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

Carl A. Mears and Frank J. Wentz 2005

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

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.climateconversation.wordshine.co.nz/2011/07/species-decline-or-scaremongering/#comment-65088

Analysis of longer-term records show decadal-scale decreases in temperature and ozone near the tropical tropopause that are similar to, and in fact strongly influenced by, the changes observed since 2001. We note that the 25- or 20-year records in Figures 12 and 13 are relatively short and strongly influenced by end values, and it is unclear if the recent changes reflect low-frequency natural variability or an accentuation and continuation of monotonic trends.

So even though there may be the cooling bias he claims (although NOAA have not made any adjustments to their data as a result), he still concludes that the troposphere is cooling.

I would be interested to look at the work of Mears et al, which you say finds the hot spot. Please provide a link as I could not determine which paper you were referencing.

“Our 1979–2005 trends for 850–300 hPa in the tropics are 0.15° +/- 0.07°C decade. This is within uncertainty of the roughly 0.17°–0.22° expected on the basis of surface trends of 0.12°–0.14°C decade”

If you only look at the pictures in the paper then I admit the colour coding might be confusing but since it was clearly labelled accusations of fraud are a little over the top.

You also confuse random error (+/- 0.1 as you say) with systematic error (which could be much larger). A systematic error is possibly what is causing the divergence between the radiosonde measurements and the multiple indirect measurements I have mentioned.

As for causes of the systematic errors let me quote the abstract from Randel and Wu (2006)

“Temperature trends derived from historical radiosonde data often show substantial differences compared to satellite measurements. These differences are especially large for stratospheric levels, and for data in the Tropics, where results are based on relatively few stations. Detailed comparisons of one radiosonde dataset with collocated satellite measurements from the Microwave Sounding Unit reveal time series differences that occur as step functions or jumps at many stations. These jumps occur at different times for different stations, suggesting that the differences are primarily related to problems in the radiosonde data, rather than in the satellite record. As a result of these jumps, the radiosondes exhibit systematic cooling biases relative to the satellites. A large number of the radiosonde stations in the Tropics are influenced by these biases, suggesting that cooling in the tropical lower stratosphere is substantially overestimated in these radiosonde data. Comparison of trends from stations with larger and smaller biases suggests the cooling bias extends into the tropical upper troposphere. Significant biases are observed in both daytime and nighttime radiosonde measurements.”

I would also like to add that direct satellite measurements also find the hot spot, Mears et al. (2005)

So we have multiple lines of evidence from a variety of direct and indirect sources for the hot spot on the one hand and a single data set containing known errors and biases on the other.

Abraham Lincoln.

1) They’re inadvertently plotting the reciprocal of the data.

2) They should be employing Micheal Mann style data inversion techniques.

3) Sceptic controlled nanobots have infiltrated the electronics for the last 60 years and are mischievously and systematically manipulating electron polarities.

Your reasons Nick?