We add some of the CMIP6 models to the updateable MSU [and SST] comparisons.
After my annual update, I was pointed to some MSU-related diagnostics for many of the CMIP6 models (24 of them at least) from Po-Chedley et al. (2022) courtesy of Ben Santer. These are slightly different to what we have shown for CMIP5 in that the diagnostic is the tropical corrected-TMT (following Fu et al., 2004) which is a better representation of the mid-troposphere than the classic TMT diagnostic through an adjustment using the lower stratosphere record (i.e. ).
This data for the historical and SSP3-70 scenario (135 simulations) is for the region 20ºS-20ºN. This allows us to provide an updateable comparison to the equivalent satellite temperature diagnostics from RSS v4, UAH v6 and the new NOAA STAR v5. As with the earlier CMIP6 comparisons, I’ll plot the observational time series against both the full ensemble and the ensemble screened by the transient climate response (TCR) as we recommended in Hausfather et al (2022), and plot the time series and trend histogram.
Two things are clear. First the 24-model ensemble as a whole is clearly warming faster than the observations, but the histogram shows that this ensemble is heavily skewed by including 53 ensemble members from CanESM5/CanESM5-CanOE (green in the histogram) which unfortunately has a very high climate sensitivity (ECS 5.6ºC, TCR 2.7ºC). The TCR Screened ensemble (only including the 15 models that have 1.4ºC < TCR < 2.2ºC) is in red and is closer to the observations in terms of trends, but only 7 simulations (from 6 models) out of 53 simulations have trends within the uncertainties of the observations.
The above selection of CMIP6 models does not include the range of configurations of the GISS coupled models that we looked at in Casas et al. (2023). Since this is a somewhat differently designed ensemble, I’ll plot that similarly (45 simulations), and note too that these are global means, again, for the corrected-TMT product (for the historical and SSP2-45 scenarios after 2014). This ensemble samples model structural variability (vertical resolution, model top, interactive composition) and some aspects of forcing uncertainty (notably for aerosols and ozone), as well as the initial-condition (‘weather’) variability we are used to seeing.
As above, the GISS ensemble diverges slightly from the observations. I’ve also included a line for the AMIP ensemble mean (red) (simulations that use the observed sea surface temperatures as an additional forcing) which shows that the specifics of the interannual variability and observed trend can be matched if the sequence of El Niño and La Niña etc. are matched. For the 1979-2022 trends, the GISS ensemble is a closer match to the observations then the 24-model selection shown above – particularly the GISS-E2.2 simulations all of which are within the uncertainties of the observational spread.
The point of this exercise is first, to include CMIP6 in the comparisons. While we know that this is a trickier ensemble to work with because of the broad (and unrealistic) spread in climate sensitivity, the point in highlighting the GISS model efforts here too is to point out that we are starting to do a better job in terms of sampling different kinds of uncertainty. The CMIP ensembles are still ‘ensembles of opportunity’, but increasingly we are able to take slices through the ensemble to isolate different kinds of sensitivity that are perhaps orthogonal to what has been possible before and make a difference to many observational comparisons – not just the MSU records.
Since I’m making new graphs, here are the SST comparisons as well. The SST files come from the U. Melbourne collation and have 135 simulations from 13 separate models (when I downloaded them) (for the historical runs continued by SSP2-45). Unfortunately, a large part of the ensemble is again the CanESM5 model (52 runs), but there are 73 simulations from 9 models that pass the TCR screen used above. I’m plotting the HadSST4 and ERSSTv5 global means for the observations. The ensemble subsets don’t quite overlap (there are two additional models here – CIESN and GISS-E2.1-G, 14 missing ones, and 11 in common), but the overall picture is very similar. There is a subset of models with high TCR/ECS that warm too quickly, but the bulk of the remaining models are doing well.
The SST comparisons have popped up on twitter in the last few months, led by Roy Spencer who didn’t point out the obvious bifurcation in models, and then repeated by a number of wannabe contrarians who don’t know what they are posting and care even less. Maybe these graphs will be useful for adding some clarity?
The contrast between the excellent agreement of the screened ensemble for global SST and the slight overestimate (on average) for the tropical or global corrected-TMT is interesting. A couple of things will likely play into that. First the MSU TMT diagnostics are more dominated by changes in the tropics than the surface fields because of the effects of convection, and so the exceptional nature of the La Niña-like trend in the Eastern Pacific Lee et al (2021) is going to be magnified aloft. Second, the impact of forcings is slightly different at different layers, notably for aerosols and ozone changes, and so uncertainties there may be playing different roles in different levels.
One final caveat, I’ve been rather lazy in plotting these ensembles so that I can show the impact of both forced changes and the spread due to internal variability and structural uncertainty. Unfortunately, when you have an ensemble that has fifty runs from a single model and then a handful of models with only one or two runs, then it’s hard to know what’s best. Some papers have taken a single run from each model which seems fair, but actually confuses structural uncertainty and internal variability. Some take the ensemble mean of each model and then plots the average of the averages, which might be a good estimate of the forced change, but loses the information from the weather. Maybe these things need to be estimated separately and put together artificially. Another post though…
- S. Po-Chedley, J.T. Fasullo, N. Siler, Z.M. Labe, E.A. Barnes, C.J.W. Bonfils, and B.D. Santer, "Internal variability and forcing influence model–satellite differences in the rate of tropical tropospheric warming", Proceedings of the National Academy of Sciences, vol. 119, 2022. http://dx.doi.org/10.1073/pnas.2209431119
- Q. Fu, C.M. Johanson, S.G. Warren, and D.J. Seidel, "Contribution of stratospheric cooling to satellite-inferred tropospheric temperature trends", Nature, vol. 429, pp. 55-58, 2004. http://dx.doi.org/10.1038/nature02524
- Z. Hausfather, K. Marvel, G.A. Schmidt, J.W. Nielsen-Gammon, and M. Zelinka, "Climate simulations: recognize the ‘hot model’ problem", Nature, vol. 605, pp. 26-29, 2022. http://dx.doi.org/10.1038/d41586-022-01192-2
- M.C. Casas, G.A. Schmidt, R.L. Miller, C. Orbe, K. Tsigaridis, L.S. Nazarenko, S.E. Bauer, and D.T. Shindell, "Understanding Model‐Observation Discrepancies in Satellite Retrievals of Atmospheric Temperature Using GISS ModelE", Journal of Geophysical Research: Atmospheres, vol. 128, 2022. http://dx.doi.org/10.1029/2022JD037523
- S. Lee, M. L’Heureux, A.T. Wittenberg, R. Seager, P.A. O’Gorman, and N.C. Johnson, "On the future zonal contrasts of equatorial Pacific climate: Perspectives from Observations, Simulations, and Theories", npj Climate and Atmospheric Science, vol. 5, 2022. http://dx.doi.org/10.1038/s41612-022-00301-2
14 Responses to "Some new CMIP6 MSU comparisons"
E. Schaffer says
I wonder what difference it would make to include primary energy consumption (PEC). We use about 600 exajoules of PEC, corresponding to a forcing of 0.037W/m2 on a global sclae. By all accounts that is a small forcing, and a fraction of it even comes from solar energy eventually. However, similar small forcings have been taken into account, but not this one.
More importantly PEC is concentrated in certain regions. PEC in Germany for instance amounts to 1.09W/m2, not a negligible figure. In the more poplulated third of China it is even 1.5W/m2.
The problem is, this forcing is highly significant in the industrious regions of the planet. There it may well mask other assumed causes of causes of “global” warming, despite being a regional issue. There and then the models may produce an erroneous “good fit”..
Larry Menkes says
I appreciate any refinement in the data. However, for those of us concerned with global warming the Keeling Curve is an essential marker. Methane emissions, while much harder to fully track sit on top of the CO2 data and show evidence that its moving in the direction of exponential upward change and merits deep concern. Arctic amplification may also pose great concern for the average person.
Donald L. Klipstein says
What’s up with the pale gray text? The main text in the article is coming up at 80% of the brightness of whitest white, and the text I see while typing this comment is even whiter.
Donald L. Klipstein says
Update: After I left the above comment, the gray text became much less white, but it still seems to be an example of the recent fad of making regular text gray instead of black.
Barton Paul Levenson says
When I access RealClimate on my old flip phone, I can’t read the pages or the comments at all. Completely invisible. I wish they’d go to black print.
Paul S says
A likely factor in observed TMT is the apparently ongoing negative ENSO trend since 1979. From some brief tests East Pacific SST trends now appear to be comfortably outside the envelope of all CMIP5 model runs. Either this is a spectacularly low probability period of natural variability or the models are missing/getting wrong some important dynamics.
I recall Mike Mann writing years ago arguing that paleo data pointed to a tendency for strong forced warming to cause a La Nina-like SST pattern. Have there been many developments on that idea?
macias shurly says
@Paul S says: – ” the models are missing/getting wrong some important dynamics. ”
ms: — La Nina years can be ~0.5°C cooler as they push & hide large amounts of energy into the western Pacific, while El Nino years release more energy stored in the Pacific Ocean.
The Earth Energy Imbalance @ Top Of Atmosphere (EEI @ TOA) was close to zero W/m² (neither warming nor cooling) in 2010, also a very hot El Nino year, while in 2011-12, a strong El Nina year, the EEI was increased again to almost 1.5W/m². El Nino years thus moderate the increase in global warming and are therefore more advantageous for the earth’s climate in the long term.
This is IMO due to the generally higher relative humidity and cloud cover of El Ninos. In the graph of the Met Office for RH you can recognize the El Nino years 1998, 2010, 2016 by the maximum peaks.
My suspicion is that evaporation, relative humidity and cloud cover are not represented dynamically enough in most models. Note that the Clausius-Clapeyron equation is only valid in closed energy systems and our atmosphere is an open system.
Clausius Clappeyrons principle follows from your cooling effect of evapotranspiration kept in combination with the permanence of matter and the permanence of energy in open systems,…..
……… and not by the permanence of die Deutsche Demokratische Rerpublik and its Anerkennung as you have been brought up to it in that closed system,……
……… where the Matter and its energies is dia- lectic, created, mooved and annihilated again by the Godfather, the anonymeous Party Secretary deputee.
macias shurly says
— Are you crazy …my friend ???
Gehst du in die Anstalt – ! ohne LSD !
macias shurly says
The Clausius–Clapeyron relation characterizes behavior of a closed system during a phase change at constant temperature and pressure. — https://en.wikipedia.org/wiki/Closed_system
He is telling us that a wet towel in air will not dry up faster when heated up, and a pool of water will not freeze faster when the weather cools down.. Because both are no closed systems.
In that way, the fameous Arbeiter and Bauern- faculty of the late peoples republic can reject any critical remark to their propaganda..
Dia Lectic Materialism has allways the upper hand on what is what and what is not and what is appliciable to what or not.
His peculiar style of thought betrays his learnings bacground all the way.
I often find . the fanatic blatant surrealists in the climate dispute in that peculiar class-
It hardly surprizes me anymore.
They are simply lacking legally absolved Mittlere reife, Bachelor 1 because they visited the grand old partys new and progressive political KADRE or “leading class” substitute for it instead.. .
Brian Gideon (bdgwx) says
NOAA STARv5 uses the formula TTT = 1.15*TMT – 0.15*TLS and gets +0.142 C/decade from 1979/01 to 2023/02. Using the same formula on UAHv6 we get +0.147 C/decade. RSSv4 provides TTT directly and gets +0.167 C/decade.
STARv5 provides a TLT product from 1981/01 to 2023/02 which shows +0.129 C/decade as compared to UAHv6 of +0.139 C/decade and RSSv4 of +0.219 C/decade.
UAH uses the formula TLT = 1.538*TMT – 0.548*TUT + 0.10*TLS and gets +0.139 C/decade. Using the same formula on STARv5 we get +0.135 C/decade. There is a small discrepancy between the STARv5 official TLT trend +0.129 C/decade and the calculated trend of +0.135 C/decade using the Spencer et al. 2017 method. This is all for the period 1981/01 to 2023/02 since STAR only provides TUT starting in 1981.
Does anyone know why STARv5 uses the :Spencer et al. 2017 method for their TLT product?
Zou et al. 2023 – DOI: 10.1029/2022JD037472
Spencer et al. 2017 – DOI: 10.1007/s13143-017-0010-y
Rick Winters says
This is all so real and people walk around saying that they want change for the climate bit they do nothing, just talk. SoI took the time to call for a real revolution and wrote a song about negligence when it comes to climate change. So many people protest but still buy the newest cell phones, clothes and splurge. It is time for them to really make a stand, start a real revolution and fight for those making cents an hour just to feed their families.
In case you all wanna share here is a link to the music video: https://www.youtube.com/watch?v=eYfNaKx4g5U&list=PLpuc4AxAeGBHmauqqkIcj90VpU6Q9YHOf&index=1
Thank you all and lets stand together for real change,
Wouldn’t it make sense to use all the data but use weights so that each of multiple runs from the same model have low weight compared to one run from a different model?