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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.