Welcome everyone to the last colloquium of the semester. It's been a fantastic, diverse and really interesting talks. And so we thought we would finish off the semester with some of our own faculty. Today, we're going to hear from her own She Fang now. Carlos who's going to also talk, has has taught us that he's sick, he's had to cancel. So we've given Xi Fang the the full hour to talk and for questions. But please understand that we just let him know that earlier this morning. So nice. Welcome to use the timess as bus. Once. Jing Fang got his PhD from Columbia in 2012, he joined UDL from the University of Florida in 2018. By the way, giving this introduction for the, mainly for the students who might not know. And yet he's, he's been awarded the Sloan Research Fellowship and Ocean Sciences and the nasa New Investigator Award in earth science. Dr. Lang's research is on connecting the deep ocean to the changing climate, including the role of ocean mixing and heat transport and lot of other things. And he has offered to let us know some of the great stuff that he and his level than doing. So please take it away. Okay. Thanks game. Huh? Thank you for being here everybody. Today I'm gonna talk about the sum works my group has been doing over the past few years. Or the Studies my group has been doing a kind of around the topic of this, of the title of this talk that's connecting the climate change to the depot. So here's a kind of an outline of my talk. There. They're going to be three topics. Let's start from this diagram. That's kind of the motivation of all those studies. We know that the AP, the ocean, has an Apple II and deeply here the appellee or apportion is roughly defined, at least in Physical Geography. At the LEA, about 2000 meters. Okay, for this year, we have a lot of very good observation, decent spatial and temporal coverage. So we know distally you're changing this be relatively well. However, on the deep ocean, that's layer below 2000 meters, only have very limited data, it both spatial and temporal coverage. So we do know something about the changes in the deep ocean by the uncertainties are apparently much, much larger than the upper ocean. So my group, my group has been doing it. I'm going to first look at the changes of the upper ocean and at the end of the potion. For the upper ocean, as a measure is relative you were observed. So our focus is not on, not on the robust feature, not only on the robust to changes people already know. We focus more on the disagreement on the certainties of the upper ocean changes. We want to identify the uncertainties and the disagreements between exist existing dataset and provide some useful reference for designing or improving the already pretty good upper ocean observing system. For the deep ocean. What we try to do is to identify the buyers of the repeat of the existing data or me. Some results already know about the poultry change, but they are mainly based on very limited data. So you expect the big bias, big uncertainties associated with the existing convolutions. But how to quantify is a difficult issue. So my group has been using some saplings apartments, tried to quantify where you have the largest, the bias, where you likely underestimate or overestimate the deep ocean changes and how we do something to improve our situation. So those are the ocean changes or ability part of my study, Zomba. And also we, very interesting about the vertigo is change happening in the ocean, basically how the upper ocean and deep ocean connecting. The reason we look at this, one reason is we know the upper ocean change relatable well. So if we can understand the word go is change better. Basically how the apportion, the bullshit, exchange, the properties of materials. We can infer the possible change about deep ocean so we can have a better understanding of what the torturing changes. And of course, there are a lot of very interesting dynamical process we're comfortable related to the water goes changes like vertical mixing. Our vertical velocity is 0. So up. That's why after looking at the change of the total heat flux. So the flux is 0, we're going to look out some specific dynamical processes that's responsible for the vertigo is change. In this talk, I mean, he talks about what he go. Velocity, which is a term. Everybody seems to know they are quite important. But because they are really difficult, they're almost impossible to measure directly with the current-day measurement. So we had to find a way to estimate that. And the exam that. So that's the third topic, the topic of this talk. So let's look at the upper ocean changes in the variability. So this is the first me. The importance of the upper ocean changes. First is ocean heat content change. Those are sentences I got directly from the last IPCC report. I see the ocean warming dominates, increase the energy stored in the climate system account accounting for more than 90 percent of the energy accumulated between 1971, the two sums 10. And this is the major reader, a media evidence indicate that the global climate has been warming. Okay, This is the one of the most important evidence we have Support global climate change. This figure basically summarize some of the studies. Look at the upper ocean heat content check. And you can see that after 2070 you have a very robust upward trend. Those are where this conclusion come from. But if we look carefully, you can find they are. There are differences between all the different studies, which are those bands you can see go down senescence associated with those different product. So like I said, the thing we really interesting, we want to identify where those are suddenly come from, but the region responsible for that. And what do we can do to improve those, to reduce those uncertainties. For the abolition? As I mentioned, it's relatively observed. The reason is, we have good observation. We have particularly this very kind of extensive global, uh, re, system. That's the R-group system for people who are not familiar with Argo. Those are kind of autonomous. Blog launch and float. Kind of OB, can move up and down in the ocean every, every roughly every 10 days to provide us profiles, temperature, salinity, and other you get on some of the Argo. They can also provide some other biogeochemical of variables. And it here just to show the distribution of the Argo float. And you can see is quietly evenly distributed except maybe the highlighted region. So it's quite an intensive. It continuously provide us data. So and this prove this Argo rape. He was really reliable, very nice data from surface to about 2000 meters. After two sons, five, most of the ocean changes. We know the ocean temperatures on the needles are fundamental variables we know are from those are the flux. That said those are good flow that you can see that the even they distributed randomly, okay, So they are a Lagrangian flow that you can't let them stay in one particular point so they can move around and it's correct. So directly use of Argo data is very difficult. That's why there are many groups. They produce this gridded Argo product, or largely based on Argo product. So here I just list a bunch of the data currently available, can refer to provide this regularly agree data, data which are much easier to use. And that's that's basically the data people are going to use, how we use it. So you can see there are many, many data available. So what we do is we look out, we get all those data. Folks on the time period after two thousand, two thousand five. That's a time period, Abraham, really good observation. Okay. Then we're going to look kind of robust the features in the temperature, so many patterns. In the meantime, we're going to look at the disagreement between all the different products. So the year, the same raw data, but the January something subtle differences exist, the betrayals, the different product. So we're going to see one that can, or what they're going to be and what that can help us to improve the datasets. Here just to show the ocean heat content of change after two sons five. That first row show the example mean basically average, you average all the data product. Look at the temporal trend, ocean heat content trend in three different levels. So this is 0 to 300 meter. In 300 to 700 meter. This is 700 to those other meters. And you can see some robust partners. Those are, can see the inner popliteus a warming patches on the eastern side of the Pacific Ocean. And he also see many KM for cooling patches or that time period. So those are robust features. They have a different layer. You can see they have different products within the cave that differently your response to the climate change very differently. And the SPD here show the disagreement between Hold up. Product. Keep in mind most of the data product we use after some 2005 are based on the same Argo flows Benjamin array data. But they do show many, many differences. And you look at the red Apaches, those are the regions show the most significant disagreement between all those product. And you can found out those red patch it differently. Years are mainly associated with a very dynamic region of the ocher. And here you can see, here you can see the the, the Gulf Stream is a red, is one of the hotspot showing the big dif disagreement and the crucial you and Asana ocher. Okay, So that's major dynamical currents have big impact on the agreements of those different dataset. So how the deal, whether those dynamical region when the generator this greeted at data, how pretty big, significant impact on the data itself. So that basically knee, if you want to understand the changes he builds region, you choose one data and get some result each with another dataset that you may get a slightly different result. So that's why when deal with those changes in those regions, you gotta be careful because the conclusion is going to be data dependent. And also this uncertainty regions. Reminder, observational community. We're going to deploy more Argo data of overflows. For instance, those Reader seems to be the region they need to pay attention to. Okay? Because those are associated with the high-frequency wearability, you want to get more data in those reader to really reduce the discriminant. So this is a kind of provide some useful information for the observation or community. We also look at the ocean is salt content, change and uncertainty. The left column basically show the global mean salinity from here, seven different product and also their example means. And you can see the kind of, uh, show the long-term we're ability seems to agree quite well. And this is the three different layer, again, top layer, middle layer and deep layer. And one particularly interesting feature, we find that even though the number for the Argo float has being slightly increase over time, like after two sounds five, you have more and more Argo floats in the ocean. But after 2015, if you look at a week folks on agreements between the the all come argued base a data product, that disagreement actually increases significantly. Okay. Yes. You can see here they diverged from each other. After 2005. It's cosine the fact that you have more Oracle data, more dense distribution, but you have more disagreement. So that's indicated there was quite a significant issues existing of 2015. Are here. You just like another way to quantify this disagreement. Those bars just show the quantify the disagreement between this salmon are where we're well known or use product. You can see that after 2015, the disagreement or the spread of this data in salinity dramatically increase. Okay? So again, confirm there's a big issue of 2015 that we actually look at the possible reasons for that. And we found out after two sons, 15 bunch of Argo floats. Because a match manufacturer problem, that salinity sensor has some drift issue because after 2015 menu for the new flop, new army flow The put into the ocean actually has an issue in a Selenium measurement to the cylindrical, slightly drifted, too high, salinity, overt high. And that's why give us this upward. It's a linear trend. Okay? And in the meantime, different group process this drift, the issue different delay, become, generate a bigger difference in their products from each other. So that's likely the reason for that. So we have a manuscript in preparation, try to remind the community this is a big issue because, uh, you know, if you use this data to understand ocean water cycle for instance, because ocher Salafi, directly linked to global water cycle information, people use ocean salinity changes to you for global water cycle. And then you use this one Beta or problematic data. You're going to get it wrong information or abolish global plan for the cycle check. So this is the some, some issue. The opposite we should not community really need to deal with. In addition to that, we'll also look at the ocean stratification check hundreds saw certainty. So it's quite natural. We look at a temporary to change the locale of salinity change. And we know that density, the function of the temperature salinity and stratifications determined by temp, by density. So we're going to look at the strategic in check. And we know that stratification, particularly the upper ocean stratification, is very important for our determine the exchange of Adam Fear, informational material to the deep ocean. So the ocean stratification and act as a barrier between the atmosphere deep ocean. So here we basically look at the certificate and change after two sons are for our two sons, 2005. And look at the trend, we're going to find intensifies stratification in the tropical region as some kind of a decrease of stratification and subtropical regions. Of course there are many details visual pattern with air. And we also look Abby, contribution from the temperature change and the contribution from the salinity check. So you can see most of power of the ocean that's ready we're going to change is related to the temporary change as expected by a lot of people in the community are usually colorful. Expect global warming will increase the stratifications through temperature. But here we also showed I in addition to temperature for some region, the salinity changes which are going to be related to the AFC freshwater kind of fluxes. So also plays a role in some rejects. Him can see the acquiring important for the art, again, the eastern tropical Pacific Ocean and some coastal region where the salinity change could become significant in determining the global stratification check. Similar to the previous study, we look out there are uncertainties from all the different datasets. And you're going to see are they are quite a significant uncertainty. Usually associated with a coastal region where you, how big a river runoff, for instance, here, may know you have the Amazon River come up from here. So you have a lot of fresh water in this region. The Hindus had to deal with that different dataset. I'll give you a different result. Those are the regions show pretty pick our uncertainties. And also coastal regions are against those are regions show big uncertainty. Hopefully by improving the measurement of a temporary cement in the future, we can have a better agreement in the ocean stratification as well. Okay. So now let's go the, the, the, the previous slide to show the region we have good observation. Okay? You can see that even we have very good observation. The arguer way does the regions that you have issues that need to be improved. Now let's go to the deep ocean. The deep ocean like a sad, it's a totally different story from the apples. For the deep ocean here defined at the layer below 2000 years. The story is totally different. This is the data we have for the deep ocean. We owe with them. Major data source for the layer below 2000 meters are the historical repeated hydrographic measurement. We take the CTD measurement along those lines. Okay? Visually, you can tell the bars than the arc of a really appropriate. In addition to that, the temporal resolution is very low. The reason is it's very expensive to carry out this. You can cause a lot of money. So are the current aim. The go sheep, our project, basically the project, the international collaborative project, try to carry out a measurement, aim to have will have each section measured once each decade. Okay, so with every chain year, you likely to how one repeated measurement of a long one of the sections. So we know if we want to understand the global ocean changes, you at least need two ports, right? Mom polyhedral, don't even know how to calculate the change. So you at least have 22 data points at one location to understand the changes. So that's why. Oh, okay, anyway, let's talk about this first. It's even we have a, such a limited data, but people use those data to get some information about the deep ocean changes. Now this is a very influential paper by Sarah perky and Greg Johnson in 2010. They basically use those and repeated hydrographic measurement until that time to look at the possible changes of the deep ocean temperature or deep ocean heat content, how this is the result I get on the find some quite a significant warning, particularly in the southern ocean. And they are cooling, warming patches, but always uncertainties in other regions of the global ocean. If you do a global Min on here just to show the heat content change for the global ocean or in the deep ocean, you can see their conclusion is that they are, They're warming, okay? But here they show the sound notion. You can see it's our notions of warming as well. But I might have mentioned their results are based on very limited observation. Okay, That's the hardware. Hadoop repeated a hydrographic measurement. Every quantify that limitation here. This is the way to quantify the limitation of deep and this ocean measurements. If at least one CDD station exist within 60 kilometer times 60 kilometer box over this time period. Okay, if we defined our way, if we find the CDD stationary top box, we haven't defined our warrior potion volume is observed. Then we can do some quantification of the, this observed ocher body. It turns out for this time period below 2000 liter, there are 21% of the ocean volume was observed. But if we look at the region or the side opposite more than once, which is the data. We at least know that this is the data we need to understand changes. You find there only 1.4% of the ocean volume below 2000 meter. Observer twice. Okay. If we go even deeper below 3600 me that I only want put a 1% over the water volume was absorbed twice. So those are the data people use to understand the deep ocean changes. And you can imagine they're going to be huge uncertainties, right? So what we tried to do is to quantify this constraint x. What do we use it? This ocean state estimate called an anchor. So the Ocean State acids made, or some people call that ocean re-analysis. The idea of those products is the uses are the specific generous ocean circulation model. In this case, the use MIT GCM thing I would say, ocean circulation model, then assimilate all kinds of measurement. We have global scale measurement that we have, like the Argo flows. We talk about the repeated hydrographic measurement and we talk about even satellite measurement like of the TP. Jason provided the sea surface height measurement and the grace data provide them the seafloor or pressure. So there are also other, a lot of data. They assimilate data, data to provide this very dynamically consistent estimates. This dynamically consistent, basically me, everything in this product. I could temperature salinity, the windows to as the precipitation. Are. Everything gone full closely followed the control equations. Okay, everything goes perfectly follow that the control equations. So that's called a dynamic consistent. So with this product, you presumably can propagate some well-deserved apportioning information to the deep ocean, throw the dynamical processes, he included it in the equations. Okay, So at least theoretically, we have better information about the deep ocean changes. Okay? Then the way we use this echo data for this particular purpose is we do a sampling experiments. The assembly experiment aids. We go to the model. We just pretend that we have many crusades. Week. We just pick up the data happening along the repeated the hydrographic section at a time. The handle persuasion, we're just basically get a bunch of pseudo observation from the model. Following the observation, the GOCE for the repeated a cartographic metrics that we gathered data from the model. Then we calculate the ocean heat content change the same way the procaine Johnson did. Okay, just a USDA. Pretend the measurement in a model and category we should heat uncommon change. On the other hand, we know the real changes in the model because we have everything in a model. I, not only the, the, the, the, the measurement or the locations for the repeated graphic measurement. But we have measurements from everywhere, the data from everywhere. So we have the truth of the model results, that we have the result based on the sample, this sample data. So this is the choose for the layer below 4000 meters in the echo. Our state estimates. And this is a stop order. This is the result of bees on that sampled, presumably the Hadoop graphic sections in echo, we, we generate that. We compare those two together. Buyers does it do to the sapling issue? Okay. And visually you can see the major pattern roughly the same, right? You're cooling warming pattern roughly the same. But if you look at the buyer's, you're going to find that for the abyssal ocean, basically a below 4000 meter. Here, the rat, now the kind of warm color basically me, you have a wall bias. It's the, does the sample, the result of mindless or choose give you a warm patch? How basically me, the sample result have warming buyers in the warming patches. But for the green patches mean the sample, the result on the estimate a warmer. So can we, you are come cooling bias. So here look at this figure. Basically show data for the North Pacific Ocean. They're very significant warming bias. So that means the repeat, historical repeated had graphic measurement. Likely give you FEC warming. Okay, maybe not that much for the Southern Ocean on the other hand, on this morning. And I hear this figure, we do the global, obese global and a base, an average, which actually give us more director, our estimator for the buyers. You can see here the black curve is the wheel if they choose. The red curve, if the sample, the result, you can clear see below about 4 thousand year this sample, the result clearly overestimates the heat content check. Okay? And if you look at the special panel, is overestimated of deep ocean warming are likely will be so whoami is related to the Pacific Ocean or the show in the previous slide. Loan some notion. On the other hand, the sample result, I actually own the estimate of warming. So we think that could be important because as I mentioned, ocean heat content change was used. To constant during the whole radiation balance of the climate system. So if you kind of an estimate on the SMA, the heat content change, you'd likely over or under estimate the, the, the response of the whole climate system to the greenhouse you are emitted into the atmosphere. Okay, so here's a summary about the changes of our ability in the global oceans. For the upper ocean, we see that even though they are abundant data, another amount of data, significant uncertainties of the very fundamental variable still exist. So this uncertainty are usually associated with the EDI, which are very dynamic region, okay? For the deep ocean, due to the limited spatial and temporal coverage of the existing data, it's very likely the previous studies overestimate the deep ocean heat uptake. So and as a consequence, the energy imbalance of the Canvas system maybe overestimated. Okay? And both the result, particularly our emphasize on or certainty in up and deep ocean, could provide a useful information for future design of the Global Observing System, like where you deploy more Argo float. And now the VOD play D bar goes away. Put the paragraph, we should do. If you have a limited amount of money, limited amount of instrument, we need to deploy that those are information in our study could be helpful for them to figure out that question. Now let's look at the change of the upper deep ocean. Are, as I said, these are, look at this arrows. They exchange between opposing deep ocean could connect the aperture and the potion. So on one hand, they help us to better interpret the observed upper ocean changes, right? So why you how double genius. So they gotta be related to both the young fee ocean change and the deep ocean apprehension is doing on standing desk, help us to understand the upper ocean changes. In the meantime, it going to help us to kind of get some possible changes about deep option because this heat exchanger Saudis change exedra going to affect the deep ocean check, throw a very simple budget point of view. So that's why we want to look at the vertical changes. So for the vertigo, key transport, transport, the tour, we use DOD echo estimate. What do we do? We have we first looked at them or legal heat transport. And the left column show the long-term me what your heat flux at two different levels. How this is near the surface, there's a 200 meter. So near the surface you find a very conventional idea about the vertical motion. So you see you have will be subtropical downwelling. In a subtropical ocean basin. You also have a strong upwelling. So you have this very conventional pattern and look around awesome's ever wear matters for the vertical. He does change in surface because the everywhere can find some quite a significant number. However, if you go to deep ocean, for instance, or two times a meter, you found out most significant vertical change only occur in the cell notion for so for some notion. And also the highlight you North Atlantic Ocean. So in the deep and peace ocean, the region really matter for the app, the upper peoples and change are those highlight you to liters via the reader, you're going to have very strong, very significant and verticals check other regions. They are still some where it goes change, but their magnitude are much smaller. So this highlights the important rule for the highlighted region, for the vertical exchange of heat sought and all likely or the other material or the right upon are basically show the temporal variability of this vertical heat flux. And you can kinda see where the largest the temporal variability appear. Urea, equator, equatorial region, or some asymmetric feature in a deep puncture. Okay? So the, the key information here, the deep ocean a highlight you, the reader, are really the key for the vertical heated transport. If we do a global integral, are like here, show global vertical fluxes of the global mean, vertical heat flux. And we found nine in the deep ocean. This would you go heat flux shows a positive number. Okay, look at numbers like 0, 1, something like that. And that means at any location for incentives, go back to this diagram. If you care about the upper ocean change, it's cut off at 2000 meter. You're going to have our upward a heat flux from the deep ocean to the upper ocher. So this Abdullah heat flux and contribute to the well-known apportion warming. So the upper ocean warming is not a simple consequence of the global admin fee Awami, the heat get the ocher, but also part of that actually related to the redistribution of heat inside the ocean. Okay? So this basically remind us, the ocean, it's not just very acquired bucket of water. You gotta heat to the surface. They get to the deep ocean through diffusion. In the ocean is very dynamic. They are many, many process, we're abilities or upward and downward arrow going on. All over sudden time period, It's certainly possible you have upward transport, rather simple, diffusive idea from top to bottom. Okay? We also do the same thing for the salinity or salt transport. This is the result. The vertical saw the transport for the 500 meter as well, the 2 thousand meter. Again, this is just confirm. Look at the deep ocean one. You're going to see the highlighted region are the key regions for the vertical salt trust for the same conclusion as the heat cockpit key content transport. So again, I've highlighted the important rule of the highlighted region. We do the global mean again for the salinity of the soil transport. We found that if we care about the upper ocean salinity change with people, how be using to infer the global water cycle change. So that people usually just a tribute all this linear chain toward cycle change. But here we showed that if you use certain depth for instead of up to 100 upper 400 meter salinity. Phrasing easy case found that the salinity change over the upper ocean actually partially due to the solid transport, uphill transport from the layer below it. So if you are truly the oldest linear channel in Apollo, ocean to water cycle, you're going on the estimate, the water cycle change. Okay? So there's basically how remind us. If you want to link the upper ocean changes in temperature salinity to global climate change. You need to take into account the, the deeper part, how the chain of the APOE sure how they contribute to the upper ocean changes. Ignore that part. You're going to over or under estimate the unfair exchange. So here's a summary of the, or this part. For the worry or heat transport we feed our global integral shows our net upward heap transporting in the deep ocean. So that means part of the observed apportion Wally could be due to the vertical redistribution of ocean heat. Okay, these are the pretty big impact or implication for infirmary. The global energy budget. Energy imbalances Agile. For the vertical solid transport, the global integral show a net upward assault, transporting the potion. And the upward transport to the ocean compensated a freshening induced by the net of fresh input, melting of ice, etc. So if you want to quantify this mounting by looking at the ocean salinity, you really need to consider the vertical salt transport as well. So both the vertical movement of heat and salt show very complex spatial patterns. Confirming that the ocean is a passive reservoir, is not a passive reservoir, but an active heat I have sought is changer. So it's not like acquired bucket of water getting diffused to the bottom. It's not like that. There are many things going on. We really neat on standard failures happening inside the ocean. That's why we cannot go to the next topic, the global vertical velocity, the global ocean. What do your velocity like I had been doing is I just analyze the body global assay from a curve. The reason is the vertical velocity in the ocean are associated with large-scale circulation are very, very tiny. The strongest mom maybe like one meter per day in the ocean. So you can imagine with the current name, the equipment we have. It's almost impossible to measure them directly. So we have to infer based on some as Ocean City dads to me like hacker. So here we just look at the long-term, what you got velocity from the left to the column show the shirley would equal velocity are three different depths to our meter surface of vertical velocity v0 commercial pattern, equatorial upwelling, subtropical downwelling due to Ekman pumping. So very conventional pattern. If you go to deep ocean, he found that most of the straw upwelling or downwelling swap occur in the sound notion. As well as the North Atlantic Ocean highlighted North Atlantic Ocean can look at different, perhaps a little different. So again, this highlights the importance of those highlighted region for the vertical transport of material, intellectual property, okay, particularly for the solution. If we look carefully, if you folks are more particular patch here, you've got to find those patch actually extended from the sea floor up to couple of 100 meter per 1000 meters. Those Apaches are really related to the bathymetry for pictures, we know that a sound and show you how very strong ACC, by the ACC move around the sun, the ocean. And then you have a lot of mid-ocean ridges or the bathymetric features on the bottom. So I'm an ACC flow over those bathymetry. You're going to have up and down. You're going to generate this vertically is 10 the pipes, if an upward pipe are done with the pipe. So they could be very important and for transporting climate a signal from the upper ocean to the deep ocean. You're much faster, way to magnitude. It's much faster. So it can take about 20 or 30 years for a signal from surface of gathered bar. So it's very fast and this could be the reason we have the deep ocean warming happening. It's our notion because they can propagate the signal to the deep ocean, read fast. Again, arrive, just show the temporal reliability. I want to teach you about that. During the process, if we look at the global vertical velocity, we found very interesting pattern. I mean the Southern Ocean. Everybody seems no, they'll draw our vertical motions. Will you tutorial, we know that equatorial up money. But when we fairly interesting is along the western boundary currents, they are interesting upward patches from surface to about one thousand, two thousand meters. But that's only based on ACO. So we want to know it is the artifact in Geico. This western boundary are promoting patch or artifact or they are likely robust features that basically are seldom discussed in later richer. Then we'll go to other ocean we analysis product and we found a whole bunch of different other products. It provided vertical velocity acid. So what do we do is we find the overlapping time period over those product and look at the woody go, we'll ask the answer. These are the what are we found? You see they are these are the 300s, around 300 meters of the vertigo velocity estimate from six product, five of them, these five are ocean re-analysis products. This one is a very high resolution, similar ocean model simulation. You can find out even though the DTO to patch detail pattern are different, but they all show very significant upwelling patterns. Random means upwelling patterns alone, the major subtropical Western Boundary Current. So this kind of give us confidence that this western boundary upwelling likely a very robust feature in the ocean than how be ignored or overlooked in previous literature. Here we look at the vertical structure of those upwelling from the four major Western Boundary Current, the gulf stream from the six different product, cool shield us. The six different product. Same thing here. You can see that even though they all show some differences there, which we expect, right? But they all show this very robust upwelling pattern, the West boundary of the subtropical ocean basin. Okay? And this worry go up. Why? If you look at this, the vertical extended, they are extending from 2020, 50 meters down to one farmlands or 2000 meters, very, very kind of four. What do you is tending upward? So they provide a good way to connect the surface to the deep ocean. We also try to quantify the contribution to volume transport of the different upwelling systems. Some of them are well-known ones like the Eastern Boundary Current, upwelling, equatorial upwelling sound, the ocean upwelling on them. We also, you kind of show the Western boreal planning. So they were quantify their contribution to the body go volume of transport or compare them at different depths. Are you going to see that this western boundary upwelling from like some 220 meter, 50 meter down to about 200 meters. The only less important and assail motion, but they are much more important than equatorial. Why? Some surface and the Eastern boundary upwelling. So this has, hasn't been discussed much in the literature. So we think this is a overlook the branch off of the global ocean circulation. There shouldn't even more study about that. Okay, So this is summary of all the ocean vertical velocity. When I want to highlight two regions, the first is sound, the ocean pipes. So in some the ocean receive what equally coherent, upwelling and downwelling. And they act as pipes connect the apportion piece ocean and the consequence, likelihood consequence of the ECC inactive with the mid-ocean ridges or lose lot of typography features and some actually are in the conventional picture of the meridional overturning circulation. Because due to low average does a pipe with the structure yearly average out. So the Haven't, they are war in the vertical transport had been largely ignored in the classical overturning circulation picture. So they should be addressed. The WBC, the Western boundary upwelling, those are the founders are robust, intense summer so if it's abnormally and they are essential by looking at the numbers of transport, but overlook the branches of a global opportunity circulation and also the WBS, the upwelling can intersect the ocean eutrophic mutually, okay? Because they have reachable 50 meters, something like that. So they could play a very important role in the vertical transport of nutrients and the biogeochemical tree search. So those have been discussing previous study and I hope more people are going to look at box. Alright, so this is the conclusion of the whole talk. For the ocean changes, we see significant uncertainty in our current estimate of ocean heat and salt content changes, particularly deep abyssal ocean. So our estimator, but those are certainly could be useful for the people who are improving the current observing system. For the vertigo is the changes we found out what evil redistribution for heat and salt inside the ocean really matter for inferring information about climate change and viability from Office wishes. And they are also very useful for describing an outstanding the data poor. Deep ocean checks for vertical velocity. We found out vessel boundary of some tropical ocean basin play a very important role in the vertical transport the ocean property and material. We call the existence of this significant upwelling system. But this system along the line, someone recurrent or overlooked. And the body, it could be important branches of a global circulation. Okay? And also I want to highlight that Italian ocean pipes, those vertical velocity associated with a bathymetry and ACC are very important process and deserve some attention in the future. That's all goes. There are some papers related to death. My students and me republish over the past few years. And thank you. Thank you so much. Great time management. I think we have time to take for some questions from students. So I encourage you guys to raise your hands and speak up. I'm not seeing any hands yet. Two threads as the students are being the questions I had. I'm really blown away by the verbal exchange of heat from the deep ocean to the surface ocean. Do we do you know where that heat is coming from? Did it proceed climate change? Is that just the yeah, that's that's one possible explanation. The thing is, when we measure the ocean or we only measure like for instance, for that beta will be, I use only 20 years long, right? It's just a snapshot of the long-term perturbations of the ocean and the Hindu inside the ocean, we know the ocean somewhere. People tell osha has memory. So the ocean can memorize things like thousands of years. So any snapshot that we have in the deep ocean or the part of the ocean. It's integral over the past couple of thousand years. So we know that over the past year. So how many climate change happening, event happening like the Little Ice Age? That's a significant cooling event, happened in couple 100 years. And thus signal definitely hide somewhere in the ocher. So if you just measure some part of the record, it's totally possible for certain dapsone level thou cooling signal stalling there. And now clearly signal in the deep ocean gonna reflected as our upward of heat flux because of cooling. You know, if think of budget point of view that are making the cooling is for the cooler regions are losing heat or losing heat. It basically our heat flux from the cooling region to the warning reach. Again, this is just mean the dot the heat transport, the Usha is not pure diffusive process and you know, diffusive process only happen from how the reagent cool region. But if you think there are not only diffusive processes in the ocean, they are the vacuum process, text circulation, transport, Zara, you add on that. It's certainly possible over certain time period. You're going to have this our upward transport, we call a simply assume that, okay, surface warming. Now, you add heat over the past few decades, the heated sure. Already continued deeper down to the bottom. It's not like now. Yeah. No. You percents and I think this was a particularly new you have a sense of how much heat flux we would expect just from the diffusive processes psyche, is it possible that, that this net heat transfer is actually is a combination of diffusive Yup going out and that he actually in that paper, we did look at that. The contribution from diffusive process and the advected process. The interesting thing is if we consider diffuse same process, including both the dye pick them mixing and also the ICP color mixing and mixing in the ocean has the one that crosses the eszopiclone. And also mixing allow me ask people if we only talk about a heat or salt. So if you add those two together, the funny thing is, the defusing process actually transport heat upward. Uipath, that's very counterintuitive. And we were very surprised about that. But if you look at the diff vertigo, diffuse our vertical, or that people are diffusive, process it totally downward because in a hot surface, high-temperature surface contemporary the bottom, so the heat transport down that. But a separation of that vertical diffusive process and the alarm has regular diffusive. We haven't done that yet. That's kind of on my to-do list for awhile, but really haven't get to the point that you really calculate it out, but that's totally can be down. Yep. Yep. Thank you. Other questions? I have a quick question. So I was this is the first time I pay very close attention to the quantitative measurement of the size of vertical exchange in terms of content, some content between up in the deep ocean, in different oceans actors. So I noticed that in your earlier plots, Southern Ocean has like really intense vertigo exchanges spatially. But there are also a lot of spatial conversation. So when we see a lot of blue color, we also see a long red color. So my question is, if we cross theta is the entire ocean sectors will suddenly oh, should still be our dominant sector now, the cedar and the overall vertical change between upper and the deep ocean around the Earth. Or it will be still like as a weaker because a special compensation ago, actually a good question, actually very important that the thing is the convention idea. When people look out the vertical change or would you go to change the conventional meridional overturning circulation picture? They do the average. You're going to average all those positive and negative number out, right? The main part of the velocity, for instance, they use the velocity times t OS get the vertical transport. But the fact that look at this picture, I show here, for instance, look at the 1000 meter, you, how positive-negative the Apaches, the policy. You can imagine W at time t at one location plus w time t unknown location. They added together is not equal to the w mean time ti me isn't non-linear process. If you use it WT or the each location, you add everything together, you're going to get a pretty big number. But if we just use a W main type of teaming, you're going to get a much smaller number. So that special patch, actually very important. It'll give you, give you win, bigger. Would you go transported and a simple pseudo average result. That's why this is important is the Juno is matching. The pattern should be taken into account, rather than simply either zooming vertical velocity times pseudo mean temperature. Because it's a non-linear process. And actually, I think after we published a paper, there's a review paper in Nature, just to review this specific dimension is asymmetric. Asymmetric and padding the sour the ocean. Why people should care about this? And the, actually if you just use a little main thing, you're just on the estimate about the transport or exedra, a bunch of stuff. And also we ignore that interesting dynamic or related Apaches are the questions that it's late in the semester and everyone is probably pretty frantic at this point. But if there aren't any other questions, then we will close up. Thank you so much for really detailed and talk and for stepping in when we had together conflation. So really appreciate it and thanks to everyone for coming to all these colloquia. And we look forward to the workmen, the new colloquium organizers starting next semester. Thank you very much. Thank you, Jim.

SMSP Spring 2022 Colloquia Speaker Series - Dr. Xinfeng Liang

From Yun Li May 13, 2022

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Zoom Recording ID: 95225027531 UUID: BYIlPhUTS1yjt8nNHO0XLg== Meeting Time: 2022-05-13 03:17:03pm

Tags: deep oceanclimate changedata analysis

Department Name: SMSP
Date Established: May 13, 2022
Appears In: SMSP Seminars

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