Regardless of what Fielding’s personal agenda may or may not be, the important point to take away from the data is that IPCC’s models and forecasts may not be accurate. That is not to say that there has not been any global warming over the last century, and not to say that there won’t be more over the next. The point is that, if the projections are not being borne out by the data over time, then the wild feedback effects that the IPCC postulates to support their cataclysmic global warming predictions may also not be accurate.

CO2 may cause some global warming, but assuming a case of zero feedback, the level of warming purely due to CO2 would be modest (somewhere close to 1 degree C). So the important question is “how strong will the feedbacks be, and will they be positive or negative”? I think that question is far from settled, and the current temperature trend supports the idea that there is more work to do on this issue.

Speaking from Australia I can confirm it is the former.

Any correlation we can infer from a line on a chart is only correct IF the model described by the chart is correct. No chart tells us whether we should have used the data as is, or graphed the log of one or the other series, or whether we should have used a linear trend line or a polynomial one. We have to use logic to construct the model, then use a regression to test the hypothesis whether our model is correctly described or not.

Most people who read charts are not aware of these subtleties. If you place the line somewhere on the chart, you’re telling these peopre that ‘these things are correlated, and here’s how’. Place the line somewhere else, and you’re telling a slightly different ( or perhaps completely different) story. Omit the line and you’re telling yet another story …one that says ‘I don’t think these 2 things have ANY correlation.

Use a linear trendline, and get one truth. Use a polynomial trendline and get another truth. Omit one variable and choosle another, and get another truth. When it comes to 2 dimensional charts, truth (or an awareness of a lack of truth) lies in the eye of the beholder, which is strongly influenced by the decisions of the chart creator.

If you look at the graph in your example about CO2 and temperature, there are so many questions unanswered:
- what are the statistics behind this graph? (error levels of the data points, regression, etc). Without the statistic you can say absolutely nothing about what is happening. You might see a “trend” of rising temperature, but you cannot know that for sure. Maybe the variance is so large that statistically there is no trend in both measures at all.
- even if you would have the statistics behind the data, you have to know if a 15 year period is relevant to what you want to say with the data. 15 years is absolutely way too short of a period to say anything about climate change. I mean, yesterday it was 26 degrees here, today 20. Does that mean the climate is getting colder?

More in general: on one side I think it’s good more and more data is being opened to the “general” public. And it’s good that that data is available and can be used in various ways. However, having a background in science, I know how incredible difficult it is to deal with that data and use it to say anything about relations between things. Causal relations is even more difficult. So there’s a big danger in data being misused, misinterpreted, etc. Your example is one of many.

Numbers may not lie, but they are always influenced by the questions we ask to obtain them. Furthermore, we can choose to interpret those numbers howsoever we please.

This is why context is so important. To Jerome’s point (above), the accuracy of the numbers is not the issue; if those numbers only focus on one perspective, we’re not telling the whole truth.

And in such a scenario, those numbers are reduced to propaganda.

Some more thoughts on that here: http://eskimon.wordpress.com/2.....pagandata/