Lab 1 GEOS______
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School
University of British Columbia *
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Course
102
Subject
Geography
Date
Apr 3, 2024
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Pages
7
Uploaded by delilahrelling
GEOS 102 Our Changing Environment: Climate and Ecosystems
PART 1: Daily Radiation Budget [12 marks]
We will be using the SURFRAD network website to access radiation data from 2 monitoring sites in the
United States. The network provides long-term, continuous measurements of the surface radiation budget
for multiple sites, and available data includes incoming and reflected shortwave radiation, incoming and
outgoing longwave radiation and net radiation. This data can be downloaded and used to inform climate
research. Also available on the website are photographs of the sites, which can be used to gain insight on
radiation budgets.
Firstly, follow the instructions in the document:
Accessing the SURFRAD network
. This document, will
show you how to obtain the data and graphs needed to complete the following questions.
Note: For date selection in step 3, choose the date following date which applies to you (based on the last 2
digits of your student number):
0-25 : June 01 2019 26-50 : July 05 2018
51-75 : May 28 2018
76-99 : August 05 2019
Fort Peck, MT
Q1.
(
upload a screenshot of the graph, acquired by following the above instructions (Accessing the
SURFRAD dataset), to canvas
)
Answer the following questions for the graph of Fort Peck, MT:
Q2.
Name 2 variables that could affect the value of SW ↓ and SW ↑. [2]
-
Time of day
-
Temperature
-
Sunlight
Q3.
Why do SW ↓ and SW ↑ have similar (if not the same) values between 3 and 11 UTC? [1]
-
During the hours of 8:00 PM to 4:00 AM there is less energy, the temperature is low and constant
which is why the graph is flat.
Q4.
Why is LW ↑ greater than LW ↓? [1]
-
The majority of the energy got reflected and reemitted into the atmosphere; the heat resulting from the
absorption of incoming SW radiation is emitted as LW radiation. The LW ↑ comes from the surface of
the earth and the LW ↓ comes from the atmosphere, the temperature of the surface of the earth is
greater than the temperature in the atmosphere.
Q5.
Describe the diurnal pattern of total net radiation (Q*), and state which component of the radiation
budget exerts the most control over Q*. [2]
-
Q* goes down slightly from about 260 watts/m^2 to -60 watts/m^2 between 0 to 3 UTC and stays
almost constant around -60 watts/m^2 between 3 to 11 UTC. Between 11 to 13 UTC, Q* slowly
increases to 90 watts/m^2. From 13 to 15.5 UTC, Q* increases rapidly from -60 watts/m^2 to 500
watts/m^2. Between 15.5 and 18.5 UTC, Q* slightly increases to around 680 watts/m^2. From 18.5
UTC to 20.5 UTC, Q* drops drastically but increases back to around 780 watts/m^2. From 20.5 UTC
to 24 UTC, Q* with lots of fluctuation unevenly drops to just over 0 watts/m^2.
-
Downwelling solar exerts the most control over the total net radiation.
Desert Rock, MV
Q6.
(
upload a screenshot of graph, acquired by following the above instructions (Accessing the
SURFRAD dataset), to canvas
)
Q7.
State 2 differences between the two sites and suggest some reasons for these differences in terms of
climate and environment. [2]
-
First, in Desert Rock, Nevada between 13 and 15.5 UTC the downwelling solar, upwelling solar, and
total net radiation is more stable compared to Fort Peck, Montana where there is lots of fluctuation
between 13 and 15.5 UTC. Second, between 0 and 2 UTC in Fort Peck, Montana the decline in the
downwelling solar and total net radiation is more stable than Desert Rock, Nevada and there is much
less fluctuation in watts/m^2.
-
In Fort Peck, Montana the temperature difference during the day and night is much greater compared
to Desert Rock, Nevada. This temperature difference indicates instability in regards to sunlight, which
can cause fluctuation of the radiation at Fort Peck between 13 and 15.5 UTC. Aswell, between 0 to 2
UTC, the temperature decreases much faster and less stable in Desert Rock compared to Fort Peck.
Calculating albedo and net radiation (see more in the endnotes)
1.
Download the
Fort Peck radiation data spreadsheet
containing radiation data for a day in July in Fort
Peck. Fill in the columns for albedo and net radiation using the following equations:
Netradiation
(
𝑸∗
) = (
?
↓ +
?
↓)– (
?
↑ +
?
↑) = (
?
↓ −
?
↑) + (
?
↓ −
?
↑) (1)
The albedo or reflectivity (α) of a surface refers to the proportion of incident short-wave radiation which
is reflected by the surface:
Albedo
(α)
=
?
↑ / ?
↓(2)
Q8.
At what time of day does the maximum Q* occur? [1]
-
The maximum Q* occurs at 1:01:01 PM with 669.4 watts/m^2
Q9.
What is the value of albedo at 0900 and 1700 hours? Express answers in percentages (e.g. 10% not
0.1). Explain how albedo changes between these hours. [3]
-
0900 hours = 25%
-
1700 hours = 30%
-
Between 0900 hours to 1700 hours, the albedo increases from 25% to 30%.
PART 2: CO2, Temperature, and Climate Change [12 marks]
Examine the updated version at the end of this document (in endnotes; Fig. 2), downloaded from the
NOAA web-site: https://www.esrl.noaa.gov/gmd/ccgg/trends/. This famous graph is often referred to as
the “Keeling Curve” after the name of first author who originally published the data. It represents
atmospheric CO2 concentration atop Mauna Loa, Hawaii (in parts per million, ppm).
Q10.
List 4 processes/activities that are responsible for the peaks and troughs observed in the keeling
curve (2 for each). [1]
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Volcano eruption
-
Seasonal changes
-
Human activity
-
Industrial release
Q11.
Find the most recent
annual mean
atmospheric CO2 concentration at Mauna Loa (i.e., for 2019). By
what percent has the atmospheric CO2 concentration increased from the pre-industrial value of 280 ppm?
[1]
-
47%
Q12.
Watch the film at: https://www.esrl.noaa.gov/gmd/ccgg/trends/history.html. Focusing on the data
shown on the left figure (snapshot, below) from about 0-2 minutes of the film, explain what is being
portrayed. Also: Can you explain why there is so much more variation in values at latitudes 30°N and
higher than those south of the equator? See clock to the right of the graph for year and month – you may
need to play the film a couple of times to become familiar with the trends) [2]
-
The left figure portrays the change of CO2 in the atmosphere over time from 1979 to 2019.
-
I think a possible reason for the variation in values at latitudes 30 ° N and higher is that there are more
human driven activities which produce gasses such as CO2.
Q13.
Examination of data used to compute the climate change index:
Download the dataset in the
climate change index spreadsheet
. This table presents the following data for
the time period 2006-2019: 1) Global average temperature, 2) Atmospheric CO2 concentration, 3) Global
Mean Sea Level (mm height with reference to a fixed datum), 4) Average Arctic Sea Ice Extent. These
variables are critical indicators of human impact on climate and affected systems. The variables are used
to compute the Climate Change Index, a single number calculated annually that can be used to show
accumulated change over time. Here, we will ignore the index and examine the data it derives from.
Open the Excel spreadsheet. Create time-series (line) graphs to illustrate the variables. Because the
variables are measured on different numerical scales, I suggest you either construct: i) four separate line
graphs, one for each variable,
or
ii) three graphs, the first with CO2; the second with Global Mean Sea
Level, and the third with Global Avg. Temp. and Average. Arctic Sea Ice Extent.
Your TA will help you with the “line graph” feature in Excel. For general graphing instructions, refer to
the handout on Constructing Line Graphs (posted in Canvas) or consult your TA in lab. Note that: if you
choose option ii), you will have a secondary y-axis on the last graph: one, on the left for the first variable
(e.g., Global avg. temp.), the second on the right, for the other variable, e.g., Average. Arctic sea ice
extent (e.g., see Fig. 1 below). Remember,
time
is always your
x-axis
variable in a time- series graph. The
following Excel help tutorial will help with adding secondary y-axes:
https://www.youtube.com/watch?v=P-mB4I16GC8
Present your graphs together (e.g., side by side; one on top of the other) so you can easily compare trends
among the variables. Be sure to include a title, axis labels indicating units of measurement and, if you
have more than one series per graph, a legend to indicate which is which. Place the graphs (copy and
paste each) into a Word document and convert to pdf. If you are unable to convert to pdf, you may submit
your Word doc. Follow the prompts (click ‘browse computer’, select your file, etc.) to attach to the
electronic submission in Canvas. [3]
1)
Global average temperature
2)
Atmospheric CO2 concentration
3)
Global Mean Sea Level
4)
Average Arctic Sea Ice Extent
Q14.
Looking at sea surface height change (represents change relative to the 1993-2008 average),
comment on the pattern over time (direction, magnitude, variability)? How are sea surface height trends
related to the other 3 variables, if at all? Explain any relationships in terms of underlying processes of
cause-effect [2].
-
From 1993 to 2002, the change of sea surface height was unstable and there was lots of fluctuation.
Since 2002, the sea level has been slowly rising without as much fluctuation as the years before.
-
Atmospheric carbon dioxide concentration and the global average temperature are rising over time,
resulting in the rise of sea surface height.
Q15.
Calculate the percent change in temperature that occurred between an earlier year and the last year
in the time sequence, as follows (you will compute this using 2 different ‘earlier’ years) [1]:
-
2006 to 2019: 2%
-
2014 to 2019: 1%
Q16.
Is comparing the percentage of change between individual years a good way to assess temporal
trends in the variable temperature? Why/why not? What would be a better temporal scale for this
variable? [2]
-
I don't think that comparing the percentage of change between individual years is a good way to
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assess temporal trends in the variable temperature. It is not good because the comparison cannot see
extremely detailed temperature changes. Choosing two different time periods of temperature changes
to compare would provide a better temporal scale to evaluate this variable in my opinion.
PART 3: What is your impact? Carbon footprint: [2 marks]
Q17.
Carbon footprint: [1]
Q18.
Justify your answer by providing details of, e.g., indicate those elements of your lifestyle that are
particularly carbon-hungry; those that are relatively conservative of energy/carbon, etc. You will not be
judged for this. Some of us have taken more than our share of international flights; or drive SUV’s! [1]
-
Both parents work about a ten minute drive from home five days a week which consumes lots of
car fuel. Family lives about two hours away, which we visit every weekend!
-
I have a brother and a father who have a diet which consists of lots of protein which means lots of
groceries.
-
My father works in construction so we save money on services because he does them all for free.
-
My family thrifts most of our clothes, etc. Reuse, reduce, recycle!