I wouldn't deny that, there's already room for improvement. It's a relative measure though and here at least it's obviously superior.
Forgive me for failing to recognize the obvious. Please demonstrate that which you consider to be apparent.
And once added it takes the now 17 year average below statistical significance. You've been mislead because you don't understand how the data is being manipulated.
How does adding more data reduce the statistical significance of the findings resulting from standard and recognized data analysis processes?
Adding data which indicates that we had one of the top 20 warmest years of the last ~130 years, even if it had been the lowest of those 20 years, doesn't reduce the statistical significance of the findings. You might lower a short-term trend or even reverse the direction of a short-term trend with the addition of more data, but the addition of good data almost always increases the statistical significance of the analysis finding.
The problem that kept the findings from analysis of the period of 1995-2009 from achieving 95% significance isn't so much the values of the yearly data, it is that there simply wasn't enough data to yeild 95% or better significance to the findings. Adding good data (whether it is the data from the last two years or the 10 years prior to 1995) adds to the statistical significance of the findings (regardless of whether your analysis of the data indicates up, down or flat)
And add the 10 years before that and it's statistically insignificant.
This seems to be the result of a fundemental flaw in understanding regarding the language and terminology of Statistics.
The following is the NCDC record of the top 20 warmest years of the last ~130 years, in order, warmest first:
1) 2005
2) 2010
3) 1998
4) 2003
5) 2002
6) 2006
7) 2009
8) 2007
9) 2004
10) 2001
11) 2011
12) 2008
13) 1997
14) 1999
15) 1995
16) 2000
17) 1990
18) 1991
19) 1988
20) 1987
Again, you don't understand how the data is being manipulated.
You are correct, I don't understand the details of how you feel that the data has been improperly and disingenuously manipulated. I know of no compelling evidence suggesting such and yet I know of several compelling supportive analyses which seem to indicate that there has been no untoward or improper data handling.
The climate goes through natural periods of variation. Some years are warmer and some years are cooler. By selecting data on on side of the upswing we can "prove" the climate is warming. Select data on the downswing side and we can show it's cooling.
This is why we typically use 30 years and average over that because ideally this is long enough for both the upswing and the downswing in the natural climate oscillation.
The first four sentences above are fair representations of mainstream understandings. The last sentence is largely accurate but is phrased in a manner that might lead to mistaken impressions. How you are using or distorting these statements in an attempt to support non-mainstream assertions and assessments is actually a textbook example of "pseudoscience," but perhaps I am just misunderstanding your application of the English language.
The primary reason that 30 years is used as a minimal time frame for good analysis is the basic statistical precept that rigor in analysis requires at least a sample size of 30. The more the better, and you can produce significant and compelling analyses with smaller sample sizes, but for broad-based rigor and good analysis within the spectrum of well established statistical analysis techniques, it is a general rule to have sample sizes of at least 30 data points.
I can't make it any simpler than that.
Simple is not the issue, though I suspect that you are under-estimating your capabilities.
Alarmists are just up in arms with the fact that now that we truly have "modern instruments" ie; satellite data the empirical evidence doesn't match their model predictions. It's not a matter of "hiding the decline", it's desperate hand waving trying to hide the fact that there's been no significant change.
Your beliefs seem at odds with the demonstrable facts of reality.
[Sample size of 30 - is a statistical RoT, when you have a sample size of 30, the 95% confidence interval for the mean is 1/3 of the standard deviation. So if your analysis of 30 data points produced a mean of 10 and a st dev of 3, then you can be 95% confident that the true/absolute mean is between 9 and 11.]
A few references that some might find interesting
"Distinct Global Patterns of Strong Positive and Negative Shifts of Seasons over the Last 6 Decades" -
http://www.scirp.org/journal/PaperDownload.aspx?paperID=17129
"Impacts of climate change on the future of biodiversity" -
http://onlinelibrary.wiley.com/doi/10.1111/j.1461-0248.2011.01736.x/full
"No way out? The double-bind in seeking global prosperity alongside
mitigated climate change" -
http://www.earth-syst-dynam.net/3/1/2012/esd-3-1-2012.pdf
"Perceptions of Climate Change: The New Climate Dice" -
http://www.columbia.edu/~jeh1/mailings/2011/20111110_NewClimateDice.pdf
. Firstly, there's the irony, and secondly, it'll have attracted the attention of some deniers who'll assume the bias is in the other direction, another example of the mainstream media's pro-AGW position that they believe exists.
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