S&P Global Inc. (SPGI) Earnings Call Transcript & Summary

We're going to wait for a moment until all participants have had a chance to join. Narelle, can you advise me on the count? I don't see the count. Yes, I do...

We're at about 50 at the moment.

All right. And I see it was rising as well. So hang on, everyone for about another minute and then we will start in earnest. All right. I think we should go ahead and get started. The count appears to have stabilized. And as I say that, it takes another jump, give me 15 or 20 more seconds. We'll see if things stabilize, excuse me. All right. I think we're 2 minutes in. We have a lot to discuss today. You can see on the screen the principal elements of why we're here today. All of the speakers today and the WCRP and S&P are concerned about linking climate change to economic and financial impacts. It's a very complex multidisciplinary question involving economic sciences, earth system science, financial aspects of many different kinds. We all come from different disciplines that have key models, data and different methods, and bridging the gap between those models, data and methods is a nontrivial and demanding exercise. Thirdly, we want to understand in the face of many different types of uncertainty, how can decision-makers be better informed, both private and public decision-makers about how to respond to climate change in the face of these uncertainties and this multidisciplinary challenge. And practically speaking, what gaps do we need to address? What are the key gaps? How can we prioritize our efforts in this direction. Next slide, please. We have four speakers today. Well, we have three speakers today, and I'm the moderator, Lars Peter Hansen from the University of Chicago; Amanda McCartney (sic) [ Amanda McCarty ], the Director of the Climate Ready Nation program at NOAA; Tom Karl, Senior Consultant, former Director of the National Center for Environmental Information in the United States; and myself, Chief Science Officer at the Climate Center of Excellence at S&P Global. I'm the moderator, the other three are our speakers. I'm going to give a very brief motivational background, just a few slides. Then Tom Karl is going to take a look at the physical science challenges that we face. Lars Peter Hansen is going to address climate and uncertainty, and how that affects economic and financial decision-making. Amanda McCartney (sic) [ Amanda McCarty ] is going to address similar questions from the perspective of linking climate change modeling to economic and financial impacts. And then we're going to have an open discussion. We're allocating 20 minutes each for the speakers and then half an hour of discussion. We'll be liberal in our discussion, and we hope that you will find this stimulating, challenging and above all interesting. Next slide, please. This webinar today is in support of an upcoming workshop entitled, coupled physical, economic and financial impact modeling that's cosponsored by WCRP and S&P Global. This will be occurring in November. The graphic here is simply a reminder that we're looking at complex interconnected coupled systems. We have a tendency to look at these through professional lenses that isolate us to a certain degree, and this is reemphasizing the multidisciplinary challenges that we face in addressing these complex interacting feedback between these systems. Next slide, please. The next couple of slides, again, just a very brief introduction by way of motivation. We all know that we are dealing with uncertainty at the level of what scenario for the future are we even talking about? Are we talking about a 1.5 degree C world? Are we talking about a 2.5 degree C world? Are we talking about something different and possibly worse. Uncertainty in simply projecting what type of future do we envision? Next slide. There's further uncertainty. This particular graphic is from IPCC AR6. There's further uncertainty in the aggregates, economic impacts of climate change. The takeaway from the graphic on the right is just notice the spread. This is a graph of end of century, net GDP impact as a function of global temperature change, and notice the vertical scale. Just an enormous difference in professional opinion about what will be the economic impacts of different degrees of climate change. Next slide. There are parts of the physical system. This is, again, an uncertainty type of Slide. There are parts of the physical system that we actually do not understand very well at all. Two key components of that are correlated and cascading events as well as tipping points. We do not understand how correlated and cascading events in any systematic way will affect economies and financial institutions. And the situation is even more egregious for tipping points. The possibility of different types of Amazonian rainforest impacts, changes in major components of Ocean circulation. Those are areas where we are just scratching the surface on our understanding of how those might occur, when they might occur, and what their impacts could possibly be. So these last three slides are essentially just motivation, and painting part of the landscape of the uncertainties that we face, the challenges that we face in this general area of coupling physical impacts, economic impacts and financial impacts. By way of introduction or by way of setting the stage, that's the end of my brief introduction. I'm going to turn things over to Thomas Karl, who's going to take a perspective on some of the physical science data and models that are currently on the table and some of the gaps that he sees. Over to you, Tom.

Thanks, Terry. And just as you said, I'm obviously coming to this problem from a physical climate hazard perspective, that's my background. But with the recognition, if we're going to make progress on understanding the economic impacts, this cross-talk between physical climate scientists, economic experts is going to be dependent on how well we understand each other's strengths and weaknesses. So in that respect, I want to talk a little bit about the assets we think we have that we bring to the table from a climate perspective and some of the barriers that we have. If we can go on to the next slide. I'll just briefly mention the assets we'll talk about from data, models, methods are an important asset that we have. But then there are some important barriers to recognize, and this is where we can get into trouble when we try to do cross-disciplined science, it's really important to link into the expertise within each of our own prospective fields, it began to feel very comfortable and interacting. Normally to have a good, strong cross-disciplinary relationship, these kinds of things take time to cultivate. They don't often happen in kind of one-off events. So this is a long-term investment that's going to be important. So I hope to briefly go through some of the assets we have and some of the barriers. And on the next slide, with tongue and cheek, I want to make sure we don't fall into Don Quixote trap of expecting every venture we go on is going to provide just marvelous results, grandeur noble adventures because there are some barriers as Sancho Panza says, you don't want to fall off your sally, you might want to buckle up. So I think our challenge is going to be to recognize those assets because there are certainly assets out there, but don't lose sight of the barriers that some of them bring. So we go on to the next slide, I'll talk a little bit about the physical climate assets that we have for temperature, every data set that we provide, and you see on the right in these diagrams, there's many data sets out there. There's a trade-off because as we increase the time span of the data set, just for temperature, we tend to reduce the spatial resolution. And similarly, for the temporal resolution, as the time span increases, we reduced the temporal resolution. So there's this trade-off that one has to make a good understanding of what each data set offers is critically important. This is for temperature, you can make the same argument on the next slide for precipitation. As the time span in years for the precipitation data sets increase, the spatial resolution decreases, and similarly for the temporal resolution. And we'll talk a little bit later on about why it's so important to get high temporal resolution if you're interested in precipitation extremes, and similarly for spatial resolution. One can be misled if one is not careful, in thinking that precipitation rates are dictated only by a specific time or space resolution. On the next slide, we have an enormous number of models we bring to the table. CMIP6 models, there's CMIP5 models. This is a sixth generation of climate model that have been run for the various scenarios of greenhouse forcing. And you can see we have many models that have run these forcing, great asset. We can do ensembles. We've got pre-industrial control runs, historical runs. We've got regional models with many variables. They span a shorter period of time, usually, readily available. We've got monthly to seasonal and multi-seasonal forecast. We've got a whole number of metrological and chronological institutes that provide these data on a regular operational basis. And we now have some hybrid models that are coming into play, those that combine both machine learning, artificial intelligence with dynamical models. So a great number of assets there. Next slide, same holds true for hydrologic models, if we're interested in the runoff from extreme precipitation events, for example, these are extremely important models and again, a variety of models, variety of resolutions and available at a variety of institutions. Next slide. And I should mention if one works with these models from hydrologic to CMIP to seasonal forecast, it's extremely important to link into the experts that are quite familiar with those models because there are many pitfalls in using those data that one should be aware of if we're doing cross-disciplinary science. There's also methods to improve resolution biases. This is frequently referred to as downscaling statistical methods. There's a whole suite of them that one can make use of. There's new methods coming on, artificial intelligent, machine learning methods. They all have varying degrees of complexity. And again, extremely important to understand how one is going to apply these and what might be the best for the intended use. Next slide, please. Recognizing these major barriers when using the physical climate hazards, can be broken down into three major components. One, huge uncertainties with the SSP scenario forcings, how those are going to vary with time. The type of forcings, including the feedbacks between the amount of forcing we see and how responsive civilization would be to making changes in those forcings. How to best bound those forcings. It's not always clear how to do that. Another uncertainty, climate model sensitivity. This uncertainty varies with the model. The good news here is that can be bounded to some extent by the observations, there's been a number of individuals who have done this, and that helps us quite a bit. Steve Sherwood is going to lead -- help lead the WCRP workshop later in November, has written an outstanding article on how one can go about bounding these uncertainties. Downscaling and bias corrections, as I mentioned, these uncertainties increase with the magnitude of the change, and it's difficult to bound these uncertainties as you go well beyond conditions that have not been observed in the historical data. So some important concepts to keep in mind. Next slide, please. I know Amanda is going to talk about this. But I just wanted to put it up here because this is extremely important. This is -- in many countries have these kinds of data sets showing the number of $1 billion weather climate disasters over the last several decades, and how they have changed and increased. And you can see 2024, just in the U.S. we're likely to be up near or above the records with the two most recent events, Milton and Helene and the year is not over yet. So if you take this data on the next slide, and try to fit the losses that have been adjusted for real GDP, and you try to do a best fit of how these might be expected to change with global temperature. The best fit is a nonlinear fit and Terry got to this on his early introductory slides showing the IPCC diagram of how these uncertainties and economic impacts vary quite substantially. Here's a case using the same data set, a linear fit, the economic losses are in the order of a few percent with 4-degree warming. And if you do a nonlinear fit, which was the best fit, you end up with 15%, 20% loss with 4-degree warming. And I should mention these losses don't include supply chain losses. They don't include anything with mental or physical health, and there's been recent work showing that it's not only the time in which these events occur, but 5 years down the line, new research have showed, there are still greater number of hospital visits in areas that have been affected by extreme events. And it doesn't include natural capital, environmental degradation. So these are probably underestimates in terms of what the actual losses are. The next slide, please. And we have to think about how these losses come about. I wanted to show one example from an extreme weather event. This is a multibillion-dollar event. You can see on the left here, December 23, 2022, the number of warnings and advisories and watches that the U.S. NOAA's National Weather Service had put out on that day, this is all due to -- on the right, you'll see on 12/22 a storm developed in the central part of the U.S., moved up into Canada, deepened tremendously. The blue dots represent substantial deepening in terms of pressure grading of the storm. So this storm affected many areas virtually simultaneously. And this is the difficult part that we need to address. It's not adequate to look at one area, one kind of condition because this hits multiple areas with multiple conditions. If we look at the next slide, this will give you an example, the type of events that we saw during this one storm, flooding, landslide, high winds, freezing rain, heavy snow, blizzard conditions with wind speed in a cold wave, depending on where you were -- with respect to that storm, you were in one of these categories. And this is what makes it so challenging to understand this inter-connectivity, these correlations, as Terry mentioned, during these events. And this is the kind of thing that we need to work very closely with our partners from the social economic side to make sure we get this right. The next slide, another aspect that's important to consider is spatial decay lengths. It's simply where does the correlation drop down the 0.37, the one over [ E scaling ] and it's quite different if you're looking at temperature. Here, the K length can be 1,000 kilometers, 2,000 kilometers. If you're looking at precipitation, it's much less in the order of hundreds of kilometers, and it changes depending on the season. Convection, much shorter. Weather events, a little bit longer. So understanding this is clearly important if one wants to understand what we just showed on the previous slides how these events link spatially. Next slide. Here's an example for temperature, just to give you an idea on the left. It's the actual temperature on the right, it's the anomalies. And you can see if you got a warm temperatures, those anomalies spread sometimes to half a continental scale. And this could be important if one is focused on, for example, energy transfer. Energy transfer from one area to the next, during a heat wave, extremely important, but you've got to get energy from places that don't need it. And if the whole country or the whole area, multi-countries, are in extremely warm conditions, it's very hard to transport that energy. Next slide, please. It is what I just mentioned is importance of transferring when you need it through the grid. And it's not only important during the event, but prior to and subsequent to extreme events. If you want to look at assistance and recovery, one really needs to understand the spatial extent of these extreme events. Next slide, please. Here's one of the barriers that's important to recognize and why it's so important to get closely linked with the experts who develop these data sets. Many data sets assume if you're interested in changes, for example, of temperature and elevation, that changes with the free atmosphere decrease of temperature, which is about 9.8 degree C per 1,000 meters. But in reality, if you actually look at the 2-meter temperature at the surface of the earth, the change in temperature with elevation is substantially less, and it varies for the minimum temperature and the maximum temperature. So it's important to keep that in mind and it also varies seasonally. And so one could be led astray if one simply assumed approximately 10 degrees C per 1,000-meter decrease of temperature with height. It really decreases if you're looking at instrument-level temperature at the height of your face, above the ground, it's substantially less as walking -- if you were walking up hill, a mountain or downhill mountain. Next slide, please. Here's another example where it's extremely important to understand the time resolution and the space resolution and how that affects precipitation rates. This is an example from Germany in the summer and you can see in the bottom left, if you look at a 1-kilometer area, and this is from radar data over the couple of summers, the greatest 5-minute rainfalls can be over 25 millimeters per hour at 1-kilometer. But if you look at -- for 5 minutes. If you look at 6 hours over on the left for 1-kilometer, you are down to 5 millimeters per hour. And similarly, if you look at the right side of the diagram, if you're at 50 kilometers, you have a 5-minute average, maybe you can get as high as 7 millimeters per hour of precipitation rate. But if you go to a 6-hour time resolution, now you're only at 1.5 millimeters per hour. And you can see if you go across horizontally, changing that spatial scale, you get quite different rainfall rate. So again, important to understand the rainfall rate that should be expected based on your time and space scale. Next slide, please. I mentioned downscaling. We still have unresolved downscaling issues. Here's an example, one of the well-used. And I'd say, well-respected downscaling method, NEX-GDDP data, where they don't scale the CMIP models. If you compare that over on the left, you can see from a great number of models shaded in gray, how that compares to ERA5 reanalysis data. And we think this is pretty close to the observational data. You can see even after we downscaled, we haven't done quite as good a job as we would like to with respect to mean daily relative humidity. And if we go over to the right for July, we see if we compare the actual observations at one place like Raleigh-Durham, North Carolina, the ERA5 data tends to underestimate at the lower parts of the distribution and a little bit of overestimate at the upper parts of the distribution, but we didn't do quite so good with the downscaling data that is used for looking at the climate of the 21st century with the CMIP6 model projections. So you can see, we overestimated relative humidity quite a bit. So what -- if you're going to use these data, you need to take another step to make sure that it's relatively consistent with the observations if you're going to use relative humidity and something that is important, something like a heat index, a wet bulb temperature, those kinds of combinatorial indices. Next slide, please. I wanted to talk a little bit about climate model sensitivity and early tipping points. This is from a diagram, pretty famous OECD showing a variety of tipping points. And you can see that tipping points are subject to occurring at various levels of global warming based on expert opinion. And I wanted to focus a little more on the permafrost, the boreal permafrost because that tipping point is closer to us in the order of somewhere between 2 and 3 degrees C. So that's for forcing from greenhouse responses. And if we look at the actual way in which we could reach that tipping point, not simply by greenhouse forcing, but also by considering natural variability on top of that greenhouse forcing. The next slide shows what can actually happen in reality. This is global temperature anomalies for the year 2020. And you can see in some locations, those anomalies can be over 5 degrees C, and that's well above the forcing of 2 to 3 degrees that we talked about for greenhouse gases. And for permafrost, what has been identified as a key predictor is the mean annual air temperature, where that mean annual air temperature is above 0-degree C, there is no permafrost. And so that can be used as a predictor for when we might lose the permafrost. So what one could do and the next example, just is a good example on the next slide, please. If one looked at simply the forcing of the mean annual air temperature, that's a solid blue line. And you can see how that changes. On the left is the permafrost coverage from 0 up to 1, 1 being 100% coverage. And you can see how it goes down over time. And this is for one model. It's a scenario with not extreme amounts of greenhouse forcing, SSP126. That's 2.6 degree C forcing by the end of the century. And one can see that there's certainly a decrease, but if one includes the possibility of a natural occurrence of extremely warm conditions that occur simultaneously in the permafrost areas, one sees that dash line. And I should say that was for two consecutive years. We ran the model for two consecutive years. If you do it for one year, you're going to get substantially less. You can do it for multiple years, 3, 4, 5 years. These are the kinds of things that we need to probe more seriously because we may find ourselves in an early tipping point scenario because simply natural variability has added to that air temperature increase. On the next slide, please. I just wanted to identify some of the recommendations that we think will be really important if we're going to really cut into these barriers and take advantage of the assets. First off, recognition, the problem set is large. There's a lot of resources out there. There's more being developed and the pace is enormous. There's a diversity of methods, diversity of products, discovering their trade-offs and assumptions. This is really important if we're going to link together across disciplines. We really need to help minimize the misuse, maximize the benefits of the data sets, cross-discipline and cross-institutional cooperation is going to be key. We need a robust set of international cross-disciplined projects and assessments that's easier said than done, takes enormous time investment and scientists will need to be encouraged and rewarded for the time it takes to work through the problems of doing cross-disciplinary science. And not only cross disciplines, but it's important to include the public in these discussions, and doing this early is to an advantage of all of us because in the end, how this is going to be made use of, is going to be through public opinion in many countries. So with that, Terry, I'll turn it over to you.

Very good. Thanks, Tom. I hope everyone can see the robust nature of both the wealth of information that we have, but also a flip side of the coin and the uncertainties associated with that wealth of information and different modeling techniques. Lars Peter Hansen is going to talk to us about how to address designing prudent policies in the face of such uncertainties. Over to you, Lars.

Thank you very much. I really appreciate the opportunity of being involved in this discussion. I think it's an incredibly important discussion to be having. So I'm going to be talking really kind of -- I'll be putting some methodological points, some challenges on the table, and I'll also talk a little bit about a few illustrations. Next slide, please. So economists, we like to talk about trade-offs that's kind of our bread and butter. And when it comes to the uncertainty, there's two trade-offs that are really important, I think, and one that we -- for the design of policy, we really need to be thinking about. One is when we use models, we can use models to make best guesses, we can use them to a assess potentially bad outcomes. We've seen lots of like evidence already in the initial talk, we'll see more evidence about giving nice characterizations of uncertainty. But how do we care about that uncertainty? How a first should society be to these uncertainties as part of the -- is a critical input in making prudent decision-making. And that involves trading off kind of best guesses versus potentially bad outcomes. How much weight do we put to the really bad outcomes. If we only focus on best guesses and potentially bad things that could happen with nontrivial chances can come and bite us. We only look at really bad outcomes, we kind of give up and not do anything. So that's an intermediate ground, we have to kind of figure out and sort out and that's a notion of uncertainty of version. The other one is a dynamic one that's very explicit. Do we act now or do we wait until we learn more? There are certain aspects of the climate change problem, which we will learn more about, things about how economies will adapt, what technological progresses will be that will help us out and like that. To what extent we want to wait until we learn to what extent we want to act now. Just the possibility of bad outcomes already gives us some incentives to want to act now. And so that trade-off again is a very important one. Next slide, please. So this is just a quote. It's consistent with our previous discussions that many economic consequences of complex risk with climate change cannot be quantified. I would say, fully quantified. The previous speaker, Tom was talking about potential bounds and stuff like this. These unquantified, poorly understood and awfully deeply uncertain risk should be included in economic evaluations and decision-making processes. So maybe we can all agree that, that should be the case. But the question is, how do you do that? And that's a very important consideration here. It's wonderful to get these different measures of uncertainty, but what do we do with them and how do we use them to shape prudent policymaking. Next slide, please. So this is what keeps me up at night. There's this Hayek, who is a very famous economist. This is part of this Nobel address, which is a very controversial Noble address. I don't agree with all of it, but I kind of like this statement, I think, kind of resonates. There's this notion in policymaking. You can observe this in across all disciplines, it's certainly true in economics and it's also true elsewhere. Now when it comes to uncertainty, there's a little bit of a fear that if we put it on the table, we're open about it, it's going to lead policymakers to just dismiss it and therefore, we shouldn't do something. And so therefore, it leads to this bias about people in order to influence policy pretending we know more than we actually do. And so I see there's lots of evidence in this. And so the question that really I think is important is how can we integrate discussions of uncertainty in the policymaking settings without having to shy away from, hide them or the like. How can we push these conversations from scientific discussions to what role they ought to play in the design and policymaking. And how do we produce methods to do that. So there's not this kind of notion that we should -- by the time to get to the policymaking realm, we should just pretend it's not there. Next slide, please. So just the type of stylist in a very abstract general setting, we can think about different channels of uncertainty that would show up here in terms of trying to really making prudent policymaking. So one is kind of just from a standard economic perspective, when we're thinking about producing goods in the future there's uncertainty in terms of productivities in the future and the like. And this is something that's well-studied, how we make a capital investment today is going to alter future output, and that's done in an uncertain way. This is kind of a standard economic contribution of uncertainty. Now we're going to add on to that type of uncertainty. One is the geoscientific side. We've already heard a lot about this, and there's some very, very interesting and some very valuable characterizations of this. In the simplest form, how do CO2 emissions today impact the future climate. All the way from temperature changes to tipping points and the like. The next one is climate change in the future is going to alter economic opportunity. We can't just do these like dynamic -- simple regression extrapolations to the future because we know economies are going to adapt in some way or another. There's going to be endogenous movements. As climate change becomes more and more pronounced, other things are going to happen, it's going to be responses and the like. However, we're in a situation in which we can look at historical evidence, it can give us some information. But we're also pushing economies in the places they haven't really experienced in the past. And that means the amount of evidence we can draw on historically is somewhat limited. There's going to be subjective uncertainties that come into play. And this is also going to require some serious modeling about -- in order to help us make guesses about what type of adaptations will be taking place. The third one, and this is left out of remarkably large number of discussions of climate change, but if you really want to talk about policy levers, I think that's central, and that is on technology. How much should we be investing today in new potentially cutting-edge technologies that are going to have really a dramatic impact to help us get out of the climate change problem. People speculate about things like nuclear fusion that it has a possibility, right now, a long way to be economically viable. It has a possibility of being a big game changer should we actually be able to cross a variety of different barriers and the like. More generally, the other type of technologies people speculate about. For me, this is central into thinking about the addressing climate change and to leave this out of the picture, I think, is a big mistake. Next slide, please. So right now, I think there's many problematic aspects of some of the current approaches to this interdisciplinary type work and there are certain obstacles to it as well. So I kind of applaud this whole session for trying to open the hood on some of these. And so let me give you some of my perspectives on this. I as an economist see many, many applications of these so-called social economic pathways. These are exogenous type of inputs stuck into different type of climate models to try to make guesses as to things that might happen. There were mentions in both the introductory comments by Terry, there's also mentioned by Tom, but so-called feedback effects. The right way to think about this, I believe, is you have to think about this as a simultaneous system, a simultaneous dynamics. You can't just say, "Well, I'm going to go show you the climate dynamics and someone else can figure out the rest." But you really have to think about these things as they interact together. I as an economists cannot give you credible social economic pathways without thinking about the interaction it's going to have between climate change and the uncertainties because economies are going to respond. There's going to be even suboptimal policy responses and the like. So one really has to think about these as kind of simultaneous systems that makes the modeling challenge all that much more harder and all the much more challenging for us. Now the economic component has one other feature here that is different than type of models of physical systems. We include forward-looking people, forward-looking policymakers, forward-looking investors. They themselves also are exposed to uncertainty. So we've built models of people inside the model is being exposed to uncertainty. And then we, as model builders ourselves, also face uncertainty. And this is stuff I've talked about, other people have talked about as kind of -- and I like the categorization of inside the model uncertainty, that we need to think about already because we have people responding to those uncertainties in various endogenous ways and then outside the model uncertainty, how do we as then model builders look at the overall uncertainty in terms of what implications are for policy and the like. So this makes this forward-looking aspect and this additional insight the model uncertainty kind of compounds or makes the analysis of uncertainty, all the more challenging, but I view this is very important. Currently, there's been many interdisciplinary approaches to things like measuring the social cost of carbon. To my taste some of these kind of stable insights across fields and if you open the hood on them and sometimes incoherent ways because they're not facing all the challenges, which completely the ones which I mentioned there. On the other hand, I believe that interdisciplinary work is vital for studying climate change, even if it's very difficult to do well. Next slide, please. So let me just kind of conceptually see what's going on here. We can start off with a basic economic model. This is one in which there might be some energy input into the production of output, along with capital out of that might come some investment. That investment might then be used to augment the capital and enhance future output. Part of the output gets put into consumption and economic well-being. So it's a highly stylized model. In the words or wisdom of Adam Smith. This is a model as is that Adam Smith's invisible hand could work for us, and markets can help to support prudent outcomes. Next slide, please. Now, these energy inputs then, of course, they, at the same time, emit carbon in the atmosphere. So this will be what our economists will call externality or it's kind of the externalities were really studied extensively by economist Pigou, who worked out tax policies in order to address them. These are things which markets don't internalize. And so here's the failure of Adam Smith's invisible hand and now this calls for policy intervention. The emissions induced changes in climate that can damage economic well-being, it could alternatively damage the productive capacity of the economy. Next slide, please. here's the piece that I'd like to see more attention applied to? Well, some of that output could be used in investment. This investment could be done by the private sector, some of the -- once technologies -- their evident economic viability is in place, the private sector can play very important roles. But sometimes they require some initial scientific advances in the form of government support for research and development. So there's a danger in just turning governments over into venture capitalist. They're probably not as good as the other private sector. On the other hand, there are certain types of investments in initial stage things like, say, nuclear fusion or the like in which you would want some type of important government investments, even if the prospect is uncertain. And then potentially, we want to go across multiple different kind of path-breaking technologies here, even if we can't be 100% confident they're going to pay off. And then once economic viability is established, we can start thinking about incorporating more private sectors. And even if you look at nuclear fusion, it's already on the radar screen of various different venture capitalists. Next slide, please So there's been some advances in decision theory, under uncertainty. And I'd like to look to these to help us to provide a guidance policy. Okay. So we talk about these deep uncertainties. We talk about all -- producing bounds and stuff like this. So what do we do with these things? I mean, how do we really make the systematic or the like? So to do this, we have to really think about uncertainty in very, very clear terms, and I like the following type of language. It's consistent with much of the recent decision theory. The first one is risk, the term risk is used for lots of things. but I like a narrow term of it. It's a situation like coin flips, rolling dice, which we don't know outcomes, but we know probabilities. Often in economic classes and analysis, we focus on risk, risk aversion and the like. The next one is ambiguity, unknown weights assigned to alternative models. So we have multiple models on the table. They have differential predictions. We have to figure out some way to weight them. And this creates a form of prior uncertainty. Now we kind of know from Bayesian statistics and the like, is that prior can eventually be dominated by data richness that likelihoods can dominate priors and therefore, prior sensitivity can eventually -- may eventually not be that important. In a lot of these climate change conversations, I think prior sensitivity is very important. So I'm a big fan of Bayesian-type methods, but they have to be coupled with notion of prior sensitivity and ambiguity. There's conceptual, I think, very conceptually nice ways to integrate that into decision-making. Finally, the one that's hardest, and may actually be the most important. In all these disciplines, we're writing down models. We're running our models that are along some dimensions are simplifications. Our models can't be full descriptions of reality, they'd be far too complex to be even useful. In the case of these integrated models, we have to make various simplifications just to even make them tractable in the short run. Yes, there's lots of machine learning type methods we can use for model solution for analysis and the like, and we're clearly importing those. But there's still lots of simplifications that are necessary. This makes the models in some sense long. How do we use models that we think are very insightful and very useful and simplifications, but at the same time, they will have mistakes in them. So we're in the business of trying to incorporate ideas for multiple disciplines, control theory, economic, statistical decision theory, probability theory and the like, in order to try to address these different components to uncertainty. The next slide, please. So for decision-makers then, they have to confront this uncertainty. And confronting uncertainty is typically some notion of aversion and some notion of averse to uncertainty there is. Now there's algorithmic ways for doing this when you don't have full probabilistic representations of things. Instead of taking a decision problem where you might just simply maximize social welfare. Now you have to explore the consequences of -- over the different ranges of uncertainties of kind of what that might have on the welfare. And so you tweak the problem from a maximization one to kind of a so-called max-min type game. And then the minimalization, what you're doing is the part of the sensitivity analysis. If I take this course of action, there's a range of possible things that might occur that are plausible and then what happens, what could really go along. And then you kind of make these calculations over various different types of policy trajectories. There's ways to do this algorithmically and tractably. And then how much you allow this exploration of the sensitivity analysis is exactly where this uncertainty aversion comes into play. And so that would require a certain type of sub-domain expertise to help us bound that or gauge that. But I think this type of framing can be very, very useful. Now of course, in climate change, there's not a single policy maker. We have multiple countries involved that each of them could be engaged in these type of calculations, but they're going to be concerned. There will be differential concerns about uncertainty, there's going to be the welfare considerations will have kind of different aspects to them as well. This makes it harder. This makes the whole policy harder. And indeed, this is a huge challenge to doing prudent climate change policy, coordination across countries and the like. To me, this makes it all the more important to be thinking hard about technological type solutions. Next slide, please. So we're engaged in some point of uncertainty quantification. Like previously, I mentioned these different type of channels of uncertainty, the productivity, geoscientific economic damages and technology. And then the question is which of these matter for policymaking. I can be inputting uncertainty from a whole lot of different sources. Some are going to be first order and some are going to be second and third order, okay? And just looking at their magnitude of histograms or like, is not enough to make this assessment, you've got to see what consequence that has for the question at hand, okay? And so once you integrate this more kind of decision policymaking framework into play, it is going to change -- it's going to influence your perspective on where are the important uncertainties. And so we're working on methods to try to actually address these type of questions, which among different sorts of uncertainty really matter for the questions at hand. And so we can compare decision-making solutions when they activate all simultaneously versus doing some just partially and that can give us some type of handle on what matters. And once you isolate the parts that really are important, maybe that's the place you can engage in more research and development, maybe that's a place that you can find more refined knowledge that can improve your decision making. Next slide, please. There's another type of uncertainty decompositions that we find to be useful. There's a lot -- economists like to think about things in terms of cost-benefit trade-offs and the like, okay? And this opens the door to so-called marginal valuations. These prudent decisions depend on your marginal assessments of the consequences of those. And they involve things like the social cost of global warming, the social value of research and development and these margin valuations give rise to potential measurements like these. It turns out that these type of measurements can be represented as asset prices, just like we think of coming out of finance. There's a set of social cash flows. There's issues about discounting that shows up into play. They are uncertain cash flows, they could be positive, they could be negative. In the case of climate change, we're thinking negative and in case of technological progress, we're thinking more positive type payoffs. But then how do we treat that uncertainty? And there's insights we can draw from not only decision theory, but also from fields like asset pricing theory that spent decades trying to figure out how uncertainty should be incorporated into valuation. We can push those same tools from market valuation into social valuation and that can be a prudent thing to be doing. And from that, we can start unpacking these different sources of uncertainty and figure out which ones really are most important to the analysis. Next slide, please. So we've had a couple of different research projects, I just kind of mentioned trying to incorporate these uncertainties. These are highly stylized projects. The problem right now with these so-called integrated assessment models is there's a lot of simplifications attached to them. And some people use this as a reason to dismiss them. I use it as a challenge to build them and make it much more rich and make them more incredible. But anyway, so some of our initial research here, we've integrated in this R&D investment. And we find the R&D investment part of the policymaking is the most important contributor to the uncertainty. With a kind of damage uncertainty or tipping point uncertainty coming in next and the geoscientific part is being kind of coming in third. But this R&D type uncertainty, the potential one will or if new technologies help us get getting out of this climate change is -- I think, should be part of the conversation. Now I think it's interesting here is you might think that this uncertainty about R&D is going to make you want to do less. And we actually document the sense that even though you might have an enhanced uncertainty concerns about this R&D because of its long-term prospects are so uncertain. You actually want to do more of it because the uncertainty concerns make you more cautious about its potential payoffs, but the payoffs when they happen can be very, very large. And this is the case of these technological payoffs for getting us out of climate change. And so this actually leads you not to be more passive, but for you being more proactive, which I think is an important insight. You still want to do things like reduce emissions because this is the only way we can get this R&D to have a really good chance for success. So it's still a very definite ambition reductions component to these type of proposals. Next slide, please. An entirely different project, people talk about nature-based solutions, so about saving rainforest and the like, and this can be part of a solution to a climate change problem. So right now, in terms of these integrated models, we're not in a position where one model can do everything. So we pay it -- just for tractability reasons. So we have a rather different type of model, special dynamic model of land allocation in the Brazilian Amazon. This land can be allocated to agriculture. It can also be allocated to growing trees that absorb carbon. Now the land use productivities within the Amazon are uncertain, but there's statistical ways to get measurements of them and try to bound them to try to figure out at least some information about the various different potential uses along the way. So we actually integrated a type of uncertainty analysis into this. And we actually find that in the case of the Amazon rainforest, it's a fairly low economic cost, one could preserve the rainforest. And so the challenge is to transfer that economic cost into prudent policymakers that preserve all the relevant incentives. But this is one that, from a standpoint of society, it would not be a hugely costly thing to address in principle. Brazil is never going to do it on their own. It will requires some form of transfers. But strictly speaking, relative to the numbers people toss around for the social cost of carbon, the costs for fixing this look to be quite modest. Now what we're missing in this is things like biodiversity consideration. So we're looking at ways to make the whole discussion much richer by incorporating biodiversity that interacts with climate change and the like, we think these type of developments are very important. And so there is an issue of tractability. Finally, let me just conclude here by saying that in the slide. Sometimes the best responsible uncertainty is to be more proactive. We shouldn't, therefore, shy away from uncertainty in policymaking. I think extensions and refinements of uncertainty quantification, including the ones I described, you can have much more general applicability beyond the example economies that I described. This type of research I view as part of a larger agenda to explore uncertainty impacts in both private and public sector decision-making. Thank you.

Thank you, Lawrence. Very stimulating. I have a number of questions, I'm dying to ask, but we need to move on to the next presentation from Amanda. Amanda is going to talk to us about NOAA and international partners and their international partners' endeavors in this general area. Over to Amanda.

Thank you, Terry, for the kind introduction. All right. So next slide. Just to ensure everybody knows what NOAA is, the National Oceanic and Atmosphere Administration's mission is to advance understanding of climate, weather and oceans and coast, share that knowledge with others and can serve marine and coastal ecosystems while supporting improved decision-making. Our organization is structured into six key line offices that specialize in everything from weather forecasting, coastal and marine research, fisheries management, satellite and data management, cross-agency research and fleet operations. So we cover the entire gamut of science to services. Next slide. On the science side, we work a lot in global observations and monitoring, and we collect information about what's happening on the planet in order to track what progress is going on. Tom shared this earlier, but this is NOAA's billion-dollar disaster set which showcases the number and costs associated with the most significant extreme events affecting the United States since 1980. Between 1980 and 2024, the United States sustained 395 weather and climate disasters, where overall damages and costs reached or exceeded $1 billion. The total cost of just these 395 events exceeded $2.7 trillion. This does not include the many disasters that cost less than $1 billion. So the cost of disasters across our country are much higher. As climate change increases the frequency and intensity of these extreme events and drives overlapping and compounding risks, we expect to see increasing financial impacts over time, especially if we do not take collective actions. Understanding the environmental conditions and drivers of these environmental changes is essential for assessing risks, and the vulnerabilities of society that drive the economic cost of extreme weather events, like our built environment and its ability to withstand extreme and the resilient society to bounce back and avoid disruptions are affected by our ability to provide high-quality information without planning for extreme events, we create vulnerabilities for citizens, property, economic activity, national and global security. Next slide. If you look at what's happening right now, many recent events showcase what's going on. We've just had Helene and Milton. We have not calculated the cost of those events yet. But if you look beyond just the monetary cost, we're seeing events that are beyond historic and beyond our traditional planning horizon. On the left-hand side of the slide, you can see flooding in New York City in 2023, and on the right-hand side, you can see a deadly heat wave in the Pacific Northwest in June 2021. These are examples of events that have reached a magnitude that we were not ready for or accustomed to, but it's consistent with what we have said will happen under a climate change scenarios. These events had real consequences and have drawn emergency managers, urban planners, public health officials, infrastructure engineers, corporate resilient officers and individuals to interact with science and forecast differently. We are increasingly realizing that the past is no longer an accurate predictor of the future, and there's increasing complexity in decision-making, which requires higher quality information about what's coming our way. Next slide. So NOAA works to support risk management. We have expertise from monitoring the Earth system to predicting what might happen in the coming days, weeks, months, years and decades, but also support in terms of capacity building technical assistance, tools and solutions that we put out there. Next slide. Users are seeking more highly detailed information, higher spatial and temporal resolution models are useful because they improve accuracy for regional and local climate features. However, developing these downscaled models faces many computational and physical limitations and need to be balanced with the need for scientifically reliable information and practical limitations of computational resources. Despite these limitations, NOAA is investing heavily in these resources to provide high-quality, verified and transparent information to its users. It is incredibly important that people understand what is going into the information and tools and models being produced in addition to what the models are saying. So we can have confidence in the results of these models and understand the uncertainty and limitations. NOAA conducts foundational research that facilitates the refinement and application of government data sets and methodologies, making them valuable assets for many, including the private sector. Examples of this range from our global models like the CMIP models, as well as SPEAR, which is looking at more shorter scale time series. We support private sector users such as The American Society of Civil Engineers, finance insurance entities, retail food, beverage, many different sectors of society are seeking out the information that NOAA has provided about historic conditions as well as our predictions for what is likely to happen in the future. Next slide. We are developing many planning tools. This shows some of NOAA tools for planning in the near-term future. On the upper left, you can see a weather hazards map. This is the type of thing that drives what the messaging that comes out of our local weather service offices. The map on the right is a monthly map that provides information about temperature outlooks. You can see the bar chart on the lower left, which is looking at forecast probabilities. And then on the bottom right, you can see more information about our ensemble forecast. All of this information is providing outlooks of what's coming over weeks and months to come. This information is often tied to El Nino and La Nina events. And the knowledge of these events is crucially important for investments in operations in diverse sectors such as water resources, agriculture and transportation. Right now, just so you know, in case you haven't heard, we're favoring the La Nina that's emerging and is expected to persist through January to March 2025, which tells us quite a bit about what's coming our way versus when we ran at El Nino previously. We also try and translate our technical information to make it more accessible. So we do things like assess how our predictions in the previous year played out and how we can do better in the future. And we share information with people about where we were correct and where we were incorrect, so that they can have confidence that we're being transparent about what information we're putting out and where we need to continue to shift and grow through investments in research, observations and tool development. Next slide. If you look on this, now I want to talk a little bit about the longer-term climate tools that we have out here. On this slide, I'm showing some illustrations from an in development tool known as Atlas 15. Our Atlases provide point estimates for frequencies of current and future extreme precipitation events. Atlas allows planners to think about buildings, infrastructure and land use, and have locally applicable estimates about potential flood risk. For example, for the first time ever, Atlas 15, which builds on Atlas 14, will include both historic data as well as climate projections, allowing us to assess what precipitation can look like in the future, under changing climate conditions. Next slide. Shifting into sea level rise, which is a slower onset event. Here you can see puts from NOAA's sea level rise viewer. Here, we're looking at Oakland, California. Light blue areas are showing where we can expect 4 feet of global sea level rise and inundation across areas of the city. Red shading indicates the degree of vulnerability of different neighborhoods and show us where residents are likely to suffer the most when exposed to flooding. We take the monitoring and research and modeling that we've put together, and we can provide tools like this that allow people to see the things that matter to them. We're also working with our interagency counterparts on coordinating sea level rise information across the U.S. government. We released a new interagency sea level resource back in September, which was the first whole of government resource for coastal communities and decision-makers on sea level rise projections, and that can be found at sealevel.globalchange.gov. Next slide. Another area where we're focusing on taking our research and monitoring and applying it to things that people care about is related to heat. Heat-related illnesses and deaths are largely preventable with proper planning, education and action. So there's so much we can do in this space. On the right, you can see a map from our National Climate Assessment that shows the additional number of days over 95 degrees Fahrenheit that would occur within a 2-degree Celsius global warming condition. Dark red colors correspond to over 30 additional days of high temperatures, which you can see are happening across much of the Southern United States. Heat.gov serves as our premier resource about heat and health information for the nation, so that we can help people reduce health, economic and infrastructural impacts of extreme heat. We've also supported the development of a National Heat Strategy that introduces the challenges posed by extreme heat and climate change as well as provides an overarching approach to the problem, describes a series of guiding principles for action and prevents four goals focused on communication, science, solutions and support so that we can continue to provide better information to prevent heat-related impacts. Next slide. In terms of wildfire, which is another hazard affiliated with climate change, NOAA plays a critical role, as a leading federal weather and climate agency, we employ a comprehensive network of surface observation satellite and predictive tools that are used to inform wildfire planning in response. Before the fire, we provide seasonal forecast and drought monitoring tools as well as actionable weather forecasts to aid fire management. During wildfires, we have on-site incident meteorologists who are deploying -- deployed on-site to deliver real-time weather updates that are crucial for firefighting tactics. And our satellites and models track wildfires, predict smoke dispersion and ensure ongoing monitoring and forecasting to support efforts. Post fire, we continue to assess flooding risk from burn scars and develop -- are working to develop next-generation flood warning systems to enhance community safety. Next slide. As you can see by the examples I just put out there, many of these hazards interact. So we recognize that hazards don't work in isolation and compound risks are truly more than the sum of their parts. You can see on the left side, a piece of art entitled under pressure. That was included in the Fifth National Climate Assessment that shows the concurrence of floodwaters in blue, extreme heat in red and how they can descend upon our homes. At the right is another figure from our National Climate Assessment, showing compounding events of just 2020 and '21, where you can see the timing of heat, drought and fire events in the U.S. West, and the East Coast facing numerous destructive tropical storms. Superimposed on this time frame were many other societal challenges, including COVID. Communities were challenged by each of these climate impacts, but also by broader societal impacts and their ability to respond and recover is truly impacted by all the events taking place. At a national scale, it require -- and global scale, it required staggering resource allocations to challenge to simultaneously deal with the pandemic and the collection of regional climate hazards. So it's important that we put our climate work in the context of these other hazards. Next slide. While extreme weather and climate events often attract our attention, there are relatively slower environmental shifts associated with climate change that are also presenting us with important risks. On this slide, we have two illustrations. At the left, you can see the projected increase of the range of Valley fever. So we need to be thinking about diseases as well. At the top is the current range for Valley fever incidences with most cases in the Southwest, and below is the map for the end of this century, with cases potentially extending into the Canadian border. As the climate warms and soils dry out West, the fungus that causes this disease will be viable in places much farther north. There are many other examples of this type of spread. On the right, we look at the way climate change is affecting biogeochemical cycles in the ocean associated with plankton. You can see the up and down arrows associated with processes, indicating that the direction of net change is actually unclear. Given over 70% of the earth is covered by oceans and plankton represent billions of tons of biomasses, understanding these changes are important for understanding our marine food web and the chemistry of the entire planet. In both cases, our ability to characterize and hopefully manage these risks is predicated on the robustness of our monitoring systems and research to fill these gaps. If we can observe and track changes, we are flying blind. Next cycle -- or next slide. In terms of looking at biodiversity a bit more, we know that there are strong interactions between climate and biodiversity. And so we need to develop national accounting systems that measure and quantify both the physical and monetary flow of natural resources in the face of climate change, and also look at how we can use biodiversity in nature to provide climate solutions. As part of these efforts led by the White House, NOAA has contributed to the national strategy to develop statistics for environmental economic decisions. It is our hope that working with our interagency counterparts, we can develop reliable, reproducible and timely statistics that we can use to track changes in the stock and physical quality of our natural resources and in turn, the changes in their economic value. We also are working to connect to natural capital information to measures of how the marine economy is contributing to the U.S. GDP, and demonstrate how resources ultimately impact economic activity. Next slide. I've highlighted some of the great resources NOAA offers for understanding the Earth system, but I'd like to pivot to talk about a little more about what's holding us back. Next slide. First, although we have an abundance of information about the future of climate, we struggled to infuse that information into the many local scale decisions that affect land use, infrastructure, housing, public health, natural resources and more. We need community scale engagement at scale to assist planners, community leaders, resource managers and others in identifying where all this great information can link up with decisions. Next slide. A second challenge is our limited understanding of economic exposure and vulnerability. Shown here are a number of reports from government agencies focused on translating physical climate risk into economic terms. While we can usually demonstrate the impacts of climate change are costly, there are still gaps in more detailed analysis and in how we articulate this information to decision makers. For example, how do impacts affect federal spending over a 10-year budgetary window, how do impact ripple through the labor market, the insurance market, housing market, credit markets and others? How will industries reallocate their resources or change priorities or the regions in which they operate following extreme events. Next slide. A third challenge is the inequities in our society are very much at play when it comes to the individuals and groups that are most exposed and vulnerable to climate. Impacts. On this slide, you can see the relationships between average temperatures and incomes in 3 cities across the United States, Atlanta, Houston and Minneapolis. In these cities, as in many cities across the country and around the world, those with the lowest income are more likely to live in the warmest parts of the city and have less shade and vegetation to offset extreme temperatures. This is just one example where addressing exposure and vulnerability requires us to confront deeper societal inequities related to wealth, job opportunities, education, political representation and access to quality health care and housing. Next slide. All right. So where do we go from here? We can't end on all the challenges. Next slide. First, we need to invest more in preparing and adapting to the future climate. You can see on this slide, estimates of the global level of finance directed towards climate solutions from both public and private sources. You can see a massive imbalance between mitigation activities that are working to reduce greenhouse gas emissions and adaptation activities, which are preparing us for future and current climate conditions. Adaptation activities speak directly to many of the climate risks we've covered today, at least in the short term over the next few decades. Mitigation is no doubt important, absolutely critical for addressing the long-term climate change. We need to limit greenhouse gas emissions and maximize things. At the risk of oversimplifying, though, I'd argue it's a bit more straightforward to do mitigation than adaptation. We can count our emissions, make choices about energy and land use that reduce emissions and set goals and targets accordingly. We also have a better understanding of relative costs and the efficacy of different policy options and solutions. Adaptation on the other hand, requires a multi-scale approach that is heavily weighted toward the local and community scale where every condition is different. It involves an economic equation where many terms are not well known and forces us to confront deep societal inequalities. Next slide. Along these lines, NOAA is prioritizing work on equitable climate services and becoming more strategic in its public private partnerships. We're aiming to support local communities in their efforts to apply climate information to better decision-making and to support technologies that build climate resilience. On this slide, you can see a variety of programs that are shown, including our Small Business Innovation Research program, and our Ocean accelerators that focus on the blue economy and industry proving grounds, which is working to provide improved climate information to the private sector. Together, these programs spread our efforts across the tech development value change from basic research and development to developing prototypes to deploying technologies at scale. My second suggestion on the next slide is that we need to walk this road together. NOAA is actively engaging our users and working with them to identify our science and serves objectives in the climate space to coproduce our science and coproduce our services to provide solutions. We're working with partners such as the American Society of Civil Engineers on engineering guidance and codes, with electric utility industry to infuse our work in their risk management approaches through an industry-proven ground targeting financial, insurance sectors, the retail sector and architecture and engineering sectors. And we're working with partners such as the Smithsonian and Marine Ecosystem managers on climate and biodiversity. We invite you to work with us. We need more partners in this space, we need to work together. Our collective expertise, experience and passion is invaluable for solving these problems. Next slide. To get to the outcomes we want to see, we truly need to work collaboratively and find ways to drive capital investments that will address climate risk. NOAA has a wealth of observations and forecast tools that inform our understanding of the Earth system and climate risks. We want to see science translate into action that reduces our risks and getting their requires greater investment in collaboration, including across the public and private sectors. Thank you.

Thank you, Amanda. Very interesting work and an enormous landscape. We only have 10 minutes left in the session. So I'm going to try to formulate a question for general discussion among all of the speakers.

Amanda, your last slide mentions greater investment in collaboration across public and private sectors. And Lars at several points in your presentation, you mentioned the importance of setting R&D priorities and by implication, I take it, increasing R&D. And Lars, if I recall correctly, your order of priorities for R&D was something like what I'll call investing in green technologies, and I take that to include both mitigation and probably adaptation. Second, I think you mentioned tipping points, and I'm going to extend that a little bit on my own cognizance to include compound events. And third and not least, I believe you mentioned general geoscientific R&D. So the question I'd like to pose goes along the lines, are we investing enough? This is for Tom, Lars and Amanda, to reduce the key uncertainties that will influence the policies, which will influence the risk. Just to expand very slightly. Are we investing enough? And if we're not, how could we increase that investment. Lars mentioned models, which are multidisciplinary being stapled together. And I've seen a lot of that, literally just stapled together. Do we need to invest in R&D in, I'll call it, a moonshot to use an overused term for integrated modeling across these disciplines. Just an idea for debate, but the general question is along the lines of where should we be investing our R&D money.

Can I just make clarification of the message I was trying to get, and then I'll keep this short because I know we're short of time. I want to draw two distinctions. One, I was talking about where the uncertainties were most important to the questions at hand. And so that ordering of uncertainty is what you certainly captured the fact we found technological uncertainty to be very important around calculations. We found certainly, that interact -- accurate however, with both things like damages and with tipping point uncertainty, with the geoscientific uncertainties of the other types, net of tipping points being more modest in terms of what impact that had on our conclusions. There's another question and that is what should society be investing in terms of resources. And so that was not what that quantification statement that I just told you was about. But indeed, I do think that there is a relevant question about where -- how should governments be involved. And I would personally be sympathetic to our notion that I think some investment in integrated science done at a very deep level, at a very deep probing level is kind of worth more investment. And it doesn't just mean you bring hundreds of scientists together and a whole magic happens, but it really requires people rolling up their sleeves and engaging in joint model building. And I think that could be tremendously important.

Very good. Tom or Amanda, would like to comment on this topic?

Sure, Terry. This is Tom. And one of the things I think we all recognize in the scientific community, probably one of the most important things we have done over the last several decades is to assemble the world's leading scientists to do climate assessment that includes economic impacts, and that has been extremely successful at bringing information that we know out to the public and policymakers. However, those are assessments. Those aren't necessarily ongoing R&D projects. In fact, you don't do R&D, when you do those assessments. So something similar to that, that actually somewhat along the lines that Lars says, if you actually look at how are you going to integrate the science and understanding. And I think something as important as what IPCC has done, but not for assessment, something in addition to assessments like R&D is really needed. And that would be a way to also bring in the public as much as IPCC does with policymakers. So that would be one area why I think we could make some progress.

And I'll come in and just say that I think the research needs are as dispersed as the challenges that we are facing. And so to answer that question, we are starting with the end users and what questions they're asking. And so if you're talking about architectures and engineers, they're looking for really different information than city planners. Sometimes it overlaps, sometimes it doesn't. But many people across the spectrum are looking for higher quality information in the 6 months to 10-year time skills. And we have some major gaps that are preventing us from being able to provide information at the spatial and temporal resolutions where decisions are made. And so continuing to work in that space to fill the many gaps we have, but also to pull the information that we have together and communicated in a way that is transparent and articulates uncertainties or areas where we're less sure, we're less confident about what we can actually say. Many of the decision-makers are very comfortable working under uncertain circumstances. They just want to know what the uncertainties are. And so we have a lot we can do already with what we have, but we also have some major gaps particularly to fill that temporal time scale and also to get down at the spatial resolution that many people want information provided at.