http://miskolczi.webs.com/MiklosZagoni_ReplyToRob.pdf

Dear Dr. Dorland, Dear Professor Forster,

Gentlemen
With full respect, I must say that your attempt to understand Miskolczi’s results correctly was only a partial success.

So what? That’s just TCWV over the ocean. The “more robust analysis” (Mears et al (2010) turns out to be merely a chapter (page 29) in the report “State of the Climate in 2009″ headed

c. Hydrological cycle
1) Total column water vapor—C. Mears, J.
Wang, S. Ho, L. Zhang, and X. Zhou

http://www.indiaenvironmentportal.org.in/files/climate-assessment-2009-lo-rez.pdf

Figure 2.11 shows the over-the-ocean-only 0.27 +/- 0.08 mm/decade TCWV trend (NOTE THAT THE TREND IS A FRACTION OF A MILLIMETRE PER DECADE) that van Dorland and Forster describe as “more robust”. But when we look at GLOBAL TCWV it’s a different story entirely. See the following plot:-

Total Column Water Vapor (cm):
21-Year Deviations and Anomalies of Region Monthly Mean From Total Period Mean Over Global

Go to the bottom windows of this page:-

http://isccp.giss.nasa.gov/products/browseatmos.html

Select a variable:[Total Column Water Vapour]

Select a geographic region: [Global]

View Plot

First, the anomaly baseline is 2.41 Centimetres and corresponds with the Climate4you TCWV plot here:-

http://climate4you.com/ (click “Greenhouse Gasses”)

Second, the data is from the file B128B129glbp.dat but I can’t open it from the ftp site and plot it to obtain a trend but eyeballing the plot reveals that over 2 1/3 decades the procession is:-

1984 24.5 mm

1988 25.1 mm

1992 24.1 mm

1996 25.0 mm
1998 26.0 mm
2000 23.0 mm

2004 23.1 mm

2008 22.1 mm

The trend from the 4 yr values is -0.1098x or -1.098 mm/decade (GET THAT? DECREASING ONE WHOLE MILLIMETRE PER DECADE AND THAT INCLUDES OVER-OCEAN DATA).

Note that this is SATELLITE data, see:-

ISCCP OVERVIEW

http://isccp.giss.nasa.gov/overview.html

TCWV is significantly influenced by evapotranspiration from Northern Hemisphere land (land area greater in the NH (70%) than in the SH (30$)). By looking at over-ocean-only TCWV van Dorland and Forster neglect this most important item.

A Comparison of MERRA and NARR Reanalyses with the DOE ARM SGP data

Aaron D. Kennedy, Xiquan Dong, and Baike Xi

Shaocheng Xie and Yunyan Zhang

Junye Chen

2011 (PRELIMINARY ACCEPTED VERSION)

257 Near the level of non-divergence (~400-500hPa), all biases change in sign from negative to positive. The MERRA bias has a peak of 8% near 300 hPa and then decreases towards 0% at 100 hPa,

6% is approximately 1 g kg-1 so MERRA is more than 1 g kg-1 too moist at 300hPa RH in the study location.

262 The MERRA moist bias in the upper troposphere is also larger during the summer months and doubles during time periods of precipitation.

264 To better understand these humidity biases, histograms were calculated at 925 hPa and 200 hPa (Fig. 2) which represent the boundary layer and near the tropopause, respectively. [...] Fig. 2a clearly shows that MERRA is dry [below 300hPa] as its distribution is shifted approximately 5-10% to the left of the other datasets.

306 MERRA captures the general shape of RH at the ARM SGP site (Fig. 4c), but with a ~5% negative bias throughout the year in the upper troposphere except during the late spring and early summer when convection is most common at the ARM SGP site. compared to ARM and NARR. Seasonal RMSE plots (not shown) demonstrate that the largest disagreement between MERRA and ARM continuous forcing for mixing ratio occur during the spring (MAM) and summer seasons (JJA) in the boundary layer and upper troposphere. The maximum RH for MERRA occurs during June when boundary layer humidity is highest. As will be shown later, cloud fraction in MERRA also peaks in June, suggesting that this may be a byproduct of the convective parameterization used in the AGCM. This is also supported by the fact that the RH bias in the upper troposphere doubles during periods of precipitation in the summer months. Like ARM and NARR, additional peaks occur during January and March. It is concluded that the seasonal cycle of RH from three different datasets generally agree during this 319 3-yr period except for the upper troposphere during the summer months. During this time period, MERRA has a considerable positive bias (~10-15%)

This effects radiation.

488 MERRA has larger biases than NARR for LW-down under both clear-sky and all-sky conditions (-20 and -19 w m-2). Compared to ARM and NARR, these negative biases are consistent with the drier conditions in MERRA as demonstrated in Figs. 1, 2, and 4 and the seasonal variations of precipitable water vapor (not shown). Atmospheric water vapor is extremely important for LW-down fluxes under both clear-sky and all-sky conditions (Dong et al. 2006) and is supported by the fact these biases are largest during the warm season.

Figure 1. Biases of ARM continuous forcing (black), NARR (red), and MERRA (blue) relative to the ARM Cloud Modeling Best Estimate (CMBE) sounding profiles during the period 1999-2001 for (a) temperature, (b) zonal wind, (c) meridional wind, and (d) relative humidity. (e)-(h) are the same as (a)-(d) except for the RMSE.

MERRA bias @ 300hPa in the study location

Relative Humidity: 5% (positive and moist)

Figure 8. Monthly total precipitation measured over the ARM SGP domain by ARM (black), NARR (red) and MERRA (blue) during the period 1999-2001.

MERRA is the outlier, not enough precipitation in the study location.

http://www.esrl.noaa.gov/psd/cgi-bin/data/timeseries/timeseries1.pl

Here’s the raw data for these parameters:-

300mb Pressure Level Specific Humidity (gr/kg)
Latitude Range used: 20.0 to -20.0
Longitude Range used: 180.0 to 180.0
Weighted area grids = yes

http://www.esrl.noaa.gov/psd/cgi-bin/data/timeseries/timeseries.pl?ntype=1&var=Specific+Humidity+%28up+to+300mb+only%29&level=300&lat1=20&lat2=-20&lon1=180&lon2=180&iseas=0&mon1=0&mon2=0&iarea=1&typeout=1&Submit=Create+Timeseries

And the plot of that data:-

http://www.esrl.noaa.gov/psd/cgi-bin/data/timeseries/timeseries.pl?ntype=1&var=Specific+Humidity+%28up+to+300mb+only%29&level=300&lat1=20&lat2=-20&lon1=180&lon2=180&iseas=0&mon1=0&mon2=0&iarea=1&typeout=2&Submit=Create+Timeseries

This plot from 1979 – 2009 does not look like the D&D plot. If we take 1980 for example, the annual average of the 12 ESRL monthly datapoints is 0.6 g/kg.but the D&D plot shows 0.5. Similarly 1990 ESRL 0.62 D&D 0.51, 1995 ESRL 0.54 D&D 0.45, 1998 ESRL 0.488 D&D 0.45, 2000 ESRL 0.4 D&D 0.4, 2005 ESRL 0.48 D&D 0.4, 2009 ESRL 0.49 D&D 0.4.

Except for 2000 where ESRL corresponds exactly to D&D, the other D&D values seem about 0.1 g/kg too low last century and 0.085 too low this century. Note that ESRL 1998 is the same as ESRL 2009 at 0.49 but D&D has 1998 0.45 and 2009 0.4.

Using the above ESRL values for D&D’s linear trend analysis might work in favour of D&D’s case (except for this century) but the difference in data ESRL vs D&D calls into question what data D&D actually used. I’ve probably got my story checked and sorted enough to make a query to Garth Paltridge now.

What you may not have picked up on is that the ERA-40 reanalysis (triangle symbol series) also uses radiosonde data and that ERA-40 is the new ECMWF 40-year reanalysis. Therefore ERA-40 supersedes the “interim” ECMWF series that D&D have plotted (dot symbol series). This leaves only MERRA and JRA in conflict with NCEP trend-wise (and JRA is essentially flat 1979 – 1998). See:-

ERA-40 Project Report Series No. 2
The long-term performance of the radiosonde observing system to be used in ERA-40
August 2000
Kazutoshi Onogi

http://www.ecmwf.int/research/era/_docs/ERA40PRS_2.pdf

Figure 1 page 25 shows radiosonde distribution by manufacturer January 1988

I cant ascertain why there is such a difference between NCEP and ERA-40 absolute values i.e. where are the ERA-40 adjustments to NCEP documented? There’s a number of plots at the bottom of the report that give a clue but that’s as far as I’ve got.

The following article describes radiosondes, their components and the operation of them:-

RADIOSONDES — An Upper Air Probe

http://www.aos.wisc.edu/~hopkins/wx-inst/wxi-raob.htm

The following is the observing requirements for the GCOS Upper-Air Network (GUAN):-

http://gosic.org/gcos/GUAN-spec.htm

Note the Design Principles and Best Practices e.g.

# Changes of bias caused by changes in instrumentation should be evaluated by a sufficient overlapping period of observation (perhaps, as much as a year) or by making use of the results of instrument intercomparisons made at designated test sites.

I note that Paltridge, Arking and Pook says this about satellites:-

“As with radiosonde measurements, satellite observations of upper-level humidity have their own problems and must be treated with caution. Most work to date concerns analysis of the channel 12 data of the High-Resolution Infrared Radiation Sounder (HIRS) of the NOAA polar orbiting satellites. Channel 12 is located near the center of the 6.7-μm rotational–vibrational emission band of water vapor and is sensitive both to the water vapor emission and to the temperature of a broad layer from about 200 to 500 hPa. Soden et al. (2005) find no trend in the global mean channel 12 radiance. Since model simulations with constant relative humidity also show no trend and simulations with constant specific humidity show a positive trend, they conclude that the HIRS observations are consistent with constant relative humidity. A potential practical issue concerns the fact that the positive trend for the constant q simulation appears only in the last 10 years of the 24-year simulation. Perhaps more to the present point, Bates and Jackson (2001) find wide variations in the HIRS-derived RH trend for different latitude zones. The trend is negative in the Southern Hemisphere between 10° and 60° S and negative in the Northern Hemisphere between 10° and 40° N. It is significantly positive only for the tropical zone from 10° N to 10° S.”

And

“There are still many problems associated with satellite retrieval of the humidity information pertaining to a particular level of the atmosphere— particularly in the upper troposphere. Basically, this is because an individual radiometric measurement is a complicated function not only of temperature and humidity (and perhaps of cloud cover because “cloud clearing”
algorithms are not perfect), but is also a function of the vertical distribution of those variables over considerable depths of atmosphere. It is difficult to assign a trend in such measurements to an individual cause.”

It’s a case of two evils but one has been around longer than the other.

http://wattsupwiththat.com/2011/08/14/its-not-about-feedback/

The countervailing scenario I am looking for is specific to radiosondes and is a plausible explanation of what the systemic error actually is that causes a 60+ year (starting way before satellites) trend to be negative instead of positive for such a large number of separate soundings. Randel and Wu speculate on “jumps” causing a cool bias but how does such a cool bias actually happen over the course of 60+ years from so many separate soundings.

It’s the radiosonde operators that you will ultimately have to convince with your reasoning not me but you may as well work it out via me first because until you have it perfected the radiosonde data still stands and AGW is disproved (and it will have nothing to do with satellites – that’s just the basis of your speculation, there’s no root cause there).

The data (as I have detailed – see up-thread) is not as conflicting as you make out. There is only 2 that conflict prior to 1998, 1 of those is interim and all are in sync after the 1998 El Nino. The D&D plot of 300 hPa NCEP looks highly suspect so until I’ve checked with the database and Garth Paltridge I don’t put much stock in their speculation or analysis

BTW, I see you citing Mears et al 2005 in support of the hotspot. If it’s Mears and Wentz, they study LOWER troposphere temperature. The hotspot should be in the UPPER troposphere (300 hPa). See this article that takes RC to task by referencing the IPCC diagrams of the predicted hotspot:-

http://rankexploits.com/musings/2008/who-expects-a-tropical-tropospheric-hot-spot-from-any-and-all-sources-of-warming/

The Effect of Diurnal Correction on Satellite-Derived Lower Tropospheric Temperature

1. Carl A. Mears and
2. Frank J. Wentz

http://www.sciencemag.org/content/309/5740/1548.short

Re this:-

“As CO2 rises in the atmosphere it traps heat and causes the earths temperature to rise. This triggers positive feedback in the form of water vapor”

Lot’s of problems with this. I suggest you join me on this thread (nobody else is) where I’m taking this scenario to task:-

http://www.climateconversation.wordshine.co.nz/2011/08/just-one-fact/#comment-64413

I’m investigating heating effect by radiation including evaporation (solar SW vs GHG DLR) in respect to Trenberth, Fasullo and Kiehl’s Earth’s Energy Budget which seems to be the de-facto AGW paper that we must address in lieu of a falsifiable hypothesis. I’ll be pursuing the topic on an on-going basis even if no-one else is. I’ve got much more to add BTW

As for my countervailing scenario it is the same as that generally accepted among climate scientists:

As CO2 rises in the atmosphere it traps heat and causes the earths temperature to rise. This triggers positive feedback in the form of water vapor causing further temperature rises until a new equilibrium temperature is reached.

The CO2 emitted by humans if unchecked is likely to increase the global temperature to dangerous levels.

Given the uncertainties of the radiosonde readings and the conflicting measurements from satellites that we have discussed I don’t think their data is sufficient do disprove the theory I have presented above.

I’m someone who, being afflicted by a natural disposition to confirmation bias from time to time and acceptance of missives from those of more lofty stature than myself when I really should be activating my BS detector, must actively cultivate a discipline of scepticism of my own and other interpretations, and statements emanating from preeminent scientists i.e. respect – yes, unquestioning acceptance – no..

For this reason (unlike almost all of the unsceptical who seem to gravitate to the AGW side) I do not accept all statements at face value from preeminent scientists or identities whether they are in the luke-warm camp (e.g. Spencer, Watts) or radically anti-AGW (e.g. Claes Johnson) even though I would place myself between the two.

Given that Spencer and Miskolczi are at loggerheads (and resolution of the issues is a long way off) and huge egos are at play (I followed their tetchy debate – see, AGW sceptics don’t just accept each others opinion without question), I’m not surprised at Spencer’s statement (it’s to be expected from a luke-warm AGW disposition) but what you quote is his opinion, not mine by default (see above). I very much doubt that Spencer did a survey for his “few people” quote and just because he personally doesn’t know anyone who subscribes to Miskolczi’s theory or aspects of it, doesn’t mean there aren’t any people who do and besides, it’s the empirical aspect that is significant to me. His numerical theory needs a peer reviewed rebuttal (none yet) from someone whose grasp of mathematics is orders of magnitude better than mine and a response from Miskolczi for me to ponder and so on (i.e. as with AGW, it will take some time to play out – the normal scientific process in action).

I accept there are uncertainties with radiosonde AND satellite humidity data but I don’t accept that the uncertainties in radiosondes are enough that if rectified would turn a 60+ year trend from negative to positive. In this respect, the radiosondes disprove AGW and support the Saturated Greenhouse Effect until if and when fundamental systemic and/or technological errors (or whatever) are found, documented and rectified etc. I’m still waiting for your countervailing scenario. It doesn’t have to be technical, just plausible reasoning. If you can’t come up with something, your condemnation of radiosondes is just an arrangement of pixels on a blog page.

Do you also extend your condemnation to ARGO data trends Nick?

Once we have covered this I’m happy to move on to Dr Hansen’s prediction but you might find a little independent research saves us all some time.