Ocean Data Assimilation Activities at NOAA/GFDL Current status and future directions Matthew...
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![Page 1: Ocean Data Assimilation Activities at NOAA/GFDL Current status and future directions Matthew Harrison, Ants Leetmaa, Anthony Rosati, Andrew Wittenberg,](https://reader033.fdocuments.in/reader033/viewer/2022051401/56649d805503460f94a64f77/html5/thumbnails/1.jpg)
Ocean Data Assimilation Activities at NOAA/GFDL
Current status and future directionsMatthew Harrison, Ants Leetmaa,
Anthony Rosati, Andrew Wittenberg, Shaoqing Zhang
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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
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… 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
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ODA Components
• Quality Control
• Model
• Assimilation algorithm
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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
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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
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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
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Towards understanding the ocean’s 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)
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OVERVIEW- ECCO Collaboration
Ocean Data Assimilation for Climate Testbed
Indicates common/additions from ECCO
OBSCommon metadata
3D-variational
Ensemble filter
Start 4d-var
Common infrastructureMOM4+
ENSO forecasts
GODAE-global change
NCEP Operations – when mature
Kalman filter
Routine Products
•Heat & salt storage•Sea level rise•Carbon storage•Initializations dec-cen forecasts
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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?
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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.
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Simulation
OI
EAKF
TAO
Seasonal Cycle of Temperature at 140W, Equator
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Time Series of Temperature at 140W, Equator
Simulation
OI
EAKF
TAO
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Simulation
OI
EAKF
TAO
1996-1999 Temperature at 140W, Equator
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CDEP ConsortiumOcean Data Assimilation Consortium for Seasonal-to-Interannual Prediction
(ODASI)
COLA, GFDL, IRI, LDEO, NCEP, GMAO
http://nsipp.gsfc.nasa.gov/ODASIhttp://nsipp.gsfc.nasa.gov/ODASI
COLAJim KinterEd SchneiderBen KirtmanBohua Huang
GMAOMichele RieneckerChaojiao SunJossy JacobNicole KurkowskiRobin KovachAnna Borovikov
GFDLTony RosatiMatt HarrisonAndrew Wittenberg
IRISteve ZebiakEli GalantiMichael Tippett
LDEOAlexey KaplanDake Chen
NCEPDave Behringer
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CGCM Forecast skill - January starts - multimodel ensemble
All TAO moorings
West TAO moorings
East TAO moorings
Obs (Reynolds)
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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
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zonal average 1980-1999 temperature trend from 3DVar
Analysis
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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
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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 T&S•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