[HN Gopher] Oceans are hugely complex: modelling marine microbes...
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Oceans are hugely complex: modelling marine microbes is key to
climate forecasts
Author : rntn
Score : 48 points
Date : 2023-11-06 14:05 UTC (6 hours ago)
(HTM) web link (www.nature.com)
(TXT) w3m dump (www.nature.com)
| argiopetech wrote:
| So, we'll save the planet by forecasting its doom by building
| hugely complex models which run on massive super computers which
| use insane quantities of energy.
|
| I'm sure I want this to happen, but it's on the level of "100
| private jets fly to a climate conference" in terms of optics.
| pvaldes wrote:
| Is this way or never. Choose one.
|
| Some people still wants to believe that we will be able to
| solve some of the most hairy problems of the human history
| without spending too much resources and that it will be cheap.
| I don't think so.
| tony_cannistra wrote:
| 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.
| loceng wrote:
| 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.
| tony_cannistra wrote:
| 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.
| loceng wrote:
| 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.
| kuhewa wrote:
| > 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.
| tony_cannistra wrote:
| 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).
| krisoft wrote:
| For a sci-fi short story I was thinking how a briefcase size
| solution to climate change could look like. In the story humanity
| would buy the "solution" to climate change from an alien high-
| tech travelling salesman.
|
| What I ended up is that it could be a small vial of some bio-
| engineered marine microorganism designed to bloom on every ocean
| surface, capture sunlight and use and use the energy to capture
| CO2 in some chemically stable molecule and then sink to the
| depths. And most of the briefcase would be actually filled with a
| manual describing how humanity can modulate the intensity of
| capture to the right level with bio-engineered viruses which
| predate on the artificial microorganism.
| klyrs wrote:
| Let the chemically stable molecule be teflon, and you've got
| yourself a sequel.
| krisoft wrote:
| Can't unfortunately. Where would the microbe get the massive
| amount of fluoride required?
| klyrs wrote:
| Likewise, where does the oxygen go? Oh well.
| photochemsyn wrote:
| Modelling is certainly going to be a bit cheaper than collecting
| comprehensive datasets from around the planet's ocean zones
| throughout the upper ~500m where most biological activity takes
| place, but without real data inputs, models tend to diverge from
| reality. See for example the Hawaiian Ocean Time Series project
| (running for ~25 years now, but just at one location, and still
| quite expensive):
|
| https://en.wikipedia.org/wiki/Hawaii_Ocean_Time-series
|
| > "The 25 year record of ocean carbon measurements at Station
| ALOHA document that the partial pressure of CO2 (pCO2) in the
| mixed layer is increasing at a rate slightly greater than the
| trend observed in the atmosphere. This has been accompanied by
| progressive decreases in seawater pH."
|
| Thus, scientific funding needs to be split at least equally
| between real-world data collection and computer modelling if we
| want to understand what's going on. The denialist crowd would
| rather not know, of course, which is the equivalent of trying to
| avoid getting cancer by never going to see a doctor.
| nvm0n2 wrote:
| When will climatologists give up and just go all-in on pure DL.
| Their current modelling approach is a dead end and correctly
| drives a lot of climate(ology) skepticism.
|
| The current approach is to try and simulate everything from
| physical first principles. They started in the 70s with just the
| atmosphere, later added the oceans. The problem they have is that
| despite many claims to the contrary, their models have
| historically not worked and not generated accurate predictions.
| There are numerous examples of this like the famous "running hot"
| problem [0], or that since the millenium west Antarctica has
| cooled rather than warmed, and that no models predicted this [1].
| Lack of detail around clouds are sometimes blamed. Climatologists
| responded by adding ever more detail to the models, in the hope
| that with bigger programs the predictions will come good and the
| (enormous) CIs will shrink.
|
| That unfortunately didn't happen. Instead as models became more
| complex they diverged from each other. We are told the science is
| settled and only the edges need to be tied up, but in reality the
| science is getting less settled over time as the simulations get
| supposedly more accurate. It's not possible for all of them to be
| right but the literature (and IPCC) often just average all the
| models together, as if they are all equally reliable.
|
| Now we are told that figuring out clouds (still very far from
| done) isn't enough, and marine microbes are also critical. Where
| does this end? We can't simulate the full complexity of Earth.
| And if more detail = less confidence continues to hold, adding
| this will just make the models even less useful.
|
| On top of all that, the models are full of numerical stability
| bugs. NASA Model E is nearly half a million lines of FORTRAN. If
| you tell it to simulate a stable pre-industrial climate many runs
| end up catastrophically diverging to a snowball or a climate
| hotter than Venus [2]. Very often these bugs are papered over
| with hacks rather than root caused and fixed. For example, it has
| a comment saying _" this routine makes sure that the temperature
| remains within reasonable bounds during the initialization
| process (sometimes the computed temperature iterated out in left
| field someplace, way outside any reasonable range)"_. Then they
| do the same for wind speeds, just using min(max(...)) to
| constrain that part of the simulation when it enters a feedback
| loop and would otherwise crater the sim. There are dozens of
| other serious errors that are patched up at the end with hacks.
| And then the model has hundreds of tunable free parameters that
| make it easy to overfit, some of which are explicitly computed
| backwards like humidity thresholds to ensure that at least some
| runs remain stable, instead of being set to their actual physical
| values.
|
| The field needs to reset its approach. The AI field has dropped
| attempting to model intelligence from first principles because it
| didn't work, and embraced DL methods that can model any function
| given enough data. Google has been training and using neural nets
| to do short range weather forecasts. Neural climate models may
| not yield more accurate results (who says there's enough data)
| but at least there's a base of relatively solid tooling and
| methodologies for training, detecting overfitting, validation etc
| and the implementations are written by pro-level developers.
|
| [0]
| https://www.nature.com/articles/d41586-022-01192-2?error=coo...
|
| [1]
| https://journals.ametsoc.org/view/journals/bams/104/6/BAMS-D...
|
| [2]
| https://www.netzerowatch.com/content/uploads/2023/07/Eschenb...
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