Hello. Welcome to Portland, Oregon. Those of you who are here in person. And for those of you who are online, my name is Bush Cunhaler, and I am a dean in the College of Earth, Ocean, and Earth Science at the Oregon State University. And we are right now in a jeep. We are right now in a building that is part of the Oregon State University campus. We call this Oregon State University's Portland Center. The building was originally a department store, Myron Frank, in the 1920s, I think, and was completely remodeled. And during the remodel, it was also represented for a seismic fix. So we are in a safe spot if the big one was to hit. But of course, before, way before then, this land was the land of tribal nations, and you might not be surprised to find out. You might not be surprised to find out that what is now Portland at the confluence of the Willamette and Columbia Rivers was a very important trading post for the Indigenous people who lived here before. And some of those folks, some of those tribes, you will see their names represented in streets and counties. So Multnomah, Wasco, Powlitz, Plachamus, Chinook, Tualatin, and Califuya, you will see all of these names all around us. So if you know that those are the people who inhabited this land for centuries, for generations. And so I do want to start out with the land acknowledgement. Oregon State University, and I'm going to read our land acknowledgement as OSU really paid attention to the wording. So OSU recognizes the impact of this land grant history on Indigenous communities in Oregon. It was the Morrill Act of 1862, which established land grant universities in the United States. Federal government sees nearly 11 million acres of land, or 250,000 tribal nations, with little or no compensation. Oregon State University in Corvallis is located within the traditional homelands of the Mary's River Ampinapu Band of Califuya. Following the Malama Valley Treaty of 1855, the Califuya people were of course leaving to reservations in western Oregon. Today, living descendants of these people are part of the confederated tribes of the Grand Broad community of Oregon and the confederated tribes of the West. Indigenous people are valued contributing members of Oregon State community and represent multiple sovereign tribes among our students, among our faculty, staff, and alumni. This is a part that really resonates with me. Oregon State University accepts its responsibility for understanding the continuing impact of that history on the communities. And we are committed in the spirit of self-reflection, learning of reconciliation, and of partnership, to ensure that this institution of higher learning will be of an enduring benefit, not only to the state of Oregon, but also to the people in our communities and your fellow community. I think this committee really takes this part, right? These are not just words. There are actions that we can take as a committee in the spirit of self-reflection, learning, reconciliation, and partnership. And in particular, over the past months or so, we've had some conversations about what it would look like to include the voices and needs of Indigenous communities, and the tribal nations here and other Indigenous communities elsewhere in the West, how to include their voices in the story, right? So if we're thinking about ocean finds priorities, we want to take into account the ocean finds priorities of tribal nations and communities. We will hear as little as that perspective here today, but primarily we as a committee decided that instead of trying to invite folks from tribal nations and Indigenous communities into the meeting and give them a few minutes to speak to us or be part of a nearly one-hour panel discussion, we have decided that instead we will go to them. And I already have some meetings set up for November and December, where I will go and some of you will join and we will go and talk to tribal caucuses, specific tribal nations who have expressed interest on their time, and of course only if they have time and willingness to engage with us. You know, they have other priorities as well. And I know there are others on the committee who are making similar contacts so that we have a better sense of nationally what the needs of tribal communities are, as well as Indigenous communities in Alaska and Hawaii, for example. Okay, so with that, I want to welcome you all here into this space both physically and virtually. It is great to see so many of our committee members here. And Kirstie, we need to make sure you have a seat at the table too. We need to space together a little bit more, rearrange the tables to make that possible. Yeah, we can do it now. We can do it at the right. Yeah, I think that's good. That's what your meeting has been one that I've been really excited about. And I will tell you I had a hard time sleeping last night so I'm caffeine depth so I feel excited. Looks like I'm a little over excited that's where that's coming from. And the reason why I've been so excited is because we've been doing a lot of work that's been focused on ocean drilling lately and I want to thank those of you who really have done a lot of sweat and tears into our interim report so far. You've been doing an amazing job. And those of us who have not contributed as much because ocean drilling is not part of our, we don't feel like it's squarely within our expertise. Please know that we have been reading and thinking about what you've written. And we want to make sure that this is the consensus reports. So even those of us who haven't written a word must must make sure that we agree to every word that report. I also promise you that we'll do a lot of heavy in the same way. Along with well coming of course you all. The reason why I was excited about this report is because we have a chance to really open our aperture a little bit more and really think about ocean science overall. Right. Number two, I will remind you and we've had this conversation many times that each one of us brings to this work is specific disciplinary length. Yes. But each one of us who that lens responsible for thinking about the ocean clients enterprise as a whole. Right. If each one of us only advocates for our piece of the pie, we will have failed. So please keep that responsibility in mind. We are here representing the entire ocean finds community. And we are here from into here from some experts about their ideas. Now, the intention that I set for this week. I thought to myself, you know, what, what is my intention this week and words that I came up with myself. Were pay attention. This week, these next 2 days, I want to pay attention. And what I mean by that is not just listen to the words. But really pay attention to the whole. And so I invite you to think about what intention you bring to these next 2 meetings. And make sure that you stick to that intention. Right. That we bring that kind of intentionality. Let's see what am I forgetting logistics. There's some coffee and pastries and fruit over just on the other side here for those of you in the room on the other side of the panel. The bathrooms are around the corner to my left. Down that way and. Oregon has is promising kind of a mixture of an October day for you, which is very difficult time of year. I love October in Oregon. So I'm really glad that we're all joined up here for the students. Let's see our agenda. We have a packed agenda today. We've got a great show tonight. We have a number of panels. We tried to really build in a few breaks here and there so we can network with the folks who are visiting with us. But mostly, I would say we have very packed agenda today. Again, try to intention, try to keep the energy level up and let's make sure we take as much out of this. We really appreciate that our panel members and visitors have joined us. So travel far away to be here in person. So let's make sure that we get it at 844. I think I'm right on time. Okay, I'm going to hand it over to Jim. Yeah, I'm Jim Yoder, my co-chair. And so I'm sort of moderating the next session, which is on ocean observing infrastructure and innovation. And so we have three people. We have a panel to talk about the ocean observing initiative, and I think we'll just let them start. I'm not sure if it's going to go first, but I hope for tonight. So I'm going to share my screen. I think we're there. Right. Hi, everyone. I'm Jim. I'm the lead PI at the program management office. We really welcome the opportunity to talk to this group to the channel. This is going to be a jam packed presentation.  Who's the most you and the insurance array that Kelly to my left, right. This is from the University of Washington. It was me, the regional cable director Anthony conference, maybe online. I'm not sure he's at OSU and he heads up our cyber infrastructure. And I'll put him in who may or may not be on either doctors. We'll see from what's all that he runs the global crystal science. So we were given a number of questions. I'm not going through all these we hit, I think just about everyone, maybe not in the exact order. And we went off menu, maybe a couple of times, talk about various things. The one bullet that we will not address because you will see after our science highlights number three, we hit all subjects of ocean science. And so, well, without much further ado, we do need to get through these. I'm just going to introduce the arrays themselves. What do I looks like. And what we're looking at here is 1234567 arrays, two of which are then discontinued. We might have some discussion about that at some point. But of the five active arrays, they support 900 sensors, 80 platforms, and we're hoping to keep this thing running for 30 years starting in 2016. And so we're about eight years in. And so what are these arrays. There are two global arrays in the Gulf of Alaska and you can determine your sea. There are two coastal arrays on the West Coast, the endurance Iran on the East Coast, the pioneer and there is our regional cable array operated on the sea floor off the Washington, Oregon coast. And you'll hear more about all of these arrays. I did want to point out the coastal pioneer array on the mid-Atlantic by is no longer there. We're going to have to put a little discontinued in 2012, I think, or 2013. I forget, I think it was fall 2012. The first one. Yeah. So that will be discontinued. What we are working on now and you can ask questions about this later is we're moving that mid-Atlantic by pioneer array as the name implies, it's on the move. It's in its chuck wagon and moving to North Carolina off the coast there. A lot of excitement is going to go around that. So do you want to make it very clear this is a collaboration between the three institutions that you see on the bottom that you can see on the bottom of my slide between what's all you do and urgency. So that's what we've got. So I love to work with you a physicist because you get some really cool photos. Lots more than that, but you get some really nice photos. This is a nice shot of hard to film event that depth group routinely makes movies of beautiful array. I as I mentioned, I'm a meteorologist bonafide meteorologist from Penn State. But I'm a marine meteorologist. And so I work on your seat interaction and I work with oceanographers on the near surface process. So this slide is meant to do thing to things. Oh, I operates and maintains sophisticated instrumentation and demanding work. And you can see a couple of lots. Yeah, so that is a, I imagine it's a three meter distance. So you're probably up. Well, I know you're up six meters. So six meters tall is where the animal is. And you ask questions. So in demanding locations like this, and we really, really try at each of these arrays to make measurements from the sea floor to the lower atmosphere everywhere. Right. That's our goal. We a lot went into designing the UI and anything in this room will involve and many meetings, white papers, you name it. There were some curve balls thrown at us at the end. But we have a truly remarkable observatory here are some of our science teams that came out of all of those meetings. I can confidently say that we've hit each and every one of those science things. Through the operation of. We will probably work on another one of these documents working with our facilities board, who is primarily responsible for this document with our health. I will probably do something just like this. And that's some again, any upcoming five years. So I'm going to do my best. Oh, and actually, I can really do my best. These are out of the slides that I said I would talk to him on. This is our, the previous pioneer, which is on the new England shelf. And he's on his way right now to the men at my inquire, but this place was pretty amazing place to do research. And it really did result in many insights related to physical processes in the region and you can read them in the butt in the bullets. But I want to make this point. The bottom line is that the dominant processes are not the ones we expected. And this is to look at how we get stuff across the sheltering from, you know, well, it turns out it's pretty easy to get stuff across sheltering from even from Block Island, where we saw influences about the sheltering from animal school. So it's something that we did not expect. There's some talk about perhaps putting an element back, but that's that's to be discussed. And the other thing that I wanted to make clear is that this started out mean is a physical oceanography experiment that in the last years, more and more biogeochemists have been using more and more and more. That is one of our goals is to get the broad community using certain rates. And now we're starting to get papers out of all of these sub-disciplines. Next one is this Armander Sea. It's a great place to do work if you're really interested in nasty conditions, which I am. High winds, high waves, you name it, we get it. We've gotten very good at maintaining these arrays out for a year, except if you look forward for a year. And a couple of highlights, very challenging environment. I think we know that. And really, really strong collaboration with OSMAP, right? And some of you are familiar with OSMAP. We formed sort of the western edge of OSMAP East is where we are. Right at the tip, you can see the tip of Greenland. Now, this is something that Al wanted me to mention. It's easy to forget the idea that Arming your sea was not a hotspot for, well, was not a hotspot for convection, like deep convection like AMO. It is an extremely important region for maintaining AMO, perhaps even more than the Labrador Sea that was its competitor. And remains its competitor, but it's very clear that the AMO receives vital for the maintenance of that and the weakening of that as we're finding. And then lastly for Al. So Papa Janie, it's been here forever and ever. And one of the key things about Papa and why you provided there, in addition to the long time series of atmospheric measurements, it didn't have so much to say about deep water in that region. So that is what OOIs providing. It is, it sort of connects with the other regional observatory that means RCA and endurance and Papa are sort of a triangle for things like modeling ex-validation and initialization of numerical models. And it's going to talk about that a little bit. And I think that's, I think I'm going to stop there. So one for the meteorologists. I don't know if there's any other meteorologists in the room, but as I said, I'm a green meteorologist. The OOIs, the only continuous network, measuring directly measuring momentum, the only one, right? We go on our research courses and occasionally we'll put these out. I now have five years of data from these various points. With that, you can do things like improved model for immunization, model physics, using this data. And some of you may be familiar with things like the drag coefficient or the buoyancy coefficient is the dolphin stanton number we use. But these are all improved learning forecasts of wind waves and currents. And then lastly, there is a group working to direct outside of the MIOs if you're helping, that are working to deploy their system like a directly measured CO2 exchange. Not just bullpen by direct Americans. Now, with that, I'd like to end this readover today. So one of the great things about the original K-literator is that it has a submarine bi-rocket cable that's unlimited power basically in bandwidth. It's used all the data for over 150 instruments from streaming in real time. And we have two-way communications. It spans the sea floor to five meters below the air sea interface. And this is just one example that this is not my world, I'm a geologist, but it provides unprecedented full waterfall measurements of ocean acidification. Most cruises go out maybe once a year and get a few samples. In this case, we have over 45,000 profiles. There's three of these that span blue ocean three hundred miles off the coast of coastal environments. And so, and the other thing about this is that it is, sorry. So this is one of the profiles that goes up and down. So it goes up and down nine times a day, three different locations. And one of the great things is it's incredibly multidisciplinary. There's nothing else that like it in the world oceans. They've been a real workhorse and it's connected. PHCO2 is connected to 16 other instruments. So it includes biological instruments. So with this platform, you can get a really amazing data set out of ecosystem and how it responds to different events. And this is the longest time scale, highest resolution continuous record of the ocean. On the other side, so this is more of what I looked at underwater volcanoes and hot springs. What are the incredible successes for regional cable array? This is 300 miles offshore. It's about a mile down. And there's a high candy here, but there's a full suite of instruments that are not only quarter instruments for the OI, but it's been a real hot spot for PI instruments on that again. So these are externally funded projects. Over 70% of the volcanism on Earth occurs underwater. The Mid-Ocean Ridge is the longest county on Earth. It's erupting all the time, but we're never in the right place at the right time to see it. So this long-term observatory has captured the very first time live underwater eruptions erupted in 1998, 2011, and then 2015. In 24 hours, there was a major seismic crisis with over 8,000 earthquakes and 24 hours sea floor fell about seven feet. And there's a really interesting microbial story where during these eruptions, there's huge microbial blooms with billions of micro-stream currents. So it's a super exciting place to operate. It's also a hotspot for many, many researchers because of the array. It's the best image underwater volcano in the oceans. And it's really changed. There was hypothesis for restful environments that the volcanoes have stacked magnet chambers. These folding tanks of lava. And this is because it's in water. We can image actually ourselves. Since it was first installed, we've had, there's 43 investigators from 23 institutions and over $21 million in funding that's been supporting this outside of OI that's been supporting this research. I'll touch on it later, but it's also approved for a drilling program on foot course observatories in the south surface to look at the sea floor bias. One of the coolest things I think again, not my world is this is probably one of the most exciting things I've seen for a long time. To me, it's like bringing the first telescope into the ocean. So with these submarine fiber optic cables, you can hook up a system in the short station. It sends light pulses basically and interrogates fiber basically the way that the fiber behaves. During that, pick up a real-time monitoring of earthquakes, volcanoes, eternal lands. And the cool thing about this is that it extends so far right now over 100 kilometers offshore and about every meter to 10 meters is basically a sensor. They're using this on land. They're using it to look at glaciers, but this was the first community experiment in oceans. And it was phenomenal. And one of the great things I saw, this is from William Wilcox. Currently, you do it when the cable is turned off. We had a maintenance grid and they measured over tens of thousands of whale calls in just four days. When we talk about big data, this is big data. It's terabytes of data per day. And now there's new systems that are coming on. So you don't have to turn the cable off. So this is a great opportunity and I think look in our future. We'll hand it over to Ed. One of the main reasons we have observatory science and oceans observatory is to look at long-term changes in things, including effects of ocean acidification, hypoxia, wave climate changes, etc. In the coastal and in the ocean. And, you know, endurance arrays is one example of this early science. And in particular, recently, there's been a lot of interest in green carbon dioxide removal, wave energy, offshore wind energy. There's interest in using long-term observations or backgrounds, environmental information. I think one of the more important things that observatory science gives to the community is the validation extrapolation of in-situ measurements. And time series measurements are quite expensive. And in particular, when you're starting to take a lot of different multi-disciplinary measurements, the way the ocean observatory system does is something that can't be replicated at every place. And one of the big things that people want to use to replicate or understand and extrapolate pushing observatories measurements are remote sensing. So this is some early work done by Hendricks by the Sol right here. And what it shows is in-situ chlorophyll on the horizontal axis and satellite retrievals on the vertical axis. And what it shows is that, you know, if you just look at the chlorophyll estimated from satellites, you get much higher estimates off of Washington than we do off of Oregon, in particular in the near shore. And the indication here from the comparison with in-situ measurements is that the scattering is actually affecting the satellite retrievals of chlorophyll. It gives us a way of better interpreting those satellite measurements. And that's one of the reasons that they go up to four now proposals to the NASA pace validation were asked to use Hawaii data or the world of Washington. And then similarly in-situ measurements can distinguish between surface phenomena or subsurface phenomena. In particular, we're seeing climate change play out not as a steady increase in temperature or decrease in oxygen, but in terms of events that happen rapidly. And the in-place measurements provided by pushing observatories are one of the ways to look at those measurements in real time. And so this is an example, you know, the famous and from this world law from the early part of the 20s. And just using satellite images, what's seen in 2014-2015 is a pretty impressive area of warm temperature water. That's that area is replicated in other events. But what really distinguishes the law is its persistence, but also the anomalies seen beneath the surface rather than just atmosphere. So that's what's shown at the bottom slide there in the space using Hawaii data from profilers at the station. Some of the kind of ongoing innovations in ocean observing, you know, I think one of the more important things we can start to do now is to, is to pull together ocean observations from a number of different networks. There weren't these specific as one of the most highly sensed areas of the world. And what's shown here, this is from a 2019 paper by Barthendoll. The figure is from Ocean Networks Canada, and it shows the number of observations it's even hard to see at this scale. There's a large number of observations that span from the northwest of the United States up into Canada and include not just Hawaii Ocean Networks Canada, but centers from government agencies, private and now even close to tribes are making sense. There's a lot of Oregon and Washington. So that's the part of improving our ability to work across these observing networks, combining data from them and make the whole greater than some of its parts. This is a worthy goal for the next 10 years. Other things, you know, on the previous slide we showed the Marine B wave, better mobile technologies, I think that's important, in situ and detection and identification with changes in ocean ecosystems. The Ocean Observatory's initiative of sensors that we purchased back in 2012, they include measures of ecosystem function, but we have much better sensors now. They're going to start to look directly at DNA, indicators of human composition, better indicators of ocean port activity, coral algal wounds, and so forth. And then, the validation of the observatory data is important, not just for remote sensing, but also for models including physical models, by the chemical models that the model that our program created is the live ocean model that's shown on the right-hand side there. And if you look carefully, not only is it just temperature and splinted, but also oxygen, this model has in it things like arachnid saturation, nitrate, any number of different parameters that include, again, measures of ecosystem function, and we can now start to validate those models using this data. One of the challenges in terms of this validation is that modelers have a tough enough job ahead of them just making their models run, and they're not expected to be experts in all the conventional sensors. As we start to get into sensors that go beyond things like temperature, humidity, wind, and velocity, it's important to find chemical sensors. We've heard from the community that modelers in the first century are poor data sets with quality and full data, regular time and space and calculation, and embedded data, including error bounds. And putting it together in that kind of data sets can be a big challenge when you're taking as many different types of measurements as something like OLI. One of the things that we do, I think, a positive impact, a broader impact on the community, is that as we operate so many different types of sensors, we have some leverage with manufacturers. And so these are some examples of ways that we've improved measurement technologies through, I'm not going to name specific vendors here, but you can see them on, as indicated on the images, we've improved the vendors, dissolved oxygen to flow calibration procedures. We've worked with provider manufacturers to update their firmware to lower power consumption and meet specifications we developed for OLI. And then, especially on the cable array, we've worked with manufacturers to improve groundfall testing for external requirements for this. We have annual meetings with vendors to coordinate maintenance schedules, to discuss product updates, issues, obsolescence, et cetera. Observatory is also possible because people research and workforce development and a number of different ways. I think it's a great plot. It's worked out by Briana Velasco back in 2020. It's part of a virtual ROU program that was run by Rutgers University. And so the Rutgers Ocean Data Labs are probably familiar with it. Many of you are probably familiar with it. Well worth checking out in terms of their ability to facilitate use of observatory data for student lessons and for student research. And what Briana did here, she was working with Rachel Hevella at Oberlin College, so going outside the bounds of sort of traditional oceanographic institutions. She's looking at ERC, PCO2 off of Washington, or the Cognitive Shelf, and you can see here the ORM shows the PCO2 in the ocean, the blue is the piece of air. And you can see here that the upwelling system is a significant state of carbon dioxide. So again, observatory data in conflict with other things can really enhance student research. So one of the great things about the ORM is it's a program out of the University of Washington to take undergrads. Grad students are the group folks to see with all of our pre-use. Our students are usually 45 days. Over the last few years, we've taken, that's the last decade, we've taken over 200 students to see. Some familiar ones, Deb Lixon. So these are a couple of highlights of Deb Lixon. We know that Deb Lixon's student is now a director of science and resources, National Academies. He's worked with us for a couple of years with an undergrad. He went out on numerous vision cruises. He's director, director for Water Instruction and the White House. And we just got a new NSF postdoc. So really forms of foundation for workforce development in many different areas. And the students go on to work in engineering. There's no requirements for the students. Typically they'll go for two weeks to 45 days a week. And many of them have gone on to work for industry, biotech firms, geotechnical firms, academics, coast guards. So really we're bringing up a lot of students say this is fundamental change in life. So I'm really happy and humbled that I get to do that. So we have, there was a question about our impact, our measurements in life. And this is a nearly a decade of ocean observations through the observatory. This is just one example. If you could hear that, you would hear that it's whale calls. It's not your phone data. So we can track mammals off. It turns out that thin whales, fossilizations have changed over the last decade. I'm not quite sure why. The best hypothesis I like is with the embargo on whaling, they don't have to yell as loud to scare their friends. And then I mentioned already one of the newer instruments that we have, which is also really exciting. So the DK sonar, DK 60 sonar, it's a solar that images organisms in the upper water column. But in this case, this is a great example during the clips where the zooplankton, they go to the surface at night, so they don't get eaten. And then during the day time they go down. And so they, they were thinking during the close to come back up again. And we just refreshed this. There was a question about fisheries, this DK 80 sonar. It has two different kinds of functions. And one of the functions, a broadband function, enables it to actually quantify fish in the upper water column. Now, so that's a really exciting technical look. So we also have a variety of cameras in near shore, deep, and then clear off with access C map, access C map. We have a dedicated fiber for that. This seems video of the hypothetical events, looking at the vent communities, how they change over time. It's completely the only place in the world. And that can have one also that looks at the biology, how that she means a lot over time. And there's a great average. There's lots of students that are working on that vent imagery. So we get to quantify the temporal spatial changes and vent environments. We also have a really unique instrument. I think except for the EPS sampler on by a bar. This is one of the few samplers in the world that we actually have a tube that goes into a hard to thimble vent. There's a lot of interest in viruses on the deep biosphere, how the microbes in the oceans evolve over time, especially when there's volcanic events or earthquakes. And so this is an in situ sampler that measures the samples of fluid temperature and chemistry. So you look at the change in microbial timings over time. And it's a NSF awarded recent Anderson, a little career award and she's been out there for about four years looking at the samples and looking at viruses. So these science series really provide a, one of the kind of time series of microbial viral viral management over multiple years. Okay. So in order to deliver all this data, we have to have a world class cyber infrastructure on the system. And we have just that at Oregon State University at their data center. It's really is keep in mind that the day, all the data we're collecting is freely available to any, as long as you're on the internet. Actually, it's democratization of the data. We're trying to be here. The center itself is just focusing on low risk, but cost effective ways of that allow us to do things like improve compute power for things like Jupyter notebooks. It's what they work on. Modernizing the storage facilities, both in Corvallis, but also in bed for additional safety where they're real big on cybersecurity in our data center. So you can see that provides a secure daily. They do file a fair and trust principles and we do provide a lot of extra storage. It's not infinite, but we are expecting the data to be quadrupled by the efforts program. And so we are planning ahead so that we can accommodate that. This is just some metrics that we use to show how users are near the type of usage of this data. The first one is, you know, I will say the old way does not require users to register before they use that was a decision made a long time. And there has been some discussion about that, but that's how it remains right now. So we have to find creative ways of counting people. And one is through Google analytics. You know, take it with a grain of salt, but this profile shows the accumulated number of distinct users based on IP addresses. Since the data explorer system that we developed was christened in 2020. And so there's, you can see it's well over 5,000 users accumulated because these are distinct. It's accumulated, but if you go in 10 times, you get counted. That's that's the deal. So you can see from that curve, lots of users. And of course, publications are our currents. And we can see there on the top right, that from starting in from OLI 2.0, which began in 2018 to the present to OLI earlier 2.5. And we've had a dramatic increase in the number of publications. The last column 2023, that's halfway through. We do have calendar year. So I have a whole another group of publications that will be added and then one more before Christmas. And I'm hoping to meet the record, which was just. This one's kind of cool. As I don't know, people realize it's very difficult to get information about funding, especially dollars. And so you can get it through NSF, but that's not the best search engine I've ever used. So what we've been doing is, is data mining, data mining acknowledgments of all of the publications OLI publications to find out if they reference a fund. And most people do. We're smart. We want NSF or O&R or DOE to know that we're using their money wisely. Here's the publication. And so what we're seeing down here is it's still primarily NSF, as you might expect, but you can see it's a broad. It's a diverse number of funding agencies, including our NASA know, but interestingly, Europe and the UK are two of our biggest users. I think they're, of what the usage is through those, all of the countries in the EU and the UK. Yeah. Does this break apart funding when it's like multiple funders? So like, let's say someone. That's a good question, right? And please note, this is not money. This is, these are people that are indicating they use the data. And to your question, though, yes, I will, because it's, it's a, it's a bit ad hoc how I'm doing it. But if someone says NOAA and NSF, I'll check both. If it's very clear, I'll check. So again, it's because it's counting and it's not money. That's a good way. So let's keep moving. Early on, it was. Iris is basically the data base that everybody puts their seismic data in. So we did that from day one as far as we go to look at the seismic data and store. And so this is just another population of really good population of people over 800. You need addresses. And you're looking at the seismic data. They also bring in low frequency, high performance on pressure data. And planning is underway to bring these data because they're coming in at real time. There's a shake alert system that's now been installed. And so bring that into early with quite boring system because we have very poor measurements. We can do it on a continent, but very poor measure. It's not, not as good as measurements in the ocean. And it's kind of critical for figuring out when a seismic wave is going to hit the map of condition. And I should also say that from the devote who are quite one of the big lessons that they really needed. But that's not being ensured. They really needed near shore measurements of pressure. And our system is one of the few systems that has that near shore environment. So we can have coastal warning on this in place for some. Market line in 2006 said, if you build it, we will cut. They will come. And one of the great things about RCA is it was designed and built for looking to build a seismic data base. And it was designed to build a seismic data base. And it was designed and built for looking towards expansion over 30 years. So we have a lot of bandwidth and power for people to add into the sun. We have over 78 total funded awards. That's PI and several awards from lots of different institutions, 38 institutions. Over $46 million of outside funding has come in on to OOIs for the RCA. And a really diverse portfolio, which is something OOIs proud of, NSF going on and NASA. And this doesn't count as ship time. So if you figure a ship and an ROV is $100,000 a day, it's a big investment outside of OOIs. And I'll put this one up. I like this one. There's always been questions about OOIs and who's taking advantage of all the data that's coming in. And there has been a tendency for our PI, so our colleagues to think that it's all going to benefit the MIOs. UDUB, OSU, and who. Yeah, well, they benefit from the data. These are just researchers at those institutions, of course. I'm going to do research for three of the biggest, three of the largest research institutions in the country. So we're going to have some large fraction of PI. But what this shows, it's not the dominant fraction. It's about a third versus two thirds publications by institutions. All of the yellow is all of, a lot of that was all of the other science highlights that we provided are from outside our home institutions. And the same can she said about the NSF awards, here it's, well, it's actually closer to a third there. It's a quarter of publications by institutions. So we hope this kind of puts to bed this notion that we're the only ones that, that is not true. Everyone benefits from OOIs. So maybe I can take this, this is, so this was the parade for us slide. I have to say we submitted a proposal in 2022 to renew the OOI for five more years. And we heard about three weeks ago, I would say, that we were provided that award. And we are another five years. We're extremely happy about that. So this running is sufficient to maintain and raise them operational for another five years. It does provide as, you know, if we get what we are hoping to get from NSF, we does provide for significant support for increased capacity for the cyber infrastructure. It does provide funding for small teams, data teams for QA, QC activities, and something we're calling data ambassadors where we will set a team of scientists and data specialists to various places, community colleges, research universities, you name it, to explain how to, what the use of OOI data and how to access it. So that's something we hope to be able to do. And then you could look at us up here. We're not getting any younger. We really like to bring in some younger scientists to learn at RD before we move on. And this would allow us to do that with three associate project scientists playing with the OOI team. And I don't know if you want to speak to this one. Go ahead. Okay. So this is a nice point. I think Debts made this. The OOI team has significant breadth of little depth, right? We do cover a lot of things, but we have to grab someone who's working on, you know, by due chemical sensor quality assurance to go to C and run with CTD, which we don't have people dedicated as much as we'd like to things like data, especially quality control, quality assurance. This would allow to have us these enhanced data team members. The OOIs get kind of old. It's a lot of the equipment has been out there for 10 years. And you can only refresh instrumentation so much before it just breaks and there's no fixing. So we are desperately in need of both tech refresh and physical infrastructure refresh along with cyber infrastructure. And also we have these real exciting new technologies that are coming online, like the flow side of OOI. People are really excited about the pioneer array and the things we've got. I'm sure it's going to find its way to the environment's array and not do this in the future. So we show these things because we have the question, what if we get level funded, like we had been level funded for five years at 44 million for 2.0, no inflation, no increase to the tech. We got it in 2.5. If that doesn't hold, if we don't get the anticipated amount of money, it's going to be very hard to conduct these sorts of things. And something's got to get if that happens. So we're hopeful that it's all going to work out just fine because we're really excited about the opportunities that the new budget provides. But let's keep it finished. And then. So one of the questions was about the interface of the, do that this regional table rate team interact with the seismic seduction zone for the community initiative. And we're super excited about this. William Wilcock put in a MSRI proposal. It goes through a lot of pre-proposal, full proposal. We had a reverse site visit with NSF. And we were, he was just awarded, their DIs are from the UW and Scripps. And this is a $10.6 million award to add on to the infrastructure to look at the seduction zone on the Cascadia margin. And what you show here is the two table arrays that come out from the city. The one on the bottom runs out to Axl-CMAT. And then there's another one that runs up to often Newport, Oregon. The exciting thing about this, so we do have a small suite of instruments. It's very rare in the world's ocean staff instruments on the subducting plate as well as on the margin. So it's very hard to look at the coupling between those processes that happen when the plate's been seducted. On the Cascadia margins, so the last major nine magnitude earthquake was in 1700. Again, tsunami came ashore. It's had multiple breaks. And it historically, when a magnitude kind of earthquake occurs, it basically unzips the whole seduction zone from Northern California all the way off to Vancouver Island. That plate has been locked for a long time since 1700, meaning that we don't see a lot of earthquakes. The only place, luckily or not, we do see a lot of earthquakes is right where our array is. And those are those green dots. So the interpretation is this is the one place in the Cascadia margin that it's not locked. And so the 4D community has also been in this. The next one. This is the infrastructure that we're putting in. These are one of the nice things about this is some of the instruments were funded by Princess. They have the P.I. instruments on an accesse mount. Would not be that they would be tested there. And then they could be transported to the Cascadia seduction zone. And that has happened. So we have lots of pressure sensors. We're putting in three new junction boxes. These are small substations on the C4. And these are big enough. They have enough power and bandwidth that there's fours available for other research scientists. So it's not just for this installation. It'll be about 13 days on installation in 2000. 26. There's also educational programs. And all these data will flow into Shackler and the Know-It's tsunami warning system. It involves the P.I. There will be a science oversight community or science advisor committee. And this includes involvement with Crescent. There's a meeting going on this week. It's a large $18 million reward to OSU for Cascadia region. There's quite a science center. There's the 4D community. I've seen a lot of endorsement letter from one's proposals. So there's been lots of communication between those. And then there's also a large program for looking at the relationships of indigenous people hazards along coastal. But there's also a question about the IODP. So there's been a long history of IODP drilling. Both on the Wadafika plate as well as along the margin there. And there was a series of three proposals that have gone in. There's one from UNC or from Canada. It goes off of Vancouver Island. There's a full proposal 947, which is to look at the four goals that court conservatories from the north and the south. And then a particular interest is, I should say, you know, there's no place in the world that's planned, spanned on one of the pipeline right now in terms of looking at cable conservatories and putting court conservatories. You guys heard about these downhole. You make downhole measurements. So high-sphere seismic, lots of different properties. Williams, again, you, there's lots of PIs. So it's not just going honestly. There's a pre-proposal that went in to quick. So the idea is that blue circles there are the drill sites and then court those in cable. So again, we would get real-time measurements over years to look at the deformation and the hydrogeology along the Cascades. And say, I want to ask you a little quick question. And you look at your NSF stats for your budget, does that include the cost of ship time or is that not included in your money? So as of this year, starting with this year, we will no longer get the ship support, you know, to our home institution, which just has been a pass through to new halls.  So it's not included in a wide budget. So it's about 220 million that is being shared amongst the MIOs. It's about 61 million. They removed that's going down the street to ship. It's not an ROV. And the other quick question, just one for all three, actually is if you had, you know, a couple of minutes with the NSF director, what would you tell them was one of your most or the most scientific discovery that you're part of the observatory that was made the last, say, five, ten years? Who wants to start? So in my, so it's sort of the lead PI, I kind of see all of these observing systems. I would say, and I've seen Al Fluteman give just amazing talks about what the pioneering is doing for science on our coast. But I've been really impressed with the armor you see. And the cooperative nature of the science being conducted with host snap. We really do help. We help them. They help us. We have gotten some really nice papers written as a result of that. And so I'll leave it for me. That's Contribution. It's contribution. Whether it's weakening, et cetera. We are making some of the data that will allow them to say definitively what's happening. From the geological sector, as they ask to see that, we had during the ridge program, there was a set of views of the stories that this is the first place, only place in the world where we're getting all the real time data. And so we have to look at the biological to physical chemical properties that happen.  But it's also the only place where because we have a time series data set on how the volcano reforms, that it's the only place right now where we can forecast when the next eruptions can take place. So again, and I think the thing to come out of that is that we get the real time data and it impacts not only the chemical nature of the hydrochemicals, but also the biological activity. And that's been there's no place right now where when a volcano is erupting that we get there fast enough to look at the some of the major things that happen in terms of heat, chemical, biological transfer from beneath the sea floor to the water. For me, it's the short time scale and space scale variability in particular the current system. The rapid changes in each CO2 and the associated kind of ecosystem changes that occur. It's not enough to have a lot of measurements couple of times a year. The real time variability is extremely important. And could I add as the meteorologists know, I'm really proud of the fact that we are improving marine forecast of ring waves occurrence. And the fact that we're directly measuring these fluxes surface stress and heat exchange is making that awesome. And so, you know, I, I, one of my missions is to bring more meteorology more atmosphere sciences into the program. And I think you can get them excited. I'm going to remind folks in the room to both respond. So we were thinking to equalize the online and in person participation that those of you in the room, if you have a question, please put it in the fly go. And if Jim choose to use that question, then you can, you can ask it yourself if you're in the room. But that way we'll have four representations. Does that make sense? Right. We'll start with the brandy on this question. Okay, thanks. So my question is, is the previous source of both was fairly critical and raised a number of key issues and made a kind of negative recognition of the program.  The question really is, you know, how have you responded to that? And where does OI in your view sit relative to those critical comments? I mean, I can speak that it's not as painful for me because I've only been at this for two years, right? And a year and a half. Because what I've seen is they've addressed the two main issues, which was difficulty with the physical infrastructure and getting things turned on time and getting sensors. And also the cyber infrastructure. I think we've done a fantastic job at turning the race. I think we've figured it out. I also think that the cyber infrastructure center at OSU is now delivering data. That was the whole point, right? Is to get data out to people to archive it so that we know that we're going to have it for 30 years. Maybe I could talk about this a little bit more. But those two things were kind of key things. Things that were not working well that have been fixed since 2018 when OI 2.0 took over to the point where in fact, the matter is NSF buy this with the opportunity to renew, not recompete. Because we were doing a much better job in the middle. Yeah. If I can just chime in on that, because when I was at NSF, it was when the transition occurred. The previous OI that the previous DSOS was responding to was administered the main pride was consortium corrosion leadership. And so COL was the pride. These three institutions were the subs. That structure was very difficult and challenged. I don't want to speak for NSF now. I can speak for NSF then. Very challenging. So it was recreated to be NSF to be OI 2.0 five years ago. And this team won that competition. That was the fundamental in my opinion. Fundamental change facilitated the type of work these groups are able to do. Okay. I'm going to go down the list here, but so too bad. Follow-ups. Yeah. So you spoke to some of the things that you did fix with 2.0 to really get the data out to the people. You know, I'm drawing some parallels here with the IODP program that we really should dive into, you know, a lot of these data, but how do you make that handshake so people can really use it? And so can you speak a little bit to the remaining barriers? And I'll also add to that question. I was impressed to see that different uptick in 2018 and publications, of course, publications lag the data being a little bit, right? So it's probably coincidence that that's right. In fact, whatever you did probably is right now is going to lead to different publications. So tell me about the remaining barriers though. Where do you feel like NSF could help you, or we could help you in the work of getting the data in the hands of the people you like? I'll take the first shot at that. I think one of the main barriers is to chemical and biological data that the sensors used are significantly more sophisticated and have significantly greater issues than sensors that measure physical and physical parameters and use a lot of parameters. And so we have a significant amount of expertise in house, but it's a limited number of people. And we are working our way through what we think of as industry standard, which is the NOAA developed core talk, quality assurance of real-time oceanographic data. We're working our way through that, but in many cases, additional quality control is needed to make that data improve the utility of that data. There aren't a lot of observatories that are making, a lot of pen series measurements of ocean are still for a relatively limited number of variables. And so we're, you know, it has a larger number of types of different variables than many other different types of observatories. Probably the closest to analog might be the Bi-Jic and the Hargos. We can learn from Bi-Jic and the Hargos, but we are active participants in ocean best practices. So we're engaging with the community. Some of the things that I think can be done include, you know, facilitating subject matter experts to use and improve regulatory data is something that we've done as well. Because with the staff we have, and we've used data numbers that we've been able to hire, it's never going to be enough. I think one of the other things is maybe a generational thing where, you know, we have such a fire hose of data, one hertz data, or higher. Imagine that with 18 instruments all at once. And so I think that some of the biggest breakthroughs we're going to require new eyes on, how do you visualize those in a 20, 30-year time series that are changing over, you know, very short to long time scales. And so we've had to move one of the stomach blocks as the data sets are so large now we've had to move our QHC on in the cloud because just to visualize the data along the QHC requires that we need to move it on the cloud. And so I think the younger folks, they don't mind, they're using Excel, right? They use, you know, Jupyter Notebooks or Python. So they're getting very enjoyed, they're super excited about it. But I think there's a generation that doesn't quite know that technology yet. And so I think one of the powerful things on my line is AI. And I think we'll also need that for QHC. A couple people have asked about the relationship between an OOI and I, if there is such a relationship. How does that work out? You get a lot of work. Yeah, so I think, Additionally, there was the start of OOI, there was a statement made by NSF that the OOI was NSF's contribution to IOS. I think they later walked that back. But having said that, we are partners with IOS in many cases, the regional arrays, our partners with their regional counterparts, the regional OOI arrays are regional counterparts. We attend a lot of the same meetings. Our data can be, because our data is only available, it can be picked up and reused in these systems and it is reused in these systems. And we also, again, kind of thinking about a broader collaboration with OOI, OOI Neurological Data is going to NBBC and that enhances the use of that data by researchers as well. It provides that data to the global telecommunication system, GKS, which despite its kind of strange password number, strange name, is the kind of operational way that oceanographic and neurological data is delivered for model forecasting. But that is getting used as a weather system. And I do want to mention that the ocean sites rule. We were also starting to engage with the ocean sites. I had a chuckle in this, I remember back in the early 2000s many arguments, no brisket. This relationship, the difference between the ocean site and the ocean site and the ocean site. And this has taken pains to tell us we are not an operational system. But nonetheless, when we are operating, we make that data available to operators. We'll also ask the share of the data. Can I have a follow-up? Yeah, go ahead. I think none of these are going to go away. I'm also interested in how Oval Eye, I used this other concerning entities. How do we get a comprehensive look at what's being measured, what's not being measured? I think we look at these program by program, but not in like a comprehensive, not a great visual from the ocean Canada that's kind of what the family was posed. But it would be interesting to know what's missing, what are those signal areas that should be looking at them. I think that's something we should be thinking about and leveraging on. Yeah, I think there's a lot of examples now. Things like GliderDoc where it's a way to bring all those people that are running gliders on both coasts into a central system. We are definitely talking more and more with like I just mentioned ocean sites and Argo. How do we bring these together? Is it just going to be a web page where we have linked all our stuff? That would be a start, right? But that's the type of conversation that we're having now. We are bringing that conversation actually to the COP 28 meeting. We are organizing a session. It's actually, it's not, it's called a site event, even though it has like 200 people are invited to be site events. And it's being co-run by Olaibu and Pogo, which is the Plymouth Marine Laboratories facility with COS. So we're bringing the community together. It's a start and it's, you know, likely we are sharing data directly with NBC. Whether we can do that on a larger scale, we'll work on. I think we would all like to see that happen. Yeah, we're running out of time. I think it's easy to do that question about ocean data assimilation. Right. I was curious to know if you have any examples of modelers who are actually assimilating data real-time through weather, ocean weather. So glider data did go to the glider data assembly center and glider back. And so that data is incorporated, I think, in data assimilating models. The person that I think of most, it's most closely associated with data assimilation. I'm not sure whether he's now using photo-eye data in an assimilated way or whether he's still using it as primarily his validation. Is Alexander Korobat, who's associated with the West Coast? I guess just that's a, the intention of every kind of question is trying to see what might be involved for assimilating data. And the people that I've talked to, including Chris Edwards and other folks that assimilate data that did point it out over and over again, the, from their perspective, the barriers are knowing the, getting a data set that's interpolated on a relatively even basis, but also knowing the error bounds. And in some cases, it's not a matter of knowing the exact sense of accuracy, because we have specifications on that. But really in the end, what's the in-situ error bounds of the data delivered as a whole. And that's a bigger question than the specific accuracy of being censored by a scientist. So there was a, at the Ocean Observatories Initiative, SOTI board, there was a lot of respect in our models there. And they are very strong opinions. But they would like to basically give them curated data sets. So completely, you know, take CO2, take all the flyers out and have it in a specific format that they can just plug it into their models. And that was a, I think it won't come out as a strong recommendation. It's been something that we've shied away from to some extent, just because of the enormity of the task. But there's also a place where quality control starts to shade into scientific interpretation about what's good and what's bad. What might be good for one purpose is not necessarily good for another. In many cases, I think data that the model, modelers they want to use could have looser error bounds on it than people that are interested in very finely looking at, you know, long-term trends. And then the evolution of 1.0, 2.0. Early on, we were told, we want you to give us the raw data. We don't want to work on the data ourselves. Okay, I think we've got to move on.  We have one of them here today with us in person, Melanie Peeing. And am I correct in that the remaining four are online? Yeah. Hello, hello. I'm now using a microphone for those of you who are on Zoom. Maybe this might improve your sound experience. Okay, I'm having some echo here. Melanie, can we invite you to be up front with us? Would you like company? Shall I sit with you or are you okay? Thank you for going alone. I'll know you're right there to my right. And do we have our other speakers online? Lauren Thompson, Craig Lee, Jack Barth, Joe Schumacher. What do you say, Zoe? Are they all with us? Okay. Excellent. Thank you. So this really, this panel is about challenges and opportunities in physical oceanography. And part of the reason why we wanted to hear specifically from physical oceanographers. Well, one is there happened to be a lot of high quality physical oceanographers in the Pacific Northwest. So we wanted to take advantage of the fact that the meeting was a little bit different. But also at some point, you know, we did hear that perhaps we didn't have a lot of physical oceanographers on our committee. Some of us feel like we know a lot about physical oceanography, but may not be card carrying physical oceanographers. But I'm inspired to hear more here from these colleagues, especially because just yesterday afternoon, we celebrated one of the great coastal physical oceanographers, John Allen, who recently passed away. And it was really great to kind of reflect on the approach that somebody like John Allen would take on combining observations, models, and really getting a deep sense of processes. So really approaching it from both in miracle modeling, theoretical and observational perspective. So, and I think this panel today will be able to give us that multifronged view of the field as well. Okay, with that, I'll pass it on to Luanne. I think you are first. Is that correct? I'm up. I assume you can hear me at this point. Very well. Take it away. Okay. So I am going to talk about dynamics, modeling and climate. I am going to give kind of a large scale perspective on this. So if you could go the next slide, please. So first of all, I wanted to say that, which I'm sure you guys have already talked about, that sustained observations are needed to detect and validate models. So things like satellite, sea surface height, sea surface salinity, sea surface temperature, et cetera. And of course, Argo, the temperature and salinity profiles. And then as we get towards longer time scales, we repeat hydrography program as well as deep Argo. So go ahead to the next slide. So I wanted to sort of set the context for why we need extra effort in this area. And to remind you that the climate model inner comparison projects that are models that are used for climate protections and projections and IPCC have about a hundred kilometer grit. And we know that that misses an awful lot of the dynamic. And in particular, I chose this figure because it shows the really large biases and sea surface temperature in Eastern boundary uphelling systems where up to 40% of global fish catch happens. So when we think about climate models and what's done in the IPCC, it's really missing and has an error. A lot of the important things that we're going to do is we're going to look at the ocean scale. So this has an error. A lot of the important regions of our ocean for people in the planet. Go ahead to the next slide. So I think the challenge for ocean modeling compared to the atmosphere is that the ocean scales are small and the motions are slow. So this is just a snapshot of sea surface temperature from August 12th, 2023. And you can see Eddie's lots of small scale variability as well as large scale imprint of climate. So can we increase the spatial resolution particularly to the sort of climate projection class of models from 100 kilometers to say 10 kilometers where we're starting to resolve Eddie's. It creates 100 times the amount of data and the time step has to be smaller by a factor of 10. And that makes those models about a thousand times slower per model day. And we have to recognize that the ocean takes centuries to spin up and that we need multiple simulations for climate projections and for climate predictions. In addition, this is big data. Really, really big data. We're talking of petabytes of data. So the analysis needs to happen where the data resides. This is not something where you're going to analyze the data and analyze it on a local computer because we're talking about petabytes of data. In addition, regional downscaling for projections can also be used, but those regional downscaling will inherit global model biases. The next slide, please. Okay, so those are sort of the big issues I think for the future for both climate projections and for climate predictions. But I wanted to bring up another issue that I've thought about lately, that workforce issues and ocean dynamics and modeling. There's been a brain drain to industry that's pretty astounding. Five scientists from the ocean section at the National Center for Atmospheric Research. So these are the people that are creating the ocean models that are sort of the community models for global climate prediction kinds of things. And one professor from Columbia University that is now working in industry and no longer working on ocean modeling and dynamics. The other thing that I've noticed recently that there's actually been little progress and maybe even backsliding in gender parity in ocean modeling and dynamics. So here's a few examples. Even before this brain drain, there were zero women on the tenure track equivalent in the ocean section at the National Center for Atmospheric Research. There are two women out of 12 in the federal workforce at NOAA GFDL. Could you go back, please? The Ocean and Cryosphere Division. And recently I went to a workshop and an invitation only and only 25% of them participants were women and it was on dynamic sub mesoscale air sea interaction workshop. Okay, next slide. If I have time. So I wanted to also put in the wild cards for thinking about physical oceanography as we go into the future. The rise in offshore wind and wave energy and thinking about what are the modeling prediction and workforce needs within that. And then also preparing for rapid sea level rise, Greenland and Arctic ice sheet melt caused by potentially regional ocean mourning and what is our role as physical oceanography as we look towards the future. Okay, I'm done. Great, thank you. Thank you very much, Luanne. I think Craig Lee from the University of Washington is next. Great, thank you. I'll show my screen here. Can you guys see that? Great. Okay. Thanks, everybody. So what I'd like to do today is just touch on four subjects briefly and then open up for discussion once we've all gone. I'd like to talk a bit about the need for sustained climate scale observing, coupled with that access to challenging environments. To me, a big part of the challenge in physical oceanography right now is it's not physical oceanography as a distinction unto itself, but doing science at the interfaces between disciplines and areas and then also sustaining expertise and I'll expand on what I mean by that. Going forward here. There we go. So first the need for climate scale observing, really sustained large-scale distributed observing through the global ocean. And I'll use a lot of Arctic examples here because it's something I've been thinking a lot about. But these are figures from one of Alexander Yan's papers. And here she's talking about climate trends, right? And Arctic freshwater balances, in particular the exports of the gateways and the storage terms run off from the river's precipitation. The different components of that climate system and you see elements of that balance in the middle panel there. And then the more interesting part of this is if you look at the right, she's looking at a long-term record from CCSL models. And in particular, looking at what you call a shift being the first year that we see a change in these fluxes or storage terms that's outside of a three-and-a-half standard deviation range from the 1800-year pre-industrial control run amine and emergence that the trend remains outside of that range. And the reason I've got that here is right, if we want to start seeing things like this, we need to start seeing other small-scale climate trends, if we want to begin to understand some of the long-time scale dynamics behind them. We really need to be looking at these time scales and these are processes that unfold over these large space and long-time scales so that the effort itself is different from the idea of monitoring an efficient exhibition or a bill that may become the duration that we're talking about exceeds the span of individual careers. But we don't really have the kind of funding models we need or we need to develop funding models and we'll facilitate these things. And culturally, we need to learn how to recognize and reward researchers for actually making these contributions. I think these are tough problems that go well beyond technologies and approaches and so that we really think about and how we execute them. One of the challenges I see going forward for physical oceanography, a couple with that is access to really difficult to get to environments. Two examples again taken from my latitudes. On the left, you see a figure that shows you the Arctic basin, right, Arctic Ocean and all the ice-tethered profiler measurements have been made from 2004 to 2016. The ice-tethered profilers have been termed Argo of the Arctic. It's a high-space instrument that extends down through the ice and profiles over the upper 800 meters or so. That looks like really good coverage until you start to look at what it means on a month-by-month basis, right? That's many years of profiles, but on the right hand side, you see a comparison between a month of profiling and the month happens to be January 1st 2013, but it could be any month, really, relative to the Arabian Sea and Argo profiling in the Arabian Sea, which is as far as we profile the area for Argo. When you look at the coverage there, it doesn't look so good for the Arctic, right? It's a few select measurements made in particular places where those instruments are drifting. We just don't have the ability to make those distributed measurements in these remote environments right now, but we're working towards it. Gliderons of floats used to sample underneath the dots and ice shawl for an annual cycle per piece are, again, ridiculously difficult places to sample despite their importance in beating down uncertainties and predictions of global sea level rise, right? This is the biggest term driving that uncertainty. To get there, we need to have a tolerance for high-risk investments, right? We need to be willing to take those risks and take those on the way there. Really kind of long-term investments in different technologies and approaches. So the other challenge I see for physical oceanography, right, when we believe in the past, had big questions like finding the missing ocean mixing and understanding what's happening. Today, I think a lot of those questions exist at the interfaces between disciplines and areas. And when I say that, I mean things like the role of physics, the modulating production and exploitive carbon or the interaction between atmosphere, sea ice and ocean or the atmosphere-ocean interactions that govern the monsoons. And I would just throw up here a couple of examples from something that Mary Jane Perry or Tercero in a bunch of us did a number of years ago, making autonomous measurements at the North Atlantic which wasn't a new problem, right? People have worked on that quite a good deal before we did it. It's one of the reasons why we chose it. But the coupling of physics in the biology, chemistry in the biology and the ability to be out there for a long time led us to some new insights. On the left, you see the idea that the slumping of horizontal gradients modulates the timing of the bloom because it modulates when stratification occurs in what just simple solar warming does. On the right, you see an actual quantification of seduction of POC along an eddy, eddy-driven seduction of POC which is where it was hard to observe and hard to get up but with a larger team we were able to do that. So I see the challenges, right, being able to communicate and work across disciplines being familiar with what other people are doing and adapting existing approaches and technologies to these tasks. But I see that as one of the real areas where we can make progress in physical oceanography. And lastly the thing I'd like to touch on is the need to really sustain technological and operational expertise. And here I don't mean PIs like those of us who are giving these talks today, but I mean are the technologists and engineers and software engineers, developers who sit as part of these teams of equal importance to the science piece, right? But who are a little more vulnerable to fluctuations in funding and other things that happen within the community. And what I've got out of here is just the glider development timeline because it's something I've been involved in for a very long time. Basically 20 years between the starter development and production use there's no Align in 2014 or so, right? So that's a very long arc you see. Right hand side you see a list of 45 people and that's probably not everybody who are involved in the development of the three production level gliders within the community today. So long development arc to produce instruments and then bring them into production. The arcs are not linear, right? A lot of the things that happen are slide projects or things that people think are going to be important down the line and so they work on it on the side over the course of several years and then it intersects with an area where the development benefits from it. There are these ideas that we should be commercializing things and sending this to industry but in reality our experience has been that the scale and the demand for the instruments that we're talking about really limit commercial viability and so it limits commercial uptake and commercial interest in producing these things. There are some exceptions and we can point to those but in general that's a tough thing to do. What isn't in debate I think is that in order to make progress here it's required really consistent long-term engagement by really highly skilled technologists, engineers software developers, right? That's what is giving us the progress and instrumentation and approaches, you know, ability to do these very involved intricate field programs over the past few decades. So the question I pose to the group is how do we nurture support and reward these individuals within our community? I think it's rather unstable at this time and a difficult thing to navigate and I'll leave it there. Thank you. Great. Thank you Craig. Thank you both, Luanne and Craig. I feel like I'm already seeing some perils here with what you're talking about. Also with the OOI session, right? There's a lot of talk about interactions with physics and biogeochemistry so physical oceanography is no longer practiced in isolation. You both have talked about challenges in the modeling how much big data we're producing how we have to do the processing on the server where the data sets also challenging environments like the Arctic, you know, we're really looking at all those places. Valuing work that we know is important that does not get valued by our current evaluation system. I think that's really important. I'd love to talk about that more. And then the workforce issues, the brain drain, the diversity, the technical staff. So thank you for those thoughts. Now we'll move on to Melanie. Melanie is here in person and I'm going to hand you this microphone so folks online can hear you well. Thank you. My name is Melanie Feuings. I'm an associate professor at Oregon State University and in this green box in the lower left are a couple of the main issues I would like to touch on. One has been touched on in all the previous presentations which is our need for long time series to understand our changing climate. And I want to talk about a solution that we've implemented in my lab but also other places are doing this historical data rescue. And then I want to talk about a problem which is what my postdoc advisor live Washburn said we have chronic data accumulation. We've talked a lot about how we don't have enough data to answer the questions we want to answer but we also have these workforce issues. We already have way more data than we are able to get what we could be getting out of it. So it's already more than we can analyze and I'm going to float an idea and I'll say what that is if you haven't already guessed. So to understand our changing climate we need long time series decades long I'm not controlling those slides. Thank you. So we want to identify long-term trends. We want to be able to calculate anomalies and know in a robust statistical sense what those anomalies are. Which means we need to have statistically robust climatologies which means we need decades of data and we need that to know what the typical conditions are and how those are changing in order to identify extreme events against those backgrounds. So for example marine heat waves including El Ninos thanks. Subarctic invasions are a thing that we see in the northern California current system when we look at unusual inputs from the north kind of the opposite of El Nino. Severe droughts and their impacts on the ocean and land and so on. And we've heard other examples in the previous presentations and I've already kind of made the second point we need robust climatologies to identify these anomalies. But we know that most geophysical spectra are red meaning that there's more and more energy as you go to longer time scales. So really no time series is ever long enough right but a community standard that NOAA and others have adopted is a convention that you should use 30 years of data for a climatology and we know how rare it is to have 30 years of data for subsurface ocean measurements and like Craig mentioned that's much greater than the length of a single typical project and so NSF is presently supporting collecting time series that approach this length through for example the long-term ecological research network and the ocean observatories initiative and other initiatives but we don't have to just wait to build up 30-year long time series at new sites we can rescue historical observations that help us fill in the past and I've listed three examples there that we're working on in my lab which I can talk more about if you want. Another one is TACA's research archiving digitizing historical tide gauge data back to the 1800s really valuable for understanding how things used to work around our coastlines and for our need to understand upcoming sea level rise and the effects of dredging on harbors and so on and so these historical oceanographic data sets including things like ADCP data collected on continental shelves or in the deep ocean by individual PIs that are retiring and passing away these are national treasures that are being lost. Next slide please so recommendations and so the first point I want to touch on something more about this issue of what is known in some communities as climate data records there are areas where Argo floats don't go and time scales that Argo floats don't sample right so if you want to know global ocean mix layer depth on daily or sub daily time resolution we would need so many Argo floats that we trip over them every time we went to sea and Argo floats don't go on continental shelves and continental slopes and the spatial scales of variability that are important there are much smaller in the open ocean so could we imagine something like a coastal Argo program and back to the historical data set rescue it's my understanding that USGS has or had an office for historical data rescue they actually had an employee whose job it was to fly around the country before COVID interviewing scientists who were retired digitizing their handwritten data sets archiving those at the USGS could the NSF support something like that for data that has been funded by NSF past and our danger of being lost because those projects have ended and also just in general with data archiving I think that if there were more support to help PIs archive the data and get that done in a consistent way it would be more likely to happen and this paradox that I already mentioned we have both not enough data and more data than we can handle I think that you know with all these data sets we've been hearing about with NSI and other efforts we could be getting a lot more scientific results from the data we already have if just as one idea there were more grad student and postdoc fellowships funded to work with those existing data and back to the LTOR idea long-term oceanographic research sites similar to the NSF's long-term ecological research sites how is this different from OOI and I don't know is this something that would be alongside OOI or would be different locations this is just the important thing that I wanted to bring up for discussion is these would be hypothesis driven and they would also involve funding for the analysis because the way the OOI works right now there has been purposefully a kind of a barrier built between the people who are collecting the data and the people who are allowed to hire grad students and postdocs you can't do it with the same money right and so I think that there's a barrier there that is slowing down the science because the people who are the most expert in collecting the data are fully occupied with collecting those data and not allowed to fund grad students and postdocs on the same grant if I understand things correctly so is there some kind of structural tweak that could be made by looking at how LTER's work that would enable us to get more science out of the OOI data faster this is me as a kind of semi-external observer because I'm at OSU but I don't work for the OOI and I'm not funded by it and on the ambassador's question I also think we need something more like data helpers I understand Ocean Networks Canada has something like this especially for the interdisciplinary work I work a lot with ecologists and fisheries people NOAA and so I work a lot in that space between physical oceanography and marine ecology biological oceanography and that it takes time and work and communication and explanation from both sides there are lots of people who want to be using OOI data but it's like a level up from the volume of data they've worked with before and so if there's someone who can help make a figure for a proposal and kind of get the effort launched that kind of thing is what I'm thinking of and I'll stop there thanks. Thank you Melanie so I want to move on to Jack Barth and Joe Schumacher who are online with us but thank you Melanie for that I think one thing you point out yet again is this whole mismatch between what we should be doing to move the science like archiving our data and what in a tenure track world were rewarded for which is not that right so we can keep thinking and talking about that okay Jack take it away Alright just Tuba just checking you can hear me alright yep. Okay good morning everybody I'm Jack Barth and I'm a oceanographer at Oregon State University and I'm just going to tell you about a particular journey working with Joe Schumacher and the Quinnell Indian Nation just off the coast here off the Washington coast as usual to do any of these things takes lots of people Craig touched on that the excellent technicians and folks that we really rely on and keep funded through our research lots of partners here at the bottom including the IUS integrated ocean observing system elements we talked about basic NOAA funding with sometimes in relation to the fisheries folks but this all really started out with an NSF single PI proposal about 20 years ago and we got involved with flying underwater gliders kind of a third of the way through that timeline that Craig Lee was showing we're going to talk a little bit about low oxygen and I'll just say right off the bat that we've kind of moved a lot past that into the multi stressors world so I'm going to be talking about oxygen but obviously the same water has low pH it's got lots of warming and of course harmful algal blooms so next slide please so we're I'm glad Luann pointed out that eastern boundary current issue they're really exciting places because of this both the closeness of the so-called oxygen minimum zones onto the continental shelf but then this very active upwelling driven by the winds which of course are in turn subject to climate change so the setting here is we're going to up well from the very top of that put the water up on the shelf the nutrients are going to cause things to bloom that material falls to the bottom and undergoes respiration so you've got this recipe for low oxygen well you know we really didn't see that much in the California current until about the last decade and a half it's really because the balance of the physics biology and chemistry is changing and we really didn't think that that low DO would come right to shore where you could almost hit a hit a baseball out into the waters that are low oxygen and the communities really care about this so let's go to the next slide so as Joe will explain more we've got a number of Native American tribes along the Pacific Northwest coasts their livelihoods and their culture are oriented around the ocean they're seeing changes they worked with us to think about how we might do a better job measuring those with the tools we had and communicating those so really the question is that they need to know where when how much these events are affecting their coastal waters next please so I don't need to dwell too much on this this is just the the slope of underwater glider it's equipped with as well as lots of bio optical and chemical sensors and I'll just throw something out here right away is what I've learned is that we don't really need ships to do CTDs anymore right we've got these gliders we can measure a 3d or a 4d volume and have ships doing much of the experimental work that we really need but the basic background view beneath the sea surface these guys can do it so next please so here's just a random section from almost 20 years ago when we were first getting started I've just colored along the track of the glider up down up down starting at Newport, Oregon on the right hand side and going out into the deep ocean with temperature salinity chlorophyll and life backscatter from top to bottom there's probably 300 profiles across this for comparison along the top side there's looks like about 8 or 10 dark ticks that were the traditional ship based measurements were made but all of a sudden you've got this amazing view under the ocean multi parameter you can see the linkages between the upwelling front and the subduction of plankton down along it you can see the difference between the live and the dead down in the life backscatter the living material in the top the settled material on the bottom that's undergoing respiration so let's go one more so what we've been doing up off washing with the quenalt is flying these gliders in patterns that map the low oxygen low oxygen zone I would having been a coastal oceanographer for 30 years now you'd think that we could say well on the sea floor here's what's happening and this is why but we really still don't have that ability to dive down get those measurements come back and report maps like that you know we should be farther along on that so this is just an example from the Nanus site the Northwest Association of Networked Ocean Observing Systems the Pacific Northwest Integrated Ocean Observing System and we just look at a line like that along the glider tracks so next please oh and I should say these are amazing things for doing community development for next generation we really ought to be doing more with that next okay so this is just a typical example comes out in near real time is available on the web and can guide decisions so on the right hand side is that low oxygen zone right near the bottom we can see it's nearly half the water column is pretty amazing this is in September at the end of the the upwelling season and then the trick is how long does it stay there how close does it get to shore but what I want to point out from a physical oceanography point of view is we get to see all those structures that my dear colleague John Allen another studied for 30 40 50 years and that is the coastal upwelling fronts the along shore propagation of signals you can even see the internal tide those waves at the bottom of the oxycline there that we see in that image so next one and this will kind of tee up Joe for some discussion about interacting with the with the tribes on the coast but as I said students love this technology right so showing some of the conalt youth about what we do in the ocean I mean it's really cool what we do and it's not just entry point for physical oceanography like myself there's the technicians and the machinists and the field folks that Craig talked about so just absolute fascination in this and we ought to be doing as much as we can to pull people into that next please so my takeaways are oceanography shapes the marine ecosystem we actually have the tools to look at these important ecosystem stressors and then of course every time we look closer we learn more the submersive scale we've known about in the coastal ocean but big studies now by NASA and NSF to look at that on the larger scales the boundary layers both top bottom and sides continue to be more important and we can do lots of these things from a single platform I'm actually doing active multi-frequency acoustics from gliders now so we can get some sense of up into the food web for fish and zooplankton I'll just pile on to the subservice data still lacking and we do need those data heroes and ambassadors to make the best use of that data and this example of co-design co-production is just teeing up for your session later in the day and then I'll hand off to Joe, thank you Excellent, thank you Jack and thank you for making those connections yourself so I wouldn't, I didn't, I don't no longer have to do that. Joe Schumacher are you online? I am online, thanks let me see if I can keep the presentation rocking there, hold on one moment and I'm just going to get the share screen really let's try this one see if this flies for us and then share are you seeing the presentation probably not aren't you, hey is there anything good in there? just a moment here hold on just one second, thank you well just as we start up here I just want to mention that you know you've got this panel of fantastic physical oceanographers and then you've got me a fishery scientist here who depends upon these folks and it's a it's the needs that I'm going to talk about here today so we're just going to go ahead and do this, hold on one moment thank you you ready there we go fun fun what happens when I have multiple screens? I know how that goes yeah by the way we're having fun here right now let's try this over here by the way Jack's pictures did take a few, I'm going to share a few of the same ones with you here whenever we get this shared appropriately oh here we go let's try it one more time we're going to see this one try to move this over to here if we can do this screen one more time just one moment folks tactical difficulties yep we're seeing your slide now thank you my goodness we got it to work first and foremost in the agenda you'll note it it has to be associated with NANUs and I have been for 20 plus years with this great team from the northwest Jack noted earlier the NANUs team is extraordinarily good for working with the tribes out here both in Puget Sound and out here on the other coast of Washington but in fact I'm employed by the Quinalt Indian Nation and have been for 24 years out here so we work on a remote environment that has not gotten a lot of attention over the years until recently so we really appreciate the fact that we have these folks here today that have helped us in this regard we live in an isolated coast and in particular this area here north of Grace Harbor on the northwest coast of Washington state is really really tough to get to and has a lot of resources but it has not had the attention paid to it for data gathering that has been needed especially for management of resources here in fact in the past all of those data collection came primarily from personal observations from tribal members that have lived here forever the ocean provides everything that the tribes need out here for them including the salmon it's the home for the salmon that return up the rivers that are so important to them but everything out here right now is in jeopardy and has been for some time and we've seen the changes and it's been noted by everybody that lives out along these coasts they're on the front line of climate change so our data needs are more pressing than ever and that's where folks like Melanie Jack Luann and Craig come in handy I mean we really absolutely need the data produced by oceanographers to help us better understanding the physical environment in this ocean that leads to the biological changes and ecological changes that are so important for us to know so that we can at least monitor for them and potentially mitigate against them so we're we're harvesters the quinoa nation we manage many many fisheries out here in the ocean quinoa is a known manager noted manager I should say that he was recognized in the federal court case that re-established their treaty rights out here the United States versus the state of Washington and in that court case the federal judge found that two tribes in the state of Washington had a history of management and that included the quinoa Indian nation and the Yakama nation and so we are co-managers and full regulators of our fisheries out here and you can see halibut, dungeness crab, sable fish, various rock fish and other demersal species on the shoreline the Pacific razor clams and down in the lower right corner the iconic blueback sockeye salmon that run up the quinoa river and these as you saw earlier from Jack's photos there of some of our events we had the first time that any of our quinoa members had ever noted a fish kill of this magnitude occurring and we went back to the elders within the tribe we went back to everybody that we could talk to up not just at quinoa but with our tribal members to the north as well from the Ho the Quileut and the McCon tribes and there were no stories no long oral history of large fish kills washing ashore on the beaches so this is an example of where as Jack noted earlier in the September we had a lighter mission off of the quinoa coast there you saw that it came in right towards the village of Tahola where I'm speaking to you from now that we had a low oxygen zone there about taking over about half of the water column at that point well this is an example where it takes over the whole water column and when this occurs we have dead fish and we've had this reoccur numerous times since this 2006 massive event that littered the beach for miles with dead fish of all species and types we have this hand in hand relationship with OA that frightens us deeply because of the future of the ocean out here we know what we know about the carbon storage in the ocean we know that upwelling is bringing this to our shores and we know that it's causing carbonate chemistry problems that are going to probably affect a lot of our resources so we need to know more and more about all of these parameters in order to better prepare for an uncertain future and to potentially mitigate for events as they occur the oceanographic data needs for Cornell are the same as much of this many of the things that have been discussed today but in particular long term data sets and I think we've heard that from a couple of folks here today we need to define these baselines and then the changes I think that it's very important that NASCM and others continue to realize the importance of traditional knowledge were available and appropriate and how they can incorporate this data into these western data sets that are available now in these spatially remote areas so we're looking for optimized or useful data off our coast out here that we can use to better manage including ocean temperature DOC level currents of waves, carbonate chemistry long term biological data including primary and secondary productivity and as Jack alluded to the determination of multi-stress on organisms out here which we're getting more and more concerned with for obvious reasons I'd like to put a plug in too for LTERs as well as LTORs as Melanie brought up but LTERs we have the one off the Newport line that's been in place for some time now off the central coast of Oregon there it's extraordinarily valuable but however between here and there we have a thing called the Columbia River and it's usefulness for the biological data in particular for our work on salmon up here could be augmented by something north of the Columbia River maybe off of Westport with that I'd just like to note the collaboration of new tools the NANUS collaboration has been extraordinarily useful for Quinalt and has led to many collaborative efforts as well as the great tools that are available on NANUS including here from the NBS this is an Aragonite saturation model off the live ocean model that shows it at the seafloor bottom out there I had it forecast to today actually 24th of October and there we go and you saw this earlier from Jack's work when we have Jack take the time as a collaborator with us to set up the missions that occur off our coast out here and to come all the way north to work with our students that is true collaboration that works not only for helping us with our data needs to help spawn that next generation of scientists that are needed to really get in place and continue the work out here off our coast I love this slide here this image on the right this is from an earlier version of the sea glider work some earlier collaborators from the center for coastal margin ocean prediction I just like the view point this is looking south at the top of that picture is the Columbia river then a little bit lower is the Willapa Bay and then Grays Harbor and so we're looking north to south there it just kind of shows that dramatic that coastal shelf and the dramatic drop down there and some glider drops in there and obviously some low oxygen zones in there in this hypoxia index image so this collaboration is essential to filling the gaps both in our knowledge and the coverage and that's just the way it works you really have to work with the folks that are out there in those locations to get the best possible information and to plan the best possible research that not only gets to the basic science needs gets to the user groups that are out here as well collaborative oceanographic sciences with non-academic entities it's not easy so I really want to thank all of our partners on the Washington coast for their support traditional knowledge should be a part of long-term data it's hard again to work on it we have strong recommendations for collaborative science with underserved communities including tribal governments and should be supported by the NAACM and others and collaboration always requires funding and resource support for all those partners involved so just a plug out there to make sure that everybody understands it we can't we want to participate but we can't do it without the potential support all of you need, thank you thank you Joe, thank you and stop sharing thank you very much for this perspective I love that you brought up traditional ecological knowledge and how that can really help move the science forward if it's put into use equal footing with the hypothesis based more western science I have a feeling we're going to talk about that more at the first session after lunch when we talk about co-design and co-development I think Joe, you and Jack really presented a case where what engaged science can look like to really benefit in immediate need so thank you for that we do have some time for questions and those of you who are online are also in the room if there's a particular question you see on Slido that you really like give it a thumbs up so it kind of moves up in the ranking here and Brad I see that your questions is up there in regards to the brain drain that Luanne talked about and Brad there's also an anonymous question that came in just before you about the brain drain maybe you can incorporate that point of view into your question as well and I think you're going to get a microphone from Eric so that the online folks can hear you really well yeah Brad that was me I didn't know how to put my name in so this is Brad Rick we'll join together and you can correct anything I get wrong so Rick's portion of this Luanne was to ask whether these people who had shifted had shifted into an activity that may still be focused on oceanography in some sense because you can go to the private sector and there's lots of potential kind of oceanographic aspects there but I was thinking of a kind of a different question as to you know on this imbalance what do we need to do to make change to this issue and how specific is this to physical oceanography I mean is it in other areas is it broadly an oceanography so for the first one the brain drain I think the reason that I'm concerned about it is that many of those were involved in the upgrade of the community or system model for the ocean component so it's been using an ocean model that's decades old and it was moving they were trying to move to mom's sex which is a newer model out of GFDL and many of those people who left are either people who develop memorizations or who were helping with the effort to modernize that model so it's more than just them leaving you know they're not many of them did go to CDR but we're losing their modeling knowledge at the institution that builds the community model in the US okay and then the second question can you remind me how I got so passionate about that second question has two parts in a sense is this specific to physical oceanography broadly but then more importantly I think the real issue maybe for all of us is how do you change this how do you correct gender imbalance because you pointed to that in a number of different areas and how do we engage or bring more people in or sustain those who are presently I think that part of I think what's happening is this sort of coding focus and machine learning that is sort of moving towards computer science which has tended to be less friendly to women and it's quite different on the theory modeling sort of computational side the gender distribution then it is in the observational side there are actually more women I mean I don't know what the but just sort of anecdotally in my experiences there are more women proportionally in the observational world than there are in the computational analysis world so there are some follow-ups to that I see let's go to Shannon here because my microphone is nearby and then Marcia can you touch no mine was more of a comment to add to that a little bit and Luanne's anecdotal evidence I think that has been proven through NSF studies that women are less likely to be found in physical oceanography or marine technology fields and to that point NSF does fund a program for retaining women in physical oceanography it's called the empower program and I think that's been pretty successful in keeping women in the field of physical oceanography but I just wanted to point out that has been a issue that has been recognized by NSF I've been involved in empower since its inception Marcia Luanne can you elaborate on why you think AI, ML and data science is unfriendly to women that might not be what you intended to say that it's unfriendly but why women aren't fitting into that field maybe yeah I don't I don't know if I have a good answer to it it's mostly an observation and if you look in at least at University of Washington computer science department it's heavily dominated by men so I'm not really sure why there's probably papers on this out there that I could explore but I'm not an expert in that it's more of an observation that I've seen the evolution over time of what physical oceanography has looked like I mean it was when I first started there were very few women physical oceanographers and then there were more and more and now it seems to be changing very anecdotal I realize but something to watch thank you Luanne clearly this is a topic that resonates with this group and maybe Melanie wants to add to that and then I want to move on to a different topic okay thanks I I just wanted to quickly say there's the article a recent article by Ranganathan at all that looks at different geosciences disciplines and to my shock as a physical oceanographer physical oceanography actually has the lowest proportion of women out of any geoscience disciplines second I totally agree the empower has helped a lot to retain women who are in physical oceanography already in fact Luanne has been my mentor for 15 years we have talked on the phone every month for 15 years where m power peer group one still going strong and the third thing is we've been talking a lot about trying to increase the numbers of women which is great but I also want to mention that there are other underrepresented groups who are so underrepresented that we don't even have statistics to talk about and so we could maybe broaden the conversation of it there but I'm glad that you brought this up Luanne. Very good points thank you Melanie so I want to move us on to a little bit of a different discussion Lahini I see that you have a question here about LTRs and LTRs do you want to go ahead and ask that. Well it was I think two part question or two part observation I was thinking as you were especially Melanie talking about long-term observations like multi-decadal scales we think about marine heat waves or decadal scale changes in the Pacific that are natural that you know I think there are more and more data sets geochemical data sets coming out now whether it's from corals down in Riviera hey hey does or sediment data sets that are high resolution enough what about interfacing with some of those data sets to really try to you know and we've done this for you know for time immemorial I guess in this discipline but I think these higher resolution data sets and also you know now with the geochemical advances where you can make these geochemical measurements on smaller and smaller sample sizes which means you can increase the temporal resolution I think you know thinking about how to combine those is valuable Melanie do you want to address that oh thank you yes I totally agree I guess my immediate thought is that it involves working often working across disciplinary boundaries so I'd like to connect it back to the issue Tuba has raised about and others about the way that our evaluation comes work right now and that you know we there's still a lot of they're not even to the point of being only vestiges right of the lone hero scientists being the most valued and and so I think in order to join together these observing efforts on different time scales and in different fields we really need to learn better how to do that kind of team work the interdisciplinary work it just takes longer as you well I'm sure no is you always having to explain what do your words mean that don't mean the same thing in the other field and vice versa and it's it's you know it's hard to know how to convey or to assess that effort yes thank you for that it seems to be coming up in the slide questions maybe we have just enough time to cover that or by going a few minutes late if that's okay Jim you have a question about commercialization let's go there and see what Craig or Jack or others think Craig just following up on your point which I think is really valid something that I've been sort of beating my head against for a while now curious to hear if you have any thoughts on how alternate modes of making new instruments available within the community work would work you think or because the idea that industry will pick up something and there'll be a market for it really doesn't seem to be there yeah thanks we're actually struggling through a part of that right now your our experience just give a really quick summary of our experience the Seabire right this designed and built at the University of Washington provided to the community through a service center University of Washington our fabrication center for a number of years commercialized through a license originally to our robot make the rumble vacuum cleaner military robots that lasted a while they let go of the license when they failed to obtain a large Navy contract Kongsburg pick that license up again with these aspirations for a big Navy contract they sold it to pundit and Ingalls maker of minutes class aircraft carriers you can imagine how that went and we've recently taken it back and started a in-house service center with a slightly different bed on how to provide service and support to the community and you know a part of this is that you know as you alluded to the the commercial market is small relative to the aspirations of most companies that would like to pick it up our relentless focus on growth is well suited to these kinds of things right it would be good living for a few people you know over a sustained period of time but it's not a good living for continue to grow a number of people with aspirations to fail ever more every year you know the models we think about are trying to empower the community to support itself by providing support and training we talk about doing open source kinds of things for these instruments seeking small ways to commercialize if you will you know not big aspirational companies but but smaller groups yeah it's a tough problem I don't really know where to go from there thank you thank you for that perspective Craig any other thoughts from the panel about that topic Jack well Craig's absolutely right about these these high end complicated robots but there's another end of the of the challenge and that is getting many sensors out for low cost into the hands of folks and we've been making a fair bit of progress and with robust inexpensive dissolved oxygen sensors of course we've been able to do temperature forever things are coming along in the biogeochemistry lots of potential there to get you know order thousand dollar instruments out there by the thousands rather than the you know the few two hundred thousand dollar instruments so I'm pretty hopeful about that end of things great perspective thank you Jack this has been a really informative panel you all I really appreciate everybody's time Luann Craig Melanie Jack and Joe thank you very much start a session on artificial intelligence and machine learning and the future of ocean science and we have three people I think are online Patrick Einbach I'm a classic on some having trouble reading Patrick persona and Kana is everyone on all three online I think you see this is Kana here I think the list has Patrick going first is that correct is that how we're going to do it Patrick are you going to go ready to go first yes that's correct hi everyone let me share my screen here okay so you're not seeing right now you're seeing the presenter mode is that right you're not seeing the now we see it the full screen well thank you very much for giving me the opportunity to talk about this subject there was actually even a bit broader the name digital twins also appeared in the request and so what I'm trying to do here in just ten minutes to cover some aspects of weaving together this concept of digital twins and then an ocean of how we use a scientific machine learning in ocean sciences and first the question here is what actually are digital twins and this brings to mind the some of you've seen this the parable of the blind man and the elephants depending on where and from how you look at this you might get quite some different answers let me start here with the perspective of this UN ocean decades effort called Ditto the digital twins of the ocean and the quote here from Martin Visbeck ditto twins of the ocean will bring together ocean data models and digital information with those who are planning and regulating human ocean interactions they will come in dispensable elements of sustainable development of the ocean space so you see how a lot of different building blocks actually weave together so it's not just one component of it but it's basically how do we have a workflow or building rocks that actually seamlessly go between the ocean observing system that is indispensable and that's still too sparse at the present day a data space on where we can seamlessly share and discover and use information then the data analytics sort of modern and evolving tools for example machine learning on how we actually rapidly and comprehensively analyze the data prediction engines which we can think of the physics models that we are that we've actually talked about today but also surrogate models or emulators then all the way to visualizations tools and the decision making tools that allow users to actually really take advantage of all of the different data spaces and so if you want to know more about this the digital website actually provides some information but I wanted to switch gear a little bit and actually take a slightly sort of somewhat complimentary perspective from an engineering perspective because doing so will actually bring us a little bit towards what is actually under the hood needed to realize sort of the concept of these digital twins and all of their different renditions and so from engineering perspective where the concept of digital twin actually started an aerospace engineering up to 50 years ago so the idea is that the digital twin is in silico replica of a system that has a number of requirements. So first it must continuously improve as it integrates new data and then provides a dynamical digital history of the assets that we're studying it must be able to issue predictions so it must be it will go beyond sort of what it's basically the scene data into unseen conditions it must do so reliably and ideally we wanted to be able to do that with quantified uncertainty we want to be able to use such little twins to support assessments of what if scenarios like we can think of these scenarios like of course that we're used to in the climate modeling community but the scenarios there are actually still the way how we can do them and how we can afford to doing them computationally are quite limited we want to be much broader again quantifying uncertainties and then we want to incorporate sort of a synergistic two-way coupling between the physical system the data collection and then the user and the social system at the other end and again here sort of what the building blocks here what they look like on the one hand we have of course high fidelity models the physical models we have advanced systems for doing data assimilation on how we integrate and merge the data with these observations advanced tools of reduced order modeling so server modeling machine learning tools that we can either use to vastly accelerate simulations or calibrate those models much better we need advanced tools of uncertainty quantification which would present major challenges and again the question of the data cyber infrastructure that provides us with the means of analyzing this vast output that can go into the petabytes for example from the C-MIP-6 suit or some very very high resolution model simulations at one kilometer resolution also take up petabytes and soon will basically also go into the petabyte range with satellite data such as the surface water ocean topography mission that NASA has just launched a year ago and so trying to move here from how can machine learning support digital twins machine learning again so it has a number of ways on how we can what machine learning actually can do for us and it's for example classification anomaly detection or it is regression which can be in the form of parameter calibration or state estimation we can think of space or time dependent state prediction we can think of autonomous systems and active sampling techniques and how we can optimize our sampling strategy given sort of the very expensive and harsh environment that often times we operate in the ocean and then emulation for example for and certainly quantification large really number of topics that we could talk about and what I was going to focus here is on point point two on the regression and model calibration and when Thompson earlier today showed how climate models still are endowed with sort of major model biases that's true for the atmosphere it's also as Luan showed for the ocean and here can we leverage machine learning tools for example to improve such model parameter parameterization calibrations this is from a review article by Laura Zana and Bolton in 2021 where they show on how we can use equation discovery or surrogate modeling to calibrate ocean models better sub grid scale parameterization, eddy parameterization in ocean models using simulated data for example from large eddy simulation to actually be able to still run course resolution models which we will not get away with for the foreseeable future even 10 kilometer resolution in some ways this course resolution if we think at the high latitudes we're still barely resolving the barricade across the rays of deformation and so we're not able to get away from the need to parameterize these models and machine learning may give us a way to to improve such parameterizations we can go much broader and actually think about how we can seamlessly integrate scientific machine learning so what's just been shown on the previous slide but also inverse method approaches that have been around for a while to also incorporate not just big data that we get from very high resolution model simulations like but also from the sparse observational and eclectic global ocean observing system that we have to do something that's called a posterior or full model or online end-to-end learning of the models and so where we actually use state of the model itself that will calibrate not just the parameterization schemes on their own and sort of the recent sort of buzzword in this context has been it's called the differentiable programming so where we're basically bringing together networks that follow this concept of differentiable programming again but then extended to the full model that we have so for example to the physical model biogeochemical model and other types of models that we wish to calibrate and confront to the data and learn from these data through the lens of those models and to do so it's really and this is really just one example and I'm wanting to use this to show how in order to make this happen make this work really requires the intense and long-term collaboration between the computational scientists, computer scientists and then the domain scientists to really leverage some of the emerging tools hardware architecture and that we have in high performance computing that enable us to run sort of GPU enabled ocean models but then also computational tools such as differentiable programming can actually use this model and seamlessly combine machine learning tools with these PDE constraint models to actually achieve something like end-to-end model learning and here this is an example from a Julia approach that basically is being applied to Julia based ocean model that's been recently run at very, very high resolution on a large number of GPUs. Finally I want to go very briefly talk about the issue of making the scientific data more usable and so that we can really fully explore the vast amounts of data that we actually have and here the point here courtesy Ryan Abernathy that he has been making is that if we talk about the science community which is the right column and the business community which is the left column, they're really disparate tools that have been used in many of the levels. So the data makes the warehouses, the storage formats, the analysis APIs, they've basically been a complete disconnect between those two communities which is unfortunate. I think Ryan thinks that there is huge gains to be made by actually really bringing some of these capabilities in the private sector where there's huge amounts of investments have gone into developing these tools bring this into the scientific community and basically merging this and think about sort of a scientific data commons that serve on the one inside the data providers that basically produce data either simulated data or observational data then data enriches which are tech companies that work in the data science, data analytics space and then all the way to the data consumers which could be increasingly the insurance companies, financial services, all kinds of ocean services for the blue economy all the way to the defense. And so like to finish here with just basically pointing sort of this brings the notion of digital twins which really tries to integrate a number of these workflow into this seamless interaction between end to end sort of data production and data data use. There is a current National Academy studies going on on the foundational research gaps in the direction for digital twins and there which has also had a session on its uses for weather and climate simulation and certainly the ocean is an integral part of this and there's a lot to be done here and the notion here of really interdisciplinary work here with the computational and computer science I think is indispensable to really make progress in that space. Thank you. So we'll now hear from Prasanna. Thank you. You see the correct wheel, right? Yes, we can see the screen. Yeah, thanks everyone and I'm Prasanna Satyari, a principal research scientist studying research and I'll be talking about trustworthy AI and its role in AI as to decision making. Yeah, I think we I think I don't have to say too much about the possibility that AI can bring to different environmental science and ocean sciences. I think the previous presentation touched upon a little bit on this just to make the point I think we can use AI in a lot of different applications and it holds a lot of promise for example we did some work around super resolution so going from low spatial grid to more high resolution enhancement using different machine learning tools. That's one possibility and we can also think of other applications where we are doing automatic classification and there are a lot of applications which are boiled down to classification and so I think my focus on my focus in the stock would be like how and when can we trust such automated systems and what are the risks associated with such systems and these are some examples they are from different domains but you can see that these models when we train some limited amount of data they can have some sort of shortcuts which could be harmful when you deploy it in real applications so if you take for example these test x-rays we see that they learn a shortcut based on some artifacts that are there and the problem with such shortcuts is that when you go slightly away from the domain that you have trained your data it kind of fails because it's relying on these shortcuts and it's making predictions and similarly it can take some feature which is not at all important and then latch on to it and make a prediction and again when these correlations which are there in the data which are not representative of the real sort of mechanisms of how we come upon some outcomes it can have like bad outcomes right so I think these are some of the things that we need to sort of think about and to sort of more build a framework around these risk we can think about is the model fair and fairness could be so different many different dimensions right it could be based on geographical regions it could be based on a lot of different aspects right so essential aspect there is is it robust to some sort of distribution shifts or when the population underlying population changes right so and why is that important right and I think the reason I'm bringing up these risk is if we identify the risk then we can enable this AI as a decision maker in a much more powerful manner right so the key thing is we use these automated systems and the model makes a prediction and we sort of want to identify if this prediction is correct or incorrect right and so what are the things we can do to enable an expert to look at models prediction and make a judgment do I trust this prediction or not and we like to think of this decision making framework using the assistance of AI in sort of two ways right so one is where the AI is just communicating its predictions to the expert and maybe they communicate slightly more things and the other more richer way of communication is where the AI and the expert are coordinating with each other they're communicating with each other and then you come up with a decision which is taking into account their strengths and weaknesses together right so so one way to sort of enable this AI human collaborator system is through uncertainty right which the previous presentation was a touch upon so if the AI system gives us a prediction saying it's let's say in this case right so if there is a contamination or not right so it's not enough for an expert to act on it it should provide some meaningful uncertainty associated with that right and let's say it gives you some confidence scores it says I have 55 confidence in one contaminated class and then I have only 0.45 in the other class then the expert can obviously determine that this is not a very confident prediction I should probably not accept the model prediction and take some other corrective measures so this paradigm has been studied under selective predictions in the literature right and I think the key thing here is uncertainty quantification right and there are a lot of different algorithms in the literature that we can get uncertainty scores we like to think in terms of intrinsic or extrinsic so intrinsic is where the model that we are using for prediction itself is able to capture different sources of uncertainty and the common source of uncertainty are either data or model right so data is something like for example if I have noisy input and so on whereas model uncertainty is something that's because of the gaps in the models knowledge so again going back to the previous example I trained my model on only some subpopulation and there is gaps in the models knowledge because it has never seen data from other subpopulation right but in this era of foundation models right and we are seeing more and more domains where foundation models are being applied where we train big models on lots of different data and then we use it