First results on two-way coupled model ICONGETM for the Central Baltic Sea
Bauer, Tobias
To understand local phenomena over the Baltic Sea such as Coastal Upwelling or Salinity Inversion, we are coupling an atmosphere and ocean model with the Earth System Modelling Framework (ESMF). For the atmosphere, the operational model of the German Weather Service (ICON) is used in a nested limited area mode. The General Estuarine Turbulence Model (GETM) has been chosen as the local ocean model. Typical coupling issues are the different grid schemes of the models and hence, a set and choice of suitable interpolation/regridding methods is required. Within our framework, the state variables (e.g. temperature) and flux data (e.g. heat flux) have to be interpolated from ICON to GETM and vice versa. This presentation will give an overview about the coupling strategy we have applied. Furthermore, the results of first model runs are presented for the Central Baltic Sea. At first, we show results of uncoupled model simulations. Additionally, we will talk about the differences for the coupled model simulations compared to the uncoupled runs.
 Yeah, thank you very much for inviting me. Well, I'm not actually invited, but for giving me this opportunity to talk about the coupled model I can get them. I can get them as an atmosphere ocean model, mainly developed for the purpose of research over the Baltic Sea. And this coupling of those two models, I can and get them, has been done by our institute and by the Institute of Baltic Sea Research and Warner Minder, mainly contributed with the work from Knut Klinkwey. And so we finished this coupling about two months ago, but we had some problems with the simulations. So this is now about actually the first simulations we have done. And in the beginning, I will motivate my talk with introduction in coastal upvailing event from the east coast of Sweden and Gotland Island in July 2012. And then I will go into the details of both uncoupled models and then, of course, the coupled model itself. And I can get them as just the combination of the name of the two individual models. And then I will show you the results of our simulations we achieved. OK. At first, let's go a bit into detail of coastal upvailing. In general, as the interactions are driven by many different factors, such as precipitation, radiation, or wind. Wind and atmospheric pressure generates waves and currents and all other things you can might imagine. There's a lot of things going on at the DRC interface. For the atmosphere, especially, it's important that there is a warming or cooling from below. That's our main purpose of interest for the troposphere. And I'm from the Tropospheric Research Institute in Leipzig. So this is why we are interested in this. And for us, we now look at the coastal upvailing. And coastal upvailing is mainly driven by the Agment transport into water. So we have a wind blowing along the coastline. And this wind has to blow in a specific direction along the coast. And then due to the Coriolis force, you actually get the energy and the surface current moves into the ocean. And then your wind actually drives the water. And it happens in a 90 degrees angle, basically. So you start your wind blowing along the coast. And then with going deeper and deeper in the ocean, the water is moved or drift away from the coast. And then if the upper water is drift away from the coast, well, there must be some other water coming up. And this water is actually then coming from below the ocean. So it brings then deep, dense, and cooler, and usual nutrient rich water towards the ocean surface. So also for fishing industries, it is very important. And if there's nutrient rich water coming up, then also the fish are coming up as well. So this is also interesting for fishing industries. And especially if cold water comes up from the bottom of the ocean, then you also have a cooling of the atmosphere. And then you have a feedback mechanism into both directions. And that's what we want to investigate. We have had a study in the beginning about available coupled models for the Baltic Sea. They have been available for climate research and for horizontal resolutions, which are, well, large enough for actually showing that these events happen. But we are now interested in actually the physical processes of what is going on. And so we are interested in a much higher resolution talking about a kilometer or even less. And that's why we have to couple two models which can handle these resolutions. And to show you that this actually also takes place, and it happens quite often, I have here two animations. On the left side, there is sea surface temperature measured by the NOAA-19 satellite. But over the three weeks in July, 2012, unfortunately, there were a lot of clouds. And I was not able to gain remote sensing data so far. That's why we have a lot of white gaps in here. And on the right side, I'm having the wind map of Central Europe. I want to stop to show you that actually there is coastal uphealing available on the dates we are interested. And hopefully, I'm not missing it. Yeah, yeah, might be possible to see. We will see it later in the results even better. But there are small green dots coming up here along the coast or here. And those green dots are saying that the temperature is about five Kelvin less than the rest of the surface. And I hope maybe there's a different there. I think you see it on the screen even better than here. There's green. And we will see it later on. And the wind has to come from mainly southwest, blowing to northeast. So in the northern hemisphere, we have to have the water or the ocean on the right-hand side of the direction of the wind. On the southern hemisphere, it's just on the left-hand side. So let's go a bit into the details of those models we are using. So I will talk about ICANN. The atmosphere model and about the ocean model. And then I will talk also, giving two slides about the coupling software we have used. ICANN is a model developed from the German weather service and the Max Planck Institute for Meteorology in Hamburg. And this mainly, the main purpose was to develop a unified model, which allows climate research, also weather forecasting and weather research, and to combine this in one major tool to handle a lot of these features. They also introduced the flexible grid-nesting capability, which allows regional nesting for higher resolutions, which we want to have. On the right-hand side, you see how this basically looks like. You have a global domain with, let's say, well, I can't really say what is the resolution of this, but it's large. And we have on this red area where we have a higher resolution. And the German weather service uses it as the global and Europe domain currently, and will soon, hopefully, replace the German domain, the domain of Germany, which is even higher-resoluted. So actually, then, yeah, we're presenting those two. And we have a triangular-stretch horizontal grid, which is unstructured. And the ocean model uses a structured rectangular model. So we have a big gap then within our grid structures, which we will handle, of course. So I'm now pointing out a small scheme of the information you have to provide your model with to do a simulation. So you have to have a orography, land use class, of course, for the land. And you have to have some reanalysis data, at least for those simulations we want to do. The first point is that you want to initialize a model. And for limited areas, if you don't want to do a global simulation, you just start with a limited area, so a regional simulation, if you want. Then you have to have some lateral boundary data, which are given by also reanalysis data. That's why we use the IFS data later on, from the EZMWF. And then you fit or you start your model, and then you get an output. So about the ocean model, the ocean model is a general extra-in-transport model, which has been developed. And with the Institute for Baltic Sea Research and other contributors, and they are focusing on estuarine transport in shallow ocean or shallow water areas. And that's why they've also used this for the Baltic Sea or the North Sea, even for the Mediterranean Sea. And they try to reproduce, well, percol-cleanic features such as upwelling or internal salinity inflows, and all other things you can imagine for the Baltic Sea. And they use, as I already said, a structural rectum circuit. And this is the area we are going to look into with our simulations later on. It's called the Central Baltic Sea. And as I've done it with the ICANN model, I'm also pointing out here a short scheme about the ocean model, which also starts with reanalysis data for the surface. So the ocean model has to be driven by methodological data, which they will get from also reanalysis data. And then they have a bathymetry. Basically, it's the orography of the ocean, and to know where that is, how deep is your water. And they have river data, so inflow from rivers, water coming into the Baltic Sea. And open boundaries, because they do only a regional simulation, so they give you a specific area. If you go back, you also have here an open boundary, here an open boundary. So you have to have some data for that as well, like the lateral boundary data for the atmosphere. It's nearly the same. And then you also get an output at the end. The coupler we have used is a software tool which has been developed by the United States for Earth system models, especially for their purpose, but it's open source, so you can use it. And we have followed these coupling strategy by simply applying all the routines they are providing us within our model. So we have to make sure that there's an interface for each model. This has been done using this specific NuOpsy interface layer. This is a bit technical, but there's a generic software tool on top of these. All these routines by the Earth system modeling framework, which allows a minimized amount of coding. So you don't have to write all the necessary routines which are already available. If it's just a standard routine, you always call. Then you can use this layer. And you only have to provide your model with some information like the grid you want to use, the variables, the data you want to exchange. So you start with an initialization. You prepare your coupling. And then the actual coupled model just drives all three components. I can get them and the coupling between those two models. And from our first simulations, we have seen that the initialization takes a bit longer, of course, than before with the uncoupled simulations. But the integration of your model system is not really slowing down. So you actually achieve almost the same computing speed, which is pretty nice. And here we have basically the strategy how we do it. So we send some data to the coupler, and then it will distribute to both models. So you can also imagine there's a specific reason why we want to do this. And I want to give you an example for that. Let's say you exchange the sea surface temperature from the ocean to the atmosphere. And you have for some reason the ocean model calculates your sea surface temperature in degrees of Celsius. And your atmosphere model does it with Kelvin. So you have a gap between those data. You can handle this in here. You don't have to handle this in the models. You can handle the difference in here. So in this case, you just add 273 Kelvin to the data, and that's it. So if you want to exchange maybe some data which you might have to add before you can put them to the next model, can handle it here and in here, you don't have to do this within your models itself. So each model just sends and receives data. So combining those two models to give us this fancy scheme now, but since we couple now, we don't need the meteorological input furthermore for the ocean because we get it from the coupler. And so we have some meteorological fields we want to send from the atmosphere model to the ocean. And we send some ocean and graphical fields like anything you can imagine. And as specific with our model, you actually can define as the user which data you want to exchange. This has not to be done before by the compare, you can do it on your simulation run. You just tell the model what you want to exchange, and then it does it for you. Might be some restrictions like if the ocean model needs two or three different sets of information like wind, pressure, and whatever, and it does not get everything, then it might stops from, because it says I need more information, but except of these restrictions, you're up to any use you want. And yeah, with this model. So let's have a look into the simulations, results of our first test. We have applied for ICANN a regional domain with three different nests. So we have three different resolutions. This is due to the fact that we received our renal as a state in the resolution of 16 kilometers horizontal resolution, which is definitely too large, or too low in the other way around, for our purpose. And with the nesting capability with an ICANN, we can handle this and getting down to 2.5 kilometers, in this case for this green area, which is the resolution and the smallest domain for atmosphere. And the blue area will be the domain of the ocean. And we have used the experience that it is helpful that we reinitialize our models after every 48 hours, but do our simulations for 72 hours, which allows us one day of spinning up our model if there's maybe some influence from the initialization, which we can have. We have not looked into the fact that if we want to run our model for, I don't know, a week, which we will do later on, a week without reinitialization, we might want to do later on. For the ocean model, it is nearly the same. We have initialized the uncoupled simulations with the renal as a state of from GFS. We have some letterry boundary data. And we also do restart after 48 hours, because if you want start one model after 48 hours, you also have to start the other one after 48 hours. As I already mentioned, this is now excluded from the coupled model. And let's have a look at the data we actually exchange so far. We exchange currently only the information, which the ocean model gets from the reanalysis data. So we have not looked into flux exchanges yet. So we have two meter temperature. We have wind at 10 meters. We have two point temperature. We have air pressure. And we have cloud cover. And the atmosphere receives only sea surface temperature. As before, we have not looked into heat fluxes yet. And we exchange our information every 15 minutes. We have thought about maybe doing it more often or even less. But so the uncoupled simulations for the ocean model receives every one hour new meteorological data. So we thought, well, we should start with less than one hour to see already some differences. But we didn't want to do this every one minute or something to exclude the possibility that we might wait a week for the data. So these are now a couple of slides I want to show you about the details we have received so far for the salts. Let's have a look at the sea surface temperature in the atmosphere. So this is the output. On the left side, we have the output from the coupled simulation. On the right side, we have the output from the uncoupled simulation. And in between a different. This is now for all slides the same. As I showed you or tried to show you with the satellite data, we have now upwelling here provided by the sea surface temperature. So if this color gets into white or even blue, then you actually see much lower temperature at the coast of Sweden or even in the Netherlands. And this is actually the sea surface temperature which is given within ICANN without any coupling by a parametrization. I believe they update their data once a day or something. And which is really nice is that we see here this upwelling coming really far into the central Baltic Sea. And just to mention that these lines here are just the border of the area where the ocean model stops. And then after outside of this area, they use, of course, the data from here again. The difference is actually really interesting because what you see is that it's not like always the coupled model is warmer or cooler. It changes. So we have here now, of course, we have cooler areas, which is upwelling. But now we have here warmer areas as well. So there is a gap in between those data, which is really nice. And we will see soon that ICANN actually reacts only on those changes. This is the only thing we have changed in the ICANN model, and it reacts already on that. We also have the sea surface temperature on the ocean model. And what we see here is nearly the same data as before because the ocean model provides ICANN with that. And we have also here this upwelling, of course. But the uncoupled simulation already showed also upwelling, of course, because that's why they have developed this model. But there is a difference in between those results, which we, of course, can see here. And it looks like that the uncoupled simulation actually is warmer than the coupled simulation. And in my opinion, I started mathematics. I'm not a meteorologist or oceanographer. But I believe this is the reason why this comes from the fact that if you provide your ocean model with free analysis data, let's say, over each hour, even after three hours, you have a linear interpolation between those data, and you have constant average wind going from one direction. So if it's coming, let's say, at midnight, and you have the next update of your meteorological data at 3 AM in the morning, and the midnight says there is coastal upwelling, a favorable wind situation, then this will happen for the next three hours. But since we do an update after every 15 minutes, there is, of course, a change of the data. And that's why I believe, it's my personal opinion, that these changes come from this fact, of course. But it is interesting that we have two caravans already here. And in the atmosphere, we already had four caravans, which is even more. We tried to look at the coast of Goldland, I think the update was taken from here. The information was taken from here. So let's look at other data we have so far. Let's look at the surface pressure from the IKIN model. On the left side, as before, we have the coupled simulation and the uncoupled simulation on the right side. And what we see is that there is local feedback, of course. It's just the local information which we provide for the atmosphere on the pressure by the change of the sea surface temperature. But the differences are really low, like a really small one hectopascal, which is not much. But at least we see something. And we also not expected that to see a big gap, because otherwise the models would have been uncoupled, not really reasonable, had not produced reasonable results. But we see also these small differences within our wind simulation. I hope this will now work, because they are getting more and more animations, and hopefully the PDF can handle. So we have here now the zonal wind in 10 meters within the atmosphere, and we see a gap in the wind speed, which is due to the fact that we have a difference in the surface and pressure, and then we have a difference in the wind, of course. And we also have to consider that the change of the information given by the sea surface temperature might also just produce statistical noise. So for that, we actually have to do kind of an example study to figure out whether these small changes in the wind speed are related more to the fact that we have a new sea surface temperature or more to the fact that we have a change in the initialization data in general. So the same applies also for the meritorial wind, so the northward wind, if you want. And as before, we also see there's only a small change of the information. About the temperature and two meters height, since we have a change in pressure and a change in wind speed, we of course have a change in the temperature. That is what we see here, because we also have just a change in about one k-mean. So it's not much. But as I said before, that's what we hope to see. We hope to see a difference, but not to see a large gap. And what is nice, that even if we have coastal uphealing, we do not see a large gap on the eastern coast of Sweden. We only have a change about one k-mean, as I said. So even this cold water coming from the, we're closing. And we don't see a lot of change in the two-meter temperature, although we have in the atmosphere now a gap of four k-means at the coast of Gotland, or Kostov. Sweden, which is interesting. Yeah, I think this not works anymore. Let's have a look at the velocities in the ocean. That is actually pretty interesting, because the ocean is pretty small. Well, the changes in the ocean are pretty small, in general. But the difference between the coupled and the uncoupled simulations are actually in the same magnitude. We have not really figured out yet why this is actually a case. And we cannot really say where is the model actually heading to. So what is the reason for that, in general? But what interesting is that even providing the ocean with this information from meteorological data, there's a big change already. So there will be some influences, especially if you look into vertical profiles, which we have not done yet, for the velocities. And we also see these changes in the salinity, which is for the ocean, of course, very interesting. The salinity, in this case, is with the coupled simulation, but changes increases over time. So continuing our coupled simulations, comparing to the uncoupled one, we actually see that the changes in the salinity increases over time. And this, I believe, is pretty interesting for the ocean genocrophys. So I hope we can gain some physical knowledge out of these simulations for the Baltic Sea. And now I want to conclude. So we have now developed, and we can make this model available for investigations, especially designed for the Baltic Sea, but we can also apply it to different areas as long as we have the data for it. What we already see is that there is a significant feedback related to the coupling that provides us with the knowledge that we can actually use it, because there was some paper in the literature that they only showed a statistical improvement, not actually a difference in the results. And our next part will be further developing of the model, for instance, like I said, flux exchanges, and then, of course, an evaluation and validation of the data. And as already mentioned, that we have to look into this noise problem. And then there might be the possibility of adding other components into the model system, like waves, or even using the multiscay atmospheric transport model from our institute within ICANN. And my final slide, my last slide, is actually the physics of the sea interface we want to achieve. So we want to exchange the fluxes, the radiations, evaporation, precipitation. And this is what is currently done within uncoupled simulations. Thank you. Thank you very much for your very nice talk. Have a great day.
 Yeah, thank you very much for inviting me.  Well, I'm not actually invited, but for giving me this opportunity  to talk about the coupled model I can get them.  I can get them as an atmosphere ocean model, mainly developed  for the purpose of research over the Baltic Sea.  And this coupling of those two models, I can and get them,  has been done by our institute and by the Institute of Baltic Sea  Research and Warner Minder, mainly contributed  with the work from Knut Klinkwey.  And so we finished this coupling about two months ago,  but we had some problems with the simulations.  So this is now about actually the first simulations  we have done.  And in the beginning, I will motivate  my talk with introduction in coastal upvailing event  from the east coast of Sweden and Gotland Island in July 2012.  And then I will go into the details of both uncoupled models  and then, of course, the coupled model itself.  And I can get them as just the combination  of the name of the two individual models.  And then I will show you the results of our simulations  we achieved.  OK.  At first, let's go a bit into detail of coastal upvailing.  In general, as the interactions are driven  by many different factors, such as precipitation, radiation,  or wind.  Wind and atmospheric pressure generates waves and currents  and all other things you can might imagine.  There's a lot of things going on at the DRC interface.  For the atmosphere, especially, it's  important that there is a warming or cooling from below.  That's our main purpose of interest for the troposphere.  And I'm from the Tropospheric Research Institute in Leipzig.  So this is why we are interested in this.  And for us, we now look at the coastal upvailing.  And coastal upvailing is mainly driven by the Agment  transport into water.  So we have a wind blowing along the coastline.  And this wind has to blow in a specific direction  along the coast.  And then due to the Coriolis force, you actually get the energy  and the surface current moves into the ocean.  And then your wind actually drives the water.  And it happens in a 90 degrees angle, basically.  So you start your wind blowing along the coast.  And then with going deeper and deeper in the ocean,  the water is moved or drift away from the coast.  And then if the upper water is drift away from the coast,  well, there must be some other water coming up.  And this water is actually then coming  from below the ocean.  So it brings then deep, dense, and cooler, and usual nutrient  rich water towards the ocean surface.  So also for fishing industries, it is very important.  And if there's nutrient rich water coming up,  then also the fish are coming up as well.  So this is also interesting for fishing industries.  And especially if cold water comes up  from the bottom of the ocean, then you also  have a cooling of the atmosphere.  And then you have a feedback mechanism  into both directions.  And that's what we want to investigate.  We have had a study in the beginning  about available coupled models for the Baltic Sea.  They have been available for climate research  and for horizontal resolutions, which are, well,  large enough for actually showing that these events happen.  But we are now interested in actually the physical processes  of what is going on.  And so we are interested in a much higher resolution talking  about a kilometer or even less.  And that's why we have to couple two models which  can handle these resolutions.  And to show you that this actually also takes place,  and it happens quite often, I have here two animations.  On the left side, there is sea surface temperature  measured by the NOAA-19 satellite.  But over the three weeks in July, 2012,  unfortunately, there were a lot of clouds.  And I was not able to gain remote sensing data so far.  That's why we have a lot of white gaps in here.  And on the right side, I'm having the wind map of Central  Europe.  I want to stop to show you that actually there is  coastal uphealing available on the dates we are interested.  And hopefully, I'm not missing it.  Yeah, yeah, might be possible to see.  We will see it later in the results even better.  But there are small green dots coming up here along the coast  or here.  And those green dots are saying that the temperature is  about five Kelvin less than the rest of the surface.  And I hope maybe there's a different there.  I think you see it on the screen even better than here.  There's green.  And we will see it later on.  And the wind has to come from mainly southwest,  blowing to northeast.  So in the northern hemisphere, we have to have the water or the  ocean on the right-hand side of the direction of the wind.  On the southern hemisphere, it's just on the left-hand side.  So let's go a bit into the details of those models we  are using.  So I will talk about ICANN.  The atmosphere model and about the ocean model.  And then I will talk also, giving two slides about the  coupling software we have used.  ICANN is a model developed from the German weather service  and the Max Planck Institute for Meteorology in Hamburg.  And this mainly, the main purpose was to develop a  unified model, which allows climate research, also weather  forecasting and weather research, and to combine this in one  major tool to handle a lot of these features.  They also introduced the flexible grid-nesting  capability, which allows regional nesting for higher  resolutions, which we want to have.  On the right-hand side, you see how this basically looks  like.  You have a global domain with, let's say, well, I can't  really say what is the resolution of this, but it's  large.  And we have on this red area where we have a higher  resolution.  And the German weather service uses it as the global and  Europe domain currently, and will soon, hopefully, replace  the German domain, the domain of Germany, which is even  higher-resoluted.  So actually, then, yeah, we're presenting those two.  And we have a triangular-stretch horizontal grid,  which is unstructured.  And the ocean model uses a structured rectangular model.  So we have a big gap then within our grid structures, which  we will handle, of course.  So I'm now pointing out a small scheme of the information  you have to provide your model with to do a simulation.  So you have to have a orography, land use class, of  course, for the land.  And you have to have some reanalysis data, at least  for those simulations we want to do.  The first point is that you want to initialize a model.  And for limited areas, if you don't want to do a global  simulation, you just start with a limited area, so a  regional simulation, if you want.  Then you have to have some lateral boundary data, which  are given by also reanalysis data.  That's why we use the IFS data later on, from the EZMWF.  And then you fit or you start your model, and then you get  an output.  So about the ocean model, the ocean model is a general  extra-in-transport model, which has been developed.  And with the Institute for Baltic Sea Research and other  contributors, and they are focusing on estuarine  transport in shallow ocean or shallow water areas.  And that's why they've also used this for the Baltic Sea or  the North Sea, even for the Mediterranean Sea.  And they try to reproduce, well, percol-cleanic features  such as upwelling or internal salinity inflows, and all  other things you can imagine for the Baltic Sea.  And they use, as I already said, a  structural rectum circuit.  And this is the area we are going to look into with our  simulations later on.  It's called the Central Baltic Sea.  And as I've done it with the ICANN model, I'm also  pointing out here a short scheme about the ocean model,  which also starts with reanalysis data for the  surface.  So the ocean model has to be driven by  methodological data, which they will get from also  reanalysis data.  And then they have a bathymetry.  Basically, it's the orography of the ocean, and to know  where that is, how deep is your water.  And they have river data, so inflow from rivers, water  coming into the Baltic Sea.  And open boundaries, because they do only a regional  simulation, so they give you a specific area.  If you go back, you also have here an open boundary, here  an open boundary.  So you have to have some data for that as well, like the  lateral boundary data for the atmosphere.  It's nearly the same.  And then you also get an output at the end.  The coupler we have used is a software tool which has been  developed by the United States for Earth system models,  especially for their purpose, but it's open source, so you  can use it.  And we have followed these coupling strategy by simply  applying all the routines they are providing us within our  model.  So we have to make sure that there's an interface for  each model.  This has been done using this specific  NuOpsy interface layer.  This is a bit technical, but there's a generic software  tool on top of these.  All these routines by the Earth system modeling framework,  which allows a minimized amount of coding.  So you don't have to write all the necessary routines which  are already available.  If it's just a standard routine, you always call.  Then you can use this layer.  And you only have to provide your model with some  information like the grid you want to use, the variables,  the data you want to exchange.  So you start with an initialization.  You prepare your coupling.  And then the actual coupled model just drives all three  components.  I can get them and the coupling between those two models.  And from our first simulations, we have seen that the  initialization takes a bit longer, of course, than before  with the uncoupled simulations.  But the integration of your model system is not really  slowing down.  So you actually achieve almost the same computing speed,  which is pretty nice.  And here we have basically the strategy how we do it.  So we send some data to the coupler, and then it will  distribute to both models.  So you can also imagine there's a specific reason why we  want to do this.  And I want to give you an example for that.  Let's say you exchange the sea surface temperature from the  ocean to the atmosphere.  And you have for some reason the ocean model calculates your  sea surface temperature in degrees of Celsius.  And your atmosphere model does it with Kelvin.  So you have a gap between those data.  You can handle this in here.  You don't have to handle this in the models.  You can handle the difference in here.  So in this case, you just add 273 Kelvin to the data, and  that's it.  So if you want to exchange maybe some data which you  might have to add before you can put them to the next model,  can handle it here and in here, you don't have to do this  within your models itself.  So each model just sends and receives data.  So combining those two models to give us this fancy scheme  now, but since we couple now, we don't need the  meteorological input furthermore for the ocean because  we get it from the coupler.  And so we have some meteorological fields we want  to send from the atmosphere model to the ocean.  And we send some ocean and graphical fields like  anything you can imagine.  And as specific with our model, you actually can define as  the user which data you want to exchange.  This has not to be done before by the compare, you can do it  on your simulation run.  You just tell the model what you want to exchange, and then  it does it for you.  Might be some restrictions like if the ocean model needs two  or three different sets of information like wind, pressure,  and whatever, and it does not get everything, then it might  stops from, because it says I need more information, but  except of these restrictions, you're up to any use you want.  And yeah, with this model.  So let's have a look into the simulations, results of our  first test.  We have applied for ICANN a regional domain with three  different nests.  So we have three different resolutions.  This is due to the fact that we received our renal as a  state in the resolution of 16 kilometers horizontal resolution,  which is definitely too large, or too low in the other way  around, for our purpose.  And with the nesting capability with an ICANN, we can  handle this and getting down to 2.5 kilometers, in this case  for this green area, which is the resolution and the smallest  domain for atmosphere.  And the blue area will be the domain of the ocean.  And we have used the experience that it is helpful that we  reinitialize our models after every 48 hours, but do our  simulations for 72 hours, which allows us one day of spinning  up our model if there's maybe some influence from the  initialization, which we can have.  We have not looked into the fact that if we want to run our  model for, I don't know, a week, which we will do later on,  a week without reinitialization, we might  want to do later on.  For the ocean model, it is nearly the same.  We have initialized the uncoupled simulations with the  renal as a state of from GFS.  We have some letterry boundary data.  And we also do restart after 48 hours, because if you want  start one model after 48 hours, you also have to start the  other one after 48 hours.  As I already mentioned, this is now excluded from the  coupled model.  And let's have a look at the data we actually exchange so  far.  We exchange currently only the information, which the  ocean model gets from the reanalysis data.  So we have not looked into flux exchanges yet.  So we have two meter temperature.  We have wind at 10 meters.  We have two point temperature.  We have air pressure.  And we have cloud cover.  And the atmosphere receives only sea surface temperature.  As before, we have not looked into heat fluxes yet.  And we exchange our information every 15 minutes.  We have thought about maybe doing it more often or even  less.  But so the uncoupled simulations for the ocean model  receives every one hour new meteorological data.  So we thought, well, we should start with less than one  hour to see already some differences.  But we didn't want to do this every one minute or something  to exclude the possibility that we might wait a week for  the data.  So these are now a couple of slides I want to show you  about the details we have received so far for the  salts.  Let's have a look at the sea surface temperature in the  atmosphere.  So this is the output.  On the left side, we have the output from the coupled  simulation.  On the right side, we have the output from the uncoupled  simulation.  And in between a different.  This is now for all slides the same.  As I showed you or tried to show you with the satellite  data, we have now upwelling here provided by the sea surface  temperature.  So if this color gets into white or even blue, then you  actually see much lower temperature at the coast of  Sweden or even in the Netherlands.  And this is actually the sea surface temperature which is  given within ICANN without any coupling by a  parametrization.  I believe they update their data once a day or something.  And which is really nice is that we see here this upwelling  coming really far into the central Baltic Sea.  And just to mention that these lines here are just the border  of the area where the ocean model stops.  And then after outside of this area, they use, of course,  the data from here again.  The difference is actually really interesting because what  you see is that it's not like always the coupled model is  warmer or cooler.  It changes.  So we have here now, of course, we have cooler areas, which  is upwelling.  But now we have here warmer areas as well.  So there is a gap in between those data, which is really  nice.  And we will see soon that ICANN actually reacts only on  those changes.  This is the only thing we have changed in the ICANN model,  and it reacts already on that.  We also have the sea surface temperature on the ocean model.  And what we see here is nearly the same data as before  because the ocean model provides ICANN with that.  And we have also here this upwelling, of course.  But the uncoupled simulation already showed also upwelling,  of course, because that's why they have developed this model.  But there is a difference in between those results, which  we, of course, can see here.  And it looks like that the uncoupled simulation actually  is warmer than the coupled simulation.  And in my opinion, I started mathematics.  I'm not a meteorologist or oceanographer.  But I believe this is the reason why this comes from the  fact that if you provide your ocean model with free  analysis data, let's say, over each hour, even after three  hours, you have a linear interpolation between those  data, and you have constant average wind going  from one direction.  So if it's coming, let's say, at midnight, and you have the  next update of your meteorological data at 3 AM  in the morning, and the midnight says there is  coastal upwelling, a favorable wind situation, then this  will happen for the next three hours.  But since we do an update after every 15 minutes, there is,  of course, a change of the data.  And that's why I believe, it's my personal opinion, that  these changes come from this fact, of course.  But it is interesting that we have two  caravans already here.  And in the atmosphere, we already had four  caravans, which is even more.  We tried to look at the coast of Goldland, I think the update  was taken from here.  The information was taken from here.  So let's look at other data we have so far.  Let's look at the surface pressure from the IKIN model.  On the left side, as before, we have the coupled simulation  and the uncoupled simulation on the right side.  And what we see is that there is local feedback, of course.  It's just the local information which we provide for the  atmosphere on the pressure by the change of the sea surface  temperature.  But the differences are really low, like a really small one  hectopascal, which is not much.  But at least we see something.  And we also not expected that to see a big gap, because  otherwise the models would have been uncoupled, not really  reasonable, had not produced reasonable results.  But we see also these small differences within our wind  simulation.  I hope this will now work, because they are getting more  and more animations, and hopefully the PDF can handle.  So we have here now the zonal wind in 10 meters within the  atmosphere, and we see a gap in the wind speed, which is  due to the fact that we have a difference in the surface and  pressure, and then we have a difference in the wind, of  course.  And we also have to consider that the change of the  information given by the sea surface temperature might also  just produce statistical noise.  So for that, we actually have to do kind of an example study  to figure out whether these small changes in the wind  speed are related more to the fact that we have a new sea  surface temperature or more to the fact that we have a  change in the initialization data in general.  So the same applies also for the meritorial wind, so the  northward wind, if you want.  And as before, we also see there's only a small change  of the information.  About the temperature and two meters height, since we have  a change in pressure and a change in wind speed, we  of course have a change in the temperature.  That is what we see here, because we also have just a  change in about one k-mean.  So it's not much.  But as I said before, that's what we hope to see.  We hope to see a difference, but not to see a large gap.  And what is nice, that even if we have coastal uphealing, we  do not see a large gap on the eastern coast of Sweden.  We only have a change about one k-mean, as I said.  So even this cold water coming from the, we're closing.  And we don't see a lot of change in the two-meter  temperature, although we have in the atmosphere now a gap of  four k-means at the coast of Gotland, or Kostov.  Sweden, which is interesting.  Yeah, I think this not works anymore.  Let's have a look at the velocities in the ocean.  That is actually pretty interesting, because the  ocean is pretty small.  Well, the changes in the ocean are pretty small, in  general.  But the difference between the coupled and the uncoupled  simulations are actually in the same magnitude.  We have not really figured out yet why this is actually a  case.  And we cannot really say where is the model actually  heading to.  So what is the reason for that, in general?  But what interesting is that even providing the ocean with  this information from meteorological data, there's  a big change already.  So there will be some influences, especially if you  look into vertical profiles, which we have not done yet,  for the velocities.  And we also see these changes in the salinity, which is for  the ocean, of course, very interesting.  The salinity, in this case, is with the coupled simulation,  but changes increases over time.  So continuing our coupled simulations, comparing to the  uncoupled one, we actually see that the changes in the  salinity increases over time.  And this, I believe, is pretty interesting for the ocean  genocrophys.  So I hope we can gain some physical knowledge out of  these simulations for the Baltic Sea.  And now I want to conclude.  So we have now developed, and we can make this model  available for investigations, especially designed for the  Baltic Sea, but we can also apply it to different areas as  long as we have the data for it.  What we already see is that there is a significant feedback  related to the coupling that provides us with the knowledge  that we can actually use it, because there was some paper  in the literature that they only showed a statistical  improvement, not actually a difference in the results.  And our next part will be further developing of the  model, for instance, like I said, flux exchanges, and then,  of course, an evaluation and validation of the data.  And as already mentioned, that we have to look into this  noise problem.  And then there might be the possibility of adding other  components into the model system, like waves, or even  using the multiscay atmospheric transport model from our  institute within ICANN.  And my final slide, my last slide, is actually the  physics of the sea interface we want to achieve.  So we want to exchange the fluxes, the radiations,  evaporation, precipitation.  And this is what is currently done within  uncoupled simulations.  Thank you.  Thank you very much for your very nice talk.  Have a great day.
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Yeah, thank you very much for inviting me. Well, I'm not actually invited, but for giving me this opportunity to talk about the coupled model I can get them. I can get them as an atmosphere ocean model, mainly developed for the purpose of research over the Baltic Sea. And this coupling of those two models, I can and get them, has been done by our institute and by the Institute of Baltic Sea Research and Warner Minder, mainly contributed with the work from Knut Klinkwey. And so we finished this coupling about two months ago, but we had some problems with the simulations. So this is now about actually the first simulations we have done. And in the beginning, I will motivate my talk with introduction in coastal upvailing event from the east coast of Sweden and Gotland Island in July 2012. And then I will go into the details of both uncoupled models and then, of course, the coupled model itself. And I can get them as just the combination of the name of the two individual models. And then I will show you the results of our simulations we achieved. OK. At first, let's go a bit into detail of coastal upvailing. In general, as the interactions are driven by many different factors, such as precipitation, radiation, or wind. Wind and atmospheric pressure generates waves and currents and all other things you can might imagine. There's a lot of things going on at the DRC interface. For the atmosphere, especially, it's important that there is a warming or cooling from below. That's our main purpose of interest for the troposphere. And I'm from the Tropospheric Research Institute in Leipzig. So this is why we are interested in this. And for us, we now look at the coastal upvailing. And coastal upvailing is mainly driven by the Agment transport into water. So we have a wind blowing along the coastline. And this wind has to blow in a specific direction along the coast. And then due to the Coriolis force, you actually get the energy and the surface current moves into the ocean. And then your wind actually drives the water. And it happens in a 90 degrees angle, basically. So you start your wind blowing along the coast. And then with going deeper and deeper in the ocean, the water is moved or drift away from the coast. And then if the upper water is drift away from the coast, well, there must be some other water coming up. And this water is actually then coming from below the ocean. So it brings then deep, dense, and cooler, and usual nutrient rich water towards the ocean surface. So also for fishing industries, it is very important. And if there's nutrient rich water coming up, then also the fish are coming up as well. So this is also interesting for fishing industries. And especially if cold water comes up from the bottom of the ocean, then you also have a cooling of the atmosphere. And then you have a feedback mechanism into both directions. And that's what we want to investigate. We have had a study in the beginning about available coupled models for the Baltic Sea. They have been available for climate research and for horizontal resolutions, which are, well, large enough for actually showing that these events happen. But we are now interested in actually the physical processes of what is going on. And so we are interested in a much higher resolution talking about a kilometer or even less. And that's why we have to couple two models which can handle these resolutions. And to show you that this actually also takes place, and it happens quite often, I have here two animations. On the left side, there is sea surface temperature measured by the NOAA-19 satellite. But over the three weeks in July, 2012, unfortunately, there were a lot of clouds. And I was not able to gain remote sensing data so far. That's why we have a lot of white gaps in here. And on the right side, I'm having the wind map of Central Europe. I want to stop to show you that actually there is coastal uphealing available on the dates we are interested. And hopefully, I'm not missing it. Yeah, yeah, might be possible to see. We will see it later in the results even better. But there are small green dots coming up here along the coast or here. And those green dots are saying that the temperature is about five Kelvin less than the rest of the surface. And I hope maybe there's a different there. I think you see it on the screen even better than here. There's green. And we will see it later on. And the wind has to come from mainly southwest, blowing to northeast. So in the northern hemisphere, we have to have the water or the ocean on the right-hand side of the direction of the wind. On the southern hemisphere, it's just on the left-hand side. So let's go a bit into the details of those models we are using. So I will talk about ICANN. The atmosphere model and about the ocean model. And then I will talk also, giving two slides about the coupling software we have used. ICANN is a model developed from the German weather service and the Max Planck Institute for Meteorology in Hamburg. And this mainly, the main purpose was to develop a unified model, which allows climate research, also weather forecasting and weather research, and to combine this in one major tool to handle a lot of these features. They also introduced the flexible grid-nesting capability, which allows regional nesting for higher resolutions, which we want to have. On the right-hand side, you see how this basically looks like. You have a global domain with, let's say, well, I can't really say what is the resolution of this, but it's large. And we have on this red area where we have a higher resolution. And the German weather service uses it as the global and Europe domain currently, and will soon, hopefully, replace the German domain, the domain of Germany, which is even higher-resoluted. So actually, then, yeah, we're presenting those two. And we have a triangular-stretch horizontal grid, which is unstructured. And the ocean model uses a structured rectangular model. So we have a big gap then within our grid structures, which we will handle, of course. So I'm now pointing out a small scheme of the information you have to provide your model with to do a simulation. So you have to have a orography, land use class, of course, for the land. And you have to have some reanalysis data, at least for those simulations we want to do. The first point is that you want to initialize a model. And for limited areas, if you don't want to do a global simulation, you just start with a limited area, so a regional simulation, if you want. Then you have to have some lateral boundary data, which are given by also reanalysis data. That's why we use the IFS data later on, from the EZMWF. And then you fit or you start your model, and then you get an output. So about the ocean model, the ocean model is a general extra-in-transport model, which has been developed. And with the Institute for Baltic Sea Research and other contributors, and they are focusing on estuarine transport in shallow ocean or shallow water areas. And that's why they've also used this for the Baltic Sea or the North Sea, even for the Mediterranean Sea. And they try to reproduce, well, percol-cleanic features such as upwelling or internal salinity inflows, and all other things you can imagine for the Baltic Sea. And they use, as I already said, a structural rectum circuit. And this is the area we are going to look into with our simulations later on. It's called the Central Baltic Sea. And as I've done it with the ICANN model, I'm also pointing out here a short scheme about the ocean model, which also starts with reanalysis data for the surface. So the ocean model has to be driven by methodological data, which they will get from also reanalysis data. And then they have a bathymetry. Basically, it's the orography of the ocean, and to know where that is, how deep is your water. And they have river data, so inflow from rivers, water coming into the Baltic Sea. And open boundaries, because they do only a regional simulation, so they give you a specific area. If you go back, you also have here an open boundary, here an open boundary. So you have to have some data for that as well, like the lateral boundary data for the atmosphere. It's nearly the same. And then you also get an output at the end. The coupler we have used is a software tool which has been developed by the United States for Earth system models, especially for their purpose, but it's open source, so you can use it. And we have followed these coupling strategy by simply applying all the routines they are providing us within our model. So we have to make sure that there's an interface for each model. This has been done using this specific NuOpsy interface layer. This is a bit technical, but there's a generic software tool on top of these. All these routines by the Earth system modeling framework, which allows a minimized amount of coding. So you don't have to write all the necessary routines which are already available. If it's just a standard routine, you always call. Then you can use this layer. And you only have to provide your model with some information like the grid you want to use, the variables, the data you want to exchange. So you start with an initialization. You prepare your coupling. And then the actual coupled model just drives all three components. I can get them and the coupling between those two models. And from our first simulations, we have seen that the initialization takes a bit longer, of course, than before with the uncoupled simulations. But the integration of your model system is not really slowing down. So you actually achieve almost the same computing speed, which is pretty nice. And here we have basically the strategy how we do it. So we send some data to the coupler, and then it will distribute to both models. So you can also imagine there's a specific reason why we want to do this. And I want to give you an example for that. Let's say you exchange the sea surface temperature from the ocean to the atmosphere. And you have for some reason the ocean model calculates your sea surface temperature in degrees of Celsius. And your atmosphere model does it with Kelvin. So you have a gap between those data. You can handle this in here. You don't have to handle this in the models. You can handle the difference in here. So in this case, you just add 273 Kelvin to the data, and that's it. So if you want to exchange maybe some data which you might have to add before you can put them to the next model, can handle it here and in here, you don't have to do this within your models itself. So each model just sends and receives data. So combining those two models to give us this fancy scheme now, but since we couple now, we don't need the meteorological input furthermore for the ocean because we get it from the coupler. And so we have some meteorological fields we want to send from the atmosphere model to the ocean. And we send some ocean and graphical fields like anything you can imagine. And as specific with our model, you actually can define as the user which data you want to exchange. This has not to be done before by the compare, you can do it on your simulation run. You just tell the model what you want to exchange, and then it does it for you. Might be some restrictions like if the ocean model needs two or three different sets of information like wind, pressure, and whatever, and it does not get everything, then it might stops from, because it says I need more information, but except of these restrictions, you're up to any use you want. And yeah, with this model. So let's have a look into the simulations, results of our first test. We have applied for ICANN a regional domain with three different nests. So we have three different resolutions. This is due to the fact that we received our renal as a state in the resolution of 16 kilometers horizontal resolution, which is definitely too large, or too low in the other way around, for our purpose. And with the nesting capability with an ICANN, we can handle this and getting down to 2.5 kilometers, in this case for this green area, which is the resolution and the smallest domain for atmosphere. And the blue area will be the domain of the ocean. And we have used the experience that it is helpful that we reinitialize our models after every 48 hours, but do our simulations for 72 hours, which allows us one day of spinning up our model if there's maybe some influence from the initialization, which we can have. We have not looked into the fact that if we want to run our model for, I don't know, a week, which we will do later on, a week without reinitialization, we might want to do later on. For the ocean model, it is nearly the same. We have initialized the uncoupled simulations with the renal as a state of from GFS. We have some letterry boundary data. And we also do restart after 48 hours, because if you want start one model after 48 hours, you also have to start the other one after 48 hours. As I already mentioned, this is now excluded from the coupled model. And let's have a look at the data we actually exchange so far. We exchange currently only the information, which the ocean model gets from the reanalysis data. So we have not looked into flux exchanges yet. So we have two meter temperature. We have wind at 10 meters. We have two point temperature. We have air pressure. And we have cloud cover. And the atmosphere receives only sea surface temperature. As before, we have not looked into heat fluxes yet. And we exchange our information every 15 minutes. We have thought about maybe doing it more often or even less. But so the uncoupled simulations for the ocean model receives every one hour new meteorological data. So we thought, well, we should start with less than one hour to see already some differences. But we didn't want to do this every one minute or something to exclude the possibility that we might wait a week for the data. So these are now a couple of slides I want to show you about the details we have received so far for the salts. Let's have a look at the sea surface temperature in the atmosphere. So this is the output. On the left side, we have the output from the coupled simulation. On the right side, we have the output from the uncoupled simulation. And in between a different. This is now for all slides the same. As I showed you or tried to show you with the satellite data, we have now upwelling here provided by the sea surface temperature. So if this color gets into white or even blue, then you actually see much lower temperature at the coast of Sweden or even in the Netherlands. And this is actually the sea surface temperature which is given within ICANN without any coupling by a parametrization. I believe they update their data once a day or something. And which is really nice is that we see here this upwelling coming really far into the central Baltic Sea. And just to mention that these lines here are just the border of the area where the ocean model stops. And then after outside of this area, they use, of course, the data from here again. The difference is actually really interesting because what you see is that it's not like always the coupled model is warmer or cooler. It changes. So we have here now, of course, we have cooler areas, which is upwelling. But now we have here warmer areas as well. So there is a gap in between those data, which is really nice. And we will see soon that ICANN actually reacts only on those changes. This is the only thing we have changed in the ICANN model, and it reacts already on that. We also have the sea surface temperature on the ocean model. And what we see here is nearly the same data as before because the ocean model provides ICANN with that. And we have also here this upwelling, of course. But the uncoupled simulation already showed also upwelling, of course, because that's why they have developed this model. But there is a difference in between those results, which we, of course, can see here. And it looks like that the uncoupled simulation actually is warmer than the coupled simulation. And in my opinion, I started mathematics. I'm not a meteorologist or oceanographer. But I believe this is the reason why this comes from the fact that if you provide your ocean model with free analysis data, let's say, over each hour, even after three hours, you have a linear interpolation between those data, and you have constant average wind going from one direction. So if it's coming, let's say, at midnight, and you have the next update of your meteorological data at 3 AM in the morning, and the midnight says there is coastal upwelling, a favorable wind situation, then this will happen for the next three hours. But since we do an update after every 15 minutes, there is, of course, a change of the data. And that's why I believe, it's my personal opinion, that these changes come from this fact, of course. But it is interesting that we have two caravans already here. And in the atmosphere, we already had four caravans, which is even more. We tried to look at the coast of Goldland, I think the update was taken from here. The information was taken from here. So let's look at other data we have so far. Let's look at the surface pressure from the IKIN model. On the left side, as before, we have the coupled simulation and the uncoupled simulation on the right side. And what we see is that there is local feedback, of course. It's just the local information which we provide for the atmosphere on the pressure by the change of the sea surface temperature. But the differences are really low, like a really small one hectopascal, which is not much. But at least we see something. And we also not expected that to see a big gap, because otherwise the models would have been uncoupled, not really reasonable, had not produced reasonable results. But we see also these small differences within our wind simulation. I hope this will now work, because they are getting more and more animations, and hopefully the PDF can handle. So we have here now the zonal wind in 10 meters within the atmosphere, and we see a gap in the wind speed, which is due to the fact that we have a difference in the surface and pressure, and then we have a difference in the wind, of course. And we also have to consider that the change of the information given by the sea surface temperature might also just produce statistical noise. So for that, we actually have to do kind of an example study to figure out whether these small changes in the wind speed are related more to the fact that we have a new sea surface temperature or more to the fact that we have a change in the initialization data in general. So the same applies also for the meritorial wind, so the northward wind, if you want. And as before, we also see there's only a small change of the information. About the temperature and two meters height, since we have a change in pressure and a change in wind speed, we of course have a change in the temperature. That is what we see here, because we also have just a change in about one k-mean. So it's not much. But as I said before, that's what we hope to see. We hope to see a difference, but not to see a large gap. And what is nice, that even if we have coastal uphealing, we do not see a large gap on the eastern coast of Sweden. We only have a change about one k-mean, as I said. So even this cold water coming from the, we're closing. And we don't see a lot of change in the two-meter temperature, although we have in the atmosphere now a gap of four k-means at the coast of Gotland, or Kostov. Sweden, which is interesting. Yeah, I think this not works anymore. Let's have a look at the velocities in the ocean. That is actually pretty interesting, because the ocean is pretty small. Well, the changes in the ocean are pretty small, in general. But the difference between the coupled and the uncoupled simulations are actually in the same magnitude. We have not really figured out yet why this is actually a case. And we cannot really say where is the model actually heading to. So what is the reason for that, in general? But what interesting is that even providing the ocean with this information from meteorological data, there's a big change already. So there will be some influences, especially if you look into vertical profiles, which we have not done yet, for the velocities. And we also see these changes in the salinity, which is for the ocean, of course, very interesting. The salinity, in this case, is with the coupled simulation, but changes increases over time. So continuing our coupled simulations, comparing to the uncoupled one, we actually see that the changes in the salinity increases over time. And this, I believe, is pretty interesting for the ocean genocrophys. So I hope we can gain some physical knowledge out of these simulations for the Baltic Sea. And now I want to conclude. So we have now developed, and we can make this model available for investigations, especially designed for the Baltic Sea, but we can also apply it to different areas as long as we have the data for it. What we already see is that there is a significant feedback related to the coupling that provides us with the knowledge that we can actually use it, because there was some paper in the literature that they only showed a statistical improvement, not actually a difference in the results. And our next part will be further developing of the model, for instance, like I said, flux exchanges, and then, of course, an evaluation and validation of the data. And as already mentioned, that we have to look into this noise problem. And then there might be the possibility of adding other components into the model system, like waves, or even using the multiscay atmospheric transport model from our institute within ICANN. And my final slide, my last slide, is actually the physics of the sea interface we want to achieve. So we want to exchange the fluxes, the radiations, evaporation, precipitation. And this is what is currently done within uncoupled simulations. Thank you. Thank you very much for your very nice talk. Have a great day.
opportunity to talk about the coupled model I can research over the Baltic Sea. And this coupling uncoupled models and then, of course, the coupled models. And then I will show you the results of our simulations we achieved. OK. At first, let's different factors, such as precipitation, I will talk about ICANN. The atmosphere model and will soon, hopefully, replace the German domain, just start with a limited area, so a regional or you start your model, and then you get an later on. It's called the Central Baltic Sea. And has been developed by the United States for Earth followed these coupling strategy by simply routines by the Earth system modeling framework, model with some information like the grid you want to use, the variables, the data you want to from the atmosphere model to the ocean. And we will be the domain of the ocean. And we have used For the ocean model, it is nearly the same. We so the uncoupled simulations for the ocean model local feedback, of course. It's just the local temperature. But the differences are really low, meters within the atmosphere, and we see a gap in