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Mikhail Voloshin writes this detailed analysis of NOAA and GISTEMP climate data processing on his Facebook page:
Random Walk analysis of NOAA global temperature anomaly data
Random Walk analysis of NOAA global temperature anomaly data
Summary
The global temperature record doesn’t demonstrate an upward trend. It doesn’t demonstrate a lack of upward trend either. Temperature readings today are about 0.75°C higher than they were when measurement began in 1880, but you can’t always slap a trendline onto a graph and declare, “See? It’s rising!” Often what you think is a pattern is actually just Brownian motion. When the global temperature record is tested against a hypothesis of random drift, the data fails to rule out the hypothesis. This doesn’t mean that there isn’t an upward trend, but it does mean that the global temperature record can be explained by simply assuming a random walk. The standard graph of temperatures over time, despite showing higher averages in recent decades than in earlier ones, doesn’t constitute a “smoking gun” for global warming, neither natural nor anthropogenic; merely drawing a straight line from beginning to end and declaring it a trend is a grossly naive and unscientific oversimplification, and shouldn’t be used as an argument in serious discussions of environmental policy.
Professor Patrick Frank's analysis of the CIMP Models, error ranges and multivariate relatedness.
The negligence that is the global average air temperature record:http://meteo.lcd.lu/globalwarming/Frank/uncertainty_in%20global_average_temperature_2010.pdf (869.8 KB)
The negligence that is the entire consensus position:http://eae.sagepub.com/content/26/3/391.abstract
The incompetence that is climate modeling: https://m.youtube.com/watch?v=THg6vGGRpvA
Tony Heller's Temperature Integrity Commentary
Click on the graph to compare NASA published Data from 2001 and 2016
Professor Carl-Otto Weiss
More on the 63 year cycle Weiss describes above from Wyatt and Curry
https://wattsupwiththat.com/2016/12/10/ocean-cycles-the-pause-and-global-warming/
Published Peer reviewed paper link is here
Paleoclimate Cycles are Key Analogs for Present Day (Holocene) Warm Period
Paleoclimate Cycles are Key Analogs for Present Day (Holocene) Warm Period
Guest Post By Renee Hannon
Abstract
Detailed pattern correlation of Earth’s temperature changes during the past 450 kyrs reveals observations about several cyclic climate patterns. The past four glacial cycles are increasing in duration from 89 kyrs to 119 kyrs.
https://wattsupwiththat.com/2017/06/10/indirect-effects-of-the-sun-of-earths-climate/
Professor William Grey on the Oceans effect
More Bill Grey on Modelling faults ( He invented the Hurricane reporting scale )
Is the supposed CO2 level rising from 300 ppm to 400 ppm as presented even correct ?
http://www.biomind.de/nogreenhouse/daten/EE%2018-2_Beck.pdf
https://rogerfromnewzealand.files.wordpress.com/2010/01/180_years_accurate_co2_chemical_methods1.pdf
Prior to the Mauna Loa Keeling Data, there were other data sets and the data doesn't look like the flat 280 ppm the IPCC would have us believe existed either.
Note the possible up to 470 ppm in the late 1820's probably from Vulcanism. The 1940's peak is due to CO2 following temperature as per the dissertation.
The published Beck Paper is from is links above.
The $100,000 contest to prove statistically AGW exists...
https://wattsupwiththat.com/2016/12/08/global-warming-fails-the-random-natural-variation-contest/
Bevan Dockery on CO2 and temperature relatedness
https://wattsupwiththat.com/2016/12/16/climate-change-debate-latest-results/
Can the IPCC really detect Human affectation ?
https://wattsupwiththat.com/2016/12/20/detection-and-attribution-of-man-made-climate-change/
How to measure "Climate"
https://wattsupwiththat.com/2017/06/19/why-climate-science-snubs-climatic-temperature/
Can the Normal Linear Regression Model Detect Global Warming?
http://scholar.lib.vt.edu/theses/available/etd-11292004-134819/unrestricted/bodymatter_akobundu.pdf
This document analyses two common data sets as used by used by Hansen and Jones and looks at Linear Regression tests and Null Hypothesis Confirmation.
If finds that the series are not Linear and not correctly specified and that the causation is not establsihed, requiring more research as the the real drivers of the variations. ie NOT CO2.
Page 33
Section 4: Conclusion
To answer the question posed at the outset, the normal linear regression model can assess the evidence for a warming trend when the model is correctly specified. The dual goal of this paper was to illustrate why the static model may be invalid for some global temperature series and to propose an alternative formulation that is statistically adequate for the data.
Concerning the first goal, the results indicate that the static model is misspecified for two widely-used temperature series and is not reliable for drawing conclusions about trends in surface temperatures using these series.
The model provides estimates of a statistically significant warming trend in temperature series that have been widely 34 reported in international policy arenas. The results indicate that these estimates may overestimate or mischaracterize the trend in temperature anomalies.
Concerning the second goal, the results indicate that the temperature series exhibit nonlinear trends rather than linear trends.
A nonlinear trend with an overall positive slope provides a different picture of global warming compared to a linear trend with a positive slope.
Given the recent sharp upward trending temperatures predicted by the dynamic model, some may claim that the better specified model provides more impressive and urgent evidence of global warming than the linear model.
The model results neither support nor refute such a conclusion because, while the model provides statistical evidence of nonlinear trends in the data, it does not explain the cause of the trends nor indicate whether the trends are temporary or permanent.
In a sense, the trends in the statistical models are a measure of the researchers’ ignorance about certain characteristics of the data generating process.
It is up to climate scientists to explore alternative possible theories, as well as tests of these theories, that can explain the confirmed nonlinear patterns in the data.
This is the basis of John Christy's comments in the reported article that our ignorance is simply enormous and biased to one direction and we simply don't understand the drivers.
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