This is in response to a discussion about population control and climate change on an e-list I’m on. In particular, it’s in response to a line by a mate, Jono:

it’s not the number of people that is important, but rather the power of the argument. Population control arguments need to be challenged wherever they occur, because they turn the climate movement into a war against human rights rather than for human rights.


Population control doesn’t have to infringe human rights. Some of the best ways of reducing the rate of population change are PRO-human rights: accessible education, equality in power relations between men and women, access to contraceptives, the aged pension.

Population is inseperable from environmental impact – if the population is low, but consumption per capita is very high, then you have a problem. If you have a really high population with small per-capita footprint, you still have a problem. At the moment, it’s obvious that the current global average per capita footprint is too high for the current population. The UN predicts 9 billion people by 2050, (150% of current population), which means that for us to have the same over all impact by then, we will need to have reduced our average percapita footprint to 2/3 of what it is now. To put this in perspective, current Australian GDP per capita is US$40-50,000, globally it’s about $10,000, so we’d have to reduce our footprints to about 15% of what it is now. That sounds doable, but that doesn’t take into account that we have to REDUCE our over-all impact, not keep it steady. (I realise I’m only talking about averages, but I think median figures would likely show even greater disparity).

There’s no reason why population control has to happen in the third world. It doesn’t matter where it happens. In fact, it’s probably better that it happens in the rich minority world, ’cause one less person here is heaps more impact reduction than the same person in the minority world. And that could potentially mean we have more room for refugees (not that population is the barrier now).

Ultimately, it’s about how you do it. Of course there’s plenty of fucked up ways to control populations. But the same can be said for any problem (Green Dictatorship, anyone?). We definitely shouldn’t be supporting any kind of punishment/penalties for people who feel the need to have more kids, but we should definitely encourage any positive measures that would help to slow down population rates, and oppose those that do the opposite (like Costello’s ” one for Mum, one for Dad, and one for the Country” – ugh… how would you feel to find out you were the one for the country?)

Seems to me that reducing populations and rates of change should definitely be a part of any broad climate campaign. We just have to make it abundantly clear how we mean to go about it – ethically and compassionately.

Terry Pratchett notes in one of his discworld books that politics is fundamentally about the running of the city. Politics – from Aristotle’s ta politika “affairs of state,”, from the Ancient Greek polis – the city state.

Something – perhaps Greg Combet’s assertion that “[it is] very widely agreed throughout domestic politics and international politics that an emissions trading scheme which fixes a carbon price by a market mechanism is the best way of getting a carbon price into the economy.” – tells me that politics just doesn’t cut it any more. Politics really does still deal on this level, the level of the ciity state. everything is one big race between competing countries, to get the best deal in the fastest time.

We need a way of dealing with systems bigger than the city state. We need ta geotika. Affairs of the Earth. A global politics – geotics.

CFMEU rejects carbon trading job claims – ABC News (Australian Broadcasting Corporation)

The Construction, Forestry, Mining and Electrical Union (CFMEU) says the release of figures warning that emissions trading will cost thousands of jobs is part of a scare campaign.

The Minerals Council says emissions trading will cost 23,000 jobs in the next decade .

But the CFMEU’s Tony Maher says the Minerals Council is using the figures irresponsibly.

“Even on their own shonky report there’s a very significant growth in employment,” he said.

It’s nice, really nice, to see Tony Maher from the CFMEU being honest. The Minerals Council are spinning this for all it’s worth, even though they’re getting more than they asked for in the CPRS. The CFMEU has run spin campaigns with the Minerals Coucil before, but obviously they aren’t as conjoined as it previously seemed.

Also worth noting that on Stateline tonight (I’ll link to the transcript when it goes up), solar researchers are planning to start a PV cell manufacturing industry, which they estimate will provide 70 construction, and 120 jobs. They also estimated that such an industry could eventually end up providing 40,000 jobs (if I remember the figure correctly).

That’s what I call an offset.

For anyone even vaguely involved in the world of blogs and climate change, logical fallacies are a familiar thing. The straw man, the appeal to authority, ad hominem attacks, the biased sample/cherrypicking, and many more are all used by both sides of the argument, to a greater or lesser degree.

On the side of climate scientists/environmentalists (Yes, I know that some won’t agree with my lumping those two groups together – it’s a crass generalisation, and it makes my case looks stronger (I am an environmental activist studying science), however it is true in the majority of cases) one of the arguments that comes up quite often is this:

Denier: “why should I trust the science – it’s biased/has vested interests/goes against my religion/philosophy.”

Greenie/scientist: “Why should you trust science? Look around you. You enjoy watching television, don’t you? And you’re using a computer right now, and I bet you drive a car. Science brought you those things.”

No. It didn’t.

Science is not technology, and technology is not science. The two are separate, although closely linked.

Science relies on certain technologies, such as microscopes, rulers and protractors, test tubes, and for more complex calculations, computers, etc. It does NOT rely on technologies like television, or the internal combustion engine, although these can make it easier.

Likewise, technology relies on science, but it also relies on the values of the individuals and societies building it, the resources that are available, and of course, the technology required to build it.

How about this:

Denier: “why should I trust the science – it’s biased/has vested interests/goes against my religion/philosophy.”

Greenie/scientist: “Why should you trust science? Think about this: The atom bombs dropped on Nagasaki and Hiroshima, engineered viruses, toxic toys, and television advertising. Science brought you those things.”

The point is that Technology isn’t brought to you by science. technology is brought to you by humans. True, the scientific understanding is a limiting factor on the technology available, but this does not mean that the technology will become available as the science advances.

Science, in it’s purest form, is just the pursuit of knowledge. More knowledge is, as far as I can work out, never a bad thing. Technology can go either way, and depends on the values of those designing it. Conflating the two is potentially a very dangerous thing to do, and even in cases where it’s safe,  to do so is still a logical fallacy.

There’s a long list of logical fallacies here: http://www.nizkor.org/features/fallacies/, but I don’t think this one features there. Perhaps it’s some kind of cause/effect fallacy. Perhaps it should be called the “Science for the Good/Bad life”.

I’d like to declare here and now that I’m sceptical about the “reality” of the round earth. There are many dissenting voices, sceptics of the current “consensus”, and significant evidence to show that the earth is not round. Not to mention that it’s bleedingly obvious – just look out the window: No curvature there, eh?

But despite this, dissenting voices in the debate are silenced. Proponents of the round earth hypothesis pursue their beliefs with a zeal unmatched even by the world’s most fundamentalist religions. While it’s true that many scientists believe that the earth is round, there are also significant dissenting voices, but were one to mention this in general conversation, or on talk back radio, one would immediately be shouted down, cut off, ostracised. In short, censored.

This is not how science should operate. Science is not decided by majority opinion, but by healthy debate. And while one side is being censored, there can be no real debate.

I’m not saying definitively that the earth flat or round – I’m still undecided, just that the debate needs to be opened up, so the true process of science can run its course, with maximum access to evidence and competing theories from both sides. Until all the information is on the table, I’ll be most skeptical of the majority-imposed “consensus”.


Sound familiar? The above arguments are frequently used by the denial-o-sphere (denial-o-plane?). While obviously climate change science is not so developed, or certain (or simple) as planetary physics, that does not mean that the above arguments have any weight in a climate context. (more…)

I’ve been starting to learn Octave, a maths programming language. Octave is similar to other packages that are often used to create nice graphs that you often see around the place, especially when it relates to climate change. This is a bit of a slap-dash tutorial on how to get some graphs happening with Octave. It probably assumes advanced high-school level maths.

If you wanna learn, I suggest you get QtOctave, which is damn nice, and in the Ubuntu repositories, and probably in most other distributions of linux (you can run Octave on windows – but if you really want to be this geeky, and are still on windows, you need to re-asses your values). QtOctave has a nice help-search function that lest you find most of what you need to know about functions, and installing it installs all the pre-requisites too, although depending on your distro, you might need some of the extra packages from octave-forge.

At the very bottom is an attachment with most of this code in it. I think most of this stuff will also work in Matlab, but you gotta pay for that…

Then read all of this excellent tutorial. That’s where I learned nearly everything for this tutorial, apart from the names of a few functions.

Crank out a graph!

Now you’re ready to go. Get yourself a copy of some temperature data to play with. I used NASA’s GISTEMP data. You can use any data you want, but I’ve attached a file that will do everything I’m talking about here, and includes octave-formatted GISTEMP data.

Ok, so assuming you’ve got your data in a matrix, you can then extract the relevant bits (Some of the variable names are different here to in the attachment, to save space):

% get the years from the first column
yr = GISTEMPdata(:,1);

(You did read that octave tutorial, right?)

% get the monthly averages
Temps =
GISTEMPdata(:,1);
% Average them, to get the yearly means (2 refers to the second dimension, ie. average rows, not columns)
AnnualTemps = mean (Temps, 2);

You can now hack out a simple graph:

plot(yr, AnnualTemps)

gistempplain

If you read tutorial, you’ll know how to adjust the axes, and add legends and titles, and all that jazz. I’m going to ignore that.

You’ll notice that the data range from -60 to 80. That’s because it’s a graph of temperature differences (anomalies) – which means that what matters isn’t the starting point, but rather, the relationships between the data. In this case, the -60 means -0.6DegC, and 80 means +0.8DegC (this is explained in the header of the GISTEMP file I linked to up top).

To change it to real values, to give it some human scale, we have to make the 1951-1980 average = 14DecC.

% Divide by 100, add 14, and subtract the average from the anomaly means
% 1951-1879 = 72, 1980 = 101
RealTemp = AnnualTemps / 100 + 14 - mean( mean( GISTEMPData(72:101,2:13) ) );

gistempsimple

Cool, huh? Okay, let’s get a Trend line going.

Getting Trendy

So, basically, a trend line is a best-fit line. You can do this automatically with a couple of functions in Octave, but since we’re going for just a straight trend line at the moment, we can just use a fairly simple one: a first degree polynomial fit. (a first degree polynomial is a straight line at any angle, from any starting point).

Polynomials are those equations you did in high school maths, that looked like:

y = x2+3x+1.5

That one would give you a basic parabola, shifted down and to the left a bit (I think, I haven’t actually graphed it). High-degree polynomials (where x is raised to the power of 2 or more) aren’t particularly useful for finding trend lines – they can look pretty, but don’t really help much. But more on that later. Simple first order polynomials (straight lines) are a good way of getting an idea of an overall trend.

To get the equation for the line, we need to get all the values for the basic form of a first degree polynomial:

y = mx+b

to get m and b from the data, we can use the polyfit() function, with 1, for 1st degree:

EQ = polyfit ( yr , TempReal , 1 ) ;

which provides us with an array, like:

0.0061271   2.1103472

The first value is m, the second is b.  Now we apply y=mx+b:

TrendLine = EQ(1) .* yr + EQ(2)

Now you can graph the trenline, with the original data:

plot(yr, AnnualTemps, yr, TrendLine)

gistemptrend

Looks ok to me. (I also note that even with the so-called “cooling since 1998/2000/2002/cherrypick”, 2008’s average temperature is almost 0.2DegC higher than the linear trend for the last 129 years..)

How Not To do Climate Stats

This is where the higher-degree polynomial equations come in. A high-degree polynomial can easily be made to fit a curve, but that doesn’t particularly mean anything, unless a high-degree polynomial cause can be hypothesised, that matches the trend. I don’t know of any that can.

All this was recently news, because the Australia published a piece of stupid masquerading as climate science.

Anyway, I want to show you how to do that same kind of stupid (albeit with 129 year data, not 30). You can try it with the last 30 if you like. Or with the last two. I don’t care, just don’t be surprised by the results, because they don’t mean anything.

So, we want a sixth-degree polynomial, that best fits the data we have. In other words, we want something like this:

y = rx6 + qx5 + px4 + ox3 + nx2 + mx + b

And we need to find r, q, p, o, n, m, and b. Again, we do it with polyfit(), this time with 6:

EQ = polyfit ( yr , TempReal , 6 ) ;
and we get something like:

1.3740e-16 -7.9135e-13 1.5165e-09 -9.6503e-07 -1.9859e-09 -2.5545e-12 -2.6291e-15

You might point out that these numbers are so small that they are ridiculous. To that, I’d reply: Good point.

Anyway, on with the stupidity, let’s whack those numbers into the above equation:

TempPoly6=EQ(1).*(yr.^6) + EQ(2).*(yr.^5) + EQ(3).*(yr.^4) + EQ(4).*(yr.^3) + EQ(5).*(yr.^2) + EQ(6).*(yr.^1) + EQ(7);

I hope that makes sense, it took me a while to get it.

Now we can graph it, along with the real data, and the linear trend line:

plot(yr, AnnualTemps, yr, TrendLine, yr, TempPoly6 );

gistemppoly6

Nice, huh? Now, any sane person would see without any stats education would see that an think: yep, that’s a pretty good match. Looks like a good fit to me.

But you already know it’s stupid, so you should be looking at it with even more critical eyes than usual. One of the best ways to be critical in a situation like this is to step back, and take a wide view. So let’s see how those trend lines look if we add another century on each end: 1700 to 2100.

to do this in Octave, you need to stretch the “years” component first, then just put it back into the same equations:

yr = [1700:2100]'

The ‘ is important, it makes the vector matrix vertical. Now you can just hit the up-key to access the same lines as before:

TrendLine = EQ(1) .* yr + EQ(2)

TempPoly6=EQ(1).*(yr.^6) + EQ(2).*(yr.^5) + EQ(3).*(yr.^4) + EQ(4).*(yr.^3) + EQ(5).*(yr.^2) + EQ(6).*(yr.^1) + EQ(7);

Then just run the last plot command again, (yr has changed length though, so go back to the GISSTEMPdata for the years for the original data:

plot(GISTEMPdata(:,1), AnnualTemps, yr, TrendLine, yr, TempPoly6 );

gistemppoly6long

That’s right. By 2100, temperatures won’t be 2DegC warmer, nor 4… Nope, it’s gonna be 21 degrees centigrade – 7 degrees warmer. And the “medieval warm period”? Didn’t exist. Was actually an ice age.

Disclaimer

I’m not a statistician, though I do hope to be doing stats at Uni this year. I’m reasonably sure this is all correct, though I haven’t used this kind of maths since high-school, more than half a decade ago. I learned what I now know in Octave in the last 2-3 days, so there might be better ways of doing this, I don’t know. I’d appreciate any corrections, if they’re needed, and feedback is always welcome.

I’d also appreciate any help on running a LOESS filter on the data. I don’t understand the maths except in the vaguest terms (moving polynomial average, or something?), but it seems like it applies a very useful smoothing, although it doesn’t provide any kind of future prediction the way a linear trend does (ie. in a very limited way).

ATTACHMENT:

gisstempdata.m – THIS IS A PLAIN TEXT FILE, NOT AN ODT. rename it to gisstempdata.m to use it in octave/matlab.

Until now, the technology hasn’t been available to obtain fine-scaled, precise measurements of CO2 in the atmosphere. But the launch next year of two carbon-detecting satellites, NASA’s Orbiting Carbon Observatory and the Japanese Greenhouse Gases Observing Satellite, should soon help to fill in this knowledge gap, which is critical to establishing a reliable carbon accounting system. – Amanda Leigh Mascarelli

There’s more info on the NASA project at http://oco.jpl.nasa.gov/, and on the Japanese project at http://www.jaxa.jp/projects/sat/gosat/index_e.html

It amazes me that this isn’t getting more attention already. It’s going to mean a massive increase in our ability to account for carbon and other greenhouse gas emissions and uptakes. Seems to me that these projects should be WAY more exciting than the Large Hadron Collider, for example, since they will so directly effect the science around one of the most important and controversial issues of this… century? millenium?

It also strikes me that images extrapolated from the data could be strikingly beautiful – in a similar way to the “earth by night” photos. Obviously carbon concentrations won’t be so strictly confined as light sources, and the images will obviously be false colour (since CO2 is invisible). But other effects, like those of coriolis winds and ocean and forest carbon sinks would be great to see in action, especially with changes over the seasons.

Reference:

Leigh, A. et al. (2008, December 18). What we’ve learned in 2008. Nature Reports Climate Change. Retrieved January 12, 2009, from http://www.nature.com/climate/2009/0901/full/climate.2008.142.html.