Another specialist in 10 year old charts?I beg to differ.
Great.
Which tipping point do we hit first?
Permafrost?
AMOC?
Glacial melt?
Rising humitidy?
Lowering cloud cover?
Another specialist in 10 year old charts?I beg to differ.
I do know who is not.You think you are respected, buddy?
i likely will never understand why any moral and just person would intentionally mislead children.Again, while its nice that you admit your inability to understand things that doesn't mean that smarter people can't figure it out.
Now that you've admitted you can't understand it you should leave it to the experts.
As for the AI, this is what AI says about its own findings.
Your reliance on AI just shows more ignorance.
AI Overview
AI models frequently produce inconsistent or opposite findings on repeated queries due to their
probabilistic (non-deterministic) nature and design as creative text generators rather than rigid databases. Studies indicate that AI search tools can provide conflicting answers over 60% of the time, often confidently presenting incorrect information.
- Probabilistic "Hallucinations": AI models operate on probability, not true understanding, which can lead to making assumptions, misinterpreting data, or even generating opposite information to the source they are citing.
AI Overview
Modeling the climate is one of the most complex scientific endeavors due to the need to simulate the intricate, non-linear interactions of the Earth's atmosphere, oceans, cryosphere, and biosphere
. Despite advancements, climate models face significant challenges, ranging from computational limitations to gaps in physical understanding.
Key challenges in climate modeling include:
Cloud Representation (The Biggest Uncertainty):
Simulating cloud systems, which can both warm and cool the planet, is a major, long-standing challenge. Clouds are complex, depend on many variables, and often exist at scales too small to be directly simulated, leading to significant variations in how models predict climate sensitivity.
Coarse Spatial Resolution:
Most global models divide the Earth into 100x100 km grid cells, which cannot resolve smaller-scale phenomena like thunderstorms, mountains, or urban heat islands. Subgrid processes, such as convection, must be parameterized, which can introduce errors.
Computational Intensity and Data Bottlenecks:
High-resolution models require immense computing power. As models become more detailed, managing and analyzing the massive data output creates significant bottlenecks, prompting researchers to seek faster, often less accurate, alternatives.
Incomplete Understanding of Feedbacks:
The climate system has feedback loops that are not fully understood or accurately modeled, such as the release of methane from thawing permafrost or the precise impact of aerosols on clouds.
"Right Answer for Wrong Reasons":
Climate models may accurately predict surface temperature by having compensating errors—for instance, overestimating cloud cooling while underestimating greenhouse warming—which means they may not be accurate when conditions change in the future.
Internal Variability:
Natural, random variations in the climate system, such as El Niño, can amplify or diminish climate change over short periods (1-30 years), making it difficult to project local, short-term trends.
"Hot Model" Problem:
Some recent generation models (CMIP6) have predicted significantly higher warming (high climate sensitivity) than observational records suggest, a phenomenon researchers are actively trying to correct.
Limited Observations for Validation:
Reliable observational data is often sparse, inhomogeneous, or spans too short a time, which limits the ability to train and validate models, especially regarding rare extreme events.
Now you are admitting you don't understand the science, can't follow the kids NASA page and have to paste and quote AI summaries that you also don't understand.So now you are claiming you know / understand the science and the specific issues/ challenges of climate modelling better than Artificial intelligence ?
that bold of you
So lets be clear
which of these specific issues/ challenges of climate modelling are you claiming is false ?
how long do you figure on claiming this intellectual supremacy over Artificial Intelligence ?Now you are admitting you don't understand the science, can't follow the kids NASA page and have to paste and quote AI summaries that you also don't understand.
This is one epic failure after another, larue.
All this shows is that you can't think yourself out of a paper bag, even the kids science page was too much.
You can't do real research and can't tell bullshit from science.
So you did the only thing left, AI, because you can't think for yourself.
View attachment 551105
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Oh, you think AI is smart and can think.how long do you figure on claiming this intellectual supremacy over Artificial Intelligence ?
Oh, you think AI is smart and can think.
Of course.
Still, when you can't think for yourself I'm sure its the best you can get right now.
Unless you checked in with the experts.....
AI Overview
Modeling the climate is one of the most complex scientific endeavors due to the need to simulate the intricate, non-linear interactions of the Earth's atmosphere, oceans, cryosphere, and biosphere
. Despite advancements, climate models face significant challenges, ranging from computational limitations to gaps in physical understanding.
Key challenges in climate modeling include:
Cloud Representation (The Biggest Uncertainty):
Simulating cloud systems, which can both warm and cool the planet, is a major, long-standing challenge. Clouds are complex, depend on many variables, and often exist at scales too small to be directly simulated, leading to significant variations in how models predict climate sensitivity.
Coarse Spatial Resolution:
Most global models divide the Earth into 100x100 km grid cells, which cannot resolve smaller-scale phenomena like thunderstorms, mountains, or urban heat islands. Subgrid processes, such as convection, must be parameterized, which can introduce errors.
Computational Intensity and Data Bottlenecks:
High-resolution models require immense computing power. As models become more detailed, managing and analyzing the massive data output creates significant bottlenecks, prompting researchers to seek faster, often less accurate, alternatives.
Incomplete Understanding of Feedbacks:
The climate system has feedback loops that are not fully understood or accurately modeled, such as the release of methane from thawing permafrost or the precise impact of aerosols on clouds.
"Right Answer for Wrong Reasons":
Climate models may accurately predict surface temperature by having compensating errors—for instance, overestimating cloud cooling while underestimating greenhouse warming—which means they may not be accurate when conditions change in the future.
Internal Variability:
Natural, random variations in the climate system, such as El Niño, can amplify or diminish climate change over short periods (1-30 years), making it difficult to project local, short-term trends.
"Hot Model" Problem:
Some recent generation models (CMIP6) have predicted significantly higher warming (high climate sensitivity) than observational records suggest, a phenomenon researchers are actively trying to correct.
Limited Observations for Validation:
Reliable observational data is often sparse, inhomogeneous, or spans too short a time, which limits the ability to train and validate models, especially regarding rare extreme events.
Buddy, you've given up the debate, admitting that you aren't smart enough to understand the science.how long do you figure on claiming your intellectual superiority over Artificial Intelligence ?
lets be clear about your supposed intellectual superiority
which of these specific issues/ challenges of climate modelling are you claiming is false ?
,Buddy
no actually i won the debate long agoyou've given up the debate,
that's just plain comicaladmitting that you aren't smart enough to understand the science.
there is no conversationNow you have to offload your thinking to AI because this conversation is way beyond your comprehension.
AI Overview
Modeling the climate is one of the most complex scientific endeavors due to the need to simulate the intricate, non-linear interactions of the Earth's atmosphere, oceans, cryosphere, and biosphere
. Despite advancements, climate models face significant challenges, ranging from computational limitations to gaps in physical understanding.
Key challenges in climate modeling include:
Cloud Representation (The Biggest Uncertainty):
Simulating cloud systems, which can both warm and cool the planet, is a major, long-standing challenge. Clouds are complex, depend on many variables, and often exist at scales too small to be directly simulated, leading to significant variations in how models predict climate sensitivity.
Coarse Spatial Resolution:
Most global models divide the Earth into 100x100 km grid cells, which cannot resolve smaller-scale phenomena like thunderstorms, mountains, or urban heat islands. Subgrid processes, such as convection, must be parameterized, which can introduce errors.
Computational Intensity and Data Bottlenecks:
High-resolution models require immense computing power. As models become more detailed, managing and analyzing the massive data output creates significant bottlenecks, prompting researchers to seek faster, often less accurate, alternatives.
Incomplete Understanding of Feedbacks:
The climate system has feedback loops that are not fully understood or accurately modeled, such as the release of methane from thawing permafrost or the precise impact of aerosols on clouds.
"Right Answer for Wrong Reasons":
Climate models may accurately predict surface temperature by having compensating errors—for instance, overestimating cloud cooling while underestimating greenhouse warming—which means they may not be accurate when conditions change in the future.
Internal Variability:
Natural, random variations in the climate system, such as El Niño, can amplify or diminish climate change over short periods (1-30 years), making it difficult to project local, short-term trends.
"Hot Model" Problem:
Some recent generation models (CMIP6) have predicted significantly higher warming (high climate sensitivity) than observational records suggest, a phenomenon researchers are actively trying to correct.
Limited Observations for Validation:
the climate models are flawed and run too hotReliable observational data is often sparse, inhomogeneous, or spans too short a time, which limits the ability to train and validate models, especially regarding rare extreme events
AI Overview
Based on data regarding the natural carbon cycle, the vast majority of carbon dioxide circulating between the Earth's surface and the atmosphere comes from natural sources, including the oceans.
no actually i won the debate long ago
i am just sitting back, observing you embarrass your self over and over again
and you are becoming more and more absurd with each post
pretending you have intellectual superiority over Artificial Intelligence ??
the problem is you believe those two are one and the same thingYou don't understand the science around this debate.
You can't even understand the NASA kid's page on the Greenhouse Effect.
You are a total kook if you think NASA's work is not science.the problem is you believe those two are one and the same thing
they are not
how long do you figure on claiming your intellectual superiority over Artificial Intelligence ?
The fact that you can't paraphrase the AI questions you asked here means you admit you don't understand them yourself.how long do you figure on claiming your intellectual superiority over Artificial Intelligence ?
how long do you figure on claiming your intellectual superiority over Artificial Intelligence ?The fact that you can't paraphrase the AI questions you asked here means you admit you don't understand them yourself.
So you bowed to an external authority that you don't understand, and can't verify, and asked questions you can't answer, hoping to get answers you won't understand.
Where did I make that claim, buddy?ow long do you figure on claiming your intellectual superiority over Artificial Intelligence ?






