Big swings and shock absorbers

Two years ago when we started this blog, the Midwest was going through a major drought and ended up eaking out just above 123 bushels per acre (bu/ac) in corn yield. Today the USDA released its latest projection for 2014, with a forecast for record corn yields of 167.4 bu/ac, due to really good weather (as Wolfram summarized in the last post.)

The difference of 44 bu/ac between two years this close apart is bigger than anything experienced in the history of US agriculture. The closest thing was in 1994, when yields were 139 bu/ac after just 101 in the flood year of 1993. When expressed as a percentage swing, to account for the fact that yields overall have been going up, the swing from 2012 to 2014 (36%) is near historic highs but still less than some of the swings seen in the 1980’s and early 1990’s (see figure below).

We’ve talked a lot on this blog about what contributes to big swings in production, and why it shouldn’t be surprising to see them increase over time. Partly it’s because really bad years like 2012 become more likely as warming occurs, and partly it’s because farmers are getting even better at producing high yields in good conditions. Sometimes I think of good weather like a hanging curveball – a decent hitter will probably manage a hit, but a really good hitter will probably hit a home run. So the difference between good and bad weather grows as farmers get better at hitting the easy pitches.

Moving on from bad analogies, the point of this post is to describe some of the changes we might see as the world comes to grips with more volatile production. What kind of shock absorbers will farmers and markets seek out? Four come to mind, though I can’t be sure which if any will actually turn out to be right. First, and most obvious, is that all forms of grain storage will increase. There are some interesting reports of new ways farmers are doing this on-site with enormous, cheap plastic bags. We have a working paper coming out soon (hopefully) on accounting for these storage responses in projections of price impacts.

Second will be new varieties that help reduce the sensitivity to drought. Mark Cooper and his team at Pioneer have some really interesting new papers here and here on their new Aquamax seeds, describing the approach behind them and how they perform across a range of conditions.

A third response is more irrigation in areas where it hasn’t been very common. I haven’t seen any great data on recent irrigation for grain crops throughout the Corn Belt, but I’ve heard some anecdotes that suggest it is becoming a fairly widespread insurance strategy to boost yields in dry years.

The fourth is a bit more speculative, but I wouldn’t be surprised to see new approaches to reducing soil evaporation, including cheap plastic mulches. There are some interesting new papers here and here testing this in China showing yield gains of 50% or more in dry years. Even in the Midwest, where farmers practice no-till and crops cover the ground fairly quickly, as much as a third of the water in the soil is commonly lost to evaporation rather than via plant uptake. That represents a big opportunity cost, and if the price of grain is high enough, and the cost of mulch is low enough, it’s possible that it will take hold as a way of raising yields in drier years. So between storage and mulching, maybe “plastics” is still the future.

Here a MIP, there a MIP, everywhere a MIP, MIP

A growing number of papers are looking at climate change impacts using multiple models. A few more are out this week in a special issue of PNAS. Mostly I just want to point readers of this blog to them if they are interested. I am generally a big fan of model intercomparisons (MIPs). I talk about them so much in my modeling course that I can usually hear the students’ collective sigh of relief when I move on to another topic.

Most of the strengths of MIPs have been demonstrated clearly with the climate MIPs, now on their 5^th rendition. They are useful for estimating uncertainties, they can point to important weaknesses in some models, and most of all they can create something that is more than the sum of its parts – the magical multi-model mean – where independent model errors cancel and estimates become more reliable. And a positive externality of these activities is usually that experiments and observational data to rigorously test models tends to improve, since each model isn’t in charge of testing their own model (“trust is, it’s great, we validated it years ago!”)

I think two reasons the climate MIPs were so successful is that the results were made available to the entire community, and relatedly that most of the groups performing the comparisons were not simultaneously working on model improvement. I’m not sure yet if AgMIP will follow this example. In conversations with them, I think they’d like to, but are not quite there.

With all of the positives going for it, I am still a little puzzled by a few things in the recent MIP papers. For one, it’s not clear to me why agriculture studies still do so much comparison of “no CO2” and “with CO2” runs, and conclude that the difference represents some indication of how much more work needs to be done on CO2. I’m not saying that I haven’t heard various explanations, but none of them are satisfying. The chance that CO2 has no effect on crops is about the same as the chance that Wolfram will show up to work tomorrow in a dress (that’s a very low chance, in case you were wondering).  If you are looking at uncertainty from CO2, you should look at various plausible responses to CO2, and zero isn’t one of them. I can see reasons to make estimates without CO2, including if your model doesn’t treat it, or if you are focused on effects of heat in order to test adaptations, but if you are trying to look at impacts of different emissions scenarios, why keep running a model without CO2? It reeks of trying to make the problem look worse than it is.

Another niggle is that the experimental design isn’t always set up to provide insight into what causes differences between models. I’m sure that will improve with time. But for now they are drawing some big conclusions from fairly weak comparisons. For example, the figure below shows the huge spread in model results is largely from two models from LPJ and one from GAEZ being very positive. They use this to conclude that models without N limitation have more positive impacts. But there are tons of differences between these and other models, why not conclude that models that start with L or G are more optimistic? The theory is that models with N limits can’t respond as much to CO2, but it should also be the case that they can’t respond as much to temperature, as work Wolfram and I did a while back concluded. (We also saw the GAEZ model was way positive in regions that shouldn't have much N stress). They’d need to have an experimental design to really demonstrate that it's nitrogen and not something else.  

Just to be clear, I really do like the MIPs and the people involved are high quality and have been very generous with their repeated offers to participate. Unfortunately, they have more meetings than Australia has poisonous snakes (which is a lot, in case you were wondering). I am participating in a wheat site-level intercomparison, which hopefully will be out this year.

On another note, I am now fully settled in to live in Brisbane (on sabbatical), and will try to post a little more often. I’m learning lots of interesting stuff, and not all of it about cricket (although there has been a curious uptick in the national team’s performance since I went to their match my first week – see “Australia’s resurgence as a world power in cricket has been swift, ruthless and dangerous”).  Mostly I’m deep into crop physiology, which readers of this blog (if there are any left) may or may not care about, but it may be the only thing I have to talk about for a while. Also, there’s the IPCC approval session coming in a few weeks which should be interesting. I think Max will also be there blogging for one of the other sites he actually writes things for.

Too close to call?

All of the attention on the presidential election has brought up some issues that are familiar to those of us who work in the world of anticipating and preparing for climate change impacts. In particular, there's been a clear contrast in the election coverage between, on the one hand, a lot of media stories that describe the race as a "toss-up" or "too close to call" and, on the other hand, careful analysis of the actual data on polls in swing states that say the odds are overwhelmingly in favor of another term for President Obama. Nate Silver has become a nerd celebrity for his analysis and daily blog posts (his new book is also really good). But there are many others who come to similar or even stronger conclusions. Like Sam Wang at Princeton who has put Obama's chances at over 98%.

I think there are a few things going on here. One is that the popular media has basically no incentive to report anything but a very close race. It keeps readers checking back frequently, and campaigns may be more likely to spend more money to media outlets for advertising if the narrative is for a very close race (although admittedly, they have so much money that the narrative may not make much difference). A more fundamental reason, though, is just a basic misunderstanding of probability. Not being able to entirely rule out something from happening (e.g., Romney winning) is not the same as saying it could easily happen. People mistake the possible for the probable. They want black and white, not shades of gray (at least not fewer than 50 shades of gray).

(Also in the news this week: hurricane Sandy. Another case where people who understand probabilities, like Mayor Bloomberg, have little trouble seeing the link to global warming, while others continue the silly argument that if it was possible for such things to happen in the past, then global warming can't play a role. In their black and white world, things can either happen or they can't. There is no understanding of probability or risk. I call this the Rava view of the world, based on the episode of Seinfeld when Elaine tries to convince Rava that there are degrees of coincidence:

RAVA: Maybe you think we're in cahoots.
ELAINE: No, no.. but it is quite a coincidence.
RAVA: Yes, that's all, a coincidence!
ELAINE: A big coincidence.
RAVA: Not a big coincidence. A coincidence!
ELAINE: No, that's a big coincidence.
RAVA: That's what a coincidence is! There are no small coincidences and big coincidences!
ELAINE: No, there are degrees of coincidences.
RAVA: No, there are only coincidences! ..Ask anyone! (Enraged, she asks everone in the elevator) Are there big coincidences and small coincidences, or just coincidences? (Silent) ..Well?! Well?!..)

Back to my point (you have a point!?), when we turn to climate impacts on agriculture, it's still quite common to hear people say that we just don't know what will happen. Usually this comes in some form of a "depends what happens to rainfall, and models aren't good with rainfall" type of argument. It's true that we do not know with complete certainty which direction climate change will push food production or hunger. But we do know a lot about the probabilities. Given what we know about how fast temperature extremes are increasing, and how sensitive crops are to these extremes, it's very probable in many cases, like U.S. corn, that impacts on crop yields will be negative. (For example, a few years back I tried with Claudia Tebaldi to estimate the probabilities that climate change would negatively impact global production of key crops by 2030. For maize, we put the odds at over 95%). Even in cases where rainfall goes up, the negatives tend to predominate. It's also also very likely that in some cases, like potatoes in England, that impacts will be positive. In either case we cannot say anything with absolute certainty, but that doesn't mean we should describe impacts as "too close to call"

Us academics can probably learn a thing or two from how Nate Silver is trying to explain risk and probability in his daily posts. But it's also fair to say that our task is a little hard for a couple of reasons. First, there are lots of data on past polls and election results, which people can use to figure out empirically how accurate their methods would have been in past cases. With climate change, we are often talking about changes that have not been seen in the past, or at least not by enough cases to develop a large sample size for testing. A second and, in my view, more critical difference is that climate impacts happen on top of many other changes in society. Elections provide a clear outcome - a candidate wins or loses. But what does a climate impact look like? How do we know if our predictions are right or not?  A lot of the entries in this blog are around that question, but the short answer is we can't directly measure impacts, we have to be clever in thinking of ways to pull them out of the data.

So maybe all of the attention to the election forecasts will help the public understand probabilities a little better. If nothing else, people should understand the difference between a 50% chance and an 80% chance of something happening. Reporting the latter as if it were the former is annoying in the context of the election, or as Paul Krugman says "Reporting that makes you stupid". But confusing the two in the case of climate impacts is more than annoying, it can lead to a lot more wishful thinking and a lot fewer smart investments than would otherwise be the case.

One final note: even when people are on board with the meaning of probabilities, it's still not so easy to get them right. Silver has the election at ~85% chances for Obama. That's high, but his chances of Romney winning are about 10 times higher than what Wang has. So just like with climate impacts, smart people can disagree, and it usually comes down to what they assume about model bias (Silver seems to admit a much higher chance that all polls are wrong in the same direction.) But even if smart analysts disagree, very few if any of them think the election results (or climate impacts) are a toss-up..

Is temperature variance changing?

People in agriculture often talk about “weather getting more variable.” It’s usually hard to know exactly what they mean – sometimes they are talking about precipitation becoming less frequent and more intense, and sometimes they're talking about hot extremes becoming more frequent. But it’s well known that what was considered “extreme” historically can become more frequent just by shifting the mean of the weather distribution, without any change in variance. The IPCC SREX figure below shows that clearly in the first panel.

We care about variance, though, not just because of its ability to increase the occurrence of historical hot extremes. If variance is increasing, this would mean more uncertainty faced each year by farmers and markets about what the growing season weather will be. Note that we are talking here strictly about weather variance. The variance in production can increase just from a shift in the distribution towards less favorable temperatures, as we showed in a recent study led by Dan Urban.

The question of whether variance per se has been changing (or is projected to change) has received much less attention than whether extremes are becoming more common. This is partly because changes in variance are harder to measure than shifts in means or increases in extreme events. But an interesting analysis by Donat and Alexander in GRL sheds some light directly on the variance question. They looked at the distribution of daily temperature anomalies for two 30-year time periods: 1951-1980 and 1981-2010. The figure below from their paper maps the change between the two time periods for three parameters of the distribution (mean, variance, and skewness), both for minimum (left) and maximum (right) temperature.

Two things seem new to me here. (Certainly the shifts in mean are not new, but it’s interesting to note that the shifts are about equal for minimum and maximum temperature.) First, the variance changes are mixed around the world, and not statistically significant in most places (the significant areas at 10% are shown with hatching). The authors also say that the variance changes depend a lot on what criteria they use to exclude grid cells without enough data.

The second interesting thing is that the skewness has increased in most parts, much more uniformly than the changes in variance. Just to be clear, we are talking about skewness in the statistical sense, not in the way it is sometimes used to mean “distorted” or “biased”. An increase in skewness means that the distribution is now less left-skewed or more right-skewed than before, which would mean that for a given average, there is a higher chance of having warm anomalies and a lower chance of having cool anomalies (see bottom panel of ipcc figure above).  

It’s hard to know what exactly is driving the skewness, but I suspect their paper will spur some more focus on this issue. Maybe it has to do with the shift in rainfall distribution toward heavier events, with less rainfall during moderate events. For now it seems safe to say that temperatures are not clearly becoming more variable for most parts of the world, but they seem to be slightly more skewed toward hotness.