What’s happening with the models of the Atlantic Meridional Overturning Circulation?

John “not Towering Inferno” Williams points to this new research article by Valentin Portmann et al., which states:

Climate models show considerable discrepancies in their future projections around the Atlantic, mainly due to uncertainties in the fate of the Atlantic Meridional Overturning Circulation (AMOC). Climate models suggest a reduction in AMOC strength of 32 ± 37% by 2100 (90% probability) . . . To refine this estimate and reduce its uncertainty, we use four different observational constraint methods. The best one, which provides the lowest leave-one-out error, integrates a large set of observable variables . . . It gives an estimate of the AMOC slowdown of 51 ± 8% (90% probability) . . .

Without knowing any of the details, this looks wrong to me. There’s a lot of uncertainty about AMOC, right? So how to you get to a slowdown of 51 +/- 8%? That just seems unrealistically precise.

The abstract continues:

This refinement mainly results from correcting a bias in South Atlantic surface salinity, consistent with recent studies emphasizing its role in the proximity to an AMOC tipping point.

OK, that’s fine, but then the key issue is not the statistical method for model averaging, it’s this particular model correction.

The paper’s kind of hard for me to read, paradoxically because it’s all about statistics. I’m reminded of something I heard back when I was a Ph.D. student, which is that the best statistical methods are invisible: the ideal is for applied researchers to be talking about the science, not about the statistics. That’s one good thing about Bayesian methods. Rather than arguing about the estimator, you’re arguing about the generative model (the data model and the prior distribution), which puts you in the realm of the science. I’d much rather have researchers talking about plausible values for parameters and predictions than talking about significance thresholds and rejection rates.

That said, the most important thing about a statistical method is not what it does with the data but rather what data it uses. So if the methods promoted in the paper under discussion allow the incorporation of additional information, that’s good.

According to the article:

Methods called emergent or observational constraint (OC) have been developed to reduce the model uncertainty of a future climate variable of interest, hereafter called projected variable. These methods constrain the estimate and model uncertainty of the projected variable using the real-world observations of one or more observable variables. This results in a constrained model uncertainty that is smaller than unconstrained one.

I see this and I’m like, Huh? They weren’t doing this already? If you have “real-world observations of one or more observable variables” that are relevant to your predictions, then, yeah, you should use this information.

Although I’m not really clear on what is meant by “real-world observations of one or more observable variables.” Isn’t that just the same as “observations”? If you’ve observed something, it’s in the real world, right? And anything you’ve observed is, by definition, an “observable variable,” right? As Bob would say, there are some difficulties of communication here.

As I said, I don’t know anything about the models or the data here. As a human, I’m concerned about the potential catastrophic effects of the decline of AMOC, and I guess that I’d go with the consensus forecasts and uncertainties. If it’s really true that there are important observational data not included in the standard approach, then I hope someone can write a paper explaining that directly.