University of Nevada, Reno Snow Albedo Spectral Analysis: A Study

University of Nevada, Reno Snow Albedo Spectral Analysis: A Study
University of Nevada, Reno
Snow Albedo Spectral Analysis:
A Study to Further Understanding of Snow and Glacier Energetics
by
Kyle J. Swanson
Dr. Patrick Arnott, Ph.D., Thesis Advisor
Dr. Bernhard Bach, Ph.D., Thesis Reader
May 15, 2014
i
Abstract
Glacier sensitivity to climate trends make them a point of critical interest for international
climate monitoring programs. Through collaboration with the American Climber Science Program
(ACSP) and the Desert Research Institute (DRI), this research will advance the knowledge of
glacier energetics. We are developing and refining methods to analyze the radiative balance at the
snow and/or glacier surface. A dual spectrometer system was developed to measure the spectral
albedo of the snow surface. At each location, of albedo measurements, the surface layer of snow
was collected to analyze in the laboratory. Snow was melted and filtered onto quartz fiber filters.
Filters are analyzed for transmission spectroscopy, elemental and black carbon analysis, and general
elemental analysis. We will look for relationships between the albedo of the snow and the chemical
composition. These relationships will allow scientists to quickly measure snow and glacier albedo
to determine the energy budget and to facilitate interpretation of satellite remote sensing. We are
evaluating snow progressively farther from the road at Tahoe Meadows as a test site. This newly
developed capability will allow groups such as the ACSP to analyze their data for snow collections
in the Cordillera Blanca region of South America, and in the Himalayas of Nepal.
ii
Acknowledgments
There are many people who I would like to take a moment to thank for making this moment
a possibility. Without the help of all these wonderful people I wouldn’t be standing where I am
today.
I would like to first acknowledge Dr. Patrick Arnott for all of his time and patience. Without
his unwavering support and guidance I would not have been able to complete the biggest project of
my life. I would also like to recognize the following people who gave me support and help along
the way:
Dr. Bach for his help, time, and the use of his equipment.
Brady and the Chemistry department for the use of equipment, without I may not have
finished
in time.
diet
Dr. Melodi Rodrigue for her help with everything. Also for supplementing my nonexistent
with nutritional delights.
The University of Nevada, Reno Physics Department for tools and support needed to
complete
my research and degree.
Anthony Bass for his help in the field.
Brandon for bouncing ideas off of his brain.
My Family for their support over the years.
My girlfriend, Samantha Galvan, for all of her help and support in the field and in the lab.
Without her constant help I may not get my shoes on in the morning. Shoes can be very
very tricky.
Writing these acknowledgments may have been the hardest part of the whole project. I could
have written a thesis of acknowledgments and appreciation for everyone and their help. Thank you
all so much!
“On the other hand you have different fingers.” - Warren Miller
iii
Table of Contents
Abstract..................................................................................................................................................i
Acknowledgments................................................................................................................................ii
Table of Contents.…...........................................................................................................................iii
List of Figures......................................................................................................................................iv
Chapter 1: Introduction.........................................................................................................................1
Climate.....................................................................................................................................1
Glaciers....................................................................................................................................2
Benefits of Glaciers................................................................................................................2
Dangerous Aspects of Glacier Melting....................................................................................3
Glacier Energetics....................................................................................................................3
Chapter 2: Theory ….........................................................................................................................5
Radiative Forcing....................................................................................................................5
Light........................................................................................................................................6
Intensity of Light...................................................................................................................6
Light and its interaction with surfaces....................................................................................7
Optical Depth...........................................................................................................................8
Aerosol.....................................................................................................................................9
Albedo and Spectral Albedo....................................................................................................9
Chapter 3: Methodology …...............................................................................................................10
Field Work............................................................................................................................10
Dual Spectrometer Albedo Measuring System...........................................................10
Snow Albedo Measuring and Sampling......................................................................11
Lab work...............................................................................................................................11
Preparatory filter characterization...............................................................................11
Filtration......................................................................................................................11
Chapter 4: Expedition..…......….........................................................................................................13
Accounts.................................................................................................................................13
Chapter 5: Data Analysis …...............................................................................................................15
Albedo Analysis....................................................................................................................15
Filtered Snow Melt Analysis................................................................................................15
Chapter 6: Observations and Results.................................................................................................17
Visual Examination...............................................................................................................17
Spectral Analysis..................................................................................................................19
NIR/Infrared Regime..................................................................................................19
Visible and NUV Regime...........................................................................................21
Chapter 7: Conclusion and Future work...........................................................................................23
Conclusion............................................................................................................................23
Future Work..........................................................................................................................23
Bibliography: ….................…......…..................................................................................................26
Appendix A: ….................…......…....................................................................................................28
Appendix B: ….................…......…....................................................................................................29
Appendix C: ….................…......…..................................................................................................32
iv
List of Figures
Figure 1:
Figure 2:
Figure 3:
Figure 4:
Figure 5:
Figure 6:
Figure 7:
Figure 8:
Figure 9:
Figure 10:
Figure 11:
Figure 12:
Figure 13:
Figure 14:
Figure 15:
Figure 16:
Figure 17:
Figure 18:
Figure 19:
Figure 20:
Figure 21:
Figure 22:
Figure 23:
Figure 24:
Figure 25:
Figure 26:
Figure 27:
Figure 29:
Figure 30:
Figure 31:
Figure 32:
Figure 33:
Figure 34:
Figure 35:
Figure 36:
Figure 37:
Global Averages...........................................................................................................pg 1
Global Glacier regions …............................................................................................pg 2
Glacier Length Variation ….........................................................................................pg 3
Radiation Transmitted by the atmosphere …...............................................................pg 5
Radiative Forcing Components....................................................................................pg 5
Electromagnetic spectrum............................................................................................pg 6
Cylinder........................................................................................................................pg 7
Solid Angle...................................................................................................................pg 7
Extinction cross section...............................................................................................pg 8
Number of particles per volume ….............................................................................pg 8
Dual Spectrometer system...........................................................................................pg 10
Quartz fiber filter.........................................................................................................pg 11
Filtration Station.........................................................................................................pg 12
Science Machine.........................................................................................................pg 14
Surface melt picture....................................................................................................pg 17
Grain size of snow picture..........................................................................................pg 18
Aerosol Agglomeration picture...................................................................................pg 18
Sample A TM3 SN1....................................................................................................pg 19
Sample B TM3 SN2....................................................................................................pg 20
Grain size model.........................................................................................................pg 20
Aerosol Affect model..................................................................................................pg 20
Filters Distance from road...........................................................................................pg 21
UV analysis Distance from Road................................................................................pg 22
Aerial shot of sample sites..........................................................................................pg 22
Transmission of beaker comparison............................................................................pg 25
Avian-B spectrum........................................................................................................pg 28
Particle absorption cross section.................................................................................pg 28
TM3-SN1....................................................................................................................pg 32
TM2-SN1....................................................................................................................pg 32
TM3-SN2....................................................................................................................pg 33
TM3-SN3....................................................................................................................pg 33
TM2-SN2....................................................................................................................pg 34
TM3-SN7....................................................................................................................pg 34
TM3 SN6....................................................................................................................pg 35
TM3-SN5....................................................................................................................pg 35
TM3-SN4....................................................................................................................pg 36
List of Tables
Table 1:
Global Averages............................................................................................................pg 9
1
Chapter 1
Introduction
Climate:
“Warming of the climate system is unequivocal, since the 1950s, many of the observed changes
are unprecedented over decades to millennia.”1 It is crucial that we learn as much as we can from these
changes through observation and documentation; Otherwise with out a clear understanding of how and
why our climate is evolving we may not be prepared to progress with it.
“Each of the last three decades has been successively warmer at the Earth's surface than any
preceding decade since 1850.”1, see fig. 1. This statement should be considered closely, for it is a
statement that was written by an eclectic group of international scientists that participate in a rigorous
system of peer review. Such statements are produced as the result of many studies that cover a wide
variety of extensively researched areas that include, however and not limited to, global and continental
surface temperature, upper ocean heat content, sea level, snow cover, climate extremes, and glacier
response. As a world community that would like to continue living in this oasis in space, it is important
that we advance and refine our ability to measure and monitor our world.
Figure 1:Global temperatures, comparison of observations and models.1
An area that we know a lot about, yet has room for advancement and sophistication, is glacier
monitoring. The measures that have been developed for quantifying glacier evolution offer a robust
method for documenting the change in our climate. Due to the acute sensitivity of glaciers to climate
trends, glaciers are recognized to be high confidence climate indicators2 by international climate
monitoring programs, such as: The Intergovernmental Panel on Climate Change(IPCC),The American
Climber Science Program(ACSP), The Mountain Research Initiative(MRI), etc. The measures provided
by glaciers for the interpretation of our climate are on the time scale of years to decades. Developing
tools that will allow us to measure instantaneous changes of glaciers will become a new immediate
source for climate analysis.
2
Glaciers
Glaciers are thick sheets of ice which slowly move across the land like a river. “The sheer
weight of a thick layer of ice and the fact that it deforms as a 'plastic' material, combined with gravity's
influence, cause glaciers to flow very slowly”.3 Material such as large boulders, varioulsy sized rocks,
and trees in a glacier's path can be transported as the glacier grows and slowly moves. As a glacier
moves it pulverizes the rock caught within the glacier and the rock beneath. This pulverization results
in the creation in a very fine powder refereed to as “rock flour” or “glacial flour”. 4 Glaciers can be
found through out the world, from the Arctic to the Antarctic, see fig. 2. “Glaciers form where snow is
accumulated in the cold/humid season and does not entirely melt during the warm/dry periods”.5
Unfortunately these special climate zones that glaciers form in have decreased substantially. “Over the
last two decades, the Greenland and Antarctic ice sheets have been losing mass, glaciers have
continued to shrink almost worldwide. And Arctic sea ice and Northern Hemisphere spring snow cover
have continued to decrease in extent (high confidence).” 1
Figure 2: The selected eleven glacierised macro-regions.6
Benefits of glaciers
The roll played by glaciers is a very important one to understand in detail. Glaciers have been
our reflective shields for tens of thousands of years. This reflectiveness has helped balance the over all
energy budget of the earth. Glaciers have been a source of water for all walks of life, they have also
been a source for farmers to use for the irrigation of crops.7 Glaciers have also been a form of historian,
trapping atmospheric conditions within the layers. These layers are like snap shots of the climate
history on Earth.
3
Dangerous Aspects of Glacier Melting
The warming of our climate has brought about dramatic changes in glacier systems, which has
developed a rather destructive and dangerous side of glaciers. Two dangerous aspects of the rapid
melting of glaciers are glacial floods and glacial avalanches. As glaciers melt quickly, lakes can form
underneath, within, and on top of glaciers.8 “In parallel to the worldwide glacier retreat, numerous
glacier lakes have been forming at a rapid rate especially on the surface of debris-covered glaciers(e.g.
in the Himalaya)”.9 These lakes formed by the extensive melting of glaciers have the potential to burst
and flood what ever is down stream from them. “In Peru, in 1941, 6,000 people perished when a glacial
lake suddenly burst open, flooding the town of Huaraz below it.”10 Similarly devastating are avalanches
that are caused by enormous amounts of ice breaking free from a glacier. Eighty eight construction
workers in Switzerland were killed in 1965. They were living in a construction camp when,without
warning, the tongue of the nearby Allalingletscher glacier broke free. 10
Glacier Energetics
Glaciers response to the climate is measured in two different fashions. Glaciers react to climatic
forcing measured by the advance or retreat of their boundaries, see fig. 3. Glaciers also respond to the
annual atmospheric conditions, measured by the change in thickness/volume11. These glaciological
measurements are the result of constant dynamic changes of many factors such as: solar radiation,
temperature,weather, etc. All of these factors influence the mass and energy balance at the glacier
surface. 12,13
Figure 3:Length variation in glaciers around the world.
14
4
The reason that our current glacier measurements are on a large time scale is that it takes a lot of
energy over long period of time to manifest in measurable changes. To shorten the time scale, we need
to switch our perspective from the observable change in the glacier size and direct our attention to the
energy balance at the glacier surface. Measuring the radiative balance at the surface of glaciers is a very
fast process, that will provide rich data sets of information.
In an effort to advance the knowledge and real time monitoring of glacier energetics, the
following research was conducted. The goal of this research was to study the relationships between the
spectral albedo of the snow/glacier surface and the surface structure morphology as well as chemical
composition. With this newly developing capability groups such as the ACSP will be able to quickly
analyze their data from snow collection expeditions in the Cordillera Blanca region of South America,
and in the Himalayas of Nepal. Further more in situ measurements will provide ground truthing to
facilitate interpretation of satellite remote sensing.
5
Chapter 2:
Theory
Radiative Forcing
The Sun at the center of our solar system is the source for all life on Earth. It is the energy from
the Sun that allows many complex processes to take a place. A very important process and possibly the
most important, is the radiation budget controlled by the Earth's atmosphere and magnetosphere. One
measure of the Earth's radiation budget is radiative forcing. Radiative forcing is the difference between
the amount of solar radiation absorbed by the earth and the amount of radiation emitted by the Earth
out into space, this is illustrated in fig. 4. The IPCC defines radiative forcing to be “a measure of the
net change in the energy balance of the Earth system in response to some external perturbation. It is
expressed in watts per square meter (Wm-2).”1 From this understanding of radiative forcing we can
quickly develop a simple understanding of radiative forcing values: A positive forcing value means that
Earth is retaining more energy than is emitting back into space, a negative forcing value means that
Earth is emitting more energy into space then being absorbed.
Since radiative forcing is generally defined for the Earth system as a whole, it is enlightening to
examine the individual components that comprise the total radiative forcing. Examining fig. 5 we can
see that a large majority of the total radiative forcing is positive, where as there are only a couple
components that result in negative forcing. These components of negative radiative forcing are snow
albedo, cloud albedo, and aerosol scattering of light.
Figure 4:The absorptive and scattering
properties of the atmosphere.
15
Figure 5:Itemized radiative forcing values.16
6
Figure 6:Electromagnetic Spectrum relative to commonly known objects.
17
Light
Light is a form of energy whether we describe it as a wave for as a particle. The energy that is
light is in the form of propagating electromagnetic waves. The wavelengths of these waves make up the
total electromagnetic spectrum from high energy short wavelengths to low energy long wavelengths see
fig. 6. Energy is measured in joules, however what is a joule? Recalling our good friend Einstein
Energy is equal to the product of mass and the speed of light squared. Does this not help? Lets go a
little further with this thought,
Eq. 1
From there we have the formulation of what energy is, wrapped around our finger tips.
Intensity of Light
There are two main ways to measure the intensity of light, either through radiance of light or
through the irradiance of light. Irradiance is the power of a cylindrical beam of light incident on a
surface, see fig. 7, formalized as:
Eq. 2
Here power is defined as watts(w), which is a joule per second. Radiance on the other hand requires the
thought of a cone, formally speaking a solid angle see fig. 8. Radiance is defined as the power of a
solid angle beam of light on a surface.
7
Eq. 3
Figure 7: Irradiance, cylindrical beam
of light.
Figure 8: Radiance, steradian
beam18
Light and its interaction with surfaces
Light has three choices when it comes to a surface. Light can reflect off a surface, transmit
through a surface, and light can be absorbed into the surface. When modeling the interaction of light on
a surface it is necessary to know the relation of these possibilities:
1 = A + R + T,
Eq. 4
where A, R, and T are coefficients of absorption, reflection, and transmission.
Reflection depending on the surface can be diffuse or specular. Specular reflection occur from
surfaces that are smooth. Mirrors are examples of surfaces that have a specular reflection. Diffuse
reflection, also known as Lambertian reflection, occur from surfaces that are not smooth and have an
irregular surface. Street surfaces are good example of an irregular surface that have a diffuse reflection
when light is incident on it. These two examples are the extreme cases for reflection, most surfaces are
a combination of specular and Lambertian. Diffuse reflection of light from an irregular surface is a
form of light scattering in which light is scattered in all directions evenly.
8
Optical Depth
The characterization and classification of how light propagates through a scattering and
absorbing medium is known as optical depth. For the relevance of this discussion, the media considered
are multiple scattering systems. Examples of such multiple scattering systems are clouds, milk, the
atmosphere, snow, paper, and quartz fiber filters.
To begin interpreting optical depth, it is enlightening to start with the interaction of light and a
single particle. This interaction has two outcomes, either the light wave is absorbed or scattered; see
fig. 9. These interactions results in the extinction of the light wave from propagating further through the
medium. The probability of either of these two interactions occurring is known as the extinction cross
section19 of the particle,σext. Now we move onto modeling a medium full of particles, see fig. 10. The
goal here is to choose a unit volume within the medium, and count the number of particles of size
“D”within the range D to Δ + dD. This is written as Λi(Di).
Figure 9: Single particle extinction
interactions.
Figure 10: Determining Λ(D).
We can now calculate the extinction coefficient of the medium. This is done by calculating the
following integral20,
.
Eq. 5
Determining the optical depth 21 is simply integrating βext over the depth of the medium.
.
Eq. 6
9
Aerosols
An aerosol is the uniform dispersion of, micron to sub-micron, solid or liquid particles through
out a gas. The size range of particles in the atmosphere range from 0.01 to 100 μm in diameter.22
Aerosols are created from a variety of natural and anthropogenic sources. Natural sources of aerosols
include: soil dust, volcanic dust, sea salt, and forest fires. For example in 2006 an estimated 25-80
kilotons of glacial flour was transported into the Gulf of Alaska by strong winds.24 Sources of
anthropogenic aerosols occur from many human related activities such as transportation, power
generation, and industry.
Albedo and Spectral Albedo
Albedo is defined as the ratio of reflected light divided by light incident on a surface. This ratio
allows a measure of the reflective properties of a surface. Unlike some forms of measurement albedo is
fairly intuitive, see table 1. White surfaces,such as snow, will generally have an average albedo less
than or very close to 1, while a black surface will have an average albedo very close to or equal to zero.
However surface albedo can change, just as snow falling on asphalt would raise the albedo, and
similarly, black carbon aerosols settling on snow would reduce the albedo.14 Spectral albedo is the
measurement of albedo at individual wavelengths. Measuring the spectral albedo of a surface will
reveal characteristics of the surface, such as morphological properties and chemical composition.
Applying this ability to measuring the spectral albedo of snow will allow the characterization of the
surface.
Table 1: Surface albedo examples
23
10
Chapter 3:
Methodology
Field Work
Due to the fact that Reno Nevada does not have the fortune to have any resident glaciers near
by, snow was chosen as the medium of study. Tahoe Meadows was chosen as the field location to
conduct this research, as it provided a large sample location with interesting conditions.
Dual Spectrometer Albedo Measuring System
In order to measure the albedo of a surface a dual spectrometer system was developed, see fig.
11. One spectrometer is used to measure the down welling irradiance of the solar spectrum. The other
spectrometer is used to measure the radiance reflected by a chosen surface. For each albedo
measurement taken of a surface, a calibration measurement is taken of a theoretically diffuse white
surface. The white surface used in these measurements was a metal plate coated with Avian-B, a highly
Lambertian reflectant coating; see Appendix A for more information. For each sample albedo
measurement four spectral measurements were taken: a radiance measurement of the white target
paired with a simultaneous irradiance measurement and a radiance measurement of the sample surface
paired with a simultaneous irradiance measurement.
Figure 11: Dual Spectrometer Albedo Measuring System
The need for these four spectral measurements are for cross calibration between the two
11
spectrometers. Also because we chose to measure reflected radiance as apposed to irradiance. If both
spectrometer systems were completely identical, this four measurement procedure would be reduced to
two spectral measurements without the need for a calibration target.
Snow Albedo Measuring and Sampling
At each sample location chosen in Tahoe Meadows the following procedure took place. First
pictures of the snow were taken with a 13 mega pixel camera, and a ruler was used for size in the
picture. Second the temperature of the snows surface was measured using an infrared thermometer.
Once these preliminary steps were finished the albedo of the snows surface was taken. The last step
was to collect the surface layer, 2.54 cm deep, of snow for further analysis.
Lab work
Preparatory filter characterization
In order to analyze the chemical composition of the sampled snow, the snow was melted and
filtered through 47 mm quartz fiber filters, fig. 12. Before the quartz fiber filters were used for filtration
each filter underwent pre-analysis. This initial analysis consisted of measuring the mass, light
transmission, and the light reflection of each individual filter. The LambdaSpec instrument was used to
do the transmission and reflection analysis. The lab work for this occurred in the facilities at DRI. Once
the filters were loaded, they were measured again for light transmission and reflection.
Figure 12: Quartz fiber filter.
Filtration
The process for the filtration of the snow melt was a symphony of steps in the following order.
Each sample was melted in a 100 ml beaker, with the aid of a hotplate. Once melted, the temperature of
the snow melt was measured using an infrared thermometer and the PH of the water was measured
using standard PH paper. Then the snow melt,in the beaker, was measured for relative transmission of
12
light. This process is explored further in chapter 7. After measuring the relative transmission, the snow
melt was poured into the filtration system. The filtration system consisted of two key elements: a
modular funnel system, and 500 ml flask. The funnel system was made up up of three pieces: A 250 ml
hopper/beaker, a funnel with a removable screen for filter support. Fig. 13 is the lab set up for the
filtration step of the process.
Figure 13:Filtration station.
13
Chapter 4:
Expeditions
Accounts
Over all there were four separate field expeditions to acquire albedo data and snow samples for
the project. All of these trips were conducted on Slide Mountain, Nevada, in various locations. After the
first trip was taken, each trip after that evolved from the reflection of the previous experiences. The
following is a brief summary and account of each trip.
The first trip to Tahoe Meadows was on February 18th, 2014. As any first trip out with gear, this
consisted mostly of instrument troubleshooting. An experience that is usually unexpected, yet expected
at the same time. This day was partly cloudy, and fairly gusty. By the time I started getting
measurements underway I was only able to take three spectral albedo measurements, because by this
time the sun was at too low of an angle.
The second trip took place at the “Slide Side” parking lot of Mt. Rose Ski Resort on March 9th
2014. This ended up being a very windy day. Even with the help of a my girlfriend the wind was too
much for operating the equipment with out worry. Only one sample was achieved during this trip. The
sky was completely filled with clouds, rendering a completely diffuse day.
The third trip took place at Tahoe Meadows, March 21, 2014. I was more prepared for this trip
and had constructed the science machine, see fig. 14. The science machine was the result of my
troubleshooting experience of the first expedition. The science machine allowed for a stable
workstation that was easy to transport. This improvement made it such that the spectrometers did not
have to be re-calibrated as much. Efficiency of the science machine allowed for eight snow samples to
be taken. The day was partly cloudy, this made recalibration a regular ordeal.
The fourth and final expedition took place at Tahoe Meadows on March 22, 2014.
Improvements this time were having the assistance of my friend Anthony Bass. Data collection
improved as the result of Anthony. I was able to get better surface temperature readings for each snow
sample. As well this time four spectral albedo measurments were taken from different areas of the
14
collected snow. Seven samples were taken all together, however the last sample only had one spectral
albedo measurement, as the battery for the computer died. It was a mostly sunny day resulting in a
mixture of diffuse and direct light measurements.
Figure 14: The science machine.
15
Chapter 5:
Data Analysis
Albedo Analysis
The albedo for a surface measured with the dual spectrometer system is calculated as the ratio
of the sample albedo over the target albedo. As mentioned previously, the need for this four shot
procedure is for cross calibration between the two spectrometer systems. Since there are inherently
differences between even the same model of spectrometer, it is necessary to account for this. This
method of calibration determines the albedo relation to that of a white lambertian surface. Then the
assumption is made that this is the highest albedo obtainable, from which we divide our sample surface
albedo measurements by.
Eq. 7
where As is the uncalibrated sample surface albedo and AT is the albedo of the calibration target.
Filtered Snow Melt Analysis
From the data collected with the Lambda Spec at DRI, transmission and reflection coefficients
of the aerosol present on the loaded filters were calculated. The transmission of light through an aerosol
on a filter, TA , can be described by :
Eq. 8
TB is the initial measurement of light transmission through the blank filter. TL is the transmission of
light through the loaded filter. This ratio is the relative transmission of the aerosol on the filter. In a
similar fashion,with respect to transmission, reflection of light through the aerosols on a filter, RA, can
be described by:
Eq. 9
16
RB is the reflection of the blank filter, and RL is the reflection of the loaded filter. This results in the
relative reflection of the aerosol on the loaded filter. Once transmission has been determined, the
absorption optical depth 'τabs' of the aerosol present on the filter, can be calculated from the following
relation:
Eq. 10
,
Eq 11
where m is the multiple scattering enhancement coefficient which accounts for the multiple scattering
affect of the medium. For a quartz fiber medium m is close to two.26
The total optical depth is a superposition of each optical depth that is characteristic of each of
the individual constituent aerosols present. The optical depth of black carbon has been shown to follow
a power law fit of,
,
Eq. 12
where “a” is a fitting constant. Therefore by subtracting τBC from τabs we can calculate the remaining
components of the total absorption optical depth, τR. From τR we can get an idea of what affect the
remaining constituent aerosol have in the reduction of the spectral albedo of snow. A discussion about
the black carbon fit is appendix A.
17
Chapter 6:
Results and Observations
Visual Examination
Due to an abnormally warm winter, late spring weather like conditions were observed in early
March. Since this years weather conditions were accelerated, the characteristics of the snow pack were
advanced as well. The most important of these characteristics, due to the fact that it facilitates sub key
processes, is the surface melting of the snow pack. Surface melting of the snow pack was visually
evident to the naked eye, see fig. 15. The morphology of the surface layer of snow was highly irregular,
crusty, jagged, and granular; Unlike the surface layer of snow earlier in the winter season of which is
very smooth and uniform. The difference between the two seasonal states of snow is analogous to that
of comparing powdered sugar and raw cane sugar. As stated previously, the process of surface melting
facilitates three key processes that influence the further evolution of the snow pack.
Figure 15: Surface melting, upper right area.
The first process that follows from surface melting is the thaw/refreeze cycle. As the individual
snow grains begin to melt they combine with their nearest neighbors. In the early stages the surface
melting is minor, therefore by nightfall very few ice grains may have combined and refrozen to form
new larger grains. As the thaw/refreeze cycle continues more and more grains will melt and combine
forming larger and larger ice grains. This cycle gives rise to the increase in granular ice size and the
granular irregularity, that is indicative of spring like snow conditions. Standard ice grain sizes range
from 50 μm-1mm26. From examination of fig. 16 ice grain sizes are on the order of 2-10 mm.
18
Figure 16: Large grain sizes.
The second of these two key processes is micro-physical agglomeration of aerosols in the snow
pack due to melting. This observation is visually apparent in the snow as seen in fig. 17. At first this
may not seem to be evident in the picture. With closer examination, non-uniformity of the particulate
on the snow is evident. As well the particulate range in sizes on the order of millimeters. As mentioned
earlier aerosol diameter sizes range from .01 -100 μm. An explanation for this size difference would be
the micro-physical agglomeration of the aerosol creating larger particles. When agglomeration occurs
albedo changes in two ways. The first way is that the effective area covered by the aerosol is reduced,
thus increasing the albedo. Second when the particles are larger they generate more heat through
absorption which facilitates the melting of snow beneath them causing further deposition. This theory
of micro-physical agglomeration as a process in snow melt and water is surrounded with some
controversial. A group from the National Ocean and Atmospheric Administration report that microphysical agglomeration is not a strong a factor27.
Figure 17: Micro-physical agglomeration of aerosol. Observe the non uniform nature of the aerosol,
as well as the large particulate sizes present.
19
The third process that occurs due to surface melting, is stronger absorption. When water fills in
the space around grains, the grains scatter more toward the forward direction21. This increases the
number of times the light may scatter in the snow, increasing the chance of absorption. Furthermore
when the index of refraction of a liquid is similar to that of the grain scattering is entirely in the
forward direction21. Therefore water in the snow has a huge affect in terms of increased absorption.
Spectral Analysis
NIR/Infrared Regime
Interesting results occurred when examining the spectral albedo of the snow samples in the near
infrared regime. A very large drop off occurs beyond 700 nm seen in samples taken closer to the road,
fig. 18, 19. Spectral albedo levels reach down low as far as 20% - 0%. At first this may seem to be the
result of inaccuracies in measuring or calculation. Snow spectral albedo models such as the one
developed by Warren and Wiscombe show albedo reaching levels of 20% - 0% starting at 1100 nm;
see fig. 20. With further examination of this model the largest grain size modeled is 1 mm in diameter.
As shown earlier, the grain sizes observed were on the order of 2 – 10 mm in diameter. Looking at fig.
20, it is not hard to imagine what plots would look like for larger grain sizes of 2 mm and up. As well,
there is stronger absorption due to the water present from surface melt. Furthermore, light may have
passed all the way through the snow to the soil surface, reducing albedo as well.
Figure 18:Sample TM3 SN1. * Sample A*
Top: Albedo(Red) vs Aerosol on Filter Transmission(Green). Bottom: Albedo vs Total Optical Depth(Green), Black
Carbon Optical Depth Fit (Blue) and Optical Depth of Remaining Aerosol,Dust (Black).Aerosol,Dust (Black).
20
Figure 19: Sample TM3 SN1. * Sample B*
Top: Albedo(Red) vs Aerosol on Filter Transmission(Green). Bottom: Albedo vs Total Optical Depth(Green), Black Carbon
Optical Depth Fit (Blue) and Optical Depth of Remaining Aerosol,Dust (Black).Aerosol,Dust (Black).
Figure 20:Albedo variation due to different grain sizses
26
Figure 21:Albedo variation due to aerosol size
and concentration. 28
21
Visible and NUV Regime:
The reduction of albedo due to aerosol content on the snow surface, manifests in the visible to
near ultraviolet region of the spectrum. Snow albedo models show that while diameter of the aerosol
does affect the reduction of albedo, aerosol concentration has a stronger affect, see fig. 21. Examining
fig. 18 and fig. 19 which will be sample A and B respectively, it is observed that sample A has a higher
albedo than that of sample B. Furthermore with this initial observation we would predict that the
aerosol concentration of sample A would be lower than sample B. Comparing the optical depth between
the two samples, sample A does have a lower optical depth than does sample B. This would indicate
that there was less aerosol content in the snow from sample A than that of sample B. Comparing the
filters from these two samples in fig. 22, the aerosol content of the sample from sample A is much less
than the aerosol content from sample B.
Fig. 23 is a plot of albedo and optical depth at 375 nm as function of distance from the road.
Initially we expected that albedo would increase as the distance from the road increased. The initial
prediction was this trend would be monotonic. The trend in albedo and optical depth as function of
distance is not strictly monotonic. However, increasing and decreasing trends are observed for albedo
and optical depth, respectively, as a function of distance. Furthermore the mirrored relationship
between albedo and optical depth is very evident. When optical depth spikes albedo drops and vice
versa, the fluctuations between the the aerosol content and the albedo in the near ultraviolet, exhibit a
strong relationship.
Figure 22: Far left sample is “A” and the sample third to the right is “B”. The complete data sets for
these filters are in Appendix C.
22
Figure 23: Graph showing the sample albedo and optical depth in order of distance
from the road. λ=375nm.
Figure 24: Aerial view of sample locations.
23
Chapter 7:
Conclusion and Future work
Conclusion
The methods used during this project confirm that relationships can be observed between the
spectral albedo of snow and the surface. Furthermore, the project has refined and advanced the methods
used as well as furthering understanding.
Since this years weather conditions were accelerated, late spring weather like conditions
occurred during early March. As a consequence the snow pack exhibited accelerated characteristics.
The most important characteristic being surface melt, which affected the physical characteristics of the
snow and the chemical composition. These effects were: increased grain size through thaw/refreeze
cycles, micro-physical agglomeration, and increased absorption due water in the snow. Generalizing, it
may well be that glaciers will rapidly recede as warming commences due to the spring like snow
conditions that are likely to occur over a long time period each year.
General relationships between snow grain size and the infrared albedo measured were
experimentally verifiable and correlated well with theoretical models. This work has shown the need
for increased sophistication of snow albedo models. Future model development should include
modeling larger grain sizes of snow along with modeling snow with water due to surface melt. General
relationships between aerosol content and albedo in the visible and near ultraviolet regimes of the
spectrum were also verified. Specifically there were strong relationships observed in near ultraviolet
albedo and aerosol content. Along with this verification of observable relationships the experience from
this research has focused future efforts and projects.
Future Work
Over the course of this project, several ideas for future work were conceptualized and partially
tested. The following is a brief summary of the main ideas for future work.
An aspect that will be enlightening would be to study the evolution of the snow through out the
year. This will illuminate how aerosols influence affects the total evolution of snow. As well this will
allow the study of the deposition of aerosol deeper into the snow pack. This project would also allow
the study of micro-physical agglomeration of aerosols in the snow pack.
24
In tandem with the time evolution observation of the snow pack, analyzing layers of snow will
be interesting. This study will bring to light the deposition affinity of specific aerosols. From this study
we will know how far down aerosols can be to still have an affect on the surface layer albedo.
Another improvement in the snow albedo data set would be to cover a larger distance when
sampling snow in Tahoe meadows. Instead of just increasing the distance covered linearly, a longer
and wider area should be covered. Covering a longer distance will allow the study of aerosol transport
over further distances. Variability in distance can be studied with sampling at points around the main
sample points through out the area.
During this project a method for measuring the optical properties of snow melt was conceived.
During the filtration process, measurement of the transmission of light through the snow melt in the
beaker was tested. This was done by placing the beaker on top of an integratin sphere that was
connected to a spectrometer via fiber optic cable. Fig. 25 shows the transmission curve the lambda spec
measurement of the loaded filter compared with the transmission measured using the beaker method.
With further development of this method, this technique may become a useful tool in the field. Such a
tool may eliminate the need for laboratory analysis all together.
The affects of instrumentation height from sample surface should be studied. Understanding
how the variation in height with relation to the surface will allow for better interpretation of the albedo
measurements taken.
25
Figure 25: Comparison of transmissions of aerosol on filter and aerosol in snow melt.
26
Bibliography
1 – Stocker, T. F., Qin, D., Plattner, G., Tignor, M., Allen, S. K., Boschung, J., & Nauels, A. (2013, December). Working
Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, 5.
2 – GTOS (2008): Terrestrial essential climate variables for climate change assessment, mitigation and adaptation. GTOS
52.http://www.fao.org/gtos/doc/pub52.pdf
3 – All About Glaciers. National Snow & Ice Data Center, n.d. Web. 20 Sep. 2013.
<http://nsidc.org/cryosphere/glaciers/questions/move.html>.
4 – Pidwirny, M. (2006). "Glossary of Terms: R". Fundamentals of Physical Geography, 2nd Edition. Date Viewed.
http://www.physicalgeography.net/physgeoglos/r.html
5 – Paterson, W.S.B. (1994): The physics of glaciers.3rd edition, Pergamon Press, Oxford: 480 pp.
6 – ESRI Digital Chart of the World (DCW), WGMS.
7 – All About Glaciers. National Snow & Ice Data Center, n.d. Web. 20 Sep. 2013.
<http://nsidc.org/cryosphere/glaciers/questions/people.html>.
8 – Benn, D.I. and Evans, D.J.A. (1998): Glaciers and Glaciation. Arnold: 734pp.
9 – Reynolds, J.M. (2000): On the formation of supraglacial lakes on debriscovered
glaciers. In: Debris-Covered Glaciers: p. 153–161.
10 – All About Glaciers. National Snow & Ice Data Center, n.d. Web. 20 Sep. 2013. <http://nsidc.org/cryosphereExtinction
coefficient of medium/glaciers/questions/dangerous.html>.
11 – Haeberli, W. and Hoelzle, M. (1995): Application of inventory data for
estimating characteristics of and regional climate-change effects on
mountain glaciers: a pilot study with the European Alps. Annals of Glaciology, 21: p. 206–212.
12 –Kuhn, M. (1981): Climate and glaciers. IAHS, 131: p. 3–20.
13 – Oerlemans, J. (2001): Glaciers and climate change. A.A. Balkema Publishers. Lisse,
Abingdon, Exton, Tokyo: 148 pp.
14 – Solomon, S, D Qin, M Manning, Z Chen, and M Marquis. Contribution of Working Group I to
the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007. United
Kingdom And New York, N: Cambridge University Press, 2007. Web. 1 Oct. 2013.
<http://www.ipcc.ch/publications_and_data/ar4/wg1/en/contents.html>.
15 – Image created by Robert A. Rohde / Global Warming Art
16 – Leland McInnes at the English language Wikipedia
17 – This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
18 – Permission is granted to copy, distribute and/or modify this document under the terms
of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation
19 – Arnott, W.p.. "Aerosol light absorption and its measurement: A review." Journal of Quantitative Spectroscopy and
Radiative Transfer 110: 844-878. Print.
27
21 – Bohren, C. F. Multiple scattering of light and some of its observable consequences. American Journal of Physics, 55,
524-533.
22 – Hinds, William C. Aerosol Technology. Second ed. N.p.: John Wiley & Sons, 1999. Print.
23 – Ahrens, C. D. 2006. Meteorology Today. An Introduction to Weather, Climate, and the Environment. Eighth Edition.
Thompson, Brooks/Cole. USA.
24 – Crusius, John, Andrew W. Schroth, Santiago Gasso, Christopher Moy, and Robert Levy.
"Glacial flour dust storms in the Gulf of Alaska: Hydrologic and meteorological controls and their
importance as a source of bioavailable iron." GEOPHYSICAL RESEARCH LETTERS 38. Print.
25 – Arnott, W. P., Hamasha, K., Moosmuller, H., & Ogren, J. A. Towards Aerosol Light-Absorption Measurements with a
7-Wavelength Aethalometer: Evaluation with a Photoacoustic Instrument and 3-Wavelength Nephelometer. Aerosol Science
and Technology, 39, 17-29.
26 -Warren, S. G., & Wiscombe, W. J. A Model for the Spectral Albedo of Snow. I: Pure Snow. Journal of the Atmospheric
Sciences, 37, 2712-2733.
27-Schwarz, J. P., Gao, R. S., Perring, A. E., Spackman, J. R., & Fahey, D. W. (2013, March 1). Black carbon aerosol size in
snow. nature, 1-5.
28-Wiscombe, W. J., & Warren, S. G. A Model for the Spectral Albedo of Snow. II: Snow Containing Atmospheric Aerosols.
Journal of the Atmospheric Sciences, 37, 2734-2745.
29 http://www.aviantechnologies.com/products/coatings/highreflectance.php
28
Appendix A
Avian-B:
Avian-B is a water based barium sulfate coating for use in diffuse reflectance applications. “The
coating is highly lambertian and exhibits reflectance of >97% over from 350-850 nm and greater than
92% from 250-1300 nm. The coating is easily applied to most metal substrates and can be applied to
other materials with proper surface preparation.”29
Figure 26:Reflection spectrum for Avian-B.
29
Appendix B
Zero Scattering Approximation:
An explanation for the Optical Depth Characteristic of Black Carbon
It has been observed that optical depth of black carbon can be fit to a power law,
,
Eq. 13
where a is a fitting value.
The following is a look into why this characteristic arises. Lets assume that any interaction of
light waves are going to result in absorption, due to the black nature of black carbon. Consequently this
leaves us with,
1 = T + A.
Eq. 14
Figure 27: Single particle interaction with light.
Through the rest of this exploration, fig. 27 will be used as our physical system. We will define
transmission of light through the particle19 of size “D” to be ,
.
The absorption cross section of a particle is, the product of the particle area with characteristic
Eq. 15
30
absorption.
;
Eq. 16
;
Eq. 17
;
Eq. 18
;
Eq. 19
where δ is the penetration depth of light at wavelength λ.
In order to see how the particle size relates to wavelength we can take two limits. The first
being the limit for particles larger than the wavelengths of light in question and the second limit for
particles that are smaller than the penetration depth of light. Starting off with the limit where particles
are smaller than the penetration depth of light,
.
Eq. 20
Since this result trivial and uninteresting, we can approximate ex by Taylor expansion,
Eq. 21
Trying the limit again with the approximation of e,
.
Eq. 22
When the particle has a diameter less than the size of the wavelengths in question, the absorption cross
31
section is the ratio of the particle volume over the penetration depth, skin depth.
Now taking the second limit for when the particle diameter is greater than the penetration depth;
.
Eq. 23
In the limit when the particle diameter is greater than the penetration depth, we see that the absorption
cross section is approximately equal to the area of the particle.
So where exactly is our power law fit...?
It was slightly hidden in the first limit. Let's reexamine this limit from a slightly different
perspective. In the limiting case that the diameter of the particle is less than the wavelength of light, the
absorption cross section is,
.
Eq. 24
We have arrived at our destination. When the particle size is much less than the penetration
depth of light as long as the ni is constant with wavelength, as is roughly valid for black carbon, the
absorption cross section goes as one over the wavelength in question. From this point we can even take
a second perspective towards this problem. If we substitute the definition,
,
Eq. 25
into the absorption cross section we arrive at,
Eq. 26
From this point of view, the absorption cross section increases as the frequency of light increases, and
decreases as the frequency of light decreases.
32
Appendix C
The following data sets are for the samples from fig. 23.
Figure 28: Sample TM3 SN1.
Top: Albedo(Red) vs Aerosol on Filter Transmission(Green). Bottom: Albedo vs Total Optical Depth(Green), Black
Carbon Optical Depth Fit (Blue) and Optical Depth of Remaining Aerosol,Dust (Black).Aerosol,Dust (Black).
Figure 29: Sample TM2 SN1.
Top: Albedo(Red) vs Aerosol on Filter Transmission(Green). Bottom: Albedo vs Total Optical Depth(Green), Black Carbon
Optical Depth Fit (Blue) and Optical Depth of Remaining Aerosol,Dust (Black).
33
Figure 30: Sample TM3 SN2.
Top: Albedo(Red) vs Aerosol on Filter Transmission(Green). Bottom: Albedo vs Total Optical Depth(Green), Black Carbon
Optical Depth Fit (Blue) and Optical Depth of Remaining Aerosol,Dust (Black).
Figure 31: Sample TM3 SN3.
Top: Albedo(Red) vs Aerosol on Filter Transmission(Green). Bottom: Albedo vs Total Optical Depth(Green), Black Carbon
Optical Depth Fit (Blue) and Optical Depth of Remaining Aerosol,Dust (Black).
34
Figure 32:Sample TM2 SN2.
Top: Albedo(Red) vs Aerosol on Filter Transmission(Green). Bottom: Albedo vs Total Optical Depth(Green), Black Carbon
Optical Depth Fit (Blue) and Optical Depth of Remaining Aerosol,Dust (Black).
Figure 33:Sample TM3 SN7.
Top: Albedo(Red) vs Aerosol on Filter Transmission(Green). Bottom: Albedo vs Total Optical Depth(Green), Black Carbon
Optical Depth Fit (Blue) and Optical Depth of Remaining Aerosol,Dust (Black).
35
Figure 34:Sample TM3 SN6.
Top: Albedo(Red) vs Aerosol on Filter Transmission(Green). Bottom: Albedo vs Total Optical Depth(Green), Black
Carbon Optical Depth Fit (Blue) and Optical Depth of Remaining Aerosol,Dust (Black).
Figure 35:Sample TM3 SN5.
Top: Albedo(Red) vs Aerosol on Filter Transmission(Green). Bottom: Albedo vs Total Optical Depth(Green), Black Carbon
Optical Depth Fit (Blue) and Optical Depth of Remaining Aerosol,Dust (Black).
36
Figure 36:Sample TM3 SN4.
Top: Albedo(Red) vs Aerosol on Filter Transmission(Green). Bottom: Albedo vs Total Optical Depth(Green), Black
Carbon Optical Depth Fit (Blue) and Optical Depth of Remaining Aerosol,Dust (Black).
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