By Nic Lewis
A comment on LC18 (recent paper by Lewis and Curry on climate sensitivity) by Cowtan and Jacobs has been published, along with our response.
Introduction
In an earlier article here I discussed the Lewis and Curry (2018) paper “The impact of recent forcing and ocean heat uptake data on estimates of climate sensitivity” (LC18) and set out its results.
The LC18 analysis used a global energy budget model to estimate the planetary equilibrium climate sensitivity (ECS) and transient climate response (TCR). ECS and TCR are estimated from changes (Δ) in global mean surface temperature [T], effective radiative forcing (ERF) [F] and the planetary radiative imbalance[1] [N] between a base and a final period, as:
ECS = F2×CO2 × ΔT /(ΔF – ΔN) and TCR = F2×CO2 × ΔT /ΔF
where F2×CO2 is the ERF for a doubling of atmospheric CO2 concentration.
The main LC18 estimates for ECS and TCR were as per Table 1. The main points of note are that they lie near the bottom end of the IPCC AR5 ‘likely’ ranges for ECS and TCR, and that they are both less uncertain and slightly lower than those given in the predecessor study, Lewis & Curry (2015) when using HadCRUT4 global surface temperature data. The LC18 best estimates based on the faster warming infilled Cowtan & Way Had4_krig_v2 temperature dataset are very similar to the HadCRUT4-based results in Lewis & Curry (2015).
Table 1 (based on Table 3 in LC18) Best estimates (medians) and uncertainty ranges for ECS and TCR using the base and final periods indicated. Values in roman type compute the temperature change involved (ΔT) using the HadCRUT4v5 dataset; values in italics compute using the infilled, globally-complete Had4_krig_v2 (Cowtan & Way) dataset. The preferred estimates are shown in bold. Ranges are stated to the nearest 0.05 K. Also shown are the comparable results (using the HadCRUT4v2 dataset) from LC15 for the first two period combinations given in that paper. ECS estimates assume that effective climate sensitivity does not change with time elapsed since imposition of forcing.
Summary of the Comment and Reply
A Comment on LC18 by Kevin Cowtan and Peter Jacobs, and a Reply from myself and Judith Curry, have just been published by Journal of Climate. A copy of the Reply is available here.
The Comment (referred to as CJ20, as it appears in the 1 January 2020 issue) is arguably more a critique of observational sea surface temperature (SST) datasets than of the methods and results of LC18. Its abstract reads as follows:
Lewis and Curry (2018) (hereafter LC18) present a method for the estimating the transient climate response (TCR) of the climate system from the temperature change between two time windows – an early baseline period in the 19th century, and a modern period primarily in the 21st century. The results suggest a lower value of TCR than estimates from climate model simulations. Previous studies have identified uncertainty in the historical forcings, the impact of the time evolution of the forcing on temperature response, and observational issues as contributory factors to this disagreement. We investigate a further factor: uncertainty in the bias corrections applied to historical sea surface temperature data. This uncertainty can particularly impact the estimation of variables on decadal timescales, and therefore impact the estimation of TCR using the window method as well as estimates of internal variability. We demonstrate that use of the whole historical record can mitigate the impacts of working with short time windows to some extent, particularly with respect to the early part of the record.
Originally, CJ20 asserted that the base and final periods – what they call early and late windows – chosen in LC18 – which were matched as regards volcanic forcing and influence from multidecadal internal variability – led to lower values of TCR (CJ20 did not address the LC18 ECS estimates). They subsequently removed that claim, which the analysis in our submitted Reply disproved. The final version of CJ20 focuses on the possible impact of using windows rather than all the historical data, in particular the impact – based on comparing warming in CMIP5 (current generation) climate models and in observations – of the choice of varying dates for the windows, and on uncertainty in bias corrections to historical SST data. CJ20 focus on use of the HadCRUT4 temperature record, but – as LC18 made clear – it is appropriate to use a globally complete record for comparison with climate model results. We accordingly used only Kevin Cowtans’s infilled version of HadCRUT4, Had4_krig_v2, in our Reply.
The abstract for my and Judith Curry’s Reply to CJ20 reads as follows:
Cowtan and Jacobs assert that the method used by Lewis and Curry in 2018 (LC18) to estimate the climate system’s transient climate response (TCR) from changes between two time windows is less robust – in particular against sea surface temperature bias correction uncertainty – than a method that uses the entire historical record. We demonstrate that TCR estimated using all data from the temperature record is closely in line with that estimated using the LC18 windows, as is the median TCR estimate using all pairs of individual years. We also show that the median TCR estimate from all pairs of decade-plus length windows is closely in line with that estimated using the LC18 windows, and that incorporating window selection uncertainty would make little difference to total uncertainty in TCR estimation. We find that when differences in the evolution of forcing are accounted for, the relationship over time between warming in CMIP5 models and observations is consistent with the relationship between CMIP5 TCR and LC18’s TCR estimate, but fluctuates due to multidecadal internal variability and volcanism. We also show that various other matters raised by Cowtan and Jacobs have negligible implications for TCR estimation in LC18.
In a nutshell, we refuted all points of substance made in CJ20. I plan to deal with the differences between observed and CMIP5 model-simulated historical warming, which formed the basis of CJ20’s numerical analysis, in a subsequent article. In this article, I will elaborate on our refutation of points in the remainder of CJ20.
Window selection related uncertainty
Regarding the claim by CJ20 concerning uncertainty induced by window choice, this is what we had to say in the Reply, having tested the effects of random selection of windows from a decade upwards in length,[2] all of which led to median TCR estimates very close to LC18’s 1.33 °C [= 1.33 K]:
For estimates with the highest (2.0 Wm−2) minimum forcing increase, which are most relevant to LC18’s TCR estimate, the 5–95% TCR uncertainty range arising from random window selection is 1.08–1.54 K, or 1.20–1.59 K using 0.55-scaled volcanic forcing. The width of these ranges – 0.103 and 0.073, respectively, in fractional standard deviation terms[3] – reflects the fact that many of the window combinations involve mismatched influences from internal variability and/or volcanism. These window selection uncertainty ranges do not imply that LC18 underestimated uncertainty in global temperature change: the 1σ fractional uncertainty in LC18’s preferred TCR estimate attributable to temperature change uncertainty (including that from internal variability) alone was 0.103.[4] Moreover, even if no allowance is made for double counting of temperature change uncertainty, estimated overall TCR uncertainty would increase little if window selection uncertainty were added. Adding (in quadrature) the 0.103 or 0.073 1σ fractional uncertainty in TCR from window selection to the 1σ fractional uncertainty of the preferred LC18 TCR estimate, would only increase it to 1.13⤬ its original level, or to 1.07⤬ that level if using 0.55-scaled volcanic forcing.[5]
This shows that uncertainty in TCR estimation arising from window selection is minor even if no allowance is made for double counting of temperature uncertainty, and negligible if allowance is made for such doubling counting.
Using data from the entire historical record
CJ20 propose use of data from the entire historical record. In fact, LC18 tested doing so, by the usual regression method, but found mismatching volcanic influence made estimation sensitive to the scaling factor used for volcanic forcing. Without scaling down volcanic forcing the TCR estimate from regression over the whole historical period is far lower than that from using the windows method. This is what we said in the Reply:
When AR5 volcanic forcing is scaled by 0.55, regression of median annual-mean temperature on forcing over 1850–2016 gives a 1.27 K Had4_krig_v2-based TCR estimate, marginally lower than LC18’s 1.33 K two-window based preferred estimate. Regressing pentadal means (over 1852–2016) significantly improves the fit (to an R2 of 0.92) and gives a TCR estimate of 1.33 K. Using such pentadal-mean regression on each of the 500,000 pairs of samples of temperature and forcing time series gives a 5–95% TCR range of 0.91–1.84 K, marginally lower and narrower than the LC18 preferred estimate range.
So, the results of TCR estimation using data from the entire historical record is closely in line with those using LC18’s window method and chosen windows, provided the volcanic forcing is scaled down as per LC18’s recommendation. However, the uncertainty induced by having to estimate the appropriate volcanic forcing scaling factor arguably makes using data from the full historical record a less satisfactory approach than using the windows method.
Issues with historical sea surface temperature data
There is indeed significant uncertainty as to the accuracy of the global SST record. However, CJ20 did not show that the LC18 TCR estimates were materially affected by any identified errors in SST bias corrections. Nor did they show that uncertainty in the SST record was greater than that estimated by the providers of the datasets used in LC18.
CJ20 make the point that coverage of the ‘water hemisphere’ was almost non-existent in the 1860s. However, the 1869–82 primary early window used in LC18 avoids the 1860s (save for 1869, when coverage was better), and provides slightly higher coverage in the (land-sparse) southern hemisphere than in the northern hemisphere.
CJ20 also state that nineteenth century temperatures are dependent on large ‘bucket corrections’ to sea surface temperature (SST) observations, however CJ20 themselves suggest that the change from wooden buckets to poorly insulated canvas buckets requiring a large bias correction occurred primarily during 1890–1910. Bucket corrections were relatively small during 1869–82, the LC18 early window.
Possible misestimation of forcings
This is what we wrote in the Reply concerning two forcing estimation issues raised in CJ20:
CJ20 claim that previous studies have identified differences in inferred forcings and in the temperature impact of historical versus transient forcing changes as potential explanatory factors for recent observational energy-budget TCR estimates being lower than average climate model TCR values. None of the three supporting studies that they cite supports either contention.
and
CJ20 claim that comparison of modeled and observed temperatures for late windows starting after 2005 is affected by overestimation of forcings in models. Since LC18 did not make any comparisons of modeled and observed temperatures over the historical period, the only issue of relevance to LC18 is whether it misestimated recent forcing. None of the three supporting studies that CJ20 cite indicate that LC18 misestimated recent forcing.
In fact, a more comprehensive study[6] found, in their CMIP5-specification historical simulations, that since the mid-2000s underestimation of changes in other forcing agents more than counteracted overestimation of changes in solar and volcanic forcing. Moreover, none of the studies cited in CJ20 addressed the real problem, of bias in CMIP5 model forcing that already existed several decades ago (due to principally to excessive aerosol forcing); none of their analyses started before 1980.
Ocean and air surface temperature in models and observations
In CMIP5 models near-surface marine air temperature warms more than the ocean surface temperature field (‘tos‘). CJ20 state that “Lewis and Curry argue that this field [tos] is not the top layer of the bulk ocean surface temperature” (to which measured SST broadly corresponds). However, this straw man argument, which CJ20 disprove, was never made in LC18. As the reply states:
CJ20’s claim that LC18 “argue that this field [tos] is not the top layer of the bulk ocean surface temperature” is incorrect. Rather, LC18 argued that the tas/tos warming difference reflects the model-simulated warming difference between tas and ocean skin temperature, which will warm differently from SST.
There are theoretical reasons for expecting air just above the ocean surface to warm slightly faster than the ocean skin temperature. However, the extent of the difference depends on many factors and is uncertain, as is the difference between the warming rates of SST and of ocean skin temperature. LC18 therefore focused on observational rather than CMIP5 model evidence in this area. We say in the Reply:
LC18 (section 7e) concluded from observational and reanalysis evidence that in the real climate system, tas warmed at most a few per cent more than a blend of tas and tos (model top ocean layer temperature), a substantially smaller difference than that claimed by CJ20. Indeed, the 1979-onwards ERA-interim reanalysis globally-complete surface air temperature record, adjusted for inhomogeneities in their SST source (Simmons et al. 2017), shows slightly lower warming over 1979–2016 than does Had4_krig_v2.
It is also worth noting that in CMIP5 models tas, unlike tos, is a diagnostic rather than a prognostic variable – it is a parameterised extraneous variable, not a variable featuring in the basic model physics.
Conclusion
None of the criticisms of LC18 in the Reply stand up to examination. I leave examination of differences between observed and CMIP5 model-simulated historical warming, which formed the basis of CJ20’s numerical analysis, to a subsequent article. Suffice to say here that such differences, when properly analysed in the light of differences in forcing evolution, are fully consistent with the LC18 TCR estimate.
Nicholas Lewis December 2019
[1] N is estimated from its counterpart, the rate of climate system heat uptake, which is mainly by the ocean.
[2] Since small inter-window forcing increases provide poor TCR estimation, minimum required inter-window forcing increases, ranging from 1.0 to 2.0 Wm−2, were imposed. (The greater the forcing increase the lower the relative uncertainty, as regards both forcing and the change in temperature that it causes. The windows used for LC18’s main ECS and TCR estimates gave a forcing increase of 2.52 Wm−2.) There were over 11,000 decade plus long window combinations giving a forcing increase of 2.0 Wm−2 or more. For computational tractability, early and late windows were specified to be of equal length. When using LC18’s suggested 0.55 scaling of volcanic forcing the median TCR estimates were even closer to 1.33 K at all levels of required forcing increase, and had lower uncertainty ranges, than when using unscaled volcanic forcing.
[3] So as to be able readily to combine uncertainties, we work with 1 standard deviation fractional uncertainties, here derived by scaling from 17-83% ranges and medians in Table 1
[4] Scaling from the 5-95% range and median for Had4_krig_v2 ΔT in Table 2 of LC18. If temperature uncertainty alone is incorporated, the fractional uncertainty in TCR equals that in ΔT.
[5] Scaling from the 17-83% range in Table 3 of LC18, giving a fractional standard deviation of 0.193 for the preferred LC18 TCR estimate. Uncertainties are taken to be normally distributed and independent for the purposes of deriving their standard deviations and combining them. Adding in quadrature a fractional standard deviation of 0.103 (0.073) to the original level of 0.193 increases it to 0.219 (0.207).
[6] Outten, S., Thorne, P., Bethke, I. and Seland, Ø., 2015. Investigating the recent apparent hiatus in surface temperature increases: 1. Construction of two 30‐member Earth System Model ensembles. Journal of Geophysical Research: Atmospheres, 120(17), pp.8575-8596.
Thanks Nic for the clarification. I state that there is no hint to any “repeatedly debunked work” ( Source: http://www.realclimate.org/index.php/archives/2019/12/how-good-have-climate-models-been-at-truly-predicting-the-future/#comment-751758) when it comes to L/C(18) up to now. Also in the latest reviewed article this approach failed. It’s good to know.
Re: “Thanks Nic for the clarification. I state that there is no hint to any “repeatedly debunked work” ( Source: http://www.realclimate.org/index.php/archives/2019/12/how-good-have-climate-models-been-at-truly-predicting-the-future/#comment-751758) when it comes to L/C(18) up to now. Also in the latest reviewed article this approach failed. It’s good to know.”
Running here to evade criticism from RealClimate is a bad look, especially if you think posting your points here will prevent the person you’re responding to from seeing your criticism.
Nice try.
http://www.realclimate.org/index.php/archives/2019/12/how-good-have-climate-models-been-at-truly-predicting-the-future/#comment-751791
The cartoon character is here and posts a content free comment. He has never pointed to anything that is deficient in Nic’s work. He’s just an anonymous internet disinformation troll casting smears against very honest and skillful scientists. So Atomic powered “expert”, tell us where it has been debunked so Nic can destroy you. I would be surprised if Nic will waste time on any clearly unsupported assertions, which is what most of your assertions are so try harder to come with something substantive.
dpy: :-) Indeed this post could win the award “most content free writing of the year” . His claims were all refuted, he favours papers with “forgotten forcing agents” but makes spam here and elsewhere. My discussion with him ended long ago.
I don’t doubt that this work has value, and correct me if I am wrong, but it looks to me like it suffers from the usual shortcoming of all ECS calculations, namely it assumes that the factors causing delta T are known. So for example you take out multidecadal internal variability and volcanic activity because they are “known” factors. If anything significant has been missed, then like every other estimate of ECS yours is going to be incorrect.
Absolutely. I assume he assumes that internal variability is random and thus sums to zero over time?
https://watertechbyrie.files.wordpress.com/2018/05/nile-e1527722953992.jpg
I don’t know where to start with Koutsoyiannis’ oeuvre – http://www.itia.ntua.gr/en/byauthor/Koutsoyiannis/0/ – but here’s a fun little piece. A random walk on water.
https://www.hydrol-earth-syst-sci.net/14/585/2010/hess-14-585-2010.pdf
“namely it assumes that the factors causing delta T are known.”
Wrong. it makes no such assumption. It works from what is known.
If you have factors you think are not represented then you have to provide
the factor; name it. not just arm wave that there might be unicorns. and you
have to quantify the factor.
Wrong. It ignores what is known. And has for far too long.
https://watertechbyrie.files.wordpress.com/2019/06/ceres_ebaf-toa_ed4.0_anom_toa_longwave_flux-all-sky_march-2000todecember-2018.png
https://watertechbyrie.files.wordpress.com/2019/06/ceres_ebaf-toa_ed4.0_anom_toa_longwave_flux-all-sky_march-2000todecember-2018.png
This one maybe.
https://watertechbyrie.files.wordpress.com/2019/06/ceres_ebaf-toa_ed4.0_anom_toa_shortwave_flux-all-sky_march-2000todecember-2018-1.png
A lot of it the positive SST/cloud feedback effect. Here’s the IPCC in 2007 being a little doubtful – but science has moved on a lot since then.
“In summary, although there is independent evidence for decadal changes in TOA radiative fluxes over the last two decades, the evidence is equivocal. Changes in the planetary and tropical TOA radiative fluxes are consistent with independent global ocean heat-storage data, and are expected to be dominated by changes in cloud radiative forcing. To the extent that they are real, they may simply reflect natural low-frequency variability of the climate system.” AR4 3.4.4.1
You would have to show how the evidence remains ‘equivocal’ – and that there isn’t a robustly demonstrated geophysical mechanism at work.
Quite apart from other very imprecisely known factors. Especially aerosols.
e.g. https://www.pnas.org/content/113/16/4243
Mike Jonas wrote:
…it looks to me like it suffers from the usual shortcoming of all ECS calculations, namely it assumes that the factors causing delta T are known.
What unknown factors would you like to include? And how?
Hi Nic. Thanks for this post. Given your analysis which as usual looks conclusive, is there anything of value in their note?
I love a good food fight
It is useful that Cowtan and Jabobs highlight problems in ERSST ocean temperature data, as used in GISTEMP and NOAAGlobalTemp. They thought, mistakenly, that LC18 used products based on ERSST data, but in the end were forced to correct that claim.
Their points about issues with post World War II HadSST3 data that is employed in the Cowtan & Way global temperature product used in LC18 (and in HadCRUT4) are probably worth airing as well, despite not being novel. However, the serious issues relate to the period up to 1975, which is not used in the main LC18 ECS and TCR estimates.
Sorry.
The mass of ice rain will be so great that it will cause great damage. The radar below shows what’s happening now.
https://www.accuweather.com/en/us/new-york/weather-radar?fbclid=IwAR2l5BXw-01NR_jrz7RaK6kRZkHMe95uFEx2Pm4OGovtd_dteTPJKjtIv24
A simple assessment that shows that CO2 has nothing to do with climate is at https://watervaporandwarming.blogspot.com
There’s a fundamental misconception afoot that thermalization of the atmosphere takes place primarily via the molecular collision of GHGs with inert bulk constituents. In reality such radiative heat transfer is miniscule compared to direct moist convection of evaporation from the surface
wrong.
The GHE hypothesis is about reducing cooling to space. not thermalization.
And the molecular mechanism is not absorption but scattering, very similiar to the one which makes the blue cloudless sky, called Rayleigh scatter. If you admit that the sky is blue you should not write about “fundamental misconception” when it comes to the GHE.
Joh,
“molecular collision of GHGs with inert bulk constituents” would be thermal conduction in the gas. Molecular collision is not “radiative heat transfer”. Thermalization consists of absorption of radiation energy by ghg molecules and the sharing of this energy with surrounding molecules via thermal conduction. Moist convection is a separate issue. I found it to be about the same as radiation in Sect 5 of http://globalclimatedrivers2.blogspot.com
I was simply challenging Pangburn’s stated misconception of atmospheric thermalization:
and not the “GHG hypothesis” per se, nor any other aspect of heat transfer that amateurs may imagine.
Frank:
“And the molecular mechanism is not absorption but scattering, ”
So does that apply to H2O?
Which I assume you accept is the “most important GHG”.
And if so how can “scattering” cause the GHE?
Tony: The earth surface -space directed LWR is scatterd when it hits GHG molecules (H2O; CO2…). Therefore it’s no more directed but diffuse, also partly back to the surface which is warmer if GHG’s are in the atmosphere.
Frank,
“Molecules of carbon dioxide (CO2) can absorb energy from infrared (IR) radiation. This animation shows a molecule of CO2 absorbing an incoming infrared photon (yellow arrows). The energy from the photon causes the CO2 molecule to vibrate. Some time later, the molecule gives up this extra energy by emitting another infrared photon. Once the extra energy has been removed by the emitted photon, the carbon dioxide molecule stops vibrating.”
https://scied.ucar.edu/carbon-dioxide-absorbs-and-re-emits-infrared-radiation
No, LWR is not scattered by the atmosphere, it is SWR that is. If that process produced heat then visible light on it’s own would heat the atmosphere, for as you say, it is scattered to produce a blue sky.
The GHE is known science as of the 19th century. And it is caused by absorption/re-emission and vibratory contact with other atmospheric gases, to ultimately escape to space above an altitude where the temperature is the Black/grey body temp and thereby does it less efficiently than if straight from the surface.
Scattering has nothing to do with it.
Tony: The outcome seems to be the same also when you call the processes in between absorption and re-emitting of LWR: The only difference for the LWR is, that the flux is no more earth-space directed but the re-emitting is diffuse. The GHG-molecule is not changed in relation to the state before, the amount of energy of the LWR is not changend but the direction of the radiation has changed, very similiar to the Rayleigh scatter. Can we agree at this point?
frankclimate, Tony, John321s, Dan, and others: Absorption and scattering are two physically distinct mechanisms by which the intensity of a beam of radiation traveling along a line-of-sight is reduced or “extinguished”. These concepts are clearest when applied to a beam of collinear radiation, such as a laboratory spectrometer. However, they apply to radiation traveling all directions in the atmosphere too, especially the LWR emitted by GHGs in the atmosphere.
1) Absorbed LWR photons increase the amount of energy in quantized vibrational and rotations states of GHGs. In the lower atmosphere, the lifetime of such excited states is so long that literally millions of molecular collisions occur in the time it takes for the average excited state to emit a photon (1 second for the first excited vibrational state of CO2). Consequently, from a practical point of view, absorbed photons are never “re-emitted”. Instead, excited states are relaxed by collisions. (Some like to say the absorbed photons are “thermalized”, but “collisional relaxation” of an excited state is a clearer description.) Therefore essentially all of the excited GHGs in the atmosphere are produced by collisions, and emission is a function only of temperature (a Boltzmann distribution of excited states) – and not of the local radiation field. When a Boltzmann distribution of excited states exists, this is called local thermodynamic equilibrium. LTE doesn’t exist everywhere – we’ve invented LEDs and fluorescent lights to obtain visible light at room temperature instead of a filament or flame at several thousand degK. And lasers and microwave ovens are not in LTE. However, the vast majority of GHGs in our climate system are in LTE with the local temperature.
2) Scattering “extinguishes” radiation traveling along a line-of-sight when a particle redirects photons onto a new path. Scattering in the atmosphere can be done by individual air molecules, aerosols, cloud water droplets, rain drops. Scattering is most important when the size of the particle and the wavelength of the radiation are similar. When particles are smaller than the wavelength, re-direction is called Raleigh scattering. When the particles are larger than the wavelength, redirection is called Mie scattering. Figure 12.1 of Grant Petty’s superb and inexpensive “A First Course in Atmospheric Radiation” shows the practical implications of this principle for our atmosphere: UV, Visible and near IR (SWR) are scattered by gas molecules; thermal IR (LWR) and microwaves are not. The wavelength of blue light is closer to the size of air molecules, so scattering makes the sky appear blue. Aerosols and clouds scatter both SWR and LWR. Rain drops scatter LWR, but are too big to have much effect on SWR. Particles can both scatter and absorb radiation.
Consequently, the slowdown in LWR radiative cooling to space by increasing GHGs is caused by absorption, not scattering, of LWR. No absorbed photons are “re-emitted” – the emission of LWR is a function of only the local temperature.
This is very much misguided. Good absorbers of LWR are invariably also good emitters at the same frequency. The appearance to the contrary is the result of GHG collisions with non-absorbing bulk constituents that return the majority of GHG molecules to their base LTE state before they can emit any excess photons. Kinetic energy is thereby transferred to the broad spectral continuum, instead of staying within the discrete bands characteristic of GHGs, which show much reduced energy densities at TOA. Nevertheless, all matter above absolute zero radiates somewhere in the EM spectrum. Aside from terrestrial radiation passing directly through certain “windows,” such broad-band re-emission of atmospheric radiation is the only ticket to space.
John321s: You are correct that GHGs emit most strongly at the wavelengths they absorbed most strongly. However, in troposphere and lower stratosphere 99+% of the excited GHG molecules were excited by collisions and 99+% of the excited states created by absorbing a photon are relaxed by collisions before they can re-emit a photon. Based on your comment, you may recognize this. Unfortunately, you also wrote:
“Aside from terrestrial radiation passing directly through certain “windows,” such broad-band re-emission of atmospheric radiation is the only ticket to space.”
The “only ticket to space” begins with collisional excitation and relaxation creating a Boltzmann distribution of excited GHG molecules. The fraction of GHGs excited depends on the local temperature. Fewer than one in a thousand of those exciting states emits a photon before being relaxed by a collision. The video linked linked by Tony is grossly misleading.
https://scied.ucar.edu/carbon-dioxide-absorbs-and-re-emits-infrared-radiation
John wrote: “Kinetic energy is thereby transferred to the broad spectral continuum, instead of staying within the discrete bands characteristic of GHGs, which show much reduced energy densities at TOA.”
Radiation in our atmosphere isn’t “broad-band”, especially higher in the atmosphere where pressure and Doppler broadening are greatly reduced. Kinetic energy can have any value (continuous), but the internal energy contained in vibrational, rotational and electronic states is quantized. The spectrum of blackbody radiation derived by Planck creates the ILLUSION that a broad spectral continuum is a normal state of affairs. Planck derived his law by assuming electromagnetic radiation IN EQUILIBRIUM with quantized oscillators, but radiation of a given wavelength can’t reach equilibrium if the absorption coefficient at that wavelength is too small. So GHGs at normal pressures don’t emit a blackbody spectrum appropriate for their temperature. And our as a whole atmosphere doesn’t emit like a blackbody either: At some wavelengths and altitudes, absorption and emissions don’t come into equilibrium with GHGs at the local temperature.
The correct physics for the interaction between radiation and matter starts with Einstein coefficients and mechanisms for broadening the discrete lines postulated by QM. When a Boltzmann distribution of excited states exists (LTE), one can arrive at Schwarzschild’s equation for radiative transfer without scattering:
dI = n*o*B(lambda,T)*ds – n*o*I*ds
where is the change (dI) in the spectral intensity (I) of radiation of wavelength lambda traveling an incremental distance ds through a medium of absorbing/emitting molecules of density n, with absorption cross-section o at temperature T. The first term is emission by the layer and the second term is absorption by the layer. When radiation has traveled far enough through a homogeneous medium that absorption and emission have come into equilibrium (the equilibrium Planck postulated), dI is zero and and I = B(lambda,T). Schwarzschild’s equation says that radiation traveling through a medium has a blackbody spectrum/intensity or is approaching a blackbody spectrum/intensity at a rate proportional to the density of absorbing/emitting molecules and the strength of their interaction with radiation of a particular wavelength (their absorption cross-section). This is what happens in our atmosphere. Unfortunately, the temperature and density of our atmosphere changes with altitude, and (temperature-dependent) equilibrium between absorption and emission is far from universal.
In solids and liquids, the density of absorbing/emitting molecules is high and the excited states are perturbed/broadened because individual molecules are in slightly different environments. This creates broad absorption bands and eventually a continuum. The same thing happens to gases at very high pressures, including to the CO2 on Venus. However, gas molecules are generally isolated from each other and in exactly the same environment.
Really? Irrigation responsible for global warming or increasing OLR in the satellite era doesn’t give you pause?
But as I understand it – Rayleigh scattering of blue light is not the result of photon absorption and emission in random directions. Scattering happens I understand as massless photons with ‘relativistic momentum’ bounce preferentially off molecules in a certain size range. Although I struggle to understand this in the light of wave/particle duality. Absorption and emission of photons with quantum jumps in electron orbits might be better thought of as randomizing photon paths. Something different again from the excitation of vibrational and rotational modes as kinetic energy induced by perfectly elastic collisions between photons and molecules. But then the first principal is – if someone thinks they understand quantum mechanics they don’t.
RIE,
Water vapor has been increasing faster than possible from temperature increase. Irrigation accounts for about 96% of the WV increase. Land area under irrigation is more than 4 times the area of France. I expect that’s an eye opener to lots of folks.
For a hydrologist sepecializing in biogeochemical cycling – the only eyebrow rise is at your conclusion. But I have commented on this before – forgive me if I don’t do it again.
Dan: Glanced at your new blog. A reasonable start, but you need to understand more about how GHGs interact with LWR. They both absorb and emit thermal IR. If you double the amount of a GHG in the atmosphere, you double the number of LWR photons being emitted and you halve the distance each photon travels between absorption and emission – to a first approximation. This is what happens at the center of all of the strong absorptions, and why the fact that there is 100-fold more water vapor than CO2 neat the surface doesn’t make any difference. The GHE and enhanced GHE from rising CO2 or water vapor arise from the shoulders of absorption lines that allow LWR to travel long distances between emission and absorption. Long distances in the atmosphere mean the GHG that absorbs a photon can be much colder or warmer that the GHG (or surface) that emitted it. Temperature has a big effect on emission; and little effect on absorption. Our atmosphere wouldn’t have a GHE, if it didn’t have a temperature gradient.
You might start by reading the Wikipedia article about the Schwarzschild equation for radiation transfer. Or perhaps the ScienceofDoom blog.
The IPCC’s view of climate is extremely “CO2-centric”. The average water vapor molecule remains in the air about 9 days between evaporation and precipitation. (Divide the total column water vapor by the daily precipitation rate.) So temperature effects the amount of water vapor in the atmosphere much faster than water vapor can warm the planet by acting as a GHG – which it certainly does in the long term. In the short term, average water vapor at any location is a function of temperature, though it varies widely from day to day and season to season.
One definition of radiative forcing is the instantaneous change in net flux at the TOA before anything else (temperature or water vapor) changes. Forcing is combined with feedback: the change in the net flux at the TOA per degC increase in GMST. That includes “water vapor feedback” – the change in flux at the TOA associated with change in GMST. So the IPCC’s typical approach does take water vapor into account.
Finally, every AOGCM calculates the effect of water vapor and CO2 in every grid cell on the LWR passing through, absorbed or emitted by that grid cell.
Hope your new blog leads to enlightenment.
“For over thirty years, climate scientists have presented a likely range for ECS that has hardly changed. The ECS range 1.5−4.5 K in 1979 (Charney 1979) is unchanged in the 2013 Fifth Assessment Scientific Report (AR5) “
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“At the heart of the difficulty surrounding the values of ECS and TCR is the substantial difference between values derived from climate models versus values derived from changes over the historical instrumental data record using energy budget models.”
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“ The median ECS given in AR5 for current generation (CMIP5) atmosphere-ocean global climate models (AOGCMs) was 3.2 K, versus 1.6K approx LC”
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This must be about the 10th recent attempt to refute a fatal flaw in one line only of AGW reasoning.
Instead of accepting the reality that climate models have most likely got it wrong the efforts get more and more bizarre.
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If the models are wrong, and ECS etc is only half of the best worse case scenario then we can all relax and rely on the fossil fuels running out before they can ever cause significant problems.
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The physics is clear.
Agreement on how to use the physics is not.
ECS without feedbacks is quite low.
Adjacent but lower than LC which is observational and thus includes feedbacks that exist.
ECS with model feedbacks incorporated (not real, parmeterised) is much higher.
At a minimum double observational.
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The sad fact is the higher one can make the ECS the higher one can scream the world is ending.
3.2K is sadly not even the median of scaremongering.
The fat tail argument allows conjecture out to ridiculous levels and RCP 8.5 doom predictions.
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You should label your work as real (observational) and CMP 5 as model (fictional).
“All fictions are wrong but some are scary” as Steven (King) might say.
You wrote: The physics is clear.
300 to 400 parts per million is one more molecule in ten thousand.
The physics of how one molecule can heat up ten thousand molecules is NOT CLEAR! That can not matter more than one divided by ten thousand, plus or minus ten or a hundred times nothing.
It works because CO2 can have an enormous cross section for absorption of IR. At its maximum it’s about 10,000 m2/kg, or 1.1 acre/lb. Since the areal density of atmospheric CO2 is 6.3 kg/m2, at such wavelengths the IR gets absorbed within 10s of cm. And CO2 has ~500,000 absorption bands in the IR.
popesclimatetheory
“You wrote: The physics is clear.
300 to 400 parts per million is one more molecule in ten thousand.
The physics of how one molecule can heat up ten thousand molecules is NOT CLEAR!”
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The physics is clear. How we use it is not.
Your argument in a good cause is misleading.
In several ways that I think you are fully aware of.
You state the amount of GHG molecules only considering CO2 to make a point the wrong way. As you well know the amount of water vapor alone is a lot higher.
For arguments sake only let us say CO2 is 5 percent of the total GHG and water is 95 percent. This would give us up to 8000 ppm of GHG molecules . Close to 1 in 800, so that sounds a bit more reasonable.
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Particularly when these are the molecules that are able to absorb and emit LWR. There is little point in mentioning 799 molecules that do not react to LWR.
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Third, you ignore the incoming force completely deliberately. If you have a matchstick you will not heat up the molecules much. If you have an enormous amount of energy from the sun you can expect the molecules to “heat up” a lot and transfer that energy to the other non absorbing molecules. Further when you consider how many millions of times in 12 hours or 12 minutes this can occur it is not just one tiny bit of energy GHG molecules receive and pass on but a lot of energy.
I am quite happy to accept and use and believe established science.
The argument about CO2 levels and a temperature response is not in dispute for most people.
The argument is about the sensitivity, first and adverse effects, if any, second.
If you wish to attack the science use science properly, mention the bits that do not agree with your proposition as well as the bits that do.
IP: I’m not quite sure if your continued weather reports are on the right place (Climare etc) here.?
Reblogged this on Climate- Science.press.
Reblogged this on Climate Collections.
Warming hiatus: The climate change myth that refuses to die
Posted on December 31, 2018
“Climate science deniers say warming stopped from 1998 to 2013, and temperatures are now falling. Both claims are blatantly false.”
Kevin Cowtan is Professor of Chemistry at the University of York.
Stephan Lewandowsky is Chair of Cognitive Psychology at the University of Bristol”.
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Hmmmm…
https://www.mdpi.com/2225-1154/6/3/62
https://i.imgur.com/yT71bCv.png
“Scare quotes”
Yea, but the Left essentially exploits scare quotes in a much bigger way, by exploiting children as walking scare quote machines. This makes their scares appear unassailable, aka Greta Thunberg.
Yawn.
And some people exploit that paper by making people believe the scientists who wrote it are acknowledging there was actually a DaPaws.
Abstract
This study examines changes in Earth’s energy budget during and after the global warming “pause” (or “hiatus”) using observations from the Clouds and the Earth’s Radiant Energy System. We find a marked 0.83 ± 0.41 Wm−2 reduction in global mean reflected shortwave (SW) top-of-atmosphere (TOA) flux during the three years following the hiatus that results in an increase in net energy into the climate system. …
Are there scare quotes there or not?
Are these ‘scare quotes’. 🤣 The interesting thing is (a) where cloud cover reduction occurred (b) the strength of the signal ‘post hiatus’ and (c) wondering about MBL stratocumulus dynamics over the upwelling region of the eastern Pacific.
Cowtan is right.
Cowtan is an apologist for AGW. He is so biased that it makes his scientific claims need to bear extra scrutiny.
“Climate science deniers say warming stopped from 1998 to 2013, and temperatures are now falling.
Both claims are blatantly false.”
The claims are obviously true, Climate science deniers do say this and they often say temperatures are falling.
And mainly true, thanks JCH.
“This study examines changes in Earth’s energy budget during and after the global warming “pause” (or “hiatus”) using observations from the Clouds and the Earth’s Radiant Energy System.”
And occasionally true, Temperatures fall or rise depending on whom wishes to do Cherry Picking.
Cowtan at Skeptikal science
“I have made available a new version of our infilled land-ocean temperature reconstruction (Cowtan & Way 2014, Richardson et al 2016) using HadSST4 (i.e. the second dataset on this page), and included it in the trend calculator (here and here). Temperature trends over the supposed “hiatus” period have increased. Estimates of temperature change compared to a pre-industrial baseline and of climate sensitivity are increased, however these depend on the longer term features of the records over which there is still substantial disagreement. The new land-ocean temperature record is compared to CMIP5 climate model simulations in Figure 3. While the CMIP5 simulations are expected to overpredict recent warming (because the emissions increases projected in 2005 were too high), the agreement between observations and projections is improved.”
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Interesting that he admits CMIP5 projections are too high.
How does he reconcile this with predicting that they might be OK?
I recommend carefully checking any statements of supposed fact made at “Skeptical Science”. Kevin Cowtan’s statement that “the CMIP5 simulations are expected to overpredict recent warming (because the emissions increases projected in 2005 were too high)” is untrue – certainly in relation to CO2, the principal greenhouse gas.
Total anthropogenic CO2 emissions over 2006-2018 136.9 GtC per the highest, RCP8.5, scenario that he uses for his CMIP5 simulation warming values. The actual total emissions over that period, per the authoritative Global Carbon Budget report published in 2019 were 139.3 GtC, 1.75% higher.
The relative increase in actual over RCP8.5 scenario emissions is slightly higher, as RCP8.5 assumed 2005 emissions of 9.17 GtC but the actual figure was 2.4% higher at 9.39 GtC. So the CO2 emissions difference implies that the CMIP5 models are expected to underpredict recent warming, not to overpredict it.
Recent emissions of non-CO2 greenhouse gases are much less well documented, but post-2005 trends in the next most important gas to CO2, methane, appear to be broadly in line with RCP8.5 over the period for which I have data.
My guess is that Cowtan’s comment is referring to all GHGs, not just CO2.
Per NOAA AGGI, the forcings from all GHGs 2005-2018 increased by 0.475 W/m2. In RCP8.5, for the same time period, the increase is 0.520 W/m2.
So for all GHGs, the actual increase in RF was about 10% lower than the RCP8.5 projection.
As Cowtan says, this would presumably suggest that yes, RCP8.5 would have overpredicted warming because the post-2005 increase in GHGs was too high.
At the other extreme, predicted forcings from all GHGs in RCP2.6 are about 10% lower than actual.
CDA: I’m not quite sure if one can reduce the projected ERF of the RCP 8.5 on GHG (all). In the RCP 8.5 pathway are all ( also LUC, ERFaero ect.) forcing agents included, see https://link.springer.com/article/10.1007/s10584-011-0149-y . When comparing the GMST development vs. the ERF (RCP 8.5) it makes sense to look at the sum: ERF antro. In my source ( http://www.pik-potsdam.de/~mmalte/rcps/ ) I found the annual global data for RCP8.5. The difference 2018-2005 gives +0.608 W/m² and the observed ERF antro change in this timepan gives +0.621 W/m². I can’t find a reason why the RCP 8.5 runs will under/overpredict the warming due to the ERF when it’s in the observations (marginally) bigger than in the ERF8.5 projections, near +2 %.
Hello, frankclimate. I was merely inferring what the quote from Kevin Cowtan was referring to — he said “emissions”, which suggests to me that he was referring to GHGs but not land use albedo etc. Also, it’s relatively easy to compare GHG forcings in the RCPs to reality, since those are reasonably well measured in recent years. I used the NOAA AGGI site for GHG RFs, which is a reasonable “apples to apples” comparison to the “All GHGs” column from the RCP X forcings tables.
That said, it would obviously be better to compare the total anthro forcings, or better yet the total overall forcings, from the RCPs to the real world values. I’m sure that this has been done, but as a casual observer I’m not aware of a good, readily available source for annual observations on the same set of forcings that are included in the “total anthro” or “all forcings” columns from the RCP 8.5 (or whatever) forcings table. So I’d be interested in seeing what you are using for that. Thanks.
CG = CDA, same person. Sorry.
CDA: To show it more understandable:
https://i.imgur.com/5nurQxc.jpg
In the end the ERF antro according to the RCP 8.5 projections are definitely not not the reason why CMIP5 runs will overpredict the recent warming. The only remaining reason is their sensitivity IMO.
CDA, CG or whatever :-) : For the RCP 8.5 ERF I used the website (PIK) with the given link, for the RCP8.5 you can scroll down and download an ASCII or XLS-file. I used http://www.pik-potsdam.de/~mmalte/rcps/data/RCP85_MIDYEAR_CONCENTRATIONS.xls . For the observed ERF I used the same data as were used in L/C18, updated to 2018, they are available here: https://www.nicholaslewis.org/wp-content/uploads/2018/05/LC18-AR5_Forc.new_.csv
I hope this will help.
Thanks
It is wise to question anything written at Skeptical Science. They admit they have a political agenda and it biases everything they touch.
It is wise to question everything written everywhere.
Real science is always skeptical and real science always does question everything said and written everywhere.
Many have political agendas, many just do not know much, many do not even suspect.
It is wise to question all of them!
It is more wise to question your questioning.
Skeptical science is a joke
They recent ran article on how sea level rice was causing a migration out of the farming community in the Mekong delta (viet nam). The peer reviewed study blamed it all on climate change and sea level rise.
I posted that the “peer reviewed study failed to take into account
1) The mekong delta, like most all other deltas was sinking
2) Viet nam was experiencing a manufacturing boom over the last 20 years, so like all societies entering the industrial age, there was a large migration from rural to urban
3) Farming was becoming more efficient, thereby less labor to produce the same yields.
I was highly critisized since by rebuttal wasnt “peer reviewed”
What a joke.
Joe
“In the Mekong Delta (‘Delta’), shared by Cambodia and Vietnam, groundwater exploitation has increased dramatically in recent decades. Up from a limited number before the 1960s, today more than one million wells access groundwater for domestic, agricultural, and industrial needs, causing hydraulic heads (i.e., groundwater levels) to steadily decline in many aquifers over extensive regions (Wagner et al 2012). Over-exploitation of groundwater exposes a densely settled population of >20 million living in the Delta to a variety of hazards associated with naturally occurring arsenic contamination (Erban et al 2013), subsurface saline intrusion (e.g., Bear et al 1999), land subsidence (e.g., Poland 1984) and potential damage to infrastructure, as well as increases in the depth and duration of annual flooding. Most of the Delta lies within 2 m of current sea level and is highly vulnerable to the additive effects of regional pumping-induced land subsidence and sea-level rise (SLR) due to global climate change.”
https://iopscience.iop.org/article/10.1088/1748-9326/9/8/084010/meta
They are so blinded by their rabid dogmatic mentality they don’t want to look at the actual science.
“They are so blinded by their rabid dogmatic mentality they don’t want to look at the actual science.”
Yes, but it’s worse. Unfortunately ignorance isn’t bliss with Skeptical Science, and others; they look at sound scientific rebuttals as an opportunity to double down with propaganda and obfuscation. SS, in particular, is the tip of the spear for public disinformation, all under the veil of championing education.
Can you provide a link to the peer-reviewed study that blamed absolute SLR as only cause of relative sea level rise at the Mekong River Delta?
JCH
You did say you wanted more studies that showed subsidence in Mekong Delta, didn’t you?
“Many major river deltas in the world are subsiding and consequently become increasingly vulnerable to flooding and storm surges, salinization and permanent inundation. For the Mekong Delta, annual subsidence rates up to several centimetres have been reported. Excessive groundwater extraction is suggested as the main driver. As groundwater levels drop, subsidence is induced through aquifer compaction. Over the past 25 years, groundwater exploitation has increased dramatically, transforming the delta from an almost undisturbed hydrogeological state to a situation with increasing aquifer depletion.”
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6192430/
“….land subsidence and sea level rise will have the greatest effect on both the middle delta and coastal areas of the VMD, and the vulnerability of flooding in the delta would increase in the coming years”
https://www.sciencedirect.com/science/article/pii/S2214581817302021
“ The present morphology of the Mekong Delta has been developed over the last 6000 years, during which time the delta has advanced around 200 km towards the sea [12]. Land subsidence is a gradual settling of the ground owing to the subsurface movement of unconsolidated sediments. The Mekong Delta as a “deltas in peril”, where reductions in aggradation and accelerated compaction will overwhelm rates of global sea-level rise. Groundwater exploitation could be a major cause of land subsidence “
https://www.mdpi.com/2071-1050/8/9/959/htm
“Similar to many of the world’s major deltas, the Mekong has experienced significant anthropogenic changes during the past decades, including land-use change [9], mainly to develop agriculture dominated by rice paddies [10]. This resulted in widespread overexploitation of freshwater resources [11], with excessive groundwater usage during the last decades resulting in various problems, such as the rapid falling of groundwater levels [12]. In such areas, overabstraction of groundwater resources can amplify natural subsidence processes [13]). In the VMD, new evidence of widespread absolute subsidence was revealed by Interferometric Synthetic Aperture Radar (InSAR) [14,15,16], which demonstrated that a steady increase of groundwater use and excessive pumping over the past decades has dramatically accelerated subsidence in this area.”
https://www.mdpi.com/2073-4441/11/1/75/htm
If the Mekong Delta ever becomes as developed as Florida, look out.
Silly.
Relative Sea Level Rise includes Vertical Land Motion, which can be anthropogenic.
The idea of climate equilibrium similar to that of inert matter is unrealistic. The size of life is constantly changing by internal stimuli. It is a very complex thermodynamic process. Conceptualizing that the climate can be defined by a one simple three-term equation is impossible.
The first differential global energy equation is 1st law of thermodynamics fundamentally true. Ein = Eout is maximum entropy at a transient energy balance.
Δ(ocean heat) ≈ Ein – Eout
“The Earth’s climate system is highly nonlinear: inputs and outputs are not proportional, change is often episodic and abrupt, rather than slow and gradual, and multiple equilibria are the norm. While this is widely accepted, there is a relatively poor understanding of the different types of nonlinearities, how they manifest under various conditions, and whether they reflect a climate system driven by astronomical forcings, by internal feedbacks, or by a combination of both.” https://www.globalcarbonproject.org/global/pdf/pep/Rial2004.NonlinearitiesCC.pdf
The first differential global energy equation is 1st law of thermodynamics fundamentally true. Ein = Eout is maximum entropy at a transient energy balance. Δ(ocean heat) ≈ Ein – Eout.
“The Earth’s climate system is highly nonlinear:”
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There is a fundamental disconnect between the way we treat the earth, atmosphere and ocean as simple heat exchange mechanisms at one, model, basic level and lose sight of the individual variations and permutations that different substances of different densities, volumes and states have in their necessary heat exchanges.
–
The amount of energy that hits the earth each day to keep it as warm as it is is almost incomprehensible. As is the amount of energy that goes out.
In that tiny effervescent boundary window we refer to as surface temperature change, because we are tiny and effervescent, the minute changes over minute time frames become very important to us. The physics is relentless though. Heat in, Heat out..
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CO2 and water vapour and other GHG do not warm the planet. Ocean currents at depth do not magically produce heat or cold though they can be hot or cold.
What they do is retain heat in certain layers of substances and lose it in others.
If the atmosphere is hotter then the surface has to be cooler than it would otherwise be with no GHG.
How much hotter it can get is a very complex interplay of physics and chemistry and position.
“We are living in a world driven out of equilibrium. Energy is constantly delivered from the sun to the earth. Some of the energy is converted chemically, while most of it is radiated back into space, or drives complex dissipative structures, with our weather being the best known example. We also find regular structures on much smaller scales, like the ripples in the windblown sand, the intricate structure of animal coats, the beautiful pattern of mollusks or even in the propagation of electrical signals in the heart muscle. It is the goal of pattern formation to understand nonequilibrium systems in which the nonlinearities conspire to generate spatio-temporal structures or pattern. Many of these systems can be described by coupled nonlinear partial differential equations, and one could argue that (…) the field of pattern formation is trying to find unifying concepts underlying these equations.” http://www.ds.mpg.de/LFPB/chaos
I’m the Khaos Kid – 😊 – but to 1st order it is the change in ocean heat that drives so many Earth processes.
https://watertechbyrie.files.wordpress.com/2018/05/ocean-heat-and-power-flux.jpg
Figure: Argo ocean heat – 0 to1900m – 65S to 65N – (blue) – degrees C – versus cumulative monthly radiant imbalance – (orange) – W/m2
CERES radiant imbalance is the difference between incoming and outgoing power flux calculated as a monthly average and accumulated. There is a ‘one off adjustment’ in CERES – some 0.6 W/m2 from memory – to enable closure of the energy (power flux over time) budget. Argo is less compromised. And yes I know they are different units – but it is no surprise that they covary.
This may be of interest.
https://journals.ametsoc.org/doi/pdf/10.1175/2009BAMS2752.1
REI,
Still using “power flux” in your graphs? W/m2 is an energy flux..
Anyway, if you want to align Argo and CERES-EBAF data, try to use the full depth 0-2000 m (2000 dbar) and full extent 70 S to 80 N.
Next step is to calculate the change in energy content which is about 620*10^22 J per degree C for the Global 0-2000 m layer.
The change in energy content per year ( in 10^22 J) can be converted to global W/m2 through dividing by 1.61
Finally divide by 0.82, which is the approximate share of the global energy imbalance that ends up in the 0-2000 m ocean layer.
Also, take the first three year of Argo data with a pinch of salt. There were widespread pressure sensor problems and not complete global coverage (the target deployment was reached in late 2007). There is large disagreement between various OHC datasets 2004-2006.
The result would be a good agreement between Argo and CERES EBAF, at least between 2007 and 2019
Flogging a dead horse I see.
W/m2 is a power – or radiant – flux. I’m afraid you lost me right there. The rest is just massively irrelevant. But feel free to make your own chart.
Your conclusion is incorrect I feel – it is the one off adjustment in CERES that introduces a bias. Incoming and outgoing power flux cannot be directly compared because of a relative imprecision of some 5W/m2. This is reduced relatively arbitrarily to a more reasonable imbalance loosely based on ocean heat. Radiant imbalances are neither constant or consistently positive. This cannot be a definitive energy budget. As interesting as CERES data products are. Argo – even to 1900 m – the full depth of the Scripps Argo Marine Atlas profiles -. seems more reliably grounded (?) even in the early years.
Converting Watts to Joules is simple – but still doesn’t match the pattern of either surface or ocean warming. 😊
https://www.mdpi.com/climate/climate-06-00062/article_deploy/html/images/climate-06-00062-g001-550.jpg
The source of Argo data – btw – is the Global Marine Argo Atlas using the Roemmich-Gilson climatology.
http://www.argo.ucsd.edu/Marine_Atlas.html
http://sio-argo.ucsd.edu/RG_Climatology.html
Flogging a dead horse I see.
W/m2 is a power – or radiant – flux. I’m afraid you lost me right there. The rest is just massively irrelevant. But feel free to make your own chart.
Your conclusion is incorrect I feel – it is the one off adjustment in CERES that introduces a bias. Incoming and outgoing power flux cannot be directly compared because of a relative imprecision in absolute values of some 5W/m2. This is reduced relatively arbitrarily to a more reasonable imbalance loosely based on ocean heat. Radiant imbalances are neither constant or consistently positive. This cannot be a definitive energy budget. As interesting as CERES data products are. Argo – even to 1900 m – the full depth of the Scripps Argo Marine Atlas profiles -. seems more reliably grounded (?) even in the early years.
Converting Watts to Joules is simple – but still doesn’t match the pattern of either surface or ocean warming. 😊
https://www.mdpi.com/climate/climate-06-00062/article_deploy/html/images/climate-06-00062-g001-550.jpg
The source of Argo data – btw – is the Global Marine Argo Atlas using the Roemmich-Gilson climatology.
http://www.argo.ucsd.edu/Marine_Atlas.html
http://sio-argo.ucsd.edu/RG_Climatology.html
Sorry.
The extremely high pressure in the Rocky Mountains.
https://www.tropicaltidbits.com/analysis/models/gfs/2019121700/gfs_uv250_us_3.png
I warn you of severe frost in northern US.
Are the maxima in Figs 4 of LC18 the best estimates for climate sensitivity? These curves rely on various calculations of forcings derived from common modeling approximations, e.g. Manabe-Strickler.
Assumption of the existence of a temperature function implies path-independent thermodynamic properties. The basic model is of a steady-state with the transport of energy by radiation and convection between two interfaces. Associated with any steady state is its dissipation of energy, the work necessary to forestall a relaxation towards an isothermal equilibrium. Underlying the 2nd law is a principle of minimum dissipation – nature uses all unconstrained internal variables, e.g. the thermal profile, to minimize this work, qualitatively keeping the surface as cool as possible and favoring low sensitivities. All other factors equal, mathematical approximations necessarily lead to higher sensitivity values.
Perhaps, the inflection point near 1K merits closer consideration.
dpy6629 | December 17, 2019 at 12:48 pm | Reply
“It is wise to question anything written at Skeptical Science. They admit they have a political agenda and it biases everything they touch.”
–
True, but we learn far more from our enemies than from our friends. Skeptikal science sets out hundreds of arguments against AGW and does a poor job of debunking the important ones.
Plus you get to know a who’s who about the drivers of the religion.
And you get a comedy channel with lots of laughs.
Don’t have a sea level argument with an alarmist. It will last forever and you will eventually drown.
Name an important one.
Steven Mosher
“Name an important one.”
Easy,
Any of the topics that you, Zeke and Nick Make more than 2 comments on together. The ones that rile you.
At coffee at the moment.
Trump impeachment initial in.
As Anders said it is a crazy, cruel world at times.
This should cheer him up a little.
Will get back with 4 of the most important since you asked nicely
Global Warming & Climate Change Myths.
Up to 197.
That is impressive.
“Name an important one.”
Hmm. I would go with
67 “Clouds provide negative feedback” not a myth, “how dare they”
Roy Spencer does a good outline of how important cloud cover can be.
Science of Doom dodges the issue.
Alarmists trot out all manners of denial of this very important and true fact.
Ties in with 112 “Roy Spencer finds negative feedback”
186 “UAH atmospheric temperatures prove climate models are wrong”
6 “Models are unreliable”
–
We could go with a few we both agree are likely rubbish related to our concerns.
28 “Mars is warming”
83 “Jupiter is warming”
82 “Neptune is warming”
–
But you are do agree that clouds play a very important part in predicting the climate and that we do not take them into account properly, don’t you?
Yes the models are unreliable one is particularly devoid of real knowledge or domain specific experience. Now that Palmer and Stevens have given the lie to it, it is doubly debunked. There was also the misinformers page whose article on Pielke Sr. was taken apart by Fabius Maximus and essentially every point was shown to be wrong.
That’s what you have to expect from mostly young activists with a few junior and inexperienced scientists thrown in. Many of course are in fields like machine learning totally unrelated to climate or atmospheric sciences.
The Pielke one shows actual malice and a willingness to mislead. It’s basically a political smear designed to discredit.
Old Mosher notes of interest
“there is a step before gridding where 5 sigma
values are tossed.”
“The issue when you look at the detailed data is for example some record cold in the US. 5 sigma type weather.
Looking through the data you will find that in the US you have feb anomalies beyond the 5 sig mark with some regularity. And if you check google, of course it was a bitter winter. Just an example below. Much more digging is required here and other places where the method of tossing out 5 sigma events appears to cause differences (in apparently both directions)”
–
Just inquiring how you deal with clouds in your data since you don’t exactly see them and the potential for them to cause 5 sigma events. Also to cause them locally.
Do they cause 5 sigma events?
Can you see them when you correlate your daily data with a cloud cover map for the day?
Why when clouds drop temperatures by so much do you rule these changes out so much?
I presume clouds cause either drops in temperature which can be quite severe persistent and local or buffer warmth [ they don’t exactly increase it].
When removing low 5 sigma events why are there so many compared to high 5 sigma events?
dpy6629
‘”Yes the models are unreliable, one is particularly devoid of real knowledge or domain specific experience.”
–
I agree that is a very important one.
Unlikely to drag an argument out of Steven as that view of yours is one of only a few he would agree with.
Now that Palmer and Stevens have given the lie to it, it is doubly debunked. …
Tim Palmer said, this was after the article was written, that the climate models were “remarkably accurate” at the job they were called upon to do.
His exact words.
December so far in 2019:
https://i.imgur.com/DQ2jTqU.png
Hilarious.
JCH Thanks for the graph
December so far in 2019:
An inquiry, there is a global map temp anomalies floating around which agrees with your Dec Temps but all of Australia is very, very hot compared to the rest of the world.
Out of kilter by several degrees from anywhere else and the surrounding ocean.
It is summer so it is hot here but this anomaly sticks out like a sore thumb as one of Steven’s 5 sigma events.
What is the BOM doing for heaven’s sake??
Is this all satellite data ie shows up on Roy’s or all airport data?
I thought science is a consensus. I’ve heard plenty of times that the science is settled. I’m so confused now… Skeptical Science told me that 97% of climate scientists agree on humans impacting the climate, I forget the correct terminology they used when sending out the surveys. I’m sure y’all know which one I’m referring to (cook et. al). Now that I think about it, isn’t Cook the creator of skeptical science…?
David, which “10 aging scientists” do mean in person?
Yes, Appell needs to define “aging”.
Actually, Mr. Appell, repeated surveys of climate scientists show a) that the ‘consensus’ consists of about 2/3rds of scientists who agree that humans are responsible for ‘over half’ of the current warming and b) that older scientists are more aligned with the consensus than their younger colleagues.
thomasw…: However, the context of his comment confuses me and I would appreciate if he would be more on target: Which “aging 10 scientists” are excepted in his opinion from the “consensus”? What is his definition of “aging” and who are those scientists in person?
I am not sure whether my Word Document symbols will come through here and thus I might have to correct what I have posted.
Nic, I hope your work is appreciated here for all the details that have to be covered in the use of EBM in estimating the observed ECS and TCR. I have used your delta T method in determining GMST changes from observed and modeled data and compared the results to that derived from using Singular Spectrum Analysis (SSA) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). The agreement was very good.
When comparing the observed derived ECS and TCR values with those derived from CMIP5 models in the observed period, I am wondering how this can be accomplished. We obviously can estimate the modeled ECS from special model experiments like the 4XCO2 CMIP5 experiment and the modeled TCR from the 1% CO2 experiment. The problem I see for a model to observed comparison in the observed period is that the forcing used in the models differs over a range of individual models and can vary to a large amount from that of the observed forcing.
The differing forcings are covered in Forster et al. (2013) where individual model forcings are determined (and referred to as adjusted forcings) using the EBM in conjunction with model GMST change and R and the feedback parameter determined from the 4XCO2 CMIP5 experiment.
Forster et al. (2013), Journal of Geophysical Research: Atmospheres
Volume 118, Issue 3, Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/jgrd.50174
In my analysis I found that for CMIP5 models with the RCP 4.5, 6.0 and 8.5 scenarios, the change in GMST in the future period (2006-2100) correlates very well with either F/ρ or (F-R)/λ , where F is either the individual model F2XCO2 or the change in forcing (and applied at the same value for all models), R is the change in the TOA net radiation, ρ is the climate resistance and λ is the feedback parameter. Unfortunately that is not the case in the historical period (1861-2005) where obviously different forcings (from Forster) were applied to individual models.
There exists a disconnection for the models in general between the historical and future periods. We can thus compare the observed ECS and TCR to modeled values using the special CMIP5 experiments and/or the modeled future period. A direct comparison of these values in the historical period for the observed and modeled is beyond my current comprehension. Using Forster GMST changes and forcings would be circular since forcings were generated in part from changes in GMST.
My analysis noted above is written up in the link:
https://www.dropbox.com/s/jvps550us39npd1/Disconnect2.pdf?dl=0
Ken
Our Reply deals with comparing warming in models and observations, and related forcing estimates, and I hope to highlight that in a follow up article here. It does use the Forster method for deriving forcings, but with feedback calculated over a period-length more relevant to the historical period. I agree that this does give rise to a substantial (but incomplete) element of circularity, however for two models where forcing has been independently diagnosed the method is surprising accurate, apart from in relation to volcanic forcing, which is therefore dealt with separately in the relevant part of the Reply.
Current temperature anomalies in the north of the US reach over a dozen degrees C.
https://www.tropicaltidbits.com/analysis/models/gfs/2019121806/gfs_T2ma_us_1.png
Comparison of results the planet Te calculated by the Incomplete Formula:
Te = [ (1-a) S / 4 σ ]¹∕ ⁴
the planet Te calculated by the Complete Formula:
Te = [ Φ (1-a) S (β*N*cp)¹∕ ⁴ /4σ ]¹∕ ⁴ (1)
and the planet Te (Tsat.mean) measured by satellites:
Planet or…….Te.incomplete……….Te.complete………..Te.satellites
…moon………….formula……………formula………………measured
Mercury…………..437 K…………….346,11 K…………….340 K
Earth……………..255 K……………..288,36 K……………288 K
Moon…………….271 Κ……………..221,74 Κ………….. 220 Κ
Mars……………..211,52 K………….215,23 K……………210 K
http://www.cristos-vournas.com
Current temperature anomalies in the US.
https://www.tropicaltidbits.com/analysis/models/gfs/2019121812/gfs_T2ma_us_1.png
Nic Lewis, thank you for this essay.
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Nic have you had a chance to look at the Connolly’s studies yet?
Yeah!
1. Earth’s-Without-Atmosphere Effective Temperature Calculation:
So = 1.362 W/m² (So is the Solar constant)
Earth’s albedo: aearth = 0,30
Earth is a rocky planet, Earth’s surface solar irradiation accepting factor Φearth = 0,47
(Accepted by a Smooth Hemisphere with radius r sunlight is S*Φ*π*r²(1-a), where Φ = 0,47)
β = 150 days*gr*oC/rotation*cal – is a Rotating Planet Surface Solar Irradiation Absorbing-Emitting Universal Law constant
N = 1 rotation per day, is Earth’s sidereal rotation period
cp.earth = 1 cal/gr*oC, it is because Earth has a vast ocean. Generally speaking almost the whole Earth’s surface is wet. We can call Earth a Planet Ocean.
σ = 5,67*10⁻⁸ W/m²K⁴, the Stefan-Boltzmann constant
Earth’s-Without-Atmosphere Effective Temperature Complete Formula Te.earth is:
Te.earth = [ Φ (1-a) So (β*N*cp)¹∕ ⁴ /4σ ]¹∕ ⁴
Τe.earth = [ 0,47(1-0,30)1.362 W/m²(150 days*gr*oC/rotation*cal *1rotations/day*1 cal/gr*oC)¹∕ ⁴ /4*5,67*10⁻⁸ W/m²K⁴ ]¹∕ ⁴ =
Τe.earth = [ 0,47(1-0,30)1.362 W/m²(150*1*1)¹∕ ⁴ /4*5,67*10⁻⁸ W/m²K⁴ ]¹∕ ⁴ =
Te.earth = 288,36 Κ
And we compare it with the
Tsat.mean.earth = 288 K, measured by satellites.
These two temperatures, the calculated one, and the measured by satellites are almost identical.
http://www.cristos-vournas.com
Φ is simply an ad hoc free parameter that you use to get the correct temperature. Yes?
Φ – parameter explanation
“Φ” is an important factor in the Planet Effective Temperature Complete Formula:
Te = [ Φ (1-a) S (β*N*cp)¹∕ ⁴ /4σ ]¹∕ ⁴ (K)
It is very important the understanding what is really going on with by planets the solar irradiation absorption.
The solar irradiation reflection, when integrated over a planet sunlit hemisphere is:
0,53 [ ( a ) S ] π r²
The fraction left for hemisphere to absorb is:
Φ = 1 – 0,53 = 0,47
or
Jabs = Φ (1 – a ) S π r²
The factor Φ = 0,47 translates the absorption of a disk into the absorption of a hemisphere with the same radius.
When covering a disk with a hemisphere of the same radius the hemisphere’s surface area is 2π r². The incoming Solar energy on the hemisphere’s area is the same as on disk:
Jdirect = π r² S
The absorbed Solar energy by the hemisphere’s area of 2π r² is:
Jabs = 0,47*( 1 – a) π r² S
It happens because a hemisphere of the same radius “r” absorbs only the 0,47 part of the directly induced on the disk of the same radius Solar irradiation.
In spite of hemisphere having twice the area of the disk, it absorbs only the 0,47 part of the directly induced on the disk Solar irradiation.
Jabs = Φ (1 – a ) S π r² ,
where Φ = 0,47 for smooth without atmosphere planets.
and Φ = 1 for gaseous planets, as Jupiter, Saturn, Neptune, Uranus, Venus, Titan. Gaseous planets do not have a surface to reflect radiation. The solar irradiation is captured in the thousands of kilometers gaseous abyss. The gaseous planets have only the albedo “a”.
And Φ = 1 for heavy cratered planets, as Calisto and Rhea ( not smooth surface planets, without atmosphere ). The heavy cratered planets have the ability to capture the incoming light in their multiple craters and canyons. The heavy cratered planets have only the albedo “a”.
Another thing that I should explain is that planet’s albedo actually doesn’t represent a primer reflection. It is a kind of a secondary reflection ( a homogenous dispersion of light also out into space ).
That light is visible and measurable and is called albedo.
The primer reflection from a spherical hemisphere cannot be seen from some distance from the planet.
It can only be seen by an observer being on the planet’s surface. It is the blinding surface reflection right in the observer’s eye.
That is why the albedo “a” and the factor “Φ” we consider as different values.
Both of them, the albedo “a” and the factor “Φ” cooperate in the Planet Rotating Surface Solar Irradiation Absorbing-Emitting Universal Law:
Φ*S*(1-a) = 4σTe⁴ /(β*N*cp)¹∕ ⁴
And they are also cooperate in the Planet Effective Temperature Complete Formula:
Te = [ Φ (1-a) S (β*N*cp)¹∕ ⁴ /4σ ]¹∕ ⁴ ( K )
Planet Energy Budget:
Solar energy absorbed by a Hemisphere with radius “r” after reflection and dispersion:
Jabs = Φ*πr²S (1-a) ( W )
Total energy emitted to space from a whole planet:
Jemit = A*σΤe⁴ /(β*N*cp)¹∕ ⁴ ( W )
Φ – is a dimensionless Solar Irradiation accepting factor
(1 – Φ) – is the reflected fraction of the incident on the planet solar flux
S – is a Solar Flux at the top of atmosphere ( W/m² )
Α – is the total planet surface area ( m² )
A = 4πr² (m²), where r – is the planet’s radius
Te – is a Planet Effective Temperature ( K )
(β*N*cp)¹∕ ⁴ – dimensionless, is a Rotating Planet Surface Solar Irradiation Warming Ability
Jabs = Φ (1- a ) S W/m² sunlit hemisphere
Jabs.earth = 0,47 ( 1 – 0,30 ) So π r² =
0,47*0,70 * 1.362* π r² ( W ) =
Jabs.earth = 0,329 So π r² =
= 0,329* 1.362 π r² =
= 448,10 π r² ( W )
What is going on here is that instead of
Jabs.earth = 0,7* 1.362 π r² ( W )
we should consider
Jabs.earth = 0,329* 1.362 π r² ( W )
http://www.cristos-vournas.com
Quora
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How many of the 9 climate tipping points are now a problem?
Paul Noel
Paul Noel, former Research Scientist 6 Level 2 UAH Huntsville Al. (2009-2014)
Written Dec 4
Nine active tipping points:
1.Arctic sea ice . This is a crap claim! Proof???
This is just one of 4 times since 1957 the ice has melted. This is a non-claim in fact a fraud aga…
Read More »
I like number nine. It is growing vertically.
Robert Clark wrote:
“This is just one of 4 times since 1957 the ice has melted.”
Let’s see your data.
Mr. Noel wrote on Quarel Digest that the the ice buildup in Antarctica has been growing over the last 18,000 years. The Vostok Ice Core shows 250 meters of new Ice was deposited over the last 18,000 years.
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I noticed that in the Lewis/Curry Reply1 paper featured here, Nic references the use of fractional uncertainties. Some of you reading here might understand and appreciate the implications of that use when calculating propagation of errors, but for those who are less familiar with those methods I think the link below explains in easily understood language and by way of examples how fractional uncertainty is applied in different situations.
http://ipl.physics.harvard.edu/wp-uploads/2013/03/PS3_Error_Propagation_sp13.pdf
thomaswfuller2 commented:
Actually, Mr. Appell, repeated surveys of climate scientists show a) that the ‘consensus’ consists of about 2/3rds of scientists who agree that humans are responsible for ‘over half’ of the current warming and b) that older scientists are more aligned with the consensus than their younger colleagues.
Which surveys?
This study found the consensus to be 99.94% of scientists.
“The Consensus on Anthropogenic Global Warming Matters,” James Lawrence Powell, Bulletin of Science, Technology & Society, May 24, 2017. https://doi.org/10.1177/0270467617707079
http://journals.sagepub.com/doi/abs/10.1177/0270467617707079?journalCode=bsta
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“There is indeed significant uncertainty as to the accuracy of the global SST record. However, CJ20 did not show that the LC18 TCR estimates were materially affected by any identified errors in SST bias corrections. Nor did they show that uncertainty in the SST record was greater than that estimated by the providers of the datasets used in LC18.”
As one of the “providers” of HadCRUT4 and HadSST3, I draw your attention to the following text from the conclusions of Part 2 of the HadSST3 paper (https://www.metoffice.gov.uk/hadobs/hadsst3/):
“Finally, the estimates of biases and other uncertaintes presented here should not be interpreted as providing a comprehensive estimate of uncertainty in historical sea-surface temperature measurements. They are simply a first estimate. Where multiple analyses of the biases in other climatological variables have been produced, for example tropospheric temperatures (Thorne et al. [2011]) and ocean heat content (Palmer et al. [2009]), the resulting spread in the estimates of key parameters such as the long-term trend has typically been significantly larger than initial estimates of the uncertainty suggested. Until multiple, independent estimates of SST biases exist, a significant contribution to the total uncertainty will remain unexplored. This remains a key weakness of historical SST analysis.”
and this text from the HadCRUT4 paper (https://www.metoffice.gov.uk/hadobs/hadcrut4/):
“The assessment of uncertainties in HadCRUT4 is based upon the assessment of uncertainties in the choice of parameters used in forming the data set, such as the scale of random measurement errors or uncertainties in large-scale bias adjustments applied to measurements. This model cannot take into account structural uncertainties arising from fundamental choices made in constructing the data set. These choices are many and varied, including: data quality control methods; methods of homogenization of measurement data; the choice of whether or not to use in situ measurements or to include satellite based measurements; the use of sea-surface temperature anomalies as a proxy for near-surface air temperature anomalies over water; choices of whether to interpolate data into data sparse regions of the world; or the exclusion of any as yet unidentified processing steps that may improve the measurement record. That the reduction of the four data sets compared in Section 7.4 to the same observational coverage does not resolve discrepancies between time series and linear
trends is evidence that choices in analysis techniques result in small but appreciable differences in derived analyses of surface temperature development, particularly over short time scales. As these differences are not captured by the HadCRUT4 uncertainty model, it is important that multiple
temperature data sets are maintained so that the sensitivity of studies based on historical temperature records to data set construction methodologies can be explored. This requirement is recognised in the upper air observation community (“No matter how august the responsible research ground, one version of a data sets cannot give a measure of the structural uncertainty inherent in the information”, [Thorne et al., 2011]) and applies no less to near-surface temperature records.”
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