FutureBites with Dr. Bruce McCabe
FutureBites is a podcast exploring promising and exciting pathways to a better future. Hosted by Dr Bruce McCabe, a futurist and keynote speaker who travels the world presenting his unique insights and research on the future to corporations, governments, and audiences in all industries. In each FutureBites episode, Bruce meets with an inspiring leader in science, technology, economics or the social sciences to talk about ideas, game-changers and opportunities to create a better future.
FutureBites with Dr. Bruce McCabe
Modeling Our Climate Future – with Andreas Prein
How do we model the climate? How to make predictions at a local level? What role does AI play? What do our models predict about the future of extreme weather events?
We sit down with world-leading atmospheric scientist Prof. Andreas Prein to pull back the curtain on how weather and climate models really work. Andreas describes the evolution of weather and climate modelling, the mechanics of prediction, where AI shines and struggles, the complex interconnections with other earth systems, and the important considerations when undertaking 'climate change attribution' for extreme weather events.
Inspired by first-hand experiences of extreme weather in his youth, Andreas dedicated his career to improving the understanding atmospheric systems. Nowadays he leads the High-Resolution Weather and Climate Modeling group at the Institute for Atmospheric and Climate Science (IAC), part of the Federal Institute of Technology Zurich (ETH Zurich).
It’s a fascinating and wide-ranging conversation. We've all wondered what’s behind the predictions about the biggest force of change shaping our future, and this episode has the answers!
Enjoy the podcast!
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As always, additional commentary and takeaways and the full transcript will be on the Future Bites page soon. More on my work as a Futurist Speaker, and why I do what I do, at www.brucemccabe.com
Welcome to FutureBytes, where we explore pathways to a better future. I'm Bruce McCabe, your global futurist, and today we're talking about climate modeling and modeling extreme weather events here with my special guest, Professor Andreas Pryne. Welcome to the podcast. Yeah, thank you very much for having me, Bruce. It's absolutely my privilege to talk to you today. I'm just going to give a very brief bio, uh and I'll include some links to some of your work as well in the show notes of the podcast. But uh you lead the high-resolution weather and climate modeling group here at the Institute for Atmospheric and Climate Science at the Federal Institute of Technology for Zurich. Is that right? That's exactly right, yes. You've only taken on one of the most complex and difficult areas, I would think, in modeling in the world.
SPEAKER_00:It is complex, right? It's extremely complex, yeah. The natural modeling the natural natural system is very difficult, yeah.
SPEAKER_01:So how let's just start with you. How did you get to be doing this sort of work? What drove you here? What was the inspiration? Or maybe who was the inspiration? Was there something that led you down this path?
SPEAKER_00:You know, uh I grew up in Austria on a dairy farm. And I can I was always interested in weather, and I can just really vigorously remember looking at the weather forecast in summer, for example, and when we cut the grass and we try to get the hay in, and you don't want the rain to come because then you have to turn the grass again and again and again, and the hay gets rotten. Yeah. So in my father, I can remember that. Like we always this was always a gamble, and that that was really sticking with me. Like this, also your livelihood is depending on that. Like you have cows, you have to feed them in the winter. So this really influenced me. And then later on, I went to the Austrian military, and during my service, there was a big, big flood event in Austria, 20 uh 20 um 21. Ah, 2001. Okay, and we were called in this area, and this was really the first time I saw devastation on at this scale, and just yeah, just seeing that and just also helping people there uh had a really lasting impact. So this really sparked my interest in extreme events. I really wanted to understand what's happening in the atmosphere uh when this is happening, and of course, climate change was already a topic in the early 2000s. So this drove me into physics and climate research at the end. That's really interesting.
SPEAKER_01:Did you have a sense from your family or your the farming sort of background that was there already a family knowledge that weather events were increasing, a sense of that, or not yet?
SPEAKER_00:We did never talk about that. Okay, though. Like this was really 1990s, and like I can remember like other environmental topics like acid rain, yeah, like these kind of things. There were so novel, like yeah, in Australia you saw these pictures all the time in the newspaper, but climate change was not really that big. And I think this really changed. I think when I went to high school, uh I had physics classes, and the teacher there basically brought this up, and I thought this is fascinating. And yeah, um, yeah, then it just basically continued on this physics path afterwards.
SPEAKER_01:And important. I mean, not only at a local level, but uh to me, it's the greatest force of change of all. I mean, we can talk about artificial intelligence, we can talk about editing life with CRISPR, and there's lots of things that I I do in my work, but there's nothing bigger as a force of change than a changing climate that's planet-wide. Is there? It changes everything.
SPEAKER_00:Just thinking about the energy, like you know, like a single thunderstorm has much more energy than any atomic bomb. Like it's just the energy that's in the system that we are changing now, and it's it's just it's also mind-blowing that some like small creatures like humans can inf impact the entire planet, yeah. Um and how fast this goes, actually, the rate of change is the one that like this this is the most problematic thing, like how fast the temperature is rising and things are changing.
SPEAKER_01:How quickly we're gonna die. Because I often get that. I'm constantly uh in the US talking to people and encountering people who've been educated that climate change is isn't real. And one of the cherry-picked pieces of information they use is oh, but geologically the planets warmed and cooled before. And you sit there and go, but never this fast! Like never ever this fast. We have to adapt to this. Um, so yeah, nuts. So tell me about the modeling side. You've got into, to me, this is mathematically, physically, uh statistically, one of the most, it has to be one of the most challenging things to model. Um, the climate. So tell me about that journey and and maybe we can lead into just some of the methods and the inputs that you take into models today, or or or that in general we're using for for better climate modeling today. Because to me it's endless. Um yeah, where do we start? Satellites? What?
SPEAKER_00:Yeah, no, no, I think much earlier than that. Like it's it's like the weather forecasting was always important. And this actually it started really getting serious with sailing. And of course, like people on sail ships back in the like 17th century, they wanted to know how the wind blows and if there are storms coming and so on. So they this was like in the 19th century, the first meteorological organization started to emerge in France, in Austria, actually, and also in the UK. Um, and it was for a long time really difficult to forecast the weather. So and this this changed once we like really had breakthroughs in physics where we understood the equations of how air is moving in in the atmosphere, and this comes from fluid dynamics. Yep. So we have the Navier-Stokes equations, these are the equations that we use, and these these are used everywhere. If if you do anything with fluids or gas, these are the equations that you look at.
SPEAKER_01:And if you're ever looking at any weather forecast, this is behind that forecast.
SPEAKER_00:Yeah, so like until 2023, true. But then AI came on the scene. And like we can talk about that. Yeah, we'll get to that. Interesting. So there's a transition, yeah. There's a transition, but like early on, like that there were people really formalizing, and they tried to like there was Louis Fra Richardson was the first one who imagined we can forecast the weather with these equations, and he basically put them on scorecards. And his vision, this was in the 1920, was to build a really big forecast factory with something like 60,000 computers. And back then computers were humans, yeah, and they calculate and they try to beat the weather. Like you have to do a faster calculation than real time because you want to forecast, not Heincast. So you have to beat the weather or the real time. And he had this sorted out this whole thing. It never really took off, of course. I love the imagery though. Like graphics like artists painted this forecast factory, and Lucifer Richardson wrote a book about it. So it's it's really interesting to read about it. But this really was the foundation of numerical weather forecasting as we saw it later. And then actually, the next step was in during World War II, uh, the first computers came on the scene. And John von Neumann actually he was a student here at ETH. I like to say, but he joined the Manhattan Project later on and he worked on explosive simulations on AIAC, one of the first computers that the Americans used to. ANIAC, yes. Yeah, yeah, yeah. Yeah. And he was successful. So and he developed lots of these methodologies that you use to solve these equations on computers. And he immediately saw that there's a potential not only for explosions, but also for weather forecasting on computers. So he actually hired a person, Jules Jarny, and he this Jules Jarny was then the first one who really wrote a, or one of the first ones who wrote a weather forecasting model and did a forecast on a computer that was okay-ish.
SPEAKER_01:Roughly when would that have been?
SPEAKER_00:Oh, this was uh I think 50s, 1950s. Yeah, yeah, in Princeton.
SPEAKER_01:First computer weather forecast.
SPEAKER_00:But the point here is really like computational advancements and weather focused casting, climate modeling were really closely linked.
SPEAKER_02:Yep.
SPEAKER_00:And still are. Like we still run our climate models on the biggest computers that we have. Got it. And like Jules Chan, he's an interesting character because he he wrote a very famous report, the Charney report, to US government. This was, I think, also in the 50s. You can look it up afterwards. And he warned the government back then about climate change.
SPEAKER_01:Yeah.
SPEAKER_00:Because he wrote like this this model and he simulated climate and he basically tried to understand if you double CO2 in the atmosphere, how would the temperature change?
SPEAKER_02:Yeah.
SPEAKER_00:And he got quite close to the estimate that we still have.
SPEAKER_02:Really?
SPEAKER_00:Yeah, yeah, it's it's fascinating. Like it didn't change a lot over the years. Our models, however, really changed a lot. Like they got way more sophisticated. He's just started out with a simple atmospheric model. Nowadays, if we simulate climate, of course, you have to simulate not only the atmosphere, but also the ocean. Yes, the ice, the water cycle, this water cycle, ecosphere, all of that, the interplay because they're feedbacks. Yes. These are very important. Yes. So nowadays, these are what we call Earth system models. And again, like still, that's why also my professorship is named weather and climate modeling. I really try to keep those two fields together.
SPEAKER_02:Got it.
SPEAKER_00:Because they're fundamentally linked. It's just different timescales that we're looking at. In weather forecast, you think about the next couple of weeks. In climate, you think about the next couple of decades or even millennia.
SPEAKER_01:Yeah.
SPEAKER_00:But yeah, still, it's it's still this the biggest computers that we need to solve.
SPEAKER_01:So are we just adding inputs? I mean, I just think when we look at uh the fluid dynamics of how the gases are going to behave in the atmosphere, how much heat they're going to trap on a molecular basis, then at a group basis, and how much energy causes how much wind, moving things around, you know, then we're adding inputs in the water cycle, and uh um uh both the uh the carrying capacity of the atmosphere for water has got to be in that in that model, and that changes dramatically with temperature. I think one degree, seven percent more water.
SPEAKER_00:Exactly, yeah, the closest clapper on relationship. Another ETH is that like a closer.
SPEAKER_01:I mean, it's such a remarkable statistic, just that one. One degree of temperature increase, an atmosphere can hold seven percent more water, which says straight away why we're getting more extreme weather events. It's one of the things, right? More energy and more water. Yeah, many extremes are linked to that. Yeah. Isn't that it? Yeah, and that came from here. So, but you can see where I'm going. Like is that what we've done? We've started just just adding more complexity or expanding the model. Is that what happened over time? We've just added more inputs, and then we account for re-radiation, reflectivity. Um, you know, it keeps going.
SPEAKER_00:Yeah, so we we definitely made the models more complex and more complete, as we also gained understanding of the climate system. Like at the beginning, like there was very little understanding, actually. Yeah, and nowadays we have much better understanding, still not far from complete. Um, but for example, how much energy, like most of the energy is stored in the ocean, not in the atmosphere. So you really have to capture that. And like ocean, the turnover, the circulation in the ocean is on time scales of many thousand years. Yeah, like it's very different from the atmosphere, where you have like a turnover of a couple of weeks, one, two weeks, the atmosphere changed completely. Uh so now you have to couple all these things together. Like there's of course, as you said, like there's also chemistry, um, but there's also like the whole carbon cycle, so how carbon gets emitted but also really absorbed again. Like this is now modeled, nitrogen cycle. So these these all these things they come in and this the systems become more and more complex. Nowadays, for example, we work on dynamic vegetation modeling, where you really have vegetation that's responding to the warming and to the change in the environment.
SPEAKER_01:And you've got to model the forecast or model and try and predict what the impacts to vegetation will be, to then predict what the impacts to the climate of the vegetation layer will be.
SPEAKER_00:Yeah, because it's all linked. It's all linked. So the Amazon rainforest is a good example there. Like we see, or like this is deforestration driven to a large extent, but also climate change driven. Yeah. And the fear there is really that we are close to a tipping point where the rainforest will die off. Because if you cut off down all the trees, like the rainforest to a large extent creates its own rainfall. Because the trees are evapotranspirating, so they basically suck water out of the ground and put it in the atmosphere, and this rains down, so that the whole this is really an ecosystem. If you cut down the trees, this this evaporation stops, and then you get in a very different regime, and the rainforest will likely not grow back. Um, so this you have to simulate that, of course, if you're serious about climate change. But this is extremely challenging. It's extremely challenging.
SPEAKER_01:I don't know how you even begin to do. I guess there's if I picture you and your colleagues around the world, it's a huge community of people that are constantly contributing to the same models. Would that be fair? There's no, there's many models.
SPEAKER_00:Many models, yeah. So developing a model is extremely costly. Um, so there are we call we we talk about model families. So often what is happening, like one institute, the earliest models that we're still using were developed at UCLA, um, University of Los Angeles. This was one of the most earliest models. The UK developed one. Also, my previous uh employer, um the National Center for Atmospheric Research, developed one. And then what happened over time, like additional institutes developed their own models, but often these codes were copied. Yeah. So somebody, you know, like this is science. Like we travel to a place, we copy, we basically just take the model with us and then we use it in another place. And then these models got further developed in another place, but they're still from the same family, and you can still see that actually.
SPEAKER_02:Yeah.
SPEAKER_00:Um, but nowadays, in this IPCC reports that we have, um this intergovernmental panel for climate change reports, we have approximately 60 models that contribute these global models. 60 models contribute to the IPCC. Yeah, yeah. Not all of them are on the same, like on par. Like there's definitely models that are further developed than others, yeah. But it's it's a really big ensemble, and it's really important to have that because it's independent verification, isn't it? It's independent verification, and it's not it's not clear how we, for example, just the Navy-Stokes equations, we talked about those. How do you discretize those? Like, how do you get get those equations onto a computer? There are multiple approaches how to do that. So now people taking different approaches, and then you test what does one approach tell you about climate change, and how does another approach simulate climate change? But this is like and this is almost with all components of the system. Yeah, the cloud microphysics, radiation, like there are different approaches to simulate that. And then you get an uncertainty out of that. It's basically we call this model formulation uncertainty. Yeah. So it's basically how well uh what's the uncertainty of how you formulate natural physics equations onto the computer.
SPEAKER_01:And just to clarify, was it 6-0 or 1-6? It's 6-0. 6D models. Wow. So how have we been doing on accuracy? Um, when we look at this huge history of modeling, I mentioned some of the early models are actually pretty much on target. Um clearly, I imagine we're getting more accurate as we go along with complexity. And my naive understanding of this when I read the different outputs for models is that in general we've been too conservative with our climate change model in that some of the feedback loops have been bigger than if we take a trend, the feedback loops have been worse than we expected. Um would that be fair?
SPEAKER_00:It depends on what we are looking at, to be honest. Like um Well, warming. Let's start with just warming, predicting the level. Yeah, like the models definitely got better over time. Yeah. So that there's clear evidence for that. But they also got more complex. So you have to factor this in. Like if you start with a fairly simple model, and all of these models are somehow tuned. Yep. Like what what all every modeling center what they want to see is a very good representation of the 20th century warming or the historic data.
SPEAKER_01:And they want to go back over the data and make sure it fits.
SPEAKER_00:Of course. Yeah, yeah. So you you you tune your models to some extent to fit the historic data. Because if you don't, if you're not able to simulate the past, how can you trust into the future? Like and tuning is is is a delicate thing. But um the thing is, if you add more complexity to those models, you add more degrees of freedom.
SPEAKER_02:Yeah.
SPEAKER_00:Which basically means your uncertainty could increase because now you better understand the global system. For instance, like the ecosphere can do something, like the feedback, atmosphere, and ecosphere's feedback could kick in and you get into a very different regime. This can happen in the coupled model.
SPEAKER_02:Yeah.
SPEAKER_00:Um, so it's actually it's it's a good thing that we're still on track. I think I I would I would argue that if you have a more complex model and you can still project the future with a similar curacy, that's a good thing because you you understand the system way better. Yeah. And you didn't increase the error bones that we have.
SPEAKER_01:I just when you talk there, it reminds me. I dug into, this is a while ago, dug into chaos theory to try and understand a bit more. And I finished a popular book, James Glick, uh his book on chaos is uh just easily accessible for the average reader. And uh really, I mean, the thesis with chaos theory is that some systems don't return to equilibrium. You tip them out of equilibrium and they don't come back. And by the end of the book, I was uh utterly terrified when thinking about this particular subject, because that's the possibility, right? We could tip a climate into an instability that never even if we stop all carbon emissions tomorrow and all greenhouse gas emissions tomorrow, it could be tipped already into a place that it doesn't come back. That's that's sort of the fear there, isn't it?
SPEAKER_00:Yeah, so it's it I think it depends on the time scale that we're talking about. Like we if you're not there now, but failure climate records. Like we we were in very different climate states over the last millennia and and like millions of years. Um and like like the Earth system is self-regulating to a large extent. So the earth was several times already a snowball. Yeah, like it completely froze over, and then it melted again, and then like you get more in the vegetated state, and then you took it's a forth and back. So in climate time, these these are geological timescales. Yeah, like on human timescales, that's totally true. Like if we tip some like key elements, I see that there's like a point of no return on on life on scales that are relevant for humans.
SPEAKER_01:Yes, got it. We could tip it into something that's quite chaotic and with sharp, a sharp increase or dramatic change. Yeah, yeah. That's even much more dramatic than we're seeing now, and it would be catastrophic with now times. Yeah, yeah, yeah.
SPEAKER_00:Then this is one of the biggest concerns, like this this tipping points and when they occur, like this is also one of the main reasons why during the Paris Agreement the international um governments agreed to try to keep the warming to 1.5 degrees Celsius and well below two. This is exactly what they say. Yeah, um, this is not where we're heading at the moment, but um the the the risk of this catastrophic changes are getting way higher once you get across two degrees. Yeah, yeah, it's just a lot of like it doesn't mean that they immediately happen, but just the chances, like it's it's a very risky game that we're playing once we get beyond.
SPEAKER_01:Yeah, well, just getting back to the biosphere and biodiversity, um right now we're going through a uh the the biggest bleaching event for coral reefs around the planet ever. And um I've been spending time with ocean scientists looking at at that, and they are they are very, very distraught because there's real potential to have um catastrophic knock-on effects to the the food chains and uh food production out of the ocean. So it could be it could be like a famine, an ocean famine situation, if you like. Um that plus you know acidification effects more broadly with fish. So that would be an example to me of a um a tip over point which we're playing with fire around, which could be very, very disastrous, I guess.
SPEAKER_00:Certainly, yeah. Like the ecosystem, like we don't in this models, we don't model the ecosystem very well. Yeah, these are very complex interactions. So this is, I think, an area where uncertainties are very high. And again, if you have very high uncertainty, it's a very high risk. If you tip over, you want to stay away from the area where you tip over. Again, it's it's just a very risky game that you play. Um, because like we only have one planet. Yeah, and once once we cross these tipping points, yeah, the the consequences can be very dire.
SPEAKER_01:Yeah. Now, there's lots of things I want to get into. Um just briefly, just we talked about this transition to AI. Can we just talk about that in modeling? And then I want to come back to um yeah, some of the tipping points and some of the forecasts a little bit, some of the projections, and as they look today, we'll probably scare ourselves a little bit there. But um, yeah, so more recently we've used machine learning in these models and started to apply it. Has it become quite dominant quite quickly? Is that the subtext I'm hearing?
SPEAKER_00:Yeah, so I think this is the the biggest revolution in specifically weather forecasting since this 1940-50 period where we got computers and we were able to s to solve these equations numerically on the computers. And the like the approach that we use nowadays is basically still still the same as in the early 50s. Yeah, um, and then AI-based models came on board, and like this was around 2023, and Google was the first one with this GraphCast model. Yeah, and they are fundamentally different, like there's no real physics in those models. It's basically it's trained on historic weather data, and then you use this training to predict the future. And the mind-blowing thing is this works better than the physical models for weather forecasting to some extent, like not often not for extreme events. Okay, like it's it's again like you train with the AI in general, you need massive amounts of data to train. Yeah, and then you want to have a big sample size of the things that you want to simulate, predict. Extreme events are per definition rare events, yeah. So you don't have a lot of training there. And again, like if you go into these extreme situations, these models can often fail. And that's one of the big issues nowadays. Like I know like our Swiss um weather forecasting office, Meteor Swiss, yeah, like all weather forecasting offices that I know, they are really trying to not only catch up but lead this AI development of AI models. Yeah, um because there's a really big potential, but they still, I think there's still a trust issue. So we we still run physical models, and we will do this into the future. Because if you have a big extreme event coming and you have to evacuate, you really want to trust your model. And with the AI models, my sense is you we're not there yet. We cannot really fully trust them. And it's specifically an issue if you think about climate change, because we're pushing the weather and climate system into unknown territory. Yeah, like historic data is less useful in 10 years because we're in a different regime. Yeah, so as we increase temperature and the climate is changing, this historic data gets less and less relevant.
SPEAKER_01:Absolutely. Uh I there's a couple of discussions recently that maybe give some clues as to where we go with that. I don't know if that fits with what you're thinking, but um one is you know, because uh every AI is trained once, and then it can be tuned a little bit, but that's it, you know, and then you have to retrain it on a new data set if you want it to be better or a bigger data set. Um but I recently had a conversation with the Professor Richard Sutton, he won the Turing Award this year in AI, and he's basically introducing plasticity to AI models so they can keep learning. Uh yeah, so um and weather was one of the things we talked about uh because clearly the weather's changing, the climate is changing, so a weather forecasting AI trained on last year's data will be useless or or less and less accurate over time. It has to be in the current form. But his idea was you keep feeding it new data every day. Yeah. And it's just you know, like we learn, that sort of same bioplasticity that we have, he thinks we can get that going in another few years in in AR. So that should help, I would.
SPEAKER_00:Like it depends. Like again, like if you think about weather forecasting, yes. Because the forecasting is on the horizon of a couple of days, weeks, maybe months.
SPEAKER_01:Exactly. Yeah.
SPEAKER_00:If you think about climate projections where you want to simulate something at the end of the century at plus four degrees Celsius, like the historic again, like you really have to learn how the weather looks like in this situation. And people like there are models that can do that, but they learn from this physical-based models. So it seems like they like you anchor your AI-based solution on the solution that you get from this numerical models that we are using already, yeah, which is ultimately also a limitation. Like the big benefit of AI models, as far as I can see it now, is they they're way cheaper. Like it's like we run on the biggest supercomputers. You can run these AI models on your laptop, and they give you extremely accurate forecasts.
unknown:Interesting.
SPEAKER_00:But um, again, the training is is a big effort, and then the gener, like how how well they generalize. This is basically how well they can predict states that they haven't seen in the training. This this is really a major concern. And one thing I also wanted to mention is like a big for me, like I'm looking at high-resolution modeling, and some of the motivations that we have there is we really want to look at local scale extremes like flooding, heavy thunderstorms, hail, yeah, um, orographic influences, like topography and flows. And the nice thing with physical models, if you increase the grid spacing, so if you go to higher and higher resolution, new phenomena emerge. Okay. For example, like we have a simulation of a tornado, and nobody told the model that there should be a tornado under this thunderstorm. It it basically simulated it because it's in the solution of the physics. It's a physical-based model. If you allow it to go to high enough resolution, it will basically depict a tornado. And the AI model would never do that.
SPEAKER_01:Interesting.
SPEAKER_00:At least not the ones that we have nowadays. So you have to teach them this is what a tropical cyclone looks like, this is what an extratropical cyclone looks like, this is what a thunderstorm looks like, and then it knows, but it's not really able to discover these things. Not really emergence a physical-based model would have.
SPEAKER_01:So maybe the future is um a hybrid, I guess, of very, very powerful models and algorithms supplemented by little bits of machine learning at the edges or in local conditions, or like this is already there.
SPEAKER_00:I think that the future will be very broad.
SPEAKER_02:Yeah.
SPEAKER_00:Like the physic I don't see that the physical-based models are going away, especially for research. Um, and there will be purely AI-based models at the same time, and then there will be this interface, like this hybrid models. And this is already happening, so we see this already. Um, but like I think then this is I got this question often. Like when I present at conferences, are physical-based models over and AI will take over? And for me, this is really the wrong question. Like, these are really complementary tools. Got it. Like we have a new tool in our toolbox now, and we should use both tools to our best ability to better predict the weather or what the climate will be in a hundred or two hundred years or even ten years.
SPEAKER_01:Yeah, that makes sense. And you've really helped me understand why there are limits to what AI does. I mean, it learns by example. Yeah. So there's a limits, an inherent limiting factor there. If you're not getting into the systems and the systemic and the physics of the interconnections, then you can't use it to predict the outlying events and the things that matter to us. Like, yeah.
SPEAKER_00:You know, like I'm I'm I'm not an expert in this AI area, but that there are debates on if these models learned the physics or not. And it seems, in some way, it seems like they did. But it feels like it. It feels like it, but you know, like they're of course it wouldn't be able to write down the Navy-Stokes equations at the end. Uh, but there's some like again, like this this field is developing extremely fast. So it's really hard to know what what will happen in the next two or three or ten years.
SPEAKER_01:I agree. And there's there's yeah, I come across all sorts of people who are doing new forms of AI or working on old forms of AI, trying to make them work, still, symbolic learning, other sort of pathways. I would not rule out um AI modules that that are mathematical and purely physical and so forth, and combine with learn by example. And yeah, I would not rule out for the future. I agree, I agree. It's an interesting one. So let's get into um there's two things I want to talk to you about. I don't know what we handle first. One is how we get into the attribution for extreme events. And these are the tornadoes, the hurricanes, the cyclones, um, flooding, things that really matter, people care about today. We've got great data showing they're increasing dramatically because of climate change. But attribution is sort of A part of that. And I'd also like to get into some of the I guess what the models are telling us broadly about where we're going in the next 10 or 20 years. What makes sense to tackle first?
SPEAKER_00:We we can start with the attribution, certainly. Okay. I think that there are two types of attribution. The first one, and this is also in this IPCC report, you try to understand what greenhouse gas emissions and other sorts of change, land surface changes, did to our historic climate. And of course, the problem is that we cannot roll back time and just repeat history without interfering with the with the natural system. So you do this in the model. So you basically don't change CO2 emissions, you don't change the land surface, and you rerun everything, and then you compare. This is basically attribution is always you have to have a counterfactual. So these there is an effect of greenhouse gas emissions on the atmosphere, and then you remove this effect, and then you produce a counterfactual, but this never happened.
SPEAKER_02:Yeah.
SPEAKER_00:And then you compare the two, and then you can basically attribute like how much did greenhouse gas impact global temperatures. Yeah. And this is this is done since a long time already. Um, and like good stuff. There's in in scientific communities, no debate. Like greenhouse gas human greenhouse gas emissions increase temperature and change the climate. Yeah. Um, and then the second one is is more this extreme event attribution, where you, for example, Valencia, um, you have this massive event, and we we just published a paper on that, and then you try to understand how did climate change impact. Yeah, and there are two things the intensity of the event, like how much more did it rain, yeah, and then the frequency. Because how it could also be how much more often or how how more likely is this event nowadays? And it depends on which type of extreme you look at. It's it's more or less challenging to do this attribution. Everything that's related to thermodynamics, to the temperature change, is easier because it's simpler physics. For example, heat waves. We have quite a good understanding. Heat waves have intensified in frequency but also in intensity uh over the last 50 hundred years. And they're like all attribution studies would tell you the same. Uh, once you get to more like smaller scale processes like thunderstorms, where there's really an interplay, like we talked about, this 7% increase per degree Kelvin, like you have more moisture. This is more, this is again anchoring this in thermodynamics because it's it's related to temperature change. But then there's also like this thunderstorm or this like this in Valencia, for example, this was a very cold uh extratropical cyclone. Um, how more often do those cyclones develop nowadays? That that's a very difficult question. Like it's more in the dynamics of the system. Like, how often do we get these cyclones? And if we have these cyclones and they have more moisture, how do the thunderstorms react? Because then you have this interplay between the moisture gets sucked into this cyclone, but then you have thunderstorms that lift the moisture and release a lot of water, but also the moisture, the condensation in these storms, they feed the intensity of the storm. So there's very complex interaction. Sure is. So um that's why like heat waves, cold waves, we have pretty good understanding and can attribute those very well. Extreme rainfall, like tropical cyclones are easier and extra tropical cyclones than small-scale thunderstorms. You talked about tornadoes, that's almost impossible. Yeah, too small and too small. We cannot simulate them um in the models, not at least not in the weather models. Yeah, um, and there, our understanding, or even hail, like I work on hail a lot, like larger hail. Um, we made some progress, but even there, it's extreme. Like if you have a big hail storm hitting Sydney, here you had hail, yeah, um, or here in Zurich. Um it's very it's it's extremely difficult to make this attribution statement.
SPEAKER_01:So um the Valencia example is a good one because I just I think I left Valencia two days before that hit, and uh I think a couple hundred people died or something in the Germany. It was unbelievable.
SPEAKER_00:That was such a huge event. But like just to tip on that, like um often it's not the forecast. I don't know if you saw, did you were you aware that there is no no, there was no forecast of but like actually that like the forecast was not bad. Okay. And it the same, like we had a big event in Germany, for example, 2021, many people are are dying. Flooding. The flooding, yeah. Many people died. The forecast was actually quite good. It's the warning often afterwards. Uh-huh. Like where you say, like now you know that something is happening. How do you get people out of this region? When do you start to warn them? When do you start to evacuate? In Valencia, it was way too late. Like, often like the people were locked in their houses, the water was already halfway up the door, and then they got the warning to evacuate, and then of course it's too late. Right. And we're gonna all have to get a lot better at that around the planet because there's more of it coming. So, yeah, this is often the problem. Like this, like there are enough historic examples where like some big event happened, and you can only hope that you learn out of those. Yeah, this is quite proactive. Like, I I would really hope that we got into a more proactive approach rather than reactive. So something big happens, but at least then you have a chance to learn and change. And often this warning change chains are much better thought through afterwards, and when the next event happens, like we have examples for that. There are way less fatalities and things are handled better.
SPEAKER_01:Interesting. So when we talk about attribution for um, so an extreme a huge cyclone or tropical cyclone hits somewhere, and the question hits the news media, you know, how much is it, you know, it's a classic thing, it happens every time. And then uh someone says, well, it's highly likely to be uh climate change enhanced. Um, you know, what's the sensible way of talking about it? Statistically, I guess these storms are X percent more likely to be bigger, or or in general, they're this much bigger now than they were 10 years ago. Is that the most sensible way of talking about it?
SPEAKER_00:Yeah, it depends a little bit on the event. Like it's really event from event to event, it can be different. Um with tropical cyclones, it's the extreme rainfall is often easier to attribute because it's linked to this clauses cloudron relationship.
SPEAKER_01:So, how would you attribute in in what sort of language would be the right type of language to use when you're talking about attribution of rainfall like that?
SPEAKER_00:Like I like first when I talk about attribution, I always stress that every weather went that we have nowadays is already affected by climate change because we altered the base state. Like we are not in the same climate as we were in 1850 before we started the industrial revolution. Uh so that that's a fact, and there's enough studies on that. And then the question is like, how different are these storms now? And like I would never do an attribution statement without backing it up with science. Like this of course, but they're different approaches. And for example, with tropical cyclones, what we use nowadays quite often is it's basically to use a weather forecasting model. So you use the same model that produced the forecast, but then you basically warm up the atmosphere in the model or you cool it down. So if you want to know how much more intense the cyclone is compared to 1850, now we have something like 1.3, 1.2 degrees warmer on average global. So you you cool the atmosphere down, you run the forecast again, you look at the precipitation, you do this a couple of times to just know how certain it is, and then you have you can make an attribution. You can say, like now 50% higher rainfall intensities or more volume. So you get this out of the model. You do the same thing with the future, you can do like three degrees warming, and how would this look like? Yeah, and people are doing this, and I think that's a good approach. Yeah, it doesn't really help you with the frequency part. So you this is not what like how likely or more likely was this cyclone to have like to form. It's more if it if it's there, like how much more uh rainfall comes out of it. So, like again, and you can also see this. Like, if there are big events, there are always multiple attribution statements are published, and they sometimes vary widely in how like how the attribute, how much 50% more, it's three times more likely. Um, but often with these events, at least like extreme rainfall, heat, it's all in the same direction. Like climate change intensified these events.
SPEAKER_01:You know, can I check uh share a couple of data points with you that I they shocked me when uh I had the conversation and they were financial data points. So they were just a different angle on it, and uh it resonates really well with uh my US audiences, because they get the politicization of climate change, but they kind of often relate more directly to insurance and pricing and the costs. And uh this came from Swiss Re, the reinsurer. Yeah, they're just over here. Um and and uh it blew me away. But they they said over 40 years, so it's 1983 to 2022, so about 40 years, the increase of frequency for catastrophic weather events for them now that's uh level two. So everything uh or catastrophic insurance events really, but it's mainly weather because uh earthquakes were excluded from this. So it's you know cyclones, storms, floods. The increase in frequency is 457 percent. Uh yeah. For what they catastro uh class as catastrophic. So that gives us some financial uh um uh measure anyway, a different type of look at it. But then the increase in intensity, which they measure financially, so there's some caveats um for the same period, 1,500 percent. Now that's uh a good half of that is because more people moving to cities on floodplains, and there's other reasons, you know, the claims are getting bigger. But they're shocking. That's to me, that's it brings it home, I think, makes it real to a lot of people in a different way.
SPEAKER_00:Yeah, yeah, and it's also of course done for politics. Like in I lived in the US for a long time, and of course you have these emergency funds, and like you have to release emergency help m more and more often. Yeah, and it's it's for all kinds of extreme events, like it's uh tropical cyclones. Like I worked at uh on wildfires, for example, we had catastrophic like you had them as well in Australia, but also in in California specifically, and like some of these places they cannot get insurance anymore, like even in Boulder, Colorado, where I live, like insurance almost doubled after we had a big fire went in 2021 or two. Yeah.
SPEAKER_02:Yeah.
SPEAKER_00:So these places become uninsurable in in some way. And then, of course, if you cannot insure them anymore from a private side, the state often has to bail in. And it's it's becoming really expensive.
SPEAKER_01:It's it's almost an it's a financial feedback loop, yeah, another reinforcing effect which is which is uh hurting. Um yeah, really interesting. Can we now look at maybe just some of the forecasts, just looking forward? How we I'm just gonna do a quick time check and make sure I'm not taking too much of your fine. Um as we look at what your models are suggesting, 10 years from now, I don't know what's the most useful, 2050. Um, can you give us a sense of whether there's any? I mean, it I've my conclusion so far, so you can correct me please, um, but I feel like there's no chance of remaining under two degrees, for example, planetary warming. Um there is a chance of g remaining under three degrees, but it's uh slipping away. That's sort of my my view. Um I'd love to hear your views of what the models are saying, and then also what they're saying about frequency of uh yeah catastrophic weather events as well.
SPEAKER_00:Yeah, so this emission scenarios are really they are developed outside of our models. Like this is basically like you you place through. That's another model. That's another model, yeah. And then basically you develop these scenarios, this plausible futures, and then you plug those scenarios into this physical models, and then you run them. So this is basically what what you do. And I agree with you, like staying on under two is is is probably not gonna happen. So what what what we are preparing now at the moment for is what we call an overshoot. So since we like the Paris Agreement will likely not be met. So what we will likely happen is that we go beyond two degrees for a certain amount of time, but the hope is that we can bring the temperature back down as soon as possible. So to basically go into this danger zone for a short period of time, but then you cool down the planet again with technology, and like it's it's again like this is will be extremely challenging. It would be way easier not to overshoot.
SPEAKER_01:Uh way easier not to do it in the first place.
SPEAKER_00:Yeah. Yeah, yeah. And then also like much cheaper, and then also how realistic this overshoot scenarios are. It's like this is based on technology that we don't have. Like you know, like it's it's again, it's a risky experiment that we're running. Yeah, um, like in my research and many of my colleagues' research, we we are now looking more and more at something like a three-degree warming globally as a realistic future pathway. Um, and once you look at that, like changes are dramatic. Like you can see this already. What we had 1.2, 1.3 global warming now, and you can already feel like it for me, it was interesting. The last 10 years were really a change where you now you really live through climate change. Yeah, like you see it, like this extreme events that we get over and over again. Um, they're just out of what we have experienced in the past.
SPEAKER_01:And I can tell you, I travel all over the world, and every country I go to, you can see it.
SPEAKER_00:Yeah, yeah. So yeah, everybody is affected by that. Um, and it's it's really like climate change is happening now. And now imagine we had 1.3, and now you add 1.7. Um, so the consequences will be quite dire um in every kind of aspect, like extreme heat, uh certainly, but also flooding, um the downstream effects on droughts and food production and so on. Like, and of course, this has rippling effects of on society and migration and all of that, like that we have very little understanding. So yeah, like we we are in the middle of it, and it looks like we will continue for quite a while.
SPEAKER_01:And it seems like, for example, the numbers just then on uh frequency and intensity of storm events, it seems like we're in for at least another doubling of that, you know, just it's at a minimum.
SPEAKER_00:Um, so yeah, it's yeah, the problem is really also like like, for example, we we the infrastructure that we have, uh drainage systems, but also like dams, power plants, they are built to withstand historic events. And often like they are basically really planned with observed data that we collected over the last hundred years. And as we talked already with AI, like this data gets less and less useful. And the more you warm, the closer you like the higher the extremes get, and the closer you get to a chance that these infrastructure fails, yeah, and it's aging as well. And then like we have a lot of discussions with city planners, for example, what should we prepare for? Like, of course, like sit like coastal cities like Sydney are especially vulnerable because you also have sea level rise. So now you have to invest a lot of money to protect the infrastructure that you have and to make it resilient. Because if you build and use a flood wall, for example, like this will be there for the next hundred years, and yeah. But what will the weather in a hundred years look like? And that's highly uncertain.
SPEAKER_01:Yeah, you know, Sydney is a good example because right now lots of controversy very close to my home base where I live. Um, when I'm at home, there's um seawalls being built where there used to be beaches that have to be built to preserve the properties. There's huge controversy because the erosion is moving to different areas now because of the seawalls. It's just the start. And the other one recently uh uh we spent some time in the Maldives looking at sea level rise there. And um, it's easy to say, oh, you know, it's only half a meter or whatever. Then you look at the capital, Male, in in the Maldives, and there's an entire city built about one meter above sea level, you know, and it's it's a city, you can't elevate it, you can't you can start building walls, but then you've got groundwater issues, and uh it's unbelievable.
SPEAKER_00:You know, it's catastrophic for them. Yeah, especially for more uh global south countries, like yeah, like often in the Western Hemisphere we have the financial resources to protect ourselves, and we are basically responsible for the change mostly. Yeah, and then the people that suffer are really in this more vulnerable areas that don't have the resilience that we have. Bangladesh doesn't have the same resources, yeah. Exactly. Like I talked, like I work with people in the Philippines, and like same thing, like that this uh the cyclones that pass over the Philippines all the time. It's it's it's crazy. Um it's really extremely difficult to recover for them as well. Like if they get hit, um they're already like the financial system is already quite thinly spread, and then you have to recover, and this this can go into a downward spiral, certainly. Yeah.
SPEAKER_01:So one of the tipping point ones I wanted to just just pull aside and talk to you about um was ocean currents because it's my understanding that they're almost one of the most complex things to model and they do change, and right now we're seeing the Atlantic conveyor potentially reversing or changing dramatically. Um or the amok, I haven't got the ammo, yeah, yeah, yeah. The terminology there, but the the potential change for local climate is massive when the current changes. So if the current changes off Spain, the climate of Spain will change. If the current changes on the east coast of Australia, the climate, sorry, if the current changes going down the north-south down the east coast of Australia, the local climate in Sydney and Melbourne and uh Hobart and Brisbane all change. Um and potentially quite dramatically. I just wanted your take on that. Is is it something we can model accurately or does it define modelling?
SPEAKER_00:Um I'm I'm again like just like I'm not an ocean modeler, but like I follow this area. It interacts with each other. Oh, certainly, yeah. And that one thing we know is that this can happen. Like we see this in paleoclimate data, and it it happened in the past. Right, okay. So we know that climate uh currents can change dramatically. It can change dramatically, and the impacts on Europe, for example, when the um AMORP is collapsing, are quite dramatic and quite abrupt. Like just a massive cooling. Um if this will happen is still up to the debate. Like it's at least this is what my take on this field is. Um depending on who you talk to, we're close to a collapse, or we are not as close as we think. The models that we have can do that actually, yeah, but this often happens quite late. Like you really have to have a lot of warming, and then in 2200, 2300, you get a collapse. So you can simulate this in models, but it's it's quite slow. But the question is is this realistically simulated in the model or not?
SPEAKER_02:Yeah.
SPEAKER_00:Um, and again, like there is a lot of active research going on there. But if this happens, and there was a recent study, um like it basically would offset climate change in Europe. Yes, it'd be colder here. It would be colder, and you you would get closer from the temperature side to pre-industrial um situation. So actually, yeah, it's and then but someone else is gonna suffer. That's the problem, that the heat goes somewhere else, and then we actually don't know what this would do to storm tracks or to extreme rainfall because there's still a lot of warm air around Europe, and sometimes, and this is also what happens in very extreme events, you get this warm air moved over Europe, attracted over Europe. And when this happened, like it doesn't help you that you're on average colder. It means basically, and this is basically a broadening of the distribution. So you can get extremely hot but extremely quite cold as well. Yeah, um, so it would be very different climate to what we experienced pre-industrial, and again, like we don't actually know what this would mean for food supplies for like yeah, for extreme events and so on.
SPEAKER_01:And again, it's the speed of change, which is the potential disaster. How do we it's all about accommodating that change and what infrastructure, crops, food supply, all that stuff has changed dramatically.
SPEAKER_00:But the AMOC already slowed down, so we know that from observations, and also the models show that. What we also know from observations is that it slows down for a long time and then it suddenly gets way lower. So it never shuts down completely. Like the shutdown is it's bad terminology because there's it's just operating on a way lower level. I see. Yeah, but this this transition goes quite fast, and this is what we this is the tipping point that we are concerned about.
SPEAKER_01:I'm struggling to remember the so it's Atlantic, what's the M? Meridian? No, what's it meridianal overturning circulation, yeah. Okay, yeah, there you go. Um, and basically that's taking uh warmer water north. I'm trying to remember. Yeah, north, yeah, yeah.
SPEAKER_00:North, and then the cooler cooler goes south. That's the reason why the UK or Scandinavia is so much warmer than the east coast of the US at the same latitudes.
SPEAKER_02:Yeah.
SPEAKER_00:Like it's this transport of very warm air to those regions. So it's really from Central Europe northwards, this would be the regions that are mostly impacted by collapse.
SPEAKER_01:So where do we go? I know some of your work. I'd like to just capture a little bit of your future research sort of direction because I know some of your work is about multi-level modeling or hierarchies, yeah. Yeah, what's all that about?
SPEAKER_00:Yeah, so like for as a scientist, we I'm really interested in the fundamental processes. Like I'm a trained physicist, and I want to understand like if I see climate change, and for example, a tropical cyclone gets way more intense in the model, I want to understand why. That's also the power of physical models because you can look at the equations, you can do uh more um targeted experiments where you basically try to control some of the changes, and then you get a better understanding what's driving what. Um, and you often do this if you really focus on single processes, you do this in quite simple models where you can control, like you basically you turn off, for example, the condensational heating. So if you if you condensate moisture, you release a lot of heating, like a lot of heat. And you can just turn this off in the model and see what what would happen. And if you do this in an idealized model, you can basically go into the equations and find out like physically, this is what happens, and this is the approach often. So we do this and then we look at more complex models and see if this is the same thing that we can find there. And so this is basically how we gain trust. Um, and also my models are simpler models than that the models that I use often are simpler models than what is used in the IPCC reports. Okay, like my group, we use really more weather forecasting models that are very high resolution but often not coupled to the ocean or have not very sophisticated carbon cycle and things like that. But they can really do thunderstorms very well, for example, or very local scale extremes. And like the the goal for my research, we're building these models now up to get more and more like an earth system model. So at the moment, we're actually coupling the high-resolution atmosphere model to a high-resolution ocean model, trying to get biochemistry and to see like the carbon cycle in the ocean, but also the food cycle in the ocean to simulate that at very high resolution. And for me, like the the primary motivation to do that is really to provide local scale climate change information um globally.
SPEAKER_01:This would be the so if I'm uh a policymaker or a farmer or a resident or an industry person in um uh Barcelona or in uh Colorado or whatever, I should be able to come to that model cluster of models and ask specific questions about what might happen in my area. Is that what that means?
SPEAKER_00:Uh yeah, like a little bit like that. Like a classic example, I worked with the Nuclear Regulatory Commission in the US. And they are very concerned about flooding of nuclear power plants. Okay. We actually got quite close a couple of times. Um, and of course, climate change is making extremes more um extreme, so there's a real concern there. Yeah, and um for example, we work with them using high-resolution model to provide additional data that they can use in their resilience planning. Yeah, like this is a typical example. We will work a lot with insurance companies, for example, as well, that they have a better understanding of risks, but also with city planners, for example, especially if you think about infrastructure. But of course, it's also true, like the farmer on the ground, um, to understand like how how extreme can a drought become in this region, or how much more likely are hail storms that ruin your grape harvest, for example.
SPEAKER_01:But it goes it goes forever industrially. I was just talking to people building data centers, and their key issue is not only electricity requirements now, this huge exponential increase in electricity required for AI in data centers, but water. Yeah. So they're all interested in water availability and forecasting that at a local level.
SPEAKER_00:Yeah, yeah. Uh yeah. Yeah, water. Like I worked a lot with um the Bureau of Reclamation in the US, and they they are operating all these dams at Lake Mead, um, the the Hoover Dam or like all these massive reservoirs that they have in the Western US. And this is like, of course, like this is a major concern. Yeah. Like if a region runs out of water, like you are you're in a really big trouble. Again, like western states often can cope better. Um, but yeah, water availability is and and there, for example, in the Western US, it's all about it's mostly about how much snow there is in the Rocky Mountains. And again, that this is changing a lot as well. Like because like the snow that the snow line is going up because it's warming and they get less and less snow and more and more rainfall, it's running off more and more quickly. And there are like really good studies that show every degree of warming contributes. I think it was 14% less runoff in the Colorado River.
SPEAKER_01:Wow.
SPEAKER_00:Yeah, yeah. So quite stark changes. And that's that's an example of a very localized um uh insight. Yeah, yeah, you you you really have to simulate snow then in these mountains, and like again, like them this the models that I'm using can do this quite quite well. So it really opens up a complete new area of research where you can look at very small-scale processes like how snow accumulates during during the the winter season and how this changes into the future, um, but also rain and snow transition. So, yeah, yeah.
SPEAKER_01:So can governments and industry come and uh work with you, consult with you on a uh project basis with the center here?
SPEAKER_00:Certainly, yeah, yeah. Like we we work a lot with industry. Um, but of course, like we also have research grants. So this is if you have a research grant, you basically work for the government. I would say possible. We try to do everything that we do open. There it's public. So, and but it's it's there, it's really important if you really work with stakeholders to get them involved as early as possible. Yeah, like in a city planner, for example, you you really don't want to just do your research, and at the end of your study you say, This is the data, use it or do whatever you want. Uh that that's really a lost opportunity. So often you get them involved very early on, you team up with them, uh, you talk to them, what their interests are, you get to a mutual understanding of what you can do and what they need, and then the the outcomes that you can deliver are way more impactful. And of course, it's often a trust relationship as well.
SPEAKER_01:So, um, is there anything else about the center that you wish more people would would know about that we haven't covered and your and your research?
SPEAKER_00:Um, like I think what I what I would hope, I don't know, like or what I often try to convey is like this this models that we the modeling capabilities that we have nowadays are really stunning. And I think we we can put more trust in these models nowadays. Then many people are still very skeptical. And right list, like there's good reasons for that, but like we really have and it's it's a new regime of modeling that we do that can provide very localized data. Like, for example, extreme rainfall is a good example. Often people working in the hydrologic community are very skeptical against model data, and they they rather use some observations and modify the observations for the future than using something that comes out of the model.
SPEAKER_01:Ah, interesting.
SPEAKER_00:I think we we can do much more and much better than that nowadays. But it's it's it's again like a trust issue, like a communication issue. But I think there's huge potential, um, especially when you think about resilience and to improve our resilience to extreme events with these new types of models.
SPEAKER_01:So if there was one thing that you could have leaders do, you know, to make this sort of uh make the future or create a better future, uh, what would you pick? Leaders, yeah. Yeah, I don't know. We are just just uh on the back of our conversation.
SPEAKER_00:Like I I I really like I'm more what I can do rather than leaders can do. Okay, four. Yeah, but um so I what's what's the biggest thing? What's really concerning me is if I I work a lot with students, young people.
SPEAKER_02:Yeah.
SPEAKER_00:And when I was in my teens and twenties, I always had this vision of the future. The future will be better than the past and the current. This seems to erode nowadays. It seems like young generation have they have really um a crisis of perspective. Or and when I talk like it's really shocking to me when I talk to them, like everything in the future will be better worse, like the good times are over. They've given up. That's it's that's you know it's heartbreaking because it's really like this is self-fulfill fulfilling prophecy. If if enough people think that we go there, and and it's really like often this hopelessness is is really bad. So what I try to during my lectures are talking, working with young people, empowering them. Like be part of the solution. Do you have something to give, you have something to a role to play, don't give up, look into the future, see the bright future you want to have and work on it. And I I strongly believe in that. Like if if enough people think like that and we work together, we can we can achieve that. For sure. Yeah. And it really starts at a very fundamental level.
SPEAKER_01:Yeah, yeah, I like that very much. And you know, before we started recording, we were talking about how well you were saying, you know, we have all the information we need. We don't need any more information really um to fix this. Uh we have enough data and and we have the technologies, you know, to fix most of it already. So it's just a matter of acting on it.
SPEAKER_00:It's very true. Yeah, like my take on that is adding more information has a marginal, leads to marginal improvements nowadays. Like we we know what to do, we know where we're heading, we know it's not a good place. It's not really to take action and act on it. Yeah.
SPEAKER_01:Professor Andreas Prine, thank you so much for spending so much time with us this morning. It's wonderful.
SPEAKER_00:Thank you, Bruce. This was a pleasure.
