Where Earth is brightening and dimming

1:15:20it's a paper that's published in nature proper of this year. Here's what they did. So using daily observations from 2014 through 2022, nearly a decade, they asked whether the familiar story of an ever brightening Earth survives the actual data. >> Yeah. >> That we've taken. Right. There light pollution is getting bad. >> Yeah. Right. Right. Right. That's what everyone says. >> Uh who who says it the most? Was it the uh the astronomers were trying >> astronomers were trying to say that. I mean they're complaining about the star link that that's up there. They've like but also they're also saying that light pollution has gotten bad and right and like all the um you know people who want

1:16:01to live by the stars are saying that light pollution has gotten very bad and I can't see >> Cassopia anymore right from New York City. It's like maybe don't live in New York City. I don't know >> what does the data say. >> Yeah. So let's see what the data says. Here's what it says. It's not a simple story as nothing really is when it comes to human civilization. Right? The paper applies continuous change detection to every single pixel >> that the satellite looks at for the past >> 10 years, >> right? And it looks at a sequence, not just pairs of annual screenshots. >> And the techno the technical idea is to fit an expected pattern that includes

1:16:42seasonal cycles. So you got to take care of the seasonal cycles. You got to take care of like trends. You got to account for the viewing angle as I was saying. You've got to examine observations persistently when they depart from some model that you've created that's based on the moon, the seasonal cycles and so on and so forth. Um and the headline result is more interesting than a single net percentage. Okay. So across the study they showed that a brightening contributed a radiance gain of approximately 34% of baseline in 2014 but there was a dimming offset of 18%. Some areas got brighter other areas got dimmer. It's not that simple >> is the idea. >> Everyone's moving away from the coast.

1:17:23>> Yeah. So here over here um what we're looking at is the the color actually codes the year of the most recently detected abrupt change >> in luminescence. Okay. >> The bluer year means it's closer to 2014 and the redder year means it's closer to 2022. And what we're looking at is within a year was there a significant change >> in how much activity was happening. So from here we can actually see specific spots on Earth that will show kind of interesting stories. So in the next photo we'll see these are panels that have very specific spots on the Earth highlighted. I'm

1:18:05going to go from left to right so you can kind of understand what we're looking at. On the left is Guangha, China. This is the urban area around Hong Kong. That's right outside of Hong Kong. You can see urbanization happening. >> Yeah, >> very very tightly. Let me just go through the colors real quick. Blue means that there was a net reduction and red means there was a net gain in the amount of light that was coming through. You can see the Guangha China is insanely >> Yep. >> lighting up. Okay, the next one. Armed conflicts in Kiev, Ukraine. That's the second row. You can see it's mostly blue. >> Yeah, >> right. Yeah, >> because there's a lot of dimming that's

1:18:46happening. The third row shows development of dragon fruit architecture, agriculture in Vietnam. >> Interesting. >> So, you're seeing a little bit of lighting up and a little bit of dimming based on like where that's happening. Environmental policies in Paris, France show a lot of dimming because they they've changed the lights that are in the city. Okay. The next one is gas flare changes in the Middle East region. massive massive flare-ups and also massive massive shutdowns. Yeah. Of where the gas is happening. Um next one you've got power outages in Puerto Rico as I was showing. A lot of that is blue and then there's some of it is red

1:19:27because it came back up. >> Mhm. >> Um urban expansion and decentralization in Charlotte, North Carolina. >> Y >> is the one right after. >> Y >> Charlotte's changing a lot apparently. >> Shout out Tabo who's in my brother who's in Charlotte right now. Yeah. So that's the second to the second to last column. And then finally, the economic collapse of Valencia and Caracas in Venezuela. >> Yeah. Wow. >> A lot of dimming happening in Venezuela as you can see, right? A lot of blue, which means there's a lot of dimming that's happening in Venezuela. And this is before 2022. So this is before any of the recent shenanigans that have been happening. >> Yep. >> Um, but you know, it tells a compelling

1:20:07story. each of these locations. It's it's also important to like also recognize that and it it kind of reminds me a little bit of our Navier Stokes episode. You have the larger system and then you have the systems the smaller systems within that larger system. And depending on what >> level of zoom you look at >> different stories can be told cuz when you look at the whole thing >> it's one story but when you kind of you can tie these real world social political economic cultural events or happenings to again this empirical. This is so good. >> It's so cool. This is the the most recent paper that they have. So I just

1:20:47want to go on a little bit of a deeper dive here. So this is figure two. How often does a place change and why? So um first point if you look at the small pie chart on the lower left that shows that roughly 80% of places with an abrupt change have it in a single year. >> Things happen fast. Okay. >> Only 20% >> have it over multiple years. So things happen fast when it comes to lighting and when it comes to human civilization, which I think is interesting. And then they label these like sort of labeled boxes in the world map. You've got the gas flares in central United States. That's the box that you see in the central United States. That's centered around Texas. >> Um those are the the the Peran basin in

1:21:29Texas that has all of the oil. You've got the grid failure in Venezuela. That's the next one. >> Um then you've got conflicts around the Middle East. So a lot of changes happening in the Middle East and then you've got construction and demolition happening in India and in China. One of the interesting things that I found about India was a lot of the change was concentrated in the northern part of India in Uttar Pradesh and Hariana and like that the Ganges Valley. Um, now now I'm talking completely out of my you know what I the the the time period is 2014 to 2022. >> And I wonder if this has something to do

1:22:11with Modi >> and the BJP coming to power, >> right? M >> and a lot of their strength is centered in northern India >> and they want to like they diverted a lot of resources into the >> construction and >> you know upsurgence of those areas in India because I don't see the similar things happening down south and down south is where Hyderabad is Bangalore these are tech cities >> what's going on >> but I'm not seeing >> I'm just saying no it's >> now this is again there's yo if there's like Indians in the comments who who are modi worshippers come at me. Okay, I don't care. All right, but I'm just saying that it seems maybe correlated

1:22:51>> and it's a worthy hypothesis that now this data set allows us to actually ask that question because people have been saying that independent of you bringing it up now. >> Yeah. And if there are alternate explanations, let me know. But this seems to me the simplest sort of explanation like why is it all centered around the Ganges Valley? >> Yep. >> Uttar Pradesh, Bihar, Jarand. Like I know my stuff. Okay. Hariana, like these are all BJP strongholds. Like why is all of the lighting up happening in BJP strongholds? Sorry, I I think maybe I spent a little too too much. >> No, no, no, no. It's great. >> But I mean the this I think this gets back to what I was saying earlier, which is you can the the data gives you a reference point to ask a whole variety

1:23:32of questions. >> Yeah. >> And because we can now see this, it's happening. >> Yeah. >> Like this, it's happening. >> Yeah. and the and the time period the 2014 to 2022 right this is right around when when Modi was elected as prime minister and like his first few >> decades I guess the first decade in power right so I don't know >> we can start asking these questions >> yeah um so figure three why the global average is deceptive you can't just take a global average right cuz the original question was oh like the the world is getting brighter >> right right >> n you know as we just saw stuff is getting dimmer, stuff is getting brighter. This figure, um, it's a large

1:24:14map. The red is accumulated brightening and the blue is accumulated dimming, and the pale and whitish areas are substantial amounts of both. >> What's going on in Europe, >> right? I didn't mean to cut you off, but the immediate thing I see you some areas you expect, >> but the thing I'm looking at is like, what happens? What's >> a lot of dimming, right? That probably has to do with policy changes in the types of lights that they're allowed to make, right? That's fair. Um, maybe they're doing more efficient street lights, things like that. Uh, maybe the headlights themselves are more efficient and lower power so they don't bleed out into the night sky. >> Um, so I think that's what's happening. But if you look, that's a very different

1:24:55situation than Venezuela. >> Yeah. >> In Venezuela, there's the same amount of dimming, but that's actually because of the economics and the economic collapse. >> Right. And what's interesting is like the economics of the region is not necessarily correlated to a brightening or dimming. Exactly. >> There there can be a number of other reasons. >> It could be policy, >> right? There's so many multiple faceted things. And in Europe actually, we see a 33% reduction in France, 22% reduction in the UK, and 21% in the Netherlands over this study period. And that connects to this broader pattern of lighting technology and policy. Venezuela, as I said, strong dimming dimming, >> but that's probably because of economic and infrastructure decline. The a few

1:25:36things that I want to mention here, um, the United States, there's a difference between the western brightening >> that's happening >> and the eastern parts that are dimming. In the west, you've got the the Peran Basin in Texas. You've got Backan in North Dakota that has the oil fields. >> Um, extraction and flaring can create these alternating increases and decreases. And those are some of the stuff that you're seeing in the North Dakota and the Texas areas. Um again in India and China you zoom in onto the eastern parts of China and and then India again big northern part all I'm just saying the authors describe strong development related brightening >> with different timing in northern and south southern India. So the authors of

1:26:17this paper are noticing what I was noticing which is that northern and southern India have a difference that is pretty stark in the data. I I I think this also for folks who you know may be into uh larger geopolitical conversations right there's this there's been this um conversation around you know reonshoring in the United States as an example because we've offshored so much of our manufacturing capacity >> uh to a variety of areas in regions where we're seeing the brightness increasing significantly the rise of China, a lot of these other

1:26:57again conversations and narratives, but the data is very like it's it's so interesting. The West, you know, is in a very different place. >> Yeah. >> As it relates to the data set, you can make you can extrapolate a variety of things from that. Yeah. >> areas that people talk about all the time bricks, you know, and this and it becoming a a block that is challenging on the world stage. And it is interesting for me as someone who's sort of like a >> uh a political junkie to see empirical data that kind of has interesting implications as it relates to how people

1:27:37think about that conversation. >> Exactly. Yeah. And you can see the stark difference between like sort of the west and the BRICS nations and the and the developing nation. If you look at the aggregate top plot and the the aggregate >> plot on the right hand side. So those are showing by is this I've always longit this is longitude. >> Oh we got we got a comment on this last. >> No this. Yeah. No this is longitude. This is latitude right. And so by longitude you're seeing the the western hemisphere >> like America and South America don't have that much change. compared to the the massive changes happening because of the Middle East >> 100% >> and it's happening because of China. >> Yep. >> On the on the longitude scale and on the

1:28:18latitude scale well China, India and the Middle East are all on the same sort of longitude. So you're getting massive longitude like in the tropics in the northern hemisphere and nothing elsewhere. >> And then the global south in this context is effectively in both both >> in both views is and again this is the conversation where the global south is trying to become a part of the conversation. Again, there is I think the interesting thing you note here is the Middle East on both axes >> is just massive >> is massive. >> But that's that that's a that's a factor of the fact that they have oil. So like the refineries and the and the oil fields are extremely bright at night, you know, >> and as well as a whole variety of other things that are not necessarily uh

1:29:00>> net positive. Exactly. Uh so it it's it's it's complex. >> Yeah. >> But I think >> but it's incredible that we have the data, right? We can >> because we we are no longer bound by what we gather from satellites. We have now a very sophisticated algorithm that can take care of all of the riff raff and tell us what the human activity actually is. So we can start answering these questions and know that is it is not because of artifacts. It's not because of the moon. It's not because of clouds. It's not because of snow or the aurora borealis. No, this is human activity that we're seeing >> from from space. Yeah, >> it's that's quite it's quite nice. >> Quite cool. >> Yeah. And the last thing I want to do is

1:29:41just return to the first image that we have of Puerto Rico after Maria. >> It's visually arresting to see the darkened island. It tells us immediately that something terrible has happened >> and it tells us the potential of this kind of technology. If there are future disasters like this, this is something that we have an immediate data access to right? >> 100%. >> So, um it's it's actually incredible the stuff that we can do for the betterment of humanity, not just to study how humans are living on this planet, >> but you know, as this algorithm gets more and more sophisticated, as it starts incorporating more and more data,

1:30:21I mean, right now it's just incorporating satellite data and some population level data. Imagine it can start incorporating like localized data, right? >> That was immediately what I was thinking about. >> Yeah. Like the more sophisticated this algorithm gets, the more granular we're going to have an understanding of human disasters, human influence on the environment, human influence on our natural environment like forests, forest deforestation, like all sorts of stuff that I really care about. Uh actually because you talked about the the the trees earlier, deforestation seems like an obvious one that is going to be very easy to see. >> Yeah. And um the other one that I I I

1:31:03didn't really talk about because it's not like one in highlighted in one of the papers, but they were talking about how offshore fishing, illegal offshore fishing is something that they've >> like literally caught >> where like there are countries that will, you know, >> Yeah. Yeah. Yeah.

1:31:27>> We've all agreed to protect on the open ocean. And this kind of data has access to, you know, finding out whether whether that's actually happening and if we can actually do something about it. >> What's what's and I'll make this the last note here. What's interesting about that is, you know, folks have seen the imagery because a lot of it's a lot of times it's night fishing and so they need these Yeah. >> They need lighting. >> They need lighting. Yeah. >> Um and it's over the ocean. >> Deadliest catch. >> Right. Right. Right. Right. Right. And it's over the ocean versus land. And so >> and that's like obvious. What do you >> Where did Where did this come from? >> There's no land there. >> Um and that's an >> and they usually travel in packs packs.

1:32:08Oh yeah, you know it it would be the the the fundamentals of how they do it make it actually very visible in this data set. Uh congratulations to the NASA black marble team uh on the golden goose. Uh again we will be having a conversation uh with Miguel about some of these questions. Very interested to dig into it but I think we did great coverage. we're gonna move into. We somehow always find a way to be making two to three hour episodes despite our >> This was supposed to be a short one. Sorry. >> Despite our endeavor to try to make it tight, but there's just so much interesting things to talk about. We will be back with our last Golden Goose

1:32:49Award recipient and talking about something that's a little bit different than our first two stories, which is disordered systems. Our final story is