Coupled Data Assimilation

Michele Rienecker

Global Modeling and Assimilation OfficeNASA/GSFC

WMO CAS WorkshopSub-seasonal to Seasonal Prediction

Met Office, Exeter1 to 3 December 2010

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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) - VitartCommunity ConsiderationsCoupled Data Assimilation WorkshopPortland, OR April 21-23, 2003Sponsored by NOAA/OGP

Upper Ocean-Atmosphere Interactions on Weather and Climate TimescalesMet Office, Exeter, 1-2 December 2009

GODAE OceanViewTask team for short- to medium-range coupled predictionUnder developmentFoci include: Short- to medium-range prediction of the ocean, marine boundary layer, surface waves and sea-ice

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Coupled Data Assimilation WorkshopPortland, OR April 21-23, 2003Sponsored by NOAA/OGPWorkshop goal: explore the merits of developing a program for coupled ocean-atmosphere 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.

Upper Ocean-Atmosphere Interactions on Weather and Climate TimescalesMet 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 cyclec. 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 layerf. 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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GMAO ODAS-3

From Smith et al., 2010, Mercator Ocean Newsletter

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)

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• 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

Fully Coupled GCMs

Ocn Obs

Atmospheric modeluo, vo, to, qo, pso

Ocean modelT,S,U,V

Sea-Ice model

Land model

(τx,τy)(Qt,Qq)

(T,S)obs

GHG + NA radiative forcing

u, v, t, q, ps

ADA

ODA

(u,v)sobs,ηobs

ECDA - Fully-coupled data assimilation system

LDA

Atm Obs

IDA

Courtesy Zhang & Rosati

3Dvar

Anomaly Correlation Coeff norm RMS errors0.6

1.0

Initial TimeJan Dec Jan DecInitial Time

Lead Time

1

12

12

1

Climate predictions – from SI to decadal time scales ENSO forecast: NINO3 SSTA skills

ECDA

Courtesy Zhang & Rosati

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Climate diagnostics - Upper oceanAverage 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• 9-month assimilation window

€

τ =αDCD v v

From Sugiura et al. (2008)

τx anomalies 2S-2N First guess Analysis OBS

Jan96

Jan97

Jan98

Configuration of NCEP’s Climate System Forecast Reanalysis (CFSR)

From Saha et al., BAMS 2010

Ocean assimilates data from previous 10-day windowExcept that SST is relaxed to external SST analysis

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Precipitation – SST relation improved by coupled nature of CFSRTropical 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 ForecastsFrom 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 issueDoes 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 incrementsAtmospheric 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.

18From Edson et al., BAMS 2007

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Diurnal layer

Td

Ti

TML

T(zbuoy)

T(zmw) Skin Layer

First OGCM Layer (5-10m)

Log

dept

h (m

)

~10m

~1mm

10μmLo

g de

pth

(m)

~1cm

~1m

Temperature

✪

✪

✪

✪

✪✪✪

[following Donlon et al., 2002]

Implicated in atmos data assim

Estimates from ocean data assim

NCEP now generates a 2D SST analysis using the GSI analysis, but it is uncoupled to ocean analysisDiurnal models:

Fairall, Gentemann, Zeng&Beljaars, Takaya et al.

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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⇒