initialization techniques in seasonal forecasting - … · initialization techniques in seasonal...

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


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


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,…


Impact of Sea Ice: 2007 2008

Ice Cover anomaly

Observed Z500 anomaly

Courtesy of Sarah Keely

CI 1Dm

CI .1


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


Potential Energy for Tropical Cyclones


End-To-End Seasonal forecasting System

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PRODUCTS

Initialization Forward Integration Forecast Calibration

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ECMWF forecast from 1 Jan 2007

NINO3.4 SST anomaly plume

Produced from real-time forecast data

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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


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?

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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


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


Need to Initialize the subsurface of the ocean

2OC Isotherm Depth Eq Anomaly SST Eq Anomaly


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


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


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


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


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


The Assimilation corrects the ocean mean state

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MAGICS 6.9.1 hyrokkin - neh Tue Jul 25 19:19:38 2006

Mean Assimation Temperature Increment

Free model

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Data assimilation corrects the slope and mean depth of the equatorial thermocline


Data coverage for Nov 2005 60°S 60°S

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Today’s Observations

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◊ ARGO floats: Subsurface Temperature and Salinity

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Data coverage for June 1982

Ocean Observing System


EQATL Depth of the 20 degrees isotherm

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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


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

(C/h): Min= -1.2e-03, Max= 7.5e-04, Int= 4.0e-05

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Temperature Bias Estimation from WOA05: 300m-700mTemperature Bias Estimation from WOA05: 300m-700m

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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

unbiased

f f

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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


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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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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Impact on Seasonal Forecast Skill

ORAS4

CNTL

Consistent Improvement everywhere. Even in the Atlantic, traditionally challenging area


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)


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


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


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


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


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.


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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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?


Initialization Shock and Skill

Forecast lead time

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Real World

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Initialization Shock and non linearities

Forecast lead time

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Empirical Flux

Corrections


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


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


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


Comparison of Forecast Strategies: Drift

Analysis Full Ini Anomaly Ini U Correction U+H correction

Nino 3 SST Drift 1-14 month forecast


Comparison of Forecast Strategies: Variability

Analysis Full Ini Anomaly Ini U Correction U+H correction

FC sdv / AN sdv


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


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


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?


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

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