by Nic Lewis
The four constraints that Caldwell assessed as credible.
In Part 1 the nature and validity of emergent constraints on equilibrium climate sensitivity (ECS) in GCMs were discussed, drawing mainly on the analysis and assessment of 19 such constraints in Caldwell et al (2018; henceforth Caldwell), who concluded that only four of them were credible. All those four constraints favoured ECS in the upper half of the CMIP5 range (3.4–4.7°C). An extract of the rows of Table 1 of Part 1 detailing those four emergent constraints is given below.
Caldwell regarded a proposed emergent constraint as not credible if it lacks an identifiable physical mechanism; is not robust to change of model ensemble; or if its correlation with ECS is not due to its proposed physical mechanism. The credible constraints identified in Caldwell are all related to tropical/subtropical low clouds and all except Brient Shal are significantly correlated with each other.
Figure 1 shows the geographical distribution of the dominant sources of correlation for each of the emergent constraints assessed as credible. In each case the principal source is cloud feedback, dominantly shortwave (SW). The spatial pattern of correlation between the emergent constraint and ECS arising from cloud feedback is strikingly similar for all four constraints, despite their metrics being based on differing regions. Inter-model variation in forcing for a doubling of CO2 also has a non-negligible contribution for two of the four constraints. Other feedbacks account for that part of the total correlation not attributable to cloud feedback and 2⤬ CO2 forcing, and in aggregate are small except for Brient Shal.
Figure 1. Geographical distribution of dominant sources of correlation with ECS of emergent constraints assessed as credible, per Figure 2 of the Caldwell supplementary material. The name of each constraint and its overall correlation Σ with ECS in GCMs are in bold. The correlations with ECS attributable to Net Cloud feedback and to Forcing are included in the title above each panel. Forcing is not shown where its absolute correlation is < 0.15.
Of the studies proposing the four emergent constraints that pass Caldwell’s tests, Sherwood et al. has over four times as many citations (313) as the other three studies between them, so I will start by examining the validity of its emergent constraints. They are also discussed in an informative earlier review of emergent constraints, Klein and Hall (2015), which (unlike the Caldwell paper) is open-access.
Sherwood et al. summed two physically-based metrics, D and S, to form a lower-tropospheric mixing index (LTMI) and used that as their principal emergent constraint. Caldwell found that while Sherwood S is meant to operate through SW cloud feedback, in CMIP5 models it actually gains correlation almost entirely through other terms. They therefore assessed both Sherwood S and Sherwood LTMI as not being not credible.
However, Caldwell assessed Sherwood D, which predicts tropical low cloud changes due to boundary layer (BL) drying by convection, as credible. Nevertheless, while Sherwood D is computed using data from the tropical Atlantic and East Pacific, Caldwell found that its correlation with cloud feedback there is smaller than in the West Pacific (see top LH panel of Figure 1). That might cast doubt on the credibility of D, since its physical mechanism was predicted to occur over cooler oceans where mid-level outflows of ascending air occur, rather than in warm tropical oceans such as the West Pacific where such outflows do not usually occur.  Moreover, Brient et al. (2015) found that while the convective mixing strength mechanism proposed by Sherwood et al. occurs in all GCMs, it only controls the low cloud response in about half the CMIP5 models.
Zhao et al (2016) showed that when they varied the convective precipitation parameterization in the new GFDL AM4 model, Sherwood LTMI and D changed little, and non-monotonically, between their high, medium and low ECS model variants. They say “It is clear that the two low-sensitivity models (M and L) produce a larger increase in upward water flux near the top of the boundary layer (800–900 hPa) than the high-sensitivity model (H)”, which is the opposite of the fundamental physical mechanism posited in Sherwood et al., being “that an increase of upward transport of moisture near the top of the boundary layer over the convective regions should result in decrease of low clouds because of dehydration of the boundary layer”.
If one takes Sherwood D at face value, all CMIP3 models and all but two CMIP5 models (ACCESS1-3 and CSIRO-Mk3-6-0) are unsatisfactory, in that they are inconsistent with the reanalysis-based estimates of D. Alternatively, if the reanalysis values are contaminated by model biases and a majority of models are actually consistent with the true value of D, then the Sherwood D constraint would not rule out low sensitivity models. In addition to this problem, the Sherwood D constraint seems to lack robustness to changes in the ensemble of models. Caldwell noted that Kamae et al. (2016) found that Sherwood LTMI explained low cloud feedback but not ECS in a perturbed physics ensemble (PPE), suggesting a lack of robustness. Of direct relevance to Sherwood D, Kamae et al. find no clear relationship between D and ECS in their multiple model-variant PPE. Figure 2 compares the relationship between D and ECS found by Sherwood et al. with that found by Kamae et al. in two subsets of their 8-model variant PPE, being those model-variants with respectively old and new convection schemes (OldCnv and NewCnv).
Figure 2. Relationship between Sherwood D and ECS found in Sherwood et al (LH panel; circles are CMIP3 models; triangles are CMIP5 models) and in Kamae et al (middle panel: OldCnv; RH panel: NewCnv; colours show model variants). The black squares and diamonds show the two reanalysis-derived D values. Kamae values at top left show correlations between D and ECS for individual model-variant PPEs as parameter values are changed. Statistically significant regression lines are shown.
While only two of the models that Sherwood studied (both with higher than average ECS) have D values that are consistent with the reanalysis-based values, three of Kamae’s model-variants have, for some parameter values, D values consistent with the reanalysis-derived values. These D-value consistent cases have widely varying ECS values, many of them being between 2.1°C and 3°C. Moreover, the sign of the relationship between D and ECS varies between the model variants, being positive for five and negative for three. If one were to add the Kamae data to Sherwood et al.’s data the implications for ECS of the Sherwood D constraint would be very different.
The doubt arising from the geographical source of much of the ECS correlation with the Sherwood D constraint arguably not being consistent with the proposed physical mechanism, the finding by Zhao et al. that that mechanism has the opposite effect on ECS across their three model-variants to that proposed in Sherwood et al., and the non-robustness of the correlation between the CMIP3/CMIP5 data and the Kamae data, all point to Sherwood D not actually being a credible emergent constraint. In any event, Sherwood D only explains 16% of the CMIP5 intermodel ECS variance, so it is a weak constraint.
Brient Shal is based on CMIP5 models with shallower tropical low clouds in weak-subsidence regimes tending to have a higher ECS. The paper’s authors did not view it as being about an emergent constraint, as they were fully aware of the limitations of climatological shallowness as an emergent constraint.(e.g., all models may misrepresent the relevant real-world dynamics in their parameterization schemes). The point of the paper was that different models produce different low-cloud structures because of differences in the parameterizations; some of these differences in the present-day climatology correlate with the models’ response to warming. Brient et al. (2015) emphasised the importance of the competition between convective drying and turbulent moistening. They found that the climatological shallowness of low clouds was an indicator of the relative importance of parameterized convective and turbulent mixing in climate models, while the competition between these two mechanisms was a key determinant of the change in low cloud extent with surface warming and hence SW cloud feedback and ECS. These are interesting findings. However, the shallowness index has limited ability to discriminate between models with differing climate sensitivity. Brient et al. found that CMIP5 models that had shallowness indexes consistent with observational estimates had ECS values spanning almost the entire CMIP5 range. Brient Shal explains even less of the CMIP5 intermodel ECS variance than Sherwood D – only 14%. Moreover, its rather weak 0.38 correlation with ECS in CMIP5 models (it could not be tested on CMIP3 models) arises entirely from the inclusion of four models that fail Caldwell’s clear-sky linearity test. The correlation for the 17 models that pass that test is negligible – only 0.05. There is no contradiction between any of this and what Brient et al. say in their paper – they did not claim that the shallowness measure is useful as an emergent constraint. I will examine the other two constraints that Caldwell considered credible, Brient Alb and Zhai, and set out conclusions, in Part 3 of this article.
 An emergent constraint on ECS is a quantitative measure of an aspect of GCMs’ behaviour (a metric) that is well correlated with ECS values in an ensemble of GCMs and can be compared with observations, enabling the derivation of a narrower (constrained) range of GCM ECS values that correspond to GCMs whose metrics are statistically-consistent with the observations.
 Caldwell, P, M Zelinka and S Klein, 2018. Evaluating Emergent Constraints on Equilibrium Climate Sensitivity. J. Climate. doi:10.1175/JCLI-D-17-0631.1, in press.
 The four studies involved are:
Brient, F., T. Schneider, Z. Tan, S. Bony, X. Qu, and A. Hall, 2015: Shallowness of tropical low clouds as a predictor of climate models’ response to warming. Climate Dynamics, 1–17, doi:10.1007/s00382-015-2846-0, URL http://dx.doi.org/10.1007/s00382-015-2846-0.
Brient, F., and T. Schneider, 2016: Constraints on climate sensitivity from space-based measurements of low-cloud reflection. Journal of Climate, 29 (16), 5821–5835, doi:10.1175/JCLI-D-15-0897.1, URL https://doi.org/10.1175/JCLI-D-15-0897.1.
Sherwood, S.C., Bony, S. and Dufresne, J.L., 2014. Spread in model climate sensitivity traced to atmospheric convective mixing. Nature, 505(7481), p.37-42.
Zhai, C., J. H. Jiang, and H. Su, 2015: Long-term cloud change imprinted in seasonal cloud variation: More evidence of high climate sensitivity. Geophysical Research Letters, 42 (20), 8729–8737, doi:10.1002/2015GL065911.
 In Brient Shal the major contributors to the remaining correlation contribution of –0.23 are the residual error terms arising from model and equation non-linearity.
 Klein, S. A., & Hall, A., 2015. Emergent constraints for cloud feedbacks. Current Climate Change Reports, 1(4), 276-287.
 Against that, Sherwood et al. argue that it is not expected nor required for the feedbacks to be concentrated in the same regions the constraints are measured.
 Zhao, M., Golaz, J. C., Held, I. M., Ramaswamy, V., Lin, S. J., Ming, Y., … & Guo, H. (2016). Uncertainty in model climate sensitivity traced to representations of cumulus precipitation microphysics. Journal of Climate, 29(2), 543-560
 Kamae, Y., Shiogama, H., Watanabe, M., Ogura, T., Yokohata, T., & Kimoto, M. (2016). Lower-tropospheric mixing as a constraint on cloud feedback in a multiparameter multiphysics ensemble. Journal of Climate, 29(17), 6259-6275.
 The Kamie study involved creating 8 model variants with noticeably different key parameterization and closure schemes that affect cloud and convective behaviour, and then undertaking a PPE investigation for each. As a result of including 8 permutations of these structural model aspects they sampled a far wider range of model behaviour than a single-model PPE, which involves varying key parameters in a model but not structural aspects of it.
 Tapio Schneider, personal communication, 2018.
Moderation note: as with all guest posts, please keep your comments civil and relevant.
It seems to me that before racking our brains, we must demonstrate that these variables are dependent of the CO2. And since from my point of view, the CO2 is essential dependent on the temperature, all that means nothing. The debat should stop here. I’m wrong????
Reblogged this on Climate Collections.
There is apparently a move away from the ECS to the more stable and reliable TCRE proportionality between temperature and cumulative emissions.
However, there may be a problem with the TCRE concept because the proportionality is also seen when irrelevant data are substituted for emissions data.
An emergent constraint on ECS is a quantitative measure of an aspect of GCMs’ behaviour (a metric) that is well correlated with ECS values in an ensemble of GCMs
“Emergent constraints” sound more like a cheat in a computer game, a clue to how to find the treasure or kill the endboss left accidentally by the programmer. I don’t see how it tells us anything about the real world.
Lets see if I can explain the logic.
You have system you want to model. Every group tries different approaches, there are over 100. The models have spread. Since they all use the same inputs, the spread is a result of modelling choices and inherent uncertainty in the complex process. To reduce the spread ( structural plus inherent uncertainty) you have some options: run each model many times ( wall time kills you) or find a model selection criteria.
The first obvious criteria is using temperature as a constraint. Pick those models that get temperature correct. Then we you look at the ECS value of the selected models you get an answer constrained by matching temperature.
But temperature change is a part of ECS calculation and some models are indirectlyy tuned by temperature or closely related variables.
So you look for an EMERGENT property: That is a property or metric that is not directly tied to inputs or tied to temperature. You select models based on this emergent ( develops as a result of programmed physics) property, so in the end you are using one emergent metric to constrain a different emergent metric (ECS)
Interesting approach to reducing structural uncertainty.
The good thing is it focuses your attention on areas not directly related to your inputs ( forcings– the denominator of ECS) or temperature ( the numerator of ECS)
“.. you are using one emergent metric to constrain a different emergent metric …”
The problem/fallacy of course is that this assumes that the model subset that produce the more accurate first metric is the one that produces more accurate metrics more generally, emergent or otherwise. Since model development involves all kinds of compromises with reality this won’t be so (otherwise why are the other models still in the game).
(if two men sat they’re jesus, then one of them must be wrong)…
Could an emergent constraint parameter be replicated from instrumental climate data at a particular time point? If so, then presumably the emergent constraint could be tested against historic and future data. This would also validate the model.
‘Temperature trends with reduced impact of ocean air temperature ‘
“Temperature data 1900–2010 from meteorological stations across the world have been analyzed and it has been found that all land areas generally have two different valid temperature trends. Coastal stations and hill stations facing ocean winds are normally more warm-trended than the valley stations that are sheltered from dominant oceans winds.
Thus, we found that in any area with variation in the topography, we can divide the stations into the more warm trended ocean air-affected stations, and the more cold-trended ocean air-sheltered stations. We find that the distinction between ocean air-affected and ocean air-sheltered stations can be used to identify the influence of the oceans on land surface. We can then use this knowledge as a tool to better study climate variability on the land surface without the moderating effects of the ocean.
We find a lack of warming in the ocean air sheltered temperature data – with less impact of ocean temperature trends – after 1950. The lack of warming in the ocean air sheltered temperature trends after 1950 should be considered when evaluating the climatic effects of changes in the Earth’s atmospheric trace amounts of greenhouse gasses as well as variations in solar conditions.”
Brient, F., and T. Schneider, 2016:
“Ground- and space-based observations point toward weakening shortwave reflection by TLCs under warming and hence an amplifying feedback”
That is hardly the right dynamic recipe for the temperature pause 2002-2014, in fact the pause suggests that the tropical iris opened to allow more surface warming, and then found a new and higher equilibrium.
“An increasing TLC cover dampens global warming, exerting a negative feedback, because the primary energetic effect of low clouds is to reflect shortwave radiation. Conversely, a decreasing TLC cover amplifies global warming, exerting a positive feedback.”
In the same time frame as the changes in tropical cloud cover, there has also been changes in the vertical distribution of atmospheric water vapour. Reductions in the upper troposphere and lower stratosphere, and gains in the lower to mid troposphere. If this is all teleconnected to the change of AMO phase, then it’s all negative feedbacks to a net reduction in climate forcing, indicated by the generally negative North Atlantic Oscillation regime since the mid 1990’s.
Nic: The current relative humidity of the boundary layer over the ocean is a net result of turbulent mixing in the boundary layer over the ocean and convective drying of the boundary layer by transport into the free atmosphere. The question is how do these phenomena change in response to higher SSTs. Turbulent mixing in the boundary layer ought to be a function of wind speed. The evaporation rate is proportional to wind speed and boundary layer undersaturation (1-RH).
The net heat loss across the TOA (from OLR and reflected SWR) can only rise at a rate of about -1 W/m2/K (according to the ECS of AOGCMs) or about -2 W/m2/K (according to the ESC of EBMs). The change in net heat flux across the TOA per degK of surface warming must be equal to the net increase from surface to atmosphere. If the relative humidity in the boundary layer and wind speed remain unchanged, then the latent heat flux from the surface will rise with warming by 7%/K or about -5.6 W/m2/K. A fairly huge 1%/K decrease in albedo with warming is only +1 W/m2/K. The change in OLR-DLR per K of surface warming is trivial according to Modtran and depends on which sounding is used.
So it appears as if climate sensitivity depends on the RATE at which convective drying of the boundary layer slows with warming. This increases boundary layer humidity (about 1%), reducing undersaturation from 20% to 19% (a 5% change), suppressing the increase in evaporation and precipitation to about 2%/K from 7%/K. Any increase in relative humidity in the BL over the ocean where boundary layer clouds are common should produce more of them. (Numbers from Held’s blog; my interpretation.)
However, if I understand correctly the constraints being discussed have nothing to do the RATES of change with change Ts (except perhaps for Brient Alb’s constraint). Is there any reason to believe a model’s ability to reproduce a static descriptor (an annual average?) for an atmospheric phenomena has anything to do with how that static phenomena will evolve on a warmer planet? If one pictures a graph of constraint vs. GMST, the constraint appears to be a single point on a line and we want to know the slope of the line.
Frank, the behaviour of the boundary layer under global warming is clearly complex and I won’t attempt to discuss it here. But one comment. The suppression on the increase in evaporation and precipitation to way below the 7%/K implied by the CC relationship may well be due more to a reduction in wind speed than to an increase in near-surface relative humidity over the ocean.
Both the Brient Albedo and Zhia emergent constraints involve the rate of change of cloud cover with SST, so they do involve the slope of the line. While the Brient Shallowness and Sherwood constraints do indeed not directly estimate the slope of any line, their authors present arguments that the values of those constraints is linked to the strength of feedbacks (particularly low cloud feedback), and hence to ECS, at least in GCMs. However, as discussed in my article, they extent to which that is the case appears be rather limited.
It turns out that the 7% only relates to the water vapor content in the atmosphere, not the evaporation and precipitation rates, which are more like flows through the system, and these are expressed as rates like mm/day. In climate models, these increase only about 2% per degree globally averaged. An early reference to it here from 2003.
Nic: Thanks for the reply. I need to keep reminding myself that changes in the boundary layer are a consequence (not the driver) of the change in radiative balance at the TOA with changes in surface warming, the climate feedback parameter. Nevertheless, these consequences involve large amounts of energy (W/m2/K) and are closely associated with the fundamental problem, clouds. The consensus seems to treat the slowing of the hydraulic cycle as a “feature” of climate models, not something that is mathematically linked with high ECS. Due to the magnitude of the latent heat flux involved, models that get this slowing wrong must have the wrong ECS.
You write: “their authors present arguments that the values of those constraints is linked to the strength of feedbacks (particularly low cloud feedback)”. That seems dubious. The ability to reproduce (via tuning?) the annual average of a current phenomena has nothing to do with with the ability to predict how that phenomena will change as the planet warms. Just as the y-coordinate of any data point (that can be tuned) tells us nothing about the slope of the line that contains that datapoint. IMO, nothing measured in W/m2 (without regard changing temperature) can provide useful information about W/m2/K.
Nic wrote: “While the Brient Shallowness and Sherwood constraints do indeed not directly estimate the slope of any line, their authors present arguments that the values of those constraints is linked to the strength of feedbacks (particularly low cloud feedback), and hence to ECS, at least in GCMs.”
When I expressed doubts about constraints, I was referring only to the interpretation of this two constraints, not all four constraints, nor to doubts about Nic’s conclusion. My concerns were meant to supplement his concerns, not contradict them.
JimD: If climate sensitivity is 3.7, then the increase in OLR emission across the TOA and reflection of SWR must be 1 W/m2/K. This is the climate feedback parameter. The increase in heat flux at the surface must also be 1 W/m2/K. Latent heat removes roughly 80 W/m2 of heat from the surface. The difference between a 2%/K and 3%/K change in precipitation with surface warming is an 0.8 W/m2/K increase in the climate feedback parameter and a decrease in ECS to 2.0 K. Climate sensitivity is intimately linked to the change in precipitation with warming.
It would probably be impossible to create a sensible AOGCM in which precipitation increases at the CC rate with temperature. That model would have 5.6 W/m2/K more heat exiting the top of the TOA, before any other feedbacks are included. If I include the traditional negative sign for heat lost, that is -5.6 W/m2/K to add to Planck feedback of -3.2 W/m2/K. That is an ECS of 0.4 K/doubling. Positive feedback for WV +2 W/m2/K. A large 1%/K decrease in albedo is only +1 W/m2/K. There are massive energetic constraints on how precipitation can change in response to warming.
People don’t add evaporation to the feedback parameter, and if you did, it would be a cooling effect opposed to the Planck feedback requiring even more surface warming to counter it. However, with that cooling there is also a warming in the atmosphere where it condenses, so it is largely self-canceling in terms of energy added. The additional condensation is part of the lapse rate feedback.
Jim D: Imagine a K-T energy balance diagram for a planet at equilibrium with doubled CO2, 3.7 K warmer at the surface due to an ECS of 3.7 K/doubling. At the TOA, the sum of the increase in OLR plus reflected SWR will be 3.7 W/m2, enough to negate the radiative forcing from 2XCO2. That is -1 W/m2/K expressed as a feedback. If albedo decreases a large 1%/K (+1 W/m2/K), reflected SWR will decrease by 3.7 W/m2, with perhaps 2.4 W/m of that reaching the surface. With this maximum decrease in albedo (positive SWR feedback), OLR could carry 7.4 W/m2 (-2 W/m2/K) more heat to space. (1%/K seems large when you remember that the GLM was 5K colder.)
Now look at the surface energy balance. Using Modtran, you will find that the net radiative flux (OLR-DLR) will show a small change. A full explanation is long, but OLR and DLR increase with Ts at nearly the same rate in our very IR-opaque atmosphere. If precipitation rises 7%/K, that will be today’s 80 W/m2 * 7%/K * 3.7K = 21 W/m2 more latent heat entering the atmosphere. The convection that carries that latent heat will also remove more sensible heat from the 3.7 K warmer surface.
So a 7%/K increase in precipitation appears impossible. A 2%/K increase bring the increase in latent heat flux down to 6 W/m2. Add some increase in sensible heat, possibly small change in OLR-DLR, and strong positive SWR feedback and the surface and TOA changes will be equal. If you don’t have strongly positive SWR feedback (1%/K = +1 W/m2/K), the latent heat flux will need to be lower. If SWR feedback is neutral, precipitation will like need to rise less than 2%/K.
The consensus recognizes that some mechanism of suppressing the logical 7% CC rise in evaporation/precipitation, but – in my limited experience – doesn’t explain its connection to climate sensitivity. Climate sensitivity is indeed controlled by the change in net flux at the TOA with the change in surface temperature (W/m2/K), but that drives very large changes in fluxes. Since clouds are critical to both TOA and surface latent heat fluxes, these phenomena are linked by energetic constraints.
Jim D wrote: “However, with that cooling there is also a warming in the atmosphere where it condenses, so it is largely self-canceling in terms of energy added.”
And the additional heat released by condensation (7%/K * 80 W/m2/K) must be radiatively cooled to space. So it adds to the 3.2 W/m2/K of Planck feedback and other feedbacks that must also reach space.
In the real world, the rate-limiting step in removing heat from our planet is radiative cooling from the troposphere to space. If the rate of radiative cooling with surface warming (the climate feedback parameter) is -1 W/m2/K, ECS is 3.7 K/doubling. If -2 W/m2/K, ECS is 1.85 K/doubling. If -3.2 W/m2/K, 1.16 K/doubling (the no-feedbacks ECS). At the TOA, there is no flux of latent heat to worry about, everything is radiative.
I’m conducting a thought experiment where heat transport from the surface to the atmosphere is rate limiting. The climate feedback parameter is unknown and can adopt any value needed. (Before Manabe and Weatherall in the 1960’s, everyone approached GHGs from this surface balance perspective, not the TOA.) I’m asking what would happen if precipitation rose 7%/K. Answer: An addition 5.6 W/m2/K would leave the surface as latent heat (carrying more sensible heat along with it). That heat must then be radiated to space or convection would slow. Heat from Planck feedback also needs to reach space (-3.2 W/m2/K). The remaining feedbacks total only about +1.4 W/m2/K. So, on an imaginary planet where the rate-limiting step is heat transport is from the surface to the atmosphere and evaporation rises at 7%/K, the climate feedback parameter would be -7.4 W/m2/K and ECS will be 0.5 K/doubling.
Since we know ECS is likely to be higher than this, my thought experiment shows that climate models and our planet must have some mechanism for suppressing the increase in evaporation from 7%/K to about 2%/K. That mechanism is critical to ECS. Evaporation is controlled by wind speed and “undersaturation” of the boundary layer, so evaporation can only be suppress by a decrease in wind speed or an increase in humidity near the surface over oceans. An increase in relative humidity over the ocean is caused by reduced “convective drying” or transport out of the boundary layer (an emergent constraint mentioned in this post). And the amount of change is tightly linked to climate sensitivity. Any AOGCM that doesn’t properly model the details of this suppression correctly won’t have the correct ECS.
So, it is clear there is no consensus of opinion– got it.
So AMOC collapse doesn’t quite do it? Nor perhaps meriodional NH cooling equivalent to the difference between RCP 4.5 and RCP 8.5? Nor perhaps a cooling Pacific from these intensified north/south with a cooling Sun?
Nothing quite does it for these people – they are addicted to doom.
… north/south patterns… I put it in the wrong place anyway.
Even latter day warming seems largely natural. Related to low level cloud change largely in the Pacific (Wong et al 2006, Clements et al 2009) – likely involving open and closed cell cloud formation in Rayleigh–Bénard convection in a fluid (the atmospher) heated from below (Koren et al 2017). That and a drought spike right at the end – less latent and more sensible heat flux at the surface (Pielke 2004).
I am encouraged that they are modeling this.
Even latter day warming seems largely natural.
All climate warming and cooling has been all natural for billions of years, we have not changed that. In fact, if we cause anything, we are part of nature too. The mass of CO2 in the atmosphere is small enough that we can ignore it except for how it makes green things grow. Water, in all its states is abundant, look there to understand climate.
Not strictly true. Anthropogenic gases are changing the atmosphere, oceans, hydrology and biology across the planet. We have little enough understanding on how these changes will play out – including CO2 ‘greening’ that has unknowable implications for terrestrial hydrology and biology.
Earth has been greener before, that is why we have an abundance of fossil fuels. Greener is more food for all the creatures on earth. War against CO2 is war against life on earth, especially us.
Do you have a simple scientific summary?
Re emergent correlation you say
“Sherwood S is meant to operate through SW cloud feedback, in CMIP5 models it actually gains correlation almost entirely through other terms”
So it did correlate? And the SW bit lacked the expected correlation, had no correlation or was actively negative wiping out the other correlation?
Mind you negative correlation is still scientifically very important.
Sherwood D, which predicts tropical low cloud changes due to boundary layer (BL) drying by convection is credible??
In 2 models only.
So in your opinion, and others, not really.
And does not help with ECS.
A better paper to look at for clarity is Qu et al. written by some of the same co-authors.
They find that “The relationship between each metric and ECS is largely attributable to a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread.”
Their paper ends with this.
“In such ensembles, the emergent constraints of ECS may be related to other factors influencing ECS. However, we deem this possibility unlikely in a multi-model ensemble like CMIP5, given the continuing uncertainty surrounding tropical low cloud feedback.”
In the end it comes down to how the models treat the tropical low-cloud shortwave feedback which is still quite variable due to the number of physical processes involved as well as large-scale factors. Specifically the ones that perform better in the current climate have positive cloud feedbacks.
angtech, sorry, it is a complicated subject. The abstract of the Caldwell 2018 review paper gives a short scientific summary, but it doesn’t say anything about the 4 emergent constraints that they find potentially credible, other than that they all predict relatively high ECS.
As stated in Part 1, Sherwood S had a correlation of 0.37 with ECS in CMIP5 models; that it, it explained 14% of the total variance in their ECS – not very much of it. The SW cloud feedback element of the correlation was almost zero.
Agree re Sherwood D, except that I find it not credible for the reasons given in the article.
Dr. Curry, I’ve written a rebuttal to Dr. Myles Allen’s presentation, but because you have a much larger following, it would be great if you or one of your frequent guests would do the same.
Sophistry In San Francisco; Half-Truths are Twice the Lie