Testable predictions

As our blog wakes up from an apparent winter hibernation, I’ve been thinking about some predictions for the spring that came out last fall. One on my mind, of course, is that the Warriors will dominate the NBA. That one seems a pretty safe bet at this point.

A more daring bet was one put out last November on the corn season in 2016 by Weather Trends 360. It’s a pretty entertaining video, which I link to below. I'm not sure it's convincing, but I have to give them credit for making a very testable series of predictions. Specifically:

1) An unusually wet March and April, leading to delayed planting

2) A widespread freeze in early May

3) Very high temperatures and low rainfall in June and July, leading to low yields

4) Corn prices of $7 per bushel by July 2016 (or roughly double what they are right now)

So let’s see what happens. These are very specific and, in some cases, pretty bold predictions. If they turn out to be right, I’ll definitely be taking notice next year. Meanwhile, back to enjoying the Warriors.

El Niño is coming, make this time different

Kyle Meng and I published an op-ed in the Guardian today trying to raise awareness of the potential socioeconomic impacts, and policy responses, to the emerging El Niño.  Forecasts this year are extraordinary.  In particular, for folks who aren't climate wonks and who live in temperate locations, it is challenging to visualize the scale and scope of what might come down the pipeline this year in the tropics and subtropics. Read the op-ed here.

Countries where the majority of the population experience hotter conditions under El Niño are shown in red. Countries that get cooler under El Niño are shown in blue (reproduced from Hsiang and Meng, AER 2015)

Observing Climate Change Impacts

One can tell that David is on sabbatical. He is cracking jokes left and right and they are funny! We will be posting weekly from now on and since my last name starts with A, I start. I also get to be lead author on all papers we write together.

I have spent the last three years with a very talented group of individuals, writing a chapter on detection and attribution of climate change impacts on natural and human systems for the IPCC. The chapter will be released in a few weeks in Yokohama and I will blog live from the meeting. The fancy term detection and attribution can be casually interpreted as "what observable impact has climate change already had on [insert favorite system.outcome here] ". This is harder than I thought. Let me outline some of the issues:

1) Just because your system's changing doesn't mean climate is to blame for negative trends and can't possibly be an issue if your experiencing favorable trends. Think about crop yields for example. As has been pointed out again and again, better management practices, fertilizers, irrigation and pesticides have doubled the yields of many crops over the past 40 years. Growing yields does not mean that climate change cannot be a problem. Yields could have grown even faster in its absence. A slowdown in yield growth is possibly consistent with climate change, but could be due to worsening of other factors. So in order to blame climate change, you need to show two things. 1) A sensitivity of your system to climate change while properly controlling for all confounders. This is really hard. Wolfram is really good at this. 2) A changed climate in the region under study.

2) That opens the question, what is a changed climate? Many of the systems we are concerned with are localized systems. AR3 and AR4 in their treatment of detection and attribution focused on just "climate change", not necessarily "anthropogenic climate change". A lot of the detection and attribution literature does this. I think that this is good for now. As Marshall and Kyle have pointed out, there is plenty of local climate change happening, which they in turn use to identify a climate sensitivity.

3) The data for most sectors are not very good and weather data required are thin at best in many areas. Just because you have a gridded dataset, which provides a number, doesn't mean that number has anything to do with local temperature. There are large swaths of land and time with no reliable measure of temperature or rainfall. On the outcomes side, AR4 focused largely on detecting changes in phenologies, which was a highly publicized result. But while I love butterflies, frogs and flowers, as a social scientist I am also keenly interested in what is happening to human health, agricultural yields, fisheries and economic growth. These literatures do a decent job at characterizing sensitivities of these sectors to fluctuations in weather (and sometimes even climate), but the vast majority of them focus on projecting 100 years into the future. A very small number of papers actually turn around and take a look back.

So here is what I think we should do over the next 5-6 years until someone else gets to write the D&A paper for AR6:

1) When you are estimating sensitivities using your fancy econometric models, be clear what the sensitivities are and what they capture. Are they weather sensitivities? Climate sensitivities? What omitted variables should we worry about that you could not control for? Can we expect these sensitivities to remain stable over the next 100 years?

2) When you do your projections use multiple climate models. Relying on a single model is for suckers. OK. I was a sucker until I met Marshall Burke.

3) Don't just download the projections of climate, download the historical values too. They are freely available.

4) Simulate the changes in [insert favourite outcome here] with and without anthropogenic climate change. Daithi Stone and the gang can tell you how to do this. This essentially means you leave on Volcanoes etc. and assume away human emissions. Then you turn on the humans. The difference is anthropogenic climate change. Do this for the past 30 years and calculate impacts. Then do this to your heart's delight for the future.

5) Put the word detection and attribution in either your title, keywords or paper to make sure we can find you when we are looking for you.

I think these studies are very powerful and important. I am working on four of them now. If you are in the business of projecting climate impacts using multistep models you should do the same. It's not hard.

The future of crop demand

It is common to hear statements about needing to increase food production by 70%, or even to double it, by 2050. In the past I’ve tried to avoid such statements, mainly because I hadn’t really dug into the sources behind them. But as part of prepping for a new class I’m teaching in the fall, I decided to take a closer look. And what I found, maybe not surprisingly, is a lot of confusion.

For starters, the baselines are often different, with some using 2000, some 2005-07, and some 2010. More importantly, the definition of “food” can be quite different, with some referring to cereal production, some to all calorie production, and some simply to the total value of agricultural output. And the studies use different assumptions about drivers of demand, like incomes or biofuel policies, so it’s often not clear how much differences are driven by assumptions in the models themselves vs. the inputs into the models.

Here’s a quick rundown of the common citations. First and foremost is the FAO, which produced the commonly cited 70% number. Last year they actually revised this down to 60% in their new projections, but not because the projected demand changed very much, but because they up-revised their estimate of actual output in 2006. The baseline for the FAO number is still 2006, so the 60% refers to an increase over a 44-year period. And the 60% refers to price-weighted aggregate of output, so that part of the 60% is simply a shift toward producing higher value stuff. Total cereal production only rises 46%, from roughly 2 to 3 billion tons per year. About two-thirds of that increase occurs by 2030. In terms of calorie consumption, global per capita consumption rises by 11%, and total calorie consumption rises by 54%.

The “doubling” statement, as far as I can tell, comes mainly from a 2011 paper by David Tilman and colleagues that said calorie demand would double between 2010 and 2050, and protein demand would rise by 110%. That was mainly based on extrapolating historical patterns of cereal and protein demand as a function of income, combined with projections of income growth. Coincidentally, doubling of production is also what we found in the baseline simulations we did for a climate adaptation analysis published earlier this year in ERL, and discussed in a prior post.

I won’t take time here to bore you with details of the methods in FAO vs. Tilman vs. others. But it seems a lot of the disparity is not so much the methods as the input assumptions. For example, FAO has world GDP per capita growing at an average rate of 1.4%, which they acknowledge as conservative. In contrast, Tilman has a global per capita GDP growth rate of 2.5% per year. Over a 40-year period, that translates to an increase of about 75% for FAO but 170% for Tilman! In our paper, we had a rate in between of 2% per year based on USDA projections. The reason we still get a doubling with lower income growth than Tilman is probably because we had a larger biofuel demand. (Note that my coauthors Uris Baldos and Tom Hertel have since switched to using income projections from a French group, which - maybe not surprisingly - are a little more pessimistic than the American ones.)  Now, global per capita growth rates only tell us so much, because what mainly matters for demand is how incomes grow at lower and middle income levels where people most rapidly increase consumption with higher income. Unfortunately, studies don’t usually report for the same sets of countries, and I’m too lazy to try to recompute. But the global numbers suggest pretty important differences at all income levels.

To me, it’s always useful to compare these projections to historical growth rates. Below I plot global cereal production from FAO since 1961. A couple of things are clear. First, production was about 150% higher in 2010 than 50 years earlier. Second, the growth rate appears pretty linear at a clip of roughly 28 million tons per year. A naive extrapolation gives an increase of 1.1 billion tons over a 40 year period from 2010 to 2050. For reference, I show what a 50% and 100% increase from 2010 trend levels would be. Obviously the 50% number (or the FAO’s 46%) are closer to this naive extrapolation than a doubling. 

This isn’t to say that the doubling number is definitely wrong, but just that it would mean a significant acceleration of cereal demand, and/or a significant shift of calorie consumption away from cereal-based products. It would be really nice if someone could systematically explore the sources of uncertainty, but my guess for now is that income growth is a big part of it. Unfortunately, this means our hope for narrowing uncertainty is largely in the hands of economists, and we know how good they or anyone else are at predicting GDP growth . But for those who work mainly on supply side questions, it’s mostly good enough just to know that demand for crop production will rise by 50% or more, because even 50% is a pretty big challenge.

(Note: for anyone interested in a summary of an unrelated recent paper on extreme heat, see here. And for an exchange we had about adaptation in the US see here. Wolfram told me a blog about the latter is coming, but as I told him, so is Christmas.)

Climate data and projections at your fingertips

Do you ever get jealous of Wolfram's pretty graphs on this blog or just want to know what March rainfall will look like in New Zealand at midcentury -- but you just don't have the time or energy to sort through all the various climate data sets or learn how to use GIS software?

Lucky for you, the Nature Conservancy has teamed up with scientists at the University of Washington and the University of Southern Mississippi to develop Climate Wizard, a graphical user interface available through your browser window that lets you surf real climate model projections and historical data for both the USA and the world. According to the website:

With ClimateWizard you can:

• view historic temperature and rainfall maps for anywhere in the world

• view state-of-the-art future predictions of temperature and rainfall around the world

• view and download climate change maps in a few easy steps 

ClimateWizard enables technical and non-technical audiences alike to access leading climate change information and visualize the impacts anywhere on Earth.  The first generation of this web-based program allows the user to choose a state or country and both assess how climate has changed over time and to project what future changes are predicted to occur in a given area. ClimateWizard represents the first time ever the full range of climate history and impacts for a landscape have been brought together in a user-friendly format. 

The data sets underlying behind the pictures are well documented on the "about us" page, and the data in each map is easily exportable.

If this had come out four years ago, I probably could have shaved six months off of my phd...

h/t Bob Kopp

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..

Probabilistic forecast of direct damage from Hurricane Sandy

These models are pretty preliminary, but Marshall and David convinced me to post this. I've been working with landfall statistics for only a couple of weeks, but I had enough data to put together a simple probabilistic forecast this morning for Sandy's direct damage (the number that will eventually appear on Wikipedia) based on landfall parameters (as they were forecast at around noon).  The distribution of outcomes is pretty wide, but the most likely outcome and expected loss are both at around $20B.  Below is the cumulative distribution function (left) and probability density function (right). 

click to enlarge

It will probably take several weeks for official estimates to converge. If I'm anywhere near right, I'll be sure to remind you.  Rather than explaining and caveating, I'm posting now since the power-outage frontier is two blocks away (it's dark south of 24th Street).