasts with coupled models

•Improving the seasonal forecasts by assimilating ocean data has been a slow process. Limiting factors have been (are)

•Balance constraints between variables

•Spurious inter-annual variability due to non-stationary nature of observing system

•Quality of coupled models

•With the current generation of ocean data assimilation systems and coupled models it is possible to demonstrate the benefits of assimilating ocean data for the seasonal forecast skill

•The initialization shock remains a problem. There are currently several initiatives aiming at a more coupled initialization.

•Another challenge is the initialization of a seamless prediction system: from days-months to decades.

godae final symposium, 12 – 15 november 2008, nice, france ocean initialization for seasonal...

Documents

francegodae final symposium

healthgodae final symposium

oceangodae final symposium

seasonal forecasts products

seasonal forecasts of

ocean subsurface

seasonal forecast skill

ocean estateimpact