Reposted from Dr. Judith Curry’s Climate Etc.
Posted on August 24, 2020 by niclewis |
By Nic Lewis
An important new paper by Thorsten Mauritsen, Associate Professor at Stockholm University[i] and myself has just been accepted for publication (Lewis and Mauritsen 2020)[ii]. Its abstract reads:
Recently it has been suggested that natural variability in sea surface temperature (SST) patterns over the historical period causes a low bias in estimates of climate sensitivity based on instrumental records, in addition to that suggested by time-variation of the climate feedback parameter in atmospheric general circulation models (GCMs) coupled to dynamic oceans. This excess, unforced, historical “pattern effect” (the effect of evolving surface temperature patterns on climate feedback strength) has been found in simulations performed using GCMs driven by AMIPII SST and sea ice changes (amipPiForcing). Here we show in both amipPiForcing experiments with one GCM and through using Green’s functions derived from another GCM, that whether such an unforced historical pattern effect is found depends on the underlying SST dataset used. When replacing the usual AMIPII SSTs with those from the HadISST1 dataset in amipPiForcing experiments, with sea ice changes unaltered, the first GCM indicates pattern effects that are indistinguishable from the forced pattern effect of the corresponding coupled GCM. Diagnosis of pattern effects using Green’s functions derived from the second GCM supports this result for five out of six non-AMIPII SST reconstruction datasets. Moreover, internal variability in coupled GCMs is rarely sufficient to account for an unforced historical pattern effect of even one-quarter the strength previously reported. The presented evidence indicates that, if unforced pattern effects have been as small over the historical record as our findings suggest, they are unlikely to significantly bias climate sensitivity estimates that are based on long-term instrumental observations and account for forced pattern effects obtained from GCMs.
In this article I explain in more detail what Lewis and Mauritsen (2020) is all about and what its main findings and conclusions are. For a full picture, please read the paper, which is open-access; it is available in a reformatted version here.
The back-story is concerns have been expressed that accounting for changing temperature patterns increases historical period energy budget based estimates of climate sensitivity.[iii] [iv] [v] This idea is now being used in assessments of climate sensitivity to increase significantly estimates based on historical period warming (e.g., Sherwood et al. 2020[vi]).
As I explained in a detailed 2018 article,[vii] the key paper making and quantifying this effect (Andrews et al 2018) was based on simulations driven by an observationally-based estimate of the evolution of SST and sea-ice over the historical period (amipPiForcing experiments, over 1871–2010), that showed, in six models, their climate feedback strength (λ, here λamip) on average to be substantially greater, and hence their effective climate sensitivity (EffCS[viii], here EffCSamip), substantially lower, than when responding to long-term CO2 forcing.
Only a relatively small part of the differences reported in Andrews et al. (2018) can be attributed to effective climate sensitivity to CO2 forcing increasing over time in most atmosphere-ocean global climate models (AOGCMs). Based on typical CMIP5 AOGCM behaviour, that factor, which is allowed for in some historical period energy budget based estimates, such as Lewis and Curry (2018)[ix], would only account for approximately 5% out of the 40% shortfall in effective climate sensitivity that Andrews et al. found,[x] with a further 7% due to their use of mismatching CO2 forcing values.7 This implies that the bulk of the average difference they found was instead attributable to unforced (internal) climate variability having affected SST patterns – a (positive) unforced historical pattern effect. Although I put forward arguments, both in Lewis and Curry 2018 and in my 2018 article, against claims of such an effect having occurred, such claims have become widely accepted by climate scientists.
There are other possible explanations for the differences reported in Andrews et al. (2018). One is that the AOGCMs’ simulated long-term SST and sea-ice patterns, and the resulting radiative responses, are unrealistic. Another is that the GCM radiative responses in amipPiForcing experiments are unrealistic. In this article I shall put those questions aside. A further possibility is that the forced response of the climate system to the historical mixture of forcings differs significantly from that to pure CO2 with the same time-profile of evolving effective radiative forcing. LC18 put forward evidence against that being the case. Moreover, I produced evidence in a subsequent article that, in the two models for which radiative response in standard CMIP5 climate model historical experiment was accurately known, there was no evidence that the response to the mix of anthropogenic forcings differed from that to pure CO2 forcing.[xi]
I wrote in my 2018 article that to justify the a existence of a dampening unforced historical pattern effect one would need – even assuming long-term SST and sea-ice patterns, and the radiative response to them, simulated by AOGCMs to be realistic – to establish:
- that correctly-calculated EffCSamip estimates are adequately robust to choice of historical SST and sea-ice observational dataset;
- that the differences between climate feedback strength over the historical period in amipPiForcing simulations (λamip) and when AOGCMs generate their own SST and sea-ice patterns in response to radiative forcing (λhist) could feasibly be due to natural internal climate system variability.
It is standard to use the AMIPII SST and sea-ice dataset[xii] to drive GCMs in amipPiForcing experiments. The AMIPII sea-ice dataset is based closely on HadISST1 data throughout the historical period, with only minor modifications. However, the AMIPII SST dataset is only based on HadISST1 data until late 1981, after which it is based on OIv2 SST data[xiii]. The OIv2 post-1980 SST dataset is based largely on the same in situ and satellite data as HadISST1, but with different bias corrections and a different interpolation method for reconstructing SSTs in areas lacking in situ observations.
Andrews et al. (2108) showed, in their Supporting Information, that EffCSamip estimates are not robust to choice of historical sea-ice observational dataset. They found that climate feedback in amipPiForcing simulations by two UK Meteorological Office GCMs was much weaker – λamip was smaller, and hence EffCSamip was higher[xiv] – when the HadISST2 rather than the AMIPII sea-ice dataset was used, in conjunction with HadISST2 SST data. The difference was mainly due to the change in sea-ice data rather than in SST data, and was large enough to reverse the sign of the unforced historical pattern effect, making it negative.[xv] While sea-ice variation is thus an important contributor to differences in climate feedback, we do not explore sensitivity to sea-ice dataset in Lewis and Mauritsen (2020), caveating our results in that respect. Instead we use consistent sea-ice data, enabling isolation of the influence of SST dataset on historical climate feedback.
Thorsten and I show in our paper that λamip estimates are far from robust to choice of historical SST dataset, and that when the widely used HadISST1 dataset is used in place of the AMIPII SST dataset – with unchanged AMIPII sea-ice data – λamip and λhist are indistinguishable: no unforced historical pattern effect is found with the models we used. We also investigated the unforced historical pattern effect using five other SST datasets, finding a significant estimated effect only in one case.[xvi]
Although the Lewis and Mauritsen (2020) findings are based on simulations by only two GCMs, directly for ECHAM6.3 and indirectly (via “Green’s functions”) for CAM5.3,[xvii] they are consistent with historical warming in the Indo-Pacific warm pool, relative to that over the ice-free ocean as a whole, being much higher in the AMIPII than in the HadISST1 SST dataset.[xviii]
There is evidence, at least in CMIP5 models, that climate feedback strength is strongly positively related to relative warming in the Indo-Pacific warm pool.[xix] On physical grounds, warming in tropical ascent regions, of which the most important is the Indo-Pacific warm pool, relative to elsewhere is expected to produce a strong increase in outgoing radiation at the top of atmosphere.[xx] This effect, as estimated using the CAM5.3 Green’s functions, is illustrated in Figure 1 of our paper, reproduced below.
Figure 1. CAM5.3 Green’s functions: panels (a) and (b) show the change in respectively global mean Ts (K) and in global mean R (Wm−2) per 1K increase in local grid-cell SST, while panel (c) shows the global climate feedback parameter λ (Wm−2K−1) for a change in local grid-cell SST (the ratio of the values plotted in panel (b) to those plotted in panel (a)).
The Lewis and Mauritsen (2020) main results are set out in its Tables 1 and 2, reproduced below:
Table 1. Excess Indo-Pacific warm pool SST trends and climate feedback, in ECHAM6.3 amipPiForcing simulations and in MPI-ESM1.1 coupled 1pctCO2 and historical simulations. All values are based on ensemble mean Ts and R data (save for AMIPII and HadISST1 SST trends and standard deviations of individual run feedback estimates). Feedback estimates are from OLS regression, of pentadal mean data for amipPiForcing simulations. Values in brackets are standard errors of the OLS regression feedback estimates, which reflect underlying deviations from a linear relationship as well as internal variability.
Table 2. Excess Indo-Pacific warm pool SST trends and Green’s function derived estimates of climate feedback in CAM5.3 AMIPII-based amipPiForcing simulations, in CESM1-CAM5 coupled 1pctCO2 and historical/RCP8.5 simulations, and for warming in six observational SST datasets, along with feedback estimated from the actual CAM5.3 AMIPII-based amipPiForcing simulation data. Feedback estimates are from OLS regression of pentadal mean R and Ts values derived from the evolving SST warming patterns in the relevant simulation or observationally-based dataset. Data over 1871-2010, the amipPiForcing experiment period, is used, with data from the historical experiment extended using RCP8.5 experiment data, save in the 1pctCO2 simulation case where years 1–70 data is used.
It has been found in CMIP5 AOGCMs that on average approximately 60% of the change in feedback parameter over time during abrupt4×CO2 simulations comes from the tropics (30°N–30°S),[xxi] due in particular to the west tropical Pacific warming significantly less than the east tropical Pacific, with the tropical pattern becoming more El Niño like as simulations progress. However, the Green’s function feedback estimates for the seven observationally-based SST datasets are strongly correlated with warm pool SST trends relative to those over the tropics and mid-latitudes (r=0.90), but not relative to those over the tropics alone (r=−0.10) (Figure 2).
Figure 2: a reproduction of Figure 4 of Lewis and Mauritsen (2020). The relationship between climate feedback strength, estimated using the CAM5.3 Green’s functions and pentadal regression, and the warming trend in the Indo-Pacific Warm Pool relative to that over either 30°S–30°N (blue circles) or 50°S–50°N (red circles), both over 1871–2010, for SST per seven observational datasets (AMIPII, HadISST1, HadISST2, Had4_krig_v2, HadSST4_krig_v2, COBE-SST2, ERSSTv5). The red line shows a linear fit between the warming trend in the IPWP relative to that over 50°S–50°N and estimated climate feedback strength (r = 0.90). No equivalent fit is shown for the warming trend in the IPWP relative to that over 30°S–30°N, as the relationship is very weak (r = −0.10).
The Andrews et al. (2018) AMIPII-based λamip values, which are based on regressing annual ensemble mean simulated values of outgoing radiation R on surface air temperature T, exceed our estimates on the same basis of the corresponding λhist values for all six of the AGCMs involved. That implies a positive unforced historical pattern effect in all cases when using the AMIPII dataset. However, we found that regressing annual mean data, as is standard practice, non-negligibly biased λamip estimates (although not λhist estimates) in some cases. We therefore used instead estimates from regressing pentadal mean data. Using pentadal mean data substantially reduces noise in the regressor variable, which through regression dilution causes a downward bias in the slope coefficient, and also greatly diminishes the effect of responses to interannual fluctuations, thus providing more robust estimation.[xxii] As we show in our paper, the Andrews et al. λamip estimates are up to 9% too strong relative to those based on pentadal mean data, due to responses to interannual fluctuations.
We show in our paper that, for the five GCMs featured in Andrews et al. (2018) for which the estimated AMIPII-based unforced historical pattern effect derived from regressing pentadal data was positive, unforced variability in preindustrial control run segments from 43 CMIP5 AOGCMs is in all but 0.06% of cases inadequate to account for that unforced historical pattern effect. Moreover, in only 10% of cases is such variability sufficient to capture unforced pattern effects of one-quarter their strength. Of course, the realism of multidecadal internal variability in AOGCMs could be questioned. However, we concluded that if internal variability in at least some CMIP5 AOGCMs is realistic, it seems highly probable that either the AMIPII SST dataset is flawed or at least part of the historical pattern effect detected when using AMIPII SST data is forced.
Our principle conclusion is:
‘In this study we have found no evidence for a substantial unforced pattern effect over the historical period, arising from internal variability, in the available sea surface temperature datasets, save for when the AMIPII and ERSSTv5 datasets are used. Our results imply that the evidence suggesting existing constraints on EffCS from historical period energy budget considerations are biased low due to unusual internal variability in SST warming patterns is too weak to support such conclusion, and suggest that any such bias is likely to be small and of uncertain sign.’
We also say:
‘The various datasets try, in different ways, to take advantage of the satellite observations from when they become available around 1980. The post-1981 AMIPII dataset interpolation method, however does so in a way that emphasizes small scale features at the expense of the large scale patterns central to the study of pattern effects (Hurrell et al. 2008). Perhaps as a result, AMIPII warms more in the western tropical ocean basins and less in the eastern subsidence regions when compared to HadISST1. Earlier studies have in other contexts pointed to issues with the patterns of tropical warming in AMIPII’
‘It is unclear from our results to what extent there is a robust relationship between stronger climate feedback and higher SST trends in the Indo-Pacific warm pool compared with elsewhere, at least where the comparison is limited to the tropics.’
Nicholas Lewis 24 August 2020
Originally posted here, where a pdf copy is also available
[i] Meteorology Department. Previously at the Max Planck Institute for Meteorology in Hamburg, where he worked closely with Bjorn Stevens.
[ii] Lewis, N. and Mauritsen, T., 2020: Negligible unforced historical pattern effect on climate feedback strength found in HadISST-based AMIP simulations. Journal of Climate, 1-52, https://doi.org/10.1175/JCLI-D-19-0941.1
[iii] Gregory , J. M. , and T. Andrews , 2016 : Variation in climate sensitivity and feedback parameters during the historical period . Geophys. Res. Lett., 43 , 3911 –3920 , https://doi.org/10.1002/2016GL068406
[iv] Andrews T. et al., 2018 Accounting for changing temperature patterns increases historical estimates of climate sensitivity. Geophys. Res. Lett. https://doi.org/10.1029/2018GL078887
[v] Gregory, J.M., Andrews, T., Ceppi, P., Mauritsen, T. and Webb, M.J., 2019. How accurately can the climate sensitivity to CO₂ be estimated from historical climate change? Climate Dynamics. https://doi.org/10.1007/s00382-019-04991-y
[vi] Sherwood, S., et al. “An assessment of Earth’s climate sensitivity using multiple lines of evidence.” Reviews of Geophysics (2020): e2019RG000678. https://doi.org/10.1029/2019RG000678
[vii] Warming patterns are unlikely to explain low historical estimates of climate sensitivity, 5 September 2018.
[viii] Effective climate sensitivity (EffCS) is an estimate of equilibrium climate sensitivity (ECS) derived by estimating climate feedback strength (λ) in a non-equilibrium situation and dividing it into an appropriately-derived estimate of the effective radiative forcing (ERF) from a doubling of preindustrial CO2 concentration. In an AOGCM experiment involving a step increase in CO2 concentration, this equates to linearly projecting warming to the point where the Earth’s radiation balance has been fully restored, and then scaling it appropriately if the increase in CO2 was not a doubling.
[ix] Lewis, N. and J. Curry, 2018: The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity. J. Climate, 31, 6051–6071, https://doi.org/10.1175/JCLI-D-17-0667.1; also Masters, T., 2014: Observational estimate of climate sensitivity from changes in the rate of ocean heat uptake and comparison to CMIP5 models. Climate Dyn., 42, 2173 –2181. https://doi.org/10.1007/s00382-013-1770-4
[x] Lewis and Curry (2018) estimated a 10% difference when using long term EffCS estimates based on regression over years 21–150 of CMIP5 abrupt4xCO2 simulations, but that would reduce to 5% if instead basing them on regression over years 1–150, the method used in Andrews et al. (2018).
[xi] Gregory et al 2019: Does climate feedback really vary in AOGCM historical simulations? 31 October 2019
[xii] Hurrell, J.W., Hack, J.J., Shea, D., Caron, J.M. and Rosinski, J., 2008: A new sea surface temperature and sea ice boundary dataset for the Community Atmosphere Model. J. Climate, 21(19), 5145-5153. https://doi.org/10.1175/2008JCLI2292.1
[xiii] Reynolds, R.W., Rayner, N.A., Smith, T.M., Stokes, D.C. and Wang, W., 2002. An improved in situ and satellite SST analysis for climate. Journal of climate, 15(13), pp.1609-1625. https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2
[xiv] Climate feedback strength λ is reciprocally related to EffCS. Note that we use a positive sign convention for climate feedback, but Andrews et al. (2018) use a negative sign convention, so care is needed in interpreting their statements about it.
[xv] Using feedback estimated by regression over years 1-50 of the parent AOGCMs’ abrupt4xCO2 simulations as a proxy for their forced historical feedback over 1871-2010.
[xvi] We computed feedback using surface skin temperature (Ts) rather than near-surface air temperature (T), for the reasons set out in our paper, save when working with data from Andrews et al. (2018), who used T.
[xvii] Through amipPiForcing simulations by ECHAM6.3, and through applying Green’s functions derived from multiple patch-warming simulations by CAM5.3. The Green’s function approach exploits the apparent linear superpositionality in space of GCM responses to warming. Global changes in surface temperature Ts and outgoing radiation R resulting from imposed evolving historical SST patterns can thus easily be emulated by the sums of the global responses to SST changes in individual locations weighted by time-invariant Green’s function values for each location, and associated climate feedback estimates derived. Sea-ice is held constant in the CAM5.3 patch-warming simulations, which reduces changes in the emulated values of both Ts and R.
[xviii] We define the Indo-Pacific warm pool as the region 15°S–15°N, 45°E–195°E, and compare its warming trend with that for the ocean from 50°S–50°N as a whole, that area being essentially ice-free all year.
[xix] Dong, Y., Proistosescu, C., Armour, K.C. and Battisti, D.S., 2019: Attributing Historical and Future Evolution of Radiative Feedbacks to Regional Warming Patterns using a Green’s Function Approach: The Pre-eminence of the Western Pacific. Journal of Climate, (2019).
[xx] That is because surface temperature in convective areas controls temperature in the tropical free troposphere, which spatially is fairly uniform, and influences temperature in the extratropics. An increase in free tropospheric temperature relative to surface temperature in descent regions strengthens the boundary layer temperature inversion, which is known to increase low cloud cover and hence reflected solar radiation.
[xxi] Andrews, T., Gregory, J. M., and Webb, M. J., 2015: The dependence of radiative forcing and feedback on evolving patterns of surface temperature change in climate models. J. Climate, 28(4), 1630-1648. https://doi.org/ 10.1175/JCLI-D-14-00545.1
[xxii] Using the ensemble mean from a number of amipPiForcing simulation runs data does not provide an adequate solution, because the noise in the SST data used to force the GCM is the same in all runs.
So probably still about three degrees per doubling.
I’ll stick with Lindzen’s about 0.7C. The 1930s were just as hot, or hotter than now.
You can say that again.
I’ll stick with Lindzen’s about 0.7C. The 1930s were just as hot, or hotter than now.
I see what you did there.
Ha ha ha
No evidence of that.. just pure anti-science speculation.
Models.. all the way down. !
Nope, paleo data and obsevations as well.
You will suggest to have understand ? 😀
And you have an idea of the correct doubling sensitivity ? 😀
And you don’t kid around ? 😀
No. The entire point and conclusion of Lewis and Mauritsen 2020 went right over your head Loydo. ECS < 2.0 K is alive and well despite attempts by Climate pseudo-Science to dismiss it.
Only if 100% of the warming over the last 200 years is caused by CO2.
Considering that most of the warming came before most of the CO2, that’s an already disproven assumption.
It might be more accurate to say that the majority of the warming occurred prior to the vast majority of CO2 being released.
You also can’t exclude large scale landmass changes done by humans over that same period resulting in enviromental changes. So caused by humans but nothing to do with CO2.
And anthropomorphic measurement bias
Well, the half-doubling of the logarithmic effect of CO2 from pre-industrial 280 ppm occurred at 396 ppm and dang, Loydo didn’t buy that $4 calculator yet.
Calculus can be difficult but simple arithmetic can be much more difficult if you have to use calculators, eh Loydo? How’s that Dunning-Kruger working out for you? Nobel Prize on the horizon?
So Loydo, your claim is that there are two answers to the question of how much energy is required to raise the temperature of 1 kg of CO2 1 C depending on whether infrared is part of the energy input?
If you are correct what is the new value of Cp for air now that CO2 has increased to about 415 ppm?
A plain English summary is desperately needed here.
I was going to post “Excellent paper, I wish I understood it”. Still, it is good to be reminded of ones limitations,
Harry Davidson August 25, 2020 at 2:37 pm
I was going to post “Excellent paper, I wish I understood it”. Still, it is good to be reminded of ones limitations
I can’t even figure out what the title is trying to say.
It’s always an intellectual ironman triathalon to slog through the posts from Judith’s blog. And let me be the first to admit, I’m not able to finish the race. But I guess that this could be digested for the layman along these lines:
Alarmists have been justifying ever-higher equilibrium climate sensitivity (ECS) numbers (the ultimate temperature rise expected from a doubling of CO2 in the atmosphere), based on a paper that claimed that sea surface temperature observations had unusually high variability. That supposedly biased ECS estimates derived from models. The researchers proposed to correct the results by increasing the estimate for ECS. The new Mauritsen and Lewis paper gives evidence that the effect does not really exist, with the implication that the latest CMIP6 (Coupled Model Intercomparison Project Phase 6) suite of models are even less accurate than prior versions which already ran too hot.
Please shred my synopsis at will. I’m also hoping to get a bit better clarity.
Even Rahmstorf had to admit, the models are running to hot.
The monumental embarrassment for the climate modelling community for 30 years has been the widening of the ECS range by pushing the top-end upwards, and inability to shrink it, i.e. decrease the uncertainty range.
Of late, mainstream Climate modeling cargo cultists have tried to dismiss the ECS < 2 K, as CMIP6 stands to push the alarmist, upper estimate to ~6 K, in order to not be seen as ECS becoming more uncertain after having spent many $billions and 30+ years.
This Lewis and Mauritsen 2020 paper as I read it says those mainstream Climate Science attempts at dismissing the low sensitivity range of ECS are unfounded.
Climate science is flawed in treating the far-from-equilibrium climate system as if it were in equilibrium. It’s not. The work of Ilya Prigigine establishes the big difference between these two types of system:
Russian-Belgian physical chemist Ilya Prigogine, who coined the term dissipative structure, received the Nobel Prize in Chemistry in 1977 for his pioneering work on these structures, which have dynamical regimes that can be regarded as thermodynamic steady states, and sometimes at least can be described by suitable extremal principles in non-equilibrium thermodynamics.
In his Nobel lecture, Prigogine explains how thermodynamic systems far from equilibrium can have drastically different behavior from systems close to equilibrium. Near equilibrium, the local equilibrium hypothesis applies and typical thermodynamic quantities such as free energy and entropy can be defined locally. One can assume linear relations between the (generalized) flux and forces of the system. Two celebrated results from linear thermodynamics are the Onsager reciprocal relations and the principle of minimum entropy production. After efforts to extend such results to systems far from equilibrium, it was found that they do not hold in this regime and opposite results were obtained.
One way to rigorously analyze such systems is by studying the stability of the system far from equilibrium. Close to equilibrium, one can show the existence of a Lyapunov function which ensures that the entropy tends to a stable maximum. Fluctuations are damped in the neighborhood of the fixed point and a macroscopic description suffices. However, far from equilibrium stability is no longer a universal property and can be broken. In chemical systems, this occurs with the presence of autocatalytic reactions, such as in the example of the Brusselator. If the system is driven beyond a certain threshold, oscillations are no longer damped out, but may be amplified. Mathematically, this corresponds to a Hopf bifurcation where increasing one of the parameters beyond a certain value leads to limit cycle behavior. If spatial effects are taken into account through a reaction-diffusion equation, long-range correlations and spatially ordered patterns arise, such as in the case of the Belousov–Zhabotinsky reaction. Systems with such dynamic states of matter that arise as the result of irreversible processes are dissipative structures.
Clouds, ocean and atmospheric circulation patterns are dissipative structures. They are constantly changing and don’t obey rules of linear behaviour.
Ignoring Prigogine doesn’t make him wrong.
Hint – it’s not just noise. Dissipative spatio-temporal structures can have millennial timescales.
The deep oceans and their millennium time scale circulation-overturning provides the always far from equilibrium dynamical system response. GCMs using modelled slab oceans are hopelessly always wrong then in output response, aka Trenberth’s hidden heat.
So, looking at the original paper, the conclusion is that due to complexities, variation, and inconsistencies in various data sets, it’s hard to say for certain what is going on with sea surface temperatures at a scale that makes any difference in any practical way.
If I got that wrong, someone please set me straight. Thanks.
Agree with Richard. This ECS chasing game has no logical end game logic, sequence, criterion. It needs a “so what” summary.
Richard Greene August 25, 2020 at 2:32 pm
A plain English summary is desperately needed here
Also, why force this gcm issue when climate science appears to have rejected the slab ocean model in the ESM construct?
Here is an example where Mr Lewis does a video to explain what his paper says and that does make it easier to understand.
Maybe he will do one for this paper.
Postscript: i know it’s the ipcc and not the ipc but i wrote what he said in the video.
Can anyone please explain their conclusions point by point in very plain language? Come on Willis etc, can you please help us out?
We again see confusion and argument arising from ignorance of how to properly measure errors and to show overall uncertainty of the conclusions. One can justifiably dismiss the Andrews work because of selective choice of data and because of use of numbers that are treated as data when they are little more than guesses.
There comes a point where interpolated numbers, as in global SST data sets, have to cease to be regarded as valid, certainly not valid data equal in error to carefully measured actual values elsewhere. There has been a lot of work done in one resource estimation, for example, to assign uncertainty to interpolated values and in such estimations, to reconcile uncertainties with post-mining values.
Interpolated variables like some SST are transient and not capable of validation like post-mining values. Because they cannot be validated they have to be classed as guesses. One then has a reasonable question of what caveats are needed to express the uncertainties of formal statistical analysis if guesswork.
At a minimum, it should NEVER be used for purposes like setting national government policies, as is the current, but wrong application.
In several scientific fields there is a choice of approaches between reductionist and observation based. Reductionist approaches seek to construct fully working models where every moving part is understood and forms a cog in the machine. Observation based approaches do not try to make a complete theory but instead confine analysis only to what can be measured.
A good example is in particle physics and the work of Werner Heisenberg. Heisenberg developed an approach called the “S matrix” (S for scattering). The details go way over my head but essentially he analysed the results of a scattering experiment using only observable quantities, strictly excluding any theoretical or modelled entities.
Heisenberg’s approach – confine your analysis to what can actually be observed – is sometimes called an “Effective Theory”. The term “Bootstrap” is also applied to this kind of approach in a very general way in fields from computer programming to biology to quantum physics. Using the metaphor of (impossibly) lifting yourself up by ones bootstrap, it implies finding a solution to any problem using available resources not bring it in external resources.
There must be a climate analog of the S matrix, the Effective theory and the Bootstrap. One can refuse to deal with hypothetical quasi-real entities and develop an analytical approach like Heisenberg’s S matrix that only engages with measurable and measured entities. I am writing this (thinking out loud) because I think that this is what you are alluding to also.
My feeling about models is that they are only useful if they have many points of contact with measured entities. However some in climate modelling get so carried away with their models that they start considering them as real entities. This can’t be good.
As an added point we always talk about a CO2 doubling. we have gone from 290 ppm and a doubling would be 600ppm or so.
from the rate of growth of CO2 emissions to date, much greater than initially modelled, and the modest growth up to 400ppm due in a large part to organic processes absorbing it and the likely even greater rate of absorption as more is tipped in can we even get past 500ppm.
How is this balance between natural sequestration and release determined.
Maybe the “problem” is in fact just a lot of chlorophyll away from a solution. Just asking.
The IPCC uses a clearly failed model called the Bern model for the global carbon cycle. It was recently updated to the Bern SCM v1.0 for use by the upcoming IPCC AR6.
The bottom line of the Bern model says that roughly half global CO2 emissions end up in the annual atmospheric CO2 increase while the rest goes into global sinks.
The Bern Model though of sources and sinks kinetics were utterly demolished though by the OCO-2 findings released in 2017, about the same time the Bern SCM authors submitted their manuscript. Since then, the OCO-2 team’s work on CO2 data from the mission has been put on ice. Everyone in the community is now ignoring the clear discrepancies that OCO-2 data visualization created in the Bern model’s picture of sources and sinks.
I’m not saying I know what the answer is on sources and sinks. But it is clear the Bern model assumptions conflicts with observation. And Dr Richard Feynman’s science lessons tells us how we need to view the Bern Model.
Well, the half-doubling of the logarithmic effect of CO2 from pre-industrial 280 ppm occurred at 396 ppm and dang, Loydo didn’t buy that $4 calculator yet.
Calculus can be difficult but simple arithmetic can be much more difficult if you have to use calculators, eh Loydo? How’s that Dunning-Kruger working out for you? Nobel Prize on the horizon?
Oooops, sorry for the double-post. My computer was having a conniption, but still relevant here. Bill, it’s theoretically relevant but I don’t think the Beer-Lambert Law is operating at these levels because of potentially linear feedbacks that are to climate scientists what silver crosses and garlic are to vampires. Whatever though, you’re right, if we get to 560 ppm in 200 years, whoo hoo will it save us from the descent into the temperature abyss. NO, but we won’t have the idiots bedwetting about global warming either ….. well it would be a good idea for them to have STFU by then.
The only people who give any credence to climate models are the people who use them. They are riddled with non-physical claptrap.
A year ago I wondered why the sea surface temperature peaked in late July or early August each year. It did not fit with the fact that the sun has long gone from zenith over the Southern Hemisphere, where most of the ocean is located. Also the planet was furtherest from the sun in its annual orbit.
I tested my theory of why the sea surface temperature increased in July using the attached chart showing changes in global sea surface temperature in 2018 from June to July:
It shows that the surface of the oceans adjacent to the large land masses in the northern hemisphere have a massive increase in surface temperature in just 1 month – up to 6C increase in temperature.
So the river run off in the northern hemisphere in just one month can cause an increase in the global sea surface temperature.
This raises the question – which climate models include river run-off into oceans, the run-off temperature and its mixing regime upon entering the oceans to give a meaningful presentation of reality. This is not trivial in terms of the measured global sea surface temperature. It makes that measurement much noisier than it would be without the run-off because the amount varies from year-to-year as well as variation in its location.
Which climate models have any realistic level and distribution of precipitation and evaporation to give realistic level of river run-off. The CMIP5 models do not even integrate precipitation minus evaporation to zero over time. The average of all models removes all water vapour from the atmosphere in about 4 years:
Take a close look at where the zero line is located. Note there is a large net precipitation. The excess precipitation modelled would deplete the water vapour in the atmosphere after 4 years – unphysical claptrap.
The oceans continue to warm even after the longest for the same reason that they keep cooling after the shortest day.
The oceans warm after the longest day, and cool after the shortest day for the exact same reason the land does.
It has nothing to do with rivers.
Oceans have mass and they don’t respond to changes in incoming energy instantly.
The ocean thermal inertia is a variable depending on the surface layer mixing. Low density water is just going to sit on the surface. The heat input is all retained in a thin surface layer; less mixing than when there is more even density distribution.
The thermal inertia is clearly apparent in the southern ocean during its heating phase. The SST does not start to increase until December and reaches its first maximum in March as would be expected by axis tilt and rotational eccentricity; almost 90 degrees out of phase with the solar input. However the next maximum in SST in July is hotter than March:
The point is that not all Sea Surface Temperatures are not created the same and are not representative of total ocean heat uptake. Assuming homogenous conditions over the surface of the oceans is an invalid assumption. Assuming the variation is the same from year-to-year is also invalid because the amount of precipitation, evaporation and run-off varies each year.
And what do we actually know about layer mixing ?
Discovery of an unrecognized pathway carrying overflow waters toward the Faroe Bank Channel
So far to settled science 😀
The presented evidence indicates that, if unforced pattern effects have been as small over the historical record as our findings suggest, they are unlikely to significantly bias climate sensitivity estimates that are based on long-term instrumental observations and account for forced pattern effects obtained from GCMs.
So GCM divergence from observation can be attributed to improper models, parameters (e.g. sensitivity), or tuning. Climate science has evolved as a cargo cult, where its perspective is excessively narrow, or forcibly constrained to be convenient.
I mostly agree, except I would rewrite your statement:
“So GCM divergence from observation can be attributed to improper models, parameters (e.g. sensitivity), AND tuning.”
I was going to say “and/or” but realized the probability all of them are wrong is near 100%.
I take exception to the phrase “of climate sensitivity” which is clapgarble.
What is happening at the Greenwich meridian?
Many people like me live on coastal flats which often have low cloud cover that often seems to have a base around 1,000 feet. Why is this plane around that height so common? Why does the cloud base not vary during an ordinary day, from the ground to several thousand feet up? Why is the base often so planar?
Those who study meteorology might have ready answers, but to me, there seem to be some potentially complex processes including feedbacks that need to be incorporated into climate models, given the importance of clouds. Do climate models speak of this ceiling?
Climate sensitvity based on observational measurements have a profound theoretical shortcoming: How do you know the magnitude of other drivers behind the temperature changes? Just a recent example from 2000 to 2019: the increase of shortwave radiation caused the forcing of 1.68 W/m2 that is the same as the forcing by CO2 during the time span from 1750 to 2011 (1.66 W/m2). And this a simple fact based on the AR5 and CERES radiation measurements but it has not been reported by the climate establishment. Maybe you can figure out the reason.
ECS has been exaggerated and lied about from Day One of the Great Climate Con. Without it the racket collapses.
No high climate sensitivity.
No high positive feedbacks or forcings.
No high amplification of atmospheric water vapour.
No ‘runaway’ warming.
No ‘missing heat’.
No explanation for the Pause.
Heavily biased models are extrapolated decades into the future to deliver entirely bogus numbers designed solely to deliver screaming media headlines. hey are fantasies bearing no resemblance to reality.
These are alarmists’ coordinated weapons of choice, yet they’ve consistently failed, while observed reality laughs in their faces.
ECS isn’t high. The models are consistently wrong. There’s no runaway warming. Seriously, what is there left of AGW theory?
I’ll try to point some messages: The “unforced pattern effect” from the natural variability is (was, after the release of the Lewis/Mautisen paper) a main arguement to dismiss the lower sensitivity values when one takes advantage of the observed warming, as it did Lewis/Curry in their paper 2018. They found an ECS of <2°C/2*CO2. The argument goes this way: The observed pattern of ocean warming is only one possible "trajectory", we were in luck due to a "freindly internal variability" in the past. It could also change rapidly and the "Climate feedback" gets lower and the sensitivity increases. This is what models expect. One of the most aggressive proponents of this argument is Andy Dessler, see this article: https://journals.ametsoc.org/jcli/article/33/6/2237/347141 . Lewis/Mautitsen (2020) were able to reject it with multiple lines of evidence. This means: The "unforced pattern argument" failed dramaticaly. To understand the whole thing you should read the acknowledgements : "We thank… Andrew Dessler and Stephen Po-Chedley for valueable discussions…" 🙂
The paper is a defeat of some scientists which promote a high sensitivity when trying to dismiss the real world observations and it's implication for the climate sensitivity.
Both sky-is-falling AND skeptical papers in climate science (?) have become more and more other-worldly as time goes on. The Climate Wroughters set the rules of the game and both sides engage with their different choices of official, highly artificial data sets for impotent, meaningless clashes.
The datasets are governed by algorithms that are constantly changing the sets all the way back to 1880. As Mark Steyn famously quipped at a Senate hearing on climate data that the clime- syndicate knows with high certainty what the weather will be like in 2100 but are uncertain about what it will be in 1950!
Nic is a smart guy but wastes his talent on this Neo-Astrology.
Nic, Thank you. But it’s your “principal” conclusion.
My and Thorsten Mauritsen’s principal conclusion is that assertions by climate scientists that natural (internal) variability in SST patterns over the historical period cause a low bias in estimates of effective climate sensitivity based on instrumental records are based on weak evidence, are inconsistent with the extent of natural variability in CMIP5 climate models, and that any such effect is likely small and of uncertain direction.
This finding relates purely to unforced natural variability in SST patterns. It says nothing about whether effective climate sensitivity estimated over the historical period is lower than equilibrium climate sensitivity (ECS), as it is in most CMIP5 climate models – due mainly to a forced change in SST warming patterns (a forced pattern effect).