Let's talk for a minute about generational collaboration in research.
This morning, before going back to coding, I met with two younger research scientists and their boss, a senior professor. They do ecological systems modeling in the Department of Wildlife and Fisheries, one floor down from my office. One of their projects tries to understand the seasonal movement of the Invasive Sugarcane Aphid, a bug that damages crops, with the goal of creating forecasts and warnings for farmers who can then apply pesticides more targeted. Their work is not too different from what we do, so they were interested in learning about what we do. We had a wonderful, three hour long conversation about our project and theirs, and only stopped because, despite the jaffa cakes they brought, we eventually got hungry for lunch.
The way the three described their work made me think about how different generations often approach problems differently. The two researchers are much younger than their boss and probably grew up in a time when computers were ubiquitous in colleges and maybe even schools. They know who to code, run models, and build web interfaces for users to get information over the internet. The professor, on the other hand, did his early research using punch cards (if you're under 20: punch cards). A simple calculation of data, not more than a few lines of code in today's world, would require a stack of punch cards that needed to be inserted in a machine and took minutes to process. The obvious comparison for me – thanks to this week's 50th anniversary of the moon landing – is that of the Apollo spacecraft computer to the super computers we carry around today in our pockets as smartphones.
Their situation reminded me of my own. They're are young tech wizards, bursting with ideas and enthusiasm. The professor, much like my supervisor, has decades of wisdom and the accumulated expertise of thousands of books and papers he read and wrote over the years.
Much like interdisciplinary teams, inter-generational teams, in my opinion, can be incredibly innovative and efficient, but only if we allow ourselves to admit that we need each other. Us young(-ish) folks are tech geeks, fluent in R, NCL, Python, and GitHub, and full of ideas for tools and gadgets, but often lack the experience and context what to innovate for. We like to make cool stuff for cool stuff's sake. Our supervisors, on the other hand, might lack some technical skills, but they know much better what problems need to be addressed.
And that's a wrap. This was one week of work, and it was fun for me to write. If you found it insightful and/or fun to read, please feel free to drop me a note, and I might do this again in the future (maybe during a conference or workshop).
Once a month, I get a special email from the National Oceanic and Atmospheric Administration's Office of Communication in Washington, D.C. As a federal agency, part of NOAA's job is to provide environmental information to the public. To do that, NOAA has data portals, like ncei.noaa.gov, and issues forecasts, warnings, advisories, and general press releases, for example through its website www.noaa.gov and via email, which are often published by media outlets. NOAA also holds press calls with their own scientists for journalists to learn about and discuss current topics that relate to weather and climate. And that's what this special email was about. This morning was such a press call, and I attended as listener.
Today's call discussed the recent heat wave in Europe (June set a new record there as the hottest recorded June ever, surpassing the previous record by almost 1ºC or 1.8ºF) and conditions and outlook in the U.S. For me as a researcher and science blogger, these calls are really interesting. They're a good way to learn about current developments in the fields of weather and climate, and they're great to experience how scientists and journalists interact, what vocabulary, jargon, and graphics are common, to track what information makes it into the news, and to understand the news cycle in general.
Clear graphics are essential, and ahead of time NOAA publishes a PDF with presentation slides that their researchers discuss on the call. These figures could end up in Tweets or Facebook posts (with citation of course), so communicating a clear message is key. Short summary statements in the slides can also get copied into tweets, or make headlines or highlights in an article. Here are some examples:
But good graphics are only half the story. Answering essential questions with confidence and good language is also key in these calls. What, where, when, why, who cares, and so on, without diving into methodological details or listing all the limitations of the results (although, touching on them might not be a bad idea).
Being too much of a scientist (i.e., going on and on about minute details and using too much jargon) you could run the risk of "losing" a person, or even worse, make them misunderstand and misinterpret important information, and then unknowingly misinform millions of people. One of the journalists on the call, Seth Borenstein, writes for the Associated Press, and his reporting from today (all accurate by what I could tell) was published by the Washington Post, several regional news organizations in the U.S., and even an Italian news website.
After 45 minutes, I got back to my own research, made some more progress, and at the end of the day (literally, 5.45 pm) finally got some graphic results myself. They're not quite ready for sharing, yet, but they generally suggest that in the next decades, drought years similar to 2011 or 2012 could occur much more frequently, with extremely dry or wet years representing up to five years per decade in the Southern Plains, especially after the 2050s, and one to two years in the Northern Plains.
I still have a lot more analysis ahead of me, most importantly to understand what this means for ranchers, but this is a very important and valuable step.
Here are Friday, Monday, Tuesday, and Wednesday. And if you wonder what this about, here is the intro blog post.
Getting my script from yesterday to work almost went into day three. Almost. At 5.17 pm, after writing, testing, and debugging 983 lines of code, all errors and typos were corrected, and the script worked like a charm. Sadly, there is not muchto show, yet (also because I have to double-check the results). So in place of graphs and maps, this is what my two monitors looked like for most of today and yesterday.
The one the left has Safari running R Studio, which I use to write code and manage files. The one on the right has three blue Terminal windows up and a data viewing app. Terminal is a Mac OS tool that emulates a command-line interface, which I use to tell the computer which files to run and to see some results. The fourth window, the data viewer, is Panoply, a very handy app by NASA to view NetCDF data, the type of data I work with. It comes in handy when I check results.
Despite the looks, my computer doesn't actually do much work. All of my commands and every line of code are sent 360 miles north to the South Central Climate Adaptation Science Center at the University of Oklahoma. There, all our data and most of my code is stored on a data server. Folks there also have NCL and several other programming languages, which makes our life here a lot easier.
A good day always becomes better when it ends well. On the third Wednesday of every month, the Texas A&M Postdoc Association organizes a "networking event," a fun hangout with fellow postdocs to chat over snacks and drinks at a bar or restaurant in town, courtesy of the organization. It's a good way to meet peers and make friends, to bring up workplace problems, or just to talk nerdy to fellow nerds. I always enjoy these gatherings! However, today I had to pass it up for something even better – a dinner at an Italian restaurant with my wife.
Here are Thursday, Monday, and Tuesday. And if you wonder what this about, here is the intro blog post.
A quick note about the heatwave that is bound to hit many parts of the U.S. starting Thursday. If you live between Oklahoma and New England, it will get much warmer than usual in the next few days. Please avoid staying outside for long periods of time, and if you do, make sure you take a water bottle, put on a cap, and try to stay out of direct sunlight.
If you want to know how climate change is changing the odds for heatwaves like this, check out yesterday's episode of the "Got Science" podcast by the Union of Concerned Scientists.
Every Tuesday and Thursday morning, I join a group of colleagues for what we coined "The Write Stuff," a writing group. We meet down the hall from my office in a conference room with large windows that overlook a beautiful garden, and spend two hours not chatting (mostly), but working on whatever everyone is working on. It sounds banal, but it's incredibly effective. The peer pressure of "Everyone else is working!" really makes you push through and avoid distractions. Also, the room is a pleasant change from my windowless office situation.
My goal for today was to write a script that analyzed rainfall projections for the next 80 years (2020 to 2099) and find years with extremely high and extremely low amounts of rainfall in the Great Plains. We found previously that rainfall variability is very different across the Great Plains. For example, in the Southern Plains, annual rainfall can vary a lot more than in the Northern Plains. Now we were interested in whether these super dry and super wet years will occur more or less often in the future. Specifically, we wanted to see if the frequency of years and the number of consecutive years with rainfall of more than 20 percent above and below the decadal average will change over time.
Two assumptions were important for us going into this. We assumed that ranchers are used to some degree of year-to-year variability - they could handle some wet and dry years. But the really bad ones, especially when several occur back to back, like the droughts around 2012 or in the late 1980s, would be a real challenge. Because of that, we chose a threshold of 20 percent above or below the decadal average. It's an arbitrary value, really, and we'll change it once we find a more meaningful number that's based on previous studies. But for now we'll run with it. We also used an average that will change every decade, instead of one that runs from 2020 through 2099. Why? Because of our second thought: whichever way things will change, even if it continues to get drier, ranchers will find ways to adapt to a new normal over time, just like they have in the past. Using something like a running average will account for this.
Now, of course a drought is more severe the hotter it is. So looking at rainfall without temperature will only give us half the picture. But for now, it is the first step to understand the effect of climate change (and climate variability) on ranching. We will repeat this process with temperature, and eventually with a suitable drought index that combines temperature and rainfall.
Once we had this figured out, the real challenge was writing the script, telling the computer what to do. I am using a programming language called NCL, which stands for NCAR Command Language (and NCAR is short for National Center for Atmospheric Research in Boulder, Colorado – we really know how to make things complicated). NCL is a programming language specifically for climate and model data, and widely used around the world. I had written a script a few weeks ago that did something very similar, only with monthly data that were compared to rainfall observations, and three thresholds, 10, 20, and 40 percent, instead of one. So this should be similar, but simpler. It's always easier to have a foundation to work from than to start from scratch, because some parts stay almost always the same. Most scripts have three parts: (1) open a data file and extract the necessary variables, (2) process the data in a certain way, and (3) save the result in a new data file. Some scripts create graphs or maps instead of data files, or in addition to it.
Writing a script is like giving a blind person directions for where to go. The computer knows basic operating rules, but it doesn't know what you want to do. It knows pre-defined commands that are hard-wired into the language, like how to open one or more files and how to add or subtract, and basic rules, for example don't divide by zero. But from there it is up to the programmer to make sure the computer does what it should. The smallest typo, a comma instead of a period or a space where there shouldn't be one, can crash the program, or worse, give out incorrect results without anyone noticing. I went step by step, wrote some code, ran it, checked the result, debugged if necessary, ran it again, checked again, wrote some more, ran it, checked it, and so on, until eventually, the script was complete.
Or rather, will be complete. This process can take all day, or longer. After 687 lines of code and eight hours of staring at a screen, I was square-eyed and decided to call it a day and unwind on a long run.
Tomorrow is another day.
Here are Wednesday and Monday, and if you wonder what this about, here is the intro blog post.
The nice thing about working at a big university is there is always something happening, even during summer break.
This morning, a professor from Ethiopia, Dr. Seifu Tilahun, who was visiting the college, gave a seminar about irrigation challenges in Ethiopia at the Borlaug Institute, one building over from my office. It's always fascinating to learn about research in other countries, and to discuss common ideas and challenges. Similar to the U.S., water quality in agricultural areas is a big problem in Ethiopia. Heavy rainfall washes crop fertilizer into streams and lakes and pollutes the groundwater, often the only source of drinking water, especially in rural areas. Practices that can reduce erosion and over-fertilizing, like no-till farming, are known and available, but adoption in the real world takes a long time, no matter how much they make sense to ecologists and economists.
Back in my office, I had to sort out two things before I get started with coding. The first involved paperwork and some physical exercise, two things I got used to quickly in a large department like mine. I needed to register for a coding workshop on campus in August, but I didn't know how pay for it. Most faculty have work credits cards, but for some reason I didn't. So, a quick walk to our business admins in another building, and a few signatures later I had a temporary credit card and could register for the workshop (sadly, I'll have to return it tomorrow).
The second involved Dr. Cait Rottler, a fellow postdoc in Oklahoma with the Agricultural Research Service, the research branch of the U.S. Department of Agriculture. Cait and I are planning a science communication workshop at a rangeland conference next February in Colorado. Good communication is important in research, so that scientists from different fields understand each other and work well together. Cait and I both work in climate change adaptation and know what a challenge this is. More than probably most areas, climate change is one where collaboration between disciplines is key to get things done, from engineering to social sciences to economics and ecology. And our workshop will help with that – or at least that's what we think. Today was the deadline to submit proposals – and with most things that have a deadline, we submitted it in time, but only just :-) We should find out in September if our workshop concept got accepted.
Eventually, after a late lunch and much later than I had hoped, I sat down to work on my data analysis. I only had about three hours before my day was over, which isn't enough to start coding. But that was enough time sketch things out and get started. It's important to know the bigger picture and to develop smaller goals, before starting to write the code to get there. That's what I did today, and tomorrow I will home in on this more.
Here is Tuesday. Wonder what this is about? Here's the intro blog post.
High school students on Skype a Scientist have asked me a few times what an average day looks like for me. But it's hard to find an "average" day, so what I'll do instead is describe an average week. This week should fit pretty nicely.
If you read my About Me page or my Research page, you know I study how climate change affects cattle production in the U.S. Great Plains, a large agricultural region in the central US, between Canada, Mexico and the Gulf Coast, the Rocky Mountains, and the Mississippi. I study data to understand how future changes in temperature and rainfall affect where and how well natural vegetation grows in the Great Plains. Because we're concerned about ranching, I'm not so much interested in shrubland and forests, but mostly grassland, which ranchers use as feed for their cattle.
I'm mostly a data analyst, and our first goal is to publish our work in scientific journals so other researchers can read about it and use it in their own work. These journals are like newspapers for scientists, except they're much harder to understand. We also go to scientific conferences a few times a year to present our work, meet colleagues, and learn about their research (and we get to see some fascinating places, too). We just finished working on two papers, about how past droughts have affected cow numbers in the Great Plains and how grasslands in the Great Plains will change in the future, and we submitted them to two journals for review. It'll probably take a month or so until we hear back from them.
As one of the leaders in our project, I am also interested in the bigger picture, and I am responsible to determine what to do next. Last week, after we finished our second paper, my postdoctoral advisor and I brainstormed about what to do next.
One thing we are trying to understand is what our projections mean for ranchers and their operations. Our data are really just millions of numbers, for every year from 2020 to 2099 arranged in a raster grid across the Great Plains. They can be really abstract if you don't organize them in a smart way.
That's what I'll do this week, plus dealing with smaller things that occur each day.