Modeling Our Climate Future – with Andreas Prein

WEBVTT
00:00:05.759 --> 00:00:11.039
Welcome to FutureBytes, where we explore pathways to a better future.
00:00:11.359 --> 00:00:24.320
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.
00:00:24.719 --> 00:00:25.679
Welcome to the podcast.
00:00:25.920 --> 00:00:28.160
Yeah, thank you very much for having me, Bruce.
00:00:28.719 --> 00:00:31.359
It's absolutely my privilege to talk to you today.
00:00:31.440 --> 00:00:39.200
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.
00:00:39.280 --> 00:00:52.000
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.
00:00:52.159 --> 00:00:52.640
Is that right?
00:00:52.880 --> 00:00:54.399
That's exactly right, yes.
00:00:55.039 --> 00:01:01.679
You've only taken on one of the most complex and difficult areas, I would think, in modeling in the world.
00:01:01.840 --> 00:01:02.719
It is complex, right?
00:01:03.200 --> 00:01:04.480
It's extremely complex, yeah.
00:01:04.560 --> 00:01:09.359
The natural modeling the natural natural system is very difficult, yeah.
00:01:09.680 --> 00:01:11.439
So how let's just start with you.
00:01:11.599 --> 00:01:13.920
How did you get to be doing this sort of work?
00:01:14.000 --> 00:01:14.799
What drove you here?
00:01:14.879 --> 00:01:16.000
What was the inspiration?
00:01:16.159 --> 00:01:17.519
Or maybe who was the inspiration?
00:01:17.680 --> 00:01:19.439
Was there something that led you down this path?
00:01:19.760 --> 00:01:23.920
You know, uh I grew up in Austria on a dairy farm.
00:01:24.159 --> 00:01:43.280
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.
00:01:43.359 --> 00:01:43.519
Yeah.
00:01:43.680 --> 00:01:45.280
So in my father, I can remember that.
00:01:45.359 --> 00:01:49.040
Like we always this was always a gamble, and that that was really sticking with me.
00:01:49.120 --> 00:01:51.200
Like this, also your livelihood is depending on that.
00:01:51.280 --> 00:01:53.280
Like you have cows, you have to feed them in the winter.
00:01:53.359 --> 00:01:55.040
So this really influenced me.
00:01:55.120 --> 00:02:03.519
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.
00:02:03.920 --> 00:02:05.599
Ah, 2001.
00:02:05.760 --> 00:02:20.000
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.
00:02:20.159 --> 00:02:22.960
So this really sparked my interest in extreme events.
00:02:23.039 --> 00:02:31.439
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.
00:02:31.520 --> 00:02:34.879
So this drove me into physics and climate research at the end.
00:02:35.199 --> 00:02:35.840
That's really interesting.
00:02:35.919 --> 00:02:47.439
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?
00:02:47.759 --> 00:02:49.199
We did never talk about that.
00:02:49.280 --> 00:02:49.759
Okay, though.
00:02:49.919 --> 00:02:59.360
Like this was really 1990s, and like I can remember like other environmental topics like acid rain, yeah, like these kind of things.
00:02:59.520 --> 00:03:06.800
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.
00:03:06.960 --> 00:03:08.800
And I think this really changed.
00:03:08.960 --> 00:03:16.560
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.
00:03:16.639 --> 00:03:21.919
And yeah, um, yeah, then it just basically continued on this physics path afterwards.
00:03:22.240 --> 00:03:22.639
And important.
00:03:22.960 --> 00:03:29.599
I mean, not only at a local level, but uh to me, it's the greatest force of change of all.
00:03:29.919 --> 00:03:42.800
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.
00:03:42.879 --> 00:03:43.280
Is there?
00:03:43.360 --> 00:03:44.000
It changes everything.
00:03:45.360 --> 00:03:52.000
Just thinking about the energy, like you know, like a single thunderstorm has much more energy than any atomic bomb.
00:03:52.080 --> 00:04:04.879
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.
00:04:05.039 --> 00:04:15.360
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.
00:04:15.680 --> 00:04:16.560
How quickly we're gonna die.
00:04:16.800 --> 00:04:18.000
Because I often get that.
00:04:18.240 --> 00:04:25.120
I'm constantly uh in the US talking to people and encountering people who've been educated that climate change is isn't real.
00:04:25.360 --> 00:04:32.480
And one of the cherry-picked pieces of information they use is oh, but geologically the planets warmed and cooled before.
00:04:32.720 --> 00:04:37.120
And you sit there and go, but never this fast! Like never ever this fast.
00:04:37.360 --> 00:04:38.959
We have to adapt to this.
00:04:39.199 --> 00:04:41.199
Um, so yeah, nuts.
00:04:41.519 --> 00:04:43.199
So tell me about the modeling side.
00:04:43.360 --> 00:04:53.839
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.
00:04:54.480 --> 00:04:55.600
Um, the climate.
00:04:55.759 --> 00:05:06.319
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.
00:05:06.399 --> 00:05:08.319
Because to me it's endless.
00:05:08.480 --> 00:05:10.000
Um yeah, where do we start?
00:05:10.240 --> 00:05:10.720
Satellites?
00:05:10.879 --> 00:05:11.120
What?
00:05:11.439 --> 00:05:13.360
Yeah, no, no, I think much earlier than that.
00:05:13.519 --> 00:05:16.160
Like it's it's like the weather forecasting was always important.
00:05:16.240 --> 00:05:20.000
And this actually it started really getting serious with sailing.
00:05:20.079 --> 00:05:27.920
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.
00:05:28.000 --> 00:05:37.920
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.
00:05:38.160 --> 00:05:43.120
Um, and it was for a long time really difficult to forecast the weather.
00:05:43.199 --> 00:05:55.279
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.
00:05:55.360 --> 00:05:55.600
Yep.
00:05:55.839 --> 00:06:00.879
So we have the Navier-Stokes equations, these are the equations that we use, and these these are used everywhere.
00:06:00.959 --> 00:06:05.600
If if you do anything with fluids or gas, these are the equations that you look at.
00:06:05.839 --> 00:06:09.439
And if you're ever looking at any weather forecast, this is behind that forecast.
00:06:09.759 --> 00:06:12.639
Yeah, so like until 2023, true.
00:06:12.879 --> 00:06:14.480
But then AI came on the scene.
00:06:14.639 --> 00:06:15.519
And like we can talk about that.
00:06:15.759 --> 00:06:16.399
Yeah, we'll get to that.
00:06:16.560 --> 00:06:16.879
Interesting.
00:06:16.959 --> 00:06:17.759
So there's a transition, yeah.
00:06:18.000 --> 00:06:32.399
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.
00:06:32.480 --> 00:06:40.560
And his vision, this was in the 1920, was to build a really big forecast factory with something like 60,000 computers.
00:06:40.720 --> 00:06:46.639
And back then computers were humans, yeah, and they calculate and they try to beat the weather.
00:06:46.800 --> 00:06:52.000
Like you have to do a faster calculation than real time because you want to forecast, not Heincast.
00:06:52.319 --> 00:06:55.519
So you have to beat the weather or the real time.
00:06:56.000 --> 00:06:58.560
And he had this sorted out this whole thing.
00:06:58.720 --> 00:07:00.319
It never really took off, of course.
00:07:00.720 --> 00:07:01.519
I love the imagery though.
00:07:03.920 --> 00:07:09.360
Like graphics like artists painted this forecast factory, and Lucifer Richardson wrote a book about it.
00:07:09.439 --> 00:07:11.199
So it's it's really interesting to read about it.
00:07:11.279 --> 00:07:16.800
But this really was the foundation of numerical weather forecasting as we saw it later.
00:07:16.879 --> 00:07:22.959
And then actually, the next step was in during World War II, uh, the first computers came on the scene.
00:07:23.040 --> 00:07:26.240
And John von Neumann actually he was a student here at ETH.
00:07:26.399 --> 00:07:36.560
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.
00:07:36.800 --> 00:07:37.439
ANIAC, yes.
00:07:37.600 --> 00:07:38.000
Yeah, yeah, yeah.
00:07:38.079 --> 00:07:38.160
Yeah.
00:07:38.480 --> 00:07:39.680
And he was successful.
00:07:39.920 --> 00:07:47.120
So and he developed lots of these methodologies that you use to solve these equations on computers.
00:07:47.360 --> 00:07:54.240
And he immediately saw that there's a potential not only for explosions, but also for weather forecasting on computers.
00:07:54.480 --> 00:08:07.600
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.
00:08:08.560 --> 00:08:09.839
Roughly when would that have been?
00:08:10.240 --> 00:08:13.600
Oh, this was uh I think 50s, 1950s.
00:08:13.680 --> 00:08:14.560
Yeah, yeah, in Princeton.
00:08:15.120 --> 00:08:16.160
First computer weather forecast.
00:08:16.639 --> 00:08:23.519
But the point here is really like computational advancements and weather focused casting, climate modeling were really closely linked.
00:08:23.600 --> 00:08:23.920
Yep.
00:08:24.160 --> 00:08:25.120
And still are.
00:08:25.279 --> 00:08:29.360
Like we still run our climate models on the biggest computers that we have.
00:08:29.600 --> 00:08:30.240
Got it.
00:08:30.480 --> 00:08:38.480
And like Jules Chan, he's an interesting character because he he wrote a very famous report, the Charney report, to US government.
00:08:38.559 --> 00:08:40.240
This was, I think, also in the 50s.
00:08:40.399 --> 00:08:41.759
You can look it up afterwards.
00:08:42.159 --> 00:08:45.360
And he warned the government back then about climate change.
00:08:45.519 --> 00:08:45.759
Yeah.
00:08:46.000 --> 00:08:54.799
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?
00:08:54.960 --> 00:08:55.120
Yeah.
00:08:55.279 --> 00:08:58.559
And he got quite close to the estimate that we still have.
00:08:58.799 --> 00:08:59.120
Really?
00:08:59.279 --> 00:09:00.720
Yeah, yeah, it's it's fascinating.
00:09:00.879 --> 00:09:02.639
Like it didn't change a lot over the years.
00:09:02.879 --> 00:09:04.799
Our models, however, really changed a lot.
00:09:04.879 --> 00:09:06.960
Like they got way more sophisticated.
00:09:07.200 --> 00:09:10.080
He's just started out with a simple atmospheric model.
00:09:10.159 --> 00:09:15.600
Nowadays, if we simulate climate, of course, you have to simulate not only the atmosphere, but also the ocean.
00:09:15.759 --> 00:09:22.159
Yes, the ice, the water cycle, this water cycle, ecosphere, all of that, the interplay because they're feedbacks.
00:09:22.240 --> 00:09:22.399
Yes.
00:09:22.559 --> 00:09:23.600
These are very important.
00:09:23.840 --> 00:09:24.159
Yes.
00:09:24.559 --> 00:09:28.000
So nowadays, these are what we call Earth system models.
00:09:28.639 --> 00:09:34.320
And again, like still, that's why also my professorship is named weather and climate modeling.
00:09:34.480 --> 00:09:36.879
I really try to keep those two fields together.
00:09:37.039 --> 00:09:37.360
Got it.
00:09:37.519 --> 00:09:39.039
Because they're fundamentally linked.
00:09:39.120 --> 00:09:41.120
It's just different timescales that we're looking at.
00:09:41.279 --> 00:09:43.519
In weather forecast, you think about the next couple of weeks.
00:09:43.679 --> 00:09:47.279
In climate, you think about the next couple of decades or even millennia.
00:09:47.360 --> 00:09:47.679
Yeah.
00:09:48.240 --> 00:09:52.559
But yeah, still, it's it's still this the biggest computers that we need to solve.
00:09:52.879 --> 00:09:54.399
So are we just adding inputs?
00:09:54.480 --> 00:10:20.799
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.
00:10:20.960 --> 00:10:23.120
I think one degree, seven percent more water.
00:10:23.360 --> 00:10:25.279
Exactly, yeah, the closest clapper on relationship.
00:10:25.360 --> 00:10:27.840
Another ETH is that like a closer.
00:10:28.879 --> 00:10:31.759
I mean, it's such a remarkable statistic, just that one.
00:10:32.159 --> 00:10:40.320
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.
00:10:40.399 --> 00:10:41.200
It's one of the things, right?
00:10:41.279 --> 00:10:42.159
More energy and more water.
00:10:42.320 --> 00:10:43.759
Yeah, many extremes are linked to that.
00:10:43.840 --> 00:10:43.919
Yeah.
00:10:44.240 --> 00:10:44.720
Isn't that it?
00:10:44.799 --> 00:10:45.919
Yeah, and that came from here.
00:10:49.840 --> 00:10:51.360
So, but you can see where I'm going.
00:10:51.440 --> 00:10:52.720
Like is that what we've done?
00:10:52.799 --> 00:10:57.120
We've started just just adding more complexity or expanding the model.
00:10:57.200 --> 00:10:58.399
Is that what happened over time?
00:10:58.480 --> 00:11:04.080
We've just added more inputs, and then we account for re-radiation, reflectivity.
00:11:04.399 --> 00:11:06.320
Um, you know, it keeps going.
00:11:06.720 --> 00:11:13.360
Yeah, so we we definitely made the models more complex and more complete, as we also gained understanding of the climate system.
00:11:13.440 --> 00:11:16.480
Like at the beginning, like there was very little understanding, actually.
00:11:16.559 --> 00:11:21.840
Yeah, and nowadays we have much better understanding, still not far from complete.
00:11:22.000 --> 00:11:26.639
Um, but for example, how much energy, like most of the energy is stored in the ocean, not in the atmosphere.
00:11:26.720 --> 00:11:28.240
So you really have to capture that.
00:11:28.320 --> 00:11:34.879
And like ocean, the turnover, the circulation in the ocean is on time scales of many thousand years.
00:11:35.039 --> 00:11:42.480
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.
00:11:42.559 --> 00:11:45.200
Uh so now you have to couple all these things together.
00:11:45.919 --> 00:11:55.200
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.
00:11:55.279 --> 00:11:57.679
Like this is now modeled, nitrogen cycle.
00:11:57.759 --> 00:12:02.159
So these these all these things they come in and this the systems become more and more complex.
00:12:02.240 --> 00:12:11.679
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.
00:12:12.000 --> 00:12:23.120
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.
00:12:23.360 --> 00:12:24.240
Yeah, because it's all linked.
00:12:24.320 --> 00:12:24.720
It's all linked.
00:12:24.879 --> 00:12:26.960
So the Amazon rainforest is a good example there.
00:12:27.039 --> 00:12:31.519
Like we see, or like this is deforestration driven to a large extent, but also climate change driven.
00:12:31.600 --> 00:12:31.759
Yeah.
00:12:31.919 --> 00:12:36.879
And the fear there is really that we are close to a tipping point where the rainforest will die off.
00:12:36.960 --> 00:12:43.279
Because if you cut off down all the trees, like the rainforest to a large extent creates its own rainfall.
00:12:43.840 --> 00:12:52.879
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.
00:12:53.200 --> 00:13:00.960
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.
00:13:01.600 --> 00:13:05.840
Um, so this you have to simulate that, of course, if you're serious about climate change.
00:13:05.919 --> 00:13:07.200
But this is extremely challenging.
00:13:07.519 --> 00:13:08.720
It's extremely challenging.
00:13:09.039 --> 00:13:10.960
I don't know how you even begin to do.
00:13:11.279 --> 00:13:20.159
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.
00:13:20.240 --> 00:13:20.799
Would that be fair?
00:13:20.960 --> 00:13:22.480
There's no, there's many models.
00:13:22.799 --> 00:13:23.840
Many models, yeah.
00:13:24.080 --> 00:13:26.639
So developing a model is extremely costly.
00:13:26.799 --> 00:13:30.080
Um, so there are we call we we talk about model families.
00:13:30.240 --> 00:13:38.960
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.
00:13:39.120 --> 00:13:40.960
This was one of the most earliest models.
00:13:41.039 --> 00:13:42.559
The UK developed one.
00:13:42.799 --> 00:13:47.919
Also, my previous uh employer, um the National Center for Atmospheric Research, developed one.
00:13:48.000 --> 00:13:53.840
And then what happened over time, like additional institutes developed their own models, but often these codes were copied.
00:13:54.000 --> 00:13:54.159
Yeah.
00:13:54.399 --> 00:13:56.480
So somebody, you know, like this is science.
00:13:56.559 --> 00:14:02.159
Like we travel to a place, we copy, we basically just take the model with us and then we use it in another place.
00:14:02.320 --> 00:14:09.120
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.
00:14:09.279 --> 00:14:09.600
Yeah.
00:14:09.840 --> 00:14:20.399
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.
00:14:20.799 --> 00:14:23.120
60 models contribute to the IPCC.
00:14:23.360 --> 00:14:23.759
Yeah, yeah.
00:14:23.840 --> 00:14:26.159
Not all of them are on the same, like on par.
00:14:26.240 --> 00:14:30.080
Like there's definitely models that are further developed than others, yeah.
00:14:30.240 --> 00:14:35.440
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?
00:14:35.759 --> 00:14:42.320
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.
00:14:42.399 --> 00:14:43.679
How do you discretize those?
00:14:43.840 --> 00:14:46.559
Like, how do you get get those equations onto a computer?
00:14:46.720 --> 00:14:48.720
There are multiple approaches how to do that.
00:14:48.799 --> 00:14:59.039
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?
00:14:59.200 --> 00:15:02.399
But this is like and this is almost with all components of the system.
00:15:02.480 --> 00:15:07.440
Yeah, the cloud microphysics, radiation, like there are different approaches to simulate that.
00:15:07.519 --> 00:15:09.919
And then you get an uncertainty out of that.
00:15:10.080 --> 00:15:12.960
It's basically we call this model formulation uncertainty.
00:15:13.120 --> 00:15:13.279
Yeah.
00:15:13.440 --> 00:15:21.279
So it's basically how well uh what's the uncertainty of how you formulate natural physics equations onto the computer.
00:15:21.679 --> 00:15:24.000
And just to clarify, was it 6-0 or 1-6?
00:15:24.320 --> 00:15:24.879
It's 6-0.
00:15:25.200 --> 00:15:26.000
6D models.
00:15:26.320 --> 00:15:26.639
Wow.
00:15:27.600 --> 00:15:31.600
So how have we been doing on accuracy?
00:15:31.840 --> 00:15:38.960
Um, when we look at this huge history of modeling, I mentioned some of the early models are actually pretty much on target.
00:15:39.519 --> 00:15:44.559
Um clearly, I imagine we're getting more accurate as we go along with complexity.
00:15:44.639 --> 00:16:02.639
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.
00:16:02.879 --> 00:16:04.159
Um would that be fair?
00:16:04.480 --> 00:16:06.720
It depends on what we are looking at, to be honest.
00:16:06.879 --> 00:16:09.200
Like um Well, warming.
00:16:09.360 --> 00:16:11.360
Let's start with just warming, predicting the level.
00:16:11.759 --> 00:16:14.000
Yeah, like the models definitely got better over time.
00:16:14.080 --> 00:16:14.159
Yeah.
00:16:14.320 --> 00:16:15.919
So that there's clear evidence for that.
00:16:16.000 --> 00:16:17.360
But they also got more complex.
00:16:17.519 --> 00:16:18.879
So you have to factor this in.
00:16:18.960 --> 00:16:24.000
Like if you start with a fairly simple model, and all of these models are somehow tuned.
00:16:24.159 --> 00:16:24.480
Yep.
00:16:24.720 --> 00:16:32.799
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.
00:16:33.039 --> 00:16:35.039
And they want to go back over the data and make sure it fits.
00:16:35.360 --> 00:16:35.600
Of course.
00:16:35.679 --> 00:16:36.000
Yeah, yeah.
00:16:36.159 --> 00:16:40.240
So you you you tune your models to some extent to fit the historic data.
00:16:40.399 --> 00:16:44.720
Because if you don't, if you're not able to simulate the past, how can you trust into the future?
00:16:44.960 --> 00:16:47.519
Like and tuning is is is a delicate thing.
00:16:48.320 --> 00:16:54.159
But um the thing is, if you add more complexity to those models, you add more degrees of freedom.
00:16:54.320 --> 00:16:54.639
Yeah.
00:16:54.879 --> 00:17:01.840
Which basically means your uncertainty could increase because now you better understand the global system.
00:17:02.159 --> 00:17:10.480
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.
00:17:10.720 --> 00:17:12.400
This can happen in the coupled model.
00:17:12.480 --> 00:17:12.640
Yeah.
00:17:12.799 --> 00:17:15.920
Um, so it's actually it's it's a good thing that we're still on track.
00:17:16.000 --> 00:17:26.960
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.
00:17:27.039 --> 00:17:27.119
Yeah.
00:17:27.359 --> 00:17:30.720
And you didn't increase the error bones that we have.
00:17:31.200 --> 00:17:33.519
I just when you talk there, it reminds me.
00:17:34.079 --> 00:17:38.480
I dug into, this is a while ago, dug into chaos theory to try and understand a bit more.
00:17:38.640 --> 00:17:46.240
And I finished a popular book, James Glick, uh his book on chaos is uh just easily accessible for the average reader.
00:17:47.759 --> 00:17:52.319
And uh really, I mean, the thesis with chaos theory is that some systems don't return to equilibrium.
00:17:52.400 --> 00:17:54.480
You tip them out of equilibrium and they don't come back.
00:17:54.880 --> 00:18:02.640
And by the end of the book, I was uh utterly terrified when thinking about this particular subject, because that's the possibility, right?
00:18:02.799 --> 00:18:17.279
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.
00:18:17.680 --> 00:18:19.680
That's that's sort of the fear there, isn't it?
00:18:19.920 --> 00:18:22.799
Yeah, so it's it I think it depends on the time scale that we're talking about.
00:18:22.960 --> 00:18:26.000
Like we if you're not there now, but failure climate records.
00:18:26.079 --> 00:18:32.480
Like we we were in very different climate states over the last millennia and and like millions of years.
00:18:32.640 --> 00:18:37.200
Um and like like the Earth system is self-regulating to a large extent.
00:18:37.359 --> 00:18:39.759
So the earth was several times already a snowball.
00:18:39.920 --> 00:18:46.960
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.
00:18:47.039 --> 00:18:49.680
So in climate time, these these are geological timescales.
00:18:49.759 --> 00:18:52.000
Yeah, like on human timescales, that's totally true.
00:18:52.079 --> 00:19:02.480
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.
00:19:02.799 --> 00:19:03.440
Yes, got it.
00:19:03.519 --> 00:19:09.440
We could tip it into something that's quite chaotic and with sharp, a sharp increase or dramatic change.
00:19:09.599 --> 00:19:09.920
Yeah, yeah.
00:19:10.079 --> 00:19:14.160
That's even much more dramatic than we're seeing now, and it would be catastrophic with now times.
00:19:14.400 --> 00:19:14.799
Yeah, yeah, yeah.
00:19:14.960 --> 00:19:31.680
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.
00:19:32.000 --> 00:19:33.200
This is exactly what they say.
00:19:33.359 --> 00:19:44.880
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.
00:19:45.119 --> 00:19:53.039
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.
00:19:53.119 --> 00:20:03.759
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.
00:20:04.319 --> 00:20:20.160
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.
00:20:20.240 --> 00:20:24.000
So it could be it could be like a famine, an ocean famine situation, if you like.
00:20:24.480 --> 00:20:28.640
Um that plus you know acidification effects more broadly with fish.
00:20:28.880 --> 00:20:40.000
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.
00:20:40.400 --> 00:20:41.119
Certainly, yeah.
00:20:41.200 --> 00:20:46.880
Like the ecosystem, like we don't in this models, we don't model the ecosystem very well.
00:20:47.119 --> 00:20:49.200
Yeah, these are very complex interactions.
00:20:49.359 --> 00:20:52.480
So this is, I think, an area where uncertainties are very high.
00:20:52.640 --> 00:20:55.519
And again, if you have very high uncertainty, it's a very high risk.
00:20:55.599 --> 00:20:59.680
If you tip over, you want to stay away from the area where you tip over.
00:20:59.839 --> 00:21:02.720
Again, it's it's just a very risky game that you play.
00:21:02.960 --> 00:21:05.279
Um, because like we only have one planet.
00:21:05.440 --> 00:21:11.359
Yeah, and once once we cross these tipping points, yeah, the the consequences can be very dire.
00:21:11.519 --> 00:21:12.079
Yeah.
00:21:12.400 --> 00:21:14.559
Now, there's lots of things I want to get into.
00:21:14.720 --> 00:21:19.519
Um just briefly, just we talked about this transition to AI.
00:21:19.680 --> 00:21:21.680
Can we just talk about that in modeling?
00:21:22.000 --> 00:21:31.359
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.
00:21:31.440 --> 00:21:37.839
But um, yeah, so more recently we've used machine learning in these models and started to apply it.
00:21:38.000 --> 00:21:39.680
Has it become quite dominant quite quickly?
00:21:39.759 --> 00:21:42.079
Is that the subtext I'm hearing?
00:21:42.400 --> 00:21:56.960
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.
00:21:57.039 --> 00:22:02.960
And the like the approach that we use nowadays is basically still still the same as in the early 50s.
00:22:03.119 --> 00:22:12.400
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.
00:22:12.480 --> 00:22:17.599
Yeah, and they are fundamentally different, like there's no real physics in those models.
00:22:17.680 --> 00:22:24.319
It's basically it's trained on historic weather data, and then you use this training to predict the future.
00:22:24.400 --> 00:22:33.119
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.
00:22:33.279 --> 00:22:40.799
Okay, like it's it's again like you train with the AI in general, you need massive amounts of data to train.
00:22:40.880 --> 00:22:46.720
Yeah, and then you want to have a big sample size of the things that you want to simulate, predict.
00:22:47.119 --> 00:22:50.240
Extreme events are per definition rare events, yeah.
00:22:50.559 --> 00:22:52.400
So you don't have a lot of training there.
00:22:52.559 --> 00:22:57.039
And again, like if you go into these extreme situations, these models can often fail.
00:22:57.440 --> 00:22:59.119
And that's one of the big issues nowadays.
00:22:59.200 --> 00:23:15.359
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.
00:23:15.440 --> 00:23:20.720
Yeah, um because there's a really big potential, but they still, I think there's still a trust issue.
00:23:20.880 --> 00:23:25.440
So we we still run physical models, and we will do this into the future.
00:23:25.759 --> 00:23:32.640
Because if you have a big extreme event coming and you have to evacuate, you really want to trust your model.
00:23:32.720 --> 00:23:35.599
And with the AI models, my sense is you we're not there yet.
00:23:35.680 --> 00:23:37.279
We cannot really fully trust them.
00:23:37.519 --> 00:23:45.039
And it's specifically an issue if you think about climate change, because we're pushing the weather and climate system into unknown territory.
00:23:45.119 --> 00:23:50.240
Yeah, like historic data is less useful in 10 years because we're in a different regime.
00:23:50.319 --> 00:23:57.359
Yeah, so as we increase temperature and the climate is changing, this historic data gets less and less relevant.
00:23:57.759 --> 00:23:58.319
Absolutely.
00:23:58.400 --> 00:24:04.160
Uh I there's a couple of discussions recently that maybe give some clues as to where we go with that.
00:24:04.319 --> 00:24:18.559
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.
00:24:19.200 --> 00:24:31.119
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.
00:24:31.839 --> 00:24:47.359
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.
00:24:47.920 --> 00:24:49.440
It has to be in the current form.
00:24:50.160 --> 00:24:52.799
But his idea was you keep feeding it new data every day.
00:24:52.960 --> 00:24:53.119
Yeah.
00:24:53.519 --> 00:25:03.440
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.
00:25:03.519 --> 00:25:04.319
So that should help, I would.
00:25:04.720 --> 00:25:05.279
Like it depends.
00:25:05.359 --> 00:25:08.079
Like again, like if you think about weather forecasting, yes.
00:25:08.720 --> 00:25:13.119
Because the forecasting is on the horizon of a couple of days, weeks, maybe months.
00:25:13.279 --> 00:25:13.599
Exactly.
00:25:13.680 --> 00:25:13.920
Yeah.
00:25:14.079 --> 00:25:27.039
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.
00:25:27.119 --> 00:25:33.839
And people like there are models that can do that, but they learn from this physical-based models.
00:25:33.920 --> 00:25:44.000
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.
00:25:44.160 --> 00:25:49.599
Like the big benefit of AI models, as far as I can see it now, is they they're way cheaper.
00:25:49.759 --> 00:25:52.319
Like it's like we run on the biggest supercomputers.
00:25:52.400 --> 00:25:57.920
You can run these AI models on your laptop, and they give you extremely accurate forecasts.
00:25:58.319 --> 00:25:58.880
Interesting.
00:25:59.200 --> 00:26:06.480
But um, again, the training is is a big effort, and then the gener, like how how well they generalize.
00:26:06.720 --> 00:26:11.279
This is basically how well they can predict states that they haven't seen in the training.
00:26:11.359 --> 00:26:13.200
This this is really a major concern.
00:26:13.519 --> 00:26:33.759
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.
00:26:34.079 --> 00:26:42.720
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.
00:26:43.039 --> 00:26:43.200
Okay.
00:26:43.680 --> 00:26:52.079
For example, like we have a simulation of a tornado, and nobody told the model that there should be a tornado under this thunderstorm.
00:26:52.240 --> 00:26:56.079
It it basically simulated it because it's in the solution of the physics.
00:26:56.240 --> 00:26:57.599
It's a physical-based model.
00:26:57.680 --> 00:27:03.359
If you allow it to go to high enough resolution, it will basically depict a tornado.
00:27:03.680 --> 00:27:05.599
And the AI model would never do that.
00:27:05.839 --> 00:27:06.160
Interesting.
00:27:06.640 --> 00:27:08.319
At least not the ones that we have nowadays.
00:27:08.480 --> 00:27:19.119
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.
00:27:19.440 --> 00:27:23.039
Not really emergence a physical-based model would have.
00:27:23.359 --> 00:27:36.799
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.
00:27:36.880 --> 00:27:38.559
I think that the future will be very broad.
00:27:38.720 --> 00:27:38.960
Yeah.
00:27:39.119 --> 00:27:44.319
Like the physic I don't see that the physical-based models are going away, especially for research.
00:27:44.559 --> 00:27:51.599
Um, and there will be purely AI-based models at the same time, and then there will be this interface, like this hybrid models.
00:27:51.680 --> 00:27:54.720
And this is already happening, so we see this already.
00:27:55.039 --> 00:27:59.200
Um, but like I think then this is I got this question often.
00:27:59.359 --> 00:28:06.240
Like when I present at conferences, are physical-based models over and AI will take over?
00:28:06.319 --> 00:28:07.920
And for me, this is really the wrong question.
00:28:08.000 --> 00:28:09.599
Like, these are really complementary tools.
00:28:09.839 --> 00:28:10.079
Got it.
00:28:10.240 --> 00:28:21.119
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.
00:28:21.359 --> 00:28:22.160
Yeah, that makes sense.
00:28:22.240 --> 00:28:26.559
And you've really helped me understand why there are limits to what AI does.
00:28:26.640 --> 00:28:28.000
I mean, it learns by example.
00:28:28.079 --> 00:28:28.319
Yeah.
00:28:28.480 --> 00:28:31.759
So there's a limits, an inherent limiting factor there.
00:28:31.839 --> 00:28:41.599
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.
00:28:41.680 --> 00:28:42.240
Like, yeah.
00:28:42.640 --> 00:28:50.640
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.
00:28:50.799 --> 00:28:53.200
And it seems, in some way, it seems like they did.
00:28:53.279 --> 00:28:54.400
But it feels like it.
00:28:54.880 --> 00:29:00.799
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.
00:29:00.880 --> 00:29:05.039
Uh, but there's some like again, like this this field is developing extremely fast.
00:29:05.119 --> 00:29:09.039
So it's really hard to know what what will happen in the next two or three or ten years.
00:29:09.359 --> 00:29:09.680
I agree.
00:29:09.759 --> 00:29:20.480
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.
00:29:20.640 --> 00:29:30.079
I would not rule out um AI modules that that are mathematical and purely physical and so forth, and combine with learn by example.
00:29:30.160 --> 00:29:32.960
And yeah, I would not rule out for the future.
00:29:33.039 --> 00:29:33.839
I agree, I agree.
00:29:33.920 --> 00:29:35.279
It's an interesting one.
00:29:35.599 --> 00:29:40.480
So let's get into um there's two things I want to talk to you about.
00:29:40.559 --> 00:29:42.000
I don't know what we handle first.
00:29:42.160 --> 00:29:46.319
One is how we get into the attribution for extreme events.
00:29:46.799 --> 00:29:54.000
And these are the tornadoes, the hurricanes, the cyclones, um, flooding, things that really matter, people care about today.
00:29:54.160 --> 00:29:58.559
We've got great data showing they're increasing dramatically because of climate change.
00:29:58.720 --> 00:30:01.039
But attribution is sort of A part of that.
00:30:01.279 --> 00:30:10.880
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.
00:30:11.039 --> 00:30:12.799
What makes sense to tackle first?
00:30:13.440 --> 00:30:15.359
We we can start with the attribution, certainly.
00:30:15.519 --> 00:30:15.759
Okay.
00:30:16.559 --> 00:30:18.480
I think that there are two types of attribution.
00:30:18.559 --> 00:30:30.240
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.
00:30:30.400 --> 00:30:37.119
And of course, the problem is that we cannot roll back time and just repeat history without interfering with the with the natural system.
00:30:37.519 --> 00:30:38.720
So you do this in the model.
00:30:38.880 --> 00:30:46.880
So you basically don't change CO2 emissions, you don't change the land surface, and you rerun everything, and then you compare.
00:30:46.960 --> 00:30:50.559
This is basically attribution is always you have to have a counterfactual.
00:30:54.319 --> 00:31:03.440
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.
00:31:03.519 --> 00:31:03.680
Yeah.
00:31:03.920 --> 00:31:10.400
And then you compare the two, and then you can basically attribute like how much did greenhouse gas impact global temperatures.
00:31:10.480 --> 00:31:10.640
Yeah.
00:31:10.799 --> 00:31:13.119
And this is this is done since a long time already.
00:31:13.279 --> 00:31:14.720
Um, and like good stuff.
00:31:15.119 --> 00:31:17.920
There's in in scientific communities, no debate.
00:31:18.079 --> 00:31:24.160
Like greenhouse gas human greenhouse gas emissions increase temperature and change the climate.
00:31:24.240 --> 00:31:24.400
Yeah.
00:31:24.640 --> 00:31:38.480
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.
00:31:38.799 --> 00:31:44.799
Yeah, and there are two things the intensity of the event, like how much more did it rain, yeah, and then the frequency.
00:31:45.039 --> 00:31:50.240
Because how it could also be how much more often or how how more likely is this event nowadays?
00:31:51.039 --> 00:31:55.359
And it depends on which type of extreme you look at.
00:31:55.519 --> 00:31:58.640
It's it's more or less challenging to do this attribution.
00:31:59.279 --> 00:32:06.559
Everything that's related to thermodynamics, to the temperature change, is easier because it's simpler physics.
00:32:06.720 --> 00:32:08.240
For example, heat waves.
00:32:08.400 --> 00:32:09.680
We have quite a good understanding.
00:32:09.839 --> 00:32:18.000
Heat waves have intensified in frequency but also in intensity uh over the last 50 hundred years.
00:32:18.319 --> 00:32:22.240
And they're like all attribution studies would tell you the same.
00:32:22.880 --> 00:32:33.680
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.
00:32:33.759 --> 00:32:39.599
This is more, this is again anchoring this in thermodynamics because it's it's related to temperature change.
00:32:39.759 --> 00:32:47.759
But then there's also like this thunderstorm or this like this in Valencia, for example, this was a very cold uh extratropical cyclone.
00:32:48.240 --> 00:32:52.000
Um, how more often do those cyclones develop nowadays?
00:32:52.079 --> 00:32:53.599
That that's a very difficult question.
00:32:53.680 --> 00:32:55.599
Like it's more in the dynamics of the system.
00:32:55.680 --> 00:32:57.839
Like, how often do we get these cyclones?
00:32:58.000 --> 00:33:02.799
And if we have these cyclones and they have more moisture, how do the thunderstorms react?
00:33:02.960 --> 00:33:17.519
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.
00:33:17.599 --> 00:33:19.279
So there's very complex interaction.
00:33:19.519 --> 00:33:20.000
Sure is.
00:33:20.480 --> 00:33:28.160
So um that's why like heat waves, cold waves, we have pretty good understanding and can attribute those very well.
00:33:28.559 --> 00:33:34.559
Extreme rainfall, like tropical cyclones are easier and extra tropical cyclones than small-scale thunderstorms.
00:33:34.720 --> 00:33:37.119
You talked about tornadoes, that's almost impossible.
00:33:37.359 --> 00:33:38.880
Yeah, too small and too small.
00:33:38.960 --> 00:33:43.519
We cannot simulate them um in the models, not at least not in the weather models.
00:33:43.599 --> 00:33:50.079
Yeah, um, and there, our understanding, or even hail, like I work on hail a lot, like larger hail.
00:33:50.240 --> 00:33:53.440
Um, we made some progress, but even there, it's extreme.
00:33:53.519 --> 00:33:58.480
Like if you have a big hail storm hitting Sydney, here you had hail, yeah, um, or here in Zurich.
00:33:58.640 --> 00:34:03.200
Um it's very it's it's extremely difficult to make this attribution statement.
00:34:03.519 --> 00:34:13.920
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.
00:34:14.239 --> 00:34:15.360
It was unbelievable.
00:34:15.440 --> 00:34:16.960
That was such a huge event.
00:34:17.280 --> 00:34:22.639
But like just to tip on that, like um often it's not the forecast.
00:34:22.880 --> 00:34:29.440
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.
00:34:29.599 --> 00:34:29.920
Okay.
00:34:30.239 --> 00:34:35.519
And it the same, like we had a big event in Germany, for example, 2021, many people are are dying.
00:34:35.840 --> 00:34:36.079
Flooding.
00:34:36.239 --> 00:34:36.960
The flooding, yeah.
00:34:37.119 --> 00:34:37.920
Many people died.
00:34:38.079 --> 00:34:39.920
The forecast was actually quite good.
00:34:40.159 --> 00:34:41.920
It's the warning often afterwards.
00:34:42.000 --> 00:34:42.159
Uh-huh.
00:34:42.239 --> 00:34:44.559
Like where you say, like now you know that something is happening.
00:34:44.719 --> 00:34:46.559
How do you get people out of this region?
00:34:46.719 --> 00:34:48.000
When do you start to warn them?
00:34:48.159 --> 00:34:49.440
When do you start to evacuate?
00:34:50.159 --> 00:34:52.480
In Valencia, it was way too late.
00:34:52.559 --> 00:35:00.800
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.
00:35:00.960 --> 00:35:01.119
Right.
00:35:01.199 --> 00:35:04.800
And we're gonna all have to get a lot better at that around the planet because there's more of it coming.
00:35:05.039 --> 00:35:06.639
So, yeah, this is often the problem.
00:35:06.719 --> 00:35:15.280
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.
00:35:15.440 --> 00:35:16.880
Yeah, this is quite proactive.
00:35:17.039 --> 00:35:23.280
Like, I I would really hope that we got into a more proactive approach rather than reactive.
00:35:23.519 --> 00:35:27.280
So something big happens, but at least then you have a chance to learn and change.
00:35:27.519 --> 00:35:35.840
And often this warning change chains are much better thought through afterwards, and when the next event happens, like we have examples for that.
00:35:36.000 --> 00:35:39.440
There are way less fatalities and things are handled better.
00:35:39.760 --> 00:35:40.239
Interesting.
00:35:40.400 --> 00:35:56.719
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.
00:35:56.800 --> 00:36:02.400
And then uh someone says, well, it's highly likely to be uh climate change enhanced.
00:36:02.639 --> 00:36:04.800
Um, you know, what's the sensible way of talking about it?
00:36:04.960 --> 00:36:13.199
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.
00:36:13.360 --> 00:36:15.199
Is that the most sensible way of talking about it?
00:36:15.920 --> 00:36:18.239
Yeah, it depends a little bit on the event.
00:36:18.320 --> 00:36:22.239
Like it's really event from event to event, it can be different.
00:36:22.559 --> 00:36:32.559
Um with tropical cyclones, it's the extreme rainfall is often easier to attribute because it's linked to this clauses cloudron relationship.
00:36:32.800 --> 00:36:40.800
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?
00:36:41.280 --> 00:36:51.280
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.
00:36:51.440 --> 00:36:57.360
Like we are not in the same climate as we were in 1850 before we started the industrial revolution.
00:36:57.440 --> 00:37:00.239
Uh so that that's a fact, and there's enough studies on that.
00:37:00.559 --> 00:37:03.920
And then the question is like, how different are these storms now?
00:37:04.159 --> 00:37:08.639
And like I would never do an attribution statement without backing it up with science.
00:37:08.800 --> 00:37:10.639
Like this of course, but they're different approaches.
00:37:10.719 --> 00:37:16.079
And for example, with tropical cyclones, what we use nowadays quite often is it's basically to use a weather forecasting model.
00:37:16.159 --> 00:37:23.519
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.
00:37:23.679 --> 00:37:34.000
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.
00:37:34.079 --> 00:37:45.360
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.
00:37:45.440 --> 00:37:49.280
You can say, like now 50% higher rainfall intensities or more volume.
00:37:49.599 --> 00:37:50.880
So you get this out of the model.
00:37:50.960 --> 00:37:55.039
You do the same thing with the future, you can do like three degrees warming, and how would this look like?
00:37:55.199 --> 00:37:58.000
Yeah, and people are doing this, and I think that's a good approach.
00:37:58.079 --> 00:38:01.039
Yeah, it doesn't really help you with the frequency part.
00:38:01.440 --> 00:38:07.519
So you this is not what like how likely or more likely was this cyclone to have like to form.
00:38:07.840 --> 00:38:13.039
It's more if it if it's there, like how much more uh rainfall comes out of it.
00:38:13.360 --> 00:38:15.760
So, like again, and you can also see this.
00:38:15.920 --> 00:38:29.679
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.
00:38:29.840 --> 00:38:36.000
Um, but often with these events, at least like extreme rainfall, heat, it's all in the same direction.
00:38:36.079 --> 00:38:38.480
Like climate change intensified these events.
00:38:38.800 --> 00:38:47.199
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.
00:38:47.360 --> 00:39:02.320
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.
00:39:02.880 --> 00:39:05.920
And uh this came from Swiss Re, the reinsurer.
00:39:07.360 --> 00:39:08.480
Yeah, they're just over here.
00:39:08.639 --> 00:39:11.440
Um and and uh it blew me away.
00:39:11.519 --> 00:39:26.159
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.
00:39:26.400 --> 00:39:32.559
So everything uh or catastrophic insurance events really, but it's mainly weather because uh earthquakes were excluded from this.
00:39:32.960 --> 00:39:35.519
So it's you know cyclones, storms, floods.
00:39:36.079 --> 00:39:38.800
The increase in frequency is 457 percent.
00:39:39.039 --> 00:39:39.840
Uh yeah.
00:39:40.079 --> 00:39:42.480
For what they catastro uh class as catastrophic.
00:39:43.119 --> 00:39:50.960
So that gives us some financial uh um uh measure anyway, a different type of look at it.
00:39:51.360 --> 00:39:58.960
But then the increase in intensity, which they measure financially, so there's some caveats um for the same period, 1,500 percent.
00:39:59.920 --> 00:40:07.840
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.
00:40:09.039 --> 00:40:10.400
But they're shocking.
00:40:10.639 --> 00:40:15.440
That's to me, that's it brings it home, I think, makes it real to a lot of people in a different way.
00:40:15.679 --> 00:40:17.679
Yeah, yeah, and it's also of course done for politics.
00:40:17.760 --> 00:40:24.639
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.
00:40:24.800 --> 00:40:28.960
Yeah, and it's it's for all kinds of extreme events, like it's uh tropical cyclones.
00:40:29.119 --> 00:40:48.159
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.
00:40:48.320 --> 00:40:48.719
Yeah.
00:40:48.960 --> 00:40:49.199
Yeah.
00:40:49.440 --> 00:40:52.559
So these places become uninsurable in in some way.
00:40:52.639 --> 00:40:58.320
And then, of course, if you cannot insure them anymore from a private side, the state often has to bail in.
00:40:58.400 --> 00:41:00.559
And it's it's becoming really expensive.
00:41:00.880 --> 00:41:06.480
It's it's almost an it's a financial feedback loop, yeah, another reinforcing effect which is which is uh hurting.
00:41:06.559 --> 00:41:08.400
Um yeah, really interesting.
00:41:08.719 --> 00:41:13.119
Can we now look at maybe just some of the forecasts, just looking forward?
00:41:13.440 --> 00:41:19.360
How we I'm just gonna do a quick time check and make sure I'm not taking too much of your fine.
00:41:19.679 --> 00:41:28.639
Um as we look at what your models are suggesting, 10 years from now, I don't know what's the most useful, 2050.
00:41:28.719 --> 00:41:32.559
Um, can you give us a sense of whether there's any?
00:41:32.800 --> 00:41:42.960
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.
00:41:43.199 --> 00:41:49.360
Um there is a chance of g remaining under three degrees, but it's uh slipping away.
00:41:49.440 --> 00:41:51.920
That's sort of my my view.
00:41:52.079 --> 00:42:01.280
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.
00:42:01.519 --> 00:42:08.480
Yeah, so this emission scenarios are really they are developed outside of our models.
00:42:08.800 --> 00:42:11.360
Like this is basically like you you place through.
00:42:11.840 --> 00:42:12.559
That's another model.
00:42:12.880 --> 00:42:14.000
That's another model, yeah.
00:42:14.239 --> 00:42:22.320
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.
00:42:22.400 --> 00:42:24.320
So this is basically what what you do.
00:42:24.559 --> 00:42:30.719
And I agree with you, like staying on under two is is is probably not gonna happen.
00:42:30.960 --> 00:42:37.039
So what what what we are preparing now at the moment for is what we call an overshoot.
00:42:37.840 --> 00:42:41.199
So since we like the Paris Agreement will likely not be met.
00:42:41.519 --> 00:42:51.039
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.
00:42:51.280 --> 00:43:01.920
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.
00:43:02.000 --> 00:43:03.920
It would be way easier not to overshoot.
00:43:04.000 --> 00:43:05.679
Uh way easier not to do it in the first place.
00:43:05.760 --> 00:43:05.840
Yeah.
00:43:06.000 --> 00:43:06.239
Yeah, yeah.
00:43:06.400 --> 00:43:12.960
And then also like much cheaper, and then also how realistic this overshoot scenarios are.
00:43:13.039 --> 00:43:15.280
It's like this is based on technology that we don't have.
00:43:15.360 --> 00:43:18.559
Like you know, like it's it's again, it's a risky experiment that we're running.
00:43:18.800 --> 00:43:29.840
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.
00:43:30.000 --> 00:43:34.239
Um, and once you look at that, like changes are dramatic.
00:43:34.320 --> 00:43:35.519
Like you can see this already.
00:43:35.599 --> 00:43:41.840
What we had 1.2, 1.3 global warming now, and you can already feel like it for me, it was interesting.
00:43:41.920 --> 00:43:48.960
The last 10 years were really a change where you now you really live through climate change.
00:43:49.039 --> 00:43:53.599
Yeah, like you see it, like this extreme events that we get over and over again.
00:43:53.679 --> 00:43:56.639
Um, they're just out of what we have experienced in the past.
00:43:56.880 --> 00:44:00.559
And I can tell you, I travel all over the world, and every country I go to, you can see it.
00:44:00.719 --> 00:44:01.039
Yeah, yeah.
00:44:01.519 --> 00:44:03.199
So yeah, everybody is affected by that.
00:44:03.519 --> 00:44:06.320
Um, and it's it's really like climate change is happening now.
00:44:06.400 --> 00:44:09.920
And now imagine we had 1.3, and now you add 1.7.
00:44:10.239 --> 00:44:22.960
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.
00:44:23.039 --> 00:44:30.239
Like, and of course, this has rippling effects of on society and migration and all of that, like that we have very little understanding.
00:44:30.559 --> 00:44:37.119
So yeah, like we we are in the middle of it, and it looks like we will continue for quite a while.
00:44:37.440 --> 00:44:47.519
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.
00:44:47.679 --> 00:45:01.280
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.
00:45:01.360 --> 00:45:06.960
And often like they are basically really planned with observed data that we collected over the last hundred years.
00:45:07.039 --> 00:45:10.960
And as we talked already with AI, like this data gets less and less useful.
00:45:11.280 --> 00:45:21.039
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.
00:45:21.199 --> 00:45:26.159
And then like we have a lot of discussions with city planners, for example, what should we prepare for?
00:45:26.480 --> 00:45:32.079
Like, of course, like sit like coastal cities like Sydney are especially vulnerable because you also have sea level rise.
00:45:32.159 --> 00:45:38.239
So now you have to invest a lot of money to protect the infrastructure that you have and to make it resilient.
00:45:38.400 --> 00:45:45.039
Because if you build and use a flood wall, for example, like this will be there for the next hundred years, and yeah.
00:45:45.280 --> 00:45:47.519
But what will the weather in a hundred years look like?
00:45:47.599 --> 00:45:48.559
And that's highly uncertain.
00:45:48.719 --> 00:45:56.320
Yeah, you know, Sydney is a good example because right now lots of controversy very close to my home base where I live.
00:45:56.400 --> 00:46:04.239
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.
00:46:04.639 --> 00:46:09.199
There's huge controversy because the erosion is moving to different areas now because of the seawalls.
00:46:10.000 --> 00:46:11.599
It's just the start.
00:46:11.840 --> 00:46:18.159
And the other one recently uh uh we spent some time in the Maldives looking at sea level rise there.
00:46:18.639 --> 00:46:22.880
And um, it's easy to say, oh, you know, it's only half a meter or whatever.
00:46:23.039 --> 00:46:37.519
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.
00:46:37.679 --> 00:46:38.960
You know, it's catastrophic for them.
00:46:39.199 --> 00:46:50.159
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.
00:46:50.320 --> 00:46:56.000
Yeah, and then the people that suffer are really in this more vulnerable areas that don't have the resilience that we have.
00:46:56.559 --> 00:46:58.559
Bangladesh doesn't have the same resources, yeah.
00:46:58.800 --> 00:46:59.119
Exactly.
00:46:59.199 --> 00:47:06.559
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.
00:47:06.639 --> 00:47:08.000
It's it's it's crazy.
00:47:08.400 --> 00:47:12.239
Um it's really extremely difficult to recover for them as well.
00:47:12.320 --> 00:47:20.960
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.
00:47:21.199 --> 00:47:21.360
Yeah.
00:47:22.000 --> 00:47:44.239
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.
00:47:45.039 --> 00:47:48.079
Um or the amok, I haven't got the ammo, yeah, yeah, yeah.
00:47:48.719 --> 00:47:56.719
The terminology there, but the the potential change for local climate is massive when the current changes.
00:47:56.960 --> 00:48:00.719
So if the current changes off Spain, the climate of Spain will change.
00:48:00.800 --> 00:48:14.960
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.
00:48:15.760 --> 00:48:17.599
Um and potentially quite dramatically.
00:48:17.679 --> 00:48:18.880
I just wanted your take on that.
00:48:18.960 --> 00:48:23.199
Is is it something we can model accurately or does it define modelling?
00:48:23.360 --> 00:48:28.719
Um I'm I'm again like just like I'm not an ocean modeler, but like I follow this area.
00:48:29.039 --> 00:48:29.679
It interacts with each other.
00:48:29.840 --> 00:48:30.480
Oh, certainly, yeah.
00:48:30.960 --> 00:48:33.840
And that one thing we know is that this can happen.
00:48:34.159 --> 00:48:37.119
Like we see this in paleoclimate data, and it it happened in the past.
00:48:37.519 --> 00:48:38.000
Right, okay.
00:48:38.079 --> 00:48:41.840
So we know that climate uh currents can change dramatically.
00:48:42.239 --> 00:48:51.599
It can change dramatically, and the impacts on Europe, for example, when the um AMORP is collapsing, are quite dramatic and quite abrupt.
00:48:51.760 --> 00:48:54.000
Like just a massive cooling.
00:48:54.320 --> 00:48:58.800
Um if this will happen is still up to the debate.
00:48:58.880 --> 00:49:01.920
Like it's at least this is what my take on this field is.
00:49:02.079 --> 00:49:09.519
Um depending on who you talk to, we're close to a collapse, or we are not as close as we think.
00:49:09.679 --> 00:49:15.119
The models that we have can do that actually, yeah, but this often happens quite late.
00:49:15.199 --> 00:49:22.000
Like you really have to have a lot of warming, and then in 2200, 2300, you get a collapse.
00:49:22.400 --> 00:49:25.599
So you can simulate this in models, but it's it's quite slow.
00:49:25.760 --> 00:49:29.199
But the question is is this realistically simulated in the model or not?
00:49:29.280 --> 00:49:29.599
Yeah.
00:49:29.840 --> 00:49:33.760
Um, and again, like there is a lot of active research going on there.
00:49:33.840 --> 00:49:40.480
But if this happens, and there was a recent study, um like it basically would offset climate change in Europe.
00:49:41.119 --> 00:49:42.239
Yes, it'd be colder here.
00:49:42.559 --> 00:49:49.199
It would be colder, and you you would get closer from the temperature side to pre-industrial um situation.
00:49:49.360 --> 00:49:53.119
So actually, yeah, it's and then but someone else is gonna suffer.
00:49:54.079 --> 00:50:11.840
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.
00:50:12.079 --> 00:50:16.400
And when this happened, like it doesn't help you that you're on average colder.
00:50:16.480 --> 00:50:19.920
It means basically, and this is basically a broadening of the distribution.
00:50:20.400 --> 00:50:23.599
So you can get extremely hot but extremely quite cold as well.
00:50:23.679 --> 00:50:35.119
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.
00:50:35.360 --> 00:50:38.800
And again, it's the speed of change, which is the potential disaster.
00:50:38.880 --> 00:50:45.360
How do we it's all about accommodating that change and what infrastructure, crops, food supply, all that stuff has changed dramatically.
00:50:45.679 --> 00:50:51.199
But the AMOC already slowed down, so we know that from observations, and also the models show that.
00:50:51.760 --> 00:50:58.880
What we also know from observations is that it slows down for a long time and then it suddenly gets way lower.
00:50:58.960 --> 00:51:00.480
So it never shuts down completely.
00:51:00.559 --> 00:51:05.519
Like the shutdown is it's bad terminology because there's it's just operating on a way lower level.
00:51:05.760 --> 00:51:06.159
I see.
00:51:06.320 --> 00:51:11.760
Yeah, but this this transition goes quite fast, and this is what we this is the tipping point that we are concerned about.
00:51:12.719 --> 00:51:16.159
I'm struggling to remember the so it's Atlantic, what's the M?
00:51:16.320 --> 00:51:16.800
Meridian?
00:51:16.880 --> 00:51:19.360
No, what's it meridianal overturning circulation, yeah.
00:51:19.840 --> 00:51:20.800
Okay, yeah, there you go.
00:51:22.639 --> 00:51:28.719
Um, and basically that's taking uh warmer water north.
00:51:28.800 --> 00:51:29.280
I'm trying to remember.
00:51:29.599 --> 00:51:30.400
Yeah, north, yeah, yeah.
00:51:30.639 --> 00:51:32.400
North, and then the cooler cooler goes south.
00:51:32.719 --> 00:51:38.960
That's the reason why the UK or Scandinavia is so much warmer than the east coast of the US at the same latitudes.
00:51:39.039 --> 00:51:39.199
Yeah.
00:51:39.360 --> 00:51:43.039
Like it's this transport of very warm air to those regions.
00:51:43.119 --> 00:51:49.039
So it's really from Central Europe northwards, this would be the regions that are mostly impacted by collapse.
00:51:50.239 --> 00:51:52.320
So where do we go?
00:51:52.719 --> 00:51:53.760
I know some of your work.
00:51:53.840 --> 00:52:02.159
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.
00:52:02.320 --> 00:52:03.280
Yeah, what's all that about?
00:52:03.519 --> 00:52:09.039
Yeah, so like for as a scientist, we I'm really interested in the fundamental processes.
00:52:09.119 --> 00:52:18.000
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.
00:52:18.320 --> 00:52:31.679
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.
00:52:32.480 --> 00:52:44.400
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.
00:52:44.480 --> 00:52:49.440
So if you if you condensate moisture, you release a lot of heating, like a lot of heat.
00:52:49.599 --> 00:52:54.000
And you can just turn this off in the model and see what what would happen.
00:52:54.159 --> 00:53:02.159
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.
00:53:02.239 --> 00:53:06.880
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.
00:53:07.199 --> 00:53:10.079
And so this is basically how we gain trust.
00:53:10.320 --> 00:53:19.760
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.
00:53:19.920 --> 00:53:32.880
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.
00:53:33.039 --> 00:53:39.199
But they can really do thunderstorms very well, for example, or very local scale extremes.
00:53:39.599 --> 00:53:46.000
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.
00:53:46.079 --> 00:53:59.920
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.
00:54:00.159 --> 00:54:10.000
And for me, like the the primary motivation to do that is really to provide local scale climate change information um globally.
00:54:10.159 --> 00:54:29.760
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.
00:54:29.840 --> 00:54:30.719
Is that what that means?
00:54:30.960 --> 00:54:33.519
Uh yeah, like a little bit like that.
00:54:33.679 --> 00:54:38.559
Like a classic example, I worked with the Nuclear Regulatory Commission in the US.
00:54:38.719 --> 00:54:41.679
And they are very concerned about flooding of nuclear power plants.
00:54:41.840 --> 00:54:42.000
Okay.
00:54:42.400 --> 00:54:44.400
We actually got quite close a couple of times.
00:54:44.559 --> 00:54:51.119
Um, and of course, climate change is making extremes more um extreme, so there's a real concern there.
00:54:51.199 --> 00:54:59.920
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.
00:55:00.000 --> 00:55:01.519
Yeah, like this is a typical example.
00:55:01.599 --> 00:55:11.440
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.
00:55:11.599 --> 00:55:24.159
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.
00:55:24.400 --> 00:55:26.159
But it goes it goes forever industrially.
00:55:26.239 --> 00:55:39.440
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.
00:55:39.760 --> 00:55:40.079
Yeah.
00:55:40.480 --> 00:55:47.039
So they're all interested in water availability and forecasting that at a local level.
00:55:47.199 --> 00:55:47.599
Yeah, yeah.
00:55:47.760 --> 00:55:48.159
Uh yeah.
00:55:48.400 --> 00:55:49.039
Yeah, water.
00:55:49.199 --> 00:56:01.920
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.
00:56:02.000 --> 00:56:04.400
And this is like, of course, like this is a major concern.
00:56:04.559 --> 00:56:04.719
Yeah.
00:56:04.960 --> 00:56:09.519
Like if a region runs out of water, like you are you're in a really big trouble.
00:56:09.679 --> 00:56:12.800
Again, like western states often can cope better.
00:56:13.039 --> 00:56:22.639
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.
00:56:22.880 --> 00:56:24.880
And again, that this is changing a lot as well.
00:56:25.039 --> 00:56:33.440
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.
00:56:33.599 --> 00:56:38.639
And there are like really good studies that show every degree of warming contributes.
00:56:38.719 --> 00:56:42.239
I think it was 14% less runoff in the Colorado River.
00:56:42.480 --> 00:56:42.719
Wow.
00:56:42.960 --> 00:56:43.360
Yeah, yeah.
00:56:43.440 --> 00:56:45.039
So quite stark changes.
00:56:45.360 --> 00:56:50.079
And that's that's an example of a very localized um uh insight.
00:56:50.400 --> 00:56:58.239
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.
00:56:58.400 --> 00:57:12.639
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.
00:57:12.719 --> 00:57:13.280
So, yeah, yeah.
00:57:13.840 --> 00:57:21.199
So can governments and industry come and uh work with you, consult with you on a uh project basis with the center here?
00:57:21.679 --> 00:57:22.400
Certainly, yeah, yeah.
00:57:22.480 --> 00:57:24.400
Like we we work a lot with industry.
00:57:24.559 --> 00:57:26.800
Um, but of course, like we also have research grants.
00:57:26.960 --> 00:57:29.840
So this is if you have a research grant, you basically work for the government.
00:57:30.000 --> 00:57:30.639
I would say possible.
00:57:31.599 --> 00:57:33.519
We try to do everything that we do open.
00:57:33.840 --> 00:57:34.639
There it's public.
00:57:34.800 --> 00:57:41.760
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.
00:57:41.920 --> 00:57:52.400
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.
00:57:52.480 --> 00:57:55.199
Uh that that's really a lost opportunity.
00:57:55.440 --> 00:58:09.199
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.
00:58:09.280 --> 00:58:11.760
And of course, it's often a trust relationship as well.
00:58:12.159 --> 00:58:19.360
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?
00:58:19.599 --> 00:58:30.800
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.
00:58:30.960 --> 00:58:35.440
And I think we we can put more trust in these models nowadays.
00:58:35.519 --> 00:58:37.840
Then many people are still very skeptical.
00:58:38.079 --> 00:58:46.960
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.
00:58:47.039 --> 00:58:49.840
Like, for example, extreme rainfall is a good example.
00:58:50.079 --> 00:59:02.079
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.
00:59:02.320 --> 00:59:02.800
Ah, interesting.
00:59:03.119 --> 00:59:06.719
I think we we can do much more and much better than that nowadays.
00:59:06.880 --> 00:59:10.559
But it's it's it's again like a trust issue, like a communication issue.
00:59:11.280 --> 00:59:19.760
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.
00:59:20.079 --> 00:59:30.000
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?
00:59:30.400 --> 00:59:31.199
Leaders, yeah.
00:59:31.360 --> 00:59:32.239
Yeah, I don't know.
00:59:32.320 --> 00:59:34.639
We are just just uh on the back of our conversation.
00:59:35.039 --> 00:59:40.400
Like I I I really like I'm more what I can do rather than leaders can do.
00:59:40.639 --> 00:59:41.119
Okay, four.
00:59:41.599 --> 00:59:44.960
Yeah, but um so I what's what's the biggest thing?
00:59:45.280 --> 00:59:50.480
What's really concerning me is if I I work a lot with students, young people.
00:59:50.639 --> 00:59:50.960
Yeah.
00:59:51.119 --> 00:59:56.639
And when I was in my teens and twenties, I always had this vision of the future.
00:59:56.880 --> 00:59:59.599
The future will be better than the past and the current.
01:00:00.239 --> 01:00:02.159
This seems to erode nowadays.
01:00:02.320 --> 01:00:09.599
It seems like young generation have they have really um a crisis of perspective.
01:00:09.840 --> 01:00:17.360
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.
01:00:17.599 --> 01:00:18.320
They've given up.
01:00:18.559 --> 01:00:24.079
That's it's that's you know it's heartbreaking because it's really like this is self-fulfill fulfilling prophecy.
01:00:24.159 --> 01:00:33.039
If if enough people think that we go there, and and it's really like often this hopelessness is is really bad.
01:00:33.280 --> 01:00:39.840
So what I try to during my lectures are talking, working with young people, empowering them.
01:00:40.239 --> 01:00:41.840
Like be part of the solution.
01:00:42.079 --> 01:00:51.920
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.
01:00:52.239 --> 01:00:54.239
And I I strongly believe in that.
01:00:54.400 --> 01:01:00.000
Like if if enough people think like that and we work together, we can we can achieve that.
01:01:00.239 --> 01:01:00.719
For sure.
01:01:00.960 --> 01:01:01.360
Yeah.
01:01:01.599 --> 01:01:04.159
And it really starts at a very fundamental level.
01:01:04.400 --> 01:01:06.079
Yeah, yeah, I like that very much.
01:01:06.239 --> 01:01:14.159
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.
01:01:14.400 --> 01:01:17.519
We don't need any more information really um to fix this.
01:01:17.679 --> 01:01:24.159
Uh we have enough data and and we have the technologies, you know, to fix most of it already.
01:01:24.239 --> 01:01:25.920
So it's just a matter of acting on it.
01:01:26.239 --> 01:01:26.800
It's very true.
01:01:26.880 --> 01:01:33.599
Yeah, like my take on that is adding more information has a marginal, leads to marginal improvements nowadays.
01:01:33.679 --> 01:01:37.440
Like we we know what to do, we know where we're heading, we know it's not a good place.
01:01:37.679 --> 01:01:40.800
It's not really to take action and act on it.
01:01:40.880 --> 01:01:41.360
Yeah.
01:01:41.840 --> 01:01:46.239
Professor Andreas Prine, thank you so much for spending so much time with us this morning.
01:01:46.320 --> 01:01:46.800
It's wonderful.
01:01:47.039 --> 01:01:47.760
Thank you, Bruce.
01:01:47.840 --> 01:01:49.039
This was a pleasure.