One more dot on the graphs for our annual model-observations comparisons updates. Given how extraordinary the last two years have been, there are a few highlights to note.
First, we have updated the versions of a few of the observational datasets: UAH TLT/TMT are now on version 6.1, and the NOAA NCEI surface temperature data are now version 6. We use the same collations of Hansen81/Hansen88/CMIP3/CMIP5/CMIP6 model output as previously. The comparisons cover surface air temperatures, sea surface temperatures, tropospheric atmospheric temperatures (TLT, TMT), stratospheric temperatures, and a few variations on these themes that have been of interest in the past. (It would be nice to have some non-temperature variables in the mix – feel free to suggest some if you can point to (or post) an archive of the models historical+projected results).
With respect to the GMSAT, it’s striking how close the real world is to the Hansen et al. (1988) ‘Scenario B’ (this scenario had ‘business as usual’ concentration rises in CO2, but too much growth in CFCs and CH4. However, the prize for most skillful projection still goes to the CMIP3 ensemble; even after 20 years, it’s still pretty much spot on.
The detailed issues that lead to some angst around the CMIP5 models – mis-specifications of the forcings, the importance of the SST/SAT blend vs. SAT trends have somewhat faded in importance. These are/were real issues, but they are small compared to the ongoing trends. With respect to CMIP6, the observations (across a swath of temperature related diagnostics) are still best matched by the sub-sample of screened models (i.e. discarding those that ‘ran hot’).
The updates with respect to the atmospheric temperature profiles (MSU/AMSU derived diagnostics), have become slightly more favorable to the models, though the structural variation between the RSS data and the UAH/NOAA STAR retrievals is still clear. For the sea surface temperature, the real world seems to warming at the upper end of expectations, but still (just!) within the screened spread.
One of the main reasons to maintain these comparisons is to see where discrepancies arise. To that end, multiple versions of the observational data are obviously useful since they can give an estimate of the structural uncertainty (this has been very important in the MSU/AMSU comparisons for instance). In other instances, we have less concerns about the observational data, but we are concerned that the models are not being given the right inputs. For example, since the internal variability in stratospheric temperatures is much less than in the lower atmosphere, incorrect forced signals can emerge faster. I think we may be seeing some of that in the SSU comparisons…
The match to the models is very good over the historical period (to 2014), but post 2015, there is some mismatch between the model variance and the obs. There are two potential issues – the timing of the solar cycle 25 (a solar max warms the stratosphere) – which happened earlier and bigger than expected by CMIP6, and the presence of the Hunga Tonga volcano (from 2021) which is having complex impacts on the stratospheric temperatures. Nonetheless, the long term trends are still well-modeled.
As always, if someone knows of expanded model diagnostics and relevant observational data sets to compare with, let me know and we can add it to the page.
Thanks as always to the data centers who provide the observational data, the CMIP committees who organised this storage of the outputs, the modeling centers that did the runs, and the authors who produced the derived data sets we are using directly here (full refs on the above listed page).
See you next year!
Not a scientist, just an interest in weather models… However one aspect that is becoming important is the number of high winds we are seeing. Of course this is directly related to the overall temperatures, but it is also becoming the cause of a number of wildfires across the Western US, and the intensity of hurricanes.
I have not run across any models that try to predict how the continued global warming will affect these winds, but it seems like that information would be useful for predicting trends towards future disasters.
This is also affecting the arctic blasts we are seeing so spotlighting the trends towards rapid cooling in areas, especially when those downward spikes are sudden and severe, may be a good indicator of how the climate has shifted. For example, here in Colorado a few years back we had one day where the temperature dropped by 70F overnight, killing off otherwise hardy pines. And on the opposite side, I watched the temperature rise by 50F over a period of less than four hours one morning. What happens to the plant life if these trends get worse or more frequent?
S: I have not run across any models that try to predict how the continued global warming will affect these winds, but it seems like that information would be useful for predicting trends towards future disasters.
BPL: I don’t know of a time series for global average winds, although I have seen some estimates. Perhaps some exist for winds in particular areas. If you can find one, you can then apply time series analysis to see how it might be related to temperatures or other factors.
I’m not sure I’D want to try THAT time series analysis without a lot of time and resources (and a grad student or two!)! Just operationalizing wind fields in some sensible and well defined way kicks the math to a whole new level!
Send a note to Tamino…he could likely do it in a few days!!!
Thank you for all the interesting work. However it would be nice if some high resolution versions of the figures were available, as I like to show some of them to my students in class (Climate change Biology course).
[Response: All of them are 300 dpi png files on the main page. Is that not sufficient? – gavin]
i) Open Image in New Tab and ii) Save As. (or some such).
They are okay but still a bit blurred when projected onto a big screen in a classroom. I might be too picky. Thanks anyway.
“There are two potential issues – the timing of the solar cycle 25 (a solar max warms the stratosphere) – which happened earlier and bigger than expected by CMIP6”
That’s embarrassing to mention sunspots. Attributing solar sunspot cycles to climate variation is the equivalent of prescribing Ivermectin to a medical condition. Perhaps worse because you guys claim to understand the physics.
Not sure I understand your point. You seem to be denying the link between the solar cycle and fluctuations in temperature. I thought it was well established that the 11 year sunspot cycle does give rise to an 11 year cycle in global temperatures, but that is overlaid on whatever other trends are apparent: in this case, a steady warming trend. But perhaps I have missed something. Perhaps you could clarify? And in what sense does the posting misunderstand the physics, as you seem to imply? Thank you.
The key is how much solar cycles influence climate variability. Not very much. Your comparison to Ivermectin is unwarranted.
It’s not clear if the current sunspot cycle had much to do with this particular heat spike, but in general sunspots do increase the temperature in the stratosphere. The effect is very nonlinear.
https://localartist.org/media/StratCooling.png
Paul Pukite: “ That’s embarrassing” , “equivalent of prescribing Ivermectin”, “you guys claim to understand the physics”
Extraordinary claims demand extraordinary evidence. Your seething contempt toward Gavin and other “you guys” is as extreme as they come. Yet your extraordinary evidence is …absent.
So put your money where your mouth is – PROVE beyond ANY doubt (“extraordinary evidence”) that the solar cycle does NOT have any effect on the Earth temperature
Since you described Gavin’s “a solar max warms the stratosphere”, to be “embarrassing” and “equivalent of prescribing Ivermectin” – how hard could this be for you, Mr. Pukite?
Not so.
See, for example, Foster and Rahmstorf, 2011:
https://www.researchgate.net/publication/254496419_Global_temperature_evolution_1979-2010
” When the data are adjusted to remove the estimated impact of known factors on short-term temperature variations (El Nino/southern oscillation, volcanic aerosols and solar variability), the global warming signal becomes even more evident as noise is reduced”
https://www.merriam-webster.com/dictionary/hubris
WHUT I am still willing to read your research on ENSO when it has been published in a non-predatory peer reviewed journal.
Note the quote specifically mentions stratospheric temperatures “(a solar max warms the stratosphere) “. IIRC there is a big increase in UV associated with sunspots and that UV is absorbed by ozone in the stratosphere. So while it has a modest effect on surface temperatures, I can see why it would have a greater impact on the stratosphere. So it seems pretty reasonable to me (especially as there is less internal variability in the stratosphere, so small changes in forcing will be more easily detected there).
in re to Paul Pukite, 28 Jan 2025 at 2:25 AM,
https://www.realclimate.org/index.php/archives/2025/01/comparison-update-2024/#comment-829608
Dear Paul,
The relationship between climate variations and sun activity cycles was the Ph.D. thesis topics of notable Czech astronomer Ladislav Křivský in the year 1948:
https://www.astro.cz/spolecnost/sin-slavy/ladislav-krivsky.html
40 years ago, I read in one of his books an explanation why the power output of Sun is slightly higher at the maximum of sunspot number than in minimum of teh solar cycle (although sunspots are colder than the rest of chromosphere.
It is because the opposite effect of much less remarkable hot solar flares accompanying the sunspots prevails. It appears that respectable information sources like
https://www.weather.gov/fsd/sunspots
https://www.climate.gov/news-features/understanding-climate/climate-change-incoming-sunlight
https://www.landgate.com/news/the-impact-of-sunspots-and-solar-flares-on-solar-energy
https://spaceplace.nasa.gov/solar-activity/en/
still share this view.
I therefore join Rory Allen in asking you for clarification of your assertions.
Best regards
Tomáš
Hi Gavin, is there a site that i can get the maximum and minimum worlds average temperatures on a yearly basis?
Cheers.
Simon,
If you’re after annual averages of daily high temperature and daily low temperature (which would apply only to land SAT), Berkeley Earth have both graphed out and link to the data HERE.
If you’re after the average over the land and oceans, you are presumably after the absolute temperature version of the monthly or perhaps daily global SATs showing the annual cycle with its max & min. The absolute temperatures require the temperature of the anomaly base by month or perhaps day. This will require a bit of data manipulation by you and the absolute anomaly base data can be well tucked away. For GISTEMP this page appears to show it. Berkeley Earth show it in the header to the data HERE. (Note there are two sets of anomaly data.) ERA5’s Climate Pulse site graphs out absolute daily global temperature and below the graph is a download button for the 85 years of daily data.
It would be helpful to provide the conversion from 1979-1983 to 1850-1900 baselines.
It would have been helpful if the antebellum United States had launched weather balloons or satellites so that we’d know the 1850-1900 baseline. As it is, surface temperatures are about the only thing measured well enough to provide a global baseline for 1850-1900, and even then just barely.
While the globally averaged T anomaly is managed to be kept on track at the surface and stratosphere, how do the screened models compare to the mechanisms of energy accumulation, i.e. the ambiguous forcing and feedbacks observable as TOA all-sky trends of absorbed solar radiation and canonical greenhouse effects? (a la CERESMIP). Thanks
NASA have provided a handy explainer Paul (minus the physics of why more sunspots = more irradiation): https://spacemath.gsfc.nasa.gov/sun/Earth8.pdf
If one takes the NASA GISTemp annual (J-D) anomaly data from 1970 forward and plots the year-over-year rate of change values the strong upward trend becomes substantially lowered…moderated. Perhaps someone can explain that phenomenon.
If one actually shows their work to experts, then experts have a much easier time explaining what you’ve done to you that you didn’t understand.
What specific variables did you construct using what specific level of aggregation” What was the level of significance of your “substantially lowered” value?
Should be only about a 10 line R-script to show your work. Please do said and show the world.
Probably something to do with wishful thinking, unreliable memory, confirmation bias, and looking at it through a mirror while standing on your head and squinting through a tiny gap in your fingers, Ken.