Note: This is my analysis of a new paper by Joe D’Aleo, I’ve tried to simplify and explain certain terms where possible so that it can reach the broadest audience of readers. You can read the entire paper here.
Joe D’Aleo, an AMS Certified Consulting Meteorologist, one of the founders of The Weather Channel and who operates the website ICECAP took it upon himself to do an analysis of the newly released USHCN2 surface temperature data set and compare it against measured trends of CO2, Pacific Decadal Oscillation, and Solar Irradiance. to see which one matched better.
It’s a simple experiment; compare the trends by running an R2 correlation on the different data sets. The result is a coefficient of determination that tells you how well the trend curves match. When the correlation is 1.0, you have a perfect match between two curves. The lower the number, the lower the trend correlation.
Understanding R2 correlation
|R2 Coefficient||Match between data trends|
|0 or negative||no match at all|
If CO2 is the main driver of climate change this last century, it stands to reason that the trend of surface temperatures would follow the trend of CO2, and thus the R2 correlation between the two trends would be high. Since NCDC has recently released the new USHCN2 data set for surface temperatures, which promises improved detection and removal of false trends introduced by change points in the data, such as station moves, it seemed like an opportune time to test the correlation.
At the same time, R2 correlation tests were run on other possible drivers of climate; Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and Total Solar Irradiance (TSI).
First lets look at the surface temperature record. Here we see the familiar plot of temperature over the last century as it has been plotted by NASA GISS:
The temperature trend is unmistakeably upwards, and the change over the last century is about +0.8°C.
Now lets look at the familiar carbon dioxide graph, known as the Keeling Curve, which plots atmospheric CO2 concentration measure at the Mauna Loa Observatory:
A comparison of the 11year running mean of the USHCN version 2 annual mean temperatures with the running mean of CO2 from CDIAC. An r-squared of 0.44 was found.
We know both the Pacific and Atlantic undergo multidecadal cycles the order of 50 to 70 years. In the Pacific this cycle is called the Pacific Decadal Oscillation. A warm Pacific (positive PDO Index) as we found from 1922 to 1947 and again 1977 to 1997 has been found to be accompanied by more El Ninos, while a cool Pacific more La Ninas (in both cases a frequency difference of close to a factor of 2). Since El Ninos have been shown to lead to global warming and La Ninas global cooling, this should have an affect on annual mean temperature trends in North America.
This PDO and TSI to surface temperature connection has also been pointed out in previous post I made here, for former California State Climatologist, Jim Goodridge. PDO affects the USA more than the Atlantic cycle (AMO) because we have prevailing westerly wind flow.
Here is how Joe did the data correlation:
Since the warm modes of the PDO and AMO both favor warming and their cold modes cooling, I though the sum of the two may provide a useful index of ocean induced warming for the hemisphere (and US). I standardized the two data bases and summed them and correlated with the USHCN data, again using a 11 point smoothing as with the CO2 and TSI.
This was the jackpot correlation with the highest value of r-squared (0.83!!!).
An R2 correlation of 0.83 would be considered “good”. This indicates that PDO and our surface temperature is more closely tied together than Co2 to surface temperature by almost a factor of 2.
But he didn’t stop there. He also looked at the last decade where it has been commonly opined that the Top 11 Warmest Years On Record Have All Been In Last 13 Years to see how well the correlation was in the last decade:
Since temperatures have stabilized in the last decade, we looked at the correlation of the CO2 with HCSN data. Greenhouse theory and models predict an accelerated warming with the increasing carbon dioxide.
Instead, a negative correlation between USHCN and CO2 was found in the last decade with an R or Pearson Coefficient of -0.14, yielding an r-squared of 0.02.
According to CO2 theory, we should see long term rise of mean temperatures, and while there may be yearly patterns of weather that diminish the effect of the short term, one would expect to see some sort of correlation over a decade. But it appears that with an R2 correlation of only 0.02, there isn’t any match over the past ten years.
As another test, this analysis was also done on Britain’s Hadley Climate Research Unit (CRU) data and MSU’s (John Christy) satellite temperature data:
To ensure that was not just an artifact of the United States data, we did a similar correlation of the CO2 with the CRU global and MSU lower tropospheric monthlies over the same period. We found a similar non existent correlation of just 0.02 for CRU and 0.01 for the MSU over troposphere.
So with R2 correlations of .01 and .02 what this shows is that the rising CO2 trend does not match the satellite data either.
Here are the different test correlations in a summary table:
And his conclusion:
Clearly the US annual temperatures over the last century have correlated far better with cycles in the sun and oceans than carbon dioxide. The correlation with carbon dioxide seems to have vanished or even reversed in the last decade.
Given the recent cooling of the Pacific and Atlantic and rapid decline in solar activity, we might anticipate given these correlations, temperatures to accelerate downwards shortly.
While this isn’t a “smoking gun” it is as close as anything I’ve seen. Time will give us the qualified answer as we have expectations of a lower Solar Cycle 24 and changes in the Pacific now happening.
US Temperatures and Climate Factors since 1895 , Joeseph D’Aleo, 2008
Persistence in California Weather Patterns, Jim Goodridge, 2007
The USHCN Version 2 Serial Monthly Dataset, National Climatic Data Center, 2007