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Köppen-Geiger classifications of paleoclimate model simulations

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a Christian women of PhD student at the University of Cologne in and did some work on a cooking Gaia classifications on pile you'll climate model simulations on which I submitted a paper for the academic track here and I'm very happy to be able to trees and my talk now to you here and so on a structure this talk in 5 parts so I will 1st introduce a bit the project environment in which no we conducted this research but then I would explain the what cooking Geiger classifications actually are and how they're used and I will talk a bit about the pollen climate models which I and use as the basis for their classifications and the the main part of the talk will be the implementation of how we derived this from the model data and finally I will so from some of the maps showing the results it yeah OK then
I'm a working in a large research project called the Collaborative Research center at 8 0 6 which is funded by the German Research Foundation and test so around 200 scientists working in this research center it's sort of um um research project concerning cultural environment interaction and human mobility in the late Quaternary uh there are scientists from of universities of often born in Cologne working in this project and myself and the group I'm working in our basic alone and you can find lots of information on the UI you find here that's a hazard DIY ending but the content of some english so you the so you can all understand all so the main the main um point here is that I'm working and no yeah ocular archeology G on scientific and of how you environment background for which a highly environmental classification then of course are useful databases for further research and this why we conducted this so only yeah but I
wanted knowledge to give a short introdution to cooking Gaia classication the map you see here this section classification alone the created by a cop out into some 6 from a the yeah compensation available conversation data which is the mean by the very well and good density but around the world and to the he is this is the status on the basis of of 50 year uh um a monthly means for precipitation and temperature which are the main variables for the Köppen-Geiger classifications and this is what at the sum these maps look like was supposed today state of this is based on data from 1952 to 1 I guess summary written here in 1951 to 2000 and the the yet the so if you are a the familiar with like a plant retrieving distributions and the ecology the the view be by your maps which are also very popular look very similar so this classification seems to the I'll show the same pattern as so by true by only some MEPs or ecosystem it's can call them for different the ecosystems the covering I go classifications has sort of 5 mines main classes which are the tropics which is the mostly in red the temperate which greenish the parrot which are like a brownish on this map and the cold snowy climates which I like of probably the leash and the Frost so the small and polar which blue so this is 1 the main types and then there are several mean subsequently distinguish nations based on temperature and precipitation and the seasonality of both the transportation cost me so and to compute this
term classifications so of a permanent with the the diver is some Wallace based on the use of these 11 variables to tendency defined here uh and these are all based on temperature and climate of which the you can see here I will further explain them later but there are some so that the thing is you know you have to do the climate and and temperature the the temperature precipitation data and to compute ERM with um cross mark the rise of these variables for the rest of us for the distribution and then you use them in a try in the In if expressions so to distinguish the classes of
here you see the criteria the big from from this variables defined before uh 0 from which you then define the servants of classes and there are lots of it so I've listed them here for completeness and you can think of to can put this into court and this is actually what I did and term but we applied it to of climate data and to the uh yeah Malone yet to to derive these maps them to panic
climate data we acquired from the the from the declares that no at all so yes cheerful Assistant director of the foundation the decoder centers the Dodgers cumulation sent home note there are several nodes you can see listed on the web by page 1 4 of them use some notes in the US of course and around the world and these descriptors also some Holzer data for and for the time of models which are for example useful the IPCC reports and we use actually the the same models which are which producing the data for the IPCC reports but modeled for pi times so the sum of these models have a certain boundary conditions from which for forecasting project like trace cells gasses and ocean salinity and to further things like an orbiter para meters of the US and a 10 and from these models also for boundary conditions for the past for example if you get these parameters from the z demands of archives or ice cost is have error you can run them for the yeah past tense and this system I we require that these 3
models through which is that the control run this lecture on from them on the conditions from the pre industrial society at the time this is the end of the assumed for a 1850 the hundred so before the onset of industrialisation from the main onset of it and the the pictures of but a system and then we have the mid-Holocene time slices of of x 6 thousand years before present and the energy and this is the last glacial maximum the CIA some parameters stated that almost all the same uh I took the atmosphere once the real data because precipitation and uh and uh um and to a temperature are in the atmosphere and and to see the Tampa resolution so that the control run has of 150 years ago from simulation and both of us have for a hundred years simulation for each month so that they have this this year's time stressful 12 months a year and a half the number of actual Russell layers for a modern and then you have the spatial resolution which is not very find so it's 192 were 96 for all complete and ourself so famous and to come and this version must so you have links on the last page of the slide switch from upload to the actual data so you you can in the access exams and data and talk about you and to them only as
user a models paramater side talked before about this is that this this are the boundary permit on there doing the models so this is the new again a but the then guess so as I already told taken adjustments to then past times the yeah uh and um does the processing tool chain for for deriving the classifications Newman so we have here on the ominous uh and to review the input to messier 5 so you get the metadata files for each variable the which share the use of their and thousand 200 or so that the 1800 2 layers data that for for each month 100 or 150 years and every have yeah Python scripts computing the monthly means for its all you for for each variable which then
it's very the recession that results in 12 monthly mean of Russell layers so uh for for the temperature T S and precipitation and then I applied to an interpolation in which a talk on the next slide to the to increase the information the from the data this is the crucial point of this approach and some a then the then we detect actually a calculation of the classifications of the 2 criteria are presented before they're in from related than another uh Python script and from this classification speed that the pants and so this is
some on the left side you see that the original resolution for the input data and on on the on the right side you you see the increased resolution we resampled at to centered degree grid so point 1 times . 1 degrees uh um uh resolution and so we applied the bilinear interpolation and to we think this is the Egitim because the input data are modeled the a model outputs some con continues script and if you think about the gradient between 1 of the great said in the other uh this is sort continues of slope of so it's not curve it's a continuous slope and this why we believe this a bilinear inter polation smell to increase the and the information in that sense that the boundaries of the classes resulting from this class classification of and more finite so if you have large gradients between 2 of them uh the grid cells the that the boundary would that would be a good different from from the original of gridset and this results in yeah in the news in some way in the high amount of information and spatial resolution so yeah
the of here are some examples of what the prices could just look like it's going you see an example so the calculation of the mean annual presentation and the ones below this calculation of a some of the the summer winter differentiation forces amenities so the seeds of a metKod expression from grass which could put in using the grass findings in the uh the uh In this up but of Python code and the can you you you you see here that the precipitation data is given in the in an MIT from millimeter 1st 2nd 3rd grade of square meter and to and other classification deficiency of available are also on the and on the on and Justin millimeter expressions so so this the so what this what given in In the flux and the just it computers 5 to 2 of daily missions the so for me may just day a perspective and the other thing is to distinguish between us from last winter the who to do just that the mean of the temperatures and look which of hence the yeah the the higher value this is the sum of indices can distinguish between autonomous bensonhurst from mostly the balloon because some of the use of expression shown before of based on this is the so what you see here a
drive to that the a map of the Aegean classification for the worldwide you don't see the high resolution of which the test because it's what right here but what you can see as we applied for their jamming on different coastlines based on the minus a hundred 20 meters by symmetry contour according to several publications it is but 1 this the we are given in the community that during the last glacial maximum that means the letters that kind of about 120 meters of today and we additionally included it in the dataset from enough that i which all have cited that the submitted paper and for the and ice-sheet the distributions and so but and you few of the northern European and North American ice sheets and I issues and aren't artist for example spent said mask and you can see for example very well here and East Asia the margin and answers that this has of course also effects on northern Europe for example work written lost part of you book in mainland in 21 thousand years ago or so and this is a very useful information if you will to research on yeah of the culture and environment from the last glacial maximum to see what the environment of patterns of where were like in this times and so on here we have the the the map for the mid-Holocene witches that has the same phone coastline so like from today and as soon have partly different from the control run if you see the controller under some differences in the the yeah and a snowy climates and to it out in a Sub-Saharan Africa you see some differences from the distributions 1 so what this sort of that is assumed that in like 8 thousand years ago at the this the today some Zahara was Savannah and 10 to at what was marketed as if it was the the habitable for humans and animals so um year was something yeah there's
so I have the colt and data and every single online so you can check that out of broadening the tutorial which confined of no this you and and to some can access surprise surprise of picky and that the data is also published in our project out the base this you I so the share price and the resulting Judas and here I'm happy about comments and especially maybe for the interpolation approach which I think this pelleted interred anything as it may be that are some people more or has better have some something which I didn't confronted so I what we have about the plate and
then short sorry I have to do a little bit advertising and left the November we have that and management workshop organized by our group in Cologne and the yard I'm happy to welcome you there if you want to visit beautiful Cologne and here's some stuff on the state of the art better management you are the only a common invited yes and
no I want to thank you for your attention and if it so sorry for that limit confusing and all of the slides I recognized with you the I thank you very much I had a question about you out for comments about the interpolation yes but actually I think I don't fully
understand what you if you go back 1 slide about they're the moment we need to the
interpolation yeah a the because it seems to me that you into interpolate part of data and then go further with the analysis of that interpolates using the interpolated data are correct e yeah so this is so because what makes sense sense to interpolate the result intensification and because it's not it's not a question of ordinal data the government temperature the monster temperature improvement in the presentation of and the various thank you for the then proceed with some the only you you the interpolation for example the temperature uh guest 0 purely based only on the roster size and then uh did the values being the linear interpolation of integration of the values you don't take into account any any other data are high in the cell the 2nd just based on presentation and now also the only equipment the variables on 100 years ago and I I with that company didn't interpolate Russell they back he did have received what you do if you interpolate for example between 2 of the big blocks of temperature you interpolate that there's a smooth and I am sure the difference because they are already on the battle of the i th that it is a continuous like yeah straight line they did did that might not be true that with me and then you thinking so but especially uh if you do that for for a especially for and uh temperature and and then spatially precipitation you know that in in real life the fact that that for example a mountain Rachel mail or a cowshed would would make the so the difference is much less smooth ride you assume that this authority or a modest nor from the description of the model this is taking into account for the for the model to a more they don't have pay is of the view of and because the rest of in life let us from and some effect this is right gradient in small states that information in the and sold to go through the motions of OK so that it's possible to have a hamster at yeah version of the thank you
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Formale Metadaten

Titel Köppen-Geiger classifications of paleoclimate model simulations
Serientitel FOSS4G 2014 Portland
Autor Willmes, Christian
Lizenz CC-Namensnennung 3.0 Deutschland:
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DOI 10.5446/31679
Herausgeber FOSS4G, Open Source Geospatial Foundation (OSGeo)
Erscheinungsjahr 2014
Sprache Englisch
Produzent Foss4G
Open Source Geospatial Foundation (OSGeo)
Produktionsjahr 2014
Produktionsort Portland, Oregon, United States of America

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract 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.
Schlagwörter climate model
paleoclimate
GRASS GIS
Python

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