UPDATE: for those visiting here via links, see my recent letter regarding Dr. Richard Muller and BEST.

I have some quiet time this Sunday morning in my hotel room after a hectic week on the road, so it seems like a good time and place to bring up statistician William Briggs’ recent essay and to add some thoughts of my own. Briggs has taken a look at what he thinks will be going on with the Berekeley Earth Surface Temperature project (BEST). He points out the work of David Brillinger whom I met with for about an hour during my visit. Briggs isn’t far off.

Brillinger, another affable Canadian from Toronto, with an office covered in posters to remind him of his roots, has not even a hint of the arrogance and advance certainty that we’ve seen from people like Dr. Kevin Trenberth. He’s much more like Steve McIntyre in his demeanor and approach. In fact, the entire team seems dedicated to providing an open source, fully transparent, and replicable method no matter whether their new metric shows a trend of warming, cooling, or no trend at all, which is how it should be. I’ve seen some of the methodology, and I’m pleased to say that their design handles many of the issues skeptics have raised and has done so in ways that are unique to the problem.

Mind you, these scientists at LBNL (Lawrence Berkeley National Labs) are used to working with huge particle accelerator datasets to find minute signals in the midst of seas of noise. Another person on the team, Dr. Robert Jacobsen, is an expert in analysis of large data sets. His expertise in managing reams of noisy data is being applied to the problem of the very noisy and very sporadic station data. The approaches that I’ve seen during my visit give me far more confidence than the “homogenization solves all” claims from NOAA and NASA GISS, and that the BEST result will be closer to the ground truth that anything we’ve seen.

But as the famous saying goes, “there’s more than one way to skin a cat”. Different methods yield different results. In science, sometimes methods are tried, published, and then discarded when superior methods become known and accepted. I think, based on what I’ve seen, that BEST has a superior method. Of course that is just my opinion, with all of it’s baggage; it remains to be seen how the rest of the scientific community will react when they publish.

In the meantime, never mind the yipping from climate chihuahuas like Joe Romm over at Climate Progress who are trying to destroy the credibility of the project before it even produces a result (hmmm, where have we seen that before?) , it is simply the modus operandi of the fearful, who don’t want anything to compete with the “certainty” of climate change they have been pushing courtesy NOAA and GISS results.

One thing Romm won’t tell you, but I will, is that one of the team members is a serious AGW proponent, one who yields some very great influence because his work has been seen by millions. Yet many people don’t know of him, so I’ll introduce him by his work.

We’ve all seen this:

It’s one of the many works of global warming art that pervade Wikipedia. In the description page for this graph we have this:

The original version of this figure was prepared by Robert A. Rohde from publicly available data, and is incorporated into the Global Warming Art project.

And who is the lead scientist for BEST? One and the same. Now contrast Rohde with Dr. Muller who has gone on record as saying that he disagrees with some of the methods seen in previous science related to the issue. We have what some would call a “warmist” and a “skeptic” both leading a project. When has that ever happened in Climate Science?

Other than making a lot of graphical art that represents the data at hand, Rohde hasn’t been very outspoken, which is why few people have heard of him. I met with him and I can say that Mann, Hansen, Jones, or Trenberth he isn’t. What struck me most about Rohde, besides his quiet demeanor, was the fact that is was he who came up with a method to deal with one of the greatest problems in the surface temperature record that skeptics have been discussing. His method, which I’ve been given in confidence and agreed not to discuss, gave me me one of those “Gee whiz, why didn’t I think of that?” moments. So, the fact that he was willing to look at the problem fresh, and come up with a solution that speaks to skeptical concerns, gives me greater confidence that he isn’t just another Hansen and Jones re-run.

But here’s the thing: I have no certainty nor expectations in the results. Like them, I have no idea whether it will show more warming, about the same, no change, or cooling in the land surface temperature record they are analyzing. Neither do they, as they have not run the full data set, only small test runs on certain areas to evaluate the code. However, I can say that having examined the method, on the surface it seems to be a novel approach that handles many of the issues that have been raised.

As a reflection of my increased confidence, I have provided them with my surfacestations.org dataset to allow them to use it to run a comparisons against their data. The only caveat being that they won’t release my data publicly until our upcoming paper and the supplemental info (SI) has been published. Unlike NCDC and Menne et al, they respect my right to first publication of my own data and have agreed.

And, I’m prepared to accept whatever result they produce, even if it proves my premise wrong. I’m taking this bold step because the method has promise. So let’s not pay attention to the little yippers who want to tear it down before they even see the results. I haven’t seen the global result, nobody has, not even the home team, but the method isn’t the madness that we’ve seen from NOAA, NCDC, GISS, and CRU, and, there aren’t any monetary strings attached to the result that I can tell. If the project was terminated tomorrow, nobody loses jobs, no large government programs get shut down, and no dependent programs crash either. That lack of strings attached to funding, plus the broad mix of people involved especially those who have previous experience in handling large data sets gives me greater confidence in the result being closer to a bona fide ground truth than anything we’ve seen yet. Dr. Fred Singer also gives a tentative endorsement of the methods.

My gut feeling? The possibility that we may get the elusive “grand unified temperature” for the planet is higher than ever before. Let’s give it a chance.

I’ve already said way too much, but it was finally a moment of peace where I could put my thoughts about BEST to print. Climate related website owners, I give you carte blanche to repost this.

I’ll let William Briggs have a say now, excerpts from his article:

=============================================================

Word is going round that Richard Muller is leading a group of physicists, statisticians, and climatologists to re-estimate the yearly global average temperature, from which we can say such things like this year was warmer than last but not warmer than three years ago. Muller’s project is a good idea, and his named team are certainly up to it.

The statistician on Muller’s team is David Brillinger, an expert in time series, which is just the right genre to attack the global-temperature-average problem. Dr Brillinger certainly knows what I am about to show, but many of the climatologists who have used statistics before do not. It is for their benefit that I present this brief primer on how not to display the eventual estimate. I only want to make one major point here: that the common statistical methods produce estimates that are too certain.

…

We are much more certain of where the parameter lies: the peak is in about the same spot, but the variability is much smaller. Obviously, if we were to continue increasing the number of stations the uncertainty in the parameter would disappear. That is, we would have a picture which looked like a spike over the true value (here 0.3). We could then confidently announce to the world that we know the parameter which estimates global average temperature with near certainty.

Are we done? Not hardly.

Read the full article here

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