An Unexpected Friendship with Numbers, and Why They Aren’t So Boring After All
This year’s Climate and Society class is out in the field (or lab or office) completing a summer internship or thesis. They’ll be documenting their experiences one blog post at a time. Read on to see what they’re up to.
By Sarah Lynagh, C+S ’16
I have a confession to make.
I do not like data. Even the word “data” makes me cringe.
This may not be a run to the press, tell all your neighbors kind of confession, but for someone with a background in meteorology who just spent the past year studying climate science, it probably sounds a bit surprising.
Data is such an elemental component of any science, and atmospheric and climate science are certainly not exceptions. Data allows us to test hypotheses, to document the world around us, and quantify observations. Data is what allows us to say the planet is unequivocally warming, or that it rained 1 inch in Central Park last night or that yes, a certain percentage of U.S. voters actually do support Donald Trump.
Each of these statements seems relatively interesting, or useful at the very least. So what’s wrong with data? What did data ever do to me? The truth is nothing, really. So data, I’d like you to know, “it’s not you, it’s me.”
As an undergraduate studying atmospheric science, I experienced a large push towards research as a career since I was fairly certain that I was not headed towards the path of becoming an operational meteorologist. I spent a summer at the National Weather Center in Norman, Oklahoma testing out my research capabilities and deciding whether or not I enjoyed the modeling, the hours sitting in front of a computer screen running code…and dealing with data. While my research was not explicitly focused on the physical equations that culminate in a model that reproduces, say, the inner workings of a tornadic supercell, I was fully exposed to the process by which this type of research is completed. And I did not like what I saw. It seemed too tedious. Too slow. Too much work for too few or possibly no results. And too little storm chasing to make it all worthwhile.
Data became, for me, the random numbers that were passed along to me in computer programming classes in order to practice certain coding skills. The data, while attached to some physical concept, such as temperature or precipitation, did not hold any real meaning. The codes we created and ran were real, but the applications were not. I felt more like the lab rat than the scientist.
But then along came my interest in climate science and the global impacts of climate change. I instantly cared more about this topic than much of the atmospheric science to which I had been exposed. The impact and the application of the science was right in front of me. Phrases like climate refugees, rising sea levels, species extinction, vector-borne disease, and water insecurity drew me in and gave the science such immediate purpose. The passion that I began to develop in college has only developed more fully over the years, and has culminated in my year at Columbia in the Climate and Society program.
Through my experiences this past year in the C+S program and this summer working at the International Research Institute for Climate and Society (IRI), I have learned to see and even appreciate another side of data. What is simply a list of carbon dioxide concentration measurements from the Mauna Loa Observatory in Hawaii is also the physical evidence for increasing carbon dioxide concentrations in the atmosphere due to the combustion of fossil fuels. This list of numbers is also a record and reminder of the fact that our atmosphere and our earth are very much alive, as the Keeling Curve (the compilation of this data) clearly depicts the “breathing” of the atmosphere, as plants become more abundant and make more productive use of carbon dioxide during certain times of the year. This dataset also sparked much of the initial research on climate change and the impact of increasing carbon dioxide on our planet.
The enormous lists of numbers that we store on elevation, tide levels, glacial ice mass, and temperature combine to tell us where we should and should not develop along the coast. And should we choose to ignore this data right now, then it tells us which of our coastal developments will eventually be underwater whether its 20 years or 100 years from now.
My work this summer at IRI on the Subseasonal-to-Seasonal Prediction Project (S2S) — a project seeking to improve forecast skill in the two-week to two-month range — would be impossible without extensive data and the resultant physical modeling of the atmosphere and oceans. This data excites me. This data means that with some hard work, we will hopefully be able to provide vulnerable communities with advanced flood warnings up to one month in advance, giving them enough time to prepare and prevent the loss of life and property. In this way, data is transformed into something incredibly useful and even lifesaving.
Luckily, this type of transformation is not rare. Observations, measurements, and lists of numbers drive us towards innovation and progress every day. In the climate sector, data is enabling us to plan ahead for rising sea levels and rising global temperatures. It enables us to model the world as it will be in 100 years and prepare for the future. Without data, we could not address issues such as climate refugees, rising sea levels, species extinction, vector-borne disease, or water insecurity, each of which is so important. And this is why I have made my peace with data and even come to embrace it. Data enables society to address the issues that truly matter.
So data, I’ll be seeing you around.