1 00:00:02,520 --> 00:00:07,000 Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. 2 00:00:08,160 --> 00:00:11,879 Speaker 2: You're listening to Bloomberg Business Week with Carol Masser and 3 00:00:11,960 --> 00:00:14,400 Speaker 2: Tim Steneveek on Bloomberg Radio. 4 00:00:14,920 --> 00:00:17,000 Speaker 1: I'm going to be hot tomorrow. At last check, my 5 00:00:17,239 --> 00:00:19,840 Speaker 1: weather app said ninety seven degrees here in New York. 6 00:00:20,239 --> 00:00:20,880 Speaker 3: It's crazy. 7 00:00:20,960 --> 00:00:22,520 Speaker 1: Yeah, who knows if that's actually accurate. 8 00:00:22,720 --> 00:00:25,480 Speaker 3: It's our reality though of where we are. Yeah, And 9 00:00:25,480 --> 00:00:28,320 Speaker 3: what's interesting is we did see a couple of headlines 10 00:00:28,320 --> 00:00:30,840 Speaker 3: crossing earlier, the biggest US power grid failing for a 11 00:00:30,920 --> 00:00:34,320 Speaker 3: third straight time to secure enough future supply commitments to 12 00:00:34,360 --> 00:00:37,760 Speaker 3: ensure reliability in coming years amid a boom in data 13 00:00:37,840 --> 00:00:44,680 Speaker 3: centered demand. So we're talking about PGM, that power grid, 14 00:00:44,720 --> 00:00:47,559 Speaker 3: and it's something that we've talked about a lot. They 15 00:00:47,560 --> 00:00:50,800 Speaker 3: see total capacity costs tying a record of sixteen point 16 00:00:50,840 --> 00:00:55,840 Speaker 3: four billion. So the power graph continues, and it doesn't 17 00:00:55,880 --> 00:00:59,040 Speaker 3: help that when you've got really hot temperatures, you're seeing 18 00:00:59,080 --> 00:01:01,440 Speaker 3: really kind of stress in terms of air conditioning. We 19 00:01:01,480 --> 00:01:04,160 Speaker 3: may not need it here in this studio because it's 20 00:01:04,400 --> 00:01:05,800 Speaker 3: my fingers are known right now. 21 00:01:06,240 --> 00:01:07,840 Speaker 1: I think it's a perfect temperature. 22 00:01:07,600 --> 00:01:11,040 Speaker 3: I'm sorry, I have hand warmers. 23 00:01:11,120 --> 00:01:13,280 Speaker 1: Well, you heard me mention you heard me mention that 24 00:01:13,319 --> 00:01:15,000 Speaker 1: I don't know if the weather app is actually going 25 00:01:15,080 --> 00:01:19,240 Speaker 1: to be accurate, because who knows if that is actually accurate. 26 00:01:19,240 --> 00:01:20,920 Speaker 1: Who knows that the data that they're getting from the 27 00:01:20,920 --> 00:01:23,640 Speaker 1: government right now is actually as good as that data 28 00:01:23,640 --> 00:01:25,840 Speaker 1: can be. That's what Mark Gongloff writes about. He's a 29 00:01:25,840 --> 00:01:29,120 Speaker 1: Bloomberg opinion editor. In columnists, he covers climate change. He 30 00:01:29,240 --> 00:01:32,520 Speaker 1: joins us now from New Jersey. Mark, I'm so glad 31 00:01:32,560 --> 00:01:34,920 Speaker 1: that you wrote this story. You actually reference a piece 32 00:01:34,959 --> 00:01:38,240 Speaker 1: that made the rounds in the Stenovic family chat, which 33 00:01:38,400 --> 00:01:40,800 Speaker 1: was from his motto last week. My brother sent it 34 00:01:40,840 --> 00:01:43,800 Speaker 1: around it and and as you write about in the 35 00:01:43,840 --> 00:01:46,200 Speaker 1: piece too, you take the piece of step further. This 36 00:01:46,280 --> 00:01:50,040 Speaker 1: piece basically said the Doge firings and the cuts that 37 00:01:50,040 --> 00:01:52,840 Speaker 1: we've seen at the National Weather Service have really affected 38 00:01:52,840 --> 00:01:54,680 Speaker 1: the number of weather balloons that can go up and 39 00:01:54,960 --> 00:01:58,520 Speaker 1: gather data. What what did this report find? And then 40 00:01:58,600 --> 00:02:00,800 Speaker 1: and then what did you find in your column? 41 00:02:02,160 --> 00:02:05,640 Speaker 2: The report found that there are there's a big chunk 42 00:02:05,880 --> 00:02:08,640 Speaker 2: of the United States from the Great Planes up to 43 00:02:08,840 --> 00:02:12,640 Speaker 2: the Midwest and the Pacific Northwest where you're getting about 44 00:02:12,680 --> 00:02:15,359 Speaker 2: half the number of weather balloon launches that you used 45 00:02:15,360 --> 00:02:18,240 Speaker 2: to get. And why this matters is you're not getting 46 00:02:18,280 --> 00:02:20,480 Speaker 2: them in the morning and you're not and it's a 47 00:02:20,480 --> 00:02:23,200 Speaker 2: big chunk of the west that's not getting weather balloons. 48 00:02:23,360 --> 00:02:25,480 Speaker 2: And what these things are is they're like MRIs for 49 00:02:25,600 --> 00:02:28,040 Speaker 2: the atmosphere, going from this ground all the way to 50 00:02:28,080 --> 00:02:34,079 Speaker 2: the troposphere. So, uh, the the what you lose then 51 00:02:34,360 --> 00:02:38,399 Speaker 2: is weather that develops early that could affect your weather later, 52 00:02:38,680 --> 00:02:41,000 Speaker 2: and whether that develops in the west that could affect 53 00:02:41,040 --> 00:02:43,519 Speaker 2: weather in the east, which it almost always does. Weather 54 00:02:43,600 --> 00:02:46,440 Speaker 2: moves from west to east. So you have this huge 55 00:02:46,480 --> 00:02:48,600 Speaker 2: gap where you've only got about half the data you 56 00:02:48,720 --> 00:02:52,239 Speaker 2: used to get. We haven't seen nobody's done a study 57 00:02:52,320 --> 00:02:56,560 Speaker 2: yet that says the you know, forecasts are any worse. 58 00:02:57,480 --> 00:02:59,639 Speaker 2: There are a lot of there's a lot of anecdotal evidence. 59 00:03:00,040 --> 00:03:03,239 Speaker 2: A lot of people are saying, you know, forecasts don't 60 00:03:03,240 --> 00:03:04,680 Speaker 2: seem right to me. And there was a big thing 61 00:03:04,720 --> 00:03:06,720 Speaker 2: that happened in April and Kansas where all of a sudden, 62 00:03:06,720 --> 00:03:09,400 Speaker 2: these tornadoes hit in Kansas, which Kansas is you know, 63 00:03:09,520 --> 00:03:13,480 Speaker 2: obviously prone to tornadoes, as we all know, but these 64 00:03:13,520 --> 00:03:15,920 Speaker 2: sort of seem to come out of almost nowhere to 65 00:03:16,000 --> 00:03:18,679 Speaker 2: some extent, and some people were starting to blame the 66 00:03:18,760 --> 00:03:21,639 Speaker 2: lack of weather balloons on the Great Plains for that. 67 00:03:22,200 --> 00:03:24,640 Speaker 3: So maybe we haven't missed too much yet, But is 68 00:03:24,680 --> 00:03:27,639 Speaker 3: the possibility of a miss mark now greater if we're 69 00:03:27,639 --> 00:03:31,840 Speaker 3: not launching more balloons weather balloons into the atmosphere. 70 00:03:32,919 --> 00:03:35,960 Speaker 2: Yeah, that's what meteorologists are saying. Again, not all of them, 71 00:03:36,000 --> 00:03:38,920 Speaker 2: because the jury is still kind of out. But the 72 00:03:38,960 --> 00:03:41,720 Speaker 2: thing with these balloons is that what you miss most 73 00:03:41,720 --> 00:03:44,680 Speaker 2: often is extreme weather, and that's the thing we care 74 00:03:44,720 --> 00:03:48,040 Speaker 2: about the most unfortunately, and so things like flash floods, 75 00:03:48,960 --> 00:03:51,800 Speaker 2: a bunch of sudden tornadoes, and also hurricanes and you know, 76 00:03:51,920 --> 00:03:56,200 Speaker 2: hurricanes intensifying. So those weather balloons aren't affecting that. But 77 00:03:56,200 --> 00:03:59,000 Speaker 2: there are other issues with the National Weather Service, a 78 00:03:59,000 --> 00:04:01,880 Speaker 2: lot of layoffs, a lot of places are stressed out, 79 00:04:01,920 --> 00:04:05,600 Speaker 2: they don't have enough people, they're not keeping up websites, 80 00:04:05,640 --> 00:04:08,440 Speaker 2: that sort of thing. So everything's just kind of like 81 00:04:08,880 --> 00:04:11,680 Speaker 2: on edge right now. And so, yes, we haven't necessarily 82 00:04:11,720 --> 00:04:14,640 Speaker 2: there's nothing there's no fingerprints on any big mess yet, 83 00:04:14,680 --> 00:04:17,280 Speaker 2: but the odds are going up, and then of course 84 00:04:17,320 --> 00:04:21,839 Speaker 2: you have the long term effect of people being stressed meteorologists, 85 00:04:21,880 --> 00:04:26,920 Speaker 2: you know, leaving a lot of very experienced meteorologists were 86 00:04:26,920 --> 00:04:29,479 Speaker 2: fired last year, and you don't get that institutional knowledge 87 00:04:29,520 --> 00:04:30,800 Speaker 2: back very quickly. 88 00:04:30,640 --> 00:04:32,640 Speaker 1: Mark, come on camp, Prediction markets fill the void. 89 00:04:34,960 --> 00:04:38,359 Speaker 2: Sure, yes, Well that's another interesting thing is that you know, 90 00:04:38,400 --> 00:04:44,440 Speaker 2: these prediction markets are based on knowing how much how 91 00:04:44,520 --> 00:04:46,320 Speaker 2: much wind you're going to get in a hurricane, or 92 00:04:46,320 --> 00:04:47,800 Speaker 2: how many these tornators a you're going to get, or 93 00:04:47,800 --> 00:04:50,839 Speaker 2: where flooding is going to happen. And so I personally 94 00:04:50,880 --> 00:04:53,680 Speaker 2: find that kind of a not good morally to bet 95 00:04:53,720 --> 00:04:55,919 Speaker 2: on those kinds of disasters. But people are doing that, 96 00:04:55,960 --> 00:04:58,600 Speaker 2: and so there's money at stake. More importantly, there's money 97 00:04:58,600 --> 00:05:00,520 Speaker 2: at stake in the insurance market, and that's where you 98 00:05:00,600 --> 00:05:04,200 Speaker 2: start to get into real issues where if you have 99 00:05:04,320 --> 00:05:07,560 Speaker 2: less data, if the forecasts start to deteriorate over time, 100 00:05:08,000 --> 00:05:10,440 Speaker 2: insurance companies say, hey, I've got to listen certainty now 101 00:05:10,440 --> 00:05:12,960 Speaker 2: about where tornadoes and such are going to land. I've 102 00:05:12,960 --> 00:05:16,359 Speaker 2: got a price that uncertainty into your home insurance premiums, 103 00:05:16,360 --> 00:05:19,080 Speaker 2: which are already very high right now, right. 104 00:05:19,080 --> 00:05:21,400 Speaker 1: So we're paying for the uncertainty. 105 00:05:20,960 --> 00:05:24,400 Speaker 3: Yeah, right, Yes, and more people say all the uncertainty 106 00:05:24,520 --> 00:05:26,440 Speaker 3: right if you don't know, if you actually don't know, 107 00:05:26,560 --> 00:05:29,360 Speaker 3: especially since we know it's gotten more volatile and weather 108 00:05:29,440 --> 00:05:33,800 Speaker 3: events are happening in places that didn't used to happen. Yeah, 109 00:05:33,839 --> 00:05:35,840 Speaker 3: so where does this all leave us? I mean, I 110 00:05:35,839 --> 00:05:38,120 Speaker 3: don't know. Do we all just wait for another administration, 111 00:05:38,240 --> 00:05:40,440 Speaker 3: or do some folks wait for another administration and then 112 00:05:40,440 --> 00:05:42,280 Speaker 3: we start to see the weather balloons go back up? 113 00:05:42,480 --> 00:05:44,560 Speaker 3: Or does the private sector get involved? I mean how 114 00:05:44,560 --> 00:05:45,280 Speaker 3: do I don't know. 115 00:05:47,160 --> 00:05:49,400 Speaker 2: I don't know. For I can't predict the future either. 116 00:05:49,880 --> 00:05:52,200 Speaker 2: It would be I could on the surface say yeah, 117 00:05:52,240 --> 00:05:53,760 Speaker 2: we just have to wait till twenty thirty, but we 118 00:05:53,880 --> 00:05:56,599 Speaker 2: really can't wait till twenty thirty. There could be a 119 00:05:56,600 --> 00:05:59,400 Speaker 2: disaster that comes. And you know, this stuff does not 120 00:05:59,520 --> 00:06:02,760 Speaker 2: go un noticed. And there have been issues that, like 121 00:06:02,800 --> 00:06:06,200 Speaker 2: with the ocean monitoring that the Trump administration wanted to 122 00:06:06,240 --> 00:06:08,040 Speaker 2: tear up, there are a lot of people that said, hey, 123 00:06:08,080 --> 00:06:10,280 Speaker 2: wait a minute, I need some of these forecasts that 124 00:06:10,279 --> 00:06:13,320 Speaker 2: they're producing. And so businesses need these things. This causes 125 00:06:13,400 --> 00:06:15,599 Speaker 2: you know, whether extreme weather, whether you believe in climate 126 00:06:15,640 --> 00:06:19,039 Speaker 2: change or not, extreme weather causes business disruptions. People want 127 00:06:19,080 --> 00:06:22,720 Speaker 2: good weather, and so there could before twenty thirty beat 128 00:06:22,760 --> 00:06:25,960 Speaker 2: political pressure and say hey, let's kind of juice up 129 00:06:26,880 --> 00:06:28,920 Speaker 2: what we've got going on at the government level, because 130 00:06:29,160 --> 00:06:31,880 Speaker 2: you can't replace this with the private sector unfortunately at 131 00:06:31,880 --> 00:06:34,240 Speaker 2: the moment, because all the private sector gets this stuff 132 00:06:34,320 --> 00:06:37,840 Speaker 2: straight from the NWS, straight from the US government, So 133 00:06:38,240 --> 00:06:39,800 Speaker 2: you're kind of at a loss if you don't have 134 00:06:39,839 --> 00:06:40,560 Speaker 2: that stuff real. 135 00:06:40,440 --> 00:06:44,160 Speaker 3: Quick, just under thirty seconds. I know US weather data 136 00:06:44,200 --> 00:06:48,520 Speaker 3: really has been top notch, but are other weather knows 137 00:06:48,560 --> 00:06:51,240 Speaker 3: no borders, so are other countries doing things that we 138 00:06:51,240 --> 00:06:53,320 Speaker 3: can kind of rely on and just real quickly please? 139 00:06:54,360 --> 00:06:56,720 Speaker 2: Yeah, I mean Europe is doing some great things right now, 140 00:06:56,760 --> 00:06:59,080 Speaker 2: and they've got some good models that help predict things. 141 00:06:59,120 --> 00:07:03,239 Speaker 2: Other places are going to eventually catch up. The problem 142 00:07:03,320 --> 00:07:06,560 Speaker 2: is that that takes time. It took lots of tax 143 00:07:06,560 --> 00:07:08,359 Speaker 2: to baihillion dollars, lots of years to build up what 144 00:07:08,400 --> 00:07:10,360 Speaker 2: we've got now, so it's gonna be hard to replicate that. 145 00:07:10,560 --> 00:07:12,840 Speaker 3: Well, we appreciate being able to check in with you 146 00:07:12,880 --> 00:07:15,880 Speaker 3: and staying on top of this. Mark Onngloffi's calmunist for 147 00:07:15,880 --> 00:07:19,760 Speaker 3: Bloomberg Opinion covering all things when it comes to climate change. 148 00:07:19,800 --> 00:07:20,760 Speaker 3: Really appreciate it.