 to be told the difference between, no, we will all be told the difference between an impact model and a climate model and we're going to be told this difference by Kala Berind. There you go. Thank you. Hello and welcome everyone. I would like to see my slides. Are these slides? Ah, okay. Nice. So welcome everyone to my talk about climate modeling and the science behind climate reports. First of all, I will shortly introduce myself and what I do. I work at the U of Z. That's the Hammer Center for Environmental Research in Leipzig and I work for the ESM project which is short for Advanced Earth System Modeling Capacity. I'm also a PhD student at the University of Potsdam and I'm part of the developer team for the Misescale Hydro-Lotic Model which is an impact model and I'm also a scientist for future and an artist. So what this talk is about, this talk is partitioned into three sections mainly. First is the introduction where I will introduce some nomenclature like what is weather, what is climate and what we can say about predictions, for example, why we can tell the weather in three years but we can say something about the climate and what are climate models. Then the second part will be the longest. The science behind warming graphs, I will show you a graph that's typically shown when people tell you about climate change and I will explain that graph in detail and what is behind it. The third part would be installing an impact model to your local PC if there's time, if there's no time I will skip that and in the end there's as always a summary and conclusion. So starting with the introduction, weather is defined as the physical state of the atmosphere at a given time whilst climate is average weather. Most of the time period of 30 years is taken for that averaging but also other time periods could be taken. So while the main question was while we are not able to predict weather at a specific date and a decade, for example, let's say the 27th of December in 50 years or so, why does it still make sense to propose general trends for the climate? That is a question that often arises and I'll answer that. So first of all it is about average. Average cloud coverage gives us information on average reflection and average reflection has an impact on the warmth on the earth and the same is true for another scenario, for example, average precipitation meaning rain or snow and temperature has an impact on vegetation and vegetation influences the carbon cycle and that again influences the warming or cooling and that has an influence on the ice coverage and that again on the reflection. So there are lots of processes that are connected to each other and if we know something about the average of some of this physical state of the atmosphere we can say something about the climate trends. So the question is what is a climate model? And the AR-5 defines a climate model as a numerical representation of the climate system. The AR-5 is a source I will cite quite often, so I have one slide with the whole citation. It's the fifth IPCC report. IPCC is the Intergovernmental Panel on Climate Change and the fifth assessment report is, so AR-5 is the abbreviation for fifth assessment report. By coming back to a climate model, so a climate model could for example be a GCM, a general circulation model, which is a global climate model that usually consists of an ocean and atmosphere circulation. And the fifth assessment ocean and atmosphere circulation. An RCM is not a GCM but it's a regional climate model, meaning a climate model at a limited area and mainly it has a higher resolution and for it is at a limited area that usually means that there is some input and output going in because it's not a closed system. And an impact model again has usually a higher resolution in time and space and it's not a climate model but it's for simulating extreme weather events like floods. So if you want to build a dam or a dyke and you want to know how high this dyke or dam should be, then you would usually run an impact model that gives you information about water heights over decades or longer or so and then you would decide on the height. So this is the use for impact models. So that's for the introduction part. Now I come to the main part and I will start with a question. Is it proven or with the climate graph? As said, I will show you a graph, a typical image people would show you when they address climate change. This graph has an x-axis with a time scale and you see it's reaching far into the future and it also has three or four regions and the first region is only in the past. And the y-axis is the global surface temperature change meaning how much degrees and Celsius or in Kelvin if it's different it's the same. We will have in future or we had already. And then you see several lines in different colors and with the names RCP something and I will explain all the numbers and everything about this graph because it's a pretty important graph. So first of all I will tell you something about the numbers and uncertainties. The uncertainties are the transparent colors behind the lines. I will tell you something about the representative concentration pathways which is short RCP and so it's reflecting the colors of the lines. I will tell you something about the source of the graphs so where does this graph actually come from so I will tell you something about the assessment report and first of all I will answer the question is it proven or is there scientific evidence that we will face that climate change. So you will see that graph quite often. First of all I took a definition for proof for scientific evidence from Wikipedia. The strength of scientific evidence is generally based on the results of statistical analysis and the strength of scientific controls meaning you make an experiment over and over again and you change basically some influences on the experience where you want to know that these does not influence the output so you can narrow it down and know what is the source of your results and so you can prove a physical law or something. Yeah I took this comic from XKCD because it's a nice it's somehow connected so there's a person who pulls a trigger and then got thrown by a bolt or something. Something bad happens for example climate change and then yeah there are two scenarios for example a person usually would decide okay okay I would not pull the lever again but scientists usually or more often would say okay maybe does that happen every time if I do so because yeah that's basically how you prove something or that's experiments but in case of climate change even scientists say you shouldn't although it's pretty interesting for us from a scientific perspective but the problem is we only have one earth we cannot do this experiment very often except we had a time machine then we could go back but we haven't so we shouldn't do that experiment and that's something a scientist before long ago in 1957 said already human beings are now carrying out a large-scale geophysical experiment of a kind that could not have happened in the past nor be reproduced reproduced in the future so another question is yeah if you ask this question if it is proven or that it probably is not happening or so to climate bias they usually would tell you okay maybe it's not happening and the other side would take the position and ask you okay if you stand in front of a road and you want to cross the road and there is a car approaching very fast would you cross that road because it could happen that the car stops or makes a new turn or something but well usually it doesn't and sadly we know lots about this this experiment because it's done very often before we know something about traffic and that is pretty dangerous so let's change the factors a little so that we don't know so much about that situation let's say a cube approaches us with a high velocity on something that is maybe not a road would you still cross the something and the answer is you still probably wouldn't and why wouldn't you do so although you know nothing about this situation well you do know something you know you know conservation of the momentum which is a physical law you know about so you have a situation you know not so much about you have never had an experience before but you still are able to make some assumptions because you know the physical laws behind it and that's basically the same we do with in fact in the context of climate so we have let's say just an earth and the sun and the sun yeah has some radiation and that comes to the earth and gets partially reflected and the earth radiates itself because it has some temperature we know something about this sun we know the solar installation and we know parts of the light is reflected and the factor that is reflected is usually called albedo so the reflected energy is albedo times the solar insulation and albedo is something about 30% and we know then that the light that is observed must be all the remaining energy so the energy of the surface is one minus albedo times the solar insulation then knowing Stefan Boltzmann law for energy emissions where the temperature goes into the power of 4 and with Stefan Boltzmann constant we can actually find out the surface temperature which then is derived to minus 19.5 degree Celsius well we know probably we know that the earth is much warmer and that's because our model in this case which is maybe a climate model is far too simple so we change something about that we add atmosphere and atmosphere has some interesting thing impact so atmosphere consists has some trace greenhouse gases for example CO2 but also H2O ozone methane O2 and nitrous oxide and these greenhouse gases reflect the radiation of earth back to earth partially meaning the atmosphere has a transparency and this transparency we call T is something between 15% and 30% so it's not fixed and that's another interesting fact the atmosphere emits energy which we call J-atmos and that goes goes out in space and to earth and the energy that goes into the atmosphere is one minus the transparency times the energy so we know two equations the first is the energy that goes into the atmosphere also goes out of the atmosphere the second is that the surface temperature the surface energy of the earth is the term we had before one minus albedo times the solar insulation plus the energy the one part of the energy that is reflected by the atmosphere and so we have two formulas two equations with two unknown and with a Stefan Boltzmann law from before we can divide the surface temperature which is 15 degrees of Celsius and that actually is not so far from what it actually is in 2000 in 2000 it was measured that the surface temperature is 14.5 degrees so I did this for a specific T which is 22.5% but when we changed that T a little to for example 20% so we add more CO2 because for example we would add a factory that would do carbon emissions then the transparency goes down and the temperature rises to for example 16.6 degree in case of 20%. This is also a very old knowledge so this is maybe a little much on a slide but it's still very interesting because it's copied directly from a paper that was published from Svante Ahenius in 1896 already and it's on the influence of carbon acid and the air upon the temperature of the ground and carbon acid is the old term for carbon dioxide so if we have a look to the percentage so he investigated what if we change carbon dioxide so what is the impact of our behavior let's say carbon dioxide would the carbon dioxide in our atmosphere would double so that would increase by a factor of two then the average temperature rise in Leipzig in December so Leipzig I choose the region for Leipzig would be 6.1 degree well that's probably a little high but what we can see is already that Ahenius back then already knew that there is seasonal impact on climate that is climate change is seasonal and also spatial so it is not just one not the average temperature is the only interesting knowledge we get so Ahenius said something like the temperature in case of carbon acid doubled would be around 4 to 6 degrees and the current models predict something like an increase for 2 to 4 degrees for that scenario so there's maybe a overlap already with that simple model from back then so then I come to the question a climate model represents physical laws that's what we learned where do the uncertainties come from so if we know all the physical laws and we would just calculate everything with these physical laws why are there even uncertainties and there are some reasons for that for example the initial conditions is one main source of uncertainties meaning how is the current state of the climate system now how fast does something move where are the clouds exactly and so on we don't know these precise initial conditions and therefore from arrows here second would be the resolution and of a model so the temporal and spatial step length meaning we can't so we always represent our climate system with differential equations and we approximate everything we have not the movement of every molecule but we have some average on cells and if we increase the resolution then usually the uncertainties go down but sometimes they even don't for some question for some questions it's better to have a lower resolution but mostly it's better to have a higher then truncation so there we have computation the limits and lack of understanding for example clouds clouds are not understood pretty well and when I read the fifth assessment report I found the sentence I found a little amusing climate model clouds and climate models usually tend to rain too early yeah so but if you know all these sources of uncertainty why is there no such thing as the one best climate model meaning why can't we go to the highest resolution and to the best the the best computer we get and do everything just in the best way and then we would have our best climate model and there are some reasons for that for example the so-called dynamic core including the method for differential equations or something like grids for example if we have a triangular grid or a rectangular grid on rectangular grids we usually can be can calculate faster but on triangular grids we could for example increase the resolution locally that might be differences and both have advantages and disadvantages also the parameterization parameters and our last slides were for example the t and the albedo which will probably be not the final parameters because they are derived from other parameters but physical laws or something are often represented by rules with parameters and these parameters can be estimated and they can be calibrated with different error measures and there this is another reason for uncertainties and differences in climate models and then there are schemes for example there are different formulations of physical processes for example again clouds and last the truncation again we can also decide how we limit due to our lack of computational power so yeah what do we do we investigate all the models we have so there are different climate models that are representing our climate and we take all the models that match certain conditions I come to that later and we average the output and then we get a climate projection and also that uncertainty band you see so what climate models do we investigate they are so-called coordinated GCMs or that are so climate models are compared in so-called coupled model intercomparison projects in different phases these coupled model intercomparison projects are showed CMIP 4, 5 and 6 so there might have been for early ones but currently for the AR6 so for the six assessment report CMIP 6 is investigated and for I showed you on the map the research centers which took part in CMIP 6 so which take part in the six assessment report these research centers are mainly specialized research centers university and a meteorological offices but in January it's open for any institution to participate as long as they follow a protocol for their contribution where there's some rules so you cannot just do anything these institutions need to produce variables for a set of defined experiments and a historical simulation from 1850 to present this blue part is a link so if you go to my slides afterwards you can see these variables you need to reproduce and then you can do something like this so we have a graph here again on the x-axis we see again a timescale that reaches from 1850 to today and on the y-axis we again see the temperature anomaly or the temperature difference between so the exactly the temperature difference so how much the earth has warmed up we see CMIP 3 and CMIP 5 compared which were the models that were investigated for the AR5 um so we see a band um so this uncertainty with the yellow and bluish in the background and then we see these two lines the blue and the red one from CMIP 3 and CMIP 5 and then we see the black one and that is what actually was observed and we see that this differs quite a lot and that's due to there was only investigated the natural forcings meaning excluded what the human did and if we also put the human forcing into it then it's quite matching and that is the best kind of proof we can get and again I said we investigate the physical laws um and the physical laws were actually results of scientific experiments and so yeah there's this kind of proof and yeah um so maybe a little addition um there are also other coordinated model entire comparison projects uh then so outside of the IPCC uh and uh so as all one are inside the APCC where the scientific focus is on uh subtopic uh on something like land surface for example and that's what uh I do uh and um there are also uh there's also published work outside from IPCC so back to the graph we talked about the part is it proven and I hope I convinced you that this it is and um now I will talk about the sources of the graph so I talked a lot about the IPCC um the IPCC uh the Intergovernmental Panel on Climate Change um published uh reports so the fifth um for example the fifth assessment report and what you see here is part of the cover but um there have been four ones before as the name fifth um suggests um the first assessment report FAR was published in 1990 uh the second SAR in 1995 uh then there was the TAR and then for the first fourth assessment report they changed the name scheme for some reason to AR4 and then there was AR5 which I'm talking about the IPCC consists of several working groups including uh working group one to three uh providing the assessment reports and I mainly focus on the assessment report from a working group one um which investigates the scientific aspects of the climate system and climate change but there's also a working group uh and investigating on vulnerability vulnerability uh and economic impact and the third one on the options of limiting greenhouse gas emissions and others so I shortly show you a history of um the climate models for in something the mid uh 70s uh climate models were investigated where there was just an atmosphere the sun rain clouds were missing and CO2 emissions and uh I hope you believe that the sun is behind the atmosphere and not in this atmosphere um for in the mid 80s there was uh prescribed ice added and already clouds and land surface and yeah you see a nice mountain but actually in that time um the resolution was so low that uh the alps only had one or two um grid cells meaning there was not so much about land surface but it was added and for the first uh first assessment report uh there was a swamp ocean added meaning an ocean was added but it was had no depth um for the second assessment report uh the ocean got some depth so it was a normal ocean with surface uh circulation and there was added volcano activity and sulfates for the third assessment report that was added so um this is all about um which kind of processes were um they are on um climate models there that were investigated in this uh assessment reports meaning there were climate models before that already those uh processes uh included but they were not investigated under assessment reports so this is a history of which climate models or which processes and climate models were investigated in assessment reports and the third assessment report there was uh another circulation added for the ocean the overturning circulations and there were rivers added which is interesting because I do something with rivers uh and um there were aerosols added and um a carbon cycle meaning that the carbon that goes into the atmosphere also goes out but yeah not everything or the half the half time is not so good uh for the ar-5 uh four uh there was uh chemistry added in the atmosphere and interactive vegetation and for the ar-5 there was ozone added and biomass burning emissions and uh there's a history of processes but there's also a history of computer modeling that might be really interesting it started more or less in 1904 uh with uh Wilhelm Diakens who um found equations that could be solved to obtain future states of the atmosphere and he thought about that um yeah maybe these equations are really hard to solve and that task should be split and distributed to many people so he basically imagined a human computer and then Louis Fry Richardson came in 1922 and did actually calculate all this did a six hour forecast solving the equations by hand alone and 42 days user time meaning he himself calculated 42 days on it but that 42 days were distributed over two years in total um so he was a little behind the weather only to find out that it didn't give the correct answer that was long forgotten um but uh and people said yeah that's not quite practical we cannot do that but then computers came in uh 1950 the first successful weather model was run on a computer called ENYAC and in 1950 weather predictions were run twice a day on an IBM 701 nowadays we use supercomputers much larger and yeah there there's a whole list in the rank and I will shortly introduce Jules to you uh the Jülich wizard for European leadership science that's a supercomputer in Jülich and I would have shown you a picture but you are not allowed usually you are not simply allowed to take pictures pictures on that campus uh but since supercomputers are fancy shiny cupboards anyway I thought this is okay um so we have these cupboards that look yeah like shiny cupboards and in these cupboards there are blades and each blade uh is called a standard node and consists of in case of Jules 2 times 24 cores uh with 2.7 Gigahertz uh and it's hyper threaded meaning you can actually run uh 96 threads or processes on one of these nodes and these nodes have 12 times 8 gigabyte of run uh memory um and that's not quite uh much if you want to run a climate model but I will come to that a little later and in fact in case of Jules you have um like three rows of five of these cupboards or something and uh so they are in total 2,271 standard nodes uh 240 large memory nodes and 56 accelerated nodes consistent having something like GPUs um and I tell you about Jules not because it's the fastest actually it's maybe the 30th uh not even because it's the fastest in Germany it was when it was built but that's a while ago um but I told you about that because uh Jules provides actually computing budget for the ESM project the advanced earth system modeling capacity and so they're actually um a system model's run on that machine um so what I told you before there's not so much uh memory on each node so what you need to do is you need to cut down your problem and distribute it about the over the nodes and then there needs to be some communication so usually you if the task is so simple you can uh cut down your grid and put a number of grid cells to each node and then there's communication between the nodes on the boundaries to solve the differential equations um talking about grids um I would talk about the resolution also again a history of resolution of the climate models for the first assessment report the region resolution was 500 kilometers times 500 kilometers and as I said before you see these um two yellow yellowish um cells in the middle that are the alps um for the second assessment report the resolution already doubled or half depends on how you want to phrase it for the tar it was 180 kilometers and for ar4 it was 120 kilometers uh for the ar5 it's a little bit a different section I show you uh and also um I show you two resolutions there is the resolution for the higher um models which is um 87 for example 87.5 kilometers and for the real uh very high resolution uh with 30 kilometers and that's because um climate models are not just one model but there are different kinds of model that are coupled and uh each model has its own resolution um so it's more less like something like this uh so we have a model for ice we have a model for atmosphere for ocean and for a terrestrial uh and this is coupled so they all sent their data to a coupler or something and that's set as an input to the other model um so this is more or less like um how climate models look and each of the models again has several layers for example the terrestrial layer um has a groundwater part and the atmosphere and um so there's some input from the atmosphere to um the soil and plant system and then there's some water that is sinking into the groundwater and then coming out to the rivers um and uh yeah so then we have the runoff so meaning rivers get um water and then if you have a look to rivers and want to parallelize rivers then it's not so easy because um the um we have a source somewhere and the water has to go from the source or something that happens at the source has an impact to the sink meaning um this has to communicate all the way along uh to the sink and uh that's where I come in I actually do um um so I show you uh the genube which you probably know better with the name Donau um at a resolution of five kilometers um and uh basically cut down the genube into sub river domains um and we need if we parallelize these we need to calculate uh the uh sub river domains that are farther away from the sink first and uh you see that in the first um a graph a little so the grayish areas are calculated first and then it goes down um farther to uh the sink so just to tell you what I do um the now we come back to the my question so we answered where the sources of uh where the graphs come from now we uh under the question what is uh a representative concentration pathway um meaning what we all did before was more or less telling how we get to that black line in the first section and now we concentrate on the color part where we have more graphs than one uh the so um the working group one of the IPCC generally tests a selection of couple more climate models that is what I told you before uh matching specific conditions and investigates the output assuming different emission scenarios meaning we have uh couple climate models that are somehow different for example in their um grid and then we have input data the input scenarios would be for example the first one um where we just do business as usual and don't reduce uh carbon emissions the second would be we um start with our way we do it today but we will slowly change to uh renewable energy and the third one would be a scenario where we do it spontaneously now or so and that is an input scenario that we put into the systems uh and then we get out uh a model output that says something about the future so there's a black line that says okay this uh was uh our history until today and from that on uh we have three scenarios and uh they represented upper to lower so uh the upper upper and right uh line represent the um the way where we do nothing or so uh and uh yeah so this is basically what we do with uh scenarios and uh the rcps are scenarios the rcp with the synthesis consultation pathways are scenarios that include time series of emissions and concentrations of the full suite of greenhouse gases and aerosols and chemical active gases as well as land use and land cover um so that is another graph from the ar uh five and it shows again uh in the x axis the years it's the same time scale as before but on the y axis we now have the radiative rate of forcing uh that is basically having this impact on uh our climate and so each of the rcp scenarios has uh some kind of equivalent equivalent and uh radiative forcing and uh the uh uh yeah so we have four four of these scenarios um the data for the rcp scenarios is coordinated coordinated by again the input form MIPS input database for model and comparison projects that I told you before and most of it is freely available and I gave you the link so uh if you want to run your own climate model and uh tested with these input uh you can find it there and now I will explain the last part uh the numbers and uncertainties so first of all again to the graph from before the numbers beside the rcp refer to the radiative forcing at the end of the modeling clearing of 2100 meaning if you follow one of these lines for example the red run uh to where it crosses the um 2100 uh line then the number there is 8.5 so rcp 8.5 represent is uh the name for this um rcp scenario then um the numbers in this uh different sections are the numbers of models used for this scenario in the in this time period um yes uh so uh as I said there are lots of models intercompared uh compared and uh we even have different uh models um for the different time periods so uh until 200 and 100 they're own there are 39 uh models for the rcp 8.5 and for the uh all the rest uh there are 12 and you see this little gap um this uh with this line break uh at 200 and 100 uh 2100 and that is caused by the change of numbers of uh models that took place uh that took part in this project and uh another interesting thing that we see here and maybe the most important is um we have quite uh quite huge model uncertainties so if we compare all the models there's a huge band um when we can't exactly say okay it's like this or that but this band is still um about human uncertainties are uh more important uh than this model uncertainties we see tiny overlap but mainly we can say uh how the human behave derives our future and um that there will be this climate change we were talking about so that was the main part about these three parts and it's also um it is also the um most important part now I could probably uh show you uh how you can and sell an impact model to your local pc but probably I will uh I have um maybe something like three minutes left and we'll switch to the conclusion um and uh yeah maybe if it's arising as a question I can do it um so what have we learned um whether is the physical state of the atmosphere at a given time while climate is average weather it is over 30 years a climate model is a numerical representation of the climate system um and we learned that the main uncertainty uh is the way we solve uh differential equations I would probably have told you what a differential equation is uh in particular about that would have taken maybe another lecture climate change is not proven throughout repeating one real experiment over and over again so that it is only one earth as said but models simulate our past climate pretty well based on physical laws that were proven in real experiments and then maybe the most important message human behavior is the primary source of climate change therefore we talk about projections and not predictions meaning if we wanted to um predict the climate then we needed to simulate all human minds and what we will decide in future but we don't that would be another talk again um we um take what human will decide in future as an input scenario and with this input scenarios we create different output scenarios so with different input scenarios we create this different output scenarios where we can tell okay when we behave like that this is the output and human behavior scenarios dominate model uncertainties meaning the question is what do we want and um if you go to an demonstration the answer is usually climate justice and I think that is a good answer thank you