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Tuning and Validation of Ocean Mixed Layer ModelsDavid Acreman

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In partnership to provide world-class ocean forecasting and research

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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

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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

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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).

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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.

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Mixed Layer Depth diagnostic

Figure from Kara et al, 2000, JGR, 105 (C7), 16803

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.

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Annual cycle of mixed layer depth from 1 degree global FOAM

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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.

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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.

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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)

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Performance at OWS Papa (0.5m resolution)

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Performance at OWS Papa (2m resolution)

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Performance at OWS Papa (5m resolution)

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Performance at OWS Papa (10m resolution)

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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).

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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

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OWS Papa Tuning Results (10m resolution)

RMS errors Mean errors

Minimum RMS errors with =0.775, =40m

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OWS Papa Tuning Results (2m resolution)

RMS errors Mean errors

Minimum RMS errors with =1.275, =30m

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OWS Papa Tuning Results (0.5m resolution)

RMS errors Mean errors

Minimum RMS errors with =1.225, =30m

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Performance at OWS Papa (0.5m resolution)

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

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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.

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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 .

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Tuning results: all floats

RMS errors Mean errors

Smallest RMS errors with =1.5, =40m

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Tuning results: assimilation of one profile only

Smallest RMS errors with =1.1, =40m

Mean errorsRMS errors

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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.

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Case study: Argo float Q4900131 (2)

• K-T model uses =0.7, =100m.

• GOTM version 3.2• GOTM results courtesy

of Chris Jeffery (NOC).

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Case study: Argo float Q4900131 (3)

• KT model uses =1.5, =40m.

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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

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Difference in mixed layer depth

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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.