Of interest is AQUA channel 5. Channel 5 measures a portion of the atmosphere which is largely the troposphere. The weighting function graph for channel 5 (red circle), and the other AQUA channels, is shown below.
The plot indicates that channel 5 largely measures the free troposphere, with some contribution from the lower stratosphere and from the near-surface layer.
AQUA has been operational since mid-2002. The UAH website provides daily AQUA data dating to mid-2002, including channel 5.
What does the AQUA daily data look like? First, let’s look at the time series of daily channel 5 values since January 2003. Then let’s look at the same data by day of the year,:
The plot resembles colorful spaghetti, with year-to-year variation. Now suppose that those daily values are averaged over 2003-2010 so as to present the “normal” tropospheric temperature for any day of the year. I realize that eight years of data hardly constitutes a robust climatology, but hey it’s what we have. Here’s a plot of that daily average:
The image shows a clear annual cycle. Global temperature on channel 5 peaks in July and August and reaches a minimum around January. That seems a bit odd, since global TOA insolation peaks in the Northern Hemisphere winter, when Earth is closest to the sun.
One potential explanation of this channel 5 annual pattern is based on the facts that (1) Earth’s land fraction varies by latitude (with a greater land fraction in the Northern Hemisphere than in the Southern Hemisphere) and (2) land has a lower heat capacity than the oceans.
Here’s the thought: if the fraction of TOA (“top of atmosphere”) sunlight falling on land varies through the year and if the land, due to its low heat capacity, readily transfers that energy to the adjacent troposphere, then the hemisphere with the greater land surface would show greater tropospheric warming in local summer and greater cooling in local winter than the other hemisphere. This would induce an annual cycle into the global data.
Let’s explore that. It is possible to create an approximation of insolation falling on land for each day of the year. This is done by using two sources of information. One is the NOAA insolation calculator, which provides TOA data by latitude, and the other is Figure 4 from this Milankovitch article.
I choose the middle latitude of each latitude band to represent the entire band’s insolation. To this I applied the fraction of land in the latitude band.
Here is a plot of the scaled “landfalling insolation” (actually, it’s the TOA insolation above land masses) and the annual cycle provided by AQUA channel 5:
The visual impression is that the two cycles are similar, with global tropospheric temperature lagging landfalling insolation by a month or two. The r-squared correlation value for the two is 0.72 with no lag, which is not shabby.
Here’s a scatterplot of insolation above land vs. temperature for each day of the year. This type of plot, in my opinion, communicates well the presence of a lagged relationship between insolation and temperature.
Suppose that we lag temperature 30 days behind insolation above land. What do we get? We get the plot below, with an r-squared value of 0.95:
So, it looks like insolation above land provides a pretty good explanation for the annual variation in global tropospheric temperature. as indicated on channel 5. And, global temperature appears to lag insolation above land by around 30 days, more or less.
One final observation of this data – there is a wiggly area in the Northern hemisphere winter, where the correlation seems to break down a bit. Why is that? Engineers are always curious about odd data wiggles – we can’t help ourselves.
A thought – possibly the wiggliness is related to Northern Hemisphere snow cover and its impact on absorbtion of sunlight. Here is an annual plot of NH snowcover by day of the year:
The plot indicates that NH snowcover remains more or less constant until about March. This happens even though high-latitude NH landfalling insolation is increasing. The albedo of the snow may effectively slow any warming of the land and associated troposphere until late winter, when the snow and ice melt. I have not explored this in detail to see if the hypothesis holds water, but it is plausible.
All in all, a basic seasonal pattern in AQUA channel 5 data can be reasonably explained. It’s nice when data behaves as-expected.