Coastal Data Assimilation: progress and challenges in state estimation for circulation and BGC models Emlyn Jones, Roger Scott, Mark Baird, Frank Colberg, Paul Sandery, Pavel Sakov, Gary Brassington, Mathieu Mongin, Jenny Skerratt, Mike Herzfeld and Peter Oke CSIRO Oceans and Atmosphere
Case Study: GBR why is this a good testbed? Typical traits of many coastal regions Areas of broad/narrow shelf Numerous large rivers Steep shelf break Genesis region for a WBC (East Australian Current) Sparse in-situ observing system Atypical traits: Longest coral reef in the world (World Heritage) Large tides inshore (up to 8m in Broad Sound) Wet season (Nov - Mar) / Dry season (Apr Oct) 2 Coastal DA: progress and challenges
ereefs modelling system: https://research.csiro.au/ereefs/ All code is open source and available in GitHUB: https://github.com/csiro-coasts/ems/releases Circulation/hydrodynamics: Model: SHOC (https://research.csiro.au/cem/software/ems/hydro/) Horizontal Resolution: ~4km Vertical grid: z-grid 47 levels OBC Forcing: BRAN 2015 Surface Forcing: ACCESS-R (BoM) Sediment Model: Model: MecoSED (https://research.csiro.au/cem/software/ems/sediments/) 5 sediment layers 4 particulate classes BGC Model: Model: EMS (https://research.csiro.au/cem/software/ems/biogeochemistry/) Details: Its complicated Substantial effort has been invested in tuning the free running models! As a result they have excellent predictive skill without DA switched on. Hydrodynamic RMSE: SLA ~11cm, SST RMSE ~0.7 deg C, Depth averaged ARGO Temp ~1 deg C, Salinity 0.2 PSU 3 BGC RMSE: ~ OC3M 0.19-0.21 mg Chl-a/m 3 (corresponds to 50% relative error)
Assimilation System Configuration: We use EnKF-C (https://github.com/sakov/enkf-c), developed Pavel Sakov and supported by BoM. 100 Members Covariance Localisation: Variable Dependent Inflation factor 1.05 Perturbations: Initial Conditions River loads Wind forcing Model Parameters Open Ocean Boundary conditions 2 Day Forecast cycle Asynchronous assimilation using FGAT ~1,000 100,000 remote sensing super-obs per cycle Hydrodynamic model constrain by: SLA, SST, ARGO and Gliders De-tiding the model to assimilate SLA is non-trivial! We only assimilate SLA in regions deeper than 500m. BGC observations: bio-optical assimilation of Remote Sensing Reflectance (simulated OC3M). All results are for a weakly coupled DA systems. BGC observations only update the BGC state. Hydro observations only update the hydro state. 4 Coastal DA: progress and challenges
Assimilation System Configuration: We use EnKF-C (https://github.com/sakov/enkf-c), developed Pavel Sakov and supported by BoM. 100 Members Covariance Localisation: Variable Dependent Inflation factor 1.05 Perturbations: Initial Conditions River loads Wind forcing Model Parameters Open Ocean Boundary conditions 2 Day Forecast cycle Asynchronous assimilation using FGAT ~1,000 100,000 remote sensing super-obs per cycle Baird, M.E., Cherukuru, N., Jones, E., Margvelashvili, N., Mongin, M., Oubelkheir, K., Ralph, P.J., Rizwi, F., Robson, B.J., Schroeder, T. and Skerratt, J., 2016. Remote-sensing reflectance and true colour produced by a coupled hydrodynamic, optical, sediment, biogeochemical model of the Great Barrier Reef, Australia: comparison with satellite data. Environmental Modelling & Software, 78, pp.79-96. Jones, E.M., Baird, M.E., Mongin, M., Parslow, J., Skerratt, J., Lovell, J., Margvelashvili, N., Matear, R.J., Wild-Allen, K., Robson, B. and Rizwi, F., 2016. Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef. Biogeosciences, 13(23), pp.6441-6469. Hydrodynamic model constrain by: SLA, SST, ARGO and Gliders - Detiding the model to assimilate SLA is non-trivial! We only assimilate SLA in regions deeper than 500m. BGC observations: bio-optical assimilation of Remote Sensing Reflectance (simulated OC3M). All results are for a weakly coupled DA systems. BGC observations only update the BGC state. Hydro observations only update the hydro state. 5 Coastal DA: progress and challenges
Challenge 1: Assimilating altimetry in tide resolving models During the 3-years of lifetime mission, the main goals are: Provide sea surface heights (SSH) and terrestrial water heights over a 120 km wide swath with a +/-10 km gap at the nadir track. Over the deep oceans, provide SSH within each swath with a posting every 1 km x 1 km Over land, produce a water mask able to resolve 100 meter wide rivers and lakes of 250x250 m in size, wetlands, or reservoirs. Associated with this mask will be water level elevations with an accuracy of 10 cm and a slope accuracy of 1.7 cm/1 km (when averaging over water area >1 km²). Cover at least 90 percent of the globe. Gaps are not to exceed 10 percent of Earth's surface. More details: https://www.aviso.altimetry.fr/en/missions/future-missions/swot.html Ocean Topography Observations of the sub-mesoscale (dynamics time scales hours) 6 Coastal DA: progress and challenges
Assimilating altimetry in tide resolving models Two options: 1.) leave the tide in, and assimilate SSH; or, 2.) remove the tide, and assimilate SLA. sla = eta tide mdt eta: instantaneous sea surface height (ssh) tide: tidal component of the sea surface elevation mdt: mean dynamic topography (estimate from a long free run of the model/observations) Method 1: sla = eta 3D tide 2D E(eta 3D ) 5years BoM use this method in their EnKF system Method 2: sla = E(eta 3D ) 3days mdt BRAN Developed for use in ROAM 7 Coastal DA: progress and challenges
Assimilating altimetry in tide resolving models Deep water (offshore) 8 Coastal DA: progress and challenges
Assimilating altimetry in tide resolving models Shallow water (coastal central-gbr) 9 Coastal DA: progress and challenges
Assimilating altimetry in tide resolving models Summary: Method 1: is ok for assimilating the along track altimeter observations in deep and shallow water. But requires a twin 2D barotropic model. o Forecast MAE: ~8.5cm (June 2013 Oct 2013) o Forecast Bias: ~0.1cm (June 2013 Oct 2013) Method 2: is sub-optimal but cheap for assimilating along track altimeter observations in deep water. o Forecast MAE: ~10cm (June 2013 Oct 2013) o Forecast Bias: ~-6cm (June 2013 Oct 2013) Neither method is suitable for assimilation of swath altimetry (e.g. SWOT) 10 Coastal DA: progress and challenges
Challenge 2: Coupled (hydrodynamics/bgc) DA Experiment Hydro BGC Exp A 1 member DA Off 100 members DA Off Exp B 1 member DA Off 100 members DA On Exp 1 100 members DA Off 100 members DA Off Exp 2 100 members DA Off 100 members DA On Exp 3 100 members DA On 100 members DA Off Exp 4 100 members DA On 100 members DA On 11 Coastal DA: progress and challenges
Ensemble experiments: This configuration has the highest MAE and Bias DA in the hydro model only (no DA in the BGC), yields the worst results by far. The configuration with a free running hydro model beats the configuration with DA on in the hydro. 12 Coastal DA: progress and challenges
Ensemble experiments: These configurations have the lowest MAE and Bias The weakly coupled system (green) yields the best forecast error stats. BUT, the BGC DA is suppressing spurious primary production! 13 Coastal DA: progress and challenges
Domain Mean Timeseries: DIN 14 Coastal DA: progress and challenges
Effect of assimilation on vertical velocity The ensemble mean vertical velocity (in geographic [domain wide, upper 150m]) timeseries for the final 5 cycles of the experiment. Data assimilating experiments Free running experiments The non-assimilating runs have a 40% lower mean vertical velocity. 15 Coastal DA: progress and challenges The assimilating runs have more energetic vertical velocities, entraining sub-nutricline water into the photic zone.
CSIRO EnKF system vertical velocity: 23rd Sept 2013 16 Coastal DA: progress and challenges Instantaneous values immediately after analysis cycle.
BoM EnOI system vertical velocity: 23rd Sept 2013 6 hr mean values after analysis cycle 17 Coastal DA: progress and challenges
Summary Having access to two modelling systems in this area helps diagnose and improve/reduce model error. Assimilating altimetry in the presence of tides is difficult: o Current methods are unlikely to work well with swath based observations. DA into the hydro model can cause elevated vertical velocities, likely due to unbalanced analysis fields. o Supported by recent work of Pilo et al., (2018) on the BRAN Global system. o However, the BoM EnOI system does not appear to substantial enhance the vertical velocities. The increased vertical velocities enhance the supply of nutrients to the photic zone. In nutrient limited systems, the BGC model responds by increasing primary production. Weakly coupled assimilation into both (hydro and BGC) models shows the greatest reduction in MAE and bias across all variables, however: o The DA in the BGC is reducing spurious elevated primary production, but this system requires regular observations to keep it on track. Multi-week forecasts from such a system would be problematic. 18 Coastal DA: progress and challenges
Questions? More information, model output and source code can be found at these web pages: https://research.csiro.au/cem/ http://ereefs.info https://github.com/csiro-coasts/ems/releases https://github.com/sakov/enkf-c CSIRO Oceans and Atmosphere t +61 3 6232 5483 E emlyn.jones@csiro.au CSIRO Oceans and Atmosphere