1 00:00:00,000 --> 00:00:12,817 *rC3 preroll music* 2 00:00:12,817 --> 00:00:19,670 Herald: It is with much pleasure that I can now introduce our next speaker, so 3 00:00:19,670 --> 00:00:26,500 it's just started raining outside, but this heavy rain is not at all probably the 4 00:00:26,500 --> 00:00:32,980 extreme weather effects that we will hear about right now. The weather, the talk 5 00:00:32,980 --> 00:00:40,540 that we are being presented next will deal with extreme weather effects and how they 6 00:00:40,540 --> 00:00:45,180 are linked with climate change and how we even know about that. Our speaker today 7 00:00:45,180 --> 00:00:51,800 is Fredi Otto. She's associate director of the Environmental Change Institute of the 8 00:00:51,800 --> 00:00:57,760 University of Oxford, and she's also the lead author of the upcoming IPCC 9 00:00:57,760 --> 00:01:04,640 assessment report, AR6. And without with no further ado, I give you the stage 10 00:01:04,640 --> 00:01:07,350 Fredi, please. 11 00:01:07,350 --> 00:01:12,280 Frederike Otto: OK, thank you. Yeah. Hi. It's stopped raining here in Oxford, just 12 00:01:12,280 --> 00:01:16,280 about, but it's definitely flooded, so that might actually be something to come 13 00:01:16,280 --> 00:01:24,670 back to and talk about with respect to climate change. So. Whenever we hear or 14 00:01:24,670 --> 00:01:31,740 whenever today an extreme weather event happens, we hear about hurricanes, 15 00:01:31,740 --> 00:01:39,780 wildfires, droughts, etc., the question that is immediately asked is, was this, 16 00:01:39,780 --> 00:01:47,830 what is the role of climate change? And to answer that, for quite a long time, 17 00:01:47,830 --> 00:01:55,580 scientists gave an answer that we cannot attribute individual weather events to 18 00:01:55,580 --> 00:02:07,720 climate change. But… Sorry, OK. But this… Because the first, the one answer that 19 00:02:07,720 --> 00:02:14,650 people were giving were that, well, you can't attribute individual weather events 20 00:02:14,650 --> 00:02:20,950 or they were saying in a world where climate change happens, of course, every 21 00:02:20,950 --> 00:02:25,120 extreme weather event is somewhat affected by climate change. And the latter is 22 00:02:25,120 --> 00:02:30,960 attributed too, but that does not obviously provide much information, 23 00:02:30,960 --> 00:02:34,940 because it doesn't say anything about whether the event was made more likely or 24 00:02:34,940 --> 00:02:42,209 less likely or what the role of climate change was. And the first answer that you 25 00:02:42,209 --> 00:02:49,359 can't attribute individual events is not true any longer. And this is... why that has 26 00:02:49,359 --> 00:02:55,129 changed and how that has changed. And what we can say is what the content of this 27 00:02:55,129 --> 00:03:03,700 talk will be. So ultimately, every weather event, extreme or not, is if you 28 00:03:03,700 --> 00:03:10,589 absolutely boil down to it is unique and they all have many different causes. So 29 00:03:10,589 --> 00:03:16,730 there is always the role of just the natural chaotic variability of the climate 30 00:03:16,730 --> 00:03:22,279 system and climate and weather system that plays a role. There's always a causal 31 00:03:22,279 --> 00:03:28,419 factor in where the event happens, whether it's over land, over a 32 00:03:28,419 --> 00:03:37,010 desert, over a city or a forest, but also man-made climate change can have an 33 00:03:37,010 --> 00:03:46,620 influence on the likelihood and intensity of extreme weather events to occur. And so 34 00:03:46,620 --> 00:03:52,279 what we can say now, and what we mean when we talk about attribution of extreme 35 00:03:52,279 --> 00:04:00,059 weather events to climate change is how the magnitude and likelihood of an event 36 00:04:00,059 --> 00:04:07,430 to occur has changed because of man-made climate change. And in order to do that, 37 00:04:07,430 --> 00:04:14,299 we first of all need to know, what is possible weather in the world we live in 38 00:04:14,299 --> 00:04:21,160 today? So say we have a flooding event in Oxford and the question is, was this 39 00:04:21,160 --> 00:04:27,290 climate change or not? So the first question is we need to find out what type 40 00:04:27,290 --> 00:04:34,240 or what kind of event is the heavy rainfall event that leads to the flooding. 41 00:04:34,240 --> 00:04:39,800 So is it a 1 in 10 year event? Is it a 1 in 100 year event? And in order to do 42 00:04:39,800 --> 00:04:44,782 that, you can't just look at the observed weather records because that will tell you 43 00:04:44,782 --> 00:04:50,340 what the actual weather that occurred is. But it doesn't tell you what the possible 44 00:04:50,340 --> 00:04:55,700 weather under the same current climate conditions are. And so we need to find out 45 00:04:55,700 --> 00:05:02,680 what is possible weather. And to do that, we use different climate models. So we 46 00:05:02,680 --> 00:05:07,630 simulate under the same climate conditions that we have today, possible rainfall 47 00:05:07,630 --> 00:05:14,750 events in December in Oxford. And we might find out that the event that we have 48 00:05:14,750 --> 00:05:23,210 observed today is a one in 10 year event. And so if you do this, look at all the 49 00:05:23,210 --> 00:05:27,520 possible weather events, you get a distribution of possible weather under 50 00:05:27,520 --> 00:05:33,311 certain conditions, which is shown in the schematic on the slide here in the red 51 00:05:33,311 --> 00:05:40,340 curve. And then you know that when it rains above, say, 30 millimeters a day in 52 00:05:40,340 --> 00:05:45,140 Oxford, then you have a real problem with flooding. So you define that this is your 53 00:05:45,140 --> 00:05:49,750 threshold from when you speak about an extreme event. And so you have a 54 00:05:49,750 --> 00:05:57,650 probability of this event to occur in the world we live in today. Of course, that 55 00:05:57,650 --> 00:06:02,700 does not tell you the role of climate change, because in order to know that, you 56 00:06:02,700 --> 00:06:07,950 would also you will also need to know what would the likelihood of this event to 57 00:06:07,950 --> 00:06:15,290 occur have been without man-made climate change, and so. But because we know very 58 00:06:15,290 --> 00:06:22,310 well how many greenhouse gases have been introduced into the atmosphere since the 59 00:06:22,310 --> 00:06:27,910 beginning of the industrial revolution, we can actually remove these additional 60 00:06:27,910 --> 00:06:34,330 greenhouse gases from the climate models atmospheres that we use and simulate a 61 00:06:34,330 --> 00:06:41,300 world that would have been exactly as it is today, but without the greenhouse gases 62 00:06:41,300 --> 00:06:46,570 from the burning of fossil fuels. And in that world, we can then also ask the 63 00:06:46,570 --> 00:06:54,440 question, what are possible heavy rainfall events in December in Oxford? And we might 64 00:06:54,440 --> 00:07:00,540 find that the event that we are interested in is in that world, not a one in 10 year 65 00:07:00,540 --> 00:07:06,760 event, but a one in 20 year event. And because everything else is held the same, 66 00:07:06,760 --> 00:07:11,660 we can then attribute the difference between these two likelihoods of 67 00:07:11,660 --> 00:07:19,070 occurrence of the extreme event in question to man-made climate change. And 68 00:07:19,070 --> 00:07:26,389 so with this fake example that I've just used, we would then say climate change has 69 00:07:26,389 --> 00:07:31,830 doubled the likelihood of the event to occur because one that was one in 20 year 70 00:07:31,830 --> 00:07:41,680 event is now one in 10 years. So that is basically the whole theoretical idea 71 00:07:41,680 --> 00:07:47,710 behind attributing extreme events and this method can be used. And so, for example, 72 00:07:47,710 --> 00:07:53,110 with our initiative that's called World Weather Attribution, we have looked this 73 00:07:53,110 --> 00:08:02,710 year at the extreme heat in Siberia, the beginning of this year that, among other 74 00:08:02,710 --> 00:08:08,311 things, led to temperatures above 38 degrees in the city of Verkhoyansk, but 75 00:08:08,311 --> 00:08:16,780 also let to permafrost thawing and large wildfires. And that event was made so much 76 00:08:16,780 --> 00:08:23,190 more likely because of climate change that it's almost would have been impossible 77 00:08:23,190 --> 00:08:29,540 without climate change. So when we did the experiments that the models it's a one in 78 00:08:29,540 --> 00:08:34,990 80 million year event in a world without climate change. And it's still a 79 00:08:34,990 --> 00:08:40,569 relatively extreme event in today's world, but it is possible. So this is the type of 80 00:08:40,569 --> 00:08:46,740 event where climate change really is a game changer. Another event that we have 81 00:08:46,740 --> 00:08:56,689 looked at is Hurricane Harvey that hit the Houston and Texas in 2017 and caused huge 82 00:08:56,689 --> 00:09:05,190 amounts of damage with the rainfall amounts it brought. And several attribution 83 00:09:05,190 --> 00:09:11,649 studies doing exactly what I've just described found that this type of, so this 84 00:09:11,649 --> 00:09:16,680 extreme rainfall associated with a hurricane like Harvey has been made three 85 00:09:16,680 --> 00:09:22,769 times more likely because of climate change. And colleagues of mine, Dave Frame 86 00:09:22,769 --> 00:09:29,760 and his team, have then used these studies to figure out how much of the economic 87 00:09:29,760 --> 00:09:36,079 costs this hurricane can be attributed to climate change, and found that of the 90 88 00:09:36,079 --> 00:09:43,540 billion US dollars that were associated, that were associated with the flood damage 89 00:09:43,540 --> 00:09:51,089 from Harvey, 67 billion can be attributed to climate change, which is in particular 90 00:09:51,089 --> 00:09:58,720 interesting when you compare that to the state of the art economic cost estimations 91 00:09:58,720 --> 00:10:05,899 of climate change in general, which had estimated only 20 billion US dollars for 92 00:10:05,899 --> 00:10:12,670 2017 in the US from climate change. And of course, not every year is an event like 93 00:10:12,670 --> 00:10:19,600 Harvey, but it shows that when you look at the impact of climate change in a more 94 00:10:19,600 --> 00:10:24,490 bottom up approach, so looking at the extreme events, which are how climate 95 00:10:24,490 --> 00:10:30,420 change manifests and affect people, you get very different numbers, as if you just 96 00:10:30,420 --> 00:10:39,420 look at large scale changes in temperature and precipitation. But of course, not 97 00:10:39,420 --> 00:10:45,850 every extreme event that occurs today has been made worse because of climate change. 98 00:10:45,850 --> 00:10:51,560 So this is an example of a drought in southeast Brazil that happened in 2014, 99 00:10:51,560 --> 00:11:00,089 2015, where we found that Climate change did not change the likelihood of this 100 00:11:00,089 --> 00:11:07,850 drought to occur, so it was a one in 10 year event in 2014, 2015, and also without 101 00:11:07,850 --> 00:11:14,139 climate change, it has a very similar likelihood of occurrence. However, what we 102 00:11:14,139 --> 00:11:20,329 did find when we looked at, OK, what else has changed? Why has this drought that has 103 00:11:20,329 --> 00:11:27,079 occurred in a very similar way earlier in the 2000s and also in the 1970s with much 104 00:11:27,079 --> 00:11:33,110 less impacts. We looked at other factors and found that the population has 105 00:11:33,110 --> 00:11:38,869 increased a lot over the last or over the beginning of the 21st century, but in 106 00:11:38,869 --> 00:11:45,529 particular, the water consumption in in the area and the water usage has increased 107 00:11:45,529 --> 00:11:54,269 almost exponentially. And that explains why the impacts were so large. So this is 108 00:11:54,269 --> 00:12:00,679 what I've just said is sort of basically the the very basic idea and and how in 109 00:12:00,679 --> 00:12:09,779 theory these studies work and how and some results that we find. In practice, it is 110 00:12:09,779 --> 00:12:14,600 usually not quite as straightforward, because while the idea is still the 111 00:12:14,600 --> 00:12:21,649 same, we need to use climate models and statistical models for observational data 112 00:12:21,649 --> 00:12:25,990 to simulate possible weather in the world we live in and possible weather in the 113 00:12:25,990 --> 00:12:31,230 world that might have been. That is, in theory, straight forward, in practice, 114 00:12:31,230 --> 00:12:37,079 it's often relatively difficult, and what you see here is how the results of these 115 00:12:37,079 --> 00:12:42,980 studies look when you don't use schematic and if you're not a hydrologist, this 116 00:12:42,980 --> 00:12:49,929 might be a bit of an unfriendly plot. But it's it's basically the same as the 117 00:12:49,929 --> 00:12:57,470 schematic that I've showed at the beginning, but just plotted in a way that 118 00:12:57,470 --> 00:13:03,218 you can see the tails of the distribution particularly well, so where the extreme 119 00:13:03,218 --> 00:13:09,279 events are. So on the X-axis, we have the return time of the event in years on a 120 00:13:09,279 --> 00:13:18,029 logarithmic scale and on the Y-axis, you see the magnitude of the event and that 121 00:13:18,029 --> 00:13:27,589 defines what our extreme event is. And this is actually a real example from heavy 122 00:13:27,589 --> 00:13:35,699 rainfall in the south of the U.K. And you can see here in red, each of these red 123 00:13:35,699 --> 00:13:43,199 dots that that you see on the red curve is a simulation of one possible rainfall 124 00:13:43,199 --> 00:13:49,399 event in the South of the U.K. in the year 2015 in the world we live in today with 125 00:13:49,399 --> 00:13:57,279 climate change and the dashed line indicates the threshold that led to to 126 00:13:57,279 --> 00:14:04,739 flooding in in that year. And on the X-axis, when you go down from the dashed 127 00:14:04,739 --> 00:14:10,079 line, you can then see that this is roughly a one in 20 year event in the 128 00:14:10,079 --> 00:14:15,519 world we live in today. And all the blue dots on the blue curve are simulations of 129 00:14:15,519 --> 00:14:22,530 possible heavy rainfall in the South of the U.K. in 2015, in a world without man- 130 00:14:22,530 --> 00:14:28,290 made climate change. And you can see that these two curves are different and 131 00:14:28,290 --> 00:14:33,469 significantly different, but they are still relatively close together. And so 132 00:14:33,469 --> 00:14:38,720 the event in the world without climate change would have been a bit less likely, 133 00:14:38,720 --> 00:14:45,629 so we have roughly a 40 percent increase in the likelihood. But still other factors 134 00:14:45,629 --> 00:14:52,300 like, yeah, just the chaotic variability of the weather and also, of course, than 135 00:14:52,300 --> 00:14:57,660 other factors on the ground where houses build in floodplains and so on play an 136 00:14:57,660 --> 00:15:07,620 important role. So this is the actual attribution step. So when we find 137 00:15:07,620 --> 00:15:13,040 out what the role of climate change is, but of course, in order to do that, there 138 00:15:13,040 --> 00:15:20,660 are a few steps before that are crucially important and absolutely determine the 139 00:15:20,660 --> 00:15:27,720 outcome. And the first step, the first thing to find out is what has actually 140 00:15:27,720 --> 00:15:32,089 happened, because usually when we read about extreme weather events or when we 141 00:15:32,089 --> 00:15:39,129 hear about extreme weather events, you see pictures in newspapers of flooded parts of 142 00:15:39,129 --> 00:15:47,170 the world. And so you don't usually have observed weather recordings reported in 143 00:15:47,170 --> 00:15:53,360 the media. And the same actually is true for us. So when we are, so we work a 144 00:15:53,360 --> 00:16:00,660 lot with the Red Cross and they ask us: OK, we have this large flooding event, can 145 00:16:00,660 --> 00:16:05,199 you do an attribution study? Can you tell us what the role of climate change is? 146 00:16:05,199 --> 00:16:09,910 Then we also just know: OK, there is flooding. And so the first step is we need 147 00:16:09,910 --> 00:16:15,390 to find out what is the weather event that actually caused that flooding. And that is 148 00:16:15,390 --> 00:16:21,920 not always that straightforward. And this is what you see here on this map, on this 149 00:16:21,920 --> 00:16:29,999 slide is a relatively stark example, but not an untypical. So it's of an extreme 150 00:16:29,999 --> 00:16:35,439 rainfall event on the 10th of November 2018 in Kenya. And on the left hand side 151 00:16:35,439 --> 00:16:41,079 is one data product of observational data, of observational rainfall data that is 152 00:16:41,079 --> 00:16:49,649 available and on the right hand side is another showing the same event. And the 153 00:16:49,649 --> 00:16:57,960 scale which I failed to to say on the slide in millimeters per day. And so on 154 00:16:57,960 --> 00:17:03,790 the left hand side, you have extreme rainfall of above 50 millimeters per day, 155 00:17:03,790 --> 00:17:10,780 which is considering that, for example, in in my home town of Kiel in Schleswig- 156 00:17:10,780 --> 00:17:17,940 Holstein, there is about 700 millimeters of rainfall per year. You can see that 50 157 00:17:17,940 --> 00:17:24,050 millimeters in a single day is very heavy rainfall, whereas in the other data 158 00:17:24,050 --> 00:17:32,420 product, you don't see as much rain. You still see large rain, but it's not in 159 00:17:32,420 --> 00:17:39,860 the same magnitude, and it's also not exactly in the same place. And so given 160 00:17:39,860 --> 00:17:44,990 that most countries in the world do not have an open data policy, so you can't 161 00:17:44,990 --> 00:17:50,890 actually get access to the observed station data, but you have to use 162 00:17:50,890 --> 00:17:56,490 available, publicly available products like the two have shown here, you have to 163 00:17:56,490 --> 00:18:03,840 know and you have to work with experts in the region to actually know who hopefully 164 00:18:03,840 --> 00:18:08,640 has access to the data to actually find out what has happened in the first place. 165 00:18:08,640 --> 00:18:15,040 But of course, if you don't know that or there is not always a perfect answer, then 166 00:18:15,040 --> 00:18:21,410 if you don't know what event that is. It's very difficult to do an attribution study. 167 00:18:21,410 --> 00:18:27,360 Assuming though you have found a data product that you trust, the next question 168 00:18:27,360 --> 00:18:34,220 then is what is actually the right threshold to use for the event? So if you 169 00:18:34,220 --> 00:18:39,290 have flooding that was pretty obviously caused by one day extreme rainfall event, 170 00:18:39,290 --> 00:18:43,870 then that would be your definition of the event. But it could also be that the 171 00:18:43,870 --> 00:18:50,920 flooding has been caused by a very soggy, rainy season. So actually, the really the 172 00:18:50,920 --> 00:18:57,850 real event you would want to look at is over a much longer time scale or if the 173 00:18:57,850 --> 00:19:02,360 flooding occurred mainly because of some water management in the rivers and has 174 00:19:02,360 --> 00:19:08,050 actually flooded further upstream, your spatial definition of the event would be 175 00:19:08,050 --> 00:19:13,770 very different. And so and what you see here on this plot is an example of a heat 176 00:19:13,770 --> 00:19:22,430 wave in Europe in 2019. And there, what usually makes the headlines is the maximum 177 00:19:22,430 --> 00:19:27,580 daily temperature. So if records are broken, so you could use that as a 178 00:19:27,580 --> 00:19:32,800 definition of the event that you're interested in. But of course, what really 179 00:19:32,800 --> 00:19:38,861 causes the losses and damages from extreme events is not necessarily the one day 180 00:19:38,861 --> 00:19:43,770 maximum temperature, but it is when heat waves last for longer, and especially when 181 00:19:43,770 --> 00:19:49,190 the night temperatures are also high and not just the daytime temperatures. So you 182 00:19:49,190 --> 00:19:54,770 might want to look at an event over five day period instead of just the maximum 183 00:19:54,770 --> 00:20:02,790 daily temperatures. Or, and this is sort of why I have shown the pressure plot on 184 00:20:02,790 --> 00:20:06,430 the right hand side, which is really just an illustration, it's not terribly 185 00:20:06,430 --> 00:20:11,430 important what's on there. But there are, of course, different weather systems that 186 00:20:11,430 --> 00:20:18,120 can cause heat waves, especially in the area here in the south of France. It could 187 00:20:18,120 --> 00:20:26,580 be a relatively short lived high pressure system bringing hot air from the 188 00:20:26,580 --> 00:20:32,470 Mediterranean. Or it could be something that is caused from a long time stationary 189 00:20:32,470 --> 00:20:38,730 high pressure system over all of Europe. If you want to take that into account, 190 00:20:38,730 --> 00:20:44,800 obviously also your event is different. And there is no right or wrong way to 191 00:20:44,800 --> 00:20:50,500 define the event because there are legitimate interests in the maximum 192 00:20:50,500 --> 00:20:57,920 daily temperatures, legitimate interest in just a specific type of pressure system 193 00:20:57,920 --> 00:21:04,600 and interest in what actually causes more excess mortality on people, what would be 194 00:21:04,600 --> 00:21:11,260 the three day or longer type of heat waves. But whichever definition you 195 00:21:11,260 --> 00:21:19,270 choose, it will determine the outcome of the study. And here are some typical 196 00:21:19,270 --> 00:21:28,060 results of attribution studies when you look at them in a slightly more scientific 197 00:21:28,060 --> 00:21:33,870 way and slightly less just the headline way as the ones that I've shown earlier. 198 00:21:33,870 --> 00:21:39,620 Because, of course, what also is important is not only how you define the event, 199 00:21:39,620 --> 00:21:44,760 depending on the impacts and depending on what you're interested in. The extreme 200 00:21:44,760 --> 00:21:48,950 event and what observational data you have available. But of course, there's also 201 00:21:48,950 --> 00:21:53,950 then the question of what models, what climate models do we have available? And 202 00:21:53,950 --> 00:21:58,560 there's always some tradeoff between what exactly caused the event and what we can 203 00:21:58,560 --> 00:22:04,600 meaningfully simulate in a climate model. And then all climate models are good for 204 00:22:04,600 --> 00:22:10,740 something and bad for other things. So there always need to be a model evaluation 205 00:22:10,740 --> 00:22:15,130 stage. So where you test if the models that you have available are actually able 206 00:22:15,130 --> 00:22:20,690 to simulate in a reliable way the event that you're interested in. But even if you 207 00:22:20,690 --> 00:22:26,980 have done all this, it can sometimes be that the models and the observations that 208 00:22:26,980 --> 00:22:34,191 you have show very different things. And so the heat wave in Germany in 2019, which 209 00:22:34,191 --> 00:22:39,520 was also on the slide before, is an example of that. When we 210 00:22:39,520 --> 00:22:48,310 look at the long term observations of extreme, of high temperatures and see how 211 00:22:48,310 --> 00:22:55,190 they have changed over time, we find that, because of the change in climate, we have 212 00:22:55,190 --> 00:23:02,680 observed, the likelihood of this type of heat wave has increased more, yeah, about 213 00:23:02,680 --> 00:23:10,410 300 times. So you see this in the black bar, the black bar in the 214 00:23:10,410 --> 00:23:14,900 middle of the blue bar, on the left hand side, at the very top where it says DWD 215 00:23:14,900 --> 00:23:19,770 obs, that's the Deutscher Wetterdienst observations and we see that where this 216 00:23:19,770 --> 00:23:25,240 black bar is about, again, a logarithmic scale, about 300 hundred times more 217 00:23:25,240 --> 00:23:30,800 likely. But of course, because we have only 100 years worth of 218 00:23:30,800 --> 00:23:38,510 observations and summer temperatures are extremely variable, there is a large 219 00:23:38,510 --> 00:23:43,820 uncertainty around this change. And so we cannot, from the observations alone, we 220 00:23:43,820 --> 00:23:50,300 cannot exclude 100.000 times change in the likelihood of this heat wave. But 221 00:23:50,300 --> 00:23:55,910 similarly, also not a 20 times heat wave. But what the main point is, that in all 222 00:23:55,910 --> 00:24:01,760 the climate models and all the red bars that you see on there are the same 223 00:24:01,760 --> 00:24:08,330 results, but for climate models where we have compared today's likelihood of the 224 00:24:08,330 --> 00:24:13,200 event to occur with the likelihood in the world without climate change, and you see 225 00:24:13,200 --> 00:24:18,180 that the change is much lower. And of course, climate change is not the only 226 00:24:18,180 --> 00:24:23,940 thing that has changed and that has affected observed temperatures. But other 227 00:24:23,940 --> 00:24:30,820 factors like land use change and things like that are much smaller in the size 228 00:24:30,820 --> 00:24:36,260 than the climate signal. So they cannot explain this discrepancy. So this means 229 00:24:36,260 --> 00:24:42,860 that the climate models we have available for this type of study have obviously a 230 00:24:42,860 --> 00:24:51,390 problem with the extreme temperatures in a small scale. And there are effects that we 231 00:24:51,390 --> 00:24:56,320 don't yet understand. And so we can't say: OK, this heat wave was made 10 times more 232 00:24:56,320 --> 00:25:03,530 likely. But we can only say, that with our current knowledge and understanding, we 233 00:25:03,530 --> 00:25:07,280 can say that climate change was an absolute game changer for this type of 234 00:25:07,280 --> 00:25:14,350 heat wave, but we can't really quantify it. On the right hand side is a much nicer 235 00:25:14,350 --> 00:25:21,110 result on the top one, which is for extreme rainfall, in Texas 2019 and nicer 236 00:25:21,110 --> 00:25:27,660 result I mean now for a scientist and in a scientific way. So we have in blue 237 00:25:27,660 --> 00:25:35,530 two different types of observations from the heavy rainfall event, and they both 238 00:25:35,530 --> 00:25:43,650 show pretty much exactly the same result. And also the two climate models that we 239 00:25:43,650 --> 00:25:51,500 had available that passed the model evaluation tests show an increase in the 240 00:25:51,500 --> 00:25:56,640 likelihood of this event to occur. That is very similar to that in the observations 241 00:25:56,640 --> 00:26:04,190 in terms of order of magnitude. And so in that case, we can just synthesize the 242 00:26:04,190 --> 00:26:09,760 results and give an overarching answer, which is that the likelihood of this event 243 00:26:09,760 --> 00:26:18,250 to occur has about doubled because of man- made climate change. And the last example 244 00:26:18,250 --> 00:26:27,080 that I, that is here is for drought in Somalia in 2010, where not only the 245 00:26:27,080 --> 00:26:32,850 observations are extremely uncertain. So from the observations, you could say we 246 00:26:32,850 --> 00:26:37,540 could have both an increase in likelihood or a decrease in likelihood by a factor of 247 00:26:37,540 --> 00:26:45,330 10. But also the climate models show a very, very mixed picture where you can't 248 00:26:45,330 --> 00:26:51,720 even see a sign that that is conclusive. So in that case, you can say, we can 249 00:26:51,720 --> 00:26:59,740 exclude that climate change made this event more or less than 10 times, more 250 00:26:59,740 --> 00:27:05,720 than 10 times or less than 10 times more likely. But we can't say anything more. So 251 00:27:05,720 --> 00:27:09,560 we can exclude that it's a complete game- changer like we have for heat waves, for 252 00:27:09,560 --> 00:27:14,030 example. But that's about the only thing that you can say for a result 253 00:27:14,030 --> 00:27:24,050 like this. So this was sort of the most detailed scientific stuff that I 254 00:27:24,050 --> 00:27:29,780 would like to show, because I think it's important to get some background behind 255 00:27:29,780 --> 00:27:35,310 the headline results that would just be climate change doubled the likelihood 256 00:27:35,310 --> 00:27:42,840 of this event. So there are always four possible outcomes of an attribution study 257 00:27:42,840 --> 00:27:51,780 a priori. And that is because climate change affects extreme weather in 258 00:27:51,780 --> 00:27:58,381 two ways basically. One way is what we would call the thermodynamic way, which 259 00:27:58,381 --> 00:28:02,170 means that because we have more greenhouse gases in the atmosphere, the atmosphere 260 00:28:02,170 --> 00:28:07,160 overall gets warmer. So you have, on average, an increase in the likelihood of 261 00:28:07,160 --> 00:28:12,380 heat waves decrease in the likelihood of cold waves. A warmer atmosphere can also 262 00:28:12,380 --> 00:28:17,550 hold more water vapor that needs to get out of the atmosphere as rainfall. 263 00:28:17,550 --> 00:28:24,270 So on average, from the warming alone, we would also have more extreme rainfall. But 264 00:28:24,270 --> 00:28:28,240 then there's the second effect, which I call the dynamic effect, and that is 265 00:28:28,240 --> 00:28:33,500 because we've changed the composition of the atmosphere, that affects the atmospheric 266 00:28:33,500 --> 00:28:38,780 circulation. So where weather systems develop, how they develop and and how they 267 00:28:38,780 --> 00:28:44,230 move. And this effect can either be in the same direction as the warming effect. So it 268 00:28:44,230 --> 00:28:51,990 can be that we expect more extreme rainfall, but we also get more low pressure systems 269 00:28:51,990 --> 00:28:57,350 bring rain to get even more extreme rainfall. But these two effects can also 270 00:28:57,350 --> 00:29:03,380 counteract each other. And so you can expect more rainfall on 271 00:29:03,380 --> 00:29:07,860 average. But if you don't get the weather systems that bring rain, you either have 272 00:29:07,860 --> 00:29:13,580 no change in likelihood and intensity or, if the dynamics win, you have actually 273 00:29:13,580 --> 00:29:19,450 decrease in the likelihood of extreme rainfall in a particular season or region. 274 00:29:19,450 --> 00:29:24,550 And so this is why a priori, that can always be four outcomes: It can be that 275 00:29:24,550 --> 00:29:29,010 the event was made more likely. It can be that it was made less likely. It can be 276 00:29:29,010 --> 00:29:34,330 there's no change. Or it can be that with our current understanding and tools, we 277 00:29:34,330 --> 00:29:46,760 can't actually answer the question. And so this has been possible to do now for 278 00:29:46,760 --> 00:29:52,860 about a decade, but only in the last five years really have many, many people or 279 00:29:52,860 --> 00:29:57,380 many scientists started to do these studies. And so there is actually a 280 00:29:57,380 --> 00:30:05,370 relatively large, there are lots of attribution studies on different 281 00:30:05,370 --> 00:30:12,150 kinds of extreme events. And what you can see on this map here is what the news and 282 00:30:12,150 --> 00:30:17,510 energy outlet CarbonBrief has put all these studies together. And you can see in 283 00:30:17,510 --> 00:30:22,401 red where climate change played an important role, and blue where climate 284 00:30:22,401 --> 00:30:33,930 change did not play a role. And in gray, that was an inconclusive result. This is 285 00:30:33,930 --> 00:30:39,750 very important, though, that this is not representative of the extreme events that 286 00:30:39,750 --> 00:30:46,580 have happened. This is just represents the studies that have been done by scientists 287 00:30:46,580 --> 00:30:59,559 and they are, of course biased towards where scientists live 288 00:30:59,559 --> 00:31:05,280 and also towards extreme events that are relatively easy to simulate with climate 289 00:31:05,280 --> 00:31:12,780 models. So there are lots of heat waves in Europe, Australia and North America 290 00:31:12,780 --> 00:31:21,309 because that is where people live. And on this next map, I have tried to 291 00:31:21,309 --> 00:31:26,390 show the discrepancy between the extreme events that have happened and those for 292 00:31:26,390 --> 00:31:33,850 which we actually do know the role of climate change. So here in red are deaths 293 00:31:33,850 --> 00:31:39,570 associated with extreme events since 2003. So since the first event attribution 294 00:31:39,570 --> 00:31:49,340 study. And it's death from heat waves, storms, heavy rainfall events and droughts 295 00:31:49,340 --> 00:31:54,940 primarily in different parts of the world, the bubble is always on the capital of the 296 00:31:54,940 --> 00:31:59,820 country. And the larger the bubble, the more deaths due to extreme events in those 297 00:31:59,820 --> 00:32:07,600 years. And in black overlaying that are those deaths for which we know the role of 298 00:32:07,600 --> 00:32:11,300 climate change. So that doesn't mean that the deaths are attributed to 299 00:32:11,300 --> 00:32:17,250 climate change, but it means that there we do know whether or not to what 300 00:32:17,250 --> 00:32:23,381 extent climate change played a role. And you can see that most of the European 301 00:32:23,381 --> 00:32:28,780 countries, the black circle is almost as large as the red one. So for most of the 302 00:32:28,780 --> 00:32:32,440 extremes or most of the deaths associated with extreme events, we do know the role 303 00:32:32,440 --> 00:32:39,740 of climate change. But for many other parts of the world, 304 00:32:39,740 --> 00:32:44,470 there are no or very small black circles. So for most of the events and the deaths 305 00:32:44,470 --> 00:32:49,090 associated with them, we don't know what the role of climate change is. And I've 306 00:32:49,090 --> 00:32:52,951 used death here not because I'm particularly morbid, but because it's 307 00:32:52,951 --> 00:32:58,640 an indicator of the impacts of extreme weather that is relatively good 308 00:32:58,640 --> 00:33:05,990 comparable between countries. So this means that with event attribution methods 309 00:33:05,990 --> 00:33:12,130 that we have developed over the last decade, we now have the tools available to 310 00:33:12,130 --> 00:33:19,950 do, to provide an inventory of the impacts of climate change on our livelihoods. But 311 00:33:19,950 --> 00:33:25,680 we are very far from having such an inventory at the moment because most of 312 00:33:25,680 --> 00:33:30,000 the events that have happened, we actually don't know what the role of climate change 313 00:33:30,000 --> 00:33:37,960 is. And so we don't know in detail on country scale and on the scale where 314 00:33:37,960 --> 00:33:46,710 people live and make decisions, what the role of climate change is today. There's 315 00:33:46,710 --> 00:33:56,530 another slightly related issue with that is, that the extreme events that I've used 316 00:33:56,530 --> 00:34:01,510 to create the map are shown before with the death of climate change, with the 317 00:34:01,510 --> 00:34:07,670 death of extreme weather events. They are from a database called EM-DAT, which is a 318 00:34:07,670 --> 00:34:16,290 publicly available database where losses and damages associated with disasters 319 00:34:16,290 --> 00:34:20,310 technological disasters, but also disasters associated with weather are 320 00:34:20,310 --> 00:34:31,290 recorded. But, of course, they only can record losses and damages if these losses 321 00:34:31,290 --> 00:34:36,590 and damages are recorded in the first place. And so what you see on this map is 322 00:34:36,590 --> 00:34:44,640 in grey and then overlayed with different with different circles are heat waves that 323 00:34:44,640 --> 00:34:50,580 have occurred, they have occurred between 1986 and 2015 on this map. But you could 324 00:34:50,580 --> 00:34:56,330 draw a map from 1900 to today, and it would look very similar. And that shows 325 00:34:56,330 --> 00:35:03,510 lots and lots of heat waves reported in Europe and in the US, India, but there are 326 00:35:03,510 --> 00:35:09,170 no heat waves reported in most of sub- Saharan Africa. However, when you look at 327 00:35:09,170 --> 00:35:17,420 observations, and also we see that extreme heat has increased quite dramatically in 328 00:35:17,420 --> 00:35:24,320 most parts of the world and a particular hotspot is sub-Saharan Africa. So, we know 329 00:35:24,320 --> 00:35:29,400 from when we look at the weather that heat waves are happening, but it's not 330 00:35:29,400 --> 00:35:35,200 registered and it's not recorded. So we have no idea how many people are actually 331 00:35:35,200 --> 00:35:41,060 affected by these heat waves. And so we then, of course, don't do attribution 332 00:35:41,060 --> 00:35:46,080 studies and don't find out what the role of climate change in these heat waves is. 333 00:35:46,080 --> 00:35:52,750 So in order to really understand the whole picture, we would also need to start 334 00:35:52,750 --> 00:36:01,820 recording these type of events in other parts of the world. And so my very last 335 00:36:01,820 --> 00:36:08,700 point, before, I hope that you have questions for me, is: Of course, 336 00:36:08,700 --> 00:36:14,270 everything I've said so far was talking about the hazards, so talking about the 337 00:36:14,270 --> 00:36:19,760 weather event and how climate change affects the hazard. But of course that is 338 00:36:19,760 --> 00:36:25,790 not the same or translates immediately into losses and damages, because whether 339 00:36:25,790 --> 00:36:32,119 or not a weather event actually has any impact at all is completely driven by 340 00:36:32,119 --> 00:36:38,440 exposure and vulnerability. So who and what is in harm's way. And I've already 341 00:36:38,440 --> 00:36:46,160 shown, I've already mentioned the example early on with the drought in Brazil, where 342 00:36:46,160 --> 00:36:52,110 the huge losses and damages were to a large degree attributable to the increase 343 00:36:52,110 --> 00:37:01,630 in water consumption. And thus, therefore, in order to really find out how 344 00:37:01,630 --> 00:37:07,950 climate change is affecting us today, we not only need to define the extreme events 345 00:37:07,950 --> 00:37:14,930 so that it connects to the impacts, but also look into vulnerability and exposure: 346 00:37:14,930 --> 00:37:21,520 What is changing, what's there and what are the important factors. But we can 347 00:37:21,520 --> 00:37:28,330 do that. And so we have really made a lot of progress in understanding of how 348 00:37:28,330 --> 00:37:35,010 climate change not only affects global mean temperature, which we have known for 349 00:37:35,010 --> 00:37:41,830 centuries, and how it affects large scale changes in temperature and 350 00:37:41,830 --> 00:37:46,869 precipitation, which we have also known for a very long time. But we now have 351 00:37:46,869 --> 00:37:52,110 actually all the puzzle pieces together to really understand what climate change 352 00:37:52,110 --> 00:37:58,740 means on the scale where people live and where decisions are made. We just need to 353 00:37:58,740 --> 00:38:07,190 put them together. And one lens or one way of where they are currently put together 354 00:38:07,190 --> 00:38:16,619 is, for example, in courts. And so because it's obviously people who experience 355 00:38:16,619 --> 00:38:23,010 losses and damages from climate change. And so one way to address that is going 356 00:38:23,010 --> 00:38:28,910 through national governments, local governments, hoping for adaptation 357 00:38:28,910 --> 00:38:35,070 measures to be put in place. But if that's not forthcoming quickly enough, there is 358 00:38:35,070 --> 00:38:40,510 the option to sue. And so this is one example which is currently happening in 359 00:38:40,510 --> 00:38:53,820 Germany where a peruvian farmer is suing RWE to basically pay their share of a 360 00:38:53,820 --> 00:39:01,100 adaptation because of largely increased flood risk from glacier melt in the area. 361 00:39:01,100 --> 00:39:09,360 And they want RWE to pay from their contribution to climate change, where 362 00:39:09,360 --> 00:39:16,260 their emissions and then have some funding for the adaptation measures from them. And 363 00:39:16,260 --> 00:39:21,970 that is one example of where these kind of attribution studies can be used in a very 364 00:39:21,970 --> 00:39:29,220 direct way to hopefully change something in the real world. And with 365 00:39:29,220 --> 00:39:36,350 this, I would like to end and yeah, leave you with some references, and hope you 366 00:39:36,350 --> 00:39:39,010 have some questions for me. 367 00:40:01,132 --> 00:40:14,912 Herald: Sind wir durch? So, ja. Herzlichen Dank für den Vortrag. Ich hab, bevor wir 368 00:40:14,912 --> 00:40:20,782 zum Q&A kommen muss ich einmal mich im Namen der Produktion bei den Zuschauern 369 00:40:20,782 --> 00:40:25,382 entschuldigen, ich glaube ihr hattet etwas Produktionssound auf den Ohren, das sollte 370 00:40:25,382 --> 00:40:34,201 natürlich nicht so sein. Gut, wir haben jetzt keine Fragen aus dem Chat bisher. 371 00:40:42,141 --> 00:40:50,941 Aber vielleicht eine Frage von mir, das letzte Beispiel war ja ein Fall 372 00:40:50,941 --> 00:41:00,661 einer Klage über Ländergrenzen hinaus quasi, ist das ein Ansatz, den man, den 373 00:41:00,661 --> 00:41:06,560 wir in Zukunft öfter sehen würden, das heißt, dass über Ländergrenzen hinweg 374 00:41:06,560 --> 00:41:13,540 Menschen oder Organisationen sich gegenseitig versuchen quasi über den 375 00:41:13,540 --> 00:41:20,490 Klageweg auf den richtigen Weg zu bringen. FO: Also es ist tatsächlich ein, eine 376 00:41:20,490 --> 00:41:31,940 Ausnahme, dass das im Fall RWE und Lliuya funktioniert, denn das deutsche Recht 377 00:41:31,940 --> 00:41:36,330 sieht vor, dass Firmen, die in Deutschland ansässig sind auch verschieden 378 00:41:36,330 --> 00:41:39,340 verantworlich sind, die nicht in Deutschland stattfinden. 379 00:41:39,340 --> 00:41:44,750 H: So sorry to interrupt. I just realized that we are still in English talk. Sorry 380 00:41:44,750 --> 00:41:48,810 for that. FO: OK. No worries. So your question was 381 00:41:48,810 --> 00:41:56,119 if we're going to see more international court cases where across 382 00:41:56,119 --> 00:42:03,040 countries, across nation states we have climate litigation. And this type of 383 00:42:03,040 --> 00:42:07,369 litigation that I've just shown as the example is in so far an 384 00:42:07,369 --> 00:42:13,869 exception, as in German law, a company is also responsible for the damages caused 385 00:42:13,869 --> 00:42:20,060 outside of Germany. Which is not the case, for example, for companies in the US 386 00:42:20,060 --> 00:42:30,150 or so. So, and this is why Lliuya sued RWE and not, for example, ExxonMobil. But 387 00:42:30,150 --> 00:42:40,780 these type of cases, where this Lliuya case is an example. We see a lot of 388 00:42:40,780 --> 00:42:48,380 a lot of them, an increasing number of them each year. And they are difficult to 389 00:42:48,380 --> 00:42:57,940 do across nations because this, the German law is exceptional on that case. But there 390 00:42:57,940 --> 00:43:03,340 are other ways, like, for example, why are human rights courts that can be done 391 00:43:03,340 --> 00:43:11,230 across nation states and that is also happening. So it's at the moment, it is 392 00:43:11,230 --> 00:43:18,560 still legally not super straightforward to to actually win these cases, but 393 00:43:18,560 --> 00:43:24,320 increasingly a lot of lawyers working on that so that we will see a lot of 394 00:43:24,320 --> 00:43:31,580 change in that in the coming years. H: OK, thank you. In the meantime, there 395 00:43:31,580 --> 00:43:37,860 appeared some questions from the chat and from the Internet. I will go through them. 396 00:43:37,860 --> 00:43:42,910 First question is: are the results of the individual attribution studies published 397 00:43:42,910 --> 00:43:50,450 as open data in a machine readable format? FO: *laughter* So all the studies that 398 00:43:50,450 --> 00:43:57,620 we do that that I've done with my team, with world weather attribution. So 399 00:43:57,620 --> 00:44:03,020 there all the data is available, and it's available on a 400 00:44:03,020 --> 00:44:11,000 platform that's called Climate Explorer. So that should be machine readable. So and 401 00:44:11,000 --> 00:44:17,790 this is deliberately because yeah, because we want to make it as transparent as 402 00:44:17,790 --> 00:44:23,760 possible so everyone can go away, use our data, and redo our studies, and find out 403 00:44:23,760 --> 00:44:29,450 if we made any mistakes. But this is not the case for all the studies that exist, 404 00:44:29,450 --> 00:44:34,830 because most of them or many of them are published in peer reviewed journals and 405 00:44:34,830 --> 00:44:39,070 not all peer reviewed journals have open data and open access policies. 406 00:44:39,070 --> 00:44:45,950 But increasingly, journals have. So if you, for example, go to the 407 00:44:45,950 --> 00:44:51,410 CarbonBrief website and look at the map of studies, there you have links to all 408 00:44:51,410 --> 00:44:56,330 the studies. And a lot of them have the data available. 409 00:44:56,330 --> 00:45:05,000 H: OK, maybe a follow up to this one. The next question is, are the models somehow 410 00:45:05,000 --> 00:45:11,910 available or usable for a wider interest public or is APC required? I'm not quite 411 00:45:11,910 --> 00:45:18,020 sure what APC means. FO: So the model data is publicly 412 00:45:18,020 --> 00:45:25,780 available from–and this is one reason why we have been able to do these studies 413 00:45:25,780 --> 00:45:31,280 because until relatively recently, model data was not publicly available and only 414 00:45:31,280 --> 00:45:36,390 scientist working in a specific country could use the model developed in that 415 00:45:36,390 --> 00:45:44,810 country–but now all the model data is shared publicly and people can use it. So 416 00:45:44,810 --> 00:45:50,830 it's definitely there and usable. It just requires some expertise to make sense of 417 00:45:50,830 --> 00:46:00,000 it. But it's, yeah, people can use it. H: OK, the next question is: to what 418 00:46:00,000 --> 00:46:05,450 certainty can you set up counterfactual models, which are an important reference 419 00:46:05,450 --> 00:46:12,915 to your percentage value, and what data are the basis for these models? 420 00:46:12,915 --> 00:46:19,760 FO: So the counterfactual simulations are- the climate models we use are basically the 421 00:46:19,760 --> 00:46:23,970 same models that are used also for the weather forecast. They are just run in 422 00:46:23,970 --> 00:46:30,520 lower resolution. So, which I guess most of this audience knows what that means. So 423 00:46:30,520 --> 00:46:36,670 the data points for the part, so that it's not so computing intensive. And these 424 00:46:36,670 --> 00:46:43,390 models, they are tested against observed data. And so that is how we do the model 425 00:46:43,390 --> 00:46:48,600 evaluation. So that is some simulations of the present day. And for the 426 00:46:48,600 --> 00:46:57,430 counterfactual, we know extremely well how many greenhouse gases have been included 427 00:46:57,430 --> 00:47:02,010 into the atmosphere since the beginning of the Industrial Revolution, so that there 428 00:47:02,010 --> 00:47:07,740 is some very large certainty with that number and we remove that from the models' 429 00:47:07,740 --> 00:47:13,080 atmospheres. So the models have exactly the same set up, but the lower 430 00:47:13,080 --> 00:47:16,720 greenhouse gases, lower amount of greenhouse gases in the atmosphere, and 431 00:47:16,720 --> 00:47:24,580 then are spun up and run in exactly the same way. So, they, but of course, we 432 00:47:24,580 --> 00:47:33,620 can't test the counterfactual. And so that means that we assume that the sort of the 433 00:47:33,620 --> 00:47:40,510 the weather was still the same, physics will still hold in the counterfactual. And 434 00:47:40,510 --> 00:47:45,800 that the models that are developed using present day represent the 435 00:47:45,800 --> 00:47:48,880 counterfactual. Which is, which is an assumption. 436 00:47:48,880 --> 00:47:51,820 But it is not a completely unreasonable assumption, because of 437 00:47:51,820 --> 00:48:00,740 course, we have now decades of model development and have seen that, in fact, 438 00:48:00,740 --> 00:48:05,990 that indeed climate model projections that have been made 30 years ago have actually 439 00:48:05,990 --> 00:48:13,110 come… come to… have been realized, and pretty much the same way on a large scale 440 00:48:13,110 --> 00:48:18,980 that they have, as they had been predicted 30 years ago. And so that assumption 441 00:48:18,980 --> 00:48:24,890 is not, yeah, it's not a big assumption. So the counterfactual itself is not a 442 00:48:24,890 --> 00:48:29,700 problem. But of course, also the present day model simulations, they are 443 00:48:29,700 --> 00:48:34,990 not… they are very far from perfect. And there are some types of events which state 444 00:48:34,990 --> 00:48:41,040 of the art climate models just can't simulate. And so, where we can- what 445 00:48:41,040 --> 00:48:46,560 we can say very little. So well, for example, for hurricanes, we can say 446 00:48:46,560 --> 00:48:51,730 with high certainty about the rainfall associated with hurricanes, the 447 00:48:51,730 --> 00:48:56,670 hurricane strength itself and the frequency of hurricanes is something 448 00:48:56,670 --> 00:49:01,970 which is very difficult to simulate with state of the art models. So our 449 00:49:01,970 --> 00:49:12,640 uncertainty there is much higher. H: OK. And then, well, some, one question 450 00:49:12,640 --> 00:49:20,170 that emerges from all of this is, of course, if we know this much and way 451 00:49:20,170 --> 00:49:26,720 more than in the past, how are politicians still ignoring that 452 00:49:26,720 --> 00:49:34,944 information? And how can we convey that into their minds? 453 00:49:34,944 --> 00:49:39,880 FO: Well, if I knew the answer to that, I would probably not be standing here, 454 00:49:39,880 --> 00:49:49,480 but actually doing politics. But I think it takes a frustratingly long time 455 00:49:49,480 --> 00:49:56,849 for things to change and things should change much faster. But we actually- the 456 00:49:56,849 --> 00:50:03,510 last two years have shown huge progress, I think, in terms of also putting climate 457 00:50:03,510 --> 00:50:11,830 change on the agenda of every politician. Because, and that's largely due to the 458 00:50:11,830 --> 00:50:17,740 Fridays For Future movement, but also to a degree, I think, due to the fact that we 459 00:50:17,740 --> 00:50:23,800 now actually know that the weather that people experience in their backyard–and 460 00:50:23,800 --> 00:50:29,400 pretty much independent of where their backyard is–is not the same as it used to 461 00:50:29,400 --> 00:50:37,430 be. And so people do experience today climate change. And I think that 462 00:50:37,430 --> 00:50:42,700 does help to bring a bit more urgency. And, of course, I would have said everyone 463 00:50:42,700 --> 00:50:47,630 has climate change on their agenda, which was very different even two years ago, 464 00:50:47,630 --> 00:50:52,140 where there were lots of people who would never talk about climate change and 465 00:50:52,140 --> 00:50:57,880 their political agendas has played no role. It doesn't mean that it 466 00:50:57,880 --> 00:51:05,790 has the right priority on that agenda, but it's still a huge step forward that 467 00:51:05,790 --> 00:51:18,710 has been made. And so I think we do know some things that do work, but we just have 468 00:51:18,710 --> 00:51:28,080 to just keep doing that. Yeah, I don't think I can say more. I don't have a magic 469 00:51:28,080 --> 00:51:35,200 wand to change it otherwise. H: Maybe some other point of impact. 470 00:51:35,200 --> 00:51:40,140 One of the question is, is it possible to turn the results of attribution studies 471 00:51:40,140 --> 00:51:47,920 into recommendations for farmers and people who are affected in a financial way 472 00:51:47,920 --> 00:51:53,359 by extreme weather and how to change agriculture to reduce losses from extreme 473 00:51:53,359 --> 00:51:56,580 weather effects? FO: Yes, absolutely. So that is 474 00:51:56,580 --> 00:52:03,730 one of the most useful things of these studies is well, on the one hand, to raise 475 00:52:03,730 --> 00:52:07,960 awareness. But on the other hand, if you know that a drought that you have 476 00:52:07,960 --> 00:52:16,930 experienced that has led to losses is a harbinger of what is to come, then that is 477 00:52:16,930 --> 00:52:22,630 incredibly helpful to know how agricultural practices might need to be 478 00:52:22,630 --> 00:52:29,880 changed. Or that insurance for losses from agriculture might need to be changed. And 479 00:52:29,880 --> 00:52:36,009 so this is exactly why we do these attribution studies. Because not 480 00:52:36,009 --> 00:52:42,770 every extreme event has always shows the fingerprints of 481 00:52:42,770 --> 00:52:47,990 climate change. And if you know which of the events are the ones where 482 00:52:47,990 --> 00:52:53,920 climate change is a real game changer, you also do know where to put your efforts and 483 00:52:53,920 --> 00:53:00,491 resources to be more resilient in the future. And for financial losses, it 484 00:53:00,491 --> 00:53:06,070 is on the one hand, yeah, you can use these studies to find out what your 485 00:53:06,070 --> 00:53:12,930 physical risks are for your assets. And how they, and of course, everything that 486 00:53:12,930 --> 00:53:17,710 I've said, comparing the counterfactual with the present we can do, and we do this 487 00:53:17,710 --> 00:53:23,950 also with the future. So you can also see how in a two degree world, the events, 488 00:53:23,950 --> 00:53:29,220 the likelihood and intensities are changing. And of course, you can then 489 00:53:29,220 --> 00:53:35,250 also, in a less direct way, use this kind of information to see, to assess what 490 00:53:35,250 --> 00:53:42,880 might be other risks from- where might be stranded assets, what are other risks 491 00:53:42,880 --> 00:53:48,910 for the financial sector, for the financial planning. 492 00:53:48,910 --> 00:53:57,190 Where could liability risks be and how could they look like. So there is, because 493 00:53:57,190 --> 00:54:02,450 extreme weather events and their changes in intensity and magnitude is how climate 494 00:54:02,450 --> 00:54:10,360 change is manifesting, it really connects all these aspects of where the 495 00:54:10,360 --> 00:54:21,920 impacts of climate change are. H: OK, last question for today. I hope I 496 00:54:21,920 --> 00:54:30,020 can get that right. I think the question is if there are study, if there are 497 00:54:30,020 --> 00:54:40,520 studies on how we cultivates fields and agriculture. How does this impact the 498 00:54:40,520 --> 00:54:48,950 overall climate in that area? The example here is that only an increase in water 499 00:54:48,950 --> 00:54:57,849 consumption was directed to São Paulo. Or might there also be a warm world created 500 00:54:57,849 --> 00:55:06,440 by monoculture in central Brazil? FO: So, yeah, I don't know details, but 501 00:55:06,440 --> 00:55:13,460 there are, but land use changes and land use does play a role. On the one hand, it 502 00:55:13,460 --> 00:55:20,210 affects the climate. So if you have, if you have a rainforest, you have a very 503 00:55:20,210 --> 00:55:26,940 different climate in that location as if there is a savanna or plantation. And 504 00:55:26,940 --> 00:55:35,230 also, of course, if you have monocultures, you are much more, your losses are 505 00:55:35,230 --> 00:55:42,100 larger usually as if you have different types of agriculture. Because 506 00:55:42,100 --> 00:55:46,830 in a monoculture everything is in exactly the same way vulnerable and 507 00:55:46,830 --> 00:55:52,030 so that, yeah. So that does, land use change plays a hugely important 508 00:55:52,030 --> 00:55:59,390 role with respect to the impacts of extreme weather. And that is one thing to 509 00:55:59,390 --> 00:56:03,570 look at. When I was saying, talking about looking at vulnerability and exposure, and 510 00:56:03,570 --> 00:56:08,520 of course also changes in the hazard are not just because of climate change, but 511 00:56:08,520 --> 00:56:12,610 also because of land use change. And you can use exactly the same methods, but 512 00:56:12,610 --> 00:56:17,040 instead of changing the CO2 or the greenhouse gases in the atmosphere of your 513 00:56:17,040 --> 00:56:22,790 model, you can change the land use and then disentangle these different drivers 514 00:56:22,790 --> 00:56:29,870 in and hazards. H: OK, Fredi Otto thank you very much for 515 00:56:29,870 --> 00:56:37,290 your presentation and for the Q&A. It was a pleasure to have you with us. And yeah, 516 00:56:37,290 --> 00:56:43,800 if you have any questions, any more questions, I guess there are ways to 517 00:56:43,800 --> 00:56:47,250 contact you. FO: *laughter* 518 00:56:47,250 --> 00:56:52,540 H: I think your email address and contact details are in the Fahrplan for all the 519 00:56:52,540 --> 00:56:58,930 viewers that have way more questions. And, I don't know, do you have access to the 2D 520 00:56:58,930 --> 00:57:05,520 world and do you explore that? FO: Given that I don't know what you mean, 521 00:57:05,520 --> 00:57:07,350 probably not, but… *laughter* 522 00:57:07,350 --> 00:57:12,070 H: OK. FO: That can also be changed. 523 00:57:12,070 --> 00:57:20,950 H: Yeah, it's the the replacement for the congress place itself. But anyway, 524 00:57:20,950 --> 00:57:26,700 if you viewers and you people out there have any more questions, contact Fredi 525 00:57:26,700 --> 00:57:32,930 Otto. And thank you again very much for your talk. And, yeah. Have a 526 00:57:32,930 --> 00:57:35,330 nice congress, all of you. 527 00:57:35,330 --> 00:57:39,080 *rc3 postroll music* 528 00:57:39,080 --> 00:58:13,960 Subtitles created by c3subtitles.de in the year 2020. Join, and help us!