The US federal government goes to quite a lot of effort to (mostly successfully) keep sensitive but unclassified (SBU) information (like personal data) out of the hands of people who would abuse it. But when it comes to the latest climate models, quite a few are SBU as well.
The results from climate models that are being run for CMIP6 have been talked about for a few months as the papers describing them have made it in to the literature, and the first assessments of the multi-model ensemble have been done. For those of you not familiar with the CMIP process, it is a periodic exercise for any climate model groups who want to have their results compared with other models and observations in a consistent manner. CMIP6 is the 5th iteration of this exercise (we skipped CMIP4 for reasons that remain a little obscure) that has been going since the 1990s.
The main focus has been on the climate sensitivity of these models – not necessarily because it’s the most important diagnostic, but it is an easily calculated short-hand to encapsulate the total feedbacks that occur as you increase CO2.
The first public hint of something strange going on, was at the Barcelona CMIP6 meeting in March earlier this year where this graphic showing Equilibrium Climate Sensitivity (ECS) for the models was prepared:
This showed that quite a few of the models were possibly coming in with sensitivities above 5ºC (grey bars were self-reported, green bars were calculated coherently from the archive). At about the same time, developers at the Hadley Centre and IPSL, wrote about their preliminary results. This was news because the previous IPCC report (and most assessments) have found the likely range of climate sensitivity is roughly 2 to 4.5ºC. For contrast the range in CMIP5 models was 2.1 to 4.6ºC.
As more models have been put into the database (all of which is publically available), more consistent estimates are possible, for instance:
By applying the python scripts by Angie @apuffycloud, and incorporating more models available now, here is a summary of ECS from abrupt-4xCO2 for 20 CMIP6 models up to date (with time-varying feedbacks taken into consideration) pic.twitter.com/XbDcBvW3Gh
— Yue Dong (董 玥) (@YueDong35680721) August 29, 2019
So what should people make of this? Here are some options:
- These new higher numbers might be correct. As cloud micro-physical understanding has improved and models better match the real climate, they will converge on a higher ECS.
- These new numbers are not correct. There are however many ways in which this might have manifest:
- The high ECS models have all included something new and wrong.
- They have all neglected a key process that should have been included with the package they did implement.
- There has been some overfitting to imperfect observations.
- The experimental set-up from which the ECS numbers are calculated is flawed.
There are arguments pro and con for each of these possibilities, and it is premature to decide which of them are relevant. It isn’t even clear that there is one answer that will explain all the high values – it might all be a coincidence – a catalogue of unfortunate choices that give this emergent pattern. We probably won’t find out for a while – though many people are now looking at this.
Why might the numbers be correct? All the preliminary analyses I’ve seen with respect to matches to present day climatologies and variability indicate that the skill scores of the new models (collectively, not just the high ECS ones) are improved over the previous versions. This is discussed in Gettelman et al. (2019) (CESM2), Sellar et al (2019) (UKESM1) etc. Indeed, this is a generic pattern in model development. However, up until now, there has not been any clear relationship between overall skill and climate sensitivity. Whether this will now change is (as yet) unclear.
Why might these numbers be wrong? Well, the independent constraints from the historical changes since the 19th C, or from paleo-climate or from emergent constraints in the CMIP5 models collectively suggest lower numbers (classically 2 to 4.5ºC) and new assessments of these constraints are likely to confirm it. For all these constraints to be wrong, a lot of things have to fall out just right (forcings at the LGM would have to be wrong by a factor of two, asymmetries between cooling and warming might need to be larger than we think, pattern effects need to be very important etc.). That seems unlikely.
But if these numbers are wrong, what is the explanation? Discussions with multiple groups indicates that there isn’t one new thing that all of these groups have included (and the other groups have not) or vice versa. Neither is there some dataset to which they have all tuned their models to that is flawed. The closest might be the CERES TOA radiation, or perhaps CloudSAT/CALIPSO data, but there is no indication there are any fundamental issues with them.
There is some indication that for the models with higher ECS that the changes in the abrupt4xCO2 runs are changing so much (more than 10ºC warming) that the models might be exceeding the bounds for which some aspects are valid. Note these are the runs from which the ECS is calculated. What do I mean by this? Take the HadGEM3 model. The Hardiman et al. (2019) paper reports on an artifact in the standard runs related to the rising of the tropopause that ends up putting (fixed) high stratospheric ozone in the troposphere causing an incorrect warming of the tropopause and a massive change of stratospheric water vapor – leading to a positive (and erroneous) amplification of the warming (by about 0.6ºC). Are there other assumptions in these runs that are no longer valid at 10ºC warming? Almost certainly. Is that the explanation? Perhaps not – it turns out that most (though not all) high ECS models also have high transient climate responses (TCR) which happen at much smaller global mean changes (< 3ºC).
What is clear is that (for the first time) the discord between the GCMs and the external constraints is going to cause a headache for the upcoming IPCC report. The deadline for papers to be submitted for consideration for the second order draft is in December 2019, and while there will be some papers on this topic submitted by then. I am not confident that the basic conundrums will be resolved. Thus the chapter on climate sensitivity is going to be contrasted strongly with the chapter on model projections. Model democracy (one model, one vote) is a obviously a terrible idea and if adopted in AR6, will be even more problematic. However, no other scheme has been demonstrated to work better.
Interesting times ahead.
References
- A. Gettelman, C. Hannay, J.T. Bacmeister, R.B. Neale, A.G. Pendergrass, G. Danabasoglu, J. Lamarque, J.T. Fasullo, D.A. Bailey, D.M. Lawrence, and M.J. Mills, "High Climate Sensitivity in the Community Earth System Model Version 2 (CESM2)", Geophysical Research Letters, vol. 46, pp. 8329-8337, 2019. http://dx.doi.org/10.1029/2019GL083978
- A.A. Sellar, C.G. Jones, J.P. Mulcahy, Y. Tang, A. Yool, A. Wiltshire, F.M. O'Connor, M. Stringer, R. Hill, J. Palmieri, S. Woodward, L. de Mora, T. Kuhlbrodt, S.T. Rumbold, D.I. Kelley, R. Ellis, C.E. Johnson, J. Walton, N.L. Abraham, M.B. Andrews, T. Andrews, A.T. Archibald, S. Berthou, E. Burke, E. Blockley, K. Carslaw, M. Dalvi, J. Edwards, G.A. Folberth, N. Gedney, P.T. Griffiths, A.B. Harper, M.A. Hendry, A.J. Hewitt, B. Johnson, A. Jones, C.D. Jones, J. Keeble, S. Liddicoat, O. Morgenstern, R.J. Parker, V. Predoi, E. Robertson, A. Siahaan, R.S. Smith, R. Swaminathan, M.T. Woodhouse, G. Zeng, and M. Zerroukat, "UKESM1: Description and Evaluation of the U.K. Earth System Model", Journal of Advances in Modeling Earth Systems, vol. 11, pp. 4513-4558, 2019. http://dx.doi.org/10.1029/2019MS001739
- S.C. Hardiman, M.B. Andrews, T. Andrews, A.C. Bushell, N.J. Dunstone, H. Dyson, G.S. Jones, J.R. Knight, E. Neininger, F.M. O'Connor, J.K. Ridley, M.A. Ringer, A.A. Scaife, C.A. Senior, and R.A. Wood, "The Impact of Prescribed Ozone in Climate Projections Run With HadGEM3‐GC3.1", Journal of Advances in Modeling Earth Systems, vol. 11, pp. 3443-3453, 2019. http://dx.doi.org/10.1029/2019MS001714
DDS 45: * the reason models overpredict temperature
BPL: They didn’t. The model ensemble is right in the groove.
* the reason adjustments lower past temperatures
BPL: They don’t always. This is another denier myth.
* the reason polar bears are dying off but they are not
BPL: How do you know? Cite a source.
* the reason more CO2 is bad for plants but it is good
BPL: CO2 in a hothouse can increase plant growth. In the outside world plant growth is more likely to be limited by available water.
* the reason ocean records are modified to show more sea-level rise
BPL: Prove it. Cite a source.
* and the reason every new finding is “worse than expected”
BPL: Maybe that’s because global warming is turning out to be worse than expected.
#29 Mr. DaSilva,
You may or may not have something to contribute to this technical discussion, but as soon as you utter or write: “post-modern Marxist theory” (whatever that could be) all of your credibility is lost.
Think of it as a far stronger corollary to Godwin’s Law…
DDS continues to regurgitate many of the same tired and distracting talking points that I first encountered over a decade ago when I started to read climate change comment threads here and elsewhere. He brings nothing new to the table, only long debunked assertions. He is only here to waste your time and disperse doubt.
#45, DDS–
They don’t.
They frequently don’t.
Some populations of polar bears are indeed declining, including the most southerly population in Hudson Bay, which is strongly affected by increasing sea ice loss. Others are not, and for many we do not have sufficient data to determine trends.
Define “bad” for plants. Certainly no-one has ever claimed that CO2 was toxic to plants at anything like current levels, and pretty much everyone agrees that elevated CO2 can promote plant growth if other nutrients are not limited–and if environmental conditions in general remain favorable.
They aren’t.
They aren’t.
I’d say you have pretty good reason to fear the borehole, DDS; you’re evidently quite prone to saying “that which is not true.”
As to my best guess as to why many things have indeed turned out to be “worse than we thought”: it would be that there has been a lingering bias against “catastrophism”. (E.g., and at the outlandish end of the spectrum, Emmanuel Velikovsky.)
Gavin writes: “What is clear is that (for the first time) the discord between the GCMs and the external constraints is going to cause a headache for the upcoming IPCC report.”
You may want a mechanism for rejecting or discounting models that don’t produce realistic feedbacks. The most easily measured feedbacks are those associated with the seasonal cycle in GMST. This cycle is 3.5 K in amplitude, so the changes in OLR and OSR measured by CERES are huge (about 8 W/m2 in LWR and 5 W/m2 in OSR). While the magnitude of feedbacks in response to seasonal warming may differ from those in response to global warming, if a model can’t reproduce the large changes we observe every year, should we rely upon that model to predict the climate a century from now? Tsushima and Manabe performed such an analysis of CMIP5 models and found that all models did a good job with LWR feedback through clear skies, but over estimated LWR feedback from cloudy skies. SWR feedback through clear and cloudy skies were problematic, because the response appears have at least some lagged components in both cases, but models performed poorly and mutually-inconsistently. Surface albedo feedback should lag temperature in regions with seasonal snow and ice, so a linear fits to this data seems inappropriate. (Lindzen and Spenser have reported marginally better lagged correlation between OSR and temperature. Marine boundary layer clouds are created by subsiding air masses that have traveled long distances and may not be in convective contact with the SST below or elsewhere.)
https://doi.org/10.1073/pnas.1216174110
Seasonal warming is, of course, is warming in the NH accompanied by cooling in the SH and the change is much greater in the extra-tropics than the tropics. There is little land in the SH with seasonal snow cover, so some fraction of change in OSR is not driven by GMST, but by winter moving to the hemisphere with more land. Observed tropical “warming” may be mostly driven by the internal variability associated with ENSO rather than “seasonal warming”. It may be possible to learn more by analyzing the hemispheres or latitude bands separately (but then we aren’t observing the result only of heat transport across the TOA).
Tsushima and Manabe wrote: “One can argue whether the strength of the feedback inferred from the annual variation is relevant to global warming. Nevertheless, it can provide a powerful constraint against which every climate model should be validated.” If you are looking for some way to abandon “model democracy”, this might work.
According to this study (https://www.geosci-model-dev-discuss.net/gmd-2019-282/), higher ECS in the EC-Earth model “can be attributed to the more advanced treatment of aerosols”… “The increase in climate sensitivity is unrelated to model tuning as all experiments have been performed with the same tuning parameters and only the representation of the aerosol effects has been changing”. What do you think about that ?
Re: What is your best guess? I think I have stated mine. It is the same reason as:
ooo, i know this one, I know this one. It’s a VAST LEFT WING CONSPIRACY designed to SAP OUR PRECIOUS BODILY FLUIDS.
sidd
” . . . Actually, we show that aerosol-induced cooling is currently only ~0.4°C (see 3rd figure in the CarbonBrief article). Higher aerosol sensitivity would be incompatible with the observed mid-century hiatus. Plus, current warming would be overestimated if transient sensitivity was higher than we report. The neat thing is that the temporal evolution of (warming) anthropogenic greenhouse gases and (cooling) aerosols is not a mirror image. Hence they can both be constrained fairly robustly now.”
https://www.realclimate.org/index.php/archives/2019/06/unforced-variations-vs-forced-responses/#comments
DDS,
Spelling and punctuation are fluid. Bare novice writers (K-12) are taught rules. Then they go to big-boys-and-girls’ school and learn that creative writing includes deliberately breaking and rewriting the rules. For example, ee cummings tended to expunge capital letters. I happen to prefer leaving dots off the abbreviations for doctor and mister. It looks and flows better IMO.
Ya know that someone has nothing to contribute when they resort to whining about a dot.
On sensitivity:
Sensitivity is a seriously important value to nail down but it tends to distract from the inconvient truth that CO2 is both a forcing and a feedback. As Killian said, simultaneously falling dominoes is a new thing. Why on Earth would one assume that if humanity stops increasing GHGs at 500PPMe then GHGs won’t keep rising? It’s a competition between ocean drawdown (with an assist from weedy plants) and CO2 and CH4 feedbacks, as assisted by refrigeration and other chemicals leaking from dead appliances, landfills, and whatnot.
Perhaps we’re Japan in WW2. Perhaps we’re waking a sleeping giant. Perhaps 500PPMe today equals 1000PPMe tomorrow.
WJ 50: Why does some one call themselves KIA and prove in his posts that he knows nothing of value? The whole world wants to know….
BPL: It’s a Jay Ward reference. Rocky and Bullwinkle cartoons in the early 1960s occasionally had segments with a guy calling himself Mr. Know-It-All. KIA is suggesting that climate scientists are elitists for claiming to know anything.
#58 NOOOOOO!!!! lol!
#49, DDS–
OK, be a jerk by smearing people far more competent and sincere than you’ll ever be, with the utterly unfounded “weasel words” tag. I don’t know if you are actually unaware that the authors define “extremely likely” and similar verbal probabilistic descriptions, but as a long-time participant, you really should know that by now.
(Footnote 2, p. 121, from link below.)
https://www.ipcc.ch/site/assets/uploads/2017/09/WG1AR5_Chapter01_FINAL.pdf
So, “extremely likely” means a 95% chance or better. As for “best estimate,” would you have the Panel pretend to a certainty that does not exist? I suspect you’d be among the first to howl about it if they did.
I quoted from the SPM as a quick reference, but here’s what the full Chapter 10 bottom line is:
As for ECS:
See pages 420-426, here:
https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter10_FINAL.pdf
“Likely”, of course, means a 66% chance or better, as noted above.
Now, you may say that 1.5-4.5°C represents a “wide range of value” if you like; unlike the IPCC’s “likely”, your term is quantitatively undefined. However, no reasonable person would claim that that range can be equated with “no idea”–however you want to try to define the latter.
P.S. Since you ask for grammar lessons, I will say that sentences are supposed to end with periods. No need to thank me.
you just admitted being a troll, DDS.
Gavin, thank you for a fine essay.
Sidd @58, ha ha very true.
#49 Dan
*410*
Jai Mitchell: #49 Dan *410*
AB: Actually, you were just being psychic. Somehow you knew that if you put in “41” a zero would chime in.
DDS (@46)
“What is the ECS of the model INM-CM5? I could not find it.”
You have found the reason why the model “can not play with the others”. If the ECS is not published / unknown it can not be be included in a comparison.
The requirement for the model to be used is that the developers/users of the model publishes and make available model data for pre-industrial conditions; 4xCO2 i.e. ECS;1 % CO2 increase i.e TCR; historical
BPL: It’s a Jay Ward reference. Rocky and Bullwinkle cartoons in the early 1960s occasionally had segments with a guy calling himself Mr. Know-It-All. KIA is suggesting that climate scientists are elitists for claiming to know anything.
AB: Bless your heart. You’re making a typical human mistake by seeking rational explanations for an idiot’s stupidity. As if mrkia could ever understand Bullwinkle.
I think this is my first comment on here.
I just wanted to mention that Dr. Carolyn Snyder recently authored a paper on climate sensitivity:
“The paleoclimate sensitivity parameter estimates (S[GHG,LI,AE,VG]) are 0.84 °C/W/m2 (0.20 to 1.9 °C/W/m2, 95% interval) for interglacial periods and intermediate glacial climates and 0.53 °C/W/m2 (0.08 to 1.5 °C/W/m2, 95% interval) for full glacial climates, 37% lower at the median.”
https://link.springer.com/article/10.1007/s10584-019-02536-0
I thought that would be relevant here, given the previous saga on RealClimate and elsewhere, regarding Dr. Snyder’s 2016 paper. For those who aren’t familiar with that, I’ve listed some of the relevant material on this below:
Snyder 2016: “Evolution of global temperature over the past two million years”
RealClimate critique of Snyder 2016:
https://www.realclimate.org/index.php/archives/2016/09/the-snyder-sensitivity-situation/
Comment from RealClimate authors: “Overestimate of committed warming” (doi: 10.1038/nature19798)
Snyder’s reply to the comment: “Snyder replies” (doi: 10.1038/nature22803 (2017))