More Model Set-Up

Since the object of the Climate portion of this blog is to develop a model and description of climate change so simple that 51% of Ivy League graduates can understand, I need a few simple visual aids.

This will be my two dimensional energy flow diagram.  As you can see, there is a thermal equator separate from the true geographical equator.  The energy flow arrows for the atmosphere are roughly symmetrical with the true equator. There are no ocean energy flow arrows yet since the thermal equator shifts. When the thermal equator is closest to the true equator, it is warmer globally.  When the thermal equator is further away from the true equator, it is cooler globally.  Since the rate of energy loss to space is more constant south of the thermal equator, the southern portion of the Earth, both oceans and atmosphere, are more stable thermally.  That does not imply that the weather is stable.  Actually, the small changes in thermal energy in the Antarctic would cause greater variations in extreme weather as the Antarctic sinks the energy to space.

Because of the Earth's elliptical orbit around the Sun and ~23.5 degree axial tilt, the solar energy felt at the surface of the Earth is ~1413 Wm-2 in Austral Summer and ~1321Wm-2 during Austral Winter.  The asymmetrical energy input along with the difference in thermal mass due to the location of the continents creates a little issue with a simple model approach.

The GISS LOTI regional data illustrates the differences in response to climate change due to the asymmetry of the Earth.  Unfortunately, the regions are divided based on the geographical equator.  To better illustrate climate change, I will need to modify the regional data to reflect the thermal equator.  That may not be an easy concept for the Ivy Leaguers to grasp.

Here is another perspective.  Using the GISS LOTI regional data, high is the difference in the high latitudes, 44-64 North minus South, mid is 24-44 North minus South and the equa is 24-equ North minus south.  As noted, the plots are based on the satellite era 1995 to 2010 that I prefer mainly because of the AQUA data starting in 2002 which while not a part of this plot, is useful for other purposes.  As I noted earlier, the 44-64 South regional data is the most stable so the "high" being the most variable of the three plots indicates that 44-64 North is very variable.