doctor of philosophy היפוסוליפל רוטקוד · cloud fields; one that will be resilient...
TRANSCRIPT
Thesis for the degree
Doctor of Philosophy
By
Rotem Bar-Or
Name in English
Advisor: Prof. Ilan Koren
July, 2012
Submitted to the Scientific Council of the
Weizmann Institute of Science
Rehovot, Israel
השפעתם של אירוסולים על התכונות האופטיות
והמיקרו פיסיקליות של שדה עננים
The effect of aerosols on the optical and
microphysical properties of a cloud field
לתואר (תזה)עבודת גמר
דוקטור לפילוסופיה
מאת
אור-רותם בר
שם בעברית
ב"התשע, תמוז
מוגשת למועצה המדעית של
מכון ויצמן למדע
ישראל, רחובות
אילן קורן' פרופ :המנח
List of abbreviations
AOD Aerosol optical depth
Cb Cumulonimbus cloud
CCN Cloud condensation nuclei
CF Cloud fraction
CFF Cloud field fraction
Ci Cirrus cloud
Cu Cumulus cloud
DC Deep convective cloud
FMF Aerosol fine-mode fraction
GCM Global circulation model
IC Ice content
IGRA Integrated global radiosonde archive
IN Ice nuclei
LCL Lifted condensation level
LCT Lower cloudy troposphere
LES Large eddy simulation
LWC Liquid water content
MBL Marine boundary layer
MBP Moist bounding parameter
MODIS Moderate resolution imaging spectroradiometer
NASA National aeronautics and space administration
NOAA National oceanic and atmospheric administration
RH Relative humidity
SALR Saturated adiabatic lapse rate
Sc Stratocumulus cloud
SRH Sub-saturated relative humidity
WMO World meteorological organization
Abstract
Clouds and aerosols play key roles in the global climate system by altering Earth‟s energy
budget and by controlling the hydrological cycle. Both numerical simulations and observations are
used for studying processes related to clouds, aerosol and the interactions in between. A major
difficulty in observational analyses is the separation in space between clouds and aerosol, as the
border between these is not well defined and often not practically indictable. Such problem begs for
a different approach that will take into account spatial and spectral features of whole cloud fields,
including both clouds and aerosols. Thus, this study offers a new classification of the observed
atmosphere into cloud-fields (including clouds and their surrounding influenced zone) and cloud-
free.
For this purpose, a new morphological method for spatially determining cloud field
boundaries is presented and validated. Using this method, extensive analysis reveals global cloud
field coverage of 88%, while the corresponding cloud fraction is only 51%. It shows a strong
latitudinal dependence, pointing to a very small probability to sample a cloud field free atmosphere
in most regions over the globe. Furthermore, we find that cloud fields extend on average ~30 km
from cloud edges, as supported by independent studies.
Close examination of MODIS aerosol retrievals shows a clear trend linking the retrieved
aerosol properties with their distance from clouds, which is commonly believed to be mainly a
result of aerosol humidification. We study the optical significance of aerosol humidification using
large eddy simulations of cloud fields, for developing a parameterization of the relative humidity
(RH) as a function of the distance from clouds. Following this parameterization, atmospheric
radiative transfer simulations show that the optical significance of aerosol humidification in cloud
fields is limited to the area within 500 m from cloud edge, with a strong sensitivity of the retrieval
bias near clouds to the aerosol chemical and physical properties.
Finally, for studying the distribution of RH within cloud fields from measurements, we
develop a method using atmospheric sounding data for characterizing the sub-saturated RH vertical
profiles in cloudy atmosphere. Such method allows a calculation of the potential for aerosol
humidification directly from the best available RH measurements. It enables an estimation of the
impact of aerosol humidification on aerosol-cloud interaction analyses. In this study we show that
aerosol humidification can explain only a small fraction of the measured trends between aerosol and
convective clouds properties.
תקציר
א ובשל "לעננים ולאירוסולים ישנה השפעה מכרעת על האקלים בשל השפעתם על מאזן האנרגיה של כדה
, הן סימולציות נומריות והן תצפיות משמשות לחקר תהליכים הקשורים לעננים. תפקידם במחזור ההידרולוגי
הצורך להבחין בין עננים לאירוסולים מהווה כיום קושי עיקרי בניתוח . הגומלין ביניהם-לאירוסולים וליחסי
קושי זה דורש גישה . מכיוון שהגבול ביניהם לא מוגדר כהלכה ולעתים קרובות אף בלתי ניתן להערכה, תצפיות
. הכוללים עננים לצד אירוסולים, עננים שלמים-שונה שתתחשב במאפיינים המרחביים והספקטראליים של שדות
הכוללים את העננים עצמם )עננים -לשדות: במחקר זה אנו מציעים סיווג חדש של האטמוספרה הנצפית, ואכן
.ולאטמוספרה נקייה מעננים (אשר מושפע מנוכחותם, ואת האזור סביבם
שימוש בשיטה זו . מוצגת כאן שיטה מורפולוגית הקובעת את גבולותיהם המרחביים של שדות עננים, לצורך זה
-א מכוסים בשדות" משטח כדה88%-כ , בלבד51%בניתוח נתונים נרחב מגלה כי כאשר כיסוי העננים עומד על
אבחנה זו מראה כי ההסתברות . העננים בקו הרוחב-מאובחנת תלות ברורה של אחוז כיסוי שדות, בנוסף. עננים
-ניתוח התצפיות מראה ששדות, כן-כמו. ברוב העולם, לדגימת אטמוספרה נקייה משדות עננים הינה נמוכה מאד
.תלויים-כפי שעולה גם ממחקרים אחרים בלתי, מ מגבולות העננים" ק30עננים מתפרשים בממוצע למרחק של
מעלה קשר ברור בין תכונות האירוסולים המוערכות לבין MODISעיון מדוקדק בתצפיות אירוסולים מלווין
. קשר זה מיוחס ברובו לגידול האירוסולים עקב ספיחת מים בסביבה לחה, כיום. מרחק הדגימה מהענן הקרוב
תוך , עננים-אנו בודקים עד כמה משמעותית התרומה האופטית של גידול האירוסולים הלחים בשדות, במחקר זה
מנת לפתח פרמטריזציה של -על, (large eddy simulations)שימוש בסימולציות נומריות המדמות שדות עננים
המתבססות על , הדמיות של מעברי קרינה באטמוספרה. היחסית כתלות במרחק מהענן הקרוב-הלחות
המטרים 500-עננים מוגבלת ל-מראות שהתרומה האופטית של גידול אירוסולים לחים בשדות, פרמטריזציה זו
הדמיות אלה מראות בנוסף כי סטיית מדידות האירוסולים בקרבת הענן רגישה מאד . הקרובים ביותר לענן
.לתכונות הכימיות והפיסיקליות של האירוסולים אותם מודדים
פיתחנו שיטה המבוססת על , עננים-היחסית בשדות-בכדי להעריך את המאפיינים של התפלגות הלחות, לבסוף
. עננים-בשדות (מחוץ לענן)רוויה -היחסית בתת-אשר מחשבת את הפרופיל האנכי של הלחות, מדידות רדיוסונדה
ולהעריך את ההשפעות , שיטה זו מאפשרת לחשב את הפוטנציאל האופטי הגלום בגידול אירוסולים לחים
היחסית הישירות והטובות ביותר אשר -תוך שימוש במדידות הלחות, אירוסולים-האפשריות על חקר יחסי עננים
.זמינות היום
במחקר זה אנו מראים שגידול אירוסולים לחים יכול לספק הסבר רק לחלק קטן יחסית של המגמות הנמדדות
.בין אירוסולים ובין תכונותיהם של עננים קונבקטיביים
Table of Contents
1 Introduction ............................................................................................................ 1
1.1 Research objectives ............................................................................................ 1
1.2 Background ........................................................................................................ 2
1.3 Clouds ................................................................................................................ 4
1.3.1 Detection of clouds and cloud fields ........................................................... 5
1.3.2 Aerosol effects on clouds ............................................................................ 6
1.4 The twilight zone.............................................................................................. 10
1.5 Aerosol hygroscopic growth in cloud fields .................................................... 11
1.5.1 Humidification effects on aerosol size distribution .................................. 11
1.5.2 The relative humidity in cloud fields ........................................................ 14
2 Methods and data .................................................................................. 17
2.1 Cloud field masking algorithm ........................................................................ 17
2.1.1 Cloud field masking algorithm - description ............................................ 20
2.1.2 Cloud field masking algorithm - sensitivity study .................................... 22
2.2 Global cloud field coverage ............................................................................. 23
2.3 Aerosol retrieval inside cloud fields ................................................................ 24
2.4 Radiative effects of aerosol humidification on cloud fields............................. 25
2.4.1 Parameterization of the RH spatial distribution in cloud fields ................ 25
2.4.2 Atmospheric radiative transfer simulations of humidified aerosols ......... 26
2.5 Characterizing the RH in cloud fields .............................................................. 29
2.5.1 Locating the cloudy layer with radiosonde data ....................................... 29
2.5.2 Characterizing the SRH values in cloud fields ......................................... 32
2.5.3 The lower cloudy troposphere SRH and cloud development ................... 33
3 Results ............................................................................................................ 34
3.1 Global cloud field coverage ............................................................................. 34
3.2 Aerosol retrieved optical properties in cloud fields ......................................... 37
3.3 Radiative effects of aerosol humidification on cloud fields............................. 40
3.3.1 RH spatial distribution in cloud fields ...................................................... 40
3.3.2 Humidified aerosol properties in cloudy environment ............................. 42
3.3.3 Absorbing humidified aerosol properties in cloud fields .......................... 48
3.4 Upper-air measurements of the RH spatial distribution in cloud fields ........... 53
3.4.1 RH mean vertical profile in potentially cloudy layers .............................. 53
3.4.2 SRH profile and cloud development ......................................................... 56
4 Summary and discussion ...................................................................... 59
Tables ............................................................................................................ 64
5 References ............................................................................................................ 70
6 List of publications ............................................................................... 79
7 Declaration ............................................................................................................ 80
Appendix A ............................................................................................................ 81
Appendix B ............................................................................................................ 84
1
1 Introduction
1.1 Research objectives
The primary aim of this study is to better estimate (qualitatively and quantitatively)
the radiative effects in cloud fields, with the consideration of suspended aerosols.
Within this framework, the specific objectives are:
1.1.1 Presentation of an objective metric that defines the extent and coverage of
cloud fields; one that will be resilient to differences in the exact definition of
what is a cloud.
1.1.2 Development of a robust cloud field masking algorithm which can accurately
determine the cloud field boundaries, including the detectable clouds and the
transition zone between clouds and cloud free atmosphere (i.e. the twilight
zone).
1.1.3 Observation of the global cloud field coverage above land and oceans, with its
latitudinal dependence.
1.1.4 Assessment of the trends between remotely sensed aerosol retrievals and the
distance from clouds.
1.1.5 Estimation of the aerosol humidification contribution to the apparent aerosol
radiative features in cloud fields.
1.1.6 Acquiring better understanding of the spatial distribution of relative humidity
(RH) in cloud fields.
1.1.7 Evaluation of the relative humidity vertical profiles in cloud fields, based on
long-term atmospheric sounding measurement record, and estimation of the
humidified aerosol retrieval biases in cloud fields.
2
1.2 Background
The Earth's climate is changing. It has changed in the past, as observed in ice-core
data [Jouzel et al., 2007], and it is currently changing, as shown in many continuous
measurements taken during the last two centuries [Forster et al., 2007]. The main
driver for these climatic variations, as known today, is the divergence from
equilibrium of the Earth's global energy budget [Trenberth et al., 2009]. The energy
budget summarizes the incoming solar radiation, Earth's outgoing thermal emission,
and the interactions of radiation with atmospheric and surface features according to
their radiative properties, as demonstrated in Figure 1.1. Any perturbation from the
delicate equilibrium (namely “radiative forcing”) may directly alter the global
climate.
Figure 1.1 The global annual mean Earth‟s energy budget for the March 2000 to May
2004 period (W ⋅ m−2). The broad arrows indicate the schematic flow of energy in
proportion to their importance (Fig 1. in Trenberth et al., 2009).
3
During the last decades, many studies have tried to properly estimate the total
radiative forcing, including assessment of the anthropogenic contribution compared to
the natural one, in order to find the causes for global climate changes. The total
forcing is the sum of several components (anthropogenic - greenhouse gases, jet
contrails, and natural - solar flux fluctuations, volcanic activity, ocean thermo-
dynamical variances, and more), of which the aerosol radiative effects (directly and
through impact on clouds) have one of the greatest uncertainty [Forster et al., 2007].
Hence, the potentially crucial climatic signature of aerosols has been extensively
explored for decades, in attempt to better estimate the overall atmospheric radiative
effects of aerosols; either directly by scattering or absorbing radiation [Kaufman et al.,
2002], or indirectly by their effects on cloud spatial and physical properties (described
in detail in section 1.3.2).
Studying the variety, the non-linearity, and the intricate nature of these indirect
feedbacks requires accurate and reliable measurements and observations, which adds
difficulties to the assessment of their overall effect.
The regions of interest for studying aerosol-cloud interactions are difficult for
examination; as they are naturally contain both aerosols and clouds which spatially
and spectrally overlap, and therefore are difficult for separation. Aerosol and cloud
observations, commonly suffer from retrieval accuracy and quality uncertainties,
which may disqualify conclusions.
In this study, the radiative properties of whole cloud fields are addressed. A first
attempt to spatially bound cloud fields based on their morphological features is
presented and validated (Section 2.1), enabling the estimation of the global cloud-
field coverage (Section 3.1). The aerosol retrieval biases in the vicinity of clouds are
observed using satellite data (Section 3.2), and the net contribution of aerosol
hygroscopic growth (humidification) to these biases is examined (Section 3.3). As a
part of the assessment of aerosol humidification effect, the spatial distribution of the
relative humidity (RH) near clouds is simulated and parameterized (Section 3.3).
Finally, the RH values in the lower troposphere are measured for cloud fields, using
vast radiosonde data sets, and studied in respect to cloud vertical development
(Section 3.4).
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1.3 Clouds
Clouds, in Earth‟s atmosphere, are commonly defined as “a detectable mass of
water droplets or ice crystals suspended in the atmosphere”. The formation of clouds
depends on the atmospheric thermo-dynamical conditions as super-saturation is
required for enabling the formation and existence of liquid or solid water particles.
The phase transition of water vapor into water droplets or ice particles, within an air
parcel, mostly occurs due to a vertical lift of the air parcel that is sufficient to form
super-saturation due to expansion and cooling.
There are several mechanisms that may contribute to air lifting. Meteorological
conditions may drive dynamic vertical lift (e.g. frontal systems), any source of
differential surface heating initiates vertical lifting, and physical geographic obstacles
efficiently serve as vertical lifting agents (namely aerographic lifting).
The phase transition of water vapor into liquid water or solid ice particles
releases latent heat and increases the buoyancy of the air parcel, which then increases
the parcel‟s vertical velocity. The developing stage of the cloud ends when the cloud
starts loosing water (or ice) by evaporation and precipitation [Rogers, 1989]. When
decaying, the cloud exhibits the stronger precipitation intensity. The maximal cloud
top height, during it's lifetime, is determined by the atmospheric thermo-dynamical
conditions; where a dry or stable layer stops supporting vertical movement and
hydrometeors existence. The size distribution of the droplets/ice particles varies in
time due to growth by diffusion, evaporation (mainly in the cloud top and edges),
collision-coalescence processes, breakup and sedimentation.
Cloud processes span the physical scales between 10−6 and 105 m, from aerosol
size through processes in clouds‟ turbulent eddy scales through cloud fields and up to
synoptic scale of hundreds of kilometers. The temporal scale of cloud lifetime may
spread between a few minutes (e.g. for a small cumulus in relatively dry environment)
to several days, as observed in marine decks of stratiform clouds.
Past studies showed that clouds have fractal features [Cahalan and Joseph, 1989;
Cahalan et al., 1994; Marshak et al., 1995; Borde and Isaka, 1996; Lovejoy and
Schertzer, 2006], characterized by a power-law size distribution [Kuo et al., 1993;
5
Koren et al., 2008b]. Cloud fields, are clusters of clouds that form in the same
environmental conditions and due to that have similar physical properties. Cloud field
is usually a cloud system with spatial characteristic scale larger than the typical scale
of its clouds. Cloud fields can be characterized by their total size, their cloud types
and spatial distribution and by their development in time. Recent studies suggested
that some cloud fields can be described as complex systems, that their organization
pattern is determined by interactions between the clouds, and influenced by the
environmental conditions such as aerosol loading [Koren and Feingold, 2011].
Constantly covering ~60% of Earth's surface, clouds serve as a primer
component in the climate system. Their effects on the radiation budget depend on
their type. Clouds reflect solar radiation back to space, causing cooling of the
atmosphere, and absorb the Earth‟s IR radiation, causing warming of the atmosphere.
Low-level shallow clouds cool the surface by reflecting solar radiation back to space
and emitting thermal radiation in a temperature similar to the earth surface beneath
them. High altitude clouds (e.g. Cirrus clouds) release less thermal energy to space,
and cause heating of the atmosphere [Koren et al., 2010] (see Figure 1.1). Clouds are
the main engine of the water cycle. Any change in precipitation patterns and amounts
would influence significantly the hydrological cycle as it determines how much is
absorbed by the ground and how much will flow over it.
1.3.1 Detection of clouds and cloud fields
The growing usage of space-borne platforms for remote sensing instruments for
cloud and aerosol research during the last decades [Kaufman et al., 2002; King et al.,
2003] raised the need to develop accurate methods to retrieve cloud and aerosol
properties [Platnick et al., 2003; Remer et al., 2005; Levy et al., 2007]. In order to do
so, it is common to classify the imaged domain into two: detectable cloud and
"assumed to be" cloud-free.
The commonly used method for detecting clouds and providing cloud mask
maps, using remote sensing instruments, involves the location of reflectance
signatures of suspended liquid water drops or ice particles, followed by the usage of a
6
reflectance threshold in order to separate the cloudy atmosphere from cloud-free
[Ackerman et al., 1998; Platnick et al., 2003; Dybbroe et al., 2005; Luo et al., 2008].
Wide swath imagers may add spatial analysis information to improve the threshold
choice using multiple pixel retrievals in a given domain [Martins et al., 2002].
In situ observations can use the sampled atmosphere and estimate the amount of
suspended liquid water or ice, which enables the separation between cloudy and
cloud-free samples. For these observations, the main challenges are the instrument
capabilities and accuracy [Verver et al., 2006].
Cloud masks may also be calculated for cloud resolving numerical simulation
output, which provide the liquid water content (LWC) or ice content (IC) for every
grid cell. However, recent study has shown that even simulated apparent cloud-free
regions within cloud fields contain some LWC [Jiang et al., 2009; Koren et al.,
2009].
While most previous studies that targeted cloud fields focused on cloud field
inner properties [Sengupta et al., 1990; Weger et al., 1992; Zhu et al., 1992; Weger et
al., 1993; Lee et al., 1994; Nair et al., 1998], our study presents a first attempt for
analytically defining the cloud field boundaries based on its inner cloud spatial
distribution (Section 2.1).
1.3.2 Aerosol effects on clouds
Aerosol may serve as cloud condensation nuclei (CCN) or ice nuclei (IN), which
produce cloud droplets or ice crystals in clouds. Therefore, changes in the aerosol
concentration affect the cloud droplet and ice particles concentration and size
distribution. An evidence for this connection was found in the observed effect of
aerosols on the cloud reflectance in the shortwave spectrum, namely „the Twomey
effect” [Twomey, 1977].
Twomey [1977] showed that an increase of the aerosol concentration (for a given
LWC) leads to an increase in the droplet number, which decreases their mean radius
and therefore enhances their optical cross section and optical depth. An example for
7
such observation is given in Figure 1.2, presenting satellite observed ship-tracks
[Coakley et al., 1987; Wang and Feingold, 2009] above the Atlantic Ocean,
demonstrating both radiative and microphysical effects of anthropogenic aerosols on
clouds. The particles from the ship engines act as CCNs, reducing the mean droplet
size (lower right panel), increasing the solar reflectance (upper panel) and the optical
thickness (lower left panel).
Figure 1.2 An unusually high number of ship tracks, as observed off of the coasts of
France and Spain in true-color (upper panel) and false-color images, representing the
retrievals of cloud optical thickness (lower left panel), and cloud effective particle
radius (lower right panel), from MODIS on the Aqua satellite on January 27, 2003
(source: MODIS Rapid Response website).
8
The effect of aerosol concentration values on the mean cloud droplet size drives
additional important dynamical and microphysical feedbacks. High aerosol loading,
which leads to smaller drops, reduces the efficiency of the collision-coalescence
process and due to that cause suppression of the warm rain. It may extend cloud
lifetime. This finding was observed and simulated for shallow clouds [Albrecht, 1989;
Jiang et al., 2006]. The warm rain suppression due to aerosol concentration increase
[Rosenfeld, 2000] was found also to be the first step in chain of events that cause
invigoration of convective clouds [Kaufman et al., 2005a; Koren et al., 2005]. This
chain of events includes smaller drops and smaller falling velocities, causing the
drops to reach higher altitudes, freeze at higher altitudes and release latent heat higher
in the atmosphere. All of this produces more vertically developed clouds with higher
tops, higher center of gravity of their rain column [Heiblum et al., 2012], bigger hail
particles, more intense electrical activity [Rosenfeld, 2000; Andreae et al., 2004;
Altaratz et al., 2010], and stronger rain rates [Koren et al., 2012].
Figure 1.3 schematically presents the observed differences between polluted
(high aerosol concentration) and pristine convective clouds, during their evolution in
time.
9
Figure 1.3 (Figure 2 of Rosenfeld et al., 2008): Evolution of deep convective clouds
developing in the pristine (top) and polluted (bottom) atmosphere. Cloud droplets
coalesce into raindrops that rain out from the pristine clouds. The smaller drops in the
polluted air do not precipitate before reaching the super-cooled levels, where they
freeze onto ice precipitation that falls and melts at lower levels. The additional release
of latent heat of freezing aloft and reabsorbed heat at lower levels by the melting ice
implies greater upward heat transport for the same amount of surface precipitation in
the more polluted atmosphere. This means consumption of more instability for the
same amount of rainfall. The inevitable result is invigoration of the convective clouds
and additional rainfall, despite the slower conversion of cloud droplets to raindrops
[Tao et al., 2007].
A different aerosol effect on clouds' processes is caused by aerosols which
absorb solar radiation and heat their surrounding, modifying the atmospheric
temperature and humidity profiles. [Hansen et al., 1997; Ackerman et al., 2000;
10
Koren et al., 2004; Koren et al., 2008a; Davidi et al., 2012]. By radiation absorption,
aerosols are able to heat their atmospheric layer, and influence the vertical
temperature gradient, stabilizing the underlining layers and destabilizing the layers
above their location. The result is weakening of shallow convective clouds [Koren et
al., 2004; Feingold et al., 2005].
On the other hand, in-cloud processes affect the aerosol properties. Those
processes affect the size distribution, spatial distribution, chemical composition and
optical properties, by transport, coalescence, and chemical processes [Feingold and
Morley, 2003].
1.4 The twilight zone
The regions for studying cloud-aerosol interactions by remote sensing methods
include the detectable clouds and their surrounding areas, where one can observe
clouds and aerosol properties in the vicinity of clouds. Those areas near clouds form a
continuous transition zone between cloud and cloud-free atmosphere (namely the
twilight zone). The twilight zone [Koren et al., 2007] has been recently recognized
and characterized as a wide belt surrounding detectable clouds, which contains small
clouds and cloud fragments, decaying and hesitant clouds, different levels of
humidified aerosols [Charlson et al., 2007; Koren et al., 2009]. The twilight zone‟s
content was shown to affect the apparent aerosol optical retrievals up to 30 𝑘𝑚 from
the nearest cloud edge [Charlson et al., 2007; Koren et al., 2007; Twohy et al., 2009a;
Varnai and Marshak, 2009; Bar-Or et al., 2010; Bar-Or et al., 2011].
In this region in the vicinity of clouds, the apparent aerosol optical and physical
retrievals from remote sensing instruments may be affected by the following
components: (1) aerosols may change their physical and optical properties due to
hygroscopic growth, where the relative humidity (RH) reaches high values [Twohy et
al., 2009a], (2) the apparent aerosol optical signal may be modified by contributions
from clouds which are too thin or too small to be detected as clouds by the instrument
or retrieval algorithm [Kaufman et al., 2005b], and (3) three-dimensional (3D)
radiative effects from multiple scattering inside clouds may be reflected into the
11
surrounding environment and contribute an additional signal to the apparent aerosol
retrievals [Wen et al., 2007; Marshak et al., 2008; Marshak et al., 2009].
The gaining interest in the twilight zone, accompanied by the recent observations
that show aerosol retrieval dependence on the distance from the nearest cloud, raised
the need to take this parameter into account when observing aerosols. Therefore, the
distance from the nearest cloud will be added to the new coming MODIS atmosphere
level 2 standard products (Robert Levy, personal communication).
The twilight zone was identified using remote sensing observations, but the
separation and the estimation of each of the three mentioned components contribution
to aerosol retrievals has not been accomplished yet. In the study described in Sections
2.4 and 3.3, a single component of the twilight zone is isolated (namely aerosol
humidification), and its contributions to the apparent aerosol optical depth (AOD) and
to the aerosol fine-mode fraction (FMF) are qualitatively estimated as a function of
the distance from the nearest cloud (dc).
In this study, the twilight zone is treated as a part of the cloud field. A cloud field
masking algorithm is presented in Section 2.1, and implemented in Section 3.1 for
estimating the global cloud field coverage and the average extent of the twilight zone
from the closest cloud. The unique features of aerosol retrievals in the twilight zone
are examined and described in Section 3.3.
1.5 Aerosol hygroscopic growth in cloud fields
Aerosol water uptake from their surrounding environment mainly depends on the
atmospheric relative humidity (RH) and on the aerosol chemical properties. Clouds,
being pockets of liquid water (or ice), naturally form in high RH regimes. Therefore,
one expects higher RH values near clouds, which may affect the suspended aerosol
size distributions and optical properties.
1.5.1 Humidification effects on aerosol size distribution
The water content of atmospheric aerosols determines their size, density, and
refractive index [Tang, 1996]. When dry aerosols are put in a humid environment,
12
they uptake water and grow. The growth rate as a function of the surrounding relative
humidity, namely the aerosol hygroscopic growth factor 𝑔 𝑅𝐻 , is defined in
Equation 1.1:
g RH =Dwet
Ddry=
Vwet
Vdry
13
= Vw + Va
Va
13
= 1 +Vw
Va
13
1.1
Where 𝐷𝑑𝑟𝑦 and 𝐷𝑤𝑒𝑡 are the diameters of the dry and the wet aerosols, respectively,
𝑉𝑑𝑟𝑦 and 𝑉𝑤𝑒𝑡 are the volumes of the dry and wet aerosol, respectively, 𝑉𝑎 is the
volume of the dry aerosol, and 𝑉𝑊 is the volume of the taken additional water content.
The amount of water that aerosols uptake from their surrounding environment
also depends on the aerosol hygroscopic properties, derived by the aerosol chemical
composition and microphysical shape. Rissler et al. [2006], followed by Petters and
Kreidenweis [2007], offered a single parameter representation of the hygroscopic
growth factor as a function of the water activity (𝑎𝑊), as presented in Equation 1.2:
1
aW= 1 + κ
Va
VW= 1 +
κ
g3 − 1 1.2
Where 𝑎𝑊 is the water activity, 𝑉𝑎 and 𝑉𝑊 are the volume of the dry aerosol and of
the water addition, respectively, 𝑔 is the hygroscopic growth factor, and 𝜅 is defined
as the aerosol hygroscopicity parameter.
When neglecting the Kelvin effect, whose contribution to the growth of
aerosols with radii larger than 0.1 μm is smaller than 1% for a temperature range
relevant to this study [Hinds, 1999], the water activity is identical to the environment
RH value, and the hygroscopic growth factor as a function of RH may be evaluated as
follows (Equation 1.3):
g κ, RH = 1 + κ ∙RH
1 − RH
13
1.3
13
Where g is the hygroscopic growth factor, 𝜅 is the aerosol hygroscopicity parameter,
and RH is the relative humidity that the aerosol experience.
Using the kappa parameterization, the hygroscopicity (𝜅) values of a wide variety
of aerosol types and compositions were measured both in laboratory and in field
campaigns. Average marine aerosols were estimated to have kappa values of 0.7 ±
0.2 [Andreae and Rosenfeld, 2008], while coarse-mode sea salt is even more
hygroscopic and may reach 𝜅 = 1.2 [Kreidenweis et al., 2005; Andreae and
Rosenfeld, 2008; Sorooshian et al., 2008]. Dust particles are much less hygroscopic,
with values of 0.01 − 0.08 [Gasparini et al., 2006; Andreae and Rosenfeld, 2008;
Koehler et al., 2009; Twohy et al., 2009b; Yan et al., 2009]. Fine-mode biomass
burning aerosol 𝜅 values vary between 0.06 − 0.7 while the majority of the biomass
burning products obtain values of 0.06 − 0.33 [Andreae and Rosenfeld, 2008; Petters
et al., 2009; Carrico et al., 2010]. Pure black carbon is assumed to be absolutely non-
hygroscopic 𝜅 = 0 . Figure 1.4 shows the hygroscopic growth factor g for various
hygroscopicity parameter and relative humidity values, and Figure 1.5 presents the
change in the aerosol size distribution caused by a hygroscopic growth for a bimodal
lognormal distribution.
14
Figure 1.4 The hygroscopic growth factor g as a function of the relative humidity RH
and the hygroscopic growth factor 𝜅, following Equation 1.3. The g values around 2
are marked (black) to demonstrate the RH and 𝜅 values that will result in doubling of
the dry aerosol radius, by water uptake.
Figure 1.5 A demonstration of dry (blue line) and humidified (RH = 95%, red line)
aerosol bimodal log-normal size distributions, normalized to the total particle number.
The fine mode contains biomass burning particles rg = 0.08; σ = 0.7; κ = 0.3 , and
the coarse mode contains sea salt particles rg = 0.6; σ = 0.6; κ = 0.7 . The gray
dashed line represents the boundary between fine and coarse mode distributions
(when σ = 0.7). Note that the total particle number of the two presented aerosol
distributions is equal.
1.5.2 The relative humidity in cloud fields
As shown above in Section 1.5.1, the relative humidity (RH) may determine the
aerosol size distribution in cloud fields, as derived in Equation 1.3.
15
When estimating the aerosol humidification effect, it is necessary to evaluate the
dependence of RH in the distance from clouds. Very few studies had provided in-situ
measurements of this feature [Twohy et al., 2009b; Wang and Geerts, 2010],
encouraging further research in order to spatially characterize the RH in cloud fields.
According to these few recent studies, areas of high RH (RH ≳ 95%) which would
cause significant aerosol humidification effect are limited to the closest vicinity of
clouds, while most of the cloud field exhibits much lower RH values, whose average
is closer to the background RH value (far from clouds). Therefore, clouds' presence
efficiently marks areas of high RH values, as expected.
In Section 3.3.1 of this study, the RH in cloud fields is simulated using large eddy
simulation (LES) models, and a new parameterization of the RH as a function of the
distance from clouds is presented.
Cloud-aerosol interaction studies examine trends between the observed properties
of clouds and aerosols, trying to determine causality between aerosols and clouds.
These studies need to check all possible reasons for these trends before they can
conclude about causality. Other possible reasons for such observed trends can be
meteorology that drives them both or aerosol and clouds retrievals artifacts. Recent
studies identified an aerosol invigoration effect on convective clouds, showing that
increase in aerosol loading causes an increase in cloud horizontal and vertical
dimensions [Koren et al., 2005; Heiblum et al., 2012]. It was also shown that in
polluted environments with higher aerosol loading the rain rate is stronger [Koren et
al., 2012], and that the precipitation particles are located higher in the atmosphere
[Heiblum et al., 2012]. These studies examined carefully two factors that can
influence the observed trends: meteorological variance and cloud contamination, by
stratifying the observed data for different meteorological conditions, and filtering out
all AOD data that were suspected as “cloud contaminated”. However, the significance
of aerosol humidification (as an effect that influence the aerosol measured properties
in the vicinity of clouds) to the observed trends in these type of studies has not been
accurately evaluated yet.
Therefore, the significance of the aerosol humidification effect is studied in this
work. Several recent studies, using Global Circulation Models (GCM), had referred to
16
this question, concluding that aerosol humidification is a dominant generator of any
observed trend between aerosol and cloud properties [Quaas et al., 2008; Quaas et
al., 2010]. These findings were presented under the limitations derived by the non-
linearity features of this problem, and by the poor parameterizations that are available
in GCMs for this purpose, as stated in Quaas et al. [2008]. On the other hand, the few
in-situ measurements of RH and specific humidity near clouds [Twohy et al., 2009a;
Wang and Geerts, 2010] suggest that high values of RH exhibit only near clouds.
In this study, we wish to describe as accurately as possible the RH properties of
the cloudy atmosphere form the best available measurements and to estimate expected
AOD biases due to RH variations in the vicinity of clouds.
The most accurate and statistically valid RH profile measurement, to the best of
our knowledge, are atmospheric sounding (radiosonde) data sets. Analyzing the vast
number of measured atmospheric profiles, which include vertical RH profiles and
information about the extent of the cloudy layers, enables the study cloudy
atmosphere RH trends. Our research extends over 14 globally distributes atmospheric
sounding stations, and over 32 year long period.
First, we estimate the mean and the variance of the sub-saturated RH values in the
lower cloudy troposphere. The lower troposphere is likely to host most aerosol mass
(95% up to 2 km from surface, [Blanchard and Woodcock, 1980]), and was shown to
have the highest Kappa values, suggesting that most of humidification effect is likely
to be there.
Then we use the information that is folded within these profiles regarding the
thickness of the cloudy layer, and analyze the local differences per each station
between the mean sub-saturated RH values of the shallower subset and the more
developed (vertically thicker) clouds. This analysis examines the potential for
possible RH driven biases in the observed trends of cloud aerosol interactions.
17
2 Methods and data
2.1 Cloud field masking algorithm
Cloud field is defined in this study as the area that contains both detectable clouds
and the space around them, within a characteristic distance from each detectable
cloud. It is assumed that the likelihood to have an undetectable cloud (weak optical
signature and/or small relative to the sensor resolution) increases as one approach
detectable clouds [Koren et al., 2007; Koren et al., 2008b]. Therefore, this likelihood
is higher in a cloud field and decreases as moving away from it. Moreover, the same
behavior applies for pockets of high relative humidity and extra illumination coming
from the sides of clouds [Marshak et al., 2006; Wen et al., 2007; Marshak et al.,
2008; Zinner et al., 2008; Marshak et al., 2009; Varnai and Marshak, 2009].
A robust and simple to implement cloud field masking algorithm should comply
the following requirements: (1) the algorithm should use basic input data on cloud
distribution, like binary cloud mask, (2) the algorithm should be based on a spatial
analysis scheme, (3) the algorithm should be applicable to any informative input data
resolution (similar to characteristic cloud size), (4) the algorithm should be valid to all
cloud types.
All previous studies of cloud fields spatial structure have focused on the spatial
distributions of clouds inside cloud fields, and usually considered the entire examined
domain as a part of a cloud field (without any definition of the cloud field
boundaries). Therefore, this study focuses on developing a robust and easy to
implement cloud field boundary detection algorithm.
A few existing spatial analysis methods were considered for this research. For
example, one proposed a method used joint statistics and K-Nearest-Neighbor (kNN)
techniques [Sankaranarayanan et al., 2007] over binary cloud mask data, in order to
isolate the largest clusters in the examined domain, and then define the cloud field
boundaries as the cluster edges. This method lacks the ability to treat isolated small
cloud fields, and ignores these important fields. In addition, this method focuses on
clouds only and does not include the important twilight zone in the cloud field area.
Other methods that do mark a characteristic distance from the cloud field center lack
18
the ability to mark reasonable borders when the cloud field is not rounded shape (most
cloud fields are not rounded).
A cloud field masking algorithm requires a flexible method that will follow any
cloud field shape. Here, that requirement is defined as "locality", i.e. the algorithm
should be sensitive to scales which are higher than the scale of the whole cloud field
in order to mask fields with relatively complex shape. Therefore, the preferred metric
should rely on local properties of the clouds distribution. The best metric that meets
this requirement was found to be the distribution of the distance-from-nearest-cloud
[Koren et al., 2007], where each element represents the Euclidian distance of the
center of the pixel to the nearest cloud. Such metric is local by definition and it
considers nothing but the distances in the vicinity of each cloud.
Euclidian distance transform methods are being used in a wide range of spatial
analysis applications, such as linear and edge detection of objects in digital images
[Rosin, 2009], fractal dimension analysis of 2D objects [Adler and Hancock, 1994],
and recently – for cloud spatial and radiative properties [Koren et al., 2007; Marshak
et al., 2008; Bar-Or et al., 2010]. The last is gaining an increased interest, and a
dataset of the distance-from-the-nearest-cloud is planned to be added to the MODIS
atmosphere level 2 products. Deeper mathematical discussion and further synthetic
examples of the distance field distribution can be found in Ripley (1981).
In the presented algorithm, the probability distribution of the Euclidian distance
map is being used for distinguishing the inner cloud field area from the surrounding
cloud-free area. Examining the whole domain (including the cloud-free area), the
probability distribution shows two different regimes: (1) the intra cloud-field regime,
characterized by the distance probability distribution of clouds inside the field
(describing the cloud spatial distribution), and (2) the extra-field regime, which
asymptotically approximate distance probability distribution of a single giant cloud.
While the distance probability distribution inside the field has a maximum point,
representing the most common distance from a cloud inside the field, following by a
decrease in likelihood of larger distances, the distance probability distribution outside
the fields is monotonically increasing (with a slope that asymptotically goes to 2π,
away from the cloud field as the smoothed perimeter approximate a circle).
19
Having a local maximum in the intra cloud field distribution and monotonic
increase out of the cloud field defines a minimum in the transition between the
distributions.
The distance value corresponding to the local minimum is defined here as the field
distance parameter (R0), and it represents the largest distance-from-the-nearest-cloud
that is still considered to be part of the cloud field. The contour defined by R0 marks
the cloud field boundaries, distinguishing the cloud field from the surrounding cloud-
free area. Figure 2.1 shows a distance map and a distance from the nearest cloud
probability distribution for a synthetic cloud field. It clearly shows the different
distribution properties inside and outside of the cloud field. A true data example for
the extraction of the field distance parameter is presented in Figure 2.1.
Figure 2.1 (Fig. 1 in Bar-Or et al., 2011) Distance map of a synthetic cloud field,
composed of randomly distributed pixel-size clouds (a), and a zoom on its interior
distance map (b). The different distance probability distributions of the whole
synthetic field and of the inner field only (c and d, respectively) demonstrate the
transition point between the inner field distances and the complete field distances. The
20
decrease of the distance probability function in panel c (from distances of 100 pixels
and more) is due to the restricted domain size where larger distances are needed.
Figure 2.2 (Fig. 2 in Bar-Or et al., 2011) Analysis of an observed cloud field,
including the distance probability distribution (blue line), the filtered distance
probability distribution (green line), the distance cumulative probability (red line), and
the transition point, identified by the minimum of the filtered distance probability
distribution, and defining the field distance parameter (marked with orange arrow).
2.1.1 Cloud field masking algorithm - description
The complete cloud field bounding algorithm for the MODIS data is described
below:
Step 1. Data projection on an equal-area matrix
The large footprint of the MODIS instrument results in slight geometrical
distortion. The first step of the algorithm is a projection of all product granules (data
blocks) on an equal-area matrix, in order to avoid any errors due to geometrical
21
differences. The input data for this study is the MODIS cloud mask product
[Ackerman et al., 1998; Platnick et al., 2003]. The projection to 1 km equal-area
cloud mask and to 1 km ocean/land masks are done using MODIS Geolocation
product.
Step 2. Distance map extraction
The Euclidian distance from the nearest cloud is calculated, based on the 1
km/pixel equal-area cloud mask.
Step 3. Calculating the distance probability distribution
The distance cumulative distribution A(r) is calculated for varying distance
parameter values (r). A(r) is the total area that is closer than r from any cloud in the
observed domain. Then, the distance probability distribution is calculated as the
derivative d
dr(A r ).
Step 4. Noise corrections for the distance probability distribution
The generated distance probability function d
dr(A r ) may suffer from high noise
levels, mostly for low r values when the distances calculated for integer number of
pixels may create discontinuities in the distribution. In this step, a Gaussian filter is
applied on the distance probability distribution function in order to filter out high
frequency variations, and to enable the calculation of field distance parameter R0.
Figure 2.2 demonstrates the distance cumulative function A(r), the distance
probability distribution d
dr(A r ), and the smoothed distance probability function.
Step 5. Extracting the field distance parameter R0
The local minimum of the smoothed distance probability distribution is used to
determine the field distance parameter R0, as demonstrated in Figure 2.2.
Step 6. Calculating the cloud field boundaries and coverage
After determining R0, the Cloud Field Fraction (CFF) is calculated. The cloud
field fraction is a unit-less normalized ratio that represents the portion of cloud field
22
covered area in the whole examined domain and defined as: CFF =A(R0)
AD, where AD
is the domain‟s area. The CFF is analogue to the cloud fraction measure. CFF=0
represents an absolute cloud-free domain and CFF=1 a domain that contains only
cloud field area. Given that the domain‟s cloud fraction is A(r=0)
AD, the domain‟s CFF is
always equal or larger than the domain‟s cloud fraction.
The boundaries of the cloud field are represented by the contour r=R0 where all
the pixels whose distance from the nearest cloud is smaller than R0 are part of the
field.
2.1.2 Cloud field masking algorithm - sensitivity study
The proposed algorithm was examined by an extensive set of sensitivity tests,
verifying that the algorithm is not sensitive to the resolution of the input cloud mask
data or to the clouds' spatial distribution. The tests were conducted on both synthetic
and realistic (observed by MODIS) cloud fields. For these tests, the resolution of each
cloud mask data was reduced by a simple averaging of pixels, verifying that the cloud
fraction is constant. After reducing the data resolution (i.e. increasing the data pixel
size), the algorithm was used for calculating the field distance parameter. The
algorithm was found to be stable for varying data resolutions, provided that the data
pixel size is smaller than the characteristic length scale of the examined cloud field.
Figure 2.3 demonstrates the described resolution sensitivity test for a MODIS
observed cloud field (the same as was analyzed in Figure 2.3). In this case, the field
distance parameters calculated by the algorithm are in the range of 17.5-22.0 km, for
pixel size smaller than 7 km. The cloud fraction is stable in the range of 62.6%-
64.6%, as expected by a resolution reduction of a binary cloud mask.
23
Figure 2.3 (Fig. 3 in Bar-Or et al., 2011) The calculated field distance parameter R0
(blue line) and the relative cloud fraction (green line), as a function of MODIS data
resolution.
For the sake of completeness, the theoretical case of a cloud field that contains
only one circular cloud is also considered. In this case the distance probability
function has only one minimum at r=0, and therefore the calculated field distance
parameter is zero. These scenarios are easily identified by the algorithm and gain field
distance parameters that are defined by the mean field distance parameters of the
neighboring cloud fields. However, the probability to find such cloud fields in
realistic datasets is negligible.
2.2 Global cloud field coverage
The data analyzed in this section are MODIS-Terra atmosphere level 2 products
[Platnick et al., 2003; Remer et al., 2005; Levy et al., 2007], for one day at July 28th
,
2008. This study is based on analysis of 66 granules (dataset blocks), containing day-
24
light information, for this specific day, between latitudes 50°S-50°N. The cloud mask
input data are based on the MODIS cloud mask product [Ackerman et al., 1998;
Platnick et al., 2003], the aerosol properties data are based on the MODIS aerosol
product [Remer et al., 2005; Levy et al., 2007], and both sea/land mask and geo-
location data are based on MODIS Geolocation product.
The global CFF (50°S to 50°N) is estimated over land and ocean. In order to find
whether there is any dependence of the field distance parameter on the cloud type,
several cloud fields in each granule are manually classified. This classification is done
using the MODIS provided true-color images, based on the familiar spatial
morphology of the clouds type.
The classification includes four cloud types: Stratocumulus (Sc), shallow Cumulus
(Cu), Cirrus (Ci), and deep convective (DC). The field distance parameter of each of
the selected 170 cloud fields is calculated using the described algorithm; which treats
all cloud types in a similar way.
2.3 Aerosol retrieval inside cloud fields
A daily global MODIS dataset is analyzed in order to evaluate the total radiative
effect of the twilight zone on the aerosol retrieved physical and optical properties.
Both MODIS aerosol optical depth (AOD) and aerosol fine-mode fraction (FMF) are
examined as a function of the distance from the nearest cloud (dc). The distance map
generated for this analysis is based on the MODIS 1 km cloud mask product [Platnick
et al., 2003], and the aerosol retrievals are analyzed separately over Oceanic and land
surfaces, with latitudinal dependence. The surface type and geolocation data are given
by the MODIS geolocation product.
Furthermore, the sensitivity of the MODIS aerosol fine mode fraction (FMF) to
the distance from the nearest cloud is examined. The data for this sensitivity study is
selected to be only above oceans due to the high uncertainties in the aerosol fine-
mode fraction product retrieved above land.
25
2.4 Radiative effects of aerosol humidification on cloud fields
2.4.1 Parameterization of the RH spatial distribution in cloud fields
The current lack of high-resolution in-situ measurements of RH as a function of
the relative location of neighboring clouds (dc), urges the use of numerical simulation
based data for description of RH dc . For this purpose, the liquid water content
(LWC) and the RH values are calculated by the Regional Atmospheric Modeling
System (RAMS, Cotton et al., 2003) and by the Weather Research & Forcasting
model (WRF version 3, Skamarock et al., 2008) , for warm Cumulus cloud fields. The
cloud masking algorithm uses a LWC threshold of 0.01 g ∙ kg−1, and the RH field is
given by the model. The mean RH as a function of the distance to the nearest cloud
RH dc is calculated for every cloud-containing layer, and fitted to an exponential
function:
RH dc = RH0 + RHE − RH0 ∙ e−
dcδ
2.1
Where RH0 is the background relative humidity far from clouds, RHE is the relative
humidity on the cloud edge, and 𝐵 is the e-folding exponential distance scale of the
relative humidity growth near clouds. This fit represents the general averaged RH
field in the cloud layer.
The estimation of RH(dc) in this work is performed using outputs of two
different large eddy simulations. The first simulation (LES simulation A), was carried
out by the WRF (version 3) model, using the two-moment bulk microphysical scheme
(Morrison et al., 2009). This simulation was initialized based on August 21th
, 2007,
00:00 UTC sounding of temperature and moisture from Lihue, Hawaii (WMO station
number 91165). It covers a 15 × 15 km warm Cumulus field, with a horizontal
resolution of 100 m, and 149 terrain following vertical levels in the domain reaching
the model top at 8 km. The vertical resolution varies from 37 m at the lowest layer to
about 83 m at the highest layer. The second simulation used for this research (LES
simulation B), was carried out using the RAMS6 model, with the two-moment bulk
microphysical scheme [Walko et al., 1995; Meyers et al., 1997; Walko et al., 2000].
This simulation was initialized based on June 26th
2010, 12:00 UTC sounding of
26
temperature and moisture, measured in Bet Dagan, Israel (WMO station number
40179). It covers a 12.4 × 12.4 km warm Cumulus field, with a horizontal resolution
of 100 m and vertical resolution of 50 m below 4700 m (the total domain height is 9
km). The total duration of the input data is 1.5 hours, with a 5 minute average (for
simulation B), in order to include a variety of cloud sizes and ages.
2.4.2 Atmospheric radiative transfer simulations of humidified aerosols
When dry aerosol population experiences an increase in the environmental RH, it
is assumes that the water uptake by aerosols does not reduce the RH, and that all
particles are simultaneously growing with the same hygroscopic growth factor, as
shown in Equation 2.2. These assumptions are appropriate for aged, internally-mixed
aerosols.
r RH = rdry ∙ g RH 2.2
Where r(RH) is the humidified aerosol radius, rdry is the initial dry aerosol radius,
and g RH is the hygroscopic growth factor, as presented in Equation 1.3.
Similar to the aerosol size distribution, the aerosol mass distribution is also
modified by the gain of additional mass due to uptake of water content in a varying
RH environment, as expressed in Equation 2.3:
M RH = Mdry ∙ 1 +ρ
w
ρa
g3 − 1 2.3
Where M(RH) is the humidified aerosol mass, Mdry is the initial dry aerosol mass,
ρw
and ρa are the bulk densities of pure water and of the dry aerosol, respectively, and
g is the hygroscopic growth factor, as presented in Equation 1.3.
By adding water mass content to the aerosols, the humidification process also
impacts both bulk density and refractive index of the humidified aerosol. These
27
properties are calculated using volume weighted means of the dry aerosol and pure
water properties (Equations 2.4-2.5):
ρ RH = ρw
+1
g3 ρ
a− ρ
w 2.4
Ref RH = nwet − i ∙ kwet =
= nw +1
g3 na − nw − i ∙ kw +
1
g3 ka − kw
2.5
Where Ref RH is the humidified aerosol refractive index, comprising the real
component nwet , and the imaginary component kwet . nw , kw , na , and ka are the real
and imaginary components of pure water, and dry aerosol refractive indices,
respectively, and g is the aerosol hygroscopic growth factor.
The volume weighted mean for both density and refractive index calculations is
commonly used in chemical, cloud and radiation models [Tang, 1996; Levoni et al.,
1997], and satisfies accuracy standards in recent experimental research [Levoni et al.,
1997; Michel Flores et al., 2012]. In this study, concentrated on the wavelength of
0.550 micron, pure water parameters are set to be: ρw
= 1 g ∙ cm−3; nw =
1.33; kw = 0.
For simulating the MODIS retrieval of the humidified aerosol properties the
Spherical Harmonic Discrete Ordinate Method – SHDOM [Evans, 1998] is used. In
these simulations, all suspended aerosols are assumed to be uniformly distributed
between heights of 1-2.7 km, which represent the cloud layer height in the LES
simulated cloud field in case A1, described in Section 3.3.1.
This assumption about aerosol location in the cloud layer only represents the
maximum humidification effect as in nature the aerosol is distributed from the ground
up, throughout the boundary layer to the free atmosphere, where the humidity may be
lower. The initial dry aerosol size distribution is set to be a bimodal log-normal
distribution, comprising fine-mode and coarse mode aerosol distributions. In order to
better simulate MODIS instruments retrievals, all radiative transfer simulations are
conducted at a wavelength Gaussian band of 545-565 μm , similar to MODIS band 4
28
[Remer et al., 2005]. The aerosol fine-mode fraction (FMF) is calculated using two
separate simulations: the first contains only a single fine-mode aerosol distribution,
and the second contains a bi-modal distribution. After acquiring the aerosol optical
depth for each simulation, the FMF is directly from its definition, using Equation 2.6:
𝐹𝑀𝐹 =𝐴𝑂𝐷𝑓𝑖 (𝜆 = 550𝑛𝑚)
𝐴𝑂𝐷𝑡𝑜𝑡 (𝜆 = 550 𝑛𝑚) 2.6
Where 𝐴𝑂𝐷𝑓𝑖 and 𝐴𝑂𝐷𝑡𝑜𝑡 is the aerosol optical depth of the fine mode aerosol and
the total aerosol distribution, respectively, and 𝜆 is the used wavelength.
The above scheme, which its conceptual model is demonstrated in Figure 2.4,
where as the outcome, calculates both AOD(dc) and FMF(dc) for any given aerosol
distribution. The sensitivity of both AOD and FMF to the varying RH is estimated by
running an extensive set of radiative transfer simulations for different size
distributions.
Figure 2.4 (Fig. 1 in Bar-Or et al.[2012]) The conceptual model operated in Section
3.3. Input data (gray circles): the cloud mask field, the relative humidity field, the
aerosol hygroscopic growth parameterization (growth param.), and the dry aerosol
physical and optical properties. Analyzed data (green rectangles): the field of
distances from the nearest cloud - dc, the hygroscopic growth factor as a function of
relative humidity - g RH , the relative humidity as a function of dc - RH dc , the
29
hygroscopic growth factor as a function of dc - g dc , and the humidified aerosol
optical and physical properties due to hygroscopic growth. Simulated data based on
the modified aerosol properties as a function of dc (blue rectangles): the humidified
aerosol optical depth - AOD dc , and aerosol fine-mode fraction - FMF dc .
2.5 Characterizing the RH in cloud fields
The input datasets for this analysis are the records of the World Meteorological
Organization (WMO) registered upper-air measurement stations, that are freely
available at the University of Wyoming Atmospheric Soundings database
(http://weather.uwyo.edu/upperair/sounding.html), or at the website of NOAA‟s
Integrated Global Radiosonde Archive (IGRA, Durre et al., 2006). These vast datasets
provide a continuous record of upper-air vertical profiles of pressure, temperature,
relative humidity, wind speed, and wind direction. In most stations, radiosondes are
released at least twice a day, at 00:00 UTC and at 12:00 UTC (i.e. 00Z and 12Z),
without any consideration of the local atmospheric conditions. This independent
sampling procedure enables the analogy of a long radiosonde record to a pseudo
Monte-Carlo experiment, which examines the statistical properties of several
atmospheric measures in a specific location.
In addition to the estimation of the RH vertical profile for the selected locations
(and seasons), it is possible to extract the height and thickness of the lowest (by
altitude) potentially cloudy layer the radiosonde sampled, enabling the assessment of
RH vertical profiles inside cloud fields (see Section 1.5.2). This potentially cloudy
layer is characterized by suitable conditions for cloud formation.
2.5.1 Locating the cloudy layer with radiosonde data
The lowest (by altitude) atmospheric cloudy layer is expected to extend from the
Lifted Condensation Level (LCL), which is calculated using Equation 2.7 [Bolton,
1980]. This equation is an approximated iteratively solved solution of the equations
that describe the dry adiabatic lapse rate and the dew point lapse rate (i.e. the point of
which an adiabatic parcel‟s temperature equals the dew point temperature).
30
𝑇𝐿𝐶𝐿 =1
1𝑇𝑑 − 56
+𝑙𝑛 𝑇/𝑇𝑑
800
+ 56 2.7
Where TLCL is the temperature at the LCL (K), and T and Td are the temperature and
the dew-point temperature at the surface or at any chosen altitude below the LCL.
For each radiosonde profile, the cloudy layer base is set as the Lifted
Condensation Level (LCL [Bolton, 1980] ), which is defined as the height of
saturation for a rising air parcel based on the average humidity conditions at the
lowest 500 m above surface. In cases where the surface initiated LCL computation
results in an LCL that is higher than an atmospheric stable layer (where the observed
temperature lapse rate is lower than 3 𝐾 ∙ 𝑘𝑚−1), the calculation is considered
incorrect, and a corrected LCL is calculated using T and Td values at the top of this
stable layer (using Equation 2.7). The calculated LCL is set as the base of the cloudy
layer.
The upper limit of the cloudy layer is defined by two different methods. The
Saturated Adiabatic Lapse Rate (SALR) limit is the bound of the unstable layer for
saturated air above the LCL, a layer that enables cloud formation. The cloudy layer,
as defined by the SALR method describes the atmospheric layer that is conditionally
unstable for wet air and enables cloud formation (computed from the LCL height
using Equations 2.8-2.9):
𝑇𝑆𝐴𝐿𝑅 = 𝑇 𝐿𝐶𝐿 + ΓSALR h −𝐿𝐶𝐿
0
𝑑 2.8
ΓSALR r, T = g ∙ 1 +Hvr
Rsd T ∙ Cpd +
Hv2rε
Rsd T2
−1
2.9
Where 𝑇𝑆𝐴𝐿𝑅 is the temperature following the saturated adiabatic lapse rate from the
LCL (𝑑𝑒𝑔𝐾), T(h) is the temperature vertical profile (𝑑𝑒𝑔𝐾), 𝑟() is the water vapor
mixing ratio vertical profile (𝑔 ∙ 𝑘𝑔−1), ΓSALR is the saturated adiabatic lapse rate
(𝐾 ∙ 𝑚−1), g is the Earth‟s gravitational acceleration (𝑚 ∙ 𝑠−2), 𝐻𝑣 is the heat of
evaporation of water (𝐽 ∙ 𝑘𝑔−1), 𝑅𝑠𝑑 is the specific gas constant of dry air (𝐽 ∙
𝑘𝑔−1𝐾−1), 𝑅𝑠𝑤 is the specific gas constant for water vapor (𝐽 ∙ 𝑘𝑔−1𝐾−1), 𝐶𝑝𝑑 is the
specific heat of dry air at constant pressure (𝐽 ∙ 𝑘𝑔−1𝐾−1), and 𝜀 is the ratio of the
31
specific gas constant of dry air to the specific gas constant for water vapor
(dimensionless).
The second measure takes in account that often convective clouds overshoot
the conditionally unstable layer as long as the conditions of the layer above the SALR
are close to saturation. For that purpose, we define here a new measure namely the
Moist Boundary Parameterization (MBP, Equation 2.10). Convective clouds
occasionally exceed the SALR layer and penetrate the layer above, as long as it is
humid enough to keep the clouds from complete evaporation. The MBP is calculated
for each altitude using the temperature difference between the measured temperature
(T) and the dew point temperature (Td), divided by the measured relative humidity
(RH). This method is sensitive to both reduction in Td that will increase the numerator
and the reduction in RH that will decrease the denominator and therefore found to
detect small shifts in the profile well. The likelihood for cloud existence decreases
rapidly as the MBP increases. After inspecting the sensitivity of the profiles to the
MBP values were tested, a threshold value of 5 ℃/% was found to well define the
MBP cloudy layer upper limit for this study.
𝑀𝐵𝑃 𝑇, 𝑇𝑑 , 𝑅𝐻 = 𝑇 − 𝑇𝑑 /𝑅𝐻 2.10
The Moist Bounding Parameter (MBP) as a function of the temperature (T), the dew
point temperature (Td), and the relative humidity (RH).
An example of one analyzed radiosonde sample is presented in Figure 2.5. The
two different methods for identifying potentially cloudy layers enable the treatment of
all convective cloud types. While most convective cloud layers have a similar top
boundary using the two methods, the temperature profile of deep convective cloud
layers in humid environment (e.g. tropical Cb cloud fields in the Amazon), is very
similar to the saturated adiabatic lapse rate. The similarity between the saturated
adiabatic lapse rate and the temperature profile in these clouds will cause the
algorithm to bound the layer lower than necessary, and therefore only the moist
bounding parameter threshold can find its physical limit.
32
Figure 2.5 A single radiosonde sample, obtained in Lihue, Hawaii (see Table 4), on
November 25th
, 1993, 00:00:00 UTC. The shown vertical profiles are the measured
relative humidity (left panel), the calculated moist bounding parameter (MBP, middle
panel), the measured temperature (right panel, red line), the measured dew-point
temperature (right panel, black dashed line), the saturated adiabatic lapse rate from the
estimated LCL (right panel, blue line), the LCL as calculated from the lowest 500m of
the sample (rigt panel, black horizontal line), and the upper boundaries of the cloud
layer as calculated by the SALR method and by the moist bounding parameter
threshold (right panel, brown and green horizontal lines).
2.5.2 Characterizing the SRH values in cloud fields
Integrating 32 year record of AS profiles for a given station, at a given
measurement time, during a specific season (presuming low meteorological variance),
is analogue to a Monte Carlo experiment which samples the atmosphere in a specific
location and time, uninfluenced by the atmospheric conditions. For a long 32 year
sample period this pseudo Monte Carlo experiment enables the calculation of the
33
mean sub-saturated (not in cloud) RH profiles - 𝑆𝑅𝐻 (𝑧), and of the RH standard
deviation profiles 𝜎𝑆𝑅𝐻(𝑧). These RH measurements in cloud fields, yet not in clouds,
provide the mean RH values that retrieved aerosols experience in the vicinity of
clouds.
2.5.3 The lower cloudy troposphere SRH and cloud development
To further explore the possible effects of SRH in the lower cloudy troposphere
on the observation of cloud-aerosol interactions, we keep following the same line of
reasoning. We estimate the SRH values as a function of the level of the cloud
development. Since a positive correlation in expected between cloud horizontal and
vertical dimensions [Koren et al., 2010] and both are correlated with the cloudy layer
thickness (between the LCL and the SALR), the data subset with the thicker cloudy
layer is likely to represent cases of vertically developed clouds and high cloud
fraction.
For each station, all radiosonde profiles are sorted by their cloudy layer thickness
(measured from the LCL to the SALR upper limit). The profiles are classified for
more developed cloud profiles, and shallower cloud profiles, separated by median of
the cloudy layer thickness, for each station.
A calculation shows that hygroscopic aerosols (e.g. sea salt aerosols with 𝜅 = 0.7
experience geometric cross section difference of 19.5% when their environment RH
value changes from 80% to 85%, and experience AOD change of 18.2% when the RH
value changes from 60% to 70%. Non-hygroscopic particles (e.g. biomass burning
aerosol – 𝜅 = 0.1) gain geometric cross section difference of 7.8% for RH increase of
80% to 85%, and AOD change of 4.8% for RH increase of 60% to 70%. The
geometric cross section difference is a first order approximation to the total expected
AOD change, neglecting the modification of the refractive indices due to water
uptake, and the aerosol size parameters.
34
3 Results
The results of Sections 3.1-3.4 below are included in the following manuscripts:
Bar-Or, R. Z., I. Koren, and O. Altaratz (2010) Estimating cloud field
coverage using morphological analysis, Environ. Res. Lett., 5, 014022,
doi:10.1088/1748-9326/5/1/014022.
Bar-Or, R. Z., O. Altaratz, and I. Koren (2011) Global analysis of cloud field
coverage and radiative properties, using morphological methods and MODIS
observations, Atmos. Chem. Phys., 11(1), 191, doi:10.5194/acp-11-191-2011.
Flores, J. M., R. Z. Bar-Or, N. Bluvshtein, A. Abu-Riziq, A. Kostinski, S.
Borrmann, I. Koren, and Y. Rudich (2012) Absorbing aerosols at high relative
humidity: closure between hygroscopic growth and optical properties, Atmos.
Chem. Phys., 12, 5511-5521, doi:10.5194/acp-12-5511-2012.
Bar-Or, R. Z., I. Koren, O. Altaratz, and E. Fredj: Humidified aerosol
properties in cloudy environment, Atmos. Res., 118, 280-294, doi:
10.1016/j.atmosres.2012.07.014.
Bar-Or, R. Z., I. Koren, O. Altaratz: Characterizing the relative humidity in
the lower cloudy troposphere, Geophys. Res. Lett., submitted.
3.1 Global cloud field coverage
The results (Table 1), which are based on the selected 170 cloud fields, show a
mean global field distance parameter of 29±1 km, in agreement with the results of
Bar-Or et al. (2010), that found a monthly averaged field distance parameter of 30 km
over the Atlantic Ocean during July 2008 (the error is calculated as a standard mean
error). The results also agree with Koren et al. [2007], that found a reflectance signal
effect up to 30 km from the nearest cloud edges. The comparison between the field
distance parameters of different cloud field types shows that Sc fields have the
smallest distance parameter, probably due to their sharp transition to cloud-free
atmosphere at their edges. The calculated field distance parameter for Cirrus cloud
fields is very close to the mean value of all cloud type, possibly because Cirrus field
may be located above a wider field of different cloud type. Such setting would lead
35
the algorithm to be sensitive to the clouds that appear on the border of the fields. All
calculated field distance parameter values are in the same order of magnitude of 29±1
km, with the extreme results being 17 and 39 km.
The global cloud field fraction for July 28th
, 2008, is 88%, calculated using the
algorithm generated field distance parameter R0, and 81% when using the constant R0
= 10 km for the whole dataset. These results are in line with Twohy et al., [2009a]
that found that only 8% of the detected cloud-free area above oceans is located in a
distance larger than 20km from the nearest detected cloud. This suggests that the CFF
over oceans, using a constant R0 of 20 km, is approximately 82%. Koren et al., (2007)
showed that an addition of 30 km belt around all clouds when the global cloud
fraction is 51%, will cover 81% of Earth‟s surface. It means a global CFF of
approximately 82% when using a constant R0 of 30 km.
Next, the calculated global CFF and the distance-from-the-nearest-cloud above
land and above ocean were separated and compared for different latitudes (with a
latitudinal resolution of 1°). The calculated latitudinal mean CFF, and the latitudinal
mean distance-from-the-nearest-cloud are presented in Figure 3.1.
36
Figure 3.1 (Fig. 4 in Bar-Or et al., 2011) Latitudinal mean cloud fraction (left panel,
lines), cloud field fraction (left panel, dots) and distance from the nearest cloud (right
panel), above land (red) and ocean (blue), based on MODIS Terra observations for
July 28th
, 2008.
The results show a clear difference between cloud fields above oceans and lands.
While the CFF above oceans is 80% or higher in all latitudes, the CFF above land
carries a strong signature of the global atmospheric circulation. The CFF above land
significantly decreases in the Hadley subsidence (desert belt) latitudes (10°S-25°S and
20°N-35°N). The northward shift of the Hadley cells is expected considering the date
of observed data, during the boreal summer (July 28th
, 2008). Moreover, a closer
examination of the differences between the CF and the CFF curves shows that while
the CFF over land has a similar trend to the CF, the CFF over oceans is uncorrelated
to its corresponding CF; this indicates that the spatial cloud field structures over land
significantly differ from the structures over oceans.
37
In spite of the low latitudinal variance of the distance-from-the-nearest-cloud
parameter over oceans, the mean latitudinal CFF reveals a clear difference between
the Hadley subsidence latitudes (10°S-25°S and 20°N-35°N) and the Inter Tropical
Convergence Zone (ITCZ, 5°S-15°N). This transition is probably a result of the
difference between the spatial properties of the marine cloud fields over the ITCZ
(mostly deep convective), and the cloud fields in the subsidence zone latitudes
(mostly shallow clouds). The ITCZ‟s distinguished marine CFF behavior
demonstrates the ability of the algorithm to indentify different cloud fields by their
distance probability distribution, in spite of their similar mean distance-from-the-
nearest-cloud values.
Furthermore, these results indicate that while the likelihood to sample a total
cloud-free (away from cloud fields) pixel above oceans is ~12%, the likelihood to
sample such a pixel above land varies between ~10% in the ITCZ latitudes, and ~80%
in the central latitudes of the Southern subsidence zone (around 20°S, in the observed
day).
3.2 Aerosol retrieved optical properties in cloud fields
A daily global dataset is used to show these effects, separately above land and
ocean. Figure 3.2 presents the mean aerosol optical depth (AOD) as a function of the
distance-from-the-nearest-cloud, showing a clear exponential decay. The higher mean
AOD values above land (compared to the oceans) are expected, because of the higher
aerosol concentrations observed above land, and agree with past research [Remer et
al., 2008].
In addition to the analysis presented in Bar-Or et al., [2010], where Cirrus cloud
fields are excluded from the input data, a global analysis of all cloud types is
included. The optical contribution of the twilight zone around Cirrus clouds is
considered in this global analysis because undetectable Cirrus clouds often appear in
the vicinity of detectable clouds and affect the aerosol optical retrieval.
The global mean AOD as a function of the distance-from-the-nearest-cloud is
found to be monotonically decreasing (Figure 3.2). An interesting finding is the clear
38
change in the decrease rate, around 30 km distance from the nearest cloud. Given the
large size of the dataset (more than 1 million pixels), this sharp rate change may point
to a characteristic influence scale of cloud fields, supporting the findings presented in
Section 3.1, and previous studies [Koren et al., 2007; Remer et al., 2008; Twohy et al.,
2009a]. The exponential decay found here agrees with previous findings and the
coefficients presented here agree with the values calculated by Bar-Or et al. [2010].
The global mean AOD values found here are also supported by the MODIS-Terra
long-term global analysis of Remer et al. [2008] that found a global mean AOD of
0.19 above land, and of 0.13 above Ocean.
Figure 3.2 (Fig. 5 in Bar-Or et al., 2011) Global mean aerosol optical depth (AOD)
as a function of the distance from the nearest cloud, retrieved above land (red dots)
and above ocean (blue dots), and the matching exponential fitting functions (purple
and green lines, respectively), for distances of 0-30 km from the nearest cloud. The
error bars represent the standard mean error. All data are based on MODIS Terra
observation for July 28th
, 2008. The constant distance lines highlight the values for
R0=10 km and for the calculated global characteristic value (R0=30 km, see Section
3.1).
39
The mean FMF values retrieved above ocean as a function of the distance-from-
the-nearest-cloud is presented in Figure 3.3. The results clearly show two regimes in
the graph. The first, in the range of distances between 0~33 km from the nearest
cloud, that shows an exponential increase of the FMF with the distance from the
nearest cloud, varying from 37.1% at 0-1 km to 53.4%, 32-33 km from the nearest
cloud. The calculated exponential fit in this regime saturates at FMF of ~54%. The
second regime in the graph is for distances that are larger than ~33 km that shows a
slow decrease of the FMF when increasing the distance from the nearest cloud (in the
range of 53.4%-51.7%).
Figure 3.3 (Fig. 6 in Bar-Or et al., 2011) Global mean aerosol fine-mode fraction
(FMF) as a function of the distance from the nearest cloud, retrieved above ocean
(blue dots), and its exponential fitting function (green line), for distances of 0-30 km
from the nearest cloud. The error bars represent the standard mean error. All data are
based on MODIS Terra observation for July 28th
, 2008. Similarly to Figure 3.2, the
constant distance lines highlight the values for R0=10 km and for the calculated global
characteristic value.
40
The results of the first regime in the graph can be explained by the theoretical
superposition of two effects. The first is the aerosol swelling process that produces
sharp exponential decay in the aerosol size, as the distance to the nearest cloud
increases [Pahlow et al., 2006]. The second is the effect of undetectable clouds that
increase the retrieved aerosol apparent size.
Both AOD and FMF trends in cloud fields strengthen the need in distinguishing
between measurements of aerosols inside and outside cloud fields, because of the
significant difference in aerosol properties and its measured optical characteristics
near detectable clouds.
3.3 Radiative effects of aerosol humidification on cloud fields
3.3.1 RH spatial distribution in cloud fields
The RH simulated data of cases A1 and B (see Section 2.4.1) are fitted to
exponential RH(dc) functions, as described by Equation 2.1. The extracted mean
background relative humidity (RH0) values, based on all cloud containing layers in
cases A and B respectively, are 64.9% and 59.7%, and the extracted mean exponential
distance scale (δ) values are 85 m and 93 m.
In both cases, the lower RH0 values are mostly attributed by calculations of RH
change around tops of clouds within the inversion layer. In this layer the background
RH is relatively low (due to the sharp decrease in RH with altitude), and increases
rapidly when approaching a cloud that happened to break into the inversion layer.
This may result in the relative low RH0 values found. The higher atmospheric layers
drag the mean RH0 function to lower RH0 values, and shorter exponential distance
scale (δ) values. A counter example is shown when the analysis in case A1 is done
only with the lower atmosphere (below 2.6 km), which includes marine boundary
layer cumulus clouds in a moist environment. The mean RH0 value is higher (78.4%),
and the derived exponential distance scale (δ) is longer (87.5 m). Figure 3.4 presents
the derived RH(dc) functions based on filtered case A1, and on case B.
41
Figure 3.4 Parameterization of the mean relative humidity as a function of the
distance from the nearest cloud - RH dc (thick lines) and the corresponding standard
deviation (shadowed area), as estimated by LES simulation A1 (pink), and B
(yellow), as described in Section 3.3.1 and Equation 2.1. The presented data for
simulation A includes only the lower atmosphere (below 2.6 km), while simulation B
includes all altitudes.
The simulated results agree with in-situ measured relative humidity values in the
vicinity of clouds (RH(dc)). Twohy et al. [2009a] presented RH(dc) air borne
measurements sampled in warm trade cumulus cloud field, during INDOEX
campaign [Clarke et al., 2002]. Fitting the results of Twohy et al. [2009a] to Equation
2.1 finds exponential distance scale (δ) values of 98 m – 278 m , and background
relative humidity (RH0) values of 87%-89%. Additional results, from Wang and
Geerts [2010], averaged the specific humidity as a function of the distance from cloud
edge qv(dc), sampled in warm trade Cumulus field, during the RICO campaign
42
[Rauber et al., 2007]. Fitting these results to Equation 2.1 (assuming curve trend
similarity of qv (dc) and RH dc functions) extracts B values of 106 m – 120 m.
3.3.2 Humidified aerosol properties in cloudy environment
Using SHDOM radiative transfer model [Evans, 1998], extensive sensitivity
simulations calculate both aerosol optical depth (AOD) and aerosol fine-mode
fraction (FMF), as a function of the distance from the nearest cloud (dc). The
simulations are performed for bimodal log-normal aerosol distributions, and the
varying parameters are the physical and optical properties of the fine-mode aerosols
(see Table 2). All sensitivity simulations used coarse-mode of sea salt aerosols (sets
S1-S4 in Table 2), or of desert dust aerosols (sets D1-D4 in Table 2).
Figure 3.5 Simulated aerosol optical depth (AOD, upper panel) and fine-mode
fraction (FMF, lower panel) as a function of the distance from the nearest cloud (dc),
for relative humidity exponential parameterization RH dc that vary between sloped
(lowest background values, green), mean (red), and mild (highest background values,
43
blue), as described in Section 3.3.1. The simulated bimodal lognormal distribution
contains coarse mode NaCl, and fine-mode biomass burning aerosols (set R1 in Table
2).
Figure 3.6 Simulated aerosol optical depth (AOD, upper panel) and fine-mode
fraction (FMF, lower panel) as a function of the distance from the nearest cloud (dc),
for fine-mode hygroscopicity parameter κfi that vary between 0-1.1. The simulated
bimodal lognormal distribution contains coarse-mode NaCl, and fine-mode biomass
burning aerosols (set S1 in Table 2).
44
Figure 3.7 Simulated aerosol optical depth (AOD, upper panel) and fine-mode
fraction (FMF, lower panel) as a function of the distance from the nearest cloud (dc),
for fine-mode mass content mfit values that vary between 5 − 50 μg ∙ m−3. The
simulated bimodal lognormal distribution contains coarse-mode NaCl, and fine-mode
biomass burning aerosols (set S2 in Table 2).
45
Figure 3.8 Simulated aerosol optical depth (AOD, upper panel) and fine-mode
fraction (FMF, lower panel) as a function of the distance from the nearest cloud (dc),
for fine-mode mass content mfit values that vary between 5 − 50 μg ∙ m−3. The
simulated bimodal lognormal distribution contains coarse-mode NaCl, and fine-mode
biomass burning aerosols (set S3 in Table 2).
46
Figure 3.9 Simulated aerosol optical depth (AOD, upper panel) and fine-mode
fraction (FMF, lower panel) as a function of the distance from the nearest cloud (dc),
for fine-mode imaginary part of the refractive index Im Reffi values that vary
between 0 and 1. The simulated bimodal lognormal distribution contains coarse-
mode NaCl, and fine-mode biomass burning aerosols (set S4 in Table 2).
The simulation results show that aerosol optical depth and fine-mode fraction
trends near clouds are more sensitive to the hygroscopicity of the participating
aerosols (simulation sets S1 and D1 in Table 2) than to the mass content ratio between
fine-mode and coarse-mode aerosols (mfi mco ). The second most effective parameter
is the fine-mode aerosol geometric radius (rg,fi, sets S3 and D3 in Table 2); this
sensitivity is mainly due to the characteristic size scale of the fine-mode aerosol,
which its interaction with light at the wavelength of 0.550 μm is more efficient than
the coarse-mode's interaction.
The curve trend of FMF(dc) is found to significantly vary with the suspended
aerosol properties. The simulation results (summarized in Table 3) point to a delicate
47
balance between the two represented aerosol modes, which may be affected by their
hygroscopicity properties, and by their radiative interaction in the given wavelength.
The results of simulation sets D1-D4 (Table 2), as described in Section 3.3.2, are
presented in Appendix A. This set of sensitivity simulations is performed for different
fine-mode aerosol properties, while the coarse-mode aerosol properties are kept
constant with the values commonly used for desert dust aerosols (Dubovik et al.,
2002). Figure 0.1 presents the sensitivity of both 𝐴𝑂𝐷(𝑑𝑐) and 𝐹𝑀𝐹(𝑑𝑐) to the fine-
mode aerosol hygroscopicity parameter 𝜅𝑓𝑖 , for the value range between 0-0.7 (set D1
in Table 2), which is a possible hygroscopicity range for different biomass burning
aerosols (Petters et al., 2009; Carrico et al., 2010). The results agree with the findings
presented in Figure 3.6 (set S1 in Table 2), but show that 𝐹𝑀𝐹(𝑑𝑐) switches shape
only for less hygroscopic fine-mode aerosols (𝜅𝑓𝑖 ≲ 0.03). The 𝐹𝑀𝐹 decreases near
cloud edges, unlike the findings of set S1, when using sea-salt aerosols as coarse-
mode. A possible reason for the lower 𝜅𝑓𝑖 threshold for the 𝐹𝑀𝐹 𝑑𝑐 shape switch
may be the lower hygroscopicity of the coarse mode in this set (𝜅𝑐𝑜=0.03), which
cancels the coarse-mode 𝐴𝑂𝐷 increase contribution in the vicinity of clouds, unless
the fine-mode's hygroscopicity effect is smaller.
The sensitivity of the simulated AOD(dc) and FMF(dc) curve shapes to the fine-
mode aerosol mass content mfi, combined with coarse-mode desert dust (set D2 in
Table 2), agrees with the results of set S2 (Figure 3.5), with sea-salt aerosol. We find
no sensitivity of AOD(dc) and FMF(dc) curve shapes to the fine-mode mass content.
Figure 0.2 presents the sensitivity of AOD(dc) and FMF(dc) to the fine-mode
aerosol geometric mean radius rg , while considering coarse-mode desert dust aerosols
(set D3 in Table 2). We find that unlike the equivalent sensitivity simulation done
with sea-salt (set S3 in Table 2), there is no sensitivity of AOD(dc) and FMF(dc)
curve shapes to changes in the fine-mode geometric mean radius rg . This behavior is
most likely due to the low hygroscopicity of desert dust (κco = 0.03). The low
hygroscopicity of the coarse-mode aerosols, compared to that of the fine-mode
aerosols, increases the relative contribution of the fine-mode aerosols to any optical or
physical change of the whole aerosol distribution in a high RH environment. Hence,
48
when the coarse-mode aerosols are non-hygroscopic, the fine-mode aerosols are more
dominant in changing the total aerosol optical contribution near clouds.
The simulation set results presented in Figure 0.3 show the sensitivity of
AOD(dc) and FMF(dc) to the changes in the values of the fine-mode aerosol
absorption ability, via the imaginary part of the refractive index Im Reffi , with
coarse-mode desert dust aerosols (set D4 in Table 2). Unlike the results of the
equivalent simulation, done with coarse-mode sea salt aerosols (set S4 in Table 2),
the AOD(dc) and FMF(dc) curve shapes appear to be insensitive to changes
in Im Reffi . This finding may be explained by the low hygroscopicity of desert dust
(κco = 0.03), which decreases the relative influence of the coarse-mode aerosols in
high relative humidity environments, enabling the fine-mode aerosol hygroscopic
growth to drive both AOD(dc) and FMF(dc) in the vicinity of clouds.
3.3.3 Absorbing humidified aerosol properties in cloud fields
The optical behavior of different absorbing aerosol types is explored in vicinity of
clouds, using the Spherical Harmonic Discrete Ordinate Method model (SHDOM,
Evans, 1998) for atmospheric radiative transfer. The simulations focus on the change
in total extinction (in units of 𝑘𝑚−1), the single scattering albedo (ω), and the
asymmetry parameter (g) as a function of distance from the nearest cloud (dc) at
wavelengths of 532 nm and 355 nm.
In each simulation the hygroscopicity parameter κ [Petters and Kreidenweis,
2007], the real part of the dry aerosol complex refractive index, the dry effective
diameter (Deff-dry), assuming a single-mode log-normal distribution with a ln(σ) = 0.7,
and the mass of the aerosol layer, set at 5 μg m-3, are kept constant while varying the
imaginary part of the dry aerosol RI. Moreover, two different RH fields are used in
the model: one typical for the marine boundary layer (MBL), where the background
RH reaches ~88%, and the other describing continental cumulus cloud field (CCF)
where the background RH reaches 60%. Both RH fields are extracted from large-
eddy-simulation model results (UCLA-LES, Xue and Feingold, JAS, 2006, and
RAMS6, [Cotton et al., 2003], and supported by in-situ measurements [Twohy et al.,
49
2009a; Wang and Geerts, 2010]. The κ values used are κ=0.6, typical of ammonium
sulfate [Petters and Kreidenweis, 2007] and κ=0.15, typical of organic aerosols
[Petters et al., 2009]. The real part of the refractive index (RI) and the dry effective
diameters used are n = 1.504 and n = 1.626, and Deff-dry=0.1 μm and Deff-dry=0.2 μm.
The imaginary component of the RI for the dry aerosols was varied from 0.0 to 0.4 in
0.05 steps. The results for the extinction as a function of distance from the nearest
cloud (dc) are shown in Figure 3.10, for SSA (ω) vs. dc in Figure 3.11, and for the
asymmetry parameter vs. dc in Figure 3.12. All figures show 4 pairs of graphs, where
within each pair all variables are the same except the RH field; left side MBL and
right side CCF. The top graphs (a-MBL, a-CCF, b-MBL, b-CCF) show the
simulations for a Deff-dry = 0.1 μm, where the lower graphs (c-MBL, c-CCF, d-MBL,
d-CCF) for a Deff-dry = 0.2 μm. The differences within the top and lower graphs are the
κ and n values. The left graphs have a κ = 0.6 and n=1.504, where the right graphs
have a κ = 0.15 and n = 1.626.
Figure 3.10 shows that the extinction is practically independent from the
imaginary component of the complex refractive index, and that for a Deff-dry = 0.2 μm
there is even no spectral dependence. Furthermore, the greatest differences in
extinction occur in the first 50 m near the cloud edge, with a steep exponential
increase. Further away, the extinction remains practically constant for all of the CCF
cases, and it decreases slightly for the MBL cases; a maximum of 0.07 km-1 for the a-
MBL case. For the MBL case study the differences in extinction are dominated more
by the size of the effective dry diameter than by differences in κ or n, the results show
that between a-MBL and b-MBL the shape and value of the extinction are basically
the same (as well as between c-MBL and d-MBL). This suggests that the dry size
distribution of the aerosols present in the twilight zone is the dominant factor in the
total extinction. For the CCF cases, a greater difference is observed in the extinction
between the constant Deff-dry with different κ and n; where below an RH of 80% the
real part of the RI dominates. For example, there is around a 0.01 km-1 difference in
extinction between a-CCF and b-CCF, which means that for an aerosol layer of 5 km
there is an optical depth difference of 0.05 which is small but not negligible.
On the contrary to the extinction as a function of dc, a clear difference is shown
in the single scattering albedo (Figure 3.11) and asymmetry parameter (Figure 3.12)
50
vs. dc, for different degrees of absorption of the present aerosols. Figure 3.11 shows
that in the first 50 m from the cloud edge there are significant differences between the
highly absorbing (k=0.4) and lightly absorbing (k = 0.05) aerosol. Within this
distance, the single scattering albedo may decrease down to ω = 0.45 for an imaginary
component of k = 0.4, and down to ω = 0.75 for k = 0.05 at a wavelength of 355 nm
(see d-CCF) and 532 nm (see b-CCF). The value of ω for a specific imaginary
component at different dc‟s varies for each of the 8 cases presented. For the MBL
cases, there is a constant decrease in ω as the dc increases, whereas for the CCF cases
after the first 100 m the single scattering albedo remains constant. Another distinct
feature in the behavior of ω is that it is always lower at 355 nm than at 532 nm for the
same k value, with the exception of the k = 0.4 value of the b-CCF case. For the Deff-
dry = 0.2μm cases the differences between the wavelengths is greater than at Deff-dry =
0.1μm.
Figure 3.10 Extinction as a function of distance from the nearest cloud (dc), for four
different scenarios: a) the hygroscopicity parameter (κ) set at 0.6, the real part of the
refractive index (n) set at 1.504, and the dry effective diameter (Deff-dry) set at 0.1 μm;
51
b) κ=0.15, n=1.626, and Deff-dry= 0.1 μm; c) κ=0.6, n=1.504, and Deff-dry= 0.2 μm; d)
κ=0.15, n=1.626, and Deff-dry= 0.2 μm. For each scenario two different relative
humidity fields were used: one typical for the marine boundary layer (MBL), and the
other describing a continental Cumulus cloud field (CCF). Two wavelengths: 355 nm
(solid lines) and 532 nm (dashed lines), are shown. The imaginary component (color
scale) was varied in each case from 0 to 0.4.
Figure 3.11 Single scattering albedo (ω) as a function of distance from the nearest
cloud (dc), for the same scenarios as in Figure 3.10. See Figure 3.10 caption for full
description.
52
Figure 3.12 Asymmetry parameter (g) as a function of distance from the nearest
cloud (dc), for the same scenarios as in Figure 3.10 and in Figure 3.11. See Figure
3.10 caption for full description.
The asymmetry parameter results (Figure 3.12) suggest that the light is
predominantly scattered to the forward direction for both wavelengths, with generally
being larger for 355 nm than for 532 nm for the same k value (see c-MBL for the few
exceptions). Furthermore, the asymmetry parameter goes from a minimum value for
purely scattering aerosols, with values as low as g = 0.66 at 532 nm (b-CCF), to a
maximum value for the highly absorbing aerosols; i.e., as the imaginary component is
increased the scattered light is directed more in the forward direction. Furthermore,
there is no clear pattern that describes the changes of the asymmetry parameter with
dc.
53
3.4 Upper-air measurements of the RH spatial distribution in cloud fields
3.4.1 RH mean vertical profile in potentially cloudy layers
A long record of upper-air measurements between 1980 and 2011 is examined,
for the stations listed in Table 4, using the methods described in Section 2.5. The
chosen season for this analysis are the month June-July-August. We select a short
season in order to avoid biases due to sharp changes in the meteorological
characteristics. Moreover, June-July-August are tested to be meteorologically stable
for most station, showing small variances in the cloudy layer heights, the 500 hPa
height, and the 850 hPa and 500 hPa temperatures.
Figure 3.13 shows the daily mean heights of the calculated cloudy layer (using
the SALR bounding method, described in Section 2.5), as observed in Hilo, Hawaii
(PHTO, see Table 4) between 1980 and 2011. The seasonal variability in this station
is clearly small, as expected by a tropical oceanic station, so this station is found
suitable for the initial assessment of the RH mean vertical profile in cloud layers.
54
Figure 3.13 Daily mean cloud layer base (red line) and top (blue line), calculated
from 32 year long upper-air measurement record over Lihue, Hawaii (see Table 4), at
00:00 UTC.
The mean vertical profiles for the Hawaiian station (PHTO, see Table 4),
between June and August, are presented in detail in Figure 3.14. First, the observed
vertical probability distribution of cloudy layer sampling (Figure 3.14, left panel)
shows a maximum of 87% at the altitude of 1700 m above sea level. This value
indicates a cloud-rich lower atmosphere, as predicted for a humid maritime tropical
station.
Figure 3.14 Vertical profiles of the sampled cloud layer fraction (left panel), the
mean SRH values (right panel, black line), and the mean SRH values that are in cloud
layers, but not inside clouds (right panel, blue line). The gap between each dashed line
pair represents two standard deviations (one for each direction). The analyzed data are
55
all 00:00 UTC radiosonde observations of Hilo, Hawaii (see Table 4), between June
and August, from 1980 to 2011.
The observed mean SRH vertical profile of this specific station (Figure 3.14,
right panel, black line) shows a significant decrease in the SRH values in the free
atmosphere, suggesting that the relative humidity drops rapidly and reaches extremely
low values above ~2500 m above sea level, even above tropical oceanic areas. The
RH mean vertical profiles within cloudy layers (Figure 3.14, right panel, blue line) is
always significantly more humid than the general mean SRH profile, but still shows a
sharp decrease above ~2500, suggesting that the mean SRH values within the lower
(by altitude) cloudy layers (outside clouds), is not sufficient for aerosol radiative
effects due to hygroscopic growth, according to our findings in section 3.3.
Similar analyses are conducted on all 14 station data (Appendix B), showing
various SRH vertical profiles with different cloudy layer statistical and seasonal
features.
Since most aerosols are concentrated in the lower troposphere (95% up to 2 km
from surface [Blanchard and Woodcock, 1980], with the exception of long-range
aerosol transport, further analysis focuses on the lower cloudy troposphere (LCT),
which is introduced in this study, for the first time, as the atmospheric layer that
inhibits most aerosols and has the largest contribution to cloud-aerosol interactions.
Figure 3.15 summarizes the SRH standard deviation vs. the mean SRH, in the
LCT of all 14 stations during June-July-August for LCT upper height limits of 1 km,
2 km and 3 km above surface. A clear negative trend between the SRH and its
standard deviation is shown for all cases, with a slope that ranges between -0.317
(lowest 2 km) and –0.372 (lowest 1 km). Specifically, the results show that most
maritime stations are characterized by relatively high mean SRH values in the LCT,
accompanied by low SRH standard deviation values, and the vice versa for most of
the continental stations. This trend is kept along the lowest 1 km, 2 km and 3 km, with
lower mean SRH values, and higher standard deviation values for increased LCT
layer thickness.
56
Figure 3.15 The mean SRH standard deviation as a function of the mean SRH, as
calculated for the lower cloudy troposphere (LCT) of 14 globally distributed
continental (circles) and maritime (square) stations, using LCT upper height limits of
1 km (upper left), 2 km (lower left), and 3 km (lower right). The red dashed lines of
each panel represent the linear fit. All presented data are based on day time
measurements during the months June-July-August.
3.4.2 SRH profile and cloud development
In order to further explore the possible effects of SRH in the lower cloudy
troposphere on the observation of cloud-aerosol interactions, we estimate the SRH
values as a function of the level of the cloud development. Since a positive correlation
in expected between cloud horizontal and vertical dimensions [Wang and Rossow,
1995; Koren et al., 2010] and both of these are correlated with the cloudy layer
thickness (between the LCL and the SALR), a thicker cloudy layer is likely to
represent cases of vertically developed clouds and high cloud fraction.
For each station, all radiosonde profiles are sorted by their cloudy layer
thickness (measured from the LCL to the SALR upper limit). The profiles are
57
separated into two equally number of samples of thicker and shallower sets for each
station, classified by the cloudy layer thickness median. The cloudy layer thickness
values range from 859 m to 2266 m in the maritime stations with median values
between 481 m and 1900 m, and from 829 m to 2789 m in the continental stations
with median values of 500 m and 2929 m (Table 5). Note that especially in case of
convective clouds, these thicknesses do not capture the whole extent of the clouds but
the conditionally unstable layer of their profiles.
Figure 3.16 shows the difference between the mean SRH values in the lower
cloudy troposphere of developed and shallow cloud profiles (Δ(𝑆𝑅𝐻)𝐿𝐶𝑇), as a
function of the mean SRH values in the lower cloudy atmosphere (𝑆𝑅𝐻𝐿𝐶𝑇), for LCT
upper height limits of 1 km, 2 km and 3 km above surface.
Figure 3.16 The difference between the mean SRH values in the lower cloudy
troposphere of developed and shallow profiles (Δ(𝑆𝑅𝐻)𝐿𝐶𝑇), as a function of the
58
mean SRH values in the lower cloudy atmosphere (𝑆𝑅𝐻𝐿𝐶𝑇), for 14 stations, in the
months of June-July-August, during 1980-2011, for LCT upper height limits of 1 km
(upper panel), 2 km (middle panel) and 3 km (lower panel) above surface.
The results show that the differences of the mean SRH in the lower cloudy
troposphere between developed and shallower cases are around 5% (Figure 3.16, and
Table 5 in the supplementary material). A calculation shows that hygroscopic aerosols
(e.g. sea salt aerosols with 𝜅 = 0.7 experience geometric cross section difference of
19.5% when their environment RH value changes from 80% to 85%, and experience
AOD change of 18.2% when the RH value changes from 60% to 70%. Non-
hygroscopic particles (e.g. biomass burning aerosol – 𝜅 = 0.1) gain geometric cross
section difference of 7.8% for RH increase of 80% to 85%, and AOD change of 4.8%
for RH increase of 60% to 70%. The geometric cross section difference is a first order
approximation to the total expected AOD change, neglecting the modification of the
refractive indices due to water uptake, and the aerosol size parameters.
59
4 Summary and discussion
The recent recognition of the importance of the transition zone between detected
clouds and cloud-free atmosphere (the twilight zone, see Section 1.4) upraised
questions regarding its nature and its climatic implications. Some of these questions
deal with issues as How to determine a cloud field that includes the twilight zone?
What are the optical properties of the whole cloud field and of its twilight zone? and
how are the aerosol properties and retrievals affected when located in the vicinity of
clouds? This research was aimed at the determination and exploration of a cloud field
as an entity, including clouds and aerosol. We studied clouds and aerosols spatial
features and radiative properties related to their location in the field and with respect
to the RH spatial variation.
In this study, the twilight zone was considered as an integral part of a cloud field,
and the observed atmosphere was classified into cloud fields (detectable clouds
surrounded by twilight zone) and cloud-free. The new offered definition had required
a suitable analytical tool that enables quantitative research of the atmosphere under
the new classification. This tool was presented in this study, namely the cloud field
masking algorithm (Section 2.1), and was validated as a robust method for defining
the boundaries of any cloud field, based on its detectable clouds' spatial distribution.
The core assumption behind the cloud field masking algorithm is that every cloud
field has a characteristic length scale that represents the maximal distance between
clouds, defining the cloud spatial distribution inside the field. This characteristic scale
was named here “the distance parameter (𝑟0)”, and its calculation was done by
operating the Euclidian distance transform on the detected cloud mask, followed by a
discrimination between the inner field and extra-field cloud distributions. The exact
distance that marked the boundary between the distributions was set as 𝑟0, and
enabled the spatial bounding of the observed cloud field. Detailed graphic description
is found in Figure 2.1.
Analytically masking cloud fields also enabled the presentation of a new metric to
evaluate cloud field coverage. We have named this metric “the cloud field fraction”
60
(CFF), and similar to the well-known cloud fraction (CF) metric, it was defined as the
ratio between the cloud fields covered area to the area of the whole observed domain.
The cloud field masking algorithm was used for the estimation of the global cloud
field coverage for a single selected day. A global mosaic of 1 km cloud mask data, as
observed by MODIS Terra satellite was analyzed in order to extract the global CFF,
and the mean 𝑟0values for different cloud field types. The total observed global cloud
field coverage was 87%, while the observed cloud fraction for that day was only 51%
(Section 3.1). This finding emphasizes the low likelihood to randomly sample a cloud
field free atmosphere above Earth. It also stresses out the need to better quantify the
optical and the physical properties of aerosols which are located in cloud fields as
they are affected by the humid environment there.
It was also found that on a global average cloud fields extend ~30 km from the
detectable cloud edge (𝑟0 ≅ 30 𝑘𝑚), while different types of cloud fields experience
different 𝑟0 values in accordance to their typical spatial distribution of clouds (Table
1). The 30 km scale, found here using a morphological analysis, supports previous
studies that found similar distance scales using different types of analysis on remote-
sensing data [Koren et al., 2007; Bar-Or et al., 2010]. A closer examination of the
calculated data showed a clear latitudinal dependency of the mean CF, the mean CFF
and the mean distance from clouds (𝑑𝑐), above lands and oceans. It was found that a
high probability to sample a cloud-free pixel is limited only to some desert areas
under the Hadley subsidence zone (Figure 3.1).
The global MODIS 1 km resolution data set of distance from cloud (𝑑𝑐) was then
used in order to observe the trends in the aerosol optical properties in the vicinity of
clouds. Based on global MODIS aerosol product for the matching day, it was shown
that both aerosol optical depth (AOD) and the aerosol fine-mode fraction (FMF)
strongly depend on the sample‟s distance from the nearest detectable cloud (𝑑𝑐).
While the AOD and 𝑑𝑐 has a negative exponential trend (Figure 3.2), the FMF and 𝑑𝑐
has a positive exponential trend (Figure 3.3, Section 3.2). Moreover, both AOD and
FMF retrievals showed a scale break in the distance of ~30 km from the nearest cloud,
which may point to a natural distance scale of cloud fields. The consideration of this
finding, with the observed vast global cloud field coverage, may be used for better
61
future assessment of cloud field radiation budgets. The addition of cloud distance
dependent aerosol optical depth into large scale (coarse resolution) numerical models
following the findings presented here, is able to improve the estimation of aerosol
radiative forcing, which is currently poorly understood [Forster et al., 2007],
An attempt to explain the aerosol retrieval biases in clouds' vicinity raises three
main features that affect the optical and physical properties of aerosols in this zone:
(1) aerosol hygroscopic growth (aerosol humidification) due to high RH values near
clouds [Feingold and Morley, 2003; Twohy et al., 2009a; Quaas et al., 2010], (2)
signal contribution by clouds which are too small or too optically thin to be identified
as clouds, and (3) 3D radiative effects from light scattered by neighboring clouds
[Marshak et al., 2006; Varnai and Marshak, 2009]. For better understanding of the
physical mechanisms that cause the twilight zone‟s radiative effects, we isolated the
aerosol humidification component and investigated its net contribution.
For this purpose, a new detailed parameterization of the relative humidity (RH)
values as a function of the distance from clouds (𝑑𝑐) was presented (Section 3.3.1) for
warm Cumulus cloud fields (Sections 2.4.1 and 3.3.1). This parameterization was
developed using two different large eddy simulation models (LES), and supported by
the few in-situ observations that measured RH in respect to the closest cloud location
[Korolev and Isaac, 2006; Twohy et al., 2009a; Wang and Geerts, 2010]. In these
cloud fields, the RH was found to exponentially decrease as the distance from cloud
increases, with an e-fold distance scale of 98-278 m. Therefore, we conclude that the
RH high values range, that is relevant to significant aerosol humidification effect is
estimated to extend up to 0.5 km from the nearest cloud (Section 3.3.1), suggesting
that aerosol humidification contribution to biases in aerosol retrievals in the twilight
zone is not the dominant one in distances of 0.5-30 km from clouds.
Additionally to the aerosol humidification research implications, this RH
parameterization can be used to improve the representation of cloud fields‟ internal
properties in large scale numerical simulations, like in Global Circulation Models
(GCM). The lack of similar parameterizations forces GCM models to assume simple
spatial relative humidity distribution within grid cells, leading to unavoidable errors in
estimations of radiative properties of clouds and humidified aerosols. Although the
62
weaknesses of the present RH distribution were acknowledged by GCM studies
[Quaas et al., 2008; Quaas et al., 2010], it is still used [Quaas, 2012]. Therefore, the
new RH parameterization presented here may improve the representation of sub-grid
spatial humidity and cloud distributions in GCM's.
Using the new RH parameterization in a cloud field, a close examination of
aerosol humidification radiative effect near clouds, as a function of aerosol physical
and chemical properties was conducted (Section 3.3.2 and Section 3.3.3). This study
was carried out using an atmospheric radiative transfer model (SHDOM, Evans,
1998), to simulate the MODIS aerosol retrieval variance near clouds, due to aerosol
humidification. The results showed variant radiative behaviors of both AOD and FMF
as a function of the distance from clouds, suggesting that the net contribution of
aerosol humidification depends strongly on the aerosol properties, and not only on the
environmental conditions. This finding suggests that the overall trends of aerosol
optical properties near clouds, even in small distance from clouds (Section 3.2), are
not necessarily due to aerosol humidification. Further possible future application of
this result may be the retrieval of aerosol properties based on their observed optical
signatures and on their location in respect to clouds.
The next part of this research studied the RH characteristics inside cloud fields
(yet outside clouds), for better estimations of the radiative contribution of humidified
aerosols that will enable also a better representation of aerosol humidification effects
in climate models. For that purpose, an extensive 32 year long atmospheric sounding
record was analyzed and the sub-saturated relative humidity (SRH) vertical profiles
inside cloud fields were examined (Section 3.4). The results showed that the mean
sub-saturated (out of clouds) relative humidity value range (SRH), in the lower
troposphere of profiles that exhibit cloudy layer, is ~80% accompanied by a standard
deviation of ~7% for most maritime stations, and ~67% with standard deviation of
~14% for most continental stations. Furthermore, it was found that the mean SRH and
the SRH variance have a clear negative trend (the stations whose mean SRH is higher
are characterized by low SRH variance). The findings presented in Section 3.4.2
suggest that the measured SRH values in cloud fields, by their mean values, cannot
result in significant radiative effect due to aerosol humidification.
63
Finally, the dependence of SRH values in the cloud vertical development was
explored, for estimating the likelihood for biases of aerosol-cloud interaction study
conclusion due to aerosol humidification radiative effects. In this part of our study we
examined the probability to wrongly misinterpret trends between cloud and aerosol
properties as products of aerosol-cloud physical feedbacks, due to aerosol
humidification near clouds. We divided each station‟s data into developed and
shallow cloudy layers, using the median cloudy layer thickness, in order to examine
the dependence of the RH properties in cloud vertical development. The results
showed that the difference in range, in the mean SRH of the lower cloudy troposphere
between profiles with shallow and developed cloudy layers for all stations, is 1%-9%
for LCT of 1 km above surface, 3%-10% for LCT of 2 km above surface and 6%-14%
for LCT of 3 km above surface. This finding suggests that the mean SRH values in
the lower cloudy troposphere are relatively similar for different cloudy layer
thicknesses. Therefore, we conclude that observed trends between aerosol and cloud
properties are not likely to be a result of aerosol humidification near clouds. Thus, this
result supports past studies which examined physical aerosol-cloud feedback using
observed trends between aerosol and cloud properties in cloud fields [Koren et al.,
2005; Koren et al., 2008a; Heiblum et al., 2012; Koren et al., 2012].
This thesis explored some of the twilight zone‟s core questions; beginning with
the fundamentals of cloud and cloud field spatial definitions, through the observed
optical trends of aerosols near clouds, continuing with the isolation and assessment of
the radiative contribution of aerosol humidification in cloud fields. The outcomes of
this research are new tools for future study of cloud fields and aerosol-cloud
interactions, using simulations, observations and measurements. We believe that the
findings presented above improve our understanding of cloud field spatial and
radiative properties, enabling better evaluation of aerosol and cloud properties and
aerosol-cloud interactions. There is great potential in implementation of the new
methods and knowledge presented here in future climate research.
64
Tables
Table 1 (Table 1 in Bar-Or et al., 2011) Granule cloud fraction (CF), cloud field
fraction (CFF), and field distance parameters (R0) as calculated globally for July 28th
,
2008. The cloud fields are classified to: Cirrus (Ci), Stratocumulus (Sc), Cumulus
(Cu), and Deep Convective (DC). All values refer both to fields and granules,
respectively.
Mean Standard
deviation
Number
of
Samples
Granule Cloud Fraction 51% 23% 66
Granule CFF (Calculated R0) 88% 66
Granule CFF (R0=10km) 80% 23% 66
R0 – All 29 km 9 km 170
R0 – Ci 30 km 9 km 38
R0 – Sc 25 km 8 km 21
R0 – Cu 29 km 9 km 80
R0 – DC 31 km 8 km 31
65
Table 2 The input physical and optical aerosol properties, used for the results described in Section 3.3.2. Both fine-mode and coarse-mode
aerosol are assumed to have a log-normal distribution, characterized by the geometric mean radius 𝑟𝑔 , the log-standard deviation 𝜎, and the total
mass content 𝑚. The aerosol physical properties listed are the aerosol hygroscopicity parameter 𝜅, the aerosol complex refractive index 𝑅𝑒𝑓, and
the aerosol bulk density 𝜌. All values describe dry aerosol.
fine-mode aerosol coarse-mode aerosol
Set
𝑟𝑔 ,𝑓𝑖
𝜇𝑚
𝜎𝑓𝑖 𝜅𝑓𝑖 𝑅𝑒𝑓𝑓𝑖 𝜌𝑓𝑖
𝑔
𝑐𝑚3
𝑚𝑓𝑖
𝜇𝑔
𝑚3
𝑟𝑔 ,𝑐𝑜
𝜇𝑚
𝜎𝑐𝑜 𝜅𝑐𝑜 𝑅𝑒𝑓𝑐𝑜 𝜌𝑐𝑜
𝑔
𝑐𝑚3
𝑚𝑐𝑜
𝜇𝑔
𝑚3
R1 0.06 0.7 0.3 1.510-i·0.021 1.5 5 0.6 0.6 0.7 1.546-i·0.003 2.17 50
S1 0.06 0.7 0-1.1 1.510-i·0.021 1.5 5 0.6 0.6 0.7 1.546-i·0.003 2.17 50
S2 0.06 0.7 0.3 1.510-i·0.021 1.5 5-50 0.6 0.6 0.7 1.546-i·0.003 2.17 50
S3 0.06-0.3 0.7 0.3 1.510-i·0.021 1.5 5 0.6 0.6 0.7 1.546-i·0.003 2.17 50
S4 0.06 0.7 0.3 1.510-i·(0--1) 1.5 5 0.6 0.6 0.7 1.546-i·0.003 2.17 50
D1 0.06 0.7 0-0.7 1.510-i·0.021 1.5 5 2.3 0.6 0.03 1.550-i·0.002 2 50
D2 0.06 0.7 0.3 1.510-i·0.021 1.5 1-25 2.3 0.6 0.03 1.550-i·0.002 2 50
D3 0.04-0.3 0.7 0.3 1.510-i·0.021 1.5 5 2.3 0.6 0.03 1.550-i·0.002 2 50
D4 0.06 0.7 0.3 1.510-i·(0--1) 1.5 5 2.3 0.6 0.03 1.550-i·0.002 2 50
66
Table 3 Result summary for Section 3.3.2
Set examined
parameter
coarse-
mode
aerosol
Curve shape sensitivity of 𝑨𝑶𝑫(𝒅𝒄) and 𝑭𝑴𝑭(𝒅𝒄)
R1 𝑅𝐻( 𝑑𝑐 sea salt
𝐴𝑂𝐷(𝑑𝑐) and 𝐹𝑀𝐹(𝑑𝑐) curve shapes and values are
sensitive to the slope of the exponential
𝑅𝐻 𝑑𝑐 parameterization far from clouds 𝑑𝑐 >0.1 𝑘𝑚). In the close vicinity of clouds there is no
sensitivity to the 𝑅𝐻 𝑑𝑐 parameterization (Figure 3.5).
S1 𝜅𝑓𝑖 sea salt
𝐹𝑀𝐹(𝑑𝑐) curve shape is sensitive to the fine-mode
hygroscopicity. 𝜅𝑓𝑖 > 0.3 leads to increasing FMF near
clouds, while 𝜅𝑓𝑖 < 0.3 leads to decreasing FMF near
clouds (Figure 3.6).
S2 𝑚𝑓𝑖 sea salt
Both 𝐹𝑀𝐹(𝑑𝑐) and 𝐴𝑂𝐷(𝑑𝑐) curve shapes are not
sensitive to the fine-mode aerosol mass content 𝑚𝑓𝑖
(Figure 3.7).
S3 𝑟𝑔 ,𝑓𝑖 sea salt
The curve behavior of 𝐹𝑀𝐹 𝑑𝑐 is sensitive to the fine-
mode aerosol dry geometric mean radius, as a result of
the better scattering efficiency small aerosol in the
wavelength of 550 𝑛𝑚 (Figure 3.8).
S4 𝐼𝑚 𝑅𝑒𝑓𝑓𝑖 sea salt
Near clouds, where the hygroscopic growth is dominant,
𝐹𝑀𝐹(𝑑𝑐) and 𝐴𝑂𝐷(𝑑𝑐) curve shapes are not sensitive
to the fine-mode aerosol absorption
efficiency 𝐼𝑚 𝑅𝑒𝑓𝑓𝑖 . Far from clouds, the contribution
of fine-mode aerosol to the total AOD increases with its
absorption efficiency (Figure 3.9).
D1 𝜅𝑓𝑖 desert dust
Only non-hygroscopic fine-mode aerosol shows a
decrease of the 𝐹𝑀𝐹(𝑑𝑐) near clouds, due to the low
hygroscopicity of desert dust.
D2 𝑚𝑓𝑖 desert dust
In agreement with the equivalent simulation set S2, no
curve shape sensitivity was found in response to
changes in the fine-mode aerosol mass content 𝑚𝑓𝑖 .
D3 𝑟𝑔 ,𝑓𝑖 desert dust
Unlike the equivalent simulation set S3, with coarse-
mode sea salt, no sensitivity was found to the fine-mode
aerosol geometric mean radius. The fine-mode aerosols
are more effectively scattering and more hygroscopic,
and therefore drive the curve shapes of 𝐴𝑂𝐷(𝑑𝑐) and
𝐹𝑀𝐹(𝑑𝑐).
D4 𝐼𝑚 𝑅𝑒𝑓𝑓𝑖 desert dust
The low hygroscopicity of desert dust keeps the fine-
mode aerosol absorption efficiency 𝐼𝑚 𝑅𝑒𝑓𝑓𝑖 as the
main contributor of the curve shapes of 𝐴𝑂𝐷 𝑑𝑐 and
𝐹𝑀𝐹(𝑑𝑐).
67
Table 4 The selected 14 atmospheric sounding station names, the WMO station
number, the station geo location (latitude, longitude), the station elevation above sea
level (m), and the total number of all obtained vertical measurement profiles between
1980 and 2011.
Station
WMO
Station
#
Location
(lat , lon)
Elev.
(m)
# of all
profiles
Lihue, Hawaii 91165 21.97 , -159.35 45 31364
Hilo, Hawaii 91285 19.70 , -155.05 11 23178
Marshall Islands 91376 7.07 , 171.38 3 18990
Darwin, Australia 94120 -12.40 , 130.88 30 19276
Le Raizet, Guadeloupe 78897 16.26 , -61.51 11 7799
Lord Howe Island, Australia 94995 28.37 , 129.55 295 27938
Naze-Funchatoge, Japan 47909 -31.53 , 159.05 7 11436
Budapest, Hungary 12843 40.52 , -80.23 357 22488
Munich, Germany 10868 48.25 , 11.55 489 22919
Nashville, Tennessee 72469 36.25 , -86.55 210 23278
Manaus, Brazil 82332 -3.15 , -59.97 84 11796
Blacksburg, Virginia 72318 37.20 , -80.41 654 11484
Pittsburgh, Pennsylvania 72520 40.52 , -80.23 357 22488
Nairobi, Kenya 63741 -1.29 , 36.75 1798 14359
68
Table 5 The selected 14 atmospheric sounding station names, the WMO station number, the station geo location (latitude, longitude), the station elevation above sea level
(m), the total number of all obtained vertical measurement profiles between 1980 and 2011, the selected measurement time (UTC), the number of profiles in the selected
season (June-July-August) and hour, the number of cloudy-atmosphere profiles in the selected season and hour, the calculated mean LCL height above sea level for the
selected season and hour (m), and the LCL standard deviation for the selected season and hour (m).
Station WMO
Station #
Location
(lat , lon)
Elev.
(m)
# of all
profiles
Time
UTC
# of
selected
profiles
# of cloudy
selected
profiles
Mean
LCL
(m)
LCL
std
(m)
Lihue, Hawaii 91165 21.97 , -159.35 45 31364 00Z 3954 3774 877 257
Hilo, Hawaii 91285 19.70 , -155.05 11 23178 00Z 2887 2807 912 316
Marshall Islands 91376 7.07 , 171.38 3 18990 00Z 2765 2659 679 283
Darwin, Australia 94120 -12.40 , 130.88 30 19276 00Z 2857 1495 1265 720
Le Raizet, Guadeloupe 78897 16.26 , -61.51 11 7799 12Z 1051 1039 791 269
Lord Howe Island, Australia 94995 28.37 , 129.55 295 27938 00Z 2896 2734 1042 564
Naze-Funchatoge, Japan 47909 -31.53 , 159.05 7 11436 00Z 2618 2458 1217 647
Budapest, Hungary 12843 40.52 , -80.23 357 22488 12Z 2768 2236 1313 528
Munich, Germany 10868 48.25 , 11.55 489 22919 12Z 2781 2402 1985 940
Nashville, Tennessee 72469 36.25 , -86.55 210 23278 00Z 2880 2548 1713 945
Manaus, Brazil 82332 -3.15 , -59.97 84 11796 00Z 908 882 1055 385
Blacksburg, Virginia 72318 37.20 , -80.41 654 11484 00Z 1410 1224 2040 913
Pittsburgh, Pennsylvania 72520 40.52 , -80.23 357 22488 00Z 2782 2425 1938 1179
Nairobi, Kenya 63741 -1.29 , 36.75 1798 14359 12Z 1310 1239 3184 1132
69
Table 6 The selected 14 atmospheric sounding station names, and the analyzed properties for the selected hour (see Table 1), during the months June-July-August: the mean
relative humidity value in the lower cloudy troposphere (%), the standard deviation of relative humidity value in the lower cloudy troposphere (%), the SALR cloudy layer
mean depth (m), depth standard deviation (m), and depth median (m), the MBP cloudy layer mean depth (m), depth standard deviation (m), and depth median (m), and the
difference of the mean relative humidity values in the lower cloudy troposphere, between developed SALR cloudy layer (whose SALR is deeper than the median), and
shallow SALR cloudy layer (%).
Station
mean
𝑹𝑯𝑳𝑪𝑻
(%)
𝑹𝑯𝑳𝑪𝑻
Std
(%)
SALR
mean
depth (m)
SALR
depth
std
(m)
SALR
depth
median
(m)
MBP
mean
depth
(m)
MBP
depth
std
(m)
MBP
depth
median
(m)
𝚫(𝑹𝑯𝑳𝑪𝑻)
(%)
Lihue, Hawaii 80.90 7.36 984 660 950 1564 856 1425 3.37
Hilo, Hawaii 79.41 9.29 1168 865 1100 1707 939 1525 6.53
Marshall Islands 86.42 6.53 2266 1805 1900 4694 1747 4875 2.04
Darwin, Australia 65.65 15.15 722 1003 481 1259 890 1101 6.80
Le Raizet, Guadeloupe 79.95 7.63 1663 1194 1375 2971 1941 2450 4.33
Lord Howe Island, Australia 85.02 8.75 1202 1270 775 3309 1997 3100 5.30
Naze-Funchatoge, Japan 70.29 11.49 859 974 650 1599 1167 1300 5.02
Budapest, Hungary 77.75 13.98 1153 1258 725 2515 1679 2243 2.11
Munich, Germany 69.35 16.85 973 975 700 2458 1473 2325 2.33
Nashville, Tennessee 68.92 14.56 1932 1579 1625 2970 1900 2875 8.04
Manaus, Brazil 75.34 11.52 2789 1620 2929 4255 1675 4157 7.50
Blacksburg, Virginia 71.11 14.69 1454 1333 1050 2694 1801 2425 4.77
Pittsburgh, Pennsylvania 69.92 16.01 1408 1322 1050 2370 1696 2075 7.67
Nairobi, Kenya 75.00 13.23 829 956 500 1752 1081 1625 5.77
70
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6 List of publications
6.1 Bar-Or, R. Z., I. Koren, and O. Altaratz (2010) Estimating cloud field coverage
using morphological analysis, Environ. Res. Lett., 5, 014022, doi:10.1088/1748-
9326/5/1/014022.
6.2 Bar-Or, R. Z., O. Altaratz, and I. Koren (2011) Global analysis of cloud field
coverage and radiative properties, using morphological methods and MODIS
observations, Atmos. Chem. Phys., 11(1), 191, doi:10.5194/acp-11-191-2011.
6.3 Flores, J. M., R. Z. Bar-Or, N. Bluvshtein, A. Abu-Riziq, A. Kostinski, S.
Borrmann, I. Koren, and Y. Rudich (2012) Absorbing aerosols at high relative
humidity: closure between hygroscopic growth and optical properties, Atmos.
Chem. Phys., 12, 5511-5521, doi:10.5194/acp-12-5511-2012.
6.4 Bar-Or, R. Z., I. Koren, O. Altaratz, and E. Fredj: Humidified aerosol
properties in cloudy environment, Atmos. Res., 118, 280-294, doi:
10.1016/j.atmosres.2012.07.014.
6.5 Bar-Or, R. Z., I. Koren, O. Altaratz: Characterizing the relative humidity in the
lower cloudy troposphere, Geophys. Res. Lett., submitted.
80
7 Declaration
I declare that the thesis summarizes my independent research, under the
supervision of Prof. Ilan Koren and with the continuous consultancy of Dr. Orit
Altaratz. The following sections of the thesis have been conducted in collaboration
with additional researchers:
Sections 2.4.1 and 3.3.1 were supported by Prof. Erick Fredj, who provided
the WRF simulation results.
Section 3.3.3 was co-authored with Dr. Michel Flores, Mr. Nir Bluvshtein,
Prof. Alex Kostinski, Prof. Stephan Borrmann, Prof. Ilan Koren, and Prof.
Yinon Rudich (PI of this study).
81
Appendix A
Figure 0.1 Simulated aerosol optical depth (AOD, upper panel) and fine-mode
fraction (FMF, lower panel) as a function of the distance from the nearest cloud (dc),
for fine-mode hygroscopicity parameter κfi that varies between 0-1.1. The simulated
bimodal log-normal distribution contains coarse-mode desert dust, and fine-mode
biomass burning aerosols (set D1 in Table 2).
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Figure 0.2 Simulated aerosol optical depth (AOD, upper panel) and fine-mode
fraction (FMF, lower panel) as a function of the distance from the nearest cloud (𝑑𝑐),
for fine-mode geometric mean radius 𝑟𝑔 ,𝑓𝑖 values that vary between 0.06 − 0.3 𝜇𝑚.
The simulated bimodal log-normal distribution contains coarse-mode desert dust, and
fine-mode biomass burning aerosols (set D3 in Table 2).
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Figure 0.3 Simulated aerosol optical depth (AOD, upper panel) and fine-mode
fraction (FMF, lower panel) as a function of the distance from the nearest cloud (𝑑𝑐),
for the fine-mode imaginary part of the refractive index 𝐼𝑚 𝑅𝑒𝑓𝑓𝑖 values that vary
between 0 − 1. The simulated bimodal log-normal distribution contains coarse-mode
desert dust, and fine-mode biomass burning aerosols (set D4 in Table 2).
84
Appendix B
The results of all atmospheric sounding stations (listed in Table 4), as described in
Section 3.4.1, are presented below.
Figure 0.1 Vertical profiles of the sampled cloud layer fraction (left panel), the mean
RH values (right panel, black line), and the mean RH values that are in cloud layers,
but not inside clouds (right panel, blue line). The gap between each dashed line pair
represents two standard deviations (one for each direction). The analyzed data are all
00:00 UTC radiosonde observations of Lihue, Hawaii, between June and August,
from 1980 to 2011.
85
Figure 0.2 Vertical profiles of cloudy layer and RH values, see Figure 0.1 caption for
full description. The analyzed data are all 00:00 UTC radiosonde observations of
Marshall Islands, between June and August, from 1980 to 2011.
86
Figure 0.3 Vertical profiles of cloudy layer and RH values, see Figure 0.1 caption for
full description. The analyzed data are all 00:00 UTC radiosonde observations of
Darwin, Australia, between June and August, from 1980 to 2011.
87
Figure 0.4 Vertical profiles of cloudy layer and RH values, see Figure 0.1 caption for
full description. The analyzed data are all 00:00 UTC radiosonde observations of Le
Raizet, Guadeloupe, between June and August, from 1980 to 2011.
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Figure 0.5 Vertical profiles of cloudy layer and RH values, see Figure 0.1 caption for
full description. The analyzed data are all 00:00 UTC radiosonde observations of Lord
Howe Island, Australia, between June and August, from 1980 to 2011.
89
Figure 0.6 Vertical profiles of cloudy layer and RH values, see Figure 0.1 caption for
full description. The analyzed data are all 00:00 UTC radiosonde observations of
Naze-Funchatoge, Japan, between June and August, from 1980 to 2011.
90
Figure 0.7 Vertical profiles of cloudy layer and RH values, see Figure 0.1 caption for
full description. The analyzed data are all 12:00 UTC radiosonde observations of
Budapest, Hungary, between June and August, from 1980 to 2011.
91
Figure 0.8 Vertical profiles of cloudy layer and RH values, see Figure 0.1 caption for
full description. The analyzed data are all 12:00 UTC radiosonde observations of
Munich, Germany, between June and August, from 1980 to 2011.
92
Figure 0.9 Vertical profiles of cloudy layer and RH values, see Figure 0.1 caption for
full description. The analyzed data are all 00:00 UTC radiosonde observations of
Nashville, Tennessee, between June and August, from 1980 to 2011.
93
Figure 0.10 Vertical profiles of cloudy layer and RH values, see Figure 0.1 caption
for full description. The analyzed data are all 00:00 UTC radiosonde observations of
Manaus, Brazil, between June and August, from 1980 to 2011.
94
Figure 0.11 Vertical profiles of cloudy layer and RH values, see Figure 0.1 caption
for full description. The analyzed data are all 00:00 UTC radiosonde observations of
Blacksburg, Virginia, between June and August, from 1980 to 2011.
95
Figure 0.12 Vertical profiles of cloudy layer and RH values, see Figure 0.1 caption
for full description. The analyzed data are all 00:00 UTC radiosonde observations of
Pittsburgh, Pennsylvania, between June and August, from 1980 to 2011.
96
Figure 0.13 Vertical profiles of cloudy layer and RH values, see Figure 0.1 caption
for full description. The analyzed data are all 12:00 UTC radiosonde observations of
Nairobi, Kenya, between June and August, from 1980 to 2011.