There’s a reason no one can agree about climate change, and on some level, it’s the same reason you curse the gods on rainy days when the forecast said sunny.
Meteorology and climate science, in general, are complicated and speculative — reliant on mathematical models that aren’t always fully accurate to paint pictures of yesterday, today, and tomorrow.
When the weather is wrong
Take how we come up with forecasts, for example. Equations for forecast models include wind speed, temperature, geographical features and more to outline the probability of future weather conditions. And as we know, predicting the future is hard.
As it stands, neither the models themselves, nor the data input, are ever perfect — and so there is always a level of uncertainty in the results.
If we can’t trust meteorologists to get the tomorrow’s weather right, how can we trust their predictions for the next decade? Furthermore, how can we believe models regarding past climate conditions and their current trajectory?
Skepticism in climate science[contextly_auto_sidebar id=”PU050xKfnKPXFzJjFKWwtjZ2kSK3n7ti”]The term “skeptic” can be used in various ways. The skeptic community, and data skeptics in particular, acknowledge that certain knowledge is impossible; they approach numbers and information, especially when stated as fact, with an inquisitive attitude.
“Climate skeptics,” or those that disagree that the Earth is warming due to human activity (or at all), don’t inherently fit into this category. In fact, many of such people believe data selectively along ideological lines, rather than looking at it objectively and deriving conclusions.
To be fair, those that blindly believe climate change (or anything for that matter) just because MSNBC tells them to, without doing due diligence, are just as bad.
The point is, skeptics of all belief systems should inform themselves to the fullest extent; they should examine data from every angle in attempt to get as close as possible to a rational truth.
The truth about climate models
Just like the forecast will never be 100 percent correct, models and equations cannot be either.
So when NASA stated, for example, that 2014 was the hottest year on record, this naturally perked up skeptics’ ears. In fact, NASA was only 38 to 48 percent certain of this; there was also a 23 percent chance the hottest year was 2010, a 17 percent chance it was 2005, and so forth.
All statistical proclamations have margins of error — including them in headlines just doesn’t make for as snappy clickbait. Margins of error may take away from the punch, but in context, the implication is still sound.
Because even if 2014 were not the warmest year on record, mountains of data still indicate that it probably was — and even if it wasn’t, the warmest years since 1880 still all fall within last decade, in step with the global warming trend.
Though better than its ever been – and improving every year – it’s true that data and the conclusions drawn from it are imperfect. But generally the more information you have from different sources using different methods, the more accurate your reading is.
That is to say that when a majority of models produce similar conclusions, you can be fairly sure of a trend or pattern, if not its exact details.
For example, both NASA and the NOAA identify near-identical warming patterns, lending the trend even more credibility:
And for another example, when rising CO2 levels correspond with rising temperatures in the atmosphere, the skeptic can deduce a relationship between differing datasets:
It could be a fluke, of course, if it didn’t have concrete backup: a warming trend hasn’t just been indicated by ground temperature measurements, but by weather balloons, satellite measurements, ocean temperatures and more. And this is data not just from NASA, the NOAA, but the IPCC, hundreds of scientists and thousands of expert reviewers. It’s all these factors more that form a consensus.
Though correlation does not equal causation, as we know, add into the mix reams of peer-reviewed research, and the the lack of a model that can replicate warming without greenhouse gas – the skeptic can draw their own conclusions.
If it’s June 5, scientists can guess with pretty good certainty what tomorrow will be like based off complex models. It’s definitely not going to snow.
However, it still could rain, and next year could be cooler than this year. But data skeptics need to look at the larger picture: your weatherman probably isn’t a buffoon, after all, and NASA scientists certainly are not. If then, a skeptic still chooses not to trust scientists or their iPhone apps, that’s in their right. It might even be in their interest.
But there’s a far cry from denial and skepticism, and the latter is a lot more becoming.