Measuring Temperature Experiment Number 2 Measuring the temperature at the same place

Overview

In the last post I introduced Red and Green, my two digital temperature loggers.

In this really simple experiment I’ve put them next to each other on the window sill of my office and let them run for as long as they can. According to the manufacturer, they can measure and record the temperature every ten seconds for a total of 28,800 measurements. That’s a period of 3 days and 8 hours.

The questions I want to answer are:

How much of the data I collect is actually useful?

What issues do I have in collecting and comparing the data?

Since I’ve placed them next to each other, I expect them to record the same temperature.  Do they?

You may remember the manufacturer claims they measure temperature to within 0.5 º C, that is, within one-half of one degree Celsius. Our instrumental error is 0.5 º C.

Comparing the measurements

In the last post I pointed out that Red and Green each have a button on their fronts to start collecting data. I haven’t mastered pressing the two buttons at the same time and in this experiment I started Red at 18/03/2015 16:08:28, but started Green five seconds later at 18/03/2015 16:09:33.

This is a systematic error. My measurement of the difference in temperature between the two thermometers will be wrong in every single case because they’re measuring temperatures at different times. As a experimenter I can only hope the difference isn’t too much.

Here’s the first few sets of measurements:

Data Points	Date/Time	Temperature(°C)
1	18/03/2015 16:08:28	29.2
2	18/03/2015 16:08:38	29.2
3	18/03/2015 16:08:48	29.2
4	18/03/2015 16:08:58	29.1
5	18/03/2015 16:09:08	29.1
6	18/03/2015 16:09:18	29	Data Points	Date/Time	Temperature(°C)
7	18/03/2015 16:09:28	29	1	18/03/2015 16:09:33	27
8	18/03/2015 16:09:38	29	2	18/03/2015 16:09:43	27
9	18/03/2015 16:09:48	29	3	18/03/2015 16:09:53	27
10	18/03/2015 16:09:58	29	4	18/03/2015 16:10:03	27.1
11	18/03/2015 16:10:08	29	5	18/03/2015 16:10:13	27.2
12	18/03/2015 16:10:18	29	6	18/03/2015 16:10:23	27.3
13	18/03/2015 16:10:28	29	7	18/03/2015 16:10:33	27.4
14	18/03/2015 16:10:38	29	8	18/03/2015 16:10:43	27.4
15	18/03/2015 16:10:48	29	9	18/03/2015 16:10:53	27.5

As you can see the first six measurements of Red don’t have any corresponding values in Green.  Similarly, Green has six extra measurements that Red doesn’t.

To make a fair comparison, I need to exclude the six pairs of non-matched measurements, reducing my total from 28,800 to 28,794. Remember I’m talking about comparing pairs of measurements. When I exclude six measurements from each set, that’s six pairs, not, twelve measurements.

This is a missing data problem. There are lots of ways of dealing with missing data. This is the simplest: just don’t use data pairs with missing data.  Other techniques try to replace missing data with some ‘appropriate’ value. The Australian Bureau of Meteorology and other weather ‘authorities’ use homogenisation, where they replace the temperature from one weather station with that from another station, possibly one hundreds of kilometres away.   

OK, what does the data look like? The best way to ‘see’ the relationships in data is to draw a graph. Plus, I LOVE graphs.

There are several things about graphs that should be ticked off in the mind when looking at one:

1.  The graph’s title should tell you what the graph shows.  This one is about measured temperature on the vertical axis against time on the horizontal axis.       
2. The axes should have labels that tell you how the compared quantities are measured. Temperature in º C and time is in tens of thousands of seconds starting at a particular day and time..     
3.  The colour of the line tells you which temperature logger is reported. Note that the lines are so similar that they’re indistinguishable except for the little corner right at the start.

The Graph tells us lots of things.  Here’s a few:

1.      As noted, the loggers report close to the same temperature.  We’ll look at how close later.
2.      The temperature goes up and down as we expect. We all know it’s hotter during the day and colder at night. To answer more detailed questions like “What’s the hottest time of the day?” we’ll have to look more closely at the data.
3.     It looks like the temperature got generally cooler as time progressed. The top temperature the first day (the first up-pointing lump) appears to be around 30 º C. The top temperature for the second day is about 25 º C and the top for the third day is about 23 º C. The lowest temperature for each day also appears to be cooler than that on the previous day.

If I got back to the data, I can extract the highest and lowest temperatures. Since there’s lots of data (28,794 measurements to be exact) I won’t do the extraction myself. I’ll do one of my favourite things: I’ll write a program to do it.  Here’s the results:

 

Note that I’m using Australian date format with the day first, then the month.

For each day I’ve shown:

• The time of the first and last temperature reading for the day (Start and Finish); 
•  The number of measurements made that day (Count);    
•  The minimum temperature that day and the first time that temperature was reached (Minimum and Time of Minimum);   
• The maximum temperature that day and the first time that temperature was reached (Maximum and Time of Maximum);     
• The average temperature reached that day (Average); and  
•  The range of temperatures for that day, that is, the difference between the maximum and minimum temperatures (Range);

The Time of Minimum and Time of Maximum are the earliest times when that temperature was reached. On 19/3/2015, for example, the minimum was actually reached nine different times, in the 10 minutes 25 seconds between 07:16:38 and 07:27:03. During that period the temperature bounced between 18.6 and 18.7 º C.

I could have also reported the latest time the lowest temperature was reached or the time of the middle of the 10 minute period. All would be correct.

Also, of course, the clocks in the thermometers aren’t exact either and may well be slightly different to the clock on my laptop, so there’s instrumental error in the time as well as in the temperature.

Finally, we can compare how the two, supposedly identical thermometers measured the same temperature over the same time.

How well do the two temperature loggers agree? This graph shows us. For most of the measurements, they differ by either + 0.1 or – 0.2 º C. The differences at the start were considerable larger as noted on the graph. These differences are well within the manufacturer’s stated accuracy of 0.5 º C

All this seems pretty pedantic. Most of us just see the pattern in the wavy line and have a feel for what it represents. The tabular data adds details that (or may not) be of interest.

There’s one important thing we can check. For many years, the average temperature was calculated as the average of the highest and lowest temperatures.

As the picture shows, it’s not all that hard to produce a thermometer that measures the high and low temperatures for a particular day.

The table below shows the minimum, maximum and average temperatures calculated from the data we collected every ten seconds over a three-day period. I’ve added a column for ‘Calculated Average’ that’s computed by adding the maximum and minimum and dividing by two.

None of the averages calculated this way agree with the average of the 8,640 measurements taken each day. In one case,, on the 19^th, the difference is 1.2 º C.  That’s more than twice the error in accuracy that the manufacturer claims for the temperature logger.

Comparison of average temperature measurement Day Maximum Minimum Average Calculated average Difference 19/03/2015 29.8 18.6 23.0 24.2 1.2 20/03/2015 24.7 17.9 21.0 21.3 0.3 21/03/2015 22.4 14.4 18.3 18.4 0.1

Finally

In this mind numbingly simple experiment, we actually uncovered a few important matters:

•   The measurements contained several sources of error.
•    There was instrumental error; the temperature can only measure accurately to 0.5 º C.  The clocks in the loggers also will have some error, but we don’t have any estimate of that.
•    We had missing data. Not all of the data we gathered could be compared as the loggers gathered data from slightly different periods of time.
•  We the data loggers experienced a systematic errors, where Red started about 2 º C warmer and about five seconds earlier than Green.  The first of these systemic errors got smaller as the experiment unfolded but the second remained constant throughout.
• Calculating the average for the day based on just on two values, the maximum and minimum temperature, does not give the same value as calculating the average of 8,640 separate measurements.

Based on this simple experiment, we can certainly wonder how accurate the average temperatures claimed in the past would compare to their true values.  In other words, what’s the uncertainty in past temperature records?

After all, we asked to believe in dangerous, anthropogenic (human induced) global warming. Uncertainties in the temperature record need to be clearly understood before policy decisions are made. It doesn’t matter what scientists believe.  What matters is, how good are their numbers?

Extraordinary Claim Number 1

Extraordinary Claim: The whole world is hotter than it was in 1880 and we know by how much.

“Extraordinary claims require extraordinary evidence”[1]. Carl Sagan

The International Panel on Climate Change (IPCC) tells us: “It is certain that the Global Mean Surface Temperature has increased since the late 19^th century.”[2]
 


A few sentences later, we’re told by how much: “The globally averaged combined land and ocean surface temperature data as calculated by a linear trend, show a warming of 0.85 [0.65 to 1.06] º C over the period 1880-2012.” I’ll explain some of this in a minute.

This statement includes references to some fascinating footnotes that explain what “certain” means. “Virtually certain” means the authors are 99-100% certain that the statement is correct, so I suppose “certain” is even more certain, that is, more than 100% certain. Wow, that’s certainly certain.

The next footnote tells us that the numbers in the square brackets are 90% confidence limits. I remember from statistics that this translates into ‘if I sampled the rise in the worlds temperature over this 132 year period, one hundred different times, I would get a number between 0.65 and 1.06 ninety times. Of course I’d also get a number outside of this range ten times.’ I might even get 0.85 occasionally. Or not.

To me this range of uncertainty is a crucial aspect of the dogma related to climate change.

We are told to accept, without question, that it is possible to measure a change less than one degree, in the average temperature of every point on the Earth’s surface, over a period of 132 years. What n extraordinary claim!

OK, let’s start simple.

Question: How do we measure temperature? Answer: with thermometers.

Question: How accurate are they? Answer: That depends.

I’ll come back to how temperatures were measured back in 1880 in a later post, but how about now?



I got hold of a couple of inexpensive temperature loggers. A Chinese company, Tempsen, makes them for putting in trucks or restaurant fridges to keep a record of the temperature food’s kept at.

They claim an accuracy of 0.5 º C, not as good as the claimed accuracy of the 0.2 º C for the Hadley Research Centre data, but good enough for us.

I’ve added green and red dots so I can tell them apart. They’re actually named DB19M31238 and DB19M31236 but I call them “Red’ and ‘Green’ for short.

OK then, what can we do with them?

1. Look at how the temperature varies over a few days. Each logger can store about three days worth of data with the temperature sampled every 10 seconds. How cool is that?

2. Look at how the location of the thermometer affects the readings. We’d expect that being in shade or directly exposed to the sun would make a difference. It will be fun to see how much. Weather stations are housed in enclosures called Stevenson Screens. You can buy a nice one for $A990. I won’t be. I’ll look at the details of these later and I might have a crack at building suitable substitutes for Red and Green.

3. Look at how objects near the thermometer affects the readings. Does a thermometer surrounded by grass get the same readings as one surrounded by bitumen? Probably not. A key adjustment to ‘official’ temperature records is adjustment for the urban heat-island effect (UHI). We can use Red and Green to check this effect ourselves.

4. How much does temperature vary over distance? A key practice of the ‘official’ temperature record keepers is to ‘homogenise’ readings. (No, there’s no mixing of milk and cream involved.) If a weather station doesn’t show sufficient global warming, a simple way to fix that is to use the readings from another, hotter, station instead. https://www.facebook.com/denial101x has a video from Kevin Cowton from January 26, 2015 explaining how the temperature records for all of the weather stations in Paraguay were adjusted to match weather stations outside of Paraguay and to turn an inconvenient cooling trend into a proper warming one. I won’t be going to South America (or even South Australia) but I can explore how temperature varies over even short distances, like between my house and the Traralgon Airport.
I’m sure other experiments will occur to you and me as things progress. I’d love your suggestions.
I’ll finish this post with a bit of data collected from Red and Green just to show you what it looks like.
The day I got them, I set them running and the next morning worked out how to transfer the data from the loggers to my PC. I collected data from 3:36 PM on 17/3/2015 to 08:12 AM on 18/3/2015.

Here are the first few rows of the data:

Colour		Device	Dataset		Colour	Device	Dataset
Red		DB19M31236	EC12153551		Green	DB19M31236	EC17153551
Points	Elapsed Time	Date/Time	Temperature(°C)		Temperature(°C)	Delta Tt
1	0s	17/03/2015 15:36:34	26.3
2	10s	17/03/2015 15:36:44	26.4
3	20s	17/03/2015 15:36:54	26.5		25.3	1.200
4	30s	17/03/2015 15:37:04	26.6		25.3	1.300
5	40s	17/03/2015 15:37:14	26.6		25.5	1.100
6	50s	17/03/2015 15:37:24	26.7		25.5	1.200
7	1m	17/03/2015 15:37:34	26.7		25.5	1.200
8	1m10s	17/03/2015 15:37:44	26.7		25.5	1.200
9	1m20s	17/03/2015 15:37:54	26.6		25.5	1.100
10	1m30s	17/03/2015 15:38:04	26.6		25.5	1.100
11	1m40s	17/03/2015 15:38:14	26.5		25.5	1.000
12	1m50s	17/03/2015 15:38:24	26.5		25.5	1.000
13	2m	17/03/2015 15:38:34	26.4		25.5	0.900
14	2m10s	17/03/2015 15:38:44	26.3		25.4	0.900
15	2m20s	17/03/2015 15:38:54	26.3		25.4	0.900
16	2m30s	17/03/2015 15:39:04	26.2		25.3	0.900
17	2m40s	17/03/2015 15:39:14	26.1		25.3	0.800
18	2m50s	17/03/2015 15:39:24	26.0		25.2	0.800
19	3m	17/03/2015 15:39:34	26.0		25.2	0.800

A couple of things to notice:

The temperature records have to be lined up. They didn’t start at exactly the same time. Green started a bit after Red. That’s easily fixed with our modern thermometers, but could be a problem with older ones that only got read twice per day.

Red and Green are identical and both have a error of 0.5 º C. The column marked Delta T is the difference between the temperature readings. Why is this so? Well, I handled them. I must have had Red in my hand for longer than I held Green and warmed it up more. It actually took about 4.5 hours for the two to read within 0.5 º C of each other and over ten hours before the reported difference was zero. When was it exactly? We don’t know because of the error inherent in the two thermometers. 
 

To me that’s critical: All of the readings are just numbers. They are only useful measurements when the errors and uncertainty are taken into account.




Finally, here’s a graph of the results. The numbers on the horizontal axis are just the number of each point starting at one. Points are ten seconds apart so the numbers represent tens of seconds. Minutes or hours would have been better, but this graph is just here to show what the data looks like, the trend, if you will.
If you look carefully, you can just see the difference between Red and Green at the far left, near 25 on the temperature scale.

Next time we’ll do something a bit more interesting: I’m running Red and Green for three days on the window sill of my office. We’ll look at how the temperature varies over that period and how the readings from the two loggers compare.

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[2] Fifth Assessment Report, Climate Change 2013, Working Group I: The Physical Science Basis, Chapter 2, Observations: Surface and Atmosphere. Page 161. http://www.climatechange2013.org/report/full-report/ contains the full report.

Journalism award of the week or Play the man, not the ball

Pat Bagley, a cartoonist for the Salt Lake City Tribune published this charming cartoon recently. I found it on Jo Nova's site: http://joannenova.com.au/  She's cropped out the rest of the cartoon.
Nothing like a nice, reasoned scientific argument, is there?