Let's see now. One thing they neglect to mention above is that the Holocene maximum was a 4,000 year long period, starting 9,000 years ago. So what they're saying is that for almost half of the past 10,000 years, the Arctic temperatures were about 5°C (9°F) warmer than they are today. I don't think those higher temperatures back then were caused by SUVs and private jets!For example, during the 'Holocene thermal maximum,' the warmest period of the past 10,000 years, the Arctic average temperature was two to three degrees warmer than it is today, while the global average was only a degree or so warmer.
"But based on lake sediments from Baffin Island, our data show that this area of the Arctic experienced temperatures five degrees warmer than today," said Briner.
There's a consistent pattern amongst these research reports that just doesn't add up:
- Climatologists doing actual research (as opposed to bulding computer models) consistently find historical data indicating that (a) the Earth's climate has broad long-term natural swings, not caused by human activity, (b) the Earth's climate has smaller short-term swings that correlate well with the sun spot count, and (c) patterns of climate change that don't jibe with the patterns found in the models.
- Climatologists “back-testing” computer climate models consistently find that the models don't match reality. In back-testing, the researchers take data from some past period (say, 2,000 years ago), feed it into the model, and check to see if the model successfully predicts what happened afterward. If the model did succeed in such predictions, you'd have some reason to believe that by feeding in today's data you could predict what will happen down the road. It's the gold standard used for testing all models that have the ambition to predict the future. Nobody has built a climate model yet that survives back-testing.
- Climatologists building computer climate models consistently throw out (or arbitrarily “adjust”) data that doesn't produce the results they believe are correct. The two glaring examples I know of are (a) tossing out satellite based atmospheric temperature measurements, and (b) adjusting historical temperature records before feeding them into their models.
I just don't get it...





















