 Good evening everybody. Thank you for coming out to tonight's lecture by Dr. Ross Dixon and also I hope you have a chance if you haven't been upstairs to love north to see the exhibit. It will be there till the end of the semester. So if you have time tonight go see it if you haven't already or sometime when you're cruising through campus stop and stop in and visit the exhibit that's the reason for this talk so Dr. Dixon joined the faculty at UNL January a year ago. He's been here for a little over a year now. He's a regional climate modeler and that's the reason we recruited and hired him. Ross spent, well I should start I guess at the beginning right, not that far back. Ross got his bachelor's degree and master's degree in physics and atmospheric physics, respectively, at University of Maryland Baltimore County, and then went on to the University of Wisconsin for his PhD, which you received in 2017. Yeah. And then he spent two and a half years at the National Center for meteorological research and to lose France. I didn't try to pronounce the French name for the center because that would just be awful for everybody. And then after he finished that he happened to stop by Lincoln and interviewed for his job, and then went on to another postdoc at the University of Arizona, before he came back for the faculty position in January of 2021. So, Ross is going to talk today about climate models and how we use climate models to project future climates for Earth. Do you have any further ado? Thanks for that kind introduction, Clint. And thank you all for coming out tonight. I know the weather might not be the nicest tonight so I really appreciate you coming out for my talk. I'm Ross. I'm really interested in climate dynamics, atmospheric dynamics and climate modeling. So it's a real pleasure to be here tonight to tell you a bit about what computer models can tell us about Earth's future climate. We need to click here. There we go. We live in a changing world. You might be aware of this. This figure here comes from the most recent IPCC report which was released last summer from the first working group, and it shows change in time for several different parts of the Earth system. So, for example, at the top here we've got the atmosphere. One important component is the carbon dioxide concentration. And so here we've got an increase, a dramatic increase in CO2 concentration with time in the cryosphere. There's observed glacial mass loss in the recent past. The ocean is increasing in heat content. We see sea levels rising. All of these things are connected, especially through global surface temperature, which we can see warming in these colors, blue colors are cooler here. Red colors are warmer. So we want to focus down here into this global surface temperature. Let's look at this, this data in a different lens. This might be easier for some people to picture. We've got changes in global surface temperature on the y-axis here relative to the 1850 to 1900 period. What's been observed in these observational records is around one degree Celsius of warming since that period which ends in 1900. And if we extend our record back 2000 years using proxy data for temperature from ice cores, tree rings, and other records, which we can derive temperature from. We can see that this warming is unprecedented in more than 2000 years. This is a global signal that we can observe in the climate record. This is another way of looking at these global temperature changes. I don't know if you've seen these before. This was called the warming stripes. This was put together by Ed Hawkins. And it's essentially the exact same data from this figure here. But we've put the warming colors, right? Colors that are a positive on a figure like this in red and ones that are negative in blue. The averaging is done over a slightly different period. So you're going to see red and blue in slightly different places. And this signal is clear warming towards the end of this time series, dramatic warming. In the next couple of slides, I want to flip through and think about going to smaller and smaller regions because when you think about it, it's really important to think about what global temperature change is going to be, but it's even more important to think about what the change that's happening in your own area is, right? No one wakes up and says, well, you know, I really noticed that the global temperature was a little bit warmer. What they notice is how temperature is changing for them. So this is what a similar plot looks like for the entirety of the United States. Sorry, 1895. And what we see is the same pattern warming towards the end of the record, but there's more noise here, right? There's a lot more variability in the year to year variations in warm and cooler years. And this continues as we look at the state of Nebraska. And so you can see the same signal warming towards the end of the record, but there's some really, really strong warm years towards the earlier period, some cool years mixed in here. This is kind of highlighting how understanding regional climate is a serious challenge and is so important for understanding human impacts. I really enjoyed these visualizations. Make sure you come by Adele Hall Learning Commons on April 27 from three to 5pm. The library is helping organize some visual histories. These are some visualizations of temperature using yarn. And I'm super excited about this. I've seen some examples from the past and this is going to be a really cool activity. I'm definitely planning on swinging by. Okay, so this is what these global warming stripes look like and regional, global, national, regional warming stripes look like. What are some other important observed signals, observed changes in climate? We can think about precipitation changes. So this is an image taken from the third national climate assessment. And what I want to draw your eye to in this figure is that there's a lot more spatial variability here in terms of the green colors which represent an increase in precipitation in terms of percent here. And the brown regions, which is a drying a decrease in precipitation. And what they've done here in this climate assessment is separated the United States down into further regions and created these bar graphs. So this is the United States average with each of these bars representing a single decade and each of these different regions. So you might be interested in finding a region that's a particular interest to you. My eye is drawn of course to Midwest where I live for a good amount of time and of course the Great Plains North where I currently am. And I see that for several of these regions there has been an increase in precipitation in the last century, perhaps a more important thing to look at is changing heavy precipitation. Because these extreme precipitation events are a particular interest to human impacts flooding, right. And those that really have strong implications for human health and well being. So, if we think about these indices for very heavy precipitation, we can see that there's also a tendency for there to be an increase in these heavy precipitation events in the observed changes in climate. So, I want to show you the distribution temperature these are important for agriculture as well. I wanted to show you this figure of hardiness zones produced by the National Arbor Day Foundation. If there are any gardeners in the room I'm sure you're familiar with how important the understanding of the hardiness is right the lower numbers, the cooler colors indicate regions where the winters are cold and you can't over winter plants. And what we see between 1990 and 2015 is that these hardiness zones have shifted northward, meaning that winters aren't as cold and harsh as they were. Have a look at Nebraska, where in 1990 there was a lot of blue and green, and in 2015, it's pretty much all green now. And some of the other sort of human implications well why do we care about these changes that we see in the climatic record well these are just a couple of news articles I grabbed from the last couple days as I was preparing this talk. We've got Lake Powell's water level plummeting due to the extreme mega drop that's being seen in the Southwest that has a fingerprint from anthropogenic climate change climate change has challenges to the outer banks barrier islands. So, a very a lot of economic sort of decisions to be made here. He waves at both of Earth's poles alarm climate scientists. This has just been going on the last several days is very interesting and terrifying signals that are being seen. And of course the IPCC is discussing new reports and new information and suggestions for policy that will be coming out in the next couple weeks. If you have want to explore more about different observed signals and human implications would highly recommend you check out the traveling exhibit. I'm sorry this is really driving me crazy. And perhaps just use the. Thank you. Is that all right. I'm sure it was driving some of you crazy as well. I would highly recommend checking out the exhibit on the second floor of the Love Library will be here to the end of the semester called real people real climate real changes it's a traveling exhibit that was put together by the National Center for Atmospheric Research, and it digs into some of these ideas a little bit further, but what I where I want to go is what are the causes of these observed changes in climate okay right we've seen that there's observational evidence for these changes. What's causing them and so I want to start with this image, which is a time series of carbon dioxide concentration and parts per million. Taken at the monoloa observatory, it's a great place to get a good record of well mixed carbon dioxide in the atmosphere because it's in the middle of the Pacific Ocean right away from a lot of major sources of carbon dioxide. And what we can see in this figure is since they've started taking records back in 1958. Nice long term record carbon dioxide concentration has increased from around 315 parts per million, all the way up to over 420. This is a really large increase in carbon dioxide and this is driven by burning a fossil fuels and for causes, you burn fossil fuels for energy and greenhouse gases, including carbon dioxide are emitted. So what does this have to do with temperature, the third national climate assessment put together this is cool figure where we see a this solid curve is our carbon dioxide concentration. Placed on top of the changes in global temperature. So you can probably see that there is some sort of similarity in these two signals. And you might be thinking, well, well, what if that's just a coincidence. So in order to really connect anthropogenic human emissions of CO2 to temperature change, we can run climate simulations. We can enforce them with just natural for things and different human forcing so that's what's shown in this figure we have global surface temperature change since 19, sorry, 1850 on the y axis and time on the x axis and the observations you've already seen this black curve a couple times in this talk. This is an increase of around one degree Celsius, since the beginning of this record, and what's shown in the colors are model simulations of the global temperature with different forcing is applied. So this green curve here only has for things from natural causes for things like volcanic activity, changes in solar activity sunspots, etc. So when we look at the green curve we aren't able to reproduce the observed record. We need to add human forcing is from human activity into our global climate models if we want to reproduce the observed temperature record, and this includes the greenhouse gases, which are shown in the right here. This has a tendency to warm the global climate and aerosols particles which are also created during the combustion process, which reflect energy and resolved in a cooling of the climate system. And if we put all of these things together natural causes and human. We end up being able to reproduce well the observed warming pattern. We can use these simulations to think about future projections of climate. So this figure here shows temperature global surface temperature on the y axis and projecting these temperature changes into the future for several different scenarios. Right what our choices we make now, in terms of how much carbon we're going to put into the atmosphere are going to have implications for how much warmer the climate system is going to get. So if we're able to curb our emissions, perhaps we can keep warming to one to two degrees Celsius, but if we continue to admit at higher very high levels we're thinking more around more around five degrees Celsius by the end of the century. I bet some of you are thinking to yourself well hold up. Last couple of slides have right relied completely on model simulations. Why must we rely on computer models. Well, we only have one earth, there is no second earth. There's no way for us to you know set up another earth and and say well let's let's change the carbon dioxide concentration in it and see how that planet changes right is no planet be no second earth there's no way to run controlled experiments. So the goal of climate modeling is to think about representing the earth system is to think about representing the earth system as accurately as possible. These simulations are not earth. But the goal is to create simulations that best represent earth, so that we can investigate many different climate scenarios. What are the fundamental concepts in climate science, and how do we use them to build useful models of global and regional climate. And I want to start with a very simple model, and think about after we build this model, you'll be aware of three things that control the equilibrium temperature of a planet. The first one is how much solar energy arrives at the planet. How much of that energy is reflected back in the space, and how much of the energy that that planet emits is absorbed by its atmosphere and trapped. So the simple model we're going to create is just a simple energy balance model. This is going to be a simple model for the average temperature of the planet so we're thinking about a average over the entire planet, which is going to have some sort of surface and surface temperature T sub s. And then we're going to consider the energy coming into this system and the energy leaving the system, and we're going to equate them energy in equals energy going out think of this like your bank account right. If you're coming in and money going out and those things are equal, your savings doesn't change at all. And so in this situation, if we have energy in is balanced with energy going out our surface temperature is going to have an equilibrium. So what are the sources of energy in for the simple model. What's the source of energy that that that warms the planet, but it's energy from the sun. The sun produces a tremendous amount of energy, and the planet intercepts some of it. And so we're just going to call that f sub s here, this is the energy flux of the sun. This value changes. This number has different amounts of solar activity and outputs more or less energy. This number also changes as the orbit of the earth changes and gets closer or further from the sun at 100,000 year time cycle. So, of that energy, some of it is absorbed by the surface and some of it is reflected back into space. This is the satellite image of the earth which shows that there are a lot of climate features which are light and reflective clouds and ice and snow and those aerosols that I was talking about in the earlier figure that are often emitted by combustion and volcanic activity, these things reflect incoming solar radiation back into space. This is the albedo of the planet. We represented this with a letter a times our solar flux here. And the final thing we have to consider in this model is energy that's being emitted by the planet, right so some of this energy from the sun is absorbed and warms the surface the surface has a temperature, all things that have a temperature emit radiation. It goes to the amount of energy that's emitted is proportional to temperature to the fourth power. So that's what's represented here. And so now we've got these three different energy fluxes. We can do our energy in equals energy out and come up with this equation. You don't need to worry about what this equation says but I wanted to put it here so that you all know that I'm just not making this stuff up. We know what the solar fluxes we can measure that we can measure what the albedo of the planet is. And so we're able to compute the temperature of this system, the surface temperature, and we get a number of 255 Kelvin, we like to use Kelvin in science right because the absolute. Sorry, sorry, the zero point is absolute. But I've also placed the temperatures here in Celsius and Fahrenheit we have negative 18 degrees Celsius and zero degrees Fahrenheit. So what's the problem here, this is, this is really cold life on this planet would not exist as we know it. If, if this was the temperature. So what aren't we including in this model. We didn't include the fact that our planet has an atmosphere. So if we enter just a simple slab atmosphere which absorbs all of that outgoing radiation from the surface and then reemits it to space and back down towards the surface. We can do this energy balance again, and we find that we have an equilibrium temperature of 303 Kelvin, or around 85 degrees Fahrenheit. Well this is too hot. It ends up that the atmosphere doesn't absorb all of the outgoing radiation from the surface. It only absorbs part of it and if you take that into account, then you're able to get much closer to what the observed temperature of the planet is. Of course, how much of the radiation that's outgoing as observed is absorbed by the atmosphere depends on the constituents of the atmosphere, carbon dioxide, water vapor, our greenhouse gases. This is the atmospheric greenhouse effect, which causes the surface of the planet to be warmer than it would be if there was no atmosphere. So to go back to this idea, the three things that control the equilibrium temperature of a planet, how much solar energy arrives at the planet, our sunspots, the orbit of the planet. How much of that energy is reflected back into space, ice, snow, clouds, these aerosols. And then finally, how much of the energy that the planet emits is absorbed by the atmosphere and trapped. This is our atmosphere greenhouse effect that depends on the composition of the atmosphere. So we're probably thinking, oh, and this is this is not a new idea right we've been using the simple energy balance models for over 100 years. Here's a paper by Arrhenius on the influence of carbonic acid and the air upon the temperature of the ground. Carbonic acid is our carbon dioxide here. This is this is not a new idea. And you're probably thinking to yourself, well that's great that we have a simple model for the global temperature but the planet doesn't have the same temperature everywhere. This is something that that you're probably thinking of, well how useful is this model really so let's think about expanding this model and thinking about spatial variations in temperature so here's a map that shows that the tropics are warmer and the the warmer colors and the pose are cooler and the cooler colors. So let's see if we can create a simple model that will allow us to reproduce this temperature distribution. And so we're just going to think about this temperature distribution, and then we're going to think about it intuitively by thinking about just averaging across the horizontal direction on this plot here the zonal direction. Here's a latitudinal plot of energy in and energy out what we see is the energy in. There's a lot more energy in in the tropical regions than in the polar regions. This is due to the curvature of the earth. There's a much flatter curve right it's depending on temperature that T to the fourth relationship. So in the tropics, there's a lot more energy in and energy out there's an energy surplus here. And in the polar regions there's a lot more energy out than energy in, you have an energy deficit. And if there wasn't anything that was able to rearrange on the planet what would end up happening is that the tropics would just get warmer and warmer and warmer and the poles would colder and colder and colder until they were extremely cold, and the tropics were extremely warm. But fortunately there is energy transport from the tropics to the poles, north and south energy transport by atmospheric and oceanic motions. This is the general circulation of the atmosphere and ocean. And you could spend an entire course thinking about the complexities of the general circulation of the planet. But what I want us to think about is just a simple sort of transport of energy between latitude and no bands. And we can think about breaking this system up into discrete regions right create a bunch of columns here. And for each of these columns we're going to consider energy in energy out and transport in the north south direction. This is a latitudinally dependent energy balance model. And this was a technique that was used in the late 1960s this is one one paper that produced a model like this, William sellers a global climatic model based on the energy balance of the earth atmosphere system. I'm going to show just one of the pages from this paper, this is the whole model. Okay, so this is we've gone from one equation to a page of equations. And when you use this equation. The dots here are the output from this model, compared with the line which is observations reproduces the latitudinal distribution of temperatures really well. At this point you're probably thinking okay well is this much better we now have an idea of going from a global to now we've got different latitude bands. How can we continue to add complexity to the system. What about cool things that like convection and clouds and storms, how are these things how might we represent these processes in a model. And so I grabbed this image. This is over some islands in Indonesia. We see clouds and convection upward motion. There's precipitation I'm sure occurring under some of these. We split this sort of system into areas which we can model, we can't just take this continuous system and model every particle every little bit of the system we have to think about breaking this domain into some sort of grid, just like we just took that horizontal domain and sliced it into latitude bands. We're going to take this horizontal domain and split it into different boxes. And what you'll notice is that I've outlined regions of around 100 kilometers. The resolution of occurring climate models global models. And what you'll notice is that there's a lot of stuff that is happening at a smaller resolution than that 100 kilometer grid box. Right, some of these boxes have, you know, are all ocean but some of them have some land and some ocean. Right, we've got some that have these small lower level clouds some of them have a large amount of convection but it's not only confined to half of that. How do we think about taking these complex things which are happening at scale smaller than the grid that we're modeling and represent them properly. These are what's known as our parameterizations and models and a useful tool for developing them and understanding how, how they represent different processes in the climate system is a single column model so we're just going to think about one of these grid boxes here I've outlined this one in yellow and creating a vertical column of boxes. This is a single column model. I'm going to consider the globe here and then taking just a region and just one place in that region and building a vertical model for for the that for for the atmosphere. This was a very popular tool in the early stages of model development. This is a very famous paper by my knob and whether all where they use a single column model. They apply various changes, which I've outlined in red here changing the solar constant changing carbon dioxide concentrations ozone concentrations and cloudiness and why do you think they chose these things to change in the model. These are the three knobs that we discovered were important forcing for important drivers of climate in our energy balance model right we've got the solar constant this is energy in from the sun. We've got cloudiness which has to do with that Albedo the reflection of of energy back into space and we've got constituents such as CO2 ozone, which are important for the greenhouse effect. What they were able to show is changing is how vertical temperature profiles so here we've got temperature along the x axis and height along the y axis, how these temperature profiles changed when you were to alter these different values of climate and this one here is for carbon dioxide with the the triangle line here with is 150 parts per million and the circles 600 and we see cooling and the stratosphere warming in the stratosphere and that these signals were much stronger when you were able to take moisture feedbacks into account and these is a very simple model right that has that energy balance but also some simple representation of convection and for the work that we're doing and others did in the 60s for the physical for developing physical modeling of Earth's climate quantifying variability and reliably predicting global warming. He was awarded with with other researchers the Nobel Prize in physics just last year. This is really important work that was done. We've gone from a global model for climate, a latitudinal model for climate, a very local one has single column model for climate. How do we go back to a full global model, which is perhaps more complicated. So if you can imagine a single column, you might be able to imagine lots and lots of columns that are all next to each other. So our global Earth system model, the atmospheric component is going to consist of a lot of these columns, right which are able to exchange energy and mass in the vertical direction in the horizontal direction, turning the earth into a gridded space. And so there. So, so we've gone from from something that is, well, yeah, kind of simple to something that's much more complex here all the different things. Not all, not all of them this is a sum of the important things that need to be considered when you're developing a global earth system model we no longer call them climate models because we're trying to capture all of the important components of the earth system. For example, the ocean circulation deep ocean circulation ocean ice, snow and aspects of the biosphere which are important for for climate convective clouds stratiform clouds, these aerosols right in the stratosphere and the troposphere that I was talking about fluxes between all these different components. And of course, the constituents, which are important for radiation and that forcing that we were just talking about water vapor CO2 greenhouse gases. And here's a schematic so what does this actually look like when you, you put together a client model here is the schematic for the Community Earth System model. This is the model that's developed at NCAR. And a lot of scientists have been working for a long time on developing this model here are just all the different components in it. We have atmosphere sea ice land ice, the ocean river runoff land and of course biogeochemistry the importance of life. And all these are coupled together. This code is, or at least it was as of several years ago, 1.5 million lines of code. And I know it is many more lines now. So we went from a single equation to a page of equations. So now we're talking about a model which is millions of lines of code, in order to try to represent all the processes in the earth system which are important for understanding past, present and future climate. So you're probably thinking to yourself well this is probably an extremely a computationally expensive thing to run, and you would be correct. This is the machine that's being used at NCAR right now this is Cheyenne. It's a computer that's able to do 5.34 petaflops. This is, is fun to say what is a petaflop well a flop is a floating point operation per second. And PETA is a quadrillion or 10 to the 15th a quadrillion. So 5.34 quadrillion calculations per second in 2016 when it was put online. It was the 20th most powerful supercomputer in the world. And currently it's about to be replaced by an even stronger machine known as an even faster machine known as the ratio, which is around just under 20 petaflops and incredible amount of computing power. We are fortunate here at UNL to have the Holland computing center, which also has some very strong high performance computing equipment. The machine here is 1.21 petaflops which is less but it's nothing to sneeze at. This is a really powerful resource and I run simulations on this machine. And if you're interested in high performance computing would highly recommend checking out their different courses and figure out ways to learn more about this. So where are these earth system models being developed and run. This figure here shows all the different modeling groups, which have contributed simulations to the coupled model inner comparison project. All these different places are developing and running their own climate simulations just like the CSM but they've used different techniques to represent these sub grid scale processes and large scale dynamics. And these simulations all with the same sort of experiment the same type of forcing, so then they can be compared. And this is important because like I said, none of these simulations are earth. They're all earth like planets, they're all the, there are attempt to create a simulation which as close to earth as possible. So instead of relying on just one it's important for us to have a large number of these simulations to think about what might, what might occur in the future. So what are these. What does this ensemble of simulations show for the future. One way we can think about this is by breaking the earth down into different regions. This is new in the most recent IPCC report. This is taking the earth and turning it into a bunch of regions, and then thinking about how many of those regions have high or medium confidence in the change and increase or decrease in different climatic drivers that we're interested in, for example, mean surface temperature, extreme heat, heavy precipitation and flooding hydrological drought. And so what we can see in this chart over here is that these ones which are associated with temperature, there are a lot more regions with high or medium confidence in an increase or decrease in our future projections. And for some of these which are more focused on precipitation, for example mean precipitation hydrological drought. Agricultural ecological drought. The, there's a lot more uncertainty in these simulations in terms of precipitation change. This is what I mean by uncertainty, as this is what my research is really about is understanding regional uncertainty and projections of climate. And so here's a figure from a recent paper showing the ensemble mean precipitation change for a large number of these CMIP models. So let me remind us a historical period. The green colors show where precipitation is expected to increase, and the brown color show where it's expected to decrease. But this, this hatching that goes across here indicates regions where models agree, where the, the sign of the change in many of these simulations is the same. So I went to the, the CMIP archive and grabbed a bunch of these simulations, and just for this region across the central part of the United States and, and Mexico, I just plotted a couple of profiles of these individual model ensembles. This is being shown from 20 degrees to 50 degrees north along this axis of the just the change in precipitation at the end of the century. And what we can see is that in the southern part of the domain. There is this decrease in precipitation right these brown colors here, and the hatching which shows that the models are in good agreement. And in the northern part we have an increase in precipitation. And it shows that most of the models are in agreement, but we have a lot of disagreement in terms of where the models are saying there might be an increase or decrease in this transition area region around 30 degrees north, right, where there's not a lot of differences in this plot. And so the question is, well, if you have, if you're growing crops in that region and you're interested in what the precipitation change might be in the next few decades. How do we think about understanding this uncertainty. And this is a really challenging problem because precipitation change is forced by a lot of different things. In reality, it can be forced by changes in large scale circulation patterns changes in regional circulation patterns, different boundary conditions land usage change, and then of course, the local physics, how clouds and convection change. And in the model we have all these things this is, I put commas here instead of plus because this is all very non linear and these are all interacting with each other. In the model we have the same things but we have slight errors and biases in all these fields so to understand model precipitation change. We have to consider all of these different for things for regional precipitation change and in my research I like to use a large variety of model complexities to think about this problem, ranging from single column simulations. The two dimensional simulations, even using aqua planets, basically taking a global model and removing all land, land is a source of complexity. So if you can remove some complexity from a simulation, it might be easier to understand different drivers of these regional precipitation changes all the way up to using global climate models to run, design and run experiments to think about controls on local precipitation. And I wanted to show you just one example from my research. This is using a two dimensional model to understand drivers of regional climate. One of the regions of research that I'm very interested in is West African climate. Here we've got a satellite image from August of 2006. This is during the height of the monsoon season we can see the Saharan desert here and of course green across the Sahel, this boundary, the lower boundary of the Saharan desert, where precipitation falls for three months of the year. And the people who live in this region are highly dependent on that monsoonal precipitation. So a major question is how is that precipitation going to change as climate changes. And it's a complicated region. The precipitation this region is impacted by processes across the Saharan, Saharan desert, a low level pattern, a low level circular, a low pressure circulation pattern which develops due to the hot temperatures there. So the general circulations driven by gradients in sea surface temperature and the temperature across the Guinean coast, large scale circulations, land surface properties, it's a very complicated region of the world. So how do we think about understanding how precipitation might change. Approach has been to create a two dimensional model. So this is thinking about that latitudinally dependent energy model, but just for this region that goes across West Africa. So we're just taking a north south and vertical transect across the region. And we're going to use that and force it with these different large scale and regional circulation patterns to see how changing each of these individually and then all together changes precipitation in the region. And so there's a lot going on this is this is a plot that I actually took from one of my publications so I'm going to slowly walk you through don't freak out. Okay. So these plots are showing precipitation change with increased sea surface temperatures so here we have these latitudes from 10 degrees south to 25 degrees north. Taking that north south transect across West Africa. And on the, the y axis here we have precipitation. And that contour shows precipitation for full global climate simulations. And we see what we expect, right that precipitation for the month of August is heavy across the, the guinean coast and Sahel region tapers off here at the edge of the Sahara. However, if we warm sea surface temperatures everywhere by 40 degrees Celsius. We're in the blue curve. And what we see in that precipitation pattern is a decrease in precipitation and a shift of precipitation towards the south. So this is a really interesting signal, what makes precipitation shift to the south and decrease. And so with our two dimensional model, we were able to separate out regional changes in the circulation, and large scale changes in the precipitation and apply them separately and together. So that's what's shown in the second plot here, we have latitude along the x axis in the same manner and precipitation along the y axis, our control simulation is shown in black here. It produces a similar a similar pattern and precipitation as shown in these full three dimensional models. We apply just a regional forcing to this model by just changing sea surface temperatures and seeing what circulation changes. Due to that, we get a southward shift in the precipitation but also an increase in the precipitation. When we only apply large scale forcing without changing any of the regional circulation pattern, we're able to see a decrease in the precipitation, but not that southward shift in the blue line here. And only when we combine the regional and large scale forcings together, we're able to reproduce the signal that's seen in the full three dimension 3D model, shown in this orange line here. And so this gives us some information about what is causing the southward shift in the precipitation band, and what is causing the decrease. It's two different things, the regional forcing is really responsible for the southward shift. And large scale forcing is responsible for that decrease. And in this paper we were able to go through and think about more dynamical mechanisms in terms of moisture transport and temperature transport that were associated with these two different drivers of the regional climate to really understand what processes were resulting in this very interesting signal we see in the full 3D model. So this has given you an idea of how models of varying complexity can be useful for understanding regional climate. I just wanted to mention one other type of simulation, which I'm getting into this is something that is a little bit new for me but I'm thinking about regional climate modeling in terms of running regional climate models. These are models which are only run for a regional domain. And in this figure we see some global climate model output this is our standard 100 kilometer output from the simulations I was discussing earlier these are global simulations. And this other figure here on the right shows the output from a simulation which is just for the central US region, these simulations were actually done by an undergraduate student, Ali Berry, who worked with me on a you care project last summer. And what you can see is that these have a much higher resolution, and you can do different things about how we force them from the boundary conditions to really think about running innovative experiments to increase understanding of climate dynamics and understand the projection of uncertainty into the future. These are very useful for creating high resolution outputs which are useful for driving impact models, flooding and agriculture. So this is a new direction of my research which I'm very excited about. There's a lot of knowledge here at UNL about regional climate modeling so I'm, I'm very pleased to be here and and starting to work on projects like this. In summary, hopefully what you've gotten from this talk is an appreciation for for some knowledge and appreciation about how climate models think about developing models from the simplest models to the most complex, and the importance of understanding differences between global and regional climate and have come away with the idea that computer models are useful tools for understanding changes in global and regional climate. After all we only have one planet to work with here right so we need to use these simulations and we need to consider how the choices we are making are going to end up with forcing climates of the future which someone is going to have to deal with. And I know something I didn't mention in this talk is thinking about solutions and approaches to solving this idea of of climate change, but I wanted to advertise a fortunately, there is a virtual event that's occurring next week on March 30 for the solve climate by 2030 organization on campus and would highly recommend checking that out if you're interested in thinking about solutions to climate change. And so if you're a student or a member of the community and you have some further questions about climate modeling climate change or interested in taking courses or doing research about climate change. Feel free to email me contact me I'd be glad to answer your questions or or talk about different opportunities that might be available. So with that, I'd like to thank you for your time and would be glad to take questions. And I'll bring the mic to you so we can record the question as well. Don't be shy. Enjoy the talk Ross thanks. So it seems like I read that when people are trying to do a shorter range, like extending the standard weather forecast from you know the six to seven days to two weeks to one month or whatever. But in terms of computational power or their commonalities to weather forecasting and the line of the long range projections that you're making. Yeah, so, so in terms of thinking about what computation is needed for these shorter scale for shorter scale forecasts and longer scale. The fundamentals are the same. But there are a lot of differences in terms of how these models are set up. What really needs to be considered. If you want a really good forecast of whether or even a seasonal forecast, compared to these longer time time scale. Time scale has a lot to do with it. These to run these simulations on a climatological time scale for hundreds of years. This is when you start getting into a lot of computational expense. So even though whether models can often be run at these higher simulate higher resolutions, it's because they're being done for much shorter periods. That's a great question. Thank you. You did a very good job. And also my question is, if us as humans continue to do the same things that we have been doing. How do you see the conclusion ending. Okay, well, yeah, so it is true that it can be rather depressing being a climate scientist. There's a lot of singles that are extremely troubling. But there's also a lot of human ingenuity and resilience. So, it's a hard question. It depends on whether I'm feeling being an optimist or a pessimist in the day. But ultimately, we need to think about how to stop emitting carbon dioxide and greenhouse gases into the atmosphere and think about developing technologies to remove greenhouse gases, right. People come up to me and say, Oh, you're a climate scientist, you're going to solve climate change. No, that's, that's not really my job. My job is to think about understanding how that climate change is going to impact people where I where I have hope. And I know that there's a lot of debate and discussion about this, but that perhaps technology might be able to lend a hand, but we've been saying this for a long time now. Thank you for that question. And on that note. Thank you again for coming out tonight. Really appreciate your presence. And if you have other questions, feel free to email me or come up and ask. Thanks. Thank you again, Ross.