Glad Zeke (and Andrew) have joined the climate scrum on Substack. I sure hope reporters and editors read this post before the flood of overheated headlines asserting the world has crossed into climate doom as the global average touches 1.5 when the next #ElNino comes atop CO2 heating. More on Twitter: https://twitter.com/Revkin/status/1653097497198227456
So the models predict an El Nino with the index somewhere between zero and two … that's like predicting the weather tomorrow will be either warmer or colder.
And of course, as far as I know, not one of these models predicted that we'd get three years worth of La Nina conditions, but that colossal failure hasn't seemed to cool your ardor for these Tinkertoy™ models.
I just went to the ICI website. Their track record for predicting El Niños is abysmal. Often they don't even get the sign right for their 11-month-out predictions, much less the amplitude.
Do you not know this about the junk models, or do you simply not care?
Y'all are hilarious. All you've demonstrated are that the models aren't worth a bucket of warm spit but you'll push their lies without regards to consequences for either the world or your reputations.
The nice thing about making near-term projections as we won't have to wait too long to see if I'm right or wrong here. But it seems pretty clear that El Nino is developing at the moment (and evidence has gotten stronger since the plume models were last updated in mid-April); the question is how strong it will end up being.
Sooner or later the mean global surface temperature will increase 1.5K and speculating about the effect of El Nino will not change that. Of course if El Nino does make it happen sooner that might wake people up now rather than later.
Meantime the oceans steadily absorb nearly all of the "extra" energy retained by Earth. They are not listening to you.
A linear trend should work fine for estimating global-average warming trends for the next decade or so, because a local tangent linear approximation works well even for a nonlinear curve. The effects of carbon emissions are cumulative. So the impact of emission scenario build up slowly. We need the billion$ models for projecting regional climate change, needed for quantitative adaptation decisions. Global-average temperature is useful for mitigation discussions, but not so much adaptation.
Climate models are optimized for long-term rather then near-term climate projections. The fact that they are initialized awhile back (e.g. in 2015 for CMIP6) means that they have less knowledge of recent years than a statistical model. CMIP-style models also have their own internal variability (ENSO and whatnot) that will not match the real-world cycle of variability in time (though it ideally should in amplitude), so most CMIP models will not have a moderate or strong El Nino in 2024.
actually there is little rationale, because a linear extrapolation has to be based on the assumptions that everything will continue as before and that all effects are linear. Both of these are awful assumptions. A couple of for instances. 1. Melting of glaciers takes up a lot of heat, but once the land beneath them is exposed the earth's albedo is reduced leading to more heating. So as glaciers melt away the heating accelerates. 2. Since 1959 the Mauna Loa annual average CO2 measured concentration has been increasing quadratically (R -squared > 0.999) which if continued would lead to accelerated warming. But IF we alter our behavior appropriately stabilization and even reversal of heating will occur at some point. Linear projections include neither the physics nor the sociology and so can be misleading
thank you for taking on this task!
Glad Zeke (and Andrew) have joined the climate scrum on Substack. I sure hope reporters and editors read this post before the flood of overheated headlines asserting the world has crossed into climate doom as the global average touches 1.5 when the next #ElNino comes atop CO2 heating. More on Twitter: https://twitter.com/Revkin/status/1653097497198227456
So the models predict an El Nino with the index somewhere between zero and two … that's like predicting the weather tomorrow will be either warmer or colder.
And of course, as far as I know, not one of these models predicted that we'd get three years worth of La Nina conditions, but that colossal failure hasn't seemed to cool your ardor for these Tinkertoy™ models.
I just went to the ICI website. Their track record for predicting El Niños is abysmal. Often they don't even get the sign right for their 11-month-out predictions, much less the amplitude.
Do you not know this about the junk models, or do you simply not care?
Y'all are hilarious. All you've demonstrated are that the models aren't worth a bucket of warm spit but you'll push their lies without regards to consequences for either the world or your reputations.
w.
The nice thing about making near-term projections as we won't have to wait too long to see if I'm right or wrong here. But it seems pretty clear that El Nino is developing at the moment (and evidence has gotten stronger since the plume models were last updated in mid-April); the question is how strong it will end up being.
Sooner or later the mean global surface temperature will increase 1.5K and speculating about the effect of El Nino will not change that. Of course if El Nino does make it happen sooner that might wake people up now rather than later.
Meantime the oceans steadily absorb nearly all of the "extra" energy retained by Earth. They are not listening to you.
This is a weather report, not an article about the climate.
It's an article about models that have proven again and again to underestimate felt climate impacts.
Great article. You did miss out the Tonga volcano and the solar maximum, both of which add to temperature rise.
See https://therenwhere.substack.com/p/climate-change-triple-whammy
In the UK this has been the coldest spring in several years where I live :) That aside, late 2023 to the end of 2024 will be very interesting.
A linear trend should work fine for estimating global-average warming trends for the next decade or so, because a local tangent linear approximation works well even for a nonlinear curve. The effects of carbon emissions are cumulative. So the impact of emission scenario build up slowly. We need the billion$ models for projecting regional climate change, needed for quantitative adaptation decisions. Global-average temperature is useful for mitigation discussions, but not so much adaptation.
Climate models are optimized for long-term rather then near-term climate projections. The fact that they are initialized awhile back (e.g. in 2015 for CMIP6) means that they have less knowledge of recent years than a statistical model. CMIP-style models also have their own internal variability (ENSO and whatnot) that will not match the real-world cycle of variability in time (though it ideally should in amplitude), so most CMIP models will not have a moderate or strong El Nino in 2024.
That being said, there are some efforts out there to use dynamic models for subdecadal-scale predictions: https://hadleyserver.metoffice.gov.uk/wmolc/
actually there is little rationale, because a linear extrapolation has to be based on the assumptions that everything will continue as before and that all effects are linear. Both of these are awful assumptions. A couple of for instances. 1. Melting of glaciers takes up a lot of heat, but once the land beneath them is exposed the earth's albedo is reduced leading to more heating. So as glaciers melt away the heating accelerates. 2. Since 1959 the Mauna Loa annual average CO2 measured concentration has been increasing quadratically (R -squared > 0.999) which if continued would lead to accelerated warming. But IF we alter our behavior appropriately stabilization and even reversal of heating will occur at some point. Linear projections include neither the physics nor the sociology and so can be misleading