Eddy Flux Observations of Evaporation and Vapor

Eddy flux observations of evaporation

and vapor advection in the Gulf of

Aqaba (Eilat), Red Sea

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Dekel Shlomo

Graduate Program in Civil Engineering

The Ohio State University

2011

Master's Examination Committee:

Dr. Gil Bohrer, Advisor Dr. Ethan Kubatko Dr. Linda Weavers

Dekel Shlomo

2011

ABSTRACT

There is a large uncertainty around the rates of evaporation from desert enclosed seas, and in particular the Red Sea. The Gulf of Aqaba is long and narrow and is partially isolated from the Red Sea and the Indian Ocean by shallow and narrow straits. The long fringing reef of the Red Sea has a large economic importance to the region's tourism. In the northern part of the Red Sea, vertical mixing of the water column, which is driven by evaporation, supplies nutrients to the shallow water and, at high levels, results in favorable conditions for algae over corals, and thus, will lead to the destruction of corals.

The local weather and sea surface temperature are also affected by the rates of evaporation. There are two compounding phenomena that complicate the estimation of evaporation rate in this region: (1) Although the wind is mostly oriented along the long axis of the narrow Gulf, advection of water vapor towards the dry desert surrounding the

Red Sea may account for large amounts of water. (2) In the summer, the mean sea surface temperature is colder than the warm, dry desert air, leading to a thin stable boundary layer above the sea that may suppress evaporation. Atmospheric and oceanic models of the Red Sea area have run into difficulties in estimating the evaporation rates. There are very few locations where the temperature and humidity are measured routinely. Direct measurements of evaporation or on-shore advection of water vapor were not previously conducted in this region.

In March 2009 we set up two eddy flux towers at the Inter-University Institute for Marine Science in Eilat, Israel, at the north western shore of the Gulf of Aqaba. We conducted measurements of water vapor wind and other meteorological conditions. We used the eddy-covariance technique to calculate the mass balance of water in the atmosphere above the coral lagoon near the shore. Our measurements show that a combination of advection toward land and stability conditions of the boundary layer due to negative water-air temperature gradient in hot days significantly affects the evaporation rates.

iii

Dedication

This document is dedicated to my family.

Acknowledgements

The work was funded by Research Award #181 from the PADI Foundation to Dr. Gil

Bohrer. The project was hosted, coordinated and made possible by Amatzia Genin, HUJI, and IUI, Israel. Hezi Gildor provided critical advice and assistance in the setup of the project and data interpretation. Daniel Carlson and Eliyahu Biton assisted in building the eddy-flux towers. Shahar Yair, Igal Berenshtein and Margarita Zarubin assisted in construction and maintenance of the towers and continuous data collection. I also thank

Ran Nathan, Ithak Mahrer and Elad Shilo for providing additional equipment to the station. Yoav Bartan, Moti Ohavia and Timor Catz assisted in construction of and infrastructure for power supply to the towers. Ronit Bohrer-Hillel and Eyal Hillel provided transportation to the field site. I thank Ziv Bohrer and Patrice Allen for their time and effort in coordination of the expedition. Dan Vehr and the Region-1

Computational Laboratory of the OSU College of Engineering for printing presentation media. Partial support for developing the data analysis methods used in my work was provided through the OSU Institute for Energy and the Environment 2009 Seed grant to

Dr. Gil Bohrer and Peter Curtis, NSF grant #DEB0918869, NASA Earth and Space

Science Graduate Training Fellowship #NNX09AO26H to Anthony S. Bova and Gil

Bohrer, and the U.S. Department of Energy Midwestern Regional Center of the National

Institute for Climatic Change Research (NICCR) at Michigan Technological University, under Award No. DE-FC02-06ER64158.

A special gratitude to my advisor, Dr. Gil Bohrer, for his patience, guidance and support throughout the course of this research.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National

Science Foundation.

Vita

1998...... Yeshivat Hadrom High school

2008…...... B.Sc. Biotechnology Engineering, Ben

Gurion University of the Negev

2009 to present ...... Graduate Research Associate, Department of Civil and Environmental Engineering, The Ohio State University

Fields of Study

Major Field: Civil Engineering.

vii

Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgments...... v

Vita ...... vii

List of Tables ...... ix

List of Figures ...... x

Chapter 1: Introduction ...... 1

Chapter 2: Methods ...... 10

Chapter 3: Results…...... 32

Chapter 4: Conclutions and Future work ...... 77

References ...... 80

viii

List of Tables

Table 1. H2O budget for Eshel and Heavens and Ben-Sasson models ...... 8

Table 2. Sensors on micro-meteorological towers at the IUI site ...... 14

Table 3. Despiking values and standard deviation...... 18

Table 4. Average H2O budget ...... 63

Table 5. Comparing H2O budget ...... 67

Table 6. Average CO2 budget ...... 76

List of Figures

Figure 1. Location of the Gulf of Aqaba...... 2

Figure 2. Formation of sea breezes ...... 5

Figure 3. Daily mean sea and air temperature in the Gulf of Aqaba from 2006-2009 ..... 6

Figure 4. Schematic map of the measurement setup...... 11

Figure 5. Eddy flux towers located at the IUI ...... 13

Figure 6. Control volume diagram ...... 16

Figure 7. Mean daily dynamics of temperature measurements during the spring ...... 34

Figure 8. Mean daily dynamics of temperature measurements during the summer ...... 35

Figure 9. Mean daily dynamics of temperature measurements during the storm ...... 36

Figure 10. Mean daily dynamics of temperature measurements ...... 37

Figure 11. Potential temperature during the summer ...... 40

Figure 12. Potential temperature during the storm ...... 41

Figure 13. Potential temperature during the spring ...... 42

Figure 14. Wind rose diagram for the spring ...... 44

Figure 15. Wind rose diagram for the summer ...... 45

Figure 16. Wind rose diagram for the storm ...... 46

Figure 17. Mean daily dynamics of advective wind measurements ...... 48

Figure 18. Mean daily dynamics of change of storage ...... 51

Figure 19. Mean daily dynamics of water vapor during the storm ...... 52

Figure 20. Mean daily dynamics of water vapor during the summer ...... 53

Figure 21. Mean daily dynamics of water vapor during the spring ...... 54

Figure 22. Mean daily dynamics of advection ...... 55

Figure 23. Mean daily dynamics of horizontal flux divergence ...... 58

Figure 24. Mean daily dynamics of vertical moisture flux ...... 59

Figure 25. Mean daily dynamics of total evaporation rates ...... 61

Figure 26. Mean daily dynamics distribution of the water budget for spring ...... 62

Figure 27. Mean daily dynamics distribution of the water budget for summer ...... 63

Figure 28. Mean daily dynamics distribution of the water budget for storm ...... 64

Figure 29. Total mean daily dynamics of evaporation rates ...... 66

Figure 30. Mean daily dynamics of carbon change of storage ...... 67

Figure 31. Mean daily dynamics of carbon advection ...... 68

Figure 32. Mean daily dynamics of carbon flux divergence ...... 72

Figure 33. Mean daily dynamics of carbon vertical flux gradient ...... 73

Figure 34. Mean daily dynamics of carbon budget...... 75

CHAPTER 1

INTRODUCTION

There is a growing concern about climate change and the impact it may have on ecosystems and the people which depend on them. Climate change has also been increasingly linked to degradation of coral reefs with the number and scale of reported coral bleaching episodes trending upward with global temperature, most notably over the past 30 years (MacKellar et al. 2010).

The Gulf of Aqaba (Eilat) is an inlet of the Red Sea and is surrounded by warm and arid desert. It is 1820 m deep, 180 km long and 6-25 km wide, and nearly rectangular. The Red Sea is a seawater inlet of the Indian Ocean, lying between Africa and Asia (Fig.1).

Israel

Jordan Egypt Eilat

Golf of Aqaba

Saudi Arabia 115 mi Red Sea

Figure 1: Map of the location of the Gulf of Aqaba, the City of Eilat, where the meteorological experiment was conducted during the year 2009. (Image from Google

Earth).

The reef systems along the Red Sea are highly developed because of its depths and an efficient water circulation pattern. The Red Sea exchanges water with the Arabian

Sea, Indian Ocean via the Gulf of Aden. These physical factors reduce the effect of high salinity caused by evaporation in the north and relatively hot water in the south.

Nevertheless, very high surface temperatures coupled with high salinities makes this one of the hottest and saltiest bodies of seawater in the world. The average surface water temperature of the Red Sea during the summer is about 26 °C in the north and 30 °C in the south, with only about 2 °C variation during the winter months. The overall average water temperature is 22 °C. The rainfall over the Red Sea and its coasts is extremely low, averaging 0.06 m per year.

Corals, the foundation of tropical marine ecosystems, exist in a symbiotic relationship with zooxanthellae (algae). This symbiosis is very sensitive to subtle changes in environment, such as increased ocean acidity, temperature, and light. When unduly stressed, the colorful algae are expelled from the corals, causing the corals to “bleach” and potentially die (Oppen et al. 2009). Because of the sensitivity of corals to environmental factors, bleaching events are seen as indicators of the changing state of the world’s oceans as climate warms (Heron et al. 2010). Warm sea-surface-temperature

(SST) anomalies during summer have long been linked to coral bleaching events.

Recently, the influence of light has also been implicated in bleaching (Oppen et al. 2009).

In the Gulf of Aqaba, corals are threatened by algae growth in the open water. Algal blooms diminish light and oxygen in the water, leading to coral death (Genin et al 1995).

The low levels of vertical mixing in the narrow Gulf of Aqaba keep nutrient levels low and prevent algal blooms. Changes in turbulence mixing of the water column could be driven by changes to the wind direction and intensity, or to the rates of evaporation.

A sea-breeze is a wind from the sea that develops over land near coasts. It is formed by increasing temperature differences between the land and water, which create a pressure minimum over the land due to its relative warmth and forces higher pressure, cooler air from the sea to move inland. Generally, air temperature gets cooler relative to nearby locations as one moves closer to a large body of water (Fig. 2A) (Stull , 1988).The sea has a greater specific heat capacity than land and therefore is more able to absorb heat than the land during the day, so the surface of the sea warms up slower than the land surface. At night, the land cools off quicker than the ocean due to the differences in their specific heat values, which stops the daytime sea breeze. If the land cools below that of the adjacent sea-surface temperature, the pressure over the water will be lower than that of the land, setting up a land breeze as long as the environmental surface wind pattern is not strong enough to oppose it (Fig. 2B).

Figure 2: The formation of sea breezes. Diagram A: Sea breeze, cooler air from the sea moves inland (typically during the day). Diagram B: Land breeze, cooler air from the land move to the sea (typically during the night), (Image from Wikimedia commons).

40 air water

35 C]

o 30

20 Temperature[

9/22/06 1/22/07 3/22/07 5/22/07 7/22/07 9/22/07 1/22/08 3/22/08 5/22/08 7/22/08 9/22/08 1/22/09 3/22/09 5/22/09 7/22/09 9/22/09

11/22/06 11/22/07 11/22/08 11/22/09

Figure 3: Daily mean sea and air temperature at the IUI meteorological station in the

Gulf of Aqaba throughout 3 years from 2006-2009. In the summer the sea mean surface temperature is colder than the warm dry desert air and in the winter the air temperature is colder than the sea mean surface temperature allowing high evaporation rates.

Vertical mixing provides an efficient mechanism to redistribute surface heat through the water column, potentially mitigating thermal stress (Skirving et al. 2006). In the west coast of the Gulf of Aqaba, evaporation controls vertical mixing. Evaporation increases the salinity near the surface, leading to denser layer of water that sinks.

Evaporation is most effective when the water surface is warmer than the air. This occurs at the Gulf of Aqaba only during the winter (Fig 3). Studies showed that an anomalous cold air-temperature during the winter in the Gulf of Aqaba in 1992 (following Mt. Pina

Tubo eruption in 1991) led to an unusually deep vertical mixing, resulting in increased supply of nutrients to surface waters, which fuelled extraordinarily large algal and phytoplankton blooms and extensive coral mortality with further detrimental effects to marine life in the Gulf (Genin et al. 1995). Low evaporation in the summer is caused by a stable boundary layer above the relatively cold water. Advection of vapor to the land may offset this effect. Advection is defined as a movement along a concentration gradient, and therefore, cannot be quantified from point measurements in meteorological stations (at least two points along the gradient are needed). One of the critically unknown questions is the affect of the advection by horizontal winds on the evaporation rates from the water of the Gulf of Aqaba. Several studies have provided models of the evaporation in the northern Red Sea during the day and the night (Eshel and Heavens, 2007; Ben-Sasson et al. 2007). They tried to predict the difference of the evaporation rates during the summer and winter and used a separate model for each one. Ben-Sasson used the point measurements of the average humidity in his model. According to his model the

7 evaporation rates in the Red Sea as shown in Table 1 are 3-4 m/year in the winter, 1 m/year in the summer and an annual of 1.6-1.8 m/year (Ben-Sasson et al. 2007). Eshel and Heavens used an atmospheric model to estimate the evaporation rate in the Red Sea.

The evaporation rates according to their model as shown in Table 1 are 2.9 m/year for the winter, 1.3 m/year in the summer and an annual of 2 m/year (Eshel and Heavens, 2007).

Table 1: The volume-averaged H2O budget for the winter, summer and the total in Eshel and Heavens and Ben-Sasson models.

Period Winter Summer Total

Model

[m/year]

Ben-Sasson 3-4 1 1.6-1.8

Eshel and Heavens 2.9 1.3 2

Nakamura and Mahrt (2001) have claimed that the Monin-Obukhov similarity theory for fluxes of heat and momentum in the turbulent surface layer, which is typically used by surface energy and flux exchange models, is not valid for heterogeneous surface, such as the Gulf of Aqaba because it is very narrow and, thus, violates the assumption of horizontal homogeneity, needed for Monin-Obukhov similarity (Mahrt et al., 1998). Both of these studies are important to understand the relationship of the advective winds and evaporation rates. In this study, we investigate the fundamental methodology of how to

8 calculate evaporation eddy-flux using the water mass-budget method with observations over the Red Sea. We focus on the affects of advection on the water transport over the

Red Sea. The results of this study could be useful to evaluate the evaporation rate in the

Red Sea, and similar narrow bays and desert enclosed seas.

CHAPTER 2

METHODS

We conducted a field campaign in the Inter-University Institute for Marine Research

(IUI), in Eilat, Israel, at the north western shore of the Gulf of Aqaba, from March 21st

2009 to August 25th 2009 (Fig. 4). Our goal was to measure the evaporation rate of water and the rate of CO2 exchange over a coral reef lagoon. We constructed two eddy flux towers, one on a pier, 10 meters from the shore, the other 7 meters inland. The IUI already had one meteorological station on the pier, next to our tower (Fig. 5). The IUI meteorological station provides measurements of water level (tides), sea surface and air temperatures, humidity and wind speed and direction at 10 m above the mean sea level.

We measured net radiation 6 m above the sea level at the edge of the pier. Temperature and humidity were measured from a pier at 3 levels (4, 6 and10 m) above the mean sea level and at 2 levels (4 and 6 m) above ground. We used sonic anemometers at two levels

(4 and 6 m) for high frequency measurements of 3-D wind velocities and an open-path

Infra-Red Gas Analyzer (IRGA) for fast concentration fluctuations of CO2 and H2O. See

Table 2 for models and manufactures of all instruments. We combined the fast wind and concentration measurements, using the eddy covariance method to measure water vapor and CO2 flux (at 6 m) in both stations (Table 2).

Figure 4: Schematic map of the measurement setup. The shoreline (solid line) separates the two meteorological towers (green dots) next to the campus of the Inter-University

Institute for Marine Science (box) in Eilat. In order to calculate the advection of moisture from the sea to land, we decomposed the wind coordinate system to the direction corresponding with the mean orientation of the Gulf of Aqaba (17o N) (dashed blue lines). As a simplification, we assume that the humidity and wind are uniform along bands parallel to the beach and therefore the measurements at the actual station location

(green dots) is representative of a line at equal distance from the shoreline (dashed black line). The advective component of the distance between the stations is measured along the perpendicular direction of the advective coordinate system (red line).

In order to calculate the advective wind we decomposed the wind speed along the advective coordinate system, corresponding with the mean orientation of the Gulf of

Aqaba (17o N). We first rotated the wind measurements such that the mean vertical wind would be 0 to account for leveling errors in the construction of the tower and placement of the sensor. The wind coordinate system was further rotated such that the 30 minutes mean U is equal to the wind speed along the mean wind direction Wd, (in degrees from north) and therefore, V, the 30-minutes mean cross-wind component (perpendicular to

Wd) is 0. Then, U was decomposed along the advective coordinate system and the advective wind component (u), directed from the sea, perpendicular to the mean orientation of the shoreline is, where WD is the wind direction:

WD 17   (1) u  U sin     180 180 

Figure 5: Beach tower (front), Pier tower (background) and the Meteorological Station located at the Inter-University Institute for Marine Research, Eilat, Israel. On the Beach tower (6 m height): Open-path gas analyzer – CO2/H2O, 2 Ultrasonic anemometers, 2

Temp./Hum. Probes with aspirated radiation sheilds. On the Pier tower (6 m height): Net radiometer, Open-path gas analyzer – CO2/H2O, 2 Ultrasonic anemometers, 2

Temp./Hum. Probes. On the Meteorological station (Pier, 10 m height): Propeller anemometer, Temp./Hum. Probe, water level and temperature sensor (not showing).

Table 2: Sensors on micro-meteorological towers at the IUI site

Towers/Station Measured Height Measurement Sensor Model, variable (m) frequency Manufacturer

CO2, H2O, Air 6 10 Hz Infra-Red LI-7500, LI- pressure Open Path gas COR, Lincoln, Beach and Pier Analyzer NE

3-D Wind 4, 6 10 Hz 3D Ultra-sonic RM-Young velocity anemometer 81000, RM- (U,V,W) and Young, temperature Traverse City, MI Temperature 4, 6 1 min-1 Probe HMPC45, & Humidity VAISALA, Helsinki, Finland Temperature 10 1 min-1 Probe HMPC45, & Humidity VAISALA Helsinki, Finland Wind speed 10 10 Hz Propeller RM-Young IUI and direction Anemometer 7000, Meteorological RM-Young, Station Traverse City, MI Tide height 0.79m 1 min-1 Water pressure CS408, below Campbell sea Scientific, level Logan, UT Net radiation 6 10 Hz Global CM11B, Kipp radiation & Zonen, Delft, Holland Water 0.79m 1 min-1 Mercury- 108, temperature below thermometer Campbell sea Scientific, level Logan, UT Water 0.79m 1 min-1 Water CS408, pressure below pressure, Campbell fluctuation sea Campbell Scientific, level Scientific Logan, UT

The data was collected on a Data Logger (Campbell Scientific, CR-3000, Logan

Utah) and stored on memory cards in the logger. Data was downloaded from the cards by

IUI students every 3 days and uploaded through scp to a storage server in OSU. Then it was divided to daily files, and processed to half-hour block averages of the measurements, and processed to calculate fluxes and other statistics, as described below.

The control volume diagram (Fig. 6) is describing the directions of the terms in the volume-averaged water mass budget equation (Eq. 11). It is a rectangular, where x is the distance along the advective direction (17m) and z is the height above ground or sea level (about 10m). This is not a 3-D diagram because we assume homogeneity in the direction parallel to shore (y-direction).

Season periods

We started the experiment from March 21st 2009 to August 25th 2009. The spring period started from March 21st and ended on May 31st, the next day, June 1st, is the first day that the minimum air temperature excided the sea surface temperature, so June 1st is the first day of the summer that continued through the end of our research, August 25th. The storm period is the days that we had an extremely strong wind 11 meter/sec or high temperature, above 41 °C.

Beach Pier wq wq 10m q p3 uq q u uq x z3

q p2 6m 6m qb2

q z2

t q 4m qb1 p1 4m

z1 S X a Ground Sea level level X coast

Figure 6: The control volume diagram is describing the directions of the terms in the

volume-averaged water mass budget equation (Eq. 11). Where a subscript marks the level

and station from which the measurement is taken, such that: The bottom tower(4m) – 1.;

top tower(6m) –2; IUI Meteorological Station tower(10m) – 3.; Pier– p; Beach– b. The

distance between the pier and the beach tower along the advective direction X a =17m.

q The distance between the shore line to the pier is X =10m. q- water vapor; - coast t

q Change of storage; u - Advection; uq - Horizontal flux divergence; wq -Vertical x

flux; S- Evaporation.

Data despiking and quality control

Despiking eliminates spikes in the data and smoothes a signal in the time series. Data despiking prior to processing can result in significantly more reliable estimates of variance and covariance of high frequency measurements. Spikes can occur because of electrostatic noise in the sensor and cables leading to the datalogger, or through physical obstructions to the sensors (e.g., insect crossing the sonic path). Despiking involves two steps: 1. detecting the spike and 2. replacing the spike. The two steps are independent so they are considered separately. Since we can be generally confident of the signal except during the spike itself, we can replace only the bad data with some interpolation of the good data. This does not affect the signal outside the spike. The despike program removes data that meets the criteria of having more than a specified amplitude range

(spike amplitude) in a given depth interval (spike width).

An example for despiking the data from the Sonic Anemometer, this program check that the data is within reasonable bounds and removes spikes of the wind speed that are greater/lower than the maximum/minimum value (in this case ±25 m/s), next the program checks that the data within a prescribed timeframe (200 seconds in this study) does not exceed a threshold of a given number (6 in this example) of standard deviations away from the mean for that timeframe. When we have a spike we will replace it with

NaN (not a number) this process eliminates bad data. In Table 3 we show the parameters of the despike algorithm in our program.

Table 3: Despiking values and standard deviation

Value Minimum Maximum Standard

Term deviations

U -25 m/s 25 m/s 6

V -25 m/s 25 m/s 6

W -10 m/s 10 m/s 6

T 0 [deg C] 55 [deg C] 6

Temperature Humidity and net radiation:

Sonic anemometer-driven measurements of the speed of sound (SOS) where converted to temperature in Kelvin using the equation:

SOS 2 T   273.16 . (2) 402.789

The Schotanus correction (Liu et al., 2001; Schotanus et al., 1983) was applied to the temperature to correct for cross-wind bias:

0.75U 2  0.75V 2  0.5W 2 Ts  T  . (3) 402.789

Where U, V and W are the wind velocity components (m/s) in the westward, southward and upward directions (respectively). This equation accounts for the dependency of the measurements of the sensor geometry and wind velocity. We applied the correction after de-spiking on the data. For the calculation of heat fluxes, temperature observations from the sonic anemometers were converted to "real" temperature, Tr, in degrees Celsius, to account for the effects of water vapor and air density changes using the method provided by Kaimal and Gaynor (1991):

Tk Tr   273.16 . (4) 1 (0.32  E / P)

Where Tk is temperature from the Sonic anemometer in Kelvin, both E the vapor pressure and P the pressure are in Pascals.

Water vapor and CO2 concentrations from the IRGA measurements were adjusted using the Webb, Pearman and Leuning (WPL) correction (Leuning, 2004; Webb et al.,

1980) in a modified form derived by Detto and Katul (2008) as a correction for the 10 Hz time series of the scalar (CO2 or water vapor), rather than a correction for the half-hour mean.The WPL correction for water vapor is calculated using the equation:

 w  w qWPL  q  (q  q)    ( )  q  (1    ( ))  (T  T ) / T (5)  da  da

kg Where qWPL -the WPL corrected water vapor perturbations in [ ], q - Water vapor [ m3 kg ], q is the 30-minute block average of q in ,  - [unitless] it is the molar mass of m3

kg kg dry air[ ] divided by the molar mass of water vapor [ ],  - the water vapor mol mol w

density [ ], da -the dry air density [ ], T is the mean of T the air temperature in

Kelvin.

The WPL correction for CO2 is:

 c  w cWPL  c  (q  q)    ( )   c  (1    ( ))  (T  T ) / T (6)  da  da

Where cWPL -the WPL corrected CO2 flux perturbations in [ ], c is the observed

instantaneous CO density [ , c is the mean of c [ ,  is the mean density of 2 ] ] c

the CO2 fraction of the air, as an ideal gas [ ].

Eddy flux analysis

The eddy flux method, is the most widely used and direct method presently available for quantifying exchanges of carbon dioxide, water vapor, methane, various other gases, and energy between the surface of earth and the atmosphere. Flux describes how much of an entity of interest moves through a unit area per unit time. For example, gas flux is often presented in mg per square meter per second. Measurements of ecosystem fluxes are important for constructing energy, water and carbon budgets, understanding key factors governing ecosystem functioning, and modeling the processes of water, and gas exchange. The eddy flux method, also known as eddy covariance technique or eddy correlation, is one of the most direct, defensible ways to measure and calculate ecosystem fluxes within the atmospheric boundary layer (Burba et al., 2010). Eddy Flux provides a way to measure surface to atmosphere fluxes, gas exchange budgets, and emissions from a variety of ecosystems, including agricultural and urban plots, landfills, and various water surfaces. Emissions and fluxes can be measured by instrumentation on either a stationary or mobile tower, floating vessel (such as a ship or buoy), or aircraft (Foken,

2008).

The eddy covariance method relies on the combined high-speed measurements of gas concentrations, temperature, and wind speed, followed by data analysis. In physical terms, "eddy flux" is computed by measuring how many molecules, moles, or milligrams of a gas went up with upward wind movement at one moment and how many went down with downward wind movement in the next moment. In mathematical terms, "eddy flux" is computed as a covariance between the instantaneous deviation in vertical wind speed

(w’) from the mean value ( w ) and the instantaneous deviation in gas mixing ratio (s’), from its mean value ( s ), multiplied by mean air density (ρa ) (Baldocchi, 2010).

In turbulent flow, vertical flux can be presented as:

 c F   a ws ( s  is a mixing ratio of substance c in the air).  a

Reynolds decomposition is used then to break into means and deviations:

F  (a  a ')(w  w')(s  s')

After opening parenthesis and canceling terms that go to zero, as averages of fluctuations

equal zero by definition ( a ws', a sw',swa ' ) and w's'  a ' , triple moments are negligible :

F  a w's'  a ws  ws'a '  sw'a '

Assessing Vertical Drift Velocity, w Flux density of dry air is zero and is equal to

 w'  ' w  w'  '  w  0 , and therefore w  a After this substitution the terms a a a  a .

th a ws,sw'a ' cancels each other, the term ws'a ' , becomes the 4 order of moment and is assumed negligible. Then we get the formal definition of eddy flux, calculated as a covariance:

F  a w's' (7)

Governing Equations

The volume-averaged H2O budget equation can be expressed via the mass balance equation in Cartesian coordinates by using the continuity equation of conservation of water vapor (q):

dq q (qu) (qv) (qw)      0 . (8) dt t x y z

After applying the product rule we get:

q q q q u v w  u  v  w  q  q  q  0 . (9) t x y z x y z

Using Reynolds decomposition rules:

q q q q uq vq wq  u  v  w     0 . (10) t x y z x y z

After integrating we get the governing equation (Sun et al. 2007):

q q q q uq vq wq  u  v  w    dxdydz  S x, y,z   (11)  t x y z x y z 

Where: x- Distance along the advective direction (m) y- Distance parallel to the shore (m) z- Height above ground or sea level(m) t-time (sec)

q - Change of storage t

q u -Advection x

uq vq - Horizontal flux divergence in the advective and parallel directions x , x

wq -Vertical flux gradient z

S- Evaporation rate, it is unknown (objective of measurements)

Assumptions:

a. dy =1. We assume homogeneity in the direction parallel to shore. q b. v =0 because it is parallel to the beach so the humidity is constant. y vq c. =0 because it is parallel to the beach so the humidity is constant. y q d. w =0 because w =0. z

After applying these assumptions the governing equation becomes:

q q uq wq   u   dxdz  S x,z t x x z (12)  

We divide the evaluation of the integral to different numerical cases, which differ by the geometry of the interpolation between measurement points and the sea surface.

These two equations (Eqs. 12-13) are used when the height of the beach tower is higher or equal to the pier. We want to adjust the humidity measurement. In order to do that we are using a numerical approximation from the measurements in the stations to calculate the humidity in the pier tower. In a case that beach tower is lower than the pier tower we use the humidity from the pier tower without any adjustments, because we do not have any measurements below the pier tower:

 Hb1  H p1 :      H  H   H  H    b1 p1 b1 p1  q p1I ' q p1  1   q p2     q   H  H H  H  (13) p1I    p2 p1   p2 p1   H  H :   b1 p1    q p1I ' q p1 

 H b2  H p2 :      H b2  H p2   H b2  H p2   q ' q  1    q     p2I p2  H  H  p3  H  H     p3 p2   p3 p2   (14) q p2I  H  H :   b2 p2    H b1  H p1   H b1  H p1   q ' q  1    q     p2I p1  H  H  p2  H  H     p2 p1   p2 p1  

Where a subscript marks the level and station from which the measurement is taken, such that: The Sea Surface – 0; Bottom tower – 1; Top tower – 2; IUI Meteorological Station tower – 3; Pier– p; Beach– b. Hb1 – is the height of the bottom beach tower= 4.2 [m]; Hb2

– is the height of the top beach tower =6.4 [m]; Hw – tide level from IUI meteorological station data [m]; Hb3– is the height of the IUI Meteorological Station tower from the sea surface= 10 – Hw [m]; Hp1 – is the height of the bottom pier tower from the sea surface

=4.01 – Hw [m]; Hp2 – is the height of the top pier tower from the sea surface =6.32 – Hw

[m]; Hp3 – is the height of the IUI Meteorological Station tower from the sea surface =10

– Hw [m]; the averaging timestep, t 1800 [s]; the distance between the pier and the beach tower along the advective direction Xa=17[m].

The following equations are used when the height of the beach tower is lower than the pier tower. We adjust the mean wind speed from the measurement. To do that we are using a numerical approximation from the measurements in the stations to calculate the wind speed in the pier tower.

 H  ln b1   z  (15) u   0  u H  H p1I  H  p1 b1 p1 ln p1   z   0 

Where z0 - Roughness length for sea in meters:

2 u* z0  0.015 (16) g

H 2H 3H 4H and H  b1 ; H  b1 ; H  b1 ; H  b1 . Here we break the height of the b01 5 b02 5 b03 5 b04 5 beach tower to 5 heights in order to make a better evaluation of the wind speed:

 Hb01  ln   z0  u  u H  H b01   b1 b01 b1 (17) Hb1 ln   z0 

 Hb02  ln   z0  u  u H  H b02   b1 b02 b1 (18) Hb1 ln   z0 

 Hb03  ln   z0  u  u H  H b03   b1 b03 b1 (19) Hb1 ln   z0   H  ln b04   z  u   0  u H  H b04  H  b1 b04 b1 (20) ln b1   z   0  27

The change of storage term from the governing equation can be expressed by the following equation:

(21)

(t1) (t) (t1) (t) qb1  qb1  q p1I  q p1I  2H b1        q  (t1) (t) (t1) (t) (t1) (t) (t1) (t)   qb1  qb1  qb2  qb2  q p1I  q p1I  q p2I  q p2I  H b2  H b1   x,z t       (t1) (t) (t1) (t) (t1) (t) (t1) (t) qb2  qb2  qb3  qb3  q p2I  q p2I  q p3I  q p3I  H b3  H b2   X  a 6t

The advection term from the governing equation can be expressed by the following equation:

(22)

    ub01  u p01I / 2 q p01I  qb01  H b01  Z 0          ub02  u p02I / 2 q p02I  qb02     H b02  H b01   ub01  u p01I / 2 q p01I  qb01          ub03  u p03I / 2 q p03I  qb03     H  H    b03 b02  ub02  u p02I / 2 q p02I  qb02         ub04  u p04I / 2 q p04I  qb04    H  H     b04 b03  ub03  u p03I / 2 q p03I  qb03   q   u    / 6 x,z x   ub05  u p05I / 2 q p05I  qb05      H  H    u  u / 2 q  q  b05 b04   b04 p04I p04I b04       ub1  u p1I / 2 q p1I  qb1       H b1  H b05    u  u / 2  q  q  b05 p05I   p05I b05       ub2  u p2I / 2 q p2I  qb2      H b2  H b1    ub1  u p1I / 2 q p1I  qb1          ub3  u p3 / 2 q p3  qb3     H  H    u  u / 2  q  q b3 b2  b2 p2I   p2I b2  

The horizontal flux divergence term from the governing equation can be expressed by the following equation:

uq (23)  (uqp  uqb ) H p2  x,z x

The vertical flux term from the governing equation can be expressed by the following equation:

wq wqb  wqb  (24)    X a x,z z 2  

In a similar way to the volume-averaged H2O budget equation we can develop the volume-averaged CO2 budget equation in Cartesian coordinates as:

c c uc wc (25)  u   dxdz  S x,z    t x x z 

The change of storage term from the governing equation can be expressed by the following equation:

c (t1) (t) (t1) (t) X a (26)  cb2  cb2  cp2  cp2  Hb2  x,z t 2t

The advection term from the governing equation can be expressed by the following equation:

c (27) u  ub2  u p2I / 2cp2  cb2 Hb2 / 2 x,z x

The horizontal flux divergence term from the governing equation can be expressed by the following equation:

uc (28)  (ucp ucb ) H p2  x,z x

The vertical flux term from the governing equation can be expressed by the following equation:

wc wcb  wcb  (29)     X a x,z z 2  

CHAPTER 3

RESULTS

We have developed a Matlab program to collect the fast and slow data from all the 3 stations and convert, calculate and average all the variables from the sensors. The next step was to use another Matlab program with the equations and information as described in the methods chapter. The last step was to plot all the mean variables for the spring, summer, storm and the total period using another Matlab program. The spring period had

60 days, the summer had 93 days and the "storm" period included 32 not necessarily sequential days when there were southern storms. The total measurement period was 153 days.

Temperature

The mean daily dynamics of the observed temperatures are shown in Figures 7, 8 and 9 for the spring, summer and the storm periods. It is clear from the measurements that the air temperature is lower than the Sea Surface Temperature (SST) only during the night in the spring, and never during the summer or the storm periods. These results are entirely consistent with those reported in the chilling effects of desert enclosed seas in previous studies (Genin et al. 1995). Storm periods were the warmest, then summer then spring

(Figure 10). We observe a similar pattern when we compare the sea surface temperature

(SST) during the same periods as described in Figure 10. Sea surface temperature varies slower than the air temperature. These results are common, particularly in a sea surrounded by desert, where air temperatures are high during the day and low during the night. Nonetheless, the hot air temperatures during the day do not cool enough during the night during the summer to drop below the sea surface temperature.

34 SST 32 T 4m T 6m 30 T 10m 28 Spring

Temprature[Deg C] Temprature[Deg 24

20 0 5 10 15 20 Hour of Day

Figure 7: Mean daily dynamics pattern of temperature measurements in 10m, 6m, 4m and the Sea Surface in the spring. The air temperature at dawn is lower than the sea surface temperature, but warmer during the rest of the day. An inversion (a layer with lower temperatures at lower elevations) develops during the day.

40 SST Summer T 4m 35 T 6m T 10m

Temprature[Deg C] Temprature[Deg 25

20 0 5 10 15 20 Hour of Day

Figure 8: Mean daily dynamics patterns of temperature measurements in 10m, 6m, 4m and the Sea Surface Temperature during the summer. The air temperature is always higher than the sea surface temperature. A thin inversion (higher than 10 m) develops during the day, but during the night a thin stable (inverted) layer develops between the sea surface and below 10 m, while a convective layer (lower temperature with height) exist above 6 m.

40 SST T 4m T 6m 35 T 10m

30 Temprature[Deg C] Temprature[Deg 25 Storm

20 0 5 10 15 20 Hour of Day

Figure 9: Mean daily dynamics patterns of temperature measurements in 10m, 6m, 4m and the Sea Surface Temperature during the storm. The air temperature is always higher than the sea surface temperature. A thin inversion (higher than 10 m) develops during the day, but during the night a thin stable (inverted) layer develops between the sea surface and below 10 m, while a convective layer (lower temperature with height) exist above 6 m.

]

Degree C Degree

[

Degree C Degree

[

Temperature Temperature

] 20

15 0 5 10 15 20 Hour of Day

Degree C Degree Spring [ Summer

Storm

Temperature Temperature 35

10 0 5 10 15 20 Hour of Day

Figure 10: Mean daily dynamics of temperature during the spring the summer and the storm periods. The temperature in the SST (Lower Figure) and in 10 meters (Upper

Figure) in the storm is always higher than the summer. The temperature in the SST and in

10 meters during the spring is always lower than the storm and the summer. Dashed lines show the spread (+- standard deviation) of the data around the mean.

The cooler sea surface temperatures (except during spring nights) lead to the development of an inversion layer above the water that suppresses evaporation. There are three cases of stability in the boundary layer (Stull 1988):

(1) Stable (sub-adiabatic): potential temperature increases with height

(2) Neutral (adiabatic): potential temperature keeps constant with height

(3) Unstable (super-adiabatic, convective): potential temperature decreases with height

Potential temperature, θ [K], is defined as:

R  P  C p (30)   T 0   P 

Where P0 is standard sea-level pressure, P is the atmospheric pressure at the measurement height, R is the specific gas constant for air, and Cp is the heat capacity of air.

We calculated the potential temperatures from the temperature and pressure measurements in our experiment. Results are shown in Figures 11-13. Potential temperatures are increasing with height during most of the measurement period, which indicate the development of a stable boundary layer (Fig 11-13). Only at night and until dawn during spring neutral or slightly convective boundary layers develop above the sea

(Fig. 13).

During the day, particularly at the summer (including storm periods), a deep stable boundary layer develops, thicker than our highest tower measurement at 10 m above sea level. This is apparent by the monotonous increase of potential temperature with height from the sea level to 10 m. However, during the night, a mixed boundary layer is observed, with a thin (less than 10 m) stable layer and a slightly convective

(negative relationship between height and potential temperature) above 6 m, indicated by the potential temperature profile that shows a maximum at 6 m.

Summer, Dawn

Summer, Noon

Height above sea level[m] sea Heightabove

Summer, Dusk

Potential Temperature (Degree C)

Figure 11: Potential temperature above the sea level at dawn, noon and dusk during the summer. The potential temperature is rising with the height during the dawn, noon and dusk in the summer. The structure is describing a Stable boundary layer in the Red Sea during the summer.

Storm, Dawn

Storm, Noon Height above sea level[m] sea Heightabove

Storm, Dusk

Potential Temperature (Degree C)

Figure 12: Potential temperature above the sea level at dawn, noon and dusk during the storm. The potential temperature is rising with the height during the dawn, noon and dusk in the storm. The structure is describing a Stable boundary layer in the Red Sea during the storm.

Spring, Dawn

Spring, Noon

Height above sea level[m] sea Heightabove Spring, Dusk

Potential Temperature (Degree C)

Figure 13: Potential temperature above the sea level at dawn, noon and dusk during the spring. The potential temperature is almost constant with the height during the dawn and it is rising with the height during the noon and dusk in the spring. This indicates that we have a neutral boundary layer during the dawn and a stable boundary layer during the noon and dusk in the spring.

Winds and advective wind

When computing the wind speed and the wind direction for the spring, summer and the storm periods as described in Figures 14-16, we notice that during the day time we have stronger winds than during the night for all the periods as expected due to the wind breeze at that time. Also the wind direction during the day for all the periods is North-

East, this means that the wind breeze has a big affect on the wind direction. When we compare the wind direction during the night as described in Figures 14-16, we notice a difference between the spring, summer and the storm periods. During the spring the wind direction is mostly to the West tending a little to the North (Fig. 14), while during the summer the wind direction is also mostly to the West but here the wind is tending more to the South(Fig. 15), this can be explained by the fact that during the summer the temperatures are higher and the difference between the day and night are higher, so that is why the land breeze is stronger at night and affect the wind direction to be a little more to the South during the summer. The affect of the land breeze is becoming even stronger due to higher temperatures during the storm and that is why the wind direction during the night is mostly to the South (Fig. 16). The wind speed during the storm periods are the strongest during the day and the night compared to the spring and summer periods. This is due to higher temperature the increases the affect of the sea and land breezes.

Figure 14: Wind rose diagram describes the wind speed in meter/sec and the wind directions during the day and the night in the spring. The winds during the day are usually stronger than during the night. The wind direction during the day is mostly North-

East, while during the night it is mostly to the West.

Figure 15: Wind rose diagram describes the wind speed in meter/sec and the wind directions during the day and the night in the summer. The winds during the day are usually stronger than during the night. The wind direction during the day is mostly North-

East, while during the night it is mostly to the West.

Figure 16: Wind rose diagram describes the wind speed in meter/sec and the wind directions during the day and the night in the storm. The winds during the day are usually stronger than during the night. The wind direction during the day is mostly North-East, while during the night it is mostly to the South.

The advective wind during the day hours through all the periods is relatively high and positive (Fig. 17), transporting moisture from the sea toward the land. The advective wind is negative and low during the evening/night hours through all the periods. Despite the fact that the wind blows north-northeast almost all the time, observing only the advective component of the wind does show the effects of sea and land breezes. During storms the advective wind is usually stronger than during the summer or the spring. There is a small difference between the night time advective wind during the summer and the spring, with the advective wind in the summer usually slightly stronger than the spring.

-2 u[m/sec]

Spring -4 Summer

Storm -6

0 5 10 15 20 Hour of Day

Figure 17: Mean daily dynamics of the advective wind (u) in the spring the summer and the storm periods. The advective wind during the storm is usually stronger than during the summer or the spring. During the day hours u is positive and relatively high but during the evening/night u is negative and relatively low during all the 3 periods. Dashed lines show the spread (+- standard deviation) of the data around the mean.

Mass Budget of Water – Calculating Total Evaporation

We used the volume-averaged water mass budget equation (Eq. 12) to quantify the evaporation rates from the Red Sea. The water budget equation contains 4 terms that add up to total evaporation: change of storage; advection; horizontal flux divergence; and vertical flux (Eqs. 21-24, respectively).

Change of storage

Change of storage is very low during all the 3 periods (Fig. 18). The change of storage is negative during the day, indicating humidity is decreasing within our control volume and positive during the evening/night in all the 3 periods. As expected by the fact that our control volume has finite (and rather small) size, the mean 24-hours integral of the change of storage during the day is close to 0 and does not have a significant effect on the total evaporation rate. However, this indicates that our sensors performed well and that slow drift in the calibration of the sensors did not occur and did not bias our measurements.

On-shore advection of moisture

The on-shore advection result is a combination of the daily dynamics of the advective wind, driven by sea and land breezes, and the daily patterns of humidity gradient between the air above sea and the air above land (Figs. 19-20). The advection is positive and high during the daytime and negative and low during the evening/night hours for all the 3

49 periods (Fig. 22). Advection during the storm is the highest and during the spring is the lowest, due to the advective wind in those periods. The overall sum of the advection during the day is not very small and does have an effect on the evaporation rate in the

Red Sea.

0.5

0.4

0.3

0.2

0.1

-0.1

-0.2

-0.3 Spring

Change of Storage[m/year] of Change -0.4 Summer -0.5 Storm 0 5 10 15 20 Hour of Day

Figure 18: Mean daily dynamics of the change of storage in meter/year for the spring, the summer and the storm in the Red Sea. The change of storage is very low during all the 3 periods. It is negative during the day hours and positive during the evening/night in all the 3 periods. Dashed lines show the spread (+- standard deviation) of the data around the mean.

0.022 ]

3 Pier 0.02 Beach 0.018

0.016

0.014

0.012

0.01

0.008

0.006 Storm 0.004

Water vapor concentration[kg/m vapor Water 0.002

0 5 10 15 20 Hour of Day

Figure 19: Describes the mean daily dynamics of water vapor concentration in kg/m3 for the storm in the Red Sea. The water vapor concentration in the pier is always higher than the beach. The concentration is increasing in the evening and decreasing a few hours after dawn. Dashed lines show the spread (+- standard deviation) of the data around the mean.

0.022 ] 3 Pier 0.02 Summer 0.018 Beach

0.016

0.014

0.012

0.01

0.008

0.006

0.004

Water vapor concentration[kg/m vapor Water 0.002

0 5 10 15 20 Hour of Day

Figure 20: Describes the mean daily dynamics of water vapor concentration in kg/m3 for the summer in the Red Sea. The water vapor concentration in the pier is always higher than the beach. The concentration is increasing in the evening and decreasing a few hours after dawn. Dashed lines show the spread (+- standard deviation) of the data around the mean.

-3 x 10

] 14 3 Pier

12 Beach

4 Spring

Water vapor concentration[kg/m vapor Water 2

0 5 10 15 20 Hour of Day

Figure 21: Describes the mean daily dynamics of water vapor concentration in kg/m3 for the spring in the Red Sea. The water vapor concentration in the pier is always higher than the beach. The concentration is increasing in the evening and decreasing a few hours after dawn. Dashed lines show the spread (+- standard deviation) of the data around the mean.

Advection[m/year] Spring -2 Summer -4 Storm -6

0 5 10 15 20 Hour of Day

Figure 22: Describes the mean daily dynamics of the advection in meter/year for the spring, the summer and the storm in the Red Sea. Advection is positive and high during the day hours and negative and low during the evening/night hours for all the 3 periods during the day. The advection during the storm is the highest and during the spring is the lowest. Dashed lines show the spread (+- standard deviation) of the data around the mean.

Horizontal flux divergence

The Horizontal flux divergence is very low during all the 3 periods. Furthermore, the mean 24-hour integral of the flux divergence is very close to 0 because the differences between the horizontal fluxes over the beach and water are negative during the daytime and positive during nighttime during all the 3 periods (Fig. 23).Horizontal flux divergence, therefore, does not have a significant effect on the total evaporation rate in the Red Sea.

Vertical flux

Vertical water vapor flux is consistently positive (Fig. 24), indicating that water vapor almost never condenses from the air into the sea. Note that despite the large difference between the colder sea surface temperature and the very warm air temperature, the sea surface is relatively warm (23-29 oC, Fig. 3) and the air is very dry (20-60% Hum). There was no precipitation during the last 5 years prior to the experiment. Similar to a diurnal cycle over land the vertical flux is higher during the daytime and lower during the evening/night. This was the case during all the 3 periods. Unlike typical terrestrial diurnal cycles, the peak of vertical water vapor flux rate over the Red Sea is not at noon-2 PM but late in the afternoon/early evening. At these times, cooler air temperature thins the stable layer over the water surface and promotes vertical mixing. Despite the strong daytime inversion of the boundary layer, water vapor is mixed vertically with the strong wind that generate sheer eddies, further enhanced by the extreme gradient of humidity in

56 the air between the desert air aloft and the roughness sub-layer. The roughness sub-layer is the lowest part of the boundary layer, where the surface roughness, (over the sea it is caused by waves) leads to sheer which generates turbulence. With strong winds most of the time and particularly during the daytime, the friction velocity is relatively high. This is rather typical given that the boundary layer is mostly stable. The vertical flux during the storm is the highest as the wind is strongest during storms. From dawn to noon during the spring the vertical flux is higher than at the same time during the summer, this happens because during the summer a thick stable layer exists all the time while in the spring, there is a slightly convective boundary layer during the night and the stable layer develop slowly during the daytime, when hot desert air is heated by the sun and advected over the colder sea.

-1

-2 Spring

Flux Divergence[m/year] Flux Summer -3 Storm -4 0 5 10 15 20 Hour of Day

Figure 23: Describes the mean daily dynamics of the horizontal flux divergence in meter/year for the spring, the summer and the storm in the Red Sea. The Horizontal flux divergence is very low during all the 3 periods and also it is negative during the day hours and positive during the evening/night in all the 3 periods. Dashed lines show the spread (+- standard deviation) of the data around the mean.

12 Spring 10 Summer

8 Storm

0 Vertical moisture flux[m/year] moisture Vertical

-2

0 5 10 15 20 Hour of Day

Figure 24: Describes the mean daily dynamics of the vertical moisture flux in meter/year for the spring, the summer and the storm in the Red Sea. The Vertical flux gradient is positive all the time but it is higher during the day hours and lower during the evening/night hours for all the 3 periods. The vertical flux during the storm is the highest and during the spring and the summer it is almost the same, part for a few hours from dawn to noon that the vertical flux during the spring is higher than the summer. Dashed lines show the spread (+- standard deviation) of the data around the mean.

The total evaporation rate (Fig. 25) is mostly affected by advection and the vertical flux for all the 3 periods (Fig. 26-28). Both terms have a strong diurnal cycle with a peak at the afternoon. Advection tends to be negative during the night while vertical flux is weak but positive, which leads to overall small but negative total evaporation during nighttime and strong positive daytime total evaporation. During the spring (Fig.

26), slightly stronger (compared to summer (Fig. 27) and storm (Fig. 28)) nighttime vertical flux, coupled to weak nighttime negative advection leads to positive total evaporation at all times of day, including the night.

In Table 4 we arrange all the terms in the governing equation for the volume- averaged H2O budget equation and took the average for each term in each period and the total of all of them. The storm has the highest evaporation rate 3.5 [m/year], but it is interesting that even though we have stronger winds and higher temperature during the summer compared to the spring, we still get a higher evaporation rate during the spring

3.1 [m/year] while in the summer we get only 2.8 [m/year], this is because of the bigger affect of the vertical flux on the evaporation rate compare to the 3 other terms. From

Table 4 it is clear that the advection cannot be neglected when calculating the evaporation rate. On the other hand we can neglect the change of storage and the horizontal flux divergence when calculating the evaporation rate in the Red Sea, because they are very small and nearly zero.

20 Spring Summer 15 Storm

0 Total Evaporation[m/year] Total

-5 0 5 10 15 20 Hour of Day

Figure 25: Describes the mean daily dynamics of total evaporation rates in meter/year for the spring, the summer and the storm in the Red Sea. The evaporation rate is positive and high during the day hours and low during the evening/night hours for all the 3 periods during the day. The evaporation during the storm is the highest and during the spring is the lowest during the day hours, but during the evening/night the evaporation during the spring is bigger than the storm and the summer. Dashed lines show the spread

(+- standard deviation) of the data around the mean.

9 ChangeofStorage 8 Spring FluxDivegence 7 Advection VerticalFlux 6 TotalEvaporation

3 [m/year] 2

-1

-2 0 5 10 15 20 Hour of Day

Figure 26: Describes the mean daily dynamics distribution of all the terms in the water budget in meter/year for the spring in the Red Sea. The vertical flux and the advection mostly affect the total evaporation rate causing the total evaporation rate to be positive and high during the day hours and low during the evening/night hours. The change of storage is very low. The flux divergence through the day combines to almost zero.

10 Summer ChangeofStorage FluxDivegence 8 Advection VerticalFlux TotalEvaporation 6

4 [m/year] 2

-2

0 5 10 15 20 Hour of Day

Figure 27: Describes the mean daily dynamics distribution of all the terms in the water budget in meter/year for the summer in the Red Sea. The vertical flux and the advection mostly affect the total evaporation rate causing the total evaporation rate to be positive and high during the day hours and low during the evening/night hours. . The changes of storage and flux divergence are very low.

14 ChangeofStorage FluxDivegence 12 Storm Advection VerticalFlux 10 TotalEvaporation

4 [m/year]

-2

0 5 10 15 20 Hour of Day

Figure 28: Describes the mean daily dynamics distribution of all the terms in the water budget in meter/year for the storm in the Red Sea. The vertical flux and the advection mostly affect the total evaporation rate causing the total evaporation rate to be positive and high during the day hours and low during the evening/night hours. The change of storage is very low. The flux divergence through the day combines to almost zero.

Table 4: The average of all the terms in the volume-averaged H2O budget equation for the spring, summer, storm and the total of all of them.

Period Spring Summer Storm Total

Term

[m/year]

Change of Storage - 4.2104 7.4105 -0.0025 4 10 5

Advection 0.84 0.89 0.98 0.85

Flux divergence -0.13 -0.0093 -0.086 -0.058

Vertical flux 2.4 1.9 2.6 2.1

Total evaporation 3.1 2.8 3.5 2.9

When we break the total evaporation to a daily average through the entire measurement period, we can notice that the total daily evaporation in all the 3 periods is almost always positive (Fig. 29), this is because of the strong evaporation during the day hours that makes the daily average evaporation positive. The high daily evaporation during the entire period in Figure 29 is describing the storm periods during our measurements, because during the storm we get stronger winds and higher temperatures.

When we compare the total daily evaporation between the spring (till day 151) and the summer (from day 152) in Figure 29, without the high evaporation rates during the storm days, we can notice that the total daily evaporation during the spring is usually higher than the summer, this is because of the bigger affect of the vertical flux on the evaporation rate, as we explained above.

Total Daily Evaporation[m/year] Daily Total 0

-1 100 120 140 160 180 200 220 240 Day of Year, 2009

Figure 29: Describes the total mean daily dynamics of evaporation rates in meter/year during our measurement period. The spring ends on day 151 and on day 151 the summer period starts. The high daily evaporation during the entire period is describing the storm periods.

When comparing the early studies that tried to evaluate the evaporation rate using some models as shown in Table 5. Ben-Sasson et al. (2007) presented in their model that the annual mean evaporation in the Red Sea is 1.6-1.8 m/year with a minimum of 1 m/year during the summer and a maximum of 3-4 m/year during the winter. Eshel and

Heavens (2007) showed in their model that evaporation rate for the summer is 1.3 m/year, 2.9 m/year during the winter and an annual mean of 2 m/year. In our research the total evaporation rates we observed were higher for the summer, 2.8 m/year and the observed spring rates, 3.1 m/year; the total evaporation is 2.9 m/year. We can explain some of this discrepancy due to the fact that our measurements were taken close to the shore and represent evaporation from the coral lagoon.

Table 5: The volume-averaged H2O budget for the winter, summer and the total for

Eshel and Heavens, Ben-Sasson and our study .

Period Spring Summer Total

Model

[m/year]

Ben-Sasson 3-4 (Winter) 1 1.6-1.8

Eshel and Heavens 2.9 (Winter) 1.3 2

Our study 3.1 2.8 2.9

CO2 budget

In a similar way to which we analyzed the evaporation rate above, we will describe the

CO2 budget in the Red Sea according to the CO2 mass budget equation (Eqs. 25-29).

Figure 30 describes the change of storage in kg/m2/year for the 3 periods during the day.

The change of storage is very low during all 3 periods and also the change of storage is negative from dawn to noon during all 3 periods. The change of storage is stronger during the spring, and the summer has the lowest change of storage. The overall sum of the change of storage during the day is very small and does not have an effect on the CO2 budget.

Because of malfunctions of one of the LI7500 sensors we are missing some measurements during the summer and the storm periods. In order to calculate the advection of carbon, two measurements are needed and therefore we could calculate the advection term during these periods. During the spring the advection is positive and high during the evening hours but it becomes negatively high during the dawn (Fig. 31).

Advection is negative most of the day, bringing CO2 from the terrestrial atmosphere toward the coral reef lagoon. The overall sum of the advection during the day is not small and does have a big effect on the CO2 budget in the Red Sea.

0.15 Spring 0.1 Summer

Storm

/year] 2 0.05

-0.05

-0.1 Change of Storage[kg/m of Change

0 5 10 15 20 Hour of Day

Figure 30: Describes the mean daily dynamics of the change of storage in kg/m2/year for the spring, the summer and the storm in the Red Sea. The change of storage is low during all the 3 periods. The change of storage is negative from dawn to noon during all 3 periods. The spring has the highest change of storage and the summer has the lowest change of storage.

Spring /year]

2 5

-5 Carbon Advection[kg/m Carbon -10

0 5 10 15 20 Hour of Day

Figure 31: Describes the mean daily dynamics of carbon advection in kg/m2/year for the spring in the Red Sea. The advection is positive and high during the evening hours for the spring but it becomes negatively high during the dawn. The advection is negative most of the day.

Horizontal carbon flux divergence is also dependent on simultaneous measurements from both sensors and is therefore missing for those periods. During the spring the carbon horizontal flux divergence is very low (Fig. 32). The overall sum of the horizontal carbon flux divergence during the day does not have a significant effect on the

CO2 budget in the Red Sea.

The vertical flux of carbon, as described in Figure 33, is negative most of the time for the spring and the storm periods, indicating carbon uptake by the ocean. It is higher during the morning hours and lower during the evening/night hours for all 3 periods. The vertical flux during the summer is small and positive during the day. The spring shows a slightly higher negative vertical flux than the storm periods. This is because during the spring we have lower temperatures and winds. The overall sum of the vertical flux during the day is not small and does have an effect on the CO2 budget in the Red Sea.

-5 x 10

Spring

/year] 2

-2 Carbon flux divergence[kg/m flux Carbon

-4 0 5 10 15 20 Hour of Day

Figure 32: Describes the mean daily dynamics of the carbon flux divergence in kg/m2/year for the spring in the Red Sea. The horizontal flux divergence is very low, also the flux divergence is mostly negative during the evening/night and positive for a few hours during the day hours.

1 Spring 0.5 Summer

Storm

/year] 2 0

-0.5

-1

-1.5 Carbon vertical flux[kg/m vertical Carbon

-2 0 5 10 15 20 Hour of Day

Figure 33: Describes the mean daily dynamics of the carbon vertical flux gradient in kg/m2/year for the spring, the summer and the storm in the Red Sea. The vertical flux gradient is negative most of the time for the spring and the storm periods, it is higher during the morning hours and lower during the evening/night hours for all 3 periods during the day. The vertical flux during the summer is small and positive during the day.

The spring and the storm are almost the same, when the spring has a higher negative vertical flux.

After analyzing all 4 terms in the governing equation for the volume-averaged

CO2 budget equation we can see in Figure 34 that the CO2 budget is mostly affected from the carbon advection. The CO2 budget during the spring looks like the advection plot in

Figure 34. It is positive and high during the evening hours for the spring but it becomes negatively high during the dawn. The storm and the summer are positive, opposite to the spring, during the night. The missing/bad data during the summer and the fact that we could not approximate the missing/bad data from the measurements can affect these results.

In Table 6 we arrange all the terms in the governing equation for the CO2 budget equation and took the average for each term in each period and the total of all of them.

2 The spring has a high negative CO2 budget -7.5 [kg/m /year], the CO2 budget for the summer and the storm is not available due to malfunctions of one of the LI7500 sensors, but we can notice that the CO2 budget is mostly affected from the carbon advection compare to the 3 other terms. From Table 6 it is clear that the advection has a big influence when calculating the CO2 budget. On the other hand we can neglect the change of storage, the horizontal flux divergence but not the vertical flux when calculating the

CO2 budget in the Red Sea.

10 Spring Summer Storm

/year] 5 2

-5 Carbon Budget[kg/m Carbon

-10

0 5 10 15 20 Hour of Day

Figure 34: Describes the mean daily dynamics of the carbon budget in kg/m2/year for the spring, the summer and the storm in the Red Sea. The CO2 budget is positive and high during the evening hours for the spring but it becomes negatively high during the dawn.

The storm and the summer are positive, opposite to the spring, during the night. The summer has the lowest carbon budget. The carbon budget is negative most of the day for all 3 periods.

Table 6: The average of all the terms in the CO2 budget equation for the spring, summer, storm and the total of all of them.

Season Spring Summer Storm Total

Term

[kg/m2/year]

Change of Storage -0.015 -3.410 4 -0.0016 -0.013

Advection -6.5 NA NA NA

Flux divergence 4.110 6 NA NA NA

Vertical flux -1.06 0.15 -1.7 -0.87

Total CO2 flux -7.5 NA NA NA

CHAPTER 4

CONCLUSIONS AND FUTURE WORK

The goal of this research was to evaluate the evaporation rate and to determine the terms in the water vapor mass budget that most influence the evaporation rate in the Gulf of

Aqaba (Eilat), Red Sea. Even though we did not conduct our experiment through the whole year of 2009, we can still use this research as a good reference to understand the behavior of the winds and the temperature that has a direct influence on the evaporation rate in the Red Sea.

Early studies tried to evaluate the evaporation rate using some models. Ben-

Sasson et al. (2007) presented in their model that the annual mean evaporation in the Red

Sea is 1.6-1.8 m/year with a minimum of 1 m/year during the summer and a maximum of

3-4 m/year during the winter. They determined that advection rates are not important and used a vertical column bulk-formulae models and meteorological measurements from 3 stations on the Red Sea coast (one of which is the 10 m pier measurements used in our study) to evaluate the vertical flux only as an approximation to the total evaporation.

Eshel and Heavens (2007) based their evaluation of the total evaporation rate in the Red

Sea on the moisture budget and a regional wind model, the values he showed in his research are: 1.3 m/year during the summer, 2.9 m/year during the winter and an annual mean of 2 m/year.

In our research we use more accurate measurements than the ones used to force

Eshel's and Ben-Sasson's models, and explicitly measure vertical flux and advection. We show that advection is a relatively large term that should not be ignored, but the vertical flux is somewhat lower than predicted by Ben-Sasson (et al 2007) probably due to the effects of the inversion layer above the sea surface. The total evaporation rates we observed were higher for the summer, 2.8 m/year and the observed spring rates, 3.1 m/year, were close to the annual maximum. We can explain some of this discrepancy due to the fact that our measurements were taken close to the shore and represent evaporation from the coral lagoon. Evaporation rates over the deeper open water, where sea surface temperature is lower are expected to be less.

This study focused mainly on the evaporation rate in the Red Sea, but by analyzing the CO2 budget we can see some interesting behavior of the advection. It has a big influence on the CO2 budget. We also notice that we cannot ignore the carbon vertical flux when calculating the CO2 budget. When comparing the carbon budget in the forest in the University of Michigan Biological Station (UMBS), we can notice a big difference,

2 the CO2 budget there for the annual there is -0.17 kg/m /year, where we calculated -6.5 kg/m2/year, this is due to malfunctions of one of the LI7500 sensors and also we had less sensors to calculate the CO2 budget. We can notice from our results that the carbon

2 vertical flux, -0.87 kg/m /year, is close to the annual CO2 budget in the UMBS, -0.17 kg/m2/year .These results are not sufficient, because we had a lot of missing/bad data when we evaluate the CO2 budget in the Red Sea, this data could not be approximated 78 from other measurements as we did for the H2O budget. In order to get a better understanding of the evaporation rate and the CO2 budget in the Red Sea it is recommended to conduct an experiment through the whole year, where the pier tower will be further in the sea. Also it will be advised to have a few licors to collect more CO2 on the towers.

This research can help us understand the relationship between weather, sea and air temperature and evaporation, and thus, link the predictions of changing regional climate over the Red Sea region to their impact on vertical mixing, water nutrient levels and coral reef survival. We notice that during the storm period we have the highest evaporation rate where the temperature during that time is also the highest as the advective wind. This leads to a conclusion that if local climate change will be characterized by increase in storm frequency, it will lead to a higher evaporation rates in the Red Sea that may affect the coral reef.

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