http://wattsupwiththat.com/2011/01/02/do-solar-scientists-still-think-that-recent-warming-is-too-large-to-explain-by-solar-activity/

“That’s the big update that all of our solar scientists need to make. They need to stop tolerating this crazy charade that allows the CO2 alarmists to ignore the impact of decades of grand maximum solar activity and misattribute the resulting warming to fossil fuel burning. It is a scientific fraud of the most disastrous proportions, giving the eco-lunatics the excuse they need to unplug the modern world.”

Ouch.

“But no adjustments are made by a calculation based on the change in altitude.”
Because that’s not how you do it – and Niwa have not done it like that. The temperature difference caused by the lapse rate is an explanation for why the Kelburn site is cooler, but also serves to make the difference plausible. If the difference would have been, say, much smaller than what you expect from the lapse rate it would have raised a red flag and required more detailed research why that might be the case.
The latest report does actually mention this:
“The monthly mean temperature at Thorndon (Site 5) in December 1927 was 14.7 °C, while at Kelburn (Site 6) it was 13.7 °C, a difference which is close to that which would be expected for sites with an elevation difference of 122 m.”

“Yes, R & H is cited, but not Salinger’s thesis. Not that I’ve seen!”
Than look at page 4 and the references on page 12.
And even if they would not have cited his 1981 thesis, that does not mean that “[...][NIWA] ignored Salinger’s ‘methods’ in producing the new series, abandoning their support of him.”
First R&S (not R&H) is based on his what was developed in Salinger’s thesis, secondly: Science has moved on in the last 30 years and if there are better homogenisation methods available now, they will of course be used.

BTW. why did you put methods in quotes in your comment?

I totally disagree that “the science has not been done”. If you don’t want to create your own series, fair enough, but there are 169 pages with references that document how the temperature record has been created and what adjustments where made. There is more than enough that you can peer review. The descriptions of how uncertainties are measured can be reviewed separately.

D.

But it didn’t did it, especially the 1940s warm trend and it it hasn’t done so well the last decade either. Plus if you look at the AR4 table of forcings you will discover that Natural Category contains TSI only

“If you don’t provide evidence for you mistaken claim”

The 7SS is averages of the means of each years data (smoothing) and it also NOT the “original data” – that’s raw data.

“No smoothing is required – and certainly it is incorrect to statistically analyse smoothed data.”

See “On the trend, detrending, and variability of nonlinear and nonstationary time series”

Wu, Norden, Huang, Steven, Long, and Peng, 2007

Proceedings of National Academy of Sciences

http://www.climateconversation.wordshine.co.nz/2011/01/rotted-minds-at-hot-topic/#comment-35071

“Yes, especially when the those natural factors are so inadequately modeled (TSI only).”

How come the modeling worked extremely well until 50 years ago??

And this is just an outright lie:

“I should point out too, that the 7SS has already had smoothing applied by NIWA.” If you don’t provide evidence for you mistaken claim I will assume you acknowledge that.

No smoothing is required – and certainly it is incorrect to statistically analyse smoothed data.

Your deduction of a global model from regional data was completely unwarranted. A simple reading of NIWA’s report will show you missed huge natural but regional effects.

All you have done is the simple sum 0.9-0.5=0.4 – but have fooled yourself with an unnecessary and incorrect spreadsheet. And the result is meaningless anyway.

It is possible to obtain a good fit of actual temperatures to a whole range of models. However, only if human influences are included. When they aren’t there is a huge discrepancy over the last 50 years. Natural influences can’t explain this by themselves.

Go and look at the figures. I provided a link in my comment – of go to Climate change is complex

http://sidstation.loudet.org/emd-en.xhtml

EMD flow chart here

Analysis of Temperature Change under Global Warming Impact using Empirical Mode Decomposition

Md. Khademul Islam Molla, Akimasa Sumi and M. Sayedur Rahman, 2007

http://www.waset.org/journals/ijice/v3/v3-6-59.pdf

http://www.google.co.nz/#sclient=psy&hl=en&safe=off&q=Empirical+Mode+Decomposition+for+Determining+Intrinsic+Climate+Trend&aq=&aqi=&aql=&oq=&gs_rfai=&pbx=1&fp=28f6b3b14a1a140e

Turns up plenty of links to climate applications

i.e. Out

Straight line best fit

Moving mean

Linear regression

Nonlinear regression

Fourier filtering

In

Empirical mode decomposition
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See “Climate Science’s Dirtiest Secret”

“With the climate science party-line case for global warming rapidly unwinding there is growing interest by researchers from outside the climate change community in applying advanced statistical techniques to climate data. It has long been recognized that statistical acumen is lacking among mainstream climate scientists. This dirty little secret was first publicly disclosed during Congressional hearings regarding the 2006 Wegman Report. Even newer analyses have revealed that many of the predictions made by the IPCC reports and other global warming boosters are wrong, often because inappropriate statistical techniques were applied”

[Snip]

Empirical Mode Decomposition

[Snip]

In 2007, Zhaohua Wu et al. published a paper on the use of EMD to illustrate the determination of the intrinsic trend and natural variability in climate data (see PNAS “On the Trend, Detrending, and Variability of Nonlinear and Nonstationary Time Series”). In their paper they explain the complexities of analyzing climate related data:

[Snip]

The assertion that simplistic analysis (that is a very relative term here) will not work unless the underlying physical processes are completely understood is a warning to all those who attempt to relate trends in climate with trends in other physical phenomena. Wu et al. extracted linear and multidecadal trend data for a set of temperature data using EMD. The data used were the annual global surface temperature anomalies analyzed by Jones et al. and posted at the web site of the Climate Research Unit, University of East Anglia, UK

[Snip]

Breaking down the underlying signals in the temperature data yields an interesting statistical explanation for the anomalous temperature increases in the 1990s: “The extreme temperature records in the 1990s stand out mainly because the general global warming trend over the whole data length coincides with the warming phase of the 65-year cycle.” Here are the major conclusions from the paper:

* The linear trend gives a warming value of 0.5°C per century.
* The overall adaptive trend was close to no warming in the mid-19th century and is ≈0.8°C per century currently.
* The multidecadal trend rate of change is much larger than the overall adaptive trend but oscillates between warming and cooling.
* The higher frequency part of the record in recent years is not more variable than that in the 1800s.

From these analyses the predicted temperature rise by 2100, assuming the current trends continue, would only be 0.8°C, significantly less than even the low-end IPCC predictions. This is in fairly close agreement with the statistical break analysis previously reported. From the figure above is would appear that the long-term trend rate of increase has flattened out while the multidecadal trend, which peaked in 1998, is headed downward and is expected to drive the overall temperature trend into cooling. If the current trend is maintained, the peak of the cooling period will be around 2030, followed by a warming trend that will peak around 2060. As with all non-stationary data, the underlying system may change and the future trends vary in unpredictable ways.

One of the most interesting things they found was that the time scale of the multidecadal trend was not constant, but varied from 50 to 80 years with a mean value slightly higher than 65 years. “Significantly, other than the familiar overall global warming trend, the 65-year cycle really stands out,” state the authors. “The origin of this 65-year time scale is not completely clear because there is no known external force that varies with such a time scale.” Remember, all of this is strictly from analyzing historical data. It is in no way calculated from the underlying mechanisms that cause Earth’s climate to change, as GCM attempt to do.

http://www.theresilientearth.com/?q=content/climate-sciences-dirtiest-secret
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Really looking forward to applying EMD to the 7SS in view of all that.