[Trish] This is being recorded, trying to figure out a way to make the presentations available after the fact.
If you would like to unmute yourself if you're online once we come to the end of questions, please do so or you can type questions into the chat.
So I'm happy to introduce as you all know, Sam Laney.
He's our new Director of Engineering Development Division, started in January.
He got his Bachelor's in Agricultural and Biological Engineering from Cornell. He has a Master's and a PhD

in Oceanography from OSU. And probably most interestingly out of the Master's of Education from the University of Massachusetts.
Before coming to PMEL, he had a research program at WHOI, which he can describe better than I can.
He was also a director of WHOI's AVAST initiative with the goal of fostering new ocean technology innovations in autonomous vehicles and sensors.

[Sam Laney] Sure.
[Trish] Thank you, Sam.

[Sam Laney] Thanks everybody for being
here and those of you online.

I hope you feel free to send your questions in at the end as well.

Like Trish said, I've been here for three months and I really looked forward to the opportunity to present myself to you, to meet people in the lab sort of on a more formal basis outside of the hall.
I have a very unusual background. And I find that it's difficult to describe sometimes because people ask me what I've done in life and it doesn't really fall into categories that people are familiar with.

It kind of looks like a hopping around of a bunch of different activities and professions.

And in the sense it is, but it's also been very intentional through my life. 
I'm a very interdisciplinary thinker.
I'm a very interdisciplinary doer.

As I start describing my life and my professional career today, I think you'll get a sense of that, that there's some value to being at this interface. 
I mean there's some bad things about being in the interface as well. 
People sometimes don't know where to put you.
For me as an oceanographer and engineer, it was almost impossible to find a job at a graduate school because I wasn't engineering enough for an engineering professor job.
I wasn't oceanographic enough for a purely oceanographic job. So there were a lot of those job searches before I actually found a place to land.
But there are a lot of benefits too and that's one of the things I want to communicate today is, how I will be coming at this job at least my mindset in, you know, a role not necessarily as a supervisory engineer, although that's my title, but more of like a chief technologist or a matchmaker, somebody who brings technology and science together. And I'll give you some examples today, some of the ways that's happened over the course of my career. Just in case you're interested in this picture.
It's me with some oceanographic technology that was returned from the Arctic Ocean.
The background is the Alaska Pipeline and the very north tip of what's called West Dock in Prudhoe Bay. 
I just bring this picture up because I've been using it for years and I didn't realize until yesterday that I'm wearing a NOAA hat. I've got a Dyson hat on.

So I've been apparently advertising NOAA a lot in my talks and I'll try to point that out as I go along.
But I got the Dyson hat after being on this cruise, 2007 BASIS cruise, the first one where Dyson went to the Arctic Circle, we all got Bluenose. 
This was advertised in NOAA-wide emails back then. 
And that's where I first got my contact with them. 
And I'm actually quite happy to be back in the blue. 
So these are my goals for the seminar.
It's gonna be light on science, it's gonna be light on technology, but I'm gonna try to focus instead on first giving you this perspective about my career to date and my professional interests my perspectives on technology and geoscience.
 
I'll then next have a little bit of a discussion about how I see these things coming together
 
We talk a lot about interdisciplinary science in oceanography and other fields of geoscience, but people who study this use terms like "integrative" and I'm striking a favor using these terms and you'll hear them throughout the talk.
And then a little bit about how, like I was saying, my perspective about how mine might fit in here as less of a pure engineer and more of a technologist.
And then I'll just talk about three prior projects that I've worked on where these two fields have come together in different ways.
 
Examples that have, in all cases I feel, opened up new avenues of ocean science and in some cases technology, and provided new insight into important things about the ocean environment.
 
And then the subtext here of course is, I hope this enthuses you so that you will come to me if you have similar interests or it sparks some idea, you will come and talk to me or Scott or Noah, other people on the team and start bouncing ideas around that, hey, you know, this is a direction we'd like to move forward with, how can EDD help?

So please put that on your plate for after this talk, even immediately or in the future.

Okay, I consider myself somebody who wears three hats.

I started out my career as an R&D design engineer at Department of Energy, working on a certain class of oceanographic instrumentation.
My job was basically a translator because I had an engineering background and a science background.

I would go ask the scientists what they needed to happen.

I would go tell the engineers what scientists' dreams were.

The engineers would tell me that it could not physically be made because a circuit won't work and there's not a glue strong enough.

And then doing this dance is translating back and forth to get both sides to harmonize to something that can be achieved and ultimately scientifically valuable.

And after I worked for the DOE, I went into industry and private consulting as a technology transfer engineer on this technology, which we moved to Britain. It got a national award.

The company's still [indistinct].

I got tired after a while of being told that I should be a manager. 
I should be a manager because I want to keep my stuff. 
So I went back to graduate school at Oregon State and studied phytoplankton, and photophysiology and ecology and studied the optical properties of oceanic waters.

And while I was doing that degree, I was also earning a minor in electrical engineering.

And as Trish mentioned, I have an interest in education.
 
I've always had an interest in graduate and undergraduate education, so post-secondary education, and specifically, how do you train cross-disciplinary students?

So I haven't had many students, I've had three, but they're all excellent cross-disciplinary students, and people clearly would ask me, when am I training more?

And I would tell them, if you know how hard it was to train a cross-disciplinary student in a disciplinary institution,
you wouldn't be so free with this ask.

But that's how I see myself, a person who wears three hats.

And this balance changes over time, depends on what stage of career I'm at. 
And I think as Trish mentioned with my formal education background, this has been lifelong.

So I'm one of the few oceanographers I knew of who has basically an Ag degree as an undergraduate, but it was this weird joint program between a science college and an engineering college. 
And it was kind of a mess.
This was the early 90s, late 80s when they were trying to figure out at various universities in the U.S.

how to make what's turned out to be biological engineering as a discipline.

At Oregon State, it was also not terribly easy to do this.

I was actually rejected for the interdisciplinary program and told that that's why I should try a Master's.
And then after I succeeded with my Master's, people were very happy to let me do what I wanted to do, which was major in oceanography and minor in electrical engineering.

And even my education work is very, I would say interdisciplinary in a sense, because what I was focusing on when I was taking my Master's these last few years is how do we build models in college for training STEM specialists to work across disciplinary fields.

And the program I was in is also extremely well known for what's called social justice education, which is DEI in educational formats. It's another thing I'm very interested in. 
And as Trish said, I've spent most of my professional years at WHOI.

So I spent 17 years there. I came in as a postdoc, and I ended up as a Senior Scientist in the Biology Department, that's equivalent of a full professor.

I was placed in the Biology Department because at that time I was probably 70:30, weighted a little bit more to doing oceanography.
But over the career there, it's been roughly 50:50, half of my papers are oceanography, half of my papers are instrumentation or technology. 
I've been the PI on roughly 20 projects, mostly funded from NASA.

So I've been awarded the New Investigator Award, did a lot of work under carbon climate, carbon and climate ecosystems, and IDS is interdisciplinary science, it's a special pool that I was asked to propose to given my programs have been successful in interdisciplinary work. 
And I also floated myself with WHOI a lot on internal money.

So WHOI has a nice pot of money for fostering innovation, give out internal awards and the idea is, maybe not everyone will be great, but if you get one out of seven that's really successful, that can turn into a really impactful global scale project. 
So I was also, I mentioned teach students.
So I was on three different faculties at Woods Hole.
I was in the biology faculty, I was in the chemical, oceanography faculty and I was in the engineering faculty, teaching, introductory courses, and all of those.
And as Trish also said, my capstone, what I did last when I left was leading the new AVAST initiative.

I just want to describe what that is because I had a really good time with it and a lot of what I have lessons learned there are in my head still as I moved into PMEL.

So AVAST, like Trish said, stands for Autonomous Vehicles and Sensing Technology.

It was just a catch-all term because there were a lot of people in different units at WHOI worked on these types of things.
And the institution wanted to resource them more intelligently, resource them not just with stuff that they can use, but also people who can help them catalyze ideas into reality, into projects, into demonstrations that could be used in proposals.
And so it has a physical presence.

I had a part of the building with test equipment and engineering spaces.

This is another picture I use all the time and I realize that three of the vehicles here are basically receiving NOAA funding.
So the LRAP on the bottom was getting money for Fisheries, the two deep sea vehicles at the top [indistinct] were paid in part of a project, were
paid in part by Ocean Exploration. 
And actually outside this picture are other rooms with other scientists and engineers and under my help working on carbon sensing instruments like Andrew Shell's group had a room that we supported for two years, working on a new type of methane sensor.

So this was a way to bring people and resources together and then give them the people and resources to support them so they can just focus on their science and technology development.

So I have a team of engineers, field specialists, machinists, admin people who would help just support the researchers from behind so that they can focus on the mission. And the outcomes were really cool.
So within a year, the institution see if these types of synergies are good.
You shouldn't have to say why it's good to get people excited about innovation.
But even within a year, we had any number of projects that had people who had no exposure to technology and didn't know how to solve a problem came to us. We helped them through a solution. We matched them with the right people.
And they ended up having new valuable things that will be used in their research going forward.
And this type of thinking is what I would like to help out here at PMEL.
If there are these opportunities, I have a recipe in my head for, I think, how we can move forward on these types of things. 
Just for example, upper left is somebody who's doing sampling of methane seeps, and they needed, they need to build a little thing that Shell Oil can deploy with an ROV biomethane seep in an oil field, so we help them build that.
In the bottom left is Ted Maksym, who's a scientist, who's a sea ice geophysicist.
He needed instrumentation throughout the field, and he's not, he needed a higher level of electronics expertise than he has in his head. 
That far right is Warren Melendon.
She's a biologist who needed a high pressure thing to measure, to monitor organisms that she pulls out of deep-sea vet communities so she can look at them under the microscope.
All of these things springboarded for the research that they were tool that we built [indistinct]. 
So this is my mindset, this is one of the things I really like doing and I'm hoping that over the next six months here we'll be able to pick some of the ideas from your brain and move forward with that as well in a similar way.
So I find this, that integrating across disciplines is essential for research, certainly I think I was mentioning earlier to somebody that this wasn't actually coming up through the academic tiers, if you're in a department, they want you to do things that the department identifies so they don't like spreading out. 
And despite that, I think I've been able to show a lot of achievements, scientific and technical, in my career that are interesting because I was looking at things outside or at the margins of a traditional discipline or I was reaching out to some sort of non-research stakeholder and bringing them in, especially in the hard work.
That was a very important thing in Alaska.

Or a project that requires a team that has sort of a certain mindset of being generalists and specialists at the same time to tackle these types of problems. 
And then certainly with a background in engineering and professional experience, I fold in engineering to this recipe as well.
This has been very, very exciting.

I did promise, you know, I gave the scope of this talk, it's three decades in the interface. 
And I just made a map for this, for my career, for a talk I gave to some graduate students about six months ago who were interested in interdisciplinary lifestyle, interdisciplinary careers.
And they asked me what mine was.

I'm like, I don't think I've ever listed it down. 
But I did it, and I'm not gonna go through the laundry list, but I started out actually studying philosophy.
Then I had my bio degree.

Then I worked in the field.
Then I went back to school.

Then I got a tenure track position.
and then I went back to school at 58, another Master's, then started doing leadership.
And the way this starts to fall out is that you'll see that the mix of the natural sciences, the technology and engineering, the humanities and social science and sort of like organizational management side of things, they all kind of go together, not hopping around.
They're all knitted together.

And this is lucky in a sense.
I know a lot of people are not able to have this type of flexibility in their careers.
And I'm not saying this is the only way to do it, this just happens to be the way that I've done it over the years. 
When I try to describe what this lifestyle is like, what this type of being a scientist is like, it took me a long time to realize that I actually knew of an analog. 
And that analog is the field of medicine where they train people to be internists, people who specialize in internal medicine.
So you know a lot of physicians who are cardiologists, right?
They deal with the heart and circulatory system or neurologists, or infectious disease specialists. 
These are specialties within medicine.
But medicine also has a specialty, a non-specialty called internal medicine.
People who deal with multi-organ or multi-system disorders.
These are things like, these are things that are, now as I get older and older, more of a concern to me, obesity, diabetes, and hypertension. 
These are the types of things that a specialist can't deal with.

You need a generalist, but a generalist who's trained to get multi-organ, multi-system specialty.
So, you know, I guess the most popular one on TV, you know, is Dr.
House from the program where he solves all these complex things that fall between medical disciplines. 
And it makes you ask the question in, you know, geoscience or ocean science, what would be analogs?
What type of phenomena are there that would be likened to this in medicine?
What things are multi-system or multi-domain?

And there are any number of them.
And most of you here work on one 
or more of these.
So coastal systems, polar systems, climate systems, anything that's got to do with land-sea interactions, fisheries, things that cross the oceanographic field, fisheries or stock management field, biogeochemical cycles. 
You know, these are all areas of inquiry that, in my mind, require people to have integrative thinking. 
We don't actually have many programs to train people that have a type of specialty thinking, which is something I'm, you know, just another piece of professional interest. 
In my career, more recently, over the past, let's say, 8 to 10 years, I've been working on one of these complex multi-system phenomena, which is the spring freshet in the Alaskan Arctic. 
So a freshet is when, you know, snowpack on land melts and it goes into rivers and eventually goes into the ocean.
And in most places, most temperate parts of the world, that melted snowpack goes into a liquid ocean. 
And in the Arctic, it doesn't, because a lot of the rivers, at least here in Alaska, they're flowing north.
And when that lower latitude snowpack melts, it flows north downslope onto sea ice.
So this is a really interesting phenomenon.

It happens very quickly in a couple of weeks.
It contains a lot of organic material that's going from the land into the ocean and then there's all these really crazy dynamics that happen when this water hits the physical and ecological environment of the coastal ocean. 
To me that was a really interesting problem to tackle for 10 years. It was also a interesting technical problem. We got
this scenario where, you know, seasonally, August to 

September there is no sea ice. It starts to form in the late fall,
November or so. It thickens, thins the plates, move into spring and then it breaks up and then you have open water again. That's the, you know, the typical seasonal cycle.
Unfortunately, the period I was particularly interested in the spring freshet is one of the most difficult dangerous times to be there. 
It is very, very bad to try to be on the water when when the river is showing up in there. 
And so it calls for an autonomous technological solution in order to serve the system, not just during the hard winter, but during a really dangerous time in the spring. 
So the way we tackled this problem was just using, you know, standard bottom moorings that we built that survive ice out.
We developed some ice buoys that we could go out on, snowmobiles right before the freshet arrived, deploy them through the ice so that little sensors underneath the ice will detect the arrival of the freshet. 

The freshet happens, and in the fall, when we can go back and get our [indistinct] moorings, we grab them.
This is one approach to getting the seasonal picture of this complex phenomenon.
I won't go into the technology too much, because it's published.
Actually, I won't go into the science too much, because it's published, too, but just to give you a taste of what we did in this particular project, it was very simple.
We wanted this to be inexpensive and effectively disposable if need be.
Just a basic data logger with a GPS and a radio system that we could stick a spar through a hole in the sea ice and then put a cluster of sensors somewhere close to the bottom of the ice, which is where the freshet water will run, sitting on top of the denser sea water when it arrives in spring. 
So we plant these before the freshet arrives. And it was a very simple operation because there's very little logistical support.
And you can back on [indistinct], going on snowmobiles, and ice logger, and putting things together and sticking it in by hand. 
Sort of like, think of raising a flag on Mount Suribachi when I see this picture, but that wasn't the plan.
So this is an example of where, you know, we didn't want to use a very complicated technology. 
And that's something terribly sophisticated because we didn't need that. 
Just needed something simple and straightforward in order to make these new, as of this point never ever been done, types of observations over these scales. 
That's a theme you might see for the rest of the talk. They worked. It was very interesting. 
You could see definitely in terms of temperature, the arrival of the freshet somewhere in early June of this year because it went from the coldest seawater can get to the coldest freshwater can get and still main liquid.
And then other sensors that we've had in mind with the temperature measurement showed us some indices of dissolved organic carbon and particular organic carbon that we were interested in.
Not just the temperature of the arriving freshet, but the chemistry and the biogeochemistry of it. 
So this is just an example of the types of data and scales we were looking at. 
I also mentioned that we did these bottom moorings, it was the same operation. It was something very simple, that only the technology you need to get and the types of measurements you want. Don't spend a lot of time on inventing something custom, reuse and repurpose as much existing things as possible.
We had a special challenge here because we're only deploying in roughly eight meters of water and the ice keels can easily scrub it. 
We're very close to shore to be right in front of those rivers. 
But we also needed sensors that could be reliable for a year and that's almost never guaranteed in these environments.
We put, I want to say kitchen sink, but we put the appropriate amount of things on it for the types of observations we were trying to get. 
So ADCP that looks up to track the ice cover and its thickness, arrival and loss, a CT sensor so we can get sort of a sense of the water masses in the lagoon, the optical sensor to look at changes in different properties that tell us things about the changing environment. 
And, you know, interestingly, maybe to some people here, they gave us money to try to see if a pCO2 sensor can work.
One of the commercial ones over the year.

And I'm not gonna get into the details of observations as well. 
This is just, you know, some data about a year going under the ice where the top shows in this case, pCO2 signature under ice cover during most of the period and what it looks like during the freshet, during wintertime, during open water season, gassing events, photosynthetic events, accumulation events because CO2 is getting trapped underneath the ice. How that relates in the second panel to changes in ice thickness and presence and absence. And then the bottom how it changes with respect  to the physical hydrography of the area.
So, you know, again, as far as I know, this is the first time somebody has been able to collect this type of data in this type of environment. 
And there was a merging of technology and science here, it's just an item case to go over the simplest possible tools that we could use to get the most valuable data out as soon as possible. 
That was just one taste of a certain type of complex environment and I'm going to spend the rest of the talk just touching on three other projects that I've worked on where I was, let's say, not the primary technologist in the project, my job was more of a freeloader or a co-PI. 
These have also been extremely valuable projects and you will see, I hope, as we go through that, you know, the technology that we added to these existing initiatives is not sophisticated. It's not necessarily pretty, but the results are, you know, first ever observations of these types of the ocean. 
And again these are the types of this is the type of thinking I'd like to encourage because it's it's easy to do easy things because it's an easy solution and it requires minimal technology that's kind of what we should be doing because at the end of the day our goal is to do science and research or research learning.
But the first one will be a buoy project I worked with the Air-Sea Interactions group at WHOI.
The second would be a project that I worked on with the Nereid Under Ice team, also at WHOI, and the third, the Ice-Tethered Profiler team. 
So the buoy project was really interesting to me. 
This was early funded my participation by NASA. 
And my interest was from an ocean color remote sensing phytoplankton ecology.

I wanted to be able to measure over a year, changes in a certain optical property of the ocean that would tell me about phytoplankton photosynthesis, but like every 15 minutes, because I needed to get a diel pattern And once I could understand how the diurnal pattern is in a certain day, I can compare it to the next day, the next day, the next day. 
So it's a day-to-day comparison, but to get the day-to-day indices, you need to really look into the diurnal cycle, the full-day cycle of this optical problem.

And so the approach here was just to find a friend who does a buoy program and has a platform in the ocean and engineer with them a way that we could put hyperspectral radiometers on their existing buoy to make the measurements that I want without interfering at all with the important well-calibrated time series work that they were doing. 
This is fairly straightforward engineering I would say by commercial radiometers, hyperspectral radiometers.
You arrange them in different places on the marine halo so that you know that they're not going to interfere with let's say the anemometers on top or any of the other airflow characteristics that have been on that top part of the halo. 
And here you can see three of the radiometers labeled R and they're actually in those light tubes downward facing so they're looking at the color of the ocean emanating up into space. 
And there's also a radiance hyperspectral sensor that's labeled I, it's actually looking straight up at the sky that's giving the color of light that's impacting the ocean that's coming down on it.
And so the trick is, if you know the color that goes down, you know the color comes back, you do something useful. 
That's the basic recipe of ocean color sensors. I have some other things in there too. 
I added some subsea sensors and in a radio system there so they could get the data. 
But this was a very interesting project.
So this was this was light engineering again the first time anybody had made these observations over a year. And not that expensive.

So this was a very very easy thing to do and that data that we got were very valuable. 
And I won't go into the data too much, except to give you just another flavor of how engineering technology has been beneficial in this project.
Like I just said, I was looking at the certain optical property, a certain color property of the ocean over the course of the day and it is a fluorescence property.
So a single color of red whose magnitude changes over the course of the day due to stuff that
phytoplankton are doing photosynthetically. 
I would love to talk to you about it later if you care about phytoplankton photophysiology, but I think most people don't.
But a normal day of sunlight, you know, this part of the ocean looks sort of like a sinusoid. 
The fluorescence that's emanating out of the ocean doesn't quite exactly look like the sinusoid. 
If you were to plot the light in versus light out on an x-y axis, you'll see that there's kind of a loop there.
There's some hysteresis, there's some closed, it doesn't just change one way to noon and then exactly reverse itself in the afternoon, it gets a little toasted, it gets a little sunburned and that degree of difference in that loop has a lot to do with the physiology and ecology that I'm interested in. 
For years people have been looking at it with this kind of template, like okay there's just this like TI curve type thing and that's how we'll interpret it, but you know somebody with an electrical engineering background, say, look, this is a system where you have an input and output transfer function, and you can build a dynamical model of that behavior. 
And then you can pop this into MATLAB or Simulink and run it backwards and map it back to the data and actually get the parameters of the model extracted from the data, and there's your answer.
So this is another interesting flavor where technology and science came together in a new and different way. 
To the best of my knowledge, no one had really recommended this before because who's got a background in photo and photo physiology and signal processing, right? 
There will probably not be another person like that in the next 100 years. 
But there can be people in the world that can try to keep those people and network them together. 
So again, this will be something that you have an interest in these types of mathematical sides of engineering.

Please come talk to me.
Forgive us.

The second one I wanted to talk about just briefly is this project we've had with the NUI folks, the Nereid Under-Ice vehicle people.
I'm interested in the Arctic because of how the light passing through the sea ice affects algal communities in the side of the sea ice and underneath.
And, you know, you maybe, for those of you who've never been to the Arctic, you might think that it's kind of like to form white surface, but it's not.
There's a lot of variability in the ice and the snow that really modulates the amount of light that gets through underneath. 
And here it's just a very dramatic example of an ice edge with Polarstern in front. 
You see all the leads and the breaking up of the ice like the light field underneath that ice is really complicated.
And I'm interested in how that light field structures, why you might see algae in certain spaces or regions underneath there where they might grow well or where they might not.
And so this NUIs, this Nereid Under-Ice vehicle, seemed like a really good project to work with it's, it's about the size of a Volkswagen Beetle. 
It has its own batteries so you can motor away, but it connects back to the ships with a fiber optic cable, the type that they have in the, like when they launch a torpedo, the torpedo is guided it goes away, it's pulling out some fiber and then there's a canister on the set that also has fiber about 10 kilometers of fiber. So you could in principle drive that vehicle 10 kilometers under the ice and do some mapping. And I thought well, it sounded awesome, because as far as I know, at this time when we did this, it really never been done. 
My end, again, this is, it's not even really an engineering approach. 
It's basically it uses things to pick up truck and to ratchet strap a CTD package to the mouth.

Like if that's all it takes, that's all it takes. 
There's really no sense in making something fancy for this experiment, because this is the solution that works.
It is not pretty.

People who build million dollar pretty vehicles don't like that very much. 
I had somebody tell me it was like, you know, cable tying a milk crate to the top of your Jaguar.
I'm like, you know, but it works.

And it did work.
So I'll show you some of the data.

This is just vehicle imagery.
And I'm not, I mean, there's nothing to really learn here.
I'm actually a biologist, but you'll see a lot of coagulant and a lot of dark spots underneath the ice, dark spots or ice algae.

I'd like you to pay a little bit of attention to kind of what you might see in the water column.

There's definitely organisms skittering back and forth, things that are motile, but there's also things that stuff is not [indistinct].
These aggregates, this fluff. Some of that fluff is sinking ice algae.
Some of it is a little harder to see, but actually, phytoplankton in the water column. 
And those are the things that were of interest to me. 
So this is just one example of a track that they did. 
So the picture is looking down, it's a radar image, and the red line that's sort of going to seven o'clock is the direction of the bow of the ship, the ship's in the center. 
And overlaid on the radar image is a yellow track that shows how the vehicle is moving spatially horizontally, something out from the ship, and then it did some zigzagging on the way back. 
But the red dots indicate points where it stopped its zigzag and it did what was called a pogo. 
So it just changed its buoyancy and slowly drifted up to the surface. 
That's sort of basically doing an upside down CTD profile while I had my sensors in the mouth of the vehicle sampling water and sampling optics.
So that's just to give you a picture of what the mapping was like and just a taste of the types of data we were able to get with this cruise. 
This is just salinity.
So this is a little bit, the image is kind of skewed a little bit so that you can see the vertical relief.

But it's the same track along the bottom with these pogos.

And when it's deep, you have salty water around 34 or so PSU.

When it comes near to the surface, it's fresh.
This should be no surprise, right?

This is just proof that we were actually observing a freshwater lens in certain spots underneath this this ice sheet, but the variability in chlorophyll was very interesting.

This is just within a kilometer.
This track is not bigger than a kilometer in terms of space, but you can see considerable variability, not just [indistinct], but also spatially in the chlorophyll biomass that we're measuring with a fluorescence sensor.

And so this is where it becomes interesting to the biologists, like is this structure or is this random?
And it's hard to say.

This is a very early experiment, but we did some analyses to look at the water column structure in terms of density, and that the panel on the right show how chlorophyll-a from chlorophyll fluorometer and particle backscatter from a backscatter nautical sensor pick up the large spikes, which we assume to be aggregates versus the, what I'm gonna call background level, which are the smaller particles, the smaller algae that don't ring out as a big blip. 
But this is the type of information that we would be going forward with and again this is the type of thing that, you know, just had never been observed before because... [Audience] Where were you? [Sam] This is Fram Strait on Polarstern in 2017. Yeah, so this is part of a big German-American project. Yeah, um, [indistinct]. Okay.

Okay, so let me talk about the third one before I wrap. Um, I said, uh, the third one involved this is kind of in between, there's a little bit more engineering prep but still the vehicle is primarily brought by other people for other things and the vehicle is the Ice-Tethered Profiler. So that's the vertical tube that you see in the left hand picture. Basically an Argo float you put the motor on it so instead of floating up and down based on buoyancy that little white puck sort of at chest-level with the person next to it, that's got a gear and it clamps onto a cable that's about 800 meters long and it creeps up and down four times a day. Creeps up and down at most four times a day over an entire year and whatever sensors you put on there will measure whatever you want to measure over that amount of time, potentially up to a year or more. 
These Ice-Tethered Profilers are now into serial number 120 or so, so they've got 120 deployed in the Arctic and many of them have done about a year's worth measurement and the system that I worked on to add simple sensors to an ITP gave us this first ever view of chlorophyll distributions under Arctic sea ice at about 8N over an entire year. 
So a full season of what's going on under the sea ice.

Technically, there's not a lot that, and many of you who work with programs that have very high investments in technology and platforms know that once it's refined, you don't really want to be asking for many, many changes.
They might not even be possible, and if they are, there's always a risk of interrupting the success of the program they were originally built for.

But for me, the major design limitations on Ice- Tethered Profiler project were whatever you put on, make sure it could go down an 11-inch holes, because no one wanted to drill bigger holes.

And I said, that's fine.
If this is what it takes, then we'll do it.

We spent a lot of time going back and forth, don't want to say negotiating, but like confirming that a design addition will not in any way disrupt the types of, in this case, purely efficient physical oceanographic work that they were trying to do.

So these ITPs originally came only SeaBird 41CP, which has a CTD with a dissolved oxygen sensor in it.

And we wanted to add, I wanted to add a multiple sensor and a light meter, and in order to make this reliable, we also had to add a third thing, which was a shutter, an electromechanical shutter that didn't rely on gears or shafts, so that if it got banged in the ice, it wouldn't rip something, so it's a magnetically coupled system.
These three things are commercially available, and our deciding challenge was to make these smart things that appear dumb as possible to the ITP social controller, which is not really intended to sample extra sensors.

So we deployed maybe eight of these, two of them did really well.

Two of them we got full years around.
So number 48 and number 64. 48 was deployed in the middle of the central Arctic.
64 was in sort of over Gyre.

They both lasted for about a year.
They are at different latitudes.

So one is very, very far
north. The other is

pretty far north. And you can
see that the types of cortical signatures that we saw in these measurements would be when an algal ecologist would think when you talk about a very, very high latitude light-limited environment where the algae, in this case on the left-hand picture, stay close to the surface because there's not that much light.

Whereas further south, I know it's not really further south the way many people think, but even going down to 70 degrees or so, those algae are looking much deeper in the water around 50 meters because the light is stronger and penetrates deeper.

So this is again is not rocket science like we could, I could have predicted this. I just couldn't tell you how deep or how long or how dense like the quantitative part is what these observations are.
This is actually a really interesting project too. I said it can profile as much as four times per day and I'm sure you can profile it as fast as you want but the routine is four per day during the time of year when things are interesting.

And then you put it to sleep a little bit, and you do a profile, profile every day or so over the winter when not much is expected.
But in aggregate, you know, there are well over 1000 profiles that we've got at this time, which is really interesting, because that means I have profiles not on a daily level, but on a sub-daily level.

And being able to leverage that for some biology was really attractive.

So what, these organisms, their lifespans are not that long. It's only on a scale of days.

So by sampling shorter than that period, you can learn a lot about their ecology.

So we were able to do things like not just looking at the day-to-day changes in chlorophyll, which is on the sort of middle panel here.
So take the intensities in the top panel and vertically integrate them to 50 meters, and tell me how much chlorophyll is in the water column in the top 50 meters day by day.
Great. But once you have that time resolution, you can do things like take a derivative.
You can look at day-to-day changes or quarter day-to-day changes.
And then you can see instances where the biomass is rapidly increasing or not. And these are really interesting phenomena.

We don't know exactly what's going on. Is it in state growth?

Is it more chlorophyll in the water column because it's falling off the sea ice?

You know, there are many ways chlorophyll can get in front of your sensor.

And these are great questions for an ecologist you might want to know.

What's the water column algae versus what's the sea ice that's falling through?

And then again, as you'll hopefully get from this talk, I love using sensors in ways they weren't designed.
You can see some of these particles, some of these blips of sedimenting sea ice algae. This is what I'm stating in these optical signatures.

You can count the number of blips in, let's say the top 700 meters per caps.

That's some number of, some proxy for aggregates in the water column at that time.

And one of the things I did is I did that throughout the entire growth season.

And so, okay, how many particles, let's say, this is operation... [Silence. Audio cuts off]

...anybody wants to talk about.
I've built and worked with active fluorometers of all kinds.
If you want to measure fluorescence,

I'm interested in that.
A particular one is called variable fluorescence.
So I spent at least a decade on that.

That's basically high-speed probing, using light with biological systems.

Measuring light underwater, very interested in that.

Light of all kinds, yeah, different types of light fields.

If anybody has that type of interest, even from like a physical heat transport way. One of you that I think that's something I'd like to talk about. Hyperspectral sensors and imaging while you're measuring light [indistinct] spectrally now at the end.

I have a lot of experience with that. And you know, I did my postdoc on algal cell imaging so I know some people who are familiar with the imaging [indistinct].

I postdoc [garbled] scratch to come out on these breakers need some new measurements in parts of the world where they haven't been used before. And so there's, there's more going on if people are interested in those types of topics.
So let me just close off with this. You know, my job title is Supervisory Engineer here in the EDD and I will do that job.

I can be a Supervisory Engineer, but I can also be a supervisory technologist and that means speaking to non-engineers.
And I'm trying to find a common ground where science ideas and technology capabilities can come together in a new and different way.

And that's a lot of fun, like I think if you ask many engineers why they like being an engineer, it's the creativity, it's kind of artistic, there's a lot of choices to be made.

You can tell people's personalities in some sense by how they design stuff. And that's something I've always enjoyed about my career as an engineer.

I kind of, in my head, break engineers kind of into two categories.

One is the type of people who should be building, I don't want to say this anymore, but the type of people who should be building and waiting for the next Boeing jet.

That's a certain type of engineering that has to be done with a certain mindset.

And then there's all the other engineers like in that Apollo 11 movie where you get a big pile of stuff on the table.
It's like they need an oxygen scrubber in space right now.
Build it out of this.

And that's always been the type of engineering I've been more drawn to.

And I think many people in ocean science are like that, too.

Most technologists who come to ocean science really like that type of creativity.

So make use of it if you want to brainstorm or explore something new.

Our doors are always open.
If I were to try to give acknowledgments for everybody who's been participating, has worked with me over the past 30 years, we would be here for another hour.
but I just would say I do not take credit for everything here at all.
There are many, many collaborators and funding sources that are just hard to maintain here.
But I'd like to stop here and take questions or hear comments or whatever you would like to do, but I want to say thanks for your attention and please keep the dialogue going if you're at all interested. So thanks.

[Applause]

[Trish] Okay, very interesting. That's really great.
And I guess one, what I see from your slides is those movies are just so dramatic, really.
And I'm just wondering, I think that we need to be selling our science more.
And I'm wondering whether we need to start building into all of our ocean observing systems, monitoring capability of the wonder and curiosity and excitement of actually doing these observations.

But there is gonna be power costs and also people costs and expertise.

And I'm just wondering, coming from Woods Hole and yours and coming into us, you know, how do you see us doing that really?
And what's the balance and how do we do that?

[Sam] So you certainly won't hear any argument from me that it's always good to capture the fun of what we're doing because there are multiple audiences that should hear that.

The people who give us money, the people who authorize the people who give us money, the students who are going to be our next generation, even our colleagues whom we might want to attract here.
Like there's a lot of audiences who should hear this type of thing.
In terms of working at Woods Hole, like Woods Hole has a very sophisticated PR machine and it might not be exactly the model we would like to replicate here.
There's other ways to raise our visibility.

You know, I think one way to maybe go about it, and like I don't think there's a solution that will work for everybody's research program, but getting other people, like finding one way to get another type of just being focused, like I want to communicate to this group, or I want to energize this constituency, like just pick one, and then you can build a plan, like an outreach plan or a communications plan.
I know it sounds overly formalized, but if you have that in your mind, it might help you identify the easiest way for you to do this. Because my experience is that there's always more people you can talk to, and it's hard to see the return on that investment sometimes. But if you're jazzed with students or if you're jazzed with communities then you should focus on that. [Trish] Yeah, well, my question though is is more of how do we collect the data for doing that? Um, so, you know, are we supposed to be having a GoPro attached to our moorings or, you know, our our heads as we're going out to sea or... What is it that we need to, how do we collect that data and make that...
[Sam] So GoPros on, GoPros on buoys, lots of people are doing things like that and you're right, it's really compelling.

There's ways and if you're if you're interested in telecommunicating that data in real time we could talk about that too. There are ways to be intelligent about that without eating up all your reading time.
I don't think, I mean you and I can definitely talk about this like for your particular applications certain things might be smart and certain things might be not so great. Like for Greg I put a GoPro on his Deep Argo and you know that's not that's not gonna be the solution for him.
Oh, I don't know maybe your audience is not looking for excitement.
[Laughter]

Look, anyways I think the temperature
profiles are kind of exciting myself.

[Laughter]
[Multiple overlapping voices]

[Eugene] There was a webcam in the North Pole webcam.
It was North Pole, right, Jim?

[Jim] Yeah.
[Eugene] Or was it just Arctic?

[Man] It's on the wall out front here.
[Jim] Yeah.

[Sam] I think that's great.
I've seen a lot of that done recently, too, where people actually have taken images now underneath the ice right there.

Image on top of the, let's say, ice buoy that sees the bear when it shows up, you know, that's biofouling that you want to be aware of when it happens. It's a scientific reason to do that.

But yeah, there's a lot of...if you're interested in that, come and talk.

[Trish] Yeah, I just I think that it is difficult for us scientists sometimes to think, well, the temperature is really the exciting part about this.

But in fact, you know, getting some images or drama from say, the bubbles that happened from breaking waves can actually make it more real for stakeholders, but also provide quantitative information for processes that are happening. Anyway, it's, it's hard.

And I actually think that this is something that we need exposure to and kind of have you thought about that, you know?
[Sam] In my mind, we just described those three legs in that stool. There's the science, you know, a technology that can enable capturing some sort of thing that can be presented to the community and not a communication specialist who knows how to connect that information to the receiving audience.

So, yeah, another one of these interdisciplinary problems.

[Trish] Yes.
[Sam] Yup.

[Sam] Go ahead. Jamie.

[Jamie] Thank you. Great talk. Very interesting.
In fact, I was taking notes and like, you know, they can connect in some ahead of different areas where your background would align messages. And so that's great.
So my question, so you've been on the ground now, I think, for three months, give or take.
So it's not very long, still kind of been, you know, spin up and listen and learn about.
But I'm curious, has, but that's an interesting time, actually, to ask you this question, which sort of, you know, someone who's now kind of been around long enough to sort of get an impression of this place, of this lab, where we are, where we might be going, but still coming in with sort of outside eyes.

I'm curious to hear what are some of your initial impressions of PMEL and where you kind of see opportunities, where you see, you know.
[Sam] Yeah, so, I mean, my, I see so much potential here.

I think there is a mindset at this institution of not shying away from long-term tricky problems.

And I like working in those types of environments.

I've been privileged over my career to work in, I'd say four such places, just being like four.

And so, to me, that's something I can't, I have no control over.

If I didn't feel that, then I would go, right?
I can't, it's like when I was training students, like I can teach them how to do stuff, I can teach them how to understand material, but I can't teach anybody to be curious, and I can barely get people comfortable to work outside their comfort zone.

Like those are, in my mind, basically immutable aspects of students and people, and so knowing and seeing that attitude here to me is number one, like those are things I have no control over.

They're organizational cultural things and if they don't exist there's no hope. So I'm really excited about those types of things.
Like I've had any number of conversations with people just doing what I do like throwing stuff out like spaghetti at the wall, like what about this what about this and there's been more, you know, a lot of receptiveness to that. So that's number one.
But the second thing is kind of capacity building and resilience. So, you know, somebody like Russ would really know this.

People who work in IT. There used to be a time in the world where you could keep ahead of things because all you had to deal with was like Microsoft and, you know, maybe Adobe, right?

Like the keeping up an IT platform was straightforward because those two companies had scheduled rollouts, like it was all, it was doable. And nowadays is the complete opposite.

You know, how many network devices are in this room, you know, that's just messing around with the hardware infrastructure.

What types of software need to be working together in those [indistinct].

We now live in an environment where IT people are going nuts. It didn't used to be that way.

I just gave you that story as an analog to what I see in technology too.

Technology has moved so much since when I got into the field, since when some of my students started a decade ago, you know, back in a former job. And it's important to understand that landscape so that you will figure out how to fit best into it.

I understand also, you know, we're a government agency and we have many, many different types of stakeholders.
I'm still learning which stakeholders seem to have the most synergy potential for us.
And I think that's important. I don't think this is the type of thing we can do alone.
Just technology is so complicated now, the way it's been distributed amongst geoscience.
But I guess those are the top two things.

A lot of potential and an attitude for trying out new things, I like that.

And just being careful about how we gear up for the next decade.

I'm sure there's more.
[Joe] All right, I'll ask a science question next.

[Sam] How about a science question?
[Joe] So I thought your blips on your LSS sensor were pretty cool. [Sam] Yeah.
--But how do you turn that into sort of a meaningful number oceanographically? 
[Sam] Right. So this is this is definitely a cultural thing.

It's probably one of the hardest things to get used to.

If you're somebody with an engineering background trying to get a graduate degree in oceanotology. Like the way you collect information and the types of information that can be valuable to you differs in disciplines. So like there's no way we can do physical oceanography the way we do it now without specific quantitative numbers with error bars, right?

It's just I mean, there's descriptive physical oceanography, but by and large, those rely on accurate comparison to temperature. In ecology, it's not the same thing.

Sometimes you have to find comfort in plus or minus in order of magnitude. So I think that's half of your question. It actually depends on the field.

Sometimes you don't need to turn those things into quantifiable, precise data in order to learn something about the system.
Because for example, maybe what you want to learn is just presence or absence or did it start? Yeah, go ahead.

[Joe] So it's part of the softball question.

But when I saw your image up there, I think to myself, what's the biomass?

[Sam] Sure. 
[Joe] Are you measuring export?

[Sam] Sure.
[Joe] Those are two big questions, especially

export that we're going to need to answer if we're going to do anything in the way of green carbon

dioxide removal, understanding climate, etc., etc., and so on. The export is a big one now.

[Sam] Absolutely. And so just throwing a fluorometer into the ocean is not going to do it. Throwing the

fluorometer into the ocean shows that you can see systematic changes over the season.

You can find numbers to it that need that proxies
that do sort of like what you're expecting and

then you take the next step and try to refine those proxies.
So just to let you know if I were still doing the stuff

where I would go next was not worry about a profile,
but I would build a pumped optical sensor that

sits at a depth horizon so that I know how much water volume
goes in front of the sensor over a certain amount of time.

And that's actually one unconstrained thing about the profiling measurements and not profiling through a fixed volume, profiling through an effective field.

I don't know how fast the field is vecting, so that's affecting my measurement too.

So sort of moving it step by step, but knowing what you can do and then taking the next step of how can I make it better.
I'm not saying this is, what I was showing you was not supposed to be a quantitative proxy bell.
[Joe] No, it was just really cool that you used such a simple sensor to get at what you did.
[Sam] Yeah. Most people would call that noise.

It's like in the days when people found thin layers in the ocean, coastal Oregon, they saw these big blips and just assumed that they were noise, but they actually found that there are huge abundances of algae eating that much depth [indistinct] orders of magnitude.

Yeah, it's a good question though.
[Man] I have a follow up to Joe's question.

So you showed chlorophyll-a with ranges of below 0.4.
[Sam] Yep.

[Man] Given your background in bio-oche engineering and light... [Sam] Yeah.

[Man] Do you believe any of that?
I mean, so taking into account how the fluorometers are calibrated, can it be both fluorescence or not?

And for people in this world, many of whom don't do chlorophyll work, but do rely on sensors that say micrograms per meter or it's a drop-down meter. Do you believe any of that data, given the errors, or do you make 100%?
[Sam] I mean, if we're talking about the 80 north [indistinct] chlorophyll metrics, I believe there's chlorophyll there, in the summer, right?

[Man] Yes. But 0.4 micrograms...
[Sam] So that is down in the dirt, close to the measurement

range of those sensors, even in optimal
conditions. So I could spend another whole talk commenting on the quality of photophoreometers now versus 30 years ago.

Like, that is one area of ocean instrumentation that is no longer, and I'll just use the phrase right here, you know, appropriate for climate.
It's like, we just don't, you can't buy one off the shelf as accurate as they were 30 years ago because people have just turned to these things right they're just not good. And so no, I think in order to do our research on those questions better somebody needs to engineer a better barometer again and this is just something that we have as a scientist, you know, you have to cater to industry. Industry has not been our friend and maintaining quality of instruments that accept. In a different type of career I can imagine, leading some sort of initiative to bring commercial technology back to the scales of the accuracy that we need, because a lot of, the market doesn't build what many of us need. Yeah.

So yeah, no, I don't, yeah, I've spent a lot of time on the phone, but I don't start a company.

[Man] I don't wanna keep going on it, but my pet peeve on cruises is seeing someone pull down a menu, go for fluorescence to use that data on a paper or a talk.
It doesn't really translate into that.

[Sam] Not very accurate.
[Woman] Well, the good thing about this situation is you're here.
[Sam] I'm here.

The people in the room stay.
[Woman] Yes, you can stay. [Indistinct]

[Applause]