Ocean Data Assimilation Activities at NOAA/GFDL

1
Ocean Data Assimilation Activities at NOAA/GFDL

• Current status and future directions
• Matthew Harrison, Ants Leetmaa, Anthony Rosati,
  Andrew Wittenberg, Shaoqing Zhang

2
Ocean Modeling Needs Data Assimilation

• Uncertainty in air-sea fluxes
• Uncertainty in model physics.
• ODA produces consistent ocean states serving as
  initial conditions for model forecasts
• The reconstructed time series of ocean states
  with a 3D structure aids further understanding of
  the dynamical and physical mechanisms of ocean
  evolution
• Ocean analyses for model simulation or hindcast
  verification

3
however

• Ocean data assimilation products do not all agree
  reflecting uncertainty in models and data
  assimilation methods
• ODA does not necessarily lead to model
  improvement nor to increased understanding

4
ODA Components

• Quality Control
• Model
• Assimilation algorithm

5
Ocean Observations

• GODAE Server near real-time data stream
  (http//www.usgodae.org)
• VOS XBT
• CTD
• Argo
• TAO/PIRATA
• Altimetry
• maintain data center QC flags
• In-house quality control

6
Ocean Model

• MOM4 OM3
• Re-engineered for multi-processor platforms
• Utilize FMS infrastructure for communications,
  i/o and coupling
• Global 1 degree x 50 level z-coordinate with 1/3
  degree tropical resolution
• Tripolar grid (no polar filter required, full
  Artic Ocean)
• KPP Vertical mixing
• SWEBY advection scheme (courtesy of A. Adcroft )
• Rotated isopycnal diffusion with G-M thickness
  flux
• Coupled with Ice Model

7
Ocean Data Assimilation

• 3DVar
• Error covariance is stationary in time.
• Kalman Filter
• Error covariance evolves based on model
  linearization
• Ensemble Adjustment Kalman Filter
• Use full model equations to propagate error
• 4DVar
• strong constraint to model equations
• No source/sink terms

8
Towards understanding the oceans role in climate

• GFDL will be expanding its ocean data
  assimilation activities starting this year
  through partnership with the ECCO group
• Estimating the Circulation and Climate of the
  Ocean (http//www.ecco-group.org)
• MIT (Carl Wunsch, Patrick Heimbach, Alistair
  Adcroft) /JPL (Ichiro Fukimori, Tony Lee) /
  Harvard (Eli Tziperman, Jake Gebbe)

9
OVERVIEW- ECCO Collaboration Ocean Data
Assimilation for Climate Testbed
Indicates common/additions from ECCO
10
3DVar

• http//nomads.gfdl.noaa.gov LAS/DODS
• 1980-present analyses
• Routinely used for SI forecast initialization
• Tropical Pacific is well constrained in upper
  300m
• Mid-to-high latitudes are more problematic
• Sparse data outside of trade routes
• Few salinity measurements
• Use T/S covariance?
• Multivariate using ARGO Salinity?

11
Ensemble Adjustment Kalman Filter (EAKF)

• Initial tests in the tropical Pacific basin for
  ENSO prediction
• Linearized atmospheric model with added noise
• Each model realization is integrated between
  analysis steps which determines the PDF of the
  model guess.
• Followed by a least square adjustment based on
  the model and observational PDF of the respective
  ensemble members.

12
(No Transcript)
13
Seasonal Cycle of Temperature at 140W, Equator
Simulation
OI
EAKF
TAO
14
Time Series of Temperature at 140W, Equator
Simulation
OI
EAKF
TAO
15
1996-1999 Temperature at 140W, Equator
Simulation
OI
EAKF
TAO
16
(No Transcript)
17
(No Transcript)
18
CDEP Consortium Ocean Data Assimilation
Consortium for Seasonal-to-Interannual Prediction
(ODASI)
COLA, GFDL, IRI, LDEO, NCEP, GMAO
GMAO Michele Rienecker Chaojiao Sun Jossy
Jacob Nicole Kurkowski Robin Kovach Anna Borovikov
GFDL Tony Rosati Matt Harrison Andrew Wittenberg
COLA Jim Kinter Ed Schneider Ben Kirtman Bohua
Huang
NCEP Dave Behringer
IRI Steve Zebiak Eli Galanti Michael Tippett
LDEO Alexey Kaplan Dake Chen
http//nsipp.gsfc.nasa.gov/ODASI
19
CGCM Forecast skill - January starts - multimodel
ensemble
20
Longer term change

• Globally averaged heat content
• Blue line is the GFDL R30 Climate Model
• Red Line is R30 Model with added aerosol forcings
  including volcanic
• Black line is the Levitus analysis
• Dashed line is GFDL 3DVar analysis

21
zonal average 1980-1999 temperature trend from
3DVar Analysis
22
1992-1997 SSH Trend

• JPL Kalman Filter smoother (top)
• GFDL OI (middle)
• ECCO 1deg Iter69 (bottom)
• We need to understand the differences between
  these analyses

23
Overview of Early Activities GFDL, NCEP,
Goddard, MIT(AER), JPL, Harvard

• Year 1
• Begin global state estimation at GFDL with ECCO
• Produce and utilize routine data streams
  including ARGO TS
• Tangent linear/adjoint model. Ocean model and
  model parameterization development
• Derivation of Kalman filter and smoother for MOM4
  and Poseiden
• Year 2
• Global estimates with ECCO continue
• Optimization of ECCO and GFDL ECCO like models
  for Dec/Cen applications
• Experiments for S/I forecasts utilizing different
  assimilation schemes/models
• Experiments with new data types (GRACE)
  comparison to independent data types like length
  of day and earth polar motion
• Adjoint sensitivity analyses to various controls
  and observing systems
• Year 3
• Year 4