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March 16th, 2010
Those who can’t bear to believe that the laws of physics govern global temperature, still want to maintain that it’s a random walk. They base this on the fact that the ADF (Augmented Dickey-Fuller test) doesn’t reject the presence of a unit root, if you refuse to use the BIC (Bayesian Information Criterion) for model selection and you’re willing to ignore the Phillips-Perron unit root test.
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March 5th, 2010
Anthony:
It has now been independenly confirmed, by multiple persons, that my results regarding the impact of station dropout on global temperature are correct. Your claims, in your document with Joe D’Aleo for the SPPI, are just plain wrong.
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March 5th, 2010
I’ve finished processing the southern hemisphere GHCN data, and computed the temperature according to the simple procedure for the entire globe.
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March 1st, 2010
By now it’s clear to many readers that others have replicated my results. There’s even one at the blackboard.
Perhaps some industrious reader would consider it worthwhile to scour the net and find out just how often the results have now been confirmed. Then, of course, visit WUWT and ask Anthony Watts whether he’s willing to admit that the false claims in his document with Joe D’Aleo are wrong.
In the meantime, I’ll continue preparing for publication.
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February 25th, 2010
There’s a most interesting comment on Anthony Watts’ blog:
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February 25th, 2010
Two of the most prominent claims of global warming denialists have been proven wrong.
This raises two very serious issues. First, it’s certainly possible to “run the numbers” in order to check the truth or falsehood of their claims, but they didn’t bother to do so. I have. For them to make the claims they’ve made, without even doing the work require to find out, is fundamentally dishonest.
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February 25th, 2010
Two of the most prominent claims of global warming denialists have proven to be utterly false.
I’ve completed processing the GHCN data for the northern hemisphere. This project was undertaken to investigate two denialist claims: 1st, that the dramatic reduction in the number of reporting stations around 1990 introduced a false warming trend; 2nd, that the adjustments applied to station data also introduce a false warming trend.
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February 22nd, 2010
Steven Goddard doesn’t know when to quit. Sad.
But far more interesting than showing him the error of his ways, is to take a look at northern hemisphere snow cover. Here’s the weekly snow cover since satellite observations began:
and here’s the weekly snow cover anomaly:
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February 18th, 2010
Anthony Watts has a new post by Steven Goddard about northern hemisphere snow cover. There’s plenty of snow in the northern hemisphere right now — an exceptional amount. This last week saw the 2nd-highest weekly average ever recorded in the data available from the Rutgers University Global Snow Lab. As we’re well aware, an exceptional event doesn’t mean there’s a trend. But Steven Goddard thinks there is. He even shows this graph:
and tells us that wintertime snow cover is increasing at 100,000 km^2 per year.
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February 16th, 2010
Suppose you have a child, a son — he’s 10. You want to know whether or not he’s growing normally, so every day you measure his height with a tape measure. You’ve done so since he was 5. You even plot the data on a graph, and notice two things about it. First: the measurements show a fair amount of jitter, sometimes they’re a wee bit higher, sometimes a wee bit lower, there’s noise in the data. Second: there’s also a trend. Your kid is a lot taller at 10 than he was at 5, in fact the trend over the observed time span is upward and reasonably steady. You even do a statistical analysis, estimate the growth rate, and determine that it’s definitely statistically significant — so it’s not a false trend due to noise in the data, it’s real. Your son is growing normally.
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February 15th, 2010
One of the section titles in D’Aleo and Watts’s denialist document is “NO WARMING TREND IN THE 351-YEAR CENTRAL ENGLAND TEMPERATURE RECORD.” It comes from a site calling itself “carbon sense.” Only when you read the section do you discover that it only compares 100-year averages of the 20th century to the 18th century, and only for the summer season. In fact their entire case is just smoke and mirrors.
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February 15th, 2010
We’ve already critiqued the analytical skills of Joe D’Aleo and of course the incomparable Anthony Watts….. Incomparable.
They’ve joined forces to create a document which pretends to ask the question, “SURFACE TEMPERATURE RECORDS: POLICY DRIVEN DECEPTION?” but is really just a bunch of false claims intended to state outright that the surface temperature record is not just mistaken, it’s fraudulent.
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February 13th, 2010
I’ve decided to average the GHCN station data in gridboxes which are 10 deg. latitude tall, and approximately the same width. That makes them 600 nautical miles tall, which is a bit over 1100 km. Within that range, we can expect that all stations which inhabit the same grid box will show correlation with each other. The exception to the “10 deg. tall” rule will be stations north of 70N latitude — instead of defining separate grid boxes for stations north of 80N latitude, I’ll lump them together with the stations north of 70N latitude.
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February 8th, 2010
On a non-climate note, I congratulate the New Orleans Saints for their victory in Super Bowl XLIV. It was a well-deserved victory for the underdogs, and is a source of pride for a city which still shows massive scars from the ravages of Hurricane Katrina.
We’ve already looked at a way to combine data from different locations to obtain a grid-wide average of station anomalies. In that post, I computed anomalies for each location then combined the anomalies. It’s also possible to combine raw temperature data, then compute anomalies for the grid averages. I also combined station records in a simple way, by starting with a “reference station” then adding one new location at a time, computing an offset to align it with existing data and then incorporating it into the average. But I’ve noted that this is not the “optimal” way, that optimally (in a least-squares sense) one would compute the offsets which give the minimum sum of squared differences between all stations.
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February 6th, 2010
Tomorrow is super bowl Sunday, the day of the championship game for American football. It’s the biggest sports day of the year in the U.S., and possibly the world (I really don’t know). For those outside the U.S., the contestants are the Indiannapolis Colts and the New Orleans Saints. I guess I’m rooting for Indiannapolis, but I won’t mind if the upstart Saints take the prize.
But the “grid” for this post is a geographical grid, the 5×5-degree grid box containing Skikda. We looked at the data from Skikda (in northeast Algeria), and we discussed how different data records from the same station can be combined into a single location record. Since that city is at Lat. 36.93N, Lon. 6.95E, let’s see what else is in the GHCN from 35 to 40 deg. N.Lat, from 5 to 10 deg. E.Lon.
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February 4th, 2010
First of all, thanks to Scott Mandia for the link to the story of the duck. That sums up what denialists are doing. Plain and simple.
The very first data record in the GHCN (using the raw data, not the adjusted) is for Skikda in northeast Algeria near the Mediterranean Sea.
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January 30th, 2010
I stole the title from the Rabett. He posted an invitation to readers to put up links your mom would warn you away from. Naughty bunny!
Just to keep the blogosphere “fair and balanced” I’d like to invite readers to post links your mom would recommend. If you can hear her, in your mind’s ear, saying “Why are you looking at that? You should be reading (*blank*)!” Fill in the blank.
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January 19th, 2010
NASA GISS has released the estimated monthly temperature for December 2009, which closes out the year 2009, which closes out the decade of the 2000s. The result: 2005 is still the hottest calendar year, 2009 is the 2nd-hottest year ever, although it’s really in a statistical tie with 1998, 2002, 2003, 2006, and 2007.
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January 13th, 2010
Chapter 8 of the IPCC AR4 report lists 23 computer models which contributed to the AR4 assessment. The output of those models is available from Climate Explorer, and for most of the models multiple ensemble member runs are available. Downloading the global average temperate data from the 20C3m (20th-century) and SRES-A1B experiments provides data for 118 runs of those 23 models. This enables us to compare the models’ global average surface temperature, as well as the average of all the model runs, to the observed global temperature according to NASA GISS (using anomalies on a 1980-2000 baseline):
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December 31st, 2009
It’s way too easy to look at data and think you see cycles. After all, if it went down, then up, then down, then up — it must be cyclic, right? The amount of analysis that goes into such conclusions is often limited to “Looks pretty cyclical to me.” Such “analysis” is tantamount to seeing a rock formation on Mars that looks vaguely like a face, and concluding that aliens constructed it millions of years ago as a message to future humanity.
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