 the decision you have to make when you have to choose a specific indicator and how to choose among many. So there will be Francesca and Natalia, they both worked with me at ICTP, so Francesca was the contributing author of our chapter as well. Natalia, not because she was not here yet, which is new. And yeah, they worked with me on this project that is called I4C in which we are going to assess the European hazard at different global warming levels. Therefore, we are using this climatic impact driver also in this project as a follow-up of our IPCC expertise. Thank you. Okay. Good afternoon, everybody. Thank you for being here on the last day of last session of last and okay, I'm gonna have to sit down because there is a microphone for Zoom. Okay. So I will start with an introduction and focusing attention on some of the missions from the IPCC research report on risk. Risk is defined as the potential for adverse consequences for human or ecological systems. So in the context of climate change impacts of climate change, the risk can arise from climate change impacts as well as from human responses to climate change. In this session, we will focus the attention on impacts. So in the context of climate change impacts, risk is defined as a dynamic interaction of three kinds of hazards that are the potential periods of a natural human-induced physical event or climate on that can cause loss of life, injury, or loss of property, infrastructure, and so on. And we will focus on climate-related hazards that include extreme weather and climate events. So, besides hazards, we have vulnerability that can contribute to the risk recognition. We are really in the process of disposition to be adversely affected. And this is a concept that contains also the idea of the lack of the capacity of adaptation or the sensitivity to harm, for example, and exposure. Exposure is the presence of people or species or ecosystems, but also environmental functions, services, economic societal assets, so everything that could be adversely affected. So risk defined in this context has consequences, impacts as well as consequences of realized risks on natural and human ecosystems. And they can be adverse but also beneficial. And this last concept is strictly connected with the definition of climate impact drivers. In fact, climate impact drivers are physical climate system conditions that affect an element of societal ecosystem. But the terminology is in contrast with the term hazards, since it provides a more value-neutral characterization of climate change, that has a definition without a prejudging on the potential impacts of climate change. Extremes are a category of seeds corresponding to unusual events with respect to the range of observed values of that variable. Climate impact drivers may not be related only to extremes, but they can, for example, be related to, for example, for the rate of coastline recession. We don't have an extreme in this type of event, but we have a risk connected with this climate impact driver that is strictly connected with the sea level rise, for example. Climate impact driver indexes are numerical computable indexes that are done using one or a combination of climatic variables. And are indexes that are designed to measure the intensity of the climatic impact driver or the probability of accidents. Indexes are physical computable from observations, the analysis model calculations. But the important thing is that when we wanted to compare all these different datasets, we have to pay attention to the scale at which the index is computed. For example, for extreme precipitation event, it can have a lower magnitude if we consider a large grid cell, but it would be more, more large if we consider the same index computed in a single station inside that grid cell. So this is a list of extreme indexes that have been identified in the report, the IPCC report. And they are, it is a very complete list where indexes are divided in indexes based on temperature, on precipitation, drought, and so on. And all these indexes are then organized in categories. They have been identified in seven categories, seven categories that are heat and cold, wet and dry, wind, snow and ice, coastal, open ocean, and water. And for each of these categories, there are many indexes that have been identified. For example, now I will show you some practical examples of the indexes that have been computed in some words that we did in the past. And just to give you an idea of how they appear when we did, for example, a computation over a reference period, or when we did a change, so a plan of projection of these indexes. So this, for example, for the state categories of heat, is the heat 35, that is the number of days with maximum temperature greater than 35 degrees. This is another index that is the cooling degree days, CDD, that is the measure of the energy consumption for cooling in hot environment. This is a computed solving. This simple operation, a threshold has been fixed, like 22 degrees. And then we need to use web as input daily, minimum, maximum and minimum temperature, and then we can compute with these formulas the index. If we want the index computed for the whole year, then we need some, all the daily indexes, that's very cute. The same, for the same, but for the cold category, we have the heating degree days, that is similar to the CDD, but we have a threshold that is for a simple cold environment, and it is a threshold of 15.5 degrees. And even here, we have to use input daily, maximum and minimum temperature. For what concerns the drought category, an index that can be used, which is mostly used, even in the IPTP report is a standard precipitation index, that is, an index designed to quantify the precipitation that it is for multiple timescale. So it can reflect the impact of drought on the availability of different water resources. The calculation of P, for the calculation of P, we need a monthly precipitation 10 years, so this time is not daily, but monthly precipitation, and we need a time series that is at least of 30 years. In fact, then we have to define a time window, so that will be our time scale, and this time window can be from three months, typically to 24 months, and then for each set of timescales that we defined, we have to compute a running mean. Now, then, once we computed this running mean for each data set, each data set is fitted to the gamma distribution, and then the values from this probability distribution are then transformed into a normal distribution, so that the mean, peak, desired location and period is zero, and the standard distribution equal to one. In this way, we have an index that has these kinds of values, from minus three to plus three, and each of these values correspond to probability, and in particular, for example, positive spin values indicate precipitation that is greater than the median, so we have these values, and so the values around zero and plus three will represent categories from near-normal to extremely wet, whereas in the negative, we'll represent conditions that are less than the median locations, conditions from near-normal to extremely dry. According to the time scale that we choose, we can have an evaluation of drought that is different. For example, if we choose a time scale, so it is short, a short accumulation period from one month to three months, for example, we can speak about the meteorological drought. From three to 12 months, we can speak about agricultural droughts. In fact, in this case, the mean will be indicated for reduced spring flow and reservoir storage, and from 12 to 48, we will have a computation of ecological droughts. This is an example of a computation of these indexed in terms of drought sequences. We use the computation of six months, so we use the time window six months, and then we consider, from literature, that a drought starts in the month when this index of three-sixth was below minus one, and it ends when the same index becomes positive for at least two consecutive months. We count all the droughts in this way, and we have the drought frequency, so the number of droughts event here has been computed for decades. This is another example, so it's related to standard precipitation index. Just to show you how it is able to represent the droughts event. In this map, we have the localization of past drought events in Europe, and the recalculation of SPI for different years using the EOPS data. We can see that for each year, we have SPI well represented the distribution of the droughts event that has been recorded in the past. This is another example of a climate-experiment index, that is the SPI water index, that is an index meteorological in pace. To compute it, we need daily precipitation, daily maximum temperature, relative humidity, and maximum wind speed. So the more the SPI water index is high, the more probably we will have the conditions to increase the fire. This is the SPI water index, that is defined in terms of categories of fire danger, that are described in this table, so we can go from very low risk to maximum risk. This is an example of the fire water index computed for the projects for, and for the COPSO, for two different time slices for the historical future and the two time slices for the future media and past futures, and then an example of change in categories. So we can see that we have areas in which the probability for a future fire is well high in the future. This is just to show you that you can also use different indexes and decide to combine them and decide to analyze for a specific region. You can decide to go and look if there is a combination, a compound action between the different indexes. And for example, a copper cross would be a tool, a way to represent this combination, so it's beautiful. This is the representation of, for some indexes, that we already saw, the evolution of the climate projections, just to show you how is the change, how has been computed, the change for these indexes. So here we have heat waves, TX35, the growing degree days, that is another index very useful for agriculture, and then we have cooling degree days and heating degree days. For wet and dry indicators, we have P99, Q100, that is the indicator based on discharge, so it is the peak discharge at 100 at a return period of 100 years. We have also the number of dry days, and finally the DF, that is the drought frequency that we saw before. I don't know if it is visible, but all these maps contain some areas where we put dots, black dots. These are areas where the signal is not, the signal of change is not significant. So this is a concept that is very important when dealing with climate projection. We need to work always with an ensemble of models, and once we got an average ensemble mean of these models, we need also to provide the significance of the change, so the results that we obtain is robust or not. In this context, from Working Group 1, the robustness of change has been chosen as at least 66% of the models should have a signal to noise ratio greater than one, and at least 80% of them must agree on the sign of change. If these two conditions are satisfied, so the change can be defined as robust. The signal to noise ratio is estimated for each model and is the ratio between the change and the standard deviation of a non-overlapping 20-year period of the corresponding pre-industrial simulation. If we deal with regional simulations, we don't have the pre-industrial period, so our reference period for the standard deviation will be 1970-1999. This is another example of how to plot a model consensus, so we should put on our plot the significance, so we should compute the robustness of our signal, and then we can also show how the model among the ensemble spread according to the mean values, so this is a way to represent the model consensus through box points. Okay, and then another concept that is now being used, been using a lot, and that can be applied in order to overcome the dependency from emission pathways and single models is the concept of global warming levels. In this figure, this is an example of two different scenarios, the blue one and the orange one, and each single member of the ensemble belonging to this scenario. We can see that each member reached the degree of, for example, two global warming in different years, each of the models did this differently, so the idea is to average the, so they are among the different scenarios, and among the models the change computed in that very time slice that are typical for each of the models. So in the final stage we will have a temperature change, for example, at a precise degree of global warming, for example 42 or 1.5 degrees, and this will be an average an ensemble mean of all these members, because if we refer to global warming levels, we can compute the mean over all scenarios and over all models. Okay, this is an example of the calculation of how this global warming level for each model is calculated, but you don't need, you will never need to calculate this because there is a table calculated for all of the models, both all the same six and all the same five for all the scenarios containing the time slice corresponding to a single global warming level, so this is just an example to understand how they work. I mean you can find it, but we have it and we will use it in the lab. Natalia will explain, but it's very simple, it's very easy. But just to understand how they are calculated, here I put the change, each point is the change of this single time slice, that is a time slice of 20 years, with respect to the to the pre-industrial the 1850-1900. So this is the values of the change for each of the time slice that is running the slice of 20 years, and we can see that for these three models the first degree of global warming is reached differently in the timeline. So for this one we can, yes them, they reached in the time slice 1991-2010 in this other, for example this green CNRM like 10 years later and then the orange one other seven years later. And then and so on in the long the old time series, we can see when the each model reached the global warming level. So the idea then is that if we, you will see in the lab later that if you use, for example, this the blue model, this model, and you want to compute the change at the global warming level, for example of two, you will need to use this time slice 2075-2035 and to compute as your as your future period for a global warming level of two. But later Natalia, later now because I finished. Just one thing is that this is the this calculation is done for Siemit 6 and Siemit 5 models. And when you use the regional simulation that is driven by Siemit 5 now, you will find, you will use the the time slice of the Siemit of the driven model of the drivers, because there are no there are no computations for regional for each regional model. Okay, that's I'm done. Okay, so the idea now is that each of you are in a computer, so I expected you are already logged in your computer because you did it yesterday in the other lab maybe. And especially because the data that we're going to use now is available and I'm going to show you where. But you're not going to be able to use your data from your notebook. So it would be better if you use the computers here. So what are we going to do? We are going to approach a beginning of the computations that Francesca just told you. Yes, to connect, you have to use your username provided by when you are right here and your password. So that okay well I'm going to first explain how is everything computing and then we are going to start with the computation. So as I was saying Francesca showed a nice figures that they already compute the time slices and the changes computed according to the global warming level. So that's what we are going to try to do now. What we need now is to have data in here so climatic variables in this case we are going to use just because we don't have time to compute everything. Three different, I was going to show three different indexes that are going to use maximum and maximum temperature and precipitation both at daily scales and we are going to use the historical period and a future scenario. We are going to need the lower water level time slices, the table that Francesca has just mentioned, and we are going to use a TDO to compute these things. So if you want to have in-hand the presentation you can go to this link and you will have the pdf in case you want to copy and paste and it will be faster so I give you a minute if you want to access the presentation because I'm going to move forward and the link is going to. Are you able to get the presentation? I mean you don't really need that, I'm going to put it again when I finish but okay. So I move forward because we are going to run out of time so the first thing you need to do is to select for your computation. Okay so in this case and for this time we are going to use the period 1990-1999 and we need to choose a time size in the future to compute the difference right according to the global warming level scenario and we are going to compute the change but instead of using the traditional approach that Francesca showed in the first samples that you choose for a future mid-future we are going to choose a time size for a global warming level specific one. So the good thing is that CDO has already a package that contains the already some commands that calculate this set of indices so we are going to take advantage of the CDO package so if you go to the link here it will lead you to the presentation of this reference and you can see I go back here but there are some more that these are not everyone's here but most of the indices are already a process I mean already there's a script that runs for you so we are going to use tbo to have a simple calculation the link for the presentation. Okay so the ones that we're not able to copy if you want to take a picture of the the link and use it for reference so you can access the links that are in my presentation if you need. Okay so we continue from here as I was saying this is just a reference for the future we don't need to take this now because I'm going to give you the commands for the specific indices that we are going to use but if you want to come back home and process another different index you will have this table in which there's a description on how to use this command and you can compute another index because here we just choose a short set of things so the third thing you need to do if you are working in your in the the computers here is you need to open a terminal yeah so we are going to work in a linux environment and you need to do this first command and you don't need to follow me at this moment but I'm going to describe the steps so that you can have some time afterwards and we can guide you so the first one will open all the libraries that you need to use this is if you are working here then when you come back home you won't first you won't need this and before that it would not work and so if you want to access the data we prepare a folder that is called workshop linux and if you go to that directory you will have a data folder and another folder for users and if you ls the data you will find that inside we already provide for you and for this example data from different sources so you will have a folder with 75 data a folder with 76 data and then you will have the full expressions for example africa 44 and you will have south america 44 and europe 11 these are the codec simulations and these files the dot cfp are the tables that we okay sorry then this is for africa and south america it's the old codecs okay so these other files the dot cfp are the tables containing a tiny slide please for each model for each scenario and for each member of the cfp or the cfp file and we are going to take a look after so if you want to work in the computers you need to to switch to the folder users and you can create a directory for you with your name or whatever sorry okay i'm going to explain this test and then i'm going to pass by your computer so you can access and also francesca and going around sorry all that but we use only the name so you can make a folder for your name and to work here so you can access data and from the calculation here so if you want then you can create your your folder here the the next steps is if you take a look in how the data is stored also is then i'm going to show you where to access the data because if you are not here you will need to download if you don't already have it but the the files are divided every five years just because of the storage they come up with you so the first thing you will need to do is to do a merge time in tv o and you will need to provide this common tv o always in reverse in this case the merge time command you will need to provide the the path for the data so for example you're already here you will put all the path that i already mentioned in this part you will choose if you are going to work with the south america with afghan europe or septic primers and exists and then you will need to move to the folder that contains the data and you can of course choose one particular mission one particular model for example the story that is what we are going to focus on now the answer is so that it can find everything that matches and you will store this in an open file if you check with the ansida the the files that are provided there you will see that the precipitation is not immediately per day and we need to be immediately per day and the temperature in this case that's matched in the variable is not in degree Celsius but in kelvin so we need to convert this data so you can use these commands for example for converting the precipitation if you want to work with an index of precipitation because the studio is more see that this is multiplied by a constant i will multiply for this value to find the different millimeters per day the input side and the output side if you want to compare the temperature you will use the subspeed with this variable the input and the output and in case you want to use the mix 5 or to mix 6 data and you want to concentrate in a particular region you can use the command and set the box and provide the the limits of your box we then put side and an output side so once you have your data prepared and i've heard somebody the computation of the index humidity the maximum number of consecutive days with less than one millimeter of precipitation per day and per five years and so if you want to use your great detail the data up a merge file and then you convert the units you can select because we are going to use the reference videos 1980 1999 you can use this command to use the year and the years that you want to use as a reference the input and the output file and then you will use the common that is aside from you is in the in the list that i show you for eca cd and so you can use video web activity with the already for this video 1980 1999 and you store this information in an output file so if you do it in this way it's going to calculate the index for the entire time slice so i just call this output as total because it's not a value expressed in days per year so if you want to compare this variable just to be more like if you need to read the information if it is per year it will have to be like i see so you use the common that divides by a concept constant your input side and your output file and now you will have the cd for the reference video okay so for example another extreme index that that is also already in this commands less cd of commands is the rx one day that is represents the maximum one day participation amount per time period and you can use the same command if you already have your presentation prepared with the time size and the conversion of the and so you will use eca rx one day okay so after this you can divide by cd and you will have the rx one day for this reference time period in this case i'll show you that there's another index that is widely used and also can just show one example from one of the papers but it's not a pre-charged in CDO but you can easily compute it it's a number of days with t-max above 35 degrees so for this index you will also need to select the time size basically for this vector and you will use the common greater than a constant which is dgc and you will choose 35 because the vector is already in degrees tells you if you see the previous step so once you have this output that i just call it output time because it's not necessary to have this final store because it will contain as you say ones and zeros if it is true or false that is greater than a constant so after that you will do a year sum so this will sum all the variable containing all five of those i have all different tools i think and if it is one and zero she will compute the numbers of days that have a has been a lot of t-max sorry about t-max above 35 and in this case instead of t-max doing as we did in the previous instance you will need to do an amine to have the the mean for the reference period that is 99 so you can use this one meaning and then you will have the index is 35 for the region if you want you are using the model you are using and the reference time period okay so if we want to compute the change for this for any of the indices that you are using you will need to take a look at today this file at this table 75 warning levels and other name for example if you are using 75 and for example if we say okay i'm going to use i'm going to be dramatic i will use the scenario rcp 8.5 and the global warming level four so we need to know as francesca show when the model that i am using is going to reach that four global warming level right so in this table is already separate as this first the name of the model then the the r1 i1d1 is always the middle member the rcp 8.5 it will say the difference you will find that it's a rcp 4.5 or 2.6 and all of them and this number is the global warming level so we can see that there's a column that has one, two, three, four then one, two, three, four so this is a very long file and for this you will have this the initial year and the end year of this time cycle you want to have at your future so if i am working with this model that is half and yes i'm going to take a look at this line and i'm just going to use the this member r1 i1d5 i1 and i'm going to use the rcp 8.5 i'm going to check when it's going to be four degrees so my future period will be 2063 28 but if then i need to do an ensemble that is what we finally want to do i'm going to take a look at what happens with the other members of my ensemble so what happens with this i, b, c, l, dm5, l, r the same member at the same scenario the same global warming level and for this column it's a different time cycle so i have 2056, 2075 so for this individual member the period is going to be slightly different and you see that for the same model for the same rcp and for the same global warming level the different members are also different time cycles for the future so if you have many members inside for one particular model and then you want to do a multi-model ensemble you will need to check all of the time cycles that are always different right okay so this is another example for MPI that is the for the UH7 switch in 2070 to 2091 so what we need to do if you want to express the change for example in TDD or in TX35 is we need to have the previous calculation was for the reference period so we need to repeat this calculation for the future time slice in which you are going to change the same year the common same year instead of putting the 1980-1999 you will choose from the table for the one that you are using so finally you can subtract the two uh means for one for the future and one for the reference period and you will have your index change in this sense with CDO sub so finally what you can do here in the lab you can use the commands to compute the indices we of course we don't have time to produce very nice figures here so you can just check your output with NCVU and you will see for example in my case I am using a computation for a additional model ICTP the ICTP model XEM and this is driven by the HUB102 PS and I'm choosing as I was saying before the RCTP 8.5 for 4 degrees of global warming level and this is expressed in the three years so here I know if you can see from this screen but it goes between zero and 300 being for example 300 is in the north of South America and this is the sign for this particular index and it is done only for one model one member so if you want to compute an ensemble you will have to repeat this step for every model and for every member that you are going to use so one last thing and especially for the people that are listening in Zoom because obviously you are not going to be able to compute that ensemble here in the the couple of minutes that are remaining but if you want to go home and you want to continue with these calculations that as I was showing they seem rather simple for the computation of the indices you can access via the SSTF node and this is an example only that I usually use the French one you can select the the process that you want to analyze the mid-sixth, the mid-five, four days and there you can download all the data that you will see available now in the in your folders there in the computer so the idea now is that using the the commands that we provided here and all the ideas that Francesca also commented you can choose if you want to make it easier you can choose the ones that I already show but if you have a particular interest in others indices you can check the the table that as I was saying is bad but if you go to the links there are some more and you can say okay I want to compute for my mission is a web days per time period instead of one five day classification whatever you want and then you can compute this difference but instead of computing the change for this variable in terms of the far future or the near future you individualize yourself in this fixed period and you choose the period according to the global mid-level so this is all that we wanted to share and now we are going to pass by your computers to assist you if you will need some help for the computations or if you cannot access to the data or some other problems that might appear so if you have any questions about the first part or about this part that you want to share with the rest we can discuss it now there is any question maybe especially in the first part that it was more like examples and theories or if you want to start your exercise yes a question is about taking the contribute year around the morning so isn't it easier to take 10 years before and 10 years after 20 years so they got yes in principle is the the one year and that's the one year when you cross the Europe with each the the degree of global warming and then you take exactly that year will be the center year and then you construct the centralized of 20 years with that here in the center so 10 of time yeah that's okay uh no and 21 21 yes If you look at the day, it was at the time of the global warming, which was at 21. And we had the warning that we were going to come here, but yeah, you are considering an opportunity for the run. You should take the drivers, there's the one of the drivers. So yeah, consider the drivers, yeah. There's another question. No, to come. Okay. I thought that But the audience, because I'm their own right hander, I'm sure that we look like it's going to be interesting. I mean, it doesn't really matter that we still want to do a visit to the site, but we're very much in sliver. We need all our youths to be the best. We're going to have a good one. Okay, I'm going to leave this to Cal. I'm going to leave this to Cal. I'm going to leave this to Cal. I'm going to leave this to Cal. I'm going to leave this to Cal. I'm going to leave this to Cal. I'm going to leave this to Cal. We're going to get you in your house and we're going to take all of you by the number. We're going to get you in your house and we're going to take all of you by the number. We're going to get you in your house and we're going to take all of you by the number. We're going to get you in your house and we're going to take all of you by the number. We're going to get you in your house and we're going to take all of you by the number. We're going to get you in your house and we're going to take all of you by the number. We're going to get you in your house and we're going to take all of you by the number. I have to point out that here there is a mistake in the calculation. I mean, this is not to be considered because this is a new text that is calculated for the whole period. So we have the result, the maximum consecutive three days for the historical period. So it's not here we missed the calculation year by year and then the sum. Because you can calculate this year by year then summing together and then divide it to have the maximum number of days each year. So if you do just simply all the time slice, you will get the maximum number of consecutive three days for that time slice. So you don't need to divide it by 20. And this thing for the other thing is the one for this one. No, this one is not. I don't know what I'm going to say. I don't know what I'm going to say. I don't know what I'm going to say. You can see here. This is a supported protocol. Is this a new protocol? It is a new protocol. The current protocol is the same. Ah, it's a big problem. I don't know what to do. I don't know what to do. Okay, I'm not sure what to do. I don't know. I don't know what to do. I don't know. But we can modify the presentation so that the problem is in the