and-what a coincidence- all in the same direction
But hey , they are just non partisan folks in lab coats who just go where the the numbers take them. No agenda.
and-what a coincidence- all in the same direction
But hey , they are just non partisan folks in lab coats who just go where the the numbers take them. No agenda.
and-what a coincidence- all in the same direction
But hey , they are just non partisan folks in lab coats who just go where the the numbers take them. No agenda.
I think this guy nails it w/r to the "methodology" used to validate those 36 models - the highest of which is roughly 7x above actual:Having worked in aerospace I know a bit about modeling and simulations and what all these models all lack is a methodology to certify or validate them. We had simulations and we ran them against real flight test data. That's the ONLY way you can have confidence in your models.
The Corn Belt.
How about the rest of the world?
The corn belt isn't the world.
LOLYou took someone's tweet about the ****ing Corn Belt and used it to extrapolate some sort of idiotic conclusion about the evidence for GLOBAL warming?
and-what a coincidence- all in the same direction
But hey , they are just non partisan folks in lab coats who just go where the the numbers take them. No agenda.
Overall, the projected median is 2.2 degrees Celsius (4 degrees Fahrenheit), considerably lower than the implausible 4 to 5 degrees Celsius (7.2 to 9 degrees Fahrenheit) of future warming often touted by the media – although it’s slightly above the upper limit of 2 degrees Celsius (3.6 degrees Fahrenheit) targeted by the 2015 Paris Agreement.
But they got it wrong on 0.0001% of the earths surface...The Corn Belt.
How about the rest of the world?
They test models by running them against past known climate.Having worked in aerospace I know a bit about modeling and simulations and what all these models all lack is a methodology to certify or validate them. We had simulations and we ran them against real flight test data. That's the ONLY way you can have confidence in your models.
That would be assuming there are no positive feedbacks at all. For example, a warming ocean and its impact on atmospheric co2.The climate models will always overestimate warming anywhere, because they are simulating the wrong thing!
They simulate ECS which is based on an abrupt doubling of the CO2 level, whereas the actual CO2 levels have been increasing at ~ .65% per year. When the same models simulate emissions close to actual emissions, they come in much lower. There is not an error with the models, but what they are simulating.
That's not a 'test', rather that's a study. It's problematic for a number of reason. One is there's no way to narrow down & isolate your input variables like you can do in a carefully controlled experiment. Next, no matter what historical data you choose you're cherry picking your inputs. Consider that the earth is billions of years old, how do you pick which years to look at? The earth is large, how do you choose the locations? And last and most importantly, Past performance is no guarantee of future results. Just like the stock market or any super complex system for that matter.They test models by running them against past known climate.
I am not a data scientist, but how far you go back in terms of predicting past climate or forward in predicting future climate impacts the resolution of the models. For example, predicting climate over the past 100 when we have solid records would be a high resolution model. Going 100 million years back would be a much lower resolution model. In terms of future climate prediction, that is why there is a range in terms of predicting climate.That's not a 'test', rather that's a study. It's problematic for a number of reason. One is there's no way to narrow down & isolate your input variables like you can do in a carefully controlled experiment. Next, no matter what historical data you choose you're cherry picking your inputs. Consider that the earth is billions of years old, how do you pick which years to look at? The earth is large, how do you choose the locations? And last and most importantly, Past performance is no guarantee of future results. Just like the stock market or any super complex system for that matter.
In 1960-1970 the growth rate was slightly less than about 1 ppm/y, but the growth-rate has been steadily increasing, reaching 2.37±0.26 ppm/y (mean ± 2 std dev) at the beginning of 2023. This means that currently, the concentration of carbon dioxide is growing by about 2.37 ppm per year.Jun 5, 2023The climate models will always overestimate warming anywhere, because they are simulating the wrong thing!
They simulate ECS which is based on an abrupt doubling of the CO2 level, whereas the actual CO2 levels have been increasing at ~ .65% per year. When the same models simulate emissions close to actual emissions, they come in much lower. There is not an error with the models, but what they are simulating.
To model the earth's climate is about the most complex chaotic non-linear system ever imagined. We might choose 100 different input parameters, but who's to say that we didn't miss something important. Add to the fact that there's only one earth (n=1) and its always active in motion. A controlled experiment is impossible and looking at historical data has its problems. What we're doing with climate modeling isn't useless it's just that we're assigning for more importance to it than it deserves because it can be and probably is wrong.I am not a data scientist, but how far you go back in terms of predicting past climate or forward in predicting future climate impacts the resolution of the models. For example, predicting climate over the past 100 when we have solid records would be a high resolution model. Going 100 million years back would be a much lower resolution model. In terms of future climate prediction, that is why there is a range in terms of predicting climate.
Your stock market comparison is actually a decent one because while we cannot predict with high accuracy how the market will do on a day 10 years from now, we can reasonably predict the future rate of return for index funds over a period of several decades in that while it may fluctuate a lot in any given year, over the course of 30 years you are very likely to average 6 to 8% return per year.
No, it’s the same models and included all the feedbacks in the model. Here is one of the studies, note the simulations are run out to 1000 years.That would be assuming there are no positive feedbacks at all. For example, a warming ocean and its impact on atmospheric co2.
The .65% figure I used is the average annual growth since 2000, of 2.74 ppm per year.In 1960-1970 the growth rate was slightly less than about 1 ppm/y, but the growth-rate has been steadily increasing, reaching 2.37±0.26 ppm/y (mean ± 2 std dev) at the beginning of 2023. This means that currently, the concentration of carbon dioxide is growing by about 2.37 ppm per year.Jun 5, 2023
https://mlg.eng.cam.ac.uk/carl/words/carbon.html#:~:text=In 1960-1970 the growth,about 2.37 ppm per year.
The growth rate is increasing now by .26 ppm/y how does that figure into your calculations?
ECS examines all feedback and represent a number equal to warming from all sources at equilibrium from a given CO2 level. Oceans can take 100's of years to reach equilibrium. Currently the date of doubling is 2060. 37 years away.The .65% figure I used is the average annual growth since 2000, of 2.74 ppm per year.
At the current level of CO2 (418 ppm) 1% would be 4.18 ppm per year.
TCR is a growth of 1% per year,
ECS is an abrupt 100% increase!
Which looks closer to how humans emit CO2 at 0.65% per year?