WEBVTT - A Historic El Niño Is Coming That Could Cost the World Trillions 0:00:02.720 --> 0:00:17.720 Bloomberg Audio Studios, Podcasts, Radio News. 0:00:18.560 --> 0:00:21.320 Hello and welcome to another episode of the aud Thoughts podcast. 0:00:21.480 --> 0:00:22.840 I'm Tracy Alloway. 0:00:22.640 --> 0:00:24.520 And I'm Jill. Why isn't thal Joe. 0:00:24.560 --> 0:00:26.159 I don't know if I've ever told you about this, 0:00:26.360 --> 0:00:28.360 but I'm a little bit of a weather. 0:00:28.200 --> 0:00:30.760 Nerd Okay, I know, maybe a little bit. 0:00:30.800 --> 0:00:30.960 I know. 0:00:31.280 --> 0:00:34.519 I just get really excited by weather. I love thunderstorms. 0:00:34.640 --> 0:00:36.839 I love a good thunderstorm so great inside. 0:00:36.960 --> 0:00:39.440 Well, this is the thing. I love thunderstorms, except when 0:00:39.440 --> 0:00:41.640 I'm on an airplane or something like that. I do 0:00:41.720 --> 0:00:45.320 not like economically damaging weather. I feel like I need 0:00:45.360 --> 0:00:46.560 to throw that caveat in there. 0:00:46.920 --> 0:00:49.360 You know what I am. I'm like a weather truther. 0:00:50.200 --> 0:00:53.480 So, for example, I don't believe that there's any accurate way, 0:00:53.560 --> 0:00:56.760 for example, to measure wind chill. It's like, oh, it's 0:00:56.800 --> 0:00:59.800 like twenty five degrees out and it feels like fifteen. 0:01:00.160 --> 0:01:02.200 It's like, how do you know what fifteen feels like? 0:01:02.400 --> 0:01:04.800 You know what I'm saying, Like, I don't believe in 0:01:04.840 --> 0:01:08.040 some of these numbers. Also, humidity, what, there's water in 0:01:08.080 --> 0:01:09.160 the air. Give me a break. 0:01:09.200 --> 0:01:10.039 There's a water in there. 0:01:10.080 --> 0:01:13.360 Now, tell me you've never been to Abu Dhabi this 0:01:13.400 --> 0:01:16.520 summer without telling me you've never been to Abudabi in this. 0:01:16.280 --> 0:01:20.080 Last week in New York, I know that one was 0:01:20.080 --> 0:01:23.120 a joke. I am a Wincheal skeptic, even though I 0:01:23.120 --> 0:01:25.320 don't think that's the topic of our conversation today. 0:01:25.400 --> 0:01:27.880 It is not the topic of our conversation is actually 0:01:28.160 --> 0:01:33.600 economically damaging whether yes, and specifically the upcoming El Nino, which, 0:01:34.080 --> 0:01:37.200 by the way, it's it's not that that often that 0:01:37.280 --> 0:01:39.679 you see a lot of cell side notes start to 0:01:39.800 --> 0:01:42.360 mention weather phenomenon, But just in the past, you know, 0:01:42.360 --> 0:01:46.080 a few days, I've had a Thailand economics note from 0:01:46.120 --> 0:01:49.680 City talking about interest rates and quoting the potential impact 0:01:49.720 --> 0:01:53.360 of El Nino potentially inflationary. I've had a Bank of 0:01:53.480 --> 0:01:57.680 England outlook from Morgan Stanley that mentioned El Nino, and 0:01:57.760 --> 0:02:02.520 also a Bank of America f outlook talking about the 0:02:02.640 --> 0:02:06.640 impact on em currency. So this is something that people 0:02:06.760 --> 0:02:08.919 are expecting to have an actual impact both on the 0:02:08.919 --> 0:02:11.120 global economy and the investment landscape. 0:02:11.160 --> 0:02:13.400 You know, I will just say, even though I'm not 0:02:13.440 --> 0:02:15.120 a weather nerd the way you are, I sort of 0:02:15.160 --> 0:02:17.240 have this thought that if you look at like the 0:02:17.360 --> 0:02:20.919 really grand sweep of history, like thousands and thousands of years, 0:02:21.080 --> 0:02:24.120 maybe even hundreds of thousands of years. I sort of 0:02:24.160 --> 0:02:27.160 think there are two things that matter in human history. 0:02:27.560 --> 0:02:29.840 One of them, as you know I'm interested in, is 0:02:29.919 --> 0:02:34.079 like basically technological evolution media technology and how it reshapes 0:02:34.120 --> 0:02:36.519 how we think. And then the other one is sort 0:02:36.560 --> 0:02:41.080 of climate change, weather and storms. And I think that 0:02:41.160 --> 0:02:45.360 when we think about history, many things are contingent either 0:02:45.560 --> 0:02:49.160 on like brief or long periods of weather change, and 0:02:49.200 --> 0:02:54.680 it can reshape society in permanent, long term, unexpected ways 0:02:54.800 --> 0:02:57.600 at the time. And have we ever even done any 0:02:57.680 --> 0:03:00.680 aninio episode in like ten years or eleven years of 0:03:00.720 --> 0:03:03.080 doing this? I don't think we have, But it sounds 0:03:03.120 --> 0:03:05.160 like this is one that we should cover if it's 0:03:05.160 --> 0:03:06.200 showing up on all these notes. 0:03:06.240 --> 0:03:08.440 If we're going to cover one, this should probably be 0:03:08.520 --> 0:03:10.920 the one because at the moment, well, you have different 0:03:10.960 --> 0:03:14.760 al Nino forecasts, and you know there's a range of 0:03:14.960 --> 0:03:17.959 probabilities involved here, but a lot of the forecasting models 0:03:17.960 --> 0:03:20.480 are currently forecasting that this is going to be a 0:03:20.480 --> 0:03:24.400 potentially record breaking al nino, something we haven't seen in decades. 0:03:24.440 --> 0:03:26.000 Do you think there are any people who are like, 0:03:26.200 --> 0:03:28.640 it's interesting you bring up the El Nino. You know, 0:03:28.720 --> 0:03:32.160 I've always thought Nino's are the more underrated one. Do 0:03:32.200 --> 0:03:33.320 you think that's the type of guy? 0:03:33.680 --> 0:03:36.840 No, I hope not. But are you going to become 0:03:36.880 --> 0:03:39.040 like an Al Nino truth or are going to be like, 0:03:39.040 --> 0:03:41.040 how do you know it's El Nino unless you're standing 0:03:41.080 --> 0:03:43.280 in the Pacific Ocean measuring the water yourself. 0:03:43.320 --> 0:03:44.840 We're going to be covering guy. He's like, you know, 0:03:44.920 --> 0:03:47.560 you really should be paying attention of coming La nin Yah. 0:03:47.680 --> 0:03:51.480 Oh good, that'll be a fun transformation for you. Okay, Well, 0:03:51.640 --> 0:03:53.920 we do, in fact have the perfect guest. We're going 0:03:53.960 --> 0:03:56.880 to be speaking to someone who not only follows the 0:03:56.920 --> 0:04:00.720 weather but also tries to measure its economic impact. We 0:04:00.840 --> 0:04:04.800 have Justin Mankin. He is professor of geography at Dartmouth 0:04:04.880 --> 0:04:08.320 and director of the Climate Modeling and Impacts Group. So Justin, 0:04:08.520 --> 0:04:10.080 thank you so much for coming on odd thoughts. 0:04:10.440 --> 0:04:12.240 Yeah, happy to be here, Thanks for having me. 0:04:12.400 --> 0:04:14.480 I feel like maybe the first thing we should do 0:04:14.600 --> 0:04:17.719 is can you convince Joe that El Nino is a 0:04:17.800 --> 0:04:22.040 real thing? Apparently he needs like a first person experience 0:04:22.080 --> 0:04:22.960 of it. What is El nine? 0:04:23.320 --> 0:04:23.520 Yeah? 0:04:23.560 --> 0:04:23.840 What is it? 0:04:24.080 --> 0:04:27.479 Yeah? Like wind chill factor is, it is in fact 0:04:27.560 --> 0:04:31.600 real and measurable, and they're both kind of actually aerodynamically related. Right, 0:04:31.640 --> 0:04:34.440 So wind chill, the feeling you get on your skin 0:04:35.040 --> 0:04:37.440 is owing to the fact that the wind is wicking 0:04:37.480 --> 0:04:40.919 away latent heat from your skin, which is the heat 0:04:40.960 --> 0:04:44.160 associated with a phase change of water from a liquid 0:04:44.240 --> 0:04:48.080 to a vapor. And so that's why when you get 0:04:48.120 --> 0:04:51.680 out of a shower, you feel cooler than before you 0:04:51.720 --> 0:04:54.800 get into it. Right, So that evaporation off your skin 0:04:54.920 --> 0:04:58.640 induced by aerodynamic resistance i e. The wind, That is 0:04:58.680 --> 0:05:00.640 the thing that's cooling you down, which is why we 0:05:00.680 --> 0:05:03.800 can measure it and why we can tell you that 0:05:03.880 --> 0:05:09.000 cold exposure is a bad thing. The Almino phenomenon is 0:05:09.080 --> 0:05:11.400 an ocean atmosphere. It's kind of part of what's called 0:05:11.400 --> 0:05:15.839 the Almino Southern oscillation, which is essentially a relationship between 0:05:15.880 --> 0:05:20.640 the tropical Pacific and the overlying tropical atmosphere, and it 0:05:20.720 --> 0:05:23.479 has a tendency to be in what we call a 0:05:23.520 --> 0:05:27.160 neutral state, which is kind of average conditions where you 0:05:27.320 --> 0:05:30.279 have these easterly trade winds. So these are winds that 0:05:30.320 --> 0:05:33.080 are blowing from east to west across the tropics. So 0:05:33.200 --> 0:05:36.760 long the equator, and what they're doing is they're dragging 0:05:37.360 --> 0:05:40.680 surface water, surface water that's been warm because it's in 0:05:40.720 --> 0:05:43.719 the tropics. The sun is just shining there all year round, 0:05:44.080 --> 0:05:46.719 and so that water heats up and then the winds 0:05:46.800 --> 0:05:50.040 kind of drag that surface water and create a literally 0:05:50.080 --> 0:05:54.560 a pile such that the sea altitude is higher in 0:05:54.600 --> 0:05:58.480 the West Pacific than in the East Pacific. And that 0:05:58.720 --> 0:06:01.800 pile of warm water is something we call the West 0:06:01.839 --> 0:06:04.799 Pacific warm Pool. And so it just sits there, parked 0:06:04.800 --> 0:06:08.680 by Indonesia and Australia, and then every once in a while, 0:06:09.120 --> 0:06:13.480 those trade winds weaken and break down, and that pile 0:06:13.920 --> 0:06:17.440 of warm water that actually penetrates quite deep into the 0:06:17.440 --> 0:06:21.000 western Tropical Pacific. That pile of warm water just spreads 0:06:21.040 --> 0:06:25.560 itself out over the vast expanse that is the Tropical Pacific. 0:06:26.040 --> 0:06:30.680 And so that's actually how we measure ELMOS. We're measuring 0:06:30.720 --> 0:06:34.400 it as a function of sea surface temperature anomaly. So 0:06:34.480 --> 0:06:39.600 how much warmer than average our ocean temperatures at the surface, 0:06:40.760 --> 0:06:43.839 you know, at this time in this location. And you know, 0:06:43.880 --> 0:06:46.400 there are different ways to kind of calculate what the 0:06:46.480 --> 0:06:49.960 regional averages, and we can get into that technical nitty gritty, 0:06:50.120 --> 0:06:54.520 but essentially, all this water slashes back eastward, and in 0:06:54.600 --> 0:07:00.320 doing so reconfigures all of weather, which is just energybution 0:07:00.440 --> 0:07:04.440 from the Sun on our fluid planet. Right. It redistributes 0:07:04.520 --> 0:07:07.240 all of that energy differently than would have been the 0:07:07.279 --> 0:07:10.280 case if all that water were piled up in the 0:07:10.280 --> 0:07:13.840 Western Pacific. I guess like a really simple analogy a 0:07:13.920 --> 0:07:16.760 listener could maybe grab onto is they've walked into a 0:07:16.800 --> 0:07:20.560 cold room where there's a little space heater plugged into 0:07:20.600 --> 0:07:23.680 the corner, right, And that little space heater, yeah, it 0:07:23.720 --> 0:07:26.480 is maybe generating some heat that's warming up the room, 0:07:27.000 --> 0:07:30.480 but it's really only warming up that corner of the room. Yeah, 0:07:30.720 --> 0:07:34.200 that is akin to neutral or what are called La 0:07:34.320 --> 0:07:37.560 Nina like conditions, which is an amplification of the neutral pattern, 0:07:37.600 --> 0:07:40.840 meaning that those winds are stronger than normal and the 0:07:40.880 --> 0:07:45.200 eastern Tropical Pacific is cooler than normal. When you slosh 0:07:45.560 --> 0:07:49.720 all of that water back across the entire expanse of 0:07:49.880 --> 0:07:53.520 the Tropical Pacific, it's a kin to walking into a 0:07:53.600 --> 0:07:58.200 room that has wall to wall radiators instead the thermodynamics 0:07:58.240 --> 0:08:00.800 of that room. That's a totally differ for an experience 0:08:00.880 --> 0:08:02.760 for you than would have been the case if it 0:08:02.840 --> 0:08:04.960 was just that space heater in the corner. And so 0:08:05.000 --> 0:08:07.960 when an alnino occurs, what it is doing it is 0:08:08.080 --> 0:08:11.880 essentially the tropics are the heat engine of global weather 0:08:11.960 --> 0:08:15.680 and climate, and it's a complete reconfiguration such that so 0:08:15.840 --> 0:08:19.000 much more of our global atmosphere is exposed to that 0:08:19.120 --> 0:08:21.640 heat energy from the ocean that would have otherwise been 0:08:21.720 --> 0:08:26.080 buried in the western tropical Pacific. Does that make sense? 0:08:26.120 --> 0:08:27.119 That was an amazing answer. 0:08:27.200 --> 0:08:29.760 Yea, and I totally I understand something that I went 0:08:29.880 --> 0:08:32.319 forty five years without having any idea of and now 0:08:32.320 --> 0:08:34.280 I get it. And as someone who used to live 0:08:34.320 --> 0:08:36.959 in a drafting New England home, I have a lot 0:08:37.000 --> 0:08:40.320 of experience with space heaters that only heat up that 0:08:40.480 --> 0:08:44.080 corner of the room. What are the conditions or what 0:08:44.160 --> 0:08:49.760 are the drivers such that these aninos are semi regular? 0:08:49.920 --> 0:08:53.240 Why do they seem to come every few years? Why 0:08:53.280 --> 0:08:56.319 are they not a permanent pattern? Why is one year 0:08:56.360 --> 0:08:58.400 or al nino year and another year isn't. 0:08:58.960 --> 0:09:02.400 Yeah, there's a lot of work on kind of the 0:09:02.520 --> 0:09:07.000 drivers of the breakdown of the winds in the eastern 0:09:07.040 --> 0:09:10.839 tropical Pacific. I think a simple answer is that we 0:09:10.920 --> 0:09:16.560 live on a sphere that's rotating and has stratified and 0:09:16.600 --> 0:09:20.079 unstratified fluids in the form of the ocean and the atmosphere, 0:09:20.400 --> 0:09:25.959 and that essentially means that energy is propagated as wave phenomena, 0:09:26.120 --> 0:09:29.360 right with peaks and troughs, and El Nino is akin 0:09:29.880 --> 0:09:32.800 to a wave phenomena, right. It is kind of a 0:09:32.880 --> 0:09:37.439 pattern what we call a mode of variability. George Filander, 0:09:37.480 --> 0:09:42.040 who's this Princeton geophysicist and major scholar of el Nino, 0:09:42.200 --> 0:09:45.760 called el Nino the trunk of the variability tree when 0:09:45.760 --> 0:09:48.200 it comes to climate variation in terms of kind of 0:09:48.559 --> 0:09:54.000 regulating these pulses of energy redistribution around our planet. And so, 0:09:54.440 --> 0:09:58.360 you know, a phenomena like Elmino being modial, meaning it 0:09:58.440 --> 0:10:02.920 has a canonical path learn temporally and spatially and then 0:10:03.000 --> 0:10:08.240 induces physical responses that we can characterize and use as 0:10:08.280 --> 0:10:11.200 a means of prediction. Right Like we know what we 0:10:11.200 --> 0:10:14.320 should expect for Peru, we know what we should expect 0:10:14.880 --> 0:10:19.079 for Australia. And we're able to do that because we've 0:10:19.200 --> 0:10:23.280 diagnosed this mode of variability and given it a name 0:10:23.480 --> 0:10:26.320 and characterized its length scales and characterized what we call 0:10:26.360 --> 0:10:31.040 its teleconnections, meaning that kind of physical bridge between Elmino 0:10:31.160 --> 0:10:34.960 as a phenomenon and what it implies for the Indian 0:10:35.040 --> 0:10:39.920 monsoon or for the southwestern United States temperature and precipitation 0:10:40.040 --> 0:10:43.080 regime for that season. And we've given these things and names, 0:10:43.160 --> 0:10:46.640 and they've come instead of being purely a source of 0:10:46.720 --> 0:10:50.680 kind of stochastic uncertainty, just kind of random variation in 0:10:50.760 --> 0:10:54.000 weather and climate, they become a means of predictability. And 0:10:54.040 --> 0:10:57.679 that's what's really powerful about investigating these type of phenomena 0:10:58.120 --> 0:11:01.120 and giving them names. Right, we have of el Amino 0:11:01.440 --> 0:11:04.599 or Almino Southern oscillation, which is the kind of teleconnection 0:11:04.640 --> 0:11:08.079 with the overlying atmosphere. We have the North Atlantic oscillation, 0:11:08.160 --> 0:11:10.760 we have the Indian Ocean dipole, we have the Matt 0:11:10.800 --> 0:11:15.199 and Julian oscillation. So we've like identified these patterns and 0:11:15.280 --> 0:11:18.840 their kind of manifestation and that in terms of like 0:11:18.880 --> 0:11:22.160 their underlying drivers, which is I think the question you're asking. 0:11:22.440 --> 0:11:26.440 I think it's just a function mostly of living on 0:11:26.520 --> 0:11:31.520 a complex planet with geophysical fluid dynamics and such that 0:11:31.720 --> 0:11:35.040 these wave phenomena just kind of emerge on these characteristic 0:11:35.080 --> 0:11:38.120 time scales. The precise mechanism of action that kind of 0:11:38.200 --> 0:11:42.040 leads the breakdown of these easterly trade winds, which like 0:11:42.160 --> 0:11:45.760 triggers what's called the Birkness feedback, which is this positive 0:11:46.360 --> 0:11:50.960 feedback loops, so an amplifying feedback that tends to reinforce itself. 0:11:51.440 --> 0:11:55.120 Can come from myriad different places, from changes in ice 0:11:55.200 --> 0:11:58.760 cover to you know, a weakening of the equator to 0:11:58.800 --> 0:12:02.319 pole temperature gradient. All kinds of different things have some 0:12:02.400 --> 0:12:05.840 culpability and potentially causing a breakdown of these winds that 0:12:05.960 --> 0:12:10.280 allow the sea surface temperatures to slosh eastward again and 0:12:10.360 --> 0:12:13.440 reconfigure all severe weather on our planet. 0:12:13.640 --> 0:12:15.959 So talk to us about what the forecast actually is 0:12:16.000 --> 0:12:19.080 for this particular al nino, because I read these things 0:12:19.200 --> 0:12:22.800 like apparently the Australian Bureau of Meteorology, which would be 0:12:22.880 --> 0:12:26.400 interested in this because Australia is particularly affected by El Nino. 0:12:26.480 --> 0:12:32.280 Apparently the relative Oceanic Nino index for Ronnie for short, 0:12:32.679 --> 0:12:36.120 apparently has never reached the three point three threshold that 0:12:36.160 --> 0:12:39.800 they currently project before, so that seems extreme. Are what 0:12:39.920 --> 0:12:41.400 exactly are we expecting here? 0:12:41.760 --> 0:12:44.960 So there's a bunch of different ways to forecast almino. 0:12:45.160 --> 0:12:47.800 All of them kind of stem from some of the 0:12:47.840 --> 0:12:51.040 original innovative work in the late seventies and early eighties 0:12:51.120 --> 0:12:53.960 by Mark Cain and Steve Zediac who built the first 0:12:54.280 --> 0:12:57.120 kind of physical model of el amino to explain it 0:12:57.160 --> 0:13:00.760 as a phenomenon and then actually make predictions of its occurrence, 0:13:00.800 --> 0:13:03.640 which is pretty remarkable. But all of these are kind 0:13:03.640 --> 0:13:06.920 of based on Some of them are statistical in nature, 0:13:07.040 --> 0:13:11.000 so kind of taking temperatures at time T and making 0:13:11.000 --> 0:13:14.800 a prediction, given kind of autocorrelation in that time series, 0:13:14.800 --> 0:13:16.720 what it should look like at time T plus one. 0:13:17.240 --> 0:13:19.640 There are others that are physical models that are actually 0:13:19.640 --> 0:13:25.119 stimulating the relationship between oceanic temperatures, wins, the overlying atmosphere 0:13:25.120 --> 0:13:27.520 and what it does to the kind of pressure and 0:13:27.600 --> 0:13:30.480 circulation that actually gets air moving in the first place. 0:13:30.800 --> 0:13:33.520 And so those ones are called process based models, and 0:13:33.559 --> 0:13:36.440 those are distinct from these statistical models. But all of them, 0:13:36.800 --> 0:13:40.080 regardless of how it's being modeled, are kind of converging 0:13:40.559 --> 0:13:43.800 on a record al nino at least since nineteen fifty, 0:13:44.000 --> 0:13:48.520 but arguably since much earlier, right the eighteen seventy seven 0:13:49.120 --> 0:13:53.520 eighteen seventy eight El Nino, which was associated with the 0:13:53.920 --> 0:13:56.720 Great famines, and Mike Davis has this amazing book from 0:13:56.800 --> 0:14:01.280 two thousand and two about that al Nino event. That one, 0:14:01.480 --> 0:14:04.920 I think is, you know, kind of indistinguishable in strength 0:14:04.960 --> 0:14:07.679 from almino as we've already had, such as the ones 0:14:07.679 --> 0:14:10.960 in nineteen eighty two and eighty three, the ninety seven 0:14:11.120 --> 0:14:14.280 and ninety eight Elmino, which listeners may remember as it 0:14:14.360 --> 0:14:18.880 being associated with Chris Farley's snl skit of I am 0:14:18.920 --> 0:14:21.840 the al Nino right, and you know the twenty fifteen 0:14:21.880 --> 0:14:24.680 and twenty sixteen almino, which I believe is the one 0:14:24.720 --> 0:14:29.080 on record depending on your indices. So we've had really 0:14:29.120 --> 0:14:34.480 strong alminos, but this one is looking like it's much bigger. 0:14:34.600 --> 0:14:40.000 Potentially every forecast right now I believe puts the fourth 0:14:40.000 --> 0:14:43.000 company the unfolding l NEU, because we are in an 0:14:43.040 --> 0:14:45.960 advisory and we have been since June according to the 0:14:46.040 --> 0:14:51.680 National Oceanic and Atmospheric Administration's Climate Prediction Center. Essentially you're 0:14:51.720 --> 0:14:56.640 looking at every forecast being having a warmer Almino than 0:14:56.680 --> 0:14:59.160 the twenty sixteen el Nino. 0:15:14.880 --> 0:15:19.520 How did we actually discover el nino as a weather phenomenon? 0:15:19.640 --> 0:15:23.000 Because it happens with some regularity, but it's not like 0:15:23.040 --> 0:15:26.520 it happens every three to four years, like right on time, 0:15:26.800 --> 0:15:30.400 at the same intensity as the previous you know round. 0:15:30.520 --> 0:15:35.240 So how did people identify this as an actual measurable phenomenon. 0:15:35.400 --> 0:15:37.960 You know, in terms of kind of like the modern 0:15:37.960 --> 0:15:41.640 instrumental and climate science era. I don't actually know the 0:15:41.640 --> 0:15:43.720 answer to that, but I do know something that's actually, 0:15:44.040 --> 0:15:47.800 i'd argue even more interesting. So the person who first 0:15:48.160 --> 0:15:52.840 modeled Alminho from a physics perfective Marcaine, It's amazing geophysicist 0:15:52.960 --> 0:15:57.160 at Lamondhorty Yors Observatory at Columbia University, and as colleagues 0:15:57.280 --> 0:15:59.720 John Cheng and other climate scientists there at the time, 0:16:00.080 --> 0:16:02.840 and then an anthropologist by the name of ben Orloff 0:16:02.880 --> 0:16:07.000 who's an anthropologist at Columbia, they wrote this incredible paper 0:16:07.040 --> 0:16:11.560 I believe in Nature back in the nineties or early 0:16:11.640 --> 0:16:15.640 aughts where they essentially showed that there was really good 0:16:15.800 --> 0:16:20.720 archaeological evidence that Peruvian potato farmers as long as five 0:16:20.800 --> 0:16:24.520 hundred years ago were forecasting El Nino, and they were 0:16:24.560 --> 0:16:28.320 doing it based on the visibility of the Pleatees, which 0:16:28.400 --> 0:16:32.520 is a star system you can see from the Andes, 0:16:32.800 --> 0:16:37.160 and essentially as El Nino occurs. What it's doing is 0:16:37.200 --> 0:16:41.640 it's reconfiguring the distribution of high versus low clouds in 0:16:41.680 --> 0:16:45.120 the eastern tropical Pacific. It's essentially taking convection that was 0:16:45.160 --> 0:16:49.560 happening near Australasia and moving it eastward, and in doing so, 0:16:49.640 --> 0:16:53.320 you're creating all these kind of high serious cloud shields 0:16:53.320 --> 0:16:57.960 over the Andes and that reduces the visibility of this 0:16:58.480 --> 0:17:01.920 star pattern. They were and they would alter their their 0:17:01.960 --> 0:17:06.159 their planting dates based on that. So there it is 0:17:06.240 --> 0:17:10.320 just kind of this remarkable cultural knowledge of being able 0:17:10.320 --> 0:17:12.840 to grab onto something and be able to make a 0:17:12.880 --> 0:17:16.639 forecast and inform forecast. And so I'd argue there's a 0:17:16.680 --> 0:17:20.280 lot of cultural knowledge about these innight climate phenomena around 0:17:20.320 --> 0:17:22.879 the world that we have yet to kind of appreciate 0:17:22.920 --> 0:17:26.120 and diagnosed in the in the highly industrialized world where 0:17:26.119 --> 0:17:27.640 we're so relying on instrumentation. 0:17:28.000 --> 0:17:32.240 Yeah, folk meteorology or folk whether it is like extraordinary 0:17:32.359 --> 0:17:34.280 and like how much they were able to anticipate. I 0:17:34.320 --> 0:17:36.520 was sort of I was, you know, I imagined that there 0:17:36.520 --> 0:17:40.560 were probably various civilizations that interpret and known Nina is 0:17:40.600 --> 0:17:43.719 like the king has lost the mandate of Heaven and 0:17:43.760 --> 0:17:45.960 now you know, there's a drought. Now we have to 0:17:46.080 --> 0:17:49.120 have a new political order. Also, this Mike Davis book 0:17:49.160 --> 0:17:51.800 that you mentioned looks fascinating and sort of speaks to 0:17:51.840 --> 0:17:56.560 the point of like late Victorian holocausts, el Nino famines, 0:17:56.760 --> 0:18:00.400 and the making of the Third World talk about squent 0:18:00.520 --> 0:18:04.560 famines in India, northern China, and northeastern Brazil. Definitely going to 0:18:04.640 --> 0:18:07.320 have to read that book. When we talk about a 0:18:07.359 --> 0:18:10.600 quote record el Nina, what is the measure? Is it 0:18:10.680 --> 0:18:16.040 the heat variation? Is it the expected change in rainfall 0:18:16.119 --> 0:18:16.719 or drought? 0:18:16.880 --> 0:18:16.960 Like? 0:18:17.280 --> 0:18:20.240 What are the sort of the gauges that we use 0:18:20.600 --> 0:18:22.439 to say this is a record on Nina or not 0:18:22.640 --> 0:18:25.879 or measuring that? What what are the parameters of an 0:18:25.880 --> 0:18:26.760 el Nina? 0:18:26.920 --> 0:18:30.119 My understanding is it's all benchmarked to the magnitude of 0:18:30.200 --> 0:18:33.679 the sea surface temperature and agay. An anomaly, you know, 0:18:33.920 --> 0:18:37.719 is a departure from expectation or a departure from normal, 0:18:37.760 --> 0:18:41.879 and so what constitutes normal becomes really important there for 0:18:42.000 --> 0:18:47.040 kind of setting the magnitude of how anomalous something is. Right, 0:18:47.080 --> 0:18:49.560 And that's why you know, when Tracy was talking about 0:18:49.560 --> 0:18:54.320 the relative Neino index right, the rony right, that's kind 0:18:54.320 --> 0:18:58.160 of shifting kind of a rolling baseline because we have 0:18:58.280 --> 0:19:02.800 secular warming in the tropical Pacific owing to global warming 0:19:02.800 --> 0:19:05.920 that's occurring right as you heat up the planet. The 0:19:06.040 --> 0:19:09.679 largest reservoir of heat storage just happens to be the 0:19:09.840 --> 0:19:13.280 seventy percent volume of water that constitutes our planet's surface, 0:19:13.840 --> 0:19:16.400 and it takes a lot of energy to heat up 0:19:16.840 --> 0:19:20.920 a volume of water. It's much much more energy intensive 0:19:20.960 --> 0:19:22.720 to heat up a volume of water than it is 0:19:22.920 --> 0:19:26.159 an equivalent volume of air, so that specific heat is 0:19:26.200 --> 0:19:29.040 just much higher. So it's just a great energy reservoir. 0:19:29.080 --> 0:19:32.720 And the ocean has been doing humanity and incredible solid 0:19:32.840 --> 0:19:36.600 by absorbing all this excess heat, which is the problem 0:19:36.760 --> 0:19:40.200 of global warming right that we're not allowing our atmosphere 0:19:40.240 --> 0:19:43.240 to shed heat as efficiently as it otherwise would have 0:19:43.240 --> 0:19:46.800 been if greenhouse gases were lower in our atmosphere, and 0:19:46.840 --> 0:19:50.240 in doing so, we're having to heat up to reattain 0:19:50.320 --> 0:19:52.720 equilibrium such that the energy coming in from the sun 0:19:53.200 --> 0:19:56.320 is balanced by earthlight leaving out to space that is 0:19:56.600 --> 0:19:58.600 not in balance at the moment, and a lot of 0:19:58.600 --> 0:20:00.800 that excess heat is going into the ocean, and that's 0:20:00.840 --> 0:20:04.639 how planetary temperatures increase such that in can equal out again. 0:20:05.080 --> 0:20:08.240 That's essentially a very simple global warming one oh one. 0:20:08.480 --> 0:20:11.720 But the way we're measuring al Nino as a phenomenon 0:20:11.880 --> 0:20:15.080 is by taking a regional average to choosing an area 0:20:15.720 --> 0:20:19.840 in the tropical Pacific that is canonically associated with the 0:20:19.880 --> 0:20:24.280 manifestation of Almino, and you can choose different regions and 0:20:24.320 --> 0:20:30.400 then benchmarking those average temperatures in this month to expectation 0:20:30.800 --> 0:20:33.880 what a average has been over the time over time, 0:20:33.920 --> 0:20:35.840 and so you can choose different regions. So there's a 0:20:35.920 --> 0:20:38.840 Ninio three region which is kind of shifted towards the 0:20:38.880 --> 0:20:41.840 central Pacific. There's a Nino four region which is shifted 0:20:41.880 --> 0:20:45.000 a bit more eastward. There's a Ninio three four region, 0:20:45.040 --> 0:20:47.679 which is kind of the typical region that's now used, 0:20:47.880 --> 0:20:52.560 which characterizes al Nino over something in between those two regions. 0:20:52.760 --> 0:20:56.280 Then there's the E index, which is taking a dimension 0:20:56.320 --> 0:20:59.720 reduction technique on a spatial field and collapsing it to 0:20:59.760 --> 0:21:03.520 say these are kind of the dominant directions of variability 0:21:03.520 --> 0:21:06.479 in that field, and that E index would kind of 0:21:06.560 --> 0:21:10.680 represent something differently in the sense that al Nino, while 0:21:10.720 --> 0:21:14.280 we think of it as a singular phenomenon, it has 0:21:14.359 --> 0:21:18.639 a geography associated with it, and that geography means that 0:21:18.680 --> 0:21:22.560 it has flavors associated with it, and so these flavors 0:21:22.600 --> 0:21:26.160 mean that different parts of the tropical Pacific warm differently 0:21:27.119 --> 0:21:30.960 and look anomalously different. And so not every al nino 0:21:31.600 --> 0:21:34.840 is the same, and so it's consequences in terms of 0:21:35.119 --> 0:21:38.679 the hazard it brings for the United States or for 0:21:39.040 --> 0:21:42.480 hurricane occurrence in the Atlantic basin or whatever. It's not 0:21:42.680 --> 0:21:46.200 the same for every al nino event. This spatial pattern 0:21:46.280 --> 0:21:49.760 of those sea surface temperature anomalies also seems to matter, 0:21:49.800 --> 0:21:51.920 and so there's been a lot of work and kind 0:21:51.920 --> 0:21:55.480 of diagnosing these different flavors of almino, and the E 0:21:55.640 --> 0:21:58.000 index is intended to try to capture some of that. 0:21:58.040 --> 0:22:00.160 So there are lots of different ways for measuring it, 0:22:00.240 --> 0:22:03.679 but it's always a sea surface temperature, normally in degree 0:22:03.720 --> 0:22:07.919 celsius benchmark to some expectation or average or normal. 0:22:08.640 --> 0:22:10.159 So one of the things that Joe and I have 0:22:10.280 --> 0:22:14.359 learned in doing various agricultural episodes over the years is 0:22:14.400 --> 0:22:17.920 that when you have a supply shock, the timing matters right, 0:22:18.040 --> 0:22:21.480 So you generally don't want to declare war in the 0:22:21.520 --> 0:22:24.440 spring planting season when all your soldiers might be off 0:22:24.560 --> 0:22:28.960 planting stuff instead. Does the timing of this particular all 0:22:29.080 --> 0:22:31.919 Nino matter. I keep reading that the peak of this 0:22:32.119 --> 0:22:34.159 el Nino is expected to be in the fall in 0:22:34.200 --> 0:22:38.560 the Northern hemisphere. Is that particularly unfortunate timing? 0:22:39.000 --> 0:22:41.040 You know, there's been a lot of work that looks 0:22:41.200 --> 0:22:45.000 you know, the planting and harvest of crops tends to 0:22:45.000 --> 0:22:49.119 be something that is highly calendar dependent, and there's a 0:22:49.119 --> 0:22:51.800 bit of flexibility that I think farmers have to be 0:22:51.920 --> 0:22:57.959 responsive to their expectations for weather variability, and certainly ad 0:22:58.200 --> 0:23:02.200 forecasts have become important, and agriculture where large has become 0:23:02.240 --> 0:23:05.840 really dependent on al nino forecasts. The thing about al 0:23:05.920 --> 0:23:10.399 Ninho is that it does typically peak in December, January 0:23:10.480 --> 0:23:14.040 and February. So this is kind of the boreal meaning 0:23:14.080 --> 0:23:18.239 the Northern Hemisphere winter, and that is implications for the 0:23:18.280 --> 0:23:22.159 austral summer right for the Southern Hemisphere summer, which is 0:23:22.200 --> 0:23:25.159 in the opposite season. It also there's also kind of 0:23:25.160 --> 0:23:28.080 a time dependency associated with the impact. So let me 0:23:28.160 --> 0:23:30.240 just comcatize this with an example. We've been in an 0:23:30.280 --> 0:23:33.960 al nino advisory since June that's also coincident with the 0:23:34.000 --> 0:23:37.639 typical onset of the South Asian monsoon, which is a 0:23:37.680 --> 0:23:40.800 determinant of feast or famine for billions of people. Right, 0:23:40.880 --> 0:23:46.080 you can link monsoon onset to you know, farmer well 0:23:46.119 --> 0:23:50.159 being in farmer suicides. You can link the timing of 0:23:50.280 --> 0:23:57.800 monsoon onset to diesel prices within India and the political 0:23:57.880 --> 0:24:01.560 fates of people around diesel sung cities, because if the 0:24:01.600 --> 0:24:05.480 monsoon onset is delayed, you have a bunch of farmers 0:24:05.600 --> 0:24:09.399 suddenly needing to irrigate crops where they otherwise wouldn't have 0:24:09.440 --> 0:24:14.440 had to, and that irrigation is dependent on the generators 0:24:14.480 --> 0:24:17.880 that rely on diesel fuel, and so you can see 0:24:18.080 --> 0:24:21.720 price spikes around those types of things, and the political 0:24:21.760 --> 0:24:25.800 fortunes of like municipal and the kind of local politicians 0:24:26.280 --> 0:24:29.159 kind of surviving or not surviving in elections based on 0:24:29.200 --> 0:24:32.000 those kinds of things. So there's like lots of cascading 0:24:32.480 --> 0:24:35.560 ways in which an el nino, even at this early 0:24:35.640 --> 0:24:38.480 stage and its life cycle, which tends to kind of 0:24:38.520 --> 0:24:41.480 start after what we call the spring prediction barrier, which 0:24:41.520 --> 0:24:44.400 is kind of this period in time where it's difficult 0:24:44.440 --> 0:24:46.440 to make a prediction about whether or not an El 0:24:46.520 --> 0:24:50.520 Nino is going to occur to next February, so we're 0:24:50.560 --> 0:24:53.800 looking at kind of a wind down of this event 0:24:53.920 --> 0:24:57.760 in February March of twenty twenty seven. What's interesting about 0:24:57.760 --> 0:25:00.560 it is that it's going to lock in all sorts 0:25:00.600 --> 0:25:03.560 of phenomena. You know, twenty twenty six has a non 0:25:03.680 --> 0:25:07.440 trivial chance of being the warmest year on record, not 0:25:07.480 --> 0:25:10.760 simply because of global warming, but because of this emergent 0:25:10.800 --> 0:25:13.480 al nino. So it is already kind of just increasing 0:25:13.920 --> 0:25:17.400 global temperatures over what they would have been an absence 0:25:17.440 --> 0:25:20.800 of this event, and it's nearly locked in a virtually 0:25:20.880 --> 0:25:24.760 guaranteed record temperature for twenty twenty seven, even though we 0:25:24.840 --> 0:25:29.280 haven't actually had that year unfold yet. It's no coincidence 0:25:29.320 --> 0:25:32.480 that the hottest years on record have almost always been 0:25:33.160 --> 0:25:35.680 El Nino years. Right when you take a warming trend 0:25:35.720 --> 0:25:38.800 and then you impose an anomaly on top of that, 0:25:38.880 --> 0:25:42.640 you're just going to get these outliers. And so these 0:25:42.640 --> 0:25:46.240 impacts are arguably already manifesting. So the South Asian monsoon 0:25:46.840 --> 0:25:50.159 was delayed, that onset was delayed by this al nino, 0:25:50.440 --> 0:25:52.920 and so while the kind of major tel connections and 0:25:52.960 --> 0:25:56.399 the peak strength associated with this event is anticipated to 0:25:56.520 --> 0:25:59.520 be in the winter, which is typically the case associated 0:25:59.560 --> 0:26:02.720 with alminus, and just kind of the life cycle associated 0:26:02.720 --> 0:26:05.960 with them. It's impacts are manifesting now and we'll continue 0:26:05.960 --> 0:26:07.560 to manifest into twenty twenty seven. 0:26:23.480 --> 0:26:27.000 Let's talk a little bit about the economic costs that 0:26:27.040 --> 0:26:30.560 we associate with an el nino. Like I said, up 0:26:30.680 --> 0:26:34.600 until your first answer in this interview, I didn't know 0:26:34.640 --> 0:26:37.280 what an el nino really was. But that certainly doesn't 0:26:37.280 --> 0:26:40.480 mean I've never said it or read it before. You know, 0:26:40.520 --> 0:26:42.120 I used to be on TV you have a guest, 0:26:42.240 --> 0:26:46.560 it was like, oh, the upcoming al nino, ptificating about 0:26:46.560 --> 0:26:49.399 the al nino. Yes, but it always seems like a 0:26:49.440 --> 0:26:52.040 sort of cyclical thing. It's like, Okay, it hits every 0:26:52.080 --> 0:26:54.520 few years and there's some costs, and then it goes 0:26:54.640 --> 0:26:58.560 back to normal. But if their whole books about the 0:26:58.640 --> 0:27:03.600 long term consequence of any given hel ninho, it sounds like, 0:27:03.680 --> 0:27:07.119 at least in some serious ones, there can be quite 0:27:07.119 --> 0:27:10.879 a tale, quite a you know, ripple effects. 0:27:11.080 --> 0:27:12.840 When you go back and look. 0:27:12.600 --> 0:27:16.000 At maybe some of the more recent big el nino's 0:27:16.119 --> 0:27:18.320 that we had, I think you mentioned there was one 0:27:18.359 --> 0:27:21.239 in the early eighties. How do you think about like 0:27:21.760 --> 0:27:24.760 tallying up. I don't know what the the what the 0:27:24.760 --> 0:27:28.000 global economy, the price the global economy paid for it. 0:27:28.480 --> 0:27:31.399 Yeah, such a good question. I want to call out 0:27:31.480 --> 0:27:35.200 a key collaborator that I've had in this work, Chris Callahan, 0:27:35.600 --> 0:27:41.119 who's a professor at Indiana University and just a stellar, stellar, stellar. 0:27:41.160 --> 0:27:43.879 I couldn't have asked for a better PhD student to 0:27:43.920 --> 0:27:47.120 get to think about these hard things with. So Chris 0:27:47.119 --> 0:27:49.480 had been thinking about al Ninho and it's kind of 0:27:49.520 --> 0:27:52.920 projection uncertainty and other contact, meaning what does the future 0:27:53.000 --> 0:27:55.600 of al nino look like in a world with global warming? 0:27:56.200 --> 0:27:58.879 And how to understand that just be given that it's 0:27:59.280 --> 0:28:02.480 such an important and part of year to year variation, 0:28:03.119 --> 0:28:07.280 whether in climate globally, and it's essentially unknowable, right, It's 0:28:07.400 --> 0:28:09.840 very hard to know what al nino is going to 0:28:09.880 --> 0:28:11.720 look like in twenty fifty, what it's going to look 0:28:11.760 --> 0:28:14.600 like in twenty one hundred, and how that picture changes 0:28:14.680 --> 0:28:18.399 as a function of what your background warming happens to be. 0:28:19.240 --> 0:28:21.760 One of the things we've been investigating is just well, 0:28:22.200 --> 0:28:26.760 we kind of had pivoted from thinking about future to saying, well, 0:28:26.800 --> 0:28:29.240 we live in a world where global warming has already occurred, 0:28:29.560 --> 0:28:34.200 we can simply document what has happened. And so much 0:28:34.200 --> 0:28:37.000 of what my lab group does here at Dartmouth is 0:28:37.200 --> 0:28:42.880 investigate retrospectively kind of build that documentary evidence of here 0:28:42.920 --> 0:28:45.240 are the cost of climate change to date. And in 0:28:45.280 --> 0:28:47.400 thinking about the cost of climate change to date, one 0:28:47.400 --> 0:28:50.040 of the things you have to do is isolate those 0:28:50.120 --> 0:28:54.240 costs from the costs that would have happened anyway, such 0:28:54.240 --> 0:28:56.640 as the cost from al Nino, which is an innate 0:28:56.720 --> 0:29:00.680 form of climate variability, right. Almino occurs in absence of 0:29:00.720 --> 0:29:06.080 people and their greenhouse gas emissions. So isolating that cost 0:29:06.320 --> 0:29:09.880 and kind of partitioning it from the cost accounting of 0:29:09.880 --> 0:29:13.600 global warming to date became a pretty important exercise. So 0:29:13.640 --> 0:29:17.480 we're always in kind of detection and attribution science trying 0:29:17.520 --> 0:29:20.280 to say, well, what is consistent with the world as 0:29:20.320 --> 0:29:23.000 it has been versus the world as it would have 0:29:23.040 --> 0:29:25.760 been absent global warming, And that means having a really 0:29:25.800 --> 0:29:29.240 clear picture of what we call internal climate variabilities, such 0:29:29.240 --> 0:29:32.960 as the variability from Elmina. And so we were interested 0:29:32.960 --> 0:29:36.920 in this cost accounting of Almino just to clarify the 0:29:37.000 --> 0:29:39.600 cost of global warming to date. And when we went 0:29:39.680 --> 0:29:43.320 back and did this exercise using these causal inference techniques. 0:29:43.320 --> 0:29:45.840 So this is all observationally built. Right. We can take 0:29:46.480 --> 0:29:52.000 data of variation in almina, which is essentially a random treatment, right, 0:29:52.240 --> 0:29:56.000 it is stochastically generated while it has a life cycle 0:29:56.920 --> 0:30:01.800 against it. Our global economies aren't necessary shaping sea surface 0:30:01.840 --> 0:30:06.240 temperature anomaly patterns in places like the tropical Pacific, right, 0:30:06.280 --> 0:30:11.320 So it's effectively exogenous tour from our economy, right, And 0:30:11.360 --> 0:30:15.080 so that's really crucial in this empirical setting of trying 0:30:15.080 --> 0:30:17.760 to identify, well, what's the effective in al Nino on 0:30:17.880 --> 0:30:20.680 an economy and how does that upward aggregate to a 0:30:20.720 --> 0:30:23.480 macroeconomic toll. What you can do is just take a 0:30:23.520 --> 0:30:27.040 bunch of data of something like gross domestic product, which 0:30:27.080 --> 0:30:31.680 is summarizing all economic productivity, goods and services produced by 0:30:31.720 --> 0:30:35.239 an economy. We have pretty good data on that at 0:30:35.240 --> 0:30:38.600 the country scale for a pretty long amount of time, 0:30:38.880 --> 0:30:42.600 and you can look at differences in that economic growth. 0:30:42.720 --> 0:30:46.080 So how did economic growth vary from year to year? 0:30:46.440 --> 0:30:49.040 So you're not looking at absolute levels of GDP, You're 0:30:49.120 --> 0:30:53.520 looking at changes in economic growth as embedded in changes 0:30:53.560 --> 0:30:56.640 in GDP. And then you can look at something like 0:30:56.840 --> 0:30:59.960 al nino as a treatment. And then you have another 0:31:00.240 --> 0:31:03.160 source of variation, which is that not every country is 0:31:03.240 --> 0:31:07.080 equally linked to al Nino physically. Al Nino is kind 0:31:07.120 --> 0:31:11.360 of remarkable in that it generates many different kinds of 0:31:11.400 --> 0:31:15.880 physical hazards, from heat waves to droughts, to floods, to 0:31:16.520 --> 0:31:19.960 landslideses to wildfires. Right, and so in that sense, like 0:31:20.040 --> 0:31:23.800 its impacts interestingly look a lot like the wider impacts 0:31:23.800 --> 0:31:27.000 from global warming, which is a worthy discussion and of itself, 0:31:27.040 --> 0:31:29.240 but we'll put that to the side for a second. 0:31:29.600 --> 0:31:31.600 And what you can do is you can exploit the 0:31:31.680 --> 0:31:34.560 kind of random variation we've observed in the real world 0:31:34.880 --> 0:31:38.920 of al nino occurrence and alninio magnitude, how that relates 0:31:38.920 --> 0:31:43.520 to the teleconnection, how tightly coupled locations weather and climate 0:31:43.600 --> 0:31:46.560 is to al nino as a phenomenon, and then see 0:31:46.800 --> 0:31:51.040 what accounts for the variation in economic productivity as measured 0:31:51.280 --> 0:31:55.240 in changes in GDP. And so that's what we did 0:31:55.680 --> 0:31:58.640 back in twenty twenty three and published in a paper 0:31:58.840 --> 0:32:03.520 in Science where we showed that actually we can detect 0:32:04.120 --> 0:32:10.080 very sizable deviations in economic growth from Elmino as a 0:32:10.080 --> 0:32:15.040 phenomena and that scales in teleconnection strength, meaning countries that 0:32:15.120 --> 0:32:20.600 are most influenced climatically and from a meteorological standpoint by 0:32:20.720 --> 0:32:26.200 the phenomenon of El Amino are most impacted and negatively 0:32:26.320 --> 0:32:34.240 still so Peru with road washouts disrupting commodities from mind 0:32:34.320 --> 0:32:38.280 sights in the mountains to port induces a shock, and 0:32:38.320 --> 0:32:42.080 that shock doesn't seem to be what an economist might 0:32:42.120 --> 0:32:45.720 call a level effect, meaning a simple deviation that you 0:32:45.800 --> 0:32:49.600 then recover from in a subsequent year. What we find 0:32:49.920 --> 0:32:54.160 in this empirical research is that observationally, Almino seems to 0:32:54.240 --> 0:32:59.760 systematically depress growth, meaning your country is growing at a 0:32:59.800 --> 0:33:02.760 race and then an al Nino occurs, and then you 0:33:02.800 --> 0:33:06.800 are growing on a different trajectory, and so those losses 0:33:07.400 --> 0:33:11.360 just accrue add infinitum in time. And that is a 0:33:11.440 --> 0:33:15.880 distinct finding that what was previously understood about al nino. 0:33:16.160 --> 0:33:18.640 Right, So this is really important. I think most people 0:33:18.720 --> 0:33:22.240 are used to thinking about whether related shocks as this 0:33:22.400 --> 0:33:25.520 sort of short term thing that, as Justin just said, 0:33:25.640 --> 0:33:29.720 impacts the price level. But what the paper actually found 0:33:29.760 --> 0:33:33.840 was that there's this longer term impact on essentially output 0:33:33.960 --> 0:33:37.960 like growth in the longer term. Why is that, like, 0:33:38.000 --> 0:33:41.120 what exactly is happening here, Because again you would expect, like, Okay, 0:33:41.120 --> 0:33:44.680 a road gets washed out, it's probably inconvenient, it's probably 0:33:44.840 --> 0:33:48.120 expensive to replace, but it does get You would think 0:33:48.160 --> 0:33:50.520 it does get replaced relatively quickly, and a lot of 0:33:50.560 --> 0:33:54.719 these negative effects would subside relatively fast. 0:33:54.960 --> 0:33:59.400 I think a one off shock might look that way, Right, 0:33:59.800 --> 0:34:03.200 You loss owing to a heat wave or drought in 0:34:03.320 --> 0:34:07.160 one part of the Upper Midwest says nothing about major 0:34:07.200 --> 0:34:13.920 grain producing regions in Russia or cotton prices given growth 0:34:13.960 --> 0:34:17.360 in the Furghana Valley in Uzbekistan, Right, Like, these things 0:34:17.400 --> 0:34:20.719 are are disparate enough in space that maybe they're not 0:34:21.400 --> 0:34:25.680 experiencing simultaneous shock. What I think is kind of interesting 0:34:25.880 --> 0:34:29.880 about Alminho and why Almino is an interesting analog for 0:34:29.920 --> 0:34:34.319 the impacts of wider global warming, which is a kind 0:34:34.320 --> 0:34:38.800 of a universally experienced You know, there's a high degree 0:34:38.800 --> 0:34:42.799 of correlation in the impacts of global warming. Right, It's 0:34:42.840 --> 0:34:47.000 not simply a heat wave here. It's a heat wave here, here, here, 0:34:47.120 --> 0:34:50.840 and here all at the same time, all experiencing loss 0:34:50.840 --> 0:34:52.600 at the same time, Which is one of the reasons 0:34:52.640 --> 0:34:58.439 why I think it's this simultaneity of disparate regions experiencing 0:34:58.560 --> 0:35:03.560 hazards and experiencing losses that then can lead to these 0:35:04.280 --> 0:35:09.239 up aggregating market forces that systematically depressed growth and alter 0:35:09.880 --> 0:35:13.840 where capital investment can go. You know, the efficiency with 0:35:13.880 --> 0:35:17.000 which you're able to take labor and capital and mobilize 0:35:17.000 --> 0:35:21.240 it into productive parts of the economy. It is hard 0:35:21.440 --> 0:35:24.920 to do that when there are lots of hazards being 0:35:25.040 --> 0:35:28.120 experienced around the world, right, so it's not one thing 0:35:28.239 --> 0:35:32.560 disrupting your supply chain, it's maybe a dozen things. And 0:35:32.600 --> 0:35:35.200 I think that's the distinction. Why. You know, the ninety 0:35:35.239 --> 0:35:39.279 seven ninety eight Almino was about thirty six billion in 0:35:39.520 --> 0:35:42.680 economic losses asked as estimated at the time as a 0:35:42.760 --> 0:35:45.799 level effect, a shock and then a recovery. What we 0:35:45.840 --> 0:35:49.200 actually found is that there was empirical evidence for a 0:35:49.320 --> 0:35:53.439 systematic depression of economic growth and over a five year 0:35:53.520 --> 0:35:58.000 period that amounts to about five point seven trillion dollars 0:35:58.040 --> 0:36:01.800 in global economic losses instead by the year two thousand 0:36:01.840 --> 0:36:04.839 and three, right five years after the ninety seven ninety 0:36:04.840 --> 0:36:08.680 eight event. What's really fascinating is we played this game 0:36:08.719 --> 0:36:12.359 where we use what's called a perfect model framework, where 0:36:12.360 --> 0:36:15.680 we simply ask the question, if we know that the 0:36:15.719 --> 0:36:19.719 impact of El Nino is permanent and of this magnitude, 0:36:20.320 --> 0:36:24.319 given the observational data we have, could we prove that 0:36:24.360 --> 0:36:28.759 it's permanent. So these kinds of statistics rely on the 0:36:28.800 --> 0:36:33.120 ability to pull a signal from noise, which requires a 0:36:33.120 --> 0:36:35.960 certain amount of data, and that amount of data affects 0:36:36.000 --> 0:36:38.719 the claims you can make robustly. And so what we 0:36:38.719 --> 0:36:41.120 were trying to do there is say, even if we 0:36:41.280 --> 0:36:44.760 had a known effect, can we recover that known effect 0:36:44.800 --> 0:36:47.799 perfectly given our data record? And what we found is 0:36:47.840 --> 0:36:50.959 that we couldn't, which means that we cannot rule out 0:36:51.320 --> 0:36:55.400 the possibility that the economic impacts of al Nino at 0:36:55.400 --> 0:36:58.759 the global scale are permanent, meaning that every time in 0:36:58.840 --> 0:37:02.400 al Nino occurs, we're just knocked off our growth trajectory 0:37:02.440 --> 0:37:06.000 that would have been absent that event, which is kind 0:37:06.000 --> 0:37:09.320 of remarkable, which implies that these costs kind of a 0:37:09.440 --> 0:37:12.879 crue ad infinitum in time. In our paper, we only 0:37:12.920 --> 0:37:16.239 went out to five and eight and ten years of 0:37:16.320 --> 0:37:19.560 growth accounting, but it just says that the economic losses 0:37:19.600 --> 0:37:23.680 associated with these events are much higher than we understood before, 0:37:24.040 --> 0:37:27.320 which means, you know, is just another kind of piece 0:37:27.360 --> 0:37:30.799 of evidence that shows that weather and climate matter for 0:37:30.880 --> 0:37:35.399 our economy, and our climate system is indogenous to that 0:37:35.760 --> 0:37:39.920 economic growth, right. It is greenhouse gas emissions induced by 0:37:39.960 --> 0:37:43.480 economic growth that is altering our trajectory of climate, and 0:37:43.520 --> 0:37:47.200 our climate also has a finger on the scale of 0:37:47.239 --> 0:37:50.600 what constitutes our economy. And so that indogen eighty, I 0:37:50.600 --> 0:37:53.960 think is something really important to understand and represent, particularly 0:37:54.040 --> 0:37:57.640 around policies related to climate variability and climate change. 0:37:57.840 --> 0:38:01.800 That was a fantastic answer. When you think about the. 0:38:01.680 --> 0:38:06.200 Impacts of this Elenino or theo in the year twenty fifty, 0:38:06.320 --> 0:38:09.640 or the Elnino in the year twenty one hundred, you know, 0:38:09.800 --> 0:38:14.920 so obviously like one, you know, presumably take a country 0:38:14.960 --> 0:38:17.560 like Peru. It's probably going to be richer in the 0:38:17.640 --> 0:38:21.320 year twenty fifty, so it'll probably have more economic activity. 0:38:21.800 --> 0:38:26.320 It may also have better infrastructure in the year twenty fifty, 0:38:26.520 --> 0:38:31.400 such that road washouts are less likely. Countries after you know, 0:38:31.440 --> 0:38:35.320 you experience I presume even since eighty two or ninety seven, 0:38:35.440 --> 0:38:40.040 there's been you know, hardening against weather related shocks and 0:38:40.080 --> 0:38:44.480 so forth. As countries learn about these patterns, how do 0:38:44.560 --> 0:38:47.839 you model that aspect? Both the fact that, okay, there 0:38:47.920 --> 0:38:53.680 is more economic activity in this sort of temporarily connected areas, 0:38:54.120 --> 0:39:01.160 and also presumably governments and companies get better about anticipating 0:39:01.239 --> 0:39:05.439 the al ninos and strengthening their infrastructure such that they're 0:39:05.440 --> 0:39:06.280 not as vulnerable. 0:39:06.600 --> 0:39:09.440 It turns out it's really hard, right. The predictions of 0:39:09.600 --> 0:39:14.160 future are not falsifiable scientifically, and so. 0:39:14.600 --> 0:39:17.880 I hope perused wealthare in twenty fifty and twenty one hundred. 0:39:17.920 --> 0:39:21.319 But yeah, I mean, yeah, we didn't even talk about 0:39:21.320 --> 0:39:22.560 the fisheries implications. 0:39:22.680 --> 0:39:23.160 Yeah. 0:39:23.320 --> 0:39:25.600 One thing that's really I think what you're kind of 0:39:25.680 --> 0:39:29.640 raising is this question of adaptation and resiliency, and the 0:39:29.680 --> 0:39:33.920 way I've been thinking about that is evolving. Right, there's 0:39:33.960 --> 0:39:38.040 a story we can tell that says, Okay, if there 0:39:38.120 --> 0:39:42.320 are market and efficiencies, or we're shown to be vulnerable 0:39:42.360 --> 0:39:47.160 societally in these particular ways, we're going to develop strategies 0:39:47.280 --> 0:39:51.080 to ensure resilience. Right. Those Peruvian potato farmers noticed they 0:39:51.080 --> 0:39:54.319 were losing crops, you know, and they altered their sewing 0:39:54.440 --> 0:39:57.400 dates based on the visibility of the ple eighties overhead. 0:39:57.680 --> 0:40:02.200 Like that is an adaptation. And you can imagine autonomous atatotations, 0:40:02.239 --> 0:40:05.880 meaning individual level adaptations. You can also imagine society wide 0:40:05.960 --> 0:40:10.239 or planned adaptations such as shoring up kind of resilience, 0:40:10.280 --> 0:40:12.880 and these things are linked to alnino forecasts, right like 0:40:12.920 --> 0:40:15.279 one of the things you're seeing, you know, you said 0:40:15.280 --> 0:40:19.920 at the outset tracy of all these financial planners thinking 0:40:19.960 --> 0:40:24.200 about the implications of al Nino for markets around the 0:40:24.239 --> 0:40:27.640 world and supply chains. That is a form of kind 0:40:27.640 --> 0:40:33.200 of resilience strategy building that's occurring that is indelibly linked 0:40:33.200 --> 0:40:38.080 to the science of enso forecasting, and why al nino 0:40:38.160 --> 0:40:41.959 forecasts are so important, Right that nineteen eighty two eighty 0:40:42.000 --> 0:40:45.080 three al Nino, we didn't have the benefit of a 0:40:45.120 --> 0:40:49.400 tremendous amount of lead time in planning for its impacts. 0:40:49.480 --> 0:40:54.000 Right a tropical cyclone often the ocean, right, being able 0:40:54.040 --> 0:40:57.359 to track it and have sufficient time to get resources 0:40:57.400 --> 0:41:00.360 injected into the places you need and people lot of 0:41:00.360 --> 0:41:03.480 harm's weigh right Like, that requires lead time, which is 0:41:03.520 --> 0:41:07.520 why these types of forecasts are so essential, because they 0:41:07.560 --> 0:41:11.720 allow us to pursue resilience strategies that we otherwise wouldn't 0:41:11.760 --> 0:41:14.279 have been able to. But this wider question of like 0:41:14.719 --> 0:41:18.080 are we well adapted to climate variability? We have and 0:41:18.120 --> 0:41:22.080 shouldn't we expect that will be even better adapted to 0:41:22.120 --> 0:41:25.080 climate variability in the future. I would say the evidence 0:41:25.160 --> 0:41:27.680 is pretty mixed. There isn't a whole lot of evidence 0:41:28.160 --> 0:41:30.480 that we're well adapted to the climate we have. We 0:41:30.560 --> 0:41:36.960 are finding documenting observationally that the costs of almino are astounding, 0:41:37.440 --> 0:41:39.880 despite the fact that we've been exposed to it for 0:41:40.040 --> 0:41:43.640 hundreds and hundreds of years. Right, this is not something 0:41:43.680 --> 0:41:46.480 that is novel. Right, while the coming al nino is 0:41:47.040 --> 0:41:49.840 like seems to be out of sample and the costs 0:41:49.840 --> 0:41:53.279 associated with it are really hard to anticipate because it 0:41:53.360 --> 0:41:55.759 is so out of sample. Right, Like, when I think 0:41:55.760 --> 0:41:58.920 about the cost of this forthcoming al nino, I estimate 0:41:59.000 --> 0:42:03.800 somewhere around ten trillion dollars of global economic losses conservatively 0:42:04.600 --> 0:42:08.240 over the next five years, right up to fourteen trillion 0:42:08.280 --> 0:42:10.760 dollars of economic losses if you take some of these 0:42:11.000 --> 0:42:13.439 tailed But I could only seventy percent of that only 0:42:13.719 --> 0:42:17.320 you know about ten trillion dollars. So that is in sample, 0:42:17.440 --> 0:42:21.400 meaning supported by existing data we have on alminios and 0:42:21.440 --> 0:42:25.680 economic responses. Our global economy is larger, and so these 0:42:25.960 --> 0:42:29.560 losses are associated not just with the magnitude of the Alnino, 0:42:29.800 --> 0:42:32.120 but the size of our global economy, there's just more 0:42:32.239 --> 0:42:35.600 stuff to be impacted, right. But what that tells me 0:42:36.080 --> 0:42:39.040 is that we are not optimally adapted to the climate 0:42:39.120 --> 0:42:41.560 we have, and so there isn't much evidence that we 0:42:41.680 --> 0:42:44.759 have that we are taking on board the feedback we're 0:42:44.760 --> 0:42:48.040 getting from the climate system about how things are going 0:42:48.600 --> 0:42:53.680 and pursuing strategies that really offset or mitigate losses in 0:42:53.719 --> 0:42:57.760 a coherent way. I think that's, you know, pretty pretty telling, 0:42:57.920 --> 0:43:01.239 just given the documentary exercise. That's not to say that 0:43:01.280 --> 0:43:04.040 we couldn't be better adapted. I just think that we 0:43:04.080 --> 0:43:08.160 are not well adapted, and there isn't widespread evidence of 0:43:08.400 --> 0:43:11.120 adaptation to either El Nino as a form of climate 0:43:11.160 --> 0:43:15.800 variability or wider global warming. Since the impacts of global 0:43:15.840 --> 0:43:18.799 warming look a lot like the impacts of Almino, and 0:43:18.800 --> 0:43:20.640 so I think there's like a bunch to kind of 0:43:20.680 --> 0:43:25.120 disentangle there. But when we're thinking about these future growth 0:43:25.719 --> 0:43:30.759 impacts on fromenso we need a counterfactual world of what 0:43:31.160 --> 0:43:36.320 economic growth trajectory would have been absent Almino, but given 0:43:36.360 --> 0:43:39.400 global warming, and that is a picture of future that 0:43:39.440 --> 0:43:42.480 I think is really hard to envision, but there are 0:43:42.960 --> 0:43:47.440 economic tools that people use to create those counterfactual worlds, 0:43:47.480 --> 0:43:50.239 and then you can measure deviations as could be the 0:43:50.280 --> 0:43:53.479 case given what we know to be observationally true about 0:43:53.480 --> 0:43:55.759 the relationship between Al Nino and an economy. 0:43:56.080 --> 0:43:57.840 I know you just said there aren't a lot of 0:43:57.880 --> 0:44:02.040 examples of successful adaptation for El Nino, other than perhaps 0:44:02.160 --> 0:44:05.440 Peruvian potato farmers sowing their crops early. But give us 0:44:05.440 --> 0:44:08.440 some hope here, or some idea of what a successful 0:44:08.520 --> 0:44:13.120 adaptation could look like in an ideal scenario where this 0:44:13.239 --> 0:44:16.239 is an endogenous phenomenon as you just described, where you 0:44:16.280 --> 0:44:20.239 have it basically happening all around the world simultaneously, what 0:44:20.400 --> 0:44:22.319 could we do to try to offset some of the 0:44:22.360 --> 0:44:23.240 economic impact. 0:44:23.760 --> 0:44:25.680 Gosh, that's you know, it's such a great question. One 0:44:25.719 --> 0:44:29.759 of the ways I think about this is there's improving 0:44:29.840 --> 0:44:34.880 resiliency in global markets such that these weather and climate 0:44:34.920 --> 0:44:40.359 shocks don't alter prices and interest rates and all these 0:44:40.400 --> 0:44:43.319 things that are huge determinants of human well being. Then 0:44:43.320 --> 0:44:46.759 there is also, like the direct hazards themselves, right, a 0:44:46.800 --> 0:44:51.560 stronger than average atmospheric river event that triggers a landslide 0:44:52.000 --> 0:44:55.279 along route one in California creating a road wash out 0:44:55.320 --> 0:45:00.200 that is a massive disruption to communities. They're a wildfire, 0:45:00.480 --> 0:45:04.600 you know that rages through the bush in Australia, killing 0:45:05.160 --> 0:45:11.560 trillions of animals and raising insurance premiums on kind of 0:45:11.600 --> 0:45:14.759 wildfire protection for homes there right, There are all these 0:45:14.880 --> 0:45:20.200 kind of ways you can process trace how Elmino hurts 0:45:20.400 --> 0:45:24.040 an individual or a community, And so I think that 0:45:24.120 --> 0:45:27.359 protection of human well being from approximate sense of like 0:45:27.880 --> 0:45:31.640 are you okay given these hazards, that to me is 0:45:31.680 --> 0:45:35.680 a different question than how do you shore up international 0:45:35.719 --> 0:45:39.160 markets such that they are resilient to the impacts of almino? 0:45:39.239 --> 0:45:42.600 And I think just separating them is really really valuable. 0:45:42.640 --> 0:45:45.360 I think the fact that people are aware of almino 0:45:45.400 --> 0:45:48.120 as a phenomenon and that it can induce these cascading 0:45:48.160 --> 0:45:53.320 shocks through agriculture or through mining, or through the transport sector, 0:45:53.600 --> 0:45:55.839 and that all those things all around the world can 0:45:55.960 --> 0:45:59.160 up aggregate to a macroeconomic toll I think is important 0:45:59.239 --> 0:46:02.800 to know. And I don't know enough about how people 0:46:02.880 --> 0:46:07.360 think about these portfolios, but I know that risk diversification 0:46:08.040 --> 0:46:11.600 is a strategy to ensure returns, and I imagine that's 0:46:11.600 --> 0:46:16.080 something that people are embracing given modern portfolio theory or whatever. 0:46:16.560 --> 0:46:18.880 But I think this question of like how do you 0:46:18.920 --> 0:46:23.360 protect people from hazard that is a different question and 0:46:23.480 --> 0:46:28.000 requires different sets of warnings about the local manifestation of 0:46:28.000 --> 0:46:31.800 the hazards associated with Almino. And to me, that means 0:46:31.840 --> 0:46:37.560 early morning systems. That means emergency management and response teams 0:46:37.600 --> 0:46:41.720 that are well placed and understand these consequences. That means 0:46:41.960 --> 0:46:46.920 we have insurance markets that recognize the hazards, the unique 0:46:47.080 --> 0:46:50.520 geophyscal hazards associated with something like El Nino and its 0:46:50.600 --> 0:46:53.920 regional dependencies, and what it should imply for the types 0:46:53.960 --> 0:46:56.960 of products those people need to protect the most capital 0:46:56.960 --> 0:47:00.000 intensive asset they have, which is their home. It's many 0:47:00.040 --> 0:47:02.319 different kinds of things, and so none of this is 0:47:02.440 --> 0:47:05.960 out of the realm of possibility. There are a lot 0:47:06.000 --> 0:47:09.680 of these things we do anyway, right we build reservoirs 0:47:09.719 --> 0:47:13.480 to mitigate drought risk already. Never mind the fact that 0:47:13.600 --> 0:47:16.479 Lake Powell the central bank of the American West Water. 0:47:19.680 --> 0:47:21.640 He knows his audience anyway. 0:47:21.320 --> 0:47:23.120 I guess the point is is that you know, I 0:47:23.160 --> 0:47:25.440 think there are lots of there's lots of evidence that 0:47:25.480 --> 0:47:28.920 we these adaptations are within the realm of possibility, but 0:47:29.000 --> 0:47:33.000 the constellation and configuration they take is kind of regionally dependent. 0:47:33.320 --> 0:47:35.880 And so I think kind of a coherent global strategy 0:47:36.360 --> 0:47:39.840 about managing the impacts of El Nino is an interesting 0:47:40.000 --> 0:47:44.640 test bed for adaptation strategies that would lend itself to 0:47:44.680 --> 0:47:48.040 wider global warming. And that's something that I'm really intrigued by. 0:47:48.400 --> 0:47:51.719 So you talk about this idea that what you've discovered 0:47:51.760 --> 0:47:55.359 through your research is that al Nino's are not these 0:47:55.400 --> 0:47:58.719 sort of cycles that you bounce back from and then 0:47:58.760 --> 0:48:02.560 you're resumed to trend growth, right, that they change their trajectory. 0:48:03.000 --> 0:48:06.600 But if al Nino's are something that are very consistent 0:48:06.800 --> 0:48:11.040 and predictable throughout history, such that we've been you know, 0:48:11.120 --> 0:48:14.680 there's evidence of farmers having tried to anticipate them going 0:48:14.760 --> 0:48:19.520 back over five hundred years, why should we not assume that, 0:48:20.239 --> 0:48:23.279 you know, it's seasonal, Like you could say, for example, 0:48:23.920 --> 0:48:29.600 that cold winter months depress economic growth, which they probably do. 0:48:29.760 --> 0:48:34.440 People don't go out as much and shop and travel 0:48:34.520 --> 0:48:37.400 as much in the middle of December, and so we 0:48:37.400 --> 0:48:39.440 could say, oh, if there were no Decembers, if it 0:48:39.480 --> 0:48:43.960 never got cold, then in the counterfactual. You know, we 0:48:44.040 --> 0:48:47.200 would have had much higher growth, which is true but 0:48:47.320 --> 0:48:50.680 also absurd because the premise of not having a winter 0:48:51.080 --> 0:48:54.640 is absurd. If al Nino is something that will always 0:48:54.719 --> 0:48:58.759 and has always been with us, why do we even 0:48:58.840 --> 0:49:03.040 imagine that they are being a counter factual trend rate 0:49:03.080 --> 0:49:05.440 of growth in a world without aninos. 0:49:05.600 --> 0:49:08.319 Yeah, I think you know that comes back to the 0:49:08.440 --> 0:49:12.200 kind of empirical identification strategy. Okay, like we don't have 0:49:12.239 --> 0:49:17.000 the benefit of resimulating the world as it has been 0:49:17.360 --> 0:49:20.440 and kind of the emergence of global society and the 0:49:20.440 --> 0:49:25.880 global economy absent Alnino, and so we rely on models 0:49:25.920 --> 0:49:31.120 to create those counterfactuals. We know that al nino, while 0:49:31.160 --> 0:49:34.120 being cyclic in nature, it is not perfectly so it 0:49:34.200 --> 0:49:37.239 is as good as a random treatment, and so we 0:49:37.320 --> 0:49:42.239 can separate the experience of the world into those associated 0:49:42.239 --> 0:49:45.960 with almino and those not, and we can examine the 0:49:46.040 --> 0:49:50.319 differences in economic productivity under those two regimes. So that's 0:49:50.480 --> 0:49:54.440 essentially what we're doing. Empirically. It's akin to a medical 0:49:54.480 --> 0:49:59.000 control trial, right where you randomize a population into a 0:49:59.000 --> 0:50:02.800 treatment group that gets drug and control group that doesn't 0:50:02.880 --> 0:50:07.080 write or receives a placebo and you compare medical outcomes. Right, 0:50:07.360 --> 0:50:10.239 there are a year, there are people that may look 0:50:10.320 --> 0:50:12.880 the same in the treatment and control group, and there 0:50:12.880 --> 0:50:15.600 are people that may look different in the treatment and 0:50:15.640 --> 0:50:19.200 control group. But if you randomize effectively, those differences among 0:50:19.280 --> 0:50:23.120 individuals cancel out, and the kind of efficacy of the 0:50:23.200 --> 0:50:25.840 drug itself is just the difference between those two groups. 0:50:26.239 --> 0:50:29.759 El nino is as good as randomly assigned despite the 0:50:29.840 --> 0:50:33.359 fact that it is cyclic in nature, and so you 0:50:33.400 --> 0:50:37.120 can treat it as this natural experiment, this treatment and 0:50:37.200 --> 0:50:39.600 compare it to the control of non al nino years. 0:50:39.600 --> 0:50:42.960 And that's effectively what we're doing here when we're kind 0:50:43.000 --> 0:50:47.680 of talking about this counterfactual. We know what economic productivity 0:50:47.719 --> 0:50:50.359 looks like, and so you're right to say that, you know, 0:50:50.600 --> 0:50:54.960 economic productivity looks different in winter than in summer or 0:50:55.040 --> 0:50:58.640 in spring, right, And it looks different in the face 0:50:59.280 --> 0:51:02.400 of a tropical cyclone than it does in the face 0:51:02.480 --> 0:51:05.160 of you know, a civil conflict or something else. Right, Like, 0:51:05.239 --> 0:51:08.520 these these things have impacts. But what we're doing here 0:51:08.600 --> 0:51:13.440 is kind of exploiting natural stochastic variation in it despite 0:51:13.440 --> 0:51:16.239 its cyclic nature. And what I think is remarkable and 0:51:16.280 --> 0:51:19.000 what I what I'm kind of suggesting here is that 0:51:19.120 --> 0:51:23.799 despite the fact that al nino is not a novel phenomenon, right, 0:51:23.840 --> 0:51:28.360 it is not something that we have never experienced before, 0:51:28.960 --> 0:51:32.920 which global warming is right. Global warming is something humanity 0:51:32.960 --> 0:51:36.000 has never experienced, and it is out of sample, and 0:51:36.040 --> 0:51:39.920 it is arguably making this El Nino that's unfolding given 0:51:39.960 --> 0:51:43.239 the background warming, truly out of sample, truly out of 0:51:43.280 --> 0:51:48.080 sample event, right, something humanity has never experienced before. And 0:51:48.160 --> 0:51:51.279 I'm saying, despite the fact that we should be inured 0:51:51.800 --> 0:51:56.000 to experiencing almino, we have limited evidence of widespread adaptation 0:51:56.120 --> 0:51:57.000 and resilience too. 0:51:57.320 --> 0:51:59.520 All Right, Justin Mann can thank you so much for 0:51:59.600 --> 0:52:01.680 coming on having. 0:52:14.360 --> 0:52:14.600 Joe. 0:52:14.640 --> 0:52:18.240 I found that conversation fascinating. Love talking about the weather 0:52:18.360 --> 0:52:21.240 in always. But I think the point about this idea 0:52:21.360 --> 0:52:23.840 that like, okay, el nino is something that we have experienced, 0:52:23.880 --> 0:52:27.080 it does come with some regularity, and yet there seems 0:52:27.120 --> 0:52:32.000 to be some proof that it has this lasting impact 0:52:32.239 --> 0:52:35.120 not just on like the immediate economy, but on economic 0:52:35.160 --> 0:52:38.560 growth itself. Is a very important one when you can 0:52:38.719 --> 0:52:41.840 consider that, like again, we're about to experience a potentially 0:52:41.880 --> 0:52:45.440 record breaking in a nino and with the wider backdrop 0:52:45.560 --> 0:52:46.359 of climate change. 0:52:46.400 --> 0:52:50.200 Right, super interesting. I learned so much in that conversation 0:52:50.280 --> 0:52:52.560 that I didn't know this term that I just heard 0:52:52.600 --> 0:52:55.720 throughout my life that I really gets warmer or something 0:52:55.760 --> 0:52:57.440 like that. I didn't really know what it was, or 0:52:57.440 --> 0:52:59.719 why it was or what direction. I thought it was 0:53:00.120 --> 0:53:04.839 really interesting. Okay, what makes an El Nino different from 0:53:04.880 --> 0:53:09.120 another random weather event. The global simultaneity of it is 0:53:09.160 --> 0:53:13.440 being key, Like, Okay, other events you just bounce back from. 0:53:13.520 --> 0:53:16.000 I mean everything must have some effect. You never like 0:53:16.040 --> 0:53:19.960 bounce back from anything that perfectly back to trend. But 0:53:20.040 --> 0:53:22.840 this simultaneity, of course, we saw that during COVID and 0:53:22.880 --> 0:53:25.399 the ripple effects from that. And so when you have 0:53:25.480 --> 0:53:30.920 this weather pattern that is truly global in nature and 0:53:31.160 --> 0:53:34.160 sort of very I guess, predictable in the sense that 0:53:34.239 --> 0:53:36.719 you know what regions they're all going to get hit, 0:53:37.080 --> 0:53:39.000 it sort of makes sense to think about it in 0:53:39.040 --> 0:53:41.800 these terms of like, oh, there are real long term costs, 0:53:42.200 --> 0:53:45.840 especially if they're intensifying during a trend of global warming. 0:53:45.960 --> 0:53:48.640 Yeah, nowhere to run, nowhere to hide kind of idea. 0:53:48.800 --> 0:53:51.160 The other thing I was thinking, of course, back to 0:53:51.160 --> 0:53:54.480 some of our early agricultural episodes, from this year. All 0:53:54.520 --> 0:53:57.560 of this is happening against a backdrop of higher fertilizer 0:53:57.600 --> 0:54:03.279 prices and lower supply. You still, I think I can't 0:54:03.280 --> 0:54:07.600 remember the exact numbers of global fertilizer supply, stuck in 0:54:07.920 --> 0:54:10.799 the strait of horror moves, and that can't be helpful either, 0:54:10.920 --> 0:54:13.840 when you're talking about the biggest El Nino on record. 0:54:14.080 --> 0:54:16.440 Yeah, no, it's gonna be a Well. Now, we are 0:54:16.520 --> 0:54:17.160 well armed. 0:54:17.280 --> 0:54:20.200 Maybe we're not well adapted for the nino, but at 0:54:20.280 --> 0:54:24.480 least you're a least well armed to understand news stories 0:54:24.520 --> 0:54:28.080 that will be emerging over the weeks and months side 0:54:28.120 --> 0:54:30.600 analysts and the sell side analyst notes in the in 0:54:30.640 --> 0:54:31.400 the months ahead. 0:54:31.440 --> 0:54:32.440 All right, shall we leave it there. 0:54:32.480 --> 0:54:33.239 Let's leave it there. 0:54:33.440 --> 0:54:35.840 This has been another episode of the Odd Thoughts podcast. 0:54:35.920 --> 0:54:39.200 I'm Tracy Alloway. You can follow me at Tracy Alloway. 0:54:38.960 --> 0:54:41.920 And I'm Joe Wisenthal. You can follow me at the Stalwart. 0:54:42.239 --> 0:54:45.600 Follow our producers Carmen Rodriguez at Carman Arman, dash Ol 0:54:45.600 --> 0:54:49.040 Bennett at Dashbot, kel Brooks at Keil Brooks and Kevin 0:54:49.120 --> 0:54:52.760 Lozano at Kevin Lloyd Lozano. And for more Odd Lots content, 0:54:52.800 --> 0:54:55.160 go to Bloomberg dot com, slash odd Lots or the 0:54:55.280 --> 0:54:57.960 daily newsletter and all of our episodes, and you can 0:54:58.000 --> 0:54:59.879 shed about all of these topics twenty four to seven 0:55:00.080 --> 0:55:03.600 in our discord discord dot ggashlines. 0:55:03.719 --> 0:55:05.520 And if you enjoy odd Lots, if you like it 0:55:05.560 --> 0:55:07.560 when we talk about the weather, then please leave us 0:55:07.600 --> 0:55:10.920 a positive review on your favorite podcast platform. And remember, 0:55:10.960 --> 0:55:13.279 if you are a Bloomberg subscriber, you can listen to 0:55:13.360 --> 0:55:16.239 all of our episodes absolutely ad free. 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