1. EdGCM Lab 2: Using EdGCM to Visualize Climate Change
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2. EdGCM Lab 2 Agenda Review of global climate models (GCMs) (10 min)
More information on climate models (10 min)
Learning how to extract and visualize (30 min)
Making graphs, maps, and anomoly maps (As needed)

3. Review of Models Need to supply the GCM with information
Boundary conditions: inputs that do not change over the course of the simulation
- Examples: Topography, land/sea distribution, land ice extent, vegetation
Initial conditions: inputs prescribed at the beginning of a run that change as the simulation proceeds
- Examples: Greenhouse gas trends, solar trends, sea surface temperatures
GCMs are computer programs, mostly written in Fortran and requiring a fairly high level of programming skills to operate, let alone develop
Some model inputs, such as greenhouse gas levels, can fall into either category of input

4. Review of Models
Initial and boundary conditions put into a model are based off real world data
But the model then runs physical equations and parameters to simulate interactions in each grid cell of the globe to create its own simulated data.

5. Review of Models
This is not the same as gathering or using data from the real world… (That kind of data is gathered by physical scientists!)

6. Review of Models
The process of hindcasting (or testing a models accuracy!) is: When we compare a models simulated data versus that data collected by a physical scientist.

7. Review of Models
Testing models against the existing instrumental record suggested CO2 must cause global warming, because the models could not simulate what had already happened unless the extra CO2 was added to the model.
All other known forcings are adequate in explaining temperature variations prior to the rise in temperature over the last thirty years, while none of them are capable of explaining the rise in the past thirty years. 
CO2 does explain that rise, and explains it completely without any need for additional, as yet unknown forcings.

8. Now, More A Bit More About GCMs

9. What’s A Climate Equilibrium?
As we now know, the global climate is generally in equilibrium on human time scales (even though some factors influencing it are episodic)
We’ll see this using EdGCM in a moment!
When the climate is out of equilibrium, negative and positive feedbacks swing it back to balance.
Feedbacks usually lag behind at a slower pace.
The amount of incoming solar radiation is balanced by the amount of outgoing terrestrial radiation (section 1.2.3), so that the Earth neither continues indefinitely to heat up nor cool down.
GCMs simulate not only feedbacks, but the lag in their impact…

10. The Ice Albedo Feedback
Positive feedback --- warming tends to decrease ice cover and hence the albedo, increasing the amount of solar energy absorbed, leading to more warming
Source: NOAA, 2010

11. Two Positive Feedbacks
Water Vapor Feedback
Warmer atmosphere
Increased greenhouse warming
More evaporation
Ice-Albedo Feedback
Increased water vapor content
Warmer atmosphere
Double the increase in global average temperature
Depends on cloud temperature which is reflection of its height
Low clouds tend to reflect solar radiation (shortwave) back into space
High clouds tend to act like greenhouse gas, absorb thermal (longwave) radiation.
Less albedo
More ice melt

12. A Negative Feedback Warmer atmosphere
More reflection of incoming solar radiation
More evaporation
Increased formation of clouds in the high atmosphere
Increased water vapor content

13. What Could Prevent Equilibrium Being Re-established?
If any factor that has a long-term, continuous trend…

14. What Comes Out of a Climate Simulation?
The GCM produces raw data files of various diagnostic quantities that have been calculated by the physical equations in the model
Data files must be post-processed so that usable information can be extracted more readily
Diagnostic variables are visualized and analyzed to check the model accuracy as well as the outcome of the climate experiment
GCMs are computer programs, mostly written in Fortran and requiring a fairly high level of programming skills to operate, let alone develop

15. How Good Are GCMs, Anyway?
No model is perfect – all have limitations (including sea level rise not being included)
Certain aspects reflect uncertainty in current knowledge (clouds, sea ice)
Accurately reproduced past climates (confirmed by records), predicted modern climate effects (mt. pinatuba)…but still have limitations…
-won’t tell you when it will rain
-how local can you go?
-some simulated features are rudimentary
Still, numerical climate models as representations of
the Earth system do a very good job of helping us
understand past, present and future climates

16. A Real World Question to Investigate

17. Today’s exercise
How might we determine a question that we are interested in using EdGCM to answer?

19. Can we localize our question?
The model does have limits as to how local it can go… But climates will be impacted regionally based upon there current state and future changes.

20. Questions to Answer in Using EdGCM

21. Control vs. Treatment A control group is used as a baseline measure
The control group is identical to all other items you are examining with the exception that it does not receive the treatment or manipulation the treatment receives
So how does this work with scenarios we are examining?

22. Visualizing Using Graphs…
Let’s take 10 minutes to make sure we can all do this…I’ll show you first…
Do we need to select years for a time series?
Isn’t this the same as running the model?
Why do we need to extract these files?

23. Visualizing Output on a Map…
Let’s take 10 minutes to make sure we can all do this…I’ll show you first…
1) Why is it important to manipulate the range?
2) What about the color bar?
3) Are there cases where we might want to adjust having continents or oceans filled?
4) How do you make an anomoly map? (What is an anomoly map?)

24. Differencing These Plots
What does it show us? Why do this?

25. Should We Interpolate Our Maps?
Interpolation = “Allows the model to estimate values of data between two known values.” Does it look different?

26. What Happens If We Use A Different Map Projection?
Try a Mollweide map. What happens? It accurately portrays area but distorts shape and direction…

27. Now We Need a Control Right?
Let’s extract the same variable data for the “Modern_Predicted SST” scenario. Again, let’s only use the last five years.
Make sure you choose the same variables and the same geographic range/time range.
Now let’s finalize this map (map type, scale, color bar, continents).

28. Questions to Ask About Our Maps
Where is the 100% snow coverage? What is 100% snow coverage?
What parts of the planet show the greatest changes?
Let’s compare our two maps. What differences do we see?

29. Can We Further Compare Our Maps?
Do you remember the steps to create an anomoly map? Let’s try it with one set of the maps we just created… Now let’s finalize the map (map type, scale, color bar, continents)

30. Questions to Ask About Our Anomoly Map
What regions show the greatest change in snow/ice cover using the anomoly map?
How much will the increase or decrease be?
How does this trend compare with what’s seen by satellites and reported at the NSIDC “State of the Cryosphere” map. Go here:
Oceans absorb heat and act as a cooling system…