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MAJ Ong Ah Chuan RSN, USW

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MAJ Ong Ah Chuan RSN, USW

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MAJ Ong Ah Chuan

RSN, USW

- Problems of the Diagnostic Initialization
- Proposed Research in this Thesis
- Environment of the South China Sea
- Experiment Design
- Sensitivity Study Result and Analysis
- Conclusion

- Ocean modeling - Need reliable data for specifying initial condition
- Past observations - Contributed greatly to T & S fields

- (Tc, Sc) obtained from NODC or GDEM as initial T & S fields
- Initial Vc usually not available
- Initialization of Vc important
- To accurately predict ocean – need a reliable initialization

N Equatorial Current

South China Sea

Model output

- Widely used model initialization - diagnostic mode
- Integrates model from (Tc, Sc), zero Vc & holding (Tc, Sc) unchanged
- After diagnostic run, a quasi-steady state & Vc is established
- (Tc, Sc, Vc) are treated as the initial conditions

- Initial condition error can drastically affect the model
- Diagnostic mode initialization extensively used - need to examine reliability
- Chu & Lan [2003, GRL] has pointed out the problems:
- - Artificially adding extremely strong heat/salt sources or sinks

- Horizontal momentum equation – (1)
- Temp & Salinity equations – (2) and (3)

----- (1)

------------------ (2)

------------------ (3)

- (KM, KH) – Vertical eddy diffusivity
- (Hv, HT, HS) – Horizontal diffusion & subgrid processes causing change (V, T, S )

------- (1)

------------------ (2)

------------------ (3)

- Diagnostic initialization integrate (1)-(3):
- with T and S unchanged

------------------ (5)

------------------ (6)

- Analogous to adding heat & salt source/sink terms (FT, FS)
- (2) & (3) becomes:

Keeping :

------------------ (7)

- Combining (5), (6) & (7):

,

------------------- (8)

------------------- (9)

are artificially generated at each time step

- Examine these source/sink terms
- POM is implemented for the SCS

- POM: time-dependent, primitive equation numerical model on a 3-D
- Includes realistic topography & a free surface
- Sigma coordinate model

s ranges from s = 0 at z = h to s = -1 at z = -H

- Sigma coordinate - Dealing with significant topographical variability

- Chu & Lan [2003, GRL] had proposed criteria for strength of artificial source & sink
- Based on SCS, maximum variability of T, S: 35oC & 15 ppt
- Max rates of absolute change of T, S data:
- These values are used as standard measures for ‘source/sink’

----- (10)

- Twenty four times of (10) represents strong ‘source/sink’ :

----- (11)

- Ten times of (11) represents extremely strong ‘source/sink’

------ (12)

- (10), (11) & (12) to measure the heat/salt ‘source/sink’ terms generated

- Chu & Lan [2003] found the problem:
- Generation of spurious heat/salt sources and sinks
- Did not analyze uncertainty of initialized V to the uncertainty of horizontal eddy viscosity & duration of initialization

- Thesis Demonstrate:
- -Duration of diagnostic initialization needed to get initial V ?
- - Uncertainty of C affect artificial heat & salt sources/sinks ?
- - Uncertainty of C affect initial V from diagnostic initialization ?
- - Uncertainty of V due to uncertain duration ?

- Area of study: SCS
- POM implemented for SCS to investigate physical outcome of diagnostic initialization
- NODC annual mean (Tc, Sc)
- SCS initialized diagnostically for 90 days (C = 0.05, 0.1, 0.2 & 0.3)
- 60th Day V with C = 0.2 taken as reference

SCS Area = 3.5 x 106 km2

Sill depth:

2600 m

- Largest marginal sea in Western Pacific Ocean
- Large shelf regions & deep basins
- Deepest water confined to a bowl-type trench
- South of 5°N, depth drops to 100m

Climatological wind stress

- Subjected to seasonal monsoon system
- Summer: SW monsoon (0.1 N/m2 )
- Winter: NE monsoon (0.3 N/m2)
- Transitional periods - highly variable winds & currents

Jun

Dec

Kuroshio

Luzon Strait

Sill depth: 2600 m

South China

Sea

Jun

- Circulation of intermediate to upper layers: local monsoon systems & Kuroshio
- Kuroshio enters through southern side of channel, executes a tight, anticyclonic turn
- Kuroshio excursion near Luzon Strait, anti-cyclonic rings detached

- North: Cold, saline. Annual variability of salinity small
- South: Warmer & fresher
- Summer: 25-29°C (> 16°N)
- 29-30°C (< 16°N)
- Winter: 20-25°C (> 16°N)
- 25-27.5°C (< 16°N)

Winter

Summer

- 125 x 162 x 23 horizontally grid points with 23 s - levels
- Model domain: 3.06°S to 25.07°N, & from 98.84°E to 121.16°E
- Bottom topography: DBDB 5’ resolution
- Horizontal diffusivities are modeled using Smagorinsky form (C = 0.05, 0.1, 0.2 and 0.3)
- No atmospheric forcing

- Closed lateral boundaries
- Free slip condition
- Zero gradient condition for temp & salinity

- No advective or diffusive heat, salt or velocity fluxes through boundaries
- Open boundaries, radiative boundary condition with zero vol transport

- Analyze impact of uncertainty of C to initialized V
- 1 control run, 3 sensitivity runs of POM
- Control run: C = 0.2, Sensitivity runs: C = 0.05, 0.1 & 0.3
- Assess duration of initialization & impact on Vunder different C
- - diagnostic model was integrated 90 days
- - 60th day of model result used as reference
- - RRMSD of V between day-60 & day-i (i = 60, 61,62…...90)
- Investigate sensitivity of V to uncertainty of initialization period

- POM diagnostic mode integrated with 3 components of V = 0
- Temp & salinity specified by interpolating annual mean data
- FT & FS obtained at each time step
- Horizontal distributions of FT & FS derived & compared to measures established earlier
- Horizontal mean | FT | & | FS | to identify overall strength of heat & salt source/sink

- 30 days for mean model KE to reach quasi-steady state

Figure 7. Model Day: 90 days with C = 0.05

Figure 8. Model Day: 90 days with C = 0.1

- (FT, FS) generated on day-30, day-45, day-60 & day-90
- Identify their magnitudes & sensitivity to the integration period

Figure 9. Model Day: 90 days with C = 0.2

Figure 10. Model Day: 90 days with C = 0.3

- Horizontal distribution of FT (°C hr-1)
- - at 4 levels (surface, subsurface, mid-level, near bottom)
- - with 4 different C-values
- Show extremely strong heat sources/sinks
- Unphysical sources/sinks have various scales and strengths
- Reveal small- to meso-scale patterns

Max Value = 2.331

Min Value = - 0.987

Unit: C/hr

Max Value = 1.872

Min Value = - 2.983

Unit: C/hr

Max Value = 1.682

Min Value = - 0.591

Unit: C/hr

Max Value = 0.374

Min Value = - 0.367

Unit: C/hr

- Max Heat Source = 2778 Wm-3
- Features consistent for different C-values

Max Heat Sink = -3555 Wm-3

On day-60 with

C = 0.05

Max Value = 2.338

Min Value = - 0.595

Unit: C/hr

Max Value = 1.724

Min Value = - 2.001

Unit: C/hr

Max Value = 1.627

Min Value = - 0.595

Unit: C/hr

Max Value = 0.314

Min Value = - 0.364

Unit: C/hr

Max Heat Source = 2787 Wm-3

Max Heat Sink = -2385 Wm-3

On day-60 with

C = 0.1

Max Value = 2.337

Min Value = - 0.348

Unit: C/hr

Max Value = 1.332

Min Value = - 1.016

Unit: C/hr

Max Value = 1.632

Min Value = - 0.602

Unit: C/hr

Max Value = 0.287

Min Value = - 0.369

Unit: C/hr

Max Heat Source = 2785 Wm-3

Max Heat Sink = -1211 Wm-3

- C-value increases, FT weakens
- Still above extremely strong heat source criterion

On day-60 with

C = 0.2

Max Value = 2.331

Min Value = - 0.346

Unit: C/hr

Max Value = 1.013

Min Value = - 0.908

Unit: C/hr

Max Value = 1.661

Min Value = - 0.607

Unit: C/hr

Max Value = 0.277

Min Value = - 0.363

Unit: C/hr

Max Heat Source = 2778 Wm-3

Max Heat Sink = -1082 Wm-3

- large C cause unrealistically strong diffusion in ocean model

On day-60 with

C = 0.3

- Horizontal distribution of FS (ppt hr-1)
- - at 4 levels (surface, subsurface, mid-level, near bottom)
- - with 4 different C-values
- Show strong salinity sources/sinks
- Unphysical sources/sinks have various scales and strengths
- Reveal small- to meso-scale patterns

Max Value = 0.372

Min Value = - 0.115

Unit: ppt/hr

Max Value = 0.134

Min Value = - 0.198

Unit: ppt/hr

Max Value = 0.019

Min Value = - 0.067

Unit: ppt/hr

Max Value = 0.014

Min Value = - 0.016

Unit: ppt/hr

- Max Salinity Source = 0.372 ppt hr-1
- Features similar for different C-values

Max Salinity Sink = -0.198 ppt hr-1

On day-60 with

C = 0.05

when C-value increases, FS weakens

Max Value = 0.372

Min Value = - 0.085

Unit: ppt/hr

Max Value = 0.079

Min Value = - 0.198

Unit: ppt/hr

Max Value = 0.018

Min Value = - 0.066

Unit: ppt/hr

Max Value = 0.011

Min Value = - 0.012

Unit: ppt/hr

Max Salinity Source = 0.372 ppt hr-1

Max Salinity Sink = -0.198 ppt hr-1

On day-60 with

C = 0.1

Max Value = 0.373

Min Value = - 0.075

Unit: ppt/hr

Max Value = 0.065

Min Value = - 0.199

Unit: ppt/hr

Max Value = 0.013

Min Value = - 0.067

Unit: ppt/hr

Max Value = 0.009

Min Value = - 0.011

Unit: ppt/hr

Max Salinity Source = 0.373 ppt hr-1

Max Salinity Sink = -0.199 ppt hr-1

On day-60 with

C = 0.2

Max Salinity Sink = -0.200 ppt hr-1

when C-value increases, FS weakens

But above criterion

Max Value = 0.378

Min Value = - 0.075

Unit: ppt/hr

Max Value = 0.055

Min Value = - 0.200

Unit: ppt/hr

On day-60 with

C = 0.3

Max Value = 0.011

Min Value = - 0.068

Unit: ppt/hr

Max Value = 0.008

Min Value = - 0.011

Unit: ppt/hr

Max Salinity Source = 0.378 ppt hr-1

- Horizontal mean | FT | :
- Identify overall strength of heat source/sink
- Figure 21 to 24: temporal evolution at 4 levels:
- Near surface ( = –0.0125)
- Subsurface ( = –0.15)
- Mid-level ( = –0.5)
- Near bottom ( = –0.95)

----- (17)

- Mean |FT| increases rapidly with time
- Oscillate around quasi-stationary value
- Large - Mean |FT| based on horizontal average

Figure 21. Temporal evolution at 4 different levels with C = 0.05

- Mean |FT| increases rapidly with time
- Oscillate around quasi-stationary value
- Similar features observed at other C-values

Figure 22. Temporal evolution at 4 different levels with C = 0.1

- Mean |FT| increases rapidly with time
- Oscillate around quasi-stationary value
- Strength mean |FT| decreases across corresponding level when C increases

Figure 23. Temporal evolution at 4 different levels with C = 0.2

- Mean |FT| increases rapidly with time
- Oscillate around quasi-stationary value
- Strength mean |FT| decreases across corresponding level when C increases

Figure 24. Temporal evolution at 4 different levels with C = 0.3

- Max mean |FT| at subsurface
- Min at mid-level
- Different C values, max & min mean |FT| occurred at different levels

Figure 25. Depth Profile with C = 0.05

- Max mean |FT| at subsurface
- Min at surface
- Different C values, max & min mean |FT| occurred at different levels

Figure 26. Depth Profile with C = 0.1

- Max near bottom
- Higher value indicates a greater heat sources & sinks problem
- Min at surface

Figure 27. Depth Profile with C = 0.2

- Max at bottom
- Higher value indicates a greater heat sources & sinks problem
- Min at surface

Figure 28. Depth Profile with C = 0.3

- Horizontal mean | FS | :
- Identify overall strength of salt source/sink
- Figure 29 to 32: temporal evolution at 4 levels:
- Near surface ( = –0.0125)
- Subsurface ( = –0.15)
- Mid-level ( = –0.5)
- Near bottom ( = –0.95)

- Mean |FS| increases rapidly with time
- Peak value of 0.0137 ppt hr-1
- Oscillate around quasi-stationary value

Figure 29. Temporal evolution at 4 different levels with C = 0.05

- Mean |FS| increases rapidly with time
- Peak value of 0.0127 ppt hr-1
- Oscillate around quasi-stationary value

Figure 30. Temporal evolution at 4 different levels with C = 0.1

- Mean |FS| increases rapidly with time
- Peak value of 0.0124 ppt hr-1
- Oscillate around quasi-stationary value

Figure 31. Temporal evolution at 4 different levels with C = 0.2

- Peak value of 0.0121 ppt hr-1
- Strength of Mean |FS| decreases across corresponding level when C increases

Figure 32. Temporal evolution at 4 different levels with C = 0.3

- Mean |FS| - max value at surface
- Oscillates with decreasing value as depth increases
- Higher value indicates a greater salt sources & sinks problem
- Min occurred at bottom

Figure 33. Depth Profile with C = 0.05

- Max value at surface
- Oscillates with decreasing value as depth increases
- Min occurred at bottom
- Similar pattern for other C-values

Figure 34. Depth Profile with C = 0.1

- Max value at surface
- Oscillates with decreasing value as depth increases
- Min occurred at bottom

Figure 35. Depth Profile with C = 0.2

- Greater salting rate at surface
- Strength decreases across corresponding level when C-value increases

Figure 36. Depth Profile with C = 0.3

- Uncertainty of Diagnostically initialized V due to uncertainty of C ?
- V on 60th day for 4 levels for each of 4 C-values are plotted in Figures 37 to 40 for illustrations
- Near surface ( = –0.0125)
- Subsurface ( = –0.15)
- Mid-level ( = –0.5)
- Near bottom ( = –0.95)

- Surface & subsurface circulation heads southward in an anti-cyclonic pattern
- Large uncertainty in these V , RRMSDV > 60%
- Anti-cyclonic circulation contained within SCS
- Consistent with model set-up of 0 volume transport

Day-60 with C = 0.05

- Another anti-cyclonic eddy-like structure centered at (14N, 117E)
- Near bottom of SCS, this anti-cyclonic eddy-like structure is more pronounced when C is small

Day-60 with C = 0.1

- Near bottom of SCS, anti-cyclonic eddy-like structure more pronounced when C is small

Day-60 with C = 0.2

- Near bottom of SCS, anti-cyclonic eddy-like structure more pronounced when C is small

Day-60 with C = 0.3

- Uncertainty of C-value affect V derived from the diagnostic initiation process ?
- 4 different C-values (0.05, 0.1, 0.2 and 0.3) were used

----------- (17)

----------- (18)

Day of diagnostic run. = -0.0125

Day of diagnostic run. = -0.15

Day of diagnostic run. = -0.5

Day of diagnostic run. = -0.95

- RRMSDV(k,C) increases with time rapidly
- Oscillate around quasi-stationary value between 0.6 & 0.8
- Largest value is between C = 0.05 & C = 0.2 (control run)

Figure 41. RRMSDV(k, 0.05)

RRMSDV

RRMSDV

RRMSDV

RRMSDV

- Vertical profile of RRMSDV(k, C) has a max at mid-level for different cases of C-values
- Indicates strong variation of V in mid-level of SCS
- Decreases with depth from mid-level to bottom

Figure 42. RRMSDV(k, 0.05)

Day of diagnostic run. = -0.0125

Day of diagnostic run. = -0.15

Day of diagnostic run. = -0.5

Day of diagnostic run. = -0.95

- RRMSDW(k,C) increases with time rapidly
- Largest value is between C = 0.05 & C = 0.2 (control run)
- RRMSDW(k,C) is much larger than RRMSDV(k,C)
- Smaller magnitude & larger uncertainty of W

Figure 43. RRMSDW(k, 0.05)

RRMSDW

RRMSDW

RRMSDW

RRMSDW

- Vertical profile of RRMSDW(k, C) decreases from surface to bottom
- Decreased rate of decrease of RRMSDW(k, C)

Figure 44. RRMSDW(k, 0.05)

Day of diagnostic run. = -0.0125

Day of diagnostic run. = -0.15

Day of diagnostic run. = -0.5

Day of diagnostic run. = -0.95

- RRMSDV(k, C) decreases when C-value increases
- Max RRMSDV(k,C=0.1) > 0.5

Figure 45. RRMSDV(k, 0.1)

Day of diagnostic run. = -0.0125

Day of diagnostic run. = -0.15

Day of diagnostic run. = -0.5

Day of diagnostic run. = -0.95

- RRMSDV(k, C) decreases when C-value increases
- RRMSDV(k,C =0.3) > 0.35
- Larger C-value lead to smaller RRMSDV(k, C)
- Excessively large C cause unrealistically strong diffusion in ocean model

Figure 46. RRMSDV(k, 0.3)

Day of diagnostic run. = -0.0125

Day of diagnostic run. = -0.15

Day of diagnostic run. = -0.5

Day of diagnostic run. = -0.95

- RRMSDW(k, C) decreases when C-value increases
- RRMSDW(k, C=0.1 & C=0.05) > 1

Figure 47. RRMSDW(k, 0.1)

Day of diagnostic run. = -0.0125

Day of diagnostic run. = -0.15

Day of diagnostic run. = -0.5

Day of diagnostic run. = -0.95

- RRMSDW(k, C) decreases when C-value increases
- RRMSDW(k, C=0.3) > 0.6

Figure 48. RRMSDW(k, 0.3)

- How long diagnostic integration is needed?
- 30 days of diagnostic run, quasi-steady state is achieved
- 60th day selected to compute RRMSDV & RRMSDW

----------- (17)

----------- (18)

t = 60, 61, 62 ….90

C =0.05

C= 0.1

- RRMSDV(t) fluctuates irregularly
- Increases with time rapidly from day-60 to day-70
- Cincreases, RRMSDV(t) decreases

C=0.3

C= 0.2

Figure 49. RRMSDV(t)

C =0.05

C= 0.1

C=0.3

C= 0.2

- RRMSDW(t) fluctuates irregularly
- Increases with time rapidly
- Both RRMSDV(t) and RRMSDW(t) fluctuate irregularly with time

Figure 50. RRMSDW(t)

- Strong thermohaline source/sink terms generated for C = 0.05, 0.1, 0.2 & 0.3
- Horizontal distributions of thermohaline source/sink terms show extremely strong sources/sinks
- C increases, sources/sinks decrease in magnitude, but still above the criteria
- Larger C lead to smaller spurious sources & sinks

------------------ (5)

------------------ (6)

- Uncertainty of C-value affect Vc significantly
- Uncertainty of diagnostic integration period affects drastically the uncertainty in initialized Vc

- Extremely strong & spatially non-uniform initial heating/cooling rates are introduced into ocean models

- C is the horizontal viscosity parameter

Where

- Standard measures for ‘source/sink’

----- (10)

- Strong ‘source/sink’

----- (11)

- Extremely strong ‘source/sink’

------ (12)