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Applied Climate Research: A Conversation with Stefan Sobolowski (Part 2)

In part 1 of this interview, I talked with Columbia Water Center hydroclimatologist Stefan Sobolowski about the effects of continental snowcover on climate, and the implications of his research on climate change. In part 2, we talk about the problem of uncertainty in climate prediction models, extreme weather events, the regional variation of climate change effects and improved precipitation forecasting for India and the world.

Columbia Water Center Hydroclimatologist Stefan Sobolowski

What are you working on now?

There are a number of things I’m interested in, from really theoretical explorations of what the actual climate system is and moving towards more applications-based research on changes in precipitation patterns over different regions of the world.

I’m thinking of doing some research on the changes in variability of seasonal precipitation. Not so much the changes in the mean amount of precipitation you get, but changes in the variations. Is that band around the mean getting larger? Do you have much bigger swings? Extreme event, drought, extreme event, droughts? Or is that band getting smaller around the mean? Meaning, say your average precipitation is 10 mm per day, and it pretty much stays that way.

Modeling the Relationship of Snowcover to Climate


Do we have any idea which of those scenarios is true?

It varies. It depends on which region of the world you’re looking at, and it depends on how good your data is. There’s a lot of disagreement. Part of the research is to figure out what we know and what we don’t know, and what we can believe, because the models do not do a very good job with precipitation. They’re very poor at getting moist processes correct. So part of the challenge is to first establish what has happened over the 20th century.

In most regions the variability has gone up, especially in regions that have a decent amount of precipitation. Dry areas are dry. There’s not really anywhere to go. Once they’re near zero, they usually stay near zero. They can’t get any drier–they can’t get negative! They tend to stay pretty stable.

But the challenge is to determine what’s happened regionally, in the 20th century, and then see how the models we have are able to replicate that, if they are. If they’re not, one of the things I want to work on is improving the models, to figure out why aren’t they getting it right, and whether we can fix it.

Why aren’t the models getting it right?

Because most of the processes that affect precipitation are very very small scale. And even the models that are very high resolution tend to operate on grids that are – the smallest ones are 10 x10km. So even that, they can’t really resolve evaporation for example. They can’t resolve condensation. They can’t resolve clouds very well. I mean they just cannot resolve that kind of dynamic behavior.

Source: Kevin Dooley on Flickr
Source: Kevin Dooley on Flickr

If you go outside and you look up, if you watch clouds coming in, you see even the storms we had over the weekend, in a lot of cases, you can’t do it. You have a thunderstorm and then a mile away, sun. So those processes have to be approximated, and we think that’s where the errors are coming in. It’s a big problem.
Getting back to the question of variability around the mean – what are the implications of that when discussing climate change with the public? If climate change indicates a greater number of extreme events, people might misinterpret a cold winter with a lot of snowfall as saying there’s no climate change, when in fact the opposite is the case. Is that an oversimplification?

You may have larger swings. You may have a really anomalously cold winter rather than one that’s just a little bit colder than normal. In that sense it’s in keeping with what we expect to happen. An increase in larger swings is certainly something we expect to see. But it’s difficult to ascribe any one event to global warming. There were a lot of contributing factors to this past winter, many of them not global warming related at all. Some of them were just the variability of the atmosphere, what happens on a planet that has the kind of diversity that we have! That’s just how it goes.

But yes, we expect to see these larger swings. It’ll be really tough to tag it with a global warming signature until we have more data. We just don’t know yet. But the path seems to be in that direction, that you’ll start seeing these kind of swings. The anecdotal evidence will be there. Then hopefully people start getting their hands on empirical data and can do something more robust. But yeah, you’ll have a really cold winter, and then the next summer people will be like, “oh, gee, it’s a 105 degrees here in New York City” when it was 10 degrees below normal in February. That kind of stuff may happen more frequently.

Before you talked about application-based research. What did you mean by that?

Actually producing something that people find useful! How about that! It’s interesting to learn about the system, and eventually that knowledge trickles down and gets used, hopefully it gets used for planning and adaptation, mitigation and also for prediction. That’s where I want to start focusing.

One of the things that the scientific community has realized now is that humans aren’t really good at thinking about scenarios out past 30 years. Our brains aren’t really wired that way.

I think in all honesty that these projections in the IPCC reports, where we’re talking about temperature changes in 2050 and 2099, it’s just almost impossible for people to wrap their heads around that.  And it’s a projection. It’s based on a scenario. Really, if we’re honest about the uncertainties around that—they’re huge. So one of the things we’re starting to focus on more is a 10-year climate prediction, which is about what we can handle intellectually.  I think that’s something we can do.

And not only globally, because that’s of limited use as well. We were talking about the differences between different regions. So great, it gets 10 degrees, 5 degrees warmer globally. What does that mean to me in New York City? Well, what it means for me in New York City is going to be very different from what it means for the Tlingit tribe up in Alaska as we’re already finding out. Right now it doesn’t mean a whole lot to us. Our seasons are pretty much what they’ve always been. Now the Tlingit up in Alaska have their village sinking into the permafrost, and they’ve had to actually abandon some of their villages as a result.

I think as far as applications go, working towards multi-year predictions, and improving out ability to predict out more than a couple of seasons and inter-annually, especially for things like water resources availability, precipitation, evaporation, moisture fluxes, potential for floods and extreme events. Because you can’t predict a date for a flood, these all end up being probability-based. That’s what you end up providing to policy makers. You say hey, the probability of increased flooding for the next 10 year is X. That’s some of the application-based research I’m working on.

Were you doing some work in India?

Yes, with Toby Siegfried. We were trying to develop some models for water use from groundwater supplies. That’s something I’ll also continue working on. I provided the climate perspective on that. Basically it’s a pretty simple story.  You have shallow ground-water supplies.  Deep aquifers are stashed below a lid of rock, which is basically like mining, like pulling out oil, where it’s not coming back any time soon.

These are shallow supplies that are recharged every year by the monsoon rains. The drawdown in the dry seasons, since they now plant year-round there, is dependent on how much the rains recharge, and how much is being pulled out. If they’re pulling out more than is being recharged, then the system isn’t sustainable. You can’t keep doing that. All over India they’re doing that.

What happens when they pull water out faster than the recharge rate?

Well, for the past 30 years, not much. They’ve been able to get away with it. That’s because the Green Revolution came around, and made large infrastructure improvements and fertilizer improvements, and seed improvements, and farming techniques and you name it. But eventually, the system caught up. Before there weren’t enough people pulling from the supply so that even when recharge wasn’t enough, there was still a buffer. If you think of it like a bathtub, even though they’re pulling out more than is put in, they still weren’t close enough to be hitting the bottom.

Close to the Bottom of the Bathtub? Source: Sean A. Brady on Flickr

There’s enough recharge getting back in there that it keeps it a little above, in this region. In the region we were looking at least, I don’t think they’ve reached the point where they’ve tapped into what we call a buffering capacity. What happens is that you increase your risk, if the monsoons fail, for example, that you do lose that buffering capacity. Then your dry-season fails, and then all of a sudden you’ve lost both wet and dry-season crops, and you’d better hope that you get a big monsoon rain the next year.

Because if you get a multi-year drought, or even just a multi-year dry spell, it has potential to really mess with the system, especially in India. Unlike here, where our precipitation is fairly spread out over the course of the year, they really get it in one big dump. The whole thing just dumps on top them like a bucket for a couple of months, and that’s pretty much it, so whatever recharge they’re going to get in their groundwater, they’re going to get from those monsoon rains.

India Monsoons
India Monsoons. Paul Williams on Flickr

We did some models that use that fact to help inform the amount of area that’s being planted in the dry season. With further development we can come up with something where we can use the amount of recharge and amount of rains in the monsoons to inform best decisions. We’re not there yet though.

That could potentially have global applications.

Yes, I think for those sorts of areas, where you can really nail a predictor, that happens say six months before hand, or a season before hand, certainly for those regions it could be really useful. You could do something similar in places that are heavily reliant on snow runoff for example. Like in the Central Valley, I think that could be an application. So if you have a fairly consistent predictor, whether it’s your reservoirs, or your snow reservoir, or in this case the monsoon rains, that’s useful.

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