1 00:00:00,160 --> 00:00:03,160 Speaker 1: This is Dana Perkins and you're listening to Switched on, 2 00:00:03,480 --> 00:00:05,800 Speaker 1: the podcast brought to you by B and EF. And 3 00:00:05,840 --> 00:00:08,160 Speaker 1: today we're here to talk about the weather. While I 4 00:00:08,200 --> 00:00:09,840 Speaker 1: won't be able to tell you whether or not to 5 00:00:09,880 --> 00:00:11,559 Speaker 1: grab a coat on your way out of the house, 6 00:00:11,600 --> 00:00:14,520 Speaker 1: today we will go through some important definitions when it 7 00:00:14,560 --> 00:00:17,480 Speaker 1: comes to the weather. We'll explain the difference between weather 8 00:00:17,560 --> 00:00:19,880 Speaker 1: and climate and why it can be hard to draw 9 00:00:20,000 --> 00:00:23,880 Speaker 1: straight line between natural disasters like fires and hurricanes and 10 00:00:23,920 --> 00:00:27,760 Speaker 1: climate change. We'll also highlight why B and EF's meteorologists 11 00:00:27,800 --> 00:00:30,280 Speaker 1: are some of my colleagues who work with the widest 12 00:00:30,400 --> 00:00:34,360 Speaker 1: range of teams across BNF. Weather impacts so many things, 13 00:00:34,360 --> 00:00:37,920 Speaker 1: from power prices to natural gas stores to emissions, so 14 00:00:38,000 --> 00:00:42,320 Speaker 1: it's no surprise that many companies, especially utilities, are looking 15 00:00:42,400 --> 00:00:46,320 Speaker 1: at temperature, wind, rain and everything else that goes into 16 00:00:46,320 --> 00:00:49,920 Speaker 1: seasonal weather. As we head into another cyclical La Nina period, 17 00:00:50,240 --> 00:00:52,839 Speaker 1: what does this mean for the year ahead? Today I'm 18 00:00:52,920 --> 00:00:56,480 Speaker 1: joined by B and EF's resident meteorologists and weather analysts 19 00:00:56,640 --> 00:00:59,800 Speaker 1: Jess Hicks and Willetobin, and they share findings from their 20 00:00:59,800 --> 00:01:04,000 Speaker 1: recently published research notes titled Weather and Commodities. Nine Things 21 00:01:04,000 --> 00:01:07,320 Speaker 1: to Watch in twenty twenty five and shifting weather patterns 22 00:01:07,400 --> 00:01:10,520 Speaker 1: a black swan for US commodities B and EF Clients 23 00:01:10,560 --> 00:01:12,760 Speaker 1: will be able to find both of these at BNF 24 00:01:12,880 --> 00:01:16,399 Speaker 1: go on the Bloomberg Terminal or at BNF dot com. 25 00:01:16,480 --> 00:01:29,120 Speaker 1: Right now, let's talk about the weather. Jess, thank you 26 00:01:29,200 --> 00:01:31,440 Speaker 1: very much for coming on the show today. Thank you 27 00:01:31,480 --> 00:01:33,640 Speaker 1: for having me and Willa. Good to have you here 28 00:01:33,680 --> 00:01:34,000 Speaker 1: as well. 29 00:01:34,319 --> 00:01:35,199 Speaker 2: Yeah, thank you, Dana. 30 00:01:35,400 --> 00:01:37,480 Speaker 1: So we're here to talk about the weather, and I 31 00:01:37,560 --> 00:01:39,440 Speaker 1: will tell you right now, I'm not going to tell 32 00:01:39,440 --> 00:01:41,920 Speaker 1: you what the weather's like here because I'm recording from 33 00:01:41,959 --> 00:01:45,400 Speaker 1: London and it's the same way it is every February, 34 00:01:45,440 --> 00:01:47,680 Speaker 1: so we'll just leave it at that. Gray is the theme. 35 00:01:47,840 --> 00:01:50,720 Speaker 1: But actually I wanted to be a meteorologist as a kid, 36 00:01:50,760 --> 00:01:53,480 Speaker 1: so I'm very much looking forward to this. Ten year 37 00:01:53,520 --> 00:01:56,040 Speaker 1: old me cannot believe that my job is to sit 38 00:01:56,080 --> 00:01:58,360 Speaker 1: here and interview the two of you. And actually, you know, 39 00:01:58,400 --> 00:01:59,840 Speaker 1: I'm not going to tell you how old I am either, 40 00:01:59,840 --> 00:02:02,680 Speaker 1: but the now me is also really excited. So as 41 00:02:02,720 --> 00:02:05,440 Speaker 1: we talk about the weather, so much of our conversation 42 00:02:05,600 --> 00:02:08,720 Speaker 1: in this studio and on this show revolves around climate 43 00:02:08,800 --> 00:02:12,840 Speaker 1: and emissions targets, can we have a quick definition at 44 00:02:12,840 --> 00:02:15,880 Speaker 1: the beginning to frame our conversation about the weather and 45 00:02:16,160 --> 00:02:19,119 Speaker 1: create that distinction between weather and climate. 46 00:02:19,440 --> 00:02:22,640 Speaker 2: Sure, So the difference between weather and climate really boils 47 00:02:22,680 --> 00:02:26,240 Speaker 2: down to time horizons. Weather is technically defined as the 48 00:02:26,280 --> 00:02:28,680 Speaker 2: state of the atmosphere at a given point in time, 49 00:02:28,760 --> 00:02:31,360 Speaker 2: which can be measured by things like temperature and wind 50 00:02:31,360 --> 00:02:34,160 Speaker 2: speed and pressures. Whereas climate, on the other hand, is 51 00:02:34,280 --> 00:02:37,400 Speaker 2: a long term average of atmospheric conditions for a region. 52 00:02:37,520 --> 00:02:39,280 Speaker 2: So this is going to be more of like your 53 00:02:39,320 --> 00:02:43,400 Speaker 2: thirty year averages of those types of conditions, and so 54 00:02:43,400 --> 00:02:45,360 Speaker 2: we can think of about that as like the thirty 55 00:02:45,400 --> 00:02:47,000 Speaker 2: year average of winter temperatures. 56 00:02:47,320 --> 00:02:50,240 Speaker 1: Now, other than the fact that I'm really enthusiastic about 57 00:02:50,240 --> 00:02:52,560 Speaker 1: this topic, why is it that we at bn EF, 58 00:02:52,600 --> 00:02:55,640 Speaker 1: who are so focused on the energy transition are researching 59 00:02:55,639 --> 00:02:56,079 Speaker 1: this now? 60 00:02:56,400 --> 00:03:00,079 Speaker 3: So the reason why weather is so important and why 61 00:03:00,080 --> 00:03:02,480 Speaker 3: we're researching it is because it's a fundamental part of 62 00:03:02,520 --> 00:03:05,640 Speaker 3: our lives. It impacts things as simple as what you 63 00:03:05,680 --> 00:03:08,320 Speaker 3: wear every day to things as complicated as the net 64 00:03:08,400 --> 00:03:13,080 Speaker 3: zero energy transition. Weather drives these residential and commercial power 65 00:03:13,120 --> 00:03:16,160 Speaker 3: demands through heating and cooling needs, but it also fuels 66 00:03:16,200 --> 00:03:19,320 Speaker 3: renewable power generation for wind and solar, and can disrupt 67 00:03:19,360 --> 00:03:22,880 Speaker 3: production and transportation of oil and gas with any extreme 68 00:03:22,919 --> 00:03:25,720 Speaker 3: weather event that hits. So these are just a few 69 00:03:25,760 --> 00:03:28,280 Speaker 3: items that come to my mind when I'm thinking about 70 00:03:28,280 --> 00:03:31,560 Speaker 3: how weather has an impact. At BNF, as meteorologists, we're 71 00:03:31,600 --> 00:03:34,520 Speaker 3: looking at short term weather forecast paired with past weather 72 00:03:34,600 --> 00:03:37,680 Speaker 3: data to achieve insights on any impacts for power and 73 00:03:37,800 --> 00:03:41,160 Speaker 3: energy sectors. So when we're monitoring weather, it's important not 74 00:03:41,200 --> 00:03:44,440 Speaker 3: to just look at what's happening right here now, but 75 00:03:44,480 --> 00:03:47,920 Speaker 3: also compare it to the historical averages, so create those 76 00:03:48,000 --> 00:03:53,600 Speaker 3: moving baselines and understanding how the weather is changing with time, 77 00:03:53,880 --> 00:03:57,160 Speaker 3: so that gives us an insight as to how extreme 78 00:03:57,400 --> 00:04:00,800 Speaker 3: a potential shift or a potential upsetted trend can be. 79 00:04:01,080 --> 00:04:04,200 Speaker 3: So one example of this in the EU. I've been 80 00:04:04,240 --> 00:04:06,800 Speaker 3: keeping an eye on wind speeds in Europe this winter 81 00:04:06,960 --> 00:04:10,080 Speaker 3: and we're seeing quite the hit to wind generation in Germany. 82 00:04:10,240 --> 00:04:15,120 Speaker 3: And when this hits, there's a massive decrease in wind 83 00:04:15,120 --> 00:04:19,800 Speaker 3: power generation and we're seeing this increasingly frequent, especially in Europe. 84 00:04:19,839 --> 00:04:22,120 Speaker 3: And then will if you have an example in the US. 85 00:04:22,480 --> 00:04:25,440 Speaker 2: Yeah, So for US weather, I've been interested in how 86 00:04:25,520 --> 00:04:29,039 Speaker 2: extreme weather is impacting physical infrastructure for the US. So 87 00:04:29,279 --> 00:04:31,880 Speaker 2: most recently I looked at how the LA wildfires were 88 00:04:31,880 --> 00:04:35,040 Speaker 2: impacting power transmission lines. But then back in the fall 89 00:04:35,120 --> 00:04:38,039 Speaker 2: during hurricane season, I was also monitoring which oil and 90 00:04:38,080 --> 00:04:40,920 Speaker 2: gas platforms in the Gulf of Mexico were in swaths 91 00:04:41,040 --> 00:04:41,960 Speaker 2: of hurricanes. 92 00:04:42,360 --> 00:04:44,400 Speaker 1: So we're going to talk about what some of these 93 00:04:44,440 --> 00:04:47,760 Speaker 1: extreme weather events actually are, and you'd already highlighted a 94 00:04:47,800 --> 00:04:50,440 Speaker 1: couple of them, but before we get there, I want 95 00:04:50,480 --> 00:04:53,280 Speaker 1: to have a better understanding of actually what data as 96 00:04:53,360 --> 00:04:57,480 Speaker 1: meteorologists you call upon to really formulate your research, and 97 00:04:58,040 --> 00:05:00,760 Speaker 1: you know, what information does one need in order to 98 00:05:00,800 --> 00:05:01,960 Speaker 1: start assessing this space. 99 00:05:02,279 --> 00:05:06,000 Speaker 3: Absolutely, just to start things off, weather is a dynamic 100 00:05:06,040 --> 00:05:08,560 Speaker 3: beast to wrangle and there's a lot going on in 101 00:05:08,680 --> 00:05:11,920 Speaker 3: terms of what a meteorologist needs to monitor, so it's 102 00:05:12,080 --> 00:05:15,440 Speaker 3: important to use as much data as possible. Quite a 103 00:05:15,440 --> 00:05:17,680 Speaker 3: bit of this comes in the form of global forecast 104 00:05:17,720 --> 00:05:21,359 Speaker 3: models such as GFS, which is the Global Forecasting System 105 00:05:21,520 --> 00:05:26,160 Speaker 3: and ECMWF, the European Center for Medium Range Weather Forecasts. 106 00:05:26,360 --> 00:05:31,280 Speaker 3: What these offer our forecast data on temperature, precipitation, wind speeds, 107 00:05:31,760 --> 00:05:34,840 Speaker 3: cooling and warming, degree days, and even more and so 108 00:05:35,279 --> 00:05:40,080 Speaker 3: we have access to this as well as historically recorded 109 00:05:40,120 --> 00:05:43,360 Speaker 3: weather data, and at BNF we have access to two 110 00:05:43,440 --> 00:05:46,920 Speaker 3: thousand stations globally, so we can call upon this to 111 00:05:47,000 --> 00:05:51,440 Speaker 3: create baseline comparisons with upcoming forecast data, and this helps 112 00:05:51,480 --> 00:05:54,400 Speaker 3: us understand how abnormal and upcoming weather event will be. 113 00:05:54,680 --> 00:05:58,520 Speaker 3: There is also something we monitor called teleconnections. These are 114 00:05:58,680 --> 00:06:02,480 Speaker 3: significant relationships. There are links between weather phenomena at wildly 115 00:06:03,000 --> 00:06:07,960 Speaker 3: separated locations on Earth. Again a very technical description of 116 00:06:07,960 --> 00:06:12,080 Speaker 3: what a teleconnection is. It's basically different atmospheric patterns around 117 00:06:12,120 --> 00:06:14,960 Speaker 3: the world, and one you might be familiar with is Enzo, 118 00:06:15,080 --> 00:06:18,640 Speaker 3: the El Nino Southern oscillation, which houses El Nino and 119 00:06:18,800 --> 00:06:19,279 Speaker 3: La Nina. 120 00:06:19,600 --> 00:06:22,000 Speaker 1: So that begs the question what is El Nino and 121 00:06:22,080 --> 00:06:25,880 Speaker 1: La Nina? Because I certainly remember talking about this growing up, 122 00:06:25,880 --> 00:06:28,280 Speaker 1: where you would see these periods of extreme rain and 123 00:06:28,640 --> 00:06:32,039 Speaker 1: in California, it was part of our regular lexicon. But 124 00:06:32,200 --> 00:06:34,680 Speaker 1: now I find everybody around the world is throwing these 125 00:06:34,760 --> 00:06:37,560 Speaker 1: terms around, and it seems like every year seems to 126 00:06:37,600 --> 00:06:40,039 Speaker 1: fall into one of these two categories, which I know 127 00:06:40,160 --> 00:06:42,279 Speaker 1: surely cannot be the case. So can you talk to 128 00:06:42,360 --> 00:06:44,360 Speaker 1: us a little bit about First of all, what one 129 00:06:44,400 --> 00:06:47,560 Speaker 1: is versus the other, and the frequency and duration. 130 00:06:47,839 --> 00:06:51,200 Speaker 2: So and so. It is a multi year cycle of 131 00:06:51,240 --> 00:06:54,240 Speaker 2: atmospheric patterns. It actually has three phases, which would be 132 00:06:54,320 --> 00:06:57,440 Speaker 2: El Nino, La Nina, and the neutral phase. At the 133 00:06:57,440 --> 00:07:00,560 Speaker 2: most basic level, El Nino and La Nina are warm 134 00:07:00,600 --> 00:07:03,839 Speaker 2: and cold sea surface temperature anomalies. For a section of 135 00:07:03,880 --> 00:07:07,320 Speaker 2: the Equatorial Pacific. We are currently in a La Nina, 136 00:07:07,360 --> 00:07:09,400 Speaker 2: which is the cold phase of the cycle, but this 137 00:07:09,440 --> 00:07:12,480 Speaker 2: does not necessarily mean that the entire globe is colder 138 00:07:12,480 --> 00:07:15,520 Speaker 2: than normal. A typical La Nina year will bring wetter 139 00:07:15,560 --> 00:07:19,160 Speaker 2: weather to the western Equatorial Pacific, northern Brazil and the 140 00:07:19,160 --> 00:07:22,480 Speaker 2: Pacific Northwest for the US, and drier conditions to the 141 00:07:22,520 --> 00:07:27,040 Speaker 2: southern US and northeast China. Regional temperature shifts also become apparent, 142 00:07:27,200 --> 00:07:31,080 Speaker 2: with warmer conditions across the southern US and cooler conditions 143 00:07:31,120 --> 00:07:33,679 Speaker 2: in the US, Pacific Northwest, and on the west coast 144 00:07:33,680 --> 00:07:36,760 Speaker 2: of South America. El Nino is one of the oldest 145 00:07:36,800 --> 00:07:40,160 Speaker 2: known teleconnection patterns. It was actually first discovered in the 146 00:07:40,160 --> 00:07:43,520 Speaker 2: fifteen hundreds by Peruvian fishermen who noticed the periods of 147 00:07:43,560 --> 00:07:46,240 Speaker 2: warmer water in the Pacific, bringing fewer fish to their 148 00:07:46,240 --> 00:07:49,480 Speaker 2: nets around December, so they named it El Nino due 149 00:07:49,480 --> 00:07:51,720 Speaker 2: to the proximity to the birth of Christ in the 150 00:07:51,800 --> 00:07:54,960 Speaker 2: Christian religion. So since then there has been extensive research 151 00:07:55,040 --> 00:07:58,040 Speaker 2: into this phenomenon that now is a key factor in 152 00:07:58,080 --> 00:08:02,240 Speaker 2: our seasonal forecasting. Scientists have also discovered other similar atmospheric 153 00:08:02,280 --> 00:08:05,880 Speaker 2: patterns that inform our seasonal outlooks, such as the North 154 00:08:05,920 --> 00:08:09,240 Speaker 2: Atlantic oscillation. This pattern is a sea sawing of high 155 00:08:09,280 --> 00:08:12,360 Speaker 2: and low pressures in Iceland and the Azores and has 156 00:08:12,440 --> 00:08:16,000 Speaker 2: trended more positive over the last three months, bringing warmer 157 00:08:16,000 --> 00:08:19,320 Speaker 2: than average temperatures to Europe. Understanding these patterns can give 158 00:08:19,400 --> 00:08:21,480 Speaker 2: us clues as to what weather we can expect in 159 00:08:21,520 --> 00:08:24,000 Speaker 2: the coming months. While our seasonal forecasts are not yet 160 00:08:24,080 --> 00:08:26,280 Speaker 2: accurate enough to tell you how much snow your ski 161 00:08:26,320 --> 00:08:28,920 Speaker 2: resort is going to have in the three months prior 162 00:08:29,000 --> 00:08:30,840 Speaker 2: to when you were planning it, we can have an 163 00:08:30,920 --> 00:08:33,679 Speaker 2: idea of how much above or below normal temperatures and 164 00:08:33,800 --> 00:08:36,800 Speaker 2: precipitation will be at a regional level. So this is 165 00:08:36,880 --> 00:08:39,959 Speaker 2: really important for our energy storage levels and traders. If 166 00:08:39,960 --> 00:08:43,000 Speaker 2: the US is expecting winter temperatures to be mild with 167 00:08:43,160 --> 00:08:46,880 Speaker 2: above normal precipitation, this could lead to low gas withdrawals 168 00:08:46,920 --> 00:08:50,719 Speaker 2: from a lack of heating demand and bolstering conventional hydroelectric 169 00:08:50,840 --> 00:08:53,720 Speaker 2: reservoirs leading to an increase in renewable power generation. 170 00:08:54,120 --> 00:08:57,959 Speaker 3: And another quick anecdote for Europe with the impact that 171 00:08:58,040 --> 00:09:01,880 Speaker 3: the North Atlantic Oscillation has. We're seeing the presence of 172 00:09:01,920 --> 00:09:05,920 Speaker 3: this this year with Lanina. So, like Willis said, in 173 00:09:05,960 --> 00:09:10,160 Speaker 3: Europe during a Lanina, we'll normally see cooler than average conditions, 174 00:09:10,320 --> 00:09:14,199 Speaker 3: but this year we've actually seen warmer than average conditions, 175 00:09:14,320 --> 00:09:17,760 Speaker 3: and that's because the North Atlantic Oscillation has swung into 176 00:09:17,880 --> 00:09:22,480 Speaker 3: a positive phase. So this positive phase is dominating over Lanninia, 177 00:09:22,880 --> 00:09:27,120 Speaker 3: creating that warmer than average condition in Europe. These trends 178 00:09:27,160 --> 00:09:29,800 Speaker 3: are really important to watch for liquid natural gas usage. 179 00:09:30,000 --> 00:09:31,840 Speaker 1: And I love that you brought up the LNG part 180 00:09:31,840 --> 00:09:34,600 Speaker 1: of this because this features really heavily as we do 181 00:09:34,840 --> 00:09:37,400 Speaker 1: at b and EF twice a year, this winter gas 182 00:09:37,400 --> 00:09:40,040 Speaker 1: Outlook and Summer gas Outlook, and look at the level 183 00:09:40,120 --> 00:09:42,920 Speaker 1: of storage that we have in various parts of the world, 184 00:09:43,080 --> 00:09:45,839 Speaker 1: and as traders are looking to try and figure out 185 00:09:45,880 --> 00:09:48,120 Speaker 1: what those prices are going to be going forward. My 186 00:09:48,320 --> 00:09:51,400 Speaker 1: question really revolves around when you see this warmer weather 187 00:09:51,480 --> 00:09:53,840 Speaker 1: than one would infer you need less gas over the 188 00:09:53,840 --> 00:09:56,080 Speaker 1: course of the winter in order to get through. Does 189 00:09:56,160 --> 00:09:59,440 Speaker 1: that have or is that counterweighted and to what extent 190 00:09:59,480 --> 00:10:02,880 Speaker 1: does it count weighted by increased demand for air conditioning 191 00:10:02,880 --> 00:10:06,040 Speaker 1: on a particularly hot year, where you're entering a summer 192 00:10:06,080 --> 00:10:08,160 Speaker 1: that's going to have higher than average temperatures. 193 00:10:08,480 --> 00:10:11,720 Speaker 3: It's something to monitor for sure. So when you're noticing 194 00:10:12,000 --> 00:10:16,080 Speaker 3: that warmer than average conditions are settling into an area, 195 00:10:16,280 --> 00:10:18,760 Speaker 3: for example, in winter, you are going to notice that 196 00:10:18,760 --> 00:10:21,880 Speaker 3: there is less energy demand. Now you and I might 197 00:10:21,920 --> 00:10:24,360 Speaker 3: not feel it. You and I might not feel that 198 00:10:24,559 --> 00:10:28,319 Speaker 3: it's warmer than average because technically the temperatures are still cool, 199 00:10:28,520 --> 00:10:30,800 Speaker 3: but the markets will feel it, and that's going to 200 00:10:30,800 --> 00:10:31,719 Speaker 3: be the interesting thing. 201 00:10:32,040 --> 00:10:34,800 Speaker 1: Do you work closely with the gas team as a result. 202 00:10:34,679 --> 00:10:38,400 Speaker 3: Every single day? So I know for myself in London, 203 00:10:38,600 --> 00:10:42,559 Speaker 3: I'm working with every team on my floor, So whether 204 00:10:42,600 --> 00:10:45,240 Speaker 3: that be the gas team, the oil team, the wind team, 205 00:10:45,360 --> 00:10:48,640 Speaker 3: the hydro team, we're working every single day on passing 206 00:10:48,720 --> 00:10:51,400 Speaker 3: ideas to each other and staying on top of the context. 207 00:10:51,880 --> 00:10:54,760 Speaker 2: On the US side, we've actually done some research into 208 00:10:54,760 --> 00:10:57,760 Speaker 2: how increases in summer gas demand is not enough to 209 00:10:57,800 --> 00:11:01,440 Speaker 2: offset losses in warmer winters. One of our gas analysts 210 00:11:01,440 --> 00:11:03,760 Speaker 2: in riy Kae Gonzalez put out a report called warmer 211 00:11:03,800 --> 00:11:07,040 Speaker 2: Weather low gas prices could threaten energy transitions and it 212 00:11:07,080 --> 00:11:10,800 Speaker 2: discusses how when we have warmer winters there is less 213 00:11:10,800 --> 00:11:14,800 Speaker 2: demand for gas driven heating. We are also seeing warmer summers, 214 00:11:14,840 --> 00:11:17,320 Speaker 2: but in those summers, the increase in power demand can 215 00:11:17,360 --> 00:11:20,800 Speaker 2: be filled by renewable energy, so that increase in power 216 00:11:20,840 --> 00:11:23,400 Speaker 2: demand is not necessarily being filled by burns in the 217 00:11:23,480 --> 00:11:24,440 Speaker 2: natural gas sector. 218 00:11:24,840 --> 00:11:28,240 Speaker 1: So it's really clear how weather is so incredibly important 219 00:11:28,280 --> 00:11:31,280 Speaker 1: to a number of the different commodities that are covered 220 00:11:31,280 --> 00:11:33,360 Speaker 1: at BNF. What I want to know now is a 221 00:11:33,360 --> 00:11:37,480 Speaker 1: pivot to finance. When we looked at these most recent 222 00:11:37,600 --> 00:11:41,480 Speaker 1: fires that took place in southern California, insurance came up 223 00:11:41,559 --> 00:11:45,720 Speaker 1: quite often, and then also the role of reinsurance and 224 00:11:45,800 --> 00:11:48,800 Speaker 1: catastrophe bonds. Can you talk about some of the financial 225 00:11:48,840 --> 00:11:53,240 Speaker 1: instruments that exist and really how these interrelate with extreme 226 00:11:53,280 --> 00:11:54,000 Speaker 1: weather events. 227 00:11:54,360 --> 00:11:57,360 Speaker 2: So catastrophe bond or a cat bond is a high 228 00:11:57,400 --> 00:12:00,559 Speaker 2: yield debt instrument designed to raise money for companies in 229 00:12:00,600 --> 00:12:03,160 Speaker 2: the insurance industry in the event of a natural disaster. 230 00:12:03,440 --> 00:12:07,040 Speaker 2: A CAT bond allows the issuer to receive funding for 231 00:12:07,200 --> 00:12:09,600 Speaker 2: the bond if the conditions are met, such as like 232 00:12:09,640 --> 00:12:12,760 Speaker 2: a tornado or a hurricane or severe flooding. If an 233 00:12:12,760 --> 00:12:15,760 Speaker 2: event that's protected by the bond activates a payout to 234 00:12:15,840 --> 00:12:19,160 Speaker 2: the insurance company, the obligation to pay interest and repay 235 00:12:19,160 --> 00:12:22,800 Speaker 2: the principle is either deferred or completely forgiven. So a 236 00:12:22,840 --> 00:12:26,120 Speaker 2: CAT bond has a shorter maturity date of between three 237 00:12:26,120 --> 00:12:29,600 Speaker 2: to five years, and the primary investors in the security 238 00:12:29,640 --> 00:12:33,079 Speaker 2: are like hedge funds and pensions and other institutional investors. 239 00:12:33,160 --> 00:12:35,640 Speaker 2: But on the other hand, a reinsurance is a type 240 00:12:35,640 --> 00:12:39,400 Speaker 2: of insurance primarily purchased by insurance companies to provide a 241 00:12:39,520 --> 00:12:43,520 Speaker 2: layer of financial protection against weather events that could cause 242 00:12:43,679 --> 00:12:47,360 Speaker 2: major financial disasters, and as we're seeing with extreme events 243 00:12:47,360 --> 00:12:50,360 Speaker 2: becoming more frequent, the market for these types of bonds 244 00:12:50,440 --> 00:12:53,400 Speaker 2: is also growing. In twenty twenty four, the US had 245 00:12:53,440 --> 00:12:57,160 Speaker 2: twenty four rather related disaster events that individually caused over 246 00:12:57,200 --> 00:13:00,160 Speaker 2: a billion dollars in damages, with seventy one percent of 247 00:13:00,240 --> 00:13:03,680 Speaker 2: these events attributed to severe storms. The five year average 248 00:13:03,720 --> 00:13:06,800 Speaker 2: cost of these damages in twenty twenty four was around 249 00:13:06,840 --> 00:13:09,199 Speaker 2: one hundred and fifty billion, which was more than double 250 00:13:09,240 --> 00:13:11,880 Speaker 2: what it was ten years ago. La Nina events have 251 00:13:11,960 --> 00:13:15,360 Speaker 2: been linked to more severe storms across the US, and 252 00:13:15,400 --> 00:13:18,040 Speaker 2: so this La Nina that we are currently in could 253 00:13:18,040 --> 00:13:22,080 Speaker 2: also mean increasing market chairs for these types of bonds, 254 00:13:22,120 --> 00:13:25,440 Speaker 2: as severe events could potentially be more frequent this year. 255 00:13:25,559 --> 00:13:28,120 Speaker 1: And can you put the financial losses in context, because 256 00:13:28,160 --> 00:13:30,280 Speaker 1: you know, I brought up these fires in Los Angeles 257 00:13:30,320 --> 00:13:34,800 Speaker 1: and they were really an unprecedented amount of damage in 258 00:13:34,920 --> 00:13:37,120 Speaker 1: terms of financial loss. I mean, how much was it 259 00:13:37,200 --> 00:13:40,840 Speaker 1: and how does it compare to other natural disasters which 260 00:13:40,840 --> 00:13:42,720 Speaker 1: have taken place maybe also in the US. 261 00:13:43,040 --> 00:13:46,480 Speaker 2: Yeah, so the LA fires were a really special case 262 00:13:46,640 --> 00:13:49,560 Speaker 2: of like the kind of perfect storm of bad conditions. 263 00:13:49,840 --> 00:13:52,720 Speaker 2: So it's not that these fires were necessarily the largest 264 00:13:52,760 --> 00:13:55,320 Speaker 2: fires we've seen in US history. It's more so that 265 00:13:55,360 --> 00:13:59,200 Speaker 2: they were in Los Angeles, a fairly wealthy area, and 266 00:13:59,280 --> 00:14:03,240 Speaker 2: so the the property losses were extremely large. So the 267 00:14:03,520 --> 00:14:07,480 Speaker 2: estimated monetary toll of the LA fires is expected to 268 00:14:07,480 --> 00:14:09,880 Speaker 2: surpass two hundred and fifty billion, which would make it 269 00:14:09,920 --> 00:14:12,480 Speaker 2: the costiest weather disaster in US history, and that is 270 00:14:12,520 --> 00:14:15,720 Speaker 2: even greater than Hurricane Katrina, which reached two hundred and 271 00:14:15,760 --> 00:14:17,439 Speaker 2: one billion, dollars in damages. 272 00:14:17,840 --> 00:14:19,840 Speaker 1: So one of the things that the two of you 273 00:14:19,880 --> 00:14:22,400 Speaker 1: did headed into this year was great a Things to 274 00:14:22,480 --> 00:14:25,280 Speaker 1: Watch research piece where you kind of looked at the 275 00:14:25,360 --> 00:14:28,080 Speaker 1: year ahead. And I know it's very difficult, as you've outlined, 276 00:14:28,120 --> 00:14:31,480 Speaker 1: to actually predict the weather, but given your experience in 277 00:14:31,560 --> 00:14:35,320 Speaker 1: these kind of annual and multi year trends that take place, 278 00:14:35,400 --> 00:14:37,360 Speaker 1: what are some of the things that we can expect 279 00:14:37,360 --> 00:14:38,080 Speaker 1: in the year ahead. 280 00:14:38,360 --> 00:14:40,960 Speaker 2: Yeah, So for the coming year in twenty twenty five, 281 00:14:41,040 --> 00:14:45,080 Speaker 2: we are seeing above average fire conditions forecasted for Texas. 282 00:14:45,120 --> 00:14:48,240 Speaker 2: This is coming from the National Interagency Fire Center, which 283 00:14:48,320 --> 00:14:51,600 Speaker 2: issues wildland fire potential outlooks, and so we're seeing these 284 00:14:51,640 --> 00:14:54,560 Speaker 2: above average fire conditions through the spring. And so this 285 00:14:54,600 --> 00:14:58,440 Speaker 2: is a combination of below average precipitation, above average winds, 286 00:14:58,520 --> 00:15:01,840 Speaker 2: and above average temperatures. And so from the Bloomberg Terminal, 287 00:15:01,840 --> 00:15:04,360 Speaker 2: we have these seasonal forecasts, which is showing all three 288 00:15:04,400 --> 00:15:07,480 Speaker 2: boxes are checked for Texas for the spring. But the 289 00:15:07,560 --> 00:15:11,280 Speaker 2: National Weather Service also issues temperature and precipitation outlooks for 290 00:15:11,400 --> 00:15:14,120 Speaker 2: the next twelve months, and so in those we are 291 00:15:14,120 --> 00:15:17,480 Speaker 2: seeing above average temperatures for Texas, which means that we 292 00:15:17,560 --> 00:15:20,480 Speaker 2: could see fire conditions persisting throughout the year, and so 293 00:15:20,760 --> 00:15:23,720 Speaker 2: the only thing that is left is an igniti event 294 00:15:23,800 --> 00:15:26,560 Speaker 2: to cause another string of devastating wildfires. 295 00:15:26,880 --> 00:15:30,680 Speaker 1: So related to fires, but also related to the energy system, 296 00:15:30,760 --> 00:15:34,920 Speaker 1: I want to talk about precipitation and essentially water levels. 297 00:15:35,080 --> 00:15:37,360 Speaker 1: Can you make that connection spell it out for us 298 00:15:37,440 --> 00:15:43,160 Speaker 1: regarding how rainfall is actually connected to power and emissions. 299 00:15:43,520 --> 00:15:46,080 Speaker 3: Absolutely, So I just want to preface this with we 300 00:15:46,200 --> 00:15:49,880 Speaker 3: have a focus on the US and Europe, but in 301 00:15:49,920 --> 00:15:52,560 Speaker 3: our nine Things Weather to Watch for twenty twenty five, 302 00:15:52,720 --> 00:15:54,880 Speaker 3: we took a look at China and what we're seeing 303 00:15:55,120 --> 00:15:58,440 Speaker 3: right now in China is something very interesting. So at 304 00:15:58,480 --> 00:16:01,440 Speaker 3: the end of twenty twenty four, water levels at one 305 00:16:01,440 --> 00:16:05,520 Speaker 3: of the biggest dams, actually the biggest dam in China. 306 00:16:05,120 --> 00:16:06,200 Speaker 1: Is it the three Gorgeous Dam. 307 00:16:06,320 --> 00:16:10,360 Speaker 3: Three Gorgeous Dam, the water levels measured four meters below 308 00:16:10,400 --> 00:16:13,280 Speaker 3: the five year average. Now, this doesn't sound like a lot, 309 00:16:13,440 --> 00:16:17,880 Speaker 3: but thirteen southern Chinese provinces in the last six months 310 00:16:18,120 --> 00:16:23,080 Speaker 3: have undergone drought warnings. Thankfully though, La Nina is bringing 311 00:16:23,200 --> 00:16:27,240 Speaker 3: a much needed reprieve to this area. So I've been 312 00:16:27,280 --> 00:16:31,200 Speaker 3: monitoring the weather reports and right now in some areas 313 00:16:31,280 --> 00:16:35,440 Speaker 3: of southern China. They're registering sixteen millimeters above the average. 314 00:16:35,680 --> 00:16:42,720 Speaker 3: It has relieved the drought stress on any hydropower plants 315 00:16:42,720 --> 00:16:46,160 Speaker 3: in southern China along the Yansee River. But this drought 316 00:16:46,240 --> 00:16:51,560 Speaker 3: scare has revitalized concerns over a clean, stable energy generation, 317 00:16:51,880 --> 00:16:54,960 Speaker 3: especially during periods of low hydropower output for the nation. 318 00:16:55,200 --> 00:16:58,600 Speaker 3: So to preface this with context, the issue began in 319 00:16:58,680 --> 00:17:01,320 Speaker 3: twenty twenty two there was an tense year long drought 320 00:17:01,360 --> 00:17:05,240 Speaker 3: and into twenty twenty three that hit the southern provinces 321 00:17:05,359 --> 00:17:07,960 Speaker 3: housing the Yangsee River. So this is the largest river 322 00:17:08,160 --> 00:17:10,560 Speaker 3: in China as a whole. This river is crucial for 323 00:17:10,640 --> 00:17:14,879 Speaker 3: hydropower and specifically Sichuan Province a key upstream province for 324 00:17:14,960 --> 00:17:18,320 Speaker 3: water recharge. It's also a province that makes up thirty 325 00:17:18,320 --> 00:17:21,239 Speaker 3: percent of China's hydropower. It saw water levels drop by 326 00:17:21,240 --> 00:17:26,919 Speaker 3: thirteen meters that year, so during this period, Sichuan Hydropower 327 00:17:27,000 --> 00:17:30,960 Speaker 3: actually recorded an eleven percent drop in hydro power outputs. 328 00:17:31,160 --> 00:17:34,920 Speaker 3: So the stability of the hydropower in southern China as 329 00:17:34,960 --> 00:17:38,080 Speaker 3: a power source in that area left room for concern 330 00:17:38,280 --> 00:17:41,560 Speaker 3: thinking about future peak energy demand, and so this short 331 00:17:41,600 --> 00:17:46,080 Speaker 3: term prioritization of coal is offering some stability for China 332 00:17:46,200 --> 00:17:48,720 Speaker 3: as they're aiming to amp up the renewable energy capacity 333 00:17:48,760 --> 00:17:50,840 Speaker 3: to meet the twenty thirty renewable targets. 334 00:17:51,000 --> 00:17:53,399 Speaker 1: So very simply put, when you have low rainfall in 335 00:17:53,480 --> 00:17:56,720 Speaker 1: areas that are requiring hydropower to be a part of 336 00:17:56,720 --> 00:17:58,840 Speaker 1: their energy mix, they have to turn to other sources, 337 00:17:58,880 --> 00:18:01,840 Speaker 1: and in some circumstances that leads to higher emissions. So 338 00:18:01,920 --> 00:18:05,040 Speaker 1: rain level is definitely linked. Let's also talk about how 339 00:18:05,280 --> 00:18:08,720 Speaker 1: droughts connect to not just hydropower, but also to other 340 00:18:08,720 --> 00:18:11,520 Speaker 1: fuel sources like biofuels. Naturally a lot of them come 341 00:18:11,560 --> 00:18:15,080 Speaker 1: from sources like soybean, rape seed, So can we talk 342 00:18:15,119 --> 00:18:17,520 Speaker 1: a little bit about how there may be droughts right 343 00:18:17,560 --> 00:18:20,080 Speaker 1: now and how that's impacting the biofuels market. 344 00:18:20,359 --> 00:18:23,960 Speaker 3: It's another interesting topic we touched upon in our nine 345 00:18:23,960 --> 00:18:27,440 Speaker 3: Things to Watch, So it's important to think about upstream 346 00:18:27,480 --> 00:18:30,119 Speaker 3: as well as downstream impacts. And I really want to 347 00:18:30,119 --> 00:18:33,960 Speaker 3: put a spotlight on Brazil and Brazil's upcoming biodiesel outlook. 348 00:18:34,200 --> 00:18:37,840 Speaker 3: There's been hurdles at nearly every turn. So brazil biodiesel 349 00:18:37,920 --> 00:18:42,000 Speaker 3: relies on that nationally grown soybean and twenty eight percent 350 00:18:42,280 --> 00:18:46,040 Speaker 3: of national soybean is grown in Matagrosso, so again another 351 00:18:46,119 --> 00:18:48,760 Speaker 3: spotlight on this region in Brazil. Not only was the 352 00:18:48,800 --> 00:18:52,680 Speaker 3: planting season hit with fifty six percent below average rainfall 353 00:18:52,760 --> 00:18:55,560 Speaker 3: during the first seventy five percent of that season, harvest 354 00:18:55,600 --> 00:18:59,520 Speaker 3: season is now saying delays with one hundred percent above 355 00:18:59,560 --> 00:19:02,879 Speaker 3: average rainfall, So I just want to stop one hundred 356 00:19:02,920 --> 00:19:08,239 Speaker 3: percent above average. So farmers are seeing floods, roads are 357 00:19:08,240 --> 00:19:11,399 Speaker 3: becoming impassable, bridges are being destroyed, on top of the 358 00:19:11,440 --> 00:19:14,000 Speaker 3: fact that farmers need to get into their fields and 359 00:19:14,080 --> 00:19:17,360 Speaker 3: harvest their crop. So what's happening here is that we're 360 00:19:17,359 --> 00:19:20,879 Speaker 3: seeing not only potential impacts on germination and a shorter 361 00:19:20,960 --> 00:19:24,320 Speaker 3: growing window early on in the crop cycle, but we're 362 00:19:24,320 --> 00:19:27,520 Speaker 3: also seeing moisture control issues at harvest. So this all 363 00:19:27,640 --> 00:19:31,840 Speaker 3: leads to ending production values of Brazil and soybean potentially 364 00:19:31,840 --> 00:19:34,800 Speaker 3: going down. So what will the final output be? Will 365 00:19:34,920 --> 00:19:37,400 Speaker 3: be lower than expected, and from there, how does that 366 00:19:37,520 --> 00:19:41,040 Speaker 3: impact allocation towards biodiesel on top of other uses. So 367 00:19:41,440 --> 00:19:43,760 Speaker 3: this is really important to think about when we're thinking 368 00:19:43,800 --> 00:19:46,560 Speaker 3: about Brazil's Fuel of the Future bill that was passed 369 00:19:46,560 --> 00:19:50,040 Speaker 3: in October twenty twenty four, and the pressure on Brazil 370 00:19:50,160 --> 00:19:53,600 Speaker 3: right now is to push the biodiesel blend and diesel 371 00:19:53,600 --> 00:19:56,760 Speaker 3: oil up to fifteen percent. So with this impact on 372 00:19:57,040 --> 00:19:59,800 Speaker 3: local soybean, what will be the impact on biodiesel. 373 00:19:59,800 --> 00:20:03,080 Speaker 1: This here so floods, droughts that they don't just have 374 00:20:03,200 --> 00:20:05,960 Speaker 1: to do with water. So can you actually pivot now 375 00:20:06,040 --> 00:20:09,959 Speaker 1: to what impacts renewable energy wind power and what is 376 00:20:09,960 --> 00:20:11,600 Speaker 1: referred to as a wind drought. 377 00:20:11,920 --> 00:20:14,960 Speaker 3: So these are prolonged periods of time where wind speeds 378 00:20:15,000 --> 00:20:18,280 Speaker 3: are registering two meters per second or less, and if 379 00:20:18,320 --> 00:20:20,239 Speaker 3: we look at the data that comes out of it, 380 00:20:20,280 --> 00:20:23,320 Speaker 3: the impacts are actually pretty shocking. So during an average 381 00:20:23,320 --> 00:20:26,800 Speaker 3: wind drought, if wind speeds drop by ten percent, power 382 00:20:26,840 --> 00:20:29,439 Speaker 3: generation can drop by up to thirty percent, which is 383 00:20:29,720 --> 00:20:33,439 Speaker 3: I don't know about you, but that's a pretty shocking statistic. 384 00:20:33,800 --> 00:20:37,040 Speaker 3: And this is becoming very important for countries whose generation 385 00:20:37,160 --> 00:20:39,639 Speaker 3: capacity is made up of a high proportion of wind. 386 00:20:39,920 --> 00:20:42,920 Speaker 3: So when periods of low wind hit, this could undercut 387 00:20:42,960 --> 00:20:46,600 Speaker 3: renewable energy output and exacerbate reliance on fossil fuels. So 388 00:20:46,840 --> 00:20:49,919 Speaker 3: right now, if we can turn our spotlight to Europe, 389 00:20:50,000 --> 00:20:53,720 Speaker 3: we're seeing a particularly striking impact in countries like Germany, 390 00:20:53,800 --> 00:20:56,960 Speaker 3: Europe's top country for wind production. So in twenty twenty 391 00:20:57,000 --> 00:20:59,560 Speaker 3: three and twenty twenty four there were four separate months 392 00:20:59,560 --> 00:21:02,800 Speaker 3: that Regis stirred wind rout events in Germany. Just two 393 00:21:02,840 --> 00:21:05,000 Speaker 3: of these events in twenty twenty four led to win 394 00:21:05,119 --> 00:21:09,080 Speaker 3: generation reductions of sixteen percent against the five year average, 395 00:21:09,119 --> 00:21:12,480 Speaker 3: and as a result, total German win output for twenty 396 00:21:12,520 --> 00:21:15,680 Speaker 3: twenty four fell by six percent from the previous year, 397 00:21:15,960 --> 00:21:19,440 Speaker 3: and this was despite capacity increasing three percent year over year. 398 00:21:19,560 --> 00:21:22,600 Speaker 3: So you can see the impact something like this can 399 00:21:22,640 --> 00:21:25,200 Speaker 3: have on a country. But how do we forecast these 400 00:21:25,440 --> 00:21:30,240 Speaker 3: It's incredibly difficult to accurately predict where wind will blow 401 00:21:30,440 --> 00:21:32,640 Speaker 3: and how strong it will be. But in Europe, low 402 00:21:32,640 --> 00:21:35,080 Speaker 3: pressure systems driven by the Gulf Stream tend to bring 403 00:21:35,119 --> 00:21:39,320 Speaker 3: weather and in this case, windier conditions. Tracking rainfall tied 404 00:21:39,320 --> 00:21:42,000 Speaker 3: to these systems in long range forecast can offer insights 405 00:21:42,040 --> 00:21:43,919 Speaker 3: into when wind dear weather may develop. 406 00:21:44,320 --> 00:21:46,600 Speaker 1: So I think we've done a great job of laying 407 00:21:46,640 --> 00:21:49,919 Speaker 1: out why not only those who are covering commodities, but 408 00:21:50,160 --> 00:21:54,480 Speaker 1: financial players, companies in the energy system, anybody looking in 409 00:21:54,640 --> 00:21:58,680 Speaker 1: anything that's project power development, they are going to be 410 00:21:58,760 --> 00:22:01,320 Speaker 1: focused on actually chain changes to the weather and what 411 00:22:01,520 --> 00:22:05,520 Speaker 1: is becoming increasingly unpredictable as we extrapolate this out, and 412 00:22:05,560 --> 00:22:08,840 Speaker 1: we know that a change in climate leads to a 413 00:22:08,880 --> 00:22:12,080 Speaker 1: disruption in the water cycle and therefore a change in weather. 414 00:22:12,359 --> 00:22:15,159 Speaker 1: Often when you see extreme weather events, there's a debate 415 00:22:15,359 --> 00:22:19,000 Speaker 1: over whether or not you can actually link a specific 416 00:22:19,040 --> 00:22:22,240 Speaker 1: weather event to anthropogenic climate change. And I want to 417 00:22:22,320 --> 00:22:25,720 Speaker 1: understand why it is so difficult to make that connection. 418 00:22:26,160 --> 00:22:29,040 Speaker 1: When we zoom out, the connection seems quite obvious. But 419 00:22:29,040 --> 00:22:32,200 Speaker 1: when it comes down to a specific place and time, 420 00:22:32,560 --> 00:22:34,720 Speaker 1: why is it that there is so much debate over 421 00:22:34,840 --> 00:22:37,160 Speaker 1: tying something to a specific natural disaster. 422 00:22:37,520 --> 00:22:39,720 Speaker 2: So I think that this question really ties back to 423 00:22:39,760 --> 00:22:43,040 Speaker 2: the definition of climate versus weather. When we're talking about weather, 424 00:22:43,160 --> 00:22:46,080 Speaker 2: we're looking at the timescale of like hours to days, 425 00:22:46,160 --> 00:22:48,880 Speaker 2: So weather changes every day, you know, like yesterday's weather 426 00:22:48,920 --> 00:22:51,200 Speaker 2: is not necessarily the same as today's. But in terms 427 00:22:51,200 --> 00:22:53,199 Speaker 2: of the climate, this is thought of to be the 428 00:22:53,440 --> 00:22:56,840 Speaker 2: typical conditions for a region during a specific time of 429 00:22:56,880 --> 00:22:59,160 Speaker 2: the year, and so when we look at climate change, 430 00:22:59,160 --> 00:23:01,800 Speaker 2: we can see that over the past like thirty to 431 00:23:01,840 --> 00:23:05,000 Speaker 2: fifty twelve hundred years, the weather that we are experiencing 432 00:23:05,040 --> 00:23:06,960 Speaker 2: now is not the same as it was back then. 433 00:23:07,320 --> 00:23:09,920 Speaker 2: So extreme weather has also been happening since the dawn 434 00:23:09,960 --> 00:23:12,320 Speaker 2: of time, but our methods of data collection have only 435 00:23:12,359 --> 00:23:15,560 Speaker 2: recently become quite so sophisticated. 436 00:23:15,160 --> 00:23:20,800 Speaker 1: Delineation between averages and frequency as opposed to a specific 437 00:23:20,840 --> 00:23:23,600 Speaker 1: point in time. Where we know that extreme weather events 438 00:23:23,760 --> 00:23:27,200 Speaker 1: do happen, it's just how often and to what degree 439 00:23:27,240 --> 00:23:30,560 Speaker 1: of intensity on a global basis. That's really so we're 440 00:23:30,560 --> 00:23:34,080 Speaker 1: really ending how we began, which is this delineation between 441 00:23:34,200 --> 00:23:37,560 Speaker 1: weather and climate and what conversation each of those terms 442 00:23:37,600 --> 00:23:38,280 Speaker 1: is appropriated. 443 00:23:38,680 --> 00:23:41,159 Speaker 2: Yes, and so when we talk about extreme weather, it 444 00:23:41,200 --> 00:23:43,480 Speaker 2: can be hard to link these events to climate change 445 00:23:43,560 --> 00:23:46,400 Speaker 2: just because of the infrequency in which they happen. When 446 00:23:46,400 --> 00:23:49,320 Speaker 2: you think about hurricanes, especially like a Category five storm, 447 00:23:49,440 --> 00:23:53,119 Speaker 2: those events are only maybe happening like three times a year, 448 00:23:53,280 --> 00:23:55,880 Speaker 2: and so it can be very hard to look at 449 00:23:55,920 --> 00:23:59,439 Speaker 2: a historical trend in these events when they happen so 450 00:23:59,480 --> 00:24:01,600 Speaker 2: infrequently and to then be able to link them to 451 00:24:01,600 --> 00:24:04,760 Speaker 2: climate change. But there has been significant research in this 452 00:24:04,880 --> 00:24:08,360 Speaker 2: area in linking the intensity of these storms to climate change. 453 00:24:08,400 --> 00:24:10,880 Speaker 2: So we know that like factors that contribute to their 454 00:24:10,920 --> 00:24:14,240 Speaker 2: intensity are increasing and have been tied to climate change, 455 00:24:14,240 --> 00:24:16,880 Speaker 2: things such as sea level temperatures and sea level rise. 456 00:24:17,080 --> 00:24:20,280 Speaker 2: And so while we can measure the factors that contribute 457 00:24:20,320 --> 00:24:22,600 Speaker 2: to these extreme events and lick those to climate change, 458 00:24:22,640 --> 00:24:25,439 Speaker 2: it can be hard sometimes to link the extreme event 459 00:24:25,520 --> 00:24:29,160 Speaker 2: itself because these events happen so infrequently, and since weather 460 00:24:29,200 --> 00:24:32,000 Speaker 2: has always been happening. But it's really the intensity of 461 00:24:32,040 --> 00:24:34,159 Speaker 2: these events that we are seeing as a result of 462 00:24:34,160 --> 00:24:35,000 Speaker 2: climate change. 463 00:24:35,119 --> 00:24:39,280 Speaker 3: And so I before I was a meteorologist, I was 464 00:24:39,400 --> 00:24:43,000 Speaker 3: a paleo climatologist, which is a really fancy way of 465 00:24:43,040 --> 00:24:47,160 Speaker 3: saying I studied past climate, and when you're looking at 466 00:24:47,320 --> 00:24:51,359 Speaker 3: past levels of CO two, it pairs almost exactly with 467 00:24:51,480 --> 00:24:54,680 Speaker 3: past levels of temperature. And when we're seeing these massive 468 00:24:55,000 --> 00:24:59,439 Speaker 3: shifts upward of CO two, you see paired rises of 469 00:24:59,520 --> 00:25:03,920 Speaker 3: temperature over time. And what's really striking recently is that 470 00:25:04,200 --> 00:25:08,080 Speaker 3: the amount of CO two that we're seeing increase year 471 00:25:08,119 --> 00:25:11,960 Speaker 3: after year is happening at a much faster rate than 472 00:25:12,000 --> 00:25:16,040 Speaker 3: what we ever saw in paleoclimate history. And so this 473 00:25:16,080 --> 00:25:20,480 Speaker 3: is why we're seeing the impact of more extreme weather 474 00:25:20,840 --> 00:25:26,080 Speaker 3: tied to these increasing global temperatures, because our natural systems 475 00:25:26,160 --> 00:25:29,280 Speaker 3: aren't used to this rapid change. And so as weather 476 00:25:29,600 --> 00:25:32,960 Speaker 3: is a form of equilibrium it's trying to adjust, and 477 00:25:33,040 --> 00:25:37,600 Speaker 3: with that adjustment comes stronger, more intense storms on this occasion. 478 00:25:37,720 --> 00:25:40,120 Speaker 3: So that from my perspective, that's kind of what I'm 479 00:25:40,160 --> 00:25:40,680 Speaker 3: looking at. 480 00:25:41,280 --> 00:25:44,120 Speaker 1: So Jess Willa, thank you for giving me even more 481 00:25:44,200 --> 00:25:47,680 Speaker 1: reasons to be checking the weather report and for sharing 482 00:25:47,960 --> 00:25:52,520 Speaker 1: some insights regarding how various climate events extreme and otherwise 483 00:25:52,720 --> 00:25:55,359 Speaker 1: are connected to our energy system and so many of 484 00:25:55,400 --> 00:25:57,560 Speaker 1: the commodities that we deal with on a daily basis 485 00:25:57,640 --> 00:25:58,320 Speaker 1: here at BNF. 486 00:25:58,680 --> 00:25:59,440 Speaker 3: Thank you very much. 487 00:25:59,520 --> 00:26:09,400 Speaker 2: Dana, Yeah, thank you so much for having us. 488 00:26:10,320 --> 00:26:13,440 Speaker 1: Today's episode of Switched On was produced by Cam Gray 489 00:26:13,640 --> 00:26:17,320 Speaker 1: with production assistance from Kamala Shelling. 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