Humidity Data

Wouldn’t it be nice if we could falsify the entire Enhanced Greenhouse Effect hypothesis with just one chart?  It would describe specific humidity trends for the entire troposphere on Earth. 

Anthony Watts thought that he had exactly that (or rather close to that) with this post.  After he made that post, an interesting, but not unusual, phenomenon ocurred in the blogosphere.  There are the big blogs: Climate Audit and Watts Up With That.  And then there are dozens of smaller blogs, feeding off of those blogs.  The possibility of something so simple that could completely overturn the warmers’ hypothesis seemed surprising, and of course, the smaller blogs audited the big blogs, finding that once again things are not as simple as they seem.  Watts’ chart read “up to 300 mb” (mb being a measure of atmospheric presure).  But this wasn’t what the graph represented; what the graph actually represented was specific humidity at 300 mb.  The phrase “up to 300 mb” meant that the data was only available up to a pressure of 300 mb.

Now for some background about why humidity matters.  This post’s purpose is to discuss climate data, so I’m not going to source any of the background information; it’s common knowledge anyway.  The big question concerning how sensetive surface temperatures are to changes in CO2 is often expressed as how much temperature increase we would expect from a double of CO2 levels from pre-industrial times (280 ppm).  Currently, we’re at 380 ppm, and quite far away from 560 ppm.  Assuming that the greenhouse effect hasn’t already been saturated, we would expect 1.2 degrees C of warming from a doubling of CO2 without any feedbacks.  The catastrophic climate models base their prediction off of a large water vapor feedback, which would increase climate sensitivity by providing positive feedback to a forcing.  The extent and sign of the water vapor feedback are not agreed upon. The basic premise is that specific humidity (ratio of water vapor to air in the atmosphere) must remain the same, thus forcing increased water vapor content, or relative humidity.  This increase in relative humidity would then cause a stronger greenhouse effect.

As I did in my last post, I’m skipping over the theoretical and going to right to the observations.  By comparing observations with theory, I can decide if the theory is failing without necessarily understanding the underlying physical problems that are causing it to fail.

A few days later, after everyone had enough time to fool around with the website that Watts extracted the data from, he made this post, which quotes an article from JunkScience.  I’m not satisfied though.

Here are some of the first thoughts that came to mind:

1) Is this reliable?  Is specific humidity data reliable back to 1950?  Are there other sources of data describing specific humidity?

2) Where is the water vapor?  At what altitude and pressure? 

3) Within the troposphere, are there some altitudes that are responsible for most of the greenhouse effect?

To answer #1, I did some research and found a great summary of water vapor in the atmosphere and current measuring techniques.  It’s old, but it still is very helpful. 

This paper, entitled “Trends and variability in column-integrated atmospheric water vapor, doesn’t like the data Watts used so much. 

From the Abstract: “Only the special sensor microwave imager (SSM/I) dataset from remote sensing systems (RSS) has credible means, variability and trends for the oceans, but it is available only for the post-1988 period. Major problems are found in the means, variability and trends from 1988 to 2001 for both reanalyses from National Centers for Environmental Prediction (NCEP) and the ERA-40 reanalysis over the oceans, and for the NASA water vapor project (NVAP) dataset more generally.”



So where do we go from here?  Look for more data sources of course!  The paper I quoted seemed to look favorably upon Remote Sensing Systems (RSS)’s data set: SSM/I.  That’s for another day.

To answer #2; greenhouse-relevant watervapor is located in the troposphere.  The troposphere, though, varies is height according to lattitude, as shown in the graph below.

Therefore, if we truly want to find specific humidity trends, we need to look at 0 to 10 km from -90 to -50 degrees, 0 to13 km from -50 to -30 degrees, 0 to 16 km from -30 to 30 degrees, and 0 to 10 km from 30 to 90 degrees.  When this gets translated into atmospheric pressure, it becomes clear that we need data that encorporates even the 100 mb region. 

The NCEP data that we are using only works to the 300 mb region.

And to answer #3, at this point I am not very sure.  This is an issue for the future, once we can find a reliable dataset that provides reliable results, regional data, and data extending 16 km into the atmosphere.

And just to throw in one more conflicting graphic, heres water vapor content from Roger Pielke, Sr.

The picture caption reads: Figure caption: (a) The North American Regional Reanalysis domain-averaged de-seasoned precipitable water vapor – PWAV (blue), total precipitable water content -PWAT (brown), and lower-tropospheric temperature (red), monthly anomaly time series; (b) The North American domain-averaged PWAV, PWAT, and Tcol yearly time series by season. The dashed lines represent a linear fit, and the magnitudes of the trends are also shown. The black dashed lines indicate PWAV trends for a fixed relative humidity scenario. Note the much lower (and statistically insignificant trend in PWAV and PWAT despite a significant trend in Tcol [ from Wang, J.-W., K. Wang, R.A. Pielke, J.C. Lin, and T. Matsui, 2007: Does an atmospheric warming trend lead to a moistening trend over North America? Geophys. Res. Letts., submitted].

There are still more studies out there that support the IPCC prediction of a constant specific humidity, but that’s for another post.

In conclusion, water vapor content and humidity data are obviously very messy.  For now, that’s about all that I can say.


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: