That's inside this. This is that open space with the big oak tree right above the house in the patio. And this is the fence that on the trip before this, Barney and Dennis and I made out of telephone poles. We just finished that fence there. So then we decided that as long as we now had it fenced, we'd keep it green instead of just, you know, let it dry out in the summer. So we put in an irrigation system. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. I know. John Rogers is the youngest person in history to receive a commission of appointment at the White House. He's labored with great diligence and imagination on behalf of the institution of the president. John oversaw many improvements in the executive mansion and initiated a thorough restoration of the old executive office building, a structure of great historic value. The buildings that were in John Rogers' stewardship are national treasures. To his lasting credit, he endowed them with renewed beauty and grace. Thank you. I'm not deserving of this medal, or how can a man 20 years old, a young man 20 years old, ever display any deeds that are worthy of this recognition? And so I accept it as a symbol of the expectations that have been placed by you, my family, and my friends here. And I pledge that I will work hard and honorably that someday I may be worthy. I'm sure you will, and we think you're worthy of it.


iterative procedure to find the optimal parameters for lithospheric thickness upper mantle viscosity lower mantle viscosity and then pose a resulting global mean sea level history what they find is they get constraints on the lithospheric thickness that range there are two scenarios the red and the blue but they find this what's shown here is misfit on the y-axis and essentially these values that have lowest misfit are the ones that are most likely in best fit the data they obtain a sort of likely range for lithospheric thickness these are all one dimensional models in which the viscosity in Earth's structure only varies with depth they find that the upper mantle has a viscosity of about 10 to 20 and that the lower mantle viscosity has sort of two solutions one around 10 to 21 and the other around 10 to 23 there has been a history in the community of the debate of what the viscosity is in the lower mantle also using not just sea level observations but also geodetic observations J2.0 for example here in this specific study what the authors favorite was this higher for a couple of reasons but most notably that it required a smaller Arctic ice sheet smaller ice volume at the last glacial maximum which as you just heard from Pippa the sort of updates to the reconstruction of the ice sheet volume over the deglaciation from an Arctic sort of come in lower and lower so as in there's less and less change over the glacial cycle actually and this is sort of more in line with this higher viscosity here and we get a deglacial global mean sea level history that starts with sea level at around 130 meters during the last glacial maximum and then a deglaciation with sort of punctuated ice melt that is where in particular the past rates of sea level rise occur during Mount Water Pulse 1a Mount Water Pulse 1b for example and those have been used and studied as trying to understand speed limits on ice retreat which is of course important if we think about the future so there are details here in these reconstructions that will differ a little bit from study to study but I think this is the overall there is an overall understanding here and this is just to show this as well and again the sort of ice sheet evolution over the last half a million years with major ice sheets in North America in Fennoscandia the exact distribution of them is not known very well but we have a pretty good I would say first order understanding of this ice sheet variability you see of course here during the interglacials like this one the last deglacial where the sea is actually rebounding and you see how during glacials land bridges form and are disconnected during the deglaciation there's a lot of connections to archaeology for example as well so this is sort of you know this is the biggest picture that I think will have a pretty good sense of now we're going to move to thinking about okay some of the details that we probably that I want to argue that we don't know as well on to how we can understand them better so one of those you know I say details but it's really it's pretty pretty significant difference is actually the the exact geometry of the past ice sheets and just to demonstrate this here I'm showing you three different learned at ice sheet reconstructions at 18,000 years ago and they are fit to different observations some of them are more sort of ice physics based than others but they are quite significant differences here and this is you know I would say one of the very important areas of ongoing research really trying to understand these differences there's a lot more data that can be used in assimilated and I'll get to that when we talk about future directions along those lines of sort of outstanding questions for the deglaciation there is also a the question still of what's referred to as the missing ice problem at the last glacier maximum which means that at the if we look at the if we look at sea level records from the last glacier maximum we require an ice volume an ice equivalent volume that is larger than we can really reasonably distribute among the possible ice sheets so there's this missing ice there's some emerging some suggestions of how this could be reconciled but it's certainly not a solved problem similarly thinking about how much what the contribution is from different ice sheets to meltwater post 1a and other meltwater events there is work being done in this direction and which is really interesting but these are definitely still that's the level of detail that is really important if we want to understand ice sheet variability at the moment the other big component in the GIM modeling is of course also lateral variability and viscosity and Pip already gave you a good overview of this I'm showing you here just to give you a sense of sort of the order of magnitude variability that we're expecting you see lithospheric thickness here in the top left and then variability and viscosity at a couple of incremental depths so viscosity likely varies by an order of magnitude we can what is used here rely on seismic tomography and rheological laws again Pip talked about this there are uncertainties in this here we're using some geodynamic constraints to better map shear wave speed into temperature and then into temperature and then we have certainly uncertainties remain both in the scaling relationships as well as just the tomography itself and one aspect to mention here is that during calculations that account for this full variability and viscosity is a lot more computationally expensive and the reason for that is that we only vary whether the structure is regularly symmetric we can actually solve parts of the equations semi analytically this has to be fully numerical in finite volume, finite element pick your method of choice but it has to be fully numerical which makes these calculations the actual sea level calculation associated with this earth structure a lot more computationally expensive and as a result a lot harder to explore uncertainty and as a result that makes it a lot harder to explore uncertainties so where does it leave us so what are sort of the data model misfits that we still have for the deglaciation and I'm just plotting out a couple of sites here you see different locations here over the deglaciation you might have noticed that time for me sometimes goes in one direction the other direction so you always have to keep an eye out for that so here it goes towards the left with the present being on the left and I am showing you sea level data as sea level index points which mark sea level marine limiting which mean that sea level has to be higher than that and terrestrial limiting which means that sea level has to be lower than that from a series of locations and the locations are kind of group locations that are shown here on this plot the prediction that I'm showing here is the 6GVM5 sea level prediction and this is a great sea level model and sea level prediction that's widely used what's great about it is that it's actually very easily available and it's been shared widely which is one of the reasons why it's so widely used it fits some of the sea level observations so it's a model that assumes a radially symmetric viscosity so it fits quite well in some locations here possibly up here and there are other locations where it doesn't or it fits in Barbados very well the Barbados data also went into constructing this ice model and then it doesn't fit so well in other locations I pick these somewhat randomly just not to show that this model is not a good model but just to show any ice model that you were to choose would do better in some locations and not so well in other locations and it's a result of the uncertainty that we have in the ice reconstruction and the uncertainty that we have in the viscosity structure so how can we move forward here how can we come up with a model with a GIA model that actually fits these observations well and that does allow at the same time for variability in Earth's viscosity I'm going to kind of propose one suggestion here of an approach and just before I go into that here's sort of the setup of the problem we have our GIA model what goes in is the ice and ocean loading most notably the ice sheet reconstruction the ocean loading is calculated from the ice reconstruction and the earth reology that goes that's input into a GIA model the output is our solid earth deformation it's relative sea level change and other quantities we can we make a prediction we can compare that to sea level observations and then adjust the ice and earth model accordingly so the crux here is of course what does it mean to adjust the ice and the earth model accordingly I'm going to show you one approach of how to do this now well we're just going to be adjusting the earth model and we're going to lend the methodology from seismology essentially what we want to solve is an in risk problem and when I started working on this I thought about this in terms of seismic tomography and since this is a solid earth crowd I thought this would be a good analogy to set up this approach as you all know seismic tomography we have an earthquake we have seismic stations waves travel through earth's interior and we measure the seismic speed at the seismic station there are sensitivity kernels in the earth's interior that connect where the earthquake happens and where the seismic station records the seismic waves and that means these are the areas that whatever the seismic waves that travels through the mantle is sensitive to a fast or slow structure so we have these sensitivity kernels here which are sort of the darkened areas and if we have a structure that is a feature in the mantle that is slow the wave will arrive later than expected if it's fast it will arrive faster than expected and we can take that knowledge that mismatch between the observation what we expected and what actually happened to update the internal structure and here we go that gives us our seismic tomography we can do the same thing with sea level observations so and this is work that's been pioneered by David Alatar and and his student Ophelia Crawford Crawford so what he's calculated here is a sensitivity kernel for the viscoelastic deformation problem so if you were to imagine that the ice load is just a point load which of course in reality it isn't just for analogy sense and our rebound observation is a point observation which it is then we can calculate the sensitivity kernel for the physics of this problem and we find a sensitivity that's actually quite similar to the sort of banana donut seismic sensitivity kernels we can calculate the sensitivity kernels in a 3D earth model very cheaply by using adjoint equations which have been used in seismology and geodynamics and now are also derived for the GIA loading problem so what does the sensitivity kernel look like not for an idealized point load and point station but actually for a real world extensive ice sheet as well as kind of a sea level record and that's shown here so we're looking at sensitivity kernels that have a specific ice you know the results of a specific ice history we're looking at a station so we have you know the Laurent Hyde and the fan of scanning ice sheet as I showed you this before and we have a station here that's located in Bonaparte Gauls we're looking at the sensitivity kernel at two depths here at a thousand kilometer depth and you see that there are areas essentially you see the areas that are red and blue are the areas that this specific observation so the sea level record at this side is sensitive to and you see it's sensitive to structure right underneath where the record is where the sea level record is and it's sensitive to the area ice loading again similar to seismic tomography we can use the knowledge here where if we have if we have a positive viscosity kernel here in blue we know that if we increase viscosity in that region we will increase local sea level where else if we have a negative viscosity kernel say in red here we know that increasing viscosity will lead to a decrease in the actual sea level prediction and this is fantastic because it gives us a means of updating and inferring 3D earth structure based on a data model misfit and I'm going to show you an example of how this can work with a synthetic test so the synthetic test here is that we're going to use the same database that I showed you earlier from this land back paper so this is sort of the geographic range of where we have sea level observations and we're going to set up a a known 3D viscosity structure of the earth and we want to then we then produce data with this known 3D viscosity structure and then we use the data to back out and infer back what the viscosity variability was and compare how close we got to the known structure so this is all synthetic no real data in here so we're putting this is the viscosity model that we assume to be correct this is based on the S20RTS seismic model we use a fixed ice geometry we're not inverting for ice geometry here at all the data in the far field so details in the ice geometry probably don't matter as much but if we go to a global scale of course we don't you know there's uncertainty in this which we're connecting here in this in this simplest case scenario and we run forward to projections of sea level at these different locations and produce our synthetic data we assume the data have no uncertainty okay so perfect data again easiest setup and then we start with no variations in viscosity and use our that inversion scheme those sensitivity kernels to update what the actual earth structure what the earth structure looks like and then compared to the true I'll show you the results for that in a second this is an iterative approach because we get updates to our viscosity structure then again do forward predictions compared to opposite compared to our synthetic observations update the viscosity structure and it's if we use far field data it's pretty well behaved we use near field data it gets a little bit trickier and we can also you know test do we actually match our observations after those iterations and that's shown here so this is our synthetic data is shown in black the initial one the earth model so what we started with is in is the dash blue line and then in the red is the inverted results so we see okay our inversion met matches the observation this is great but do we actually map out the correct our structure right so I'm showing you here on the left and I'm going to go through this kind of depth by depth what the viscosity structure is that we get out and you know ideally if this is a perfect inversion it looks exactly like this in reality you will already expect that it can't it can never look like this because our data really is mostly located in these regions so we don't really have any information in big parts of the earth but let's just look at the regions where we do have data okay so this is our inverted the result of our inversion the inverted viscosity structure we see sort of higher viscosity here and around here lower viscosity here which does you know our features that do to first order match up with the with the true with the true viscosity structure we also see the sort of higher viscosity sorry lower viscosity here which is a little bit present but not quite as striking if we go to 500 kilometers depth we see quite nicely that we are mapping you know the subduction zone here we're mapping these high viscosity slabs that are in our in the true model and we continue to map the slow viscosity zone here we're starting to pick up this high viscosity which is not really present at this slice but if we go a little bit deeper we're seeing sort of some bleeding between the depth slices where it's starting to pick up this high viscosity here again continue these features and one slice deeper you know we see some of this variability in the Pacific I would argue that we're sort of actually matching and are able to map some of the first order features in the viscosity structure we can actually quantify this and look at the correlation between what we know is the true viscosity and our inverted viscosity the left shows you the correlation coefficient between the two red is we're doing really well we're doing really well in the structure blue means it's actually anti-correlated so we're getting the opposite of what we should get so blue is bad red is good you see overall in the in the whole glow we don't in some regions we do better in other regions we don't do well at all however if we only look at regions where we actually update the viscosity so we actually feeding information into our inversion we are left with these regions that all have really high correlation so we are actually able to recover some of the 3D structure through this tomography viscosity tomography here's the dream my dream is to move towards such tomographic imaging of Earth's 3D viscosity I think the key part here is that it's towards a tomographic image there are a couple of very important caveats that make this very difficult but I think still possible and I'm going to end with showing you are reflecting on a few of these complications so one is of course that we need more data and more data exists so this is a compilation of the Holocene C level data that are available there have been great community efforts from the Halsey working group to compile and standardize the Holocene C level data so we can start working with them there are obvious spatial gaps that you just thought in the inversion we don't have data we don't have any information so the more data we have over this time period the better this imaging of the viscosity will work and I should also say that the time period we are most limited to the Holocene where not that much ice change happens the glacial records will be better those are much harder to get to because most of them are submerged today we can also start incorporating present day constraints GPS gravity and there was already a lot of talk about that today ice sheet reconstructions need to be improved I talked about some of the outstanding questions here in these inversions we can explore tradeoffs between inverting for ice or inverting for the Earth's viscosity but there is I think a lot of data that aren't actually assimilated in these and this goes more into PIPA's direction on the cryospheric side but I think there is a lot of data that we can leverage to actually distinguish between which of these ice sheet reconstructions is right so beyond sea level data and present day constraints there are fluvial and pro-gacial lakes all around here that have tilted shorelines that we can use river routing across here, across here, across here that tell us something about the GIA signal could use eschers to constrain the sub-glacial hydrology of the warranted ice sheets people have also used constraints from atmospheric circulation so it's kind of an eclectic set of constraints but bringing those together and not just in with a handmade ice model but with a physics-based ice model would really I think push this field forward significantly and I should say there are people who are doing this and that's great there is of course also still a lot to be done on this assimilation techniques I had described a gradient-based optimization using adjoins there are other approaches out there and it would be great to see them the drawback there are a lot of advantages to the approach that I described to you drawbacks are that we find a global we find a local minimum that means we find one possible solution we don't actually get a good sense of uncertainty this is a problem not unique to this community but to the seismic community to the geodynamic community and approaches like looking at, there are approaches out there and actually working together across the Solar Earth Sciences I think we can learn a lot from each other here I also would advocate for better benchmarking people have done benchmarking for radially symmetric GA models and Wouter van der Waal has led some of those efforts but I think on the 3D GIA models they have not been benchmarked and I think they should be benchmarked and it's boring work but it's really important and of course on the rheological parameters the better our constraints on the viscosity structure from other disciplines is the better the starting model in such an inversion the better our end result I may dream about joint inversions between doing a seismic inversion a geodynamic inversion and a GIA inversion together which might be possible at some point and I think would be really amazing and I think this is my last point here which is also on the rheological properties and Peppa mentioned this but everything I told you was assuming a Maxwell rheology a single dashpot and a spring but it's very likely that the Earth has transient behavior for example burgers or sort of an extended burgers material which we know from laboratory experiments at high stress high strain location especially close to the ice sheet we might move into dislocation creep that means stress dependent deformation so the rheology of a Maxwell body is something that we also need to reconsider and there's work being done in this direction but I think there will be a lot more in the next decade and oh no this is the last point and I'm going to just end on this very quickly but I think and Peppa and Chris mentioned this I think one really exciting aspect of looking at the deglaciation is that the records are becoming better and better and resolving smaller scale variability and here's a record that shows you sort of centennial variability in sea level which you know might not be a GIA process but might be an oceanographic process or might be related to sedimentation terrestrial water storage variability so a lot of the processes that we will hear about tomorrow are a lot I think I think the community is starting to be able to pick those up and separate them out from GIA which I think is another avenue that I'm certainly very excited about so to summarize and finish up here we understand the general deglaciate evolution and earth's internal structure and this spittles on decades of work from a lot of you know fantastic scientists improvements that start now allow us to map 3D variability and earth's viscosity better understand some of the important detail but very important isodynamic features and interpret signals associated with ocean dynamics or I should say really just non GIA processes a lot of work I think is still needed to actually get there on the data assimilation technique side including more physics based ice model Pippa said it right getting GIA models together with ice sheet modelers and observational data people that's the dream team getting diverse assimilating diverse types of data being creative about that getting better coverage and then connecting to some of these other disciplines that have you know complementary knowledge that we need and I didn't really motivate my work at the beginning because it was motivated through all the other talks but if we have this better understanding of course it affects present-day sea level changes it affects our knowledge about the feedback of solid earth and ice sheets as Pippa elaborated on and it also affects our study of sea level during past warm periods periods of rapid change for example in that water events that are really important to understand ice dynamics and sea level change into the future and I'm going to stop here and I'm really happy to take any questions. Great, thanks Jack that was another great talk I think what I'd like to do is let's switch to both Jackie and Pippin will take questions for both together and just go into a general discussion of both talks at this point and I think to start off I think Thorsten has a question so maybe we can start with him. Thanks Mark, thanks to both of you for great talks and I'm I have a billion questions and I'm really very excited that three-dimensional variations in viscosity have now become something that people outside the geodynamics community are excited about because we've been at this forever and we kind of had a hard time getting at it but when you bring in the sea level and you realize the feedbacks with the cryosphere it becomes much more relevant obviously for society and so there are a number of ways to get at the different spatial temporal scales of viscosity from geodesy, from geoid and from sea level exchange and I guess I have a number of questions that are to do with that maybe sort of specifically starting out and then someone else can pick up on this for Jackie so you showed these really amazing androine based inversions for relational viscosity variations which is I think exactly the right approach but I was very surprised that you had deep sensitivity for viscosity and these were lost right and so my understanding is when you look at GIA alone then the length scale of the ice load gives you a pretty good idea about how deep you're sensing and then you do sort of a fairly localized average. Now GIA with the sea level equation then brings in the geoid response so I presume it's this geoid response and the flexure of the lithosphere that Pippa really nicely illustrated that gives you a bit of a deeper sensitivity but when the geonemesis tried to get at lateral viscosity variations from the geoid or dynamic topography it's always like because those are mainly sensitive to density rather than viscosity and so the rates are much better so I wonder what is it when you go to sea level that then gives you sensitivity that also appears to be sort of less localized. Yeah I would say two things come to mind here one is that what you just described we think about and the rule of thumb is the sensitivity often the depth sensitivity of the ice sheet is kind of the size of the ice sheet roughly right so for the Laurent height people say I think a thousand kilometers the depth sensitivity but this applies to if you look at viscosity in the rebounding area but we are not looking at I mean we're not only looking at area at sea level in that in the same location where we also have that low change so if you were to map out the sensitivity kernel for a site for a sea level record in Hudson Bay that's exactly what you would get you'd get sort of a sensitivity kernel that extends to about a thousand kilometers but since we're measuring kind of further away we are getting this broader sensitivity the other part is that it's not just the ice load that changes over the glacial cycle but it's also the ocean load and the ocean load is a much larger spatial feature and contributes to kind of a greater depth sensitivity that is sort of my question does ocean loading right that gives you a georic kind of feeling thing that is but why does that better than you know looking at say the effect of internal density distributions is it because the forcing is different because when we do internal density variations and internal viscosity variations the geoid doesn't much care right but your sensitivity analysis says that when you look at these surface loads it seems to be more sensitive to lateral viscosity variations yes sorry is your question whether it's the trade-off with density? No the question is where does the sensitivity come from compared to other geoid forward modeling studies that mantle dynamics have done yeah I mean I would say it's the combination of both of those I mean you're seeing if you look at the if you think about the figure that I showed you with the point load of your ice sheet and the point load or the point measurement of your sea level record getting a kernel that is sort of banana donut shaped and so that just gives you I mean there's trade-offs right if you can accommodate all of that in the upper mantle but you are getting sensitivity in the lower mantle from that as well I think next we have a question from Maya I actually have a couple questions but first of all thank you to you both that was such such fabulous informative and clear talks thank you I'll start with Pippa and both my questions are very simple compared to Thorsten's they're really just for my own clarification so Pippa you talked about how this difference in viscosity is going to affect whether or not the glacier retreats because of how it responds and I saw I know this is buried somewhere in your plots that you were looking at sort of maybe a thousand year time scale but could you expand a little bit on like exactly how much difference it makes in terms of time and how quickly it is responding to these differences in viscosity yeah I think actually what you've asked there is what we're grappling with at the moment the some people are looking at very short time scales and often they're running models which can run at higher spatial resolution and so you're sort of resolving the processes the detail of the rebound if you I think it's important to say if you run ice sheet models at different spatial resolutions you'll get different answers some of those ones which have run for very long time scales are fundamentally slightly different have different sensitivity to the forcing you applied to the climate forcing essentially so that's just something to make you aware of the what we think is happening is somewhere like Pine Island in Antarctica is that we can trigger a response to ice loss within a decade which is a couple of orders magnitude greater than just the elastic rebound which we traditionally assumed was happening and we know that because the GPS are recording rates which are a couple of orders a couple of times greater two or three times greater than the elastic response we're building that into models that have run over decadal and centennial scales but we're very much looking at specific processes there we're asking the question of what can the earth do other models will be thinking about ice shelf processes ocean processes in there as well and I think it's fair to say that there's lots of different groups tackling these different forces and some of those longer time scale ones necessarily try and combine all of those but maybe with less detail I think there's not surprising that we're getting different answers for long term projections at the moment thank you as one of the things I really loved about your talk is you so clearly illustrated how complex it is and how dependent these different feedbacks are on each other and so I guess that completely makes sense given that overall theme in your talk so thank you and I'll just switch can I ask one more question of Jackie go ahead with your seismic tomography models you know one of the things that always makes me nervous about seismic tomography models is their blobbiness particularly in the shallow mantle and particularly under the ocean basins where where we have so few measurements I mean obviously you can measure it other ways with bances but how confident are you in those shallow models and does getting the shallow viscosity right does that disproportionately impact your results versus the deeper viscosity yeah so I mean in the if we use a viscosity derived from seismic tomography really at the mercy of the resolution of the tomography right just through the process of converting to viscosity we can't really add any more resolution to it that using the sea level observations as independent constraints on the viscosity might be able to help with that but I think the resolution that we're going to get and we saw this a little bit in the synthetic case the resolution that we're going to get is also going to be very blobby or very smooth out right it's really the first order features that we possibly can start to resolve the question of whether that is a concern or something that is particularly important I guess it depends on where you're looking if you're looking in Antarctica and you're looking at a location where you have your ice sheet melt and you have your station right next to it you're going to be very sensitive what happens in the upper mantle there if you're looking at a site that's further and that also applies say looking along the U.S. east coast the upper mantle is really important the further you go away from the ice load the less important that sort of oceanic upper mantle structure is and mapping out these sensitivity kernels can actually help us answer that exact question because they're telling us which part of the of the mantle we're seeing that observation is seeing so I would say it depends on what you're looking at maybe actually just to segue off of that Jackie in terms of the spatial variations there's a question from the audience whether you can comment on the sharp spatial variation in lithospheric thickness in Africa 50 to 300 kilometers what's the evidence for it and what might have caused it yeah so we're seeing some of the strong gradients in lithospheric thickness once we move on to kind of a continental craton so you know this the models that I showed I derived from seismic tomography and they sort of map out the craton in those regions which just have a much higher you know thicker core you know thicker lithosphere then the the surrounding regions so that drives those kind of relatively stark spatial gradients great thanks we have a question from Jessica Warren thanks Jackie and Pepper for a great talk so it was really interesting and my talk kind of follows up on what Maya was asking about which is I'm curious with upper mantle rheology you know if you for example put in dislocation creep which I think the seismic data suggests in many areas is dominating what does that do and maybe the other question is why the preference for diffusion creep is it it's computationally easier and just in terms of like moving towards a closer rheology for what we think is there can you know can we go in that direction of putting dislocation creep in I can start on that but I'm also happy to then hand it off to Pepper because I think particularly in Antarctica people have looking at that more than in other places in general I think that the choice of using Maxwell is just that you know start with a simplest model that can explain the data there are definitely approach you know it's not actually that I mean that more computationally expensive to run different those different realities that aren't that are still one-dimensional so it's possible and people have are doing it you're sort of adding you know more free parameters to the inversion but I think it's it's necessary and ideally they're not fully free parameters but there are parameters that are actually constrained from by the you know the laboratory experiments but yeah the initial choice for Maxwell is just for starting with the simplest model and people have looked at this in particular in Antarctica so I don't know if you want to add to that yeah I it would be good to just flag up some work so I I run a 3D GIA model in Antarctica working with Vata van der Waal where we do include dislocation and diffusion creep as Jackie says it's it's computationally more expensive a problem that we hit there is how to parameterise things like water content and grain size we the model we have at the moment we just choose a uniform value for water content of the mantle underneath Antarctica partly because we don't know any better we find very different estimates of viscosity depending on that water content which obviously has knock on implications for the GIA model an area to flag up I'm not super familiar with it but I'm aware that Magneto lyrics can tell us a little bit about water content in the mantle and and luckily people in that field are looking to Antarctica and potentially introducing some observations there which will help constrain that bit of the problem but yeah absolutely thinking about temporal variations in mantle viscosity so the dependence of viscosity and stress is something which is sort of high up on the radar in Antarctica because we're able to get these areas of rapid ice sheet change and see the rebound and we can't quite model that rebound with a Maxwell rheology at the moment thanks I but I'm going to cut them off I'll just say that I was happy to hear water invoked as well because that seems like one of the easier parameters to allow you to have viscosity variations literally as well I think I'm going to go we have a question now from Cindy Cindy Evinger yeah I'm whoops I don't know sorry something odd just happened I was going to ask both of you how we I mean one of our roles on the committee is to find ways look for intersections or ways to support the community and it seems that particularly with both of your talks that you have strong advice to where new observations should be collected where new stations should be installed and so is there adequate dialogue between the observation list particularly say Antarctica where there's a specialist community and I'm just saying I've heard a whole series of talks on Antarctica related to research of colleagues here and I and I'm not what just seems like there's potentially disconnects and wondering how we could help with the community share the modeling results to better inform data acquisition so I'll pick that up because you mentioned Antarctica there Cindy I think you've you've almost flipped around the answer that I was going to give by saying can we share them those model predictions and that's that's actually an interesting direction and there are there are studies more around the northern hemisphere ice sheets asking the question of where could we get data that would help our modeling approaches that's not really been done so much for Antarctica I think the general approach is if we can get any data which is grateful because it's logistically so challenging but I think that's an interesting direction to be more targeted in identifying specific observations that would be helpful for the modeling would be interesting and Antarctica is an interesting location it's obviously very challenging to get to and carry out field work but it's actually also an area where perhaps there's the most collaboration in the world because of that challenge so starting those conversations is actually you often get quite a long way there I can add from the sort of far field perspective there is some work that as specifically what are the areas that are most sensitive to the parameters that we want to constrain and then go there and and look at observations but of course you're constrained by not every location has the data that we're looking for not every location is accessible it's not there's a reason why most of around Africa doesn't have any of the Holocene data in this database it's a little bit of a difficult but there is definitely that conversation and I would say a little bit less so on the where should we go to collect data but a lot more what's happening more and more over the last decade and hopefully more and more in the next decade it's sharing just starting to have that conversation between the observational science and the data modeling so the Holocene what I mentioned just collecting compiling the data in a way that is meaningful and useful for the modeling community and vice versa producing model predictions and making them available that then are also useful for observational scientists I think that's the stage we're probably at at the moment and it's picking up which is really great to see. I think we have time for one last question Jeff I think you had a question. Yeah there actually my question was almost exactly the same as Cindy's so I'll just say thanks to Pippa and Jackie for great talks really appreciated it. Great actually I'm going to use my I'm going to do it right here for just a quick second to ask you guys both one of you can maybe explain something to me that I've always wondered about so we often in like intro talk just about you know if you have an ice shelf and it breaks off it doesn't change sea level right because it's already floating but thinking about these fingerprints I'm curious how does that map is that fingerprint talking only about grounded ice that's melting I would assume though that that gravitational effect would actually also pertain to floating ice directly adjacent to an ice sheet is that true I really don't know the answer I'm very curious what it is Shall I have a go at that one Jackie? So my way of thinking about ice shelves is that they're already in the ocean so obviously as you said if they break off they don't change the sea level so I'm now struggling with the concept of the gravitational attraction of the ice shelf and I have a hunch that it doesn't affect it because it's displacing ocean water which would I would say yeah I'm struggling with this answer you're absolutely right that when they break off they don't change the sea level I don't think they are massive enough to have the same sort of effect as a change in the volume of the grounded ice sheet behind if you think about them then transporting into the ocean it's just mass moving around it's not mass changing it melts into the ocean essentially and what the models do is they do account for the self gravitation of the ocean itself so I think I may be getting back to my first statement is that if you count them as being part of the ocean then it is accounted for by these models of considering the distribution of mass throughout the system so even though an isostatic canal mountain range would deflect the gravitational attraction due to the floating that wouldn't be the case for an ice shelf I think so I think that's right it's the same mass and the mass is in the end what matters for gravity I would assume that it doesn't have a change on gravity and since you mentioned mountain glaciers if the ice melts and you allow the mountain to rebound there's actually also no gravitational effect but because there's a delay in the rebound you'll have that initial change in gravity that then is slowly being made up and I would also add which isn't exactly what you asked but the ice shelf is an important betracing role so as soon as you lose the ice shelf everything else can go great well thank you both for both great talks and stimulating discussion I'm going to throw it back now to Steve Nair who's going to do a quick wrap up for the day right thanks Mark well these talks have all been great and it's a lot to summarize I'm going to take a stab at here and I apologize if I missed some points but I think what we heard today is that first satellite measurements are providing a lot of new information about sea level change including sea surface height and ice sheet height and ice sheet mass changes that's telling us that sea level is rising and the rate of that rise is accelerating and that this is being caused by a combination of melting ice and expansion of the oceans both of which are being driven by global warming there's a lot of regional variations in sea level rise due to the figure prints of the ice sheet mass loss on sea level and variations where the oceans absorb the excess heat from global warming so I think we also heard that the big uncertainty for projecting future sea level change the elephant in the room someone said is determining how quickly the large ice sheets will melt studies of relative sea level data such as tide gauge data that are important for studying both the solid earth geophysical effects as well as sea level change and understanding isostatic adjustment, tectonics sediment compaction, subsidence etc so by combining this relative sea level data with knowledge of solid earth geophysics we can make progress towards not only understanding the solid earth but also ocean circulation and ultimately climate change changes in the ocean currents and circulation can have major impacts on sea level change along the coast for example changes in the Gulf Stream will have a big impact on how much sea level change we see along the east coast of the US we also discussed that we don't fully understand the feedbacks between ice sheet dynamics and solid earth deformation but we know that 3D variations in earth reality matter and it's going to be important for determining how solid earth deformation impacts ice sheet dynamics as well as the regional variability on sea level change due to the ice sheets and the fingerprints they make on the oceans and sea level the impact on ice sheet dynamics will depend on how quickly the solid earth deforms due to the ice mass loss and what the spatial pattern of this deformation is and so mapping mantle viscosity is critical for understanding these interactions and their feedbacks we also learned how paleo sea level records are being used to understand the earth's internal structure and the drivers of ice contributions to sea level change distribution of past ice sheet loads is a major air source here and is important for modeling glacial ecostatic adjustment and other effects approaches from seismic tomography have been used to study the earth's internal structure and so viscosity tomography is a new tool for doing this somewhere starting to map 3D variations in earth viscosity using these techniques however we also need to consider other changes in sea level not related to GIA such as ocean dynamics and so it's going to be important for scientists from many different fields to work together to solve these problems of understanding how the solid of this contributing to ice sheet dynamics and I would say in closing personally I think sea level science is very much an interdisciplinary problem and so getting oceanographers and geophysicists and geodesists and others working together to understand these problems it's going to be critical to move forward so I'll stop there and just say on behalf of the committee I want to thank all of our speakers today they were all great talks I'd also like to thank everybody in the audience for joining us today and the questions that we got so I also want to know that tomorrow we're going to be continuing this meeting with a few talks on vertical crystal motion on how that contributes to sea level change those will begin at noon eastern time tomorrow so thanks everybody and we'll see you tomorrow