Ice before 1979?

It's incredible what you can discover by reading the antiscientific sources. For instance, did you know that before October 26, 1978, nobody knew anything at all about what the Arctic sea ice cover was like? Amazing. I certainly didn't know that. I don't think the Navy realized that in the 1950s when it issued an atlas of Arctic ice cover normals. (I have a copy.) The folks fishing and shipping across high latitudes would also be surprised to discover that they'd missed the chance to use the northwest passage, or to fish in the Beaufort Sea in late summer, or .... Just incredible, in the literal sense of 'not credible'.

The reality is simple -- we do indeed have observations of the Arctic ice pack running back before what we normally call the satellite period. This includes from a different satellite (ESMR) in the early 1970s, Naval operations, shipping and fishing observations, flight observations, and so on. As an antiscientific sort, though, one ignores the possibility that there could be more data available and draws the conclusion without bothering to look.

If, instead, you go look, say at the National Snow and Ice Data Center (a name in the news fairly routinely since summer 2007 when the major Arctic decline started) for what data are available on sea ice, you can rapidly find this listing of sea ice data that they have: http://nsidc.org/data/seaice/all.html . Looking through the table for 'temporal coverage', we see the AARI maps, 1953-1990, Arctic ocean ship tracks (irregularly back to 1872), Arctic ocean back to 1901 (see qualifiers as well), US-Russian atlas back to 1950, 'Morphometric characteristics of ice and snow ...' (irregularly back to 1928), the ESMR data (1973-1976). And, for some reason not listed there but I knew of it to look for it, information in the Nordic seas back to 1750: http://www.nsidc.org/data/g02169.html

So if we have data back to 1950 or so on most or all of the Arctic, and with more qualifiers back to 1901, why do Cryosphere Today (link on right) and NSIDC (ditto) show curves only from October 1978? The answer is, because they're trying to be scientifically honest. With the start of SMMR data in October 1978, we have a nearly continuous series of data which was all collected in nearly the same way (polar orbiting microwave satellite, only slight differences in details of the instrument). Before that period, the available data and their processing were distinctly different. This, the careful scientist knows, could introduce artefacts -- false trends or steps -- in a time series that spanned different sorts of sources. Since we expected the changes in the Arctic ice pack to be slight, these artefacts could overwhelm the slight -- but real -- changes that researchers were trying to detect.

On the other hand, we have the extraordinary low covers of 2007 and 2008. These are so drastically far from what has happened before that the minor differences in extent that would be due to changes in details of map construction and data sources are entirely overwhelmed.

So yes, we do know that the minima of 2007 and 2008 are far from what's previously been observed. And our knowledge extend much farther back than the 30 years of the satellite record. You can safely weed out sources that try to imply that the 2007 and 2008 minimum isn't unusual.

Ideas are safe

There's a Hollywood stereotype about ideas in science being stolen, and people having to be terribly careful about guarding their ideas. In some areas this might be not entirely untrue. But a recent comment by Steve Bloom points to what is much more my own experience -- your ideas 'safe'.

This actually goes over to being a problem. Your idea, even after you publish it and want people to be looking at it and using it, may not attract attention and get used. Your main problem is not to protect your ideas, but to get anybody to pay attention to them.

For the paper Steve references, it actually fits very well with an idea I was developing during the 24 hour contest I mentioned. So the good news for Steve is that I'll definitely be investigating and discussing the paper fairly thoroughly. The bad news is that since it ties to that idea of mine, I won't be delving in to it until I go back to my own version -- March/April. I'm currently working on a different notion for publication, one that I started longer ago. Making good progress now, but that also means I'm not doing as much for the blog. Only so many hours for my non-(day job) activities.

Who are you?

I've mentioned a few more or less professional things about who I am. At hand, though, is genealogy. Some people look to genealogy to tell them who they are. Some go looking for famous relatives or rich ones, and a few (the 'black sheep' society) go looking for the infamous. For me, it's none of the above. I am me, no more, no less. That won't change by who I discover I'm more closely related to than others. If they were wonderful, or awful, I'm no better or worse a person myself.

Part of the process, for me, is that discovery. A bit of the chase, and more a matter of having something to direct me a little towards learning about some particular time and place -- culture, history, language, whatever. It turns out that several ancestors, for instance, lived more or less near to my current location near DC -- Baltimore to Richmond, with a couple in Philadelphia. So there are some weekend trips to see some parts of the country which my wife and I have talked about visiting anyhow.

Or, on a long drive (DC to Chicago) my wife and I thought some about the travels of one of my ancestors in the early 1800s -- Baltimore to N. Central Ohio. We were driving 55-65 miles per hour along what was probably more or less their route. That, to their 10 or so miles per day. Us being able to stop whenever we wanted, to get food, take a break. Could have stopped in a hotel, etc. My ancestors would have had to carry everything with them. If it rains, it rains. No problem for us, but how would they have handled it? What time of year would they have travelled? They were farmers, so I'd think they'd want to arrive for a spring planting. But that means travelling in late winter, which would not be a very pleasant time! Their trip was 1-2 months, assuming 10-20 miles/day (and some parts my wife and I agreed it would be hard to make much time through), to our 1 day. The world, we've heard a lot since our childhoods (my wife and I, that is, not sure about you all) is getting smaller. Thinking about our trip versus my ancestors' really brought that home for us.

Happy New Year

Happy Chinese New Year!

(a little late, and we're still in question whether it's year of the Cow or what)

Unity of science and Turkey Vultures

One of the features that makes science fun to do, and to spectate at, is that the pieces of knowledge all fit together. There's this very large interlocking structure of knowledge that we each try to build more on to. If we're luckier, we find an area where the pieces don't fit together so well. Or, if we're very lucky, get to break open a large chunk and replace it with another of our own construction.

My recent personal illustration is the paper "Movement Ecology of Migration in Turkey Vultures" (link here, full citation and abstract below). The way I know about it is that it cites a paper I was involved in. Now, I know just shy of nothing about turkey vultures (though we seem to have a lot of them around here, that's about my extent of knowledge). Nor do I know about ecology to be publishing on that. So how do ecologists studying turkey vultures come to cite something I've done professionally? Turns out, they were relating the migration of turkey vultures to meteorological conditions. Now meteorology, I know something about, and contributed to a large project that they used to understand their turkey vultures.

The authors merely using the results of our work doesn't make so much for the picture of interlocking pieces. If all they do or can do is take our results, then, effectively, they're just accepting whatever shape piece we meteorologists hand them. But, in principle at least, this is not what they're doing. The authors look in to the migration patterns of the turkey vultures, and its dependance on meteorological conditions. If the analysis were wrong in some way that affected the birds, their data would be showing this. If the analysis winds were too calm (apparently the vultures like a turbulent atmosphere) in the analysis, then the tracking data would show vultures in places the analysis said they shouldn't be.

Here's the real fun. The ecologists would then send a note to the meteorologists who did the analysis and say that there was a problem. The meteorologists' data didn't fit the vulture data, so we have to talk and figure out where the problem was. Maybe it's the vulture data, maybe it's the meteorological analysis, maybe it's both. But the pieces have to fit together. In practice, we didn't get that call (I don't think, have to check with our lead author for certainty). Their results fit with our work. So we have a little bit more confidence that we got things right in ours -- and so do any other users of our work.


Movement ecology of migration in turkey vultures
Mandel, J. T.; Bildstein, K. L.; Bohrer, G.; Winkler, D. W.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 105 (49): 19102-19107 DEC 9 2008

Abstract:
We develop individual-based movement ecology models (MEM) to explore turkey vulture (Cathartes aura) migration decisions at both hourly and daily scales. Vulture movements in 10 migration events were recorded with satellite-reporting GPS sensors, and flight behavior was observed visually, aided by on-the-ground VHF radio-tracking. We used the North American Regional Reanalysis dataset to obtain values for wind speed, turbulent kinetic energy (TKE), and cloud height and used a digital elevation model for a measure of terrain ruggedness. A turkey vulture fitted with a heart-rate logger during 124 h of flight during 38 contiguous days showed only a small increase in mean heart rate as distance traveled per day increased, which suggests that, unlike flapping, soaring flight does not lead to greatly increased metabolic costs. Data from 10 migrations for 724 hourly segments and 152 daily segments showed that vultures depended heavily upon high levels of TKE in the atmospheric boundary layer to increase flight distances and maintain preferred bearings at both hourly and daily scales. We suggest how the MEM can be extended to other spatial and temporal scales of avian migration. Our success in relating model-derived atmospheric variables to migration indicates the potential of using regional reanalysis data, as here, and potentially other regional, higher-resolution, atmospheric models in predicting changing movement patterns of soaring birds under various scenarios of climate and land use change.

Reprint Address:
Mandel, JT, Cornell Univ, Dept Ecol & Evolutionary Biol, Corson Hall, Ithaca, NY 14853 USA.

Research Institution addresses:
[Mandel, J. T.; Winkler, D. W.] Cornell Univ, Dept Ecol & Evolutionary Biol, Ithaca, NY 14853 USA; [Mandel, J. T.; Bildstein, K. L.] Acopian Ctr Conservat Learning, Orwigsburg, PA 17961 USA; [Bohrer, G.] Ohio State Univ, Dept Civil & Environm Engn & Geodet Sci, Columbus, OH 43210 USA

Asian Brown Cloud

A recent question was about the Asian Brown Cloud, which I couldn't help with at the time. But I'm now doing some reading, and see that there's a recently released major report "Atmospheric brown clouds: Regional assessment report with focus on Asia. So there's a solid place to go for information.

I'm also a bit amused by who the lead author is. Not that he's amusing, V. Ramanathan is a serious, careful scientist. But that the author I wind up referring you to was one of my doctoral committee members. Science is a small world.

2008 Reading

Some ages ago, prompted by others who talked about their reading logs, I started keeping one myself. Below are some of the better, or more interesting, or just strike my amusement, of my reading in 2008. Not counting work-related reading and other things I don't count, something like 118 books in 2008. Somewhere around my long-term average. I've bolded the books that are more relevant to the blog.

Math:
Number Theory, George E. Andrews
Lady Luck, Warren Weaver (re-read)
A Mathematician's Apology, G. H. Hardy

On Doing Science:
Creationism's Trojan Horse, Barbara Forrest and Paul R. Gross
The Devil in Dover, Lauri Lebo
Only a Theory, Kenneth R. Miller

Glaciology:
The Ice Finders, Edmund Blair Bolles


Literature:
The Voyage of Argo, Apollonius of Rhodes
Jitney, August Wilson (play)

History:
Charlemagne: A biography, Derek Wilson

Running:
Running Strong and Injury Free, Janet S. Hamilton
Run Strong, Kevin Beck, ed.
Hidden Cuases of Injury, Prevention and Correction for Running Athletes and Joggers, John Jesse

Fiction:
Good Omens, Neil Gaiman + Terry Pratchett (re-read)

Language:
A History of Language, Steven Roger Fischer
The First Word, Catherine Kenneally

Suggestions

Time for a converse to the usual question place note -- here's a place to contribute suggestions for the blog. Topics to cover, display methods, ... whatever would make for a more interesting and useful blog. I'll particularly address this to the science teachers, as I would like to be helping you and your students learn about science in general, and my science(s) in particular. But all constructive ideas are welcome.

Questions for a new year

As always, here's a place to put your questions, particularly about science.

As it's a new year, and resolutions may be present, questions about getting started exercising (especially for running, but other sports also welcome), or good things to read are also welcome.

Results on deciding trends

No, the delay was not because the results came out differently than I'd expected. Just the more mundane business that I've been doing a number of different things and getting the figures done up reasonably is taking some time as I change my mind about what constitutes reasonable.

In brief (in a journal paper, this would be the 'abstract'):

• You need 20-30 years of data to define a climate trend in global mean temperature
• Forward and backward trends are markedly different
• Therefore, to discuss climate trends in global mean temperature, you need to use 20-30 years of data centered on the date of interest.
  

As with any abstract, it's too brief to show you why any of these are true, just some simple declarations. Now, if you trust me absolutely (which I don't recommend -- and if I'm talking science, you don't need to), you can stop and move on to some other reading. But let's take a look at the whys. As before, I'm putting the data and programs on my personal web site and you can run the analysis yourself, and modify the programs to work on different assumptions, methods, data sets.

Let's consider the first point -- how long it takes to determine a climate trend in global mean temperature. We could define a trend with 2 minutes of data -- temperature at one minute, temperature at the next minute, and draw a straight line through the two numbers. We'd wind up with wildly varying trends, though, from minute to minute through the year. This is weather and turbulence. Make it daily or monthly averages, and we still have the wildly varying trends, and the magnitude of those trends will depend on what time period we chose. Rather than declare that 'this is the right period', we'll determine it by looking at the data itself.

If it is meaningful to talk about climate as opposed to weather, there has to be a time span over which our result for describing climate does not depend much on how long a time span we choose. For average climate temperature, we found 20-30 years as the appropriate time span. I didn't show the figures then, but it's in the program and output you can pick up from my web site that this is also the appropriate time span for deciding a climate temperature variance (how much scatter there is about the average; even if the average didn't change, we would probably consider it a climate change to have winter lows vary from -30 to +15 instead of -10 to -5).

Figure 1 here shows the trends for all years (remember I'm lopping off the first 31 and last 31 from the NCDC record) that I computed trends for, by all 3 methods, in terms of the length of data record used. So at 36 (months) we see a range in the computed trends between +15 C/century and -15 C/century. These are enormous values cmpared to what we think of for climate change. If I wanted to give you a wrong impression about climate, then, I could use such short records. The range declines as we take longer periods. And then flattens out for trend periods of 252-372 months (21-31 years -- remember I took only odd averaging periods). In this part of the display, the range is about +1.5 C/century to -1.5 C/century -- and it is independant of how long an average I took. This, then, supports that a) there is such a thing as a climate temperature trend and b) that you need 21-31 years to find it (we can round to 20-30, given how 19 years is close to 21 also, we expect 20 even to be so as well).



Statistical aside: To compute variance, we find the deviation of each observation from the mean, square it, and then add this value up for all observations and divide by the number of observations. (Or use the appropriate function in your spreadsheet.) This is a fundamentally meaningful quantity. If we then take the square root of this number, and the numbers have a normal distribution, then we have a standard deviation. We can always take the square root of the variance, but it will not always be a standard deviation.

In figure 2, I plot instead the maximum and minumum trends, and the square root of variance -- again in terms of how long a period is used to compute the trends. This shows fundamentally the same information, but perhaps a little more clearly. Aside: It's a good idea to look at your data from several different vantages. Sometimes the display method you use in one step can hide something that's blindingly obvious in another method. Again, we see that the figures (maximum trend, minimum trend, average trend, square root of variance in trend) all stabilize once the data length used is 20-30 years. And, conversely, that for periods of 3-13 years, the figures all depend sensitively on how long an averaging period you choose.



Choose is a key word in doing science. We try to avoid having choices. Choices can be made differently by different people, for different reasons, and not all those reasons will turn out to be good ones. Finding a scientific principle and then looking for how to satisfy that principle is far better. Here, the principle is that the length of data used should not affect your conclusion about what the climate trend is. This is a strong principle. So when you see someone violating it (say by using a 7 year span without doing some real work to justify it -- work like I'm doing here), they're probably not doing good science.

Now, in figure 3, let's look at what the trends are like if we use 7 years of data, versus using 25 years. I'm computing all these by using centered information -- data evenly on either side of the time of interest. We'll get to why this is best in a minute. The main thing I think this shows is that if you use the short period, you present a false impression that climate is highly variable, trends changing from some very high positive value to a high negative value in a span not terribly longer than the 7 years' data you used in either case. This makes no sense for climate, but does for weather, or for misleading people. Weather, we know, does change rapidly. We can be warmer than usual for a few days (or months, or years) and then cool a few days/months/years later. Nobody (scientific) has ever said weather was going to end. Go out to the 25 year data period trends and we see, instead, that the trends have more stable behavior. They do change, which is reasonable since we do expect that climate changes. But it's no longer large magnitude flip-flops. That, too, makes sense as climate is a big beast and turning on a dime has to be a rare if ever occurrence.



To look a little differently at it ... if someone shows you a trend over 3 years, about 90% of what they're showing you is weather (real trends of up to 1.5 C/century, 3 year trends of up to 15 C/century -- 90% of that 15 C/century is weather). For a 7 year trend, it's about 70% weather. Weather is interesting, but if you're interested in climate, and they're claiming to be talking about climate, then they're misleading you by those 70-90% of weather they've thrown in by using such short spans.


On to which data to use for computing trends (or, for that matter, averages, but I'm focusing on trends today). Figure 4 shows trends in degrees per century as computed with data forward from the date given, backwards from the date given, and centered on the date shown. It also shows, and this is why I went to degrees per decade -- the magnitudes come out comparable, the NCDC monthly anomalies. I computed the trends using 25 years of data (300 months).



We immediately see that, indeed, the forward and backward trends are quite different, as expected in the planning note 'deciding climate trends'. The curves themselves are actually the same -- but shifted by 25 years (the period used to compute the trend -- when I use 31 years, it's shifted by 31 years, and so on). The centered trends show, again, the same behavior, but now 12.5 years (half my data period) off from the forward, or backward trends. So, look at the data for anomalies versus the different trends as computed. If we look at, say, 1945 (month 780) -- a generally warm year, we see a modestly negative trend from the centered trends, an extremely negative trend if we look forward, and extremely positive if we look backward. What's happening? And which makes most sense for thinking about the 1945 climate trend?

Climate is about normal, our expectations. For 1945, then, would we describe the typical change as one of rapid warming? rapid cooling? a modest cooling? If we look around then, the best description of the tendency is that there's a modest cooling going on. 1930s were warm, 1945 was a particularly warm year, but going in to the 1950s, temperatures were cooler. The large trends, in opposite directions, the forward and backward trend computations give us, even though for an appropriate period, mislead us. The centered trend computation gives us the right idea of what is going on around 1945. Repeat this inspection for other years, and think you'll rapidly come to the conclusion that the best description of what is going on around any given time is the one from a centered computation. If a trend is downwards (negative numbers), then we expect that times before our year of interest are generally warmer than later times. If it's upwards (positive trend), the we expect later years to be warmer. Only the centered computation consistently gives us this result.

We now have 2 conclusions: trends should be computed with 20-30 years of data, and they should be centered on the date of interest. Let's see how it works, applied to as much data as possible. To cover the greatest time span possible, I'll take the shortest data length reasonable for computing trends -- 20 years. Since I'm doing centered computations, this lets me get to within 10 years of the start and end of the record (remember I had been skipping the first and last 31 years so that all three methods could be used, and because I didn't know if some number shorter than 31 years would be long enough). Figure 5 gives the result, in degrees per decade, and with the NCDC monthly anomalies as well.



The most recent year we can compute a good quality trend for is 1998. The trend then is a warming of 0.19 degrees per decade, 1.9 per century. We see that the trend is higher towards the end of the record (i.e., towards the present) than at other times, though we'd have to do additional work to decide whether the difference was physically meaningful. The thing which surprised me about the curve is that most of the time -- from 1890 to 1998 that we can compute a quality trend for -- the climate trend has been a warming. Not a matter of sometimes up, sometimes down, but rather a basically up except for a bit of down in mid-20th century. 70% of the time, we've been seeing warming.

By way of summary, or postscript, or some such ...

• Whether it is to compute the average climate temperature, the variance of climate temperature, or the trends in climate temperature, we need 20-30 years of data.
• On the other hand, it is possible to compute a climate with 20-30 years of data. This didn't have to be the case. I'll show in a later post a curve with no climate in the sense I've been talking about.
  
• Most of the time for the period of data, we've been experiencing a climate warming.
  

Conclusions are, of course, limited by how data were analyzed. In this analysis, I looked only at the global mean temperature data themselves. If I'd done something more sophisticated, like carefully removing the effects of some 'weather' (short term or from outside the climate system) processes such as El Nino-La Nina oscillations, volcanoes, and solar variability, and then looking at how long was needed to get a stable estimate of the mean, or trend, I might wind up with a shorter period. On the other hand, that shorter period would only apply when people did indeed do a careful analysis of the effects of those things on global mean temperatures. This can be done, but is more sophisticated than I wanted to start with. It's also vastly more sophisticated than the many blogs and such out there which are misleading people by doing sloppy short term analysis and pretending that it's climate they're talking about.

Update  13 February 2010: The data file I worked on and the program I used are in a tar file.  You'll need a fortran compiler for this, or translate it to a language of your choice.  Nothing very fortran-ish is being done and the program is short.

A running start on the new year

For someone who calls himself a runner, I didn't do much running in 2008. So for 2009, I made sure to get a running start on the year, and ran my club's Great Lengths race -- 2009 fathoms. (There was also a 2009 rods race, but that's more than I can do for a while yet.) The 2009 fathoms is a bit less than 4 km, (1 fathom = 6 feet, 1 rod = 16.5 feet). But I got out and enjoyed the race and nice, if chilly (near freezing) day.

The Great Lengths race is one I started with the club (Prince George's Running Club) back in 2002 or 2003. A chance to run an unusual distance and to get a start on resolutions, or just get a fitness check (the rods race is almost exactly 10 km). It has since been passed on to Bill Radcliffe and Don Proctor, who organize an extremely good, fun race regardless of the weather (downpour one year, near freezing this year, rather pleasant once, ...)

Alas, my post back in August was near a then-peak in my running. November I came down with a bad cold and that toasted my running 'til the end of December.

Still, those of you looking at New Year's resolutions can take a look at that old post for a guide on how to get in to running. It has worked for people who are 40-50 years out from their last time exercising. Do, of course, clear these ideas with your doctor, particularly if you're older (than 40), and weigh more (than 10% over a healthy weight for you).

Happy Perihelion

Not quite yet, but as everyone else is wishing happy New Years that seems covered. On the 3rd (usually at least) of January we are closest to the sun. That the calendar year starts near then is coincidence. Due to orbital variations (Milankovitch cycles), our date of closest approach varies through the calendar year. This is the longitude of perihelion.

Back with the simplest climate model posts we looked some at how our distance to the sun could affect the global mean temperature. In honor of our passage, a project to see how much difference our being 0.0167 astronomical units closer to the sun than normal (i.e., we'll be at 0.9833 AU instead of the average 1.0000) would make. At least, would make if the assumptions of the simplest model were all true.