Kristina Lindgren

Examensarbete vid Institutionen för geovetenskaper ISSN 1650-6553 Nr 158

The Behaviour of the Latent Heat Exchange Coefficient in the Stable Marine Boundary Layer

i

Abstract

Knowledge of the turbulent fluxes at the sea surface is important for understanding the

interaction between atmosphere and ocean. With better knowledge, improvements in the

estimation of the heat exchange coefficients can be made and hence models are able to

predict the weather and future climate with higher accuracy.

The exchange coefficients of latent and sensible heat during stable stratification vary

in the literature. Therefore it is necessary to investigate the processes influencing the

air-sea exchange of water vapour and heat in order to estimate these values. With

measurements from a tower and a directional waverider buoy at the site Östergarnsholm

in the Baltic Sea, data used in this study have been sampled from the years 2005-2007.

This site represents open-ocean conditions during most situations when the wind comes

from the south-east sector. The neutral exchange coefficients, CEN and CHN, have been

calculated along with the non-dimensional profile functions for temperature and wind,

and , to study the dependence of stability and other parameters of relevance.

It was found that CEN increased slightly with wind speed and reached a mean value

of approximately 1.45×10-3

. The highest values of CEN were observed during near

neutral conditions and low wave ages. CHN attained a mean value of approximately

0.77×10-3

and did not show any relation to wind speed or to wave age. No significant

dependence with wind or wave direction could be shown for either CEN or CHN in the

sector 80-220°. The stability correction, performed to reduce the dependence on

stratification for CEN and CHN, was well performed for stabilities higher than 0.15. The

stability is represented by a relationship between the height and the Obukhov-length

(z/L).

Validity of and showed that, for smaller stabilities, these functions gave

higher values than the corresponding functions recommended by Högström (1996).

was shown to have a larger scatter while was less scattered and deviated more from

the functions given by Högström.

ii

Sammanfattning

Kunskap om turbulenta flöden i det marina gränsskiktet är viktigt för att förstå

växelverkan mellan atmosfär och hav. Med bättre kunskap kan förbättringar i

bestämningen av utbyteskoefficienterna för latent och sensibelt värme erhållas. Det

medför att modeller kan prognostisera väder och framtida klimat med högre

noggrannhet.

Utbyteskoefficienterna för latent och sensibelt värme har för stabil skiktning olika

värden i litteraturen. Detta gör det nödvändigt att undersöka de processer som påverkar

utbytet av vattenånga och värme mellan luft och hav för att kunna bestämma dessa

värden. Data som har använts i den här studien insamlades mellan år 2005 och 2007

från en boj och ett torn vid mätplatsen Östergarnsholm i Baltiska havet. För det flesta

situationer, när vinden blåser från syd-ost, representerar mätplatsen ett förhållande

likvärdigt det över öppet hav. De neutrala utbyteskoefficienterna, CEN och CHN, och de

dimensionslösa profilfunktionera för temperatur och vind, och , har beräknats för

att studera beroendet av stabilitet samt andra relevanta parametrar.

Beräkningarna visade att CEN ökade något med vindhastighet och hamnade på ett

medelvärde av ungefär 1.45×10-3

. De högsta värdena på CEN observerades vid nära

neutrala förhållanden och låga vågåldrar. CHN uppmättes till att ha ett medelvärde på

ungefär 0.77×10-3

och uppvisade inget beroende med vindhastighet eller vågålder. Inget

märkbart beroende med vind- eller vågriktning kunde visas för CEN eller CHN i sektorn

80-220°. Stabilitetskorrektionen, utförd för att reducera beroendet av atmosfärens

skiktning för CEN och CHN, var bra för stabiliteter högre än 0.15. Stabiliteten

representeras av förhållandet mellan höjden och Obukhov-längden (z/L).

Utvärdering av och visade att dessa funktioner, för små stabiliteter, gav högre

värden än motsvarande funktioner som rekommenderas av Högström (1996). Värdena

på hade större spridning än värdena på och avvek mer från funktionerna givna av

Högström.

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Contents

1 Introduction ......................................................................................................................... 1

2 Theory .................................................................................................................................. 2

2.1 The stable boundary layer ............................................................................................. 2

2.2 Similarity theory ............................................................................................................ 3

2.3 Latent and sensible heat flux ......................................................................................... 4

2.4 Bulk formulas ................................................................................................................ 4

2.5 Stability correction ........................................................................................................ 5

2.5.1 The Bisection method ............................................................................................ 7

2.6 Wave state ..................................................................................................................... 8

3 Site and measurements ....................................................................................................... 9

3.1 The Östergarnsholm site ............................................................................................... 9

3.2 Instrumentation ........................................................................................................... 10

3.3 Data criteria ................................................................................................................. 11

4 Results ................................................................................................................................ 12

4.1 The variations of the neutral exchange coefficients for heat ...................................... 12

4.1.1 The variation of CEN and CHN with wind speed and wind direction .................... 12

4.1.2 The influence on CEN and CHN with a wave direction from 50-80° .................... 16

4.2 The stability dependence on the heat exchange coefficients....................................... 17

4.3 The influence of wave state on the neutral exchange coefficients for heat ................. 20

4.3.1 Variation of CEN and CHN with wave age ............................................................ 20

4.3.2 Variation of CEN and CHN with significant wave height ...................................... 22

4.4 Dimensionless profile functions for temperature and wind ........................................ 22

4.4.1 Calculations of and with gradients of a non linear polynomial fit .......... 23

4.4.2 Calculations of and with gradients from a linear fit ................................ 25

5 Discussion ........................................................................................................................... 27

6 Conclusions ........................................................................................................................ 29

Acknowledgements .............................................................................................................. 29

References ............................................................................................................................. 30

Appendices ............................................................................................................................ 32

Appendix A ....................................................................................................................... 32

Appendix B ....................................................................................................................... 32

1

1 Introduction

The atmosphere and the oceans form a coupled system where the air-sea interaction is of

major importance for both weather and climate. The exchange of water vapour, heat and

momentum between the atmosphere and the oceans contribute extensively to the atmospheric

and oceanic circulations. The oceans cover about 70% of the Earth’s surface but still studies

on the air-sea interface are incomplete. Thus it is important to increase the knowledge of this

complex interaction between the air and the sea.

The atmosphere gets practically all of its water vapour through evaporation of the oceans.

Latent heat of oceanic evaporation is of great importance for cloud formation and

precipitation over both sea and land. Coming to understand more of the latent heat flux over

sea is therefore important for modeling and predicting the weather and future climate. With

more information on the interaction, the models will become more accurate.

The interaction is taking place in the lowest part of the boundary layer, the surface layer,

where turbulence is the governing process in the direct air-sea exchange of water vapour, heat

and momentum. Only sparse direct measurements are made over sea since such measurements

are difficult to perform. This makes it necessary to, on the basis of Monin-Obukhov similarity

theory, parameterize the fluxes. Bulk formulas constitute a practical method for estimation of

fluxes between the sea and the atmosphere using more accessible parameters and an exchange

coefficient. The exchange coefficient used in calculations of the latent heat flux is called the

Dalton number, denoted CE. Its value is relatively uncertain as it changes through different

literature.

To compare exchange coefficients from different experiments the influence of atmospheric

stratification needs to be removed. This is done by reducing the exchange coefficients to

neutral values, expressed as CEN and CHN for latent and sensible heat, respectively. Most

earlier studies have given CEN,HN = 1.1×10-3

for both stable and unstable data but studies by

Oost et al. (1999) and Rutgersson et al. (2001) have shown CEN and CHN to have lower values

during stable stratification. The behaviour of the stable boundary layer is not yet fully

understood and hence it is of great interest to study CEN closer under this condition. For better

insight it is significant to validate the non-dimensional profile functions as they are important

functions used to reduce the stability dependence.

The data in this paper were obtained from tower and buoy measurements at the site

Östergarnsholn in the Baltic Sea from 2005 to 2007. This site represents open-ocean

conditions for most situations, when the wind direction is from 80-220°, according to a study

by Smedman et al (1999). The main objective of this work is to focus on how CEN behaves in

the marine boundary layer during stable stratification. Studies of CHN will also be included

throughout the report since the latent and sensible heat fluxes are closely related.

2

2 Theory

2.1 The stable boundary layer

The atmospheric boundary layer is the lowest part of the troposphere (Figure 1). In the

boundary layer different stratification appears depending on the atmospheric conditions. Due

to heating of the surface an unstable stratification commonly appears during the day, reaching

heights of up to a few kilometers. At sunset, when the temperature at the surface decreases, a

stable boundary layer usually begins to form and it is fully developed at night. The stable

boundary layer rises when the surface is cooler than the air or when advection of warmer air

over a cooler surface occurs. During stable stratification the surface layer may range from a

few tens of meters to a few hundreds of meters

The former description regards conditions over land. Over sea the conditions are different as

the oceans have a huge capacity of storing heat, leading to only small differences in the sea

surface temperature and hence negligible diurnal variations. Both stable and unstable

stratification appear due to air-sea temperature differences and the wind speeds are in general

higher than over land, due to a much lower roughness at the sea surface.

The knowledge about the unstable boundary layer is larger than that of the stable boundary

layer where the turbulence often is sporadic and irregular, allowing the upper part of the

boundary layer to decouple from the surface forcings [Stull, 1988]. This appears in a

complicated manner that is not yet completely understood which makes it difficult to derive

theories of general validity for the stable boundary layer. As previously mentioned, the

surface layer may be very shallow during stable stratification. This implies that only

measurements performed near the surface can be used to investigate the stable boundary layer

and similarity theory may then be used with advantage.

Figure 1. The atmospheric boundary layer over land [Stull, 1988].

3

2.2 Similarity theory

Knowledge of the physical processes in the boundary layer may sometimes be deficient or too

complex to properly describe turbulent fluxes and parameters in the layer. Use of similarity

theories may then be a good way of describing variables in the atmospheric boundary layer.

These are empirical methods used to find universal relationships between relevant variables

by dimension analysis [Stull, 1988]. The variables are then made dimensionless using

appropriate scaling factors.

The turbulent fluxes are most easily measured in the atmospheric surface layer, defined as the

lower part of the boundary layer where fluxes vary with less than 10% of their magnitude

with height [Stull, 1988]. Due to the small variations in height, the fluxes are equal to

corresponding values by the surface and an assumption that the surface layer is a constant

flux-layer can be made [Högström and Smedman, 1988]. By making this assumption, the

turbulent fluxes can be described by use of similarity theory.

The behaviour of the surface layer is well described by Monin-Obukhov similarity theory

(henceforth referred to as MOST), a theory that describes the properties of turbulence within

the surface layer. In MOST it is postulated that there are four fundamental parameters

necessary to define the relationship in the surface layer. These are: the height above the

surface, the friction velocity where and are the

kinematic momentum flux in x- and y-direction respectively, the kinematic heat flux and

the buoyancy parameter where is the acceleration of gravity and the mean absolute

temperature of the layer. The characteristic scaling parameters are for velocity and

for temperature.

From the independent variables in MOST one important parameter with the dimension of

length can be formed the Obukhov-length:

(2.1)

Here is the von Karmáns constant, equal to 0.40, and is the kinematic flux of virtual

temperature. can be used in equation (2.1) but in the case of a marine boundary layer

where the air is moist, the virtual contribution can be large. A physical interpretation of the

Obukhov-length is that it is proportional to the height at which buoyant factors begins to

dominate over mechanical production of turbulence [Stull, 1988].

The Obukhov-length is, in accordance with equation (2.1), dependent on . When the

stratification is stable, <0 which gives a positive L. During unstable stratification the

reverse is true, and L becomes negative. When the atmospheric conditions are

neutral, making L extend towards infinity. A commonly used stability parameter

that involves the Obukhov-length is and its importance will be discussed further in

section 2.5.

4

2.3 Latent and sensible heat flux

In the boundary layer turbulent fluxes are important and responsible for the exchange of

momentum, heat and water vapour. Evaporation or condensation at the surface gives rise to

flux of latent heat while fluxes of sensible heat emerge because of differences in the

temperature of the surface and the air above it. Evaporation occurs from a water surface as

well as from other surfaces whenever the air above is drier (has lower specific humidity),

while condensation often occurs on a colder surface in the form of fog.

Turbulent motions cause upward or downward flux of water vapour and heat close to the

surface of both land and sea. The latent heat flux is directed upwards during evaporation,

resulting in cooling of the surface, and directed downwards during condensation. The sensible

heat is directed upwards when the surface is warmer than the air and reversed when the

surface is cooler than the air.

The marine boundary layer differs from the boundary layer over land due to other

thermodynamic and dynamic characteristics. Over sea, the latent heat flux is usually much

larger than the sensible heat flux. During periods with upwelling, cold outbreaks over warmer

sea, ocean currents etc., the sensible heat flux is however increasing significantly.

The latent heat flux, E, is given by

(2.2)

where is the density of air, the kinematic flux of specific humidity and the latent

heat of vaporization given by the following expression:

(2.3)

where T is the temperature (in C°). The sensible heat flux, H, can be written as:

(2.4)

where cp is the specific heat of air at constant pressure equal to 1004 J kg-1

K-1

.

2.4 Bulk formulas

Of all processes taking place in the boundary layer, the turbulent fluxes of heat is the most

dominant [Geernaert, 1999]. Over sea, direct measurements of latent and sensible fluxes are

difficult to make, compared to direct measurements over land. The processes over the sea are

complicated by the fact that oceans have a dynamically active surface and a surface boundary

layer in which the motions generally are turbulent. The vertical heat flux can then, on the

basis of MOST, be expressed by parameterization with bulk formulas. In the bulk formulas,

5

the fluxes are related to measured variables at the surface with the aid of an empirically

determined exchange coefficient at a certain height.

The bulk formulas for latent and sensible heat give the following expressions:

(2.5)

(2.6)

where and are the exchange coefficients for latent and sensible heat respectively,

the averaged wind speed on 10 meters height, and the specific humidity at the sea

surface and at 10 meters height respectively and and the potential temperature at the

sea surface and 10 m height respectively.

The specific humidity at the surface, , may be calculated from the sea surface temperature

as the air closest to the sea surface is assumed to be saturated by water vapour and is given by:

(2.7)

where ε = 0.622, the atmospheric pressure and the water vapour saturation over sea.

can in turn be calculated with Magnus exact formula [Rindert, 1993]:

(2.8)

where is sea surface temperature (in K) and is given in hPa.

2.5 Stability correction

To compare measurements from different experiments, the influence of the atmospheric

stratification needs to be removed. The exchange coefficients are dependent of the stability

and may therefore be modified using a stability-dependent term from which the influence of

stratification can be estimated. The stability parameter is , which is used throughout this

paper.

The non-dimensional profile functions give a relation between the turbulent fluxes and the

corresponding vertical gradients. According to MOST, these functions can be expressed as

universal functions of . The non-dimensional profile functions for temperature, , and

wind, , is expressed as [Arya, 1988]:

(2.9)

6

(2.10)

The form of these functions cannot be solved analytically but rather be determined through

experiments in the field. For stable stratification over land, following expressions are

recommended for the profile functions that have a stability between

[Högström, 1996]:

(2.11)

(2.12)

where A1 = 8 and B1 = 5.3. For values where , MOST is not a valid theory as

several studies have shown increasing scatter with increasing stability for both profile

functions [Högström, 1996]. For larger stability, Holtslag and Debruin (1988) have found an

expression that holds for stabilities up to :

(2.13)

where a1 = 0.7, b1 = 0.75, c1 = 5 and d1 = 0.35.

The integrated non-dimensional profile function in equation 2.13 is giving the stability

correction terms and . The drag coefficient, , for momentum can then be calculated

according to:

(2.14)

where is the roughness length1.

The exchange coefficients are normalized for stability by reducing the values to neutral

stratification. The drag coefficient when reduced to neutral values, , is given by the

following formula:

(2.15)

1 The roughness length has been estimated from the COARE-model (COARE 3.0) which couples the sea and

atmosphere together [Fairall et al., 2003]. COARE (Coupled Ocean and Atmosphere Responsive Experiment) is

together with TOGA (Tropical Ocean-Global Atmosphere) an international research program that studies the

interaction of ocean and atmosphere in the western Pacific warm pool region.

7

Using measured data, the expressions for the neutral exchange coefficients for latent and

sensible heat, and respectively, can now be calculated as suggested by Geernaert

(1999):

(2.16)

(2.17)

One can expect that the stability correction term developed over land does not take the

stability into account correctly due to different conditions in the boundary layer over sea

compared to over land. Over sea, the friction is smaller (especially in the presence of swell,

defined in section 2.6) than over land which can lead to high values of the stability correction

term due to small Obukhov-lengths, i.e. large values of the stability parameter . This

might lead to incorrect values of the neutralized exchange coefficients.

2.5.1 The Bisection method

Equation (2.16) and (2.17) cannot be solved analytically and an iterative method is therefore

needed to get the neutral exchange coefficients. In this study an iteration called the bisection

method [Heath, 2002] is used. The bisection method provides a practical method to find roots

of equations difficult to solve.

To solve CEN with the bisection method, equation 2.16 is written as:

(2.18)

where the true value of satisfy . To find the root of equation 2.18, the interval

in which it lays must be known. Since the value of is approximate known, the interval is

easily set to include the root of f. The same method is used to find with equation 2.17.

In each iteration, the function f is evaluated at the midpoint and at the endpoints of the current

interval. If the interval contains a root it will also contain a sign change of the function.

Depending on where the root lays, the function at the midpoint will have different sign than at

one of the end points and the same sign as the other end point. The root is therefore in

between the changes of sign. Half of the interval can then be discarded and the process can

start over. Notice that the bisection method does not work for terrace points since these holds

change in sign.

The length of the interval is being reduced until the desired accuracy of the desired tolerance

has been reached. The tolerance that has been used in this paper is 10-15

. When changing the

tolerance to some extent, no significant difference on the results has been observed.

8

2.6 Wave state

The state of the waves influences the transfer of momentum, mass and heat and can

consequently have an impact on model predictions. A common way of describing the wave

state is by using the wave age. Wave age is a relationship between wind speed and the phase

speed of the dominant wave expressed as:

(2.19)

The phase speed, , is estimated according to the deep water dispersion relation [Arya,1988]:

(2.20)

where is the peak frequency of the wave spectrum. A correction for deep water has to be

made for waves travelling faster than 6.5 m/s. The correction, , is given by [Sahlée, 2002]:

(2.21)

When waves are not forced by the local wind and are travelling faster than the wind, they are

called swell. Swell is often defined as a wave age larger than 1.2 [Smedman et al., 1999].

When the wave age is less than 0.8 it is called growing sea and when the wave age ranges

between 0.8 and 1.2, it is called mature sea or mixed sea.

Another common way of describing the wave state is by using the significant wave height,

, described as the average of the highest one-third of the waves. It should be noted that

some individual waves might be much larger than the mean as the significant wave height is

averaged over a recording period.

9

3 Site and measurements

3.1 The Östergarnsholm site

Östergarnsholm, where the measurements are made, is a small and flat island in the Baltic Sea

(57°27’N, 18°59’E) 4 km east of Gotland, Figure 2. On the southern part of the island a 30 m

tower is situated with the base of the tower 1 m above the mean sea level. The sea level varies

slightly and is mainly changing due to synoptic weather conditions. The actual height of the

base of the tower is calculated with measurements of the sea level in Visby harbor, on the

west coast of Gotland, and the variation usually ranges between ±0.5 m [Sahlée et al., 2008a].

Figure 2. Location of the tower and the waverider buoy at Östergarnsholm (picture courtesy by Cecilia

Johansson).

A directional waverider buoy, owned and run by the Finnish Institute of Marine Research, is

moored 4 km in the direction 115° from Östergarnsholm at 36 m depth. The buoy is

measuring the wave characteristics and the water temperature, which is measured at a depth of

0.5 m making it approximately equal to the sea surface temperature [Sahlée et al., 2008a].

There is an undisturbed fetch of 150 km in the sector 80-220°, representing open-sea

conditions, and it is mainly data from this section that have been used in this study. North,

north-east of the tower there is a shoal which only represents open-sea conditions during swell

[Högström et al., 2008]. The sector 50-80° is therefore excluded from most calculations.

Winds coming from coastal areas or across the Östergarnsholm Island motivates the other

limitations in the wind sector (220-360°, 0-50°).

10

The depth in the area influencing the measured fluxes (fetch) varies but according to a study

from Smedman et al. (1999) measurements around the site represent deep-water conditions

for most situations.

3.2 Instrumentation

The tower is equipped with slow response sensors and instruments for measurements of rapid

turbulent fluctuations. Slow response sensors are placed at 8, 12.5, 15, 21 and 29 m above

mean sea level and give profile measurements of wind speed, wind direction and temperature.

Turbulent fluctuations are recorded with SOLENT 1012R2 (Gill Instrument, Lymington, UK)

sonic anemometers situated at 10, 16 and 26 m above mean sea level, giving the three wind

components and the temperature.

The sonic anemometers are affected by density changes caused by water vapour in the air and

gives virtual air temperature and virtual sensible heat flux. Due to relatively high humidity

levels, the difference between the virtual and real sensible heat fluxes can be rather large in

marine conditions. This is corrected by the following formula:

(3.1)

The sonic anemometers perform measurements with a sound pulse. A second correction of the

heat flux is therefore needed due to cross-wind effect since the cross-wind causes a signal

deflection of the sound path [Kaimal and Gaynor, 1991].

Humidity is measured at 10 m above mean sea level with a LI-7500 (LI-COR Inc., Lincoln,

NE, USA). A change in equipment took place in April 2006 by installation of a new LI-COR.

The humidity flux is corrected with the ‘Webb-correction’ which is described in detail in

Webb et al. (1980).

A horizontal distance of 0.3 m between the LI-COR and the sonic anemometers results in an

attenuation of the fluxes, which in turn results in a flux loss. The mean attenuation for the

measured fluxes due to the displacement is 5% for stable stratification [Sahlée et al., 2008b].

This is compensated for in each measurement.

The turbulent fluxes are measured directly using the eddy correlation method, explained in

Sahlée (2007) and Appendix A. The turbulence data are recorded with a frequency of 20 Hz.

A high-pass filter based on a 10 min running average is applied on the turbulence time series

to remove possible trends before the fluxes are calculated. In this study, the measurements at

the tower are averaged over 60 minute periods. Measured data from the tower and the buoy

have been put together in a common database and is therefore not fully synchronized. Data

from the buoy is given every hour, but to fit the data from the tower the corresponding time

steps have been rounded down to the nearest whole hour. The buoy data hence differ by

approximately 30 minutes from the tower data.

11

3.3 Data criteria

In this study, two different sets of data have been used. The first data set range from June to

August and from November to December 2005, and the second data set range from July to

August 2006 and from June to September 2007. The buoy is regularly removed to avoid

damages from ice during the winter and therefore no data have been available from late winter

and spring.

Data have been selected under these criteria:

a) Winds from the sector 80-220°. Because of an undisturbed fetch.

b) Wind speeds on 10 m height > 2 m/s. The wind direction is difficult to determine

when there are low wind speeds as the direction may range from one extreme value to

another over a very short period and most measurements are not considered reliable

during low wind speeds.

c) Relative humidity < 95%. The measured data are not reliable when there is too much

moisture on the instruments.

d) and holds for CEN and CHN respectively. Due to

the resolution of the instrument, very small values of and are not

considered reliable.

e) The fourth moment < 0.1 Because of a large scatter in the measurements for higher

values. A large fourth moment is indicating measurements containing large errors.

f) and should have the same sign since CHN is expected to be positive.

12

4 Results

During the calculations different data sets for CEN and CHN have been used for the wind

direction sector 80-220° because of different data criteria (section 3.3), 139 data points passed

the criteria for CEN and 198 data points passed the criteria for CHN. The measurements are in

general from a height of 10 m and include measurements from 2005 to 2007. Different

amounts of data have been used in section 4.4 due to other criteria.

4.1 The variations of the neutral exchange coefficients for heat

4.1.1 The variation of CEN and CHN with wind speed and wind direction

In addition to the data that are used for the calculations of CHN, some negative values also

exists for CHN (removed by criteria f in section 3.3) and can be observed in Figure 3. It is not

physically correct with negative values as it is in contravention of the second law of

thermodynamics, i.e. the heat flux goes across the gradient of the air-sea difference or the

vertical temperature flux (equation 2.6). Those values are therefore not included in following

analysis or in Table I.

Figure 3. CHN as a function of wind speed and wind direction (WD) with the negative values visible in the lower

region. The solid line represents the mean value for wind speed intervals and the vertical bars the standard

deviations. The negative values and the values measured for WD 50-80° are not taken into account in the

averaged line or in the standard deviations.

The buoy temperature is measured at a depth of 0.5 m (bucket temperature) and a difference

between the sea surface temperature (skin surface temperature) and the bucket temperature

sometimes appear. Due to warming of the surface layer, a heat flux against the gradient can

form indicating the appearance of a warm-layer. The warm-layer effect is thus only estimated

to give small changes in the measurements [Fairall et al., 1996]. The skin effect (estimated to

13

0.12°C from May to December 1998 at Östergarnsholm in a study by Rutgersson et al., 2001)

is thus of minor importance. In Figure 4 the negative values of CHN that correspond to

measured data, within the calculations of CEN can be seen.

Figure 4. CEN as a function of wind speed including the negative values of CHN that has a corresponding value in

CEN, represented by squares. The solid line represents the mean value for wind speed intervals and the vertical

bars the standard deviations.

The neutral exchange coefficients for humidity and temperature are shown as functions of

wind speed in Figure 5a and 5b, respectively. The values representing the wind direction 50-

80° has been excluded from the calculations of the mean and the standard deviations. These

measurements give low values of CEN with high wind speeds and are probably not

representative for the result. Those values are therefore not included in following analysis or

in Table I.

A slight increase of CEN with wind speed can be seen, in contradiction to earlier studies by

DeCosmo et al. (1996) and Oost et al. (1999) where no dependence on wind speed have been

found. The mean value of CEN in this study is also larger than these studies have shown

(giving 1.1×10-3

and 0.28×10-3

respectively), with an average of 1.45×10-3

. CHN does not

show any significant dependence on wind speed and its mean value, 0.77×10-3

, is well in line

with a study by Rutgersson et al. (2001), based on previous measurements from the

Östergarnsholm site. The mean value of CHN is smaller than what has been shown in a study

of DeCosmo et al. (1996), 1.1×10-3

, but larger than studies by Large and Pond (1982) and

Oost et al. (1999) who showed 0.66×10-3

and 0.32×10-3

respectively.

No significant difference can be seen for different wind directions within the range 80-220°

and the measurements are therefore further presented without any notification of this.

14

Figure 5. CEN (a) and CHN (b) as functions of wind speed and wind direction (WD). The solid line represents the

mean value for wind speed intervals and the vertical bars the standard deviations. The values measured for WD

50-80° are not included in the averaged line or in the standard deviations.

The mean values of CEN and CHN can be seen in Figure 6. In an earlier study by DeCosmo et

al. (1996), CEN and CHN followed each other relatively well but in this case the average values

differ by almost 0.7×10-3

(Table I). Differences between the values of CEN and CHN during

stable stratification have been found in other studies [Large and Pond, 1982, Rutgersson et al.,

2001] but commonly most studies give the same value for CEN and CHN.

15

Figure 6. The mean values of CEN and CHN as functions of wind speed.

Table I. Mean values and standard deviations of the neutral exchange coefficients and the exchange coefficients

for the heat fluxes.

CEN

CHN CE CH

2005 (1.53±0.53) ×10-3

(0.88±0.31) ×10-3

(1.46±0.44) ×10-3

(0.83±0.30) ×10-3

2006-2007 (1.23±0.60) ×10-3

(0.71±0.35) ×10-3

(1.13±0.56) ×10-3

(0.65±0.33) ×10-3

Mean value (1.45±0.53) × 10-3

(0.77±0.34) ×10-3

(1.36±0.50)×10-3

(0.71±0.32) ×10-3

The mean values of CEN and CHN differ between the measurements performed 2005 and those

performed 2006-2007. The data set from 2005 includes values of CEN that has a mean value of

0.3×10-3

higher than for the period 2006-2007 (Table I). This also applies for CHN where the

average is 0.15×10-3

higher for the measurements performed in 2005. The mean values of CEN

and CHN for the different measuring periods can be seen in Figure 7a and 7b, respectively.

16

Figure 7. CEN (a) and CHN (b) as functions of wind speed for the data sets from 2005 and 2006- 2007,

respectively. The thick lines represent the mean values for wind speed intervals.

4.1.2 The influence on CEN and CHN with a wave direction from 50-80°

In a previous study of the CO2 flux by Rutgersson et al. (2008), the measured fluxes of CO2

were larger when the waves were travelling from the sector 50-80°. A study of CEN and CHN,

too see if they show any notable deviation during influence of waves travelling from this

sector is therefore of interest. In these measurements, no dependence of the wave direction

50-80° can be seen for either CEN or CHN, Figure 8a and 8b. Neither could any trend trends for

CEN or CHN be distinguished for wave directions from the sector 80-220° (not shown in this

paper).

17

Figure 8. CEN (a) and CHN (b) as functions of wind speed. The solid line represents the mean value for wind

speed intervals and the vertical bars the standard deviations. The measurements of wave direction 50-80° are

marked with squares.

4.2 The stability dependence on the heat exchange coefficients

The stability influence on CE and CH are shown in Figure 9a and 9b. The highest values of

both CE and CH correspond to measurements made during near neutral stratification and

decreases as stability increases. There is a smaller variation in stability for CHN than for CEN.

At higher stabilities rather few measurements are available, giving an uncertainty in the result

for larger stabilities. A few values of CH, corresponding to values of z/L>0.5, are not

presented here but can be seen in Appendix B, Figure B1.

18

Figure 9. CE (a) and CH (b) as functions of stability. The solid line represents the mean value for stability

intervals and the vertical bars the standard deviations.

Figure 10a and 10b show the impact of the stability correction for CEN and CHN, respectively.

CE and CEN follow each other well close to near neural stratification but a larger difference

appears as the stability increases. The same relation exists for CH and CHN. The averaged lines

in Figure 10a and 10b have only a small slope after approximately z/L = 0.15, which implies

that the stability correction fully corrects for stratification. If the correction is well performed,

CEN and CHN would be totally independent of stability.

How well the estimated CEN and CHN follow the theory can also be seen in Figure 10a and

10b. The theoretical CE, CE,theory, has been calculated, in the same way as CEN, from equation

2.16, but for a constant average of CEN (Table I). The same has been done for CH,theory, but

with equation 2.17. The estimated exchange coefficients agree well with the theoretical

values.

19

Figure 10. Mean values of CE and CEN (a), and CH and CHN (b) for stability intervals. CE,theory and CH,theory are

calculated from equation 2.16 and 2.17 with a constant averaged value of CEN and CHN, respectively.

The variation of the stability correction term, (the integrated form of equation 2.13), can be

seen in Figure 11. This is calculated with data corresponding to the measurements of CEN

that are used. The correction term is negative during stable stratification and is small for near

neutral values. The decrease is close to linear as the stability correction term is a function of

z/L.

20

Figure 11. The stability correction term, , for CEN as a function of stability.

4.3 The influence of wave state on the neutral exchange coefficients for heat

4.3.1 Variation of CEN and CHN with wave age

The variation of CEN and CHN with wave age can be seen in Figure 12a and 12b. The highest

values of CEN are found at low wave ages and are thereafter decreasing with increasing wave

age. After reaching the state of mature sea, CEN flattens out and does not vary remarkably.

Swell occur when the wave age exceeds 1.2 but it does not affect CEN, which also has been

noted in a study by Oost et al. (1999).

There is a large scatter in the values of CHN but only a slight increase of CHN can be noticed

when mature sea reign. No relationship between CHN and the wave age can otherwise be

observed.

21

Figure 12. CEN (a) and CHN (b) as functions of wave age. The solid line represents the mean value for wave age

intervals and the vertical bars the standard deviations. The dashed lines represent wave ages > 1.2, i.e. when

swell occurs.

To know if there really is an increase of CEN with wave age, the measurements of CEN and

CHN corresponding to the same time values are investigated. CEN has been calculated for the

times of the CHN values. The resulting CEN and its dependence of the wave age are shown in

Figure 13. The highest values represent low wave ages and therefore strengthens the result

that higher values of CEN correspond to low wave ages.

Figure 13. CEN calculated with the measurements of CHN corresponding to the same time as a function of wave

age. The solid line represents the mean value for wave age intervals and the vertical bars the standard deviations.

22

4.3.2 Variation of CEN and CHN with significant wave height

In Figure 14a and 14b, CEN and CHN as functions of the significant wave height can be seen.

Slight inclines and declines in CEN and CHN with significant wave height are shown but

without showing any considerable dependence. This is in agreement with an earlier study by

Oost et al. (1999).

Figure 14. CEN (a) and CHN (b) as functions of the significant wave height. The solid line represents the mean

value for wave height intervals and the vertical bars the standard deviations.

4.4 Dimensionless profile functions for temperature and wind

For the stability correction to be well performed, it is important to validate the dimensionless

profile functions. Previously the dimensionless profile function was calculated from equation

2.13, but now the validity of equation 2.11 and 2.12 are investigated. In these calculations, the

same values that passed the criteria for CHN have been used and further reduced with other

criteria.

23

and from Högström (1996), i.e. equation 2.11 and 2.12, are compared with and

estimated in this study, i.e. equation 2.9 and 2.10. The gradients in equation 2.9 and 2.10 then

need to be calculated. This is done using a non linear polynomial and a linear fit.

4.4.1 Calculations o f and with gradients of a non linear polynomial fit

Slow profile sensors are placed at five positions on the tower, measuring variables such as

temperature and wind (section 3.2). For the profiles of potential temperature and wind,

stretching from 8 to 29 m, polynomial fits can be used to find profile gradients. The height 10

m is used as reference height and therefore the measured gradient, based on data from the five

measuring heights, between the two lowest sensor placements (8 and 12.5 m) are used. A

polynomial of third-order is applied due to best compliance with the profiles.

In Figure 15 it is shown how the polynomial behaves in three different cases for temperature.

The polynomials with the appearance like those in Figure 15a and 15b have been used in the

calculations due to the accurate fit. In Figure 15a the polynomial has a nearly perfect fit and in

Figure 15b it has an acceptable fit even though the line does not completely follow the

measurement points. Polynomials having the form as in Figure 15c have been discarded due

to its negative gradient between 8 and 12.5 m. In several cases the local mean gradient is very

small and a small difference in the polynomial can lead to a completely different result.

Figure

15. Third grade polynomials for potential temperature with a good (a), a moderate (b) and a bad (c) fit.

In Figure 16, the polynomial fit of wind can be seen for two different profiles. The slow

response sensor at 15 m did not give many useful measurements of the wind and is therefore

not included when calculating the polynomial.

24

Figure 16. Third grade polynomial fits for wind.

Calculations of the dimensionless profile functions for temperature and wind have been

performed with 147 and 187 profiles, respectively. There is a difference in the amount of

profiles because, between 8 and 12.5 m, more negative or unacceptable gradients (deviating

behaviour over a short time period) were discarded for the temperature than for the wind.

The dimensionless profile functions for temperature and wind can be seen in Figure 17a and

17b. For smaller stabilities, and estimated in this study are clearly higher and their

slopes are much steeper than and from Högström. has a larger scatter while is

not as scattered and is closer to the result of Högström, with a linear increase for smaller

stabilities. Most values of the profile functions are found near neutral stratification. Due to

rather few measurements at higher stabilities for both and there is an uncertainty in the

result in this region and this result is therefore not appropriate to use.

25

Figure 17. Dimensionless profile functions calculated with gradients from a third grade polynomial fit of

temperature profiles (a) and wind profiles (b) as functions of stability. and correspond to

equation 2.9 and 2.11 for temperature and equation 2.10 and 2.12 for wind, respectively. The solid line

represents the mean value for stability intervals and the vertical bars the standard deviations. Note the different

scales on the vertical axis.

4.4.2 Calculations of and with gradients from a linear fit

Measurements at higher heights are not always available and a calculation of the gradient with

use of only two measurement points might be necessary. Because of this, the potential

temperature is simply calculated as and the same holds for the wind, giving a

linear fit.

Figure 18a and 18b show and , estimated in this study with the gradients from a linear

fit, along with and from Högström. In these calculations, 147 values have been used

for temperature and 189 values for wind.

These calculations give higher values and scatter than the result in section 4.4.1. The

estimated and deviate more from the dimensionless profile functions from Högström

and increases slower with z/L than in the case of a third grade polynomial. Another difference

is that the estimated is very high near neutral stratifications. Most values of the profile

functions are also in this case found near neutral stratification.

26

Figure 18. Dimensionless profile functions calculated with gradients from a linear fit of temperature profiles (a)

and wind profiles (b) as functions of stability. and correspond to equation 2.9 and 2.11 for

temperature and equation 2.10 and 2.12 for wind, respectively The solid line represents the mean value for

stability intervals and the vertical bars the standard deviations. Note the different scales on the vertical axis.

Two studies performed at sites in the Baltic Sea gave linear relations during stable

stratification with and ([Bergström and Smedman,

1995] and [Högström et al., 2008]. The result of and in this

study are, in both the third grade polynomial and linear case, in contradiction to both of these

studies. The coefficients in front of the z/L are though different than those used in Högström

(1996).

27

5 Discussion

Negative values of CHN probably appear due to differences between the skin temperature and

the bucket temperature. According to Oost et al. (1999) this difference occurs due to radiative,

sensible and latent heat fluxes.

The mean value of CEN is higher during stable stratification in this study than in previous

studies that generally present a value of 1.1×10-3

. CEN also shows a tendency to increase with

wind speed which might be an explanation to the high mean value. CHN does not show any

significant trend with wind speed. The mean value of CHN is approximately equal to the one

found in Rutgersson et al. (2001), which was not unexpected as the same measuring site and

equations were used. This result indicates that the iterative method used (section 2.5.1)

presents a reliable result, even though the values of CEN are higher than expected.

Higher values of CEN and CHN were measured during 2005. The high values of CEN measured

might depend on the instrument change in April 2006, replacing the current device with a new

LI-COR. The scatter is however large for both measurement periods. For CHN it is hard to

explain the high values without investigating the particular period in detail.

No effect due to wind directions from the sector 80-220° is found for either CEN or CHN. The

tower does thus effectively represent open-ocean conditions and the measurements are not

disturbed by any effects in this sector. With an undisturbed fetch of 150 km in the sector 80-

220°, this was expected. In agreement with earlier studies by Smedman et al. (1999) and

Högström et al. (2008), measurements performed when the wind comes from the sector 50-

80° give unreliable values. The values representing the wind direction 50-80° has probably

been discarded due to too few measurements performed during swell.

After reduction to neutral conditions, there are still differences between the exchange

coefficients and the neutral exchange coefficients. Though differences appear with increasing

stability, the coefficients follow each other relatively well close to near neural stratification.

This is due to the independence of the stratification at neutral conditions. Higher values of

CEN and CHN, near neutral stratification, might depend on the appearance of higher wind

speeds during near neutral conditions than during stable conditions. The small slopes at high

stabilities imply that the values are completely corrected for the stratification.

The highest values of CEN appear during low wave ages. Because of high wind speeds or low

phase speeds this might occur according to equation 2.19. CEN shows an increase with wind

speed and the high values represent the low wave ages. Because of this, and due to high wind

speeds, a dependence on the significant wave height could have been expected. The increase

of CEN with wave age can though not be coupled to a higher significant wave height as no

considerable dependence could be seen. An eventual dependence of the phase speed has not

been studied here but might provide answers. As CHN is independent of wind speed, no

significant dependence with either wave age or significant wave height can be seen.

28

The non-dimensional profile functions for temperature and wind, estimated in this study, are

not consistent with the functions from Högström (1996). The estimated and give

higher values than the functions from Högström and do not show the same linear dependence.

This might depend on that the functions from Högström are not derived for conditions over

sea and therefore the sea state might influence the profile functions, giving higher results than

expected. Earlier studies performed at sites in the Baltic Sea [Bergström and Smedman, 1995,

Högström et al., 2008] have though given linear dependences during stable stratification. The

values on the coefficient in front of z/L, used in these functions, are different than those used

by Högström (1996). The differences of the coefficients are though too small to explain the

result observed in this study. An even higher value of the z/L-coefficient, i.e. a smaller

gradient, should give a better correspondence between the equations. The large differences

between the functions can be caused by errors in the calculations or during the measurements.

The gradients calculated from third grade polynomials are better than when calculated from

linear fits, due to better agreement with the profiles. If the third grade polynomial fit in

section 4.4.1 does not represent a somewhat straight line, use of the linear fit gives a large

uncertainty in the result and because of that large scatter in the estimated and . The

general large scatter in is probably due to the uncertainty in the small gradients, which is

almost as large as the errors of the measurements.

29

6 Conclusions

By use of tower and buoy measurements from the island Östergarnsholm in the Baltic Sea, the

neutral exchange coefficients, for the heat fluxes, and the dimensionless profile functions, for

temperature and wind, have been studied during stable stratification over sea. From this study,

the following conclusions can be drawn:

- CEN is equal to 1.45×10-3

for stable stratification and increases slightly with wind

speed.

- CHN is equal to 0.77×10-3

for stable stratification and shows no significant variation

with wind speed.

- CEN and CHN show no dependence of wind and wave directions that represents open-

ocean conditions (80-220°).

- The stability correction is well performed for stabilities higher than 0.15, i.e. z/L >

0.15.

- CEN increases with low wave ages while CHN shows no dependence with neither high

nor low wave ages.

- CEN and CHN do not depend on the significant wave height.

- and estimated in this study are higher than the dimensionless profile functions

from Högström (1996).

- have larger scatter than .

- Calculations of the gradients with a third grade polynomial fit give better results than

with a linear fit.

This study illustrates the need of improved understanding of the marine atmospheric boundary

layer during stable conditions. Different values of the exchange coefficients in the literature

indicate that the air-sea processes are not completely understood and that further

investigations are needed.

Acknowledgements

First I would like to thank my supervisor Anna Rutgersson for valuable help and support

during this work. I am thankful to Hannes Hellsborn for all help with MATLB and for giving

valuable comments on my work. Thanks to my sister Anna Lindgren for reading my work,

giving comments of importance. I would also like to thank Cecilia Johansson for letting me

use her picture of Östergarnsholm in this thesis. Finally I would like to thank my fellow

students for making my time at MIUU and in Uppsala more pleasant.

30

References

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Pettersson, H., Rutgersson, A., Tuomi, L., Zhang, F., and Johansson, M., 2008, ’Momentum

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32

Appendices

Appendix A

Eddy-correlation method:

The instruments generate time series of different variables and to get measurements that are

representative for the rapidly fluctuating fluxes, an averaging of the flow over an interval is

needed. A commonly used method for averaging is the Reynolds averaging. The variables

can by use of Reynolds averaging be separated into one mean part and one part representing

the deviation from the mean (the turbulent component). By multiplication of two

measurements, for example w and t, following expression is given by Reynolds averaging:

(A.1)

where the terms to the right is the transport due to the mean flow and the vertical kinematic

flux (representing a covariance) of temperature, respectively. Over the ocean, equation A.1

can be expressed as; , due to a very small mean vertical velocity. By measuring the

vertical velocity and the temperature or humidity, the vertical turbulent fluxes can be

calculated by averaging the product of the two fluctuating parts over some time period.

Appendix B

Figure B1. CH as a function of stability. The solid line represents the mean value for stability intervals and the

vertical bars the standard deviations.

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Nr 55 Crooked line first arrival refraction tomography near the Archean-Proterozoic inNorthern Sweden, Valentina Villoria

Nr 56 Processing and AVO Analyses of Marine Reflection Seismic Data from Vestfjorden,Norway, Octavio García Moreno

Nr 57 Pre-stack migration of seismic data from the IBERSEIS seismic profile to image theupper crust, Carlos Eduardo Jiménez Valencia

Nr 58 Spatial and Temporal Distribution of Diagenetic Alterations in the Grés de la Créche Formation (Upper Jurassic, N France), Stefan Eklund

Nr 59 Tektoniskt kontrollerade mineraliseringar i Oldenfönstret, Jämtlands län,Gunnar Rauséus

Nr 60 Neoproterozoic Radiation of Acritarchs and Environmental Perturbations around theAcraman Impact in Southern Australia, Mikael Axelsson

Nr 61 Chlorite weathering kinetics as a function of pH and grain size,Magdalena Lerczak and Karol Bajer

Nr 62 H2S Production and Sulphur Isotope Fractionation in Column Experiments with Sulphate - Reducing Bacteria, Stephan Wagner

Nr 63 Magnetotelluric Measurements in the Swedish Caledonides, Maria Jansdotter Carlsäter

Nr 64 Identification of Potential Miombo Woodlands by Remote Sensing Analysis,Ann Thorén

Nr 65 Modeling Phosphorus Transport and Retention in River Networks, Jörgen Rosberg

Nr 66 The Importance of Gravity for Integrated Geophysical Studies of Aquifers,Johan Jönberger

Nr 67 Studying the effect of climate change on the design of water supply reservoir,Gitte Berglöv

Nr 68 Source identification of nitrate in a Tertiary aquifer, western Spain: a stable-isotope ap-proach, Anna Kjellin

Nr 69 Kartläggning av bly vid Hagelgruvan, Gyttorp, Ida Florberger

Nr 70 Morphometry and environmental controls of solifluction landforms in the Abisko area, northernSweden, Hanna Ridefelt

Nr 71 Trilobite biostratigraphy of the Tremadoc Bjørkåsholmen Formation on Öland, Sweden, ÅsaFrisk

Nr 72 Skyddsområden för grundvattentäkter - granskning av hur de upprättats, Jill Fernqvist

Nr 73 Ultramafic diatremes in middle Sweden, Johan Sjöberg

Nr 74 The effect of tannery waste on soil and groundwater in Erode district, Tamil Nadu, IndiaA Minor Field Study, Janette Jönsson

Nr 75 Impact of copper- and zinc contamination in groundwater and soil, Coimbatore urbanareas, Tamil Nadu, South India A Minor Field Study, Sofia Gröhn

Nr 76 Klassificering av Low Level Jets och analys av den termiska vinden över Östergarnsholm ,Lisa Frost

Nr 77 En ny metod för att beräkna impuls- och värmeflöden vid stabila förhållanden, Anna Belking

Nr 78 Low-level jets - observationer från Näsudden på Gotland, Petra Johansson

Nr 79 Sprite observations over France in relation to their parent thunderstorm system,Lars Knutsson

Nr 80 Influence of fog on stratification and turbulent fluxes over the ocean, Linda Lennartsson

Nr 81 Statistisk undersökning av prognosmetod för stratus efter snöfall, Elisabeth Grunditz

Nr 82 An investigation of the surface fluxes and other parameters in the regional climatemodel RCA1during ice conditions, Camilla Tisell

Nr 83 An investigation of the accuracy and long term trends of ERA-40 over theBaltic Sea, Gabriella Nilsson

Nr 84 Sensitivity of conceptual hydrological models to precipitation data errors – a regionalstudy, Liselotte Tunemar

Nr 85 Spatial and temporal distribution of diagenetic modifications in Upper Paleocene deep-water marine, turbiditic sandstones of the Faeroe/Shetland basin of the North Sea,Marcos Axelsson

Nr 86 Crooked line first arrival refraction tomography in the Skellefte ore field, NorthernSweden, Enrique Pedraza

Nr 87 Tektoniken som skulptör - en strukturgeologisk tolkning av Stockholmsområdet ochdess skärgård, Peter Dahlin

Nr 88 Predicting the fate of fertilisers and pesticides applied to a golf course in centralSweden, using a GIS Tool, Cecilia Reinestam

Nr 89 Formation of Potassium Slag in Blast Furnace Pellets, Elin Eliasson

Nr 90 - Syns den globala uppvärmningen i den svenska snöstatistiken?Mattias Larsson

Nr 91 Acid neutralization reactions in mine tailings from Kristineberg, Michal Petlicki och Ewa Teklinska

Nr 92 Ravinbildning i Naris ekologiska reservat, Costa Rica, Axel Lauridsen Vang

Nr 93 Temporal variations in surface velocity and elevation of Nordenskiöldbreen,Svalbard, Ann-Marie Berggren

Nr 94 Beskrivning av naturgeografin i tre av Uppsala läns naturreservat, Emelie Nilsson

Nr 95 Water resources and water management in Mauritius, Per Berg

Nr 96 Past and future of Nordenskiöldbreen, Svalbard, Peter Kuipers Munneke

Nr 97 Micropaleontology of the Upper Bajocian Ostrea acuminata marls of Champfromier(Ain, France) and paleoenvironmental implications, Petrus Lindh

Nr 98 Calymenid trilobites (Arthropoda) from the Silurian of Gotland, Lena Söderkvist

Nr 99 Development and validation of a new mass-consistent model using terrain-influencedcoordinates, Linus Magnusson

Nr 100 The Formation of Stratus in Rain, Wiebke Frey

Nr 101 Estimation of gusty winds in RCA, Maria Nordström

Nr 102 Vädermärken och andra påståenden om vädret - sant eller falskt?, Erica Thiderström

Nr 103 A comparison between Sharp Boundary inversion and Reduced Basis OCCAM inversion for a 2-D RMT+CSTMT survey at Skediga, Sweden, Adriana Berbesi

Nr 104 Space and time evolution of crustal stress in the South Iceland Seismic Zone usingmicroearthquake focal mechanics, Mimmi Arvidsson

Nr 105 Carbon dioxide in the atmosphere: A study of mean levels and air-sea fluxes over theBaltic Sea, Cristoffer Wittskog

Nr 106 Polarized Raman Spectroscopy on Minerals, María Ángeles Benito Saz

Nr 107 Faunal changes across the Ordovician – Silurian boundary beds, OsmundsbergetQuarry, Siljan District, Dalarna, Cecilia Larsson

Nr 108 Shrews (Soricidae: Mammalia) from the Pliocene of Hambach, NW Germany,Sandra Pettersson

Nr 109 Waveform Tomography in Small Scale Near Surface Investigations,Joseph Doetsch

Nr 110 Vegetation Classification and Mapping of Glacial Erosional and Depositional FeaturesNortheastern part of Isla Santa Inés, 530S and 720W, Chile, Jenny Ampiala

Nr 111 Recent berm ridge development inside a mesotidal estuaryThe Guadalquivir River mouth case, Ulrika Åberg

Nr 112 Metodutveckling för extrahering av jod ur fasta material, Staffan Petré

Nr 113 Släntstabilitet längs Ångermanälvens dalgång, Mia Eriksson

Nr 114 Validation of remote sensing snow cover analysis, Anna Geidne

Nr 115 The Silver Mineralogy of the Garpenberg Volcanogenic Sulphide Deposit, Bergslagen, Central Sweden, Camilla Berggren

Nr 116 Satellite interferometry (InSAR) as a tool for detection of strain along End-Glacial faults in Sweden, Anders Högrelius

Nr 117 Landscape Evolution in the Po-Delta, Italy, Frida Andersson

Nr 118 Metamorphism in the Hornslandet Area, South - East Central Sweden,Karl-Johan Mattsson

Nr 119 Contaminated Land database - GIS as a tool for Contaminated LandInvestigations, Robert Elfving

Nr 120 Geofysik vid miljöteknisk markundersökning, Andreas Leander

Nr 121 Precipitation of Metal Ions in a Reactive Barrier with the Help of Sulphate - ReducingBacteria, Andreas Karlhager

Nr 122 Sensitivity Analysis of the Mesoscale Air Pollution Model TAPM, David Hirdman

Nr 123 Effects of Upwelling Events on the Atmosphere, Susanna Hagelin

Nr 124 The Accuracy of the Wind Stress over Ocean of the Rossby Centre AtmosphericModel (RCA), Alexandra Ohlsson

Nr 125 Statistical Characteristics of Convective Storms in Darwin, Northern Australia, Andreas Vallgren

Nr 126 An Extrapolation Technique of Cloud Characteristics Using Tropical Cloud Regimes, Salomon Eliasson

Nr 127 Downscaling of Wind Fields Using NCEP-NCAR-Reanalysis Data and the MesoscaleMIUU-Model, Mattias Larsson

Nr 128 Utveckling och Utvärdering av en Stokastisk Vädergenerator för Simulering avKorrelerade Temperatur- och Nederbördsserier, för Tillämpningar på den NordiskaElmarknaden, Johanna Svensson

Nr 129 Reprocessing of Reflection Seismic Data from the Skåne Area, Southern Sweden, Pedro Alfonzo Roque

Nr 130 Validation of the dynamical core of the Portable University Model of the Atmosphere(PUMA), Johan Liakka

Nr 131 Links between ENSO and particulate matter pollution for the city of Christchurch,Anna Derneryd

Nr 132 Testing of a new geomorphologic legend in the Vattholma area, Uppland, Sweden, Niels Nygaard

Nr 133 Återställandet av en utdikad våtmark, förstudie Skävresjön, Lena Eriksson, Mattias Karlsson

Nr 134 A laboratory study on the diffusion rates of stable isotopes of water inunventilated firn, Vasileios Gkinis

Nr 135 Reprocessing of Reflection Seismic Data from the Skåne Area, Southern SwedenWedissa Abdelrahman

Nr 136 On the geothermal gradient and heat production in the inner corePeter Schmidt

Nr 137 Coupling of the Weather Research and Forecasting model (WRF) with the CommunityMultiscale Air Quality model (CMAQ), and analysing the forecasted ozone and nitro-gen dioxide concentrations , Sara Johansson

Nr 138 Sikt i snöfall - En studie av siktförhållanden under perioder med snöfall,Jesper Blomster

Nr 139 Mineralogy of the hypozonal Svartliden gold deposit, northern Sweden, with emphasison the composition and paragenetic relations of electrum, Daniel Eklund

Nr 140 Kinematic analysis of ductile and brittle/ductile shear zones in Simpevarp andLaxemar subarea, Emil Lundberg

Nr 141 Wind Climate Estimates-Validation of Modelled Wind Climate and Normal YearCorrection, Martin Högström

Nr 142 An Analysis of the Local Weather Around Longyearbyen and an InstrumentalComparison, Charlotta Petersson

Nr 143 Flux Attenuation due to Sensor Displacement over Sea, Erik Nilsson

Nr 144 Undersökning av luftkvaliteten vid småskalig biobränsleförbränning i två kommuner med modellsystemet VEDAIR, Stefan Andersson

Nr 145 CO2-Variation over the Baltic Sea, Gustav Åström

Nr 146 Hur mörkt blir det? Lena Nilsson

Nr 147 Master thesis in interpretation of controlled-source radiomagnetotelluric data fromHallandsåsen, Martin Hjärten

Nr 148 A Structural and Ore Geological study of the Palaeoproterozoic Stratabound Sala Zn-Pb-Ag deposit, Bergslagen, Sweden, Nils Jansson

Nr 149 Numerical exploration of radiative-dynamic interactions in cirrus, Stina Sjöström

Nr 150 Modellering av flöden och syrgasförhållanden i Dannemorasjön och dess tillrinnings-område, Seija Stenius

Nr 151 Characteristics of convective cloud cluster formation over Thailand through satelliteimage analysis, Christian Rosander

Nr 152 Krossberg som ballast för betong - En studie av standardiserade kvalitetstestmetoderför CE-märkning av betongballast, Kristina Wikström

Nr 153 Snöns påverkarn på renarnas vinterbete - en del av projektet isis, Sofie Fredriksson

Nr 154 A Sensitivity Analysis of Groundwater Suitability Mapping of the Three-Basin Area inMaputo, Mozambique, Björn Holgersson

Nr 155 Using cloud resolving model simulations of tropical deep convection to studyturbulence in anvil cirrus, Lina Broman Beijar

Nr 156 Validation of the WAM model over the Baltic Sea, Caroline Berg

Nr 157 A Simple Method for Calculations of Wake Effects in Wind Farms with Influence ofAtmospheric Stability, Anna Lewinschal