geoalchimista

But it offers you one more language to communicate with people. Isn't that enough?

> Researchers want to be in specific locations because of their prestige. However, when all US-funded research is also available outside that location, the walled garden of prestige becomes rather porous. Especially since the reviewers typically aren't paid either.

You are assuming researchers are saintly figures dwelling in a vacuum who don't need to constantly prove to their department head or promotion evaluation committee of their worth. That is not the case. The walled gardens are desirable for some because their social functions are not easily replaceable.

One way to decouple the evaluation of scientific output from the walled garden is simply to stop using them as a gate-keeper in making hiring and research grant distribution decisions. But apart from the constant lip service, there is no momentum in doing anything concrete about this in academia.

> It’s mostly grunt work and whatever technical skills can be learned by a high school student over a summer.

Who do you think is supervising the high school student? Where does the idea for the project come from? Where does the money supporting the high school intern' experiments come from?

It isn't remotely true. Corals have persisted through the geological past when atmospheric CO2 was many times today's level. It could be true that _some_ coral species would go extinct, but corals as a class are likely to persist.

Ugh, smells like crank. Someone discovered a climatology 101 fact and then made a big fuss about it. Just ... read an introductory textbook on climate science please. It's basically all speculation disguised as a "case study". The authors should show some numbers if they are really serious about their claim, for example, how much increase in marine evaporation can be expected from an idealized sterile ocean.

I think it depends on whether you use it for operations or for data analysis. Speed is only one concern and it may not always be the most relevant concern.

A statistician/data scientist wrangling data and making plots would not have cared whether loading a CSV file takes one second or one microsecond, because they may only do it a handful of times for a project.

A data engineer has different requirements and expectations. They may need to implement an operational component that process CSV files repeatedly for billions of time a day.

If your use case is the latter, then pandas is probably not for you.

I don't think you fully appreciate that what you talk about are several different aspects of the climate system and they are true at the same time. Which one is the more salient factor at play depends on the time scale you look at.

> fossil-fuel-sourced CO2 and CH4 are steadily warming the planet

No climate scientist denies this. It is the trend happening since the industrial revolution. But say, if you have three months or three years of data, you are not gonna be able to fit a statistically robust trend. At best, you can do some Bayesian attribution of the likelihood that a climate event is attributable to the long-term climate change effect.

> Many of the supposed discoveries of periodic oscillations in the North Atlantic and the Pacific simply don't hold up over time, and as with ENSO, have zero predictive power over what phase/amplitude of the next 'period of the oscillation' will be.

You are jumping into conclusions too soon. The fact that a system has periodicity does not necessarily mean that it is perfectly deterministic. You may not be able to predict when the next El Niño is gonna happen, but you can say with confidence how many El Niño events you will likely see in a 100-year time span.

> These phenomena are likely mostly chaotic in nature

Again, being chaotic does not mean no predictability at all. The atmosphere and the ocean do follow the basic laws governing fluid dynamics. How much predictability you have very much depends on the spatial and temporal scale you are looking at. Being able to predict one event and being able to predict a statistical distribution or a power spectrum are two different things.

The title seems to imply that this problem does not exist in languages not using Latin scripts. That's not true. Take Japanese for an example, when you have hiragana, katakana, and kanji to express the same idea (and not to mention a kanji typically have more than one way of pronouncing it), the problem is gonna be orders of magnitude more complex.

All statistical inferences are estimates, and carry standard errors with them. What is your point?

With hindsight, yes. It is difficult to predict which field/researcher may become the 10x and which may not. Even an expert panel may have difficulty in judging the prospect of completely novel research. Giving funds only to 10x researchers is akin to investing in stocks that only go up; it sounds appealing, but in reality, how could a funder like NIH or NSF tell who is the 10x?

Most people pursuing a degree in physics would be more interested in core physics problems (quantum computing, particle physics, cosmology, etc.) than in climate change. Perhaps it is more difficult to motivate physics majors to turn to climate science than to teach climate scientists physics (since only a subset of physics is needed, mostly fluid dynamics). Otherwise, universities would have climate science in a physics department.

Some journals have a proofreading stage before publication. But proofreading can't save bad writing. I blame the overemphasis on "critical thinking" in modern liberal arts education at the expense of fundamental writing skills and thinking in clear terms.

The worse thing is to let academia control conference funding.