Scivillage.com Casual Discussion Science Forum

And it saved the earth from killer rays.

https://www.google.com/search?q=whatever...3hlLo,st:0

That's because atmospheric science is not about making long-term predictions based on models requiring many assumptions.

All atmospheric predictive models rely on data-refined and constantly updated assumptions, just as climate change models do. It's part of doing real science. But then you haven't a clue about that.

"Ozone hole models rely on a variety of assumptions to project and understand the depletion of the stratospheric ozone layer, particularly over polar regions. These assumptions are crucial for simulating atmospheric chemistry and dynamics and assessing the impact of human activities on ozone levels.

Here are some key assumptions made in ozone hole models:

Emission Scenarios for Ozone-Depleting Substances (ODSs): Models rely on projections of future emissions of ODSs, like CFCs and halons, based on international agreements like the Montreal Protocol, according to the NOAA Chemical Sciences Laboratory. These scenarios consider factors like production levels, existing "banks" of ODSs in old equipment, and atmospheric lifetimes. Uncertainties in these emission scenarios can impact long-term projections of ozone recovery.

Atmospheric Lifetimes of ODSs: Each ODS has a specific atmospheric lifetime, which influences how long it persists in the atmosphere and continues to deplete ozone. These lifetimes are incorporated into models to track the degradation and removal of ODSs over time.

Representations of Chemical and Physical Processes: Models simulate the complex chemical reactions and physical processes that govern ozone formation and destruction in the stratosphere. These include:

Heterogeneous Chemistry on Polar Stratospheric Clouds (PSCs): This is particularly important for polar ozone depletion, as PSCs provide surfaces for chemical reactions that convert unreactive chlorine and bromine into ozone-destroying forms.

Atmospheric Transport and Mixing: Models account for the movement of air masses, including processes like the polar vortex, which can isolate air over polar regions and influence the extent of ozone depletion.

Photodissociation Rates: The rate at which ODSs break down under ultraviolet radiation is a key factor in determining the release of ozone-destroying atoms like chlorine and bromine.

Meteorological Conditions: Stratospheric temperature and dynamics play a crucial role in ozone depletion, especially in the polar regions. Models use observed or projected meteorological data, including temperature, wind patterns (like the polar vortex), and the formation of PSCs, to simulate the conditions conducive to ozone loss.

Natural Variability and External Factors: While human-caused emissions are the primary driver of ozone depletion, natural factors like volcanic eruptions and solar activity can also influence ozone levels. Models may incorporate these factors to provide a more comprehensive picture of ozone trends.

Input Parameters and Uncertainties: Models are sensitive to various input parameters, including reaction rates, boundary conditions, and solar intensities. Uncertainties in these parameters can affect the accuracy of model projections.

In essence, ozone hole models operate on the assumption that the fundamental principles of stratospheric chemistry and dynamics are well understood. They then leverage these principles, coupled with assumptions about future emissions and meteorological conditions, to project the past, present, and future behavior of the ozone layer, says NOAA Chemical Sciences Laboratory. While there are limitations and uncertainties inherent in any modeling approach, the continued refinement and validation of these models against observational data provide confidence in their overall accuracy."

There's a difference between shorter-term assumptions of "well understood" variables and long-term predictions requiring assumptions for many confounding variables.

In climatology, confounding variables are factors that can influence both the climate patterns being studied and the outcome of interest, leading to inaccurate conclusions about cause-and-effect relationships. These variables can mask or exaggerate the true impact of the climate factor being investigated. Understanding and accounting for these confounding variables is crucial for robust climate research and accurate predictions.
- Google AI

It's trivial that the iffier the predictions the less people will trust them.
But you keep pretending you understand, just because both require assumptions. 9_9

Irrelevant. You claimed this:

Quote:atmospheric science is not about making long-term predictions based on models requiring many assumptions.

I then showed how, as in the ozone hole model itself, it does in fact make many assumptions for their predictive models, which become more reliable with more data:

"While there are limitations and uncertainties inherent in any modeling approach, the continued refinement and validation of these models against observational data provide confidence in their overall accuracy."

Hence the irrelevance of your tangent into "confounding variables".

But since you mentioned it, what are these "confounding variables" that make climate change predictive models unreliable? There must be a whole list of them somewhere. Check Ben Shapiro's website..lol

There are actually relatively few assumptions, over shorter time periods, in ozone layer science, compared with climate science.
Too bad I always give the reader the benefit of the doubt. It means you can endlessly whine about splitting hairs in what I say... even if that means completely ignoring every following elaboration I make.

The simple fact is that the more assumptions made, especially over longer time periods, increase the likelihood that more will be confounding. Seems too much for you to understand though. 9_9

Quote:The simple fact is that the more assumptions made, especially over longer time periods, increase the likelihood that more will be confounding. Seems too much for you to understand though.

You can't make predictive models without assumptions. It's the nature of good science to reduce these to the bare minimum required in order to make predictions that are accurate and testable. In this age of AI and computer modeling this science has become more reliable than ever. Generally it's undereducated Trumpers like you with no understanding of this science that become climate change deniers. That's because ignorance always breeds skepticism no matter what the field of research is.

https://sustainability.stanford.edu/news...ak-warming

"For the new study, Diffenbaugh and Barnes trained an AI system to predict how high global temperatures could climb, depending on the pace of decarbonization.

When training the AI, the researchers used temperature and greenhouse gas data from vast archives of climate model simulations. To predict future warming, however, they gave the AI the actual historical temperatures as input, along with several widely used scenarios for future greenhouse gas emissions.

“AI is emerging as an incredibly powerful tool for reducing uncertainty in future projections. It learns from the many climate model simulations that already exist, but its predictions are then further refined by real-world observations,” said Barnes, who is a professor of atmospheric science at Colorado State.

The study adds to a growing body of research indicating that the world has almost certainly missed its chance to achieve the more ambitious goal of the 2015 Paris Climate Agreement, in which nearly 200 nations pledged to keep long-term warming “well below” 2 degrees while pursuing efforts to avoid 1.5 degrees.

A second new paper from Barnes and Diffenbaugh, published Dec. 10 in Environmental Research Letters with co-author Sonia Seneviratne of ETH-Zurich, suggests many regions including South Asia, the Mediterranean, Central Europe, and parts of sub-Saharan Africa will surpass 3 degrees Celsius of warming by 2060 in a scenario in which emissions continue to increase – sooner than anticipated in earlier studies."

Yep, too much for you to understand. I've repeatedly proven you don't understand science. You just quote stuff you don't understand and it makes you feel smart (Dunning-Kruger).

No one said you can make predictions without assumptions (straw man). That doesn't change the fact that long-term climate science predictions require more numerous and more confounding assumptions than ozone layer science. We will have to wait to see if AI predictions pan out any better... ala predictions of 2060.

Quote:That doesn't change the fact that long-term climate science predictions require more numerous and more confounding assumptions than ozone layer science. We will have to wait to see if AI predictions pan out any better... ala predictions of 2060.

Climate change predictions are no longer long term any more than the ozone hole predictions were. We're talking a span of only around 30 to 40 years into the future. In fact global warming is happening right now, with every year being hotter than the last and extreme weather events increasing worldwide. And AI models are confirming all of it. You're running out of excuses to ignore it. Better invest in a powerful AC if you plan to be around in 2060. That is if you aren't living in an underground bunker somewhere with all your other World Order-fearing cretins after the Trump era sputters out of steam..

Your whining is boring. You just keep cherry-picking what I say... and then in the next post forget about that and cherry-pick another part. It's called moving the goalposts, but it just shows how intellectually dishonest, or ignorant, you are. You want to harp on "assumptions," but then if that doesn't work you want to make it about "long term." When I've already told you that climate science requires more numerous and confounding assumptions than ozone layer science and that the longer term predictions further exacerbate confounding variables.

It's like you can't keep two concepts, much less how they interact, in your head at the same time. The sad part is that you're too dumb to ever realize it.