Ocean Initialization in Seasonal Forecasting

1
Ocean Initialization in Seasonal Forecasting

• Magdalena A. Balmaseda

2
Outline

• The importance of the ocean initial conditions in
  seasonal forecasts.
• Ocean Model Initialization Requirements.
• The ECWMF ocean analysis system
• Standard Practice Assessment.
• Role of different observing systems
• Other Initialization strategies Assessment.
• How to advance further?

3
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
• Higher heat capacity (Thermodynamic argument)
• 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.

4
A tantalizing case SF of NAO and European Winters
SST anomaly Autumn 2005
5
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)
• In complex non linear systems there is no
  objective searching algorithm for optimality.
  The practical approach is to have subjective
  criteria.
• Theoretically
• 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
• Practical requirements
• If forecasts need calibration, the forecast I.C.
  should be consistent with the I.C. of the
  calibrating hindcasts. Need for historical ocean
  reanalysis
• Current Priorities
• Initialization of SST and ocean subsurface.
• Land/ice/snow potentially important. Nor much
  effort so far
• Atmospheric initial conditions play a secondary
  role.
• We choose a metric, forecasts of SST from 1-6
  months.

6
Simplest way of creating OICOcean model Atmos
fluxes
ERA15/OPS ERA40

• 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
• Does assimilation of ocean data constrain the
  ocean state?
• Does the assimilation of ocean data improve the
  ocean estimate?
• Does the assimilation of ocean data improve the
  seasonal forecasts

7
The Assimilation corrects the ocean mean state
Analysis minus Observations
Western Pacific
Equatorial Indian
DATA ASSIM NO DATA ASSIM
8
Data Assimilation Improves the Interannual
variability of the Ocean Analysis
Correlation of SL with altimeter data (which was
not assimilated)
9
Impact of Data Assimilation Forecast Skill
Ocean data assimilation also improves the
forecast skill (Alves et al 2003)
10
So far so good, but

• Progress is not monotonic
• Need good coupled models to gauge the quality of
  initial conditions
• Initialization shock can be detrimental if non
  linearities matter.

S2 NOdata S2 Assim (ERA15/OPS)
11
The Ocean Observing System
12
Main Objective to provide ocean Initial
conditions for coupled forecasts
Coupled Hindcasts, needed to estimate
climatological PDF, require a historical ocean
reanalysis
13
System-3

• Ocean model HOPE (1x1), free surface, partial
  step
• Assimilation Method OI (3D)
• Sequential assimilation
• 10 days assimilation windows, increment spread in
  time

• Features
• ERA-40 fluxes to initialize ocean
• Retrospective Ocean Reanalysis back to 1959.
• Multivariate on-line Bias Correction .
• Assimilation of temperature, salinity and
  altimeter-derived sea level anomalies and global
  mean sea level trends.
• Balanced relationships (T-S, geotrophic
  increments)
• Incremental analysis update (IAU)

14
Assimilation in the ECMWF operational system 3
Geostrophic velocity increments
15
Bias evolution vector-equation
prescribed (constant/seasonal)
Some notation
16
Impact on ECMWF-S3 Forecast Skill
S3 Nodata S3 Assim
In ECMWF S3, ocean Data Assimilation improves
forecast skill in the Equatorial Pacific,
especially in the Western Part
17
Contribution of ARGO and ALTIMETER to the skill
of seasonal forecasts
Initialization into context The Ocean Observing
System

• Observing systems are complementary
• Altimeter has largest impact in Eastern Pacific
  and Atlantic
• Argo has largest impact in Western Pacific/Indian
  Ocean

18
Perceived Paradigm for initialization of coupled
forecasts
Real world
Model attractor
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? Somewhere in the middle?
At first sight, this paradigm would not allow a
seamless prediction system.

• Experiments
• Uncoupled SST Wind Stress Ocean Observations
  (ASSIM)
• Uncoupled SST Wind Stress (CNTL)
• Coupled SST (COUP)

19
Impact of external real world information
ASSIM CNTL COUP

• Need better (more balanced) initialization
• More information corrects for model error, and
  the information is retained during the fc.
• Model errors that can not be corrected by
  initialization (intraseasonal variability)

• Relation between drift and Amplitude of
  Interannual variability.
• Possible non linearity is the warm drift
  interacting with the amplitude of ENSO?
• Other source of errors even with the correct
  mean state the I.V amplitude is small. MJO?

20
Impact of real world information on skill
The additional information about the real world
improves the forecast skill, execept in the
Equatorial Atlantic
However optimal use of the observations may
require more sophisticated assimilation
techniques, able to map the observation space
into the model space
21
Initialization of the model attractor does not
mean neglecting observations.
The transformation from observation to model
space should be scale dependent.
The challenge for a seamless prediction system is
the consistent/simultaneous initialization of the
different time scales.
22
Initialization
Uncoupled Most common
Other (potential) Strategies

• Advantages
• It is possible
• The systematic error during the initialization is
  small(-er)
• It can be used in a seamless system
• Disadvantages
• Model is different during the initialization and
  forecast
• Possibility of initialization shock
• No synergy between ocean and atmospheric
  observations

• Full Coupled Initialization
• No clear path for implementation in operational
  systems
• Need of a good algorithm to treat systematic
  error
• Coupled Anomaly Initialization (DePreSys)
• Weakly-coupled initialization?
• Atmosphere ocean mixed layer
• Ocean Atmosphere boundary layer
• Simplified coupled models?
• Initialization of slow time scales only, limited
  number of modes.
• Ocean initialization only but using the full
  coupled model.

Major challenge initialization of different time
scales
23
Initialization into context
Ocean Initial Conditions Versus Coupled Model
S2 S2ic_S3model S3
24
Final remarks

• Improvement in ocean model leads to better
  forecats mainly through the improved
  initialization (the atmosphere model controls the
  behaviour of the couple model)
• The requirements for improved assimilation
  techniques (e.g. adjoint methods) should be taken
  into account in the ocean model development

25
Summary

• Most common ocean initialization strategy is the
  uncoupled initialization
• Ocean observations are assimilated into an ocean
  model forced by atmospheric fluxes.
• In general, this strategy improves the forecast
  skill in the prediction of SST (if the coupled
  model is good/discerning enough).
• If there are serious model errors this strategy
  can lead to large initialization shocks and
  degradation of the skill (Equatorial Atlantic).
• The skill of seasonal forecasts of SST is
  steadily improving due to
• Improved quality of coupled models (ocean and
  atmosphere)
• Improved quality of atmospheric reanalysis
• Improved ocean observing system (contribution of
  ARGO and Altimeter add to the moorings)
• Improved ocean assimilation systems.
• And it can improve even further
• More sophisticated assimilation methods are
  needed for a seamless prediction system
• A balanced initialization does not mean using
  less information about the real world, but
  adequate mapping between the observed state and
  the model state.