EdGCm - Project 4 - Outgoing IR versus Surface T

Snow & ice cover versus solar constant

Goals

Diagnose outgoing infrared radiation (IR radiation)
Evaluate relationship between outgoing IR and surface temperature
Examine other factors that might influence outgoing IR

Instructions

1. This project involves further diagnosis of the simulations you have completed. As with the previous project, we want several climates so that we can evaluate the relationship between outgoing infrard radiation and global average surface temperature, as produced by EdGCM.

You should now have 5 climates simulated by EdGCM. The specific climates depend on choices you made in Projects 2 and 3. These may well be

Control run
Solar constant increased by 1%
Solar constant decreased by 1%
Solar constant increased by 2%
Solar constant decreased by 2%

but in any case, you should have 5 to evaluate.

Recall the question posed in the previous project:

How long should these simulations run?

Discussion in the course lectures about relationships between outgoing IR and other climate-system properties did not explicitly require equilibrium climates. We now have simulations in which EdGCM is not in equilibrium to start but evolves toward equilibrium. We will see how the relationship between IR radiation and surface temperature (IR-Ts relationship) changes, if at all, as the climate evolves toward equilibrium.

2. You need to pull out statistics on global average outgoing IR radiation. You should already have global average surface air temperature, but depending on how you save Ts from the previous projects, you might need to extract it again. We will want annual-average, global-average time series of the IR and Ts variables, extracted from these files:

Run_ID_SRFAIRTEMP
Run_ID_THMRADEPLAN

THMRADEPLAN has "Thermal Radiation Emitted by Planet". This should not be confused with THMRADPTOP, which is thermal (IR) radiation emitted at the top pressure of the model. This is nearly, but not quite, the same as the outgoing IR, THMRADEPLAN.

THMRADEPLAN is in the same folder where the SRFAIRTMP file resides for each run. It also has the same format for its data. Note that the IR flux has a negative sign, since the EdGCM standard treats incoming radiation as positive. Keep this in mind as you process the output.

3. We want to compare IR-Ts relationships given by EdGCM with those given in class. There are a couple of ways we can do this:

Compare IR vs. Ts for each year of a simulation. Derive a linear fit between the two, consistent with the lecture analyses looking for linear relationships.
Compare the change in IR (dIR) versus the change in Ts (dTs) between (approximate) equilibrium states of each climate.

You should do both and compare results.

4. Compare IR vs. Ts for each year of a simulation

(a) Extract the time series of annual average, global average Ts and IR from a simulation. Plot IR vs. Ts, then do a linear fit to the relationship. In Excel (both PC and Mac versions), make a scatterplot of IR vs. Ts, then in the "Chart" menu, choose "Add Trendline". In the pop-up window, under the "Type" tab, choose linear; under the "Options" tab, select options to display equation and the R**2 value on the chart.
(b) Record the linear fit and R**2 value for each of your climates.
(c) Consider all of your fits together, and answer these questions:
1. How much do R**2 vary between climates? Overall, how good is the linear fit assumption?
2. Considering the form I = A + BTs, how do the "A" values compare with those discussed in the lectures? Do they compare best with the empirical values (Lecture 3, slides 4-5) or equivalent black-body values (Lecture 3, slide 9)?
3. How do the "B" values compare with those discussed in the lectures? Do they compare best with the empirical values (Lecture 3, Slides 4-5),the equivalent black-body values (Lecture 3, Slide 9), or the one derived by Gutzler and Stone from a GCM (Lecture 3, Slide 7)?
4. Give your reasoning for your choices concerning "A" and "B". Why do you think EdGCM produced the results it gave relative to the empirical, equivalent black-body and GCM-based values?
Note: In answering these questions, you might find it useful to consider the spread among "A" and "B" values, as well as their average and median values.

5. Compare dIR vs. dTs

(a) Compute the time average I and Ts for the last several years of each of your 5 climate simulations. (You decide and defend how many years you choose to use.)
(b) With 5 climate simulations, you can produce 10 climate differences by using different pairs of climate to difference. Create the dIR and dTs for each pair.
(c) In a manner similar to part 4, plot dIR versus dTs and determine a linear relationship between the two. This is similar to the procedure followed by Gutzler and Stone (Lecture 3, Slide 7).
(d) Answer these questions:
1. How good is a linear relationship between dIR and dTs?
2. How does the "B" value compare with those discussed in the lectures? Does it compare best with the empirical values (Lecture 3, Slides 4-5),the equivalent black-body values (Lecture 3, Slide 9), or the one derived by Gutzler and Stone from a GCM (Lecture 3, Slide 7)?
3. Give your reasoning for your choice concerning "B". Why do you think EdGCM produced the results it gave relative to the empirical, equivalent black-body and GCM-based values?

6. Compare methods

Compare the two methods above for arriving at a "B" value. How close are the results? Why might we expect similar results between the two methods? Think specifically about what you are using to produce the curves in both methods - are they really all that different?

7. Sensitivity

Compute the "beta" sensitivity factor using your "B" and outgoing IR values, under the assumption of constant albedo (e.g., like Lecture 3, Slide 8). Answer these questions:

How do these values compare with the ones dervied in the Lecture?
How do they compare with your "beta" computed for EdGCM Project 2? What might cause differences between the "beta" computed here and that in Project 2? (Think about your assumptions going into each one.)

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