Köppen-Geiger classifications of paleoclimate model simulations
Willmes, Christian
A pyGRASS implementation of the Kšppen-Geiger climate classification applied to paleoclimate model simulations from the Paleoclimate Model Intercomparison Project III (PMIP III) will be presented. The talk will show the details of how Kšppen-Geiger classifications are practically implemented and applied to climate model simulations using GRASS GIS, the python library pyGRASS and QGIS for the cartography.
 My name is Christian Wilmes. I'm a PhD student at the University of Cologne. And I did some work on Copenhagen classifications on paleoclimat model simulations, on which I submitted a paper for the academic track here. And I'm very happy to be able to present my talk now to you here. And I structured this talk in five parts. So I will first introduce a bit the project environment in which we conducted this research. Then I will explain what Copenhagen classifications actually are and how they're used. Then I will talk a bit about the paleoclimat models, which I use as the basis for the classifications. And the main part of the talk will be the implementation of how we derived this from the model data. And finally, I will show some maps showing the results. OK. I'm working in a large research project called the Collaborative Research Center 806, which is funded by the German Research Foundation. It has around 100 scientists working in this research center. It's an interdisciplinary research project concerning the culture, environment interaction, and human mobility in the late-quartinary. They are scientists from universities of Aachen, Bonn, and Cologne working in this project. And myself and the group I'm working in are based in Cologne. And you can find lots of information on the URL you find here. It has a DE ending, but the content is in English. So you can understand it all. So the main point here is I'm working in an archaeology, geoscientific paleo-environmental background for which paleo-environmental classification then, of course, are useful databases for further research. And that's why we conducted this. So I wanted now to give a short introduction to Copenhagen classification. The map you see here is such a classification created by Kotek et al. in 2006 from a climate station, available climate station data, which is, meanwhile, worldwide available in good density around the world. And this data is on the basis of a 50-year monthly means for precipitation and temperature, which are the main variables for the Copenhagen classifications. And this is what these maps look like with today's data. This is based on data from 1950 to 2001, I guess. Somewhere written here. 1951 to 2000. And so if you are familiar with plant regime distributions and ecology, the biome maps, which are also very popular, look very similar. So this classification seems to show the same pattern as actual biome maps or ecosystem maps, you can call them, for different ecosystems. The Copenhagen classification has five main classes, which are the tropics, which you see are mostly in red. The temperate, which are greenish. The arid, which are like brownish on this map. And the cold, snowy climates, which are like purpleish. And frost, polar climates, which are blue. So these are the main types. And then there are several subsequently distinguishings based on temperature, precipitation, and the seasonality of both precipitation mostly. And to compute this classifications, the algorithms are more or less based on the use of these 11 variables you can see defined here. These are all based on temperature and climate, of which you can see here. I will further explain them later. So the thing is you have the climate and the temperature and precipitation data, and you compute with grass map algebra these variables for the rusters, for the distribution. And then you use them in if expressions to distinguish the classes. Here you see the criteria built from these variables defined before, from which you then define the several subclasses. And there are lots of it. So I listed them here for completeness. And you can put this into code. And this is actually what I did. And but we applied it to paleoclimate data. Yeah, to derive these maps. The paleoclimate data we acquired from the DICAR-SZ node of the ESGF, a system grid foundation. The DICAR-SZ is the Deutsches Klimarechenzentrum node. There are several nodes. You can see a list on the web page for them, some nodes in the US, of course, and around the world. And this grid also holds the data for the climate models, which are, for example, used for the IPCC reports. And we use actually the same models, which producing the data for the IPCC reports, but modeled for paleotimes. So these models have certain boundary conditions, which they're for forecasting project, like trace house gases and ocean salinity, and further things like orbital parameters of the Earth. And they can run these models also for boundary conditions for the past. For example, if you get these parameters from sediment archives or ice cores, et cetera, you can run them for past times. And we acquired these three models, which is the control run is an actual run from boundary conditions from pre-industrial time. This is then assumed for 180050 or 1800. So before the onset of industrialization, the main onset of it, and the feedback to the Earth system, then we have the metallocene time slices. This is 6,000 years before present. And the LGM, this is the last glacial maximum. You see here some parameters here. They're almost all the same. I took the atmosphere monthly realm data because precipitation and temperature are in the atmosphere realm. And you see the temperature resolution. The control run has a 150-year simulation. And both others have 100-year simulation for each month. So they have this year's times 12, so 12 months a year. You have the number of actual raster layers per model. And then you have the spatial resolution, which is not very fine. So it's 192 by 96 for complete Earth surface. And there's a version number. So I have links on the last page of the slides, which I will upload to the actual data. So you can access the same data I'm talking about here. And oh, yes, here's the model parameters I talked before about. These are the boundary parameters on which they are doing the models. So I listed them here again. So as I already told, they can adjust the models to then pass times. Yeah, and this is a processing tool chain for deriving the classifications. So we have here on the, there it is. So we have here the input net CDF files. You get net CDF files for each variable, which have these 1,200 or 1,800 layers of data for each month, 100 or 150 years. And we have a Python script computing the monthly means for each variable, which then, oops, sorry, results in 12 monthly mean raster layers for the temperature, TIS, and precipitation. And then I applied an interpolation, in which I talk on the next slide, to increase information from the data. This is a crucial point of this approach. And then we did the actual calculation of the classifications of the criteria I presented before, formulated in another Python script. And from this classifications, we did the maps. So this is on the left side. You see the original resolution for the import data on the right side. You see the increased resolution. We resampled it to the centintegree grid, so 0.1 times 0.1 degrees resolution. And we applied a B-linear interpolation. And we think this is legitimate because the import data are model outputs on continuous grid. And if you think about the gradient between one grid cell and the other, this is a continuous slope. So it's not like a curve. It's a continuous slope. And this, why we believe this B-linear interpolation is valid to increase the information in that sense, that the boundaries of the class is resulting from this classification are more finite. So if you have large gradients between two grid cells, the boundary would be different from the original grid cells. And this results in some way in a higher amount of information in spatial resolution. Here are some examples of what the Python script just looked like. You see an example of the calculation of the mean annual precipitation. And one below it is a calculation of the summer winter differentiation for the seasonality. So you see it's a map calc expression from grass, which you can put in using the grass bindings in this bit of Python code. And you see here the precipitation data is given in millimeter per second per square meter. And the classification definitions available are all on just in millimeter expressions. So this is what is given in a flux entry just computed by two daily measures. Yeah. Yeah. So for millimeters per day, not per second. And the other thing is to distinguish between summer and winter to just add the mean temperatures and look which has the higher value. This is a summer then. And this also can distinguish between northern hemisphere and southern hemisphere mostly because some of these expressions shown before are based on the seasonality. So what you see here is a map of the LGM classification. For worldwide, you don't see the high resolution, which it has. Because it's worldwide here. But what you can see is we applied for the LGM in different coastlines based on the minus 120 meters basymetry contour. According to several publications, it is more or less given in the community that during the last national maximum, the sea levels were about 120 meters below from today. And we additionally included data set from Ilast et al, which all have cited in the submitted paper for the ice sheet distributions. So you have here the northern European and the North American ice shields and the ice shields and the Antarctis, for example, as landmass. And you can see, for example, very well here in Southeast Asia, the larger land masses. This has, of course, also effects on northern Europe, for example, Great Britain was part of the European mainland in 21,000 years ago. So this is very useful information. If you do research on culture and environment from the last national maximum to see what the environmental patterns were like in these times. And we have here the map for the mid-hollow scene, which is has the same coastlines like from today. And it's partly different from the control run. If you see the control run, there are some differences in the snowy climates. And in sub-Saharan Africa, you see some differences from the distributions. So that is assumed that in like 8,000 years ago, today Sahara was savanna and it was not yet the desert. It was habitable for humans and animals. So yeah, that was something. Yeah. So I have all the code and the data and everything online. So you can check that out. I've wrote a tutorial which you can find under this URL. And can access the Python files directly. And the data is also published in our project database with DOIs, so the shapefiles and the resulting geotest. And yeah, I'm happy about comments and especially maybe for the interpolation approach, which I think is valid. I didn't turn anything else yet. But maybe there are some people who know more or have better have some something which I didn't concern yet. So I would be happy about feedback. And then sorry, I have to do a little bit advertising in November. We have a data management workshop organized by our group in Cologne. And we are happy to welcome you there if you want to visit beautiful Cologne. And here's some stuff on state-of-the-art data management. You are normally welcomed and invited. Yeah, and I want to thank you for your attention. So sorry for a little bit confusing in the order of the slides. I recognize myself. Thank you very much. I had a question about you asked for comments about the interpolation. Yes. But I actually think I don't fully understand what you, if you go back one slide, about the moment when you do the interpolation. Yeah. Because it seems to me that you interpolate part of the data and then go further with analysis of that interpolate, using the interpolated data. Am I correct? Yeah. So that's just because it wouldn't make sense to interpolate the resulting classification. Because it's not the continuous data, it's ordinary data. So we interpolated the temperature, the monthly temperature, and the monthly precipitation. Yeah, data layers, 24, then proceed with some calculations on the interpolated data sets. And you do the interpolation, for example, on the temperature, just purely based on the roster size and then the values, the linear interpolation of the values. But you don't take into account any other data, I understand. But the Copenhagen classifications are just based on precipitation and temperature. This was the only data Kupmet available some 100 years ago from climate. Yeah, yeah. But Kupmet didn't interpolate roster data. He didn't have roster data. Yeah. And what you do if you interpolate, for example, between two of these big blocks of temperature, you interpolate that there is a smooth difference. Because they are already monthly mean values. Yeah. So I assume that there is a continuous slope. Yeah. Like a straight line. But that might not be true. And you have 20 degrees mean temperature and then here is 20 degrees. But especially if you do that for, especially for temperature and especially precipitation, you know that in real life, the fact that there's, for example, a mountain rate somewhere or a coast would make these differences much less smooth. Right. But we assume that this is already, or we more or less know from the descriptions of the models that this is taken into account for the, for the model data. It's already taken into account. Ah, OK. This is the topography and all the boundary conditions. And because there's monthly mean values like weather fronts and some effects like this. We have great gradients between small scales and spatial resolution are not, are are so smoothed out through the monthly means. OK. So that it's possible to do this. OK. I understand. Here we are. OK. Thank you. Thank you.
 My name is Christian Wilmes.  I'm a PhD student at the University of Cologne.  And I did some work on Copenhagen classifications  on paleoclimat model simulations, on which I submitted  a paper for the academic track here.  And I'm very happy to be able to present my talk now to you  here.  And I structured this talk in five parts.  So I will first introduce a bit the project environment in which  we conducted this research.  Then I will explain what Copenhagen classifications actually  are and how they're used.  Then I will talk a bit about the paleoclimat models, which  I use as the basis for the classifications.  And the main part of the talk will  be the implementation of how we derived this from the model  data.  And finally, I will show some maps showing the results.  OK.  I'm working in a large research project called  the Collaborative Research Center 806, which is funded  by the German Research Foundation.  It has around 100 scientists working in this research center.  It's an interdisciplinary research project concerning  the culture, environment interaction, and human  mobility in the late-quartinary.  They are scientists from universities of Aachen,  Bonn, and Cologne working in this project.  And myself and the group I'm working in are based in Cologne.  And you can find lots of information on the URL  you find here.  It has a DE ending, but the content is in English.  So you can understand it all.  So the main point here is I'm working in an archaeology,  geoscientific paleo-environmental background  for which paleo-environmental classification then,  of course, are useful databases for further research.  And that's why we conducted this.  So I wanted now to give a short introduction  to Copenhagen classification.  The map you see here is such a classification created  by Kotek et al. in 2006 from a climate station,  available climate station data, which is, meanwhile,  worldwide available in good density around the world.  And this data is on the basis of a 50-year monthly means  for precipitation and temperature,  which are the main variables for the Copenhagen  classifications.  And this is what these maps look like with today's data.  This is based on data from 1950 to 2001, I guess.  Somewhere written here.  1951 to 2000.  And so if you are familiar with plant regime distributions  and ecology, the biome maps, which are also very popular,  look very similar.  So this classification seems to show the same pattern  as actual biome maps or ecosystem maps,  you can call them, for different ecosystems.  The Copenhagen classification has five main classes,  which are the tropics, which you see are mostly in red.  The temperate, which are greenish.  The arid, which are like brownish on this map.  And the cold, snowy climates, which are like purpleish.  And frost, polar climates, which are blue.  So these are the main types.  And then there are several subsequently  distinguishings based on temperature, precipitation,  and the seasonality of both precipitation mostly.  And to compute this classifications,  the algorithms are more or less based  on the use of these 11 variables you can see defined here.  These are all based on temperature and climate,  of which you can see here.  I will further explain them later.  So the thing is you have the climate and the temperature  and precipitation data, and you compute with grass map  algebra these variables for the rusters, for the distribution.  And then you use them in if expressions  to distinguish the classes.  Here you see the criteria built from these variables  defined before, from which you then  define the several subclasses.  And there are lots of it.  So I listed them here for completeness.  And you can put this into code.  And this is actually what I did.  And but we applied it to paleoclimate data.  Yeah, to derive these maps.  The paleoclimate data we acquired from the DICAR-SZ node  of the ESGF, a system grid foundation.  The DICAR-SZ is the Deutsches Klimarechenzentrum node.  There are several nodes.  You can see a list on the web page for them,  some nodes in the US, of course, and around the world.  And this grid also holds the data for the climate models,  which are, for example, used for the IPCC reports.  And we use actually the same models, which  producing the data for the IPCC reports,  but modeled for paleotimes.  So these models have certain boundary conditions,  which they're for forecasting project,  like trace house gases and ocean salinity,  and further things like orbital parameters of the Earth.  And they can run these models also  for boundary conditions for the past.  For example, if you get these parameters  from sediment archives or ice cores, et cetera,  you can run them for past times.  And we acquired these three models,  which is the control run is an actual run  from boundary conditions from pre-industrial time.  This is then assumed for 180050 or 1800.  So before the onset of industrialization,  the main onset of it, and the feedback to the Earth system,  then we have the metallocene time slices.  This is 6,000 years before present.  And the LGM, this is the last glacial maximum.  You see here some parameters here.  They're almost all the same.  I took the atmosphere monthly realm data  because precipitation and temperature are in the atmosphere  realm.  And you see the temperature resolution.  The control run has a 150-year simulation.  And both others have 100-year simulation for each month.  So they have this year's times 12, so 12 months a year.  You have the number of actual raster layers per model.  And then you have the spatial resolution,  which is not very fine.  So it's 192 by 96 for complete Earth surface.  And there's a version number.  So I have links on the last page of the slides,  which I will upload to the actual data.  So you can access the same data I'm talking about here.  And oh, yes, here's the model parameters  I talked before about.  These are the boundary parameters on which  they are doing the models.  So I listed them here again.  So as I already told, they can adjust the models  to then pass times.  Yeah, and this is a processing tool chain  for deriving the classifications.  So we have here on the, there it is.  So we have here the input net CDF files.  You get net CDF files for each variable,  which have these 1,200 or 1,800 layers of data for each month,  100 or 150 years.  And we have a Python script computing the monthly means  for each variable, which then, oops, sorry,  results in 12 monthly mean raster layers for the temperature,  TIS, and precipitation.  And then I applied an interpolation,  in which I talk on the next slide,  to increase information from the data.  This is a crucial point of this approach.  And then we did the actual calculation  of the classifications of the criteria I presented before,  formulated in another Python script.  And from this classifications, we did the maps.  So this is on the left side.  You see the original resolution for the import data  on the right side.  You see the increased resolution.  We resampled it to the centintegree grid,  so 0.1 times 0.1 degrees resolution.  And we applied a B-linear interpolation.  And we think this is legitimate because the import data  are model outputs on continuous grid.  And if you think about the gradient  between one grid cell and the other,  this is a continuous slope.  So it's not like a curve.  It's a continuous slope.  And this, why we believe this B-linear interpolation  is valid to increase the information in that sense,  that the boundaries of the class is resulting  from this classification are more finite.  So if you have large gradients between two grid cells,  the boundary would be different from the original grid cells.  And this results in some way in a higher amount of information  in spatial resolution.  Here are some examples of what the Python script just  looked like.  You see an example of the calculation  of the mean annual precipitation.  And one below it is a calculation of the summer winter  differentiation for the seasonality.  So you see it's a map calc expression from grass,  which you can put in using the grass bindings  in this bit of Python code.  And you see here the precipitation data  is given in millimeter per second per square meter.  And the classification definitions  available are all on just in millimeter expressions.  So this is what is given in a flux entry just computed  by two daily measures.  Yeah.  Yeah.  So for millimeters per day, not per second.  And the other thing is to distinguish  between summer and winter to just add the mean temperatures  and look which has the higher value.  This is a summer then.  And this also can distinguish between northern hemisphere  and southern hemisphere mostly because some of these expressions  shown before are based on the seasonality.  So what you see here is a map of the LGM classification.  For worldwide, you don't see the high resolution, which it has.  Because it's worldwide here.  But what you can see is we applied for the LGM  in different coastlines based on the minus 120 meters  basymetry contour.  According to several publications,  it is more or less given in the community  that during the last national maximum,  the sea levels were about 120 meters below from today.  And we additionally included data set  from Ilast et al, which all have cited in the submitted paper  for the ice sheet distributions.  So you have here the northern European  and the North American ice shields and the ice shields  and the Antarctis, for example, as landmass.  And you can see, for example, very well here in Southeast Asia,  the larger land masses.  This has, of course, also effects on northern Europe,  for example, Great Britain was part of the European mainland  in 21,000 years ago.  So this is very useful information.  If you do research on culture and environment  from the last national maximum to see what  the environmental patterns were like in these times.  And we have here the map for the mid-hollow scene, which  is has the same coastlines like from today.  And it's partly different from the control run.  If you see the control run, there are some differences  in the snowy climates.  And in sub-Saharan Africa, you see some differences  from the distributions.  So that is assumed that in like 8,000 years ago,  today Sahara was savanna and it was not yet the desert.  It was habitable for humans and animals.  So yeah, that was something.  Yeah.  So I have all the code and the data and everything online.  So you can check that out.  I've wrote a tutorial which you can find under this URL.  And can access the Python files directly.  And the data is also published in our project database  with DOIs, so the shapefiles and the resulting geotest.  And yeah, I'm happy about comments and especially  maybe for the interpolation approach, which I think is valid.  I didn't turn anything else yet.  But maybe there are some people who know more or have better  have some something which I didn't concern yet.  So I would be happy about feedback.  And then sorry, I have to do a little bit advertising in November.  We have a data management workshop organized by our group in Cologne.  And we are happy to welcome you there if you want to visit beautiful Cologne.  And here's some stuff on state-of-the-art data management.  You are normally welcomed and invited.  Yeah, and I want to thank you for your attention.  So sorry for a little bit confusing in the order of the slides.  I recognize myself.  Thank you very much.  I had a question about you asked for comments about the interpolation.  Yes.  But I actually think I don't fully understand what you, if you go back one slide,  about the moment when you do the interpolation.  Yeah.  Because it seems to me that you interpolate part of the data  and then go further with analysis of that interpolate, using the interpolated data.  Am I correct?  Yeah.  So that's just because it wouldn't make sense to interpolate the resulting classification.  Because it's not the continuous data, it's ordinary data.  So we interpolated the temperature, the monthly temperature, and the monthly precipitation.  Yeah, data layers, 24, then proceed with some calculations on the interpolated data sets.  And you do the interpolation, for example, on the temperature, just purely based on the  roster size and then the values, the linear interpolation of the values.  But you don't take into account any other data, I understand.  But the Copenhagen classifications are just based on precipitation and temperature.  This was the only data Kupmet available some 100 years ago from climate.  Yeah, yeah.  But Kupmet didn't interpolate roster data.  He didn't have roster data.  Yeah.  And what you do if you interpolate, for example, between two of these big blocks of temperature,  you interpolate that there is a smooth difference.  Because they are already monthly mean values.  Yeah.  So I assume that there is a continuous slope.  Yeah.  Like a straight line.  But that might not be true.  And you have 20 degrees mean temperature and then here is 20 degrees.  But especially if you do that for, especially for temperature and especially precipitation,  you know that in real life, the fact that there's, for example, a mountain rate somewhere or a  coast would make these differences much less smooth.  Right.  But we assume that this is already, or we more or less know from the descriptions of the  models that this is taken into account for the, for the model data.  It's already taken into account.  Ah, OK.  This is the topography and all the boundary conditions.  And because there's monthly mean values like weather fronts and some effects like this.  We have great gradients between small scales and spatial resolution are not, are are so  smoothed out through the monthly means.  OK.  So that it's possible to do this.  OK.  I understand.  Here we are.  OK.  Thank you.  Thank you.
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My name is Christian Wilmes. I'm a PhD student at the University of Cologne. And I did some work on Copenhagen classifications on paleoclimat model simulations, on which I submitted a paper for the academic track here. And I'm very happy to be able to present my talk now to you here. And I structured this talk in five parts. So I will first introduce a bit the project environment in which we conducted this research. Then I will explain what Copenhagen classifications actually are and how they're used. Then I will talk a bit about the paleoclimat models, which I use as the basis for the classifications. And the main part of the talk will be the implementation of how we derived this from the model data. And finally, I will show some maps showing the results. OK. I'm working in a large research project called the Collaborative Research Center 806, which is funded by the German Research Foundation. It has around 100 scientists working in this research center. It's an interdisciplinary research project concerning the culture, environment interaction, and human mobility in the late-quartinary. They are scientists from universities of Aachen, Bonn, and Cologne working in this project. And myself and the group I'm working in are based in Cologne. And you can find lots of information on the URL you find here. It has a DE ending, but the content is in English. So you can understand it all. So the main point here is I'm working in an archaeology, geoscientific paleo-environmental background for which paleo-environmental classification then, of course, are useful databases for further research. And that's why we conducted this. So I wanted now to give a short introduction to Copenhagen classification. The map you see here is such a classification created by Kotek et al. in 2006 from a climate station, available climate station data, which is, meanwhile, worldwide available in good density around the world. And this data is on the basis of a 50-year monthly means for precipitation and temperature, which are the main variables for the Copenhagen classifications. And this is what these maps look like with today's data. This is based on data from 1950 to 2001, I guess. Somewhere written here. 1951 to 2000. And so if you are familiar with plant regime distributions and ecology, the biome maps, which are also very popular, look very similar. So this classification seems to show the same pattern as actual biome maps or ecosystem maps, you can call them, for different ecosystems. The Copenhagen classification has five main classes, which are the tropics, which you see are mostly in red. The temperate, which are greenish. The arid, which are like brownish on this map. And the cold, snowy climates, which are like purpleish. And frost, polar climates, which are blue. So these are the main types. And then there are several subsequently distinguishings based on temperature, precipitation, and the seasonality of both precipitation mostly. And to compute this classifications, the algorithms are more or less based on the use of these 11 variables you can see defined here. These are all based on temperature and climate, of which you can see here. I will further explain them later. So the thing is you have the climate and the temperature and precipitation data, and you compute with grass map algebra these variables for the rusters, for the distribution. And then you use them in if expressions to distinguish the classes. Here you see the criteria built from these variables defined before, from which you then define the several subclasses. And there are lots of it. So I listed them here for completeness. And you can put this into code. And this is actually what I did. And but we applied it to paleoclimate data. Yeah, to derive these maps. The paleoclimate data we acquired from the DICAR-SZ node of the ESGF, a system grid foundation. The DICAR-SZ is the Deutsches Klimarechenzentrum node. There are several nodes. You can see a list on the web page for them, some nodes in the US, of course, and around the world. And this grid also holds the data for the climate models, which are, for example, used for the IPCC reports. And we use actually the same models, which producing the data for the IPCC reports, but modeled for paleotimes. So these models have certain boundary conditions, which they're for forecasting project, like trace house gases and ocean salinity, and further things like orbital parameters of the Earth. And they can run these models also for boundary conditions for the past. For example, if you get these parameters from sediment archives or ice cores, et cetera, you can run them for past times. And we acquired these three models, which is the control run is an actual run from boundary conditions from pre-industrial time. This is then assumed for 180050 or 1800. So before the onset of industrialization, the main onset of it, and the feedback to the Earth system, then we have the metallocene time slices. This is 6,000 years before present. And the LGM, this is the last glacial maximum. You see here some parameters here. They're almost all the same. I took the atmosphere monthly realm data because precipitation and temperature are in the atmosphere realm. And you see the temperature resolution. The control run has a 150-year simulation. And both others have 100-year simulation for each month. So they have this year's times 12, so 12 months a year. You have the number of actual raster layers per model. And then you have the spatial resolution, which is not very fine. So it's 192 by 96 for complete Earth surface. And there's a version number. So I have links on the last page of the slides, which I will upload to the actual data. So you can access the same data I'm talking about here. And oh, yes, here's the model parameters I talked before about. These are the boundary parameters on which they are doing the models. So I listed them here again. So as I already told, they can adjust the models to then pass times. Yeah, and this is a processing tool chain for deriving the classifications. So we have here on the, there it is. So we have here the input net CDF files. You get net CDF files for each variable, which have these 1,200 or 1,800 layers of data for each month, 100 or 150 years. And we have a Python script computing the monthly means for each variable, which then, oops, sorry, results in 12 monthly mean raster layers for the temperature, TIS, and precipitation. And then I applied an interpolation, in which I talk on the next slide, to increase information from the data. This is a crucial point of this approach. And then we did the actual calculation of the classifications of the criteria I presented before, formulated in another Python script. And from this classifications, we did the maps. So this is on the left side. You see the original resolution for the import data on the right side. You see the increased resolution. We resampled it to the centintegree grid, so 0.1 times 0.1 degrees resolution. And we applied a B-linear interpolation. And we think this is legitimate because the import data are model outputs on continuous grid. And if you think about the gradient between one grid cell and the other, this is a continuous slope. So it's not like a curve. It's a continuous slope. And this, why we believe this B-linear interpolation is valid to increase the information in that sense, that the boundaries of the class is resulting from this classification are more finite. So if you have large gradients between two grid cells, the boundary would be different from the original grid cells. And this results in some way in a higher amount of information in spatial resolution. Here are some examples of what the Python script just looked like. You see an example of the calculation of the mean annual precipitation. And one below it is a calculation of the summer winter differentiation for the seasonality. So you see it's a map calc expression from grass, which you can put in using the grass bindings in this bit of Python code. And you see here the precipitation data is given in millimeter per second per square meter. And the classification definitions available are all on just in millimeter expressions. So this is what is given in a flux entry just computed by two daily measures. Yeah. Yeah. So for millimeters per day, not per second. And the other thing is to distinguish between summer and winter to just add the mean temperatures and look which has the higher value. This is a summer then. And this also can distinguish between northern hemisphere and southern hemisphere mostly because some of these expressions shown before are based on the seasonality. So what you see here is a map of the LGM classification. For worldwide, you don't see the high resolution, which it has. Because it's worldwide here. But what you can see is we applied for the LGM in different coastlines based on the minus 120 meters basymetry contour. According to several publications, it is more or less given in the community that during the last national maximum, the sea levels were about 120 meters below from today. And we additionally included data set from Ilast et al, which all have cited in the submitted paper for the ice sheet distributions. So you have here the northern European and the North American ice shields and the ice shields and the Antarctis, for example, as landmass. And you can see, for example, very well here in Southeast Asia, the larger land masses. This has, of course, also effects on northern Europe, for example, Great Britain was part of the European mainland in 21,000 years ago. So this is very useful information. If you do research on culture and environment from the last national maximum to see what the environmental patterns were like in these times. And we have here the map for the mid-hollow scene, which is has the same coastlines like from today. And it's partly different from the control run. If you see the control run, there are some differences in the snowy climates. And in sub-Saharan Africa, you see some differences from the distributions. So that is assumed that in like 8,000 years ago, today Sahara was savanna and it was not yet the desert. It was habitable for humans and animals. So yeah, that was something. Yeah. So I have all the code and the data and everything online. So you can check that out. I've wrote a tutorial which you can find under this URL. And can access the Python files directly. And the data is also published in our project database with DOIs, so the shapefiles and the resulting geotest. And yeah, I'm happy about comments and especially maybe for the interpolation approach, which I think is valid. I didn't turn anything else yet. But maybe there are some people who know more or have better have some something which I didn't concern yet. So I would be happy about feedback. And then sorry, I have to do a little bit advertising in November. We have a data management workshop organized by our group in Cologne. And we are happy to welcome you there if you want to visit beautiful Cologne. And here's some stuff on state-of-the-art data management. You are normally welcomed and invited. Yeah, and I want to thank you for your attention. So sorry for a little bit confusing in the order of the slides. I recognize myself. Thank you very much. I had a question about you asked for comments about the interpolation. Yes. But I actually think I don't fully understand what you, if you go back one slide, about the moment when you do the interpolation. Yeah. Because it seems to me that you interpolate part of the data and then go further with analysis of that interpolate, using the interpolated data. Am I correct? Yeah. So that's just because it wouldn't make sense to interpolate the resulting classification. Because it's not the continuous data, it's ordinary data. So we interpolated the temperature, the monthly temperature, and the monthly precipitation. Yeah, data layers, 24, then proceed with some calculations on the interpolated data sets. And you do the interpolation, for example, on the temperature, just purely based on the roster size and then the values, the linear interpolation of the values. But you don't take into account any other data, I understand. But the Copenhagen classifications are just based on precipitation and temperature. This was the only data Kupmet available some 100 years ago from climate. Yeah, yeah. But Kupmet didn't interpolate roster data. He didn't have roster data. Yeah. And what you do if you interpolate, for example, between two of these big blocks of temperature, you interpolate that there is a smooth difference. Because they are already monthly mean values. Yeah. So I assume that there is a continuous slope. Yeah. Like a straight line. But that might not be true. And you have 20 degrees mean temperature and then here is 20 degrees. But especially if you do that for, especially for temperature and especially precipitation, you know that in real life, the fact that there's, for example, a mountain rate somewhere or a coast would make these differences much less smooth. Right. But we assume that this is already, or we more or less know from the descriptions of the models that this is taken into account for the, for the model data. It's already taken into account. Ah, OK. This is the topography and all the boundary conditions. And because there's monthly mean values like weather fronts and some effects like this. We have great gradients between small scales and spatial resolution are not, are are so smoothed out through the monthly means. OK. So that it's possible to do this. OK. I understand. Here we are. OK. Thank you. Thank you.
Copenhagen classifications on paleoclimat model of the talk will be the implementation of how we are the main variables for the Copenhagen ecosystems. The Copenhagen classification has and environment from the last national maximum to