Understanding Uncertainty in Climate Change and Sea Level Rise, with Vivek Srikrishnan

Notable quotes:

• Three major factors contribute to sea level rise—land-water storage, ice melt, and thermal expansion: “The land-water storage part is fairly uncertain, because a lot of that depends on how we manage water. But ice sheets and thermal expansion are the result of physical processes. These are things that we can, in principle, [use to] try to constrain some of those uncertainties a little bit better.” (4:09)
• Factors impact one another, creating even more variability in projecting sea level rise: “All of these uncertainties are themselves correlated … You can imagine that these offset each other, and that makes it hard to disentangle … These uncertainties end up compensating for each other in ways that are really interesting statistically, and really annoying if you’re trying to make projections.” (16:00)
• Short-lived greenhouse gases can contribute more to sea level rise than persistent greenhouse gases: “Strong but short-lived greenhouse gases like methane give you a higher chance of crossing over those tipping points … Even if they degrade to carbon dioxide over time, you might trigger some of these nonreversible dynamics to start occurring, because the methane has caused a larger temperature change in the short term.” (25:23)

Top of the Stack

• “The Interplay of Future Emissions and Geophysical Uncertainties for Projections of Sea-Level Rise” by Chloe Darnell, Lisa Rennels, Frank Errickson, Tony Wong, and Vivek Srikrishnan
• The Earth Transformed by Peter Frankopan

The Full Transcript

Daniel Raimi: Hello, and welcome to Resources Radio, a weekly podcast from Resources for the Future (RFF). I’m your host, Daniel Raimi. Today, we talk with Vivek Srikrishnan, assistant professor in the Department of Biological and Environmental Engineering at Cornell University.

Along with several coauthors, Vivek recently published a paper called “The Interplay of Future Emissions and Geophysical Uncertainties for Projections of Sea-Level Rise.” It’s a fascinating and complex paper on a fascinating and complex topic.

In today’s episode, I’ll ask Vivek to explain why there’s so much uncertainty over future sea level rise, what are the key drivers of those uncertainties, and how much sea level might rise under different scenarios over the next 150 years. We’ll also talk about the implications for how to prioritize reducing carbon dioxide versus more powerful—but shorter lived—greenhouse gases, like methane. Stay with us.

All right, Vivek Srikrishnan from Cornell University, welcome to Resources Radio.

Vivek Srikrishnan: Thanks for having me.

Daniel Raimi: It’s a pleasure to have you. I’m really looking forward to today’s discussion. We actually haven’t talked about sea level rise on this show for quite some time, so I’m looking forward to learning a lot today.

Before we get into the substance of our conversation, we always ask our guests how they got interested in working on environmental issues. So, how did it all play out for you?

Vivek Srikrishnan: That’s a good question. I actually don’t know when I started thinking or caring about environmental issues; it just sort of goes back a long way.

My family always went on vacations to national parks, and I was an Eagle Scout, so there was a lot of outdoor exposure. Also, the School of Hinduism that I was raised in really emphasizes the interconnection of everything in the world. So, it just always seemed very natural to think about our stewardship, or care of the environment, as part and parcel of everything else that we might do.

Then, academically, I started studying a pretty pure branch of mathematics in grad school, but it always kind of felt like I should be doing something with a little more societal relevance. Not to say anything negative about pure mathematics—it’s beautiful and wonderful, and you always find applications for things later, even if you don’t intentionally study them with that goal. But I ended up pivoting over time—first to thinking about energy systems and solar energy, and then, later, climate risk.

Daniel Raimi: Yeah, really interesting. Where did you grow up by the way? On the West Coast, or somewhere near the national parks?

Vivek Srikrishnan: Hudson Valley, New York.

Daniel Raimi: Oh, okay.

Vivek Srikrishnan: So not a lot of national parks near us, but we would go on trips to Acadia, out West, or in the South, and then we would just see whatever parks were in that area, or would structure trips around parks.

I remember we went on one really long road trip that involved seeing Jasper and Banff in Canada, and then Glacier National Park, Yellowstone, and Salt Lake City—just kind of a really, really long trip that was really organized around seeing as many parks as possible.

Daniel Raimi: Awesome. So, we’re going to talk today, as I mentioned, about a paper you recently published with coauthors about sea level rise and uncertainty.

But before we talk about the paper, it would be great to talk about some background info. Listeners will know that sea level rise is one of the major risks from climate change, but can you give us a general sense of, and a little bit of background on, why there’s so much uncertainty about future sea level rise—particularly, why there’s a lot of uncertainty in the long run?

Vivek Srikrishnan: We can broadly think about there being three major sources of increased global sea level that are related to climate change, or human activity.

The first is the thermal expansion of the ocean—as there’s more heat when the ocean’s warm, the water swells, so that’s a factor. The second is ice melt—so, the contribution of new water into the ocean from the melting of ice sheets and glaciers. Then, lastly, there are changes to land-water storage—the discharge of water from land, whether that’s through reservoir operations or natural occurrences through rivers, changes in how much water soils hold, and so on.

Those are the three major factors. The land-water storage part is fairly uncertain, because a lot of that depends on how we manage water, but ice sheets and thermal expansion are things that are the result of physical processes.These are things that we can, in principle, [use to] try to constrain some of those uncertainties a little bit better.

Thermal expansion is something that is uncertain, but relatively predictable. We have some historical record of how much that has contributed to rising sea level over time, and it’s a relatively steady physical process.

But the ice sheets are major sources of uncertainty, because our observational records are relatively limited. We obviously haven’t exposed these ice sheets to the ranges of warming that we’re potentially looking at in the future. We have some reconstructed data from … paleoclimatic reconstructions that do give us some insight into how these ice sheets may have responded during historical periods when levels of warming were greater. But, again, those are really uncertain, as well—even as data to use. We don’t want to overinterpret those findings, and there are data sets that give us different reconstructions—different levels.

What we do know is that, if we were to completely melt the Greenland ice sheet, that would result in about seven meters or more of increased global mean sea levels. Completely melting the West Antarctic ice sheet would result in about three meters of sea level rise. And completely melting the East Antarctic would result in about 53 meters of increased sea level rise.

Now, that’s fairly unlikely. The East Antarctic is a lot thicker than the West Antarctic and Greenland, and it is more ground based, so it has less exposure to ocean water—which is one of the things that we’ll talk about as one of the mechanisms by which ice sheets respond to warming. So, that’s less likely, but there is some contribution there as well.

We have all these different sources of uncertainty. How exactly do these ice sheets respond to different levels of warming? Are there more complicated physics or ice dynamics that would accelerate, or buffer, some of those responses? These are things that we have some understanding of, but there’s also additional uncertainty—especially, how those mechanisms play out in very specific ice sheets, and very specific regions of ice sheets, is still the subject of a lot of very active cryosphere research.

The other complication here is that these ice sheets have what appears as some type of “memory” related to the historical climate system—impacts that are related to global mean temperatures are directly related to global mean temperatures. If we think about maximum heat, minimum temperature, and so on, these are impacts that are relatively predictable from whatever the delta in the temperature is. So, there’s uncertainty in what those extremes might look like, but we have some understanding of how they might change as a function of the global mean temperature anomaly.

With ice sheets, it’s not specifically the temperature that makes the difference between continued exposure to ocean water, continued exposure to increased air, or some of the changes that can occur in the ice physics. There’s somewhat of an inertia in that response as well, and that’s one of the complicating factors here that was interesting for us to look into a little bit in this study: To what extent do you have that sort of inertia of that response?

There’s also just much more complicated local dynamics as well. One mechanism by which you can get accelerated melting, or increases in melting, is warm ocean waters—because the ocean is a large heat sink—go to sea-based ice, and that causes some additional melting as the heat moves closer and closer to the ice sheet.

However, there is some observational evidence. There was a recent paper by Davis et al. in 2023 in Nature [showing] that there’s increased stratification due to changes in density of the water layers near the Thwaites Glacier—which is one of these glaciers that has been the subject of considerable research, because it’s considered one of the more vulnerable glaciers in Antarctica. The heat is actually kind of kept away from the ice because of the density changes in the ocean water. That means that the melt rate has actually been a little bit less for that specific glacier than we might have predicted, based on the underlying models that we use.

So, those types of really local, physical dynamics can play a role in both directions. In some cases, there’s greater levels of melting than we might’ve predicted. In some cases, like that one, there’s less. And then, there’s also other complex geological processes that are hard to get observations of, and are hard to constrain.

There was a recent paper, last year, on how the changes to the solid earth underneath Antarctica can cause changes to ice melts and ice flows as well, which plays a role in how different masses of ice end up being exposed to either warm ocean or warm air. So, these are all factors that can be really difficult to get observations of. They can be really hard to integrate into some of these models to help constrain uncertainties.

And then, that’s only at the global mean level. When we start looking at local levels, things get even more complicated. There, you’ve got vertical land motion, and there’s glacial isostatic adjustment, where the mass of glaciers on Greenland or Antarctica ends up causing … Well, there’s two points here.

One is the rebounding from the ice ages and the glaciers that existed in places like the Finger Lakes—where they kind of pressed down on the earth, and so there’s some rebound effect that plays out very slowly over time. There’s also the impact of gravitational force exerted by the ice masses on Greenland and Antarctica, which have implications for local sea levels. Then, there’s subsidence. For example, in Norfolk, the rate of local sea level is mostly driven by subsidence due to aquifer depletion. So, those also impact just the level of local sea level change versus the global sea level change, which is what we really focused on in this paper.

Daniel Raimi: Got it. So, let’s talk now about some of the specifics that you get into in your paper that we talked about earlier. One of the things that you point out is that the rate of greenhouse gas emissions, rather than just the total amount of emissions, actually matters for how quickly these ice sheets melt or don’t melt. Can you just talk us through how that works?

Vivek Srikrishnan: Yeah, so one of the key things about the ice-sheet response is that there are a number of feedbacks (as I previously mentioned) that can lead to accelerations, or buffering mechanisms, within how ice sheets respond.

A good example of this—and a relatively classic example—is the so-called ice albedo feedback. As ice melts, you expose more land (brown land and dark land versus white ice), which results in more local absorption of solar radiation, which warms the local area, and results in even more melting from underneath the ice, as well. There’s also other feedbacks. As the surface of the ice sheet melts, there’s more exposure to greater temperatures because the elevation of the top of the ice sheet is reduced, and near-surface temperatures are higher than they were at a higher elevation.

The reason I mentioned those feedbacks is, it matters a lot how much time we spend at a given temperature, not just what the actual temperature is. That’s one reason why the cumulative level of emissions itself doesn’t necessarily matter in the way that it does in, for example, trying to predict global mean temperature anomalies—where cumulative emissions map pretty directly into what that global mean temperature change is. So, how much time do we spend at different temperature anomalies that influence the rate of sea level largely due to those ice sheet mechanisms?

That’s one reason why we really have to think about the pathway by which we get to a certain level of emissions, not just the level of cumulative emissions itself. You can think about the difference between just holding emissions constant over time, versus allowing emissions to increase quite a bit in the near term, and then drawing them down to get to the same level of cumulative carbon dioxide emissions. In the first case, we have a much higher chance of increasing exposure of the ice sheets to these higher temperatures, to warmer waters, and triggering some of these instabilities.

Daniel Raimi: Right. And, is the basic logic like: A process gets put in motion that you have a harder time reversing because you hit that higher temperature at an earlier point in time? Is that about right?

Vivek Srikrishnan: Yeah, exactly. And there’s also some other forms of hysteresis, or basically the idea that the process isn’t reversible, where you melt the ice, and that doesn’t mean it’s going to grow back in the same way when you reduce temperatures down the road.

Daniel Raimi: Right. So, in your paper, there are a variety of uncertainties about both the climate system and the ice sheets that you take into account under different emissions trajectories. And there’s a lot of detail here, but I’m hoping you can just give us a general sense of what are the key uncertainties that you try to account for in the analysis.

Vivek Srikrishnan: Yeah, so one of our goals was to really do the full pipeline of uncertainties. One of the challenges in a lot of climate risk research, I found, is in linking mitigation choices to adaptation choices—largely because a lot of the high-resolution climate outputs that we have (that you need) for local adaptation decisions are related to the specific model in our comparison runs for climate models, which have some very specific climate forcings, or emissions trajectories, that are associated with them.

Because global mean sea levels have not as much of a local effect, there’s a lot we can draw on from just the global temperature change and the global response. We figured we could take the opportunity to use this whole pipeline. We start by looking at uncertainties in what the emissions trajectories themselves look like. So, how much do we allow emissions to increase in the near term? What year do we start to reduce emissions? And how rapidly do we do that? We do ignore negative emissions because those are really difficult to constrain. Exactly how negative can they go, what years do they become plausible, and so on? So, we just chose not to go there.

We also look at, okay, What are uncertainties in the carbon cycle? Is the respiration rate (the ability of carbon sinks to absorb carbon dioxide) to the degree to which those are impacted by changes to temperature? We look at changes in the climate response to changes in carbon concentrations. The big uncertainty there is equilibrium climate sensitivity, but also the rate at which the oceans uptake heat and the degree to which aerosols cool the planet.

Daniel Raimi: And, just real quick—sorry to interrupt—but for folks who don’t know what equilibrium climate sensitivity is, Can you just explain that?

Vivek Srikrishnan: Yes, thanks. Equilibrium climate sensitivity, basically, is a measure of, if we were to double carbon dioxide concentrations, How much more would the planet warm? So it’s basically the sensitivity of the planet to an increase in carbon dioxide emissions once the planet settles into equilibrium, and all the heat fluxes between the ocean, atmosphere, and everywhere else kind of stabilize—this is a much longer-term level of change.

It’s contrasted with the transient climate response, which is what happens in kind of the shorter term—how much we may expect temperatures to increase—but it gives us a way to look at the rate of warming that we might expect from a certain level of carbon dioxide increase as a measure of how strong that response might be.

Daniel Raimi: Right. That’s great. And just as a quick point, and then I’ll ask you to please continue, is that even though we understand the basic principles of global warming quite well, there’s actually still a lot of uncertainty about equilibrium climate sensitivity because these systems are so complex, and the effects unfold over time. But Vivek, please keep going.

Vivek Srikrishnan: So what I wanted to say actually bridges off of that directly, which is that that’s been a very stubborn uncertainty. The distribution of equilibrium climate sensitivity has not changed very much in the last several decades of research. There was a little bit of an update to it in the last assessment report from the Intergovernmental Panel on Climate Change (IPCC), but overall, it’s been a very difficult thing to constrain. And part of that reason is that all of these uncertainties are themselves correlated.

So I mentioned the climate sensitivity, but then also the rate at which the oceans take up heat. You can imagine that these offset each other, and that makes it hard to disentangle. Is it that the planet is less sensitive to warming, or is it that the oceans are absorbing a lot more heat in the short term? And that, in equilibrium, might balance itself out to a higher level of warming down the road. But these uncertainties end up compensating for each other in ways that are really interesting statistically. And really annoying if you’re trying to make projections.

Daniel Raimi: To get an answer, yeah.

Vivek Srikrishnan: Yeah, and so we look at those. We also look at a variety of uncertainties related to the sea level response. In terms of the ice sheet, for both Greenland and Antarctica, How did those respond to different levels of warming, thermal expansion, and potential changes to land-water storage?

As I mentioned, that’s not one we can really constrain, but we just look at a distribution of what those could look like. We calibrated all the geophysical parts of that to a variety of observational data sets, and then combined those with just some potential probability distributions on the emissions side.

Daniel Raimi: Great. So, as listeners can imagine, there’s lots of complexity here, richness, and tons of data. We’d certainly encourage people to check out the full paper to get a sense of everything that’s going on, but let’s jump into the results.

So, you provide three scenarios that I found pretty helpful in just organizing my thinking. The scenarios are a baseline scenario, optimistic scenario, and pessimistic scenario. Can you give us a sense of what your results were, in terms of global temperature rise and global average sea level rise, under those three different scenarios, and maybe how they varied at different points in time that you modeled? You looked at the year 2100 and 2200, and other years like that. We’d love to hear just a summary of your results.

Vivek Srikrishnan: So, the differences between these three scenarios (just for context) really has to do with the plausibility of different emissions scenarios—which has been an issue that has been fairly hotly debated in and outside of the scientific literature over the last five years or so, I would say.

For example, this question about how plausible is RCP8.5 or SSP5-8.5 as a scenario. We didn’t go into that, but we just wanted to look at how sensitive these results were to some different regions of the emissions space. Our baseline scenario sort of attempted to be consistent with some of the consensus that may be emerging in the literature about what emissions are more plausible. So, for those of you who know something about where these scenarios sit in the sort of overall emissions space, medium-near-term emissions for the scenario are sort of in between SSP2-4.5 and SSP3-7.0.

For the optimistic scenario, we sort of assumed there was a good chance of keeping emissions to the level that might be needed to keep temperatures below 2°C. So we started with that probability, but then there’s some probability that you don’t—but overall, the level of emissions is lower. And for the pessimistic scenario, we basically said: okay, there’s no chance that we’re keeping temperatures below two degrees, and there’s even a high risk tail where emissions can go up above that SSP5-8.5 level. So, it allows us to look at different regions of the space, because sensitivity analyses in particular can often be very sensitive to how you set up that design of experiments.

Daniel Raimi: And real quick—Vivek, again, sorry to interrupt—but just so folks can follow along at home, RCP is a Representative Concentration Pathway. SSP is Shared Socioeconomic Pathway, and these are sort of the scenarios that the IPCC and various scientists have sort of drawn up over the years to look at the range of potential future pathways in terms of global population, economic growth, emissions, and things like that. So sorry to interrupt, but please keep going.

Vivek Srikrishnan: Yep—and they are used to benchmark differences between the climate models as well, since they’re fixed inputs that you can use to drive them.

And so the one other thing I want to note, since we may have some numbers here, is that all models have biases. All models are approximations of reality. Our models, in particular, are fairly reduced form models—which is a benefit for doing this kind of large-scale uncertainty analysis, but they do have certain biases that we diagnose as part of the paper.

One thing that I would note is that our upper range of the temperature response is a little bit lower, for equivalent scenarios, than what is in the sixth assessment report from IPCC, and that there’s sort of a compensating bias toward higher and more sensitive ice-sheet responses as well. In aggregate, that calibration kind of works out, but different parts of the model have slightly different biases, and those come out in different ways. So, we really tried to not overinterpret the specific values that we got, and focus more on the trends, differences, and uncertainty to composition instead.

So basically, at a very high level, there’s not that much difference out to 2050. The emissions scenarios have a little bit of variability, but there’s just not that much time for things to change all that much over the next several decades. And one of the consequences of that is that those changes are relatively locked in, and we kind of need to adapt our way out of things in the next few decades. We can’t mitigate our way to reducing sea level risk.

By 2100, there are larger changes. So, in the optimistic scenario, we ended up with about 2°C of warming—which, as a median, the uncertainty is anywhere plus or minus a half a degree to a degree. And then we had 2.5° instead at the baseline scenario for the ensemble. Again, a similar level of uncertainty there, and 2.7°C in the pessimistic scenario. So, we’re kind of seeing a little bit more separation at the median as well as at the high end of what the outcomes might look like.

And by 2200, perhaps as expected, we see that they get even more pronounced. So the 95 percent confidence interval for the baseline scenario extends up to about 4°C; for the pessimistic scenario, about 5°C; and the optimistic scenario doesn’t really warm too much more than 3°C, because we assume that we’ve started to at least draw down emissions well prior to 2100 in that scenario.

And so, for global mean sea level, it’s kind of a similar story. There’s not much difference out to 2050. Even the overall uncertainty ranges aren’t that different between 2100; they’re all roughly between 0.25 meters to 2.5 to 3 meters of sea level rise.

In terms of the range, though, the medians do change quite a bit. So the probability mass is much more concentrated at the high end for the pessimistic scenario, and much more at the lower end for the optimistic scenario (even within that range). By 2200, in the baseline scenario, the median is about two meters of sea level rise. The optimistic scenario is about one meter, and the pessimistic is about three meters. The uncertainties, though, are just gigantic. I mean, they all range from below one meter of change at the low end across all the scenarios to six to eight meters depending on the scenario or the ensemble that you’re looking at at the high end.

Daniel Raimi: Right. And for people who think in feet rather than meters, you roughly multiply that by three. So at the high end of the distribution, we’re looking at 18 to 24 feet of potential sea level rise by 2200 under the high end of the uncertainty range.

Vivek Srikrishnan: Yeah.

Daniel Raimi: I’d love to ask you now about another really important part of the paper. As you said, the paper’s partly about the results, but it’s also really focused on the uncertainties. So you rank the three key uncertainties in the paper when it comes to predicting future sea level rise. Which ones do you find to be sort of the most important, the most consequential, and how do they actually change over time?

Vivek Srikrishnan: Yeah, so on the geophysical side, equilibrium climate sensitivity is important no matter what, because that is the mechanism by which our emissions are translated to temperatures. So that has a constant presence over the entire time.

One of the things that we see early on is that the uncertainties that matter a little bit more come from Greenland, as well as the climate sensitivity. So Greenland seems to be melting a little bit faster than maybe we had thought it might in the past. And there’s uncertainty about what that might do in the near term. Antarctica really depends on the triggering of some of these instabilities that have been debated and brought up in the literature and that are deeply, deeply uncertain. But that requires us crossing a certain temperature threshold, and we don’t really see that emerge as a really important uncertainty until we get closer to 2070 and 2075—where there’s a chance for temperatures to kind of rise above some of those thresholds.

Similarly, that’s when the emissions uncertainty starts to become really important, and mitigation starts to become a really critical tool—largely because of its influence on determining whether we trigger those Antarctic accelerations or not.

In particular, one of the things that was surprising to me (I didn’t expect this at all) is that, when it comes to the emissions uncertainties, the timing of when we decrease emissions becomes more important than the actual rates of increase or the rates of decrease. And largely that’s because of this issue of integrated temperatures—that we’re just starting to reduce how long we’re keeping temperatures above a certain level, or we’re triggering those temperature changes. That was a little bit surprising to me.

It seems like it’s better for us to figure out ways to reduce emissions as soon as possible, rather than kind of holding things steady and hoping for a decarbonization silver bullet.

Daniel Raimi: That’s super interesting. And that brings me to the sort of the last substantive question I wanted to ask you, which is about the implications of this for policies that aim to reduce long-lived greenhouse gases like carbon dioxide, versus shorter-lived greenhouse gases like methane—which are more powerful over a short time period, but then degrade in the atmosphere over time and become less important. What does your analysis tell us about that?

Vivek Srikrishnan: In and of itself, the analysis doesn’t say very much. We didn’t really look at the differences between methane, for example, and carbon dioxide.

However … because of the really central role of these tipping points, really strong but short-lived greenhouse gases like methane give you a higher chance of crossing over those tipping points, even if your carbon dioxide emissions have started to keep you away from that a little bit. Even if they degrade to carbon dioxide over time, you might trigger some of these nonreversible dynamics to start occurring, because the methane has caused that larger temperature change in the short term.

One of the framings that we use in the paper is the idea of a safe operating space, that there are things we can control—which are largely emissions—and there’s geophysical uncertainties that we can’t control. And the really strong greenhouse gases just kind of further mean that you have less control over things because you might cross a tipping point without fully realizing it.

Interestingly, this actually has implications for hydrogen, as well, because if we vent or leak hydrogen, that extends the atmospheric lifetime of methane. So, you’re allowing that methane to force a larger temperature change than it would in the absence of that increased amount of hydrogen.

Daniel Raimi: That’s interesting. I didn’t know that about hydrogen. Super interesting. Well, one more time, Vivek Srikrishnan from Cornell, this has been a fascinating conversation. And, as listeners will know, there’s tons of richness here under the hood, so we’d really encourage you to check out the paper.

But before we say goodbye, we’d like to ask you the same question we ask all of our guests on the show, which is to recommend something that you think is really great and that you think our listeners might enjoy. So what’s at the top of your literal or your metaphorical reading stack?

Vivek Srikrishnan: So I’ve been working very slowly—because it’s a very long book, but one that’s very interesting—through a book called The Earth Transformed by Peter Frankopan. It’s a history of how environmental changes have kind of influenced socioeconomic and political aspects of different societies over time—oing back to myths, interpreting myths, and looking at them in terms of environmental changes all the way to more modern changes as well.

So it, of course, focuses on environmental drivers, and less on politics—though the author, I think, does try to warn us against being overly reductive and thinking that environmental changes determine outcomes. But it’s really fascinating, and I think this area of environmental history is something that I think is really, really fascinating.

Daniel Raimi: Yeah, that does sound fascinating. Well, thanks so much. One more time, Vivek Srikrishnan from Cornell, we really appreciate you coming on the show and sharing this fascinating work with us.

Vivek Srikrishnan: Yeah, thanks so much. Thank you for the conversation.

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