Archive for the 'global warming' Category

Colouring Doubt’s Flag


Judith Curry is keen to frame doubt in the form of an italian flag. Specifically with reference to this statement from IPCC WG1 Summary for Policy Makers:

Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations.

Curry’s flag interpretation of this statement is that we could colour the flag 5% white (uncommitted belief), 67% green (anthropogenic forcing), 28% red (natural variability). A minor quibble: the opposite of “due to increase in anthropogenic GHG” is not “natural variability” as that excludes other anthropogenic activies such as sulphate emissions and secondary effects like ozone increases due to Montreal protocol. Anyway, her flag would look like this (if she drew it):

Does that seem right, can we be almost certain that there is only 5% wiggle room for doubt? Also, if I say that 70% of the variation is anthropogenic that doesn’t mean the rest (or almost all the rest) is natural, it just means I don’t know. I interpret the IPCC statement as meaning that there are a wide range of supportable beliefs about the anthropogenic cause of 20th century warming, but 95% (ish) of those will have more than 50% of the flag coloured green. Amongst the population of possible flags is this one:

Note that this flag already represents quite an extreme position with respect to the IPCC statement, because whilst it’s compatible with the IPCC statement, only 5% of the flags have a smaller green area than this. Here’s a more median position:

How can we represent the range of beliefs that are compatible with the IPCC statement. Like this?

Screw the planet!


The earth is getting warmer. Ecosystems are changing. Food webs around the world are upset. We are in the middle of mass extinction.

For years the green lobby have been complaining about this, finally over the last decade their voice can be heard as global warming becomes a political talking point. We can save the Earth.

Screw that! The greens are getting way too much political capital out of global warming. Sending your money to Friends of the Earth, World Wildlife Fund for Nature, the RSPB, and so on, will not solve global warming. They are not in the game.

The planet is not under threat. Our life on this planet is under threat. Runaway global warming will be bad. For any metazoan. Everything larger than than a hyrax will be wiped out. But life will go on, the Earth will be claimed once more by the Archaea and it will be business as usual really. Nature’s brief experimental dalliance with multi-cellular life will have ended. She will conclude that “further research is necessary”.

So the debate around global warming is not about saving the Earth, it is about saving our way of life on the Earth. And let’s get this straight, once we’ve “solved” global warming, we’ll have put a nuclear power plant on every (100 Km or so of) coastline, we’ll have replaced inefficient sheep pastures with huge plantations of biofuel crops, we’ll have peppered the entire countryside with wind turbines, and glazed vast deserts with solar PV farms.

We will not have saved the polar bear, the poison arrow tree frogs. All those bromeliads teetering on the brink of their fragile hilltop ecosystems in the South American rain forest? All gone. It will be our fault, collectively. And it will be sad. But solving global warming and saving your favourite obscure cute species are not the same problem.

What is IsItHotOrNot?


This is the landing page for the Twitter account IsItHotOrNot. I’ll probably move it to a proper home at some point.

IsItHotOrNot displays the global temperature anomaly, month by month. Currently the data is sourced from GISTEMP, specifically the “Global-mean monthly land-ocean temperature index”. The file, GLB.Ts+dSST.txt, also has annual and seasonal values, but I don’t currently use those.

What does the number mean in “2009 Nov +0.680, hottest November on record”? It’s November’s temperature anomaly (in degrees C). The anomaly for November is the difference between this November and the average November temperature between 1951 and 1980, the base or reference period. This year’s November was 0.68 degrees C warmer than the average between 1951 and 1980. Obviously the anomalies for other months are calculated in a similar way. Note that this method takes account of the fact that Novembers are, on the whole, colder than Junes, by using the average November temperature between 1951 and 1980, not the average temperature. In other words it does seasonal correction.

It would be nice to say “about 1.0 degrees C” warmer than pre-industrial levels, like the EEA do, and I can do that by simply adding 0.3 to all the values, but since the errors for the 1850 to 1899 (which is what is usually used when you see the phrase “pre-industrial”) average are larger (on account of having fewer records), I decided not to do that. You can wing the error and add 0.3 to all the values if you like.

There are other temperature indexes available: NCDC do one; there’s the HadCRUT3 diagnostics; the Japanese Metereological Agency have their global average surface temperature anomalies. The different indexes use different reference periods (NCDC use 1900 to 1999, HadCRUT3 use 1961 to 1990, JMA use 1971 to 2000), the only effect of which is to move the graphs up and down the page (it does mean that if you want to compare the time series from two different providers, you need to rebase them to give them a common reference period).

Where does the data come from? Well, from GISTEMP, but the land and ocean index is a data product synthesized from other sources:GHCN (land), USHCN (USA land), HadSST2 (ocean), Reynolds SST (ocean), SCAR READER (antarctic land). The other indexes use more or less the same data, the differences are mostly in how it is processed.

Why GISTEMP? All the indexes are basically the same, so it doesn’t really matter which one I pick. Because of my work on Clear Climate Code I happened to be familiar with their datasets, so that was simpler from a programming perspective. The differences between the indexes are typically very small, but it does mean that the statements like: “second hottest September on record” are very sensitive to the exact dataset used. The JMA had September 2009 tying for first place, whereas GISTEMP have it second. I regard the “hottest November on record” stuff as nothing more than a popular discussion point, not an absolute truth.

Carbon into Trees


The BBC report that the Forestry Commission want to afforest 4% of the UK. And thereby get us 10% of the way towards our 80% emissions reduction target. Their wording is slightly odd, but see paragraph 12:

It is hoped the latest plan would absorb 10% of the UK’s target of slashing its emissions of greenhouse gases by 80% by 2050.

Alarm bells ringing. 1 million hectares (4% of the UK land) can sequester 8% (10% of an 80% emissions reduction) of the UK’s current CO2 emissions? No. My earlier article on coppicing willow suggests that an optimistic estimate for sequestration is 18 tonnes CO2 per hectare. So with 4% of the UK land, we could sequester 18 million tonnes, or about 3% of our (600 million tonnes of) emissions. I think my 3% figure is a really top end estimate. It’s not like willow grows particularly well in this country (but it is one of the best crops for sequestration) and with 4% of the UK covered, we may have to afforest some sub-optimal sites; short rotation coppicing is also different from growing mature forest, but I have a hard time believing that growing mature forest pulls down more carbon (yeah yeah, soil, nitrogen).

So where do the Forestry Commission get 8% from? I have no idea. And as usual the clueless journalists at the BBC fail to use the power of hyperlinking (welcome to the 1990’s) and they don’t have a link to the Forestry Commission research. Or even their press release (I suppose that would let everyone know they copied their homework).

Oh wait, here’s the first paragraph of the Forestry Commision press release: (ewgh Lotus Notes)

If an extra four per cent of the United Kingdom’s land were planted with new woodland over the next 40 years, it could be locking up ten per cent of the nation’s predicted greenhouse gas emissions by the 2050s.

Oh. So they mean 10% of our 2050 emissions. Which, as you know, are going to be 80% less than our current emissions. So 10% of 20% of our current emissions. Or 2%. Yeah, I buy that (just about, but at least it’s biologically plausible).

So the BBC mangled the press release. Does the BBC version seem very unclear to anyone else?

Food Chain Emissions


Friends of the Earth have sent our household a postcard. It says «The meat and dairy industry produces more climate-changing emissions than all the planes, cars and lorries on the planet.» They don’t quote a study, or any other source. Just a bold assertion which, on the face it, seems implausible. Even if you eat a gargantuan 250 g of meat a day (in other words, the typical US diet; Europeans eat about half that), does that really compare to all that driving round? It also seems a little bit mean to exclude trains and ships on the “transport” side. Is the balance between transport and food really so close that those 2 modes make all the difference? In the UK, rail and water account for about 4% of the total transport energy budget, so I would hope that the question isn’t so close that adding them back in tips the scales the other way. For one thing, any reasonable quantification of errors is bound to swamp that.

I think the FoE statement is false, here’s my homework.

David MacKay stacks up the UK’s energy consumption (Sustainable Energy – Without the Hot Air, Chapter 18, page 103), he has (per person): car 40 kWh/d, plane 30 kWh/d, food 15 kWh/d. So with 70 kWh/d (82 if we add the other transport modes) on the side of transport, and 15 kWh/d on the side of food then it does indeed seem implausible that food chain emissions would be higher. Note that we have all food production on one side, I can’t be bothered separating out meat from the rest, clearly meat forms the bulk of the energy consumption anyway. But wait…

As well as emissions related to the energy required to maintain the animals, they produce carbon-dioxide and methane all by themselves. In other words the food industry has emissions not related to its energy inputs (even if all the energy was produced sustainably, there would still be emissions). Non-energy related emissions show a weakness in David MacKay’s book; he neglects them completely. That’s okay, because his focus is Sustainable Energy, but be aware that it’s not the whole picture. Food, concrete, deforestation all have non-energy emissions. For animals I think we can neglect the CO2 emissions because the carbon originally came from the atmosphere anyway (respiration forms part of a close carbon cycle). Methane however is not negligible.

I reckon 1 kg of lamb produced between 60 g and 180 g of methane when it was walking about in the Peak District. That’s equivalent to about 2.4 kg of CO2. Let’s say I eat 100g of lamb a day. That’s (methane emissions equivalent to) emissions of 240g CO2, or about 1kWh of diesel. That’s roughly 0.1 litres; if you fill up 40 litres (about the size of my small car’s tank) every two weeks then that’s 3 litres a day. How often do you fill up? From a personal perspective, It looks like food-related methane emissions are not even close (to transport emissions).

Okay. So much for the ovine. What about the bovine, porcine, and, er, chickens? Well, I’m no veterinarian so this will take a lot of piecemeal research. Bugger that, lets go to a (competent?) summary: The UK’s Fourth National
Communication under the United Nations Framework Convention On Climate Change
. In 2004 UK agriculture (note: not just meat and dairy) emitted 13.8 MtC (megatonnes of carbon equivalent); transport emitted 37.4 MtC. Just what are these Friends of the Earth smoking that makes them think they can claim “The meat and dairy industry produces more climate-changing emissions than all the planes, cars and lorries on the planet” when it is so out of line with the UNFCCC GHG inventory. Is the UK really so atypical?

I suspect that what’s really happening is that the FoE are doing some clever accounting. There’s probably a little bit of double accounting (example, counting transport of feed on both sides), and I suspect some land use change. Perhaps they include chopping down ancient forest to grow soya beans for animal feed as an emission on the food change? I just don’t know, because they don’t show their homework. But I have a couple of points to make anyway. The first is that it’s not at all clear that the beef industry is too blame. If there was less demand for beef (and hence soya beans to feed the cows), then I think it’s likely that the same companies would have chopped down the same forest to grow something else. Miscanthus perhaps. The second is that while this land use change will be an emission (the UNFCCC recognises land use and land use change as a carbon source / sink), this emission occurs only once. Once the forest is cleared to grow soya, there will be no land use change emissions. So the emissions from the single land use change should be amortised over all future soya bean seasons. I think.

So FoE, how do you make the sums add up?

Appendix for the pedantic

«250g of meat a day … the typical US diet»

A quote from USDA Agriculture Factbook 2001-2002, Chapter 2, “Profiling Food Consumption in America”, :

“In 2000, total meat consumption … reached 195 pounds … per person”. That’s 242 g per person per day (2000 was a leap year).

«rail and water account for about 4% of the total transport energy budget»

Department for Transport, TSGB Chapter 3:

«1kg of lamb produced between 60g and 180g of methane»:

One 60 kg ewe produces about 20 litres methane a day (see below). Boned and trimmed meat is about 2/3 of the animal’s weight, so 0.5 litres / kg (boned). Lamb is generally defined as less than 12 month’s old or less than 18 month’s old for export. 360 days × 0.5 litres = 180 litres. × (the density of methane gas) 0.717 g/l = 129 g. 60 g to 180 g gives a range around this (to account for younger and older lambs, for one thing).

«one 60 kg ewe produces about 20 litres methane a day»

See Proceedings of the Nutrition Society, Volume 41, page 9A, meeting of 1981-07-17, “Methane production in lambs fed high- and low-roughage diets”. It depends on their diet: about 23 litres for high roughage; about 9 litres for low roughage. Two things: 1) when did you last see sheep being fed lucerne hay? 2) using 20 litres per day favours the FoE case anyway.

«equivalent to about 2.4 kg of CO2»

In terms of greenhouse gas warming potential, per kilo, methane is 20 times more potent than CO2. So 120 g methane equivalent to 2.4 kg CO2.

Warning: Generation Green could be in a classroom near you!


British Gas sent me a link to Generation Green. A classic greenwashing move: they seem to have paid a charity to produce a load of “green” lesson plans as a way to get their trademark embedded into every classroom, and therefore also into the minds of every future energy consumer. Leaving aside, for now, the ethics of corporate sponsorship of the classroom (hint: it’s wrong), what is the content like?

I had a look at Lesson 4 – Exploring sources of energy (part 1). One of the resources for this lesson is the “Energy Source Information Cards”: a series of 10 cards, one for each source of energy (coal, nukes, wind, and so on). There’s a Word document containing these that you can download (lower right of the web page I linked to).

It is from these cards that the students will be taking the factoids and copying them onto their posters in colourful crayon so that the posters can be displayed on the corridor walls in time for the first parents’ evening of term.

So, how are they? Well, a bit poor. On the whole, I’m a bit disappointed that “facts” like these are getting fed to children (and, more worryingly, their teachers). The perfect antidote to this generationgreen nonsense would be to use David MacKay’s book, Without Hot Air (go on, it’s free!). The chapters are bite-sized (especially the earlier ones), and they contain facts, and references, and good stuff.

The howlers in the “Information Cards” are Wind (mechanism wrong way round), Biomass (written by two people that never saw each other’s work), and Wave (written by someone who has no idea where the energy in waves is).

Overall there is a confusion between power and electricity. Every card has a section on how electricity is generated using that source. The Natural Gas card points out that gas can be piped into people’s homes, but there’s no mention of the fact that this is then used for heating not electricity generation. Coal, gas, oil, and biomass can be used more efficiently for heating applications directly than via conversion to electricity, but this is never mentioned. Nor is the fact that this is only of limited use because we only exploit a limited amount of low-grade heat.

There is also confusion about cost. Sometimes high capital cost (hydro) is mentioned, sometimes it isn’t (nukes). Often zero running cost is mentioned (wave) without mentioning capital. The important cost, total cost per kWh over the entire lifetime of the plant, is never mentioned.

There is also some confusion about pollution and global warming. Pollution is bad, global warming is bad. But these things are completely separate. There’s a tendency in the cards to assume that anything emitted into the air is bad, because of global warming and pollution; they’re not always specific enough about which it is.

Perhaps you can pick a different lesson and mock that, then at the end we can collect all our answers together and have a chat and make a nice poster?

Page 1 – Traditional Coal

This card is basically fine. The only things worth mentioning:

“Hard black substance that is found buried deep underground.”

Coal is not always hard (anthracite is, but it’s not the only form of coal), and it’s not always buried deep underground (I have picked it up on beaches).

Page 2 – Natural Gas

Basically fine.

Page 3 – Crude Oil

Typo: “most well knows” should be “most well known”.

There’s a double count in the disadvantages: “Burning oil pollutes the air”, and “Burning crude oil produces other emissions e.g. sulphur dioxide”. The “other emissions” are the pollution from burning oil. Perhaps better would be “Burning oil pollutes the air with sulphur dioxide and other emissions” (as does coal, by the way).

Page 4 – Wind Energy

“Wind is the effect of air flowing from low pressure to high pressure.” No, no, no, no, no. Bzzt. You’re wrong. Following is “The air in the warm regions rises and the cool air rushes in to replace it and this is what we know as wind”. A somewhat simplistic explanation, but that’s okay. The “this” is a horribly ambiguous reference; “this movement of air” would be better.

“As one of the windiest countries in Europe, it is perfect for our climate”. Yes, assuming we want to carpet bomb the British Isles with wind turbines. David MacKay’s ludicrously optimistic sketch of using 1/3 of our offshore coast for wind power (including uneconomical deep offshore wind) and carpeting 10% of our land (!) with onshore wind gives 58 kWh/day per person, or nearly half of the UK consumption. Perfect.

“Once it is built the fuel costs nothing”. Not true: offshore wind turbines need frequent replacement of the gear boxes due to sea-salt corrosion (and this should go in the disadvantages section).

Page 5 – Geothermal

Basically fine.

Page 6 – Biomass

In advantages: “It supports farmers because they can sell their crops for biomass fuel”. Whilst this is true it is seems silly to single out farmers. An advantage of wind energy is that it supports turbine blade manufacturers because they can sell their turbine blades as parts; an advantage of crude oil is that it supports oil drillers because they can sell their oil for fuel. It’s just a silly argument. What if crack cocaine was a fuel, would we be saying “it supports drug dealers because they can sell their stash for fuel”?

The “advantages” contradict the “disadvantages”. “Biomass fuel tends to be cheap” versus “Biomass can be relatively expensive compared to other sources of energy”. “Burning biomass produces carbon dioxide gas which contributes towards global warming”, strictly true but as the same card explains in the “advantages” section: “Although carbon dioxide is released when biomass is burned, it is still a carbon neutral source of energy. The amount of carbon dioxide that is released when biomass fuel is burnt is the same as the amount of carbon dioxide absorbed by the plants when they were growing.”

Page 7 – Uranium

“It does not contribute to the greenhouse effect because it does not produce smoke or carbon dioxide”. Mentioning “smoke” is absurd. The smoke produced by other sorts of power generation does not contribute to the greenhouse effect, quite the opposite. Smoke is an aerosol that has a cooling effect. Smoke is of course a pollutant, so nukes avoid air pollution, which is worth mentioning.

In advantages: “It produces small amounts of waste”. True, but so misleading. They make up for it in the disadvantages.

“It is not renewable; when the uranium is used it can not be replaced”. True, but worth mentioning the possibility of sea-dissolved uranium, which is replaced (er, I think).

“It is very difficult to turn off a nuclear power station”. Again, true, but it would be good to say a little bit on why this is a disadvantage. The reason it’s a problem is that no-one wants electricity at night but the nuclear power stations generate it anyway; you have to throw it away.

Page 8 – Solar Energy

“Every second, the Sun turns millions of tonnes of hydrogen into energy”. Well intuitively this didn’t seem right to me, but it turns out to be both right and wrong. The sun converts mass into energy at the rate of 4.4e9 kg per second (or 4.4 million tonnes, if you’d rather), and of course that mass is hydrogen. But it’s a little bit misleading not to mention the 600e9 kg of hydrogen that get converted to helium in the process. In other words every second, the Sun turns 600 million tonnes of hydrogen into helium, producing some energy in the process.

Only talks about PV, doesn’t mention solar concentration electricity generation such as the 11 MW PS10 tower in Spain (warning, EU press release).

Page 9 – Hydroelectric Energy

Hmm, it says here “Solar power can be used to create electricity in remote places where it might be very hard to get
electricity through cables”. Oh rly? What’s that got to do with hydro? Nothing, that’s what. Cut-and-paste hack-job.

Then the voice changes. Suddenly we see “we”: “We can control when the electricity is made by opening and closing the dam gates.”, and “Electricity can be generated 24 hours a day as long as we have the water”. It just hasn’t been proofread.

Disadvantages: “It is very expensive to build a dam”. Oh rly? Well, it is very expensive to build a nuclear reactor, and very expensive to build a wind farm the size of Wales, but you didn’t seem to mention that. Just casting about for disadvantages were we?

Another disadvantage: “There can be negative environmental impacts as water quality and quantity downstream can be affected and have a knock on effect on wildlife”. True, but there can be a positive effect on wildlife as well, as water habitats are created upstream of the dam and they are exploited by suitable species.

Page 10 – Wave Energy

“Wave energy is harnessed from the movement of the surface water of lakes, rivers and oceans.” Wrong. Should read “oceans” for “lakes, rivers, and oceans”. You cannot get usable energy from a wave on a lake. And as for rivers, stop laughing at the back. “Turbines can be placed by the shore, where the movement is at its strongest.” The latter bit, “where the movement is at its strongest” seems like a dubious claim to me. Surely the Atlantic waves have just as much movement a few miles offshore? The advantage of shore placement is shorely (sorry!) shorter cables?

“The wave acts like a piston that pushes air up and down an oscillating water column.” Well, that’s one way to get energy out of a wave, and it’s (kind of) how the Islay LIMPET works, but there are many other ways. Pelamis works by using the flexion of a linear body floating on the surface to drive hydraulic rams. CETO works by having a submerged buoy drive a piston to pump seawater inland at high pressure which then drives generating turbines. Salter’s Duck works, as far as I can tell, a bit like a self-winding watch.

“As an island we have lots of access to the coast and therefore could harness a lot of wave energy.” Yeah man, a lot of energy. According to MacKay, the total Atlantic wave energy hitting Great Britain amounts to 16 KWh/d per person or about 1/8 of our total consumption. If we exploited all of that then the Newquay tourism industry would be very annoyed (a disadvantage not mentioned, incidentally).

“It can be unreliable because it depends on the waves – sometimes you’ll get loads of energy, sometimes nothing”. Ah, no. Wave power is about the most reliable source of energy derived from a moving mass. Thousands of kilometres of Atlantic fetch can’t be wrong. There are always waves.

“Some designs can be very noisy”. Surely bogus, because no-one is proposing living next to them. Visually distracting, maybe, and a menace to fishing and shipping, but those disadvantages aren’t mentioned.

That’s all folks! Don’t forget your homework now, pick a lesson and tear it apart!

Putting the Heat on Wheat


Wherein I play with the lovely Google Charts API and expose my total incompetence in statistics, economics, agriculture, and geography. And quite possibly other things too.

So I was reading the Open Knowledge Foundation blog and came across this article featuring US wheat production, which points to this dataset of wheaty goodness. My recent work on Clear Climate Code had made me already aware of the availability of GISTEMP’s summary data products.

So it occurred to me that this could be used to answer the question “when the weather is warmer, does more wheat grow?”.

So the wheat data is US wheat production, including yields in bushels/acre, sigh. GISTEMP even do a dataset that shows the temperature anomaly for the US. I think this is incredibly parochial, but it happens to be just what I want.

So the wheat yield (volume of wheat per harvested unit area) has a general upward trend. At least from the mid 1930’s or so. Because I’m only interested in the local variation I have detrended the wheat data:

My hypothesis is that any deviation of the temperature from the long term average will lower wheat yields. I think this because I would expect that over the thousands of years of selection humans will have cultivated a variety of wheat that is optimised to grow at the average temperatures and it will do less well when temperatures deviate.

So what do we see? Here’s wheat yields and temperatures together:

Well, there’s no obvious correlation to eyeball. Scattergram:

(which is almost just changing ‘cht=lc’ to ‘cht=s’ in the above chart URL)

Bit of a blurry mess. If anything a slight negative trend, which would mean that colder temperatures gave a higher wheat yield. And indeed Pearson’s correlation is about -0.3 (assuming my calculations are correct) indicating a weak negative correlation.

There are problems. One problem is that I have no p-value. That’s partly because I haven’t read that far on the Wikipedia page (I’m not using some fancy stats package for my analysis; everything is hand-coded in Python), and partly because I have a degrees of freedom problem. Temperature is autocorrelated, so whilst I have 128 samples, that’s fewer than 128 degrees of freedom, so the standard assumption of independent variables is incorrect.

The other problem is that it looks like the detrending might have introduced a bit of an alarming feature into the wheat anomalies. There’s a gentle hump from 1866 to about 1940 and a similar one from about 1940 to 2000. This is almost certainly because I’ve used a cubic polynomial to fit to the data to detrend it. It looks like a two-leg linear fit would be better (with a kink around 1942), but I haven’t found how to do that. I have a sneaking suspicion I have some FORTRAN code lying around here to do it, but I’m too scared to look.

Final tiny problem almost too small to be worth mentioning: the wheat data is for the entire US, whereas the temperature data is for the contiguous 48. I’m guessing that Alaska and Hawaii make so little wheat contribution that it doesn’t matter.

In any case it doesn’t really look like fixing these problems would ever indicate a strong positive trend between temperature anomalies and wheat yields. So we can reject the notion that warmer weather means higher wheat yields. Of course warmer weather might mean we can grow more of something else (possibly just a different variety of wheat); it also might mean that the available belt of land for growing wheat is larger (but this is unlikely since it probably means the available belt of land for growing wheat has moved North).

My PyCon UK talks


My «Embedded Programming with Python» talk was the first Saturday morning slot, and really it was about manipulating hex-files and ‘scope dumps with Python:

Slides as PDF, 2.5e6 octets.

On Sunday Nick B gave a presentation of Clear Climate Code. I didn’t do much during the presentation; I was the OmniGraffle monkey. Here’s the presentation at the Clear Climate Code site.

BBC: remove errors bars for better headline


In this article from the BBC Richard Black claims “This year appears set to be the coolest globally this century”. There is no basis for this claim, and moreover the very notion of picking warmest and coolest years amounts to bickering about global warming.

Black appears to be making this claim on the basis of looking at column 2 of the HadCRUT data. Here’s a graph, freshly minted from the Google Chart API:

The data is taken from HadCRUT, here’s a relevant extract:

2000  0.238  0.249  0.227  0.333  0.144  0.238  0.233  0.334  0.143  0.334  0.143
2001  0.400  0.411  0.388  0.495  0.304  0.400  0.394  0.495  0.304  0.495  0.303
2002  0.455  0.466  0.445  0.553  0.358  0.455  0.450  0.553  0.358  0.553  0.357
2003  0.457  0.468  0.447  0.556  0.359  0.457  0.452  0.556  0.358  0.556  0.358
2004  0.432  0.444  0.421  0.530  0.335  0.432  0.426  0.530  0.334  0.530  0.334
2005  0.479  0.490  0.469  0.580  0.378  0.479  0.473  0.581  0.378  0.581  0.378
2006  0.422  0.432  0.412  0.517  0.327  0.422  0.416  0.518  0.326  0.518  0.326
2007  0.404  0.414  0.394  0.501  0.307  0.404  0.398  0.502  0.307  0.502  0.307
2008  0.281  0.292  0.270  0.428  0.134  0.281  0.275  0.429  0.134  0.429  0.134

The format of the data is described here, by Hadley.

In the graph the red line is the best estimate, the pink lines shows the combined 95% uncertainty from all sources. You can get more, or possibly just different, graphs from Hadley.

The first thing to notice is that Black’s claim is false if you include the year 2000. Okay so technically I know that “this century” starts in XX01 but I also know we all celebrated the beginning of the millennium in 2000 and we accepted then that although 2001 was technically the beginning of the millennium (and hence the century) it was much hipper to celebrate 2000. So that deserves a mention at least.

But really my gripe is about not observing the error bars. The uncertainty in the data is such that the error bars all overlap! The data does not support the claim that 2008 is warmer than 2005 for example; if we take as our null hypothesis that these two years are the same temperature then we cannot reject it with any confidence. The same is true about any other pair of years (except possibly for 2005 versus 2000; we might be able to claim that 2005 was warmer than 2000).

Neglecting 2000, as Black obviously does, the data are consistent with a constant anomaly of +0.4°C. That’s just an example, many other temperature series would be consistent with the data, including ones which make 2005 the coolest year.

And that’s the problem with trying to “rank” years. The uncertainties in the data are all so large compared to the yearly changes that it’s totally meaningless to talk about the warmest year or the coolest year. We just don’t know.

Of course, if Richard Black had thought about the uncertainties in the data then he would’ve had to say “latest HadCRUT data shows 2008 about as warm as any other year this century”, and that’s not a very controversial thing to say. All this dramatic concentration on the yearly, monthly, daily ups and downs of global temperatures, greenhouse gas levels, what-have-you is nonsense. It’s just talking about the weather while the planet burns.

Brake with your left foot


The driving manual has one recommended procedure where you should brake using your left foot:

After fording you should keep your right foot on the accelerator (to avoid stalling) and test your brakes with your left foot.

Needless to say, do this gently and after informing other people in the car that you are about to test your brakes using your left foot.