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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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)
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
Importance of Salinity
Results from MRIFujii et al 2008
T+S: both temperature and salinity corrections
NOS: No Salinity corrections, only temperature
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
• 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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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.
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
Impact on Initialiazation on SF SkillECMWF S3
NINO3.4 RMS ERRORALL NO-OCOBS SST-ONLY
Adding information about the real world improves ENSO forecasts
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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.
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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.
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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
GODAE Final Symposium, 12 – 15 November 2008, Nice, France
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.
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