1. NCOF Development Workshop 2008 Assessments of Ecosystem Models using Assimilation Techniques John Hemmings, Peter Challenor, Ian Robinson & Tom Anderson

2. What is the “Ecosystem Model” in Ecosystem Model Assessment ? Free-running model Free-running model Assimilation system (sequential D.A.) Assimilation system (sequential D.A.) Ocean Biogeochemical General Circulation Model Ecosystem Sub-model Fixed parameter model Fixed parameter model Model structure and formulation Model structure and formulation

3. Outline The Calibration Process (Inverse D.A. Scheme) The Calibration Process (Inverse D.A. Scheme) Allowing for Uncertainty Allowing for Uncertainty Assessment of D.A. Scheme and Model Assessment of D.A. Scheme and Model Combining Data from Different Locations Combining Data from Different Locations Sequential Assimilation of Ocean Colour Sequential Assimilation of Ocean Colour Improving Forecasts and Hindcasts Improving Forecasts and Hindcasts

4. The Calibration Process ECO. MODEL OPTIMIZER COST FUNC. Simulated Obs. Misfit Cost Calibration Obs. Boundary Conditions Forcing Initial Conditions Free Parameters Science Output Sensitivity Analysis Validation Obs.

5. Allowing for Uncertainty The Misfit Formulation Estimate  2 SIM by: 1)Characterizing uncertainty in IC, physical forcing & boundary fluxes 2)Propagating through model by ensemble runs Misfit = (x SIM - x OBS ) 2  2 DEP  2 DEP =  2 OBS +  2 SIM For a given parameter set,  2 SIM is uncertainty due to IC, physical forcing & boundary fluxes

6. Allowing for Uncertainty External Input Data for 1-D Simulations Biogeochemical tracer profiles B i (z, member) Biogeochemical tracer profiles B i (z, member) Initial conditions: Forcing data: Sea-surface PAR I (t, member) Sea-surface PAR I (t, member) Sea-surface salinity S (t, member) Sea-surface salinity S (t, member) Mixed layer depth M (t, member) Mixed layer depth M (t, member) Temperature T (z, t, member) Temperature T (z, t, member) Vertical diffusion coefficient k (z, t, member) Vertical diffusion coefficient k (z, t, member) Vertical velocity w (z,t, member) Vertical velocity w (z,t, member) Boundary fluxes: Horizontal biogeochemical tracer fluxes H i (z, t, member) Horizontal biogeochemical tracer fluxes H i (z, t, member)

7. Allowing for Uncertainty Marine Model Optimization Test-bed (MarMOT) INPUT ITEMS (1 or more instances of each) physical forcing run options: ecosystem model, time-step, misfit spec. … initial conditions boundary conditions observations fixed parameters MODEL SPECIFIC N SITES misfit cost other validation stats. model output M CASES free parameters (posterior) MODEL SPECIFIC free parameters (prior) MODEL SPECIFIC case table Generic Function Analyzer Model Evaluator (1-D) Optimizer misfit cost

8. Assessment Criteria Assimilation Scheme & Calibration Data Set Fit to data from non-calibration years Fit to data from non-calibration years - better than prior parameter set  TWIN EXPERIMENTS REAL-WORLD EXPERIMENTS True solution known Can test parameter recovery Ecosystem is real Idealized scenario may be unrepresentative Uncertainty in IC, forcing, horizontal fluxes and observations affects validation misfit + - No. of parameters constrained (with acceptable repeatability) No. of parameters constrained (with acceptable repeatability)

9. Assessment Criteria Ecosystem Model Calibrated Model: Fit to data from non-calibration years Fit to data from non-calibration years - better than cal. data climatology  Model Structure and Formulation: Fit to data from non-calibration years Fit to data from non-calibration years - better than alternative model with same cal. data  Limitation: optimal calibration not possible for complex models

10. Ecosystem Model Assessment An Example Model Comparison Experiment OG99 NPZD: Oschlies and Garçon (1999) HadOCC NPZD: Hadley Centre Ocean Carbon Cycle Model, Palmer and Totterdell (2001) - modified Thanks to Ben Ward & Andrew Yool for providing OCCAM output at BATS

11. Combining Data from Different Locations Identifying Calibration Provinces NERC Data Assimilation Thematic Programme Zero-D NPZ model fit to daily chlorophyll + winter nitrate at calibration stations Split-domain calibration method (Hemmings, Srokosz, Challenor & Fasham, 2004): identifies optimal geographic ranges for single parameter sets by selecting promising stations to aggregate Final provinces chosen by misfit cost at validation stations

12. Sequential Assimilation of Ocean Colour CASIX Chlorophyll Assimilation Scheme in FOAM-HadOCC 3D analysis 2D analysis of log(Chl) 2D analysis of P ΔNΔN ΔPΔP ΔZΔZ ΔDΔD Δalk ΔDIC Model forecast N:Chl Observations Aim: improve air-sea CO 2 flux by improving surface DIC and alkalinity, hence pCO 2 Aim: improve air-sea CO 2 flux by improving surface DIC and alkalinity, hence pCO 2 2-D analysis of log 10 (Chl) uses FOAM analysis correction scheme (as for SST) 2-D analysis of log 10 (Chl) uses FOAM analysis correction scheme (as for SST) Surface phytoplankton increments derived using model nitrogen:chl (dynamic) Surface phytoplankton increments derived using model nitrogen:chl (dynamic) Other variables adjusted by a new material balancing scheme (Hemmings, Barciela & Bell, 2008) Other variables adjusted by a new material balancing scheme (Hemmings, Barciela & Bell, 2008) Rosa Barciela, Matt Martin, Mike Bell, Adrian Hines (Met Office) John Hemmings (NOCS) DAILY ANALYSIS CYCLE

13. Sequential Assimilation of Ocean Colour Material Balancing Scheme for Nitrogen and Carbon Surface phytoplankton increment given as input Relative increments to other nitrogen pools depend on the likely contributions to phytoplankton error from growth and loss Relative increments to other nitrogen pools depend on the likely contributions to phytoplankton error from growth and loss Nitrogen conserved at each grid point (if possible) Nitrogen conserved at each grid point (if possible) DIC increment conserves carbon DIC increment conserves carbon Sub-surface scheme prevents formation of unrealistic sub-surface minima in DIN Sub-surface scheme prevents formation of unrealistic sub-surface minima in DIN

14. Sequential Assimilation of Ocean Colour Evaluation of Material Balancing in 1-D Twin Experiments Free run Assimilating Chl & P Assimilating Chl only 60ºN 40ºN 50ºN 30ºN

15. Sequential Assimilation of Ocean Colour 3-D Evaluation of Chlorophyll Assimilation Scheme Biogeochemical errors due to excessive vertical transport of nutrients not corrected by chlorophyll assimilation (intentionally) TWIN EXPERIMENTSREAL-WORLD EXPERIMENTS Surface Chlorophyll Un-assimilated Variables Need biogeochemical balancing scheme when assimilating T&S profiles Impact of Physical D.A. (link to MARQUEST) DIN Chlorophyll physics DA on DA off physics DA on DA off  ?  

16. Improving Forecasts and Hindcasts: the Role of Parameter Optimization A Non-identical Twin Experiment Truth: HadOCC Ecosystem Model: Simplified HadOCC with 4 free parameters Calibration data: Chlorophyll (daily), DIN & pCO 2 (monthly)

17. Improving Forecasts and Hindcasts: the Role of Parameter Optimization Sequential Chlorophyll Assimilation Results TRUTH ORIGINAL ORIGINAL + CHL D.A. OPTIMIZED OPTIMIZED + CHL D.A. Surface Chlorophyll Surface Phytoplankton Surface DIN Surface pCO 2

18. Improving Forecasts and Hindcasts Application of Different Assimilation Methods Sequential Data Assimilation Improve hindcast state Improve hindcast state Improve initial conditions for short-term forecasts Improve initial conditions for short-term forecasts Parameter Optimization (Inverse D.A. Methods) Improve long-term forecast Improve long-term forecast Improve performance of sequential schemes Improve performance of sequential schemes

19. An Example Model Comparison Experiment Comparison with Observations OG99 Pre-calibration HadOCC Pre-calibration OG99 Post-calibration HadOCC Post-calibration Val. Year Cal. Year Primary Production (mg C m -3 d -1 ) Cal. Year Val. Year DIN (mmol N m -3 ) observational estimator is nitrate!