INTRO: I’m Lia Ferguson from the Univeristy of Alaska Fairbanks, and this is Alaska Voices, a place where community members, friends and scientists can share stories and place-based knowledge in order to build a better tomorrow for Alaskan’s and the world.
Originally I grew up in Gakona, Alaska…Tyonek, Egypt, Idaho, Rampart, St. Laurence Island, St. Paul…and I’m here with my student…my science buddy, my teacher, my homie…I’m his daughter. 
CHARLIE: Okay. My name is Charlie Parr and I’m a geospatial data guru programmer scientist person at the University of Alaska Fairbanks. I’m from Fairbanks, Alaska. I’m here with my colleague, Josh.
JOSH: I’m Josh Paul, I’m also a programmer, primarily with geospatial data. I work for Scenarios Network for Alaska and Arctic Planning at UAF here.
 I didn’t used to be a programmer. I used to be a soil scientist who made maps and started using GIS and realized pointing and clicking my way through the processing pipeline for data is the slow way to do stuff. So if you learn how to program the computer, tell the computer what you want to do, you can actually do a lot more when a computer can work for you. 
So, I think you had a pretty similar journey, Charlie, right? Like you didn’t start out as a computer programmer science type guy when you got out of high school and decided to start a career, right?
CHARLIE: Yeah, that’s correct. My undergraduate degree is in geography and in that experience I used a lot of GIS software. I was lucky enough to get some advice where someone told me if you really want to own your geospatial situation, learn to program, specifically learn to program in Python. And that was probably around 2013 or 2014. So I got a job working as a snow scientist, which is like being a soil scientist, probably, there’s shovels involved. 
JOSH: It’s just soil that melts.
CHARLIE: There you go. Yeah. 
JOSH: Lot of shovels involved.
CHARLIE: And its water content is 100%. And I had a lot of latitude to create my own tools to analyze the data that we were collecting. And it was right at the time that some of these Python programming tools were becoming mature to use for this type of geospatial work. 
JOSH: This is common. This is a common way to get into programming. Sucks people in, I think, from different disciplines where you have a problem to solve and you realize, man, I need to learn a programming language to do this, or else it’s going to take me forever. Or it’s just an impossible problem to solve without a programming language. Then you start programming and all of a sudden, you leave your field that you started in and then you’re a programmer. You can help out scientists from all sorts of different disciplines because you know how to speak the language of data. 
Also, a lot of programming is problem-solving, and being able to research different programming tools and looking at examples of work other people have done and use that information to your own ends, and those are skills that you can use in all sorts of different disciplines. 
CHARLIE: I think it would be difficult or it would have been difficult for me to just learn programming or get my start in programming in an abstract or academic way. I think having a very real-world science type of objective that I was trying to accomplish really motivated me and made it interesting and really engaging for me to learn. 
Josh. Question. Are you a climate modeler?
JOSH: No, absolutely not. [laughs] I’m more of a data wrangler. I don’t make the models, I don’t know a lot about climate science, atmospheric sciences.  I see myself as more of a technician data guy, trying to take those modeling products and do things with them. What about you? 
CHARLIE: Same. So we’re not climate modelers, we work with climate data, we do a lot of things to streamline access and distill it and make it more accessible to people. 
There are dozens if not hundreds of different climate models that are available throughout the world. And all these climate models have different assumptions about how certain types of atmospheric physics work. And then of course, you can have different assumptions built into the climate models about how humans are likely to function in the future and contribute or not contribute greenhouse gasses to the atmosphere, and what those feedbacks might be like in the climate system. We call those scenarios, generally. 
So there’s many, many different climate models, different emissions scenarios. But luckily, there’s some global cooperation to create experiments where you take a bunch of different climate models and give them the same set of inputs and see what happens. And that allows people to compare results across all these different models and then you can start to determine for your geographic area, say Alaska or the Arctic, what are the most skilled models for predicting things like sea surface temperature or sea level pressure or temperature or precipitation, whatever variable you might be interested in, which of those big group of models do the best? 
JOSH: We throw around a lot of acronyms and one of them is CMIP, but this is the coupled model intercomparison project. The group that runs the CMIP project was able to put out the call and say we’re doing this project, these are the parameters, and then all the modeling groups all over the globe to do their same experiments, then you can compare them apples to apples. It’s incredible that it happens at all. 
CHARLIE: And then we also distill this into easy-to-get packages of data that researchers can grab and use for their research, or people that are writing grants, or community members can then get this information, get some context around it, and then be able to use climate change information in their work.
JOSH: We often describe that as, in a word we say that’s curation. 
You could be a highly decorated PhD in a specific field like biology or something, and you want to use climate data in your work, but that’s not really your specialty. You’re more used to, you’re counting newts or something. 
So having some support staff, science support staff like us in between the hard-to-parse climate data and your discipline, as a scientist, we do that sort of work all the time. 
We have a little bit of experience in the field and so this isn’t just data, it’s not just numbers. We know it’s actually trying to represent something real on the ground that we may have experienced or had a little bit of real-life experience of and that’s super helpful. 
If you were just purely a programmer and had never actually been in the field in Alaska, and you’re dealing with all this Alaska data, you might not realize when some values are unrealistic or wrong without running it by an expert. 
We run into this a lot. It wasn’t long ago we were looking at a model, it was projected precipitation. And there were some pixels in Southeast Alaska that were kind of a bonkers number like 500 inches of rain a year. And we were just like, is that realistic? Is that okay for the model to predict that? The models aren’t perfect, and they’re global models. So are the values going to be realistic everywhere? Maybe not. Maybe the modeling at the top of Mount Denali is going to be a little weird because it’s just a pretty extreme place on the globe. And Southeast Alaska can be that way for precipitation, too.
CHARLIE: And then we do something called downscaling, which is bring those models from a really coarse resolution where maybe you have one prediction for every 1x1 degree square for latitude and longitude, okay, now you have a prediction for every square kilometer.  
JOSH: I think a lot of people don’t realize how many people are doing this, and the level of cooperation that happens is pretty amazing.
CHARLIE: It really underscores how important climate is as a prime driver of all of our human activities. Climate really determines where people live and what types of industries we engage in and how we recreate, and our culture, and our food, and our agriculture. 
And so climate to me, I really view it as a prime driver of life on Earth. Not just human life, of course. But plant and animal life as well. And so I think that’s why there’s such incredible international cooperation to try to get a glimpse of what some climate futures might be that are ahead of us. 
I think it’s important, sometimes you see it written and said that weather and climate is this unpredictable thing. And it’s not, we actually predict weather and climate all the time with a lot of skill. We do it really well, but there are some limits to how credibly you can do that, so it’s certainly the further out you go in time, the less certain you can become. 
Figuring out how to make sense of those predictions and projections and models is a lot of work, but at SNAP if you live in Alaska or in the Arctic, we’ve done a lot of that hard work for you already. 
Sometimes we might make something that’s a bit more technical for engineers. We have a tool, the Arctic EDS, Arcticeds.org, and sometimes it’s for researchers, which is the case with the Arctic Data Science Collaborative. So different tools for different users.
And we have tools or we have web applications that people can use to get a handle on how a change in climate might impact where they live and places they care about, where they work, where they recreate. 
JOSH: I think especially Northern Climate Reports. I mean we’re talking a lot about data and numbers and stuff, but that tool is plain language summaries of how a bunch of different climate factors are predicted to change in the next 100 years or so. That’s NorthernClimateReports.org if you were interested in checking it out. 
CHARLIE: I would encourage people to not despair in this prospect of an unknowable climate future. We have tools, good tools, to get an idea of what might be coming, and of course we hope that people can use that to then plan and compare and adapt and navigate whatever is going to come our way from a climate perspective with more skill than would otherwise be possible.
OUTRO: Alaska Voices is a place for communities to connect through conversation. This podcast was the brainchild of Jesie Young-Robertson and Bob Bolton with support from the Alaska Climate Adaptation Science Center, who also funded today’s episode. The Alaska CASC is committed to providing regionally relevant science for Alaska’s changing climate. Alaska Voices would not be possible without the efforts of an amazing group of people. Our producer and audio engineer is Kelsey Skönberg of Mossy Stone Media. The Alaska Voices team includes Micah Hahn at the University of Alaska Anchorage, and Lia Ferguson, Mike Delue, Annika Ord and Diego Noreña at the University of Alaska Fairbanks. If you are interested in more conversations or information, please visit our website at AlaskaVoices.org.