DOI: 10.5281/zenodo.10933149
title: NASA/GMAO Subseasonal to Seasonal Version 3.0 SYNOBS Contribution

description: This dataset is a subset of the output of the NASA Global Modelling and Assimilation Office (GMAO) Sub-seasonal-To-Seasonal (S2S) Version 3 coupled ocean/atmosphere forecasting system [see Hackert et al., 2023 for full details].  This system, consists of the Goddard Earth Observing System (GEOS) general circulation atmosphere model which is coupled to the GFDL MOM5-based general circulation ocean model.  The S2S-3 data assimilation is weakly coupled assimilation meaning both the ocean and atmosphere have complete data assimilation systems that are coupled through the model.   Both the ocean and atmosphere also have comprehensive observation data sets as well as diagnostic codes. 

Many of the features of this latest version of the model are duplicated from the previous version [Molod et al., 2020].   However, S2S-3 has several improvements upon the previous version and these are highlighted below.   

•   Ocean Model: Although the S2S-3 continues to use the  GFDL Modular Ocean Model-5 (MOM5), the resolution is improved to global 0.25°x0.25° in the horizontal and 50 levels.  ([Griffies et al., 2005], [Griffies, 2012]). The ocean component has nominal 10 m resolution in the upper 100 m, with expanding thicknesses down to ~5600 m, and employs the non-local K-profile parameterization of [Large et al., 1994] and a parameterization of tidal mixing.  Horizontal mixing uses the isoneutral method of [Gent and McWilliams, 1990].  The horizontal viscosity uses the anisotropic scheme of [Large et al., 2001] for better representation of equatorial currents, upwelling and mixing.  •    Atmosphere Model: The atmospheric model is now the “Icarus generation” Goddard Earth Observing System (GEOS) atmospheric general circulation model with 72 layers and approximately 0.5° resolution ([Rienecker et al., 2008], [Molod et al., 2015]).  The atmosphere is replayed [Orbe et al., 2017] (i.e., similar to nudging) to an atmospheric model known as the GEOS-IT (for Goddard Earth Observing System, Instument Team) using the technique of “Dual Ocean”. •    Dual Ocean: The S2S-3 weakly coupled data assimilation system includes a new feature that is called "Dual Ocean". The term Dual Ocean refers to the use of both a "Data Ocean" component that reads the observation-based surface temperature and sea ice fraction that the atmospheric assimilation system's model used, and a "real ocean" component, or MOM5. In the first component of the Dual Ocean scheme the atmospheric model component "sees" the observed sea surface temperature (SST) and sea ice (SICE) rather than the predicted values from MOM5 and CICE4 (i.e., the ice model). As the S2S-3 coupled assimilation "replays" to a pre-computed atmospheric assimilation, it is critical for the computation of the turbulent surface fluxes to preserve the near-surface gradients. •    Atmosphere Ocean Interface Layer: Since the ocean model’s vertical grid is 10m thick at the top layer and up to 100m depth, its representation of SST diurnal cycle is inadequate.  The Atmospheric Ocean Interface Layer (AOIL) in GEOS implements a prognostic model for skin SST which builds the ocean model top level temperature, and it does that in a way that preserves the heat flux budget [Akella and Suarez, 2018].  

The control S2S-3 system routinely assimilates a wide range of global ocean in situ and satellite observations. Here we briefly list all assimilated observational data sets. In situ temperature and salinity are provided by 1) tropical moorings from Tropical Atmosphere Ocean/Triangle Trans Ocean Buoy Network (TAO/TRITRON - [McPhaden et al., 2010]), Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA - [McPhaden et al., 2009]), and the PIlot Research moored Array in the Tropical Atlantic (PIRATA - [Servain et al., 1998]) for the Pacific, Indian, and Atlantic Oceans, respectively.  All moorings maintain an array of surface meteorological observations and subsurface thermistor chains, while many moorings include salinity measurements. 2) The Argo float array, which provides profiles of temperature and salinity to 2 km depth every few degrees on average, every 10 days ([Roemmich et al., 2009]).   These in situ measurements are supplemented by a smaller amount of shipborne quality-controlled profile temperature and salinity observations from Conductivity/Temperature/Depth (CTD) profilers and temperature profiles from expendable bathythermographs (XBT) [Good et al., 2013].   

Along-track (Level 2) sea level (SL) data are obtained from the Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO, https://www.aviso.altimetry.fr/data/products/sea-surface-height-products/global/along-track-sea-level-heights.html) ), combined with gravity data from the Gravity and Ocean Explorer (GOCE - [Johannessen et al., 2003]) and the Gravity Recovery and Climate Experiment (GRACE - [Tapley et al., 2004]), and assimilated as absolute dynamic topography (ADT).  Over the period of our reanalysis experiments, we include all available satellite sea level data that were available.

In addition to all in situ profiles of temperature and salinity and altimetry data, the production GEOS-S2S-3 system routinely assimilates all available along-track satellite SSS from Aquarius ([NASA_Aquarius_Project, 2017])  for 2014 – June, 2015, the Soil Moisture Active Passive (SMAP) [Fore et al., 2016] for April 2015-present, and Soil Moisture/Ocean Salinity (SMOS) [Boutin et al., 2018] for the entire period of this study from June 2014 to the end of 2015.    

The GMAO ocean reanalysis system  assimilates the ocean observation sets using a technique similar to the Local Ensemble Transform Kalman Filter (LETKF) implementation of [Penny et al., 2013].  Our implementation of the LETKF is applied on a 5-day assimilation cycle with twenty fixed ensemble members from a free-running coupled experiment (with similar model setup as S2S-3 but without any assimilation) which has realistic ENSO characteristics. The advantage of this ensemble Kalman Filter ocean data assimilation system (ODAS) over a less expensive deterministic filter such as the 3-dimensional variational (3DVar) data assimilation approach is that it allows the error covariances to evolve with the seasonal cycle and the phase of ENSO more accurately since the twenty ensembles are recentered around the current model ocean state. We localize these error covariances to eliminate spurious correlations between distant grid points and inflate the error covariances to prevent the ensemble members from becoming too similar [Houtekamer and Zhang, 2016].   For profile data, we only localize in the horizontal, with a decorrelation length-scale that is proportional to the Rossby deformation radius [Chelton et al., 1998].     The quality of the initial conditions also depends on our specification of observation error (the sum of the intrinsic instrument error and the error due to physical processes such as internal waves that are unresolved in our system  [Janjić et al., 2018]).  Within the data assimilation code, profile data are assigned observational error depending on the depth gradient of the observation. For the S2S-3 ODAS code, vertical temperature and salinity gradients are scaled by a factor of 10 to give the final profile observation error. In this way, the highest observation errors are assigned at depths where the thermocline and hence the greatest uncertainty resides. Vertical localization is turned off for profile data. This has the benefit of calculating the analysis only once (as opposed to 40 times for 40 levels) and unique vertical localization profiles for each observation type are no longer required.  This technique has the additional benefit of allowing assimilation of vertical profiles and satellite altimetry data within a single ODAS process.

While the assimilation of most profile observations is quite straightforward, assimilating sea level as absolute dynamic topography (ADT) is a little more complicated. ADT’s main impact on the ocean state is through its covariance with temperature and salinity (and thus the depth of the pycnocline). Because of the sheer volume of the data, ADT observations are thinned prior to assimilation (see https://www.aviso.altimetry.fr/data/products/sea-surface-height-products/global/along-track-sea-level-heights.html for resolution details).  A Gaussian weighted mean is calculated for the central point of +/-10 along-track observations using a decorrelation scale of 1000 km.  This mean is then output for assimilation. The observation error we assign to ADT is estimated via the variability of the data within the Gaussian length scale and increases from < 2 cm at the equator to a maximum of 10 cm at high latitudes to reflect the decreasing error covariance between ADT and density. Finally, the observed mean sea level trend is added back after assimilation to prevent the sea level observations from affecting the time-mean barotropic circulation. 

For SSS assimilation, SMOS, Aquarius, and SMAP have effective resolutions of 50 km, 100 km, and 40 km, respectively. However, the observation error is treated differently than for ADT. For SSS assimilation, we simply use the error provided by the various product teams.