# Autocorrelation in CO2 and Temperature Time Series

by A. May, Nov 14, 2021 in WUWT

In my last post I plotted the NASA CO2 and the HadCRUT5 records from 1850 to 2020 and compared them. This was in response to a plot posted on twitter by Robert Rohde implying they correlated well. The two records appear to correlate because the resulting R2 is 0.87. The least square’s function used made the global temperature anomaly a function of the logarithm to the base 2 of the CO2 concentration (or ‘log2CO2‘). This means the temperature change was assumed to be linear with the doubling of the CO2concentration, a common assumption. The least squares (or ‘LS’) methodology assumes there is no error in the measurements of the CO2 concentration and all error resulting from the correlation (the residuals) resides in the HadCRUT5 global average surface temperature estimates.

In the comments to the previous post, it became clear that some readers understood the computed R2(often called the coefficient of determination), from LS, was artificially inflated because both X (log2CO2) and Y (HadCRUT5) were autocorrelated and increased with time. But a few did not understand this vital point. As most investors, engineers, and geoscientists know, two time series that are both autocorrelated and increase with time will almost always have an inflated R2. This is one type of “spurious correlation.” In other words, the high R2 does not necessarily mean the variables are related to one another. Autocorrelation is a big deal in time series analysis and in climate science, but too frequently ignored. To judge any correlation between CO2 and HadCRUT5 we must look for autocorrelation effects. The most tool used is the Durbin-Watson statistic.

The Durbin-Watson statistic tests the null hypothesis that the residuals from a LS regression are not autocorrelated against the alternative that they are. The statistic is a number between 0 and 4, a value of 2 indicates non-autocorrelation and a value < 2 suggests positive autocorrelation and a value >2 suggests negative autocorrelation. Since the computation of R2 assumes that each observation is independent of the others, we hope that we get a value of 2, that way the R2 is valid. If the regression residuals are autocorrelated and not random—that is normally distributed about the mean—the R2 is invalid and too high. In the statistical program R, this is done—using a linear fit—with only one statement, as shown below: