More Grumbine Science

Quickies

2013-05-17T06:30:00.000-04:00

Lots going on in the blogosphere, little of which I'll really take up in favor of mentioning that I'll be visiting Charlotte, NC next week. Any of you in the area are welcome to drop me a note and maybe show me the area some. If there's a Science Cafe in the area with an opening, I'd be glad to stop in and chat.

In the mean time, I've continued at a slow pace my work on the ESMR sea ice. See the comment by MMM about the progress that the NSIDC has made on integrating it to the record with other satellites. That's no reason to stop my effort here, though, because we're always better off if there are more lines of evidence, or more mehtods of analyzing the data, that independently come to the same conclusion. Or, perhaps, it turns out that they don't support the same conclusions. Either way, we learn something, which is the key for science.

A major European effort to re-examine prospects on sea level rise has completed and announced figures higher than the IPCC 4th report, but lower than some of those considered possible previously. See ice2sea for details.

A 'citizen science' effort from Skeptical Science has confirmed the unsurprising, to those of us reading the scientific literature, that the overwhelming majority of the scientific literature either doesn't mention whether climate change is occurring and is human-caused, or that it supports the conclusion. See 97% consensus for the details on what they did.

Climate change, by way of sea level rise, is starting to get attention of towns in the US, as Newtok, Alaska is now facing exile from its traditional locale since it'll be under water in the next few years. I'm actually involved in a proposal that'll help provide improved information for the west coast of Alaska. Doesn't affect this situation, but some towns farther inland, or where sea level isn't as obvious a factor, might be helped in their decision process.

The one-sided political divide continues on climate science. Barry Bickmore, scientist and former GOP party official in Utah, has replied to a former GOP senator about the science.

Some time in the last week or two, we've reached 400 ppm CO2 in the atmosphere. On the one hand, it's a milestone of sorts; the digits ticked over a round number. On the other hand, there hasn't been a question of whether we would do so for over 30 years. Purely a question of when. As many have noted, levels have not been this high in human history. Now, if you take history to mean the written record of 10,000 years, that's true, but no surprise. We've been past the highest levels in history ever since the industrial revolution. Call it the 280 ppm of 'pre-industrial'. It's more surprising if you consider that it's longer than our species has existed -- the about 200,000 years 'anatomically modern' humans been around. And, for that matter, we didn't see such levels even in our ancestors' times, for Homo erectus, back to around 1.8 million years. Last time such levels existed is perhaps 2.5 million years ago, when the nearest thing to us was Homo habilis -- a species with less than half our brain size, and averaging perhaps 1.3 m tall (Wikipedia).

Assessing forecasts

2013-05-01T05:30:00.000-04:00

This is actually part of pursuing whether ESMR was screwy, but I decided that to show that nothing was up my sleeve, it was time to talk some about assessing forecasts. That, and it's something I've been meaning to talk about for a while. The thing is, forecast assessment is not nearly as simple as we sometimes think. Having judged many a science fair project that is comparing weather forecasts, I've seen many of the same issues come up there, too.

For precipitation forecasts, people (science fairs included) often think about either 'probability of detection' -- i.e., what fraction of the time that there's rain did the weather forecast call for rain, and 'false alarm rate' -- what fraction of the time did you get no rain even though the forecast called for rain. Both are potentially meaningful, and both have serious problems if used alone.
The meaningful side is that someone who really doesn't like rain, or needs it not to rain (say farmers after applying certain fertilizers/insecticides/...), wants a very high probability of detection (PoD). Conversely, if you take some expensive actions given a forecast of rain (rearrange your schedule, and you don't like doing that, apply some insecticides that need rain, ...) and it doesn't materialize, you want a very low false alarm rate (FAR).

But it is very easy to cheat either one of those measures of skill. My PoD score will be perfect if I say, every day, that it will rain. It's guaranteed on the days that it rains, that this will have been my forecast. But I'll have called for rain a lot of time that it didn't happen. Conversely, I can get a perfect FAR score easily -- forecast that it will never rain. If it ever does rain, then I'll be wrong, but the FAR doesn't care about that error. PoD and FAR cover each other in this respect -- each is sensitive to the cheating you might do against one of them.

There are actually 4 conditions that can occur:

we say that there will be rain, and there is
we say there will be rain, but there isn't
we say no rain, but there is
we say no rain, and there isn't

In cases 1 and 4, we're correct, and in 2 and 3 we're wrong. So, another score we can use is % correct. The benefit to this is that it measures both failure to predict rain and predicting rain that doesn't happen. The drawback is that it gives equal weight to both (either is a 'miss') -- while most of us are more concerned about one error than the other. In other words, no one score will tell us everything we want or need to know.

There are many more scores for this kind of situation. But this is enough complexity for us to start looking at using black and white vision to analyze sea ice extent, that is ice / no ice analysis with the satellite data.

For my first trial, I took the SSMI on F-15, the same 19 GHz, horizontal polarization, channel that ESMR had, for August 1, 2011. Then I lopped off all land points (no reason to give credit to an algorithm for noticing there's no sea ice on land), all northern hemisphere points (it's the Antarctic that's of most concern for the ESMR period), and all southern hemisphere points north of 48 S (an arbitrary round number -- the point being that we know before starting that there isn't and wasn't ice that far north from Antarctica). That trimmed things down to about 200,000 observations.

To start with, I just made a scatter plot of all the observed brightness temperatures against the concentration:

Given about 200,000 points, this isn't enormously clear. But at least there's a sense that warmer temperatures correspond to more sea ice. This is reassuring, surprisingly. The thing is, the ocean is a very poor emitter of microwave energy, so it looks very cold if you use a microwave channel -- as we are. On the other hand, sea ice is a pretty good emitter of microwave energy, so it looks much 'warmer' in terms of brightness temperatures. Remember, a brightness temperature tells us how hot an ideal black body would have to be to send us as much energy as we observe -- energy is the observation, not thermometer temperature. It's also true, however, that warmer ice emits more energy than colder ice. So part of what is happening in that plot is that high concentrations of very cold ice are showing up in different parts of the diagram than high concentrations of warm ice.

Having reassured myself that even with only 1 channel, and even with the ice temperature effect being ignored (for now), we get a plausible scatter plot, next is to think of some algorithm (method) for going from the observed brightness temperature to an ice / no ice decision. What I'll do is take the algorithm "If the temperature is above this number, ice is present in the field of view". And define "ice is present" to mean that the ice analysis has greater than 15% ice concentration. The 'this number', the critical temperature in my algorithm, I'll simply vary all the way from 80 K (colder than the coldest in the diagram) to 273 K (melting point of ice) -- and see what the scores come out to be.

If I take an extremely cold brightness temperature, I can get perfect Probability of Detection. But the False Alarm Rate it horrible, as is the % correct. The interesting zone, for our algorithm evaluation is between about 120 and 160 K. Probability of Detection is getting worse all the time through that range, but False Alarm Rate is improving (getting smaller). The % correct rises rapidly to its peak, at a temperature around 140 K and then declines slowly. Let's magnify the upper parts of the curves for PoD and % correct:

Our PoD is steadily worsening, but, thanks to improving FAR, the % correct is improving up to about 140-150 K, where the scores are about the same, 97% correct, regardless of the critical temperature we choose.

We're far from done, of course. This is just one day, we've paid no attention to the possibility of using surface temperature estimates (climatology, weather analysis, other satellites, ...) to improve our estimate, the algorithm is extremely simple, and so on. Also, this is a check for each individual observation. For obtaining estimates of Antarctic sea ice extents, we want full grids of ice / no ice decisions. Some of the observations are in the same cell as others, so perhaps the few we're getting wrong would be corrected by others that were in the same cell. And ... add your own in the comments!

Still, the first exploration here suggests that the 1 channel only method can give a probability of detection better than 95%, and % correct around 97%. We'll have to think some about whether false alarms are worse than failure to detect, and see how this holds up as we look at more days and methods.

Was ESMR screwy?

2013-04-23T05:30:00.000-04:00

A reader here also asked about the pre-1979 satellite data over at my question place. The thing is, we do have pre-1979 satellite sea ice data -- the ESMR (Electrically Scanning Microwave Radiometer) 1973-1976. It was a much simpler instrument than the SMMR, SSMI, SSMI-S, and AMSR which started flying in 1978 and since. The more recent ones have two very important improvements over the ESMR -- they use multiple channels (think of it as colors) and they use both horizontal and vertical polarizations rather than just total power.

Ok, translation to English. Our eyes look in three channels -- red, green, and blue. Different creatures use different numbers of channels. Dogs use only one, black and white (as we do if the lighting is very low). Mantis shrimp use 10 channels. Bees use ultraviolet. And so forth. The key is that the eyes respond to some number of colors. Numbers vary, and what color band also varies.

The other thing about electromagnetic radiation is that it can be polarized -- vibrating in one way versus another. The two linear polarizations are horizontal and vertical. ESMR just lumped them together and measured total power. SMMR and the rest measure horizontal and vertical polarization separately at most of the channels. Basically, SMMR and all the more recent instruments have high quality color vision versus ESMR being a rather fuzzy black and white.

But ... black and white is still better than not being able to see at all. The question then arises in retrospect whether we can use the black and white instrument, ESMR, like our more recent color vision instruments. Now, in part, I know the answer already -- you can't. More precisely, you can't do it well enough to satisfy my colleagues at NASA-Goddard. Perhaps, though, it can be done accurately enough to answer some questions of interest, even if not accurately enough to be entirely comparable to the modern instruments.

I have some ideas, naturally, and have been been pursuing them a bit -- enough to know that there's a fair chance of getting useful answers. Whether it's useful enough to answer questions of interest ... well, I'll also invite questions you all find of interest.

Forecast Contests

2013-04-22T05:00:00.000-04:00

I'll invite your suggestions for forecast contests to hold. In the mean time, some results from forecast contests at my work.

The winter contest was to predict the date of the first 2 inch (5 cm) snowfall at our official weather station. It never happened. I came close to predicting the date, sort of. Since we've had some memorable storms on or near President's day (February 18th this year), I went with that. Nothing noteworthy that day this year. But the next guesser was for May 1, so when we were getting forecasts of significant snow (4-8 inches, 10-20 cm) in mid-March, I was hopeful. Only 1.7" at the official station, though, so no luck for me. (If only we'd used any of the other area stations! All beat 2".) This is our second straight year of not having even one day with 2" of snow. Should probably adjust the standards to 1" (2.5 cm).

The summer contest had a winner before entries had even closed. One of the contests was to predict the first day that the official station would exceed 90 F (32 C). Entries open to the 30th of April, it happened the 10th if I remember correctly. The 7th earliest date ever. Spring here, apparently was April 8th and 9th. We're now on summer. Note to future: have to close entries on the summer forecast contest on April 1st or earlier. (Our earliest ever 90 F day was apparently late March -- 27th, iirc).

Both contests suggest that traditional weather forecast contests need some updating for changing climate.

For here, a couple of contests that came to mind, in addition to the 'traditional' guessing of the September average Arctic sea ice extent, are to guess when the atmospheric CO2 levels for Mauna Loa monthly average will pass 400 ppm, and when it will pass 150% of pre-industrial (420 ppm). One that can be done annually, guess the first week when Arctic sea ice extent will fall below the climatological (1979-2000) minimum extent, and guess how many weeks the ice will remain below that minimum.

Other ideas?

Life isn't simple even for a coral

2013-04-11T09:12:00.003-04:00

Heard an interesting talk yesterday about coral, and remote sensing of water temperatures and light levels as a means of tracking how they're doing. The effort was prompted by the major coral bleaching events in the last decade. I'm a physical, rather than biological, oceanographer, so my prior knowledge of corals is well-covered by a) coral bleaching events are bad for coral and b) coral are pretty. A good entry point on the web for the coral monitoring efforts is NOAA Coral Reef Watch, where you can find out much more.

One of the things that is important for coral is water temperatures. If water gets too hot, it's bad for the coral. That's old news at this point. The addition from this presentation was that coral also care about light levels. If it's too bright, that's also bad for the coral. They can adapt to some degree, over time, to high light levels.

As I suggest in the title, the situation is not simply those two things. High temperatures aren't good. But if the lighting isn't too strong, it's survivable. The presentation included observation of a time that had prolonged high temperatures, but the lighting wasn't very strong and the coral survived ok. A situation that didn't have as high temperatures (though still high) but did have excessive lighting resulted in much more bleaching.

It's also the case for coral, as for people, that it is sustained extreme conditions which matter. So, again, the concern is for heat waves, not hot individual days.

Consequences of the abnormal normal climate

2013-04-10T05:30:00.000-04:00

In last Monday's note, I concluded that climate was only 'normal' from 1936-1977. As with any science conclusion, this is not past discussion. But, as we often do in science, let's take that part as true and see where it leads us. If it leads us to something silly, then we have (more) reason to question our original conclusion. On the other hand, if it leads us to things that make sense, it suggests that the original, tentative, conclusion is possibly better than we originally thought.

So, if climate were 'normal' only between that span, give or take, is there anything else that can be concluded? Two things occurred to me pretty quickly; please do add more that you think of! One is about psychology and the other is engineering.

The psychology angle is captured by a phrase regarding "The Golden Age of science fiction". The comment being that the golden age is '13' -- as opposed to 1945-1955, or 'new wave', or 'cyberpunk', etc. Namely, the defining feature of the golden age is what the reader was reading when they were about 13 years old, what was current at that time in their life, not particularly the period of the writing itself. Applied to climate, this suggests that people who were 13 in 1936-1977 have in mind a period when the climate was 'normal' in last Monday's sense -- no real trend and just bouncing back and forth across a stable average. That's, now, people 49-90 years old.

The engineering side is that a huge fraction of national and international infrastructure was built between 1936 and 1977. To the extent it wasn't built then, it was designed by engineering standards that were developed within that span (I'm guessing some -- engineers in the audience please update). The US interstate highway system, for instance, was designed in the 1950s. But even engineering designs of the 1990s were built for '30 year storms' that refer to tables originally written decades earlier. This is eminently sensible if one thinks of climate as something that behaved the way it did 1936-1977. Not so sensible if you consider climate to be something that is changing on a routine basis.

It occurs to me that the ages 49-90 observation corresponds to some degree with the polling observation that disbelief in climate change, and disbelief in human involvement in climate change, shows distinct age connection. With age groups over 50 showing much more disbelief than those under. It certainly captures folks like Richard Lindzen, Pat Michaels, and Fred Singer. They did grow up / come of weather age in that climatologically unusual span.

Thoughts?

When was climate normal?

2013-04-01T05:30:00.000-04:00

It's been a couple years since I took up the question of normal climate, so time for another go. At that time, I used monthly data from Hadley, and arrived at the observation that if you're younger than 26, you've never seen a month where the global average as as cold as the 1850-2011 average, 317 consecutive months (at that point, now over 330) of warmer than 'normal' temperatures. I'll cheat and give you some answers first, read on to see how they're established:

Climate was 'normal' only between 1936-1977
Every year 1987-present has been warmer than any year before that
1976 was warmer than any year before 1926
1978 (next coldest year of the recent run) was warmer than any year before 1940

Do read on to see what 'normal' winds up meaning; it's important! One part of 'normal', as we intuitively think about it, is that you should some times above it, and sometimes below. Having many consecutive years above 'normal' says that normal isn't really very good. To help get quantitative about how to proceed, consider this plot of NCDC's data (warmer/colder than the 1880-2012 average -- the length of the entire record).

From this we see that every year 1977-present is above the zero line, much the same as we had in my earlier note. But, which I paid little attention to at the time, every year in the early part of the record -- everything to 1936 -- is also below the 'normal' (0 anomaly) line. A long run of negatives is as good a disproof of things being part of the 'normal' as a long run of positives. Further, 1976 is much colder than all the years which follow and 1937 is much warmer than all the preceding years. So this makes a good span, 1936-1977, to try to call 'normal'. It does still satisfy our requirement that to call temperatures climate we want at least 20-30 years, though, at 42, we can't afford to lose many off either end of the record.

This satisfies one of the traits for 'normal' -- no long runs of always above or always below normal at the start or end of the record. But another trait we often require is no trend. That is a continuation of the idea that climate is stable in some meaningful sense. The trend for 1936-1977 is indeed zero. So for this span, it is indeed reasonable to say that there's a normal climate that weather is bouncing around. Here's what the data themselves look like, using 1936-1977 for the reference:

This also looks a lot like what intuitions say should be the case for a 'normal' climate, not just the math coming out that way. As it's 42 years, it's long enough to satisfy our usual requirements for a climate reference. So now let's look at the whole record against this 'normal':

After seeing this, I have to say that our intuitive demands lead us to a highly unusual 'normal'. The 1936-1977 span is the only long span with a trend of zero. The first 30ish years show cooling, next 30ish are warming, the 42 year plateau 1936-1977, and then a 35 (and counting) year warming trend.

If by normal we mean what happens most often, then (for 30 year trends) what's most common is for climate to be changing, not to be some stable reference around which weather bounces.

Buy my son's book

2013-02-25T21:34:00.001-05:00

My son (step son to be technical) is now published in book form. The first of, I expect, many. _The Machine: A Field Guide to the Resurgent Right_, Lee Fang. It is available on Amazon. The publisher is www.thenewpress.com

In keeping with my blog, this is a research book. For Lee, that means research on money and messaging in politics. The text is good and readable. And there are plenty of citations to support the points in the text. As always, follow the citations.

Another point for the book is that in several cases, Lee is the one who did the original research. One of the more amusing parts is that some of that research, an interview with one of the Koch brothers, was played as part of an episode the Newsroom.

So yes, buy my sons book. But do so because it is well-researched and will shed much light on how US politics is now run.

Question Place

2013-02-11T05:30:00.000-05:00

Ok, looks like life is moving in a more blog-friendly way. So I'll hang out the shingle again for your questions, suggestions, comments.

In the mean time, I'll note a few additions and losses. Among the losses is that the blogger widget for showing the 10 most recent comments is broken. You can still subscribe to the comments. My notes about sites which seem to be inactive, but which have material you can still read and is worth reading, are now a 'page' -- one of the tabs near the top of the page, so a loss and an addition.

Also added to the tabs near the top are "The Simplest Climate Model", and "What is Climate?", which collect in one place, finally, the several posts I've made on each topic. There'll be more.

Time to go do some science

2013-02-07T10:17:00.000-05:00

NOAA/NWS/NCEP/Environmental Modeling Center has a request for data out, one which gives anyone near water who can read a thermometer a chance to do some science. There's a science history behind why this is a request and I'll give my own, biased, view of that.

All data have errors and are messy. Though George Box's comment is often repeated at modelers ("All models are wrong, some models are useful.") it is equally applicable to data and data analysis. All data are wrong, some data are useful.

In the case of sea surface temperatures (sst), efforts to analyze global ocean sst started with badly distributed data sources -- ships. They give you a fair idea of what the temperature is along the paths the ships take. So the ship route between New York and London is pretty well-observed by ship and has been for a long time. But not many ships go through the south Pacific towards Antarctica. If you want to know what's happening down there, you need a different data source. One such is buoys. Though, again, buoys are distributed in a biased way, being mostly towards shore (so that they can be maintained and repaired).

Then came satellites and all was good, eventually, for a while. Polar orbiting satellites see the entire globe. Starting with instruments launched in the early 1980s, it has been possible to make pretty good analyses of global sst, at least on grid cells 50-200 km on a side. Since that is as good or better than any of the ship+buoy analyses could do, that was a great triumph. The ship and buoy data, though, remained and remains important. One of the problems that satellite information faces is that the instruments can 'drift', that is, read progressively too warm, or too cold. To counter that possibility and other issues, the surface data (in situ data) is used as a reference. So for a time in, say, the early 2000s, all was good.

But both scientists and users of scientific information are never satisfied for long. For sst, some of the users are fishermen -- some fish have very particular temperature preferences. As it became possible to do a pretty good global 50 km analysis, with new data over about 2/3rds of the ocean every day, scientists and users started demanding more frequent updates of information, and on a finer grid. They also got increasingly annoyed about the parts of the ocean that only got new observations every 5-20 days. This includes areas like the Gulf Stream, where it is often cloudy for extended periods. The traditional satellites are great, but don't see through cloud.

Another major user of sst information is numerical weather prediction. When weather models were using cells 80-200 km on a side, the sst at 100 km (say) was a pretty good match. But weather models continued to push to higher resolution, so that by the early 2000s, 10 km grids weren't unheard of. The reason for such small grid spacing in weather prediction models is that weather 'cares' about events at very small scale. If weather cares about those smaller scales, then it become important to provide information about sst at the smaller scales. An inadvertent proof of that was made when a model made a bad forecast for a December 2000 storm, and the cause was traced back to an sst analysis that was too coarse. See Thiebaux and others, 2003 for the full analysis.

Plus, of course, there is interesting oceanography that requires much finer scale observations than 100 km. So a couple of different efforts developed. One was to use microwave data to derive sea surface temperatures. AMSR-E was the first microwave instrument used for sst in operations, as far as I know. (Sea ice isn't the only thing that you can see with microwaves!) That addressed the issue of seeing the Gulf Stream (and other cloudy areas) most days. The other was to start pushing for higher resolution sst analysis. This lead to an international effort to analyze the global ocean at high (say 25 km and finer, sometimes 10 km and finer) grid spacing. More is involved in that than just changing a parameter in the program. (You'll get an answer if you do that, but it won't be as good as you had at the coarser grid spacing).

On the ocean side, this quality of the high resolution analyses is doing relatively well. But as you go to finer grid spacings, new matters appear. The Great Lakes are very large, so they can be seen by satellite easily, and they have buoy data through at least part of the year, so that the satellite observations can be corrected at need. But ... go to a finer grid spacing weather model and you discover that there are a lot of lakes smaller than the Great Lakes. For a 4 km model, there are some thousands of lakes just in North America. And none of them have buoys, and almost none even have climatologies. Also at this grid spacing, you start seeing the wider parts of rivers.

Here's where an opportunity arises for people who live near a shore (whether river, lake, or ocean). NOAA/NWS/NCEP/Environmental Modeling Center is requesting observations of water surface temperatures to use as a check on their analysis of temperatures in areas close to shore. (close equals, say, up to 50 km (30 miles), and at least 400m (quarter mile) from shore). Check out the project's web page at Near Shore Lake Project

As always, I don't speak for my employer or any groups I might be a member of. I'm pretty certain that all people who work on sst would disagree with at least parts of my above mini-history. Be that as it may, it should be a fun project.

Sea level's climate time scale

2013-01-31T05:30:00.000-05:00

My 'reality-based decision making' post prompted a comment asking for my thoughts about sea level rise, which is more than sufficient excuse to turn to that. An additional excuse is that it provides a chance to look at how to decide climate time scales for something other than temperatures. For global mean temperature trends, I found that you need 20-30 years to determine a climate trend. We'll see that it is 40-60 years, 60 for preference, for sea level.

My starting point for data was the University of Colorado sea level group. They provide satellite data back to late 1992. High quality data, but only for a short period of time. If global sea level's time scales are like global mean temperature's, then it's only just gotten long enough to provide a climate number. Fortunately they list links to other sea level groups, including the Permanent Service for Mean Sea Level. They have three global reconstructions available. I'll take this one -- published in the scientific literature as: Recent global sea level acceleration started over 200 years ago?", Jevrejeva, S., J. C. Moore, A. Grinsted, and P. L. Woodworth (2008), Geophys. Res. Lett., 35, L08715, doi:10.1029/2008GL033611 -- on the grounds that it covers the longest time period and has the most recent literature publication date. It will be a good project for a reader to see if the conclusions here change, and how, if you use one of the others instead.

My starting point with any data is, plot it and start to get a sense of what we're looking at. This includes not to draw lines between data points, as the lines can mislead me as to what's going on. Try it some time. The lines typically make the data look much smoother than they do if you leave them off. That's not always a help, as it can push you to thinking that you have a significant relationship that you don't. Anyhow, the first plot of the 300 years of data is:

Two things seem obvious from this figure. First, the data before about 1880 have a lot more scatter than the more recent data. Second, the change in sea level is markedly more rapid in the more recent half than the first half. Read the paper for the discussion of the change in rise, how confident you can be that it's truly different and not just a matter of noisy data, and so forth.

For our purposes, I'll start with 1880, same as the NCDC global temperature record, and look from there for the climate time scale on sea level. The straightforward plot of the data is now:

This is markedly better-behaved. Looking. But it also puts me in mind of an old post of mine on climate normals using cumulative sums, where Tamino observed at his blog that there are distinct problems with using cumulative sums. I didn't/don't think that his concerns truly applied to my application then, and you can follow that up in some update articles I wrote later (links in updates at the end of the article.) But here ... I'm more concerned about those issues.

Sea level is the cumulative sum of changes in sea level. When you get to calculus, you'll find the term is sea level is the integral of sea level change, or, equally, sea level change is the first derivative of sea level itself. Physically, we're not so much concerned with sea level itself, at least in the sense of how many meters from the top of the ocean to the center of the earth. We're more concerned with the change -- if sea level gets to be higher than it used to be, it can drown ports, amplify flooding from storm surge, and so forth. As we eyeball the data above, we see that there's a strong general trend towards higher sea level. On the other hand, if we look very closely, we see that there are some large changes year to year above and below the main body of the trend.

Since, again, eyeballs can be fooled, let's do the more direct thing and compute how much sea level changed from one year to the next and plot that:

Whew! This is some serious scatter! The average is 1.9 mm/year over the 123 years (1880-2002) of the record. But in any given year, the rate can easily be 5 times that -- either as a rise or a fall. (The standard deviation is 9.2 mm/year.) This suggests (an exercise left to the reader*) a time scale of about 60 years for looking at sea level's climate.

I'll return, though, to my simple approach of looking for a time period around which the estimates of trend don't change much as you change the length of the period. Below are figures for trends ending in 2002, starting from the noted year (working with the whole time series now, not just the recent segment), and for trends ending in 1940 (from 1700-1939) or starting from 1940 (ending in the noted year).

It's no challenge to see that within 20 years of the year we reference, the trends are wildly variable and sensitive to just how long a time period you take. So anything less than 20 years needs some much more sophisticated analysis methods than I'm using on this blog. (You'll see some examples of what more sophisticated means when you hit the scientific literature.) To get some more or less consistent trend estimates, you need 40-60 years of global sea level data. 60 for preference, according pretty well, even though it didn't have to, with the estimate above from mean vs. standard deviation.

In some of my fooling around with the data, there was some eyeball suggestion of a 60-ish year cycle in sea level change. That encouraged me to look to the scientific literature for whether a 60-ish year cycle has been noticed as a possibility already. The answer is yes, and it appears to be even more complex than this simple analysis would suggest. See Is there a 60 year oscillation in global mean sea level?, also at the University of Colorado sea level group's page.

If there's a strong possibility of 60 year cycle in the data, then you definitely want to average over the 60 years. My simple fooling around with it suggested that the magnitude of the cycle is about 20 mm, with recent years having been at the bottom of the cycle. That would suggest that using only simple analysis of the data for recent years would be leading to sizeable underestimates of sea level rates of change. As the scientific literature is already suggesting the rate of sea level rise has been increasing, that's not comforting news.

So, where are we? Well, on one hand, it suggests that trying to understand sea level change only by looking at data is a much more difficult matter than looking at global mean surface air temperatures is. Not least, that you need 60ish years of data to establish a 'climate' in sea level, while temperature can be done in 20-30 years. On the second hand, the large variability in year to year sea level change says that there are very large sources and sinks for water that can operate for a year or two (or several). "What are they exactly?" On another hand, it says that there must be something interesting to learn about the climate system -- "Why is sea level change so variable as compared to surface air temperature?

Therefore ... more posts will be coming on sea level. I was quite surprised by the year to year variability in sea level.

* 'exercise for the reader' is notorious textbook-speak for "this can be done, but it'll take a lot of time and be more involved than I feel like doing here". If you know your college intro. to statistics material, the math I'm referring to is obvious. If you don't, it'll take a fair amount of time to explain it.

Looking back at blogs 1

2013-01-11T05:30:00.000-05:00

Some of these might still be active, at least the author might be. Please let me know the new locations if I don't have one. If not, well, they still wrote some articles worth consideration.

Trees for the Forest

Looking at GHCN temperatures

Rust Never Sleeps

Old Man in a Cave

mutantClimate

Rationally Thinking Out Loud (new location) http://rationallythinkingoutloud.wordpress.com/ -- old location

Respectful Insolence -- The blog certainly has continued, this is the link to the new feed location. But some good older articles on science, in particular medicine, vs. nonscience.

The Questionable Authority Old location: http://scienceblogs.com/authority/, New location: The Questionable Authority

The Middle Way (low volume, more philosophical, blog, by a friend)

Why won't they listen?

Vickie's Prostitution Blog (my wife)

Bits from 2012

2013-01-10T06:06:00.000-05:00

Some items that interested me in 2012 that I never made full posts from ...

As you've no doubt heard, the 5th review from IPCC is in progress. I passed up yet another opportunity to be an expert reviewer.

One method of getting science together is a special issue or theme issue of a scientific journal. One that crossed my desk and I thought might be interesting to blog readers are:

Call for Papers – Climate Consensus: Steps Toward a Global Understanding of Climate

And, of course, educational meetings/workshops/'summer school':

European Earth System and Climate Modelling School sponsored by ENES

A major method (with much advertising, hence the longer list) is scientific meetings. It sometimes seems like people think there are only 5-10 scientists in the world, or at least in any given area. Below are some sessions at meetings from 2012. Each session probably has something like 100 people present (anywhere from 30-300).

EGU 2012 - 22-27 April '12, Vienna, Austria

Projections of future atmospheric composition
RECONSTRUCTING THE CLIMATE OF THE LAST TWO MILLENNIA
Decadal, seasonal and monthly forecasts
Towards a full GHG (CO2, N2O and CH4) balance from the biosphere.
Stochasticity and Statistical Physics in Climate Dynamics
Ice-sheet and climate interactions
Earth Radiation Budget, Radiative Forcings and Climate Change
Precipitation uncertainty and variability: observations, ensemble simulation and downscaling
Complex networks: Theory and methods applied to geophysical systems
Hydrogen and oxygen isotopes: From process studies to climate reconstructions
Climate Sensitivity
Geostatistics for Space-time analysis of hydrological events and environmental problems
Nonlinear, scaling and complex Physical and Biogeophysical Processes in the Atmosphere and Ocean
Modelling paleoclimates from the Cretaceous to the Holocene: learning from numerical experiments and model-data comparisons
Homogenization of Climate Data Series and Assessment of Variability in Climate Trends

ESF conference "Modes of Variability in the Climate System: Past-Present-Future"
The Max Planck Institute for Meteorology and the COMBINE project are pleased to announce the 3rd International Conference on Earth System Modelling (3ICESM). The 3rd ICESM will take place from 17-21 September 2012 in Hamburg, Germany, with the objective of advancing discourse on Earth system modelling prior to the 5th Assessment Report of the IPCC.
We would like to invite you to attend the first meeting of a new working group associated with NCAR's Community Earth System Model (CESM) that is focused on societal dimensions of earth system modeling. Development and application of CESM is organized into twelve working groups addressing various aspects of the model (<http://www.cesm.ucar.edu/working_groups/>). Earlier this year, the CESM Scientific Steering Committee approved the new Societal Dimensions Working Group (SDWG)
4th WCRP International Conference on Reanalyses 7-11 May 2012
The 7th Antarctic Meteorological Observation, Modeling, and Forecasting Workshop
Alaska Weather Symposium
The CONVEX project () is investigating the skill of climate and weather models in the simulation of extreme rainfall
Insights to the modern and palaeo carbon cycle: an isotopic and biomarker perspective
Spanish Association of Climatology (AEC): Climate Change. Extremes and Impacts that will be held in Salamanca, Spain from 25-28 September 2012.
Antarctic Science and Policy Advice in a Changing World
Urban Futures for a Sustainable World
Call for Abstracts: The 69^th annual meeting of the Eastern Snow Conference
Climate Change and Extreme Events
Uncertainty in Climate Change Research: An Integrated Approach
Seasonal to decadal climate predictability and prediction
Climate Informatics 2012: The Second International Workshop on Climate Informatics
AGU Fall Meeting

Observational Needs for Polar Climate Modeling
Climate 2.0: Usable Science for Society
Links Between Rapid Arctic Change and Mid-Latitude Weather Patterns
STREAM TEMPERATURE: UNDERSTANDING DRIVERS OF CHANGE AND IMPROVING PREDICTION
Quantifying Heterogeneity of Landscapes and Ecosystems in Earth System Models
When Winter Changes: Hydrological, Ecological, and Biogeochemical Responses
The Cryosphere and Seasonal to Decadal Prediction
Large-Scale Data Analytics in Earth System Science
The International Polar Initiative

Abrupt Changes and Extreme Events - Assessment and Risks
Arctic Hydrology in a Changing Climate (2013)
First Conference of the International Society for Atmospheric Research using Remotely Piloted Aircraft

And, again, these are a modest subset of those I got email regarding, and happened to save for blog purposes. The AGU fall meeting has something like 10,000 scientists, discussing everything from mineralogy of volcanoes to aurorae to climate to fossils. I expect the EGU meeting is similar in size and scope. (AGU = American Geophysical Union, EGU = European Geophysical Union).

Happy New Year 2013

2013-01-09T09:20:00.003-05:00

Happy near year!

I see that I have been away from here for quite a while, including past a spectacular (again) new minimum in Arctic sea ice extent this past September. Certainly lost me at least one of my bets with Alastair, maybe both. I'll be looking in to it and write up a full evaluation of the predictions I worked with.

One of the reasons for the quiet here is that I've been working on articles for professional journals. 2 now out in Deep Sea Research, 1 to be appearing in Ocean Modeling, 1 already in review at Weather and Forecasting. And two in pre-submission review, one for Climate Dynamics and another for Weather and Forecasting. 4 more in progress towards the internal review stage -- one a technical note on what I've been doing the last 15 years for sea ice concentration analysis, two for the Journal of Climate, and one for sea ice modeling. Not sure where/how I'll be publishing that. Somehow the brain cells for writing get fatigued, so if I'm writing much at work, I don't do so much here. The exercise is starting to pay off, though, so they seem to feel like they can do both now.

Good news in the background is that I'll be producing better plots when I do start more regular posting here. Had to bite some bullets to get figured out how to make publishable plots for the journals. More of an issue than it should have been. But now resolved well enough, with the side benefit that it also lets me make better plots for blogging.

The Relativity of Wrong

2012-09-06T05:30:00.000-04:00

Recent political comment brings me back to a point I'd written about before, under the label of Successive Approximation. The better title, unsurprisingly, is Isaac Asimov's The Relativity of Wrong.

I first became aware of Paul Ryan's lie when my wife asked me, having mentioned nothing about context, whether a runner could conceivably mis-remember his marathon of somewhat over 4 hours as having been somewhat under 3 hours. As has been reported in many places, and some reporters having done some experiments, the answer is no. It is completely implausible that someone, especially someone with only one marathon, could make that mistake as an honest mistake. Confusing a little over 4 for a little under ... maybe. You would likely have trained for some time under 4 and that number could have stayed with you longer than your disappointment in not making your goal.

But confusing your over-4 hours, a perfectly respectable time to cover a marathon if nothing to brag about for an open class man, for sub-3 -- a time that entitles you to some bragging (as a friend worked to earn the right to) and is achievable only with some serious training and talent -- is not an accident.

The political side of this, well, not so very interesting. Except for the fact, perhaps, that Ryan is supposed to be a leading light for Republican number-crunching. Number types are even less likely to make a mistake like that. At least as a mistake.

More interesting, and perhaps useful, to the local purpose is to look some at how to assess the relativity of wrong. The route I took in the successive approximation post was quantitative. Some articles have taken something like that, observing that his claimed time was about 70% of his actual time, or, conversely, that he lied by about 30%. That's actually much too generous to his lie. The problem with that is that if he had claimed the world record time (a bit less than 2 hours faster than he ran, as opposed to the somewhat more than 1 hour of his actual lie), it would only be scored as 50% wrong. You can't run faster than the world record, or you'd be the record holder, which even Ryan didn't claim. So, the most he could have lied by is about 2 hours (iirc his actual time was 4:01, and the world record back then was about 2:07, so 114 minutes). His actual lie is about 65 minutes (taking 2:50-something as 2:56) wrong, so 57%, more than half, of the span that he could possibly have lied by. That could be refined some by comparison against, say, the race winner's time, or the Olympic qualifying time. But you get the idea. And the 57% is about double what the other approach shows. Compare how wrong something is with how wrong it could be.

But some things, and this is where I'll especially invite comments, don't lend themselves to that kind of quantification. For instance, I'm comfortable with saying that it is more wrong to say that the earth has no greenhouse effect than it is to say that CO2 is not a greenhouse gas. But how can we assess this comparative wrongness with the error in saying that CO2 levels have not risen over the past 200 years?

So I'll invite description of approaches to assessing relative errors, preferably with examples. Note, too, that it's best to have them be objective examples -- so which candidate is better kinds of things are not going to work. Marathon times and the radiative properties of the earth's atmosphere aren't partisan matters.

There is no greenhouse effect

2012-07-23T05:49:00.000-04:00

Quote-miners will love that subject line, and it isn't a statement of my belief. But it's been recurring some that there are people denying that anybody believes that there is no greenhouse effect. Yet, typically on the same day as that claim, I keep seeing people deny that there is a greenhouse effect. It's also common enough that Fred Singer, who probably would label me as a 'warmist', has made his own complaints about people denying that there is a greenhouse effect.

Nevertheless, it's always a good idea to check in more systematically to what is really out there. The search here will limit itself to Google searches which show up for the exact phrase "there is no greenhouse effect" and are within the past year. Alas (I keep giving away the surprise ending) it turns out that there really is no difficulty at all in finding sites which claim that there is no greenhouse effect. And, of course, are wrong in doing so. If someone were to demonstrate it and be correct, I'd have to be nominating them for major scientific medals. No such concern with these.
The arguments claiming to 'disprove' the greenhouse effect's existence seem to fall mainly in to 3 groups. I add the usual 'other' category since a philosopher friend has noted that all classification schemes wind up with one.

The first group, the 'linguistic argument' is the silliest. The problem with it is to mistake the words used to describe something with the thing itself. And then consider that if you can find a problem with the words, that there's nothing being described. Poof, it's gone. In this case, that if greenhouses don't operate by the 'greenhouse effect', that there is no greenhouse effect in the earth's atmosphere. I discuss it at more length in Greenhouse misnomer. The thing is, the words we use don't change the reality we're trying to deal with. The earth's atmosphere, due to water vapor, carbon dioxide, and some other rare gases, is fairly transparent to solar radiation and absorbs the earth's radiation pretty well. It's been suggested that we call it 'atmosphere effect' or 'Callendar effect' instead. They might be better names, but, regardless, whatever words you use, the fact of selective absorption of energy by the atmosphere remains.

The second argument also relies on giving words supremacy over the reality they're working to describe. One of the may verbal descriptions of the second law of thermodynamics is that 'heat doesn't spontaneously flow from a colder source to a warmer one'. But that's only a partial description -- as usual, the statement requires that you make some assumptions. Those assumptions aren't all true when considering the flow of energy by radiation in the atmosphere. In order to apply the second law properly, you have to sit down with the mathematics. If you don't want to, or can't apply the mathematics, at least remember that the first law of thermodynamics regards the conservation of energy, not 'heat'. Radiation carries energy, as does the motion of particles, the elevation of those particles (such as make up the atmosphere above ground), and other things. 'heat' refers only to temperature. The conservation of energy applies to all, and means that if radiation goes from here to there, there gets hotter (has more energy).

Venus supplies the third argument, which strikes me as bizarre, but, then, so does denying that there is a greenhouse effect. If you look at Venus, particularly at the surface, it is exceptionally hot. Far hotter than its blackbody temperature (about 224 K, colder than the earth's 255 K !) would suggest, and far hotter than Mercury -- which is closer to the sun and you'd expect to be hotter than Venus. The reason for that exceptional warmth is the extreme greenhouse Venus has due to its extremely heavy greenhouse atmosphere. It has about 90 times the surface pressure of the earth, and almost all of that is due to carbon dioxide, versus the Earth's about 0.04% Ok, that makes it apparent why someone who would want to deny that there's a greenhouse effect (or at least that CO2 isn't a greenhouse gas) would go to Venus.

The argument, however, is absurd. I haven't gone in to detail about this yet, but there's a concept called 'potential temperature'. This is the temperature that a blob of gas potentially has -- if you moved it in a plastic bag that perfectly insulated it against heat conduction or radiation but was fine with shrinking to fit your blob as you moved it from where it was to the surface. There is an old saying that 'hot air rises', which runs in to a bit of a problem with the fact that at 10 km elevation (the tropopause in mid-latitudes) the local temperature is far colder than the surface is. If hot air rises, why is that much higher air so cold? Because the potential temperature is so high for that air. If you lowered that blob to the surface, it would be much warmer than the surface air. Take a tropopause temperatures of, say, 225 K, versus surface temperature of 300 K. By the time you brought that blob down to the surface it would be 325 K -- it really is the hotter air.

The argument relies on a ... well, I don't know what to call it, but it isn't honest or accurate. The argument relies on taking the (observed) temperature at some large height and then bringing it down to the surface and saying that this potential temperature explains why the surface is hot. It's a falsehood, though, because it doesn't explain why that temperature isn't reached until the great (observed) elevation. If there were fewer greenhouse gases in the atmosphere, that elevation would be lower is the truth that is being ignored. It is the balance between incoming energy, albedo (reflection), and greenhouse effect which determines the temperature through the depth of the atmosphere.

Linguistic argument

Second Law Argument

Claes Johnson
Climate Clash
Antigreen
Slaying the Sky Dragon (John O'Sullivan)
Webcommentary (John O'Sullivan again)
Climate Change Dispatch (O'Sullivan yet again, in yet another location; he also claims that a 33 C warming is 91.4 F, illustrating a certain lack of familiarity with at least one of the temperature scales and temperature change [1]).
Principia-Scientific (and again)
World-Mysteries (not O'Sullivan)
Radical Green Watch (back to O'Sullivan)

Venus is warm because of surface pressure / lapse rate, not a greenhouse effect

Other / Multiple

Climateclash
.pdf argument from Hans
BMX Forum
Incentiveseverywhere
Icecap (Wednesday, August 3, 2011 -- scroll down)

The links show some overlap, citing each other or the same, somewhat older, sources. This takes us past the 20 links standard. Peruse them yourself, of course. That's rather the point. That, and the reference for future use that there are indeed people (and sites to publish them) who deny that there is such a thing as a greenhouse effect.

We also see that some of the same names are showing up. We've previously seen icecap and 'climaterealists' on the blog here as unreliable sources. More of the same. And several others up there, I've seen in my other looking around -- such as the oft-reprinted + rewritten John O'Sullivan. There's a certain persistence involved.

In doing this look-around, I also noticed the 'there is no greenhouse effect' argument getting unfriendly response from WUWT and Jo Nova's. Notice also that I'm quoting Fred Singer above, and Roy Spencer for one of the physics descriptions.

Career Day Educational Paths

2012-07-18T04:00:00.000-04:00

Scientists spend a lot of time learning things, so it isn't unreasonable that the path to a career in science includes a lot of time in school. Along the way, though, remember that it is the learning things that is the important side, not so much the grades. For me this meant, for instance, taking optional classes that I was not necessarily going to get good grades in. But I learned a lot in them, more than if I'd taken the safer, easier classes. That has served me well.

College is the first part of the path. College expenses have soared since I was in school. But the method that worked for me is still available. Namely, we had very little money at home, to the point where no 4 year school was affordable. I had worked my junior and senior years of high school, not that it would have come near covering college costs, but it helped give me at least some spending money in college. The main thing was to select several schools and see who would come up with a good enough financial aid package for me to afford to attend. I wound up with the maximum in loans, the maximum in state and federal grants, the maximum in summer job earning requirement, maximum in work-study, and an aid plan that meant I'd graduate with zero dollars in savings. The rest, which was a lot, was scholarships from my school -- Northwestern University. That meant that I'd wound up at the most expensive school I'd applied to. The least expensive was my state school, which said that they'd give me much less than they thought I needed (and agreed with Northwestern about how much I and my family could come up with). Easy decision, even though they'd originally been my first choice.

Something that is more an option now than back then is to spend your first two years at a community college. Expenses are much lower, and the standard freshman chemistry/biology/physics/calculus are taught by people who are interested in teaching them, versus four year schools where it's often viewed as undesirable to teach such classes. I took Calculus III and Ordinary Differential Equations at my local community college and was very happy with the results.

As I was selecting colleges, I heard that the typical college student changes major 3-4 times. So in addition to the Electrical Engineering and Computer Science that I planned to major in, I also required that the school have a good Astronomy department and one or two other things. This helped narrow the field, and it ensured that if I decided I didn't like what I started with, I could change major to something else and still be in a good department. First I changed to just Electrical Engineering. Then to Applied Math. My area of application was originally supposed to be fluid dynamics, but that sequence was cancelled. So I jumped over to Astrophysics for my application area.

A couple of things I did in college worked out very well, and even better for my sons since they didn't have to take time to figure them out after they got to college. First, regardless of what area(s) you're interested in, join up and be active in the student groups for that interest. Different fields have different personalities, so you can get clues about whether you'd be happy in that field early on. The student groups also have more information on just what the field is like. Also join the more general groups, like Society of Women Engineers or National Society of Black Engineers (two excellent groups on my campus, probably our best-run).

Second, is to make some kind of connection -- maybe a job, maybe volunteering -- to work with someone in research. This is what I did for work-study the last two years of college. It gave me excellent practice at doing science as opposed to just taking classes about science. And it gave me a good working relationship with someone active in a field I was interested in (ice ages and climate change).

Thanks to my experience working with a professor while I was an undergraduate, I realized that my graduate school experience would depend strongly on whether my adviser was someone I could work happily with. You spend a lot of time with your adviser. If you're dreading each meeting, every day, it's going to be a very long and unpleasant time in school if you even get the degree. On the other hand, there are a lot of different people and types of people, even within the department. And many different departments in the country. Again, I was not as concerned about exact area of research I would do -- the universe is interesting. At one school, I'd have been doing theoretical climatology, at another I'd have been doing numerical models of tornadoes. At the school I went to, the University of Chicago, it was polar oceanography. While I was happy enough with the people I talked to at the other schools, my adviser and several other faculty were a step above in our conversations.

After graduate school, it's likely that you'll spend time in a postdoctoral position. Almost certain in biological sciences, likely in physical sciences. I earned an unrestricted ocean modeling fellowship -- meaning that I could do my ocean modeling at any school, with any adviser, that I chose. It's a great setup, though rare. More typically, you'll be reading help wanted pages of your professional society's web site. Anyhow, during this phase, be looking for your next job starting from day one. (I waited, which was not a good idea.) Most postdocs are only a year or two, so you'll need to be looking either for your next postdoc or a longer term position.

Every two or three years, there is a flurry of reports about the 'looming terrible shortage' of math/science/engineering people. Often, they include comments about how anybody who earns a degree (or, specifically, doctorate) in these areas will be flooded with offers. These articles have been common since I was an undergraduate, and I've seen sources saying so since the mid-1960s. There has never been a shortage in the sense of fewer applicants than jobs. The major report that came out when I was in school, which contributed to a surge in graduate students in math/science/engineering, turned out to have used fewer than 3 applicants for every 2 jobs as its definition of 'shortage'. My friends who were among the 1 in 3 who did not get jobs in science disagreed with that definition. Things are better now, but there is not, and never has been, a guarantee, or a shortage. So thinking about your job hunt much earlier than I did (not until after I defended my thesis) is a good idea, basically a requirement.

Career Day Biographical Notes

2012-07-17T04:00:00.000-04:00

I'll be talking with a career day crowd Friday, which reminded me that many of the questions the coordinator offered for the speakers to consider are also relevant to my purposes in the blog. For this note, I'll take up the more biographical side of things.

The only thing I can suggest is universal in scientist biographies is that we all think, and did so from an early age, that the universe is very interesting. Or at least some part of it is. I wasn't very excited about insects when I was young (they're more interesting to me these days, now that I'm ... less young). But a friend who is an entomologist, with particular interest in bees, has always been. It seems common, which saddens me, for kids to be taught not to ask questions, and not to find the universe so interesting, somewhere between, say, 10 and 18 years old. Scientists are ones who never lost that interest. The proverbial childlike sense of wonder about the universe is with us still.

Often that wonder and interest translates to doing a lot of learning. Sometimes we did it in school, and sometimes on our own. Not all of us were interested in school, or got particularly good grades in it when young. If not in school, then many did their learning by a lot of reading on our own (my path) or going out and observing the world (my biologist friend). But there are also scientists who weren't terribly interested in studying or practicing science prior to college; interested in the universe, but not so much or in a way that they'd start watching the bees in their back yard, or reading their way through the library.

My path also included a small telescope, messing around with electronics, taking apart clocks (they were mechanical in those days!), playing and watching baseball, running around, swimming, watching some good TV shows, and watching a lot of bad TV shows. And I read a lot -- some math, science, and history, and a lot of science fiction and mysteries. The telescope was the sort of 'Christmas' telescope that serious amateur astronomers intensely dislike -- poor mount and not very good optics. Worse, I sometimes used it watching through the window (you can hear their wails from here). But ... bad as it was, and my use of it ... it opened a new universe to me. I could see Jupiter's moons, that Saturn was blobby (not good enough to show me rings), and a huge increase in number of craters on the moon. I was practically Galileo!

Through the end of high school, at least, I haven't noticed much difference between the people who eventually became scientists and those became engineers. All the preceding applies to both. Indeed, in high school, I'd decided I was going to be an engineer -- Electrical Engineering and Computer Science (I was going to get both degrees). Conversely, the descriptions above apply to many people I know who never went in to science or engineering. My exchange student son, for instance, went in to business and now works in IT at the Deutscher Bank.

You don't have to be good at math to be good in science. Needs saying. I was, so I do kinds of science that use a lot of math. But not everyone is, and even those who are ok with math don't always like to do it. There are areas of science that don't use much math.

Which brings up the suggestion end of things: Try a lot of different things. Try math, biology, physics, chemistry, meteorology, oceanography, just plain walking through the woods, and watching city pigeons. Make mud pies, run, play sports, learn a musical instrument, learn languages. Do some reading, some observing. Fool around with ideas from my project folder. Make up your own projects and see what happens.

For the parents, do support and encourage your kids in trying things, but don't suspend your parental judgement. The idea of trying lots of different things, without worrying about whether you're good at them, is one my mother applied in raising my sisters and me. It was one of her most brilliant ideas, which I've stolen for my own parenting. But this didn't stop her from steering me away from inventing my own rocket fuel when I (a very clumsy 10 year old) was interested in trying that.

Reality Based Decision Making

2012-07-16T06:06:00.000-04:00

I have a radical idea - "reality-based decision making". I'm in favor of it, is the apparently radical part.

Reality: Local sea level is rising, and the rate of rise has been increasing. (Note, by the way, that this isn't true of all areas. Global mean sea level is rising and accelerating, but some local areas are seeing a local sea level fall -- the land is rising faster than the water due to solid earth activity. But the examples below come from areas where it is true.)
Reality: This increases the area that can be affected by storm surge if nothing changes.
Reality: It means some areas currently occupied will go below sea level if nothing changes.
Reality: Those previous 3 mean that if nothing changes, more people will be affected, possibly killed, than already are, each year or decade.

Decision? Well, I don't know. Of course I know what I prefer, but that's neither here nor there. I live more than 30 meters (100 feet) above sea level. No plausible storm surge or sea level rise for the next several centuries is a risk to my house.

Given those realities, it would be reality based decision making to respond:

I don't care, let the low-lying areas drown.
Let the buyer beware.
We should rezone to have less property in the way of the storm surge.
No new building in the areas that already flood more than once a decade.
If anything bad happens, we'll declare a state of emergency and let taxpayers from the rest of the country bail us out. Too bad, though, for the people who are killed.
We'll build a dike to keep the water out. After all, that's what the Dutch have done.
The cost of response is greater than the value of the lives and property that would be destroyed, so don't respond.
The cost of rebuilding after this predictable event destroys the area is good for the economy.
....

What isn't reality based decision making is to respond:

You can't make any decision about sea level response for at least 4 more years (passed both houses in NC, might be vetoed by the governor)
You cannot use your scientific understanding of the issue to make your projections for the future (only past trends were allowed by this bill) (this passed the NC senate, but did not pass both houses)
Sea level rise is a UN Agenda 21 conspiracy
Ice melt can't change sea level
God won't let climate change hurt us*
Delete reference to climate change from scientific reports. "We stayed away from human-induced climate change, but we felt like we had to talk about sea-level rise," he said. "After all, it's been happening for 12,000 years. We were surprised the data on sea-level rise became a contentious issue."
... your contributions here ...

* Whether this is a reality depends on your religion and religious faith, which is why I include it here. Whether sea level is changing does not depend on your religion, merely on whether you can read water level records.

The reality-based responses cover the range of what political groups in the US have argued, I think. I don't think acknowledging reality on sea level dictates your response. It seems many, whether they agree with me or not on preferred response, disagree with that.

In any case, I don't think that we're going to be making good decisions for ourselves and our posterity if we ignore reality. It seems one motive for the unreal arguments and mandates is a desperate desire to get a particular result. It's the school of "The ends justify the means".

Summer Questions

2012-07-10T06:36:00.000-04:00

Been a while since I hung out the shingle -- so here's a place for your questions/comments/suggestions that don't fit with any particular post.

On a different matter, I've noticed that my older posts don't get comments. This strikes me as odd because I don't close comments. And recent comments are always shown at the bottom of the page, along with a subscription to comments rss feed on the right hand side. If they weren't being read, no surprise that there are no comments. But some posts have had most of their reads months and even years after the original appearance. Any ideas?

Logical Fallacies and Scientific Method

2012-07-09T06:34:00.000-04:00

Cracked had a very nice article on logical fallacies -- that we all make as a matter of course. Also some good illustrations and suggestions. Aside from the fact that it was a humor magazine that had such a nice article on rational thought, I was struck by the fact that each of the points mentioned are ones that the practice of science has addressed.

The 5 natural fallacies mentioned are:
5. We're Not Programmed to Seek "Truth," We're Programmed to "Win"
4. Our Brains Don't Understand Probability
3. We Think Everyone's Out to Get Us
2. We're Hard-Wired to Have a Double Standard
1. Facts Don't Change Our Minds

Let's take a look at what science method does to combat these:

5. We're Not Programmed to Seek "Truth," We're Programmed to "Win"

In science, the 'win' is changed to be the successful seeking for 'truth'. Out-talking someone, as in a public debate, or out-wording them on an internet argument, or just browbeating them enough that they leave in either, is not a win. Hence the lack of interest from scientists in 'debate'. Putting forth an idea that is seen, eventually, to match reality better than what came before is the win.

4. Our Brains Don't Understand Probability

Therefore, we go through the occasionally ugly math to nail down the probabilities of our results. We just can't trust our intuitions about probability -- our brains don't naturally handle it at all well. We go back and work through that math, and then after publishing, many people read the news article and say 'everybody knew that already'. While everybody may have thought it in the first place, we do the work because it's also common for a reader to see the same article and say 'that's absurd, everybody knows it isn't so'. We may not agree that it was obviously true, or true at all, but we can agree on whether 2 * 3 = 6.

3. We Think Everyone's Out to Get Us

In the sense, the article notes, that "If you're smart and savvy, you know not to trust anyone. This is why we can excuse ourselves for using shady or flat-out dishonest tactics to win an argument. We're sure the other guy is doing much, much worse."

So how do you work towards truth if everybody is out to get you? One part is that when someone is found lying in their work, they're out of the field. Contrast that with, say, business or politics. Another is to enlist the help of other people who are knowledgeable in the topic and have them read the new work to ensure that there aren't any obvious mistakes or frauds. Peer review. Bad papers still make it in to the scientific literature, but it improves the chances that what you're reading is not too badly flawed. When the peer review process fails, the editors in charge usually take it very seriously. When's the last time a corporate president resigned because a vice president let a salesman lie about their product?

A different, major, part of the method is that experiments must be repeatable. Good enough a fake to get past a reviewer is not enough. Somebody, somewhere, must be able to repeat your experiment and get sufficiently similar results. For preference, someone should actually do so, but funding agencies don't like paying two or more groups to run the same experiment -- a failing in funding agencies and those who allocate funds for research. But if the experiment is not even in principle repeatable, if the answer is 'trust me', you're in trouble.

2. We're Hard-Wired to Have a Double Standard
My science example is different than the article's, but the same principle is involved. It is natural to consider evidence in favor of your position to be better than the evidence against it. It is so natural that it's also natural to simply ignore the evidence against your position outright, and only look at the evidence that supports it. Even if you have to make it up, or use for your source someone who did.

To combat this natural double-standard, in science, unlike politics/business/law*/..., you are supposed to present the evidence without regard for whether it supports your position or not. And if you fail to present evidence that is against it, you're in trouble (#3, #5).

1. Facts Don't Change Our Minds
Being able to do this is what I called the central skill of a scientist. It is so important because it is so unnatural to us humans. Quoting some pieces of the original article:

.... Let's go back to the beginning for a moment, and the theory that people figured out how to build arguments as a form of verbal bullying rather than a method of spreading correct information. That means that there are actually two reasons somebody might be arguing with you: because they actually want to get you to think the right thing, and because they're trying to establish dominance over you to lower your status in the tribe (or office or forum) and elevate their own. That means there's a pretty severe cost to being on the wrong side of an issue completely separate from the issue itself. ....
So During Your Next Argument, Remember ...You won't remember this. You're hard-wired to remain entrenched, and the Internet makes it worse because your political beliefs are pasted all over Facebook and wherever else you post your opinions. Backing down means going back on all that. It means letting down your team. Every inch of your psychology will fight it.

Doread the original article in full.

* Law has its own standards on proof and approach to truth. And it must. My wife is a lawyer, so we've had some fun talks about the differences and where they came from. One where it differs most strongly from science is how it handles the natural double standard. In science, we take the side of making practitioners do the highly un-natural thing of avoiding the double standard. Law, at least in common law countries like the US (and UK, ...), takes the opposite -- if everyone is predisposed to some level of double standard, and side-taking in their arguments, let's take it out to the extreme -- each side presents the best possible case for its own position, and only that. Then have a judge or jury assess who made the better case. The people deciding which is the stronger case are not the ones who make the case in the first place, so (the design hopes) they won't be subject to the double-standard problem. In science, the same people who would be deciding which case is stronger are the ones making (some of) the cases.

The article helped me understand a conflict I'd encountered. On one hand, doing science is very natural. We are all disposed to learning how the universe around us works, starting from birth. On the other hand, what I see in internet discussions bears strong resemblance to the 5 fallacies discussed above, even when the topic is scientific ("Is CO2 a greenhouse gas?"). Even though trying to find out more about the universe is natural, the methods that evolved over the last few thousand years to help us do so have required us to do it in differently than we reflexively choose.

In the weekend heat

2012-07-07T07:20:00.000-04:00

Sometimes it's easy to take the weather personally. Yesterday, DC set its record for consecutive days 95 F or above (35 C), with 9, and will likely obliterate it by going to 11 straight days, adding in today and tomorrow. The record it breaks, 8, was first set in 1987 -- the first summer I was here. It was tied twice more, first in 1993 -- my third summer. At the time, the summer of 1987 set many of the all-time records for sustained heat. Probably several of the summers since then would have as well, if 1987 hadn't beat them to the punch. Rephrase: many of the past 25 years would have set sustained heat records if compared only to the observations from 1880-1980. What used to be unheard of is now 'normal', or at least common. I'll be pulling down the data once NCDC is working again.

In the midst of the heat, well, we're hot here, and thunderstorms, I'll remind folks of the fact that you shouldn't run in thunderstorms. That also applies to biking and walking and other outdoor activities. If it's only heat you're dealing with, remember to drink enough fluids. And, one trick for keeping cool while exercising is to dump some cool/cold water on your head. Preferably to get a hat wet (it soaks up more water than my ever-thinning hair). For more extreme cases, some ice cubes under your hat.

While my power came back after about 20 hours, friends didn't regain it until Tuesday (~90 hours) or Thursday (~140 hours). Again, this is for wealthy, and densely populated, areas of a wealthy country. West Virginia has not been faring as 'well'. See this also. Noticed in passing (unfortunately I don't remember exact source, quite possibly a Capital Weather Gang tweet*) was that US average was 214 minutes of power failure per year. Unfortunately, even the better parts of this area are likely far in excess of that. Certainly my 20 hours this time are not balanced off with 6 previous years of 0. In other wealthy countries, it's order 20 minutes per year. In other words, US average is 10 times worse than other countries, and the Capitol area is closer to 100 times. This is not the first time we've lost power this year, and won't be the last.

* My apologies to the original source if it wasn't this. In any case, Capital Weather Gang is well worth reading, and has a blog as well.

At the same time as we were obliterating our record for sustained extreme heat, a friend bragged that Phoenix, Arizona, had its coldest 4th of July in 100 years -- 76 F (25 C). We were 20 F hotter. A different friend commented that he'd escaped the DC heat by going to the Bahamas. Summer in the Caribbean to escape the heat ...

Century storms

2012-07-06T06:43:00.000-04:00

"That's two straight years we've had a 'storm of the century'; those weather guys are idiots!" Not long after I moved to the Washington, DC area, this happened, and the quote is real. I'm not sure that the storms involved really were 'storm of the century' events -- events that if we had a long enough record, we'd see happen about 10 times per 1000 years -- but it's something to think of a little quantitatively, particularly in light of my normally abnormal note.

Let's suppose that we're building a house and would like it to last 30 years. Well, to be specific, let's say we'd like a 99% chance of it lasting that long. Obviously it has to be able to survive events that we'd expect to happen once per year. And we can probably ignore things that we'd expect only once in a million years. But what about a once in 100 year event? The name misleads us in to thinking that the next time such an event would happen is 100 years after the last time. While natural reading, it's wrong mathematics. We could easily be in the unlucky 30 years that sees a 100 year event. We could even see it twice. But is there less than a 1% chance of having one 100 year event in a span of 30 years? That's our design requirement. If it can be expected more often than that, our house design is not reliable enough. We need something better. And we'll need to get quantitative.

To deal with situations like this, where we have an expected number of events in a time span, whether it's number of 100 year events in the 30 years we want our house to last, or number of times a phone will ring in the next hour given how many times it usually does, we use the Poisson distribution. For the Poisson expectation value (lambda) take the number of years you want and divide it by the N in N-year event. 100 at the moment. In 30 years, then, we expect to see 0.3 '100 year' events. Of course you can't have 0.3 of an event, you have 0, 1, 2, ... events. One of the things the Poisson distribution does is to make the translation from our expectation in to something that could happen. The other, of course, is that it tells us how likely it is to happen.

Number	Probability	Cumulative
0	74.1%	0
1	22.2%	22.2%
2	3.3%	25.5%
3	0.3%	25.8%

More than a 1 in 4 chance (greater than 25% chance) of at least one 100 year storm hitting our house in 30 years! If we want that confidence, only a 1% risk of our house being destroyed in 30 years, we need to plan not for 100 year storms (which give us 26 times that risk!) but for 3000 year storms!

I used the online calculator at Graphpad Software. There are a huge number of online calculators, just search on Poisson distribution calculator. I encourage you to play around with this some, for events you're interested in, and levels of confidence you consider reasonable.

I was surprised at the results here. One thing it suggests, as my house is more than 30 years old, is that we are already doing some design and construction to these levels. It isn't a radical new concept. The other is, when we are considering planning against catastrophic failure, we have to consider extremely rare events. Most of us expect to live more than 30 years. The related thing, just ask insurance companies, is that changes to the probabilities of extremely rare events are extremely important. Changing the average temperature from 15 to 17 C (60 to 63 F) might be ignorable or even welcome, depending on who you are. But turning the extremely rare 100 year storm in to a rare 10 year storm gives you only a 5% chance of your 'storm of the century' house surviving 30 years. (And instead of 0.4% chance of 3 or more such storms hitting you, it becomes 58%!)

Normally abnormal

2012-07-05T08:27:00.001-04:00

Normal and abnormal are among those words that don't mean a lot without knowing the context; 'rapid', I avoid entirely having hung around with both nuclear physicists and astrophysicists, for whom rapid can mean a femtosecond, or 100 million years. It's normal to be hotter or colder than normal. It would be highly abnormal to always be right at normal. While perfectly good English, let's do some work to make this real for understanding weather and climate.

One sense of 'normal' we have is the average value. The average temperature for July 2nd in my area is, say, 86 F (30 C). This is a useful figure, at least in the sense that we then expect temperatures to be closer to that than, say, 50 C, or 10 C. But it would be highly abnormal -- something seldom seen -- for the temperature to be exactly 86 F for five consecutive July 2nds.

To take a comment of mine from Tuesday, we had about 1 hour of winds averaging 50 mph (22 m/s) in the recent storm. Such winds are highly abnormal, in that the average is 5-10 mph. But there are 8766 hours in a year. It is normal, I believe (haven't pulled down the full data set), for at least 1 hour in the year to average 50 mph here. One sense of normal is the arithmetic average. Another is 'what are the winds you see less than 1% of the time'? That would be the 99th percentile winds -- you get that or faster 87.66 hours per year. 99.9th percentile is 8.766 hours, which I think is about right for 50 mph in this area. 99.99th percentile is something you expect to see about 50 minutes each year (maybe this is where we were). In other words, it is normal to be that abnormal.

One importance of this is that if you are designing a building, or power grid, windmill, or anything else exposed to the winds, and would like it to last at least one year, you need to design for over 3 days of those rare, 99th percentile winds. And still almost an hour of the exceedingly rare 99.99th percentile winds. It's normal, predictable, expectable, and expected, to get those winds. If you want it to last a decade, you need to design for (at least) an hour of the extremely uncommon 99.999th percentile winds. If you want it to last 100 years, which a number of things -- Statue of Liberty (thank you France) and Brooklyn Bridge, for instance -- have endured, you need to be prepared for 99.9999th percentile winds. That's a lot of 9s!

The fact that humans have built structures that have lasted that long shows that we are capable of doing so. We even have some that are around seven 9s -- have lasted 1000 years. Then again, all of these long-lived structures have also had maintenance routinely through their lives. Design for endurance without maintenance is a different and more challenging matter.

But even as we are capable of designing structures to last for decades or centuries, we often don't. Or, even if designed for decades -- with routine maintenance -- we don't always do that maintenance. In both cases, there's the reality of money. It costs to do maintenance, and it costs to build for greater expected endurance. If we know what to expect, we can make the best decisions of how to balance the costs of maintenance and building for endurance versus the weather. No big deal if my backyard shed is flattened by a once in a decade wind. But it's a very big deal if my house is.

Happy 4th of July

2012-07-04T10:42:00.000-04:00

Hope everyone is having a happy 4th of July. For the USA readers, two sets of words in reminder:

1)
IN CONGRESS, July 4, 1776.
The unanimous Declaration of the thirteen united States of America,

When in the Course of human events, it becomes necessary for one people to dissolve the political bands which have connected them with another, and to assume among the powers of the earth, the separate and equal station to which the Laws of Nature and of Nature's God entitle them, a decent respect to the opinions of mankind requires that they should declare the causes which impel them to the separation.

We hold these truths to be self-evident, that all men are created equal, that they are endowed by their Creator with certain unalienable Rights, that among these are Life, Liberty and the pursuit of Happiness.--That to secure these rights, Governments are instituted among Men, deriving their just powers from the consent of the governed, --That whenever any Form of Government becomes destructive of these ends, it is the Right of the People to alter or to abolish it, and to institute new Government, laying its foundation on such principles and organizing its powers in such form, as to them shall seem most likely to effect their Safety and Happiness. Prudence, indeed, will dictate that Governments long established should not be changed for light and transient causes; and accordingly all experience hath shewn, that mankind are more disposed to suffer, while evils are sufferable, than to right themselves by abolishing the forms to which they are accustomed.
(et seq.)

2)
The Constitution of the United States
Preamble

We the People of the United States, in Order to form a more perfect Union, establish Justice, insure domestic Tranquility, provide for the common defence, promote the general Welfare, and secure the Blessings of Liberty to ourselves and our Posterity, do ordain and establish this Constitution for the United States of America.

Back to me:

Though today is the day officially designated for the first, I think the second is the more significant for what it is we are celebrating. The constitution is the basis on which we celebrate being a nation.