How Not to Analyze Data, part Deux

Recently a reader commented that Anthony Watts had been deleting comments from his blog, after the fact of having not only approved them, but responded to them. So I went to see for myself, and found out that the entire post in question was gone. Then a comment arrived here saying that not only was that post gone, so was part 2 of that series. I went to find out, but I hadn’t saved a link to part 2 of that series, so I waded through all the posts on Watts’ blog subsequent to part 1. Indeed I failed to find part 2 of the series. But what I did find, astounded me.

Not long after, the absent posts reappeared. I don’t know whether there was a technical glitch, or Watts was removing then restoring entire posts, but that’s neither here nor there. This post is about what I found.

Get Real!

The last post witnessed the utterly ridiculous (by which I mean, worthy of ridicule) attempt to justify the garbage analysis by Jim Goodridge. Much of the vain attempt focused on trying to say that the accumulated departure from average of sunspot counts, as a proxy for accumulated departure from average of solar irradiance, was somehow a meaningful indicator of heat content of the climate system — as though heat loss from the climate system were somehow constant. Of course, under this model if solar irradiance remains above average, even by a little, heat content will increase without bound, as will temperature. If you actually believe that under those conditions, temperature will continue to rise at a roughly constant rate forever, then you’re in need of remedial critical-thinking classes. Sit in the front so you can pay better attention, and try to help Anthony Watts — he needs it. And by the way, Goodridge doesn’t suggest that model; he doesn’t suggest any model, instead he suggests that his analysis is a valid way to characterize trends in solar output. That’s even more ridiculous.

How Not to Analyze Data, part 1

Anthony Watts has had a number of posts suggesting that solar activity is the primary driver of climate change. It appears that most of the real work has been done by others, including Jim Goodridge, who contributed this one. It provides an almost unbelievable example of how not to analyze data.

Recent Climate Observations Compared to (IPCC) Projections

There’s been some hoopla in the blogosphere lately about comparing projections of temperature from the IPCC TAR (third assessment report), published in 2001, to observed temperature. The comparisons have been made to temperature data since 2001, on the basis of the claim that that’s when the projections start so that’s when the comparison should start. It appears such claims are in error.

Autocorrelation

Global temperature shows a lot of variations; it goes up and down in a manner which is partly predictable and partly unpredictable. One of the important issues of global warming is how temperature will change over the long run. For this issue, we’re less interested in the short-term, year-to-year or month-to-month, day-to-day or moment-to-moment fluctuations which go up and down but average out to zero, and more interested in the persistent changes that behave more steadily and consistently over many years, decades, or longer. We’re interested in the trend.

Open Thread

It seems some people want to discuss things in threads to which they don’t belong. So, here’s another open thread about climate science. If you want to discuss political candidates, talk about their climate-science policies but not other things; that’s just too big a can of worms. And if you want to discuss other climate-related topics, feel free.

A More Perfect Union

Listen to this speech by Barack Obama. The whole thing.

I used to think we’d have to go back to JFK to find Obama’s equal in politics. Now I know better. We’d have to go back a lot further than that.

PCA part 5: Non-Centered PCA, and Multiple Regressions

Time for more equations. Yay!!! (or Boo!, depending on your perspective)

Water World

That’s the title of a film about a world in which the one thing humanity can’t seem to find is: dry land. I saw it on TV recently, and it lived up to its reputation as one of the worst movies ever. Thank goodness that’s not the topic of this post!

This post is about sea level rise. Climate change has a profound impact on sea level, primarily in two ways. First, water expands when it heats up, so a warmer climate and warmer seas will make the oceans expand and sea level rise. Second, during colder times there’s more water locked up as ice on land, both in glaciers and ice sheets.

PCA part 4: non-centered hockey sticks

In parts 1, 2, and 3 we’ve looked into the basics of PCA. The essence is that we can view multiple data sets as a vector data set, or as points occupying a (sometimes very) high-dimensional space. The original basis for this space is simply the variables with which we express the raw data; we could call this the canonical basis. In this sense, a “basis” is like a coordinate system for the space, and the canonical basis is the set of variables which define the raw data.