1 00:00:00,280 --> 00:00:05,439 Speaker 1: Welcome to zero. I'm Akshatrati this week weather data, volcanic 2 00:00:05,480 --> 00:00:22,280 Speaker 1: skies and conflict in Timbuctu. Here in the UK, chatting 3 00:00:22,320 --> 00:00:26,159 Speaker 1: about the weather is basically national pastime. When is it 4 00:00:26,200 --> 00:00:28,760 Speaker 1: going to rain? When will we see the sun again? 5 00:00:29,320 --> 00:00:32,920 Speaker 1: And why have we gone through all four seasons? Before lunch? 6 00:00:33,680 --> 00:00:37,280 Speaker 1: And thanks to weather forecasting we can have those conversations 7 00:00:37,320 --> 00:00:40,080 Speaker 1: not just with good humor but also a good level 8 00:00:40,120 --> 00:00:44,120 Speaker 1: of confidence. We get these forecasts because the UK has 9 00:00:44,120 --> 00:00:47,879 Speaker 1: a network of more than four hundred weather stations spanning 10 00:00:47,880 --> 00:00:50,440 Speaker 1: the length of the country, from the Orkney Islands in 11 00:00:50,479 --> 00:00:53,000 Speaker 1: the far north to the Scilly Isles in the south. 12 00:00:54,120 --> 00:00:57,520 Speaker 1: The data the weather stations like these collect are invaluable. 13 00:00:57,840 --> 00:01:01,160 Speaker 1: It influences the decisions of governments and companies around the 14 00:01:01,200 --> 00:01:03,840 Speaker 1: world and can be used to make models that predict 15 00:01:04,000 --> 00:01:08,360 Speaker 1: energy consumption, harvests, and even when countries might go to war. 16 00:01:08,959 --> 00:01:12,319 Speaker 1: As my colleague at Bloombergrain, Laura Milan puts it, what 17 00:01:12,440 --> 00:01:15,400 Speaker 1: they do is really important because these data fits then 18 00:01:15,520 --> 00:01:19,039 Speaker 1: into all these climate models, into the research that climate 19 00:01:19,080 --> 00:01:21,600 Speaker 1: sciences do to try to figure out how the world 20 00:01:22,160 --> 00:01:25,640 Speaker 1: works today, and these is the data key to figure 21 00:01:25,680 --> 00:01:28,800 Speaker 1: out how it will change in the future. When it 22 00:01:28,800 --> 00:01:31,560 Speaker 1: comes to making climate models, the more data you have 23 00:01:31,720 --> 00:01:34,440 Speaker 1: and the longer you've been collecting it for the better 24 00:01:34,520 --> 00:01:38,520 Speaker 1: those models become. But weather stations are also expensive to 25 00:01:38,520 --> 00:01:42,039 Speaker 1: set up and maintain, and many countries can't afford them 26 00:01:42,080 --> 00:01:46,000 Speaker 1: in great numbers. Without these stations, it becomes difficult to 27 00:01:46,040 --> 00:01:49,960 Speaker 1: provide accurate weather forecasts and makes it even harder to 28 00:01:50,000 --> 00:01:53,160 Speaker 1: work out how a country will be affected by climate change. 29 00:01:54,000 --> 00:01:57,400 Speaker 1: And while the UK benefits from an abundance of weather stations, 30 00:01:57,480 --> 00:02:01,160 Speaker 1: many countries in Africa are severely lack the resources to 31 00:02:01,280 --> 00:02:06,360 Speaker 1: produce reliable weather and climate data. This week, Laura tells 32 00:02:06,400 --> 00:02:09,200 Speaker 1: the story of weather Station six one two to three 33 00:02:09,360 --> 00:02:12,600 Speaker 1: in Timbec two and what its sudden closure means for 34 00:02:12,639 --> 00:02:16,480 Speaker 1: the people of Mali and climate science across the African continent. 35 00:02:17,440 --> 00:02:20,639 Speaker 1: The story starts with the Arab Spring, which sparked revolutions 36 00:02:20,639 --> 00:02:23,960 Speaker 1: across North Africa, and ends with how the lack of 37 00:02:24,000 --> 00:02:35,920 Speaker 1: African weather data will affect the discussions at COP twenty seven. Laura, 38 00:02:36,120 --> 00:02:38,200 Speaker 1: welcome to the show. Thank you, thank you for having 39 00:02:38,200 --> 00:02:41,280 Speaker 1: me tell me about weather Station six one two to three. 40 00:02:42,240 --> 00:02:45,640 Speaker 1: Weather Station six one two to three was one of 41 00:02:45,960 --> 00:02:48,760 Speaker 1: five weather stations in Mali that had been active for 42 00:02:48,840 --> 00:02:52,200 Speaker 1: more than one hundred years. And these stations that have 43 00:02:52,360 --> 00:02:54,519 Speaker 1: been operating for more than a hundred years, they're really 44 00:02:54,600 --> 00:02:57,400 Speaker 1: valuable because the data that they provide is very consistence 45 00:02:57,440 --> 00:03:00,840 Speaker 1: for a time. This station, in particular world was close 46 00:03:00,919 --> 00:03:04,359 Speaker 1: to the airport in Timbukto. It was set up by 47 00:03:04,520 --> 00:03:09,520 Speaker 1: the French colonialists and it was in a very discret building, 48 00:03:09,720 --> 00:03:13,280 Speaker 1: concrete building near the airport, so you know, no signs, 49 00:03:13,320 --> 00:03:16,919 Speaker 1: know anything. One could have confused it with a warehouse 50 00:03:17,400 --> 00:03:21,040 Speaker 1: or something like that. When I think of Timbuctoo, the 51 00:03:21,160 --> 00:03:30,240 Speaker 1: thing that comes to mind is how Bollywood treats at Hullo. 52 00:03:31,639 --> 00:03:35,040 Speaker 1: So from India. Many Bollywood songs have the word, and 53 00:03:35,080 --> 00:03:37,600 Speaker 1: then it's treated as this like far far away place, 54 00:03:37,880 --> 00:03:40,560 Speaker 1: which many people don't think it's real, right, And I 55 00:03:40,600 --> 00:03:45,120 Speaker 1: think it's the way it resonates in people's imagination. You 56 00:03:45,200 --> 00:03:48,200 Speaker 1: talk about Timbukto in Europe and it's also this far 57 00:03:48,240 --> 00:03:50,480 Speaker 1: away place, this city in the middle of the desert 58 00:03:50,800 --> 00:03:52,680 Speaker 1: that no one has been to, that it's a bit 59 00:03:52,840 --> 00:03:56,480 Speaker 1: mythological even, but it's an actual place. It's an actual city. 60 00:03:56,760 --> 00:04:00,360 Speaker 1: It's home to around thirty thousand people. It's next to 61 00:04:00,400 --> 00:04:04,360 Speaker 1: the Nija River in northern Malis. And the reason why 62 00:04:04,400 --> 00:04:06,600 Speaker 1: there are so many stories around it is because it 63 00:04:06,680 --> 00:04:10,520 Speaker 1: used to be the capital of an ancient empire, and 64 00:04:10,560 --> 00:04:13,240 Speaker 1: it used to be a center for knowledge and culture, 65 00:04:13,520 --> 00:04:17,920 Speaker 1: especially religious studies in the Middle Ages. And so this 66 00:04:18,400 --> 00:04:20,840 Speaker 1: Vali station six one two to three, why did it 67 00:04:20,920 --> 00:04:24,560 Speaker 1: go silent and after one hundred and some years, Well, 68 00:04:24,560 --> 00:04:27,120 Speaker 1: the reason is that the staff had to abandon it. 69 00:04:27,200 --> 00:04:30,640 Speaker 1: The people that were maintaining it, that were taking the data, 70 00:04:30,800 --> 00:04:34,520 Speaker 1: maintaining the equipment, making sure everything was running smoothly, that 71 00:04:34,600 --> 00:04:37,159 Speaker 1: they had been doing that for many years, they had 72 00:04:37,200 --> 00:04:41,280 Speaker 1: to run. They had to fleet Timbuktu. And the reason 73 00:04:41,400 --> 00:04:46,800 Speaker 1: why this happened starts in twenty eleven with the Arab spring. 74 00:04:47,120 --> 00:04:50,440 Speaker 1: One is easy set himself on fire ten years ago, 75 00:04:50,600 --> 00:04:52,920 Speaker 1: which he couldn't have known that a suicide into Nija 76 00:04:53,080 --> 00:04:59,560 Speaker 1: would ignize the entire region. When the regime of Mamagadafi 77 00:04:59,640 --> 00:05:04,000 Speaker 1: collapse in Libya, many of the desert tribes that Khadafi 78 00:05:04,120 --> 00:05:07,920 Speaker 1: had been sponsoring and supporting through the decades had to 79 00:05:08,000 --> 00:05:12,200 Speaker 1: flee and became rebel fighters. Over the last few years, 80 00:05:12,360 --> 00:05:16,320 Speaker 1: Mali has had a problem with militancy. In October twenty eleven, 81 00:05:16,560 --> 00:05:20,239 Speaker 1: ethnic Tuaregs, mostly from the north of the country, launched 82 00:05:20,279 --> 00:05:23,719 Speaker 1: a rebellion. So one of these groups is the National 83 00:05:23,760 --> 00:05:27,760 Speaker 1: Movement for the Liberation of the Azawat and their Tareg fighters, 84 00:05:27,880 --> 00:05:30,560 Speaker 1: and what this group did was surround the city of 85 00:05:30,600 --> 00:05:34,080 Speaker 1: Timbucto and on the first of April twenty twelve, they 86 00:05:34,240 --> 00:05:36,839 Speaker 1: entered the city and took over it, took over the 87 00:05:36,880 --> 00:05:41,359 Speaker 1: main institutions. Then a few days later, the Jihadis of 88 00:05:41,440 --> 00:05:45,240 Speaker 1: a radical Islamic group called ant Sharin followed in and 89 00:05:45,440 --> 00:05:50,080 Speaker 1: they were waiving the black flags that were later characteristic 90 00:05:50,160 --> 00:05:53,320 Speaker 1: of the Islamic state in Syria and in other places. 91 00:05:53,360 --> 00:05:56,840 Speaker 1: So that meant that radical Islamis had taken over the 92 00:05:56,880 --> 00:06:00,599 Speaker 1: city of timbuktwo. And that made the employees of the state, 93 00:06:00,880 --> 00:06:05,159 Speaker 1: from you know, top top government officials to the people 94 00:06:05,240 --> 00:06:09,520 Speaker 1: that were just maintaining the weather station six one, two 95 00:06:09,520 --> 00:06:12,840 Speaker 1: to three, they made them enemies of the invaders and 96 00:06:12,839 --> 00:06:15,000 Speaker 1: so they had to flee. And so what you're describing 97 00:06:15,080 --> 00:06:17,880 Speaker 1: is the start of the Mali War, which is still ongoing. 98 00:06:18,000 --> 00:06:21,839 Speaker 1: And there have been multiple good thoughts that have happened since. 99 00:06:22,400 --> 00:06:25,840 Speaker 1: But when this happened in twenty twelve. What was going 100 00:06:25,880 --> 00:06:28,880 Speaker 1: on in the minds of the people who were working 101 00:06:28,880 --> 00:06:32,120 Speaker 1: on this weather station. Well, I didn't talk directly with 102 00:06:32,160 --> 00:06:35,080 Speaker 1: the people that were working on the station, but I 103 00:06:35,120 --> 00:06:40,200 Speaker 1: did talk to the person in the meteorological agency in 104 00:06:40,320 --> 00:06:53,000 Speaker 1: Mali who was responsible for these people commodity, And obviously 105 00:06:53,080 --> 00:06:56,400 Speaker 1: there was great concern. You have to imagine seeing all 106 00:06:56,400 --> 00:07:00,920 Speaker 1: these news from Bamaco, from the capital news coming in. 107 00:07:01,000 --> 00:07:05,680 Speaker 1: It wasn't an immediate conquest. So the forces from the 108 00:07:05,720 --> 00:07:09,680 Speaker 1: tourreg surrounded the city for a few days. The elders 109 00:07:09,760 --> 00:07:13,440 Speaker 1: from Timbook too went out and started to negotiate with 110 00:07:13,520 --> 00:07:16,400 Speaker 1: them to make sure that they wouldn't destroy some of 111 00:07:16,440 --> 00:07:19,760 Speaker 1: the city's valuable ancient monuments and so on. So these 112 00:07:19,800 --> 00:07:22,400 Speaker 1: went on for a few days, and obviously rumors came 113 00:07:22,440 --> 00:07:27,760 Speaker 1: into the capital, and so this person from Mali's meteorological agency, 114 00:07:28,360 --> 00:07:34,200 Speaker 1: his first concern was to get the people out of there. 115 00:07:35,320 --> 00:07:41,680 Speaker 1: He not law. Obviously, it was very important that Mali 116 00:07:41,800 --> 00:07:45,000 Speaker 1: had a centennial weather station there, but it wasn't more 117 00:07:45,040 --> 00:07:47,640 Speaker 1: important than the lives of that people. So he made 118 00:07:47,640 --> 00:07:50,840 Speaker 1: sure he got them out of Timbook too safely and 119 00:07:51,120 --> 00:07:55,080 Speaker 1: luckily he did that. Now, let's talk about this weather station. 120 00:07:55,480 --> 00:07:59,120 Speaker 1: Why is it that it's so important to have had 121 00:07:59,160 --> 00:08:03,200 Speaker 1: that weather station there? So for any weather station, the 122 00:08:03,200 --> 00:08:06,720 Speaker 1: most important thing is not just the data that it 123 00:08:06,760 --> 00:08:10,480 Speaker 1: gathers is accurate, but that that data at any moment 124 00:08:10,560 --> 00:08:13,280 Speaker 1: in time can be compared with data that has been 125 00:08:13,320 --> 00:08:16,880 Speaker 1: recorded previously. Right, the weather stations that have been running 126 00:08:16,920 --> 00:08:20,280 Speaker 1: for more than a hundred years, that's why they're really relevant. 127 00:08:20,280 --> 00:08:24,280 Speaker 1: They're considered like the most the most valuable ones because 128 00:08:24,280 --> 00:08:28,000 Speaker 1: they allow scientists to compare what's happening today with what 129 00:08:28,200 --> 00:08:31,200 Speaker 1: used to happen like kids and more than a century ago. 130 00:08:31,760 --> 00:08:35,360 Speaker 1: So the loss of weather station six one, two two 131 00:08:35,440 --> 00:08:38,760 Speaker 1: three was tragic not just because of that, but because 132 00:08:38,760 --> 00:08:41,960 Speaker 1: they're really There are very few of those in Mali 133 00:08:42,480 --> 00:08:46,920 Speaker 1: and in Africa. So if a centennial station was lost, 134 00:08:47,040 --> 00:08:50,920 Speaker 1: let's say in China, where there are actually many, or 135 00:08:50,960 --> 00:08:53,960 Speaker 1: in Europe or in Russia, then it's always a tragedy 136 00:08:54,000 --> 00:08:57,200 Speaker 1: because a source of information gets lost. But in Africa, 137 00:08:57,200 --> 00:09:00,000 Speaker 1: where there are very very few of them, then that's 138 00:09:00,000 --> 00:09:03,680 Speaker 1: an actual problem because that means that the set of 139 00:09:03,800 --> 00:09:07,600 Speaker 1: data that this station has been gathering through time gets discontinued. 140 00:09:08,400 --> 00:09:12,480 Speaker 1: So scientists don't have a way anymore of knowing what's 141 00:09:12,480 --> 00:09:17,600 Speaker 1: happening in that place, and even immediately because this weather 142 00:09:18,080 --> 00:09:22,080 Speaker 1: station is collecting local data and also helping local people, 143 00:09:22,559 --> 00:09:26,600 Speaker 1: even immediately after going offline, there were impacts, right, Yeah. Absolutely, 144 00:09:27,080 --> 00:09:30,760 Speaker 1: it's not just a long term thing for climate scientists 145 00:09:30,760 --> 00:09:34,720 Speaker 1: to understand a phenomenon that might run for decades or 146 00:09:34,720 --> 00:09:38,200 Speaker 1: for many years, but it's actually weather stations have a 147 00:09:38,320 --> 00:09:41,480 Speaker 1: very immediate application. And in the case of station six 148 00:09:41,559 --> 00:09:45,160 Speaker 1: one to two three, it was very important because it 149 00:09:45,320 --> 00:09:49,760 Speaker 1: helped understand when a very strong gust of wind would 150 00:09:49,800 --> 00:09:53,320 Speaker 1: come through the desert into a lake Deboat, which is 151 00:09:53,600 --> 00:09:57,320 Speaker 1: Mali's largest lake. And so when the people around the 152 00:09:57,400 --> 00:09:59,720 Speaker 1: lake would receive an alert from the weather station that 153 00:09:59,760 --> 00:10:01,880 Speaker 1: this kind of extreme weather it was coming, they were 154 00:10:01,920 --> 00:10:07,040 Speaker 1: able to alert local fishermen and then people traveling in pinas, 155 00:10:07,160 --> 00:10:10,280 Speaker 1: which are kind of long boats that people traveling that 156 00:10:10,400 --> 00:10:14,520 Speaker 1: people used to transport goods and transport themselves. Even so, 157 00:10:14,600 --> 00:10:18,280 Speaker 1: in twenty eleven about ten people died in wind related 158 00:10:18,360 --> 00:10:22,600 Speaker 1: incidents around the lake Devout, but that number increased to 159 00:10:22,800 --> 00:10:26,360 Speaker 1: seventy and the year after Station six one two three 160 00:10:26,400 --> 00:10:30,400 Speaker 1: went offline, so went from ten to seventy just because 161 00:10:30,440 --> 00:10:33,360 Speaker 1: that station wasn't able to alert the people around the 162 00:10:33,440 --> 00:10:44,640 Speaker 1: lake zooming back. There's the local phenomena which we talked about, 163 00:10:45,040 --> 00:10:49,240 Speaker 1: but losing data from weather stations anywhere in the world, 164 00:10:49,240 --> 00:10:51,840 Speaker 1: but more so in places like Mali where there are 165 00:10:52,000 --> 00:10:55,679 Speaker 1: so few, also has international impacts. Right, yeah, absolutely, So 166 00:10:56,280 --> 00:10:59,880 Speaker 1: all these data, all these tiny data points that weather 167 00:11:00,080 --> 00:11:03,800 Speaker 1: stations gather every day. I think about them like little ants, right, 168 00:11:03,880 --> 00:11:07,199 Speaker 1: Like they do their work every day. They do always 169 00:11:07,200 --> 00:11:10,520 Speaker 1: the same. It's not very shiny, but what they do 170 00:11:10,640 --> 00:11:13,920 Speaker 1: is really important because these data fits then into all 171 00:11:13,960 --> 00:11:17,480 Speaker 1: these climate models, into the research that climate sciences do 172 00:11:17,679 --> 00:11:20,880 Speaker 1: to try to figure out how the world works today, 173 00:11:20,960 --> 00:11:23,960 Speaker 1: how the climate of the world works today, how it's changing, 174 00:11:24,120 --> 00:11:27,680 Speaker 1: and these is the data key to figure out how 175 00:11:27,720 --> 00:11:30,079 Speaker 1: it will change in the future. Right, And so this 176 00:11:30,360 --> 00:11:32,440 Speaker 1: whether data sort of becomes the ant hill, and the 177 00:11:32,480 --> 00:11:37,120 Speaker 1: ant hill certainly the thing of marvel. We know. There 178 00:11:37,160 --> 00:11:40,120 Speaker 1: was a very recent study earlier this year where they 179 00:11:40,160 --> 00:11:44,199 Speaker 1: found that only five percent of the deaths caused by 180 00:11:44,240 --> 00:11:48,680 Speaker 1: heat happened in tropical countries where eighty five percent of 181 00:11:48,760 --> 00:11:53,840 Speaker 1: people live. And that just is astonishing because that shouldn't 182 00:11:53,840 --> 00:11:56,520 Speaker 1: be statistically right, and when we asked a scientist about it, 183 00:11:57,320 --> 00:12:00,400 Speaker 1: she said, that's ridiculous, and the reason is because we 184 00:12:00,440 --> 00:12:04,000 Speaker 1: don't have data. That's right. And there is another study 185 00:12:04,040 --> 00:12:07,800 Speaker 1: actually that looked at heatwaves around the world and it 186 00:12:07,920 --> 00:12:13,319 Speaker 1: found that around the Sahara region there were no heatwaves, 187 00:12:13,320 --> 00:12:17,040 Speaker 1: no heatwaves had happened according to researchers. But that wasn't 188 00:12:17,080 --> 00:12:20,560 Speaker 1: the case. Obviously, in the world's biggest desert, there are heatwaves. 189 00:12:20,840 --> 00:12:23,120 Speaker 1: The problem is that there are no weather stations to 190 00:12:23,160 --> 00:12:26,920 Speaker 1: record them. Now, for the story, you produced a map 191 00:12:27,200 --> 00:12:30,560 Speaker 1: which I remember because it was a stunning map, and 192 00:12:31,080 --> 00:12:34,520 Speaker 1: it noted the density of weather data stations around the world. 193 00:12:35,200 --> 00:12:38,560 Speaker 1: And as with many other things, the continent of Africa, 194 00:12:38,760 --> 00:12:41,760 Speaker 1: not just a few countries, but the continent of Africa 195 00:12:42,080 --> 00:12:46,199 Speaker 1: was dark. Yes, And that like a very obvious way 196 00:12:46,280 --> 00:12:49,640 Speaker 1: of showing that there are very, very very few weather 197 00:12:49,720 --> 00:12:54,080 Speaker 1: stations in Africa. In particular Mali has I said before 198 00:12:54,160 --> 00:12:57,960 Speaker 1: it used to have five centennial stations, for after the 199 00:12:58,000 --> 00:13:01,640 Speaker 1: one that we've mentioned in Timbuktu went. But in total 200 00:13:01,840 --> 00:13:07,560 Speaker 1: it has thirteen active weather stations, compared to Germany, for example, 201 00:13:07,760 --> 00:13:12,280 Speaker 1: in Europe, a country that's one third the size of Mali. 202 00:13:12,480 --> 00:13:17,320 Speaker 1: Germany has almost two hundred main weather stations, so that's 203 00:13:17,360 --> 00:13:20,280 Speaker 1: more than ten times what Mali has. And then it's 204 00:13:20,320 --> 00:13:22,960 Speaker 1: not just a matter of quantity but also of quality. 205 00:13:23,400 --> 00:13:28,240 Speaker 1: So the World Meteorological Organization says that weather infrastructure in 206 00:13:28,280 --> 00:13:31,920 Speaker 1: Africa is literiorating very fast, only twenty two percent of 207 00:13:32,000 --> 00:13:35,800 Speaker 1: the station's met global reporting standards in twenty nineteen. And 208 00:13:35,840 --> 00:13:39,479 Speaker 1: again this represents a very big problem for the scientists 209 00:13:39,480 --> 00:13:42,880 Speaker 1: trying to study the phenomenon, weather phenomenon and climate phenomenon 210 00:13:42,960 --> 00:13:45,440 Speaker 1: going on in Africa and in the rest of the world. 211 00:13:45,840 --> 00:13:49,520 Speaker 1: So this lack of data, how is that feeding into 212 00:13:49,760 --> 00:13:53,560 Speaker 1: or not feeding into climate science that looks at global 213 00:13:53,600 --> 00:13:57,560 Speaker 1: phenomena and looks at many different time periods. So the 214 00:13:57,640 --> 00:14:03,760 Speaker 1: data is necessary to produce climate science, and all the 215 00:14:04,320 --> 00:14:08,720 Speaker 1: scientific papers are gathered once every five years by the 216 00:14:08,840 --> 00:14:12,720 Speaker 1: IPCC into climate reports. The problem is that if there 217 00:14:12,920 --> 00:14:19,359 Speaker 1: is no data from Africa, the representation Africa has on reports, 218 00:14:19,560 --> 00:14:23,080 Speaker 1: very important reports like the IPCC is very very small. Now, 219 00:14:23,120 --> 00:14:26,080 Speaker 1: what's being done about the situation? The good thing about 220 00:14:26,200 --> 00:14:28,040 Speaker 1: all of this is that there's a lot of very 221 00:14:28,080 --> 00:14:31,600 Speaker 1: smart people trying to fix this issue. One of the 222 00:14:31,640 --> 00:14:38,080 Speaker 1: difficulties of this problem is that national meteorological organizations in 223 00:14:38,160 --> 00:14:43,160 Speaker 1: Africa are often underfunded, and setting up a station that 224 00:14:43,720 --> 00:14:48,360 Speaker 1: is valid for the WMO that can take reliable whether 225 00:14:48,480 --> 00:14:51,400 Speaker 1: data is not cheap. It can cost more or less 226 00:14:51,520 --> 00:14:55,520 Speaker 1: around twenty thousand dollars for every station just to set 227 00:14:55,560 --> 00:14:58,320 Speaker 1: it up, and then it comes maintenance and the staff 228 00:14:58,400 --> 00:15:02,280 Speaker 1: needed to maintain in it. Weather stations are not always 229 00:15:02,320 --> 00:15:05,240 Speaker 1: a priority. If you have a government that's struggling with 230 00:15:05,360 --> 00:15:09,160 Speaker 1: conflict in the case of Mali, or with natural disasters 231 00:15:09,440 --> 00:15:13,160 Speaker 1: in the case of many other African nations, then weather 232 00:15:13,240 --> 00:15:15,880 Speaker 1: stations figuring out how hot or how cold or how 233 00:15:15,960 --> 00:15:20,040 Speaker 1: humid is in certain places doesn't necessarily come on top 234 00:15:20,080 --> 00:15:23,320 Speaker 1: of mind for the people governing, right if you would 235 00:15:23,400 --> 00:15:25,840 Speaker 1: use that money twenty thousand dollars, there's a lot of 236 00:15:25,880 --> 00:15:28,800 Speaker 1: money I do think that are really important on the 237 00:15:28,840 --> 00:15:32,360 Speaker 1: ground as needed, that's it, and and that are more 238 00:15:32,480 --> 00:15:35,760 Speaker 1: urgent or that feel more important, like for example, you'd 239 00:15:35,760 --> 00:15:38,920 Speaker 1: set up schools, or you're set up food relief programs 240 00:15:39,040 --> 00:15:42,840 Speaker 1: or any any sort of program that solves an immediate need, 241 00:15:42,920 --> 00:15:46,920 Speaker 1: whereas often the weather and climate are seen as something 242 00:15:47,080 --> 00:15:51,800 Speaker 1: that's almost a luxury. But there is technological programs that's 243 00:15:51,840 --> 00:15:56,080 Speaker 1: happening that's helping. Yes, that's right. So what's been happening 244 00:15:56,120 --> 00:15:59,000 Speaker 1: in the past few years is that technology has made 245 00:15:59,040 --> 00:16:03,840 Speaker 1: some of these instruments used to measure climate and weather data. 246 00:16:03,960 --> 00:16:06,760 Speaker 1: It's made them cheaper, and it's also made it possible 247 00:16:06,920 --> 00:16:11,560 Speaker 1: for these instruments to actually send the data remotely, so 248 00:16:11,680 --> 00:16:15,560 Speaker 1: you wouldn't need a person doing the maintenance or being 249 00:16:15,640 --> 00:16:19,280 Speaker 1: there every day all day recording the sets of data. 250 00:16:19,480 --> 00:16:22,080 Speaker 1: One of the people I found in my reporting is 251 00:16:22,440 --> 00:16:26,160 Speaker 1: Nick Vanda Gisen. He's a professor in the Netherlands and 252 00:16:26,200 --> 00:16:30,920 Speaker 1: he has set up something called the Trans African Hydrometeorological Observatory, 253 00:16:31,160 --> 00:16:34,920 Speaker 1: and this is a network of weather stations across Africa. 254 00:16:35,160 --> 00:16:38,840 Speaker 1: They can work remotely, they use modern equipment, and they 255 00:16:38,880 --> 00:16:41,760 Speaker 1: are much much cheaper than the traditional ones. And how 256 00:16:41,840 --> 00:16:44,560 Speaker 1: much cheaper are we talking here? So we've said that 257 00:16:44,600 --> 00:16:48,680 Speaker 1: a traditional weather station costs twenty thousand dollars. Their goal, 258 00:16:48,920 --> 00:16:52,560 Speaker 1: Tahama's goal is for one station to cost around two 259 00:16:52,640 --> 00:16:55,840 Speaker 1: hundred dollars. That's a lot, that's a lot cheaper, and 260 00:16:55,880 --> 00:16:58,800 Speaker 1: they're not there yet. So they've been able to produce 261 00:16:59,320 --> 00:17:02,960 Speaker 1: weather stations at the price of around two thousand dollars 262 00:17:03,000 --> 00:17:06,840 Speaker 1: and they have installed around six hundred of these across 263 00:17:06,880 --> 00:17:10,280 Speaker 1: Africa since you reported the story last year. Have there 264 00:17:10,320 --> 00:17:13,479 Speaker 1: been any updates? Yesn't know. The main headline is that 265 00:17:13,600 --> 00:17:17,120 Speaker 1: station six one, two two three is still offline. When 266 00:17:17,119 --> 00:17:22,800 Speaker 1: I talk to the people at Malimtoto, you fully maintaine 267 00:17:25,280 --> 00:17:29,399 Speaker 1: or minimum. What they told me was that bringing it 268 00:17:29,440 --> 00:17:33,320 Speaker 1: back online would require a significant investment because this sort 269 00:17:33,359 --> 00:17:35,640 Speaker 1: of equipment is very delicate, and so when it goes 270 00:17:35,680 --> 00:17:37,920 Speaker 1: offline for a while and no one maintains it, then 271 00:17:37,960 --> 00:17:42,360 Speaker 1: it requires either huge maintenance or just completely new equipment. 272 00:17:42,720 --> 00:17:47,280 Speaker 1: That hasn't happened. The security situation in Mali hasn't improved either, 273 00:17:47,440 --> 00:17:50,600 Speaker 1: so people might have read in the news how French 274 00:17:50,640 --> 00:17:53,760 Speaker 1: troops and troops from the European Union have left Mali 275 00:17:53,840 --> 00:17:56,080 Speaker 1: as well. And actually one thing that happened is that 276 00:17:56,119 --> 00:17:58,720 Speaker 1: I wanted to go physically to Mali to report on 277 00:17:58,760 --> 00:18:02,920 Speaker 1: this story, and a few weeks, I think even just 278 00:18:02,960 --> 00:18:06,520 Speaker 1: a few days before, I was thinking about that tree 279 00:18:06,840 --> 00:18:09,120 Speaker 1: and trying to figure out how the logistics would work. 280 00:18:09,480 --> 00:18:15,040 Speaker 1: A French journalist was kidnapped by rebel groups, and then 281 00:18:15,160 --> 00:18:18,480 Speaker 1: that meant that the safety of journalists in that part 282 00:18:18,480 --> 00:18:20,880 Speaker 1: of Molly could not be guaranteed, so we could never 283 00:18:20,920 --> 00:18:29,119 Speaker 1: travel there after the break our. Timbuctoo's historical records the 284 00:18:29,160 --> 00:18:31,960 Speaker 1: answer to Mali's lack of weather data and what does 285 00:18:32,000 --> 00:18:35,080 Speaker 1: the lack of accurate climate models across the continent of 286 00:18:35,119 --> 00:18:48,119 Speaker 1: Africa mean for discussions of loss and damage. Of course, 287 00:18:48,320 --> 00:18:51,240 Speaker 1: weather stations aren't the only way in which we can 288 00:18:51,320 --> 00:18:54,800 Speaker 1: gather data. Of course, current data is gathered from weather stations, 289 00:18:55,080 --> 00:18:59,040 Speaker 1: but there are other approaches to try and understand what's 290 00:18:59,080 --> 00:19:04,159 Speaker 1: happening to the continent right absolutely, and one way to 291 00:19:04,240 --> 00:19:06,320 Speaker 1: do that, and it's not just in Africa, it's being 292 00:19:06,359 --> 00:19:09,680 Speaker 1: done everywhere are historical documents. So if you go back 293 00:19:09,680 --> 00:19:12,600 Speaker 1: in time, people have been recording what the weather has 294 00:19:12,640 --> 00:19:15,760 Speaker 1: been like for hundreds and even thousands of years, and 295 00:19:15,800 --> 00:19:19,679 Speaker 1: there are clues in historic documents. In the case of 296 00:19:19,760 --> 00:19:24,240 Speaker 1: Mali and of Timbuktuo specifically, Timbuktu was a cultural center 297 00:19:24,280 --> 00:19:27,440 Speaker 1: and one of the main cities in North Africa, so 298 00:19:27,560 --> 00:19:30,919 Speaker 1: people would go there to study many many things and 299 00:19:30,960 --> 00:19:34,360 Speaker 1: they would leave written record of what was going on. 300 00:19:34,960 --> 00:19:41,600 Speaker 1: There are some stunning architectural buildings that still stand in Timbuctwo. Yeah, absolutely, 301 00:19:41,600 --> 00:19:45,680 Speaker 1: and they're actually protected by UNESCO. So they are ancient 302 00:19:45,720 --> 00:19:49,560 Speaker 1: shrines and churches that could be visited, and they're of 303 00:19:49,600 --> 00:19:55,439 Speaker 1: a characteristic architecture made with mud, very very typical of Timbuktu. 304 00:19:55,760 --> 00:19:59,120 Speaker 1: And so what used to happen in around the sixteenth 305 00:19:59,160 --> 00:20:01,800 Speaker 1: centuries that people like I said they would travel for 306 00:20:01,880 --> 00:20:05,200 Speaker 1: weeks and for months through the desert to learn from 307 00:20:05,400 --> 00:20:09,600 Speaker 1: the wise men in the city. They would learn everything 308 00:20:09,680 --> 00:20:14,800 Speaker 1: from Islamic theology, history, philosophy, anything. And then the city 309 00:20:14,920 --> 00:20:19,040 Speaker 1: was also across roads for tribes that lived in the desert, 310 00:20:19,119 --> 00:20:22,080 Speaker 1: so you can think that they used to be lots 311 00:20:22,080 --> 00:20:25,919 Speaker 1: of camel caravans and that would carry salt and gold, 312 00:20:26,240 --> 00:20:29,360 Speaker 1: even slaves across the desert and they would be traded 313 00:20:29,560 --> 00:20:33,240 Speaker 1: in Timbuktu. That left a very big pay portrayal in 314 00:20:33,280 --> 00:20:36,960 Speaker 1: the form of manuscripts that describe what life was like 315 00:20:37,240 --> 00:20:40,840 Speaker 1: at the time and that tried to register things from 316 00:20:41,280 --> 00:20:45,119 Speaker 1: agriculture techniques and obviously the weather. So we have these 317 00:20:45,240 --> 00:20:49,920 Speaker 1: historical documents from centuries ago that have detailed weather conditions 318 00:20:49,920 --> 00:20:53,000 Speaker 1: in them. Have we ended up using them in some form? 319 00:20:53,560 --> 00:20:57,240 Speaker 1: Not yet, And that's another fascinating part of the stories, 320 00:20:57,320 --> 00:21:01,600 Speaker 1: and something I find fascinating about Timbook two, and it's 321 00:21:01,680 --> 00:21:04,399 Speaker 1: that what we call the Timbook two manuscripts that people 322 00:21:04,480 --> 00:21:07,399 Speaker 1: might have heard or read about them, is a huge 323 00:21:07,440 --> 00:21:10,680 Speaker 1: collection of documents, not just from Timbook two, but from 324 00:21:10,760 --> 00:21:17,040 Speaker 1: the whole region that have been preserved within families for centuries. 325 00:21:17,400 --> 00:21:21,800 Speaker 1: So families will be guardians or custodians of a certain 326 00:21:21,800 --> 00:21:26,600 Speaker 1: set of documents that gets passed through generations, and academics 327 00:21:26,680 --> 00:21:30,600 Speaker 1: have only started to describe the surface of the wisdom 328 00:21:30,840 --> 00:21:34,159 Speaker 1: and the contents of these manuscripts. So I talk to 329 00:21:34,200 --> 00:21:38,480 Speaker 1: some researchers in the US that are digitalizing these documents, 330 00:21:38,520 --> 00:21:42,200 Speaker 1: so basically scanning them and making sure they don't get lost, 331 00:21:42,240 --> 00:21:44,840 Speaker 1: they don't get burned or stolen or anything like that, 332 00:21:45,080 --> 00:21:48,960 Speaker 1: and then analyzing the contents. They have made it possible 333 00:21:49,160 --> 00:21:54,200 Speaker 1: for people to search online certain keywords on these documents. 334 00:21:54,320 --> 00:21:57,200 Speaker 1: So if you do that search for rain, for example, 335 00:21:57,240 --> 00:21:58,960 Speaker 1: you'll be able to find that there are a bunch 336 00:21:58,960 --> 00:22:02,240 Speaker 1: of documents that rain and rain changes and so on. 337 00:22:02,359 --> 00:22:05,000 Speaker 1: But no one as far as I know, has gone 338 00:22:05,040 --> 00:22:09,639 Speaker 1: as far as to put that into scientific research. But 339 00:22:09,760 --> 00:22:14,199 Speaker 1: that has been done with ancient documents. In European nations 340 00:22:14,280 --> 00:22:18,400 Speaker 1: like Germany and the UK, people have gone to monasteries 341 00:22:18,480 --> 00:22:21,840 Speaker 1: and looked at the annotations of what the weather was 342 00:22:21,880 --> 00:22:24,800 Speaker 1: like centuries ago and produce scientific research out of this. 343 00:22:25,119 --> 00:22:28,000 Speaker 1: So it's certainly possible to find better models eventually once 344 00:22:28,040 --> 00:22:32,080 Speaker 1: the stata has been translated into a usable form. Yes, absolutely, 345 00:22:32,119 --> 00:22:34,800 Speaker 1: it won't be as thorough as if you had had 346 00:22:35,600 --> 00:22:39,360 Speaker 1: someone recording the temperatures every day for the past five 347 00:22:39,440 --> 00:22:42,640 Speaker 1: hundred years, or water levels for the past five hundred years, 348 00:22:42,640 --> 00:22:44,800 Speaker 1: which you know there are places in the world that 349 00:22:44,880 --> 00:22:48,199 Speaker 1: have been recording for many centuries. But at least you 350 00:22:48,240 --> 00:22:52,520 Speaker 1: can have an idea on whether rivers were bigger or smaller, 351 00:22:52,600 --> 00:22:55,920 Speaker 1: whether rain was more frequent, the winds, etc. And then 352 00:22:55,960 --> 00:22:59,800 Speaker 1: that could help scientific research definitely. So there's the story 353 00:23:00,160 --> 00:23:04,320 Speaker 1: I find fascinating, which is in eighteen fifteen, Mount Tambora, 354 00:23:04,400 --> 00:23:06,919 Speaker 1: which is a huge volcano, went off and put all 355 00:23:06,960 --> 00:23:12,800 Speaker 1: these sulfur related compounds into the atmosphere. Caused what is 356 00:23:12,840 --> 00:23:16,040 Speaker 1: now known as the year that had no summer across 357 00:23:16,080 --> 00:23:20,000 Speaker 1: the world. There were famines, deaths, etc. But it also 358 00:23:20,160 --> 00:23:24,320 Speaker 1: changed art because researchers have now analyzed paintings from that 359 00:23:24,400 --> 00:23:28,080 Speaker 1: era compared them to the pre eighteen fifteen era, and 360 00:23:28,240 --> 00:23:32,400 Speaker 1: essentially the skies turned more orange because there was more 361 00:23:32,440 --> 00:23:36,120 Speaker 1: sulfur and that's what the painters were reflecting in their paintings. 362 00:23:36,600 --> 00:23:39,280 Speaker 1: So there's all these downstream impacts that happened from whether 363 00:23:39,400 --> 00:23:42,719 Speaker 1: and are they're recorded in these weird forms which may 364 00:23:42,760 --> 00:23:45,200 Speaker 1: not be data, but it's still data if you want 365 00:23:45,200 --> 00:23:48,000 Speaker 1: to interpret it that way. Yeah, and maybe we're going 366 00:23:48,000 --> 00:23:51,080 Speaker 1: a bit tough topic either know. But there's also research 367 00:23:51,280 --> 00:23:54,760 Speaker 1: on the legends that Aboriginal Australians tell each other and 368 00:23:54,840 --> 00:23:58,280 Speaker 1: have been telling each other for millennia, because you might know, 369 00:23:58,359 --> 00:24:02,680 Speaker 1: the Aboriginal Australians are the longest running people or civilization 370 00:24:03,000 --> 00:24:07,440 Speaker 1: on Earth, and researchers have analyzed what they thought were 371 00:24:07,560 --> 00:24:10,480 Speaker 1: legends and found that they actually tell the story of 372 00:24:10,560 --> 00:24:14,960 Speaker 1: the land. And so that these stories correspond with changes 373 00:24:15,160 --> 00:24:18,280 Speaker 1: on rivers and mountains and on the sea that actually 374 00:24:18,320 --> 00:24:21,440 Speaker 1: happened eighties and eighties ago. It's not just weather station's 375 00:24:21,520 --> 00:24:26,280 Speaker 1: recording the changes and the data there's these changes can 376 00:24:26,280 --> 00:24:28,480 Speaker 1: be found everywhere. Now we are about to head into 377 00:24:28,520 --> 00:24:32,000 Speaker 1: another cop meeting, which is this annual climate conference that 378 00:24:32,040 --> 00:24:35,600 Speaker 1: the UN organizers. This time it's in Egypt November, called 379 00:24:35,600 --> 00:24:38,200 Speaker 1: COP twenty seven because it's a twenty seventh time it's 380 00:24:38,200 --> 00:24:41,280 Speaker 1: happening and it's the fifth time it's being hosted by 381 00:24:41,400 --> 00:24:46,680 Speaker 1: a country in Africa. How does the lack of good 382 00:24:46,720 --> 00:24:51,600 Speaker 1: weather data fit into this international climate discussions and negotiations. 383 00:24:51,920 --> 00:24:54,560 Speaker 1: It's at the heart of it, because if you don't 384 00:24:54,600 --> 00:24:57,960 Speaker 1: have the data, then there is no discussion possible. We 385 00:24:58,119 --> 00:25:01,560 Speaker 1: tend to say that Africa is the continent suffering the 386 00:25:01,600 --> 00:25:04,719 Speaker 1: most from climate change, but the one that has contributed 387 00:25:05,000 --> 00:25:09,119 Speaker 1: less to it. So the second part we know for 388 00:25:09,200 --> 00:25:11,720 Speaker 1: a fact that it's the one that has contributed less 389 00:25:11,760 --> 00:25:14,760 Speaker 1: to it. The first part of the sentence, it's suffering 390 00:25:14,800 --> 00:25:18,480 Speaker 1: the most, is the hard one to prove because we 391 00:25:18,720 --> 00:25:22,879 Speaker 1: have this intuition, but actually there isn't that much hard 392 00:25:23,000 --> 00:25:26,879 Speaker 1: data on it. And the reason is again weather stations. 393 00:25:27,280 --> 00:25:30,280 Speaker 1: So if you don't know whether a heat wave is happening, 394 00:25:30,480 --> 00:25:33,159 Speaker 1: or why people are dying in a certain place, or 395 00:25:33,160 --> 00:25:37,040 Speaker 1: why crops are failing, then it's really hard to attribute 396 00:25:37,160 --> 00:25:41,760 Speaker 1: these effects to climate change. And so a lot of 397 00:25:41,920 --> 00:25:45,639 Speaker 1: what will be discussed in COP this year will hopefully 398 00:25:45,680 --> 00:25:48,720 Speaker 1: be around that, around the need to have better data 399 00:25:48,840 --> 00:25:52,960 Speaker 1: coming from Africa in order to know more the effects 400 00:25:52,960 --> 00:25:56,480 Speaker 1: of climate change and what's happening there. And then the 401 00:25:56,560 --> 00:25:59,879 Speaker 1: other effect that the lack of data is having is 402 00:26:00,280 --> 00:26:04,320 Speaker 1: that in the IPCC reports there is lots of research 403 00:26:04,640 --> 00:26:08,600 Speaker 1: reflected on America and Europe and developed nations, but then 404 00:26:08,640 --> 00:26:12,520 Speaker 1: developing nations have less of an importance because of that 405 00:26:12,720 --> 00:26:15,320 Speaker 1: lack of data. Something that's happened over the last few 406 00:26:15,400 --> 00:26:18,359 Speaker 1: years that's really changed the way we talk about climate 407 00:26:18,440 --> 00:26:24,160 Speaker 1: change is this phenomena of attribution studies where climate scientists 408 00:26:24,200 --> 00:26:26,879 Speaker 1: can look at an extreme weather event, maybe that's a 409 00:26:26,920 --> 00:26:29,920 Speaker 1: heat wave, maybe it's a flood, and tell you just 410 00:26:30,080 --> 00:26:33,320 Speaker 1: how much worse they were made by climate change. Yes, 411 00:26:33,400 --> 00:26:37,760 Speaker 1: and again Africa, it's so important to have these sort 412 00:26:37,800 --> 00:26:41,439 Speaker 1: of studies about things that happen in Africa, about extreme 413 00:26:41,480 --> 00:26:44,359 Speaker 1: weather events in Africa, and we're not seeing them. And 414 00:26:44,400 --> 00:26:46,639 Speaker 1: again we're not seeing them because we don't have the 415 00:26:46,760 --> 00:26:51,080 Speaker 1: data to produce these studies. And the implications of that 416 00:26:51,119 --> 00:26:54,280 Speaker 1: are huge. It's not just a matter of scientific knowledge, 417 00:26:54,760 --> 00:26:57,720 Speaker 1: but African nations and this is going to be a 418 00:26:57,760 --> 00:27:00,720 Speaker 1: really important issue at COP twenty seven, that meeting you 419 00:27:00,760 --> 00:27:04,720 Speaker 1: mentioned before in Egypt, in November African nations. One of 420 00:27:04,720 --> 00:27:07,280 Speaker 1: the things that they want is something that again sorry 421 00:27:07,280 --> 00:27:09,800 Speaker 1: to bring up the jargon, but it's something called loss 422 00:27:09,800 --> 00:27:15,240 Speaker 1: and damage, which is the developing nations suffering from the 423 00:27:15,280 --> 00:27:20,960 Speaker 1: effects of climate change should be compensated by developed nations 424 00:27:21,000 --> 00:27:23,760 Speaker 1: that caused climate change in the first place, or that 425 00:27:23,840 --> 00:27:27,080 Speaker 1: are the main contributors to climate change. But if you 426 00:27:27,119 --> 00:27:30,600 Speaker 1: can approve that something has been caused by climate change, 427 00:27:30,600 --> 00:27:33,480 Speaker 1: and then how can you get compensated? Right? So this 428 00:27:33,680 --> 00:27:37,240 Speaker 1: is why again data is so important, so you can 429 00:27:37,240 --> 00:27:41,200 Speaker 1: produce studies, including attribution studies, that would then later allow 430 00:27:41,280 --> 00:27:45,000 Speaker 1: a country to go to developed nation and say these 431 00:27:45,280 --> 00:27:49,879 Speaker 1: extreme events, say a heat wave or a typhoon, or 432 00:27:50,080 --> 00:27:55,159 Speaker 1: a storm, or a flawed or anything, caused this amount 433 00:27:55,200 --> 00:28:00,720 Speaker 1: of damage to my GDP, to my crop production, an 434 00:28:00,720 --> 00:28:04,320 Speaker 1: ex amount of deaths of that number of people lost 435 00:28:04,359 --> 00:28:08,959 Speaker 1: their homes, all these very tangible, very real effects. And 436 00:28:09,359 --> 00:28:12,800 Speaker 1: I have this scientific paper which has been peer reviewed 437 00:28:12,840 --> 00:28:17,440 Speaker 1: and authored by reputable scientists saying this event was made 438 00:28:17,560 --> 00:28:19,960 Speaker 1: much worse by climate change. So how are you planning 439 00:28:19,960 --> 00:28:22,199 Speaker 1: to compensate me? So if a country cannot do that, 440 00:28:22,760 --> 00:28:28,880 Speaker 1: then the injustice in the system remains what's a climate 441 00:28:28,920 --> 00:28:32,119 Speaker 1: story that you found meaningful? I think the story that 442 00:28:32,240 --> 00:28:34,560 Speaker 1: changes the way I see climate and the way see 443 00:28:34,560 --> 00:28:37,920 Speaker 1: climate journalism. Was the first story that I read about 444 00:28:38,040 --> 00:28:42,520 Speaker 1: how climate had impacted and contributed to the Arab Spring. 445 00:28:43,160 --> 00:28:47,040 Speaker 1: So that's when I started to think that climate and 446 00:28:47,200 --> 00:28:53,520 Speaker 1: climate change had a huge impact into everything that happens 447 00:28:53,560 --> 00:28:56,120 Speaker 1: in our lives, in a small way but also in 448 00:28:56,160 --> 00:28:59,160 Speaker 1: a big way. And there's a more personal connection there. 449 00:28:59,200 --> 00:29:02,400 Speaker 1: Because the Arab Spring is what drew you into journalism, 450 00:29:02,440 --> 00:29:06,600 Speaker 1: that's it. Well, I was a journalist before, but I 451 00:29:06,800 --> 00:29:09,840 Speaker 1: happened to be in Cairo when the Arab Spring broke. 452 00:29:10,680 --> 00:29:14,560 Speaker 1: I wrote about it for two years, and I am 453 00:29:15,440 --> 00:29:18,800 Speaker 1: slightly ashamed to say that I never made the climate connection. 454 00:29:18,920 --> 00:29:21,880 Speaker 1: It was later when the reports started to come out 455 00:29:21,920 --> 00:29:25,520 Speaker 1: about how if I remember, well, two thousand and eight 456 00:29:25,960 --> 00:29:28,440 Speaker 1: had been a really dry year, and that drought had 457 00:29:28,480 --> 00:29:33,400 Speaker 1: continued and had impacted with prices and how bread had 458 00:29:33,440 --> 00:29:38,520 Speaker 1: become more expensive. That's when I made the connection. But yeah, absolutely, 459 00:29:38,600 --> 00:29:40,760 Speaker 1: there's there's a very personal side to it that this 460 00:29:40,880 --> 00:29:44,200 Speaker 1: event that I lived in such an intense way was 461 00:29:44,440 --> 00:29:48,200 Speaker 1: so affected by climate change, and I just lived through 462 00:29:48,240 --> 00:29:59,959 Speaker 1: it without realizing it. A lack of good climate dation 463 00:30:00,320 --> 00:30:03,960 Speaker 1: might sound like a wonky subject, but as Laura's reporting shows, 464 00:30:04,080 --> 00:30:07,840 Speaker 1: it has huge consequences for those without it. Access to 465 00:30:07,880 --> 00:30:10,360 Speaker 1: good weather data should be as much a part of 466 00:30:10,400 --> 00:30:14,680 Speaker 1: climate justice discussions as say, ensuring coal miners are not 467 00:30:14,800 --> 00:30:18,960 Speaker 1: left behind or developing countries have enough funding to move 468 00:30:19,000 --> 00:30:22,440 Speaker 1: to clean energy. For more. You can read Laura's story 469 00:30:22,520 --> 00:30:25,880 Speaker 1: on bloomberg dot com slash green. It's also linked in 470 00:30:25,920 --> 00:30:30,040 Speaker 1: the show notes. Thanks so much for listening to Zero. 471 00:30:30,440 --> 00:30:33,360 Speaker 1: If you like the show, please rate, review and subscribe, 472 00:30:33,840 --> 00:30:36,280 Speaker 1: tell a friend, or write it in a diary that 473 00:30:36,560 --> 00:30:39,520 Speaker 1: may be found in five hundred years. If you've got 474 00:30:39,520 --> 00:30:42,000 Speaker 1: a suggestion for a guest or topic, or something you 475 00:30:42,080 --> 00:30:44,440 Speaker 1: just want us to look into, get in touch at 476 00:30:44,560 --> 00:30:48,160 Speaker 1: Zero Pod at Bloomberg dot net. Zero's producer is Oscar 477 00:30:48,200 --> 00:30:51,840 Speaker 1: Boyd and senior producer is Christine driscoll Our. Theme music 478 00:30:51,960 --> 00:30:55,560 Speaker 1: is composed by Wonderlely. Many people help make the show 479 00:30:55,560 --> 00:30:59,320 Speaker 1: a success this week, thanks to my colleague at Bloombergreen, 480 00:30:59,640 --> 00:31:02,680 Speaker 1: Eric Austin, who has everything in his brain that I 481 00:31:02,760 --> 00:31:06,880 Speaker 1: wish I had. I'm Aukshatrati back next week