Modeling study of the coastal upwelling system of the Monterey Bay area during 1999 and 2000. I. Shulman (1), J.D. Paduan (2), L. K. Rosenfeld (2), S. R. Ramp (2), J. C. Kindle (1) (1)Naval Research Laboratory, Stennis Space Center, MS (2) Naval Postgraduate School, Monterey, CA SUPPORT: NOPP “Innovative Coastal-Ocean Observing Network (ICON)” ONR Ocean Modeling and Prediction ONR Biological and Chemical Oceanography
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Modeling study of the coastal upwelling system of the Monterey Bay area during 1999 and 2000.
Modeling study of the coastal upwelling system of the Monterey Bay area during 1999 and 2000. I. Shulman (1), J.D. Paduan (2), L. K. Rosenfeld (2), S. R. Ramp (2), J. C. Kindle (1). Naval Research Laboratory, Stennis Space Center, MS (2) Naval Postgraduate School, Monterey, CA. SUPPORT : - PowerPoint PPT Presentation
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Modeling study of the coastal upwelling system of the Monterey Bay area during
1999 and 2000.
I. Shulman (1), J.D. Paduan (2), L. K. Rosenfeld (2),
S. R. Ramp (2), J. C. Kindle (1)
(1) Naval Research Laboratory, Stennis Space Center, MS
ICON Model Runs in 2000 (January 1 – October 1) 10 COAMPS COAMPS PWC10.9 No 11 COAMPS COAMPS PWC10.9 Yes
* 9km resolution COAMPS used ** MCSST surface temperatures always assimilated into PWC but only assimilated in ICON model where indicated. *** PWC0.0 is forced with NOGAPS wind, PWC2.1 and PWC10.9 are forces with 27 km, operational COAMPS wind in 1999 and 2000 respectively. **** Runs 8, 9 and 11 were done with the use of several CODAR data assimilation schemes.
Impact of high-resolution wind forcing on ICON model
predictions
PWC and ICON forced with ~90km wind (NOGAPS)
ICON forced with 9km wind ( COAMPS)
PWC forced with ~ 90km wind (NOGAPS)
ICON forced with 9km wind (COAMPS)
PWC forced with 27km wind (COAMPS)
Standard Deviation of SST. 4-6/99, energy at periods > 90 d removed
0.1C
0.8CNOGAPS (91km)
COAMPS (9km)
D. Blencoe, MS thesis
The model run with COAMPS 9km wind forcing better captured the influence of the complex coastline and topography structure.
The model run with COAMPS 9km wind displayed more details and
produced stronger headland effects.
Coupling with larger-scale PWC modelComparison ADCP and model-predicted currents
at buoy M2
Magnitude of complex correlation Angular displacements
Impact of surface heat forcing on ICON model predictions.
Observed and model predicted MLDs (m)
0.1 ˚C 0.2 ˚C 0.1 ˚C 0.2 ˚C
Offshore core of the California current
California Undercurrent
California UndercurrentRAFOS floats vs ICON model currents, 300m
Conclusion
• With high-resolution atmospheric forcing the ICON model captures “the essence” but not the “details”of observed variability.
• Data Assimilation (“blending” of observations and model predictions) is needed
HIGH FREQUENCY RADAR (CODAR) DATA ASSIMILATION
IN THE MONTEREY BAY.
APPROACH
Methods of using HF radar data to provide corrections to
the model wind forcing are investigated.
Figure 4. Alongshore component of wind at the M1 mooring and the mode 1 amplitude for the radar-derived (CODAR) surface velocity fields as a scatter plot (left panel) and versus time (right panel).
Inadequate knowledge of the wind stress is probably a significant source of error in the model solutions.
Figure 6. CODAR data footprints (dots) and locations of M1 and M2 moorings
Magnitudes of complex correlation (a) and angular displacements (b) between model-
predicted currents and those observed at M2.
Map of complex-correlation magnitudes between observed currents at M2 and HF radar-derived surface currents (upper level in each panel) or
model-predicted currents at various depths.
No assim. With assim.
Map of complex-correlation magnitudes between observed currents at M1 and HF radar-derived surface currents (upper level in each panel) or
model-predicted currents at various depths.
No assim. With assim.
W M Av2 f
Us curlzUs .
Div M ICON forced with ~90km wind
(NOGAPS)
242d day
245th day
Along-shore model velocitiesSection AA
Section BB
Bioluminescence
AA BBBioluminescence maximums at 242d and 245th days are located in the frontal areas representing a strong reversal of flow direction.
FUTURE
• Use of circulation model for optimal and adaptive sampling
• Bio-optical and physical modeling
• Data Assimilation: CODAR, SSTs, glider and mooring data, estimation and modeling covariances.