Earth's Energy Budget and the Greenhouse Effect
Objectives:
 Extend knowledge of several aspects of the earth's global average surface,
atmospheric, and wholeearth energy budgets to consider spatial variability.
 Investigate connections between spatial variations in the energy budget
and properties of the earth, such as albedo, vegetative cover, rainfall distribution,
distribution of water vapor, and topography.
Introduction. In Lab
Activity #5, "LongTerm Average Energy Budgets for the Earth's Atmosphere
and Surface" we examined the global, longterm average energy budgets
for the earth's surface and atmosphere, accounting for radiative energy as
well
as other forms of energy. The numbers come from Meteorology, 1998, an
introductory textbook by Danielson et al., and are similar to those found in
most recent texts. In this lab you will investigate how these numbers compare
to the observations in the My World GIS data sets; investigate spatial variability
in several aspects of these budgets; and investigate possible connections between
some of these variations and properties of the earth.
Part 1: Albedo
(1) Annualaverage albedo plot. From the "Energy Balance" data library in My World,
drag the latitude, countries, and monthlyaverage radiative budget terms data sets onto the layer list. Calculate two new fields: (i) annualaverage flux of reflected solar radiation in 1987 (though
don't include the fields labeled "Reflected Solar Clear", for completely cloudfree conditions); and (ii) the annualaverage flux of
incoming solar radiation for 1987. From these two fields, calculate and plot another new field, the annualaverage planetary albedo. (Of course, to do this you have to know how albedo is defined!)
Modify
the color scheme for your annualaverage planetary albedo plot as follows:
 change the color scheme to "Gray"
 set the range of values on the color scheme ("Color Count") to 0 to 0.8
 specify 48 colors (instead of the default, 16)
 check the "Reverse" check box (so that loweralbedo areas appear dark, higheralbedo areas appear light, a more intuitive scheme)
 expand the plot to as large a size as you can
 if you've got continents plotted, hide them (their borders are thick enough to compete with the albedo information)

Suspicious albedos. Do you see any values greater than 1.0 or undefined values? Should
there be any such values, and if there are, what might account for them?
 Global, annualaverage albedo. Calculate the areaweighted global mean value of annualaverage albedo and compare it to the value given in the
global energy budget diagrams provided in class with Lab
Activity #5. Are they very
different? (If so, what might account for the difference?)
 Spatial patterns of albedo. On the (unweighted) annualaverage albedo plot, where does albedo tend to be relatively low and relatively high,
compared to immediately surrounding areas? (Focus on larger scale areas and deemphasize small, individual spots.) Pose some hypotheses about
what might account for some of these variations.

Comparison with spatial patterns of precipitation. Open a child window containing the annualaverage albedo, then hide the radiation budget terms layer. Now, from the "Climatology" data library, drag the monthlyaverage precipitation data set onto the layer list. Calculate and plot a new field: annualaverage precipitation for 1987. Does this plot help you test any of
your hypotheses in (1)(b)? If so, how?
(2)
Annualaverage clearsky albedo plot. If your child window is still open, close it. Now, repeat the plot in (1), but this time calculate the annualaverage albedo by first calculating the annualaverage
reflected solar radiation from Earth under "clear sky" conditions
(that is, with clouds removed).

Spatial patterns of clearsky albedo. Where does the clearsky albedo tend to be relatively low and relatively high?
Does this plot help you test any of your hypotheses about the reasons
for spatial variations in albedo in (1)(b) above, and if so, how?
 Comparison with spatial patterns of vegetation. Open a new child window with the clearsky albedo plot in it, then hide the clearsky albedo layer. Next, from the "Climatology" data library, drag the "Terrestrial Biomes" data set onto the layer list, and plot the "Dominant Vegetation" field. Comparing this plot with the clearsky albedo plot in the child window, would you say that
this
plot
helps
you
test
any
of
your
hypotheses in (1)(b)? If so, how?
(3) Animations of monthlyaverage albedo plots. In a Web browser, access a 12month animation (movie) of individual, monthlyaverage albedo plots from the G/M/O 405 class backup Web site at: http://funnel.sfsu.edu/courses/gmo405/MyWorldPlots/Albedo_1987.gif.
Open a separate
window in the browser (pull down the "File" menu and select "New..." or "New Window") and access a second movie, a 12month animation of individual, monthlyaverage clearsky albedo plots at: http://funnel.sfsu.edu/courses/gmo405/MyWorldPlots/AlbedoClear_1987.gif.
 What temporal patterns do you see in each animation? Pose
hypotheses about what might cause them. Do the two animations together
help you test any of your hypotheses? If so, how?
 Do any of these patterns help you test any of your hypotheses in (1)(a)?
If so, how?
Part 2: Greenhouse Effect
(4) Comparisons among energy budget calculations. In Lab Activity
#4, "Introduction to the Earth's Energy Budget", you used monthlyaverage
surface temperature data and the StefanBoltzmann Law to estimate the global,
annualaverage flux of longwave IR emitted by Earth's surface. You also used
My World GIS to plot annualaverage outgoing longwave IR from the top of Earth's
atmosphere and got a global average from it. You have also plotted the global,
annualaverage flux of incoming solar radiation.
How do these three values compare with the ones in the energy budget figures
provided in Lab Activity
#5, "LongTerm Average Energy Budgets for the Earth's Atmosphere and Surface"?
[Note: to make this comparison, you'll have take into account the fact that the energy budget numbers that appear in the figures provided in class are percentages of
the incoming solar radiation, not fluxes. Hence, you'll have to apply those percentages to the solar constant to get the energy budget figures as fluxes instead of percentages.] If the two sets of figures seem
significantly different, can you think of any reasons to explain those differences?
(5) Annualaverage greenhouse effect and greenhouse increase. Make a plot of "greenhouse effect" as follows:
 In My World GIS, remove the monthlyaverage radiation budget terms layer from the layer list.
 From the "Energy Budget" data library, drag the monthlyaverage "greenhouse effect" data set onto the layer list.
 Calculate and plot a new field, the annualaverage "greenhouse effect" for 1987.
 Access the documentation about the greenhouse effect data set (highlight the greenhouse effect layer, pull down the "layer menu" along My World's topmost menu bar, and select "Show Data Documentation"). How are these "greenhouse effect" data defined?
 Calculate the areaweighted global average greenhouse effect.
Repeat
the foregoing steps to make a plot of "greenhouse increase", a separate data set in the "Energy Budget" data library.
 Comparison of globalaverage greenhouse effect calculations. Is the areaweighted globalaverage greenhouse increase consistent with the globalaverage
surface temperature and the effective radiative temperature for the earth
(as viewed from space) that we got in Lab
Activity #4, "Introduction to the Earth's Energy Budget"?
 Spatial patterns in the greenhouse effect. What spatial/geographic patterns do you see in the annualaverage greenhouse
effect? Pose hypotheses to try to account for
some of them.
 Possible correlations between the greenhouse effect and other quantities. Plot your choice of any one or more of the following:
 Topography (pay attention to land only). (In the "World" data library, drag the "Elevation and Bathymetry" data set onto the layer list and plot the elevation and bathymetry field. Divide this field by the mean elevation above sea level of land, 677 meters, to "normalize" the land elevations.)
 Annualaverage precipitable water. (In the "Climatology" data library, drag the "MonAvg_Precipitable_H2O_Vapor" data set onto the layer list. Calculate a new field: the annualaverage precipitable water. Find out what this is by consulting the documentation. Divide the new field by the mean, unweighted global average value to get a new, "normalized" field of annualaverage precipitable water.)
 Annualaverage specific humidity. (Same procedure as for precipitable
water.)
 Annualaverage surface temperature. (In the Energy Budget data library, .... )
Create a child window with your plot in it. Hide whatever is plotted in the My World GIS main window, then "normalize" the
annualaverage greenhouse effect field by dividing it by it's unweighted global average value, and plot the result.
Do any of these four quantities seem to be relatively well correlated spatially with
the greenhouse effect? (The term "correlated" can be defined
in a mathematically rigorous way, but here all we want is a subjective
sense
of whether or not the plot patterns resemble each other closely.) Of these
four, can you think of some that might be well correlated with each other?
What physical connections do you think there might be between each pair
of wellcorrelated plots that would make the correlations more than accidental?
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