Initialization Techniques in Seasonal Forecasting. Magdalena A. Balmaseda. Outline. The importance of the ocean initial conditions in seasonal forecasts A well established case: ENSO in the Equatorial Pacific A seasonal forecasting system Ocean Model initialization
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Magdalena A. Balmaseda
The importance of the ocean initial conditions in seasonal forecasts
A well established case: ENSO in the Equatorial Pacific
A seasonal forecasting system
Ocean Model initialization
The value of observational information: fluxes, SST, ocean observations
The ECMWF Ocean Initialization system ORAS4
Assessment of Initialization strategies
Full Initialization, Anomaly Initialization
PROBABILISTIC CALIBRATED FORECAST
Tailored Forecast PRODUCTS
Real time Probabilistic Coupled Forecast
Coupled Hindcasts, needed to estimate climatological PDF, require a historical ocean reanalysis
Consistency between historical and real-time initial conditions is required.
Hindcasts are also needed for skill estimation
Half of the gain on forecast skill is due to improved ocean initialization
S1 S2 S3
Initialization into Context
Balmaseda et al 2010, OceanObs
Need of a metric: lead time, variable, region (i.e. subjective choice)
Usually forecast of SST indices, lead time 1-6 months
In complex non linear systems there is no “objective searching algorithm” for optimality. The problem is solved by subjective choices and physical insight.
I.C. should represent accurately the state of the real world.
I.C. should project into the model attractor, so the model is able to evolve them.
In case of model error the above 2 statements may seem contradictory
If forecasts need calibration, the forecast I.C. should be “consistent” with the I.C. of the calibrating hindcasts. Need for historical ocean reanalysis
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 circulation.
Constrained by SST: Fluxes from atmospheric models
have large systematic errors and a large unconstrained chaotic component
Constrained by SST+ Atmos Observations: Surface fluxes from atmospheric reanalysis
Reduced chaotic component. But still large errors/uncertainty
Constrained by SST+AtmosObservations+Ocean Observations: Ocean reanalisys
Equatorial Atlantic: Taux anomalies
Equatorial Atlantic upper heat content anomalies. No assimilation
Equatorial Atlantic upper heat content anomalies. Assimilation
In the production of the reanalysis
Need to take into account model bias. Balance Multivariate Relationships
In the production of the forecast
Ocean Observing System
Data coverage for June 1982
Changing observing system is a challenge for consistent reanalysis
Today’s Observations will be used in years to come
Impact of data assimilation on the mean
Assim of mooring data
Large impact of data in the mean state leading to spurious variability
This is largely solved by the introduction of bias correction
Mean Assimation Temperature Increment
Data assimilation corrects the slope and mean depth of the equatorial thermocline
CONTROL (no ASSIM)
Correlation with altimeter-derive sea level data.
No Data Assimilation
Impact of Data Assimilation
Ocean data assimilation also improves the forecast skill
(Alves et al 2003)
This was with S1. It has been subsequently repeated with S2, S3, S4. But it is not trivial
Ocean Reanalysis System 4 (ORAS4)
Main Objective: Initialization of seasonal forecasts
Historical reanalysis brought up-to-date => Useful to study and monitor climate variability
Slow varying term, estimated online from assimilation increments dk
Seasonal term, estimated offline from Argo Period
Bias online: Time evolution
The offline bias correction is estimated from Argo period.
The correction is applied since 1957-00-01 to present.
It is a way of extrapolating Argo information into the past.
Consistent Improvement everywhere. Even in the Atlantic, traditionally challenging area
If adding more information degrades the results, there is something wrong with the methodology (coupled/assim system)
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)
ALL ATMOS+SST SST only
Central Western Pacific
SST only produces a cold drift in both eastern and western Pacific. Large interannual variability (not shown)
ATMOS+SST does not have drift in the Eastern Pacific, but its bias cold in the Western-Central Pacific
ALL produces warm bias in the Eastern Pacific, no bias in the Western-Central Pacifi.
The warm bias in the eastern Pacific is a consequence of the unbalanced initialization:
Weak winds in coupled model -> downwelling Kelvin wave->warming in Eastern Pacific
What about the forecast skill?
Reduction in Error (MAE) in SST SF by adding observational information
WINDS = [(ATMOS+SST) – SSTonly ]/ SSTonly
OCDATA = [ALL - (ATMOS+SST)]/ (ATMOS+SST)
OC+WINDS = [ALL - SSTonly]/SSTonly
The additional information about the real world improved the forecast skill, except in the Equatorial Atlantic
Still, optimal use of the observations needs more sophisticated assimilation techniques and better models, to reduced initialization shock
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.
At first sight, this paradigm would not allow a seamless prediction system.
Model Attractor (MA)
non-linear interactions important
Real World (RW)
Forecast lead time
a: perfect initialization and perfect model
b: no initialization shock. Best skill
c: Initialization shock. Good skill until lead time L
d: model attractor ini. No initialization shock
e: initialization shock + Non linearities Different convergence
Model Attractor (MA)
non-linear interactions important
Empirical Flux Corrections
Real World (RW)
Forecast lead time
Inventory of strategies (see table)
Need to be assessed and evaluated
Nudging of anomalies from other reanalysis may not the best, but a practical solution to study sensitivities and to get started
Relation between initialization strategy and forecast strategy
Coupled initialization probably the long term solution, but more difficult to start with.
Use all possible observational information unless there is a good reason why not.
Not intention to initialize the fast time scales of the atmospheric component. The atmospheric observations are either neglected or binned in long windows. They can also be used indirectly via nudging to existing atmospheric reanalysis.
The aim is to produce better (more balanced) initial conditions rather than an accurate estimation of the ocean variability.
Observations are used to correct both the mean and the variability.
Coupled 4D-var (Suguira et al 2008). Both ocean and atmospheric observations (binned). 9 months assimilation cycle. Control vector: coupling coefficients and ocean initial conditions.
Coupled EnKF (Zhang et al 2007). Only ocean observations are used directly. Atmospheric information is nudged during the integration.
Ocean Data Assimilation with a coupled model. (Fujii et al 2009) Atmospheric model is free (AMIP). Spectral control of SST variability. Free at time scales < 1month.Coupled Data Assimilation: MEAN+ ANOMALY
Observational information is used either directly or from existing reanalysis. Usually only the ocean component is initialized.
Background given by the coupled model
To obtain observational anomalies an observational climatology is assumed. In poor observed areas the time sampling for the climatology may be limited
One-Tier anomaly initialization (Smith et al 2007). Ocean observations are assimilatated directly. Background error covariance formulation derived from coupled model. Emphasis on large spatial scales
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.
Useful to compare different ocean reanalysis with the same model (Zhu et al 2012, GRL). Widely used in decadal forecasts
Special case when only SST anomalies are used. (Keenlyside et al 2008)2) ANOMALY INITIALIZATION
Magnusson et al. 2011 ECMWF Techmemo 658
Magnusson et al. 2012 ECMWF Techmemo To appear
10m winds: model - analysis
T2m bias: 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
A) Raw Forecasts
Example of decadal forecasts: Global SST
B) Bias Estimation
C) Corrected Forecasts
Nino 3 SST Drift 1-14 month forecast
Analysis Full IniAnomaly IniU Correction U+H correction
U- and H-flux correction
Linus Magnusson et al.
Persistence Full IniAnomaly IniU Correction U+H correction
Depending on the variable and region, the forecast skill is more or less affected by Initialization and forecast strategy.
Where the non linearities are strong, having a good mean state helps. In these situations, anomaly initialization underperfoms (Precip in Nino3.4, for instance)
Representing Uncertainty without disrupting Predictability
Seasonal versus Medium Range
In Seasonal, the main source of error/uncertainty is error formulation
It uses random (but realistic) perturbations as opposed to optimal perturbations to the initial conditions
There has been some research on optimal perturbations (breeding vectors, stochastic optimals, empirical singular vectors), but it will not be covered here (some information is given as hidden slides at the end)
Burst ensemble: (as opposed to lag-ensemble)
50-member ensemble forecast first of each month
Uncertainty in the ocean surface
50 SST perturbations
Uncertainty in the Ocean Subsurface
5 different ocean analysis generated with wind perturbations
+ SV for atmospheric initial conditions
Impact during the first month
Multi-model ensemble (EUROSIP)
Uncertainties in the SST
-Create data base with errors of weekly SST anomalies,arranged by calendar week:
Error in SST product: (differences between OIv2/OI2dvar)
Errors in time resolution: weekly versus daily SST
-Random draw of weekly perturbations, applied at the beginning of the coupled forecast. Over the mixed layer (~60m)
-A centred ensemble of 50 members
Wind perturbations +p1/-p1
Effect on Ocean Subsurface (D20)
~6-12 months decorrelation time in the thermocline
Uncertainties in the ocean Subsurface
-Create data base with errors in the monthly anomalous wind stress, arranged by calendar month:
(differences between ERA40-CORE)
-Random draw of monthly perturbations, applied during the ocean analyses.
-A centered ensemble of 5 analysis is constructed with:
Is the ensemble spread sufficient? Are the forecast reliable?
Forecast System is not reliable:
RMS > Spread
How to improve the reliability of the ensemble:
a) Sampling model error: multi-model, physical parameterizations
b) Calibrating the forecast a-posteriori
2.1) Sampling model error: The Real Time Multimodel
RMS error of Nino3 SST anomalies
2.1) Uncertainties in deterministic atmospheric physics?
ECMWF stochastic physics scheme:
is a stochastic variable, constant over time intervals of 6hrs and over 10x10 lat/long boxes
Buizza, Miller and Palmer, 1999; Palmer 2001
The Stochastic Physics samples neither uncertainty in the parameters, nor model error
From Vialard et al, MWR 2005
Wind Perturbations (WP)
Stochastic Physics (SP)
Wind Perturbations No DA (WPND)