Suppose you’re an astronomer interested in variable stars — stars which change brightness. You decide to collect some data on the brightness of a newly discovered variable. It never gets brighter than magnitude 9, which is too faint to be detected with the naked eye, but you’re at a major observatory so that’s no problem. You have access to, and training in the use of, high-precision CCD photometry so your data will be outstanding. Of course, you can only see it at night and there’s stiff competition for observing time on the observatory telescope, but you manage to schedule regular observations at precisely midnight every 16 days for slightly over a year.
Eli Rabett has a post about some extremely offensive comments on Judith Curry’s blog which raises the ugly spectre of physical violence. He also posted about some rather nasty comments which may not literally threaten violence, but certainly indicate the frame of mind which spawns violent behavior.
Tim Curtin’s paper in TSWJ isn’t the first time he’s mis-applied the Durbin-Watson test in order to justify rejecting a regression of physical variables (he substituted regression of the differenced values, after which he found the regression not statistically significant). He also does so in this precursor, in which he explicitly states (regarding his first regression)
… the Durbin-Watson statistic at 1.313, which is well below the benchmark 2.0 …
I don’t see any other interpretation than that he was using two (in fact, two point zero) as his critical test value, which we have already mentioned is completely wrong.
Curtin claimed that the absence of autocorrelation is required for valid regression, which is also wrong. Nonetheless he uses that claim to justify requiring regression be performed on differenced variables. Curtin isn’t the first (and won’t be the last) to claim that regression of climate variables like global temperature should be done using differenced variables. For example, a recent commenter on RealClimate by the handle “t.marvell” did the same, justifying it by insisting that global temperature was not a stationary time series. Of course it’s not stationary — it shows a trend!
Neither of those individuals seems to understand the impact that first-differencing has on regression analysis, especially when the causal relationship we’re interested in has to do with the trends which are present. Let’s give that some consideration.
Over thirty years ago, James Hansen was lead author of a scientific paper titled Climate Impact of Increasing Atmospheric Carbon Dioxide. They estimated that doubling the amount of CO2 in the air would raise global temperature about 2.8 degrees (C, equal to about 5 degrees F). They projected that from 1980 to 2010, earth would warm a little more than 0.4 degrees C. High northern latitudes, however, would warm at a much faster rate. We would likely see the start of melting of the great ice sheets in Antarctica and Greenland. They further suggested that we could start to lose much of the sea ice in the Arctic, which might even open the Northwest and Northeast passages.
That was over thirty years ago. What has happened since then?
Our old friend Tim Curtin has published a paper in what is supposed to be a peer-reviewed scientific journal. I’m skeptical.
He regresses temperature time series against a variety of predictor variables, concluding that there is no real influence of “non-condensing greenhouse gases” (i.e., GHG except water vapor) like CO2. He achieves this by rejecting regression of temperature in favor of regression of the first-differenced temperature data. You get that by taking the difference between each data value and its predecessor.
[Note: see the UPDATE at the end of the post]
Brandon Shollenberger has written a blog post about my last post. I consider it an example of the Dunning-Kruger effect, that unskilled individuals overrate their ability.
The Committee for the Advancment of Scientific Skepticism (CASS) has issued a report on a course supposed to be about climate change, taught by Tom Harris at Carleton University in Canada. Harris is associated with the International Climate Science Coalition, and is a confirmed speaker for the upcoming climate conference to be hosted by the so-called “Heartland Institute“.
CASS reviewed video of Harris’s lectures, and found a bounty of errors as well as a consistently false portrayal of climate science. Let’s take a look at an example in which Harris indulges in one of the many ways that fake skeptics make fake arguments about global warming.