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

2 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

3 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

4 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 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 Timescales Met Office, Exeter, 1-2 December 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. 5

6 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)

7 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)

8 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 model u o, v o, t o, q o, ps o Ocean model T,S,U,V Sea-Ice model Land model (τx,τy)(τx,τy) (Q t,Q q ) (T,S) obs GHG + NA radiative forcing u, v, t, q, ps ADA ODA (u,v) s obs,η obs ECDA - Fully-coupled data assimilation system LDA Atm Obs IDA Courtesy Zhang & Rosati

3Dvar Anomaly Correlation Coeff norm RMS errors Initial Time Jan Dec Jan Dec Initial Time Lead Time Climate predictions – from SI to decadal time scales ENSO forecast: NINO3 SSTA skills ECDA Courtesy Zhang & Rosati

11 Climate diagnostics - Upper ocean Average T in upper 300m Climate diagnostics - Upper ocean Average T in upper 300m Courtesy Yan Xue, NCEP

12 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 (α E ) – multiplies Louis drag coeff., etc Ocean first guess from an ocean reanalysis 9-month assimilation window From Sugiura et al. (2008) τ x anomalies 2S-2N First guessAnalysisOBS 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 window Except that SST is relaxed to external SST analysis 13

14 Precipitation – SST relation improved by coupled nature of CFSR Tropical western Pacific – 10°S–10°N, 130°–150°E, Nov-Apr Intraseasonal signal ( days) From Saha et al., BAMS 2010

15 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

16 Climatological SST Drift in Niño-3 from ECMWF systems From Balmaseda et al. ECMWF Workshop on Ocean-Atmosphere Interaction, November 2008

17 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.

18 From Edson et al., BAMS 2007

19 Diurnal layer T  TiTi T ML T(z buoy ) T(z mw ) Skin Layer First OGCM Layer (5-10m) Log depth (m) ~10m ~1mm 10μm Log depth (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 analysis Diurnal models: Fairall, Gentemann, Zeng&Beljaars, Takaya et al.

20 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