 So, welcome everybody to our seventh press conference of the EGU General Assembly 2024. My name is Hazel Gibson and I'm the EGU's Head of Communications and I'm joined today by four excellent speakers that will be giving us a short media summary of their presentations that have been presented or are due to be presented at the conference this week. Today's press conference is titled PC7, Life in Space, Habitability in Our Solar System and Beyond and I will introduce our speakers in turn and then we will go straight through the presentations with time for question and answer at the end. If you are joining us online I would ask you to please mute your microphones. You will have the opportunity to unmute yourself and ask questions during the question and answer period. So, first of all on my far right hand side is our first speaker, Verna Grandel from Space Renaissance International Italy. We also have Stephanie Olson from the Department of Earth, Atmospheric and Planetary Sciences, Purdue University in the United States of America. We have Enrico Camporeale from CU Boulder in the United States of America and Sandra Chapman from the Center for Fusion, Space and Astrophysics in the Physics Department at the University of Warwick in the United Kingdom and the Department for Mathematics and Statistics at the University of Tromsø in Norway. So, I would like to hand over to our first speaker today, Verna Grandel. Thank you very much. Okay, thank you very much for inviting me. I just, so this is my scientific work which I have presented here. Mare Tranquiditatis Hall, a habitable place for a first lunar settlement. So, we have three lunar halls which have been discovered in the year 2009 by the Japanese lunar orbiter, Selene, which is called Selenological and Engineering Explorer. And those three lunar halls are called Mare's Hills Hall, Mare Tranquiditatis Hall and Mare Ingenie Hall. According to Mr. Haruyama at Arii, at the bottom of the halls, the temperatures range only from minus 20 degrees Celsius to plus 30 degrees Celsius. And of course, there is not so much radiation and there are not so much micrometeorites at the bottom of those halls. And those lunar halls are supposed also to be the entrances to sub-Selene lava tubes. The next one. And as a first step, I propose to build an initial modular lunar base on the lunar surface near Mare Tranquiditatis Halls, I'm sorry, for about 30 astronauts to prepare the construction site. The initial lunar base is built of cylindrical modules and spherical nodes. It is extended by inflatable elements. I shall now show it. So those are the cylindrical models and the inflatable elements are in red. And of course, the entire station is finally covered with regolith. So next slide. The second step, at the bottom of Mare Tranquiditatis Hall, five caves are excavated, if possible, by enlarging existing lava tubes. The walls of the caves are sintered with lava devices and prepared for inflatable hulls. So you can see, sorry, this is the wrong. So you have here the four, five underground structures, which we proposed. The walls of the caves are sintered with lava devices and prepared for inflatable hulls. The excavated material is filled into bags to build shieldings against cosmic rays after finishing the construction. The caves are connected by aluminum tubes and nodes, which you can see here downstairs at the bottom of the holes. And the excavated material is filled into bags to build shieldings around against rays after finishing of the construction. So the sub-stiline habitat structure is for about 50 inhabitants, for one of the five made of inflated hull and interior aluminum, light-wit trusses, light-wit collabs and floor elements. So next one. Of course, I have also tried to estimate the coasts. We assume the reduction of launching coasts by using reusable SpaceX launchers. So the reduction will be about approximately Euro 20, from Euro 20,000 per kilogram of launch weight to approximately Euro 7,000 kilograms payload. Of course, these are information from Elon Musk. So if you take those information seriously, you come to coasts for the entire project of only about 7.6 to 8 billions of euros. So this was the short description of the project I have proposed. So thank you very much. Thank you. Our next speaker will be Stephanie Olson. Just give us a couple of minutes to change the slides. Thank you. Hi, everyone. I'm Stephanie Olson. I'm an assistant professor at Purdue University, where I'm PI of FAB Lab, Purdue Habitability and Biosignatures Lab. And my group is really interested in which types of planets are most likely to host life, life that we could potentially detect. So where is life most likely to originate in the first place? If life originates, how successful will it be? And if it's potentially successful, is it actually the kind of life that produces remotely detectable signatures? So we're overwhelmingly focused on exoplanets. Our closest exoplanet neighbor is more than four light years away. And so remotely detectable ultimately means impacting atmospheric composition. All right. So the work that I'm going to talk about today focuses on how we can use climate models to understand where life might be most likely to originate. And it turns out that the ingredients for life are actually fairly simple. It's just the recipe that is wildly complex, but the basic building blocks super easy. We need liquid water. That's an excellent solvent. It facilitates all sorts of fun chemistry. We need an energy source to drive some of that chemistry. And then we need the actual elemental building blocks of life. So on earth, that is the schnapps elements, carbon, hydrogen, nitrogen, oxygen, phosphorus and sulfur. And then the good news is that these elements are actually all fairly abundant in the universe. The slight complicating factor, though, is that we need actually chemically reactive forms that are eager to participate in chemistry. And that can't always be guaranteed. So as an example, we have tons of nitrogen on the surface of earth, but it's largely in the form of atmospheric dinitrogen. So that's a nitrogen triple bonded to another nitrogen, and that triple bond is really hard to break. And so nitrogen is not chemically reactive at earth surface, and it's not willing to participate in a chemistry such as origin of life chemistry. Similarly, phosphorus is not especially reactive in surface environments because it tends to live in rocks rather than aqueous or gas phases. But fortunately, in the case of both nitrogen and phosphorus, lightning can help a lot. Right. So lightning is one of the few things that can break that triple bond. And lightning can also mobilize phosphorus and get it out of rocks and into aqueous environments. And so in addition to these basic elements, I would argue that lightning is itself a basic ingredient for life. And then finally, you need to put all of these ingredients in the right environment. And what the right environment is has been a matter of debate for decades. But a very popular idea and one that I think is especially compelling is that that environment is exposed to land surfaces. And in the case of earlier specifically, that would be volcanic islands. So that's what I'm showing there. Now, land surfaces have a number of advantages. But a big one relevant to this story is that they can concentrate lightning. So continents represent 30 percent of Earth's surface today, but more than 90 percent of lightning happens over land. And in the case of volcanic islands specifically, volcanic eruptions can actually trigger lightning. And so volcanic islands, I think, are an awesome place to originate life. But it's not enough to just put your ingredients on a volcanic island. This is kind of like building a cake. You can put all of your ingredients for cake in your kitchen, but it's never going to spontaneously assemble into an actual cake. And life is even more complicated than a cake. So you need to introduce the you need to introduce specific ingredients to each other in specific ways and sometimes in very different circumstances. So some things in your cake go in the oven. The frosting absolutely does not. And so in the case of land surfaces on early Earth or another world, a really important factor is that they can experience wet dry cycles. So water is great for the origin of life. Once life is established, water is even more important. But water is itself chemically active and has this nasty habit of inserting itself and breaking bonds. Hydrolysis reactions are referred to. And so the presence of water actually really strongly disfavors the assembly of large organic molecules. And so we need water to get the chemicals to diffuse and interact with each other in the first place. But then we need to get rid of the water actually to allow them to polymerize and build up into very large, complex molecules, things that start to look like biological molecules. And so these wet dry cycles are something that is only possible on exposed land surfaces. And so for a right that wet dry cycles are an important ingredient for the origin of life. That means that some worlds jump out as really terrible places to originate life and that's really bad places to look for life. So as an example, we think that water worlds are relatively common. These are worlds that might have a similar size to Earth, but their mass is just a little bit lower than we would expect, implying that they have tons of water. That's exciting because it meets kind of conventional definitions of habitability, which is looking for liquid water. But they can't have exposed land masses if they have oceans that are 100 kilometers deep. And so they can't have wet dry cycles. And so potentially they do not have life for that reason. Similarly, synchronously rotating M dwarf planets that have permanent day sides and permanent night sides and no seasonal cycles are also likely a bad place to look for life. And that one is kind of a bummer because M stars are of course the most common type of star near us and M dwarf planets are the best targets for JWST. And so not encouraging for life detection with JWST. But on the other hand, other types of planets stand out as potentially really great places to originate life. So for example, worlds that are tilted more on their axis of rotation or have high obliquity compared to Earth and thus experience exaggerated seasonal cycles compared to Earth. So they might have, Earth has wet dry cycles, but these worlds could have even greater potential to have wet dry cycles. And so here I'm showing some simulations from my group. On the left, we have a 30 degree obliquity planet. In the middle, we have a 60 degree obliquity planet. And on the on the right, we have a 90 degree obliquity planet. And then first thing to note here is that these are not maps. Right. The vertical axis is latitude. So we have the poles at the top and bottom equator in the middle. But along the X axis, I'm showing time. And so in the left panel here, you see that precipitation represented by the colors blue is wet, brown is dry. Precipitation is really strongly concentrated near the equator, just like on Earth. Our obliquity is a little bit less than 30, but in the same ballpark. And there's a little bit of seasonal variation of that latitude of maximum precipitation. And so near the equator, there are some areas that will experience really exaggerated wet seasons versus dry seasons. As we move to 60 degree obliquity in the middle panel, though, it looks totally different. Now we have exaggerated wet seasons versus dry seasons occurring at all latitudes, meaning now that the surface area of the planet that is potentially conducive to origin of life chemistry is much larger. And so that might be best case scenario for an origin of life. Now, as we move to even higher obliquity, now the the poles are still experiencing exaggerated seasonality. But the equator is actually pretty cold and pretty arid as a result. So there's not much going on there. So we were really excited about this because in parallel. This is not working anymore. That's it. Yeah. No. No. No. No, no, I don't know how I broke it, but I did. So anyways, I'll I'll just keep going. So we were excited about this because unrelated. We were exploring how high obliquity planets might impact the success of vegetation. It wasn't immediately clear to us whether or not extreme seasonality would be really bad for life or if maybe extreme seasonality had some unexpected benefits for life. And ultimately, what we found was that as you increase obliquity, so as you increasingly tilt the planet on its side, you increase the kind of uniformness of stellar energy distribution between the equator and the poles. So in other words, your poles warm and the kind of area over which vegetation could be successful increases quite dramatically. And that's really important because among all the types of life we have here on Earth, vegetation specifically is the most remotely detectable flavor of life. Vegetation is, by definition, sitting at the surface. It is directly interacting with the composition of Earth's atmosphere and in the the future, when we're actually directly imaging exoplanets and seeing light reflected from their surfaces, we could hope to see direct interaction between photosynthetic pigments and light bouncing from the planet's surface. So increasing both the potential for what dry cycles and the origin of life, but also the success of a remotely detectable life on these moderately high obliquity planets. So I will I will leave my summaries here and just thank you. Our next speaker is Enrico Camporeale. We'll just give us a couple of minutes to change the slides. All right. So thank you for inviting me. So I'm Enrico Camporeale. I'm actually giving this presentation on behalf of my postdoc, Andon Hu, who had some visa issues. So he couldn't be here in person, unfortunately, but most of the work is his own work. So I'd like to start this presentation with this table, which has been put together by the National Risk Register of the UK government. There are a number of there are many numbers there. Each number is a risk, either a natural hazard or a man-made accident. And this is ordered as a function of likelihood on the horizontal line, where one is less likely and five is more likely and impact on the vertical, where again, one is less impactful and five is more impactful. And as you can see, and it's not surprising to us, and now pandemic is on the very top right corner, which means it's very likely and very impactful. Now, I know that you journalists like to ask questions and answer them. But let me just ask you a quick question. Where do you think space weather sits on this table, even though you might not be able to read the numbers? Journalists are also not to be shy, so. Anyway, I'm going to answer for you. So space weather sits just a little bit below pandemic. In fact, in 2019, the US Federal Emergency Management Agency, or FEMA, concluded that only two, and this was before the pandemic, obviously, two natural hazards of the capacity to simultaneously affect the entire nation. In fact, the entire world, as we know, one is a pandemic and the other is space weather. So space weather is what I'm going to talk about. So let's start with what is space weather? It's kind of a very general term we use to identify a number of impacts due to the solar to the sun variability, impacts on our technological infrastructure that range. There's a large range of impacts we are interested in and enhanced level of radiation. I also managed to break this. OK, radiation, which might be impactful to astronauts, people wandering around in the moon, higher level of radiation, which is dangerous for polar flights and then obvious consequence to hazards for satellites, operation, genesis, communication, all the way to the ground where we can experience induced current on electric power grid, which can possibly cause a large regional blackouts. So the topic of my presentation is about machine learning for space weather and space in particular. Now there are a good number of reasons where we want to do machine learning and artificial intelligence, probably the very first reason that we have a lot of data. And for instance, as I give you a figure of merit, NASA is generating more than 12 terabytes of data every single day by collecting all data from a number of satellites and mission. Another good thing about this is that most of this data, if not all of it is publicly available. This is the website for NASA is as a similar archive. I would say about a quarter of this data from NASA is accounted for by the SDO, the Solar Dynamic Observatory, which is a satellite that takes a very high resolution images of the sun. Now, why space weather is impactful is because of the growing space economy attached to satellites in particular. So there have been some financial institutes, for instance, predicting that the total kind of US economy attached to space is going to grow, is going to pass the one trillion dollar landmark by believe 2040. If we focus in particular to the satellite industry, you know, since the birth of the space age in the late fifties to now, we have launched about 10,000 satellites in space. Now it's predicted that within the next eight or nine years, we're going to launch another 60,000 satellites. So this gives you an idea of how much exponentially growing the industry is. So it is my own view and the view of many other people in the community, that the entire space economy, when we get to a point where we have so many satellites in space, again, particularly focusing on satellites, but they said space weather affects much more than only satellites. The space economy will soon become reliant on the ability to make robust, data driven, autonomous and justifiable decision. So it sounds like a very good idea that in fact, we believe that it will only thrive when AI becomes fully integrated in the decision making process. This is not something you can do manually, unit and automatic system, possibly backed up by AI. So a few papers, a few years back, I published this review paper. I'm not going to go much into detail. So this is a review paper about using machine learning in space weather. In the last, say, five or ten years, there have been a growth of methods and application of machine learning in the field of space weather. So aside from the review, what it's included in this paper, of course, I encourage you to give a look at, it's not very technical actually, it's a list of open challenges. So kind of challenges that we think the research community needs to overcome if you want to get to that point where we use AI reliably and in a trustworthy way to support space weather forecast and the space economy operations. I'm not going to go too much into detail here, but one of the major problems is the uncertainty problem. We need to, you know, for any forecast to be reliable, you need to be able to estimate uncertainties of your forecast. This is what we in my group have been working on in the last few years. So one of the main kind of engine under the hood that is possibly the most technical slide I have here is what we call ACRU. This is a new method that stands for Accurate and Reliable Uncertainty Estimate. It's a posteriori UQ estimate, meaning that you can take a deterministic model, which can be typically it's a physics based model or it can be an empirical model, like a machine learning model. The typical way of assessing uncertainty is to run ensemble. So run many, many models slightly changing the initial conditions. For instance, this is very expensive. So this accrue method is kind of try to overcome this problem by sort of a plug and play up posteriori estimate of deterministic models. So what it does is to put an estimate of the uncertainty on top of your deterministic model. It's open source. And by plug and play, I mean that it's really model agnostic. It doesn't really need to know what the model is. You can just plug it in and it gives you the uncertainty. Now, using this sort of powerful method, we in the last few years at the University of Colorado, we have started to develop forecasting models on a range of space weather events. And you know, by the time each model is by the time we are happy enough, which one of these model we put it online as a real time model, you can check the website here of the space weather model staging platform. Again, the space weather track, University of Colorado. A couple of these, for instance, which might be interesting is the live DST is a Germanic index prediction. So it's a number that gives tells you how much activity there is in the Germanic field. And this is a probabilistic prediction one to six hours ahead. And so we expect in the near future to produce more and more of those forecasting models based on a combination of machine learning and uncertain quantification. And yeah, this is all I have. Thank you. Thank you very much. We will now move to our next speaker, Sandra Chapman. Just give us a couple of minutes to change the slides. So what I'm going to talk about is these things. So this is a nice follow on from the previous talk. These are corona mass ejections from the sun. OK. And this is the main driver of space weather. You can see that it's a big blob of plasma magnetic field burped out of the sun. It's called a corona mass ejection and it reaches the earth. Well, if it hits the earth, and that's the tricky thing for prediction, will cause all kinds of havoc. And what I want to report on is work that I've presented here. It's literally just come out in scientific reports. Just literally you're the first people I've presented it to in the press. So this is your chance. I think it's a really exciting new insight into what we can say about these events and what they do. So you've seen this just now. When one of these events hits the earth, you get a lot of impacts. And the reason you get these impacts, when this big corona mass ejection goes out, it creates a shock and so you get an instantaneous pulse of electromagnetic waves. And this can cause, you know, upsets in radio communications and things. It could almost cause a nuclear war once, because if you're looking with b-muse, you know, across the horizon, you see this burst and you think you're being jammed. That nearly happened, OK. The more slower thing, when this thing reaches the earth in a steady state a few days, you get ground impacts, you get, like, it upsets the ionosphere, so it upsets satellites. But it also cause a magnetic perturbation of earth. If you have a long piece of wire, like a power grid, you can take out parts of the power grid. And it's the magnetic effect on earth that I'm going to use to say something about space weather. So the thing about the sun is the sun has a magnetic field. You've just seen the outcome of that. It reverses. So imagine the thing the size of the sun, every 11 years, roughly, reverses its polarity. I mean, to me, that's just in itself quite stunning. And we still don't quite understand how it manages to do this. It's intrinsically tied up with how you create magnetic fields in the first place. But one of the problems for predicting space weather risk is the following. We know that when the sun's active, you're going to see more stuff, right? More space weather. And when it's quiet, you'll see less. But the cycle is never exactly 11 years. So you want to kind of compare cycles to see how likely are things at different stages of the cycle. So what we need to do is kind of create a uniform cycle. Okay, so you can see the sun spot record here. It goes back many, many years. Just to think about what this data is. It's people counting spots on the sun. So there's a lot of argument about how you normalize that data across, you know, hundreds of years. But anyway, we're going to take this data and we're going to build the clock from it. Next slide, please. And this is our clock. So skipping the detail of how I asked me off is how we do this. We put all of these at roughly 11 year cycles, not really exact 11 years, and we plot them as a clock. And so as you go around this clock, you're going around a solar cycle. And the power of this is I can now put lots of different data on top of each other because often the data we have, it's quite hard to compare it. How do you compare sun spots with activity with some guy who said, oh, my power grid blew up, you know, but we can overlay this stuff. Now, when we did this, I'll just tell you what we're looking at. So these are maxima when people look at the cycle, so that's where the maximum was or that's where the minimum was. Okay. This stuff is F 10.7. It's radio flux that people know how to do the ionosphere. We've got that for like five cycles. These bins are different classes of flare. We've only got them for a few cycles. You can see them here. This is the active region then. Now the black things, what I've done, I've taken a thing, it's called the AA index. It goes back about 150 years. It's pen charts. It's basically pen charts. People got the pen charts and they put a little bit of paper with a ruler on it and counted number one to nine and they reverse this back into what they think it is in terms of activity. 150 years with the data and these spokes, what I've done because this data is so kind of iffy, rather than take the value, I've said, when does it exceed a certain value? And so the more it exceeds it, the bigger the spoke. So the big storms are these big lines and these are the big whoppers that are going to do some damage. And when you look at this plot, when you do this, you see, well, actually, you know, you can see this thing can happen right at the edge of these lines I've drawn, switch off and switch on. Really nothing happening here. One guy, one guy in 150 years, there it is, one big chap. Okay. And when I know these times, this switch on, they still are upwards to what this is, but one of the things that happens is that the sun, when it's reversing slowly, it's got sunspots, the sunspots live in bands and they migrate towards the equator and I'll show things in a minute. But when they get to the middle, they annihilate and that's roughly when the switch on is. Okay. And when we created this clock, we kind of looked at it and said, well, if you take that angle and kind of reflect it, you get this switch off. When we first did this, I just know why we didn't know why. So the discovery is why this happens, which is really useful. So even before we get to that, when you've done this clock, when you've actually successfully done this, instead of just saying, oh, it's a bit quiet or it's a bit active, you can put numbers on it. Because I know when these times are, I can go back from the clock and map it onto the time domain. I know when it switches on and off and if I've got a model, I can put it on a model. And so I can say now, I know what the risk is in the quiet and the active times. And this is really useful for people if they know when the thing is and what the risk is if they're planning, oh, I'll do something when I satellite then. So it's actionable risk. Next slide, please. So now to figure out why it's doing this switch off. So I'm going to another long record of data here. So this is this butterfly thing now. So what I'm plotting here is time along the bottom. And this is latitude on the sun. And these are people's observations of active regions, sunspots on the sun. And you can see they start a high latitude and they can't go down and down and then the annihilate. And so what I've done is take the latitudes and plotted them as the kind of orange stuff. It's on another clock. It's the same clock. And so you can see all the storms again. And if you look, so the thing coming in like this is now spiraling in, this is latitude. So this is zero latitude in the middle. I've drawn a blue line at 15 and I'll show why that is. So you can see this stuff spirals in and spirals in. And when all these active regions have gone below about 15 degrees in solar latitude, they switch off. Okay, so next slide. This is what we think is happening. If you look at the sun, we know it's rotating. Okay, it's rotating, but it's not rotating at the same angular speed. So the equator goes faster than the poles. So this means there's a gradient in this rotation. If there's a gradient in the rotation, when you create these CMEs, it's a big piece of magnetic flux that's popping out of the sun. And if the sun is differentially rotating at different rates, it will shear it, it will twist it up. And that is what powers these CMEs. A lot of the energy in these CMEs is this twisting. And so this twisting is more pronounced where you've got a bigger gradient. And this black line is the gradient of differential rotation. And you can see it goes flat around 15. So when these active regions move but to within 15 degrees of the equator, the shear force goes away. And that is why the big CMEs disappear. And that is why these active events switch off. Next slide, please. So what we've done, we've now made a connection between the likelihood of big space weather events at Earth and actually the topology what's happening on the sun. And there's a big field of activity to try and understand the solar dipole and predict how the sun's going to behave. And so this is a real kind of new idea that we can use to get to the solar physicists to use them to help us predict space where the risk of most extreme events. And I'll stop there. We can all have questions. Thank you very much. So now we will move to the question and answer period of the press conference just as a note for everybody who is online. You can either raise your hand and I will see you in the Zoom and call on you or you can type on your chat a question and I will ask it on your behalf. If anyone in the room does have questions I will bring you the microphone for you to ask them. I will start with a question that has already been submitted through the Zoom chat which is from Jochen Stadler on the Austrian Press Agency. A question to Verna Grandel, please. How long do you propose it will take building such a habitat in Tranquillatis Hall from the first preparational landing to moving in? I think it will depend on political decisions, of course. We have now all the technology to do that but it depends on political decision, of course. I think it should be happened within this century. I think this is wide enough to make it possible. Any additional questions? A question about the detecting vegetation. So, can you explain well, it's a number of later questions. First of all, how would you be detecting this vegetation? And then given that porphylla is green, etc. that might be an evolutionary accident. What exactly are you going to how are you going to know what vegetation looks like on another planet? And a final question, this idea of obliquity affecting the amount of vegetation. Is this new? Has anyone looked at this before? Those are all great questions. So, I'll start with how if we viewed Earth as an exoplanet, what could we say about its vegetation? So, vegetation impacts the remotely characterizable features of Earth in several ways. The number one big thing is it makes oxygen and oxygen accumulates in our atmosphere to pretty high levels. High enough that if we put Earth on the other side of our telescopes, our planned telescopes, I should say, JWC can't see oxygen. But a habitable world's observatory could see that oxygen and it could see ozone produced from that oxygen in the atmosphere. A habitable world's observatory like telescope because it's a direct imaging telescope that looks at visible light that has traveled to a planet been reflected from its surface and back to the telescope could also see absorption by pigments like chlorophyll which absorbs really strongly in red light and then really strongly scatters near infrared light. So, it's not the absorption at a specific wavelength that we're looking for for something like chlorophyll. It's this really sharp spectral edge where you go from lots of absorption to lots of scattering. On Earth, that's referred to as the red edge because of the wavelength that chlorophyll absorbs at. On another world though, if the star had a different spectrum, the optimal wavelength for photosynthesis might be different and so the position of that edge might shift but it would still be a strange shape that we might hope to recognize. Was there another part that I missed? Oh yes, yeah. So, that obliquity affects climate and that climate can affect the success of plants is something that has been thought about that obliquity modifies both the success of plants and marine phytoplankton in ways that could dramatically impact the detectability of life though. It is something that's really specific to my group at the moment. Any additional questions? Yes. I'm Karl Orban, freelancing journalist for German Public Radio. I have a question about the space weather so for the two of you. So there are a lot of effort Enrico Campo Reale also talked about what NASA is investing. There's also SSR, SSA groups in many space agencies. And there is some kind of warning system in place already. So people are looking at the data. How would your work of the two of you could be used to improve this kind of work done right now? So what could be changed to have better predictions in the future than they are already done today? Yeah, I'm going to go first. So it's true. There are some forecasting models both in the US and in Europe and in other countries as well. I would say that most of those are very rudimentary still because in space weather it's a fairly new discipline. So the sort of the error bar if you wish that you should put top of those predictions oftentimes are so big that the prediction itself it's not very reliable. So from my point of view I believe and I hope that new machine learning models might improve a lot both the prediction themselves and the ability to maybe lower the error bar. And also in terms of how actionable those predictions are you need to be able to predict something far in the future. Obviously if you predict something like only 10 minutes before there's not much that the stakeholders can do. And that's another area where I believe in the machine learning can improve this lead time of prediction to several hours if not days ahead. Okay, so there's two things here. One is forecasting and the other is risk. And so what I'm talking about is risk and people want to know. So power companies need to know what the likelihood of a one in a hundred year event is and how big it's going to be. Okay. So what we've found is because we've managed to put this this variable solar cycle onto a clock and we found a really clear switch off and switch on. And the really interesting thing is some of the biggest events happen right close to the switch off and on. So it's not the normal idea as well the big stuff is at maximum right but it's not. So it's right at the switch off and on. So you kind of want to know when these are is going to be really useful. And so because we can get these timings anybody who has a prediction and a lot of people try and do is predict the sunspot number forward. You can put it on the sunspot number predictions but these predictions are all over the place. I mean if you go and look and say well you know the future predictions I mean particularly like for the current cycle I mean we did we did some some work and we were predicting quite a big you know decent sized one and everybody predicting a small one they keep adjusting this prediction to do to do you know. So the power of what we found now is there's more information than just sunspot number to get this risk. There is the latitude of the sunspots is the detailed information of what's going on on the sun. And so the idea is that we can use this extra information to improve our prediction of when the switch on and off are and you know so then we can wind it forward and give people decent estimates of these timings and that could be quite a powerful idea for planning. It's for planning purposes. I want to do this thing with my satellite. I'm going to launch people into space. This is when it's going to be quiet. And so that's the power of it in terms of risk assessment. There any other additional questions either in the room or online? Yeah. Yeah. I have a question to Stephanie Olsen also. So you have a use. You mentioned that planets without exposed rock would not be good places to look for life. But one of the places that you know is often mentioned are like icy moons like Enceladus, one of the Saturn moons. So does that mean would you would you recommend not to look for life in you know those kind of places or? For the most part I am an extreme optimist with respect to aliens. But I do have some concerns about worlds without exposed land. It's not clear to me how you could get an origin of life on Europa or Enceladus. And if that were an exoplanet even if there were life like under an ice shell for example we will never see that with a telescope. So this is like the one class of planet where I'm really quite a pessimist. That said exploring the outer solar system will teach us a lot and we should absolutely do that. And I would be delighted if I were wrong about Europa and Enceladus. Thank you. Any additional questions either in the room or online? Let's check online. Okay so that brings our press conference to an end. This was the last of our seven press conferences that we held this week at the EGU General Assembly. Next year's General Assembly will be taking place in April and early May from the 27th of April to the 2nd of May in 2025 back here in Vienna and again online. And so all that remains to be said today is to ask you to join me in thanking our excellent panelists for their contribution to the day. Thank you very much.