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Tuning and Validation of Ocean Mixed Layer Models David Acreman
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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
Forecasting the open ocean: the FOAM system 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) FOAM = Forecasting Ocean Assimilation Model Real-time data Obs QC Analysis Forecast to T+144 NWP 6 hourly fluxes Automatic verification Product delivery Input boundary data Output boundary data
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).
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.
Mixed Layer Depth diagnostic Figure from Kara et al, 2000, JGR, 105 (C7), 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.
Annual cycle of mixed layer depth from 1 degree global FOAM
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.
K-Profile Parameterisation of Large et al More sophisticated than KT. Doesnt assumed well mixed surface layer. Models turbulent fluxes as diffusion terms. Based on atmospheric boundary layer models.
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)
Performance at OWS Papa (0.5m resolution)
Performance at OWS Papa (2m resolution)
Performance at OWS Papa (5m resolution)
Performance at OWS Papa (10m resolution)
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).
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
OWS Papa Tuning Results (10m resolution) RMS errors Mean errors Minimum RMS errors with =0.775, =40m
OWS Papa Tuning Results (2m resolution) RMS errors Mean errors Minimum RMS errors with =1.275, =30m
OWS Papa Tuning Results (0.5m resolution) RMS errors Mean errors Minimum RMS errors with =1.225, =30m
Performance at OWS Papa (0.5m resolution)
Temperature and temperature error from tuned OWS Papa K-T model
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.
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.
Tuning results: all floats RMS errorsMean errors Smallest RMS errors with =1.5, =40m
Tuning results: assimilation of one profile only Smallest RMS errors with =1.1, =40m Mean errorsRMS errors
Case study: Argo float Q 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.
Case study: Argo float Q (2) K-T model uses =0.7, =100m. GOTM version 3.2 GOTM results courtesy of Chris Jeffery (NOC).
Case study: Argo float Q (3) KT model uses =1.5, =40m.
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
Difference in mixed layer depth
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.