Bits From the Latest IPCC Report

(all quotes from the summary for policymakers)

In this report, the term impacts is used primarily to refer to the effects on natural and human systems of extreme weather and climate events and of climate change

Or in other words, they are lumping together costs associated with climate change due to human action, costs associated with climate change from other causes, and costs associated with extreme climate events, whether or not due to climate change.

In recent decades, changes in climate have caused impacts on natural and human systems on all continents and across the oceans. ... Evidence of climate change impacts is strongest and most comprehensive for natural systems.  ... See supplementary Table SPM.A1 for descriptions of the impacts (B) Average rates of change in distribution (km per d ecade) for marine taxonomic groups ... . Positive distribution changes are consistent with warming (moving into previously cooler waters, generally poleward).

"Impacts" sounds scary, but, as the quote shows, only means change.

While only a few recent species extinctions have been attributed as yet to climate change (high confidence), natural  global climate change at rates slower than current anthropogenic climate change caused significant ecosystem shifts and species extinctions during the past millions of years.

Warnings about large numbers of species being driven to extinction by anthropogenic climate change have morphed into the observation that species have gone extinct in the past for reasons unrelated to human action.

Some low-lying developing countries and small island states are expected to face very high impacts that, in some cases, could have associated damage and adaptation costs of several percentage points of GDP.

Compare costs of "several percentage points of GDP" in the places most at risk due to sea level rise with past rhetoric of hundreds of millions of climate refugees, drowned island chains, and the like.

Climate change has negatively affected wheat and maize yields for many regions and in the global aggregate (medium confidence). Effects on rice and soybean yield have been smaller in major production regions and globally, with a median change of zero across all available data

They don't actually say that yields have fallen, although that is what a careless reader is likely to think they are saying, only that they are lower than they would have been without climate change. A little searching finds a scholarly article on wheat yields, published in 2012, which reports that 

Wheat yields have increased approximately linearly since the mid-twentieth century across the globe, but stagnation of these trends has now been suggested for several nations. .... With the major exception of India, the majority of leveling in wheat yields occurs within developed nations—including the United Kingdom, France and Germany—whose policies appear to have disincentivized yield increases relative to other objectives. The effects of climate change and of yields nearing their maximum potential may also be important.
...

Near the time that leveling is generally observed, the European Union shifted away from a policy that rewarded high agricultural production through price guarantees to a policy that pays flat subsidies that do not increase with production and triggers taxes when production limits are exceeded

So what has actually happened is not that yields have decreased but that in some areas they have stopped increasing, at least in part due to changes in agricultural policy.

At present the world-wide burden of human ill-health from climate change is relatively small compared with effects of other stressors and is not well quantified. However, there has been increased heat-related mortality and decreased cold-related mortality in some regions as a result of warming ... .

The first sentence makes it sound as though climate change is making things worse. The second implies that there have been both costs and benefits and offers no estimate of their relative size.

People who are socially, economically, culturally, politically, institutionally, or otherwise marginalized are especially vulnerable to climate change.

One might conclude that keeping poor people poor, for instance by pressuring poor countries to produce less energy or produce it in more expensive ways in order to hold down CO2 output, will do more damage than good.

Impacts from recent climate-related extremes, such as heat waves, droughts, floods, cyclones, and wildfires, reveal significant vulnerability and exposure of some ecosystems and many human systems to current climate variability.

Note that the extremes are not limited to those due to climate change. Floods, cyclones, et. al. do damage...and always have. 

For the major crops (wheat, rice, and maize) in tropical and temperate regions, climate change without adaptation is projected to negatively impact production for local temperature increases of 2°C or more above late-20th-century levels, ...

Emphasis mine. If farmers ignore the implications of climate change on what crops they should grow how and continue to ignore them for the next sixty years or so, output is expected to decline.

With these recognized limitations, the incomplete estimates of global annual economic losses for additional temperature increases of ~2°C are between 0.2 and 2.0% of income (±1 standard deviation around the mean)

I have not yet gotten into the full report but, judging from accounts I have seen, 2°C of additional warming is about what it suggests we can expect by 2100 if we don't do much to prevent it. So if policies to prevent warming reduce the annual growth rate of world income from (say) 2% to 1.98%, the resulting loss will just about cancel the gain. Not a compelling argument for switching from fossil fuels to solar power.

All of these quotes are from the first half of the summary for policy makers—I have not yet tried to get into the full report. My conclusion is that the IPCC's estimates of the negative effects of climate change due to human action are much smaller than the rhetoric surrounding the subject suggests, a fact the report attempts to conceal as best it can by its presentation.

And a fact that does not come through in news stories about the report, at least those I have so far looked at.

How long before the more enthusiastic true believers start accusing the IPCC of having sold out to the oil industry?

Does Climate Catastrophe Pass the Giggle Test?

The argument for doing drastic things to prevent global warming has two parts. The first has to do with climate change, with reasons to think that the earth is getting warmer and that the reason is human action, in particular the production of CO2. The second has to do with consequences of climate change for humans.

Most of the criticism I have seen, in comments to this blog and elsewhere, has to do with the first half, with critics arguing that the evidence for global warming, or at least the evidence it is caused by humans and will continue if humans do not mend their ways, is weak. I don not know enough to be sure that those criticisms are wrong; pretty clearly climate is a very complicated and not terribly well understood subject. But my best guess, from watching the debate, is that the first half of the argument is correct, that global climate is warming and that human action is at least an important part of the cause.

What I find unconvincing is the second half of the argument. More precisely, I find unconvincing the claim that climate change on the scale suggested by the results of the IPCC models would have catastrophic consequences for humans. Obviously one can imagine climate change large enough and fast enough to be a very serious problem—a rapid end of the current interglacial, for example. And if, as I believe is the case, climate is not very well understood, one cannot absolutely rule out such changes.

But most of the argument is put in terms not of what might conceivably happen but of what we have good reason to expect to happen, and I think the outer bound of that is provided by the IPCC models. They suggest a temperature increase of about two degrees centigrade over the next hundred years, resulting in a sea level rise of about a foot and a half. What I find implausible is the claim that changes on that scale at that speed would be catastrophic—sufficiently so to justify very expensive measures now to prevent them.

Human beings, after all, currently live, work, grow food in a much wider range of climates than that. Glancing over a U.S. climate map, it looks as though all of the places I have lived are within an hour or two drive of other places with an average temperature at least two degrees centigrade higher. If people can currently live, work, grow crops over a temperature range of much more than two degrees, it is hard to imagine any reason why most of them couldn't continue to do so, about as easily, if average temperature shifted up by that amount—especially if they had a century to adjust to the change. That observation raises the question with which I titled this post: Does climate change catastrophe pass the giggle test? Is the claim that climate change of that scale would have catastrophic consequences one that any reasonable person could take seriously?

I can only see two ways of defending such a claim. The first is some argument to show that present arrangements are, due to divine intervention or some alternative mechanism, optimal, so that any deviation, even a small one, can be expected to make things worse. The second, and less wildly implausible, is the observation that people have adapted their activities—the sort of houses they live in, the varieties of crops they grow—to current conditions. Put in economic terms, we have sunk costs in our present way of doing things. Even if the planet has not been optimized for us, we have optimized our activities for the planet, with the details depending in part on the local climate. Hence any change in either direction can be expected to be a worsening, making our present way of doing things less well adapted to the new conditions.

That would be a persuasive argument if we were talking about a substantial change occurring over five or ten years. But we aren't. We are talking about a not very large change occurring over a century. In the course of a century, most existing houses will be replaced. If temperatures are rising, they will be replaced with houses designed for a (slightly) warmer climate. If sea levels are rising, they will be replaced, in low lying coastal areas, with houses a little farther inland. Over a century, farmers will change at least the varieties they are growing, very possibly the kind of crop, multiple times, in response to the development of new crop varieties, shifting demand, and similar changes. If temperatures are rising, they will gradually shift to crops adapted to a (slightly) warmer climate.

Climate aside, we do not live in a static world—consider the changes that have occurred over the past century. The shifts we can expect to occur due to technological progress alone, even without allowing for political and demograpic shifts, are much larger than the shifts required to deal with climate change on the scale I am discussing.

My conclusion is that this version of climate catastrophe, at least, does not pass the giggle test. There may be other versions, based on more pessimistic predictions of climate change, that do. But the claim that we now have good reason to expect climate change on a scale that will produce not merely problems for some but catastrophe for many is one that no reasonable person should take seriously.

Future Climate and the Food Supply

I recently had an experience both rare and pleasant, a civil and informative argument about climate on FaceBook. It was started by

One of the commenters, although not prepared to defend the hysterical tone of the posted piece, was willing to argue that climate change was making the global food situation worse and threatened to make it much worse in the future. In defense of that claim, he cited "one recent study showing four major global crops declining (relative to no climate change)." The article, "Climate Trends and Global Crop Production Since 1980"  (Lobell et. al. 2011), was an attempt to separate out the effects on four major crops of different environmental changes–temperature, precipitation, and CO2 concentration–occurring from 1980 to 2008.

Reading it, I noticed that what the authors defined as the effect of climate change included temperature and precipitation but not CO2; its (positive) effect was listed separately. Including it changed the conclusion from four crops down to two down, two up. The commenter who offered the article as evidence had apparently missed that fact.

I also noticed that while they found a significant warming trend over the period, the trend in precipitation was statistically insignificant – consistent with random change. Redoing the calculation using only the two effects we knew were associated with AGW, warming and increased CO2 concentration, made the percentage increase in rice equal to the decrease in maize, the increase in soybeans larger than the decrease in wheat. The figures are shown in Table 1 from the article.

The table showed no effect of increased CO2 on the yield of Maize. Maize, as the gentleman I was arguing with pointed out, is a C4 crop, the other crops C3, the difference being in the details of the mechanism for photosynthesis. The effect of CO2 fertilization on C4 crops is substantially less than on C3 crops but not zero. Looking at another article that had been linked in the discussion, this one from the EPA, I found:

The yields for some crops, like wheat and soybeans, could increase by 30% or more under a doubling of CO₂ concentrations. The yields for other crops, such as corn, exhibit a much smaller response (less than 10% increase).

That suggests that the effect is less than a third as large as the effect on the C3 crops but still substantial. Including it on Table 1 makes the negative net effect on maize smaller than the positive effect on rice.

Looking at the EPA article I noticed that the increase in  yield due to CO2 fertilization was presented as a fact, various things that might decrease yields as possibilities.

"if temperature exceeds a crop's optimal level or if sufficient water and nutrients are not available, yield increases may be reduced or reversed." 

"Extreme events, especially floods and droughts, can harm crops and reduce yields."

 No evidence was offered that any of those things would happen or how large the effects would be if they did. It looked as though the authors wanted to give the impression that climate change would reduce agricultural yields but prudently stopped short of saying so.

I also noticed:

"Overall, climate change could make it more difficult to grow crops, raise animals, and catch fish in the same ways and same places as we have done in the past."

As conditions change, people change what they do in response. If temperatures rise, farmers will shift to crop varieties suited to a warmer environment. If rainfall increases or decreases, they will adjust crop varieties, irrigation, other details accordingly. What would happen if farmers ignored environmental changes in deciding how to farm tells us very little about what will happen in the real world. Whether or not we have global warming, it is quite unlikely that, a century from now, people will grow crops, raise animals, and catch fish in the same ways and the same places as they do now.

The same issue is relevant to the other article. The authors estimated the effect of increased temperature on yield by looking at how yields had varied with temperature, year by year, in the past. Those estimates were  used to calculate the effect of the overall increase in temperature over the period and suggest possible effects of future increases.

To see the problem with that approach, consider a farmer at planting time. He does not know how hot the year will be, how much rainfall there will be. Decisions such as when to plant and what varieties to plant can only be based on the expected value of those variables.

A farmer in 2100 knows what changes in climate have occurred over the previous century so  can take account of those changes in how he farms. It follows that models based on observations of year to year variation will show a more negative effect of climate change than can be expected from gradual change over a long period of time. The authors of the article noted that problem along with other limitations to their analysis.

Most of the time, all I learn from arguing climate with people on FaceBook is how unreasonable most people engaged in the argument, on both sides, are. This was a pleasant change. 

I will have to wait to see whether my opponent has become less confident that climate change threatens the global food supply now that he knows that the article that he thought supported that claim is, if anything, mild evidence against it.

Land Gained and Lost: A Fermi Estimate

Climate change affects the amount of land usable by humans in at least three different ways. Land is lost through sea level rise. Land is lost because it becomes too hot for human use. Land is gained because it becomes warm enough for human use. Exact calculations of the size of all three effects, if possible at all, would require much more expertise and effort than I am bringing to the problem so what I offer are Fermi estimates, numbers based on very crude approximations. For all three estimates I will be assuming warming of 3°C above current temperatures and sea level rise of .6 m above present sea level, roughly what the latest IPCC report projects for the end of the century under SSP3-7.0.

Land Lost to Sea-level Rise

The amount of land lost equals the length of coastline times the amount by which it shifts in. For the total length of the world’s coastline I found a figure of 356,000 km. The amount by which coastline shifts in with a given amount of sea level rise depends on the slope of the coastal land. I came across a figure of a hundred feet of shift for every foot of sea level rise in a book discussing the situation on the U.S. Atlantic coast; since I do not have figures for every coast in the world, I will use that.

60m coastline shift x 356,000 km of coastline = 21,436 km²

That is my very approximate estimate of land lost to sea level rise.

Land Lost to Rising Temperature

How much does temperature rise in hot parts of the world with 3° more of global warming? Figure SPM.5b of the latest IPCC report[1] shows a map of projected average temperature change due to a 4° increase relative to 1850-1900 in average global temperature, roughly 3° relative to current temperature. Parts of the Earth that are both hot and densely populated appear to warm by a little less than the global average. Table 11.SM.2 shows the effect of different levels of global warming on maximum temperatures. It looks from that as though 3° of global warming would raise the maximum temperature of the relevant regions[2] by about 3°. So if we knew at what temperature, average or maximum, the Earth’s surface becomes too hot for human habitation, we could conclude that any area currently within three degrees of that would, with our assumed level of global warming, become too hot for humans.

The simplest approach to doing this is to compare a map of global temperature (Figure1 ) to a map of population density (Figure 2) and see at what temperature population density goes to close to zero. Comparing the two maps we observe that while the coldest areas of the globe are essentially empty, the hottest are not; some, such as the Philippines, Senegal, and Malaysia, are densely populated. If there is a temperature at which the Earth’s surface becomes unliveable, these maps do not show it. Our estimate of the amount of land lost by the direct effect of heating, calculated in this way, is zero.

We may be able to do a little better by looking at data on cities. The hottest city, by average temperature, is Assab, Eritrea, at 30.5°C, with several others nearly that warm. Hence we can conclude that any city whose average temperature after climate change is less than 30.5° will not be unliveably hot while cities whose temperature is higher than that might be. There are 28 cities with an average temperature of 28°C or more. Their a combined population is about 33 million, which is roughly .7% of the urban population of the world. If we use urban population ratio as a very rough proxy for total population ratio and that as a very rough proxy for land ratio and calculate.7% of the non-arctic land area of Earth, we get 

149 million km² (Land area) – 5.5 million (Antarctica) - .8 million (Greenland) = 143 million km²

143 million km² x.007 = 1 million km²

That gives us a very approximate upper bound for the amount of land that becomes unlivable due to global temperature increasing by three degrees. It is only an upper bound because we do not know that a city would be unliveable at an average temperature of 31°, only that there are no cities that hot.

Both of these calculations are based on average temperature. Arguably what habitability depends on is be maximum temperature. If it gets unendurably hot during a summer day, the fact that winter nights are cold is little compensation.

Figure 3 is the equivalent of Figure 1 for maximum temperatures. The highest temperature regions it shows include densely populated parts of India as well as more sparsely populated parts of Africa and Arabia. Insofar as one can tell from that map, there are no places large enough to show on the map where maximum temperatures are too high for human habitation. It is possible that some would be that hot after an additional three degrees of warning but the combined evidence of Figures 2 and 3 suggests not, since some of the hottest regions are densely populated.

I have been defining usable land as land humans can live on. While there are parts of Earth that seem crowded, average land per person is about five acres, so human populations are not limited by the amount of space to put them in. They might, however, be limited by not enough land to feed them, so it might make more sense to define usable land as land suitable for growing crops.

Is there any significant amount of land that is too hot to grow crops? So far as I can tell, there is not. Maps showing yield of various crops can be found online; some regions with high average and maximum temperatures show substantial yields. The yields shown are averaged over countries, but a map of agriculture in India shows crops being grown across areas within India of both high average and high maximum temperature.

My conclusion from these calculations is that there is probably no substantial amount of land area that will become either uninhabitable or unable to grow crops solely because of temperature with global warming of 3°C.

This does not mean that there is no area that will become either uninhabitable or unable to grow crops as a result of global warming, only that there is no area where it will happen solely because of temperature. Looking at Figure 2, one observes a wide region of northern Africa with almost nobody living there — the Sahara. That area is less hot than some populated regions, so temperature is not the entire reason it is empty, but it can be, almost surely is, part of the reason, so increased temperature might expand it.

On the other hand, the latest IPCC report suggests the possibility that climate change might have the opposite effect:

Some climate model simulations suggest that under future high-emissions scenarios, CO2 radiative forcing causes rapid greening in the Sahel and Sahara regions via precipitation change (Claussen et al., 2003; Drijfhout et al., 2015). For example, in the BNU-ESM RCP8.5 simulation, the change is abrupt with the percentage of bare soil dropping from 45% to 15%, and percentage of tree cover rising from 50% to 75%, within 10 years (2050-2060) (Drijfhout et al., 2015). However, other modelling results suggest that this may  be a short-lived response to CO2 fertilization (Bathiany et al., 2014).

In summary, given outstanding uncertainties in how well the current generation of climate models capture land-surface feedbacks in the Sahel and Sahara, there is low confidence that an abrupt change to a greener state will occur in these regions before 2100 or 2300.

Figuring out all consequences of climate change for the amount of land available for human use is a much more complicated problem than I am trying to solve.

Land Gained Due to Rising Temperature

Human land use at present is limited by cold, not heat, as shown on Figure 2 above — the equator is populated, the polar regions are not. It follows that global warming, by shifting temperature contours towards the poles, should increase the amount of land warm enough for human habitation. Making Antarctica habitable would require a lot more than three degrees of global warming and the southernmost land masses north of it are already inhabited, so any land gains from warming will be in the northern hemisphere.

Figure 11.SM.1 of the sixth IPCC report shows minimum temperature of areas such as North America and Northern Asa going up by between 2 and 3.4 degrees per degree of global warming. Since warming is greater in colder climates, I take 3 degrees per degree as a reasonable guess for the increase in temperature in the northern part of those zones. It follows that three degrees of global warming will increase the temperature in the colder parts of those zones by about nine degrees. To estimate how much land will shift from not quite habitable to at least barely habitable we need two numbers — what length of the contour dividing barely habitable from not quite habitable is over land and how far a nine degree increase in temperature will shift it.

It seems likely that habitability depends more on minimal temperature than on average temperature. Figure 4 shows temperatures in January, which should be close to the minimum, with contours every five degrees — much more precise information than Figure 1 provides for average temperatures. Combining the temperature information on Figure 4 with the population density information on Figure 2, the border of habitability appears to be at about -15°C. Nine degrees of warming will raise the January temperature of land currently at -24° to -15°, so shift the land between those two contours from not quite habitable to barely habitable. I estimate the distance between the -15° and -25° contours to average about 800 km, making the distance between -15° and -24° about 720 km, and the length over land of those contours to total about 15,000 km. Hence the area between them is about 10,800,000 km².

This land is being warmed from not quite habitable to at least barely habitable, from a population density of less than two per square km to a population density of more than two but in some areas less than ten. At the same time, the land a little farther south is being warmed from barely habitable to more than barely habitable, and the land south of that …  . Combining those effects, 10.5 million square km is a rough estimate of the increase in fully usable land.

The analysis so far has used population density as the measure of habitability. As I suggested earlier, it may make more sense to use the ability to grow crops. Crop production maps for Canada and Russia show crops growing in about the same areas that appear habitable by population density, so I have not tried to redo the calculation on that basis.

Conclusion

On the basis of these calculations, I find, for the effect of climate change by the end of the century under SSP3-7.0:

Loss of usable land by flooding due to sea level rise: 21,436 km²

Loss of usable land due to the direct effect of warming: Probably close to zero, with one calculation giving an upper bound of one million km².

Increase of usable land due to the direct effect of warming: 10.8 million km².

All of these numbers are very approximate but they imply a large net increase, due to climate change, in the amount of land usable by humans — more than twice the area of the United States. They also imply that nearly five hundred times as much land is gained through warming as is lost through sea level rise, which makes it odd that only the latter is commonly included in discussions of the effects of climate change.

P.S. Two commenters on this in different places asked why I didn't discuss other work along these lines and one of them provided a link to “Climate change impacts on global agricultural land availability” by Xiao Zhang and Ximing Cai 2011 Environ. Res. Lett. 6. It is a more elaborate analysis than mine, focusing on the amount of arable land and trying to take account of a  wider range of constraints including soil quality and humidity. It finds increases in some regions, decreases in others, with the net effect, not including land not available because of population increase, ranging from -.8 million km² to +1.2 million km². Details of their analysis are difficult to extract from the article — I could not tell, for example, whether the effect of CO₂ fertilization on the water requirement of plants is one of the effects they take into account. The analysis in this chapter is less sophisticated but much easier for the reader to audit, to figure out what I am doing and whether to trust the result.

A similar calculation is done in Ramankutty N et al 2002,  “The global distribution of cultivable lands: current patterns and sensitivity to possible climate change,” Global Ecol. Biogeogr. 11 377–92. That article explicitly takes account of the reduction in water requirements due to CO₂ fertilization. The authors conclude “In the GCM-simulated climate of 2070–99, we estimate an increase in suitable cropland area of 6.6 million km².” Since I am estimating land warm enough for human use and they are estimating land suitable for cultivation, taking account of a variety of constraints, it is not surprising that their figure is lower than mine. The Sahara, for example, is warm enough for human use — there are densely populated regions that are warmer — but not suitable for cultivation.

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This is a draft of a chapter for a book I am working on. I am looking for two sorts of comments:

1. Easy ways of doing my calculations better. There are obviously ways I could make my results more accurate by more complicated calculations but since I don't really care if the real number is twice mine or half it, that isn't worth doing. On the other hand, if there are ways just as easy but smarter, giving a more reliable result, I am interested.

2. Major mistakes. My conclusions are pretty dramatic and I want to know if they are, for some reason, wildly wrong.

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[1] This and other references to IPCC figures in this chapter are to IPCC AR6 WGI Full Report.

[2] SAS, EAS, SEA, and CAF in the table.

 

Another Good Article by Dan Kahan

The source of the public conflict over climate change is not too little rationality but in a sense too much. Ordinary members of the public are too good at extracting from information the significance it has in their everyday lives. What an ordinary person does—as consumer, voter, or participant in public discussions—is too inconsequential to affect either the climate or climate-change policymaking. Accordingly, if her actions in one of those capacities reflects a misunderstanding of the basic facts on global warming, neither she nor anyone she cares about will face any greater risk. But because positions on climate change have become such a readily identifiable indicator of ones’ cultural commitments, adopting a stance toward climate change that deviates from the one that prevails among her closest associates could have devastating consequences, psychic and material. Thus, it is perfectly rational—perfectly in line with using information appropriately to achieve an important personal end—for that individual to attend to information on in a manner that more reliably connects her beliefs about climate change to the ones that predominate among her peers than to the best available scientific evidence.

His empirical claim is that disbelief in global warming, or in evolution, is not evidence of scientific ignorance. If you separate groups on roughly a left/right basis, belief in warming increases with increasing scientific intelligence (measured in other ways) in the group predisposed to believe in it (left), decreases with increasing scientific intelligence in the group predisposed not to believe in it (right). Similarly with evolution if you divide the groups into more or less religious. His explanation ...:

If that person happens to enjoy greater proficiency in the skills and dispositions necessary to make sense of such evidence, then she can simply use those capacities to do an even better job at forming identity-protective beliefs.

The article is too long and starts with an irrelevant analogy to observer effects in quantum mechanics, but it has lots of interesting stuff in it. Among other things, if you test people to see how much they understand about the theory of evolution, those who believe in it do no better than those who don't. Similarly for global warming.

Climate: The Implication of Uncertainty

Anyone who looks seriously at climate issues should recognize that the consequences of climate change are very uncertain. My own view is that they are sufficiently uncertain to raise serious doubts about the sign as well as the size of the effect, that warming due to human production of greenhouse gases might well make us better off rather than worse off. Even if I am wrong and the effect is almost certainly negative, how negative it will be is very uncertain. CO2 emissions might fall sharply due to increases in the cost of fossil fuels or decreases in the cost of alternatives. For a given value of emissions, varying estimates of climate sensitivity imply at least a factor of two range for the resulting temperature. For a given increase in temperature, the effect on humans depends on what humans will be doing for the next century. Diking against a meter of sea level change could be a serious problem for Bangladesh if it happened tomorrow. If Bangladesh follows the pattern of China, where GDP per capita has increased twenty fold since Mao's death, by the time it happens they can pay the cost out of small change.

A possible response to this point is to argue that uncertainty is no argument against action. One simply replaces the uncertain range of outcomes with the best estimate one can provide of its expected value, the average of costs weighted by their probability, and acts as if that were the known consequence of warming. If the estimate of expected cost is ten trillion dollars, then any precaution to prevent it that costs less than ten trillion is worth taking.

It is a possible response and a popular one, but it is wrong for a reason that ought to be obvious to (at least) economists. The question we are answering is not "what should we do?" but "what should we do now?" Waiting may raise the cost of dealing with the problem but it will also provide additional information. The more information we have, the better our ability to decide what precautions are worth taking. Or not worth taking. Uncertainty that will be reduced over time is an argument against immediate action.

The usual rhetorical response is to claim that we barely have time to act at all, that if we wait more than a very short time it will be too late. This claim becomes less persuasive the more times it is made, and it has  been made, by various people, quite a large number of times over the past twenty years or so. It largely depends on picking some arbitrary temperature change, most commonly two degrees C, and treating it as if it were the end of the world. As salesmen commonly put it, "Buy Now—This Is Your Very Last Chance To Take Advantage of Our Special Offer."

For a more realistic opinion, consider an estimate of the cost of waiting by William Nordhaus, an economist who has specialized in climate issues. In the course of a piece arguing for immediate action against climate change, he reported his estimate of how much greater the cost of climate change would be if we waited fifty years to deal with it instead of taking the optimal action at once.  The number was $4.1 trillion. He took that as an argument for action, writing that "Wars have been started over smaller sums." 

As I pointed out in a post here responding to Nordhaus, the cost is spread over the entire world and a long period of time. Annualized, it comes to something under .1% of world GNP.

"Thought before action, if there is time."
(quote from a character in a Dick Francis novel)

And there usually is.

Why Global Temperature Doesn’t Matter for Global Crop Yield

Looking at a little of the literature on the effect of climate change on agriculture, I noticed something that seems to be a mistake — perhaps someone here can explain why it isn’t.

Crop yields depend, among other things, on temperature, with an optimal average temperature for each crop — about 15°C for wheat, for example (Lobell 2012). If temperature goes up by a degree, yield in an area that used to be 15° and is now 16° goes down a little. This seems to be one of the effects that goes into estimates of reduced yield as a result of climate change.

But it shouldn’t. The same warming that shifts 15° up to 16° also, somewhere a little farther north in the northern hemisphere or south in the southern, warms 14° to 15°, 13° to 14°, and so on. If wheat was being grown between, say, 13° and 17°, the area of cultivation can shift by one degree towards the pole and continue to have a temperature range of 13°-17° and the same temperature-related yield as before.

I can see two possible objections to this argument. The first is that the land a little closer to the pole may be less well suited to growing wheat in respects other than temperature. That is obviously possible but why would you expect it? Is there any reason why land that happens to have the ideal temperature for growing wheat is also more likely than other land to have the ideal soil or the ideal amount of rain? If not, then on average the shift is to land about as well suited in other ways and now ideally suited in temperature. A more careful analysis might find a deviation from that in either direction, land a little closer to the poles a little better or a little worse, but why should we expect either?

The second objection is that shifting the area of cultivation is costly — wheat farms have irrigation systems suitable for growing wheat, appropriate farm machinery, are owned or managed by people experienced in growing wheat. You can’t just pick all that up and shift it a hundred miles further north.

How serious an issue this is depends in part on how fast the shift happens. Looking at maps showing average temperature, it seems to go down as you move towards the poles by about a degree every hundred miles, with a good deal of variation. At current rates of climate change, global temperature should be going up by about a degree every thirty years. So shifting the area of cultivation to keep the temperature at which wheat is being grown constant should require moving it by about three miles a year, with farms at the warm edge of the zone shifting to crops with a higher optimal temperature such as maize (18°) while farms at the cold edge are shifting from barley or vegetables to wheat.

The real pattern would, of course, be more complicated than this, but why isn’t it the right first approximation? If so, then reduced yield with warming should not be included in the effect of climate change on agriculture. What should be included is the large increase in total arable land as temperature contours shift towards the pole, since it is cold, not heat, that restricts the area of land suitable for agriculture.

Am I missing something? Alternatively, is all of this already being included in models of the effect of climate change on agricultural output? If so, perhaps someone can point me at examples.

Turning Behavioral Economics Around

I am currently involved, elsewhere online, in a discussion of behavioral economics. One point it raises is that arguments from behavioral economics—observed patterns of irrational behavior—tend to be used to support positions that those using them already believe in. Much the same is true of arguments from market failure. As I pointed out some time ago in the course of an exchange with Robert Frank, the argument he was making had a perfectly straightforward implication—that instead of subsidizing schooling, at both high school and college levels, we should tax it. It was not a conclusion that he drew, or even acknowledged and responded to when I drew it.

Consider the case of behavioral economics. One of the observed patterns is a status quo bias—a tendency to over weight potential losses relative to potential gains. I am not sure if it has occurred to any of those arguing for behavioral economics that two of the most striking examples of that pattern are the precautionary principle and the campaign to slow or prevent global warming.

The essence of the precautionary principle is that one ought not to do anything—build nuclear reactors, say, or create genetically engineered crops—unless all possibility of very bad results can be eliminated. The principle does not permit balancing some risk of very bad results from doing something against a risk of very bad results from not doing it. Still less does it prescribe always doing something unless one can show that there is no chance that failing to do it will have very bad results. Hence it makes sense only if bad effects from change are weighted much more highly than good.

Or consider the widely held view that global warming on the scale suggested by the IPCC reports—a few degrees C over about a century—would obviously be a catastrophe. It cannot be based on the idea that humans cannot live with somewhat higher temperatures, since humans already exist, indeed prosper, across a much wider temperature range. It cannot be based on the idea that increased temperature is inherently bad, since there are obviously lots of places that would be better suited to human habitation if a little warmer, including most of Canada, Alaska and Siberia. The world was not, after all, designed for our benefit, so there is no reason to believe that current climate is optimal for us. There has been a good deal of talk about higher sea levels, but most of it ignores the fact that the increase suggested by the various IPCC models is only a foot or so—much less than the usual difference between high tide and low.

Rapid climate change is presumptively undesirable, since our present way of doing things—what crops we grow where, where our housing is located and how well it is insulated—is optimized to present conditions. But over a hundred years, farmers will change crops several times over, a large fraction of the housing stock will be replaced or modified, we will change what we are doing for lots of reasons unrelated to climate change. Hence it is hard to argue any strong presumption that climate change at the rates suggested by current models is bad.

Yet discussions of the subject almost always take it for granted that it is not merely bad but catastrophically bad, worth bearing very large present costs to prevent. A clear case of status quo bias.

For one final example, consider the case of Social Security. Behavioral economics provides an argument in favor of it. Individuals badly underweight costs and benefits in the distant future—so-called hyperbolic discounting. Hence they will be less willing than they should be to provide voluntarily for their old age. Hence the government must solve the problem via a program of forced saving.

The problem with the argument is that hyperbolic discounting, insofar as it is real, applies to voters and politicians as well as to people saving for their old age. Hence it is predictable that the force will be real but the saving will be imaginary—there are always politically profitable ways of spending money that happens to be lying around—leaving the system with a trust fund full of IOU's.

Readers are invited to contribute other examples, other situations where behavioral economics provides arguments against the sort of things that most behavioral economists appear to be in favor of.

A Little More on Climate and the Food Supply

A recent post discussed the effect of climate change on the food supply. I now have a little more information.

I start with the table from Lobell et. al. 2011 which I showed in my previous post:

The issue was the effect on food supply, so it matters how much of each crop is used for food.

Of the 440 million metric tons (MMT) of polished rice produced in the world in 2010 ( Table 1), 85% went into direct human food supply ( 5 ) . By contrast, 70% of wheat and only 15% of maize production was directly consumed by humans. (Major Cereal Grains Production and Use Around the World)

 Googling around, it looks as though about 6% of soybean production is used directly as human food, 75% as animal feed, some of the rest as soy oil consumed by humans.  I can't find a figure for the total fraction used to feed humans, so am guessing 10%. We then have:

Production and yield are from Table 1 above. The bottom right cell shows a net increase in the amount of the four crops used as human food of about a million metric tons. For a more precise calculation I should have converted tons of each crop into calories. I am assuming that the ratio is not very different for the different crops, but readers are welcome to check that.

In the course of the same conversation, one of the participants insisted that all studies of the future effect of climate change on the food supply showed it to be negative. I don't generally like getting into the game of dueling citations, for reasons I will probably discuss in another post. But I was referred to the latest IPCC report so looked at it, and found a table, Figure 7.5 in Chapter 7, that showed the distribution of predictions of the effect of climate change on mean crop yield over the 21st century. For both temperate and tropical regions, the median prediction was for a negative effect but more than 25% of the studies predicted a positive effect. Looking at the estimates that included the effect of adaptation, farmers changing what they did in response to changing circumstances, the median prediction was for a reduction in yield of less than half a percent per decade.

I think that supports my view of the effect of climate change both on the food supply and more generally–that there are both positive and negative effects, both are quite uncertain, and the sum might turn out to be negative or positive, might make us worse off or better off.

Technology and the Cost of Carbon: A Second Try (revised)

 One of my chief criticisms of Rennert et. al. 2022 is that it calculates the cost imposed by an additional ton of CO2 by summing costs from now to 2300 while almost entirely ignoring technological change, in effect assuming technological stasis. That raises the question of how one ought to model technological change in trying to calculate costs over a long period of time. My initial response was that you can't do it, that any estimate of effects centuries in the future is a wild guess, science fiction not science. But that started me thinking about how, if I had to do it, I would. 

Here is my answer.

Step 1: Create a simplified procedure for deducing the Social Cost of Carbon (SCC) from your preferred climate change scenario using causal relationships, such as the effect of temperature on mortality or on crop yield, calculated with data from a single decade. The causal relationships should take account of variables other than technology, such as income, that can be expected to change over time.

Step 2: Use the procedure to produce a value for SCC using data from the most recent decade for which suitable data are available, say 2010-2020. Call this SCC(2010).

Step 3: Repeat step 2, using data from the previous decade. Call this SCC(2000).

Step 4: Calculate the ratio R1=SCC(2010)/SCC(2000)

This is essentially what Lay et. al. 2021 did for the effect of technology on temperature-related mortality.

Suppose R1=.9. That implies that changes over the decade were reducing the social cost of carbon implied by your climate change scenario at about 1%/year.

Step 5: You now abandon your simplified procedure and substitute whatever you consider the best way of calculating SCC. But instead of discounting at the discount rate you discount at the discount rate plus 1% (or whatever R1/10 turned out to be). You have now allowed, as best you can, for the reduction in cost over time due to technological change.

It is not a very good solution to the problem but better than assuming stasis. One problem with it is that it ignores the fact that, the farther into the future you go, the more uncertain your estimate of the effect of technological progress. To solve that you need ...

The Improved Version

Step 3a: Repeat step 2 using data from each earlier decade for which the necessary data exist, generating SCC(1990), SCC(1980), ...

Step 4a: Calculate R2=SCC(2000)/SCC(1990), R3=SCC(1990)/SCC(1980), R4=  ...

If you are willing to model the effect of technological progress as a constant rate of decrease of costs, use the average of your ratios R1, R2, ... instead of R1 alone to estimate the rate at which cost is being reduced due to technological change. Alternatively, if you think the rate of change due to technological change is itself changing over time and you have enough ratios, you could try fitting them to a function linear in time and use that. 

In either case, use the variation of the ratios (from the average or from the linear fit) to calculate by how much you should increase the uncertainty in cost for the later years. If you estimate the rate as 1% when it is really 2% that will have little effect on costs in the near future but produce a large overestimate of costs a century or two later.

Actually doing all of this would be a large and complicated project. Even turning my verbal sketch into a precise mathematical formulation would be quite a lot of work. I don't propose to do either, but perhaps someone more ambitious could. Having been done once, the result could be applied to a variety of different approaches for estimating future costs — from CO2 or anything else.

Have Past IPCC Temperature Projections/Predictions Been Accurate?

Arguments for or against doing things to slow climate change depend on what will happen if we don’t, a question the IPCC reports try to answer. That makes it important to know how reliable their predictions are. The latest report runs to almost four thousand pages, largely of detailed analysis depending on multiple scholarly articles for each step — Chapter 7, to pick one at random, has fifty editors and about nine hundred articles in its list of references. Someone with infinite time, energy and expertise might be able to go through all of the calculations that produced the predictions in the reports in order to see if they were done correctly, but that is not a practical option.

There is an alternative. The climate system is too complicated to make predictions on the basis of theory alone, hence the IPCC project largely consists of sophisticated curve fitting, picking a form for the relationship among observables suggested by physical theory, choosing parameters for the relationships, how strong each effect is, by finding the values that best fit historical data.  With enough tweaking of the models and adjusting of parameters that process can fit past data, but that does not tell you whether the models fit the real system well enough to correctly predict future data. As someone is supposed to have said, with enough parameters you can fit the skyline of New York.

The solution, for both the researcher who wants to know if his model is right and someone else trying to decide whether to believe him, is to test the model against data that were not used in creating it. We do not know the future, the future eventually becomes the past, so a model constructed in 1990 can be tested in 2021 against data that did not exist when the model was constructed.

The past reports are webbed. Back in 2014 I looked at each to see what someone who read it would expect future temperature to do and reported the results on my blog. If you would like to check my conclusions about what each report implied for yourself you can find links to the reports here.

What the IPCC Predicted

The executive summary of the first report (1990) contains:

Under the IPCC Business-as-Usual (Scenario A) emissions of greenhouse gases, the average rate of increase of global mean temperature during the next century is estimated to be about 0.3°C per decade (with an uncertainty range of 0.2°C to 0.5°C).

The graph shown for the increase is close to a straight line at least from 2000 on, so it seems reasonable to ask whether the average increase from 1990 to the present is within that range. 

Figure 18 from the Second Assessment Report (1995) shows the future temperature through 2020. Through that date, it rises steadily at about .14°C/decade.[1]

From the Third Assessment Report (2001):[2]

For the periods 1990 to 2025 and 1990 to 2050, the projected increases are 0.4 to 1.1°C and 0.8 to 2.6°C, respectively.

For the former period, that implies an increase of from .11 to .31 °C/decade.

The Fourth Assessment Report (2007) has[3]

For the next two decades a warming of about 0.2°C per decade is projected for a range of SRES emissions scenarios.

What Happened

When I did the calculations in 2014, I found that the IPCC had predicted high four times out of four, twice by enough so that actual warming was below the bottom of the predicted range. That looked like evidence that we should not put much weight on their predictions of future temperature.

We now have seven years more data, so I did it again. As of September of 2021, when I am writing this, the last year whose temperature is shown on the NASA page I am using is 2018; I have redone the calculations accordingly. Here are the results:

The first IPCC report was released in 1990. From then to 2018, global temperature rose .38°C for an average of .14°C/decade, well below the predicted range.

The second report was released in 1995. From then to 2018, temperature rose by .37°C, for an average rate of growth of .16 °C, a little higher than the prediction.

 

The third report was released in 2001. From then to 2018, temperature rose by .29°C for an average of .17°C/decade, towards the lower end of the predicted range.

 

The fourth report was released in 2007. From then until 2018, temperature rose by .18 degrees, .16°C/decade, below the predicted .2°C.

 

The predictions look better now than they did in 2014, high three times out of four, low once, and only once has actual warming been below the predicted range. They are still running a little high but the results look consistent with random error. That makes it at least possible that the IPCC researchers are now modeling the climate system well enough to produce reasonable estimates of its future behavior.

It is possible but far from certain because the test they passed is not a very strong one. A theory that correctly predicted the outcome of next year’s elections, including every house seat, every senate seat, and the total votes for each party, would be a very good theory indeed, since doing that well by chance is very unlikely, so we would have good reason to trust its future predictions. A theory which correctly predicted which party will end up with a senate majority after the 2022 election would be better than one that got it wrong but not much better, since one can get the right answer half the time by flipping a coin.

The IPCC reports rely on complicated models and a lot of data. One way to judge how impressive their results are, how much evidence that they have done a good job of modelling climate, is to compare their results with those of much simpler models. The simplest is the assumption that global temperature never changes. The IPCC did a little worse than that model in 1990, since it predicted warming from then to 2018 of .3°/decade and the actual value,  .14°/decade, was closer to zero. But they did much better than that model the next three times.

The next simplest model is a straight line. From 1910, about when current warming started, to 1990, when the first IPCC report came out, warming was .11 °C/decade. The rate of warming from 1990 to 2018 was .14 °C/decade, so the straight-line prediction made in 1990 predicts about 79% of warming from then to 2018. The ratio of the IPCC prediction to what actually happened was 250% for the 1990 prediction, 81% for the 1995, 125% for the 2001 and 2007 predictions.[4] So predicting that the rate of warming would continue at its average level as of 1990 does much better than the 1990 prediction, about as well as the three later ones.

One test of how good the IPCC models are is to see how well each of them did at predicting warming from then to now. The first report fails that test, the next three pass it; actual warming was within their predicted range although not equal to their best guess. That is evidence that those models can be expect to give correct predictions in the future but not very strong evidence. 

It is, however, much better than the evidence was in 2014.

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[1] The figure is on page 323 of Climate Change 1995 The Science of Climate Change. When I did my calculations in 2014 I thought it was .13°/decade but measuring the graph more carefully I now think it is .14.

[2] Page 60 of Climate Change 2001 Synthesis Report.

[3] Page 7 of Climate Change 2007 Synthesis Report.

[4] I am defining each of the IPCC predictions as the predicted value if there is one or the center of the range if there isn’t.