Examensarbete vid Institutionen för geovetenskaperISSN 1650-6553 Nr 155

Using cloud resolving modelsimulations of tropical deep

convection to study turbulence inanvil cirrus

Lina Broman Beijar

Abstract Identifying the dynamical processes that are active in tropical cirrus clouds is important for

understanding the role of cirrus in the tropical atmosphere. This study focuses on analyzing

turbulent motions inside tropical anvil cirrus with the use of a Cloud Resolving Model.

Convection in the transition from shallow to deep convection has been simulated with Colorado

State University Large Eddy Simulator/Cloud Resolving Model System for Atmospheric Model

(SAM 6.3) in a high resolution three-dimensional simulation and anvil cirrus formed in the end of

this simulation has been analyzed. For model set up, data gathered during the Tropical Rainfall

Measuring Mission Large-Scale Biosphere-Atmosphere (TRMM LBA) field experiment in

Amazonas, Brazil have been used as large scale forcing.

31 anvil clouds have been localized from a single time step of the simulation, “a snapshot”, of the

entire simulated cloud field consisting of convective clouds of different scales and subsequently

divided into three categories that represent different stages of the anvil lifetime; growing, mature

and dissipating anvil stages. The classification is based on in-cloud properties such as cloud

condensate content and vertical velocities. The simulated anvils have been analyzed both

individually and as groups to examine the transition from isotropic three-dimensional turbulence

in the convective core of the thunderstorm to stratified two-dimensional turbulence in the anvil

outflow.

A dimensionless number F is derived and used as a measure of the “isotropic” behavior of the

turbulence inside the cloud. F is expressed as the ratio between the horizontal part of TKE and the

total (horizontal + vertical)

Experiments show that SAM 6.3 clearly can resolve turbulent structures and that the transition

from isotropic three-dimensional turbulence to stratified two-dimensional turbulence occurs in

the middle layers of the mature and dissipating anvil stages.

Sammanfattning av ”Studier av turbulenta rörelser i städmoln med

hjälp av numeriska simuleringar av tropisk konvektion” Städmoln i tropikerna har stor inverkan på strålningsballansen på grund av de är så vanligt

förekommande och att de ligger på hög höjd i atmosfären. Att förstå de drivande krafterna som

är aktiva i skapandet och underhållandet av städmoln är viktiga för att få en bra bild av rollen

städmoln spelar i den tropiska atmosfären.

Den här uppsatsen fokuserar på att studera turbulenta rörelser inuti tropiska städmoln med hjälp

av en molnmodell. Tropisk konvektion har simulerats med Colorado State University’s

molnmodell SAM 6.3 i en högupplöst tredimensionell simulering. Data från en ”ögonblicksbild” av

det simulerade molnfältet har analyserats och 31 städmoln har valts ut och studerats vidare. De

simulerade städmolnen indelades i tre olika kategorier baserat på utvecklingsstadier; växande

städmoln, moget städmoln och skingrade städmoln. Stadieklassificeringen bestämdes beroende

på isvatteninnehåll och vertikalhastigheter i molnet. Städmolnen har därefter analyserats både

individuellt och som grupper för att lokalisera och analysera övergången från tredimensionell

isotropisk turbulens i kärnan av Cb-molnet till tvådimensionell stratifierad turbulens i städmolnet.

För att initiera simuleringen användes mätdata insamlade under fältexperimentet TRMM LBA

(Tropical Rainfall Measuring Mission Large-Scale Biosphere-Atmosphere) i Amazonas, Brasilien.

För att beskriva turbulenta rörelser i molnen togs det dimensionslösa talet 𝐹 fram som ett mått

på isotropin. 𝐹 uttrycks som kvoten mellan den horisontella delen av TKE och den totala

(horisontell och vertikal).

Den här studien visar att den undersökta molnmodellen SAM 6.3 klart kan simulera turbulenta i

rörelser i övergången mellan isotropisk till horisontell turbulens i olika stadier av städmolnens

livscykel. Mina analyser visar att övergången sker främst i de mellersta skikten av de mogna och

skingrade stadierna av städmolnets utveckling.

Preface This master’s thesis was carried out at the Dept. of Atmospheric Science at Colorado State

University, USA as a part of my master’s education at Dept. of Meteorology at Uppsala University

during a 6 months visit in Prof. Dave Randall’s research group.

I would like to thank Dept. of Atmospheric Science CSU for giving me the opportunity to do my

thesis at your University; it has been a valuable and exciting part of my education.

I would like to especially thank my supervisor Prof. Dave Randall, for guidance and support. Marat

Khairoutdinov, Associate professor for invaluable discussion on SAM and always having time to

answer my questions on FORTRAN programming. Kelley Wittmeyer for help with retrieving SAM

data and aid with numerous computer problems. Maike Ahlgrimm for all the help with IDL and for

being a wonderful friend. Chris Rozoff and Matt Masarik for the best adventures of my life.

Special thanks also to Dr. Cecilia Johansson and Dr. Hans Bergström at the Dept. of Meteorology

at Uppsala University for helping me complete this thesis, without their great support and

encouragement; this thesis would never have been finished. Thanks also to Dr. Anna Rutgersson

for useful comments.

And finally, my family and Martin for love and support.

Contents

1 INTRODUCTION ................................................................................................................................ 1

1.1 TROPICAL ANVIL CIRRUS ............................................................................................................................ 2

2 THE MODEL ...................................................................................................................................... 4

2.1 MODEL DESCRIPTION AND COMPUTATIONAL DESIGN ...................................................................................... 4

2.2 THE LBA-SIMULATION ............................................................................................................................. 5

3 THEORY ............................................................................................................................................ 8

3.1 A BRIEF REVIEW OF EARLIER WORK ON DYNAMICS IN THUNDERSTORM ANVIL CIRRUS ............................................. 8

3.2 TURBULENCE ........................................................................................................................................ 10

3.2.1 Turbulent Kinetic Energy ......................................................................................................... 10

4 ANALYSIS AND RESULTS.................................................................................................................. 12

4.1 TOOL FOR MEASURING THE ISOTROPIC BEHAVIOR IN SIMULATED ANVILS ........................................................... 12

4.2 ANALYSES OF THE MODEL DOMAIN ........................................................................................................... 14

4.3 ANVIL STAGE CLASSIFICATION .................................................................................................................. 16

4.4 RESULTS ............................................................................................................................................... 18

4.4.1 Vertical structure of simulated anvils ..................................................................................... 21

4.4.2 Horizontal structure of simulated anvils ................................................................................. 22

4.4.3 Gravity waves ......................................................................................................................... 23

5 DISCUSSION AND CONCLUSIONS .................................................................................................... 28

6 REFERENCES ................................................................................................................................... 30

Introduction

1

1 Introduction The parameterization of cirrus clouds in General Circulation Models (GCMs) and Numerical

Weather Prediction (NWP) models is considered as one of the biggest challenges in Global

Circulation modeling (Jacob, 2002) and to rightfully parameterize these clouds is an ongoing

research topic. Cirrus clouds are important to study because of their large effects on the radiative

fluxes of the atmosphere. Due to their inaccessibility on high altitudes they are poorly observed

both of in-situ measurements and passive remote sensing from space (Stephens, 1998).

In today’s GCMs, the grid spacing is too coarse to resolve individual clouds. Therefore the total

effects of clouds on the large-scale flow are represented in parameterized form. Randall et al.

(2003) have found that a Cloud Resolving Model (CRM) often give more accurate results than

models used in GCM’s that are based on the same parameterizations. A CRM is a numerical

model that resolves cloud scale circulations in two or three spatial dimensions.

The thermal stratification in the upper tropical troposphere is stable, which means that turbulent

kinetic energy (TKE) is consumed in this area. Turbulence at these altitudes is produced by

instabilities caused by gravitational waves and vertical shear from horizontal wind (Smith, 1997),

but can also be produces and occur in patches by latent heat release from the convective regions

and by radiative effect from the cirrus sheets originating from these convective regions. In the

tropics, daytime convection causes convective towers to reach high up in the tropical

troposphere where they spread out under the tropopause to form vast and persistent anvil cirrus

sheets.

LIDAR observations of high altitude cirrus near the tropopause show that the formation and

maintenance of these clouds are closely associated with the strength of tropospheric turbulence

(Parameswaran, 2004). The effects of turbulence and cloud-scale motions in the anvil influence

the cloud structure and therefore also the physical properties of the anvil. Small scale turbulent

motions in anvil redistribute heat, transport momentum and are responsible for mixing of scalars

such as ice in the cloud which in turn influence the radiative properties of the cloud (Lynch et al.

2002). Vertical transport of momentum by convection affects the conversion of kinetic energy in

small eddies in sub-grid-scale to the mean flow, it also affects the rate of dissipation of turbulent

kinetic energy in the atmosphere, and therefore the atmospheric energy spectrum in this region

of the atmosphere (Tao, 2007).

When the anvil starts to form under the tropopause, the vertical wind fluctuations is dampened

due to the stable thermal stratification found, making the turbulence more “stratified”.

Transition from three-dimensional isotropic fluctuations at small scales to two-dimensional

fluctuations at larger scales in anvil cirrus is discussed theoretically by Lilly (1983). Lilly predicted

that the energy in this “stratified turbulence” should be equally divided between gravity waves

Introduction

2

and chaotic turbulence. Studies of where in the cirrus anvil this transition from isotropic three-

dimensional turbulence to two-dimensional stratified turbulence and gravity waves take place is

of importance to better understand how vertical turbulent motions of various scales affect anvil

cirrus maintenance and dissipation.

To analyze where in the anvil cirrus this conversion mainly takes place, small scale variations in

air motion within thunderstorm anvils will be studied with aid of a high resolution numerical

simulation of tropical deep convection. For different stages of anvil evolution, these small-scale

variations will be located and described. As a part of this study, I will also analyze how good the

CSU System for Atmospheric Modeling Cloud Resolving Model (CSU SAM) can resolve turbulent

motions in different stages of anvil evolution.

1.1 Tropical anvil cirrus Detrainment from deep convection is the ultimate source of tropical upper-tropospheric clouds

(Webster et al. 1980). These clouds throw vast amounts of water vapor and ice crystal into the

stratosphere and feed the lower parts of the stratosphere with moist air through extensive cirrus

sheets from decaying thunder cloud anvils. The lifetime of an anvil, typically 6-12 hours

(Ackerman, 1988; Houze et al. 1980), exceeds the lifetime of deep convection by many hours.

Cirrus clouds are composed of ice crystals and form at high altitudes and low temperatures in the

troposphere. Since the height of the troposphere varies over the globe, stretching from 6 km at

the poles to 18 km at the equator, cirrus clouds can be found at various heights depending on

latitude. In the tropics, cirrus form at altitudes of 9 to 18 km.

There are many different types of cirrus clouds and ways in which they can form, but in the

tropics, the most common generator is deep convective cumulonimbus clouds (Heymsfield et al.

2002).

Deep convection redistributes heat and moisture in the tropical atmosphere. When hot and

humid air rises in areas called convective cores it cools and condensation occurs inside the cloud.

The condensation process releases latent heat which is further warming the air and contributes

to the upward motion. This produces heavy precipitation under the cloud. When the main cloud

updrafts in a tropical cumulonimbus reaches the tropopause or a layer with a strong wind shear it

is truncated vertically and forced to spread out horizontally by continuity. The updrafts are also

constrained by the convectively very stable low stratosphere. The strong wind shear stretches the

top of the cloud asymmetrically and produces the characteristic observed anvil shape (Figure 1).

The anvil can spread out to form a large widespread cloud layer and produces high-altitude cirrus

that last for several hours (McIlven, 1986). The anvil can be several times bigger in horizontal

extent than the originative cumulonimbus cloud and is positioned in the downward wind

direction from the main updraft (Short et al. 2004).

Introduction

3

Figure 1 Cumulonimbus in mature stage with an overshooting top and a clearly visible anvil. Source: www.top-wetter.de

When the cold air in the cirrus top starts to fall, it warms and dries as it sink, a process which

humidifies the upper atmosphere.

Dynamical processes in the anvil that tend to break down the anvils are precipitation fallout and

sublimation. Processes that build up and maintain the clouds are radiative cooling, condensation

and deposition due to ascent and turbulent mixing.

The Model

4

2 The Model

2.1 Model description and computational design A CRM is a numerical model that resolves cloud-scale and meso-scale motions. These models are

often used in simulations with high resolution to investigate the formation, maintenance,

structure and dissipation of cloud systems. They are also used as a tool to test new cloud

parameterizations for GCM’s.

The Cloud-resolving model used in this study is System for Atmospheric Modeling (SAM) version

6.3, which is the new version of the Colorado State Large Eddy Simulation/Cloud Resolving Model

fully described in Khairoutdinov et al. (2003). The model is three-dimensional and the dynamics is

based on the large eddy simulation (LES) model created by Khairoutdinov and Kogan

(Khairoutdinov et al. 1993). SAM is mainly used to study small- and mesoscale variability and the

organization of the clouds and their interaction with the environment. It has been used in a broad

range of experiments by cloud modelers in the US and Canada (Kuang et al. 2007; Kuang et al.

2005). The model was recently found to produce too little anvil cloud per unit of precipitation

compared to observational data (Lopez, 2007).

SAM uses the anelastic approximation to solve the equations of motion. To describe the

thermodynamics, three prognostic variables are used; non-precipitating water (water vapor,

cloud liquid and cloud ice), precipitating water (rain, snow, and graupel) and liquid/ice moist

static energy. Partitioning between these three variables is based only on temperature.

The six individual states of water (rain, snow, graupel, vapor, cloud water and cloud ice) are

diagnosed from the two prognostic water variables. The advantage of this is to speed up

computations for the simulations. SAM uses a Cartesian grid system of Arakawa C-type grid,

u (i,j,k)

w (i,j,k) v (i,j,k)

scal(i,j,k)

Figure 2 Arakawa C-type grid. The velocity vectors are defined at the center face of the corresponding grid box and the scalar quantities are defined in the center of each grid box.

The Model

5

where the wind velocity vectors u, v and w are defined at the center of the corresponding face of

each cell, and the scalar quantities are defined in the center of each grid box (Figure 2). The

model uses the multi-step Adam-Bashford scheme with a variable time step to integrate the

equations of motions. Adam-Bashford is an explicit scheme that gives good results without having

go through a large number of calculations. Monin-Obukov similarity is used to compute the

surface fluxes. The model uses periodic lateral boundaries.

2.2 The LBA-simulation The TRMM-LBA (Tropical Rainfall Measuring Mission Large-Scale Biosphere-Atmosphere) field

experiment was performed in Amazonia, Brazil, from 1 November 1998 to 28 February 1999. See

Figure 3 for location of the field campaign. This experiment was carried out in part to collect

tropical rainfall data to validate and improve products from NASA’s TRMM (Robinson et al. 2000).

The focus was on studying different aspects of tropical convection in the Amazon region. Data

collected from the TRMM-LBA experiment have been used before to validate several cloud

models. Figure 4 shows a satellite picture taken over the location of the field campaign at mid-day

where widespread cirrus sheets are clearly observed.

Figure 3 Overview of Amazonia, Brazil. Red box marks the location of the TRMM-LBA field experiment. Source

www.maps.com

The Model

6

Figure 4 Satellite picture taken over location of the TRMM-LBA field campaign on 1998 12 30 17.45 UTC=13.45 local

time. Source http://radarmet.atmos.colostate.edu/lba_trmm/sat/sat_br_19981227.html

In this study I will analyze a simulated case from SAM, based on idealized observations from the

TRMM-LBA experiment case 4. The purpose of case 4 was to investigate the development of

daytime convection and the transition from shallow cumuli to deep convection.

The simulation is three-dimensional and covers a horizontal area of (154 x 154) km2 and vertically

up to the height 25.4 km. With a horizontal grid size of uniformly 100m and a vertical resolution

gradually increasing from 50 m in the boundary layer, 100 m above 6 km, up to 250 m near the

top layer, this makes a very high-resolution simulation. The number of vertical levels in this

simulation is 256.

It is argued by Bryan et al. (2001) that the spatial resolution of the grid box must be at the most

100m to be sure that subgrid-scale parameterizations of turbulence in CRMs are valid.

To simulate deep convection, forcing has been applied uniformly in form of prescribed sensible

and latent heat fluxes from the TRMM-LBA experiment. Radiative heating rates was also

prescribed and applied evenly in the horizontal plane. Newtonian damping is applied to suppress

gravity waves in the upper third of the model.

The simulation has a time-step of 2 sec and is six hours long, from 7.30 a.m. to 12.30 p.m. Shallow

cumulus develop after 2 hours of the simulation. One hour before the end of the simulation, deep

convection emerges with many clearly distinguished anvil clouds. The anvils reach a horizontal

extent between 20 and 60 km2 in this simulation. Figure 5 shows a visualization of the three-

dimensional simulated cloud field as it would look like from the surface.

The Model

7

Figure 5 Visualization of the cloud field as would be seen from the surface, simulated by the CSU System For

Atmospheric Modeling (SAM), based on TRMM-LBA observations. (Khairoutdinov, 2006))

Theory

8

3 Theory

3.1 A brief review of earlier work on dynamics in thunderstorm anvil

cirrus A conceptual model was constructed by Lilly (1988) to better describe the dynamics and

evolution of anvil cirrus. This model is based on earlier work by Lilly on stratocumulus clouds. Lilly

uses the “wake-collapse” analogy to explain the initial thunderstorm anvil-plume intrusion into

the non-cloudy stratified layer of air under the tropopause. The “wake collapse” was early

outlined by Schooley and Stewart (Schooley et al. 1963) and is here described briefly.

A self-propelled body of large Reynolds number moving through a stratified fluid produces a

region of essentially homogenous fluid behind, a nearly isotropic three-dimensional turbulent

wake. This wake initially expands vertically, and then collapses and spreads out horizontally. The

outflow plume development from a thunderstorm is described by Lilly (1988) as an idealized case

of a two-stage process. A brief review of this case here follows.

In the first stage the plume collapses, just as in the wake collapse-analogy, externally and

internally. In the external collapse the plume is flattened and spread by the stable thermal

stratification. Figure 6 shows a sketch of an idealized anvil outflow from a cumulonimbus cloud,

where the vertical lines indicate position for the cross sections shown in Figure 7. The horizontal

lines in Figure 7 are potential temperature surfaces in the environment, and the arrows indicate

the motion field induced by the buoyancy difference between the outflow plume and the

environment. The internal collapse consists of the initially three-dimensional turbulence

transforming to larger scale quasi-two-dimensional turbulence and gravity waves.

In the second stage, strong radiative heating causes a destabilization of the plume. The heating

from infrared wavelengths is affecting the cloud unevenly thought the cloud. The top of the cloud

is cooled by atmospheric cooling and the cloud base is heated by terrestrial heating. This leads to

convectively generated turbulence inside the anvil cirrus and growth by entrainment processes at

the top and bottom of the cloud.

The internal collapse is described in detail in an earlier paper by Lilly (1983). Lilly describes a

process where the downscale cascade of three-dimensional turbulence is transformed into two-

dimensional stratified turbulence and internal gravity waves. This is valid under the presence of

static stability. The gravity waves are predicted to move away from the convective core and

finally dissipate. The two-dimensional turbulence is believed to grow in horizontal scale due to

upscale cascade, a process early predicted and described by Kraichnan (1967).

Theory

9

Figure 6 Idealized outflow of cirrus anvil from a cumulonimbus cloud. Vertical lines AA and BB indicate positions of the

cross sections in Figure 7. Adapted from Lilly (1988)

Figure 7 Collapse of a cumulonimbus anvil. The horizontal lines are potential temperature surfaces in the environment,

and the arrows indicate the motion field induced by the buoyancy difference between the outflow plume and the

environment. Adapted from Lilly (1988)

Theory

10

3.2 Turbulence Turbulence consists of irregular motions occurring in fluids, characterized as fluctuating motions

occurring in all three velocity directions and as being unpredictable in time. However, statistical

properties of turbulence can be identified and analyzed. Turbulent motions appear over a broad

range of temporal and lateral scales resulting in a mixing of fluid properties.

3.2.1 Turbulent Kinetic Energy

Reynolds averaging is used in fluid dynamics to separate turbulent fluctuations from the mean

flow. The average is usually taken over a period of time, but may also be taken over a space or an

ensemble of realizations (AMS-Glossary).

In this paper, the fluctuation, i.e. the difference between an instantaneous physical quantity and

its mean flow, is denoted by a prime, i.e. 𝑢 . The mean flow is denoted by a overline, i.e. 𝑢, and

the instantaneous wind is denoted by a lower case letter, i.e. 𝑢. Assuming that the instantaneous

wind can be separated into two parts, the instantaneous wind can be expressed as

𝑢 = 𝑢 + 𝑢′ (3.1)

Kinetic energy is defined as 1

2𝑚 𝑢2 + 𝑣2 + 𝑤2 , where 𝑚 is the mass. Assuming the flow can be

partitioned into mean and fluctuating parts; the total kinetic energy of the flow is simply the sum

of kinetic energy of the mean flow (MKE) and the kinetic energy of the turbulent flow (TKE). Per

unit mass the kinetic energy of the mean and the turbulent parts of the flow can be expressed as

𝑀𝐾𝐸 =

1

2 𝑢 2 + 𝑣 2 + 𝑤 2

(3.2)

𝑒 =1

2 𝑢′2 + 𝑣′2 + 𝑤′2 (3.3)

Where MKE is the kinetic energy of the mean flow per unit mass and 𝑒 is the kinetic energy of the

turbulent flow per unit mass. Since the instantaneous values of TKE can vary drastically, it is often

useful to average TKE over the fluctuating parts to get a more representative value of the overall

flow. This gives TKE per unit mass,

𝑇𝐾𝐸 =

1

2 𝑢′2 + 𝑣′2 + 𝑤′2

(3.4)

Velocity variance, i.e. the standard deviation of 𝑢′, 𝑣′ and 𝑤′ and can be used as indicators of

turbulence (Quante at al. 2002).

Variance is one statistical measure of the dispersion of data about a mean value, and is defined as

Theory

11

𝜎𝑥

2 =1

𝑁 𝑥𝑖 − 𝑥 2

𝑁

𝑖=1

(3.5)

, where N is the total number of grid boxes in my dataset and x is any variable

Isotropic three-dimensional turbulence is defined as turbulence in which the products and

squares of the velocity components and their derivatives are independent of direction (AMS-

Glossary).

Atmospheric turbulence is usually nonisotropic, but isotropic turbulence is what is most easily

produced in wind-tunnel experiments and the approximation is used in analysis of turbulent flow.

Analysis and Results

12

4 Analysis and Results

4.1 Tool for measuring the isotropic behavior in simulated anvils To analyze turbulent motions for any given variable, a method has to be chosen that separates

fluctuating motions from the mean value. For experimental turbulent measurements of wind,

when given a time series of wind measurements, the mean wind is calculated as a mean over

time, a temporal mean. For calculations of mean variables in this work spatial averaging has been

used instead of temporal averaging. This is because of the limitations of the given simulated data

used in this study.

The mean values for the variables in all grid boxes of the dataset were first calculated using the

running-box-mean method over a horizontal area. The running-box-mean method to calculate

mean gives a specific mean value for each grid box based on the horizontal surroundings. The

running mean box covered a horizontal area of 9 km2 (900 grid boxes). The mean value should

include many areas of the cloud regime (convective core, updraft- and downdraft-areas and

dissipating areas) and also the non-cloudy areas between the clouds. The size of the running-

mean box is chosen this big to be able to resolve fluctuations of various scales and therefore

justify averaging calculations on a spatial scale instead of the, as otherwise standard, temporal

scale.

After calculating mean values of the variables in all grid boxes, a filter was applied to smooth out

the peaks and also to make it a spatial weighted mean and therefore making the mean values

useful for calculating the fluctuating part of the variable.

The idea when constructing this filter was that when calculating the spatial mean for a given grid

box, a grid box closer to the given grid box should contribute more to the spatial mean than a grid

box further away from the given grid box. Therefore, a grid box closer to the examined grid box

obtained a higher weight than a grid box further away.

The normal distribution was used as a filter,

𝑓 𝑥 =

1

𝜎 2𝜋 𝑒−

𝑥 − 𝜇 2

2𝜎2,

(4.1)

where 𝜎 is the standard deviation, and 𝜇 is the expected value and x is the distance from the

center of the running-mean box. 𝑓(𝑥) becomes the weight factor depending of distance. A good

fit to the damping surface was found when using the standard normal distribution where 𝜎2=1

and 𝜇=0.

To illustrate the method described above, Figure 8 shows a horizontal cross section of the vertical

velocity at the height 8.7 km for a subsection of the model domain before and after applying the

Analysis and Results

13

running-mean-box averaging method with the smoothing filter. In Figure 8 (a) a towering cumulus

is visible in the center of the figure with high vertical velocities shown as red, yellow and

turquoise areas containing maximum wind speed values of 15 m/s and with a sinking air area

shown as darker blue areas with minimum wind speed values of -5 m/s surrounding the tower of

rising air. In Figure 8(b) the peaks of both rising and sinking air are still visible, but smoothed out

and easier to use for further analysis as mean values.

Figure 8 Horizontal cross section of vertical velocities for a simulated cumulus tower and surrounding cloud free areas at

the height 8.7 km. Showing (a) model data and (b) mean values of the same area when the running box mean method

width the smoothing filter was applied. Positive values indicate upward motion and negative values indicating

downward motion.

In Lilly’s theory of anvil cirrus, he outlined a method to study turbulence in anvil clouds. To

analyze if the transition between three-dimensional to two-dimensional turbulence can be

resolved in a CRM simulation, and to find when and where the transition occur, a simple

expression for calculating isotropic turbulence is constructed. This expression will later be used to

study near-isotropic turbulence and the transition between different fluctuations on the velocity-

axes in the simulated anvils.

A dimensionless number F is here introduced as a measure of the “isotropic” behavior of the

turbulence both inside and outside of the cloud. F should be interpreted as ’in a specific point,

which direction has the highest velocity fluctuations, or are all the velocity fluctuations of equal

amplitude in all directions?’ F is expressed as the ratio between the horizontal part of TKE and the

total of the horizontal and vertical TKE.

Analysis and Results

14

𝐹 =

ℎ𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙 𝑇𝐾𝐸

𝑡𝑜𝑡𝑎𝑙 𝑇𝐾𝐸

(4.2)

Rewritten as

𝐹 =

𝑢′ 2 + 𝑣′ 2

𝑢′ 2 + 𝑣′ 2 + 𝑤′ 2

(4.3)

where 𝐹 range from 0 to 1.

If given that 𝐹 is calculated to have the value 2

3 in a grid box, Eq.( 4.3) then implies that 𝑤 ′ 2 =

𝑢 ′ 2 +𝑣′ 2

2 , note that this is not the same as 𝑢′2 = 𝑣′2 = 𝑤′2 . That is, the sum of the squared

horizontal velocity fluctuation means are of equal size as the squared vertical velocity fluctuation

means. As will be shown later, 𝑢′2 is often of the same magnitude as 𝑣′2 for the analyzed levels of

this simulation. So when the value of 𝐹 is 2

3 or close to

2

3 , the turbulence in that grid box is almost

isotropic. If F is calculated to a larger value than 2/3, Eq. (4.3) then implies that 𝑤′2 < 𝑢′2 + 𝑣′2

, thus in this case the vertical velocity fluctuations dominate the horizontal. F smaller than 2/3

implies that 𝑤′2 > 𝑢′2 + 𝑣′2 , and thus the horizontal velocity fluctuations dominate the vertical.

Table 1 summaries the above discussion.

Table 1 Case table illustrating the physical meaning of various F.

F Relation Physical meaning

2/3 𝑤′2 =

𝑢′2 + 𝑣′2

2

Isotropic turbulence

<2/3 𝑤′2 ≪ 𝑢′2 + 𝑣′2 Horisontal velocity fluctuations

dominate

>2/3 𝑤′2 ≪ 𝑢′2 + 𝑣′2 , Vertical velocity fluctuations

dominate

4.2 Analyses of the model domain In Figure 9, the vertical structure of the simulated cloud field properties is shown. For the

subplot showing cloud condensate(cloud ice + cloud water), two peaks can be seen, where the

lower peek at 3 km relate to the shallow cumulus in the lower levels of the cloud field and the

higher peak relate to the anvils found under the tropopause. For the precipitation plot, this

includes rain, snow, ice and graupel, e.g. both precipitation that falls from the cloud and

Analysis and Results

15

precipitation that stay inside the cloud. Figure 9 also shows the vertical mean profiles of water

vapor mixing ratio and temperature.

In the model output of 150*150*25 km3 the average anvil height is found by defining that height

as the layer where the maximum cloud fraction occurs. The cloud fraction is calculated as fallows.

At each level, a grid box is defined as cloudy if cloud condensate (cloud ice + cloud water)

exceeds threshold 10−5 g/kg. This threshold on cloud condensate for a cloudy grid box has been

used in previous studies from simulations with SAM (Kuang et al. 2007). The average anvil height

is found to be at 8.7 km, by taking horizontal averages of the cloudy regions of the total domain

(see Figure 9, cloud condensate). At 8.7 km, the cloud cover is 12% and consists of mainly anvils

and some high convective cores.

By further analyzing vertical profiles of the vertical velocities in a zoomed-in height span around

the average anvil layer(Figure 10), clues is given of where to look for turbulent areas in the cloud

field. A peak in vertical velocity variance found 1.3 km below the average anvil layer suggests

strong turbulent motions (see Figure 10, middle). From the average anvil height and up, the

horizontal velocity fluctuations slightly increase with height. Reaching a maximum value at the

level where the highest lying anvils are found, suggesting strong turbulent horizontal motions in

and above the cloud tops. This could be explained by the strong upward air motions in the core

hitting the tropopause and than being forced to spread horizontally. Overall, the magnitude of

the vertical wind fluctuations is about twice the magnitude of the horizontal velocity fluctuations,

where the two horizontal velocity components (u-wind and v-wind) are of similar amplitude. In

Figure 10 (right), velocity fluctuations for the areas around the clouds of the simulated cloud field

is shown (note that the scale on the x-axis is smaller on this plot).

Figure 9 Vertical structure in the model atmosphere analyzed as vertical profiles of horizontally averages of total domain (including both cloudy and non-cloudy areas) (left-right) Cloud Condensate, Precipitation, Water vapor mixing ration and Temperature

Analysis and Results

16

Figure 10 Zoomed-in vertical profiles of cloud fraction (left), velocity variance in cloudy regions (middle) and velocity variance in cloud free regions (right). The figure shows profiles for u-wind (thin solid line), v-wind (thick solid line) and w-wind (dashed line).

4.3 Anvil stage classification To analyze anvils from the CRM data, the model domain must first be separated into convective

regions with anvils and surrounding air (non-convective regions). Convective regions are

identified by searching for columns with high values of vertical velocities and high values of mean

cloud condensate. Anvils are thereafter identified as the widespread sheets surrounding the

convective regions containing low values of vertical velocities and low values of mean cloud

condensate.

To analyze the dynamics in different stages of anvil evolution, the present anvils in the data set

were divided into three different stages based on cloud condensate and vertical velocities (see

Figure 11). This classification system has not been used before, but has been invented for this

study. The intervals for the evolution stages are chosen based on the assumption that amount of

cloud condensate in a grid box is denser in the beginning of anvil development, and gets more

transparent width time when the anvil dissipates towards the end.

Analysis and Results

17

In growing anvil stage, the mature cumulonimbus cloud is starting to form an anvil at the top of

the cloud. The cumulonimbus anvil cloud is still connected to the convective core that feeds the

anvil with ice and cloud debris. The core has a high concentration of cloud condensate.

Therefore, the definition classification of a growing stage anvil is an anvil containing vertical

velocities above 7.0 m/s (𝑊𝑀𝑎𝑥 ) in the middle layers of the anvil (inducing updraft region) and

also that this updraft region contains regions of cloud condensate content of at least 1.7 g/kg

(𝑄𝑛𝐶𝑜𝑟𝑒 ) to verify that the updraft is truly a cloudy core. The threshold on cloud concentrate

content to 1.7 g/kg is selected based on an examination of the total spread of cloud concentrate

in the data set. In Khairoutdinov (2006) the definition of a convective core in a cloud simulation

was a collection of grid cells, cloudy or not, with vertical velocities exceeding 5 m/s.

In the mature anvil stage, the velocities in the convective core of the decaying cumulonimbus

cloud have weakened and can no longer raise cloud condensate to the anvil-layer. With this

follows that the source of ice building up the anvil stops and the anvil enters a stage where it will

have to maintain building-up by itself. To classify this stage, the abundance from the convective

Growing anvil

Mature anvil

Dissipating

anvil

Convective core

Scattered

fragments

Figure 11 Sketch of classification of anvil stages. In growing anvil stage, a convective core is clearly distinguishable with strong updrafts and high concentration of cloud condensate. In the mature stage, the core feeding the anvil with material is no longer present. The anvil is building up by horizontal advection. In the dissipating stage, the anvil is diffuse and scattered with low lower amount of material due to precipitation fallout and spread. This figure is partly based on Machado and Rossow´s sketch of convective storms life cycles (Machado et al. 1993).

Analysis and Results

18

core has been used as the first restriction. This demands a constraint of maximum vertical

velocity to less than 7.0 m/s (𝑊𝑀𝑎𝑥 ). The second restriction is that the mean cloud condensate

content should exceed 0.3 g/kg 𝑄𝑛𝑀𝑒𝑎𝑛 at the level where the growing anvil has the maximum

horizontal extent. This restriction is based on the assumption that cloud condensation decrease

as the cloud layer spreads, due to wind shear.

The dissipating anvil stage is here classified as a stage with weaker vertical velocities than the

two other stages and with a lower amount of cloud condensate, accompanied the precipitation

fallout and dissipation. To separate this stage from the previous, restrictions are set on the mean

cloud condensate content to be less than 0.3 g/kg 𝑄𝑛𝑀𝑒𝑎𝑛 at the level where the mature anvil

has the maximum horizontal extent. Still holds that maximum vertical velocities in the cloud layer

should be less than 7.0 m/s (𝑊𝑀𝑎𝑥 ) to assure the layer doesn’t contain an active convective core.

Table 2 summaries the above discussion. Figure 12 shows cloud properties for one anvil from

each anvil stage of the classification.

Table 2 Classification of the anvil stages

Anvil stage 𝑾𝑴𝒂𝒙 𝑸𝒏𝑪𝒐𝒓𝒆

𝑸𝒏𝑴𝒆𝒂𝒏 Anvil connected to convective core

Growing > 7.0 m/s > 1.7 g/kg - x

Mature < 7.0 m/s - < 0.3 g/kg -

Dissipating < 7.0 m/s - < 0.3 g/kg -

4.4 Results One of the questions raised in the beginning of this thesis was if SAM 6.3 could distinguish

isotropic turbulence in thunderstorm anvils and when and where the transition between three-

dimensional and two-dimensional turbulence occurs. This section will present the results from my

analysis.

31 anvils from the simulation were handpicked and arranged, based on the classification, into

three categories; growing, mature and dissipating stage. The dimensionless number F has been

calculated for very grid box building up these 31 anvils, as an aid in analyzing when and where

isotropic turbulence can be found in the simulated anvils.

For every grid box in the chosen anvil clouds, the dimensionless number F was calculated. To

study when isotropic or close to isotropic turbulence is found in anvil evolution, a histogram of

the distribution of F, expressed in relative frequency of the different stages has been made

(Figure 13).

Analysis and Results

19

Figure 12 Example plots of cloud properties for anvil cirrus in growing, mature and dissipating stage. a) Cloud condensate in a growing anvil. b) Cloud condensate in a mature anvil. c) Cloud condensate in a dissipating anvil. d) Vertical velocities in a growing anvil. e) Vertical velocities in a mature anvil. f) Vertical velocities in a dissipating anvil. g) F in a growing anvil. h) F in a mature anvil. i) F in a dissipating anvil. Note that the stages in the figure are from three different anvils.

Since isotropic turbulence occurs when F has the value 2/3, the range 0.67 (+/- 0.05) covers a

range of isotropic and close to isotropic turbulence. This range, for now on referred to as

isotropic range covers 23.0% of the total number of grid boxes in the growing anvil stage. In the

b)

c)

a)

e)

f)

d)

h)

i)

g)

Analysis and Results

20

Figure 13 Histogram of the distribution, expressed in relative frequency, of F in different stages of anvil development. (Upper) growing stage, (middle) mature stage and (bottom) dissipating stage. The figure shows a clear displacement of the maximum relative frequency to higher values of F when the anvil ages.

mature stage the isotropic range covers 29.4% and in dissipating stage 17.8% of the total number

of grid boxes. Thus, a larger part of the extent of the anvils in the mature stage contain isotropic,

or close to isotropic turbulence, than for the growing and dissipating stage. The absence of grid

boxes with low values of F in the growing stage is suggesting vertical fluctuations of higher

amplitude than horizontal fluctuations and is due to the fact that the core is removed from the

dataset for the growing stage anvils.

Analysis and Results

21

4.4.1 Vertical structure of simulated anvils

Following each anvil cloud, from lower levels to higher levels and calculating a single horizontal

mean of F for each level, vertical profiles of F has been calculated for each anvil cloud. Then, all

the F-profiles in the growing stage were added to make up a mean vertical profile of growing

stage anvils. All the F-profiles for mature anvil clouds were added to make up a mean vertical

profile for the mature stage anvil clouds, and the same for dissipating anvil clouds. In Figure 14

these profiles can be seen as a function of height together with a vertical profile of the cloud

fraction in the total domain as a reference.

From this figure it can be seen that growing stage anvils have smaller values of F at every altitude

than anvils in the mature and growing stages. It can also be seen that mature stage anvils always

have smaller value of F than dissipating stage anvils. This simply means that the vertical

component of the velocity turbulence weaken at all altitudes throughout the anvil evolution.

At the height interval 7.5 km to 8 km both dissipating and mature anvils F are decreasing with

height (meaning that the vertical part of the velocity fluctuations is increasing compared to the

horizontal part of the velocity fluctuations), but F for the growing stage anvils are constant with

height or only slightly increasing. This is often found to be the bottom part of the anvil. And in the

lower parts of the anvils F is decreasing with height for the mature and dissipating stages.

Analyzing conditions higher up in the profiles, from 8.0 km to the average anvil height of 8.7 km it

is observed that F is increasing with height for the growing and dissipating stages and slightly

decreasing with height for mature stage anvils.

In the regions above the average anvil height (8.7 km) F decreases with height for all stages. The

decrease of F with height above the average anvil height begins at lowest altitude for the growing

stage anvils and begins at highest altitude for the dissipating stage anvils. This can be interpreted

as the vertical component of turbulence increases and the horizontal component of turbulence

decreases with height for all anvil stages above the average anvil height.

Analysis and Results

22

Figure 14 Vertical profiles of the modeled atmosphere showing calculations of the dimensionless number F for the tree anvil stages. Note that the figure just shows the profiles around maximum anvil height 8.7 km, a subsection of the total model height.

4.4.2 Horizontal structure of simulated anvils

This section will focus on the horizontal distribution of different types of turbulence throughout

the anvil evolution. Horizontal cross sections of calculated F at six levels for the three stages are

shown in Figure 15-17. Dark blue contour lines mark the cloudy areas as a reference to the edges

of the anvil cloud. To draw the conclusions made in this section, horizontal cross sections of many

clouds in each stage has been analyzed, but presented in this paper are just 3 examples.

Growing stage

A growing stage anvil can be seen in Figure 15 from the lower level 7777m to the higher level

10177m. In the lower levels, the core is clearly visible as the blue region (indicating values of F < 1

3

) where vertical turbulent velocity fluctuations dominate. It is also evident by studying Figure 15

that in the lower levels of a growing anvil, areas with isotropic turbulence (yellow areas)created

by the highly turbulent updrafts in the core is found outside the anvil clouds border. Areas with

low values of F are found in the center of the updraft region and outside this region F is increasing

with the distance from the center updraft region.

Mature stage

A mature stage anvil can be seen in Figure 16. In the higher levels (9477m and 10177m) of the

mature stage anvil more areas with calculated F close to 2/3 are found inside the cloud than for

Analysis and Results

23

the growing stage. More areas with isotropic turbulence are found on higher levels than in the

lower levels in the mature anvil cloud.

Areas surrounding the mature anvil are observed to contain large areas of F close to 2/3, whilst

lower areas surrounding the anvil contain mostly F larger than 2/3.

Dissipating stage

A dissipating stage anvil can be seen in Figure 17. For dissipating anvil clouds F is not as defined in

structured continuous areas as for growing stage anvils. Areas with isotropic turbulence are found

both inside and outside of the dissipating anvil. For the lower levels, areas with low F can be seen

to coincide with the location of the dissipating anvil. For middle levels (the levels where the anvil

has the widest horizontal extent) and for higher levels, this is not as evident. Areas with F ranging

from 2/3 to 1 dominate the area in and surrounding the anvil and appear scattered at this height.

Thus, for most of the areas around the highest levels of the dissipating anvil horizontal two-

dimensional turbulence is found.

4.4.3 Gravity waves

Gravity waves were predicted to occur according to Lilly’s plume theory. Analysis of vertical cross

sections of vertical velocities show that SAM 6.3 clearly resolves these waves originating from the

anvil clouds. Gravity waves have been observed to occur in all three anvil stages. Figure 18 shows

horizontal cross sections of cloud condensate content for (a) a growing stage anvil at 8.7 km, (c) a

mature stage anvil at 9.4 km and (e) dissipating stage anvil at 9.4 km. For each of these anvils,

vertical velocities have been plotted in (b), (d) and (f) respectively. Plots (b), (d) and (f) clearly

show gravity waves in the borders of the anvil clouds. The gravity waves are most frequently

found in the middle layers of the anvils in all stages.

Analysis and Results

24

Figure 15 Horizontal cross section of calculated F for an anvil in growing stage. Figure showing cross sections at six heights for the same anvil, from the lower level of the anvil (7777m) to the higher level at (10177m). Contour levels (dark blue) showing the outline of the anvil as a reference.

Analysis and Results

25

Figure 16 Horizontal cross section of calculated F for an anvil in mature stage. Figure showing cross sections at six heights for the same anvil, from the lower level of the anvil (7777m) to the higher level at (10177m). Contour levels (dark blue) showing the outline of the anvil as a reference.

Analysis and Results

26

Figure 17 Horizontal cross section of calculated F for an anvil in dissipating stage. Figure showing cross sections at six heights for the same anvil. From the lower level of the anvil (7777 m) to the higher level at (10177m). Contour levels (dark blue) showing the outline of the anvil as a reference.

Analysis and Results

27

Figure 18 Horizontal cross sections of Cloud condensate for (a) growing stage anvil at 8.7 km, (c) mature stage anvil at 9.4 km and (e) dissipating stage anvil at 9.4 km. For each of these anvils, vertical velocities have been plotted in (b), (d) and (f) respectively.) Plots (b), (d) and (f) clearly shows gravity waves

Discussion and Conclusions

28

5 Discussion and Conclusions The main purpose of this project was to investigate how accurate the CSU Cloud Resolving Model

SAM 6.3 simulates turbulence in tropical thunderstorm anvils. The focus was on studying if Lilly’s

theory on isotropic turbulence (Lilly, 1988) could be observed in the simulated anvils. Since only

data from one time step of the simulation was available for this study, and I wanted to study how

turbulence varied in the anvils for different stages in anvil lifetime, a classification was made to

sort the simulated anvils into three groups, and thereafter analyze them both individually and as

a group. By analyzing horizontal cross sections of cloud properties such as cloud condensate and

vertical velocities, in height-levels of anvils, I have been able to locate typical areas of turbulent

structures in the anvil regime.

My experiments show that SAM 6.3 clearly can resolve different turbulent motions and that the

transition from mainly three-dimensional isotropic turbulence at small scales to horizontal two-

dimensional turbulence at larger scales occurs in the mature and dissipating stages of the anvil

evolution in the middle layers of the anvil where the anvil reaches its largest horizontal extent.

The vertical profiles of cloud properties in the simulated anvils have been analyzed with respect

to the different stages. Conclusions from this analysis together with the analysis from horizontal

cross sections of calculated F are given below.

In the lower levels of the growing stage anvil, the influence of a convective tower is clearly seen.

In the middle layers of the growing anvil, the vertical part of the turbulence decreases and the

horizontal part of the turbulence grows when the anvil spreads out. At the higher levels of the

growing anvil, the vertical part of the turbulence weakens with height, but close to isotropic

turbulence is still frequently occurring at the higher levels.

Overshooting tops, caused by high vertical velocities in the top of the convective towers,

containing mostly isotropic turbulence are seen in several of the observed anvils. The transition

from three-dimensional isotropic turbulence to two-dimensional horizontal turbulence is mostly

occurring outside of the cloudy parts of the growing anvil.

For all the levels in the mature stage anvil, it is clearly seen that the absence of a cumulus tower,

earlier supporting the growing anvil with ice and kinetic energy, makes the turbulence in the

whole growing stage anvil more two-dimensional. Comparing the lower levels with the higher

levels in the mature anvil, it is seen that the kinetic energy that existed inside the anvil at the

lower levels has spread to outside the anvil at the higher levels. Thus, the isotropic turbulence in

the center of the anvil in the growing stage is transitioned to mostly two-dimensional turbulence

being transported out horizontally from the center of the cloud in the mature stage.

Discussion and Conclusions

29

For the dissipating anvil large areas of two-dimensional turbulence is surrounding the anvil at

both low and high levels. In the middle levels, a transition from three-dimensional to two-

dimensional turbulence mostly occurs inside the scattered anvil. Around the dissipating anvil in all

layers, mostly two-dimensional turbulent motions in the horizontal plane are found, and it is

impossible from this study to conclude if the reason for this is that there is no active convection in

these areas or if the reason is that the three-dimensional turbulence has been converted into

two-dimensional turbulence and gravity waves.

This study supports Lilly’s theory that the isotropic turbulence is converted to two-dimensional

turbulence and spread horizontally in the anvil, but this study can neither support nor discard

that the turbulence-conversion includes gravity waves, since gravity waves in this simulation are

found to be created in all anvil stages.

This work has focused on locating and observing turbulent motions in different stages of anvil

evolution from one single time step of a numerical simulation. More reliable results would

probably have been achieved if a time series of simulations was available to analyze so that the

anvil stage classification invented for this study would not have been required.

I have only been analyzing 31 simulated anvils, which is not sufficient to yield trustworthy

statistical results. More anvils are found in the data set, but there has been difficulties in rightfully

limit the borders of a cloud in a three-dimensional simulation output which has limited the

number of analyzed anvils.

Another error source that might have affected the results is that the anvil stage classification was

only based on 2 out of 7 available cloud properties given from the simulation. A better

classification could possibly have been made by the use of more cloud properties to classify the

boundaries of each evolution stage. However, arranging the anvils according to my classification

and observing the turbulent motions according to my invented timescale has been useful to see

indications of turbulent motion in actual anvils and to give a hint of how anvil turbulence behave

in nature.

References

30

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Nr 37 Soil respiration in sub-arctic soils – controlling factors and influence of globalchange, Evelina Selander

Nr 38 Miljöeffekter av Triorganiska tennföreningar från antifoulingfärg – med avseendepå sedimentologi, ekotoxikologi och hydrogeologi, Sara Berglund

Nr 39 Depth distribution of methanotroph activity at a mountain birch forest-tundra ecotone,northern Sweden, Erik Melander

Nr 40 Methyl tert-Butyl Ether Contamination in Groundwater, Linda Ahlström

Nr 41 Geokemisk undersökning av vattnet i Västerhavet Med avseende på metallhalter och129I, Anette Bergström

Nr 42 Fracture filling minerals and the extent of associated alteration into adjacent granitichost rock, Erik Ogenhall

Nr 43 Bi-Se minerals from the Falun Copper mine, Helena Karlsson

Nr 44 Structures and Metamorphism in the Heidal-Glittertindarea, ScandinavianCaledonides, Erik Malmqvist

Nr 45 Structure and isotope-age studies in Faddey Bay region of central Taymyr,northern Siberia, Robert Eriksson

Nr 46 Stabilitetsindex – en stabil prognosmetod för åska?, Johan Sohlberg

Nr 47 Stadsklimateffekter i Uppsala, Andreas Karelid

Nr 48 Snow or rain? - A matter of wet-bulb temperature, Arvid Olsen

Nr 49 Beräkning av turbulenta flöden enligt inertial dissipationsmetoden med mätdata frånen specialkonstruerad lättviktsanemometer samt jämförelse med turbulentautbytesmetoden, Charlotta Nilsson

Nr 50 Inverkan av det interna gränsskiktets höjd på turbulensstrukturen i ytskiktet,Ulrika Hansson

Nr 51 Evaluation of the Inertial Dissipation Method over Land, Björn Carlsson

Nr 52 Lower Ordovician Acritarchs from Jilin Province, Northeast China, Sebastian Willman

Nr 53 Methods for Estimating the Wind Climate Using the MIUU-model, Magnus Lindholm

Nr 54 Mineralogical Evolution of Kaolinite Coated Blast Furnace Pellets, Kristine Zarins

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