Changes in upper-tropospheric water vapour in response to a warming climate have been the subject of significant debate.
Due to instrumental limitations, long-term changes in water vapour in the upper troposphere are difficult to assess.

The NCEP reanalysis data is not the only source, and probably not the best.

SPM: The average atmospheric water vapour content has increased since at least the 1980s over land and ocean as well as in the upper troposphere. The increase is broadly consistent with the extra water vapour that warmer air can hold. {3.4}

WG1 S3.4: Similar upward trends in upper-tropospheric specific humidity, which considerably enhance the greenhouse effect, have also been detected from 1982 to 2004.

To summarise, the available data do not indicate a detectable trend in upper-tropospheric relative humidity. However, there is now evidence for global increases in upper-tropospheric specific humidity over the past two decades, which is consistent with the observed increases in tropospheric temperatures and the absence of any change in relative humidity. (Chap 3 sect 3.4 p40)

The Upper Tropospheric Humidity (UTH) product represents the mean relative humidity for the cloud-free upper troposphere, i.e. between 300 and 600 hPa.

Thanks. Now we can compare more easily with the text of the AR4. (I still need to get some papers from the library. I’ve been working on test plans. )

I think the various disputes on blogs are over what’s happening at high elevations– but I’m not entirely sure.

I plotted, from the same source, and on the same scale (gm/kg) the graphs of all levels from 1000 to 300mb.
Each specific humidity plot has been shifted to be zero at the present.
It shows clearly that water content of the atmosphere has been rising. The plot is here.

I’m sure they would benefit representatives in other governments. As the French are known as leaders in fashion for the world, I think we may need to send them to French Government leaders. Possibly Sarkozky’s wife, (Carly, right?) could wear them. I understand the fashionistas admire her. Being a newlywed, I’m sure Francois could persuade her — for the good of France and the world!

The longer I look at the groovy swirls on the t-shirts, the more I become convinced they are “the solution” to everything.

2. I have no idea if data collection problems invalidate trends. The ARM group who wrote the cited paper is now, and has always been focused on getting decent measurement period. That set of authors rarely later compares to models or trends. So, their concern was that the various co-located instruments gave different results even though the scientists and technicians were taking greater pains than normal to deploy the sondes in a way that would be provide the greater level of precision desired for climate modeling rather than just weather monitoring.

So that groups, after quite a bit of examination, tracked down the difficulties, and found means to correct the data from the sondes they deployed. This process involved the manufacturer, understanding of the instrument design, and a variety of other issues. The correction is physically based– meaning based on understanding of how the instruments themselves work.

Unfortunately, I suspect it’s likely that similar difficulties affect sonde measurements in the past (and even now). However, ordinarily, there isn’t any way to detect the problems, or correct them should they be detected.

3. I haven’t gotten the t-shirt yet. I also contacted Dr. Chalko to ask his opinion on the missing water vapor. I’m also trying to brainstorm ideas to save carbon when I do order my bioreasonant t-shirt. Maybe I can order 50 as Christmad presents for all my family and friends, and also contact some Australian yarn manufacturers and request them to permit Tom to include the package in a big shipment of yarn to my local yarn shop. Bundling the orders this way would surely lower the carbon footprint, and I’m sure all these businesses would understand the need for this, and be eager to expend the human energy to collect everything together for me.

Surely, if we are creating, we can all figure out how to get bioreasonant t-shirts while maintaining a low carbon footprint!

2. Do the data collection problems invalidate apparent trends? The absolute measures may suck but do they suck in a consistent way? I can’t tell from the cited materials.

3. Is the missing water vapor being sucked into the mantle along with the missing heat energy to foster the coming planetary explosion? Inquiring minds want to know… BTW did your T-shirt arrive from Australia, you of the big carbon foorprint?

By the way, did I mention instrument errors?

My husband Jim isn’t here… so I might make a hash of the history. But, let me draw your attention to: Dry Bias and Variability in Vaisala RS80-H Radiosondes: The ARM Experience, which touches on a data quality issue with balloons, which vexed Jim when he was working with ground based radiometers.

ARM was deploying ground based radiometers and also launching sondes. So, naturally, the measurements from the two were compared. The sondes and the radiometers did not match. Because Jim was noting down the serial numbers and fiduciary information about the sondes, he and others eventually tracked quite a bit of the issue down to calibration difficulties with the sondes.

Several studies intended to characterize the operational performance of the RS80-H radiosonde have been conducted by ARM since the program began in 1992. These studies included comparisons of radiosonde readings with stable reference values at the surface and of data collected during ascending and descending phases of radiosonde flights (Lesht 1995). Subsequent work (Lesht and Liljegren 1997) focused on comparison of PWV measurements obtained from the MWRs deployed at the CART site with PWV calculated from concurrent radiosonde soundings. One result of this comparison was the discovery that the discrepancies between the two measurements had a strong correlation with the radiosonde’s manufacturing date. This result was particularly important because it revealed that variations in the manufacturer’s calibration process were a possible source of measurement error and that, as a result, the uncertainty associated with the radiosonde measurements of relative humidity was greater than had previously been supposed. Comparisons between observed downwelling infrared radiance and calculations using radiosondes to define the atmospheric state also highlighted issues with the radiosonde moisture profile (Clough et al. 1996). Miloshevich et al. (2001) presented a detailed discussion of calibration issues affecting the Vaisala RS80 humidity sensors.

This discovery is circa 90-2000. They found a correction for this, but it relies on knowing the calibration data for a particular set of instruments by a particular manufacturer, using a particular design.

It seems likely to me that if measurement difficulties of this sort were discovered in the 90s, they predate the 90s. It seems unlikely that decent, physically based corrections would be possible for problems going back very far in time. The meta data (like serial numbers) may not exist. Even if they do, it may not be possible to do anything to test bias of old designs that are no longer manufactured.

It would be one thing if the errors were only random– but you’ll see they were bias errors, and the biases had a diurnal pattern. Since they are batch-to-batch variations at the manufacturer, one might even see “patterns” in humidity as users deployed one batch and then changed to another.

So, the entire issue of past measurements of humidity seems murky to me.

But… I’ll ask Jim more when he gets back from California.

How good is the Humidity?
— —- — — ——–

The humidity analysis is believed to be the weakest of the primary atmospheric analyses; i.e., Z, T, U, V, and Q. The other primary variables (Z, T, U and V) and their gradients have to be internally consistent. One can not change T without changing U, V, and Z. As a result, the internal consistency provides a check and constraints on these fields. The humidity analysis, however, is unconstrained (except for q <= q-sat) and is effectively produced by a univariate analysis with no dynamical constraint on the gradients. Then there is the sampling problem. The humidity has many small-scale features and a single measurement may not be representative of a grid box average.

By the way, did I mention instrument errors?

Trenberth doesn’t think highly of this NCEP data, and particularly says the values over ocean are unreliable. The AR4 (3.4) mainly uses other data.

Here and here are other discussions of this data and humidity issues.

Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, A. Leetmaa, B. Reynolds, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. Mo, C. Ropelewski, J. Wang, R. Jenne, and D. Joseph, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471.

Lucia,
For example, if you want gridded specific humidity data, then go to this page. Click The monthly and other derived data currently available on-line. Scroll down, click on Pressure level. Find specific humidity. Then the data file will be shum.mon.mean.nc.

This is a NetCDF file. It will contain the SH at 17 pressure levels, on a 2.5×2.5 degree grid from 1948 to present.

Relative humidity is also of interest – in all these curves it seems pretty flat (around 71% at the 400 mb level) which indicates the specific humidity changes are more a proxy for temperature changes than for changes in relative humidity – I think…

Anyway, given the IPCC commentary and the fact these data series start before 1950, it’s got to be radiosonde-based. They may have run some corrections on it though, I don’t know any details.

I was actually going to ask my husband for info on the accuracy of various measurements. He’s been pretty familiar with these ground based microwave radiometers and comparisons to the radio sondes etc. (Which will be obvious if you click link 1.) But he just flitted off to California. (Plus, he’s tired of microwave radiometry. He likes Homeland security projects better.)

But what I haven’t figure out at all is: Are the NOAA data from radiosondes? Something else? A merge? I figure I need to dig through and figure that out.

300 mb (the lowest pressure possible- which translates into way, way up high.)

– numerically, that’s about 14 km, which is somewhere around the average height of the tropopause (tropopause is higher in the tropics). So high, but still at best lower stratosphere. Lower stratosphere temperatures have been cooling, so specific humidity should be going down there at least.

I’m not sure chapter 3 of IPCC WG1 is what you were referring to? That’s discussing observations, not theory. I’ll quote one segment (p. 238):

Surface specific humidity has generally increased after 1976 in close association with higher temperatures over both land and ocean. Total column water vapour has increased over the global oceans by 1.2 ± 0.3% per decade from 1988 to 2004, consistent in pattern and amount with changes in SST and a fairly constant relative humidity. Strong correlations with SST suggest that total column water vapour has increased by 4% since 1970. Similar upward trends in upper-tropospheric specific humidity, which considerably enhance the greenhouse effect, have also been detected from 1982 to 2004.

and from section 3.4.2 (p. 271, 272):

The network of radiosonde measurements provides the longest record of water vapour measurements in the atmosphere, dating back to the mid-1940s. However, early radiosonde sensors suffered from significant measurement biases, particularly for the upper troposphere, and changes in instrumentation with time often lead to artificial discontinuities in the data record (e.g., see Elliott
et al., 2002). Consequently, most of the analysis of radiosonde humidity has focused on trends for altitudes below 500 hPa and is restricted to those stations and periods for which stable instrumentation and reliable moisture soundings are available.

(ie. altitudes below about 10 km – apparently there are known problems with the data you pulled here).

Are there reliable measurements? Yes, but not as lengthy:

For the lower troposphere, water vapour information has been available from the TOVS since 1979 and from the Scanning Multichannel Microwave Radiometer (SMMR) from 1979 to 1984. However, the main improvement occurred with the introduction of the Special Sensor Microwave/Imager (SSM/I) in mid-1987 (Wentz and Schabel, 2000). Retrievals of column-integrated water vapour from SSM/I are generally regarded as providing the most reliable measurements of lower tropospheric water vapour over the oceans, although issues pertaining to the merging of records from successive satellites do arise (Trenberth et al., 2005a; Sohn and Smith, 2003).

Significant interannual variability of column-integrated water vapour has been observed using TOVS, SMMR and SSM/I data. In particular, column water vapour over the tropical oceans increased by 1 to 2 mm during the 1982–1983, 1986–1987 and 1997–1998 El Niño events (Soden and Schroeder, 2000; Allan et al., 2003; Trenberth et al., 2005a) and decreased by a smaller magnitude in response to global cooling following the eruption of Mt. Pinatubo in 1991 (Soden et al., 2002; Trenberth and Smith, 2005; see also Section 8.6.3.1). The linear trend based on monthly SSM/I data over the oceans was 1.2% per decade (0.40 ± 0.09 mm per decade) for 1988 to 2004 (Figure 3.20).