Upwelling Dynamics off Monterey Bay: Heat Flux and Temperature Variability, and their Sensitivities

Understanding the complex dynamics of coastal upwelling is essential for coastal ocean dynamics, phytoplankton blooms, and pollution transport. Atmosphericdriven coastal upwelling often occurs when strong alongshore winds and the Coriolis force combine to displace warmer surface waters offshore, leading to upward motions of deeper cooler, nutrient-dense waters to replace these surface waters. Using the models of the MIT Multidisciplinary Simulation, Estimation, and Assimilation System (MSEAS) group, we conduct a large set of simulation sensitivity studies to determine which variables are dominant controls for upwelling events in the Monterey Bay region. Our motivations include determining the dominant atmospheric fluxes and the causes of high-frequency fluctuations found in ocean thermal balances. We focus on the first upwelling event from August 15, 2006 in Monterey Bay that occurred during the Monterey Bay 06 (MB06) at-sea experiment, for which MSEAS data-assimilative baseline simulations already existed. Using the thermal energy (temperature), salinity and momentum (velocity) conservation equations, full ocean fields in the region as well as both control volume (flux) balances and local differential term-by-term balances for the upwelling event events were computed. The studies of ocean fields concentrate on specific depths: surface-0m, thermocline-30m and undercurrent-150m. Effects of differing atmospheric forcing contributions (wind stress, surface heating/cooling, and evaporation-precipitation) on these full fields and on the volume and term-by-term balances are analyzed. Tidal effects are quantified utilizing pairs of simulations in which tides are either included or not. Effects of data assimilation are also examined. We find that the wind stress forcing is the most important dynamical parameter in explaining the extent and shape of the upwelling event. This is verified using our large set of sensitivity studies and examining the heat flux balances. The assimilation of data has also an impact because this first upwelling event occurs during the initialization. Tidal forcing and, to a lesser extent, the daily atmospheric and data assimilation cycles explain the higher frequency fluctuations found in the volume averaged time rate of change of thermal energy.

[1]  U. Send,et al.  Relaxation from upwelling in the Coastal Ocean Dynamics Experiment , 1987 .

[2]  J. Largier,et al.  Subtidal circulation over the northern California shelf , 1993 .

[3]  Pierre F. J. Lermusiaux,et al.  Multiscale two-way embedding schemes for free-surface primitive equations in the “Multidisciplinary Simulation, Estimation and Assimilation System” , 2010 .

[4]  Pierre F. J. Lermusiaux,et al.  Advanced interdisciplinary data assimilation: filtering and smoothing via error subspace statistical estimation , 2002, OCEANS '02 MTS/IEEE.

[5]  Patrick T. Drake,et al.  Local wind forcing of the Monterey Bay area inner shelf , 2005 .

[6]  Naomi Ehrich Leonard,et al.  Preparing to predict: The Second Autonomous Ocean Sampling Network (AOSN-II) experiment in the Monterey Bay , 2009 .

[7]  Russ E. Davis,et al.  Mass and heat budgets on the northern California continental shelf , 1988 .

[8]  Kathryn A. Kelly,et al.  The influence of winds and topography on the sea surface temperature patterns over the northern California slope , 1985 .

[9]  Cambridge Ma.  Advanced Interdisciplinary Data Assimilation: Filtering and Smoothing via Error Subspace Statistical Estimation , 2002 .

[10]  Pierre F. J. Lermusiaux,et al.  Estimation and study of mesoscale variability in the strait of Sicily , 1999 .

[11]  Eric Vincent Heubel.  Parameter estimation and adaptive modeling studies in ocean mixing , 2008 .

[12]  Pierre F. J. Lermusiaux,et al.  Dynamically orthogonal field equations for continuous stochastic dynamical systems , 2009 .

[13]  B. Cushman-Roisin,et al.  Introduction to geophysical fluid dynamics : physical and numerical aspects , 2011 .

[14]  Pierre F. J. Lermusiaux,et al.  Uncertainty estimation and prediction for interdisciplinary ocean dynamics , 2006, J. Comput. Phys..

[15]  James C. McWilliams,et al.  Modeling tides in Monterey Bay, California , 2009 .

[16]  S. Lentz.  A heat budget for the Northern California Shelf during CODE 2 , 1987 .

[17]  Yi Chao,et al.  High-resolution real-time modeling of the marine atmospheric boundary layer in support of the AOSN-II field campaign , 2009 .

[18]  Pierre F. J. Lermusiaux,et al.  Forecasting and Reanalysis in the Monterey Bay/California Current Region for the Autonomous Ocean Sampling Network-II Experiment , 2009 .

[19]  Arpit Agarwal,et al.  Statistical Field Estimation for Complex Coastal Regions and Archipelagos , 2011 .

[20]  Paul J. Martin,et al.  Impact of glider data assimilation on the Monterey Bay model , 2009 .

[21]  C. Storlazzi,et al.  Long-term, high-frequency current and temperature measurements along central California: Insights into upwelling/relaxation and internal waves on the inner shelf , 2003 .

[22]  Franklin B. Schwing,et al.  Bifurcated flow from an upwelling center: a cold water source for Monterey Bay , 1994 .

[23]  Pierre F. J. Lermusiaux,et al.  Adaptive modeling, adaptive data assimilation and adaptive sampling , 2007 .

[24]  S. Lentz,et al.  Heat and salt balances over the northern California shelf in winter and spring , 1994 .

[25]  Pierre FJ Lermusiaux,et al.  Coupled Ocean–Acoustic Prediction of Transmission Loss in a Continental Shelfbreak Region: Predictive Skill, Uncertainty Quantification, and Dynamical Sensitivities , 2010, IEEE Journal of Oceanic Engineering.

[26]  Michael K. Tippett,et al.  Predictability: Recent insights from information theory , 2007 .

[27]  Igor Shulman,et al.  Methodology for a regional tidal model evaluation, with application to central California , 2009 .

[28]  A. Robinson,et al.  The California Current System: A multiscale overview and the development of a feature-oriented regional modeling system (FORMS) , 2011 .