Objectives:

• Extend knowledge of several aspects of the earth's global average surface, atmospheric, and whole-earth 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, "Long-Term Average Energy Budgets for the Earth's Atmosphere and Surface" we examined the global, long-term 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) Annual-average albedo plot. From the "Energy Balance" data library in My World, drag the latitude, countries, and monthly-average radiative budget terms data sets onto the layer list. Calculate two new fields: (i) annual-average flux of reflected solar radiation in 1987 (though don't include the fields labeled "Reflected Solar Clear", for completely cloud-free conditions); and (ii) the annual-average flux of incoming solar radiation for 1987. From these two fields, calculate and plot another new field, the annual-average planetary albedo. (Of course, to do this you have to know how albedo is defined!)

Modify the color scheme for your annual-average 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 lower-albedo areas appear dark, higher-albedo 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)

1. 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?

 

2. Global, annual-average albedo. Calculate the area-weighted global mean value of annual-average 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?)

 

3. Spatial patterns of albedo. On the (unweighted) annual-average 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.

 

4. Comparison with spatial patterns of precipitation. Open a child window containing the annual-average albedo, then hide the radiation budget terms layer. Now, from the "Climatology" data library, drag the monthly-average precipitation data set onto the layer list. Calculate and plot a new field: annual-average precipitation for 1987. Does this plot help you test any of your hypotheses in (1)(b)? If so, how?

    (2) Annual-average clear-sky albedo plot. If your child window is still open, close it. Now, repeat the plot in (1), but this time calculate the annual-average albedo by first calculating the annual-average reflected solar radiation from Earth under "clear sky" conditions (that is, with clouds removed).

1. Spatial patterns of clear-sky albedo. Where does the clear-sky 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?

 

2. Comparison with spatial patterns of vegetation. Open a new child window with the clear-sky albedo plot in it, then hide the clear-sky 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 clear-sky 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 monthly-average albedo plots. In a Web browser, access a 12-month animation (movie) of individual, monthly-average 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 12-month animation of individual, monthly-average clear-sky albedo plots at: http://funnel.sfsu.edu/courses/gmo405/MyWorldPlots/AlbedoClear_1987.gif.

1. 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?

 

2. Do any of these patterns help you test any of your hypotheses in (1)(a)? If so, how?

Part 2: Greenhouse Effect

How do these three values compare with the ones in the energy budget figures provided in Lab Activity #5, "Long-Term 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) Annual-average greenhouse effect and greenhouse increase. Make a plot of "greenhouse effect" as follows:

• In My World GIS, remove the monthly-average radiation budget terms layer from the layer list.
• From the "Energy Budget" data library, drag the monthly-average "greenhouse effect" data set onto the layer list.
• Calculate and plot a new field, the annual-average "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 area-weighted 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.

1. Comparison of global-average greenhouse effect calculations. Is the area-weighted global-average greenhouse increase consistent with the global-average 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"?

2. Spatial patterns in the greenhouse effect. What spatial/geographic patterns do you see in the annual-average greenhouse effect? Pose hypotheses to try to account for some of them.

3. 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.)
   • Annual-average precipitable water. (In the "Climatology" data library, drag the "MonAvg_Precipitable_H2O_Vapor" data set onto the layer list. Calculate a new field: the annual-average 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 annual-average precipitable water.)
   • Annual-average specific humidity. (Same procedure as for precipitable water.)
   • Annual-average 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 annual-average 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 well-correlated plots that would make the correlations more than accidental?