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ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 1
Initialization Techniques in Seasonal Forecasting
Magdalena A. Balmaseda
Contributions from Linus Magnuson, Kristian Mogensen, Sarah Keeley
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 2
The importance of the ocean initial conditions in SF
Ocean Model initialization The value of observational information: fluxes, SST, ocean observations
The difficulties
The traditional Full Initialization approach: pros and cons.
Other approaches. Assessment
Full Initialization, Anomaly Initialization
Outline
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 3
The basis for extended range forecasts
•Forcing by boundary conditions changes the atmospheric circulation, modifying the large scale patterns of temperature and rainfall, so that the probability of occurrence of certain events deviates significantly from climatology.
Important to bear in mind the probabilistic nature of SF
•The boundary conditions have longer memory, thus contributing to the predictability. Important boundary forcing:
Tropical SST: ENSO, Indian Ocean Dipole, Atlantic SST
Land: snow depth, soil moisture
Sea-Ice
Mid-Latitude SST
Atmospheric composition: green house gases, aerosols,…
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 5
Impact of Sea Ice: 2007 2008
Ice Cover anomaly
Observed Z500 anomaly
Courtesy of Sarah Keely
CI 1Dm
CI .1
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 6
Low over Western Europe and Greenland high: similar response in both years. Consistent with observations
The response was conditioned by the SST, in particular the North Atlantic (Gulf Stream region), pointing
towards the need of high resolution ocean models (or flux corrections).
The question of the predictability of the sea-ice anomaly remains.
Atmos model
ObsIce-ClimIce
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From Balmaseda et al 2010
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 7
Potential Energy for Tropical Cyclones
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End-To-End Seasonal forecasting System
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COUPLED MODEL Tailored Forecast
PRODUCTS
Initialization Forward Integration Forecast Calibration
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Monthly mean anomalies relative to NCEP adjusted OIv2 1971-2000 climatology
ECMWF forecast from 1 Jan 2007
NINO3.4 SST anomaly plume
Produced from real-time forecast data
System 380°S80°S
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No Significance 90% Significance 95% Significance 99% Significance
Ensemble size = 40,climate size = 70
Forecast start reference is 01/06/2005
Tropical Storm Frequency
ECMWF Seasonal Forecast
Significance level is 90%
JASON
FORECAST CLIMATE
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 9
A decade of progress on ENSO
prediction
•Steady progress: ~1 month/decade skill gain
•How much is due to the initialization, how much to
model development?
S1 S2 S3
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ECMWF Seasonal Forecasting Systems
TOTAL GAIN OC INI MODEL
Half of the gain on forecast skill is due to improved ocean initialization
Initialization into Context
Balmaseda et al 2010, OceanObs
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 10
Importance of Initialization
•Atmospheric point of view: Boundary condition problem
Forcing by lower boundary conditions changes the PDF of the atmospheric attractor
“Loaded dice”
•Oceanic point of view: Initial value problem
Prediction of tropical SST: need to initialize the ocean subsurface.
o Emphasis on the thermal structure of the upper ocean
o Predictability is due to higher heat capacity and predictable dynamics
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 11
Need to Initialize the subsurface of the ocean
2OC Isotherm Depth Eq Anomaly SST Eq Anomaly
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 12
Initialization Problem: Production of Optimal I.C.
• Optimal Initial Conditions: those that produce the best forecast.
Need of a metric: lead time, variable, region (i.e. subjective choice)
Usually forecast of SST indices, lead time 1-6 months
• Theoretically, initial conditions should represent accurately the state of the
real world and project into the model attractor, so the model is able to
evolve them.
Difficult in the presence of model error
• Practical requirements: Consistency between re-forecasts and real time fc
Need for historical ocean reanalysis
• Current Priorities:
o Initialization of SST and ocean subsurface.
o Land/ice/snow
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 13
Dealing with model error: Hindcasts
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 conditions is required.
Hindcasts are also needed for skill estimation
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 14
Information to initialize the ocean
• Ocean model Plus:
SST
Atmospheric fluxes from atmospheric reanalysis
Subsurface ocean information
XBT’s 60’s Satellite SST Moorings/Altimeter ARGO
1982 1993 2001
Time evolution of the Ocean Observing System
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 15
How do we initialize the ocean?
To a large extent, the large scale ocean variability is forced by the
atmospheric surface fluxes.
Different ocean models forced by the same surface fluxes will produce similar tropical variability.
Daily fluxes of heat (short and long wave, latent, sensible heat), momentum and fresh water fluxes. Wind
stress is essential for the interannual variability.
1. Constrained by SST: Fluxes from atmospheric models
have large systematic errors and a large unconstrained chaotic component
2. Constrained by SST+ Atmos Observations: Surface fluxes from
atmospheric reanalysis
Reduced chaotic component. But still large errors/uncertainty
3. Constrained by SST+Atmos Observations+Ocean Observations: Ocean re-
analysis
Changing observing system and model error
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 16
Equatorial Atlantic: Taux anomalies
Equatorial Atlantic upper heat content anomalies. No assimilation
Equatorial Atlantic upper heat content anomalies. Assimilation
ERA15/OPS
ERA40
Uncertainty in Surface Fluxes:
Need for Data Assimilation
• 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? YES
2.Does the assimilation of ocean data improve the ocean estimate? YES
3.Does the assimilation of ocean data improve the seasonal forecasts. YES
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 17
The Assimilation corrects the ocean mean state
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difference from
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MAGICS 6.9.1 hyrokkin - neh Tue Jul 25 19:19:38 2006
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Free model
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Data assimilation corrects the slope and mean depth of the equatorial thermocline
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Data coverage for Nov 2005 60°S 60°S
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OBSERVATION MONITORING Changing observing
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Today’s Observations
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◊ ARGO floats: Subsurface Temperature and Salinity
+ XBT : Subsurface Temperature
Data coverage for June 1982
Ocean Observing System
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 19
EQATL Depth of the 20 degrees isotherm
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002Time
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Impact of data assimilation on the mean
Assim of mooring data
CTL=No data
Large impact of data in the mean state leading to spurious variability
This is largely solved by the introduction of bias correction
PIRATA
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 20
Need to correct model bias during assimilation
Without explicit bias correction changes in the observing system can induce
Spurious signals in the ocean reanalysis
Non-stationarity of the forecast bias, leading to forecast
errors.
Ideally, this information should be propagated during the forecast (for this the FG model and FC model should be the same, e.i. coupled model)
Temperature Bias Estimation from Argo: 300m-700mTemperature Bias Estimation from Argo: 300m-700m
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Temperature Bias Estimation from WOA05: 300m-700mTemperature Bias Estimation from WOA05: 300m-700m
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fa9p_oref_19932008_1_sossheig_stats.ps) Jun 16 2010
Number of Temperature Observations Depth= 500.0 meters
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ALLXBTCTDMOORARGOALL assimilated
There is a model for the time evolution of the bias
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This is an important difference with respect to the atmos data assimilation, where FG is assumed
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f f
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ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 21
Ocean Initialization at the ECMWF
Ocean Reanalysis System 4 (ORAS4): 1958 to present. 5 ens
members
Main Objective: Initialization of seasonal forecasts
Historical reanalysis brought up-to-date => Useful to study and monitor climate variability
Operational ORA-S4 NEMO-NEMOVAR
•ERA-40 daily fluxes (1958-1989) and ERA-Interim thereafter
•Retrospective Ocean Reanalysis back to 1958, 5 ensemble members
•Multivariate offline+on-line Bias Correction (pressure gradient, Temp,Sal,
offline from recent period )
•Assimilation of SST, temperature and salinity profiles, altimeter sea level
anomalies an global sea level trends
•Balance constrains (T/S and geostrophy)
•Sequential, 10 days analysis cycle, 3D-Var FGAT. Incremental Analysis
Update
Mogensen et al 2012, Balmaseda et al 2012
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 22
Time correlation with altimeter SL product
correl (1): fe5x sossheig ( 1993-2008 ) correl (1): fe5x sossheig ( 1993-2008 )
(ndim): Min= -0.37, Max= 1.00, Int= 0.02
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RMSE (1): fe5x sossheig (1993-2008) RMSE (1): fe5x sossheig (1993-2008)
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fa9p_oref_19932008_1_sossheig_stats.ps) Aug 4 2010
NEMOVAR T+S
ORAS4 T+S+Alti
CNTL: NoObs rms EQ2 Potential Temperature
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ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 23
Impact on Seasonal Forecast Skill
ORAS4
CNTL
Consistent Improvement everywhere. Even in the Atlantic, traditionally challenging area
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 24
Quantifying the value of observational information
• The outcome may depend on the coupled system
• In a good system information may be redundant, but not detrimental.
If adding more information degrades the results, there is something wrong with the methodology (coupled/assim system)
• Experiments conducted with the ECMWF S3
Balmaseda and Anderson 2009, GRL
SST (SYNTEX System Luo et al 2005, Decadal Forecasting Keenlyside et al, 2008)
SST+ Atmos observations (fluxes from atmos reanalysis)
SST+ Atmos observations+ Ocean Observations (ocean reanalysis)
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 25
Initialization and forecast drift
ALL ATMOS+SST SST only
Different initializations produce different drift in the same coupled model.
Warm drift in ALL caused by Kelvin Wave, triggered by the slackening of coupled model equatorial winds
SST only has very little equatorial heat content, and the SST cool s down very quickly.
SST+ATMOS seems balanced in this region. Not in others
Sign of non linearity:
The drift in the mean affects the variability
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 26
Impact of “real world” information on skill:
Optimal use of the observations needs more sophisticated assimilation techniques and better models, to reduced initialization shock
Increase (%) in MAE of SST forecasts
from removing external information
(1-7 months)
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The additional information about the real world improved the forecast skill, except in the Equatorial Atlantic
Reduction in Error (MAE) in SST SF by adding observational information
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 27
Assessing the Ocean Observing System (S3)
Important to bear in mind
1. The assessment depends on the quality of the coupled model
2. Need records long enough for results to be significant => any observing system needs to stay in
place for a long time before any assessment is possible.
From Balmaseda and Anderson 2009
See also Fujii et al 2008
1. No observation system is redundant
Not even in the Pacific, where Argo, moorings and altimeter still complement. Lessons for other basins?
2. There were obvious problems in the Eq Atlantic: model error, assimilation, and possibly insufficient observing system
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 28
Seasonal Forecasts Approach
Some caveats
• Non-stationary model error. It depends on starting date. For example, seasonal cycle dependence, which is known. There are other unknown dependences
• Drift depends of lead time. A large number of hindcasts is needed. This is even more costly in decadal forecasts.
• Initialization shock can be larger than model bias
SST FC bias CFS.v2 Kumar et al 2011 MWR
Non-linearities and non-stationarity can sometimes render the aposteriori calibration invalid
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 29
Perceived Paradigm for initialization of coupled forecasts
Real world Model world
Medium range Being close to the real world is perceived as advantageous. Model retains information for these time scales.
Model attractor and real world are close?
Decadal or longer Need to initialize the model attractor on the relevant time and spatial scales.
Model attractor different from real world.
•Seasonal traditional approach as in Medium range BUT (see next
slide)
•Not clear how to achieve initialization in model attractor Anomaly Initialization (decadal forecasts, Smith et al 2007) Full initialization with coupled models of the slow component only Other more sophisticated (EnKF, coupled DA, weakly coupled DA)
Seasonal?
At first sight, this paradigm would not allow a seamless prediction system.
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 30
Anomaly Initialization
The model climatology does not depend of forecast lead time. Cheaper in principle.
Hindcasts are still needed for skill estimation
Long
coupled
integration
Model climatology +observed anomaly
)]()[( xxHyyKxx ffa
Acknowledgment of existence of model error during initialization.
Model error is not corrected (“bias blind algorithm”):
Seasonal Approach
As Medium range but: Model bias taken into account during DA. A posteriori calibration of forecast is needed. Calibration depends on lead time.
The model for first guess during the initialization is different from the forecast model. Bias correction estimated during
initializaiton can not be applied during the forecasts
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fffa
ffffa
bxHyLbb
bxHyKbxx
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 31
Anomaly Initialization (Cont)
Two flavours
1. One-Tier anomaly initialization (Smith et al 2007). Ocean observations are assimilated
directly. Background error covariance formulation derived from coupled model (EOFs,
EnOI, EnKF). Emphasis on large spatial scales
2. Two-Tier anomaly initialization (Pohlmann et al 2009). Nudging of anomalies from
existing ocean re-analysis. The spatial structures are those provided by the source re-
analysis.
Limitations
• It assumes quasi-linear regime.
• Sampling: how to obtain an observed climatology equivalent to the model climate?
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 32
Initialization Shock and Skill
Forecast lead time
ph
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sp
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non-linear
interactions
important
Real World
Initialization
shock
b
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Model Climate
e
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L
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 33
Initialization Shock and non linearities
Forecast lead time
ph
ase
sp
ace
Real World
Model Climate
a
b
non-linear
interactions
important
Empirical Flux
Corrections
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 34
Comparison of Strategies for dealing with systematic errors
in a coupled ocean-atmosphere forecasting system
as part of the EU FP7 COMBINE project
Nature climate
Model climate
Flux correction
Anomaly initialisation
Normal initialisation
Magnusson et al. 2012, Clim Dyn Submitted. Also ECMWF Techmemo 658
Magnusson et al. 2012, Clim Dyn Sumbitted. Also ECMWF Techmemo 676
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 35
Coupled model error
SST bias: model - analysis 10m winds: model - analysis
Part of the error comes from the atmospheric component (too strong easterlies at the equator)
The error amplifies in the couped model (positive Bjerkness feedback)
Possibility of flux correction
From Magnusson et al 2012 Clim Dyn. Submitted
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 36
Different mean states
Analysis Coupled Free Coupled Ucor
Ucor: surface wind is corrected when passed to the ocean
Coupled UHcor
UHCor: surface wind and heat flux are corrected when passed to the ocean
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 37
Comparison of Forecast Strategies: Drift
Analysis Full Ini Anomaly Ini U Correction U+H correction
Nino 3 SST Drift 1-14 month forecast
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 38
Comparison of Forecast Strategies: Variability
Analysis Full Ini Anomaly Ini U Correction U+H correction
FC sdv / AN sdv
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 39
Nino3.4 SST forecasts November 1995 – November 1998
Full Initialization Anomaly Initialisation
U-flux correction
96 97 98 99
Linus Magnusson et al.
U- and H-flux correction
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 40
Impact on Forecast Skill (SST and Precip)
Persistence Full Ini Anomaly Ini U Correction U+H correction
The impact of initialization/forecast
strategy depends on the region
When the mean state matters (convective
precip), the anomaly Initialization
underperforms
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 41
What about Coupled Initialization?
• Advantages:
Hopefully more balanced ocean-atmosphere i.c and perturbations. Important for tropical convection
Framework to treat model error during initialization and fc
If the FG and FC models are the same, the (3D) bias correction estimated during the initialization can (should) be applied during the forecast.
Consistency across time scales (seamlessness):
currently, weather forecasts up to 10 days use “extreme flux correction”, since SST is prescribed. For longer lead times a free coupled model is used. More gradual transition?
• Current Approaches
Weakly Coupled Data assimilation: FG with coupled model, separate DA of ocean and atmos.
Example is NCEP with CFSR: coupled reanalysis to initialized and calibrate seasonal forecasts
Strongly Coupled Data assimilation: Coupled FG, Coupled Covariances. Usually EnKF
• Challenges:
Different time scales of ocean atmosphere . Long window weak constrain?
Cross-covariances. Ensemble methodology more natural?
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 42
Summary
• Seasonal Forecasting (SF) of SST is an initial condition problem
• Assimilation of ocean observations reduces the large uncertainty (error) due to the
forcing fluxes. Initialization of Seasonal Forecasts needs SST, subsurface temperature,
salinity and altimeter derived sea level anomalies.
• Data assimilation improves forecast skill.
• Data assimilation changes the ocean mean state. Therefore, consistent ocean
reanalysis requires an explicit treatment of the bias
• The separate initialization of the ocean and atmosphere systems can lead to
initialization shock during the forecasts. A more balance “coupled” initialization is
desirable, but it remains challenging.
• Initialization and forecast strategy go together. The best strategy may depend on the
model. The anomaly initialization used in decadal forecasts can have problems in
seasonal
”
ECMWF Seminar 2012 –Initialization Strategies in Seasonal Forecasting 43
Evolution over the last 365 days
Latest Conditions