 So my name is AJ. I'm just saying that for Sandy since we just met. And everyone else kind of knows this. But before I came to Berkeley and in the archaeology world, I did my master's in geology at Long Beach State. And I worked with a paleoclimatologist. And while I was down there, it was stressing me that the way that data works in paleoclimatology is a numbers game. And it's all about getting as much data as possible. And I see Vinisha nodding her head, because it's the same world in paleoclimatomy. It's just stacking data. And paleoclimatologists go this way. Palinologists go this way. But the idea is you get as much as possible. And you're as complete as possible. You don't want a cherry pick. If there's a site or a cave or a lake right outside of your site and there's data there that you don't present, reviewers will call you out on it. And it's just not the right thing to do. So I want to share my screen to show you kind of example of what I mean by kind of stacking data here just from a figure that I had in a publication. And since I've probably talked Vinisha's and Nika's head off about Kohokia before, I'll spare that. And just get to the point that it's all about taking as many proxies or types of paleo environmental data and just getting them together to try to find any sort of signal that pops out. In this case, what we notice is that in the 12th century towards the beginning of the 13th century, you can see there's this big decline in the records. It shows up as an increase in this record here. But there's something going on here where we to have just one of these proxies presented would not have been enough to get through publication. It might have, having been through just an archaeology focused journal. But because we're trying to make a very strong statement about what happened with the environment in the past, you get as much of these together as you can. And I talked about cherry picking. And sometimes you're tempted to not present this data. So this line here in G was from a paper where the data agree in this part and these orange data here are from my own study. But they decouple here. And where there's a negative value, we actually have a positive. And that freaked me out. I was like, should I present this? And the answer is yes, you got to. You just have to and explain what happened. I think what happened is one of our chronology starts to go off. And I think it's actually mine. So I have to address weakness of my study. But the point is it's all about lining all this data up. Now, sometimes in archaeology papers, I'm not sure if you've seen this. I'm not going to use any names. But you see a statement, sometimes a bold statement about a relationship between people and some sort of environmental change in the past. And you go to look for the data that backs it up. And I kid you not, it's a figure that shows one bit of paleo-environmental information. Maybe it's isotope data from an ice core in Greenland, which is great data. Many people use that data. But the culture is the Harappan civilization in Pakistan. And the question is, is that fair to do? And it sometimes gets by when there's not a reviewer who's coming from this training that we're talking about here. And it's just not a good thing to do, because you can't be certain that what's going on in the poles reflects what's happening at a local level. And as we talked about, you need multiple proxies to know that that signal is most likely real and not just a blip. So how do you do that? And the answer is you can either go and try to find these papers that have paleo-environmental data buried in them and then go find the supplemental information or you can find some sort of archive that has it all neatly gathered in the same place. And that's what we're going to talk about today, which is this NOAA paleoclimatology archive. And I'm not going to teach anyone, I don't think any new skills really here is more just of like, here's this place. Let's check it out together. And after we're done, here's the links for you to explore it as well. So that's why I don't think we'll take nearly the full time here, but we will, of course, have time to kind of demonstrate how to use it. And then if you want to stay on the line and we can see if you have any questions as you use it yourself, you're welcome to. So I'm going to drop some links into the chat here, which are the links that we're going to use today. So if you give me one second for me to copy paste, we'll start taking this first spin. And the first thing I want you to do is actually to click on link three, the interactive map, and just open it in a window on your computer. Ignore it for now, because it's been taking ages to load for me. I don't know if that's a problem with me or a problem with the servers. But if it is a problem on the website itself, then let's just get this in the background. And by the time we get to it, hopefully it'll be ready to go for us. So just have that in the background here. But now I want to go to link one, the products page. And we'll check out what we have to see. So I'm going to go ahead and share my screen once I get to this products page. And we will talk about what there is to see. And if you have any questions about what I'm talking about, please, of course, let me know. So what we're looking at is the NOAA Paleo-climatology database. They have it under products. And by products, they mean data that you can download. And there's this really scary scheduled maintenance bar that says it's scheduled for maintenance today. But it says it ends at 5 Eastern, which has already happened, so I hope we're in luck. And I tried using it earlier today. It was no problem for me. Anyway, so this is kind of a description page on what type of data is available on this website. Let's go check it out. So we're going to scroll down to products. And I'm just going to take maybe two minutes here to just kind of introduce these archives. I know many of you are probably comfortable with a lot of these. I'm just going to put my opinion on it. The things that I would turn to first for these different archives listed. And by the way, this is everything you could ever imagine within Paleo-climatology is any of these links. This is from NOAA. They have over 10,000 data sets contained here. So it's a good place to start if you have a specific proxy in mind. But let's go over the main ones. I suggest starting with ICE core data. Really good. It can give you annual precision. And it either tells you the isotopic composition of water in the ice or as gas. So it can tell you carbon dioxide levels, for example, through time that's trapped in ice. It's going to come from two places. The poles, so Greenland and Antarctica have excellent data heavily cited. And if we use that carbon dioxide information, that's global in scale. That's telling you what the atmosphere was like at any year. So really, really useful. However, you can't just use that, as we talked about earlier. You've got to additionally use stuff that's at least on the continent that you're interested in. Then the other place that you'll find it is in glaciers. And so you all are working in Peru, and there's a bunch of glacier ice cores, which are totally useful. So I remember thinking, oh gosh, if you're anywhere in low latitudes, what would you use ice cores for? But fortunately, there's still glaciers in the world. And many that are dying, like in Kilimanjaro, they've already gotten ice cores. So it's a sad story that some of these glaciers are disappearing. But many good scientists have gotten out and already captured a lot of that data before it melts. So yeah, that's the other thing. So ice cores applied to even places like Peru. The next big one is going to be lakes, which will tell you the sediment composition of carbonate or plastic material at the bottom of the lake, as well as things like percent organic, percent carbonate. That is also there. Oh, looks like we got a new person here. Hi, Lauren. Welcome to the talk or the workshop. Yeah, so lakes are a big one, and lakes are, of course, all over the world. So there's a lot of data to use here. The next big one is going to be under paleo-oceanography, which, like lakes, tells you this composition of carbonates at the bottom of the sea, as well as things that might be less useful to us, like currents and things that are more strictly oceanography. But it's worth checking out the paleo-oceanography stuff for isotopic data. Then the last big ones are pollen, which I'm not even going to mention, because archaeologists, if they know one thing well, I think of all of this, it's going to be pollen. Then we got speleothems, which is a word, meaning stalactites and stalagmites and caves. And just like with ice cores, how you can get, or the bottom of lakes. Let's use the bottom of lakes as the correlate here. Just like the bottom of lakes, you can look at carbonate there and look at the isotopic composition of that. You can do the same thing for carbonate and those stalactites and those stalagmites. And that's really good data because it's often annual. Every single year, there's a new layer of that stalactite that forms as you get a wet season, drips down, carbonate forms, summertime stops, next season, boom, another one. So really, really precise. And caves are all over the world. So that's another great one. And then finally tree rings, of course, tell us about drought histories. And they're extremely precise as well because of gender chronology. So those are the big six. Ice core, lakes, oceans, pollen, speleothems and tree rings that I personally trust the most and are heavily cited. Then there's a bunch of other stuff here that's more niche or isn't actual data. So in terms of that, isn't actual data. The modeling, the climate reconstructions, climate forcing, drought variability that's plotted here. These are all aggregates, interpretations and models of the other data that's on this page that have been published and presented as things like deviation of temperature from today. And you've seen that where it says, a thousand years ago, North America was half a degree warmer or cooler. I don't really have the numbers off the top of my head. That's not primary data. It's not raw data, right? That's a second order of interpretation. So just be aware of that when you click on that to know that you're using something that is itself an interpretation of something else. Then there's other raw data here that's very niche. So we've got borehole data, which is probably not gonna apply to us because it's super, that's like going down geologic lengths of time, super far down, probably not gonna help us. You've got things that might help us a lot like historical data. And by the way, you can click on any of these and they give you a little synopsis if you're not sure what the word means. So historical data, I've never used this, but this could be really useful in terms of like, harvest dates, harbor, ice-free dates, ship logs, if you're working in the colonial period, that could be super useful. And I noticed that since we have several people who joined us here, so Hibo and Lauren, let me know if you're confused where we're at. If you look at the chat, I've got some links and right now we're on the link one, so you can join us there. We're basically done with link one, but you know, that's how you get to where we are. And let me know in the chat if you got any questions about it. Okay, so anyhow, I just wanted to, as I said, introduce these types of data sets. Where did I go? Historical, that's correct because I clicked on it. Could you maybe resend the link? New people won't have the link. Oh, you're right. Thank you, Nico. I'm sorry, everyone. Let's resend it. Boom. All right, I think that should be through and it is. Okay, so assume that there's no questions about like these types of data. Let's now start to use the site. And for that, I'm gonna ask you to click on link two and for link two, you're gonna go to the main search page for this NOAA archive. And we got one more person joining us. So I'm gonna post the links like one more time. So for the person just joined, we're on link two right now. And please follow along at home. You can of course just watch too, but I think it's more helpful this way. Okay, so this is access to these, this 10,000 data sets that we talked about. There's a general search, but unless you have like a specific lake or a specific cave in mind, I don't think that's gonna be very useful. If you just type in like Peru drought or something, you're gonna get so many resources, it's not gonna really be helpful. So let's go instead to the advanced search. So you can keep scrolling down to advanced search. We're gonna skip the investigators. We don't really need to search by that, but what really helps is locations. So I mentioned at the start of the talk that I've decided to use Peru as an example here. You can of course choose with everyone you wanna do, but what if we just all stay on the same topic, let's just all use Peru. So you can search in the search bar Peru. And then if you just click on that, what shows up in this next box here is basically saying that's gonna screen everything in the world to only focus on the country of Peru, which is great, right? If you work in Morocco, you can click Morocco and it'll only give you results from Iraq, right? So that's a great way to start. If you wanna be even more specific, you can use this bar here and we're gonna go zoom into Peru. Let's fly with me in the slow down to Peru. All right, and we're gonna, sorry, I apologize. We're gonna go ahead and box. And we've made this little search box here. In fact, I'm gonna delete that. I'm gonna make it again a little... All right, here we go. So we've picked a corner of Peru. What is that? Maybe like a third of the country, a quarter of the country, something like that. You can make it as big or small as you like, but now we can say, okay, we're focusing on like the central part of the country and we'll see what's there. If we go into the entry bars on the left, we can actually search by elevation. So maybe you know that you want to get ice core data. So you might have a minimum elevation of 2000 meters or something like that or 3000 meters, whatever gets you to wherever ice is in Peru. So that's another way of specifying where you want your data to come from. And then these variables that they have at the end are even more specific, too specific for what I think I wanna talk about today. But if you wanna get data that only has to do with coccolitho spores, there's fours, coccolitho spores, I thought they're coccoliths. Anyway, point is you could highlight that and that would be the only type of data you get. So you can get really specific if you want to. But let's keep on going down to the bottom here. And I think what's really helpful for everyone to use is this time feature where you can specify the time range that you want your results to include. Now, for Peru, I know that there've been people there for a while, but let's say 10,000 years. There's just a nice even number, right? And that will get rid of things that are super old because this archive is for geologists as well. People who are studying the myosin, we don't want stuff that's millions of years old. So we're just gonna get that out of there and that will help us really narrow down the approach. However, when you do this on your own, you might wanna start a little bit broader than what I selected here. So I'd recommend maybe starting with 100,000 and then working your way down to something like 10. The reason is that sometimes you find really long archives that might cover 100,000 years and 90% of that is outside of human history, but that last 10% could still be useful to you. And that's important. But for today, I'm just gonna choose 10,000 to make things simple and easy. Okay. And then we go ahead and we click search and we can see what we get. So of course, continue to let me know if there are any questions or anything, but we'll see what we have here. And we've got our results. So we've got 20 studies that are inside this archive that show up in this box within Burroul. And that's not bad, right? 20 references just like that. Now they might not all be applicable to us but let's check them out. First one here is XRF and pollen data from a peatland, some kind of marshy area, I guess. That could be really relevant to us. That's great. Let's keep on going. There's modern surf clam. What do they mean by that? Stable isotope from surf, what do they mean by modern? Let's take a look at it. And if we keep on going to the bottom it tells you when the study starts and ends. And it says here, the earliest year is negative 61 BP which is 10 years ago. So that is definitely not of interest to us. So we're gonna scratch that, right? But the point is you can look through these and you can see pretty quickly what stuff will be useful to you and what stuff won't. Let's now go to and select this Wagapo cave, 7,200 years spheliothem stable isotope data. And let's check it out. Okay, so first of all, 7,200 years that covers a lot of time in human history in Peru could be useful to us. Let's see where it is. So you can zoom in and see, hey, it's in the valley outside of Lima. I'm not sure how far away that is. But that sure does beat an ice core from Greenland, doesn't it? Right, if we wanna talk about Peru and we've got something right here and I'm no Peruvian archeologist I would guess that there are many archeological sites in this nice river valley right here. And this could, sorry, what was that? Oh, it's just north of the Montano Valley, yeah. Is that an important archeological location? I know further south it really is. I don't know up right around in there but probably everything in Peru is just a couple. Gotcha. And so, but the point is here is that even if your site is 200 miles away this is giving you data that's gonna be much more applicable to that site than things coming from the middle of the ocean than things coming from these glaciers or not necessarily glaciers here but ice sheets that are so often used in archeology but so far away. So this is kind of the whole point here is that you can find stuff really close to where you wanna be. And then the first stuff that you see is mostly boilerplate but then you can get to where you download the data and I'd like us all to do that now. So what we're gonna do is we're gonna click and I'm gonna go ahead and use control click so it opens up in a new window or a new page but we're gonna click on Wagapo Cave P00H1. We're gonna do this first one. And a text file opens up and the beginning is all just stuff that tells you about the authors of the data, blah, blah, blah. Really important stuff if you end up using it in your vapor but for now let's get to the data. So we're gonna skip down through the text file. It's gonna tell us about how they constructed their chronology. Again, good to use if you end up publishing it to know what the uncertainty is, how good is their chronology? And by the way, it's probably gonna be good enough because all this stuff is published. A lot of this stuff has been funded by MSF so not only has it been published but it's been through a rigorous review process just to get to the study phase. So this is kind of the crème de la crème most of the time here. And we skip down and here's our data. So look at all this data, yummy, yummy data all of that there for us to use. And we can just go ahead and copy and paste it and put it into Excel. So if you have Excel, why don't you open up an Excel file real quick and we'll just start manipulating the data now. So if you just go ahead and open up a blank Excel file and I'll share what I'm doing in a second with that. And once you have the Excel file open, we can go and we can copy this data and just select it all with your mouse or whatever you have and then copy it and paste it into Excel. And you might notice right away that there's a problem. Oh, let me go ahead and stop the share and bring you over to my Excel file. Hey, Albert, how's it going? Good to see you here. Let's go into Excel. And because this is text, it all falls into one column and all of the data on the site are gonna be this way. Sometimes you can download individual Excel files where it's nice and clean, but most of the time you have to copy and paste it. And so there's a little trick you do in Excel and again, this is like the one skill and it's the most basic skill ever that I could teach but this is the skill part of the workshop. We're gonna learn how to take this data and put it into a meaningful usable form in Excel. So you highlight the column, you go to the data tab, you click text to columns and then if you click the first button that says delineated, we click next, we click space, finish and there we go. Now we have our columns and we can totally start working with this data. And I'm gonna go ahead and look through it to see if it looks funky. Sometimes there might be a problem. In this case, there's one line that was problematic where it oriented the numbers a date. So I'm just gonna delete it. But everything else should be fine. And we can start looking at paleoclimate data just like this. So we're gonna go ahead and make a chart. And here is oxygen, 18 isotopic data from a speliothem over about 1400 years. Now I mentioned in the beginning of the talk that like, I'm not gonna try to interpret what this means for climate because it's really hard to do that actually. It seems simple at first, but there's so many things that affect us. But what we can do is look for trends. Know that trends have to do with changes of some sort in the environment. And we can at least say that. And so if we look at this data, it's like, okay, we've got kind of a high about 1300 years ago and a high in the present, but there's this noticeable dip, this noticeable minimum about 300 BP, right? So I put this in the 18th century. And that might have, that might be an important climatic event. And in this in Peru, I mean, this is showing my, please don't think I'm too awful here, but I'm like, I'm gonna bring up the Incans because, you know, right? And so we know that, you know, the Incan Empire is happening around here. There's a decline in these values after that. Micahs have impacted people living there at this time. Who knows? But we're gonna, we can at least have some data to start to investigate those sort of questions. But as we talked about earlier, one isn't enough, but we've got our start. So let's go back to the search results. So I'm gonna stop my share. When I go back to where we were on that second link, under the search results and just keep looking here. Oh, did I share my screen? I don't think I shared my screen today. Whoops. Let me share my screen again here. Apologize everyone for that. So if we go back to the results, let's keep on looking and see what we got. And lo and behold, only two records down, there's another isotopic data. This time it's from Laguna Puma Cocha, which I'm gonna guess is like a lake or some sort of body of water. Let's see if it says in the abstract here. Every see, a varved lake. So this is great because the earlier record was a speliothem, which is one type of proxy. Yeah, of course. Here we go. So the speliothem is one proxy of isotopic data, and now we have lake sediment as another. And if these agree, we can be more confident about what we have been seeing in that first record. Is that decline real? Does that correspond to an instance of climatic change? So let's do what we did before and download it. So we're gonna go down, we're gonna click on PumaCocha.txt. You could click on the Excel file, but since we're gonna practice our skills with the text, and very few of these are actually Excel based. Most of them that I've seen are just the straight up just text file. Let's go find the data. So of course at the beginning, they have the chronology data, which is important, but not actual paleoclimate data. So let's go find that. And here it is. We have to scroll down kind of far, but we found it. And we've got depth in the core, the year from their chronology, and then carbon and oxygen isotope data. So we're gonna go and we're gonna bring our cursor down, get all of the data that we can, just copy paste it. And we're gonna go back to our Excel file. So I'm gonna stop the share again. And we're gonna do the same thing we did. Oh, I'm gonna restart the share again. A lot of sharing and then not sharing. Kind of like my baby. All right, so we're gonna go to the make a new tab here and do the same thing we did before, right? Where we copy it and we paste it. We go to data. We, sorry, gotta find my data bar. There it is. The text of columns, delimited finish. And voila, we have our data again. One problem though is that before, in the last archive, our data was in years BP. Now it's in years AD. So we gotta just do a quick fix for that so they can say the same thing and be potted. So we're just gonna do a quick equals 1950 minus B2. And we're just gonna drag that across here. And that way everything's gonna be in BP and it'll be a lot easier to compare. I'm just gonna keep going down and we're at the bottom. And so now we've got our years in BP and we can do option 18 and we can make a graph. Oh my gosh, I messed up somehow. Let's try that again. Insert graph. There it is. Now this, it was kind of lazy. It didn't wanna auto-adjust the axis. So I'm just gonna mess around with the axis real quick to make it a little more pretty here for us. We'll make that maybe negative 11. There we go. Now we can see the changes. And what we can do now is say, okay, let's see how well this data match up with the data set from before. And to do that, the real way to do this is to plot them on the same graph, but in Excel, it's hard to do because they'll just kind of all, yeah, it won't look that good. If you're doing this for publication, you're gonna either use graphing software or illustrator and do this in a very exact manner. So I'm not gonna do that. So don't do this at home kids for at least in the professional sense. But what I'm gonna do here is just a quick and dirty way of comparing them. We're just gonna say, all right, I'm gonna make the scales on them the same. So I'm gonna say the scale here is gonna be negative 50 to 1400 BP. And then I'm gonna make the scale on this one the same and I'm gonna change the color as well so that we can differentiate them. So we're gonna make that what I say negative 50. And I think I said 1400. Cool. And then all I'm gonna do is just copy and paste this one to the other one just to kind of look at what they, how similar or different they are. I'll make this transparent as well. So if I go to this and I actually, I did the thing I meant to not do, okay. Oops, almost there. And again, this is not the real way of doing this. I'm just kind of doing a very fast version of what I do normally. But now we have these two proxies. Their scale is the same. I think I've made it almost perfect where they start and end at the same years and we can compare them. Now I mentioned at the beginning that this is not enough. You'd wanna make a big, tall version of this with many more proxies, but this is how you start, and you can start to see, is there any agreement between them? And maybe I'll open up to y'all. What do you think? Do you see it? Would you say that these proxies are an agreement? It's bowed here. Looks like we got a little dip there around 350. I don't know if that bump at 950 is significant, but it looks like I can see a general shape there, I guess. You know, Bo, yeah, I could not agree with you more in the way that you put it. There seems to be some agreement that there is a local minimum around 350 BP. Now, as Bo also said, around 950, this record, which was the one from a lake, shows a local maximum, whereas this one from the speleothem shows really stable values over a period of 500 years. So they are not an agreement, they decouple this part of their chronology, but they agree here. Now, if we were to get more data and find that this 350 thing, there's something going on there, the more that we see shifts around this time, the more comfortable and confident we could be in saying, there's a significant thing going on in local climate in this area at 350. And at that point, maybe at that point, then we can incorporate archaeology and we can say what is happening with people at this time, right? But we first need to be sure that what's happening in local area is real. And you can see how you could get yourself into trouble. If you only use the record in red, and you said there's something important going on at 950, and if you were to base your whole paper on that and you make a big story and you've done all this work, and then someone finds this other record and says, what the hell are you talking about? It's completely flat and stable. The climate is in stasis. You got yourself a problem, right? So that is how we prevent that. By doing a good job, it really kind of seeing what the data tell us from multiple proxies. We got isotopic stuff from lakes and a cave. That's a good start. Maybe we could get a glacier, make it three. Maybe there's some other sort of stuff in terms of pollen. Maybe there's a shift in vegetation at 350 BP too. So the more that you diversify the data and they all agree, makes you even more confident that something is going on. And I did promise to not interpret what is happening here. But I mean, this is kind of like the very end of little ice age. Maybe it's something really bad, but that's more of a Northern Hemisphere thing. It's definitely more of a Northern Hemisphere thing. I shouldn't even said that. That was silly and unwise of me to deal. Okay, so we will stop sharing, but I hope that gets you an idea of how you start with the search page. You find the data, you get the data, you put new Excel and you don't stop there. You keep getting data, you keep comparing it until you find something. So the last thing, we got one more thing that I wanted to do. If anyone has any questions, please let me know. Otherwise I'm just gonna keep, just pushing through the last thing, which is to go to link three. And link three is the one that is possibly gonna be a disaster. So I saved it for the end. So go ahead and click on link three and we'll see what happens. And what link three is is, let me just get it to it on my page, is a really, really cool tool from NOAA, but it's also been frustrating to use, which is why I saved it for the end. So let me find the share screen feature. So this is this map search where instead of doing what we did before where we searched it by kind of using more text-based things, you can actually zoom into a specific latitude and longitude on planet Earth and see how close various datasets are to you. So I'm gonna try this and again, it takes forever to load most of the time, which is why I was so afraid to do it live because like what are we gonna do? Just stare at each other while the screen loads see it's happening. I was afraid of this. Point is in your own time, I'll give this maybe 30 seconds of a grace period to see if it plays ball with us. If, oh, it's starting to, it's starting to, but on your own time, check out this website, which is this map version. And you can say, hey, I know where my archeological site is in the world that I'm interested in. And I wanna know what data is available to me within 50 miles, 100 miles. And you can just scoot around and see and the different symbols say, oh, it's this type of proxy. You've got tree rings here, you've got a lake here, you've got a cave here and there's stuff from the ocean off the coast here. And then you're like, oh, great, look at this wonderful dataset that I can just compile. But as you can see, it's been super like just chugging along. And when I use this last, it was far more efficient, but I seriously use this like, I don't know, six months ago, it was doing better than this. So we might just have to call it here since it's not loading and there's no use of us sitting around a Zoom call watching a page load. So I might say that this is something for everyone to try at home and all conclude with what I have to share there. So maybe what we can do is just see if there are any questions about what we covered just then. If you wanna try it out on your own right now and say, hey, I'm gonna try searching things and can you give me a minute to try that out and see if I have any questions? I'm happy to stick around and talk you through it if you wanna do that. But yeah, I hope that wasn't too long or too short or anything like that. So thanks everybody.