Phil Plait, over at Bad Astronomy has a short note about a friend whose life was saved by science. (Ok, the technologies that scientific discovery enabled.)
My own story goes back quite a few years, to my childhood. I had a severe case of pneumonia. Two major contributors to the fact that I'm still around to blog at you were penicillin and the oxygen tent. Penicillin's story starts in 1928 with Alexander Fleming. He wasn't looking for it at the time. And it took another 12-15 years before years before other people, including Howard Florey and Earnest Chain (recipients, with Fleming, of the 1945 Nobel prize in Medicine for this work), were able to make enough penicillin for it to be used clinically in any significant amount.
Oxygen, on the other hand, owes its availability not to accidental discovery (plus over a decade of hard work), but to thermodynamics. James Dewar took an interest in liquifying gases, and in general, trying to reach absolute zero. Along the way, he invented the Dewar flask (which the Thermos company started selling, hence the name you probably know). I'm fairly confident he wasn't thinking about medicine. He was doing some interesting science, and learning more about how the universe worked. (Liquid oxygen is magnetic -- he was the first person to know this.) He also invented a way to produce industrial quantities of liquid oxygen. Once you've got that around, you have a chance to discover that, gee, it's a useful treatment to give high concentrations of oxygen to people whose lungs are seriously impaired -- like, say, me with my major pneumonia.
Two different technologies, one discovered by accident, and one not thought of for health. So it goes in science quite often. The ultimate uses can be unpredictable. But it's an awfully good bet that there will be a use down the road.
23 March 2009
19 March 2009
Does CO2 correlate with temperature?
Lately I've been seeing the assertion that carbon dioxide (CO2) levels have no correlation with temperature changes, particularly not over the last 100-150 years. I discussed a form of this, where the site was asking you to estimate the correlation by eye and doing a misleading job of it. It's a bizarre claim, however, given that even at the eyeball level the figures in that previous note of mine show a pretty good correlation.
[Update April 9, 2009: Some recent commentators don't believe, and didn't bother to check, that there are sources which claim no correlation. See 20 who deny co2 is correlated with temperature. It's actually 21.]
But let's be quantitative. Eyeball estimation only gets you so far, and eyeballs can be deceived fairly easily (such as by the co2sceptics presenting different periods of data, and drawing in a misleading curve for one set). Also, showing the variation through time, separately, of temperature and carbon dioxide, is not the best way to look at the correlation. Better is to plot the temperature against the CO2 level for that year. I use the temperatures and CO2 values from my prior note. For the period of the Siple ice core (before 1959) I filled in the missing values by straight line fill. Fortunately, in this period the CO2 concentration is changing only slowly.
Before going further, though, a couple of reminders. One is, doing this analysis is not how professional climate change predictions are done. The prediction of warming from human-released CO2 was made 60 years before the first measurements of atmospheric CO2 levels. The estimates today of warming from CO2 are done, as was done originally, by examining how the laws of conservation of energy play out in the atmosphere. Second is, this analysis is one that is demanded by the people making the claim that there is no such correlation. They are badly wrong, as we'll see. They're either lying, or failing to do their homework. Either way, not sources to keep using.
So, for eyeball inspection, annual mean global temperature (deviation from baseline) plotted against CO2 annual average:
It's awfully hard to look at this and say that there's no correlation between CO2 and temperature. Since eyeballs can be deceived, we'll be quantitative. While I think the term 'correlation' is more or less known in common language, there are a few technical points I want to be sure we all have in hand. Correlation is a measure of how much one variable (temperature, in our case) depends on another (CO2 for us). It can range from +1 (perfect correlation in a positive sense -- that is, every time you increase CO2 by 10 ppm, you would increase temperature by the same, say, 0.1 degree) to -1 (perfect correlation in a negative sense, every time CO2 increased by 10 ppm, temperature would drop by 0.1 degree). At 0 correlation, there's no connection. But ... all of this is for straight line relationships. If the real relationship is not straight line, you have more work to do. We are going to assume that the relationship is straight line.
One last bit before trotting out numbers. Correlation itself is not really our goal even if we compute that number along the way. We generally want to predict some variable given knowledge of some other variable; in our case, predict temperature given CO2 levels. Now temperature varies around year to year. If we have a good prediction method, we can explain most of that variance. If we multiply the correlation by itself (square it), that number tells us how much of the variance we can explain. If a variable explains more than 50% of the variance, we can say that it is the predominant factor. If it's over 10% of the variance, it's notable even if not predominant. Such conclusions also have to be checked for whether they're statistically significant. If I threw dice only twice, their value would 'explain' 100% of the variance in temperature for 2 years (hence the comments about 'you can always draw a straight line through 2 points'). If something isn't even statistically significant, then it is definitely not notable or predominant. It's also entirely possible for a relation to be statistically significant, but not notable -- physically.
I'm doing a little algebra and will represent the predictions as:
T = slope * (CO2 - reference)
T is the temperature anomaly in global mean surface air temperature, slope we'll compute, CO2 is the annual average CO2, and reference is a reference level of CO2. (Temperature anomalies are in centigrade, and CO2 is in ppmv)
If we simply look over the whole period of temperature data, we see 78% of the temperature variance is explained as a linear response to CO2 changes. Perhaps you don't trust ice core CO2, or older temperature values. The more recent period shows CO2 explaining 82% of all variation. For either the record as a whole, or for the more recent period, temperature shows a very strong correlation to CO2.
Research (see the citations in the IPCC working group 1, 4th report) is showing that it is in the last half century that human-derived CO2 (and others) have been the predominant drivers of climate change. Both the full 158 year record and the recent shorter 50 year record support that -- explaining 78-82% of variance definitely qualifies (at least if it passes statistical significance, which we'll get to in a minute). On the other hand, the same report says that before about 1950, CO2 is not the major driver. Here we see 28% of the temperature variance in that period being from CO2. Consistent with CO2 being a notable component of the system, but not the predominant one in that period.
Now for the tests of statistical significance, which I'm afraid is more gory in detail than I write up here. But, the result is that all three correlations are significant at better than the 0.0005 level. In more normal language, we'd take 1 and divide by that number. The result is the number of times we'd have to collect data that were just random numbers before we'd find even 1 example with this high a correlation. In this case, at least 2000. Less than a 1 in 2000 chance of just being noise. (My statistical table only goes out this far. If I had better tables, they'd show much, much, higher odds against these correlations being chance.)
The IPCC estimate for climate sensitivity to doubling CO2 levels (which they took as 550 ppm, see, for example, the technical summary at the above link) is that it is 'likely' (see their definition of the word, it probably isn't what you think) 2 to 4.5 C, with a best estimate of 3 C. Using the highly simple-minded regression above, we get estimates of 2.4 and 2.7 C. Not only does CO2 indeed correlate with temperature, proving false the unreliable sources that started us here, but the sensitivity suggested by that correlation is in the range of what the IPCC arrives at by much more meaningful models of the physics of the climate system.
One last piece. Cast your eyes back up to the graph, and maybe click on it to get the full size version. Towards the right hand end, you see a dot that's far above the straight line fit. You're not surprised that this is 1998 -- the year of the major El-Nino that was concurrent with a time of high solar activity. The bit farthest below the curve around 357 ppm CO2 is 1992-3 -- cooling effects of Mount Pinatubo. Even against a statistically strong trend, there are still other effects in the system that can give some tenths of a degree weather variations.
There are many reasons climatologists don't approach climate this way, and they're good ones. I only did it because I've been encountering sources that say that the correlation is zero (nonexistent). Those places are wrong.
[Update April 9, 2009: Some recent commentators don't believe, and didn't bother to check, that there are sources which claim no correlation. See 20 who deny co2 is correlated with temperature. It's actually 21.]
But let's be quantitative. Eyeball estimation only gets you so far, and eyeballs can be deceived fairly easily (such as by the co2sceptics presenting different periods of data, and drawing in a misleading curve for one set). Also, showing the variation through time, separately, of temperature and carbon dioxide, is not the best way to look at the correlation. Better is to plot the temperature against the CO2 level for that year. I use the temperatures and CO2 values from my prior note. For the period of the Siple ice core (before 1959) I filled in the missing values by straight line fill. Fortunately, in this period the CO2 concentration is changing only slowly.
Before going further, though, a couple of reminders. One is, doing this analysis is not how professional climate change predictions are done. The prediction of warming from human-released CO2 was made 60 years before the first measurements of atmospheric CO2 levels. The estimates today of warming from CO2 are done, as was done originally, by examining how the laws of conservation of energy play out in the atmosphere. Second is, this analysis is one that is demanded by the people making the claim that there is no such correlation. They are badly wrong, as we'll see. They're either lying, or failing to do their homework. Either way, not sources to keep using.
So, for eyeball inspection, annual mean global temperature (deviation from baseline) plotted against CO2 annual average:
It's awfully hard to look at this and say that there's no correlation between CO2 and temperature. Since eyeballs can be deceived, we'll be quantitative. While I think the term 'correlation' is more or less known in common language, there are a few technical points I want to be sure we all have in hand. Correlation is a measure of how much one variable (temperature, in our case) depends on another (CO2 for us). It can range from +1 (perfect correlation in a positive sense -- that is, every time you increase CO2 by 10 ppm, you would increase temperature by the same, say, 0.1 degree) to -1 (perfect correlation in a negative sense, every time CO2 increased by 10 ppm, temperature would drop by 0.1 degree). At 0 correlation, there's no connection. But ... all of this is for straight line relationships. If the real relationship is not straight line, you have more work to do. We are going to assume that the relationship is straight line.
One last bit before trotting out numbers. Correlation itself is not really our goal even if we compute that number along the way. We generally want to predict some variable given knowledge of some other variable; in our case, predict temperature given CO2 levels. Now temperature varies around year to year. If we have a good prediction method, we can explain most of that variance. If we multiply the correlation by itself (square it), that number tells us how much of the variance we can explain. If a variable explains more than 50% of the variance, we can say that it is the predominant factor. If it's over 10% of the variance, it's notable even if not predominant. Such conclusions also have to be checked for whether they're statistically significant. If I threw dice only twice, their value would 'explain' 100% of the variance in temperature for 2 years (hence the comments about 'you can always draw a straight line through 2 points'). If something isn't even statistically significant, then it is definitely not notable or predominant. It's also entirely possible for a relation to be statistically significant, but not notable -- physically.
I'm doing a little algebra and will represent the predictions as:
T = slope * (CO2 - reference)
T is the temperature anomaly in global mean surface air temperature, slope we'll compute, CO2 is the annual average CO2, and reference is a reference level of CO2. (Temperature anomalies are in centigrade, and CO2 is in ppmv)
| time span | % variance explained | slope (C per ppmv CO2) | slope (C per 100 ppmv CO2) | reference (ppmv) |
| 1850-2007 | 78 | 0.00868 | 0.868 | 333 |
| 1850-1958 | 28 | 0.00962 | 0.962 | 329 |
| 1959-2007 | 82 | 0.00962 | 0.962 | 335 |
If we simply look over the whole period of temperature data, we see 78% of the temperature variance is explained as a linear response to CO2 changes. Perhaps you don't trust ice core CO2, or older temperature values. The more recent period shows CO2 explaining 82% of all variation. For either the record as a whole, or for the more recent period, temperature shows a very strong correlation to CO2.
Research (see the citations in the IPCC working group 1, 4th report) is showing that it is in the last half century that human-derived CO2 (and others) have been the predominant drivers of climate change. Both the full 158 year record and the recent shorter 50 year record support that -- explaining 78-82% of variance definitely qualifies (at least if it passes statistical significance, which we'll get to in a minute). On the other hand, the same report says that before about 1950, CO2 is not the major driver. Here we see 28% of the temperature variance in that period being from CO2. Consistent with CO2 being a notable component of the system, but not the predominant one in that period.
Now for the tests of statistical significance, which I'm afraid is more gory in detail than I write up here. But, the result is that all three correlations are significant at better than the 0.0005 level. In more normal language, we'd take 1 and divide by that number. The result is the number of times we'd have to collect data that were just random numbers before we'd find even 1 example with this high a correlation. In this case, at least 2000. Less than a 1 in 2000 chance of just being noise. (My statistical table only goes out this far. If I had better tables, they'd show much, much, higher odds against these correlations being chance.)
The IPCC estimate for climate sensitivity to doubling CO2 levels (which they took as 550 ppm, see, for example, the technical summary at the above link) is that it is 'likely' (see their definition of the word, it probably isn't what you think) 2 to 4.5 C, with a best estimate of 3 C. Using the highly simple-minded regression above, we get estimates of 2.4 and 2.7 C. Not only does CO2 indeed correlate with temperature, proving false the unreliable sources that started us here, but the sensitivity suggested by that correlation is in the range of what the IPCC arrives at by much more meaningful models of the physics of the climate system.
One last piece. Cast your eyes back up to the graph, and maybe click on it to get the full size version. Towards the right hand end, you see a dot that's far above the straight line fit. You're not surprised that this is 1998 -- the year of the major El-Nino that was concurrent with a time of high solar activity. The bit farthest below the curve around 357 ppm CO2 is 1992-3 -- cooling effects of Mount Pinatubo. Even against a statistically strong trend, there are still other effects in the system that can give some tenths of a degree weather variations.
There are many reasons climatologists don't approach climate this way, and they're good ones. I only did it because I've been encountering sources that say that the correlation is zero (nonexistent). Those places are wrong.
Unreliability at co2sceptics/climaterealists
Last Tuesday I wrote up Misleading yourself with graphs, prompted by plots on the main page at co2sceptics. As I mentioned then, it could have been an honest mistake, so I sent a note to them on their site (message form).
They've neither responded nor changed their site. So I'm afraid they're simply an unreliable source. Maybe the mistake is honest, maybe not. But a reliable site would respond on being shown that they'd made a mistake and were misleading themselves and their readers.
This is less of a surprise to me now since I searched on their name and found one of the references to them is an earlier post of mine about a different unreliable source icecap.us. In that case, they were being extremely un-skeptical.
In Does CO2 Correlate with Temperature, to appear shortly, I'll be taking a more quantitative look at the correlation they want readers to believe does not exist.
They've neither responded nor changed their site. So I'm afraid they're simply an unreliable source. Maybe the mistake is honest, maybe not. But a reliable site would respond on being shown that they'd made a mistake and were misleading themselves and their readers.
This is less of a surprise to me now since I searched on their name and found one of the references to them is an earlier post of mine about a different unreliable source icecap.us. In that case, they were being extremely un-skeptical.
In Does CO2 Correlate with Temperature, to appear shortly, I'll be taking a more quantitative look at the correlation they want readers to believe does not exist.
18 March 2009
Read original sources
I've mention it here and there before, but was reminded recently of the importance of reading the original sources. The recent round was a document which said the IPCC had not discussed some points about sea surface temperature. I thought that was odd, so I looked up the IPCC section that talks about sea surface temperature (SST) and in truth, they do talk about those issues. If somebody is lying about what someone else says in such an easily identified way, time to pitch that original source and go looking for someone honest to learn from. So that's one reason to read the original sources -- to see who's lying and weed them out.
There are also more pleasant reasons to follow up to original sources. One is, often the source is being cited for something that's a minor part of what they were talking about -- and the major part is even more interesting. In oceanography, one of the things often mentioned in classes is the Ekman Layer. Don't worry about what it is exactly. Usually, a reference says that Ekman (1905) studied the formation of what we now call the Ekman layer for conditions of an infinitely deep ocean, infinitely steady winds, with absolutely constant friction through the depth of the ocean. Well, it turns out that, although he did examine that highly idealized case, this was a small part of the paper. In fact he also examined varying winds and varying friction. And the results of doing so were much more interesting than what he's normally cited for.
A second reason can be particularly important in science. In the original papers on an idea, the author is explaining something new to the audience. He can't assume nearly as much as later papers on the idea will. You'll also get to see the first matchups of the idea against observations. So I usually learn more by reading the original than by reading the textbook description. (For the geophysical fluid dynamicists out there -- Charney's paper on baroclinic instability is the one exception I've found. There also turn out to be reasons why it came out that way.)
Part three is that you'll get to see much more discussion of why the approximation can be made, and where it can be expected to fail. So, for example, a modern paper talking about the color of the sky -- perhaps for a planet orbiting some other star -- will merely mention 'We apply Rayleigh scattering law and arrive at ...'. It's only when you read Rayleigh's papers that you find out that the 'law' is an approximation, and it will fail under certain, predictable, conditions.
It can be difficult to get hold of the original sources when you start going after Reynolds 1895, Ekman 1905, etc. So the ideal can't always be met. Still, the whole IPCC working group 1 (physical science) 4th report is readily available online or you can order a print copy (also groups 2 and 3, but I'm a physical science guy, so look more to that one). So, when you encounter someone making claims about what the IPCC says, go take a look yourself. And if the point isn't clear, or it is interesting to you, start following up the citations they give. (Unfortunately, I think that after the first report, the writing declined in terms of readability by nonprofessionals. Any more, once you're past deciding if someone lied about the IPCC, I think you'll have an easier time reading by hitting the cited sources rather than the IPCC reports.)
There are also more pleasant reasons to follow up to original sources. One is, often the source is being cited for something that's a minor part of what they were talking about -- and the major part is even more interesting. In oceanography, one of the things often mentioned in classes is the Ekman Layer. Don't worry about what it is exactly. Usually, a reference says that Ekman (1905) studied the formation of what we now call the Ekman layer for conditions of an infinitely deep ocean, infinitely steady winds, with absolutely constant friction through the depth of the ocean. Well, it turns out that, although he did examine that highly idealized case, this was a small part of the paper. In fact he also examined varying winds and varying friction. And the results of doing so were much more interesting than what he's normally cited for.
A second reason can be particularly important in science. In the original papers on an idea, the author is explaining something new to the audience. He can't assume nearly as much as later papers on the idea will. You'll also get to see the first matchups of the idea against observations. So I usually learn more by reading the original than by reading the textbook description. (For the geophysical fluid dynamicists out there -- Charney's paper on baroclinic instability is the one exception I've found. There also turn out to be reasons why it came out that way.)
Part three is that you'll get to see much more discussion of why the approximation can be made, and where it can be expected to fail. So, for example, a modern paper talking about the color of the sky -- perhaps for a planet orbiting some other star -- will merely mention 'We apply Rayleigh scattering law and arrive at ...'. It's only when you read Rayleigh's papers that you find out that the 'law' is an approximation, and it will fail under certain, predictable, conditions.
It can be difficult to get hold of the original sources when you start going after Reynolds 1895, Ekman 1905, etc. So the ideal can't always be met. Still, the whole IPCC working group 1 (physical science) 4th report is readily available online or you can order a print copy (also groups 2 and 3, but I'm a physical science guy, so look more to that one). So, when you encounter someone making claims about what the IPCC says, go take a look yourself. And if the point isn't clear, or it is interesting to you, start following up the citations they give. (Unfortunately, I think that after the first report, the writing declined in terms of readability by nonprofessionals. Any more, once you're past deciding if someone lied about the IPCC, I think you'll have an easier time reading by hitting the cited sources rather than the IPCC reports.)
17 March 2009
What is a good question?
I've often heard the comment that much of science is asking good questions. That's true. I also heard often in school that there are no bad questions, which is reasonably correct also. But there can be quite a distance between not bad and good. I can't, however, remember anybody describing how to go about asking better questions. There are probably many different ways of improving, so, as usual, I invite comments.
An important part of a good question is, it will be clear that it has been answered. Start with the not-bad question "What's up with climate?" Maybe you answer talking about sea ice and polar bears. But I'm unsatisfied because what I wanted to hear about was global temperatures. So I ask the improved question "What's up with global temperatures?", and you answer talking about how the stratospheric temperatures have dropped and surface temperatures have risen lately. But I'm still unsatisfied, because I don't care about the stratosphere, and the surface temperatures I care about are the ocean's. So now I ask "What's up with ocean surface temperatures?", and you say 'not much', or, maybe, 'they've risen'. Again, I don't have an answer. You're doing your best, and being honest, but the questions just aren't strong enough.
A strong question is "How much has global mean sea surface temperature changed over the past 150 years, and what are the uncertainties in that number?" This, neither you, nor I, nor any random spectator will have any difficulty telling whether I've gotten an answer to my question. It'll be something like "risen by 0.5 C and 0.1 C standard error in that estimate". Whether 0.5 and 0.1 are correct, I don't know offhand. But, at this point, the question has an answer which is fill in the blank. We can then hit the literature (say IPCC Ch. 3 and the citations in that chapter) looking for it. If the question didn't have an answer waiting for us in the literature, we also know how to start doing our research -- look for data sources about ocean surface temperature, that are distributed over the globe, and which go back 150 years (or more, of course).
A different strong question is "Why is the sky blue?" No reason that young kids can't be asking strong questions. An answer to this question must adress why the sky is blue, as opposed to green. And a strong answer will tell you how blue it will be under different conditions. If, for instance, you have a high humidity and many aerosols, the sky tends towards white -- under the action of the same process which tells us that the sky (away from the sun) is very blue when the air is clean and dry. (Rayleigh scattering).
Questions that start "What about ...", "How about ...", "What's up with ..." are almost never strong questions. Strong questions usually start "How much ...", "How fast ...", "How big ...", or say "How well do we know ...".
If we really don't know much about an area, which is the case for us for most areas of human knowledge, it can be hard to ask strong questions. So we start with the not-bad questions aimed at learning more about the area, and keep asking them until we can ask strong questions. Hence my 'question place'.
An important part of a good question is, it will be clear that it has been answered. Start with the not-bad question "What's up with climate?" Maybe you answer talking about sea ice and polar bears. But I'm unsatisfied because what I wanted to hear about was global temperatures. So I ask the improved question "What's up with global temperatures?", and you answer talking about how the stratospheric temperatures have dropped and surface temperatures have risen lately. But I'm still unsatisfied, because I don't care about the stratosphere, and the surface temperatures I care about are the ocean's. So now I ask "What's up with ocean surface temperatures?", and you say 'not much', or, maybe, 'they've risen'. Again, I don't have an answer. You're doing your best, and being honest, but the questions just aren't strong enough.
A strong question is "How much has global mean sea surface temperature changed over the past 150 years, and what are the uncertainties in that number?" This, neither you, nor I, nor any random spectator will have any difficulty telling whether I've gotten an answer to my question. It'll be something like "risen by 0.5 C and 0.1 C standard error in that estimate". Whether 0.5 and 0.1 are correct, I don't know offhand. But, at this point, the question has an answer which is fill in the blank. We can then hit the literature (say IPCC Ch. 3 and the citations in that chapter) looking for it. If the question didn't have an answer waiting for us in the literature, we also know how to start doing our research -- look for data sources about ocean surface temperature, that are distributed over the globe, and which go back 150 years (or more, of course).
A different strong question is "Why is the sky blue?" No reason that young kids can't be asking strong questions. An answer to this question must adress why the sky is blue, as opposed to green. And a strong answer will tell you how blue it will be under different conditions. If, for instance, you have a high humidity and many aerosols, the sky tends towards white -- under the action of the same process which tells us that the sky (away from the sun) is very blue when the air is clean and dry. (Rayleigh scattering).
Questions that start "What about ...", "How about ...", "What's up with ..." are almost never strong questions. Strong questions usually start "How much ...", "How fast ...", "How big ...", or say "How well do we know ...".
If we really don't know much about an area, which is the case for us for most areas of human knowledge, it can be hard to ask strong questions. So we start with the not-bad questions aimed at learning more about the area, and keep asking them until we can ask strong questions. Hence my 'question place'.
11 March 2009
Not even wrong
Wolfgang Pauli is said to have responded That's not right. That's not even wrong. to a paper.
"Not even wrong" is a good summary of a number of psuedoscientific things. I was reminded of this by a document that wanted to toss aside most of the last 100+ years of science on climate. I'm not going to dwell on the document itself, but it seems worthwhile to look some more at the 'not even wrong' flags.
But first, the term itself. If I said 2+2 = 5, that'd be wrong. If I said 2+2 = kumquat, we're over to not even wrong. One form of 'not even wrong' is that the answer has nothing to do with the question. This shows up often in blog comments about climate where a question like "Has the temperature risen in the past 100 years?" is met with responses like "It was warmer 70 million years ago", "You're just trying to take away my SUV", "It's all natural." Not even wrong -- the response has no connection to the question.
Different version is to start with a falsehood and then draw whatever conclusion you'd like. ex: "Meteorologists don't allow for urban heat island effect, therefore there's been no real warming the last 50 years."
"Not even wrong" is a good summary of a number of psuedoscientific things. I was reminded of this by a document that wanted to toss aside most of the last 100+ years of science on climate. I'm not going to dwell on the document itself, but it seems worthwhile to look some more at the 'not even wrong' flags.
But first, the term itself. If I said 2+2 = 5, that'd be wrong. If I said 2+2 = kumquat, we're over to not even wrong. One form of 'not even wrong' is that the answer has nothing to do with the question. This shows up often in blog comments about climate where a question like "Has the temperature risen in the past 100 years?" is met with responses like "It was warmer 70 million years ago", "You're just trying to take away my SUV", "It's all natural." Not even wrong -- the response has no connection to the question.
Different version is to start with a falsehood and then draw whatever conclusion you'd like. ex: "Meteorologists don't allow for urban heat island effect, therefore there's been no real warming the last 50 years."
10 March 2009
Misleading yourself with graphs
I've mentioned before my appreciation of the book How to Lie with Statistics by Darrel Huff. One of the major sections regards graphs. Part of doing science is honesty, and, as Richard Feynman noted, the easiest person to fool is yourself.
Friday, I was doing some browsing and encountered co2sceptics. The graphs on that main page (at least on March 6th, 2009) were plots of the Hadley temperature record, from 1978 to 2008, and the atmospheric CO2 concentrations as measured at Mauna Loa from 1958 to 2008. That struck me as odd. Hadley goes back much farther than 1978, to 1850 in fact -- and co2sceptics link to this longer record so they must know that. Why show only 30 years data when you have almost 160? If nothing else, one of the How to Lie with Statistics methods is to show different time lines when comparing different information sources, and here we've got 30 years being shown against 50 years.
One thing to do, then, to try to avoid misleading yourself, is to plot 50 years for both. Here are the two plotted over the same time interval:
Let's see, the CO2 is rising steadily, and there's weather happening on top of temperature rising. Rather different from their conclusion (the quote is below).
But let's not make the same error of using too little data. Plus, with only 50 years of data, and temperatures having a climate period of 20-30 years, we might only be looking at a minor anomaly. Hadley goes back to 1850. Can we find CO2 information back that far? Yes, using the CO2 trapped in ice core bubbles, say at Siple. Here are the plots for 1850 to most recent, where I use Siple for CO2 until the start of Mauna Loa:
Now here's the text they introduce their graphs with the different time spans with:
Emphasis theirs.
Actually, given the data types and sources they chose and method of comparison they chose, it looks like CO2 does indeed drive climate on longer periods than individual years. All we had to do was show data for temperature over the full period of the CO2 record they showed. Extending the CO2 record with ice cores only makes that connection look stronger.
Now, since I think you need 20-30 years of global mean temperature data to be talking about climate rather than weather, I wouldn't trust that method of analysis very far at all, whether by eyeball or doing a regression.
Since I'm an optimist, I've sent a note to them about this post. If it's just a matter of a mistake, well, honest people can make mistakes. They can then correct their figures and update their comments to reflect a significant role for CO2 according to their already-shown methods of analysis. And if they're not concerned with honest discussion of science, they wouldn't be the first.
Mauna Loa CO2 data
Siple CO2 data
Hadley Temperature data
Friday, I was doing some browsing and encountered co2sceptics. The graphs on that main page (at least on March 6th, 2009) were plots of the Hadley temperature record, from 1978 to 2008, and the atmospheric CO2 concentrations as measured at Mauna Loa from 1958 to 2008. That struck me as odd. Hadley goes back much farther than 1978, to 1850 in fact -- and co2sceptics link to this longer record so they must know that. Why show only 30 years data when you have almost 160? If nothing else, one of the How to Lie with Statistics methods is to show different time lines when comparing different information sources, and here we've got 30 years being shown against 50 years.
One thing to do, then, to try to avoid misleading yourself, is to plot 50 years for both. Here are the two plotted over the same time interval:
Let's see, the CO2 is rising steadily, and there's weather happening on top of temperature rising. Rather different from their conclusion (the quote is below).
But let's not make the same error of using too little data. Plus, with only 50 years of data, and temperatures having a climate period of 20-30 years, we might only be looking at a minor anomaly. Hadley goes back to 1850. Can we find CO2 information back that far? Yes, using the CO2 trapped in ice core bubbles, say at Siple. Here are the plots for 1850 to most recent, where I use Siple for CO2 until the start of Mauna Loa:
Now here's the text they introduce their graphs with the different time spans with:
If you look at the Global Temperature Anomaly graph below you will notice that temperatures peaked in 1998, and have been in general decline ever since, unlike the temperature figures you will see that CO2 output is still increasing.
Now ask yourself a simple question. Is CO2 driving the world's temperature changes?
It would appear not!
Emphasis theirs.
Actually, given the data types and sources they chose and method of comparison they chose, it looks like CO2 does indeed drive climate on longer periods than individual years. All we had to do was show data for temperature over the full period of the CO2 record they showed. Extending the CO2 record with ice cores only makes that connection look stronger.
Now, since I think you need 20-30 years of global mean temperature data to be talking about climate rather than weather, I wouldn't trust that method of analysis very far at all, whether by eyeball or doing a regression.
Since I'm an optimist, I've sent a note to them about this post. If it's just a matter of a mistake, well, honest people can make mistakes. They can then correct their figures and update their comments to reflect a significant role for CO2 according to their already-shown methods of analysis. And if they're not concerned with honest discussion of science, they wouldn't be the first.
Mauna Loa CO2 data
Siple CO2 data
Hadley Temperature data
09 March 2009
Question place 5
Time to put out another shingle for questions. Note that this includes suggestions of papers I might read or comment on (like Steve's), questions for projects you have in mind (such as Bart's recent one), or whatever else comes to mind. Usual comment rules apply of course.
aside: I really will be getting to a summary of the graphing software you all suggested some time back. Been doing other things, including some home improvement. Fortunately, that project is finishing up.
Back to the main: A different part is, if you have what you think are 'stupid' questions, ask anyhow. You can do it anonymously (I permit anonymous comments). If the question is one to help improve your understanding science, or some particular part of science, it's a good one. Have at it. As I'm trying to reach to middle-school students, I realize this includes a pretty broad range of question.
aside: I really will be getting to a summary of the graphing software you all suggested some time back. Been doing other things, including some home improvement. Fortunately, that project is finishing up.
Back to the main: A different part is, if you have what you think are 'stupid' questions, ask anyhow. You can do it anonymously (I permit anonymous comments). If the question is one to help improve your understanding science, or some particular part of science, it's a good one. Have at it. As I'm trying to reach to middle-school students, I realize this includes a pretty broad range of question.
06 March 2009
Nonscience and pseudoscience
One of my interests is pseudoscience. There are a couple of lines I like to draw, between science and nonscience, and between nonscience and pseudoscience. I think science is a good thing, pseduoscience is a bad thing, and nonscience can be anything in between.
Let's start with science. Ultimately, my philosopher friend tells me, it is an impossible job to cleanly define science such that everything which is science is inside the line you draw, and nothing that isn't science is inside. Ok. So I have some fuzzy boundaries for inside vs. outside. Some things might move more cleanly inside, in time, and others will start fuzzy and then move outside. For the most part, though, the fuzziness doesn't cause me problems.
Inside science we have things where people are trying to understand the natural universe, using sharable methods and data, and is subject to further testing. Each of those elements is important. Science is about understanding. Making use of your understanding (say to build a better computer) is engineering. This is one of the many good things that isn't science. Science is about the natural universe. If you're discussing the nature of God, whether there is one, etc., you're over in theology, not science. If you, say, have an experiment where only you can get a certain result, and if I stood next to you and looked I wouldn't see what you did, then you've left science. The most difficult part, for both scientists and non-scientists, is the last -- whatever your conclusions and understanding are today, you have to be open to new data that will cause you to revise yourself tomorrow.
This last is perhaps the quickest method to find pseudoscience. The related point is, in science you don't start with your answer. The flat earth and young earth sites over in the 20 links game all start with their conclusion that the earth is flat, or young, and then assemble whatever arguments they can, however contrived and ultimately dishonest, to support their conclusion. A useful question, then, is "What evidence would cause you to change your mind?". If there's none, the person isn't interested in the science.
Having divided to science and nonscience, keeping in mind that 'nonscience' is not a slam, it's time to think about pseudoscience. I've got a shelf or two of examples on my bookcases at home. Essential to being pseudoscience is that the authors/fans/... have to claim that they are indeed doing science. Baseball, painting, theater, plumbing, ... are all good areas and aren't science. But it's also the case that none of them claim to be science. In baseball, I think the designated hitter rule is bad. This isn't a matter of science; no evidence you present will (at least nothing I've heard in 40 years on the topic) change my mind. But no problem, it isn't science.
Where we get to the pseudoscience is with the claim that it is science, even though it fails to be science. So, for instance, it is possible that the earth really is flat -- if we're being scientific we have to be open to the possibility that tomorrow we'll get a batch of fresh information about optics, gravity, etc., that will lead to the conclusion that the earth is flat after all. But the flat earthers are maintaining it in spite of the fact that there is not such evidence now.
Perhaps my favorite pseudoscience is biorhythms. It's something that could have turned out to be science. The problem only being that when it didn't, the believers didn't stop believing that it was. The idea here is that your body has certain rhythms (which it does in some things) that could predict whether you were in better or worse shape, and more or less accident prone. We're ok so far -- the topic is natural universe, the data are sharable (did people have more accidents, when were they born, where were they in their biorhythm(s), etc.). But when it came down to comparing the observations to the predictions, it failed. There were some problems with the idea (ex: how did the body maintain such a precise timing of the rhythms over a lifetime?), but if the predictions accorded with observation, that's ok. Just means more research is needed, this time to answer those questions.
I first read about this in the 1970s. At that time, one of the books mentioned that there really was a lot of data in support of the idea, but it was in a steamer trunk on a ship that sank during World War II. Bit puzzling that with almost 30 years since the sinking the proponents hadn't managed to find more data, but I was younger then and it didn't strike me as odd as it should have. In the mid-1990s, I looked again (forget the reason) and a recently published book was repeating the steamer trunk story. Come on! Another 20 years on, 50 years after the event, the fans still hadn't managed to find new data. This is 'the dog ate my homework', not science. If a process that is supposed to be going on today can't be supported with observations today, you've lost the 'sharable data' part of being science.
Pseudoscience is unfortunately common when you start looking for information about climate. On the other hand, most of it is fairly easy to identify, being not even as close to science as the biorhythm business.
Let's start with science. Ultimately, my philosopher friend tells me, it is an impossible job to cleanly define science such that everything which is science is inside the line you draw, and nothing that isn't science is inside. Ok. So I have some fuzzy boundaries for inside vs. outside. Some things might move more cleanly inside, in time, and others will start fuzzy and then move outside. For the most part, though, the fuzziness doesn't cause me problems.
Inside science we have things where people are trying to understand the natural universe, using sharable methods and data, and is subject to further testing. Each of those elements is important. Science is about understanding. Making use of your understanding (say to build a better computer) is engineering. This is one of the many good things that isn't science. Science is about the natural universe. If you're discussing the nature of God, whether there is one, etc., you're over in theology, not science. If you, say, have an experiment where only you can get a certain result, and if I stood next to you and looked I wouldn't see what you did, then you've left science. The most difficult part, for both scientists and non-scientists, is the last -- whatever your conclusions and understanding are today, you have to be open to new data that will cause you to revise yourself tomorrow.
This last is perhaps the quickest method to find pseudoscience. The related point is, in science you don't start with your answer. The flat earth and young earth sites over in the 20 links game all start with their conclusion that the earth is flat, or young, and then assemble whatever arguments they can, however contrived and ultimately dishonest, to support their conclusion. A useful question, then, is "What evidence would cause you to change your mind?". If there's none, the person isn't interested in the science.
Having divided to science and nonscience, keeping in mind that 'nonscience' is not a slam, it's time to think about pseudoscience. I've got a shelf or two of examples on my bookcases at home. Essential to being pseudoscience is that the authors/fans/... have to claim that they are indeed doing science. Baseball, painting, theater, plumbing, ... are all good areas and aren't science. But it's also the case that none of them claim to be science. In baseball, I think the designated hitter rule is bad. This isn't a matter of science; no evidence you present will (at least nothing I've heard in 40 years on the topic) change my mind. But no problem, it isn't science.
Where we get to the pseudoscience is with the claim that it is science, even though it fails to be science. So, for instance, it is possible that the earth really is flat -- if we're being scientific we have to be open to the possibility that tomorrow we'll get a batch of fresh information about optics, gravity, etc., that will lead to the conclusion that the earth is flat after all. But the flat earthers are maintaining it in spite of the fact that there is not such evidence now.
Perhaps my favorite pseudoscience is biorhythms. It's something that could have turned out to be science. The problem only being that when it didn't, the believers didn't stop believing that it was. The idea here is that your body has certain rhythms (which it does in some things) that could predict whether you were in better or worse shape, and more or less accident prone. We're ok so far -- the topic is natural universe, the data are sharable (did people have more accidents, when were they born, where were they in their biorhythm(s), etc.). But when it came down to comparing the observations to the predictions, it failed. There were some problems with the idea (ex: how did the body maintain such a precise timing of the rhythms over a lifetime?), but if the predictions accorded with observation, that's ok. Just means more research is needed, this time to answer those questions.
I first read about this in the 1970s. At that time, one of the books mentioned that there really was a lot of data in support of the idea, but it was in a steamer trunk on a ship that sank during World War II. Bit puzzling that with almost 30 years since the sinking the proponents hadn't managed to find more data, but I was younger then and it didn't strike me as odd as it should have. In the mid-1990s, I looked again (forget the reason) and a recently published book was repeating the steamer trunk story. Come on! Another 20 years on, 50 years after the event, the fans still hadn't managed to find new data. This is 'the dog ate my homework', not science. If a process that is supposed to be going on today can't be supported with observations today, you've lost the 'sharable data' part of being science.
Pseudoscience is unfortunately common when you start looking for information about climate. On the other hand, most of it is fairly easy to identify, being not even as close to science as the biorhythm business.
Usual disclaimer
Just in case anyone is thinking otherwise (I don't know why you would), I'm here as 'Bob Grumbine, private citizen'. I don't speak for my employer. If my employer should happen to say things similar to what I do, that's just their good taste. If I say things other than what they do, that's just me. It's also no accident that I don't name my employer.
05 March 2009
Weather will still happen
It looks like this is a surprise to some folks, so I guess it bears repeating: Weather will still happen in the future. This is true regardless of whether the current scientific understanding about climate change is more or less correct or not. There's still going to be weather. By that, I mean that there will still be periods when your back yard will be markedly warmer or colder than usual, you get more (or less) snow than usual, and so on.
What's bringing it to mind is that with a recent cold spell around here (Washington DC area), I've heard many more comments about how "There can't be global warming because it's cold here." (Or that it was cold when a rally was held, etc.) Now, if the people saying such things were honest (even if not correct), when we get to about 20 F warmer than usual this weekend (which is the forecast, vs. having been 20 F cooler than usual), they'd turn around and say just as loudly that there is global warming after all.
But this is one of the easier flags on whether you're dealing with an honest source. They ignore data that goes against what they want to conclude. Conversely, it's a good self-check when you start reading material that annoys you -- is it annoying because it presents evidence just as good as, or better than, you have for the conclusion you prefer? In that vein, if you think I'm ignoring important evidence, do bring it up to me.
As you know from earlier notes, if you've seen them, it's a silly thing to draw conclusions about global climate from a few days of local temperatures. Or even a few years of even global temperatures. If you haven't been here before, take a look at
What's bringing it to mind is that with a recent cold spell around here (Washington DC area), I've heard many more comments about how "There can't be global warming because it's cold here." (Or that it was cold when a rally was held, etc.) Now, if the people saying such things were honest (even if not correct), when we get to about 20 F warmer than usual this weekend (which is the forecast, vs. having been 20 F cooler than usual), they'd turn around and say just as loudly that there is global warming after all.
But this is one of the easier flags on whether you're dealing with an honest source. They ignore data that goes against what they want to conclude. Conversely, it's a good self-check when you start reading material that annoys you -- is it annoying because it presents evidence just as good as, or better than, you have for the conclusion you prefer? In that vein, if you think I'm ignoring important evidence, do bring it up to me.
As you know from earlier notes, if you've seen them, it's a silly thing to draw conclusions about global climate from a few days of local temperatures. Or even a few years of even global temperatures. If you haven't been here before, take a look at
04 March 2009
New Scienceblog
Regular commenter Kim (Hannula), whose blog Shearsensibility I've been reading and commenting at for a while has been assimilated by the Borg (scienceblogs) at
http://scienceblogs.com/stressrelated/. I'm linking this to her post inviting ideas on what skills students need in learning science (etc.) I expect you all have a fair number of ideas on that topic, so join in.
http://scienceblogs.com/stressrelated/. I'm linking this to her post inviting ideas on what skills students need in learning science (etc.) I expect you all have a fair number of ideas on that topic, so join in.
01 March 2009
The 20 links game
The Washington Post's standards of truth are, apparently, that if you can provide 20 links to support your article, that's good enough for them. (see quotes from their Ombudsman and some responses at, say, http://getenergysmartnow.com/2009/02/28/washpost-ombudsman-steps-up-and-steps-in-it-plus-another-will-fabrication/).
I boggle. Granted that this isn't that rare (part of why I'm good at the game of that name).
So the game is to find 20 links (bonus for 20 links from 20 different sites) to support:
If you post your 20, please use rel="nofollow" in the links. If you're not sure how, note that and I'll do it. Or just post the statement that you found your 20 links to support -- but only after you find the actual links (but save them, there might be later use).
I boggle. Granted that this isn't that rare (part of why I'm good at the game of that name).
So the game is to find 20 links (bonus for 20 links from 20 different sites) to support:
- The earth is flat
- The earth is the center of the solar system
- There is no greenhouse effect
- Carbon dioxide is not a greenhouse gas
- The moon landings were faked
- The earth is less than 100,000 years old
- The earth is more than 1 trillion years old
- Humans have been on the earth fewer than 10,000 years
- Humans have been on the earth more than 50,000,000 years
- Elvis is alive
- Space aliens kidnap children
- ... and any another statement of this caliber
If you post your 20, please use rel="nofollow" in the links. If you're not sure how, note that and I'll do it. Or just post the statement that you found your 20 links to support -- but only after you find the actual links (but save them, there might be later use).