WEBVTT - Weather and Commodities: A Perfect Storm 0:00:00.160 --> 0:00:03.160 This is Dana Perkins and you're listening to Switched on, 0:00:03.480 --> 0:00:05.800 the podcast brought to you by B and EF. And 0:00:05.840 --> 0:00:08.160 today we're here to talk about the weather. While I 0:00:08.200 --> 0:00:09.840 won't be able to tell you whether or not to 0:00:09.880 --> 0:00:11.559 grab a coat on your way out of the house, 0:00:11.600 --> 0:00:14.520 today we will go through some important definitions when it 0:00:14.560 --> 0:00:17.480 comes to the weather. We'll explain the difference between weather 0:00:17.560 --> 0:00:19.880 and climate and why it can be hard to draw 0:00:20.000 --> 0:00:23.880 straight line between natural disasters like fires and hurricanes and 0:00:23.920 --> 0:00:27.760 climate change. We'll also highlight why B and EF's meteorologists 0:00:27.800 --> 0:00:30.280 are some of my colleagues who work with the widest 0:00:30.400 --> 0:00:34.360 range of teams across BNF. Weather impacts so many things, 0:00:34.360 --> 0:00:37.920 from power prices to natural gas stores to emissions, so 0:00:38.000 --> 0:00:42.320 it's no surprise that many companies, especially utilities, are looking 0:00:42.400 --> 0:00:46.320 at temperature, wind, rain and everything else that goes into 0:00:46.320 --> 0:00:49.920 seasonal weather. As we head into another cyclical La Nina period, 0:00:50.240 --> 0:00:52.839 what does this mean for the year ahead? Today I'm 0:00:52.920 --> 0:00:56.480 joined by B and EF's resident meteorologists and weather analysts 0:00:56.640 --> 0:00:59.800 Jess Hicks and Willetobin, and they share findings from their 0:00:59.800 --> 0:01:04.000 recently published research notes titled Weather and Commodities. Nine Things 0:01:04.000 --> 0:01:07.320 to Watch in twenty twenty five and shifting weather patterns 0:01:07.400 --> 0:01:10.520 a black swan for US commodities B and EF Clients 0:01:10.560 --> 0:01:12.760 will be able to find both of these at BNF 0:01:12.880 --> 0:01:16.399 go on the Bloomberg Terminal or at BNF dot com. 0:01:16.480 --> 0:01:29.120 Right now, let's talk about the weather. Jess, thank you 0:01:29.200 --> 0:01:31.440 very much for coming on the show today. Thank you 0:01:31.480 --> 0:01:33.640 for having me and Willa. Good to have you here 0:01:33.680 --> 0:01:34.000 as well. 0:01:34.319 --> 0:01:35.199 Yeah, thank you, Dana. 0:01:35.400 --> 0:01:37.480 So we're here to talk about the weather, and I 0:01:37.560 --> 0:01:39.440 will tell you right now, I'm not going to tell 0:01:39.440 --> 0:01:41.920 you what the weather's like here because I'm recording from 0:01:41.959 --> 0:01:45.400 London and it's the same way it is every February, 0:01:45.440 --> 0:01:47.680 so we'll just leave it at that. Gray is the theme. 0:01:47.840 --> 0:01:50.720 But actually I wanted to be a meteorologist as a kid, 0:01:50.760 --> 0:01:53.480 so I'm very much looking forward to this. Ten year 0:01:53.520 --> 0:01:56.040 old me cannot believe that my job is to sit 0:01:56.080 --> 0:01:58.360 here and interview the two of you. And actually, you know, 0:01:58.400 --> 0:01:59.840 I'm not going to tell you how old I am either, 0:01:59.840 --> 0:02:02.680 but the now me is also really excited. So as 0:02:02.720 --> 0:02:05.440 we talk about the weather, so much of our conversation 0:02:05.600 --> 0:02:08.720 in this studio and on this show revolves around climate 0:02:08.800 --> 0:02:12.840 and emissions targets, can we have a quick definition at 0:02:12.840 --> 0:02:15.880 the beginning to frame our conversation about the weather and 0:02:16.160 --> 0:02:19.119 create that distinction between weather and climate. 0:02:19.440 --> 0:02:22.640 Sure, So the difference between weather and climate really boils 0:02:22.680 --> 0:02:26.240 down to time horizons. Weather is technically defined as the 0:02:26.280 --> 0:02:28.680 state of the atmosphere at a given point in time, 0:02:28.760 --> 0:02:31.360 which can be measured by things like temperature and wind 0:02:31.360 --> 0:02:34.160 speed and pressures. Whereas climate, on the other hand, is 0:02:34.280 --> 0:02:37.400 a long term average of atmospheric conditions for a region. 0:02:37.520 --> 0:02:39.280 So this is going to be more of like your 0:02:39.320 --> 0:02:43.400 thirty year averages of those types of conditions, and so 0:02:43.400 --> 0:02:45.360 we can think of about that as like the thirty 0:02:45.400 --> 0:02:47.000 year average of winter temperatures. 0:02:47.320 --> 0:02:50.240 Now, other than the fact that I'm really enthusiastic about 0:02:50.240 --> 0:02:52.560 this topic, why is it that we at bn EF, 0:02:52.600 --> 0:02:55.640 who are so focused on the energy transition are researching 0:02:55.639 --> 0:02:56.079 this now? 0:02:56.400 --> 0:03:00.079 So the reason why weather is so important and why 0:03:00.080 --> 0:03:02.480 we're researching it is because it's a fundamental part of 0:03:02.520 --> 0:03:05.640 our lives. It impacts things as simple as what you 0:03:05.680 --> 0:03:08.320 wear every day to things as complicated as the net 0:03:08.400 --> 0:03:13.080 zero energy transition. Weather drives these residential and commercial power 0:03:13.120 --> 0:03:16.160 demands through heating and cooling needs, but it also fuels 0:03:16.200 --> 0:03:19.320 renewable power generation for wind and solar, and can disrupt 0:03:19.360 --> 0:03:22.880 production and transportation of oil and gas with any extreme 0:03:22.919 --> 0:03:25.720 weather event that hits. So these are just a few 0:03:25.760 --> 0:03:28.280 items that come to my mind when I'm thinking about 0:03:28.280 --> 0:03:31.560 how weather has an impact. At BNF, as meteorologists, we're 0:03:31.600 --> 0:03:34.520 looking at short term weather forecast paired with past weather 0:03:34.600 --> 0:03:37.680 data to achieve insights on any impacts for power and 0:03:37.800 --> 0:03:41.160 energy sectors. So when we're monitoring weather, it's important not 0:03:41.200 --> 0:03:44.440 to just look at what's happening right here now, but 0:03:44.480 --> 0:03:47.920 also compare it to the historical averages, so create those 0:03:48.000 --> 0:03:53.600 moving baselines and understanding how the weather is changing with time, 0:03:53.880 --> 0:03:57.160 so that gives us an insight as to how extreme 0:03:57.400 --> 0:04:00.800 a potential shift or a potential upsetted trend can be. 0:04:01.080 --> 0:04:04.200 So one example of this in the EU. I've been 0:04:04.240 --> 0:04:06.800 keeping an eye on wind speeds in Europe this winter 0:04:06.960 --> 0:04:10.080 and we're seeing quite the hit to wind generation in Germany. 0:04:10.240 --> 0:04:15.120 And when this hits, there's a massive decrease in wind 0:04:15.120 --> 0:04:19.800 power generation and we're seeing this increasingly frequent, especially in Europe. 0:04:19.839 --> 0:04:22.120 And then will if you have an example in the US. 0:04:22.480 --> 0:04:25.440 Yeah, So for US weather, I've been interested in how 0:04:25.520 --> 0:04:29.039 extreme weather is impacting physical infrastructure for the US. So 0:04:29.279 --> 0:04:31.880 most recently I looked at how the LA wildfires were 0:04:31.880 --> 0:04:35.040 impacting power transmission lines. But then back in the fall 0:04:35.120 --> 0:04:38.039 during hurricane season, I was also monitoring which oil and 0:04:38.080 --> 0:04:40.920 gas platforms in the Gulf of Mexico were in swaths 0:04:41.040 --> 0:04:41.960 of hurricanes. 0:04:42.360 --> 0:04:44.400 So we're going to talk about what some of these 0:04:44.440 --> 0:04:47.760 extreme weather events actually are, and you'd already highlighted a 0:04:47.800 --> 0:04:50.440 couple of them, but before we get there, I want 0:04:50.480 --> 0:04:53.280 to have a better understanding of actually what data as 0:04:53.360 --> 0:04:57.480 meteorologists you call upon to really formulate your research, and 0:04:58.040 --> 0:05:00.760 you know, what information does one need in order to 0:05:00.800 --> 0:05:01.960 start assessing this space. 0:05:02.279 --> 0:05:06.000 Absolutely, just to start things off, weather is a dynamic 0:05:06.040 --> 0:05:08.560 beast to wrangle and there's a lot going on in 0:05:08.680 --> 0:05:11.920 terms of what a meteorologist needs to monitor, so it's 0:05:12.080 --> 0:05:15.440 important to use as much data as possible. Quite a 0:05:15.440 --> 0:05:17.680 bit of this comes in the form of global forecast 0:05:17.720 --> 0:05:21.359 models such as GFS, which is the Global Forecasting System 0:05:21.520 --> 0:05:26.160 and ECMWF, the European Center for Medium Range Weather Forecasts. 0:05:26.360 --> 0:05:31.280 What these offer our forecast data on temperature, precipitation, wind speeds, 0:05:31.760 --> 0:05:34.840 cooling and warming, degree days, and even more and so 0:05:35.279 --> 0:05:40.080 we have access to this as well as historically recorded 0:05:40.120 --> 0:05:43.360 weather data, and at BNF we have access to two 0:05:43.440 --> 0:05:46.920 thousand stations globally, so we can call upon this to 0:05:47.000 --> 0:05:51.440 create baseline comparisons with upcoming forecast data, and this helps 0:05:51.480 --> 0:05:54.400 us understand how abnormal and upcoming weather event will be. 0:05:54.680 --> 0:05:58.520 There is also something we monitor called teleconnections. These are 0:05:58.680 --> 0:06:02.480 significant relationships. There are links between weather phenomena at wildly 0:06:03.000 --> 0:06:07.960 separated locations on Earth. Again a very technical description of 0:06:07.960 --> 0:06:12.080 what a teleconnection is. It's basically different atmospheric patterns around 0:06:12.120 --> 0:06:14.960 the world, and one you might be familiar with is Enzo, 0:06:15.080 --> 0:06:18.640 the El Nino Southern oscillation, which houses El Nino and 0:06:18.800 --> 0:06:19.279 La Nina. 0:06:19.600 --> 0:06:22.000 So that begs the question what is El Nino and 0:06:22.080 --> 0:06:25.880 La Nina? Because I certainly remember talking about this growing up, 0:06:25.880 --> 0:06:28.280 where you would see these periods of extreme rain and 0:06:28.640 --> 0:06:32.039 in California, it was part of our regular lexicon. But 0:06:32.200 --> 0:06:34.680 now I find everybody around the world is throwing these 0:06:34.760 --> 0:06:37.560 terms around, and it seems like every year seems to 0:06:37.600 --> 0:06:40.039 fall into one of these two categories, which I know 0:06:40.160 --> 0:06:42.279 surely cannot be the case. So can you talk to 0:06:42.360 --> 0:06:44.360 us a little bit about First of all, what one 0:06:44.400 --> 0:06:47.560 is versus the other, and the frequency and duration. 0:06:47.839 --> 0:06:51.200 So and so. It is a multi year cycle of 0:06:51.240 --> 0:06:54.240 atmospheric patterns. It actually has three phases, which would be 0:06:54.320 --> 0:06:57.440 El Nino, La Nina, and the neutral phase. At the 0:06:57.440 --> 0:07:00.560 most basic level, El Nino and La Nina are warm 0:07:00.600 --> 0:07:03.839 and cold sea surface temperature anomalies. For a section of 0:07:03.880 --> 0:07:07.320 the Equatorial Pacific. We are currently in a La Nina, 0:07:07.360 --> 0:07:09.400 which is the cold phase of the cycle, but this 0:07:09.440 --> 0:07:12.480 does not necessarily mean that the entire globe is colder 0:07:12.480 --> 0:07:15.520 than normal. A typical La Nina year will bring wetter 0:07:15.560 --> 0:07:19.160 weather to the western Equatorial Pacific, northern Brazil and the 0:07:19.160 --> 0:07:22.480 Pacific Northwest for the US, and drier conditions to the 0:07:22.520 --> 0:07:27.040 southern US and northeast China. Regional temperature shifts also become apparent, 0:07:27.200 --> 0:07:31.080 with warmer conditions across the southern US and cooler conditions 0:07:31.120 --> 0:07:33.679 in the US, Pacific Northwest, and on the west coast 0:07:33.680 --> 0:07:36.760 of South America. El Nino is one of the oldest 0:07:36.800 --> 0:07:40.160 known teleconnection patterns. It was actually first discovered in the 0:07:40.160 --> 0:07:43.520 fifteen hundreds by Peruvian fishermen who noticed the periods of 0:07:43.560 --> 0:07:46.240 warmer water in the Pacific, bringing fewer fish to their 0:07:46.240 --> 0:07:49.480 nets around December, so they named it El Nino due 0:07:49.480 --> 0:07:51.720 to the proximity to the birth of Christ in the 0:07:51.800 --> 0:07:54.960 Christian religion. So since then there has been extensive research 0:07:55.040 --> 0:07:58.040 into this phenomenon that now is a key factor in 0:07:58.080 --> 0:08:02.240 our seasonal forecasting. Scientists have also discovered other similar atmospheric 0:08:02.280 --> 0:08:05.880 patterns that inform our seasonal outlooks, such as the North 0:08:05.920 --> 0:08:09.240 Atlantic oscillation. This pattern is a sea sawing of high 0:08:09.280 --> 0:08:12.360 and low pressures in Iceland and the Azores and has 0:08:12.440 --> 0:08:16.000 trended more positive over the last three months, bringing warmer 0:08:16.000 --> 0:08:19.320 than average temperatures to Europe. Understanding these patterns can give 0:08:19.400 --> 0:08:21.480 us clues as to what weather we can expect in 0:08:21.520 --> 0:08:24.000 the coming months. While our seasonal forecasts are not yet 0:08:24.080 --> 0:08:26.280 accurate enough to tell you how much snow your ski 0:08:26.320 --> 0:08:28.920 resort is going to have in the three months prior 0:08:29.000 --> 0:08:30.840 to when you were planning it, we can have an 0:08:30.920 --> 0:08:33.679 idea of how much above or below normal temperatures and 0:08:33.800 --> 0:08:36.800 precipitation will be at a regional level. So this is 0:08:36.880 --> 0:08:39.959 really important for our energy storage levels and traders. If 0:08:39.960 --> 0:08:43.000 the US is expecting winter temperatures to be mild with 0:08:43.160 --> 0:08:46.880 above normal precipitation, this could lead to low gas withdrawals 0:08:46.920 --> 0:08:50.719 from a lack of heating demand and bolstering conventional hydroelectric 0:08:50.840 --> 0:08:53.720 reservoirs leading to an increase in renewable power generation. 0:08:54.120 --> 0:08:57.959 And another quick anecdote for Europe with the impact that 0:08:58.040 --> 0:09:01.880 the North Atlantic Oscillation has. We're seeing the presence of 0:09:01.920 --> 0:09:05.920 this this year with Lanina. So, like Willis said, in 0:09:05.960 --> 0:09:10.160 Europe during a Lanina, we'll normally see cooler than average conditions, 0:09:10.320 --> 0:09:14.199 but this year we've actually seen warmer than average conditions, 0:09:14.320 --> 0:09:17.760 and that's because the North Atlantic Oscillation has swung into 0:09:17.880 --> 0:09:22.480 a positive phase. So this positive phase is dominating over Lanninia, 0:09:22.880 --> 0:09:27.120 creating that warmer than average condition in Europe. These trends 0:09:27.160 --> 0:09:29.800 are really important to watch for liquid natural gas usage. 0:09:30.000 --> 0:09:31.840 And I love that you brought up the LNG part 0:09:31.840 --> 0:09:34.600 of this because this features really heavily as we do 0:09:34.840 --> 0:09:37.400 at b and EF twice a year, this winter gas 0:09:37.400 --> 0:09:40.040 Outlook and Summer gas Outlook, and look at the level 0:09:40.120 --> 0:09:42.920 of storage that we have in various parts of the world, 0:09:43.080 --> 0:09:45.839 and as traders are looking to try and figure out 0:09:45.880 --> 0:09:48.120 what those prices are going to be going forward. My 0:09:48.320 --> 0:09:51.400 question really revolves around when you see this warmer weather 0:09:51.480 --> 0:09:53.840 than one would infer you need less gas over the 0:09:53.840 --> 0:09:56.080 course of the winter in order to get through. Does 0:09:56.160 --> 0:09:59.440 that have or is that counterweighted and to what extent 0:09:59.480 --> 0:10:02.880 does it count weighted by increased demand for air conditioning 0:10:02.880 --> 0:10:06.040 on a particularly hot year, where you're entering a summer 0:10:06.080 --> 0:10:08.160 that's going to have higher than average temperatures. 0:10:08.480 --> 0:10:11.720 It's something to monitor for sure. So when you're noticing 0:10:12.000 --> 0:10:16.080 that warmer than average conditions are settling into an area, 0:10:16.280 --> 0:10:18.760 for example, in winter, you are going to notice that 0:10:18.760 --> 0:10:21.880 there is less energy demand. Now you and I might 0:10:21.920 --> 0:10:24.360 not feel it. You and I might not feel that 0:10:24.559 --> 0:10:28.319 it's warmer than average because technically the temperatures are still cool, 0:10:28.520 --> 0:10:30.800 but the markets will feel it, and that's going to 0:10:30.800 --> 0:10:31.719 be the interesting thing. 0:10:32.040 --> 0:10:34.800 Do you work closely with the gas team as a result. 0:10:34.679 --> 0:10:38.400 Every single day? So I know for myself in London, 0:10:38.600 --> 0:10:42.559 I'm working with every team on my floor, So whether 0:10:42.600 --> 0:10:45.240 that be the gas team, the oil team, the wind team, 0:10:45.360 --> 0:10:48.640 the hydro team, we're working every single day on passing 0:10:48.720 --> 0:10:51.400 ideas to each other and staying on top of the context. 0:10:51.880 --> 0:10:54.760 On the US side, we've actually done some research into 0:10:54.760 --> 0:10:57.760 how increases in summer gas demand is not enough to 0:10:57.800 --> 0:11:01.440 offset losses in warmer winters. One of our gas analysts 0:11:01.440 --> 0:11:03.760 in riy Kae Gonzalez put out a report called warmer 0:11:03.800 --> 0:11:07.040 Weather low gas prices could threaten energy transitions and it 0:11:07.080 --> 0:11:10.800 discusses how when we have warmer winters there is less 0:11:10.800 --> 0:11:14.800 demand for gas driven heating. We are also seeing warmer summers, 0:11:14.840 --> 0:11:17.320 but in those summers, the increase in power demand can 0:11:17.360 --> 0:11:20.800 be filled by renewable energy, so that increase in power 0:11:20.840 --> 0:11:23.400 demand is not necessarily being filled by burns in the 0:11:23.480 --> 0:11:24.440 natural gas sector. 0:11:24.840 --> 0:11:28.240 So it's really clear how weather is so incredibly important 0:11:28.280 --> 0:11:31.280 to a number of the different commodities that are covered 0:11:31.280 --> 0:11:33.360 at BNF. What I want to know now is a 0:11:33.360 --> 0:11:37.480 pivot to finance. When we looked at these most recent 0:11:37.600 --> 0:11:41.480 fires that took place in southern California, insurance came up 0:11:41.559 --> 0:11:45.720 quite often, and then also the role of reinsurance and 0:11:45.800 --> 0:11:48.800 catastrophe bonds. Can you talk about some of the financial 0:11:48.840 --> 0:11:53.240 instruments that exist and really how these interrelate with extreme 0:11:53.280 --> 0:11:54.000 weather events. 0:11:54.360 --> 0:11:57.360 So catastrophe bond or a cat bond is a high 0:11:57.400 --> 0:12:00.559 yield debt instrument designed to raise money for companies in 0:12:00.600 --> 0:12:03.160 the insurance industry in the event of a natural disaster. 0:12:03.440 --> 0:12:07.040 A CAT bond allows the issuer to receive funding for 0:12:07.200 --> 0:12:09.600 the bond if the conditions are met, such as like 0:12:09.640 --> 0:12:12.760 a tornado or a hurricane or severe flooding. If an 0:12:12.760 --> 0:12:15.760 event that's protected by the bond activates a payout to 0:12:15.840 --> 0:12:19.160 the insurance company, the obligation to pay interest and repay 0:12:19.160 --> 0:12:22.800 the principle is either deferred or completely forgiven. So a 0:12:22.840 --> 0:12:26.120 CAT bond has a shorter maturity date of between three 0:12:26.120 --> 0:12:29.600 to five years, and the primary investors in the security 0:12:29.640 --> 0:12:33.079 are like hedge funds and pensions and other institutional investors. 0:12:33.160 --> 0:12:35.640 But on the other hand, a reinsurance is a type 0:12:35.640 --> 0:12:39.400 of insurance primarily purchased by insurance companies to provide a 0:12:39.520 --> 0:12:43.520 layer of financial protection against weather events that could cause 0:12:43.679 --> 0:12:47.360 major financial disasters, and as we're seeing with extreme events 0:12:47.360 --> 0:12:50.360 becoming more frequent, the market for these types of bonds 0:12:50.440 --> 0:12:53.400 is also growing. In twenty twenty four, the US had 0:12:53.440 --> 0:12:57.160 twenty four rather related disaster events that individually caused over 0:12:57.200 --> 0:13:00.160 a billion dollars in damages, with seventy one percent of 0:13:00.240 --> 0:13:03.680 these events attributed to severe storms. The five year average 0:13:03.720 --> 0:13:06.800 cost of these damages in twenty twenty four was around 0:13:06.840 --> 0:13:09.199 one hundred and fifty billion, which was more than double 0:13:09.240 --> 0:13:11.880 what it was ten years ago. La Nina events have 0:13:11.960 --> 0:13:15.360 been linked to more severe storms across the US, and 0:13:15.400 --> 0:13:18.040 so this La Nina that we are currently in could 0:13:18.040 --> 0:13:22.080 also mean increasing market chairs for these types of bonds, 0:13:22.120 --> 0:13:25.440 as severe events could potentially be more frequent this year. 0:13:25.559 --> 0:13:28.120 And can you put the financial losses in context, because 0:13:28.160 --> 0:13:30.280 you know, I brought up these fires in Los Angeles 0:13:30.320 --> 0:13:34.800 and they were really an unprecedented amount of damage in 0:13:34.920 --> 0:13:37.120 terms of financial loss. I mean, how much was it 0:13:37.200 --> 0:13:40.840 and how does it compare to other natural disasters which 0:13:40.840 --> 0:13:42.720 have taken place maybe also in the US. 0:13:43.040 --> 0:13:46.480 Yeah, so the LA fires were a really special case 0:13:46.640 --> 0:13:49.560 of like the kind of perfect storm of bad conditions. 0:13:49.840 --> 0:13:52.720 So it's not that these fires were necessarily the largest 0:13:52.760 --> 0:13:55.320 fires we've seen in US history. It's more so that 0:13:55.360 --> 0:13:59.200 they were in Los Angeles, a fairly wealthy area, and 0:13:59.280 --> 0:14:03.240 so the the property losses were extremely large. So the 0:14:03.520 --> 0:14:07.480 estimated monetary toll of the LA fires is expected to 0:14:07.480 --> 0:14:09.880 surpass two hundred and fifty billion, which would make it 0:14:09.920 --> 0:14:12.480 the costiest weather disaster in US history, and that is 0:14:12.520 --> 0:14:15.720 even greater than Hurricane Katrina, which reached two hundred and 0:14:15.760 --> 0:14:17.439 one billion, dollars in damages. 0:14:17.840 --> 0:14:19.840 So one of the things that the two of you 0:14:19.880 --> 0:14:22.400 did headed into this year was great a Things to 0:14:22.480 --> 0:14:25.280 Watch research piece where you kind of looked at the 0:14:25.360 --> 0:14:28.080 year ahead. And I know it's very difficult, as you've outlined, 0:14:28.120 --> 0:14:31.480 to actually predict the weather, but given your experience in 0:14:31.560 --> 0:14:35.320 these kind of annual and multi year trends that take place, 0:14:35.400 --> 0:14:37.360 what are some of the things that we can expect 0:14:37.360 --> 0:14:38.080 in the year ahead. 0:14:38.360 --> 0:14:40.960 Yeah, So for the coming year in twenty twenty five, 0:14:41.040 --> 0:14:45.080 we are seeing above average fire conditions forecasted for Texas. 0:14:45.120 --> 0:14:48.240 This is coming from the National Interagency Fire Center, which 0:14:48.320 --> 0:14:51.600 issues wildland fire potential outlooks, and so we're seeing these 0:14:51.640 --> 0:14:54.560 above average fire conditions through the spring. And so this 0:14:54.600 --> 0:14:58.440 is a combination of below average precipitation, above average winds, 0:14:58.520 --> 0:15:01.840 and above average temperatures. And so from the Bloomberg Terminal, 0:15:01.840 --> 0:15:04.360 we have these seasonal forecasts, which is showing all three 0:15:04.400 --> 0:15:07.480 boxes are checked for Texas for the spring. But the 0:15:07.560 --> 0:15:11.280 National Weather Service also issues temperature and precipitation outlooks for 0:15:11.400 --> 0:15:14.120 the next twelve months, and so in those we are 0:15:14.120 --> 0:15:17.480 seeing above average temperatures for Texas, which means that we 0:15:17.560 --> 0:15:20.480 could see fire conditions persisting throughout the year, and so 0:15:20.760 --> 0:15:23.720 the only thing that is left is an igniti event 0:15:23.800 --> 0:15:26.560 to cause another string of devastating wildfires. 0:15:26.880 --> 0:15:30.680 So related to fires, but also related to the energy system, 0:15:30.760 --> 0:15:34.920 I want to talk about precipitation and essentially water levels. 0:15:35.080 --> 0:15:37.360 Can you make that connection spell it out for us 0:15:37.440 --> 0:15:43.160 regarding how rainfall is actually connected to power and emissions. 0:15:43.520 --> 0:15:46.080 Absolutely, So I just want to preface this with we 0:15:46.200 --> 0:15:49.880 have a focus on the US and Europe, but in 0:15:49.920 --> 0:15:52.560 our nine Things Weather to Watch for twenty twenty five, 0:15:52.720 --> 0:15:54.880 we took a look at China and what we're seeing 0:15:55.120 --> 0:15:58.440 right now in China is something very interesting. So at 0:15:58.480 --> 0:16:01.440 the end of twenty twenty four, water levels at one 0:16:01.440 --> 0:16:05.520 of the biggest dams, actually the biggest dam in China. 0:16:05.120 --> 0:16:06.200 Is it the three Gorgeous Dam. 0:16:06.320 --> 0:16:10.360 Three Gorgeous Dam, the water levels measured four meters below 0:16:10.400 --> 0:16:13.280 the five year average. Now, this doesn't sound like a lot, 0:16:13.440 --> 0:16:17.880 but thirteen southern Chinese provinces in the last six months 0:16:18.120 --> 0:16:23.080 have undergone drought warnings. Thankfully though, La Nina is bringing 0:16:23.200 --> 0:16:27.240 a much needed reprieve to this area. So I've been 0:16:27.280 --> 0:16:31.200 monitoring the weather reports and right now in some areas 0:16:31.280 --> 0:16:35.440 of southern China. They're registering sixteen millimeters above the average. 0:16:35.680 --> 0:16:42.720 It has relieved the drought stress on any hydropower plants 0:16:42.720 --> 0:16:46.160 in southern China along the Yansee River. But this drought 0:16:46.240 --> 0:16:51.560 scare has revitalized concerns over a clean, stable energy generation, 0:16:51.880 --> 0:16:54.960 especially during periods of low hydropower output for the nation. 0:16:55.200 --> 0:16:58.600 So to preface this with context, the issue began in 0:16:58.680 --> 0:17:01.320 twenty twenty two there was an tense year long drought 0:17:01.360 --> 0:17:05.240 and into twenty twenty three that hit the southern provinces 0:17:05.359 --> 0:17:07.960 housing the Yangsee River. So this is the largest river 0:17:08.160 --> 0:17:10.560 in China as a whole. This river is crucial for 0:17:10.640 --> 0:17:14.879 hydropower and specifically Sichuan Province a key upstream province for 0:17:14.960 --> 0:17:18.320 water recharge. It's also a province that makes up thirty 0:17:18.320 --> 0:17:21.239 percent of China's hydropower. It saw water levels drop by 0:17:21.240 --> 0:17:26.919 thirteen meters that year, so during this period, Sichuan Hydropower 0:17:27.000 --> 0:17:30.960 actually recorded an eleven percent drop in hydro power outputs. 0:17:31.160 --> 0:17:34.920 So the stability of the hydropower in southern China as 0:17:34.960 --> 0:17:38.080 a power source in that area left room for concern 0:17:38.280 --> 0:17:41.560 thinking about future peak energy demand, and so this short 0:17:41.600 --> 0:17:46.080 term prioritization of coal is offering some stability for China 0:17:46.200 --> 0:17:48.720 as they're aiming to amp up the renewable energy capacity 0:17:48.760 --> 0:17:50.840 to meet the twenty thirty renewable targets. 0:17:51.000 --> 0:17:53.399 So very simply put, when you have low rainfall in 0:17:53.480 --> 0:17:56.720 areas that are requiring hydropower to be a part of 0:17:56.720 --> 0:17:58.840 their energy mix, they have to turn to other sources, 0:17:58.880 --> 0:18:01.840 and in some circumstances that leads to higher emissions. So 0:18:01.920 --> 0:18:05.040 rain level is definitely linked. Let's also talk about how 0:18:05.280 --> 0:18:08.720 droughts connect to not just hydropower, but also to other 0:18:08.720 --> 0:18:11.520 fuel sources like biofuels. Naturally a lot of them come 0:18:11.560 --> 0:18:15.080 from sources like soybean, rape seed, So can we talk 0:18:15.119 --> 0:18:17.520 a little bit about how there may be droughts right 0:18:17.560 --> 0:18:20.080 now and how that's impacting the biofuels market. 0:18:20.359 --> 0:18:23.960 It's another interesting topic we touched upon in our nine 0:18:23.960 --> 0:18:27.440 Things to Watch, So it's important to think about upstream 0:18:27.480 --> 0:18:30.119 as well as downstream impacts. And I really want to 0:18:30.119 --> 0:18:33.960 put a spotlight on Brazil and Brazil's upcoming biodiesel outlook. 0:18:34.200 --> 0:18:37.840 There's been hurdles at nearly every turn. So brazil biodiesel 0:18:37.920 --> 0:18:42.000 relies on that nationally grown soybean and twenty eight percent 0:18:42.280 --> 0:18:46.040 of national soybean is grown in Matagrosso, so again another 0:18:46.119 --> 0:18:48.760 spotlight on this region in Brazil. Not only was the 0:18:48.800 --> 0:18:52.680 planting season hit with fifty six percent below average rainfall 0:18:52.760 --> 0:18:55.560 during the first seventy five percent of that season, harvest 0:18:55.600 --> 0:18:59.520 season is now saying delays with one hundred percent above 0:18:59.560 --> 0:19:02.879 average rainfall, So I just want to stop one hundred 0:19:02.920 --> 0:19:08.239 percent above average. So farmers are seeing floods, roads are 0:19:08.240 --> 0:19:11.399 becoming impassable, bridges are being destroyed, on top of the 0:19:11.440 --> 0:19:14.000 fact that farmers need to get into their fields and 0:19:14.080 --> 0:19:17.360 harvest their crop. So what's happening here is that we're 0:19:17.359 --> 0:19:20.879 seeing not only potential impacts on germination and a shorter 0:19:20.960 --> 0:19:24.320 growing window early on in the crop cycle, but we're 0:19:24.320 --> 0:19:27.520 also seeing moisture control issues at harvest. So this all 0:19:27.640 --> 0:19:31.840 leads to ending production values of Brazil and soybean potentially 0:19:31.840 --> 0:19:34.800 going down. So what will the final output be? Will 0:19:34.920 --> 0:19:37.400 be lower than expected, and from there, how does that 0:19:37.520 --> 0:19:41.040 impact allocation towards biodiesel on top of other uses. So 0:19:41.440 --> 0:19:43.760 this is really important to think about when we're thinking 0:19:43.800 --> 0:19:46.560 about Brazil's Fuel of the Future bill that was passed 0:19:46.560 --> 0:19:50.040 in October twenty twenty four, and the pressure on Brazil 0:19:50.160 --> 0:19:53.600 right now is to push the biodiesel blend and diesel 0:19:53.600 --> 0:19:56.760 oil up to fifteen percent. So with this impact on 0:19:57.040 --> 0:19:59.800 local soybean, what will be the impact on biodiesel. 0:19:59.800 --> 0:20:03.080 This here so floods, droughts that they don't just have 0:20:03.200 --> 0:20:05.960 to do with water. So can you actually pivot now 0:20:06.040 --> 0:20:09.959 to what impacts renewable energy wind power and what is 0:20:09.960 --> 0:20:11.600 referred to as a wind drought. 0:20:11.920 --> 0:20:14.960 So these are prolonged periods of time where wind speeds 0:20:15.000 --> 0:20:18.280 are registering two meters per second or less, and if 0:20:18.320 --> 0:20:20.239 we look at the data that comes out of it, 0:20:20.280 --> 0:20:23.320 the impacts are actually pretty shocking. So during an average 0:20:23.320 --> 0:20:26.800 wind drought, if wind speeds drop by ten percent, power 0:20:26.840 --> 0:20:29.439 generation can drop by up to thirty percent, which is 0:20:29.720 --> 0:20:33.439 I don't know about you, but that's a pretty shocking statistic. 0:20:33.800 --> 0:20:37.040 And this is becoming very important for countries whose generation 0:20:37.160 --> 0:20:39.639 capacity is made up of a high proportion of wind. 0:20:39.920 --> 0:20:42.920 So when periods of low wind hit, this could undercut 0:20:42.960 --> 0:20:46.600 renewable energy output and exacerbate reliance on fossil fuels. So 0:20:46.840 --> 0:20:49.919 right now, if we can turn our spotlight to Europe, 0:20:50.000 --> 0:20:53.720 we're seeing a particularly striking impact in countries like Germany, 0:20:53.800 --> 0:20:56.960 Europe's top country for wind production. So in twenty twenty 0:20:57.000 --> 0:20:59.560 three and twenty twenty four there were four separate months 0:20:59.560 --> 0:21:02.800 that Regis stirred wind rout events in Germany. Just two 0:21:02.840 --> 0:21:05.000 of these events in twenty twenty four led to win 0:21:05.119 --> 0:21:09.080 generation reductions of sixteen percent against the five year average, 0:21:09.119 --> 0:21:12.480 and as a result, total German win output for twenty 0:21:12.520 --> 0:21:15.680 twenty four fell by six percent from the previous year, 0:21:15.960 --> 0:21:19.440 and this was despite capacity increasing three percent year over year. 0:21:19.560 --> 0:21:22.600 So you can see the impact something like this can 0:21:22.640 --> 0:21:25.200 have on a country. But how do we forecast these 0:21:25.440 --> 0:21:30.240 It's incredibly difficult to accurately predict where wind will blow 0:21:30.440 --> 0:21:32.640