The Pacemaker of the Chandler Wobble

Abstract: The Chandler Wobble is one of the largest circumannual periodic or quasi-periodic variations in the earth's orientation.  After over a century of searching for its forcing, it was found to be caused by atmospheric circulation and induced ocean circulation and pressure.  The question of why there should be such forcing from the atmosphere has remained open. I suggest that variations in earth-sun distance cause this forcing to the atmosphere and thence the ocean.  Analysis of earth-sun distance, earth's orientation, and atmospheric winds shows a coherent relationship between the atmosphere and earth orientation at just those periods expected from earth-sun distance variation.  As this is a general mechanism, it can be used in examining regular climatic variations on a wide range of periods and for climate parameters other than the earth's orientation.

-- -- -- -- -- -- -- 

That is the abstract for the paper I link to below.  It's not a peer-reviewed paper in the sense of being in a peer-reviewed journal.   But it has been reviewed by an expert in the field (William P. O'Connor), who was quite favorable.

I am posting the idea and paper here.  Long past time for the ideas to be discussed.  If they're shredded in the blogosphere, so be it.  I have quite a bit more than what I've put in the document. Over the next few days and weeks, I'll post more of those additional materials as well.

The Pacemaker of the Chandler Wobble, Grumbine 2014

Data Horrors

"The great tragedy of science -- the slaying of a beautiful hypothesis by an ugly fact."  Thomas H. Huxley.

Sometimes, though, you have to pay attention to just how ugly the observation (fact) is.  And even more to how ugly a collection of observations is.  Science fair project I judged a couple of years ago, the student mentioned his methods for keeping the experiment, which had to be untouched while going, out of reach of his young brother.  This student has a firm grasp of the ugliness of data and trying to collect it.  I gave him high marks.

I also mentioned a story or two I knew of data collection challenges.  I'll share them and some others here, and invite you to add your own.

One family of ocean data comes from buoys floating on top of the ocean.  A lot of the ocean is far from land, therefore far from perches for birds.  Sea gulls and other birds are often grateful for the lovely perches we're putting out for them.  Unfortunately, it does not help the accuracy of your wind speed measurements to have a bird sitting on your gauge.  Birds sitting on the solar panel reduce your energy available/recharge rate, and thence maybe lead to data outages while waiting for recharging. Guano is great for fertilizer, but wrecks havoc on the accuracy of your temperature, pressure, and moisture readings.

Walrus don't mind taking a rest every now and then either.  They're not normally a threat to wind speed measurement (which is at the top of the buoy).  But we also want to get wave measurements -- how high are they, how fast are they, what direction are they going.  Having a walrus or two on your buoy slows its ability to respond, and may suppress the peaks of the measured waves.

On land, your instrument enclosures (the Stevenson Screen for instance) provide a nice place for bees, wasps, small birds to nest.  Squirrels like to play with them too.  A beehive next to your thermometer does not help its accuracy.

Back at sea, I once got a call about a problem buoy.  It was reporting extremely high temperatures near noon because the paint had been stripped during a storm, and the now-bare metal was reflecting sunlight onto the marine thermometer.

That should get you started for remembering your own horror stories about data collection.

Recently saw someone on the web taking the line that if data wasn't perfect, you should throw out everything from that instrument or site.  Well, no.  If you did that, you'd never have any data to work with.  For my examples, you mostly just ignore the data during the period you've got a walrus infestation.  But there are other kinds of things which affect your observing, and which you might be able to compensate for.

Yabba2 -- Construction

Katherine Monroe:

Below are the full instructions on how to build exactly what I built. There is so much that could be done to improve the design. I know it is not anywhere close to perfect. The materials I used were makeshift, whatever was lying around the house or wasn’t too expensive. But that was the point. I like spontaneity. It doesn’t have to be extremely elaborate to work and to be useful. This is for anyone who wants to do anything with it or for anyone who is just interested.
  
Materials
1. Vernier Flow Rate Sensor, Order Code: FLO-BTA/FLO-CBL
2. Vernier Lab Quest by Vernier Software and Technology.13979 SW Millikan Way, Beaverton, Or 97005. 888-837-6437. (for transmitting and collecting data from the Flow Rate Sensor.)
3. 3 22” steel dowel rods
4. Compressed fiber board
5. Minwax Polyurethane Varnish
6. 24 Gauge- 100 ft. Green Floral Wire Twister
7. Small foosball
8. 2 IDEC Sensors, Magnetic Proximity Switches. Type: DPRI-019. Premium Waterproof Clear Silicone Sealant (without Acetic Acid)
10. Plugable USB to RS-232 DB9 Serial Adapter (Prolific PL2303HX Rev D Chipset)
11. RS232 Breakout - DB9 Female to Terminal Block Adapter
12. Xnote stop watch, version 1.66 (downloadable at http://www.xnotestopwatch.com/)
13. Loctite Epoxy glue
14. Drill
15. Hammer
16. 2 Brass quarter inch Phillips Head screws
17. Electric hand held reciprocating saw
18. Electrical tape
19. 4” by 3/4” strip of thin steel (cut from a can)
20. Twisted Nylon string
21. 2’ long wooden slat (to be used as a handle for carrying and placing the designed device in the water.)
22. Study Site: United States Geological Survey (0164900), Northeast Branch of Anacostia River at Riverdale, MD. (Test site was just next to the USGS data collection gauge.) (-38.961,  -76.626)

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Yabba -- Building your own stream gauge

Katherine Monroe*, the author/inventor of this stream gauge, is a graduate of Eleanor Roosevelt High School, in the same class as Elliott Rebello.  Her senior project was quite different, and you'll get to see the details in her own words.  Part 1 is today, the narrative.  Part 2 will be on Monday -- the full parts list and construction instructions.

Engineering the “Yabba Dabba Doo”

By: Katherine Monroe

June 2014

Eleanor Roosevelt High School

One year ago, as a rising senior at Eleanor Roosevelt High School in Greenbelt, MD, I was faced with the same grueling task that all students in the Science and Technology program were: RP- that is Research Practicum. This is what we had been leading up to for the past three years and now, here it was. 

RP is the year long research project that all seniors in the Science and Technology program at Roosevelt are required to complete. By the end of the year we had to have completed a science fair backboard, a laminated poster, a power point, and a five chapter paper. We had a whole class dedicated to working on all the different aspects of the project and to learning how to analyze data quantitatively and statistically. We were told to come up with a project that was interesting to us because we would be spending the entire year working on it. Some students applied for internships with NASA, USDA, NIH, the University of Maryland, the National Zoo, Walter Reed Hospital and more. Other students applied for programs established by and within the school and other students worked separately from any structured programs. 

I chose to apply to a program started by one of our school’s AP Chemistry teachers called WISP (Watershed Integrated Study Program.) It was a program which emphasized local water quality studies. Students in WISP formed groups and measured chemical and physical properties of local waterways at a bunch of sites across the county. We measured nitrate and phosphate concentrations, dissolved oxygen levels, alkalinity, ph, turbidity, total dissolved solids, temperature and took seasonal macroinvertebrate data. We then added our values to an ongoing database which students could draw from for all sorts of studies which require long term data collection.

I applied to WISP because out of the endless ocean of things I was unsure of I was sure of at least one thing and that was my love for the environment and for being outdoors. After having been accepted to WISP I began the process of deciding what to do for my project. In the end the basis of my project came from the one other thing I was sure of which was that I enjoyed building things. So I knew I wanted to build something and I knew it should relate to the local water quality movement that WISP was promoting. I looked at what we did in WISP and thought about what we measured. One aspect of water quality that I found important to a gaining a comprehensive understanding of a stream or river’s health (that we did not measure in WISP) was the speed of the water in the stream.

The water speed can provide insight into the types of organisms that can live in a stream or river, to the flow of sediment down a river, and sometimes to the oxygen levels of a river. The greater the speed of a river, the more aerated it typically is, and the higher the dissolved oxygen level. All of these can greatly affect the health of a stream or river. Stream speed can also help in understanding volume flow rate of a stream and in identifying storm water runoff patterns near and around the stream or river and in developing flood models. Overall stream speed seemed like an important factor that we did not account for in WISP due to what I believe to be a range of reasons, the expense of the necessary equipment, the complicated nature of taking stream speed measurements at a variety of points along a stream and still getting inaccurate results due to the variability of speed along an uneven stream bed, and maybe more. 

 

I decided that I wanted to design and build something that would measure stream speed; something that would be cost effective and accurate, and something that would be easy for anyone who wanted to do research, like the kind we do in WISP, to build for their own purposes. The point was to encourage citizen science by going through all the steps independently and then showing people what I had done so that they could do it too. 

 

In the end what I came up with consisted of an open track along which a light and neutrally buoyant ball was pushed by the flowing water. On either end of the track there were magnetic sensors which timed how long it took for the ball to move from one end of the track to the other. From this the speed was computed. This is where the name of the device comes in. I decided to call it the “Yabba Dabba Doo” because it looked like something out of the Flintstones (or maybe like an old-fashioned push lawn mower.) 

 

Next I had to figure out if my design actually worked. In order to do that, I compared my device to an already existing speed measurement device by the company Vernier. I assumed that the Vernier data was accurate. My null hypothesis was that the average of the speeds taken with my device would be statistically equivalent to the average of the data taken with the Vernier device. Strangely enough, I wanted to FAIL to reject the null hypothesis. Statistics are weird. I collected data with each of the devices within a half an hour period of each other (assuming that the stream speed would not change in that amount of time.) Then I analyzed the data through a statistical t-test which looked for a significant difference between the two sets of speeds and their averages. 

 

After multiple trials and readjustments to the design I got what looked like a pretty accurate result. Initially (before reaching my final design), the object moving along the track of the Yabba Dabba Doo was a metal disk attached to the metal rods of the track with metal rings. All that metal caused for a lot of friction between the disk and the track which prevented the disk from reaching the speed of the water and gave me slower averages than the Vernier averages. This also yielded a significant difference in the statistics which I did not want. In order to minimize the coefficient of friction there, I changed my design to one which consisted of that light weight, neutrally buoyant foosball (which I mentioned earlier,) that was attached to the metal track with small sections of plastic drinking straw. The foosball had no tendency to float or sink in the water and caused less friction on the track. Furthermore, the coefficient of friction between the plastic straw and the metal track was much less than between the original metal rings and the metal track. After making this design change I got averages that were much closer together between the Yabba Dabba Doo and the Vernier and in a majority of my statistical t-tests there was no significant difference. In the end I had a device that seemed to be working pretty accurately and cost $350 less to build than to buy the Vernier. 

 

Going through that process, of trial and error and trial and error and trial and then success!!! was extremely gratifying. I got to experience the life of an engineer first hand and to learn about the plethora of unforeseen problems that can arise. 

 

This entire year was a great learning experience for me. I learned what a null hypothesis was and how to go about trying to reject it (or in my case, fail to reject it.) I learned all sorts of things about the materials that I used to build my device. I learned how to bear standing out in winter weather water up to my waist, wearing my mom’s baptismal waders, for the good of science! I learned about all the things that can go wrong and need to be accounted for in a field study like this one. I learned how to access all sorts of functions on excel, power point, and word. And I learned something about myself. I learned that engineering, and I think in particular environmental engineering, is something that I could easily be passionate about and be satisfied with in the future. And for a chronically confused and disoriented teenager about to go to college, that is reassuring.

Below are the full instructions on how to build exactly what I built. There is so much that could be done to improve the design. I know it is not anywhere close to perfect. The materials I used were makeshift, whatever was lying around the house or wasn’t too expensive. But that was the point. I like spontaneity. It doesn’t have to be extremely elaborate to work and to be useful. This is for anyone who wants to do anything with it or for anyone who is just interested. 

[Back to your host: Directions on Monday ; The * is that Ms. Monroe normally goes by a more informal version of her name and I've gone with the formal here.  Formal for publication is a rule I use myself (I'm not usually Robert), and one which I've learned is helpful for women to be taken seriously.] 

Alaska LEOS and rare Mourning Doves

I finally participated in my first LEO Webinar and had a great time.  I'll be calling in for more of them as they come up monthly.

LEO is the Local Environmental Observers program/project in Alaska.  The principle being, the people actually living in an area are the prime observers for what is going on.  This includes keeping an eye on birds, among many other things.  The title comes from the September 13, 2013 observation by Richard Kuzuguk of a Mourning Dove in Shishmaref, AK.  Very unusual up there.  I don't have that species down here, but in general, they're very common here.  (Common as in 'wake up light sleepers'.)

For an idea of the rarity of Mourning Doves in Western Alaska, take a look at the distribution map at Wikipedia.

Starting a bestiary of oscillations and cycles

A bestiary originally was originally a book of pictures and descriptions, often with morals attached, of animals.  Well, that was the middle ages.  The version I've got in mind is one describing the more or less regular oscillations or cycles in the earth system, its orbit, and the sun.  For now, I'll describe just the period and its name and invite you to add to the list.  Also, I won't worry about whether the named thing is a proper oscillation (such as tides) or more of an index that may not have any particular period (PNA).  The later rendition will have some discussion of what happens in each and concern about whether the variation is a real thing or just an artefact of how people looked at the data.  For those who'd like to jump straight to discussion of weather cycles directly, I'll suggest William Burroughs' Weather Cycles, Real or Imaginary

As always, you're encouraged to add your own suggestions!

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Constructing an analysis 1: Drop in a bucket

'Analysis' is what we call an attempt to represent the state of the atmosphere/ocean/sea ice/... given a set of observations. One such analysis is the global surface air temperature analysis. That, then, spawns efforts to find a global mean temperature, or global mean temperature trends, and so forth. Several of the recently-added blogs aim to study that, in one way or another. That particular one is not my interest in two different ways.

One is, I'm an oceanographer, so I'm more interested in a sea surface temperature (sst) analysis. The other is, most of the interest in the surface air temperature analysis seems to come from its role as a detector of climate change. On the scale of things, I consider this the second weakest climate change indicator. The only thing weaker, in my view, is the so-called 'Hockey Stick'. But enough raw opinion.

Regardless of what it is you're trying to analyze, and what your reason for doing so is, there are quite a few ways of setting about doing so objectively. The fact that there are many makes this the first of something like eight notes I'll be writing up on the idea. There turn out to be many different ways of making an analysis, each objective, each with strengths, each with weaknesses.

The simplest one, if not as simple as you might think, is the 'drop in a bucket' method.

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Were the 70s cold?

I was surprised to see that the 1970s weren't particularly cold.  My surprise is partly because where I lived (Chicago area) we were busy setting all-time records for cold, and that was true for much of the US and across to the UK. 

The other part of the surprise is that it's common to hear people (see them write) something on the lines of "Of course we're seeing a warming since the 70s; it was cold in the 70s!"  Surely someone along the way did their homework and checked out what the global temperatures were?

Fortunately, if we're looking at science, we don't have to assume that other people did their work, or did it correctly.  The alternate word for it is, skepticism.  Real skeptics don't make those assumptions, they do the work themselves.  The fact that it's work also explains why there are a lot of fake skeptics -- it's much easier to pick the answer you like and reject everything else.

So let's apply some real skepticism and ask what was really going on with temperatures in the 1970s.

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Data set reproducibility

Data are messy, and all data have problems.  There's no two ways about that.  Any time you set about working seriously with data (as opposed to knocking off some fairly trivial blog comment), you have to sit down to wrestle with that fact.  I've been reminded of that from several different directions recently.  Most recent is Steve Easterbrook's note on Open Climate Science.  I will cannibalize some of my comment from there, and add things more for the local audience.

One of the concerns in Steve's note is 'openness'.  It's an important concern and related to what I'll take up here, but I'll actually shift emphasis a little.  Namely, suppose you are a scientist trying to do good work with the data you have.  I'll use data for sea ice concentration analysis for illustration because I do so at work, and am very familiar with its pitfalls.

There are very well-known methods for turning a certain type of observation (passive microwaves) in to a sea ice concentration.  So we're done, right?  All you have to do is specify what method you used?  Er, no.  And thence comes the difficulties, issues, and concerns about reproducing results.  The important thing here, and my shift of emphasis, is that it's about scientists trying to reproduce their own results (or me trying to reproduce my own).  That's an important point in its own right -- how much confidence can you have if you can't reproduce your own results, using your own data, and your own scripts+program, on your own computer?  Clearly a good starting point for doing reliable, reproducible, science.

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Where is the surface?

I just commented on my facebook status that I'm at a meeting about sea surface temperature.  That part was safe.  Rest of the comment was to observe that I'm now back to wondering whether the sea has a surface, where it is if it does, and if it does, whether it has a temperature.  That prompted a friend to comment 'Great ... this is going to bug me now.'  So for him, here's a longer version.

This sort of question is very common to science.  Of course my musing for facebook is overstated.  But there is usually a real question about what exactly it is you've observed when you take an observation.  When you have very different observing methods, they may well observe things that are different from each other.  There are, let's say 4, different ways of observing the sea surface's temperature.  For a diagram, see the wikipedia article on sea surface temperature

The standard method, and reference for others, is calibrated buoys that carry a thermometer at a known depth, typically 1 meter.  A major drawback to this method (all methods of observing have drawbacks!) is that you need a buoy.  They're not cheap, and it would take several million of them to give us a high resolution data set for global sea surface temperature (acronymed SST).

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How much detail is there really?

I'm thinking about sea surface temperature (SST) these days, but the approach here is one that can be applied to many situations, even ones outside weather and climate. A common, important, and not always easy, questions is -- just how much detail do you need? The more detail, the more expensive it is to make a good product, whether that's an analysis of sea surface temperature, a climate model, or a surface in a video game. Of course, what I'd like is the sea surface temperature every few meters over the entire globe. If that's more than necessary at some time, I could average it down. But ... it would take an awful lot of storage to save temperatures every few meters (my back yard, my neighbor's, my front yard, ...) over the whole globe.

Let's start by looking at an actual high resolution global product, though not every few meters! The SST analysis at http://polar.ncep.noaa.gov/sst/ gives a value every 1/12th of a degree in latitude and longitude, one about every 9 km (6 miles). It has about 9 million values. Let's also suppose that this is fine enough resolution that everything important is represented.

The worst resolution is to use 1 number for the entire globe, the average for all ocean points. To measure how bad this is, I'm going to compute the root mean square error. (Those who know what this is can skip to the next paragraph.) It is often abbreviated rmse. To find it, we go through every ocean point in the grid and find the difference between the value there and the average. Then we multiply this difference by itself (square it -- this avoids the marksmen statistician story*). Then add up these squares for every ocean point. This is a big and not interesting number. One thing that would be more interesting is the average value of the squared error -- the mean square error. So we divide by the number of points that were involved. This also tells us the error variance. Since we think more in terms of temperature and temperature changes than squares of temperature changes, we take the square root of the mean square error -- get the rmse. This is a figure which represents a typical magnitude of how far off we expect to be. We could be either warmer or colder by this much, but this is the magnitude.

* Two statisticians went to a shooting range and each fired at the target. The first missed by 1 meter to the left (-1 meter). The second missed by 1 meter to the right (+1 meter). They then congratulated each other on their fine marksmanship because on average they had hit the bullseye. Their average error was indeed zero. But their rms error was 1 meter.

When I compute the RMSE for using global mean temperature instead of the full resolution grid, I find 12 C. That's ... enormous. The difference between water at 20 C (68 F) and 32 C (90 F) is pretty large! So, clearly, we can't be satisfied with an RMSE of 12 C. But now we have a method for looking at the resolution we need, and a notion of how bad you can get.

Then I made my program average over smaller boxes than the whole globe, say 90 degrees on a side -- London to Chicago, equator to pole -- and found the RMSE comparing those box averages to the original temperatures in the full resolution grid. No surprise that boxes that large were pretty bad. But ... once I got down to boxes 2 degrees on a side (which is something like 200 km, or 120 miles), the RMSE was down to 0.5 degrees.

This is still definitely not zero, but it isn't bad. When a typical satellite used for the job -- such as the AVHRR instrument on NOAA-18 -- is used to make an observation, it has an RMSE (compared to a buoy's thermometer at about the same location at about the same time) of about 0.5 degrees. In other words, with boxes 2 degrees on a side, the average represents what is happening in sea surface temperature about as well as getting a single observation from satellite. We've also managed to reduce our RMS error by about 95% as compared to using only a single number. On the other hand, even though we've captured 95% of what's going on, we only need to use 16,200 numbers -- instead of the 9,331,200 we started with. 95% of the information of the full grid, with only 0.2% as much data.

We've caught 90% of what is happening (reduced the rmse by 90%) when the boxes are 6 degrees on a side (600 km, 360 miles). And it's 99% once we're down to boxes only 0.5 degrees (50 km, 30 miles) on a side (which means only about 3% as many data points are needed to represent the full data set to 99% accuracy).

Now, let's translate this back to some situations we might care about. In trying to construct climatologies of sea surface temperature, we run in to the problem that as we go back in time, there are fewer and fewer data points. On the other hand, if we have 1 observation in each box 6 degrees on a side, we've managed to capture 90% of what is happening in the sea surface temperature. In other words, a much sparser data set than we might imagine could indeed represent an awful lot of what is happening in the ocean. A global grid at 6 degrees resolution has only 1800 points, so we need only 1800 observations to fill it in our simple-minded way.

At 2 degrees resolution, we've captured 95% of what happens in sea surface temperature (at least to this quick little glance -- I only looked at 1 day, as analyzed by 1 center, etc.) So, if we had a good global ocean model at 2 degree resolution, we'd actually be pretty far along in being able to predict sea surface temperatures (model climate, etc.) well. In practice, there are processes that happen in smaller areas than the 2 degree box which can change the whole box's average and we, therefore, want finer resolution than 2 degrees. More about that in a different post.

In thinking about observing systems, if we only 'need' 1 observation every 200 km or so, and we have satellites that can take an observation every 4 km (like the one above) we're all done, right? Unfortunately, no. The problem is, that satellite looks for clouds. If there are clouds -- and cloudy areas can easily stretch for 1000 km -- the satellite can't see the sea surface to tell us what the temperature is down there. So we need other data sources -- ships, buoys, other sorts of satellite (ones that can see through clouds) to fill in even just the 200 km (2 degrees latitude-longitude) boxes each day. Plus we need to observe the detail in the oceans that are involved in those other processes I mentioned. It isn't just for models that they're important -- fishing also cares.

Happy 30th to sea ice

A belated happy 30th birthday to our continuous* record of sea ice coverage from satellite! 26 October 1978 is the first data from the SMMR instrument, so last weekend the record finally hit 30 years.

* Ok, not exactly continuous, there's a gap between SMMR and the first SSMI to follow. But it's only a matter of (quite a few) weeks, rather than years as happened between ESMR and SMMR.

Testing Ideas 1

I was invited (challenged, whatever) to take a look at a site that proposed to have disproved the 'IPCC prediction', over in comments to my cherry picking note. Here's part 1 of the look I promised in my comment reply over there. If you haven't already, please do read the cherry picking note. (Not only for my tiny little ego boost from having more page views, but because I'll be assuming here that you understand what all I mean by the term and examples of it for climate.)

Testing ideas is one of the central processes for science. Coming up with ideas is awfully easy. Supporting them takes work. Strengthening them so that they stand up to all good tests is extremely hard. But, they do have to be good tests. Same as it's harder to come up with supported ideas than just an idea, it's harder to come up with a good test than just a 'test'.

'Good test' does not mean 'comes up with the result I like'. Maybe it does, maybe it doesn't. You're usually much better off to not have specified before hand how you want the test to come out. A good test is one that is aware of the system it is studying, knowledgeable about the idea that it is testing, and has been devised carefully enough to confirm or deny the idea while also giving an idea of how firmly it is supporting or denying. Before launching in to the climate case, let's look at something very much simpler.

You might have the conjecture (even more tentative than a hypothesis), after having read my mention that I'm a distance runner, that I'm tall and thin. How would we test that? How can we make it a good test? You could just ask me. But that isn't a good test. You have no idea what I think is tall, nor do you have any idea what I think is thin. So if I say 'yes', you still don't really know anything. Poor test. You could try asking me my height and weight. But then you've made a poor test because you didn't establish what constituted tall or thin. If you're biased about the conclusion, it is far too easy to say after the fact that whatever figures I gave you do, or don't, constitute 'tall and thin'.

So you have to be precise about declaring what you're testing, what constitutes a pass and what constitutes a fail. You might arrive at something like 'taller than 80% of men your age means tall and lighter than 80% of men your height is thin'. Someone else might want those figures to be 90%, but, since you've specified how you arrived at your labels (and, of course, you will be sharing your data), they can examine for themselves whether they agree with your conclusion. You're still not out of the woods, however, because you don't know how I'm measuring my height and weight. Perhaps I'm wearing thick socks, shoes, and standing on my toes. Maybe I'm weighing myself fully clothed and carrying a backpack. You need to specify the conditions of the measurement as well. Further, you'll have to tell me how to report it. I could be 5'7" (170 cm) and round to the nearest foot/meter to tell you that I'm about 6' (2 meters) tall. That sort of ambiguity can completely ruin your test.

In any case, even for something as simple as deciding whether someone is 'tall and thin', you see that there can be quite a lot involved. One last thing, which winds up often being important in looking at people's conclusions. After going through the work of making a good test, you can only draw your conclusion about the thing you were testing. Suppose you decided that I was indeed tall and thin. That was your test, so that conclusion is reasonably good. What you cannot do, however, is conclude that because I am tall and thin, I'm a good distance runner. You never tested that, and haven't presented evidence that all tall and thin people are good distance runners. (They're not, nor is it true that all short and not-thin people are poor distance runners. Now you have to go back to the drawing board and decide what 'good distance runner' means and how to measure it.)

If something that simple involves so much work, something as complex as climate probably takes quite a bit more care. So, irrespective of much else, one thing to look for in reports about what is or isn't the case about climate is whether the author showed as much care in making their tests as I suggested for something as trivial as deciding whether someone was 'tall and thin'. In part 2, finally, I'll get to the examination I was invited to make.

Earth temperature 1

The earth's temperature is something I'll probably come back to a few times as it is a lot more involved than you might think. So this is the first part -- the temperature as observed from space.

'Observed from space' shows our first complexity. Satellites can't drop a thermometer into the earth's atmosphere, oceans, etc., to find out what the temperature is. So what can they measure from up there? Fundamentally, they can measure voltages, resistances, currents, counts of an oscillator -- electrical/electronic things like that. Not very helpful at the start. But we can arrange it so that the things we can measure have something to do with what we want to know about.

This is actually how some thermometers work. Think of a traditional old mercury thermometer. It doesn't measure 'temperature', whatever that may be; it measures the length of a thread of mercury. Alcohol thermometers use alcohol instead, but in the same idea. 'Temperature' is the property, then, which makes materials expand (when hotter) or contract (when colder). It was discovered first as a practical matter that materials do expand with temperature in a sense in agreement with our own physical ideas of hot and cold (ex. Mr. Fahrenheit and M. Celsius). So temperature could be equated to expansion of materials. In the 1800s, a firm theoretical basis for which it should be like that (and sometimes not like that at all) was laid down.

For the satellites, a similar process of trying to match up what could be measured to what was desired was involved. The little 'aim it in your ear' thermometers are a little like the satellite method. What they do (satellites more thoroughly, home ear thermometers only in a small color zone) is arrange a detector so that it gets hotter as more radiation falls on it, then measure the resistance/voltage/... of this hotter detector wall.

The satellite detector I've described relies on the Stefan-Boltzmann law to decide temperature -- it measures the energy (which causes the detector to heat up) coming from the earth, and then with the law (Energy = s * T^4, s = the Stefan-Boltzmann constant, wonder why), converts that measured energy to a temperature. That temperature is the 'Black Body' temperature of the earth. If the earth were an ideal black body, the observed amount of energy is what would be seen if the earth were radiating at the given temperature.

We can also make detectors which measure the amount of energy that's within a certain small wavelength interval (blue, for instance). This is how the ear thermometer works, except it uses infrared. Given that observation, and Planck's law (more involved than Stefan-Boltzmann, look it up), we can compute the temperature your inner ear would have to be to be radiating that much energy -- if your inner ear were a black body. It's a fair approximation to one.

In either case, we have a 'Brightness Temperature' -- the temperature the thing you're looking at would have to be to give the observed brightness (energy). In the case of the earth, it is about 255 K, -18 C, 0 F. For Venus it's about 232 K, a good deal colder than the earth. Seriously, check http://nssdc.gsfc.nasa.gov/planetary/factsheet/venusfact.html
to verify, or find some others.

What happened? Venus is supposed to be hot! The earth is seldom as cold as -18 C or 0 F anywhere, much less for a planetary average. Well, Venus is hot (over 400 C at the surface) and the earth's surface is rather warmer (about 33 C or 60 F) than the brightness temperature (black-body equivalent temperature -- same thing).

The thing is, we have to pay attention to what the satellites observe -- radiation. If that radiation comes from the surface, we see a surface temperature. But in general, the radiation comes from somewhere up in the atmosphere. For Venus, it is a very long way from the surface, so very much colder. For the earth it is typically several km (or miles) up from the surface.

If you think this is indirect and complicated, wait 'til we talk about trying to observe the temperature of layers within the atmosphere from space!