[Recording] This conference will now be recorded. [Emily Lemagie] All right. Good morning everyone and welcome to another EcoFOCI Seminar Series. I am Emily Lemagie. I am co-lead of the seminar series with Deana Crouser. And this seminar is part of NOAA's EcoFOCI bi-annual seminar series focused on ecosystems of the North Pacific Ocean, Bering Sea, and U.S. Arctic to improve understanding of ecosystem dynamics and application of that understanding to management of living marine resources. And since 1986 this seminar has provided an opportunity for research scientists and practitioners to meet, present, and provoke conversation on subjects pertaining to physical and fisheries oceanography or regional issues in Alaska's marine ecosystems. You can visit the EcoFOCI Website for more information. And we sincerely thank you for joining us today as we have this hybrid meeting. So there's a great turnout in the room and online. Thanks to all of you from both audiences. And you can see our speaker lineup at the NOAA, OneNOAA Seminar Series and on the NOAA PMEL - Calendar of Events and if you missed the seminar they'll be posted on a PMEL's YouTube Page. It takes a few weeks to get these posted but the seminars will be there eventually. And I ask that you keep your microphones muted and your cameras off. And during the talks, if you're online please feel free to type your questions into the chat and we'll be monitoring those and we'll address them all at the end of the talks. So today I'm excited to introduce two speakers. We have Emily Hayden and Jens Nielsen. And these presentations will provide an overview of ocean temperature and sea ice variability, paired with a discussion of its potential impacts on Phytoplankton Bloom Timing and zooplankton coming out of diapause, in the Bering Sea. First Emily Hayden, is a graduate research fellow at Oregon State University in the College of Earth, Ocean and Atmospheric Sciences with a concentration in physics of oceans and atmospheres. Her research focuses on the link between atmospheric variability and the ocean state and the mechanisms that drive this coupling in the subpolar Northern Pacific. [Emily Hayden] All right, so can everyone hear me okay still? [Deana Crouser] Yeah, we can hear you Emily. We can't see you. [Emily Hayden] Okay would you like me to have my video on? [Deana Crouser] If you want to, you don't have to, but if you want to do it, yes please. [Emily Hayden] Okay sure, I'll go ahead and turn that on. Okay great. Okay so today I'm going to be talking about my work. Looking at atmospheric circulation anomalies and their role in recent Bering Sea warming. So, as requested, here's my And-But-Therefore Slide. So the Bering Sea has experienced exceptional oceanic and atmospheric climate extremes, particularly over the last decade or so, so 2010 onward, and the far-reaching environmental, social and economic impacts are beginning to be well documented. However the occurrence of these climate extremes is difficult to predict largely because we don't fully understand underlying physical processes that drive them. Therefore my research aims to develop our understanding of the air-sea dynamics responsible for Bering Sea climate extremes. Which may be useful in the future for improving environmental predictive capability. Okay there we go. So some of the recent coupled ocean atmospheric streams over the recent decade, that are driving my research interests. These include extreme low sea ice extent of 2017, 2018 being the lowest on record, increased heat flux variability, elevated sea surface temperature. The upper figure on the right hand side shows spatially averaged daily sea surface temperature anomalies from 1979 to 2021. And these are from the ERA5 reanalysis fields. Over the full record length there's a warming trend about 0.02 degrees C per year. And then if we look at the more recent years so 2010 to 2021, that seems to have accelerated to about 0.1 degrees C per year. The lower figure on this slide shows the monthly mean mixed layer temperature anomalies. These are from the ECCO ocean state estimate. These are from 1992 to 2017. So this is a different climatological period, different grid scale than ERA5 but this is similarly suggestive of an increasing trend in mixed layer temperature anomalies. And some of these recent climate extremes are overlaid on top of decadal scale regime shifts, so increased sea ice extent variability, multi-year warm and cool episodes, as well as a possible system-wide regime shift that initiated in 2017. So I'm interested in the air-sea coupling in the Bering Sea. And assessing the role of the atmosphere versus the role of the ocean in driving climate anomalies. So the Air-Sea heat fluxes describe the ocean atmosphere coupling through the direct transfer of heat. And this can be used to assess whether observed air-sea heat flux anomalies are ocean driven or atmosphere driven processes. So just a brief review of air sea heat fluxes before I go on. The next air-sea heat flux is made up of the turbulent exchange of heat so the sensible light components and the radiated exchange of heat, the short wave and the long wave components. Something to keep in mind throughout this talk when I refer to positive heat fluxes or positive heat flux anomalies. This is indicating into the ocean or warming fluxes and negative heat fluxes or negative heat flux anomalies are indicating an out of the ocean or cooling flux. Okay so our research objectives in this talk are as follows. We wanted to assess the role of the atmosphere in driving ocean temperature anomalies. We also wanted to analyze the role of atmospheric variability and driving the observed air-sea heat flux anomalies. And this work focuses on the recent decade, 2010 onward. Primarily due to the climate extremes I listed previously. Okay so some of these research questions remain open. At least in part because as many of you know the Bering Sea is a remote region with extreme weather conditions. Particularly in the winter. And this has historically led to poor data coverage. In particular there are minimal in situ measurements of surface heat fluxes especially in the wintertime. So this work takes advantage of the rapidly expanding wealth of re-analysis products, Ocean State estimates, etc. and these are used to gain insight into regional air-sea dynamics. So, I'm going to be talking about two different data sources. The first is the NASA JPL ECCO Ocean State Estimate. And I use the monthly model diagnostic output to compute a balanced mixed layer heat budget for the Bering Sea. And the second is the ECMWF free analysis fifth generation or ERA5 Reanalysis Fields and I use daily averages of various oceanic and atmospheric variables to analyze the physical drivers of observed heat flux anomalies. Okay so I started by computing a balanced heat budget using ECCO. So here we have the temperature tendency. So the time derivative of temperature over time in the mixed layer is described by a sum of vertical and horizontal advection, surface forcing by air-sea heat fluxes and fresh water fluxes and horizontal and vertical diffusion. So we can take this heat budget, integrate it over time varying mixed layer depth. And then compute anomalies in each term of this balanced mix layer heat budget, relative to the 1992 to 2017 record lay. So we assess these anomalies using a balanced metric and B developed by halides goodall to compare the importance of surface forcing to ocean dynamics which we've defined as a sum of advection and diffusion of heat. And driving mixed layer temperature tendency anomalies. So in this metric is equal to one, surface forcing anomalies drive the mixed layer temperature tendency variability. And when the metric is equal to -1 ocean dynamic anomalies drive the mixed air temperature tendency variability. So on the figure on the left hand side that shows the balanced metric for mixed layer temperature tendency anomalies during the sea ice Oh sorry the upper figure. Sorry, this is the balance metric for the mixed layer temperature tendency anomalies over all time. Over all months. So 1992 to 2017. And the positive pinkish values are indicating the dominance of surface forcing anomalies in the mixed layer temperature tendency variability. So from this we found that the surface forcing anomalies are driving around 70 percent of the total mixed layer temperature anomalies over this whole data record. So the lower figure is also this balanced metric for these anomalies of the mixed air-heat budget. But this is during the sea-ice season that we've approximated as October through March and we chose this because it's a period that has experienced many of these recently observed climate extremes I mentioned at the beginning. So again the pink positive values are indicating the dominance of surface force anomalies and mixed air temperature variability. We found that in this period surface forcing anomalies account for approximately 65 to 80 percent of the mixed layer temperature tendency anomalies depending on which month we're talking about. So from, so from this we found that anomalies in surface forcing drive the majority of mixed air temperature tendency anomalies. So for the rest of this talk I'm going to focus on the air-sea heat fluxes specifically, because these are a dominant component of this surface forcing. So because we're interested in more recent events, so 2010 to the present and because ECCO is only available through 2017 we shifted to using the ERA5 daily atmospheric fields for the remainder of this analysis. And just two things to keep in mind is: ERA5 is on a finer spatial and temporal grid than ECCO, and ERA5 is available over a longer time period. So we computed our climatology and therefore anomalies relative to a different base period of 1981 to 2010. So because of the previous result that surface forcing anomalies are dominating mixed air tempature anomalies in the ECCO heat budget, particularly during the cold season. We assess the net air-sea heat flux anomalies and the Bering Sea during October through March. So the table shows, or the table describes, the amount of anomalous heat entering the Bering Sea due to heat flux anomalies during each sea ice season, 2010 to 2021. And the key things to take away here are that the net heat flux anomalies drive over 2000 exajoules excess heat into the Bering Sea over this time period. The turbulent component of the net flux so the sensible plus the latent, accounts for 85 of the net heat anomaly. And furthermore in the majority of the years that we consider the sensible component was larger than the latent component. So this figure is a visual representation of what I just showed in the table. So this is the spatially averaged daily heat flux anomalies, the net radiative and turbulent, during the sea-ice season. This is just useful as a visual representation of the data table. And so this shows the dominance of the turbulent component of the heat flux anomaly in red and driving the net anomaly shown in black. And it also shows that there's a lot of year-to-year variability over the 12-year period that we analyzed. Okay. So because the turbulent anomalies drive the majority of the observed heat flux anomaly we assess the dominant drivers of them. So first we look at the sensible heat flux. And so we computed a decomposition of a bulk flux algorithm, said solo heat flux anomaly and that's shown in the upper right hand side. So the climatologies throughout this are noted by over bars and these are calculated over the 30-year period that I mentioned 1981 to 2010. And anomalies are computed relative to the space period and those are noted by primes. So this decomposition of the sensible heat flux is composed of three terms. So we have a term that is a function of the climatological wind speed an anomalous air sea temperature difference. A term that's a function of anomalous wind speed in the climatological air-sea temperature difference. And a third term that's a function of the anomalous wind speed and anomalies in the air-sea temperature difference. So this figure shows the spatially averaged daily sensible heat flux anomaly at the top and then each term in the decomposition in the following panels. In the highlighted term in the equation that's also highlighted in the second panel of the figure, this is the term that's driven by the air-sea temperature difference anomalies. And this describes around 93 percent of the variance in the total sensible heat flux anomaly. So we further separated this term into the air temperature anomaly contribution and the sea surface temperature anomaly contribution. And that's shown in the new figure that's popped up and from this we can see that the air temperature anomaly term which is the upper part of the or the upper panel, drives the vast majority of the sensible heat flux anomaly. Okay so similar to the sensible heat flux decomposition, I just talked about in the previous slide, we did something very similar with the latent heat flux anomaly. And decomposes into three more or less analogous terms. So those terms are a function of wind magnitude and now the air-sea specific humidity difference. Again over bar is a climatologies, primes are anomalies. So this figure shows the spatially averaged daily latent heat flux anomalies at the top and each term in the decomposition. So the highlighted term in the equation and in the second panel in the figure is the air-sea specific humidity difference anomaly term and this describes around 88 percent of the variance in the latent heat flux anomaly. So we again separated this term into the air specific community anomaly term and the sea surface specific humidity anomaly term, and found again that the air specific humidity anomalies are driving the majority of the latent flux anomaly. So from these decompositions of the sensible and latent heat flux anomalies you can see that warm moist air anomalies are driving majority of the observed air, anomalous air-sea heat flux and that this is driving ocean warming over the period of interest. Right and then finally because the turbulent heat fluxes are driven by these warm moist air anomalies, we wanted to just look a little bit at the role of wind direction and driving those anomalies. So I'm only going to talk briefly about the sensible anomaly results here but just keep in mind that we found a similar relationship between the wind direction and the latent heat flux as well. So the figure on the left hand side, shows the total sensible heat flux anomaly on the upper left and then each decomposition component and these have been averaged according to the wind direction. Excuse me. So when x equals minus pi, this is easterly winds minus pi over 2 Northerly Wind. 0 Westerly Wind and pi over 2 Southerly Wind. So we start in the upper left hand side, there is an approximately sinusoidal relationship between the wind direction Theta, and the total sensible heat flux anomaly. And when the wind is southerly, so pi over two we have the strongest positive sensible heat flux anomaly. We have similarly strong positive, sensible heat flux anomalies with Easterly Wind to swell. So if we move over to the second panel, in the upper right, we found a similar result for the air-sea temperature difference term. Which we found drove the majority of the sensible anomaly and then for the other two terms which are shown in the bottom panels, there's no clear relationship between these terms and the wind direction. So our proposed mechanism is that advection of warm air from the south and the east is leading to decreased ocean heat loss and increase positive sea surface temperature anomalies. So we're also interested in whether wind direction anomalies maybe, were a dominant driver of these turbulent heat fluxes. And so we looked at a normalized histogram of wind direction Theta. So the blue bars are the 1981 to 2010 wind angle so the approximate climatology. The pink bars are the 2010 to 2021 wind angle and the purple regions are the regions where they overlap. So over the recent period there was a small increase in the southerly wind, over this decadal scale, of around half a percent and a small decrease in the Northerly Wind. But because there wasn't a very significant increase in the winds associated with the positive sea heat flux anomalies, we want to look more at atmospheric advection to determine if anomalously warm and humid air that's driving heat flux anomalies in the Bering Sea, is being advected by like a climatologically average wind. Okay so to summarize atmospheric variability plays a key role in driving oceanic anomalies. From our mixed layer key budget we found that surface forcing anomalies drive the majority of the mixed layer temperature tendency anomalies in the Bering Sea over the ECCO data record length; 1992 to 2017. Using ERA5 heat fluxes we found that during the sea-ice season 2010 to 2021 anomalies in the air-sea heat flux have generally been positive ocean warming fluxes, that have contributed to a net increase of heat into the Bering Sea. These warming fluxes are dominated by the turbulent component of the net heat flux, particularly sensible. And finally atmospheric variability particularly air temperature and specific humidity anomalies are driving ocean warming anomalies. And therefore a significant amount of the warming in the Bering Sea is driven by the atmosphere rather than simply intrinsic ocean processes. Okay, some acknowledgments. And with that I will pass it on. [Emily Lemagie] Thank you Emily. [Applause] [Emily Lemagie] Thank you. We're going to save questions for a panel with both our speakers. So right now I'm going to introduce Jens Nielsen. He's an aquatic ecologist focusing primarily on phytoplankton ecology at NOAA's Alaska Fisheries Science Center. His research aims to understand community and tropic dynamics in ecosystems, in an effort to develop biological indicators of ecosystem changes, along the U.S West Coast from California to Alaska. [Jens Nielsen] All right, can you hear me okay? [Emily Lemagie] Yes, thank you. [Jens Nielsen] Okay thank you Emily. And thank you for giving me an opportunity to speak. I'm a research scientist at CICOES and I'm going to share a sort of a project in the very early phases, but what we're trying to understand a little bit about Calanus copepod diapause dynamics and how it relates to Phytoplankton Bloom Timing. You see we have a group of co-authors here. I work with Dave Kimmel, Lisa, and Wei Cheng, on this project. But I also want to acknowledge all these names that are listed in the lower right because they've been involved in a lot of the work that kind of connects into this project. Let's see if we can get, there we go. So I'm not going to talk a lot about the climate in the eastern Bering Sea, Emily Hayden just did that very nicely. So I'm just going to show one figure here on the left where we see some sea surface temperatures and that's just from satellite and that you know we've seen this before. We have some warm periods and in the early 2000s that cold period around 2010 to 2012 and then this recent warm period. I'm curious how these warmer temperatures and reduction in sea ice influence the plankton and particularly in this talk how they influence the phenology. You might have focused on phytoplankton and zooplankton and I'm going to look a little bit about how they are right how their connections are between them. So I put up this schematic sort of starting to think a little bit about how phytoplankton and zooplankton are linked in time. The top part here is sort of a maybe our, our starting point our baseline where we have a phytoplankton bloom in spring, and then we have consumers, the zooplankton in red, reacting to that phytoplankton and grazing and so on. So the question now with warming that's my scenario one here, S1, is does that mean that everything is shifting earlier? So that both the phytoplankton is shifting and then the zooplankton, but they're still kind of linked here in kind of a nice synchrony. Or are we seeing something where one stays sort of at the same time? So the phytoplankton bloom doesn't really shift, but the zooplankton does. And of course there are many scenarios when you start thinking about predator-prey interactions, I just picked these two because I think they're relevant for what I'm going to talk about. Let's talk quickly about diapause because that's, I'm focusing on Calanus and they have this very specific life cycle. We think. So don't know so much about the Bering. A lot of our information is from the Norwegians and Calanus, but if we look at this in sort of a simple framework we have C5 stages that in late October has a small bloom. You see they feed and then they go into diapause, they go to the bottom and they lower their respiration rate, they just stay there dormant throughout the winter. The idea or the thinking is that they come up around the spring bloom, feed on the spring bloom, these C5 late stages, become adults and then they reproduce. So the question now with warming is, do we see a shift in this? Does the warming because respiration rates are tightly covered with temperature does that mean that diapause, exit basically you run out of energy reserves, and you have to get up early. And maybe you have to reproduce earlier but you don't have a lot of food. So that's really the focus of this talk. Sort of a starting point how long can these copepods Calanus physiologically stay in dormancy over the winter period and then second I'll start talking a little bit of how that links to this to the spring phytoplankton bloom. I want to start with the phytoplankton bloom because we do know quite a bit about the timing of that already. So here's a schematic that shows bloom timing. So left figure bloom timing on the left. We have all the years 2003 to 2021. Green dots are estimates of bloom timing from the mooring at M2 and the black squares are estimates of those same bloom time from an area just around the mooring from satellite data. What we see here is inter-annual variability, but not a sort of clear directional change over time or with temperature. And the reason for that is probably fairly already fairly well established, that we have this scenario where the bloom links closely to the ice retreat. So on the right figure I have ice retreat on the bottom, bloom timing on the y-axis all of this is day of year and what we see is if we have ice sort of until mid-late March the bloom basically happens as the ice retreats. So we follow this kind of dotted line and we have ice retreat very early, the ice doesn't really influence the bloom timing instead it's a combination of model stratification relaxation of winds and day length. But the bloom is not earlier so we're not seeing sort of a very clear trend with warming. That's been shown in previous papers and we see it here in this this data that we've updated. We can look at that from space so what I've done here is I've for a time period from 2003 to 2021. I estimated bloom timing from all these locations and now each little circle is where I have bloom timing and I've done a correlation relative to the sea surface temperature, from satellites in that location. A star denotes that there's a significant trend with performance warming. So a red star would mean you have earlier bloom timing, with warmer temperatures. And we really don't see a lot of change, there are some stars and on the inner shelf and a little bit in the north, where probably the link to ice retreat and thus warming influence bloom timing. But for most of the Bering Sea we don't really see a lot of shift directly to temperature with warming. So what about Calanus? Well we know respiration rates couple tightly to temperature and we also know as a consequence that the respiration you do during dormancy is very reduced, but it's still very tightly coupled to temperature. So I'm gonna now use, I constructed a simple model trying to calculate the Calanus diapause. It's really a metric of how long can you diapause? And you take into account animal size you take in you make some assumptions and some understanding about the lipid storage, because that's basically what they're slowly feeding on during the winter period. And how fast that goes so the respiration rate. I'm using an existing framework for this, I'm going to, for this talk assume that we basically going to release my diapause copepod in the first October prior year, and what we also assume is if they can diapause long enough they will somehow find out to get to the surface when the spring bloom happens. I'm going to use temperature from the Bering Sea ROMs model so the 10K bottom temperature and down here is just a nice picture of what a copepod looks like right before it goes into diapause, a lot of lipids that it can slowly respire. So directly to some results modeling so basically the color scale is how long can you diapause. And we have a cold year on the left 2011, we have a warm year on the on the right, 2019. And these numbers refer to the day of year I put in the dates sort of matching that time. We obviously see here that this is a model that's fed temperatures. So temperature has a huge effect on the extent you can diapause. Or we see a very big difference between how long you can diapause particularly in sort of the middle shelf, whether it's a warm or cold year. Now the next thing is trying to link this a little bit to the bloom timing. So what I'm going to do now is I'm going to take the same spatial projections or predictions of diapause extent, how long can you diapause, I'm going to link it to bloom timing estimates from the satellite, and I'm just using one year here as an example but I've done this for all the years. And I'm going to look at the difference in days so basically that's the figure on the right, colors here, so if you're in the red color that basically means a negative value, means diapause ended before the bloom timing. So you have to come out of diapause or the surface but there's not a bloom yet. Blue means you are able to diapause, at least bloom or even later. So we can we can look at it a little bit differently, it's the same type of scale but negative values. So if we look on the left side of the figure here, the left side will be a diapause exit, prior to the spring bloom, the zooplankton have to sort of come up, prior to the bloom. Or if you can diapause so the blue colors on the right side of this dotted line you can diapause at least onto the spring bloom. So if we look at the density distribution and I've split this into cold periods, so 2010 and 2012. I've split it into North Bering Sea and South Bering Sea. And what we see here is in the North Bering Sea, in a cold year. Basically everywhere you were except for a small little tail everywhere you are you are able to diapause to the bloom according to this model framework. If we go to the next slide. So now I have the same figure and same scale, negative values early exit from diapause, the two left figures are the North Bering Sea and the blue is the cold period and then we have red. Red is for the late one years here 2018, 2020. So we see a clear shift, I'm starting to see even in the north they're going to be areas where it does not appear you can diapause long enough to reach the spring bloom. In the south we see the same thing in the warm period. So lower right you see almost everywhere in the south. It seems like you have to exit the diapause before the spring bloom. In the cold period there are even areas where you still exit early, but there's a lot larger proportion that are able to diapause until the spring bloom. I have taken a very first stab at this, in looking at the actual survey data, because it's good to look at what's actually measured. And what we see, so what's plotted here is, the different colors. I'm very sorry, I lost the scale here. But these are different, the adults are the red. We have C3, so younger stages than C4, C3 is green. C4 is the blue and then the purple are these C5 stages that are overwintering and then coming up. And what's plotted on these bars, so what's this 40 to 60, 60 to 80. There's a time of emergence. So we have early emergence on the left side of this panel. What we see is when we've calculated for a given, location and year if we've calculated early emergence, according to the model. We also see a lot more of the actual C5 stages. So they're coming up early. There's a lot to think about when you look at survey data and development of copepods and, generally also the timing of the survey. But it's the first cut that kind of shows what we would expect. We see them earlier if there's early emergence. So, what does that mean. First of all, it's pretty clear that when we have these warm years, diapause time is substantially shorter in warm years than in cold years and it seems that to occur more frequently that diapause has to finish before you reach the bloom in spring. So what's the implications? Well it doesn't necessarily mean that as soon as you come up before the bloom you die. What's been observed before that these copepods have early emergence. What it might suggest and what we're sort of discussing, is that what's happening here is sort of an environmental filtering for different life strategies. That in some years there are going to be more copepods that just stay dormant onto the spring bloom and that's a good strategy, when it's cold. Whereas in warmer years it's a completely different strategy of getting up early. With this whole warming of the system are we starting to see some big shifts both temporally, so more years where we're going to have life strategies of very early emergence and also spatially. We're going to start seeing that further north. Why is that important? Well part of this relates to, we see these C5 stages coming up earlier. Warmer water, warmer temperatures also mean faster development times. So, starting to talk about whether it is possible to have two generations per season. Bering Sea system is kind of on the border for these species to do that. So rather than having one generation, you have two generations. That could be that could have quite some implications for the food web, particularly in late summer where smaller fish feed on these large C5 stages, if they are not there anymore because they've started reproducing in this, we have much smaller stages in the second generation scenario. Maybe there's less food. One of the challenges with doing this type of modeling is that we actually have two species here that it, that mix and are hard to distinguish. So that's another area to look into. Probably using genetics what are we dealing with and are there any shifts between these two, two types or two species. So I don't have much more at this point. I'm very happy to take any questions. [Emily Lemagie] Great thank you Jens. Yes now we have an opportunity to take questions for both speakers. I have some questions in the chat. If you guys want to share your webcams and even stop sharing your screen or you can leave that off again. [Jens Nielsen] Yeah I was trying to figure out how I do that, because my window got very small. [Emily Lemagie] All right I have two questions. Two questions in the chat. I'm going start with those ones from, James Overland. The question is for Emily. Can you comment on upstream air mass changes rather than southerly wind changes? [Emily Hayden] Sure yeah that's a really great question. And it's sort of suggestive of what we're wanting to look at in the future. So we still need to do a robust assessment of advection of anomalies, but we've seen some event scale cases of elevated temperatures, ocean temperatures, and decreased sea ice that are associated with anomalously warm (okay sorry) anomalously warm and wet air north and northwest of the Bering Sea. Also there have been things like the 2022 Siberian winter Heat Wave and other Arctic and north of the Bering Sea winter air temperature anomaly events. And these anomalies may be advected over the Bering Sea. So we're really interested in air mass changes, both north and south of the Bering Sea region partly because there's a seasonal variability in the wind direction. Yeah overall though, where the kind of early look that we've done is suggested that these northern air, northern air mass changes are important for driving some of these turbulent heat flux anomalies. But that's still something we're looking at. [Emily Lemagie] Thank you. We have a question for Jens. Can you please talk about how you obtained the diapause duration data and how it was measured? [Jens Nielsen] Yeah. So that's using, and I'm happy to share that Cody, I think Cody is the one asking. It is basically a lot of empirical data and it is based on Calanus. We look at respiration rates to temperature, there's measurements of the size of the animal and how much wax esters storage lipids they have and then we make some assumption we put that into the model and calculate how we can we can diapause. But it's some long fancy equations. I'm happy to share that and talk more about that, Cody. [Emily Lemagie] Yeah we have a question in the chat. But first I'm going to take one from the room. Al? [Al] Question for Emily. Do you happen to know was the ECCO reanalysis driven, by the ERA products for its atmospheric forcing? [Emily Hayden]  Yeah it was driven with the ERA-interim products so they're going to be slightly different. So they're driven with that and then they output a modified version from the adjoint model that ECCO uses. [Al] Okay thanks. [Emily Lemagie] And another comment in the chat. Both great talks thanks for presenting. The question for Emily, how does sea ice play a role in the heat balance? Or is your key budget analysis only over ice free area? [Emily Hayden] That's a really great question. So for our mixed layer heat budget we did this over the full, the full region. So including the sea ice areas. The MIT GCN, that ECCO uses, does include a sea ice model within that. So sea ice in a way is accounted for. We're still looking more at how sea ice variability affects the heat balance, but yeah sea ice is included in the model and so is part of our heat budget. [Emily Lemagie] Oh wait. [Participant] Yeah um, Emily thanks very much for the topic. Really nice. I was wondering, all your results is averaged over the whole Bering Sea, right? [Emily Hayden] So the heat budget results are going to be over the whole Bering Sea. But the results using ERA5 at this point we're doing the only the ice-free area, and the reason for doing that is that there are issues with ERA5 over sea ice. And so for now we're just looking at ice-free area and trying to consider how we can incorporate the sea ice covered regions in the future. [Participant] Okay great thanks yeah I think maybe northern versus southern Bering Sea may have certain nuances that this kind of analysis wouldn't be able to catch. Another maybe just a comment but I don't know if I'm totally right here, Phyllis is in the room as well, this is a very shallow and wide shelf, so in some ways it's not uh surprising that atmospheric forcing are dominant the variability. But if you zoom in to certain regions of the Bering Sea, such as those close to the Aleutian Island chain maybe and if you're building in delays in your analysis, maybe you will be able to see certain ocean influences more, more so than just atmospheric forcing. You know by that I mean maybe there's warm water coming in from the Gulf which preconditions the ocean and then you know the surface forcing anomaly came along. So there's the responses may still be somewhat different, anyway. Yeah thanks very nice for us, thank you for a nice talk. [Emily Hayden] Thank you for that comment. That's something that we're talking about a lot, is how we can look at this heat budget in more detail and what the appropriate, spatial scales and how to, you know divide the Bering Sea. Because even just looking at the heat budget and looking at the shelf versus the basin, there are you know different seasonal patterns and different things that drive those. We did find though that on the shelf and in the deep basin that the surface forcing anomalies are driving the majority of the mixed air temperature tendency anomalies but we do plan to look more detail, at more detail of the ocean dynamics. Thanks for that comment. [Emily Lemagie] Are there any other questions from online? Jiaxu? [Jiaxu] Hi Emily, this is Jiaxu and, thanks very much for the interesting talk. I have a comment, for the dataset that you're using. So I noticed that you're using the ECCO model, it's a global model. Is that correct? [Emily Hayden] Yes, it is. [Jiaxu] Yeah so, I think, the ECCO also have a Arctic regional version, which is available starting last year probably. And the lead author is Anne Inline, from Texas A&M University, so I think that model have much higher resolution in the Arctic. I'm not sure how much of the Bering Sea has been included in the model. But I think most of the Bering Sea is included and they have a huge data simulation of the observations in the model. So, and it's also, the ocean model is still advantageous yet. And I think they also have an ice model but I am not very much sure about that. So maybe that's something that you can check. Yeah that's quite common. [Emily Hayden] Thank you so much for that recommendation. I've heard a little bit about the Arctic regional version, but I'll look into it in more detail. Thanks. [Emily Lemagie] I have a question for Jens. Can you comment, so you focused on Calanus, can you comment on the likely impact of this phenology on other species? Is this a pattern that might be significant across the ecosystem, more broadly? Or is this one of the core indicators, that you're looking into for being able to look at this next match, between the phytoplankton and zooplankton? [Jens Nielsen] [Laughs] That some of the zooplankton and I think Dave should probably, chime in as well, but they have quite different life strategies. Or ways of doing the seasonal cycle. Like for example Soto-calanus, I don't think do any diapausing, they just kind of keep cycling through reproductions. And I don't know so much about Neocalanus. So I think this this particular calculation is probably specific to Calanus, but I'm looking around to see if I can get some help from Dave. [Laughter] [Overlapping voices] [Jens Nielsen]  Oh that's why I don't see him! [Dave] Yeah so Neocalanus species would be the other and they have a different diapod strategy. They instead of coming up in the spring to use the bloom to reproduce they use the bloom to put on lipids and they enter into diapause after the bloom. So any sort of warming and they're typically deeper basin species so any sort of warming would impact them as well but since they're not on the shelf, the warming might not be as big a deal for them because they diapause at much deeper depths. So. [Dave] Calanus is sort of the one we focus on because it's so important to the food web in terms of transferring lipids to commercial fish and other species. [Emily Lemagie] All the way in the back. Al? [Al] Yeah just a comment relating to both talks. Point of interest in our modeling studies, we found a strong covariance amongst air temperature and bottom temperature, on the Bering Sea Shelf and fall zooplankton and in this case less fall zooplankton when it's warmer, but that has shown up consistently over the years as a strong covariance amongst those three and others. [Emily Lemagie] Wei. [Wei] Yes, thanks for the talk. Maybe this is just a comment so what struck me on your slide, is those the differences in your PDF figure, and there's a very big difference between the northern versus southern, um, Bering Sea. How they respond to the warming. So I wondered whether it's this is only because of environmental factors. Say temperature, in north versus south, are different or it has certain biological kind of characteristics of different species of those zooplankton or whatever. But that figure is very striking to me how the north and south are very different responding to warming. I wondered, you know you guys have plans to do more analysis on that? [Jens Nielsen] I didn't expect to be questioned by my co-author but...[Laughs] No... [Laughter] [Jens Nielsen] Um, no, I think the whole you know it is very interesting I keep having this both in this bloom timing paper and here is you know I keep having to want to split, because you start seeing these different patterns. I think you know the diapause, if that's the figure you're thinking about the diapause modeling is very much temperature driven. So it might be that it's still maybe except for parts in '18, '19 the North Bering Sea is still just cold enough that you know it remained fairly cold. Like I can't, I'm trying to remember the cold pool formation plots because that is you know it's pretty tightly coupled to the diapause extent. Right? So I don't know if that fully answers it, but I don't know. Yeah go ahead. [Phyllis Stabeno] I was just going to say 2018 didn't have a cold pool. 2019 did have a cold pool, not very extensive but it was just that bit colder, than '18 and so, but there was a cold pool in the north. [Dave] So if you if you remember my seminar in the spring, which I know we all took a lot of notes and remember that closely, um basically the North Bering Sea doesn't respond very strongly until that threshold of ice is reached and then the communities respond quite strongly. So really that cold pool is acting to keep the bottom temperatures low and keep those copepods in diapause, and then even when they come up you basically don't see, you see that even distribution of stages only until it gets super warm then the development rates crank up and the whole community responds. So it looks like the Southern Bering Sea was having this variability all along but the Northern Bering Sea was insulated because of the ice and the cold pool. Once you remove that threshold then you get a pretty strong zooplankton response out of the community's warming so Matt should make a decision on that paper that I have, revision hopefully any minute. [Phyllis Stabeno] So, did you have data from '18 also? [Dave] Yeah. We had the data from '18 and that basically was the point at which the northern Bering Sea, really showed a community response. Before that there wasn't much going on even when it got really warm into, so the 2003 to 2005 period was different from the recent, low ice period. [Phyllis Stabeno] Yeah the other problem is no ice, weakens stratification by 50% now you heat the bottom layer extensively. So there's a combination of things. [Dave] And the result was, really a lot of meroplankton and neritic plankton went out onto the shelf. And so you saw a really small dominant copepod community that was not seen in the Northern Bering Sea prior to that one. [Emily Lemagie] Ned? [Ned] So I have a question for Jens and Dave. You got your crystal balls. So let's suppose we're down the future and Calanus can't make it anymore in the Bering Sea, what zooplankton would likely take over then? [Dave] Calanus. Different species, yeah. I mean, so Calanus is basically a genus that goes from the Arctic all the way down to the the subtropics. And a different genus would come in. Pacificus is a very similar species. Marshallae is very similar. Pacificus is much smaller, not a big lipid bearer. Marshallae is somewhat in between and then the Glacialis half types are generally much more ice associated with lipid. We don't know genetically what the landscape is looking like. I'm writing a proposal with the answer to look at that right now. We don't know who's actually there, so that's one of the problems in predicting what's going to happen is who's going to replace it, if it's Pacificus it's going to be a it's a borealized system. Where lipid storage is no longer a feature of the plankton community there because there's probably going to be year-round productivity enough to have continuously reproducing populations versus the post-productivity of blooms. Though those are those are prominent features in temperate systems as well. So just just depends how warming is. [Ned] So Part B of that question is that with different species of Calanus coming in, does that change the ability of small fish to eat? [Dave] So in the spring probably not. I think there's lots of larval fish feeding capability on Napoli eye and other things but later in the year if you're a fish and you're trying to pack on lipids to overwinter you could be in trouble. Because basically what we see is what Al said when you get large warm temperatures the zooplankton drop later in the year and that's really when the fish are large enough and a lot of them feed very strongly on Calanus in the Bering to get enough energetic content to overwinter and recruit to the population. [Ned] Thank you. [Emily Lemagie] Al. [Al] Yea, the present version of the NPC model we're running diapause is more fixed. It sounds like there's enough information to confidently make that temperature dependent. Is that a fair statement that we could confidently do that now? [Dave] Yeah, I think it's a fair statement. I mean there's plenty of work done in Finmarchicus stating that the lipid accumulation window hypothesis that basically diapause is determined by lipid content is widely supported though there are others who say that it's light-related. Or temperature-related so but I think it's lipid-related. I think if, if you increase the respiration rate to the point where they run out of lipids they're out of options they got to come out of diapause. So I would say that you could model that, in a temperature dependent relationship you would also need though the lipid information which we're working on gathering more information on that with the help of Cody Pinger. And Rob Syrian up in Rico? We're starting to establish a data set that has lipid amounts that we could potentially relate to to diapause durations. [Al] What would a sensible way of doing that, would be to use like degree days? [Dave] So that's been an approach that's been used, degree days. But in general the model that that Jens has worked on the song why we're in Durbin model has been the one that's been adopted in the Atlantic to explain Finmarchicus variability. The other thing is it seems to be that these copepods have a wide portfolio of who's going in and out of diapause in other words, each individual there's a lot of individual variability. And when they enter and exit diapause and that's thought to be like a portfolio strategy, in other words if a significant portion of the population is entering and exiting diapause at varying times, somebody will hit and the population will continue to recruit. So there's been individual-based models even evolutionary-based models to work on this diapause question in the Atlantic. We're just way far behind but the answer is yes. You can certainly model it in a temperature dependent relationship with some size and lipid estimate information. And Jens has done that. [Emily Lamagie] I don't know if you've looked at areas where the sea ice point range to, under the same atmospheric conditions. And is there any difference in the heat budget in those cases s a way to tease out the impact of sea ice? And I also wonder, it sounds like the phytoplankton are driven more by sea ice and I was curious, there is no, is there any projection and shifts in timing of the phytoplankton. Or is the projection that the phenology is more fixed relative to the diapause? [Emily Hayden] My sound cut out a little bit. Oh, sorry. [Jens Nielsen] Yeah, I was going to ask. I think the first question was to Emily. I had sound cut out. I heard the second part. [Emily Lemagie] Oh sorry. Yeah, Emily, I was wondering if you have any analysis of the heat budget with and without sea ice, to try to tease out the impact. [Emily Hayden] We've done a little bit of that. We primarily just looked at the heat budget without sea ice, and kind of looked at what term in this surface forcing is most important and it looks like we can see the signature of the turbulent heat flux anomalies being very, very important in driving mixed layer temperature tendency anomalies, when we don't look at the sea ice, as well. But we need to look at that in a little bit more detail because at this point it's mostly just been looking at the mixer heat budget over the whole region and we need to think a little bit more about how to separate the effects of ice. [Jens Nielsen]  And I think you asked about the bloom dynamics? Right? [Emily Lemagie] Yeah. High versus temperature. [Jens Nielsen] Yeah. I mean in the Bering Sea it and maybe there's a bit of a Southern Bering Sea lens, you know, you have kind of this, one size is not there, past mid-March it becomes winds and stratification that kind of, drives in. You need enough light, which is you can think about day lengths. So, the bloom's not going to keep switching earlier and earlier there. If you look further north, so in the Chukchi and even in the Arctic there are papers that have come out recently, really nice papers that have shown kind of, how that bloom is still so correlated to the ice retreat that as ice retreat comes earlier, you also tend to have earlier blooms. And I think Wei has shown similar things in their model projections, if I remember correctly. But in the Bering, there is kind of this early you know you're not going to keep going earlier and earlier. I think usually what they've seen at M2 for example is when you don't have sea ice the bloom is almost a few weeks later. But that is also dependent on spring storms. You need those storms to kind of not happen for a little while so you can set up some kind of stratification. Does that answer it? [Emily Lemagie] Yeah, yeah I mean I guess I wonder if the storms and the atmospheric sheeth are related too, but we have just time for one question. We're getting up on time. But if someone wants to jump in, otherwise we can thank our speakers. [Applause] Thank you for the discussion and for your presentations. I will see you guys next week.