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Comment by tony_cannistra | Hacker News Reader

Good article. One of the chapters of my dissertation focused on a small portion of this – I used remotely-sensed measurements of ocean surface temperature to drive mechanistic models of phytoplankton physiology to get a bit more detail on the effect that marine "heatwaves" have on their growth rates. I was able to validate this approach using another remotely-sensed dataset of ocean chlorophyll, which we'd expect to be linked to phytoplankton growth rates.

Besides being some interesting science, it was also an amazing testament to open data and high-quality scientific publishing. I was able to pull all of the ocean data from government sources for free, and leverage physiological model parameters published in open-access journals. And I was able to run the whole thing using Python/Dask on a VM in Google Cloud.

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One thing that modelling seems to be missing are the lag times and genetics waiting to flourish that can allow an uptick in a higher concentration of that variation that can better adapt-take advantage of a new/changing environment, e.g. increased CO2 in atmosphere will lead to more greenery, more hardy plant life, even if it takes X years for those new plants to start to, potentially on an exponential path, take advantage of the higher CO2 and capture it.

That's actually only partially true; there's quite a bit of work that attempts to capture this idea, particularly in taxa that have relatively well-understood adaptive dynamics. Here's a good general example: https://royalsocietypublishing.org/doi/full/10.1098/rspb.201...

You are correct that much of these dynamics aren't captured in long-time-scale climate models, though. But it's being modeled quite frequently in different contexts, especially ecological ones.

And then there's the unknown of what genetics might show up down the line once increased CO2 being present starts to be selected for; including I doubt taking into account externalized factors that may occur in larger steps, like once there's more plant life to capture CO2, then animals will become larger with more food (plant and other animals), which then captures-stores more CO2, etc.

Is it even remotely possible to accurately extrapolate to what the end points all may look like? It's highly likely it's not unless you're monitoring "everything" for 100s or 1000s of years to see just how far cycles will propagate.

> then animals will become larger with more food

But see the "temperature size rule".

There's too much uncertainty in most aspects of a climate forecast that far out for it to be a meaningful exercise. The same way long term climate average is a more accurate forecasts than attempting to use a weather model two weeks out.

As with many real-world modeling exercises, it's perhaps a fool's errand to attempt to capture the entire space of possibility, unless the pure notion of capturing the domain is what's interesting.

In reality there are a number of practical constraints that place limits on the search space for these kinds of models, including ones that are likely genuinely decoupled from CO2 concentrations (think of things like mutation rates).

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