HNY: The Simple Challenge to Climate Change Skeptics

A new year, a new chance to beat a horse to death and back again.

In my interactions during 2017 – on social media and elsewhere – the most common argument I’ve heard from CC skeptics was of the following form:

We simply don’t know enough about Climate Change – yet. Our model is deficient. We could be missing something – something big – that would drastically alter the predictive power of the model. What if we’re wrong?

If this is you, I’d challenge you to pinpoint, specifically, where you think the model would be acceptable to you and WHY you think the addition of this data would have an impact. If you can’t identify that point and why your addition is critical to the model’s soundness, I’d suggest you’re not approaching the issue with any kind of intellectual honesty.

Our general options here are as follows. We either:

(1) Wait for the model to account for all possible factors
(2) Make a judgement call on the data we do have to date
(3) Hope for the best, whatever the model says

(1) Is not feasible, simply because at this point, the model will no longer be a model, but a 1:1 simulation of the Earth, which we will never (theoretically) have the computer resources to construct.

(2) The most reasonable solution, seeing as the data we do have clearly indicates the Earth is warming at an alarming rate due, in large part, to human civilization. You’ll notice how the counter-narrative has shifted – and perhaps you’ve changed your views similarly – from “global warming isn’t real” to “global warming IS real but humanity’s role is unclear,” to “it’s not clear to what extent global warming will impact us.”

While these are all wrapped in the guise of skepticism, it’s a clear instance of moving the goalposts.

(3) Is, it seems, where we’re going to wind up. But don’t look at me when your ancestors are drinking fruity drinks on a Kansas beach.

Scientific skepticism is generated from within a community of scientists, not from public opinion. Your skepticism about a model is useless without an understanding of the data. No one is preventing you from learning the science or the math – so do it. See for yourself what’s real and what isn’t.

But for all of our sakes. Be honest.

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