Transcript Coupled Data Assimilation Michele Rienecker Global Modeling and Assimilation Office NASA/GSFC WMO CAS Workshop Sub-seasonal to Seasonal Prediction Met Office, Exeter 1 to 3 December 2010

Coupled Data Assimilation
Michele Rienecker
Global Modeling and Assimilation Office
NASA/GSFC
WMO CAS Workshop
Sub-seasonal to Seasonal Prediction
Met Office, Exeter
1 to 3 December 2010
What do we mean by “coupled data assimilation”?
• Assimilation into a coupled model where observations in one medium are
used to generate analysis increments in the other [minimization of a joint
cost function with controls in both media].
or
• Loosely (weakly) coupled: the first guess (background) for each medium is
generated by a coupled integration.
or
• Reduced systems: atmosphere with corrections in ocean mixed layer
model; ocean with correction of surface fluxes
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Drivers
• Ocean analyses: inadequacy of surface fluxes from atmospheric analyses
• Need to reduce initialization shocks in seasonal prediction
• Need for better surface boundary estimates for atmospheric analyses (RT)
• Evidence of improved intraseasonal forecasts with interactive ocean
surface layer (diurnal cycle) - Vitart
Community Considerations
Coupled Data Assimilation Workshop
Portland, OR April 21-23, 2003
Sponsored by NOAA/OGP
Upper Ocean-Atmosphere Interactions on Weather and Climate Timescales
Met Office, Exeter, 1-2 December 2009
GODAE OceanView
Task team for short- to medium-range coupled prediction
Under development
Foci include: Short- to medium-range prediction of the ocean, marine boundary layer, surface waves and sea-ice
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Coupled Data Assimilation Workshop
Portland, OR April 21-23, 2003
Sponsored by NOAA/OGP
Workshop goal: explore the merits of developing a program for coupled oceanatmosphere data assimilation to improve seasonal-to-interannual (S-I) forecast skill.
What are the potential benefits/problems?
Issues/Technical Difficulties Identified:
• Poor surface boundary layer formulations may preclude more accurate flux estimation
 Coupling will impact the conditioning of the estimation problem
 Coupling will obligate the need for system noise /dynamical error representation
 Component model deficiencies are amplified in coupled systems
 Costs may not be additive  increased computational resource requirements
 Mis-match in timescales has implications for 4Dvar approach :
 over long periods, the tangent linear approx. for the atmosphere may fail
 on the timescales for the atmosphere the ocean will be 3Dvar
How should the problem be approached from theoretical and practical aspects?
What are the first steps that could/should be taken?
 A loosely coupled system is the proper first step (NCEP, FNMOC)
 An incremental approach (e.g., atmosphere coupled to mixed layer; hybrid coupled models)
 Investigate coupled initialization vs coupled assimilation for forecasts
 Since one of the primary sources of ocean model errors and biases lies in parameterization
errors, particularly of vertical mixing and diffusion, we need to investigate whether
parameterization errors are random or should be considered as controls in the minimization
problem.
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Upper Ocean-Atmosphere Interactions on Weather and Climate Timescales
Met Office, Exeter, 1-2 December 2009
1. Still need to demonstrate a need for an interactive ocean on NWP timescales. To investigate the
role of a coupled ocean we should:
a. use a comparable resolution ocean and atmosphere (1/4 degree)
b. resolve or parameterize the diurnal cycle
c. test the importance of the 3D aspects of the ocean model
d. focus on likely areas of impact such as tropical cyclones, extratropical cyclones, MJO, etc…
e. consider the need for high vertical resolution in the mixed layer
f. Pay particular attention to upper ocean mixed layer processes.
2. Developments at ECMWF suggest that the advantages of having a wave model integrated into the
NWP and Climate models should be considered.
6. Coupled data assimilation should be considered. Within operational centres such as the MO,
ECMWF, NCEP, this may be done first in a sense of loose coupling.
The current atmosphere and ocean data assimilation systems should be used essentially as they are
but they should communicate during the assimilation process. Other groups may consider stronger
coupled assimilation using for example 4dvar or ensemble assimilation.
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What has been done to date? (that I know of)
• Assimilation into intermediate coupled model for tropical Pacific (Bennett et
al)
• Corrections to surface fluxes based on ocean observations – uncoupled
systems – ECCO groups, Yuan & Rienecker, …
• EnKF Ocean assimilation into hybrid coupled model – GFDL (Zhang et al.,
2005)
• Ocean assimilation in coupled model framework (no atmospheric estimation) –
MRI (Fujii et al. 2009)
• Corrections to flux drag coefficients in coupled model - 4DVar system on
slow manifold – FRCGC (Sugiura et al., 2008)
• ECDA in coupled AOGCM – assimilating prior atmospheric assimilated states
– GFDL (Zhang et al., 2007)
• EnKF and EnsOI in coupled AOGCM – constraining with prior atmospheric
assimilated states – GMAO, BOM (POAMA3)
• First guess for atmosphere and ocean from AOGCM integration - 3DVar –
NCEP (Saha et al., 2010)
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Does correcting the atmospheric forcing give a better ocean analysis?
Using Water masses as a validation metric for ocean data assimilation: pdf of
salinity mis-fits in S(T)
GMAO ODAS-3
From Smith et al., 2010, Mercator Ocean Newsletter
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Fully Coupled GCMs
• Corrections to flux drag coefficients in coupled model - 4DVar system on
slow manifold – FRCGC - Sugiura et al. (2008)
• Focused on S-I forecasts
• Approximate 4DVar: “coarse-grained” version of the model used for TLM and Adjoint
• Atmospheric variables are in the cost function, but are not control variables
• Estimate drag coefficient + ocean i.c.’s
• ECDA in coupled AOGCM – assimilating prior atmospheric assimilated states
(monthly-mean) – GFDL (Zhang et al., 2007+)
• Temporally-evolving joint PDF
• Multivariate, but not cross-component
• All coupled components are adjusted by observed data through instantaneously-exchanged
fluxes
• Minimum initial coupling shocks for numerical climate prediction
• First guess for atmosphere and ocean from AOGCM integration - 3DVar –
NCEP (Saha et al., 2010)
• EnKF and EnsOI in coupled AOGCM – constraining with prior atmospheric assimilated states –
GMAO, BOM (POAMA3) – still underway
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ECDA - Fully-coupled data assimilation system
GHG + NA radiative forcing
Atm Obs
Atmospheric model
uo, vo, to, qo, pso
u, v, t, q, ps
(Qt,Qq)
Land
model
(T,S)obs
LDA
(τx,τy)
(u,v)sobs,ηobs
Sea-Ice
model
IDA
ADA
T,S,U,V
Ocean model
Ocn Obs
ODA
Courtesy Zhang & Rosati
Climate predictions – from SI to decadal time scales
ENSO forecast: NINO3 SSTA skills
Courtesy Zhang & Rosati
Anomaly Correlation Coeff
0.6
12
1.0
norm RMS errors
3Dvar
Lead Time
1
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ECDA
1
Jan
Initial Time
Dec
Jan
Initial Time
Dec
Climate diagnostics - Upper ocean
Average T in upper 300m
Courtesy Yan Xue, NCEP
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FRCGC/JAMSTEC – coupled approximate 4DVar
• Focused on S-I forecasts
• Approximate 4DVar: “coarse-grained” version of the model used for TLM and Adjoint
• 10-day mean atmospheric variables are in the cost function, but are not control variables
• Estimate drag coefficient + ocean i.c.’s
• log (αE) – multiplies Louis drag coeff., etc
• Ocean first guess from an ocean reanalysis
τx anomalies 2S-2N
• 9-month assimilation window
First guess
Analysis
OBS
   D CD v v
Jan98

Jan97
Jan96
From Sugiura et al. (2008)
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Configuration of NCEP’s Climate System Forecast Reanalysis (CFSR)
Ocean assimilates data from previous 10-day window
Except that SST is relaxed to external SST analysis
From Saha et al., BAMS 2010
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Precipitation – SST relation improved by coupled nature of CFSR
Tropical western Pacific – 10°S–10°N, 130°–150°E, Nov-Apr
Intraseasonal signal (20-100 days)
From Saha et al., BAMS 2010
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The Importance of Atmospheric and Ocean Observations in Seasonal Forecasts
From ECMWF S3 (1-7 month forecast)
Balmaseda & Anderson (GRL, 2009)
% Reduction in MAE in SST forecasts
Forecasts initialized Jan, Apr, Jul, Oct
ATOBS: use of
atmospheric
analyses for
AGCM i.c.
OCOBS: ocean
data assimilation
for OGCM i.c.
OC+AT: both
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Climatological SST Drift in Niño-3 from ECMWF systems
From Balmaseda et al. ECMWF Workshop on Ocean-Atmosphere Interaction, 10-12 November 2008
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Issues for coupled assimilation
Drifts in coupled models are an issue
Does higher frequency assimilation (on weather timescale) ameliorate
this? [e.g., NCEP’s CFSR assimilates in both atm and ocean every 6 hours]
Boundary layer parameterizations are still an issue – improvements should
reduce drift
Model biases – even in uncoupled mode – impact assimilation increments
Atmospheric model will ignore surface corrections not consistent with
atmospheric observations and with the model itself.
Highest priority should be the atmosphere-ocean interface?
Atmosphere-ocean interactions require model of ocean diurnal cycle.
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From Edson et al., BAMS 2007
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Ti
Temperature


10μm
~1mm
Implicated in atmos data assim
T(zmw) Skin Layer
Td
Log depth (m)
Log depth (m)
~1cm
Diurnal layer

T(zbuoy)
~1m
[following Donlon et al., 2002]
T
ML
First OGCM Layer (5-10m)
Estimates from ocean data assim
~10m
Diurnal models:
Fairall, Gentemann, Zeng&Beljaars, Takaya et al.
NCEP now generates a 2D SST analysis using the
GSI analysis, but it is uncoupled to ocean analysis
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Summary/Comments
• A lot of progress has been made in ocean data assimilation!
• Many examples that coupling improves simulations and forecasts ⇒ makes
sense that paying attention to the coupled system during initialization should
help with forecasts
• There is a push for “coupled” atmosphere-ocean assimilation
• not yet clear that using ocean obs to correct atmospheric fluxes
improves the ocean state estimate (or the atmosphere)
• drifts in the coupled system are a problem & they happen fairly quickly
• need to improved modeling of atmospheric and oceanic boundary layers
and ocean’s diurnal warming layer
• loosely coupled system is still the strategy that makes most sense at
present
• coupled assimilation should focus on the air-sea interface
• Coupled assimilation is a short timescale problem, not a slow manifold
problem ⇒ “seamless” weather-climate initialization
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