Could We Get Weather Forecasts Years—Or A Decade—In Advance?

IRA FLATOW: This is Science Friday. I’m Ira Flatow, live from the Chautauqua Auditorium in Boulder, Colorado.

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Later in the hour, the latest in solar panel technology, and the science of dinosaur poop, also known as coprolites. But first, I have a prediction, something that every one of you did before coming here tonight. I know I did. And that was to check what the weather was going to be, right? My guess is that everyone here checked the weather.

And now, most of the time, we can get pretty accurate information about what the next few hours will look like, perhaps up through the next few days. But it’s possible that in the near future, we’ll be able to get accurate weather forecasts for maybe weeks or months. How about a decade ahead? Would that be cool?

Seems like science fiction, I know, but my next guests are working on that very possibility. Let me welcome both of them from the National Center for Atmospheric Research right here in Boulder. Welcome to Science Friday. Let me introduce both of you.

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Dr. Yaga Richter, lead of the Earth System Predictability Across Timescales Initiative, and Dr. Jerry Meehl, senior scientist and head of the Climate Change Research section. Welcome, both of you, to Science Friday.

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JERRY MEEHL: Thanks.

YAGA RICHTER: Thank you.

JERRY MEEHL: Good to be here.

YAGA RICHTER: It’s a pleasure to be here.

IRA FLATOW: Let me begin with you, Jerry. I know that Earth systems predictions is quite a mouthful, not only to say, but for a career field. What does it mean in simple terms we can all understand?

JERRY MEEHL: Well, when you talk about Earth system prediction, you have to go back to the 1980s, really. And that’s when we started using these, what we call Earth system models, or climate models. And in the ’80s, we had components of atmosphere, ocean, land, and sea ice. And these were basically computer programs that would communicate with each other.

And these computer programs are sets of equations from physics, and thermodynamics, and fluid dynamics. And for those of you who were forced to take calculus, the core equations in these models are partial differential equations. So you can numerically and mathematically integrate them forward in time. So that’s how we make predictions with equations. And I still can’t believe it works, but it does actually work.

IRA FLATOW: Unfortunately, I knew what you were saying. I mean–

JERRY MEEHL: You must have taken calculus.

IRA FLATOW: I did. I took calculus. And so you can actually make the mathematics string together equations to predict what’s going to happen, you think.

JERRY MEEHL: So when we first started using these Earth system models in the ’80s, we were doing an experiment where we’d start the model out from some state, say, pre-industrial state, 1850, for example, and then you’d integrate the model forward in time. And as various things happen, you’d add the ingredients as the model’s going forward.

So if a big volcanic eruption went off, you’d put the effects of the eruption in the equation to handle that. Greenhouse gases were increasing. You put the increases of greenhouse gases in that equation as you’re going forward in time. You can put in solar variability. That’s another natural aspect of the climate system that affects the outcome.

And you would go through the 20th century, and you get to the end of the 20th century. And you’d see whether your model could predict the average climate that we actually observed. And if you didn’t, if it didn’t look like planet Earth, you’d go back to the drawing board. But usually, these models, actually, even the early ones, did a pretty good job in getting the features of the climate system.

So you get to the present day and you say, well, what about the future? And we don’t know what’s going to happen in the future. So we have to come up with scenarios of different possible outcomes, evolving maybe future demographics, or economic activity, or energy sources, technologies that could come into play.

And so there’s a whole community of scientists that come up with these emission scenarios, we call them. And they give you emission pathways of future emissions of greenhouse gases. So you can run this same Earth system model into the future with the ingredients now being these future emission scenarios. So you get a range of possible outcomes.

And usually what we did– and we still do this, actually– we’d say climate change is the climate change you get at the end of the 21st century compared to pre-industrial, compared to before the Industrial Revolution started. And so we were doing– we still do this, but around the turn of the century, in the early 21st century, stakeholders were saying, “That’s great. You guys are doing this end of the 21st century thing. But we really are interested in what’s going to happen in the next few seasons, the next couple of years, the next 10 years. What can you do about that?”

Well, that then started us on a whole new area of climate science. We started using these Earth system models in a new way. And the way we were doing this now with these climate predictions is we start the model out at a specific point in time with some observed state of the system like today. Take atmosphere, ocean, land, and sea ice at the observed state, start the model out, integrate it forward in time for 10 years, and try to predict the time evolution of regional climate as it’s happening.

IRA FLATOW: I want to get into that a little bit more later about exactly what you do. But I know that, Yaga, you work on a different time scale than what Jerry does. What timescale are you working on?

YAGA RICHTER: That’s right. So I work on a subseasonal timescale, which is usually considered a couple of weeks to a few months. And that’s a much– it’s a harder time scale, because they often call it the predictability desert. So it’s–

IRA FLATOW: Why is that?

YAGA RICHTER: Because it’s really hard. There is– we still don’t understand the sources of predictability. So we know for the weather forecast, the primary source of predictability is the initial state of the atmosphere. And as Jerry explained, for the longer forecast, there’s greenhouse gases, and changes in sea surface temperatures. So we’re looking at things like interactions with the land, interactions of the oceans, the role of aerosols, the role of the upper layers of the atmosphere, like the stratosphere, so how much predictability can we get from these sources.

But this subseasonal timescale, it’s a little bit of a mystery. So we’re still trying to understand what are the key drivers in this space. And it makes it a very challenging problem.

IRA FLATOW: And you’re more liable than Jerry is, because if Jerry gets it wrong, no one’s going to know it for decades.

YAGA RICHTER: That’s right.

IRA FLATOW: You’re not right. We’ll know it next week, or in a couple of weeks, right?

YAGA RICHTER: If a heat wave comes and you don’t have snow skiing, yes, you’re going to come after me.

IRA FLATOW: And Jerry, how satisfied would you be– what record of predictability would you think would be good at the beginning of this research?

JERRY MEEHL: Well, the way we try to check out these models is we go back, and we call them hindcasts. We start the model out in 1967, run it for 10 years to 1977, and see if you could capture anything that actually happened during that time period. We can do that with climate information. So you give probabilities of certain outcomes happening at certain times in the future.

But of course, we’re trying to improve the models. We’re trying to understand what it is we’re trying to predict. And since the first paper on this was just published in 2007, this is still really a new area of climate science. And the stakes are high. Because on the 10-year time scale, this is what we have to adapt to. And these Earth system model predictions are the only way we can get information on what we’re going to have to adapt to.

IRA FLATOW: Well, let’s talk about exactly what factors go into that prediction. And I’ll ask you, Yaga, what factors– oceans, atmospheres, what kinds of things do you plug in there?

YAGA RICHTER: We apply calculus into lots of equations, and we need to use a supercomputer to solve the equations for temperature, wind, precipitation, et cetera.

IRA FLATOW: So you need all those factors, how the land works, the ocean, the atmosphere. The soil has parts to play here?

YAGA RICHTER: Yeah. And also, the humans, right? Because humans are responsible for the emissions. And they’re also responsible for planting crops. So if you replace grass with urbans and cities, that also has two-way feedbacks onto the system. So we try to capture as much of that. But the more complex you make the model, the longer it takes to run. So there is a little we have to play the game of resolution versus complexity.

IRA FLATOW: And Jerry, who would benefit mostly from these long-term predictions?

JERRY MEEHL: Well, when you think about these longer term predictions, agriculture is always one that people say, and water resource managers. The Colorado River Basin is a big one now. They really would like information on what the climate’s going to be like 5 to 10 years from now.

You can imagine infrastructure planners– people designing buildings, if the winds or temperature are going to change over the next three to five years, they need to know that. If you’re going to deploy clean energy technologies, for example, if you want to put a wind farm out there somewhere with wind turbines and the wind patterns are going to change, if they’re not going to be the same in 5 to 10 years from now, you want to know that. So you want to be optimally able to cite them.

Solar panels, same thing. It’s going to get cloudier in a place, or clearer in a place in three to five years, you want to know that.

IRA FLATOW: I mean, there are things happening now we never thought might change the weather– the forest fire soot and the smoke and things like that. Do you have to add those now more into your models than you thought before?

YAGA RICHTER: Yeah. So actually, in our institute, John Fasullo, another researcher, did a study just very recently with the Australian bushfires. And he showed that they were affecting all the way to the tropical Pacific, influencing La Niña, which is one phase of the ENSO oscillation. So these fires can– the smoke emissions can travel around the world and impact places that we never thought of.

IRA FLATOW: And what about the melting glaciers, Jerry? They’re going to change, possibly, the currents that flow around the world. Do you put those into the long-term predictions?

JERRY MEEHL: So the biggest concerns, of course, are the big ice sheets, the Greenland ice sheet and the Antarctic ice sheet, behaving in ways that humans have never observed because humans have never been around when the ice sheets were really melting like this. So people that study ice sheets are trying to figure out what this means for what they’re seeing now.

So I think, yeah, when you go into this kind of uncharted territory, you have to really be trying to understand what processes are going to affect things, and try to capture those in these prediction models.

IRA FLATOW: Plus, we’re also going to see crops moving, right? The crops are going to follow where the weather changes as the climate changes, and disease is changing, and vectors moving around. These are things I’ll bet you never thought about when you got into this business.

YAGA RICHTER: That’s right. And people are going to react differently to the forecast. So as the climate changes, people are going to take different actions. And we need to account for that in our models as well.

IRA FLATOW: And how do you do that?

YAGA RICHTER: So we work with different individuals from different sectors who study human behavior. And we need to try to project the human behavior, and then put those processes into the model. But that is very new right. Those human interactions were not in our models up to a few years ago. And we’re just at the beginning of putting those in there, especially at the urban scales.

IRA FLATOW: Oh, certainly.

YAGA RICHTER: At the scales of cities, where people are replacing grass with buildings, et cetera, that has tremendous impact on the local weather.

JERRY MEEHL: So we can model some of these things, but we have enough trouble trying to predict the physical system to predict how humans are going to react, because then geopolitical systems come into play. And it becomes a lot more complicated.

IRA FLATOW: If you had a blank check and you wanted to spend it on your research to find out something you don’t know, and you could spend it any way, how would you spend it?

YAGA RICHTER: So we would need at least $1 billion.

IRA FLATOW: $1 billion, that’s not a lot.

YAGA RICHTER: It’s not a lot.

IRA FLATOW: Not in these days.

YAGA RICHTER: But we would spend it, I think, in multiple ways. So first, we would hire more machine learning and artificial intelligence experts.

IRA FLATOW: AI.

YAGA RICHTER: AI, because AI has really grown in Earth system science in the last few years. So those scientists are helping us identify sources of predictability. Then we would need to buy a bigger supercomputer, because if we go to higher resolution, we do believe we can resolve more of the relevant processes, and produce a better forecast. And then we would need to have more observations of the area of the parts of the Earth system that we don’t have. The deep ocean is not observed, not very well. So therefore, if you start your model with limited observations, your forecasts are also not very PP

IRA FLATOW: Jerry, you got that big check. What do you do with it?

JERRY MEEHL: She has the last word on this because she was just at a workshop this last week, where she was asked that question.

IRA FLATOW: Is that right? They stole my question?

JERRY MEEHL: She was actually told by a program manager to ask for $1 billion.

IRA FLATOW: OK. There you have it. Last word– I want to thank both of you for taking time to be with us today. Dr. Yaga Richter, lead of the Earth System Predictability Across Timescales Initiative and Dr. Jerry Meehl, senior scientist, head of the climate change research section. Thank you both for taking time to be with us today.

JERRY MEEHL: Hey, thanks a lot.

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