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Heat fluxes in Tampa Bay, Florida

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Title:
Heat fluxes in Tampa Bay, Florida
Physical Description:
Book
Language:
English
Creator:
Sopkin, Kristin L
Publisher:
University of South Florida
Place of Publication:
Tampa, Fla
Publication Date:

Subjects

Subjects / Keywords:
Turbulent heat flux
Bulk algorithm
Heat budget
Radiative heat flux
BRACE
Dissertations, Academic -- Marine Science -- Masters -- USF   ( lcsh )
Genre:
non-fiction   ( marcgt )

Notes

Summary:
ABSTRACT: The Meyers et al. (2007) Tampa Bay Model produces water level and three-dimensional current and salinity fields for Tampa Bay. It is capable of computing temperature but is presently run without active thermodynamics. Variations in water temperature are driven by heat exchange at the water-atmosphere boundary and advective heat flux at the mouth of the bay. The net heat exchange surface boundary condition is required for computations of three-dimensional temperature fields. Components of the surface heat budget were measured or derived at an observational tower in Middle Tampa Bay. Net heat exchange at the surface of Tampa Bay was computed from June 2002 to May 2005. Total heat energy gained or lost at the bay-atmosphere interface includes turbulent and radiative heat fluxes. An initial examination of turbulent heat exchange, the portion of total surface heat flux driven by atmospheric turbulence, demonstrated the skill of a bulk flux algorithm (TOGA COARE v. 3.0) in predicting measured sensible heat flux over Tampa Bay (R² = 0.80 and RMSE of 11.02 W/m² from June through November of 2002).Insolation was measured directly at the observational tower. Solar radiation is reflected in proportion to sea surface albedo, computed following Payne (1972). Based upon Secchi depth readings, Tampa Bay was classified as a water body type 7. The amount of penetrating insolation reflected from the bottom was computed for this type 7 estuary. Upwelling longwave radiation is emitted in proportion to the water temperature according to the Stefan-Boltzmann law. Eleven bulk formulas for computing downwelling longwave radiation were assessed for skill in reproducing observations made at buoys moored on the West Florida Shelf. Berliand and Berliand (1952) best represented downwelling longwave heat flux measurements at the buoys and is appropriate for application over Tampa Bay.Surface heat flux dominates cooling in fall and warming in spring while advective heat exchange becomes important during the summer. Extreme events, including tropical cyclones and extratropical fronts, dramatically impact surface heat exchange, driving rapid cooling. The methods applied in computation of heat flux components are amenable to real-time modeling exercises.
Thesis:
Thesis (M.S.)--University of South Florida, 2008.
Bibliography:
Includes bibliographical references.
System Details:
Mode of access: World Wide Web.
System Details:
System requirements: World Wide Web browser and PDF reader.
Statement of Responsibility:
by Kristin L. Sopkin.
General Note:
Title from PDF of title page.
General Note:
Document formatted into pages; contains 85 pages.

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University of South Florida
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Resource Identifier:
aleph - 001992371
oclc - 316065198
usfldc doi - E14-SFE0002398
usfldc handle - e14.2398
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SFS0026716:00001


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ABSTRACT: The Meyers et al. (2007) Tampa Bay Model produces water level and three-dimensional current and salinity fields for Tampa Bay. It is capable of computing temperature but is presently run without active thermodynamics. Variations in water temperature are driven by heat exchange at the water-atmosphere boundary and advective heat flux at the mouth of the bay. The net heat exchange surface boundary condition is required for computations of three-dimensional temperature fields. Components of the surface heat budget were measured or derived at an observational tower in Middle Tampa Bay. Net heat exchange at the surface of Tampa Bay was computed from June 2002 to May 2005. Total heat energy gained or lost at the bay-atmosphere interface includes turbulent and radiative heat fluxes. An initial examination of turbulent heat exchange, the portion of total surface heat flux driven by atmospheric turbulence, demonstrated the skill of a bulk flux algorithm (TOGA COARE v. 3.0) in predicting measured sensible heat flux over Tampa Bay (R¨§ = 0.80 and RMSE of 11.02 W/m¨§ from June through November of 2002).Insolation was measured directly at the observational tower. Solar radiation is reflected in proportion to sea surface albedo, computed following Payne (1972). Based upon Secchi depth readings, Tampa Bay was classified as a water body type 7. The amount of penetrating insolation reflected from the bottom was computed for this type 7 estuary. Upwelling longwave radiation is emitted in proportion to the water temperature according to the Stefan-Boltzmann law. Eleven bulk formulas for computing downwelling longwave radiation were assessed for skill in reproducing observations made at buoys moored on the West Florida Shelf. Berliand and Berliand (1952) best represented downwelling longwave heat flux measurements at the buoys and is appropriate for application over Tampa Bay.Surface heat flux dominates cooling in fall and warming in spring while advective heat exchange becomes important during the summer. Extreme events, including tropical cyclones and extratropical fronts, dramatically impact surface heat exchange, driving rapid cooling. The methods applied in computation of heat flux components are amenable to real-time modeling exercises.
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BRACE
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Heat Fluxes in Tampa Bay, Florida by Kristin L. Sopkin A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science College of Marine Science University of South Florida Major Professor: Mark E. Luther, Ph.D. Steven D. Meyers, Ph.D. Robert H. Weisberg, Ph.D. Date of Approval: April 8, 2008 Keywords: turbulent heat flux, bulk algorithm heat budget, radiat ive heat flux, BRACE Copyright 2008, Kristin L. Sopkin

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i Table of Contents List of Tables iv List of Figures v Abstract viii Chapter One: Introduction 1 Study Objective 1 Study Area 2 Data Collection 5 Organization of Thesis 7 Chapter Two: Methods 8 Turbulent Heat Fluxes 8 Sensible Heat Exchange 8 Latent Heat Exchange 9 Quantification of Turbulent Fluxes 9 Eddy Covariance Method 9 Bulk Aerodynamic Formulas 9 Gradient Method 10 Radiative Heat Fluxes 11 Incoming Shortwave Radiation 12 Surface Reflected Shortwave Radiation 12 Bottom Reflected Shortwave Radiation 12 Incoming Longwave Radiation 15 Outgoing Longwave Radiation 19 Closing the Heat Budget 20 Evaporation Rate and Freshwater Budget Analysis 21 Chapter Three: BRACE Six-Mont h Turbulent Heat Flux Study 22 Introduction 22 Model Theory 23 TOGA COARE 23 NOAA Buoy Model 24 Model Differences 24 Specific Humidity 24 Cool Skin 25 Warm Layer 25 Wind Speed/ Gustiness/ Slip 26

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ii Sea Surface Roughness 26 Atmospheric Stability 27 Experimental Methods 27 Study Area and Modeling Period 27 Meteorological Data Collection 28 Model Application 28 QA/ QC 28 Results 28 Observed and Predicted Flux Parameters 28 Inter-Model Comparison 32 Sensible Heat Flux/ Dimensi onless Heat Transfer Coefficient 32 Friction Velocity 33 Latent Heat Flux 34 Discussion and Conclusions 35 Chapter Four: Three-Year Heat Budget Study 37 Introduction 37 Methods 38 Radiative Fluxes 40 Shortwave Radiation 40 Longwave Radiation 41 Turbulent Fluxes 42 Closing the Heat Budget 43 Results and Discussion 43 Summer – June through August 43 Fall – September through November 48 Hurricane Frances 49 Extratropical Front 51 Winter – December through February 58 Spring – March through May 61 Warming Trend 62 Summary and Conclusions 66 Chapter Five: Freshwater Balance Study 67 Introduction 67 Experimental Methods 69 Freshwater Budget Components 69 Pan Evaporation Rate 69 Evaporation Rate Produced in Latent Heat Flux Calculations 70 Results 70 Freshwater Balance in Tamp a Bay for June 2002 – December 2003 70 Inter-Annual Variability in Fr eshwater Inflow/ ENSO Impacts 74 Seasonal Variability in Freshwater Balance 75 Summary and Conclusions 75 Chapter Six: Summary and Recommendations 77

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iii List of References 79

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iv List of Tables Table 2-1 Adaptation of Jerlov’s (1968) Table XXI. Percent light remaining versus depth for coastal water types. 14 Table 3-1 Performance statistics fo r predicted and observed sensible heat (H, W/m2) and friction velocity (u*, m/s) for all data and data during stable atmospheric conditions (Kara 2005). 29

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v List of Figures Figure 1-1 The location of Tampa Bay on Florida’s west coast. 3 Figure 1-2 Photo of the BRACE observational tower. 6 Figure 2-1 Locations of EPCHC Stati ons 21 and 90 relative to the BRACE observational tower (red marker) in Tampa Bay. 14 Figure 2-2 Positions of buoys C10 a nd C14 on the WFS relative to Tampa Bay. 17 Figure 2-3 Observed and modeled (B erliand and Berliand, 1952) downwelling longwave radiation at the C10 B uoy from yearday 156 through end of year of 2003. 18 Figure 2-4 Observed and modeled (B erliand and Berliand, 1952) downwelling longwave radiation at the C14 B uoy from yearday 267 through end of year of 2003. 19 Figure 3-1 Hourly measured and mode led time series and scatterplot of sensible heat (W/m2). 30 Figure 3-2 Hourly measured and mode led time series and scatterplot of friction velocity (m/s). 31 Figure 3-3 Scatterplot of modele d hourly sensible heat flux (W/m2). 32 Figure 3-4 Scatterplot of modeled hourly DH. 33 Figure 3-5 Scatterplot of modeled hourly friction velo cities (m/s). 34 Figure 3-6 Scatterplot of modele d hourly latent heat flux (W/m2) comparisons for preand postmodification of vapor pressure. 35 Figure 4-1a Summer 2002 meteorological data. 44 Figure 4-1b Summer 2002 surface fluxes. 44 Figure 4-1c Summer 2002 net su rface and advective flux. 45

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vi Figure 4-2a Summer 2003 meteorological data. 45 Figure 4-2b Summer 2003 surface fluxes. 46 Figure 4-2c Summer 2003 net su rface and advective flux. 46 Figure 4-3a Summer 2004 meteorological data. 47 Figure 4-3b Summer 2004 surface fluxes. 47 Figure 4-3c Summer 2004 net su rface and advective flux. 48 Figure 4-4 Storm track and AVHRR image of Hurricane Frances. 50 Figure 4-5a Fall 2002 meteorological data. 53 Figure 4-5b Fall 2002 surface fluxes. 54 Figure 4-5c Fall 2002 net surface and advective flux. 54 Figure 4-6a Fall 2003 meteorological data. 55 Figure 4-6b Fall 2003 surface fluxes. 55 Figure 4-6c Fall 2003 net surface and advective flux. 56 Figure 4-7a Fall 2004 meteorological data. 56 Figure 4-7b Fall 2004 surface fluxes. 57 Figure 4-7c Fall 2004 net surface and advective flux. 57 Figure 4-8a Winter 2003/4 meteorological data. 58 Figure 4-8b Winter 2003/4 surface fluxes. 59 Figure 4-8c Winter 2003/4 net su rface and advective flux. 59 Figure 4-9a Winter 2004/5 meteorological data. 60 Figure 4-9b Winter 2004/5 surface fluxes. 60 Figure 4-9c Winter 2004/5 net su rface and advective flux. 61 Figure 4-10a Spring 2004 meteorological data. 63

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vii Figure 4-10b Spring 2004 surface fluxes. 63 Figure 4-10c Spring 2004 net surface and advective flux. 64 Figure 4-11a Spring 2 005 meteorological data. 64 Figure 4-11b Spring 2005 surface fluxes. 65 Figure 4-11c Spring 2005 net surface and advective flux. 65 Figure 5-1 Daily mean rates of evaporative loss (pan evaporimeter) out of and total freshwater inflow into Tampa Bay in cubic meters per second. 71 Figure 5-2 Daily mean estimated (bulk formula) and measured (pan evaporimeter) evaporation rates over Tampa Bay (m3/s). 72 Figure 5-3 Daily mean insolation rates (acquired from the BRACE observational tower) and pan eva poration rates over Tampa Bay. 73

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viii Heat Fluxes in Tampa Bay, Florida Kristin L. Sopkin ABSTRACT The Meyers et al. (2007) Tampa Bay model produces wate r level and threedimensional current and salinity fields fo r Tampa Bay. It is capable of computing temperature but is presently run without active thermodyna mics. Variations in water temperature are driven by heat exchange at the water-atmosphere boundary and advective heat flux at the mouth of the bay. The net heat exchange surface boundary condition is required for computations of three-dimensi onal temperature fields. Components of the surface heat budget were measured or derived at an observational to wer in Middle Tampa Bay. Net heat exchange at the surface of Tampa Bay was computed from June 2002 to May 2005. Total heat energy gained or lost at the bay-atmosphere interface includes turbulent and radia tive heat fluxes. An initial examination of turbulent heat exchange, the portion of total surface heat flux driven by atmospheric turbulence, dem onstrated the skill of a bulk flux algorithm (TOGA COARE v. 3.0) in predicting measured sensible heat flux over Tampa Bay (R2 = 0.80 and RMSE of 11.02 W/m2 from June through Novemb er of 2002). Insolation was measured directly at the observational tower. Solar radiation is refl ected in proportion to sea surface albedo, computed following Payne (1972). Based upon Secchi depth readings, Tampa Bay was classified as a water body type 7. The amount of penetrating insolation reflected from the bottom was computed fo r this type 7 estuary. Upwelling longwave radiation is emitted in proportion to the water temperat ure according to the StefanBoltzmann law. Eleven bulk formulas for computing downwelling longwave radiation were assessed for skill in reproducing observations made at buoys moored on the West Florida Shelf. Berliand and Berliand (1952) best represented downwelling longwave heat flux measurements at the buoys and is appr opriate for application over Tampa Bay. Surface heat flux dominates cooling in fall and warming in spring while advective heat exchange becomes important during the summer. Extreme events, including tropical cyclones and extratropical front s, dramatically impact surface heat exchange, driving rapid cooling. The methods applied in comput ation of heat flux components are amenable to real-time modeling exercises.

PAGE 10

1 Chapter One Introduction Study Objectives Many chemical, physical and biological parameters in marine systems are influenced by water temperature including gas solubility, chemical kinetics and speciation, phytoplankton growth and nutrien t uptake rates, and water density and stratification. The ab ility to accurately model water qua lity, biological processes, and current fields is therefore dependent upon knowledge of the three-dimensional temperature structure within a model doma in. This study focuses on changes in water temperature within Tampa Bay, Florida. Temperature variations within the bay are driven by heat transfer at the air-sea interface and at the boundary between the wate rs of the Gulf of Mexico and the Tampa Bay estuary. Prior to this study, a systematic investigation into th e processes of heat exchange at the ocean-atmosphere boundary ha s not been completed, yet is central to understanding changes in es tuarine heat storage. Net heat exchange at the water surface is the summation of the radiative and the turbulent heat fluxes. The radiative fl uxes include incoming shortwave (“solar”) radiation, a portion of which is reflected from the ocean surface as outgoing shortwave radiation, incoming longwave (“atmosphe ric”) radiation, and outgoing longwave radiation emitted at the sea surf ace. Turbulent heat transfer is partitioned into a sensible heat exchange and a latent heat flux component. Sensible heat flux occurs by conduction in response to a temperature gradient betw een the ocean surface and an overlying air mass or contacting precipitation. The latent heat flux component represents heat transfer into or out of the bay as water molecules c ondense or evaporate at the water surface. The rate of change of heat energy stored within the Tampa Bay estuary must equal the sum of the net heat exchange at the air-sea interf ace and the advective heat exchange at the mouth of the bay. This balance of energy exch ange is termed the “heat budget” of the bay. In addition, accurate paramete rization of the salinity budge t is crucial to modeling estuarine density distribution and circulation patterns. The amount of freshwater lost at the sea surface due to evapor ation is highly variable, fl uctuating with changes in humidity, wind speed, and air and water temp eratures. Rates of evaporation over the estuary are not currently directly meas ured. For modeling purposes, over-water evaporation rates are assumed equivalent to averaged daily rates obtained from an evaporation pan located near McKay Bay. Pans cannot account for physical processes that occur within a large body of water, such as the dow nward mixing and storage of heat. As one component of this study, a more realistic descripti on of the over-water

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2 evaporation rate is produced in computations of the calculated latent heat flux component of the heat budget. Study objectives include (1) ut ilizing data from a meteor ological tower located in Middle Tampa Bay to quantify net surface heat transfer over Tampa Bay and assess the contribution of individual components of this net surface flux to the total heat exchanged at the air-sea boundary, (2) estimating advect ive heat exchange at the mouth of the estuary and constructing a closed heat budge t for the estuary, and (3) determining the relative importance of freshwater loss thr ough evaporation to the fr eshwater budget of the bay. Study Area Situated on the west central Florida Gu lf coast between 27.5 N and 28.1 N and 82.3 W and 82.8 W, encompassing an area of 398 square miles at high tide, Tampa Bay is Florida’s largest estuary and is home to th e largest and busiest of the State’s ports (see Figure 1-1 for location of the Bay on Florida’ s west coast). More than two million people reside within the Bay’s watershed region. Es tuarine water quality is heavily impacted by human activity through freshwater withdrawal from rivers fe eding the estuary, chemical and oil spills into the bay, injection of hypersaline and heated waste waters from desalination facilities and co al-fired power plants and, during heavy rainfall events, overflow of human sewage and of highly aci dic process water from abandoned phosphate mines into the bay. Numerical models of bay circulation, temperatur e structure and water quality are useful tools for guiding governi ng bodies in making informed decisions by predicting the impacts of estu arine and surface water resour ce management policy to the bay.

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3 Figure 1-1: The location of Tampa Bay on Florid a’s west coast. The red marker indicates the position of the BRACE tower within Tampa Bay. The Port of Tampa is Florida’s largest por t. More than fifty million tons of cargo pass through the Port annually carrying commodities ranging from chemicals (including phosphate, anhydrous ammonia, and sulfuric acid in support of Florida’s phosphate and fertilizer industry) to coal and building materi als. In addition, the Port of Tampa is home port to several cruise lines that collectivel y transported in exce ss of 900,000 passengers in the year 2006 (Tampa Port Authority; http://www.tampaport.com/ ). Tampa Bay is characterized as a broad a nd shallow estuary with a mean depth of only about 12 ft. To facilitate the passage of large vessels, narrow shipping channels were dredged and are maintained at 43 ft dee p. The main channel extends 40 miles up the center of the bay connecting the Port to the Gulf of Mexico. The largest of the cruise vessels are over 950 ft. long and must navigate through lanes that take multiple turns and are as narrow as 200ft in places (Tampa Bay Soundings, Winter 2003). When this passage is turned treacherous by weather or poor visibility, there is increased danger of ship collisions, groundings, and cargo spills w ith potentially catastr ophic results. Model nowcasts and forecasts of water level and curr ent speed and direction can aid in the safe navigation of large commercial vessels in and out of the Port of Tampa. Tides in Tampa Bay can vary significantly from astronomical tide level predictions under forcing by strong winds in the West Florida Shelf re gion. Accurate, model-produced water level

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4 predictions provide warning of storm surge da ngers to residents in the low-lying regions surrounding Tampa Bay. As the bay area population continues to grow and Port operations expand, improved hydrodynamic and coupled models of biological, physical and chemical processes within the Bay are expected to play an increasingly important role in the safe navigation and stewardship of the Tampa Bay estuary. Earliest efforts in modeling Tampa Bay hydrodynamics neglected baroclinic circulation under the assumption that density driven circulation is unimportant in a shallow and vertically well-mixed estuar y like Tampa Bay. C. R. Goodwin (1980, 1987) of the USGS utilized such a vertically-int egrated, two-dimensional, finite-difference numerical model to examine the impacts of dredge and fill operations on circulation patterns and flushing in Tampa Bay. With a s ubsequent implementation of the first threedimensional numerical model in Tampa Bay, a relative of the Princeton Ocean Model called ECOM-3D, Galperin et al. (1991) demons trated that the strong horizontal gradient in salinity from Hillsborough Bay (where the bulk of freshwater runoff is received) to the mouth of the estuary results in a two-layer estuarine circula tion pattern that is unresolved by two-dimensional models. Vincent (2001) redeployed the ECOM-3D model in Tampa Bay with a higher resolution grid and with the ability to ingest real -time observations as part of an automated nowcast-forecast syst em of Tampa Bay hydrodynamics. Sheng et al. (2003) modeled Tampa Bay using their CH3D-I MS, a modeling system that integrates models of water quality and biological processes with a three-dimensional, finite difference numerical model of hydrodynamics. Recent modeling of Tampa Bay by Weisberg and Zheng (2006a,b) utilized an implementation of FVCOM, a threedimensional, time-dependent finite volume model. Weisberg and Zheng (2006a) used their model to analyze the residual circul ation of the bay and Weisberg and Zheng (2006b) assessed the storm surge threat in the bay. The Tampa Bay model utilized by Meye rs et al. (2007), a component of the Tampa Bay Coastal Prediction System (TBCPS) is a three-dimensional, time-dependent, nowcast-forecast implementation of the ECOM 3D finite-difference model based upon the earlier work of Vincent et al. (2000), Vincent (2001), and Galper in et al. (1991). At present, this model predicts salinity, water le vel, and currents with proven accuracy at 2,244 grid points in a domain that extends from the head of tides southward to the mouth of the estuary and resolves depth with 11 la yers in the vertical. See Vincent (2001) and Meyers et al. (2007) for descriptions of model verification studies. In order to calculate curr ent velocities, the Meyers et al. model requires specification of freshwater sources, in cluding precipitation, river discharge and underground seepage, and freshw ater loss through ev aporation at the surface. Currently, the model algorithm utilizes daily averaged evaporation rates as measured by a Southwest Florida Water Management Di strict (SWFWMD) maintained evaporation pan located near McKay Bay in hindcast studies. When obs erved evaporation rates are not available, this parameter is assigned a constant valu e in model computations. Preliminary bulk formula estimates of evaporation rate perfor med by Vincent (2001) indicated that annual freshwater volume loss via evaporation is of similar magnitude, but opposite sign, as the annual precipitation rate. Model prediction of water temperature throughout the bay requires accurate parameterization of the net heat flux at the ai r-sea interface of the estuary. Consequently,

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5 the capacity of the Meyers et al. Tampa Bay model to project temperature at each grid point within the bay is curre ntly unexploited; water temperature is assigned a constant value of 25 C. On average, the presence of a strong horizontal salini ty gradient is the main force driving large-scale estuarine circulation, however, water temperature may play a role in governing circulation in localized areas such as points of freshwater inflow, or in areas of large gradients in bathymetry leading to lateral temperature gradients during periods of st rong surface heat flux. Vincent (2001) highlighted the need fo r improved understanding of the surface heat fluxes and the freshwater budget of the Bay for specification of model boundary conditions. The present research is exp ected to allow model prediction of bay temperature, supporting future coupling of bi ological and water quality models to the hydrodynamic model. Data Collection In May of 2002, a tower located near Po rt Manatee Turn in Middle Tampa Bay (27 39.708’N, 82 35.669’W) was instrumented with meteorological and oceanographic sensors as one component of the Bay Re gional Atmospheric Chemistry Experiment (BRACE). Figure 1-1 illustrates the locati on of the BRACE tower within Tampa Bay. Developed in response to the Tampa Ba y Atmospheric Deposition Study (TBADS) finding of the importance of direct atmospheri c loading of pollutants to bay waters, the stated mission of BRACE is to estimate the rate of atmospheric deposition of nitrogen species to Tampa Bay (Poor 2000). The BRACE tower sensor array has been continuously maintained since May of 2002, providing data required for estimation of nitrogen deposition velocity and quantifica tion of air-water heat exchange. The time period of data collection encompasses three complete seasonal cycles, including periods of above and below average temperatures and rainfall. This time frame also includes extreme weather events, such as during the 2004 hurricane season in which four major hurricanes made landfall in the state of Florid a, within the three-y ear study period. Events occurring over short time scal es, such as the passage of tr opical storms and extratropical fronts, have a marked impact on the individua l components of the heat budget. Therefore, this study also demonstrates the need for frequent in situ measurements and coastal observing systems in order to adequately resolve variability in indi vidual heat budget components. Data acquired from this tower include air temperature, humidity and horizontal wind velocity measured via R. M. Young sensor s at two heights (5 meters and 10 meters above mean sea level), insolation determ ined by LI-COR LI-200SZ pyranometer, and precipitation and barometric pressure measured by R. M. Young meteorological instruments. A SeaGauge sensor, mounted at a depth of 2.5 meters below mean sea level, monitors water temperature, salinity, and water level. Additionally, a CSI CSAT3 sonic anemometer mounted at 6.9 meters above mean sea level measures the three-dimensional wind speed and the turbulent flux of sensible heat. A photograph of the BRACE tower is provided in Figure 1-2. Meteorologi cal data is gathered at 5 meters above mean sea level in addition to the standard 10-meter measurem ent height in order to generate a more complete data set. The additional data allows for estimation of the vertical gradients of

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6 temperature and moisture in the surf ace layer and computation of energy budget components via several methods. Figure 1-2: Photo of the BRA CE observational tower. Meteorological data are collected at th e BRACE tower at six-minute intervals. CSI sonic anemometer flux data are sampled at a frequency of 10 Hz and compiled into half hourly averages. Above water data are telemetered by line-of-sight radio to the Ocean Modeling and Prediction Lab (OMPL) lo cated on the University of South Florida St. Petersburg campus. Data are uploaded in real-time to the OMPL website ( http://comps1.marine.usf.edu/BRACE ) and a complete set of the data in raw form is continually appended with the most recent da ta and made available to the public on the OMPL ftp site ( ftp://comps.marine.usf.eud/pub/BRACE ). SeaGauge data are stored on the instrument and are collected when th e sensor is recovered for cleaning and recalibration. Records of instrument deploymen t and recovery, as well as certificates of calibration, are available on the OMPL ftp site.

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7 Organization of Thesis This thesis constitutes the first in-d epth heat budget study completed within Tampa Bay. Results are presented here as three independent analyses: a six-month examination of bulk sensible and latent heat flux estimations produced by the TOGA COARE 3.0 algorithm and the NOAA Buoy Model (see Chapter Three for descriptions of these algorithms), a thr ee-year investigation of the total heat budget of Tampa Bay, and an 18-month analysis of th e freshwater budget of the Bay. Chapter Two provides a detailed descrip tion of each component of the heat and freshwater budgets and outlines study methods The parameterization of each freshwater budget component supplied to the Meyers et al. Tampa Bay model is described. Chapter Three compares two bulk flux algorithms, TOGA CO ARE v. 3.0 and the NOAA Buoy Model, and explores the uti lity of each model in predicting several observed flux parameters and in estimating the atmospheric deposition of nitrogen in a coastal region. The NOAA Buoy Model has be en the preferred al gorithm for modeling turbulent heat flux during the BRACE direct deposition studies (Mizak et al. 2007, Poor et al. 2001, Evans et al. 2004, Poor et al. 2004), while the TOGA COARE algorithm is more commonly utilized within the greater scientific community. This six-month BRACE turbulent heat flux study is an adapta tion of an article recently published in Atmospheric Environment: Sopkin, K, C. Mizak, S. Gilbert, V. Subramanian, M. Luther, and N. Poor, 2007: Modeling Air/Sea Flux Para meters in a Coastal Area: A Comparative Study of Results from the TOGA COARE Model and the NOAA Buoy Model. Expanding on the BRACE turbulent heat flux study, Chapter Four includes parameterizations of the radiative heat fl ux components and extends the study to a threeyear period. Particular emphasis is placed on analysis of extreme event impacts on net heat exchange at the surface. Chapter Five compares evaporation rates produced in latent heat flux computations presented in Chapter Four to pan-measured evaporation rates supplied to the Meyers et al. Tampa Bay model and inve stigates fluctuations in the relative importance of evaporation to the freshwater budget of the bay. Chapter Six summarizes the findings of this research. Improved understanding of the heat balance in Tampa Bay is expected to permit water temperature prediction by the Meyers et al. model throughout the bay. A dditionally, verification of NOAA Buoy Model results supports previous estimates of at mospheric nitrogen deposition rates over the estuary under BRACE.

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8 Chapter Two Methods Turbulent Heat Fluxes The planetary boundary layer is the lower portion of the atmosphere that responds to surface-atmosphere interactions on a tim e scale of hours. This atmospheric layer extends approximately 10 km above the earth’ s surface and is characterized by turbulent flow. Turbulent eddies are generated by convec tive instabilities and by vertical velocity shears resulting from mean winds encounter ing surface friction and roughness elements. These atmospheric eddies, with length scales ranging from millimeters to thousands of meters, act to transport heat and pollutan ts near the surface. The fraction of oceanatmosphere heat exchange that is driven by turbulent transfer includes the sensible and latent heat fluxes. Sensible Heat Exchange The vertical flux of sensible heat, QS, is defined as: ' T w c Qp S (1) where is the density of air, cp is the heat capacity of air, w is vertical wind velocity and T is air temperature (Arya 2001). The quantity ' T w is the averaged product of measured temperature and vertical wind ve locity fluctuations away from the mean that occur in response to the passage of turbulent eddies (Fleagle and Businger 1980). This averaged vertical turbulent heat flux can differ considerably from zero, resulting in a net transport of heat to or from the estuarine surface (S tull 1984). The direction of transport depends upon the shape of the surface boundary layer temperature profile. Heat exchange resulting fr om contacting precipitation ty pically represents a heat loss from the ocean. The temperatur e of a falling droplet of rain is expected to be close to the wet bulb temperature and thus cooler than the ambient air and sea surface temperatures in tropical regions (Gosnell et al. 1995, Anderson, Hi nton and Weller 1998). On long-term average, the sensible heat fl ux due to rain accounts for only a small portion of the total heat energy lost at the surface, but sensible heat exchange due to precipitation can account for up to 60% of the net heat flux at the surface during individual rainfall events (Anderson, Hinton and Weller 1997). Sensible heat exchange was determined via three methods in this study, including 1) direct measurement of the eddy covariance of temperature and the vertical component of wind velocity, 2) bulk aerodynamic represen tation, and 3) gradient approximation. The sensible heat loss due to precip itation is computed by bulk method.

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9 Latent Heat Exchange Latent heat exchange at the air-sea in terface results from the mean vertical turbulent transport of moisture near surface. Analogously to the sensible heat flux, this turbulent transport is represented as the aver aged product of the turb ulent fluctuations of the vertical wind component and the specific humidity, ' q w where q is the specific humidity (defined as kilograms of water per kilogram of air). The latent heat flux is then given by: ' q w L Qe L (2) where Le is the latent heat of vaporization. Latent heat exchange almost always constitutes a heat loss from the ocean and is typically of much greater magnitude than the sensible heat exchange. High winds and lo w humidity accelerate evaporation from the sea surface and thus heat energy loss from the sea due to latent heat exchange. The magnitude of latent heat tr ansfer will be derived from 1) bulk aerodynamic formula and 2) gradient approximation. Quantification of Turbulent Fluxes Eddy Covariance Method – The eddy covariance method is utilized fo r direct measurement of the sensible heat flux component of turbulent heat exchange. In order to calculate QS, turbulent fluctuations of vertical wind and air temperature are measured by a CSI CSAT3 3-D sonic anemometer and FW05 fine wire ther mocouple that are mounted at the BRACE tower. The sonic anemometer measures the transit time of ultrasonic pulses between transducers mounted on three non-orthogonal axes. Wind speeds along these axes are derived from the time of flight of the ul trasonic signals and are commuted to the orthogonal wind speed components. Both averag ed wind properties and turbulent wind fluctuations are produced. The CSAT3 is mounted according to manufacturer specification at a height of 6.9 meters and is oriented into the pr edominately easterly sea breeze in order to minimize flow distortion ar ound the arms of the anemometer. Samples are acquired at the rate of 10 Hz, and sensib le heat flux is computed half-hourly using equation 1. Fast response measurements of humidity are not av ailable from BRACE tower instrumentation, and therefore, it is no t possible to determine latent heat exchange via this technique. Bulk Aerodynamic Formulas – Bulk formulas are useful for calculating bot h sensible and latent heat fluxes. This approach to determining turbulent heat tr ansfer takes advantage of the Monin-Obukhov Similarity Theory (MOST). MOST makes the assumption that within the lower approximately 10% of the planetary boundary la yer, termed the surface layer, turbulent fluxes of heat are constant with height. Additionally, MOST assumes that turbulent transfer is the dominant mechanism driving ve rtical heat exchange within the surface layer. In the bulk sensible heat transfer fo rmula, the eddy covariance of vertical wind and

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10 temperature is approximated as the differe nce between measured temperature at a predefined height within the well-mixed surface layer ( Ta) and at the surface ( Ts) multiplied by the horizontal wind speed ( U ). Bulk estimations of latent heat flux ( QL) make similar use of measured humidity at a pre-defined height ( qa) and the surface ( qs). Sensible and latent heat flux components may therefore be written as: QS= cpUCH(Ts-Ta) (3) QL= LeUCE(qs-qa) (4) where CH and CE are the dimensionless heat and mo isture transfer coefficients, respectively. Two bulk transfer algorithms (described in deta il in Chapter Three) will be used to estimate sensible and latent heat exchange, including the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) version 3.0 algorithm and the NOAA Buoy model. The TOGA COARE v3.0 algorithm utilizes rainfall rates observed at the BRACE tower and the Gosnell et al. (1995) representati on of heat transfer at the ocean’s surface due to precipitation to compute the sens ible heat flux due to precipitation. Gradient Method – Both sensible and latent heat fluxes ma y be computed with the gradient method. According to this method, turbulent fluxes may be inferred from gradients of wind, temperature and humidity between measurem ents taken at two different heights ( z1 and z2) above the influence of surface roughness elemen ts. Arya (2001) notes that this is in contrast to the bulk aerodynamic method, wh ich requires standard meteorological data from a single height as well as some pa rameterization of the wind-dependent surface roughness. In applying this method, it is assumed that the wind, temperature and humidity profiles between measurement heights are either logarithmic or linear in form and the actual gradient in the scalar values is approximated in si mple finite difference form: i. linear approximation 1 2 1 2z z M M z M z Maz (5) where za = (z1+z2)/2 is the arithmetic mean height, or ii. logarithmic approximation ) / ln(1 2 1 2z z z M M z Mm zm (6) where zm = (z1z2)1/2 is the geometric mean height and M represents scalar wind speed, temp erature or humidity values (Arya 2001). Arya (1991) demonstrated that the logari thmic gradient profile approximation is preferred in near-neutral to unstable surface layer conditions while a linear approximation performs as well or more accurately when approximating gradient profiles in a stable atmosphere. The author recommends th e logarithmic approximation for general application and notes that, fo r a reasonable ratio of meas urement heights, error is expected to be less than 2% for the logari thmic gradient approximation and twice this error for resulting flux estimates. In contrast the linear approximation may produce error

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11 as large as 8% and twice this amount fo r fluxes computed following this method. Sensible and latent heat fl uxes are computed assuming a logarithmic temperature and moisture gradient profile. From these gradient approximations, the gradient Richardson number (a dimensionless ratio of buoyant forces to shear-driven turbul ence), the Monin-Obukhov scaling parameters u* (the friction velocity, a velocity scale), T* and q* (temperature and humidity scales, respectively), and th e Monin-Obukhov stability parameter may be determined. The stability and scaling parame ters allow computation of the turbulent fluxes of latent and sensible heat. Unlike the bulk transfer algorithms, the gradient method requires no specific information a bout the surface such as surface roughness parameterization or sea surface temperature. The BRACE tower configuration satisfies Arya’s recommendation that the ratio of measurement heights does not exceed 2 in order to minimize error in profile approximations. Radiative Heat Fluxes Sources of radiative heat flux at the surface may be partitioned, according to wavelength, into longwave (emitted pr edominately in the range of 3-100 m) and shortwave (primarily spanning 0.15-4.0 m) radiation (Arya 2001). The intensity of downwelling solar (shortwave) energy arriving at the surface is dependent upon factors such as latitude, time of day and year, and absorptivity of the atmosphere. A fraction of insolation is reflected back from the ocean surface acco rding to surface albedo. The albedo, or reflectivity, of the ocean varies be tween about 0.05 and 1 (indicating complete reflection of incident shortwav e radiation) in response to s ea state and the angle of the sun above the horizon (Stull 1988). Water most effectively abso rbs incident radiation in the infrared spectrum; the majority of irradi ance in wavelengths greater than visible light is absorbed within the upper meter of the ocean (Jerlov 1976). Energy of shorter wavelengths may penetrate far more deeply into the water column depending upon the characteristics of the particular water body (turbidity, ocean color, and amount of particulate matter) and the quantity of s hortwave irradiance pa ssing through the water surface. In water that is su fficiently clear (and shallow) some amount of the shortwave radiation penetrating the ocean surface may reach the bottom and reflect upwards to reemerge at the water surface. The portion of downwelling shortwave radiation that is reflected at the ocean surface, or that reflect s from the bed and re-emerges at the surface, is unavailable for heating of estuarine waters Net shortwave radiation represents a heat gain by the ocean during daytime hours. Upwelling longwave radiation represents a loss of heat from water as energy is emitted from the ocean surface according to sea surface temperature. The atmosphere absorbs much of this outgoing longwave radiatio n in addition to a cons iderable portion of solar radiation. A percentage of the energy absorbed by the atmosphere is re-emitted in the form of downwelling l ongwave radiation and acts to warm surface waters. The magnitude of atmospheric radiation is st rongly dependent upon conditions such as amount, height, and type of cloud cover. Gill (1982) notes that the sum of upwelling and downwelling longwave radiation components typi cally acts to cool the ocean and the high heat capacity of water results in this pa rameter remaining fairly constant from day to night.

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12 Incoming Shortwave Radiation Insolation at the BRACE observational tower is measured by a LI-COR LI200SZ Silicon Pyranometer mounted at 5.8 m above m ean sea level. The pyranometer responds to wavelengths ranging from 0.4 m to 1.1 m, with the manufactur er reporting a typical error of 3% when the instrument is operating under natural daylight conditions. Measurements of downwelling shortwave radiat ion are acquired at six-minute intervals. Surface Reflected Shortwave Radiation Upwelling shortwave radiation is co mputed from measured insolation, QSW : QSW = QSW (7) given some parameterization of the sea surface albedo, Payne (1972) presented a method for determination of surface albedo based upon two factors: the atmospheric transmittance and the solar altitude The atmospheric transmittance, T is a measure of attenuation of solar radiation by atmospheric scattering and absorpti on and is written as: T = QSW / (Ssin / 2) (8) where the denominator represents the no-sky radiation. The transmittance ranges from 0 (for extremely overcast conditions) to 1 (indicating complete transmittance of solar radiation in the absence of an atmosphere). Solar altitude and estimates of no-sky solar radiation are produced by the freely avai lable Sea-Mat Air-Sea Toolbox functions for Matlab ( http://woodshole.er.usgs.gov/operations/sea-mat/ ). Payne (1972) provides, in table format, values of sea surface albedo as a function of the calculated atmospheric transmittance and sun altitude. Bottom Reflected Shortwave Radiation The percentage of surface penetrating inso lation that remains at depth in a water column depends upon factors that vary widely between water masses such as turbidity, plankton concentrations, quantity of suspende d particulate material, and the presence or absence of colored dissolved organic matte r. Jerlov (1968) developed a scheme of categorizing water masses according to their tran sparency to radiative heat flux, allowing prediction of light levels at various depths for each of 10 defined classes: five oceanic types ranging from clear, low-nutrient regions (type I) to areas of upwelling (type III) and five coastal water types, reflecting the se lective absorption of shorter wavelengths by yellow colored substances and particulate ma tter in near-shore re gions, in order of decreasing clarity from type 1 to type 9. A single coastal body of water may shift betw een classes as optical characteristics vary with seasonal or greater frequency. Low precipitation and freshwater runoff associated with unusually dry periods can lead to water that is mo re transparent than normal, while increased rains and surface wate r flow are linked with a sharp increase in concentrations of nutrients (leading to enhanced phytopl ankton growth) and colored organic matter that are injected into near-shore waters. Schmidt et al. (2001) demonstrated that precipitation rates over th e Tampa Bay watershed and river discharge volumes into the Bay are strongly modulated by El Nio – Southern Oscillation events. Under the influence of El Nio winters, th e south central area of Florida encompassing

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13 the Tampa Bay watershed region experiences increased rainfall and stormwater runoff into the Bay, while La Nia periods typical ly bring drier conditi ons. Events occurring over much shorter time scales also impact Tampa Bay such as hurricanes and tropical storms that carry large volumes of rain and bring high winds, increasi ng water turbidity. Paulson and Simpson (1977) note that, for most cases, observed Secchi depth is nearly equivalent to the level at which the i rradiance penetrating the water column falls to a tenth of the surface radiative flux value. Th is relationship between Secchi depth reading and percent radiant energy rema ining is useful for classification of Tampa Bay waters where observations of irradiance at depth ar e unavailable. As part of their mission to monitor and regulate the qua lity of Hillsborough County resources, the Environmental Resources Management divi sion of the Hillsborough Count y Environmental Protection Commission (EPCHC) analyzes chemical, biological, and p hysical parameters, including Secchi depth readings, at monthly intervals at stations located throughout Tampa Bay. These data have been collected since 1972 and are published for public usage on the EPCHC website ( http://www.epchc.org/ ). Monthly EPCHC observ ations from January 2002 through December 2004 were utilized in this study for the purpose of categorizing the coastal water types found in Tampa Bay. Two EPCHC sampling stations are situated near to, and straddling, the BRACE tower in water depths comparable to MSL at the tower of 4.61 m (see Figure 2-1 for location of these stations relative to the meteorological tower). EPCHC station number 21 is located just east-nor theast of the tower (27.663 N, 82. 564 W), in mean water depth of 5.1 m as observed by the EPCHC for the thr ee year period, and station number 90 is positioned slightly south-southwest of the to wer in water averaging 4.5 m deep (27.626 N, 82.592 W). Monthly observations of Secchi disk visibility were very similar between these EPCHC stations and were averaged to produce monthly estimated Secchi depth values in the region of the BRACE tower.

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14 Figure 2-1: Locations of EPCH C Stations 21 and 90 relative to the BRACE observational tower (red marker) in Tampa Bay. Jerlov (1968) presented percen tages of light remaining ve rsus increasing depth for each of the five coastal water types (see Ta ble 2-1 for an adaptation of Jerlov’s table XXI). For the period spanning 2002 through 2004, the averaged Secchi depth readings were compared with Jerlov’s percent irradian ce remaining values for each coastal type and the estuarine waters were categorized according to Jerlov’s scheme, assuming that observed Secchi depth is equivalent to the le vel of 10% radiant energy remaining. Tampa Bay may be variously classified as coastal water type 3 during exceptionally clear periods (8.3% of the monthly observations) to coastal type 9 (or even more opaque than a water mass described by this type) at its murkiest (27.8% of observations). Fifty percent of the thirty-six monthly Secchi depth measurem ents, as well as the mean Secchi depths observed at EPCHC stations 21 and 90 (2.68 m and 2.65 m, respectively), are consistent with Jerlov’s type 7 coastal waters. For the purpose of this heat budget study, the mean case of water type 7 is applied to all cal culations of bottom-re flected radiation. Table 2-1: Adaptation of Jerlov’s (1968) Tabl e XXI. Percent light remaining versus depth for coastal water types. Depth Type 1 Type 3 Type 5 Type 7 Type 9 0 100 100 100 100 100 1 36.9 33.0 27.8 22.6 17.6 2 27.1 22.5 16.4 11.3 7.5 5 14.2 9.3 4.6 2.1 1.0 10 5.9 2.7 0.69 0.17 0.052

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15 Assuming the bed reflects the incident radi ation completely, emergent radiation is represented by the amount of penetrating irradiance remaining af ter passing downward through the water column and reflecting from the bottom to re-emerge at the surface (a distance of 9.22 m, twice the mean sea level at the tower). Percent irradiance present at this depth can be derived from Jerlov’s ta bulated values if it is assumed there is exponential decay of radiant energy between 5 and 10 m. An estimated 0.25% of surface penetrating radiation remains after the attenuation of radi ant energy over a depth twice that of the water column at the BRACE tower. Inclusion of this component of the heat budget as a means of more completely repr esenting penetrating radiation in shallow coastal regions was recommended by Virmani (2005) and Virmani and Weisberg (2003). Tampa Bay waters are typically very turb id with visibility decreasing northward up the bay, away from the influence of tid al flushing by Gulf waters. Nutrients and suspended particles carried by riverine and stormwater runoff and sediment resuspended by maintenance dredging of the shipping channe ls contribute to poor water clarity. In Lower Tampa Bay, visibility reaches a maxi mum depth on average annually of 2.5 m as recorded by Secchi disk readings (State of the Bay, TBEP; http://www.tbep.org/ ). Estuarine waters are expected to fall into the clearest case (type 3) infrequently, during periods of unusually low rainfa ll and streamflow. On clear s ky days for brief periods of maximum insolation (1000 W/m2 incoming shortwave radiation), assuming complete reflectance of penetrating solar radiation from the bottom of the Bay, emergent radiation for maximally transparent, case 3 waters is an estimated 32.7 W/m2 at the tower. This quantity falls to 0.8 W/m2 or less reflecting from the sea floor to re-emerge at the surface when Tampa Bay corresponds to murky case 9 coastal waters. The deviation of these values from the quantity of emergent radi ation computed for the case 7 classification assumed here (an estimated 2.5 W/m2 reflecting from the bottom and lost at the surface) represents the maximum error incurred by applying coastal cas e 7 to all computations of emergent radiation at the BRACE tower. EPCHC data indicates the bay would be classifiable as a Type 3 coastal water body onl y seldom with peak er rors corresponding to brief periods of clear skies and maximum sun. Much of Tampa Bay is significantly shallower than the MSL at the tower and a higher fraction of pe netrating shortwave radiation is expected to be reflected from the bottom of the bay and lost, particularly under conditions of low surface runoff and redu ced nutrient input, in these very shallow regions. The error associated with the comput ation of penetrating radiation by the above method is expected to be small and to averag e out in the long term. A more dense set of measurements of irradiance at depth is require d to capture all of the variability in bottomreflected radiation at the BRACE tower as well as the spatial variability in bottomreflected shortwave radiation from the shoals to the shipping cha nnels of Tampa Bay. Incoming Longwave Radiation Downwelling longwave radiative heat flux is not directly measured at the BRACE tower and must therefore be estimated by bul k parameterization. As previously noted, atmospheric radiation is heavily depende nt upon cloud conditions. The presence of a cloud layer can affect the intensity of dow nwelling longwave radiative heat flux to a varying degree, depending on such propertie s as cloud base height, cloud type, water droplet size and other ch aracteristics which are seldom measured. Most bulk

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16 parameterizations of downwelling longwave ra diation require some estimate of cloud cover, introducing uncertainty into approximations of atmosp heric radiation. Reed (1977) compared methods of estimating insolati on based upon observed cloud distribution and recommended the following empirical formula instead: QSW / Q0 = 1 – 0.062C + 0.0019a (10) where the left hand side is the ratio of measured insolation at the surface to the noatmosphere radiation ( Q0), C is the observed cloud cover, and a is the noon solar altitude. Rearrangement of this relation gives: C = (1 + 0.0019a QSW / Q0)/ 0.62 (11) permitting estimation of cloud cover from m easured downwelling shortwave radiation and computed no-sky radiation. Downwe lling and net longwave radiation were determined using this approximated cloud c over in the absence of observations of cloudiness over the bay during this study. Fung et al. (1984) examined the performa nce of eight bulk ne t longwave transfer equations under clear sky settings in climat es spanning from tropical to subarctic during mean and perturbed temperature and pr ecipitation conditions. The authors give preference to the Berliand and Berliand (1952) formulation both for its skill in replicating radiative transfer equation re sults and the incorporation of a nonlinear dependence on vapor pressure and air temper ature. More currently, Josey et al. (2003) reviewed two empirical formulas for approximating atmo spheric radiation recently applied in climatological research studies. Josey et al (2003) determined that neither formula reliably reproduced measured downwelling longwave radiation and presented a new formulation for estimating atmospheric radiati on. The bulk longwave radiative heat flux parameterizations considered in these studies were evaluated in their skill at predicting incoming longwave radiation observed on the West Central Florida Shelf. The Coastal Ocean Monitoring and Predic tion System (COMPS) is a network of offshore and nearshore instrumentation main tained along the West Florida Shelf that provides real-time meteorological and oceanogr aphic data. The array is coupled with computer prediction models producing nowcasts /forecasts of current speed and direction and sea levels over the entire West Florida Sh elf from the Florida Keys northward to the Mississippi Delta region ( http://comps.marine.usf.edu/ ). The Tampa Bay PORTS and TBCPS are two components of this comprehensive coastal m onitoring system. As part of the COMPS assemblage of offshore buoys, th e C10 buoy is moored approximately 27 nm southwest of the mouth of Tampa Bay (27. 169 N, 82.926 W) in waters 24.7 m deep. A second COMPS station, the C14 buoy, is pos itioned roughly 57 nm northwest of the estuary entrance (28.306 N, 83.398 W) in a water depth of 21 m (see Figure 2-2 for locations of COMPS moorings relative to Tampa Bay). Both moorings are instrumented with pyrgeometers as well as an array of meteorological sensors supplying real-time observations at twenty-m inute intervals. Data available from these stations include air and sea surface temperatures, relative humidity, barometric pressure and incoming shortwave radiation in additi on to measured downwelling lo ngwave radiation. C10 and C14 buoy instrumentation permits applicati on of bulk longwave formulae to measured meteorological parameters and comparison of the results produced by these formulations to observed downwelling longwav e radiation at each site.

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17 Figure 2-2: Positions of buoys C10 and C14 on the WFS relative to Tampa Bay. Josey et al. (2003) suggest that air water vapor conten t is an important factor contributing to incoming longwave radiative fl ux intensity within the latitudinal band (20-35 N/S) encompassing Tampa Bay a nd the COMPS buoy network, a supposition that is substantiated by the present research. Data returned from th e C10 and C14 sensor arrays were analyzed for the covariance of th e observed flux of l ongwave radiation with each of the meteorological variables required as input to the bulk formulae, including air temperature, relative humidity, and cloudine ss represented by the ratio of measured insolation to the no-sky radi ation. The highest correla tion is found be tween relative humidity and measured downwelling longwave radiation. No clear relationship exists between incoming longwave radiation and approximated cloud cover. This close association with relative humi dity, a quantity that is dir ectly measured at the COMPS buoys and the BRACE tower, increases conf idence in the use of bulk formulae to parameterize the downwelling longwave radiation over the Tampa Bay region. Assessment of the reviewed formulas resu lted in the finding th at the Berliand and Berliand (1952) formulation, recommended by Fung et al (1984) best represents downwelling longwave heat flux measurements made at the C10 and C14 buoys. Incoming longwave heat flux measured a nd modeled (Berliand and Berliand, 1952)

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18 twenty-minutely at the C10 mooring, from day of year 156 through end of year 2003 (n = 13246), were closely correlated (r2 = 0.82) with a RMS difference of 17.0 W/m2 (Figure 2-3). A similar comparison at buoy C14, from yearday 267 through 365 (n = 4855) results in an RMS difference between mode led and observed downwelling longwave heat flux of 18.4 W/m2 with an r2 value of 0.77 (Figure 2-4). Figure 2-3: Observed and modeled (Berlia nd and Berliand, 1952) downwelling longwave radiation at the C10 Buoy from y earday 156 through end of year of 2003.

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19 Figure 2-4: Observed and modeled (Berlia nd and Berliand, 1952) downwelling longwave radiation at the C14 Buoy from y earday 267 through end of year of 2003. According to Berliand and Berlia nd (1952), the net longwave radiation, QLW may be written as: QLW = Ta 4 [0.39 – 0.05(ea)1/2] F(C) + 4 Ta 3 (Ts-Ta) (12) where is the emissivity of the ocean surface, is the Stefan-Boltzmann constant, Ta is measured air temperature in degrees Kelvin, Ts is the sea surface temperature in Kelvins, ea is the near surface vapor pressure, and F(C) is a cloud correction factor which is a function of cloud cover. Cloud conditions estim ated following Reed (1977), as described above, provide the necessary cloud cover esti mates. Fung et al. (1984) present three expressions for the cloud correction factor. Th at of Clark et al. (1974) is preferred for application to latitudes less than 50 N/S and is utilized in th is study. The Clark et al. (1974) correction factor is given by: F(C) = 1-bC2 (13) where b is an empirical constant that varies with latitude, increasing with increasing latitude in effort to parameterize the cloud t ypes that are typical for each climate regime, and C is approximated cloud cover. Outgoing Longwave Radiation Thermal radiation from the sea surfa ce is most commonly approximated by the Stefan-Boltzmann law: QLW = T4 (14)

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20 where is the emissivity of the ocean surface, is the Stefan-Boltzmann constant, and T is the temperature of the sea surface in Kelvin. Estimations of the emissivity of a body of water range from 0.95 to 0.98 though Kantha (2 000) notes that 0.97 is typically used. Closing the Heat Budget Net heat energy transfer to or from the ba y at the air-sea interf ace is written as the sum of the net radiative a nd turbulent heat fluxes: Qnet = QS + Qp + QL + QSW + QLW + Qpen (15) where Qp is the sensible heat flux due to rain fall as computed by the COARE algorithm. The net surface heat flux, Q(t) is also determined by examination of the rate of heat storage within the bay (the change in depth-averaged water temperature, T, through time), assuming there is no horizontal advec tion, following Morey and O’Brien (2002): H c t Q dt dTp) ( (16) where H is the depth of the water column. Variation in the heat content of the estuar y is the result of ocean-atmosphere heat exchange and advective heat flux between Tampa Bay and the Gulf of Mexico. The difference between the net heat transfer at the surface, Qnet, and the total heat flux, Q(t), derived from the observed change in heat content in the estuary, dt dT, represents the error incurred by neglecting a dvective heat exchange at the mout h of the bay and an error term resulting from uncertainties in the meas urement and computation of heat budget components. Tampa Bay is characteristically well mixed in the vertical and bulk sea temperature measured mid-depth at the BRA CE tower is representative of the depthaveraged temperature. Examination of water temperature measured monthly near-surface, mid-depth and near-bottom at EPCHC stations 21 and 90, in close proximity to the BRACE observational tower (see Figure 2-1), over 2004 confirms this assumption. At station 21, an average temperature gradient of only 0.14 C with a maximum gradient of 0.51 C was found and, at station 90, a mean temperature gradient over the water column of 0.18 C with a maximum gradient of 0.74 C. The EPCHC water quality monitoring protoc ol dictates that e ach station within Tampa Bay is sampled monthly. However, a pproximately one-third of the stations are visited over the course of a single day. Co mplete coverage of Tampa Bay occurs over three days of sampling, unevenly spaced throughout the month. Thus, in illustration, stations located througho ut Old Tampa Bay (the northwest arm of the bay) were sampled on the 6th of January 2004, Hillsborough Bay stations (northeastern portion of the bay) and several Middle Tampa Bay st ations were visited on the 21st, while sampling of the remainder of Middle Tampa Bay stations a nd Lower Tampa Bay stations was completed on the 28th of January 2004. On a single day, sa mpling typically begins around 8:30 AM local time with the final sample gathered around 3:30 PM. Indi vidual stations are sampled at random hours throughout the year. Despite the asynoptic nature of the EPCHC dataset, assessment of mid-depth water temperatures over 2004 reveals little horizontal variation in water temperature. Sampling occurred over 72 days (three days

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21 per month) in the year 2004. Stations sa mpled on a given day were divided in two groups: stations visited in the morning hours and those stations sampled in the afternoon. The range of mid-depth water temperature m easurements attained over the course of a morning or afternoon was, on average, 0.86 C fo r those stations in water depth of 3 ft. or greater (n = 364). Bulk sea temperature m easurements acquired at the BRACE tower, thus rates of surface heat exchange derived at the observational tower, may be considered representative of Tampa Bay. Evaporation Rate and Freshwater Budget Analysis The latent heat of vaporization of water (Le; J/kg) is the amount of energy required to convert one kilogram of water from liquid to gas phase and is a function of the temperature of the bulk liquid. Latent heat of vaporization at the BRACE tower is approximated according to measur ed bulk water temperature (Ts) following Stull (1988): Le = (2.501 – 0.00237Ts) 106 (17). Given a computed rate of heat loss from th e Bay which incorporates computations of evaporation rate (the latent heat flux; J/s.m2) and an estimate of the amount of heat contained within each kilogram of water va por formed (J/kg), the over-water rate of evaporation (E) is given: E = QL / Le (18). Daily averaged evaporation rates inferred from over-water latent heat exchange at the BRACE meteorological tower were compar ed to rates acquired via evaporation pan technique at a SWFWMD site located near Mc Kay Bay. The relative importance of this parameter to the freshwater budget of the bay is established by comparison with freshwater source parameteri zations (precipitation rate s, underground seepage, and surface runoff) supplied as boundary conditions to the Meyers et al. Tampa Bay model. A Meyers et al. model hindcast analysis of the response of ba y residual circulation to intervals of above and below average fres hwater inflow was completed for the period of 2001 through 2003 (Meyers et al. 2007). Du ring this period, precipitation rates supplied to the model were gathered from four stations located around the bay: the Sarasota/Bradenton Airport (call sign SRQ), the St. Petersburg Albert Whitted Airport (SPG), the St. Petersburg/Clearwater In ternational Airport (PIE), and Tampa International Airport (TPA). Hourly rainfall da ta from these stations were averaged to daily values for model input. There are more than one hundred surf ace sources supplying freshwater to Tampa Bay (TBEP; http://www.tbep.org). USGS daily averaged streamflow data provides surface freshwater runoff rates where point r unoff sources are gauged. Streams that are not gauged are scaled from gauges located ups tream or are assigned flow rates according to gauges located in basins that are n earby and similar in size and land cover. Groundwater seepage is parameterized as an approximate 8% augmentation of the mean flow from each surface water source following Brooks (1993). These boundary conditions, combined with evaporation rates pr oduced in computations of latent heat exchange at the BRACE Tower for the tim e interval of June 2002 through December 2003 of this hindcast study, were used in construction of a freshwater budget for Tampa Bay.

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22 Chapter Three BRACE Six-Month Turbul ent Heat Flux Study Introduction Estuaries and coastal regions are particularly susceptible to nutrient overenrichment due to their close proximity to source-rich regions (National Research Council, 2000). The impact of these nutrients poses a significant threat to sensitive ecosystems by accelerating the rate of eutrophi cation and leading to toxic algal blooms, which can affect human health and are often responsible for causing massive fish kills and aquatic species morbidity and mortality (National Research Council, 2000). As a corollary to the increase in reliance on fossil fuels and inorganic fertilizers, excess inputs of nitrogen and phosphorus to coas tal estuaries are also expected to occur. As witnessed in the Tampa Bay Estuary, this will likely result in a continual decline in ecosystem quality unless direct actions are taken to reduc e the influx of nutrients from agricultural runoff, industrial discharges, and the atmosphe ric deposition of nutri ents released from fossil fuel combustion. The BRACE study was created to focus on these issues by assessing the impacts of local sources of atmo spheric nitrogen on the health of the estuary and improving nitrogen deposition estimates over Tampa Bay. The focus of this research is on atmospheric deposition, which is a rela tively new research concept that was once considered irrelevant by the sc ientific community (TBEP, 19 96). Recently, Hicks et al. (2000) have shown that in some locations dr y atmospheric deposition may be responsible for up to 40% of the nitrogen en tering coastal water bodies. To effectively manage an ecosystem such as the Tampa Bay Estuary, a comprehensive understanding of the nutrient input pathways is necessary. In comparison to industrial discharges that ar e routinely monitored by state and local officials, it is much more challenging to monitor and estimate the atmospheric deposition of nutrients to an ecosystem, due to the spatial a nd temporal variability associat ed with this input, as well as the inherent difficulty is determining tran sfer rates over the enti re estuary. Although models have been developed to estimate air/s ea transfer rates over ope n oceans, relatively few are specific to coastal areas, which experience unique mete orological processes because they are located adjacent to land masse s. Nevertheless, many of these models are based on the bulk exchange method and parameterized according to the Monin-Obukhov Similarity Theory (MOST) for heat, moistu re, and momentum tran sfer governing the deposition process (Arya, 1988; Hicks, 1975; Hicks and Liss, 1976; and Liu and Schwab, 1987). Based on this process, air/sea mass transfer rates can be approximated as the product of the modeled heat transfer coefficient (DH) and measured wind speed (uz) (Valigura, 1995). The present research had two objectives: 1) to critically assess and validate the TOGA COARE (Fairall et al., 1996) and th e NOAA Buoy (Valigura, 1995) models, by

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23 comparing direct measurements of sensible heat and friction velocity with modeled estimates and 2) to compare and contrast several important modeled flux parameters. This study was conducted to summarize model differences and to gui de potential users through these differences so that nitrogen ai r/sea transfer rates determined during the BRACE study can more accurately be estimated over Tampa Bay. Model Theory TOGA COARE The Tropical Ocean Global Atmosphere (TOGA) Coupled Ocean-Atmosphere Response Experiment (COARE) bulk flux algorithm was initially developed by the NOAA Environmental Technology Laboratory (F airall et al., 1996). Model development began in recognition of an inability to pr edict or diagnose tropical ocean/atmosphere interactions, which are widely known to pl ay an essential role in global climate variability. Development of the bulk fl ux algorithm began on preliminary COARE cruises in 1990 as an integral part of the “interf ace component” of TOGA COARE. Of primary importance during the COARE project were ba lancing the surface radiative and turbulent heat fluxes of the tropical western Pacific re gion to within stringent limits of accuracy, and the formulation and validation of im proved flux parameterizations. Since the inception of the program, the COARE bul k flux algorithm has undergone continuous revisions and has been released for comm unity use at several stages of model construction. Although originally formul ated under the COARE Pacific Warm Pool Study and verified with data from the COARE domain centered in equatorial Pacific, the model authors note that development of the algorithm incorporated measurements from mid-latitude regions and therefore expect the C OARE code to be relevant at these higher latitudes (Lukas and Webster, 1992; Fairall et al., 1996). Among other refinements, the most recent release of the COARE algorithm used in this study (version 3.0) is tuned to an expanded data set (including observations made in North Atlantic and North Pacific regions) and introduces a variable representation of the Charnock parameter to improve model pe rformance at higher wind speeds, extending the applicability of the model from the Tropics to high latitudes (Fairall et al., 2003). The COARE algorithm, though developed for open ocean studies, has been applied to shallow water and coastal regimes prio r to the present study (Sun et al., 2003; Beardsley et al., 1998). For a complete description of the COARE bulk algorithm version 3.0 and an explanation of model verification resu lts, please see Fairall et al. (2003). The model requires input measurements of wind speed, air and bulk water temperature, relative humid ity, downward solar flux (insol ation), air pressure, and downward longwave radiation (estimated fo llowing method described in Chapter 2). Model outputs include sensible and latent heat fluxes, friction velocity, and the dimensionless heat transfer coefficient (DH) allowing for comparison of the COARE 3.0 estimation of these parameters with th e NOAA Buoy model results and permitting the external calculation of air/sea transfer rate s of soluble gases.

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24 NOAA Buoy Model The NOAA Buoy model code was produced by a BRACE scientist (Bhethanabotla 2002) from research conducted by Vali gura (1995) of the NOAA Air Resources Laboratory. It is used as a tool for estimating air/s ea transfer rates of nitric acid over coastal water bodies (Valigura, 1995). Although model applica tion was originally intended for a highly soluble compound, its appl icability has been extended to calculate the deposition of other nitrogen species to Tampa Bay, Fl orida (Bhethanabotla, 2002; Evans et al., 2004; Poor et al ., 2001). The model was formul ated based on the works of Hicks (1975), Hicks and Liss (1976), and Liu and Schwab (1987) and includes both the bulk exchange method and the flux-gradient relationships for heat, moisture and momentum. Model development included the use of near-surface, over-water coastal meteorological data obtained fr om a network of buoys to si mulate existing small-scale coastal conditions. Given that the model was developed for nitric acid, which is highly soluble in alkaline seawater, transfer wa s considered unidirectional. Additional assumptions were that surface and quasi-lamina r resistances are negligible compared to aerodynamic resistance, and therefore were not considered during model synthesis (Valigura, 1995). Finally, a major assumption in the model is that nitric acid gas and sensible heat are similarly regulated by aer odynamic resistance (Hicks and Liss, 1976). As shown in Valigura (1995), sensible heat flux is dependent, according to the bulk transfer theory, on the dimensionl ess heat transfer coefficient (DH). The DH is also used to calculate the air/sea gas exchange rates, which are calculated as the product of DH and measured wind speed at each time step. Inputs to the model include hourly wind speed, air and bulk water temperature, and relative humidity. The model begins itera tion with an initial approximation of the transfer coefficients until the modeled temperature and wind gradients match the measured wind speed and temperature diffe rentials (Valigura, 1995). Model outputs include sensible and latent heat flux, friction velocity, dimensionless heat transfer coefficient and gas transfer rate. Model Differences Specific Humidity The bulk aerodynamic formula for latent he at exchange at the air/sea interface requires specification of the sp ecific humidity difference be tween a reference height and the surface. This specific hum idity gradient is derived from vapor pressure computed from observed relative humidity at height and saturated air (assuming a 2% humidity reduction to account for salinity) at the surface. The TOGA COARE algorithm adopts Tetens’ (1930) equation for vapor pressure, incorporating an enhancement factor to comp ensate for application of the formula to moist air as opposed to pure water vapor, in the form recommended by Buck (1981) for typical use. Alternatively, the NOAA B uoy model employs Bgel’s (1979) accuracy improved adaptation of Tetens’ formula but does not include an enhancement factor. Provided the same meteorological inputs, however, these distinct vapor pressure formulations produce perfectly correlated resu lts with a maximum separation of less than

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25 1% of the range of computed hour ly vapor pressure for the six month period of this study, indicating choice of equation form does not explain modeled specific humidity differences. Tetens’ equation within the TOGA COARE code is supplied ambient air and bulk water temperatures and returns saturate d air and sea surface vapor pressures. Multiplication by observed relative humidity at height and an assumed 98% saturation humidity at the surface conve rts the respective saturation vapor pressures to vapor pressures. In contrast, the NOAA Buoy model de rives dewpoint temperatures at the sea surface from ambient air temperature and sali nity corrected saturated humidity, and at height from ambient air temperature and m easured relative humid ity, computing surface vapor pressure directly from th ese dewpoint temperatures. Cool Skin Over the ocean, turbulent fluxes are typical ly directed from ocean to atmosphere, acting in conjunction with the net longwave ra diative flux to cool the surface waters. The upward exchange of heat at the air/sea in terface generates a millimeter-scale surface film usually several tenths of a degr ee cooler than the bulk water temperature. It is this skin temperature that is required for input to the bulk aerodynamic formulas. Where the interfacial temperature is not measured, the bulk water temperature must be corrected for this cool skin anomaly. Fairall et al. (1996) represent the tota l heat loss at the surface as the sum of sensible and latent heat fluxes and the net l ongwave radiative flux. A fraction of incident shortwave radiation is absorbed in the cool skin layer, increasing the skin temperature. The TOGA COARE algorithm applies the effect ive surface cooling, adjusted for net shortwave radiative warming, to subsequent computations of cool skin layer thickness, and temperature correction, Tc. The expressions for and Tc in both shear and buoyancy driven turbulence regimes, as de scribed by Paulson and Simpson (1981), are merged for incorporation in the TOGA COARE code. Within the NOAA Buoy model the surface bou ndary heat loss is equal to the total of the sensible and latent heat fluxes and th e outgoing longwave radiation. No adjustment is made for the absorption of shortwave ra diation within the su rface film and heating contributed by atmospheric longw ave radiation is neglected. The cool skin temperature correction equation does not differ betw een shear and convective settings. Warm Layer The COARE 3.0 code includes a separate algorithm as a correction for the influence of a diurnal warm layer on the bulk water temperature. Formulated for application to coastal regimes, the NOAA B uoy model incorporates an adjustment to bulk temperature that accounts for the therma l skin but does not correct for warm layer development. To directly compare COAR E 3.0 model results with the NOAA Buoy model output, the warm la yer option within the COAR E 3.0 model was disabled. Because Tampa Bay is characterized as a shal low and vertically well-mixed estuary, the warm layer correction is seldom likely to be necessary; a warm laye r would evolve only during times of very still winds and strong solar heating.

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26 Wind Speed/ Gustiness/ Slip Both models incorporate mechanisms to deal with non-zero flux values in a low wind speed environment because theoretical ly, low wind speeds should not yield zero flux. The COARE model includes a gustiness parameter in all computations using the wind speed to overcome this issue (Godfrey and Beljaars, 1991). This parameter is strictly a function of a consta nt convective parameter and th e convective scaling velocity and is used in all subsequent computations of sensible and latent heat fluxes, friction velocity, velocity roughness length, Obukhov length scale (a nd therefore is used in stability function computati on) and transfer coefficien ts. The NOAA Buoy model eliminates conditions caused by extremel y low wind speeds by ignoring all time steps where the wind speed falls below 0.7 m/s (in the six month dataset, ~1% of wind measurements). In remaining cases, the wind sp eed includes a “slip correction” that is used in further calculations of fluxes, fr iction velocity and th e Obukhov length scale. This computation takes into account the wind-indu ced skin velocity of the water, e.g. if the skin is slipping along in the direction of the wind, being driven by the wind, then the effective wind speed available for driving surface fluxes is reduced. The TOGA COARE authors specify that wind speed model input should be the wind speed relative to the sea surface. Surface current speed and direction, however, are not available from the BRACE tower array and therefore observed rather th an effective wind speeds are passed to the TOGA COARE model, neglecting correc tion for wind driven surface slip. Sea Surface Roughness Charnock’s (1955) relation describe s sea surface roughness dependence upon wind stress and includes a consta nt that is tunable to local conditions. This parameter is assigned a large range of values (typically from 0.01 to 0.03) in the literature, but is particular to the study site, increasing in valu e from open ocean to fetch-limited and nearshore regimes (Lange and Hjstrup, 2000). Evidence of a dependence of the Charnock “constant” upon wind speed led the authors of the TOGA COARE code to incorporate a variable “constant” in the late st version (Fairall et al. 2003). The “constant” is fixed at a value of 0.011, which is generally applied to open ocean settings, during low wind speed intervals. However, at moderate to hi gh speeds between 10 and 18 m/s, the “constant” varies linearly, increasing with increasing wi nd speed. The resulting increase in estimates of surface roughness has the effect of pred icting greater near-s urface turbulence and fluxes. Above 18 m/s the parameter is agai n a fixed number at 0.018. Alternatively, within the NOAA Buoy model, this number is an invariable c onstant set equal to 0.016. At the BRACE study site, it was discovered that less than 2% of hourly winds during the time frame of data collection from June th rough November of 2002 we re greater than 10 m/s and most were observed in the month of November. Therefore, the higher value chosen by Valigura (1995) for the Charnock cons tant is likely more appropriate for flux studies in a coastal environment.

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27 Atmospheric Stability The MOST assumes that, w ithin the surface layer, tu rbulent transfer is the dominant mechanism driving vert ical heat exchange and that the turbulent fluxes of heat are constant with height. Wind, temperature and specific humidity gradients within this layer can be characterized as universal ( ) functions of a non-dimensional atmospheric stability parameter: the ratio of measur ement height, z, to the Monin-Obukhov length scale, L. The structure of the universal ( ) functions is not specifi ed and several schemes have been derived. The NOAA Buoy model assumes that the form of the universal functions is as previously applied to the study of atmospheric deposition of SO2 (Hicks and Liss 1976), namely: under neutral to stable conditions (z/L 0), the NOAA Buoy model follows the recommendations of Webb (1970) for the st ructure of Panofsky’s (1963) universal functions and for unstable settings (z/L < 0) the formulations suggested by Dyer and Hicks (1970) are adopted. Vali gura (1995) notes that MOST is considered applicable over a limited atmospheric stability parameter range (-1 z/L 1), outside of which either buoyancy driven tur bulence dominates and friction velocity becomes an inappropriate scaling parameter (in the limit of extreme instability) or turbulent vertical mixing is suppressed in a stab ly stratified atmosphere. The TOGA COARE code parameterizes a stable surface layer following the Beljaars and Holtslag (1991) universal functi on equations. These expressions have been effectively applied to conditions of extreme stability (z/L 10) and perform in nearneutral stability regimes like the Webb (1970) functions adopted by the NOAA Buoy model. For unstable environments, the TOGA COARE algorithm blends the universal ( ) functions appearing in the NOAA Buoy model code with a form applicable to highly unstable, convective settings. Experimental Methods Study Area and Modeling Period Tampa Bay is the largest open water estu ary in Florida covering approximately 645 square kilometers with an average depth of about 4 meters and a maximum depth of about 10 meters at the various shipping channe ls. Hourly meteorolog ical, sensible heat, and friction velocity measurements were made from June through November, 2002 at the Port Manatee Turn Tower, which is located in Middle Tampa Bay and is one of the Tampa Bay Physical Oceanographi c Real-Time System sites (see http://ompl.marine.usf.edu/PORTS/gt03010.html ). The tower was funded in part by the Bay Regional Atmospheric Chemistry Experime nt (BRACE) and is located at latitude 27N 39’ 50”, longitude 82W 34’ 50”, approxima tely 7 nautical miles southeast of St. Petersburg, FL, and 4 nautical miles west-northwe st of Port Manatee, FL. The tower is in water approximately 5 meters deep and extends above the water surface by 10 meters. Please visit the OMPL website ( http://comps.marine.usf.edu/BRACE ) for a complete description, photograph, and diag ram of the research tower.

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28 Meteorological Data Collection Hourly measurements of sensible heat fl ux and friction velocity were made at a height of 6.9 meters above mean sea level (MSL) with a Campbell Scientific CSAT3, 3dimensional sonic anemometer. Hourly air temperature and relative humidity were measured at a height of 5.0 meters above MSL and wind speed and wind direction were measured at a height of 10.0 mete rs above MSL using R.M. Young Company meteorological sensors. Hourly measuremen ts of the bulk water temperature were made at approximately 2.4 meters below MSL with a Sea-Bird Electronics temperature gauge. All instruments were certified calibrated by the respective manufacturers and certificates are available at the COMPS website ( ftp://comps.marine.usf.edu/pub/BRACE/seagauge_data/instr/ ). Instruments were operated, maintained and serviced according to the manufacturers’ specifications by the Ocean Modeling and Prediction Lab in the USF College of Marine Science. Model Application The micrometeorological data were av ailable in real-time via the Web at http://comps.marine.usf.edu/BRACE/ and were used as inputs to the TOGA COARE and NOAA Buoy models. The modeled outputs of se nsible heat and friction velocity were compared to the actual CSAT3 measurements (n = 4,028) made at the tower. In addition, the modeled outputs of sensible and latent heat, the dimensionless heat transfer coefficient, and friction velocity were used for inter-model comparisons of flux parameters (n = 4,126). QA/ QC Rutgersson et al. (2001) f ound that measured heat fl uxes during extreme rain events can produce biased data. Ultrasoni c anemometers are unable to measure wind during rainfall. This bias is due to sma ll water droplets beading on the transducer’s surface and interfering with signal transm ittance. Based on this information, hourly measurements of recorded rainfall were pa ired with the observed and model predicted sensible heat flux and friction velocity da ta to determine if rainfall biased those measurements. If during a rain event a measur ement spiked high or low compared to the remaining hourly measured data points, it was considered an outlier and that time step was deleted from the measured data. As a resu lt of this analysis, a total of 138 hours or approximately 3.4% of the measured to m odeled dataset were removed for a total of 3,890 data points. Results Observed and Predicted Flux Parameters The TOGA COARE and NOAA Buoy modele d results were compared with measured sensible heat flux and friction ve locity from June through November 2002 (n = 3,890). The sensible heat flux comparison is shown in both Figure 31 and Table 3-1, in

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29 which the observed and predicted values in dicate that both m odels were reasonably predictive of heat transfer at the water surface with R2 values of 0.80 and RMS model differences of 11.02 and 12.52 W/m2 for the TOGA COARE and NOAA Buoy models, respectively (Table 3-1). Average and standa rd error sensible heat values were 17.74 W/m2 and 0.34 W/m2 for the TOGA COARE model and 16.86 W/m2 and 0.39 W/m2 for the NOAA Buoy model, respectively. When th e same comparison was made for friction velocity (Figure 3-2; Table 3-1), both modeled R2 values were 0.52 and RMS differences of 0.06 m/s were found for the TOGA CO ARE and NOAA Buoy models. Average and standard error friction velocity values were 0.17 m/s and 0.0012 m/s for the TOGA COARE model and 0.16 m/s and 0.0013 m/s for the NOAA Buoy model, respectively. These results indicate that there was reas onable agreement for this flux parameter. Table 3-1: Performance statistics for pred icted and observed sensible heat (H, W/m2) and friction velocity (u*, m/s) for all data and da ta during stable atmos pheric conditions (Kara 2005). ALL DATA (n=3,890) TOGA COARE NOAA BUOY Measure H u* H u* Correlation Coefficient (R) 0.89 0.73 0.89 0.73 Root Mean Square Error (RMS) 11.02 0.06 12.52 0.06 Fractional Bias (FB) 0.21 0.05 0.26 0.08 Normalized Mean Square Error (NMSE) 0.15 0.11 0.19 0.11 Factor of Two Analysis (Fa2) 63% 92% 53% 88% STABLE DATA (n=223) TOGA COARE NOAA BUOY Measure H u* H u* Correlation Coefficient (R) 0.23 0.40 0.23 0.39 Root Mean Square Error (RMS) 9.84 0.12 12.91 0.12 Fractional Bias (FB) -1.30 0.21 -1.49 0.27 Normalized Mean Square Error (NMSE) 4.37 0.59 5.54 0.65 Factor of Two Analysis (Fa2) 33% 85% 18% 77%

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30 Figure 3-1: Hourly measured and modeled time series and sc atterplot of sensible heat (W/m2). The R2 value for both modeled series with the measured was 0.80. During summer months (June – September) the R2 value was about 0.50 for both models while the fall months (October – November) show improved agreement with an R2 value of about 0.83.

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31 Figure 3-2: Hourly measured and modeled time series and sc atterplot of friction velocity (m/s). The R2 value for both modeled series with the measured was 0.52. During summer months (June – September) the R2 value was about 0.44 for both models while the fall months (October – November) show improved agreement with an R2 value of about 0.65. Measures of model performance, as described by Ahuja and Kumar (1996), Chang and Hanna (2004), Gudivaka and Kumar (1990), Kumar et al. (1993), Kumar et al. (1999), Riswadkar and Kumar (1994) and Patel and Kuma r (1998) were used to characterize the quality and re liability of the models. Acco rding to Kumar et al. (1993), model performance is acceptable if the follo wing performance sta tistics are observed: 0.5 FB 0.5 and NMSE 0.5. The results for the entire observed to predicted data set (n = 3,890), shown in Table 3-1, support the hypothesis that both models predict reasonably well the aforementioned flux para meters. The same analysis was also completed just for hourly data points correspond ing to a stable atmosphere as described by Kara (2005), in which the air temperatur e exceeded the bulk sea temperature by at least 0.75 K (n = 223). Results indicate that for this stabil ity class, model performance declined significantly for sensible heat. For example, both the Fractional Bias and Normalized Mean Square Error, which offer a measure of the mean error and scatter in the data set, respectively, are both within recommended acceptable limits for the entire data set, but fall outside of these limits for the stable data (Table 3-1). In addition, a Factor of Two Analysis, defined as the percen tage of the predictions within a factor of two of the observed values, indi cated that for sensible heat flux and friction velocity, a majority of the predicted values were within this range for the entire data set for both the TOGA COARE and NOAA Buoy models (Table 3-1). However, when the analysis was

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32 conducted for the stable data set, a majority of the predicted values of sensible heat were outside of this range (Table 3-1). These an alyses demonstrate that because the MOST is unreliable during stable conditions, the mode ls must be used cautiously when the atmosphere is strongly stable. Inter-Model Comparison Sensible Heat Flux/ Dimensionle ss Heat Transfer Coefficient – As shown in Figure 3-3, NOAA Buoy a nd TOGA COARE modeled sensible heat flux approximations are nicely correlated with a RMS model difference of 3.7 W/m2. Specification of the dimensionle ss heat transfer coefficient (DH), which is used to calculate air/sea gas exchange, is an important variable in the calculation of sensible heat exchange via bulk algorithms (see Va ligura, 1995). The magnitude of DH is governed by atmospheric stability and wind speed. As show n in Figure 3-4 as a horizontal cluster of open circles, the NOAA Buoy model designates 7.0% (n = 292) of the data set as extremely stable (z/L > 2, Monin-Obukhov Sim ilarity Theory no l onger applies). During this period, a constant valu e is assigned to Panofsky’s W function, resulting in fairly constant and small values for the NOAA Buoy model computed DH. For 60% of those time steps classified as extremely stab le by the NOAA Buoy m odel, the TOGA COARE model classifies the stability as neutral to unstable, resulting in a large range of DH values (in contrast to NOAA Buoy m odeled results). As previous ly noted, the TOGA COARE authors extend applicability of their model to extremely stable regimes following Beljaars and Holtslag (1991). Figure 3-3: Scatterplot of mode led hourly sensible heat flux (W/m2). Models are highly correlated with very little scatter.

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33 Figure 3-4. Scatterplot of modeled hourly DH. The horizontal cluster of open circles represents, in part, model di vergence of stability classi fication. The NOAA Buoy model estimates extremely stable conditions, but for 60% of these data points, the TOGA COARE model predicts neutra l to unstable stability. Friction Velocity – The friction velocity is one of the funda mental MOST scaling parameters defined as the square root of kinematic wind stress. This velocity scale is a measure of the turbulent shear strength in the surface layer and is important to bulk estimations of heat, moisture and momentum flux at the air/s ea boundary. Results show that the NOAA Buoy and TOGA COARE model estimated friction velocity values agree very well (R2 value of 0.99) (Figure 3-5).

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34 Figure 3-5: Scatterplot of modeled hourly fr iction velocities (m/s). Models are highly correlated with very little scatter. Latent Heat Flux – NOAA Buoy model and TOGA COARE model latent heat exchange estimations agreed less satisfactorily with an R2 value of 0.47 and RMS difference of 67.90 W/m2 (Figure 3-6). A sensitivity analysis led to a basic alteration to the section of the NOAA Buoy model code that calculat es the specific humidity gr adient. The vapor pressure computations were restyled to accept ambien t air temperature at height and measured bulk water temperature as input s to the expression for vapor pressure, rather than internally computed dewpoint temperatures, as described previously. The resulting air and sea surface saturation vapor pressures were then converted to vapor pressures via multiplication by the observed relative humidity at height and assumed 98% relative humidity, respectively. No modifications were made to the form of the vapor pressure expression chosen by the NOAA Buoy model au thor. This simple adjustment to the NOAA Buoy model algorithm improved interm odel comparisons of latent heat flux dramatically (R2 value of 0.99 and a model difference RMS value of 39.4 W/m2) (Figure 3-6).

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35 Figure 3-6: Scatterplot of mode led hourly latent heat flux (W/m2) comparisons for preand postmodification of vapor pressure. Discussion and Conclusions Measured and modeled comparisons of se nsible heat flux and friction velocity over a six-month period showed that the TOGA COARE and NOAA Buoy models were reasonably predictive of these flux parame ters, although a slight under-prediction of sensible heat flux by both models was observed. As described previ ously, it is suspected that this propensity to under-predict sens ible heat will likely correspond with an underprediction of the dimensionless h eat transfer coefficient used to calculate air/sea nitrogen gas transfer rates. An evaluation of the se nsible heat flux comparisons revealed that despite this tendency, modeled results were still within recommended performance limits as described by Kumar et al (1993), although model performance is compromised when applied to stable regimes. These results str ongly caution against the use of these models during stable atmospheric conditions. It is thought that the models are unde r-predicting sensible heat due to a characteristic of the MOST and bulk transfer methods for estimating heat, moisture, and momentum exchange, which are embedded in both models. Oost et al. (2000) and Rutgersson et al. (2001) showed that the predicted heat tr ansfer coefficient gradually decreases with decreasing air/sea temperatur e differentials and low wind speeds, and is often smaller than expected under these conditio ns, resulting in a lower sensible heat flux approximation. Due to the limited fetch and relatively low wind speeds typical of a coastal estuary, it is not surprising then that th is characteristic would result in lower than expected heat transfer estimates as compared to direct sonic anemometer measurements of sensible heat exchange. To test this hypothesis with results, a diurnal frequency distribution of predicted and observed sensible heat flux ra tios less than 0.8 (n = 2120; ~54% of the dataset) was developed to de termine if lower than expected heat flux predictions occurred more frequently during the late afternoon a nd evening hours. This

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36 time frame typically contains periods of increasing stability due to low winds, very low air-sea temperature differences from the hea ting of the day and reduced sensible heat exchange between the atmosphere and oceans (Arya, 1988), particularly in the summer season. Results show that approximately 67 % of those ratios occurred in the late afternoon and evening hours (1500 0200 LST) wh en the average measured sensible heat flux decreased from 26.12 W/m2 during the daytime to 17.54 W/m2 in the evening. Likewise, the same analysis conducted for friction velocity (n = 915; ~23% of the dataset) found that approximately 60% of t hose ratios also occurred during that time period, confirming the hypothesis that similar to results from the previously mentioned studies, sensible heat transf er is under-predicted by the bulk transfer theory during periods of decreasing air-sea temperature differe ntials, or when air temperature is greater than water temperature. An additional eval uation of measured and modeled momentum transfer showed that despite frequent spikes in measured friction velocity, both the TOGA COARE and NOAA Buoy models also adequately predic ted this flux parameter. Based on this study and compared with prev ious research (Rut gersson et al., 2001; Valigura, 1995), it is believ ed that both models provide acceptable methods for estimating heat and gas exchange across the air/sea interface in coastal areas. Despite differing cool skin correction schemes resulting in disparate air/sea temperature gradients computed by each model, both models produced closely corresponding sensible heat flux values, al though the TOGA COARE predicted values were generally greater than those estima ted with the NOAA Buoy model. For the small percentage of the data set associated with periods of extreme st ability, the models disagreed in both their predicti ons of stability classificati on and the dimensionless heat transfer coefficient. Under these conditions the universal functions included in the calculation of DH are set to a constant by the NOAA Buoy model and consistently small values of DH are generated. The NOAA Buoy mo del typically under-predicts TOGA COARE produced DH for this case, leading to estimate d sensible heat flux and air/water transfer rates of lesser magnitude. Th e TOGA COARE model’s representation of Panofsky’s universal functions addresses extremely stable or unstable atmospheric states and is likely more applicable in the very stable settings that are more prominent during the summer season. In contrast, during neut ral to unstable atmos pheric conditions, the modeled values of DH closely agree. The greatest divergence in model results ap peared in estimations of latent heat exchange. Sensitivity analys is of the model algorithms le d to the discovery that the majority of model differences were attribut ed to the vapor pressure calculation. Therefore, a simple adjustment to th e NOAA Buoy model’s computation of vapor pressure, following Buck (1981) by utilizing ambient air temperatures rather than dewpoint temperatures and converting resulting saturati on vapor pressure to vapor pressure, improved modeled latent heat flux agreement. This standardized the vapor pressure calculation method between the mode ls and dramatically improved latent heat comparisons. Based on this analysis, both models are su itable for use in a coastal environment to estimate nitrogen air/ sea gas exchange, although th e NOAA Buoy model requires fewer meteorological inputs. However, if th e purpose is to conduc t more sophisticated microscale modeling of air/sea interactions, th e TOGA COARE model is r ecommended.

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37 Chapter Four Three-Year Heat Budget Study Introduction The Tampa Bay estuary, located on the west central Florida Gulf coast, is Florida’s largest open water estuary ( http://www.tbep.org/ ). In the semi-enclosed Tampa Bay basin, water temperature is modulated pr imarily by ocean-atmosphere heat transfer at the bay surface and secondarily by advective heat exchange with the waters of the Gulf of Mexico at the southern mouth of the ba y. Water temperature variations in this subtropical estuary govern the abundance and distribution of orga nisms and influence circulation dynamics. A numerical model of Tampa Bay hydr odynamics is estab lished (Vincent 2001, Meyers et al. 2007) and has been utilized in assessing the potential impacts to estuarine circulation and water quality of bay mana gement policy concerning withdrawal of freshwater from source rivers, construction of a desalination facility on Tampa Bay, and injection into the bay of process waste wate r from the Piney Point phosphate mine. This Meyers et al. model is one component of the Tampa Bay Coastal Prediction System and is a three-dimensional, time-dependent implementation of the ECOM-3D model predicting current speed and direction, wate r level, and salinity with proven accuracy. The reader is referred to Vincent (2001) and Meyers et al. (2007) for complete descriptions of model specifics and verification study results. The model is fully capable of predicting water temperature throughout the bay. However, exploitation of the thermodynamic capabilities of the model requir es specification of a previously undefined net surface heat flux boundary condition. Quantifi cation of heat exchan ge rates at the bay surface boundary will permit initiation of th e active thermodynamic component of the Meyers et al. Tampa Bay model and support future coupling of biological and water quality models to the hydrodynamic model. Recently, investigations into the nontidal circulation of the Tampa Bay emphasized the sensitivity of the residual ci rculation to changeab le boundary conditions; alterations to freshwater inflow, wind st ress, and wind direction modify the timeaveraged circulation (Meyer s et al. 2007, Wilson et al 2006). While modeled and measured parameters (water level, salinity, and current velocity) are typically in good agreement, it is suggested that isolated inconsistencies between modeled and observed variables likely result from error in boundary condition characterization, including heat transfer at the surface boundary, implyi ng a potential intermittent and localized sensitivity to a lack of active thermodynamics as well. Vincent (2001) also highlighted the need for improved understanding of the surface heat fluxes for specification of mode l boundary conditions. Inclusion of real-time net surface heat exchange computations in m odel forcing will enable calculation of water

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38 temperature everywhere in the bay. The pr imary objective of the present research is therefore quantification of th e net surface heat transfer over Tampa Bay and assessment of the contribution of individual components of this net surface flux to the total heat exchanged at the air-sea boundary. Several recent studies conducted on the West Florida Shelf (WFS) identified trends in sea temperature response to atmo spheric forcing and ocean dynamics in the eastern Gulf of Mexico (He and Weisbe rg, 2002, 2003, Virmani and Wesiberg 2003). The spring transitional period, characte rized by ocean warming and strengthening stratification, and the episodic cooling and destratification associated with the fall transition are both dominated by ocean-atmos pheric heat exchange; advective heat flux on the WFS overall plays a secondary, and of tentimes opposing, role to cooling or heating driven by the net surface flux. Convers ely, ocean circulation dominates the WFS heat budget during the summer months. Superimposed on thes e seasonal variations are interannual variations and the impacts of shorter time-scale weather events (see Virmani and Weisberg 2003). Closer to the West Florida coastline (at a station located in 15 m of water), He and Weisberg (2003) demonstrated a shift in the rela tive importance of advective and surface heat flux even more dramatically in favor of atmospheric control for the spring and fall seasons. Virmani and Weisberg ( 2003) note that the shallower, near-shore regions of the Gulf of Mexico are more responsive to su rface heat flux. This variable response to atmospheric forcing of Gulf waters shorew ard implies that the impacts on ocean heat content of surface fluxes measured and derived over the Gulf at comparable latitude are likely an incomplete characterization of the rapid response of water temperature to heat fluxes over Tampa Bay, a shallow and vertical ly well-mixed estuary with a mean depth of only about 4 m. To date a systematic invest igation into the proce sses of heat exchange at the bay-atmosphere boundary has not been completed. A second obj ective to analyze the response of Tampa Bay to short time-scale events, such as the passing of extratropical fronts during fall transition, episodes of spring transitional warming, and the approach of tropical storms and hurricanes, follows. Finally, advective heat transfer at the mouth of the bay is estimated and the shifting relative importance of heat flux due to mixing of Gulf and Bay waters to the total chan ge in heat content is examined. Methods In May of 2002, a research tower locate d near Port Manatee Turn in Middle Tampa Bay (latitude 27N 39’ 50”, longitude 82W 34’ 50; see Figure 1-1 for location of the tower within the Bay), in water approxima tely 5 m deep, was equipped with an array of sensors as one component of the Bay Regional Atmospheric Chemistry Experiment (BRACE). The tower has been continuously maintained to the present, gathering meteorological and oceanographic data at six-minute intervals. Data acquired from this tower include air temperature, relative humidity, and horizontal wind velocity measured at standard anemometric hei ght (10 meters), insolation, precipitation, and barometric pressure. Additionally, a SeaGauge sensor mounted 2.5 meters below mean sea level records bulk water temperature and salinity. Port Manatee Turn BRACE Tower data are publicly available on the Ocean Modeling and Prediction Laboratory (OMPL) ftp site ( ftp://comps.marine.usf.edu/pub/BRACE/ ) along with certificates of instrument

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39 calibration and records of sensor deployme nt, maintenance and recovery. The BRACE tower sensor array provides measurements required for complete description of surface heat fluxes in Tampa Bay. This study encompasses the three-year period spanning June of 2002 through May of 2005. An extended interruption occurs in the bulk water temperature data record during the three-year study period from December of 2002 through June of 2003. This information is required for estimation of seve ral heat flux components, resulting in a gap in the heat budget analysis. During the 2004 hurricane season, the near approach of Hurricane Jeanne damaged the research tower atmospheric pressure sensor, necessitating the substitution of barometric pressure over Tampa Bay as provided by NOAA CO-OPS (St. Petersburg Station 8726520; http://co-ops.nos.noaa.gov ) for the period of September through October of 2004. The Environmental Protection Commi ssion of Hillsborough County (EPCHC) manages a program monitoring a variety of water quality parameters, including water temperature and salinity measurements made at the surface, mid-depth, and bottom, at 56 stations throughout the Tampa Bay estuary ( http://www.epchc.org/ ). An analysis of monthly water temperature measurements made over the year 2004 confirms that there is little horizontal thermal gradient; sea temp eratures obtained at the BRACE tower are typical, on average, of temperatures throughout the bay except for brief periods during short time-scale events such as the sweeping of fronts through the bay area. Rates of ocean-atmosphere heat exchange computed at the tower are, therefore, considered representative of heat fluxes ove r the entire bay. In addition, water temperatures recorded monthly for the three year period spanning 2002 to 2004 at two EPCHC sampling stations located near to, and stradd ling, the BRACE research tower and in comparable water depth (station 21 in water 5.1 m deep and station 90 in approximately 4.5 m of water) reveal little stratificat ion (typically, order less than 0. 5 top to bottom) confirming that Tampa Bay is characteristically well mixed in the vertical. Therefore, observed temperature supplied by the SeaGauge se nsor, mounted at the BRACE tower, approximates the depth averaged bulk water temperature. Net heat exchange at the surface boundary, Qnet, is the summation of the turbulent and radiative heat fluxes. The fraction of su rface heat exchange occurring in response to the passage of turbulent eddies is comprised of the sensib le and latent heat fluxes. Sensible heat flux is the conductive transfer of heat energy down a temperature gradient between the ocean and the overlying air mass or contacting droplets of rain. Latent heat exchange is the energy flux that occurs as a result of phase shift of water at the oceanatmosphere interface. The net radiative heat flux is the sum of atmospheric and solar radiation arriving at the airsea interface less porti ons of the incoming energy reflected or emitted at the bay surface. The total energy gain ed or lost at the water surface and at the mouth of the bay due to mixing of Gulf and estuarine waters must equal the change in temperature, the total heat stored within the bay. This balance between the combined rate of heat transfer at the sea surface and the mout h of the estuary and the rate of change of heat storage in the bay constitutes the “heat budget” of Tampa Bay.

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40 Radiative Fluxes Radiative heat energy arriving at the o cean surface is apportioned, according to wavelength, into longwave (emitted pr edominately in the range of 3-100 m) and shortwave (spanning 0.15-4.0 m) radiation. Shortwave Radiation – The downwelling shortwave heat flux (insol ation) component of the heat budget, QSW is measured directly at the BRAC E observational tower by a LI-COR LI200SZ Silicon Pyranometer mounted at 5.8 m above mean sea level. Though a slight linearly decreasing trend appears in the insolation data record, it was determined that this trend is small relative to the overall variability and is therefore negligible. A fraction of the direct solar radiation is reflected from the ocean surface in proportion to the intensity of incident insolati on and the albedo, or reflectivity, of the sea surface. Freely available Sea-Mat Air-Sea Toolbox functions for Matlab ( http://woodshole. er.usgs.gov/operations/sea-mat/ ) provide the calculations of atmospheric transmittance and solar altit ude necessary to compute surface albedo, a, after Payne (1972). Surface reflected shortwave radiative heat flux, QSW follows as: QSW = QSW (19) Water most effectively absorbs solar radi ation in the waveleng ths longer than the visible light spectrum. How deeply the energy of shorter wavelengths penetrates into the water column depends upon the amount of shortwave irradiance passing through the ocean surface and characteristics of the water body such as turbidity, color, and quantity of particulate matter present. In water suffi ciently shallow and clear, a percentage of surface penetrating insolation may pass through the column, reflect from the bottom and reemerge at the surface, effectively reducing the quantity of direct solar energy that is available to warm the ocean. Secchi disc readings recorded monthly at EPCHC sampling stations 21 and 90 as part of the EPCHC Tampa Bay water qual ity monitoring program were used to approximate light penetration at the BRACE tower. Jerlov (1968) proposed a scheme of classifying water masses according to their transparency to radiative flux, allowing prediction of light levels at va rious depths for each of ten wa ter type categories, including five classes of coastal water types. For the years 2002 through 2004, Secchi depth readings were compared with Jerlov’s values for percent irradiance remaining at depth for each coastal type and the estuarine wate rs were categorized according to Jerlov’s scheme, assuming that observed Secchi depth is approximately equivale nt to the level of 10% radiant energy remaining (Paulson and Simpson 1977). Tampa Bay varies between Jerlov’s coastal water type 3 during exceptionally clear periods to coastal type 9 (or even more turbid than a water mass described by this class) at its murkiest. Both the majority and the average of Secchi depth readings at st ations 21 and 90 for this three-year period identify Tampa Bay as a coastal type 7. Fo r the purpose of this st udy, the mean case of water type 7 is applied to all calculations of bottom-reflected radiation, Qpen. Approximately 0.25% of surface penetrating ra diation remains after the attenuation of radiant energy over a depth twi ce that of the water column at the BRACE tower to be

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41 reemitted at the surface. The error associ ated with approximating bottom-reflected radiation by the above method is therefore expect ed to be small, and to average out in the long term, with greatest error occurring during brief periods of clear skies and maximum sun. Longwave Radiation – Thermal energy is radiated from the sea surface in proportion to the temperature of the water. This upwelling longw ave radiative heat flux component, QLW is typically approximated by the Stefan-Boltzmann law: QLW = T4 (20) where represents the emissivity of the ocean surface, is the Stefan-Boltzmann constant, and T is the temperature of the sea surface in Kelvin. Kantha (2000) notes that 0.97 is the value typically assigned to the emi ssivity of a body of water and this is the value chosen for this study. The atmosphere absorbs a portion of th is upwelling longwave radiation, in addition to a la rge fraction of the incoming sola r radiation, and reradiates some of this trapped heat energy as downwelling longwave radiation, QLW warming surface waters. This component is not direct ly measured at the BRACE observational tower and must be estimated by bulk parameterization. The Coastal Ocean Monitoring and Predic tion System (COMPS) is a network of offshore and nearshore instrumentation main tained along the West Florida Shelf that provides real-time meteorological and ocea nographic observations. As part of the COMPS assemblage of offshore buoys, the C10 buoy is moored approximately 27 nm southwest of the mouth of Tampa Bay and the C14 buoy, roughly 57 nm northwest of the estuary entrance (please see the COMPS websit e for the location of the buoys relative to Tampa Bay; http://comps.marine.usf.edu/ ). An assessment of the skill of eleven bulk net longwave transfer equations (reviewed in Fung et al. 1984, Josey et al. 2003) in reproducing incoming longwave radiation observations made from June through December of 2003 at the C10 and C14 buoys resu lted in the conclusi on that the Berliand and Berliand (1952) formulation, reco mmended by Fung et al (1984), accurately represents downwelling longwave heat flux at both buoys, and is appropriate for application over the Tampa Bay region. According to Berliand and Berlia nd (1952), the net longwave radiation, QLW may be written as: QLW = Ta 4 [0.39 – 0.05(ea)1/2] F(C) + 4 Ta 3 (Ts-Ta) (21) where is the emissivity of the ocean surface, is the Stefan-Boltzmann constant, Ta is measured air temperature in degrees Kelvin, Ts is the sea surface temperature in Kelvin, ea is the near surface vapor pressure, and F(C) is a cloud correction factor which is a function of cloud cover. Observations of cl oud cover over the bay were unavailable and therefore estimated, following Reed (1977), using the Sea-Mat Air-Sea Toolbox functions. The Clark et al. (1974) cloud correc tion factor is preferre d for application to latitudes less than 50 N/S and is the form utilized in this study.

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42 Turbulent Fluxes The turbulent vertical fluxes of heat (QS) and moisture (QL) at the surface are defined as 'T w c Qp S (22) ' q w L Qe L (23) where is the density of air, cp is the heat capacity of air, Le is the latent heat of vaporization and w is the vertical wind velocity. The quantities ' T wand 'q ware the averaged products of vertical wind veloci ty and measured temperature or specific humidity fluctuations away from longer term mean values. These instantaneous variations of a measured quantity from the mean,' w,' T and q occur in response to the presence of turbulent eddies in the surface layer. Where ' T wand ' q w are not directly measured, these quantities, and therefore the sensible ( QS) and latent ( QL) heat exchange rates, can be approximated by bulk transfer formulas: QS= cpUCH(Ts-Ta) (24) QL= LeUCE(qs-qa) (25) where CH and CE are the dimensionless heat and mo isture transfer coefficients, respectively, U is the horizontal wind speed, and the subscript s denotes surface observations while subscript a indicates measurements made at some pre-defined height. The Tropical Ocean Global Atmosphe re Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) bulk transfer al gorithm was initially de veloped in support of the COARE project objectiv es of better understanding of tr opical air/sea interactions, improved heat flux parameterizations and th e development of a balanced surface heat budget for the western tropical Pacific region. It is the tool most widely employed in turbulent heat flux studies and several iterations of the algor ithm have been released for public use over the course of model deve lopment. Though originally developed for application to the open ocean of the wester n central Pacific zone, the most recent incarnation of the algor ithm (version 3.0; ftp://ftp.etl.noaa.gov/user/cfairal l/wcrp_wgsf/com puter_programs/ ) has been validated with data from wind regimes ranging from calm to greater than 20 m/s, in open ocean and coastal regions, across latitudes spanning from e quatorial to greater than 50 N, and under atmospheric conditions ranging from unstable to extremely stable (Fairall et al., 1996, 2003; Edson et al. 2006). It is this latest version of th e COARE turbulent heat flux algorithm that was chosen for derivation of la tent and sensible heat exchange rates over Tampa Bay. An initial six-month study of flux pa rameters in the Tampa Bay Estuary demonstrated the skill of the COARE 3.0 bul k algorithm in reproducing sensible heat flux and friction velocity (a ve locity scale; this parameter quantifies shear stress near surface) observations made half hourly by a CSAT3 sonic anemometer mounted at the BRACE tower (Sopkin et al. 2007). Fast re sponse measurements of humidity, and therefore the latent heat flux, are not avai lable from the sonic anemometer. The COARE 3.0 algorithm produces bulk estimates of latent and sensible heat exchange rates as well as heat flux due to precipitation. Surface curre nt velocity is not monitored at the Port Manatee Turn observational towe r and therefore only the 10 m wi nds, rather than the true

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43 wind speeds relative to the sea surface, are supplied to the algorithm. Due to the limited fetch over Tampa Bay, this omission is not expected to incur significant error. Closing the Heat Budget The total surface heat budget is given by the sum of the flux terms described above: Qnet = QS + Qpen + QL + QSW + QLW (26) In the case of zero advective heat transport into or out of the ba y, the net surface heat flux, Q(t) may also be computed, followi ng Morey and O’Brien (2002), as: H c t Q dt dTp) ( (27) where dt dT is the change in the depth averaged bulk water temperature with time and H is the water column height. In the presence of advective flow, the remainder between the total surface heat flux, Qnet, and the rate of change in estuarine heat storage, Q(t) is the result of advective heat exchange between th e Gulf of Mexico and Tampa Bay plus error due to uncertainty in the measurement or es timation of the surface heat flux components. Results and Discussion Summer – June through August Light winds, warm water temperatures, and strong insolation persist over summertime, a season typified by reduced variab ility in heat exchange at the bay surface (Figures 4-1 through 4-3). The radiative su rface heat flux components all reach peak magnitude over the summer months, with incoming shortwave radiation nearing 1000 W/m2 at midday. Net longwave radiation result s in a heat loss, on average, of 30.6 W/m2 from the bay over summertime. Reduced air/water temperature gradients and low wind speeds result in typically small sensible heat loss. Sensible heat flux due to precipitation represents, on average, much less than 1 W/m2 cooling of the bay. However, convective thunderstorms in middle July of 2002 caused se veral peaks in the hour ly mean of this component of the surface heat budget of nearly –65.0 W/m2. Net surface heat exchange over the summer of 2002, a complete threemonth time series, produced warming of the bay at a mean rate of 30.5 W/m2. The residual, representing a dvective cooling plus error, over this same period was, on average, 21.7 W/m2.

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44 Figure 4-1a: Summer 20 02 meteorological data. Figure 4-1b: Summer 2002 surface fluxes.

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45 Figure 4-1c: Summer 2002 net surface and advective flux. Figure 4-2a: Summer 20 03 meteorological data.

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46 Figure 4-2b: Summer 2003 surface fluxes. Figure 4-2c: Summer 2003 net surface and advective flux.

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47 Figure 4-3a: Summer 20 04 meteorological data. Figure 4-3b: Summer 2004 surface fluxes.

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48 Figure 4-3c: Summer 2004 net surface and advective flux. Fall – September through November Weakening solar warming and sporadic high winds associated with transient tropical and extratropical systems mark the fall season (Figures 4-5, 4-6 and 4-7, a and b). Autumnal cooling of bay waters begins in Se ptember of each year, oftentimes associated with the near-approach of tropi cal weather systems. September is the peak of the Atlantic hurricane season. Over the three-year study period, the average s easonal decline in bay water temperature, from the beginning of September through November of each year, reached –10.8 C. Net surface heat flux out of the bay is, on average, remarkably similar from year to year with a mean seas onal rate of heat loss of –20.4 W/m2. Though less significant than surface heat exchange, advectiv e heat exchange (as the residual heat flux) contributed to fall cooling each ye ar at a mean rate of –6.4 W/m2 (Figures 4-5, 4-6 and 47, c). A straightforward l ook at the impact of a net surface heat loss of 20.4W/m2 on bay temperature, assuming constant density and given a simplistic representation of the bay as a volume 4 m deep with a surface area of 1030 km2, reveals that a steady heat flux of 20.4W/m2 out of the bay yields a temperature change of –9.3 C over a three month period. This is a simple check of the net surface heat budget computations presented in this study. Though advective heat exchange is not accounted for, this check demonstrates that this relatively low average net flux out of the bay is capable of producing temperature change on the order of the actua l measured temperature change (-10.8 C) when applied over a three month period. Over the fall seasons of 2002 through 2004, averaged seasonal net shor twave and net longwave radi ative fluxes were 156.0 W/m2 and

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49 –38.3 W/m2, respectively while seasonally averaged sensible and latent heat loss contributed to surface cooling of bay waters at the rates of -15.2 W/m2 and -122.3 W/m2. Early fall is often summer-like in characte r, with meteorologi cal parameters and individual heat fluxes reduced in variability However, expansive tropical storm systems bringing overcast skies and extr eme winds to the bay area may initiate rapid cooling early in the season. Extratropical fronts pass over th e bay area with increasing frequency in the latter half of the season, dramatically a ltering net surface heat exchange and causing a series of irregular drops in ocean temperat ure toward the wintertime low. On seasonal time scales, averaged individual flux compone nts are very comparable year-to-year, while shorter time scale events drive radical fluctuations in the ma gnitude and direction of heat exchange of individual heat budget components. Additionally, individual events trigger large variations in the total surface heat exchange, cautioning against applying a climatological mean net surface flux in mode l studies. The impacts of tropical and extratropical weather systems on estuarine heat content are examined in two case studies presented below. Hurricane Frances – Frances came ashore near Vero Beach on Florida’s East Coast as a category 2 hurricane in the early hours of September 5, 2004, the outer bands of the storm already reaching over Tampa Bay to the northwest (Fig ure 4-4). Near passage of the storm to the bay forced two peaks in measured wind speed (Figure 4-7a). The firs t exceeded 20 m/s to the S and SE around 15:00 UTC on the 5th of September. Nearest proximity of the eye of the storm to the BRACE tower appears as a strong signal in barometric pressure as observed at the NOAA CO-OPS St. Petersburg station. A minimum barometric pressure of 981.7 mb is recorded at 20:54 UTC on the 5th, concurrent with a temporary reduction in wind speed. As the eye of the storm ex ited the bay area, wind direction reversed abruptly, shifting to the NE, up the bay along its axis, and wind speeds observed at the BRACE tower again approached 20 m/s. This examination of the im pacts of a tropical storm system on heat exchange rates in Ta mpa Bay spans the four-day time frame early in the fall of 2004, from the 4th through 7th of September, appearing outlined in black in Figure 7a-c.

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50 Figure 4-4: Storm track and AVHRR image of Hurricane Frances. The satellite image was captured on September 5, 2004 (NOAA). St orm track image and composite courtesy of Joan David of the Nationa l Hurricane Center, Miami. The influence of Frances on individual co mponents of the heat budget is apparent. High winds and cooler air temperatures drive a maximum in latent heat transfer out of the bay of -634.5 W/m2 and peak sensible heat cooling of -154.7 W/m2. Incoming solar radiation reduced dramatically from a mean rate of 226.0 W/m2 over September 4th to an average daily mean of only 35.2 W/m2 and 71.4 W/m2 over the 5th and 6th of September, respectively (Figure 4-7b). Th e greatest daily average rate of surface cooling over the estuary, -460.8 W/m2, occurred in advance of the eye on September 5th. Net surface flux continued to drive heat loss from the bay until the storm passed from the area on the 7th. Bulk water temperature was reduced by 3.3 C over the four-day period of this case study. Virmani and Weisberg (2003) observed th at the typically nega tive net heat flux of the fall season prevents water column temper ature rise post-tropi cal storm. Hurricane Frances represented the first strong cooli ng event of this fall season and bay water temperature never recovered. A distinct signature for the passage of Hurricane Jeanne is visible as another sharp declin e in the time series of barome tric pressure corresponding to the 26th of September, 2004 (Figure 4-7a). The maximum-recorded wind speed at the BRACE tower for this storm reached 23.4 m/s. Jeanne induced another step-like decrease in bay water temperature from which the bay did not completely return. While on average both surface and advective (residual) heat exchange contributed to heat loss from the bay in res ponse to Frances (-126.4 and -66.4 W/m2, respectively; see Figure 4-7c), it is interesting to note that as net surface heat loss reached a maximum, the

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51 impact of surface cooling on water column temperature was lessened by a positive advective heat flux (daily mean = 116.5 W/m2). On the other side of the storm eye advection shifted roles abruptly once more, again enhancing surface cooling (an estimated -179.9 W/m2), with an average residual flux of -94.6 W/m2 for September 6th. Interpretation of these findings is aided by examination of the net surface and advective heat fluxes within the context of the Wilson et al. (2006) investigation into the effects of Hurricane Frances on bay re sidual circulation. The majority of freshwater input to th e bay is received in the northernmost portions of the estuary resulti ng in a horizontal salinity gradient, from the head to the saline waters of the Gulf of Mexico at the southern mouth of the bay, that drives the overturning circulation of the bay. Typically, fresher, buoyant waters from terrestrial runoff sources flow out of the bay along the su rface of the estuary wh ile salty, dense Gulf waters flow in along the deepes t portions of the bay. In th e hours preceding the passage of the eye of the hurricane, peak winds to the S and SE force a set down in water level within the bay. Wilson et al. (2006) report that, as wind spee d spikes, and in response to increased freshwater runoff into the bay, th e Meyers et al. Tampa Bay model predicts significant outflow at the bay surface and at mid-depth. An Acoustic Doppler Current Profiler deployed near the mouth of the bay in the shipping channel at depth, measures a concurrent strong inflow in the deepest por tions of the channel, in an apparent exaggeration of the general overturning circul ation. While surface waters, rapidly cooled by a combination of reduced insolation and air temperatures and sharply increased wind speeds, are pushed out of the bay, enhanced re turn flow of warmer Gulf waters at depth and increased turbulent mixing partially mitigate surficial cooling. As the eye transits the bay area and winds swing toward the NE, strong inflow is measured surface to bottom and setup of water level in the bay reach es a maximum of 1.2 m above mean sea level. Winds driving up the bay prevent, in large part, the outflow of cooled surface waters. Additionally, Gulf water forced into the bay at all depths likely represents a mixture of coastal waters also im pacted by the storm and the return of cooler water recently driven from the bay. Advectiv e heat flux again contributes to estuarine cooling during this phase. Extratropical Front – The BRACE tower recorded a dramatic sh ift in wind velocity at end of day on November 28th, 2003; in less than twenty minutes time, winds coming out of the southwest shifted to blow northwesterly and instantaneous wind speed increased by almost 6 m/s. Over most of the following tw o days, strong winds persisted out of the NW to NE over the bay, peaking on the 29th at 14.7m/s, until the front passed from the region. A detailed investigation into the effects of this event on the h eat budget of Tampa Bay encompasses the final two days of November of 2003 (demarcated by thin black lines on Figure 4-6). As cold, dry air associated with this ex tratropical frontal system moved into the bay area, relative humidity dropped to a mini mum of 28%, remaining depressed relative to the seasonal average by a mean of 23% over the two day period. Coincident with a seasonal high barometric pressure (two-day mean: 1028.2 mb), rapidly falling air temperatures generated large gradients in air-sea temperatures with an exceptional

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52 maximum air-sea temperat ure separation of 11.6 C. For the 29th and 30th of November, a mass of air with a mean temperature of only 13.5 C overlay bay waters of 20.6 C on average. The net surface heat flux response to strong atmospheric fo rcing over this short time period is extreme: net heat loss at the bay surface reached a maximum hourly mean rate of –799.8 W/m2 while the overall average total surface heat flux was –383.6 W/m2. High winds coupled with dry at mospheric conditions resulted in a mean heat loss from the bay of –316.5 W/m2 due to latent heat flux while the sudden decline in air temperature forced an average se nsible heat flux of -101.8 W/m2. Bay temperature decreased by 2.3 C in two days. A check of the impact of constant heat loss at a rate of 383.6 W/m2 over the simplistic box model of the bay described above results in a computed –3.8 C temperature change over the two da y period. Advective (residual) heat transfer contributed to bay warm ing at a mean rate of 133.6 W/m2 over the final two days of November 2003, resulting in an actual decrease in bulk wa ter temperature significantly less than the temperature decline predicted by the net surface heat flux alone in this simple verification. Such considerable heat energy losses are not unusual in response to the passage of frontal systems. Another extr atropical front, moving quickly through the bay area earlier in the same month, produced a peak hourly net surface flux of –879.8 W/m2. In the 2002 fall season, a frontal system imp acted the bay from November 16th through 18th, forcing a maximum hourly net surface cooling of –668.3 W/m2. These numbers, however, are far in excess of those reported over the bay through Hurricane Frances in the fall of 2004. Though sustained high winds and reduced insola tion contributed greatly to bay cooling during Frances, the maximum ocean/atmosphere temperature gradient attained was 4.2 C while the minimum relative humidity reached was 59%. On average, relative humidity under the influence of th e tropical system, at 81%, was slig htly higher than the mean for the fall 2004 season (76%), somewhat limiting the impacts of the storm. Virmani and Weisberg (2003) found analogous results in a comparison of the eff ects of tropical and extratropical systems on the WFS heat budget during the fall (September through October) of 2000. In middle September of 2000, Hurricane Gordon forced a maximum daily net surface heat loss of nearly –300 W/m2, yet the largest decline in water temperature over the shelf during the fall 2000 season coincided with the passage of a front in early October. Virmani and Weisbe rg (2003) report a maximum daily averaged surface heat flux of almost –1000 W/m2 for this event. For November of 2002/3, the monthly mean ra tes of heat exchange attributable to bay circulation dynamics were slightly in favor of warming (3.5 and 9.0 W/m2, respectively) while mean surface heat flux dominated, driving bay-wide cooling (-76.1 W/m2 over November 2002, under the influence of a moderate El Nio event, and –38.7 W/m2 in November of 2003). The November 20 04 data record is in complete, preventing a similar comparison. The passage of the frontal system in the latter days of November 2003 precipitated a sharp increase in the av erage rate of advective warming to 133.6 W/m2 over the 29th and 30th, exceeding average dynamical warming in advance of the eye of Frances. Analogously to the approach pha se of the hurricane, prolonged elevated winds out of the North associated with this extratropical front induced a set down in tide level relative to predicted as tronomical tides. As measured in middle Tampa Bay at the BRACE tower, observed water level is reduc ed on average 0.21 m (max. set down of

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53 0.41 m) relative to predicted ti dal elevation compared to mean set down at the tower over storm approach on September 5, 2004 of 0. 12 m (max. set down of 0.31 m). Current velocity data is unavailable ove r the frontal system transiti on, however, it is likely that increased advective warming throughout this ev ent is due to enhanced bay overturning circulation driven by a similar mechanism as during the approach of Frances; sharply cooled surface waters driven from the bay by southward winds are coupled with amplified return flow of warmer Gulf water at depth. Figure 4-5a: Fall 2002 me teorological data.

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54 Figure 4-5b: Fall 2002 surface fluxes. Figure 4-5c: Fall 2002 net surface and advective flux.

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55 Figure 4-6a: Fall 2003 me teorological data. Figure 4-6b: Fall 2003 surface fluxes.

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56 Figure 4-6c: Fall 2003 net surface and advective flux. Figure 4-7a: Fall 2004 me teorological data.

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57 Figure 4-7b: Fall 2004 surface fluxes. Figure 4-7c: Fall 2004 net surface and advective flux.

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58 Winter – December through February Surface heat flux variability remains high as water temperatures sink towards the annual minimum. Fronts continue to intermitte ntly impact the region, spiking turbulent heat exchange rates and temporarily reduci ng sea temperature (Fi gures 4-8 and 4-9). These events are interspersed with periods of minimal winds when turbulent heat exchange dips to near-zero, and bay temper atures recover. Averaged over the season, insolation and latent heat exchange reach their minimum, counterbalancing each other and resulting in only slight wintertime cooling. Figure 4-8a: Winter 2003/ 4 meteorological data.

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59 Figure 4-8b: Winter 2003/4 surface fluxes. Figure 4-8c: Winter 2003/4 ne t surface and advective flux.

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60 Figure 4-9a: Winter 2004/ 5 meteorological data. Figure 4-9b: Winter 2004/5 surface fluxes.

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61 Figure 4-9c: Winter 2004/5 ne t surface and advective flux. Spring – March through May Early spring is characterized by interv als of increasing ocean temperature under the influence of heightened downwelling solar radiation and reduced windspeed. Episodic extratropical fronts di srupt the overall warming tre nd with decreasing frequency as the season progresses. By May, surface h eat flux and meteorological parameters steady into a more summer-like pattern of reduced vari ability, essentially the converse of the fall transitional period. Bay temperature warmed by a total of 12.4 C in the spring of 2004, 8.7 C in 2005 (Figures 4-10 and 4-11, a a nd b). A break in the bay water temperature data record during the first half of the year prevents comparison with the 2003 spring season. In the following section, atmospheri c conditions contributing to ocean warming are examined in a final case study. Significant interannual variability is found, in addition to considerable differences in early and late springtime trends, in surf ace and advective (as resi dual) heat exchange. Rates of warming at the surface of the bay declined sharply from an average of 40.2 W/m2 over March through April to 12.4 W/m2 in May of 2004. Averaged net surface heat flux contributed to warming to a much lesser extent (22.5 W/m2) in early spring, March – April, of 2005 and shifted to sligh tly favor cooling in May (-1.9 W/m2). Combined, averaged rates of turbulent heat exchange opposed warming over all three months of spring: -120.3 W/m2 in 2004 and –111.1 W/m2 in 2005. Net longwave radiative heat flux is comparable year-to-year (-35.2 W/m2 in 2004 and –29.4 W/m2 in 2005). The disparity in combined, basin-averaged rainfall over the four major catchments feeding Tampa Bay

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62 (the Tampa Bay Coastal Region Basin, the Hi llsborough Basin, the Alafia River Basin, and the Little Manatee River Basin) dur ing the spring seasons of 2004 and 2005 is noteworthy; a total of 49.26 inches of precip itation fell over these four regions in March – May of 2005 as compared to 31.74 inches over the same months in 2004 (SWFWMD; http://www.swfwmd.state.fl.us/data/wmdbweb/rnfpage.htm ). Similar shifts in magnitude and directi on of advective heat exchange were found between early and late season. Bay hydrodyna mics contradict surface warming over the early spring (March through Ap ril), cooling the bay waters at a rate of –13.5 W/m2 in 2004 and –10.7 W/m2 in 2005. The latter portion of sp ring each year brought about a change in favor of advective warming. Bay circ ulation acted to heat the bay at a rate of 29.7 W/m2 in May of 2004 and 51.8 W/m2 May of 2005. Warming Trend – Following the transition of a final, late extratropical frontal system in mid-April of 2004 is an extended period of steadily risi ng water temperature in the bay. Though the autumn-like front interrupted bay warming, reducing the daily mean sea temperature by more than 2 C over a four-day interval, th e episode was succeeded by a period of light winds and strengthening insolation, from the 17th through the 25th of April (framed in black in Figure 4-10), which favored warm ing of estuarine waters. This prolonged interval of positive total heat flux into the bay drove a reversal of the frontal cooling and a continuation of the overall warming trend of the spring season; mean bay temperature is progressively higher each successive day with a total increase of 3.8 C over the nine-day period. Wind speeds were slightly lower on averag e during this warming phase (4.4 m/s) in comparison to both the spring months of 2004 overall and the preceeding winter season (each with a mean winds peed of 5.1 m/s). Observed insolation, averaging 211.8 W/m2, is significantly increased over the wi ntertime mean downwe lling solar radiation (124.6 W/m2). This period is characterized by str ong surface heat flux into the bay (nineday mean: 95.4 W/m2) while bay circulation contributes only marginally to warming (5.9 W/m2). Several days into the highlighted interval of warming, winds shifted from blowing steadily out of the E-NE to bl owing out of the E at night a nd out of the W over-water by day as a land/sea breeze pattern developed. Cooler sea breezes limited maximum daily air temperature over the bay on succeeding days. Measured incoming solar radiation was also somewhat reduced (min. daily mean of 175.5 W/m2). However, wind speeds fell to less than 4.0 m/s, turbulent heat excha nge at the surface weakened, and conditions remained favorable for warming. On April 23rd, winds shifted once more to blow easterly at night and northerly during the day, disr upting the land/sea breeze pattern. Peak air temperatures and daily mean insolation incr eased over the following days, extending the warming trend.

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63 Figure 4-10a: Spring 20 04 meteorological data. Figure 4-10b: Spring 2004 surface fluxes.

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64 Figure 4-10c: Spring 2004 net surface and advective flux. Figure 4-11a: Spring 20 05 meteorological data.

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65 Figure 4-11b: Spring 2005 surface fluxes. Figure 4-11c: Spring 2005 net surface and advective flux.

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66 Summary and Conclusions A three-year examination of the patterns of heat exchange at the surface and mouth of Tampa Bay revealed significant in terannual and seasonal variability. Short time-scale atmospheric forcing due to tropical cyclones and extratr opical frontal systems dramatically impacts net surf ace and advective heat exchange driving rapid cooling of bay waters. During such events, the surf ace fluxes are inadequately described by climatological means and active thermodynamics potentially become im portant to overall accuracy in the Meyers et al. Tampa Bay hydrodynamic model computations. Somewhat counterintuitively, the extrat ropical fronts that sweep th rough the region frequently each year often individually force gr eater change in estuarine heat content than hurricanes or tropical storms. Those components of the heat budget that are neg ligible on long-term average, such as sensible heat flux due to precipitation, can become temporarily significant. For example, heat flux due to rainfall over Tampa Bay briefly reaches a maximum hourly contribution to warming of 28.8 W/m2 in winter 2003/4, but acts to cool the bay at an hourly mean rate of 68.3 W/m2 out of the bay in the fall of 2003. As is the case for the coastal WFS (Virmani and Weisberg 2003, He and Weisberg 2002, 2003), surface heat fluxes domin ate total cooling in the fall and warming in spring in Tampa Bay. Advective heat exch ange was determined to variously enhance and oppose the direction of surface flux. On average over the spring season, advection acts to warm bay waters. However, over March and April of 2004 and 2005, advective heat flux counters surface warming before revers ing sign, contributing to warming, late in the spring season. In contrast, advective h eat exchange cools th e bay throughout the autumn season. Averaged surface heat exchan ge into and out of the bay over 2004, the most complete year of the three-year period of this study, demonstr ate that the surface heat fluxes nearly balance, as is expected ove r the annual cycle, with a mean flux into the bay of 572.4 W/m2 and an annual average heat flux out of the bay of 570.1 W/m2 at the surface. This study presents bulk methods requi ring commonly measured meteorological variables to determine model surface heat flux boundary condition. The methods chosen to estimate net surface heat exchange rates during this study are amenable to application in real-time model computations. Directions for future research include running parallel modeling studies with and without active thermodynamics in order to assess the importance of thermodynamic calculations to overall model accuracy.

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67 Chapter Five Freshwater Balance Study Introduction Tampa Bay, situated on the west central Fl orida coast, is the state’s largest open water estuary (see Figure 1-1). Within the Tampa Bay estuary freshwater from terrestrial sources meets and mixes with the saline ocean ic waters of the Gulf of Mexico. This interaction between fresh and salt water resu lts in a horizontal salinity gradient ranging from an average of 26 ppt for the fresher, buoyant waters of the nor thernmost portions of the bay, where the majority of terrestrial r unoff is received, to the Gulf waters (33 ppt mean salinity) at the southern mouth of th e bay (Meyers et al. 2007). The existence of this horizontal salinity gradient is the dominant factor co ntrolling Tampa Bay hydrodynamics. In a hindcast study of the residual circulation of Tamp a Bay, Meyers et al. (2007) demonstrated that ba y-wide salinity and circulat ion patterns are sensitive to alterations in the fresh water balance of the estuary. The long-term averaged horizontal salinity gradient is greatly reduced duri ng dry periods and amplified during wetter periods. The 2007 examination of the time-averaged circulation of the bay illustrated the impacts of highly variable fr eshwater influx volumes on baywide circulation. During conditions of reduced freshwater inflow th e residual circulation in Tampa Bay is weakened, while increased fres hwater input greatly enhances surface current velocities. The freshwater balance of the bay is comprised of several source and sink components, including precipitation, surface di scharge and groundwater seepage into the bay, and evaporation. However, Meyers et al. (2007) note that ev aporative freshwater outflow values are small as compared to th e combined freshwater sources so that a numerical model of Tampa Bay hydrodynamics is likely responsive to some longer term mean evaporation rate rath er than daily values. Maximal rainfall and river discharge us ually occur in the Tampa Bay region during the summer months, larg ely due to convective thunders torms. Winter precipitation totals are typically significantly lower: 25-50 % of summer rainfall levels. In addition to this predictable season al variability, Schmidt et al. ( 2001) demonstrated a significant response to El Nio/ La Nia events in both precipitati on and streamflow for the Tampa Bay catchment area. The authors found that ne utral winter (defined by the authors as January, February, and March) precipitati on may be increased by as much as 50-150% under the influence of an El Nio event, while the region may experi ence a winter deficit of 50-100% during La Nia episodes. Like wise, surface water discharge is greatly enhanced during El Nio winters; mean river runoff is increased by over 200%. During La Nia winters, discharge le vels were typically depressed by 70% compared to surface flow during neutral winters. Additionally, during both El Nio and La Nia events,

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68 summer (defined as July through September) mean discharge levels were reduced as compared to neutral values at most stations. Schmidt and Luther (2002) analyzed monthl y measures of salinity gathered from sixty-three stations located throughout Tampa Bay, for the years 1974 – 1999, and discovered that bay salinities are generally ne gatively correlated with both El Nio and La Nia sea surface temperature anomalies (SSTAs; positive SSTAs indicate an El Nio episode while negative SSTAs identify La Ni a events and the grea ter the magnitude of the SSTA, the stronger the event). The grea test correlation between El Nio conditions (indicated by positive SSTA) and reduced salin ities occurred during the winter (JFM) and spring (April, May and June) months, with si gnificant associations between these two parameters occurring during fa ll (October December) as well. Additionally, the authors determined that closest correlations betw een El Nio SSTAs and depressed salinity measurements are found in the northern regions (n ear the head) of the bay where the majority of surface runoff is received. Specification of surface eva poration rate is require d as a boundary condition within the Meyers et al. Tampa Bay nu merical model. Duri ng the recent 2001 – 2003 hindcast study (Meyer s et al. 2007), evaporative wa ter loss over Tampa Bay was approximated by measurements from a nearby pan evaporimeter. Availability of results from a three-year (June 2002 through May 2005) study of heat exchange over Tampa Bay permitted comparison of evaporation rates produc ed in latent heat flux computations over the bay to the evapora tive loss rates utilized during the hindcast study for the interval of study overlap, from June 2002 to December 2003. Th e first half of this study period, June of 2002 through April of 2003, is classified as a moderate El Nio event based upon a threshold Oceanic Nio Index of 0.5 C (NOAA Climate Prediction Center; http://www.cpc.noaa.gov/ ) while the remainder of the research time frame (May through December 2003) is categorized as neutral. This affords the opportunity to examine the impacts of an El Nio episode on the freshwater balance of Tampa Bay in comparison to neutral seasons. The objectives of this research were twof old: 1) to infer the magnitude of fresh water loss at the surface of Tampa Bay from es timates of daily averag ed evaporative heat loss over the bay produced by the TOGA COARE 3.0 bulk algorithm and compare estimates of evaporative water loss over the bay to evaporation rates acquired by pan evaporation technique as pr ovided to the Meyers et al Tampa Bay numerical model during the 2001-2003 hindcast residual circulati on study and 2) to consider the variable importance of evaporation as a fresh water sink in the context of fresh water balance components supplied as boundary conditions to the numerical model during June 2002 through December 2003 of the 2001-2003 hindcast study. While fresh water loss through evaporation is typically small in comparison to the collective inputs of precipitation and land surface runoff into the bay, interannual and seasonal variability of fresh water sources and sinks affect the comparative importance of evaporative water loss as a control on bay-wide salinity.

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69 Experimental Methods Freshwater Budget Components Freshwater is delivered to Tampa Ba y through several sources: surface runoff from rivers and smaller tributaries, injecti on of waste water from water treatment plants and other industrial po int sources, precipitation over th e bay, and groundwater seepage. Rainfall over the bay is supplied to the Me yers et al. Tampa Bay model as a daily composite of cumulative rainfall measur ed at four sites around the bay: the Sarasota/Bradenton Airport (SRQ), the St. Petersburg Albert Whitted Airport (SPG), the St. Petersburg/Clearwater International Airport (PIE), and Tampa International Airport (TPA). Available measurements of waste wate r released from four waste water treatment plants situated around the bay area, process water discharge rates from the Piney Point phosphate mine operation, and freshwater co ntributed via the Tampa Bypass Canal are combined with USGS daily averaged stream flow measurements, and credible estimates of streamflow rates where point source meas urements are unavailable, to produce a daily average surface water contribution. Groundwater is discharged to Tampa Bay from the surficial, intermediate and Fl oridan aquifers at an amount approximately equal to 0.081 times the total streamflow of each river and tr ibutary (Brooks et al. 1993). This additional freshwater flux is added to th e daily mean base flow rate of each point source specified within the model domain. Pan Evaporation Rate – The daily rate of evaporative freshwater loss from the surface of the estuary is assumed from a SWFWMD maintained evapor ation pan located near McKay Bay. Gaps in the pan evaporation reco rd occur spanning the end of July through September 2002 and mid-August through the middle of October of 2003 The daily rate is interpolated from monthly climatological averages where pan evaporation data is unavailable. Water loss from a pan evaporimeter responds to sim ilar environmental forcing as does loss from a large water body (atmospheric humidity, net ra diation, winds) and is therefore expected to represent an improvement to the previ ous practice of assigning a simple, static evaporation rate value (0.25 cm/day or 27.6 m3/s) over Tampa Bay in model computations. Linacre (2005) notes, however, that the pan evaporat ion technique cannot capture characteristics unique to large bodies of water such as a high thermal inertia, heat transport via currents, increased near surf ace humidity present over wide water surface areas, and the occurrence of waves. Pan evaporimeters are also sensitive to pan placement. Shading from nearby plants, shield ing from winds, and surface properties, such as aerodynamic roughness of the near by area and the low heat capacity of surrounding land, may all skew evaporation measurements (Hillel 1997). Unlike natural surfaces, pan evaporimeters are exposed to more energy per unit surface area, as the sides and bottom of the pan remain open to additi onal radiative and conduc tive heat transfer from surroundings (Kahler and Brutsaert 2006). Rates of freshwater loss produced by pan evaporimeter, similar to evaporation rates de rived from bulk formulae, do not represent a direct measurement of evaporation rate. Eva porative losses from a pan are instead related to actual evaporation from surrounding surfaces through the application of a correction

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70 coefficient. This correction factor may va ry from 0.5 to 0.85 (Hillel 1997) according to season and the environmental characteri stics specific to pan location. The reader is referred to Meyers et al. 2007 for a complete description of 20012003 model boundary condition parameterizations of freshwater sources and sinks applied during the Tampa Bay residual circ ulation study. Evaporation Rate Produced in La tent Heat Flux Calculations – The turbulent flux of latent heat at the surface typically repres ents a loss of energy from the bay in response to a gradient in specific humidity between th e saturated air at the sea surface and drier air aloft. Molecule s of water evaporati ng at the surface carry heat away from the surface, releasing this en ergy to the atmosphere and cooling estuarine waters. Latent heat transport is enhanced by high wind speeds generating increased nearsurface turbulence, low atmospheric humi dity strengthening the ocean/atmosphere moisture gradient, and elevated sea surface temperatures reducing the latent heat of vaporization. Bulk aerodynamic representati on of the latent heat flux is given by: QL= LeUCE(qs-qa) (28) where U is the wind speed qs-qa is the difference between saturated sea surface and atmospheric specific humidity, Le is the latent heat of vaporization, CE is the dimensionless moisture transfer coeffici ent, and the density of air is given by As one component of a thr ee-year investigation into the heat budget of Tampa Bay, daily averaged latent heat exchange estimates were computed via the TOGA COARE 3.0 bulk algorithm (Fai rall et al. 2003) for the pe riod spanning June of 2002 through May of 2005. Inputs to the algor ithm included measurements of water temperature and over-water winds, air temper ature and humidity gath ered at the BRACE meteorological tower located in Middle Tampa Bay (see Figure 1-1 for the location of the BRACE observational tower with in Tampa Bay). Latent heat flux data availability overlaps with the latter half of the Tamp a Bay residual circulation study: June 2002 through Dec 2003. However, for the Decembe r of 2002 through June of 2003 portion of the study, measurements of bulk water temp we re unavailable, resulting in missing latent heat exchange data. The evaporative freshwater loss, E was inferred from the estimated energy lost due to evaporation (the latent heat flux, QL) and the amount of energy contained within each kilogram of water vapor formed (the latent heat of vaporization, Le): E = QL / Le (29). Results Freshwater Balance in Tampa Bay for June 2002 – December 2003 On long term average, evaporative freshwater loss according to pan evaporimeter plays a secondary role in the freshwater balance: for June 2002 – December 2003 (n = 579 data points), the 1.5 yr mean da ily pan evaporation rate is 31.7 m3/s, removing approximately 22% of the total freshwater infl ow rate (surface runo ff, precipitation, and groundwater input) of 145.0 m3/s. The time frame of this study includes two summers, the season of peak evaporation, but only one winter and spring, where evaporative water

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71 loss is typically at a minimum according to pan evaporimeter, largely in response to reduced insolation during these seasons (see Fig 5-1). In ad dition, evaporation occurs evenly across the bay whereas the surface runoff and ground water seepage, which together account for nearly 61% of freshwat er inflow over this study period (at a daily mean rate of 88.1 m3/s), are confined to select re gions of input that are mostly concentrated in the northern head of the ba y. This heterogeneity of freshwater input results in surface waters becoming progressi vely fresher moving up the bay and is the force driving the overturning ci rculation of Tampa Bay. Precipitation, averaged over the entire bay, makes up the remaining 56.9 m3/s of freshwater infl ow. These observations support the assumption that evaporative wa ter loss does not represent a dominant controlling factor on bay hydrodynamics and th erefore a numerical model of Tampa Bay is likely not typically sensitive to dail y variations in evaporation rate. Figure 5-1: Daily mean rates of evaporative loss (pan evaporimeter) out of and total freshwater inflow into Tampa Ba y in cubic meters per second. Daily average evaporation rates pro duced in bulk latent heat flux (LHF) estimation over Tampa Bay were available for the periods spa nning June 2002 through November 2002 and July 2003 through Decembe r of 2003 of the June 2002 – December 2003 study timeframe (n = 389; s ee Figure 5-2). For these comb ined intervals, over-water evaporative water loss (daily mean rate = 47.2 m3/s) offsets a little over a third of the surface and rainfall freshwater contributions to the bay while the pan evaporimeter gives a mean evaporative loss, 31.5 m3/s, a magnitude remaining approximately 22% of the

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72 freshwater inflow. Bulk fo rmula-derived evaporation ra tes support Vincent’s (2001) preliminary findings that evaporative fres hwater volume loss from Tampa Bay is of similar magnitude and opposite sign as volum e inflow due to precipitation; the mean rainfall over the estuary for these two periods combined is 59.2 m3/s. Figure 5-2: Daily mean estimated (bulk formula) and measured (pan evaporimeter) evaporation rates over Tampa Bay (m3/s). In keeping with Linacre’ s (2005) statement that pan evaporimeter measurements are largely governed by radiation, McKay Bay pan evaporation ra tes exhibit a clear tendency toward reduced magnitude and variab ility in the fall and winter months in response to diminished insolation as compar ed to the summer months (Figure 5-3). The mean evaporation rates over June and July of 2002 (n = 54; climatological mean evaporation data excluded) and June th rough July of 2003 (n = 61) are 39.4 m3/s (std. deviation from the mean = 11.0 m3/s) and 40.4 m3/s (std. deviation of 14.8 m3/s), respectively. In contrast, aver age evaporative water loss rate, as measured at McKay Bay, is decreased during November and December of 2002 to 18.6 m3/s (std. deviation = 6.3 m3/s). Evaporation rates for the same time span in 2003 are similarly reduced in magnitude (mean of 61 time steps: 21.3 m3/s) and variability (std deviation of 5.1 m3/s).

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73 Figure 5-3: Daily mean insolation rates (a cquired from the BRACE observational tower) and pan evaporation rates over Tampa Bay. Conversely, over-water evaporation rates display the increased variability in the latter part of each year, as compared to summertime evaporative loss rates, that is expected in response to the ep isodic high winds and reduced hu midity associated with the wintertime passage of extratropical front s (see Figure 5-2). On November 16th of 2002, an approaching cold front precipitated an upsurge in rainfall over Tampa Bay to a daily average of 193.2 m3/s from a mean of only 0.4 m3/s for the prior day. Relative humidity increased by nearly 20 percen t to 88.2%. By November 18th, the rains had passed from the region and daily averaged air temperatur e, as measured at standard anemometric height at the BRACE tower, fell to 14.3 C from a mean of 21.2 C on November the 16th. The 10m relative humidity reading descende d to 50.2%. Winds th at peaked on the 17th at a mean of 9.2 m/s remained high on the 18th at 7.8 m/s, as compared to wind speeds averaged over the en tire study period (4.8 m/s). In response to this strong atmospheric forcing, evaporation rates produced in over-water LHF estima tion fell from 45.0 m3/s on November 15th to 20.9 m3/s as the rains swept through the area on the 16th, spiking to a daily mean of 112.8 m3/s on November 18th. Evaporation rates determined from pan evaporimeter exhibited a similar pattern of re sponse to the passage of the front, but to a much lesser degree in magnitude : evaporative water loss rates were at a minimum of 2.8 m3/s on the 16th and reached a maximum daily aver age of no more than 22.4 m3/s on November the 18th. Both methods of parameterizing evaporat ive freshwater loss from the surface of the bay are disadvantaged in being point estimates; pan evaporimeter measurements are

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74 acquired from a single pan located near Mc Kay Bay while over-water estimates are inferred from heat exchange rates computed at the BRACE tower in Middle Tampa Bay. In addition, pan evaporimeter observations necessitate the inclus ion of a correction coefficient and thus, like ove r-water evaporative volume lo ss rates, do not represent a direct measurement of evaporation. Evaporation rates obtained from a pan evaporimeter reflect the immediate environment of the in strument and cannot ad equately capture overwater conditions. However, evaporation rate es timates inferred from latent heat transfer rates incorporate over-water measurements of relative humidity, wind speed, bulk water and air temperatures and insolation gathered at the BRACE tower located within Tampa Bay and capture the variability expected dur ing seasonal transition periods. As such, these over-water estimates of evaporati on represent an improvement over rates determined via nearby pan evaporimeter and ar e utilized for the remainder of the Tampa Bay freshwater budget study. Inter-Annual Variability in Freshwater Inflow/ ENSO Impacts Signatures of the El Nio episode that sp ans the latter half of 2002 through the spring of 2003 may be clearly seen in the sharp contrast in combined freshwater inflow to Tampa Bay between June and December of 2002 and the correspond ing months in 2003 (a neutral season; see Figure 5-1). In agreement with Schm idt et al. (2001), bay-wide daily average winter precipitation levels are found to be greatly enhanced under the influence of this El Nio event. Mean da ily rainfall for December of 2002 is 87.1 m3/s, an amount nearly 8 times the average daily ra infall rate during the neutral month of December 2003 (11.1 m3/s). Accordingly, surface and gr ound water inflow to the estuary diminishes from an average daily rate of 194.4 m3/s during December 2002 to a mean daily flow of 21.1 m3/s for the neutral December of 2003. In contrast, while Schmidt et al. (2001) did not find a significant relationship between the pr esence of either La Nia or El Nio conditions and discharge levels, th e authors found summer river and stream discharge rates were depressed relative to rates during neutral summer seasons. Again the moderate El Nio event is evident in the di sparity between mean daily combined surface and groundwater inflow ra tes for June 2002 (31.0 m3/s) and the neutral June of 2003 (195.5 m3/s). While the influence of evaporative freshwater loss on estuarine salinity is negligible during an extremely wet El Nio wi nter, the relative impor tance of evaporation to the freshwater balance of the bay is amplified through El Nio/ La Nia summers. The contrast between freshwater input ra tes for the summer months when El Nio conditions were prevalent (Jul y, August and September of 2 002), and the same months in the neutral latter half of 2003, illustrates the impact even moderate events can exert on the freshwater balance (Figur e 5-1). During the El Nio summ er of 2002, rain fell over Tampa Bay at the mean rate of 73.4 m3/s per day while the combined surface runoff and groundwater seepage contribute d at the rate of 85.4 m3/s. The neutral summer of 2003 brought an average precipitation rate of 115.7 m3/s, while ground and surface water input to the bay swelled to a mean rate of 285.6 m3/s. Other mean meteorological parameters, such as 10-meter air temperature, relative humidity and wind speed, were remarkably similar between the summers of 2002 and 2003. Interestingly, the average rate of evaporation is identical fo r these time frames: 53.9 m3/s. While evaporative water loss counterbalances 34.0% of fres hwater inflow during the moderate El Nio summer of

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75 2002, it offsets only 18.9% of freshwater influx throughout the su mmer of 2003. Under unusually dry conditions, knowledge of the variab le rate of freshwat er volume loss from the bay surface becomes important. Bulk sea temperature measurements ar e unavailable from the BRACE tower array for the period of December of 2002 th rough June of 2003, preventing a similar comparison between El Nio and neutral wi nters. However, based upon the work of Schmidt et al. (2001), it is expected that the disparity in the relative impacts of evaporative water loss during neutral and El Ni o winter times to the overall freshwater budget of Tampa Bay would be even more pronounced. Seasonal Variability in Freshwater Balance Overlap of the three-year Tampa Bay h eat budget study data reco rds with the data set of model boundary conditions from the Meye rs et al. (2007) residual circulation hindcast study permitted intra-annual comparisons of freshwater budget components in Tampa Bay for the summer and fall 2003, a neut ral year according to ONI index. Passing extratropical fronts sweep acro ss the bay during the autumn and winter months bringing higher winds and drier air to the bay region coupled with pe riodic sharp declines in air and water temperatures. Over-w ater evaporation rates spike in response to these intense meteorological events. Standard deviation from the mean, as a measure of variability, points to significantly increased variance in evaporative volume loss from the bay during the late year months of November and December of 2003 (std. deviation = 29.7 m3/s) as compared to the mid summer months of A ugust – September of the same year (std. deviation of 13.8 m3/s). Mean evaporation rates during these periods are not vastly different. The average rate of evaporative freshwater loss from Tampa Bay through August and September (51.6 m3/s) lowered to 34.3 m3/s for November and December, likely in response to reduced mean sea temperature (down to 20.0 C from a summer mean of 29.3 C) combined with weakened insolation (an average of 119.7 W/m2 in the winter versus a mean of 184.2 W/m2 for these summertime months). Winter time rainfall, surface inflow and groundwater seepage rates, however, are dr amatically diminished from summertime values. Emphasizing the potenti al importance of evaporation in the total freshwater budget of Tampa Bay, mean total freshwat er inflow for the November – December interval is 31.4 m3/s, an amount roughly equal to evapora tive freshwater loss. In contrast, the average inflow rate from all freshwat er sources for the summer months (August through September) is 311.0 m3/s. Freshwater loss from the surface of the bay plays a much greater role in the estuarine salinity ba lance in the course of the transitional autumn season and the error incurred by assigning a constant value to the evaporative volume loss model surface boundary condition is expected to be compounded. Summary and Conclusions The present research confirms that, on long-term average, fr eshwater volume loss at the bay surface is a minor component of the freshwater balance of Tampa Bay. However, it is demonstrated that the rela tive importance of evaporative volume loss varies considerably on seasonal scales. Summe r is typically the p eak rainy season over

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76 south central Florida while wintertime pr ecipitation is usually low: 25 – 50% of summertime values. Combined freshwater input to the bay (surface and groundwater inflow and precipitation) cl early dominates the balance during the summer of 2003. Conversely, rates of evapor ation over the bay completely counterbalance depressed freshwater influx rates in th e latter months of 2003. Rainfall and runoff patterns in the Tamp a Bay catchment are drastically altered under El Nio conditions. Comparisons of tota l freshwater inflow to the bay under the influence of a moderate El Nio event and during the neutral summer and winter of 2003 caution against the application of climatol ogical seasonal mean evaporation rates as surface boundary conditions within the numer ical model. Additionally, autumn and winter extratropical fronts, bringing cold, dry air and high winds to the region, dramatically accelerate over-water evaporati on in the short term. During such events, evaporative volume loss rates, as computed in surface heat flux computations, are significantly greater than both the pan evaporimeter-derived freshwater flux rates and the constant evaporation rate applied previously in Meyers et al. numerical model studies. Bulk formula estimates of evaporation rates over Tampa Bay are readily computed realtime from over-water meteorological parameters.

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77 Chapter Six Summary and Recommendations Integration of a water quality model with the Meyers et al. Tampa Bay hydrodynamic model, the next stage in developm ent of the Tampa Bay Integrated Model, motivated the present research into bay-atmo sphere heat exchange. Definition of a heat transfer boundary condition at the bay surface is required as a precursor to computations of three-dimensional temperature fields within the bay and estuarine water quality modeling. Toward that end, qua ntification of heat exchange at the air-water interface was the primary objective of this research. An initial six-month examination of over-water turbulent heat exchange, the portion of total surface heat flux occurring in response to bay-atmosphere gradients in temperature and moisture and driven by the pa ssage of turbulent eddies, demonstrated the skill of two algorithms, the TOGA COARE v. 3.0 (TC3) algorithm and the NOAA Buoy model (NBM), in predicting directly measured sensible heat flux over Tampa Bay. At the core of each algorithm, the bulk aerodynamic me thod of computing sensible (latent) heat exchange rates relies on the product of horizontal wind speed and the temperature (moisture content) differential between n ear surface and the standard anemometric measurement height of 10 m to approxim ate the eddy covariance of vertical wind velocity and air temperature (r elative humidity). This eddy covariance of vertical wind and air temperature is also directly measur ed by sonic anemometer and used to produce sensible heat exchange at the surface. Howe ver, sensible heat flux measured by sonic anemometer is vulnerable to bias during rainfall events. Direct measurements of the turbulent fluctuations of vertical wind a nd relative humidity are unavailable and therefore latent heat flux over Tampa Bay must be approximated by the bulk aerodynamic approach described above or th e gradient method, a technique that assumes the shape of the near-surface temperature (humidity) profile based upon measurements of air temperature (moisture content) gathered at two heights over water to predict sensible (latent) heat exchange. The grad ient method of computing sensib le and latent heat flux is restricted in relevance to n ear-neutral atmospheric stabilit y regimes and was determined to be too limited for applicability to the presen t research. For the rema inder of this study, the bulk aerodynamic method of turbulent heat exchange estimation was utilized due to a lack of direct measurements of latent heat tr ansfer at the bay surface and susceptibility of the sonic anemometer to bias during fr equent rainfall events over Tampa Bay. Inter-model comparisons showed close agreement in modeled sensible heat exchange rates between the NBM and the TC3 algorithm. Modeled latent heat exchange rates agreed less satisfactorily. A minor adju stment to specific humidity computation within the NOAA Buoy model dr amatically improved mode l agreement. Though the NBM was specifically designed for coastal application, development of the TC3 algorithm incorporated model ve rification studies from the e quator to high latitudes and

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78 previous model application ranges from deep o cean to near-shore regions. In addition, the TC3 algorithm is applicable in a wider range of stability regimes. For the remainder of this study, the TOGA COARE v. 3.0 algorith m provided estimates of turbulent heat exchange for the analysis of surface heat fluxes over Tampa Bay. Net heat exchange at the surface of Ta mpa Bay has been computed for a threeyear interval spanning June 2002 to May 2005. This total heat en ergy gained or lost at the bay-atmosphere interface represents the summ ation of the turbulent and radiative heat fluxes. Changes in the total heat content of the bay encompass the net surface heat exchange and advective heat flux at the mout h of Tampa Bay with the Gulf of Mexico. Analogously to the coastal WFS region (Virma ni and Weisberg 2003, He and Weisberg 2002, 2003), surface heat fluxes dominate total co oling in the fall and warming in spring in Tampa Bay while advective heat exchange becomes relatively more important during the summer season. Short time-scale events such as tropical cyclones and extratropical frontal systems dramatically impact net surface heat exchange driving rapid cooling of bay waters. Heat budget components that are typica lly small in magnitude, for ex ample, sensible heat flux due to precipitation, may become temporarily important under the influence of these events. Interestingly, the frontal systems that regularly sweep over the bay area each year frequently exert greater influence over surf ace heat exchange rates and change in total heat content than the near approach of a tropical cyclone (Hurricane Frances). These findings recommend against the application of climatological means in Meyers et al. Tampa Bay hydrodynamic model computations and emphasize the need for frequent over-water observations in order to define model boundary conditions. Rates of evaporation over the bay fall natu rally out of calcula tions of turbulent heat exchange at the surface. Estimates of evaporation rates produ ced in over-water flux computations are preferred to either incor poration of measurements from a nearby pan evaporimeter or assignment of a constant evaporative volume lo ss in modeling of bay hydrodynamics. A secondary objective of this project was therefore improvement on the present evaporation surface bounda ry condition specification. Wh ile freshwater loss from the surface of the bay is typically small re lative to the combined contributions of precipitation, groundwater seep age and surface runoff, this study demonstrates that the rate of evaporation over Tampa Bay is highly variable and intermittently important to the overall freshwater balance of the bay. The methods applied in computation of heat flux components and evaporation rates in this research are amenable to in corporation in realtime modeling exercises. Recommended future research includes the crea tion of parallel runs of the Meyers et al. Tampa Bay Model, with and without active thermodynamics and real-time evaporation rate computations, in order to evaluate th e importance of surface heat exchange and evaporation rate informati on to overall model accuracy.

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82 Jerlov, N. G. (1976). Marine Optics. NewYork, Elsevier Scientific Pub. Co. 231 pp. Josey, S. A., R. W. Pascal, P. K. Taylor and M. J. Yelland (2003). A new formula for determining the atmospheric longwave flux at the ocean surface at mid-high latitudes. Journal of Geophysical Research. 108(C4): doi:10.1029/2002JC001418. Kahler, D. M. and W. Brutsaert (2006). Complementary relationship between daily evaporation in the environm ent and pan evaporation. Water Resources Research 42. Kantha, L. H. and C. A. Clayson (2000). Numerical Models of Oceans and Oceanic Processes. San Diego, Academic Press. Kara, A., H. Hurlburt and A. Wallcraft (2005). Stability-dependent exchange coefficients for air-sea fluxes. Journal of Atmospheric and Oceanic Technology 22(7): 10801094. Kumar, A., J. Luo and G. Bennett. (1993). St atistical Evaluation of Lower Flammability Distance (LFD) using Four Hazardous Release Models. Process Safety Progress. 12(1): 1-11. Kumar, A., N. Bellam, and A. Sud. (1999) Performance of Industrial Source Complex model in predicting long-term conc entrations in an urban area. Environmental Progress. 18(2): 93-100. Lange, B. and J. Hjstrup (2000). The influe nce of waves on the offshore wind resource. Proceedings of the European Seminar OWEMES Offshore Wind Energy in Mediterranean and other European Seas, Siracusa, Italy, 13.-15.04.2000: 491503. Linacre, E. (2005). Lake Eo, pan Ep, actual (terrestrial) Ea, potential Et and ocean evaporation rates. Proceedings of a workshop held at the Shine Dome, Australia Academy of Science, Canberra, 22 – 23.11.2004: 32-35. Liu, P. C. and D. J. Schwab (1987). A comparison of methods for estimating u* from given uz and air-sea temperature differences. Journal of Geophysical ResearchOceans 92(C6): 6488-6494. Lukas R, and P.J. Webster (1992). TOGA-COARE TROPICAL OCEAN GLOBAL ATMOSPHERE PROGRAM AND COUPLED OCEAN-ATMOSPHERE RESPONSE EXPERIMENT Oceanus 35 (2): 62-65. Meyers, S. D., M. E. Luther, M. Wilson, H. Havens, A. Linville and K. Sopkin (2007). A numerical simulation of resi dual circulation in Tampa Ba y. Part I: Low-frequency temporal variations. Estuaries and Coasts. 30(4):679-697.

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83 Mizak, C., S. Campbell, K. Sopkin, S. Gilbert, M. E. Luther and N. Poor (2007). Effect of shoreline meteorological measurem ents on NOAA Buoy model prediction of coastal air-sea gas transfer. Atmospheric Environment. 41(20): 4304-4309. Morey, S. L. and J. J. O’Brien (2002). The spring transition from horizontal to vertical thermal stratification on a midlatitude continental shelf. Journal of Geophysical Research. 107(C8): 3097 National Research Council (2000). Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution Washington, National Academy Press. Oost, W.A, C.M.J. Jacobs, and C. Van Oort ( 2000). Stability effects on heat and moisture fluxes at sea. Boundary-Layer Meteorology 95: 271-302. Panofsky, H. A. (1963). Determination of stress from wind and temperature measurements. Quarterly Journal of the Ro yal Meteorological Society. 89: 85-94. Patel, V.C. and A. Kumar. (1998). Evaluati on of Three Air Dispersion Models: ISCST2, ISCLT2, and SCREEN2 For Mercury Emissions in an Urban Area. Environmental Monitoring and Assessment. 53: 259-277. Paulson, C. A. and J. J. Simpson (1977). Irradiance measurements in the upper ocean. Journal of Physical Oceanography 7:952-956. Paulson, C. A. and J. J. Simpson (1981) The Temperature Difference Across the Cool Skin of the Ocean. Journal of Geophysical Research 86(C11): 11,044-11,054. Payne, R. E. (1972). Albedo of the Sea Surface. Journal of the Atmo spheric Sciences. 29: 959-970. Poor, N. (2000). Tampa Bay Atmospheric Deposition Study (TBADS) Final Interim Report: June 2000. 102 pp. Poor, N., R. Pribble, H. Gr eening (2001). Direct wet and dry deposition of ammonia, nitric acid, ammonium and nitrate to the Tampa Bay Estuary, FL, USA. Atmospheric Environment 35(23): 3947-3955. Poor, N., R. Tremblay, H. Kay, V. Bhetha nabotla, E. Swartz, M. E. Luther and S. Campbell (2004). Atmospheric concentra tions and dry deposition rates of polycyclic aromatic hydrocarbons (PAHs ) for Tampa Bay, Florida, USA. Atmospheric Environment 38(35): 6005-6015. Reed, R. K. (1977). On Estima ting Insolation Over the Ocean. Journal of Physical Oceanography. 7:482-485.

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84 Riswadkar, R.M. and A. Kuma r. (1994) Evaluation of the ISC Short Term Model in a Large-Scale Multiple Source Region for Different Stability Classes. Environmental Monitoring and Assessment. 1-14. Rutgersson, A., A. S. Smedman, and A. Omst edt, (2001). Measured and Simulated Latent and Sensible Heat Fluxes at Two Ma rine Sites in the Baltic Sea. Boundary Layer Meteorology 99, 53-84. Schmidt, N., E. K. Lipp, J. B. Rose and M. E. Luther (2001). ENSO influences on seasonal rainfall and river discharge in Florida. Journal of Climate. 14: 615-628. Schmidt N. and M. E. Luther (2002). ENSO impacts on salinity in Tampa Bay, Florida. Estuaries. 25(5):976-986. Sheng, Y. P., J. R. Davis, V. Paramygi n, K. Park, T. Kim and V. Alymov (2003). Integrated-process and integrated-scale modeling of large coastal and estuarine areas, pp. 407-422. In: M. L. Spaulding (ed.), Proceedings of the 8th International Conference on Estuarine and Coastal M odeling, American Society of Civil Engineers, Monterey, California. Sopkin, K., C. Mizak, S. Gilbert, V. Subram anian, M. E. Luther and N. Poor (2007). Modeling air/sea flux parameters in a coas tal area: a comparative study of results from the TOGA COARE mode l and the NOAA Buoy model. Atmospheric Environment. 41(20): 4291-4303. Stull, R. B. (1984). An introduction to boundary layer meteorology. Boston, Kluwer Academic Publishers. 666 pp. Sun, B., L. Yu, and R. A. Weller (2003) Comparisons of Surface Meteorology and Turbulent Heat Fluxes over the Atlantic : NWP Model Analyses versus Moored Buoy Observations. Journal of Climate 16: 679-695. Tampa Bay Soundings (2003). Tampa Bay After Dark. Winter 2003 3(1). Pinellas Park, FL. Tampa Port Authority (2005). The 2005 State of the Port Address. http://www.tampaport.com/ TBEP. (1996). Charting the Course for Tampa Bay Tampa Bay National Estuary Program. http://www.tbep.org. Tetens, O. (1930). ber einige meteorologisc he Begriffe. Z. Geophys. 6: 297-309. Valigura, R. A. (1995). Iterativ e bulk exchange model for estim ating air-water transfer of HNO3. Journal of Geophysica l Research-Atmospheres 100(D12): 26045-26050.

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85 Vincent, M. S. (2001). Development, Implementation and Analysis of the Tampa Bay Coastal Prediction System. Dissertation. College of Engineering. Tampa, FL, University of South Florida. Vincent, M., D. Burwell, and M. Luther (2000). The Tampa Bay Nowcast-Forecast System. In: Estuarine and Coastal Modeling, M. L. Spaulding and H. L. Butler, eds., ASCE, Reston, VA, pp 765-780. Virmani, J. I. (2005). Ocean-Atmosphere Interacti ons on the West Florida Shelf. Dissertation. College of Mari ne Science. St. Petersburg, FL, University of South Florida. Virmani, J. I. and R. H. Weisberg (2 003). Features of the observed annual oceanatmosphere flux variability on the West Florida Shelf. Journal of Climate. 16: 734-745. Webb, E.K. (1970). Profile relationships, log-linear range, and extension to strong stability. Quarterly Journal of the Royal Meteorol ogical Society 96 (407): 67. Weisberg, R. H., and L. Zheng (2006a). Ci rculation of Tampa Bay driven by buoyancy, tides, and winds, as simulated using a finite volume coastal ocean model. J. Geophys. Res. 111, C01005, doi:10.1029/2005JC003067. Weisberg, R. H. and L. Zheng (2006b). Hurricane storm surge simulations for Tampa Bay. Estuaries and Coasts 29: 899-913. Wilson, M., S. D. Meyers and M. Luther (2006). Changes in the circulation of Tampa Day due to Hurricane Frances as recorded by ADCP measurements and reproduced with a numerical ocean model. Estuaries and Coasts 29(6A): 914918.