Tuning and Validation of Ocean Mixed Layer Models

1. Tuning and Validation of Ocean Mixed Layer Models David Acreman

2. In partnership to provide world-class ocean forecasting and research

3. Overview • The FOAM system • The ocean “mixed layer” • Kraus-Turner and KPP models • Model performance and tuning at OWS Papa • Model performance and tuning vs Argo data • Effect of tuning in a global model

4. Forecasting the open ocean: the FOAM system Input boundary data NWP 6 hourly fluxes Obs QC Forecast to T+144 Analysis Output boundary data Real-time data Automatic verification Product delivery FOAM = Forecasting Ocean Assimilation Model • Operational real-time deep-ocean forecasting system • Daily analyses and forecasts out to 6 days • Low resolution global to high resolution nested configurations • Relocatable system deployable in a few weeks • Hindcast capability (back to 1997)

5. The Mixed Layer (1) • Surface layer of the ocean where temperature, salinity and density are near uniform due to turbulent mixing. • Mixed layer deepens due to wind mixing and convection. • Mixed layer shallows when winds are low and solar heating restores stratification. • The depth of the mixed layer shows seasonal variability (deepens in autumn, shallows in spring).

6. The Mixed Layer (2) • Mixed layer depth is an important output from FOAM • Properties of the mixed layer affect ocean-atmosphere fluxes. • Mixed layer depth also influences biological processes.

7. Mixed Layer Depth diagnostic Use the “Optimal mixed layer depth” definition of Kara et al. Search for a density difference which corresponds to a temperature difference of 0.8 C at the reference depth. Figure from Kara et al, 2000, JGR, 105 (C7), 16803

8. Annual cycle of mixed layer depth from 1 degree global FOAM

9. The Kraus-Turner Model • The Met Office ocean model uses a bulk mixed layer model, based on Kraus and Turner (1967), to mix tracers. • The model assumes a well mixed surface layer and uses a TKE budget to calculate mixed layer depth. • A 1D configuration was used to validate and tune the model.

10. K-Profile Parameterisation of Large et al • More sophisticated than KT. • Doesn’t assumed well mixed surface layer. • Models turbulent fluxes as diffusion terms. • Based on atmospheric boundary layer models.

11. Ocean Weather Station Papa • Frequently used for validation and tuning of 1D mixed layer models • Located in N.E. Pacific at 50N, 145W • Ran Kraus-Turner and KPP models for one year starting in March 1961 (same as Large et al 1994) • Used vertical resolutions of 0.5m, 2m, 5 and 10m • Forcing fluxes calculated using bulk formulae (met data courtesy of Paul Martin)

12. Performance at OWS Papa (0.5m resolution)

13. Performance at OWS Papa (2m resolution)

14. Performance at OWS Papa (5m resolution)

15. Performance at OWS Papa (10m resolution)

16. Tuning the Kraus-Turner Model • KT model based on a TKE budget. • Sources of TKE are wind mixing and convection. • Generation of TKE due to wind mixing given by W=u*3 • 15% of PE released by convection is converted to TKE. • TKE reduced by work done in overturning stable stratification and by dissipation. • Dissipation represented by exponential decay with depth: TKE~ exp (z/). • The free parameters  and  can be tuned to improve performance (currently =0.7, =100m in FOAM).

17. Tuning at OWS Papa • Ran many model realisations with different values of  and  parameters • Calculated mean and RMS errors in mixed layer depth • Plotted errors vs.  and  parameters • Tuned at 10m, 2m and 0.5m vertical resolutions

18. OWS Papa Tuning Results (10m resolution) Mean errors RMS errors Minimum RMS errors with =0.775, =40m

19. OWS Papa Tuning Results (2m resolution) Mean errors RMS errors Minimum RMS errors with =1.275, =30m

20. OWS Papa Tuning Results (0.5m resolution) Mean errors RMS errors Minimum RMS errors with =1.225, =30m

21. Performance at OWS Papa (0.5m resolution)

22. Temperature and temperature error from tuned OWS Papa K-T model

23. Model tuning using Argo data • Argo floats are autonomous profiling floats which record temperature and salinity profiles approximately every 10 days. • A large number of annual cycles are available for model tuning.

24. Kraus-Turner Model Tuning using Argo • Forcing from Met Office NWP fluxes. • Initial conditions from Levitus climatology. • Temperature and salinity profiles assimilated over 10 day window. • Vertical model levels based on operational FOAM system (~10m near surface). • Calculate mean and RMS errors, excluding cases with significant advection. • Average over sample of 218 floats. • Run KT model using different values of  and .

25. Tuning results: all floats RMS errors Mean errors Smallest RMS errors with =1.5, =40m

26. Tuning results: assimilation of one profile only RMS errors Mean errors Smallest RMS errors with =1.1, =40m

27. Case study: Argo float Q4900131 • Location: 46N, 134W. • Forcing from Met Office NWP fluxes. • Initial conditions from float temperature and salinity profiles. • No assimilation of data. • Compare three different models: Kraus-Turner, Large and GOTM. • Run models at high vertical resolution (0.5m) and study annual cycle.

28. Case study: Argo float Q4900131 (2) • K-T model uses =0.7, =100m. • GOTM version 3.2 • GOTM results courtesy of Chris Jeffery (NOC).

29. Case study: Argo float Q4900131 (3) • KT model uses l=1.5, d=40m.

30. New parameters in global FOAM • Ran 1 year hindcast using global 1 degree FOAM • Kraus-Turner parameters were changed to =1.5, =40m • Plotted difference in mixed layer depth between models with old and new parameters

31. Difference in mixed layer depth

32. Conclusions • The Kraus-Turner model can give a good representation of mixed layer depths when tuned. • Optimum parameters for the Kraus-Turner scheme are =1.5, =40m with assimilation. • Without ongoing assimilation the optimum value of  is reduced. • The Large et al KPP scheme tends to give mixed layers which are too shallow particularly at low vertical resolutions.