In the words of the falsetto frontman of The Four Seasons’ Frankie Valli, Oh what a night! I flew in to Las Vegas a day before the commencement of a conference on climate change, with a whole evening to be irresponsible and unaccountable. However, morning came early – it was 6 a.m. and I needed to get going in order to hear the early morning talks from such environmental celebrities as the founder of Greenpeace and the Weather Channel, meteorologists, space scientists, government and academic scientists and researchers, among others. I stumbled into the conference room and, not feeling very chatty, chose an empty table. I was sitting, quietly sipping my cappuccino, until a distinguished gentlemen sat down next to me – I guess I looked like I needed to talk to someone.
I offered a good morning greeting and he told me his name – which I should have known since I had just purchased his book a couple of weeks ago, though it was gathering dust on a side table until I could find time to read it. He was a NASA astronaut and would be speaking later in the day, and I just happened to be staring at his portrait in the brochure in front of me. Oh what a night! Anyway, I verbally fumbled around and got to the question of what inspired him to be an astronaut and what, in his view, was his particular skillset that made him successful. I have to paraphrase but essentially he was an engineer and specialized in distinguishing good data from bad data – or, in other words, what data can one rely on and what data one cannot. Slowly feeling more in the present then I did a little while ago, I found this response to be profound.
Follow the Data
I was attending this climate change conference to enhance my knowledge base as to what we know and what we do not know, and I am a glutton for a good and robust debate. Climate change science is a complicated topic and certainly no one would argue that it isn’t complex and multi-disciplinary in nature. As a geologist, I recognize the obvious fact that climate changes – that is what it does and when it stops changing, I think we really will have something to debate about. It is so multi-faceted that I simply have not been convinced that the data is clear enough to draw a conclusion as to what the climate is actually changing to and whether there is a man-induced component to climate change – I put my unconscious bias aside and I remain open-minded.
That being said, how does one determine what good data is and what bad data is when the science is multidisciplinary, multifaceted and complex? One cannot overemphasize the importance of understanding the data – how it was generated, where it came from, how it was (for lack of a better phrase) manipulated and massaged, and whether the conclusions drawn are supported by the data or if is there overreach.
I like the climate debate because it exemplifies how we as a society and science community evaluate good data from bad. It is a challenging effort to get one’s arms around the data, albeit, this particular skillset applies to all scientific endeavours.
My interest started to emerge in November 2009 with what was referred to as “Climategate.” Emails were hacked and claims of data manipulation were exchanged. Climategate was not helpful to the overall discussion and caused doubt about what was good data versus bad. It was also not helpful that everything under the sun, and everything under the sun for years to come, is also a result of man-induced climate change. In a recent issue of a prestigious science journal I came across an article that grabbed my attention: one on yellow warblers’ adaptation to climate change which started out “Human-induced climate change is causing rapidly changing global temperatures and extreme fluctuations in precipitation.” What a beginning to a paper on yellow warblers. With all the published information on climate change, a reference or two would have been nice. Statements like this, in my view are not helpful, and exemplifies unconscious bias and does a great disservice to both the author who, as I read on, seemed to be quite the expert on yellow warblers, but was not convincing in understanding the nuances of the climate change debate.
Data quality recognition skills are essential in the geosciences as they are in any science, and certainly in the climate change debate. To be of high quality, data needs to fit its intended uses in operations, decision making and planning. In other words, it needs to be useful, consistent and unambiguous. Issues with data quality often arise when a database from one line of research is merged with another. In such cases, the databases may not be compatible, and thus require what is called “data cleansing.” Yes, one has to cleanse the data to improve its quality (like with a cloth!).
A Good Place to Start
Our ability to get cozy and sieve through large databases and distinguish good data from bad data is imperative to any scientific discussion and determines whether we will be successful in our ultimate scientific pursuits. Unfortunately, although I believe the general public loves science and is enthralled by it, the public’s views and perceptions vary greatly. I believe it is a reflection of the level of understanding and appreciation of the scientific method, education, political party (this sounds so good that it deserves repeating - political party) and professional pressures, among other factors, including distinguishing good data from bad data.
I am a stout supporter of increasing funding for research to address climate change issues and concerns regardless of which way the pendulum swings. As I continue to reminisce about my night out (at least what I could recollect about my night out), and as I watch the two-inch ice cube in my Old Fashioned slowly melt, I think about where I can go to validate data – I am not ready to conclude whether man-induced climate change is occurring or not. As I listen to certain celebrities, politicians, scientists, reporters (I am being gracious not to name names) … where do I go to enhance my understanding of climate change? I continue to work toward trying to differentiate between good data and bad data – good data versus fake data – and ponder what actually is going on with climate change. But in the meantime, I spend more time trying to validate the data and separate good data from bad. I am still in the data validation stage.
I am not sure where we go from here, but I think a good place to start is talking more about the data and less about the urgency and using inflammatory rhetoric. We as environmental scientists have a long way to go in educating ourselves as scientists and the public at large.