Ad Huijser, October 2022
Summary. Analyzing the trend in the energy imbalance at the top of the atmosphere as measured by satellites, delivers a “natural” climate sensitivity of 0.3 K/W/m2. That is at, or very close to the inverse of the Planck feedback parameter as could be expected. Starting from the basic energy balance, it is shown that the high climate sensitivities as used by the IPCC are just a result from the invalid assumption that global warming is caused by greenhouse gasses only. Climate feedbacks to explain those high values are no more than necessary artifacts needed to support this mis-conception. At present conditions it is calculated from a simple analytical expression that the IPCC climate sensitivity is 3.2x too high. That implies that the global warming as measured since 1980, is for about 2/3rd the result of an increase in incoming solar power and can only for 1/3rd be attributed to an increase in GHG’s, at max. This analysis is supported by radiation data from NASA’s CERES-project (2000-2020).
A couple of years ago, I made a simple estimate of the temperature effect of the more than 10% brightening over the last 4 decades in The Netherlands [1]. The Royal Dutch Metrological Institute (KNMI) attributed only 0.2oC to that brightening [2], whereas my methodology resulted in about 1oC. That would leave only 1/3 of the observed 1.5 oC warming to the effect of greenhouse gasses (GHG’s). I coupled “brightening” to less clouds, and came to an estimate for the sensitivity to cloud change (cc) of about 0.1 K/%cc.
In the subsequent discussion with the KNMI, the only argument against my approach boiled down to: “sophisticated climate models tell us something different, so your simplistic model must be wrong”. Several other methods to determine this cloud-sensitivity, all delivered similar results. Finally, I concluded that KNMI referred to cloud-feedback results from climate models, whereas I was looking to the effect of an independent change in cloudiness. Next, I compared both views against existing trends in cloudiness, surface temperatures, etc. from satellite data [3]. When matched to trends in cloud-coverage, Global Circulation Model (GCM)-derived cloud-feedbacks delivered a climate condition close to a runaway scenario. Whereas my own idea of an independent forcing due to clouds acting as shutters (modulating solar input) delivered very surprisingly, that the sum of all feedbacks outside the basic Planck feedback parameter, became all of a sudden (almost) zero [3].
Those results confirmed my notion that high values for climate feedbacks are not real but artifacts from climate models. If temperature-induced feedbacks occur as a result of increased GHG’s, in itself a plausible idea, they have to be by definition “small”. Our climate is very stable and the Plank feedback will accommodate any perturbation from a small forcing, even from 2xCO2, easily. All those feedbacks should, and are in my opinion small, 2nd order effects, or already incorporated in that parameter as confirmed by the outcome of my feedback analysis [3]. For that reason, I also used a slightly modified Planck feedback parameter for the fundamental climate sensitivity in the recurrent relation of the Climate Model Checker (CMC) in my WUWT-contribution “Outside the Black Box” [4].
But how to prove that the IPCC/GCM climate sensitivities are fundamentally wrong?
That quest started with a kind of “reverse engineering” of my CMC [4] using the same data, TS form HadCrut5 [7], the greenhouse gas (GHG) forcing FGHG from NASA/GISS [6] and calculate the climate sensitivity as ̶ 1/λ = ∆TS/∆FGHG (see eq.3 further on) over the last century. In order to use this sensitivity as a good proxy to the Equilibrium Climate Sensitivity ECS, long periods of 15 years were applied for determining the average slopes in TS(t) and FGHG(t). Results are plotted in fig.1, but given the small ∆FGHG values before say 1920, one should take the values before that time, not too serious.
The still rocky (black) curve shows that ∆TS/∆FGHG yields “any” value for the climate sensitivity, even negative ones during 1950-1975, the years of “Global Cooling”
that climate scientists seem to have forgotten.
Our climate however, is pretty stable and accordingly, effects from just incremental amounts of extra GHG’s over a period of 15 years, will not alter the climate sensitivity dramatically. If there was just the AGW-effect warming our climate, a smooth gradually rising temperature profile was to be expected.
But what we see, looks quite different. If we translate this – 1/λ value of say the last decade 2010-2020 into a value for the ECS, the sensitivity that the IPCC is using in their communications, we get ~ 2oC. This is the supposed temperature increase from doubling the pre-industrial 280 ppm CO2 according to ΔF2xCO2 = 3.0 W/m2 from Van Wijngaarden and Happer [8]. Around 1980 that ECS would have been only about 1oC, but towards 1940 it would have been almost 8oC. To be followed by an extremely rapid decline towards -2oC around the fifties. Global Cooling was “alarming” indeed.
Once, I criticized the CMIP6 forcings [4] as being too high, but adapted values would only marginally change fig.1. It would anyhow show this “fingerprint” of natural causes for global warming. Not only that other forcings are at play, but also that they must be larger than the forcing by GHG’s. Unless of course, our climate isn’t the very stable system that I assume. So, when Willis Eschenbach was so kind to share his CERES-database on WUWT [5], I saw immediately opportunities to test that stability statement and some hypotheses I developed since these exercises described above.
That statement can indeed be easily checked with the CERES data over the period 2000-2020. All energy streams, either in the SW- or in the LW-channel are completely fixed to their prime streams SWIN and LWOUT respectively.I haven’t seen ratios for which the annual averages changed more than about 0.3% over this period. Strong variations were only found between all sky and clear sky, with surprisingly different effects of clouds in either channel, and remarkable differences between Northern- and Southern Hemispheres. Those very stable all sky ratios, show how well-controlled our climate system ultimately works. And that implies, that we don’t have to know much about what’s going on inside this “black box” that we call “climate”, to understand the effects of perturbations.
This complex climate system reflected in for instance the Trenberth type diagrams, is fully governed by these two, spectrum-wise non-overlapping energy flows SWIN and LWOUT, and their values at TOA. These flows only “touch” each other at the Earth’ surface where the first is being transferred into the latter and all the other energy flows are just “nice to know”.
But what about these large climate feedbacks? Fortunately, being stuck in a problem, there is always one way out: “back to basics”. And that climate-basics is pretty straightforward the relation between surface temperature TS, incoming shortwave solar energy SWIN and outgoing longwave IR radiation LWOUT, given by the Earth’ energy balance at the top of the atmosphere (TOA) via:
C dTS/dt = SWIN – LWOUT = FTOA (1)
In eq.1, C is the effective thermal capacity per surface area of the Earth’ system and T a system-characteristic temperature. In practice, the surface temperature TS will be regarded as the characteristic climate-temperature for obvious reasons. In equilibrium, ∂TS/∂t = FTOA = 0.
I am not going to repeat all the steps that one can find in any climate science textbook, but simply state the most important formula derived from eq.1, starting with the general assumption that changes in radiative flux at TOA are proportional to surface temperature changes:
∆FTOA = λ∆TS (2)
a 1st order linear relation between the temperature change ∆TS and changes in radiative flux ∆FTOA. It is independent from any assumption about what’s driving our climate. The inverse of the constant λ can be regarded as our basic climate sensitivity. By introducing small perturbations in eq.1, so called forcings ∆F we derive the well-known relation often used to determine the climate sensitivity:
– 1/λ = ∆TS/∆F (3)
In which ∆TS is the change in surface temperature TS, and ∆F the “forcing” that induces an imbalance. The term λ, which should in principle be equal to the one in eq.2, is now called a “feedback”, in view of the climate response to compensate that forcing, and is therefore by convention “negative”. This eq.3 holds for a complete restoration of equilibrium and that is only at “infinity”. For a dynamic analysis we often see this formula with a denominator (∆F – ∆N) where ∆N represents the (rest) imbalance at TOA. For a time period of say 2-3x the thermal relaxation time of our planet, estimated at 3-5 years, one can assume ∆N to be small and eq.3 is sufficiently accurate. I used eq.3 in fig.1 in this way to calculate -1/λ as the value of the climate sensitivity to GHG-forcings.
The last important relation to be used is the expression for the Planck feedback parameter:
– λPL = 4 SWIN/TS (4)
The shortwave solar radiation SWIN as used in eq.1. is in literature often written as (1 ̶ α)Φ0 with the albedo α and the average solar intensity Φ0 in space. The Planck feedback parameter λPL determines the way our climate reacts to disturbances in the system. It is the consequence of eq.2 for our present climate and independent from any assumptions other than that the Stefan-Boltzmann law determines the LW energy flow from the surface. Consequently, – 1/λPL should also be by definition our climate sensitivity to disturbances like the effects of GHG’s.
But apparently, climate scientists have other ideas. I shall come back on this issue, but first we are going to apply eq.2 to analyze some CERES data, in particular the radiation measurements at TOA. We’ll look at all sky data only.
In fig.2 the values for SWIN and LWOUT at TOA are plotted for the period 2000-2020. These are moving annual averages to suppress all short-term variations. Nevertheless, they are still rather “rocky”, but their trends seem stable, and in average, going up. Their absolute values can be questioned for their accuracy, but I just need their much more reliable slopes.
We rewrite eq.2 for the climate sensitivity as:
1/λ = (∂TS/∂t)/(∂FTOA /∂t) (5)
One can now directly calculate the climate sensitivity that governed our climate during that period. With the slopes that the CERES data provide: ∂/∂t (SWIN-LWOUT) = 0.41 W/m2/decade (fig.2), and from ∂TS/∂t = 0.125 K/decade, we calculate 1/λ = 0.30/K/W/m2. I could also have used the UAH LT trend of 0.13 K/decade, with 1/λ = 0.32/K/W/m2 but that wouldn’t have changed the conclusion that 1/λ is remarkably close to this “basic” Planck value of – 1/λPL = 0.30 K/W/m2 as derived from eq.4.
This cannot be a coincidence and clearly shows that the CERES data do not support the outcomes of GCM calculations: there are no large climate sensitivities, nor significant feedbacks. These CERES measurements confirm what basic climate science predicts (if not prescribes), that our climate is first and for all, controlled by the inverse of the Planck feedback parameter of about 0.3 K/W/m2.
We can also look at the “stability” of the Planck feedback parameter and see how that value evolves over time. In fig.3, – 1/λPL is plotted vs. time, as calculated through eq.4 from the values derived from the CERES data. To suppress noise, annual averages are used to calculate its value (4SWIN/TS)-1 over the period 2000-2020. Fig.3 makes immediately clear the high stability of this climate sensitivity (mind the scale) with less than 0.2% change over 20 years. But moreover, it is declining and that is contrary to what can be expected from an amplified warming effect of a high ECS with large feedbacks.
Since GHG’s don’t act on the SW-channel, the nominator of eq.4 should be constant while the denominator should increase. That implies:
– 1/λPL should increase with warming/time, if the AGW-hypothesis would be correct. It doesn’t.
It simply shows that theSWIN component is growing instead, as already clear from fig.2, and even faster than the surface temperature TS, can follow.
I haven’t put any model-assumptions in the above analysis, but just looked to the data. And those data don’t show any signs of large climate sensitivities and/or large feedbacks.
How to justify this with that “settled” climate science? Let’s first look to how and why climate feedbacks have been introduced. The derivation of eq.2-4 is based on a linear approximation so, 2nd order effects could be the reason to develop λ with extra terms as those temperature feedbacks. But then, these 2nd order feedbacks should be by definition, small.
In this case however, I assume those large feedbacks to be just a postulate to “make up” for the difference between observation/GCM calculation, and the result obtained by applying eq.3 with λPL as proportionality. Fig.1 shows, that the latter simply delivers by far not enough warming since 1980. For the calculated temperature anomalies from GCM’s it’s even worse. According to eq.3 we have apparently a large inequality, which cannot be from a 2nd order effect in our climate’s reaction:
ΔTS = – ΔFGHG/λAGW >> – ΔFGHG/λPL (6)
Here the subscript AGW is used to indicate that this reasoning is coupled to the AGW-hypothesis where all climate changes are due to increasing GHG’s only. Now to get the “correct” warming associated with this “known” forcing, the generally accepted solution is to adapt the climate sensitivity by introducing the concept of extra climate feedbacks according to:
λAGW = λPL + λ1 + λ2 + λ3 + ….. = λPL + ∑ λi = λPL + λFB (7)
The Plank feedback parameter keeps playing its role, but it is obvious from eq.6 that the combined feedbacks λFB needs to be large and with an opposite sign to λPL to get |λAGW| << |λPL|. Mind, that these combined feedbacks display a “feedforward” character and thus, enhance warming effects from GHG-forcings to fit a higher-than-expected ΔTS. The arguments that this is a good idea, are all very plausible. Take the so-called Water Vapor feedback λWV: increasing GHG’s yield warming, which enhances water-evaporation. Warmer air can contain more water vapor. Being a strong greenhouse gas itself, more water vapor yields a higher temperature. Or take the Albedo feedback λAL: higher temperatures melt the polar caps, thus decreasing the overall reflection. Less reflection implies more solar energy absorption by the Earth and so, it warms. These are all scientifically “sound” arguments.
But at what temperature will that feedforward mechanism finally stop? Moreover, we certainly had climate changes in the past with warming effects similar to those that GHG’s induce today. So, these feedbacks should already be “part and parcel” of the Planck feedback. What makes GHG-forcings then so special? The analysis of λPL and the climate sensitivity derived from the CERES radiation imbalance data, are giving a clear answer: nothing special! The real issue is: climate sensitivity is a (near) fixed parameter, and not a freely adaptable one depending on to the kind of forcing at hand. Large feedbacks are just due to the misconception that GHG’s are “the only show in town”.
The inequality in eq.6 can also be restored by changing ΔF while keeping λAGW = λPL. Just accept another forcing ΔFSW next to the GHG-forcing ΔFGHG, as I did intuitively in analyzing cloud-effects [3]:
ΔTS = – (ΔFGHG + ΔFSW)/λPL (8)
The subscript SW indicates a forcing that primarily acts on the SWIN-channel in eq.1. That is not by speculation, but the only option to explain the positive change in SWIN as well as LWOUT, as in fig.2.
The AGW-hypothesis can simply never explain an increasing LWOUT by growing GHG-forcings only!
The reasoning behind that statement is simple: although ΔFSW and ΔFGHG are both forcings that increase the surface temperature, they display rather different “fingerprints” at TOA. A GHG-forcing ΔFGHG will lower LWOUT and the climate reaction to increase TS is fed by a constant SWIN. That increase in TS will eventually restore the lowered LWOUT to its old value (see also fig.4). In case of a shortwave forcing ΔFSW, ΔTS comes directly from this additional SWIN and thus, will increase LWOUT permanently. In a dynamic situation with an increasing forcing, a GHG-forcing with e.g., ∂FGHG/∂t = constant, will yield ∂LWOUT/∂t ≈ ∂SWIN/∂t = 0. But a ∂FSW/∂t = constant i.e., ∂SWIN/∂t > 0, will yield ∂LWOUT/∂t > 0. Both positive slopes in the SW-case are the “fingerprint” at TOA as presently observed (see fig.2).
Adherents to the AGW-hypothesis will immediately claim that large feedbacks affecting the SWIN component such as Albedo- and Cloud feedback will produce a similar pattern to that ΔFSW > 0 case. True, but just in principle as there are a number of arguments against that claim. First of all, the strongest feedback i.e., from Water Vapor acts on the LW-channel suppressing LWOUT even further. Secondly, Albedo- and Cloud feedback deliver together not much more than 1 W/m2/K [13], which can never explain the 1.38 W/m2 increase in the SWIN as measured by CERES. It would require an accompanying temperature increase of 1 – 1.5 oC between 2000 and 2020, which is far beyond any observation. However, most importantly, it would only be possible when the slopes of the two trends are much closer, in line with a much larger climate sensitivity. The analysis applying eq.5 on the CERES data in fig.2 has shown already that (∂SWIN/∂t – ∂LWOUT/∂t) is determined by the Planck feedback only. Other feedbacks just don’t play much of a role in fig.2.
There are several options for such SWIN-forcings. Clouds, and in particular the low hanging clouds, are for me option #1 as they influence both SW- and LW channels, be it quite differently. From the Cloud Radiative Effect (CRE) out of CERES data, we know that the net-effect favors a ΔFSW contribution in eq.8, as also concluded in my earlier work [1][3]. Since clouds do act on SWIN in a different way than on LWOUT, we don’t even need a change in average cloudiness. A re-distribution over the various latitudes is sufficient as (SWIN – LWOUT) varies from highly positive to highly negative, going from the equator to the poles [12]. Changes in the stratospheric Ozone, and/or in UV-radiation related changes due to the cyclic behavior of the Sun, provide possibilities for solar-related forcings as well. But other explanations are certainly not to be excluded.
Eq.8 also clarifies a major characteristic of the AGW-hypothesis, namely ΔFSW = 0. Given the options for ΔFSW, one could also state that the AGW-hypothesis “denies” natural causes for global warming. This is exactly IPCC’s position [9] and implicitly, also applied in GCM calculations.
The difference between these two options, either introduce extra feedbacks (the AGW-hypothesis), or accept other forcings (this work), can be easily demonstrated. Consider a climate with the option for a step-wise change at t = 0 in the GHG forcing ΔFGHG by +/- ΔR, and for a forcing in the SW channel ΔFSW (∆SW in fig.4) variable in the same way: +/- ΔR. In fig.4 the evolution over time of the components that govern these two different views on their warming effect, is graphically displayed for the 6 most obvious combinations. The final changes in λΔTS from these two views, are also given and compared to the expected value in that particular scenario.
Scenario #6 shows what happens today in reality: a rising temperature combined with a rising LW but also a rising SW. Scenario #2 reflects todays IPCC-view. Interesting are scenario #3 and #5 with an identical “zero net-warming” response. What to recommend here? Stop emitting CO2 in case of #3? For these scenarios with canceling forces for which no warming occurs, eq. 6 produces large, non-zero results. As expected, eq.6 yields no warming from a solar forcing only. The scenarios with GHG-forcings only, are of course correctly represented by eq.6. All others are simply wrong.
As eq. 8 “delivers” in all scenarios as expected, it simply shows its validity and correctness. And thus:
the generally in climate science applied eq.6, is based on the wrong assumption of ΔFSW = 0.
No wonder, that the IPCC still keeps this wide range of ECS values. It just depends on the time and circumstances i.e., the value of ΔFSW, what ECS value eq.6 yields; just look to the facts in fig.1.
It is interesting now to calculate the ratio of the derived climate sensitivities out of both views, by eliminating ΔTS in combining eq.6 and eq.8 (with AGW and PL as the usual subscripts also for ECS):
ECSAGW/ECSPL = λPL/λAGW = (ΔFGHG + ΔFSW)/(ΔFGHG) = 1 + ΔFSW/ΔFGHG (8)
For the period 2000-2020 we find from the CERES data (fig.2) ΔFSW = ΔSWIN = 1.38 W/m2. From the CMIP6 forcings [6] we derive ΔFGHG = 0.64 W/m2, making the ratio ΔFSW/ΔFGHG = 2.2.
The climate sensitivity that the IPCC is promoting is thus 3.2x the “real” sensitivity of our climate system i.e., the inverse of the Planck feedback parameter! This factor of 3 or more sounds pretty familiar, doesn’t it? To legitimize it, the concept of climate feedbacks to bridge that gap between fake and reality had to be introduced. They look like scientifically “sound” effects but are not based on falsifiable physics. They are constructs with only one purpose: to compensate for the denial of natural effects that can cause global warming.
From the ratio between ΔFSW and ΔFGHG, it is also clear that the Sun is responsible for about 2/3 of the observed warming since 2000, or even earlier. Whereas GHG’s might be responsible for the rest. Indeed “might be”, as I have just taken ΔFGHG from an estimated/modelled forcing by NASA [6]. In “Outside the Black Box” on WUWT, I strongly questioned these data as being too high [4]. Nevertheless, this 2:1 ratio supports the assessment of the effect of brightening in The Netherlands [1] as well as my feedback analysis [3]. Globally, increasing SWIN (fig.2), must have created most of the observed warming. The growth in the atmospheric concentration of CO2 can only have played a minor role, as the rising LWOUT radiation in fig.2 confirms this much larger SW-channel effect.
Anyhow, the final question remains: “what about those wrong outcomes of GCM calculations?”
Personally, I do believe that most scientists behind climate models do, and have always done, their utmost to simulate Earth’ climate to the best of their knowledge. However, making them extremely detailed with complex surfaces, coupled oceans, melting ice-caps or whatever interactions “inside the box”, will most probably not make a big difference in calculated climate sensitivities.
On the other hand, these high sensitivities, nor these accompanying large feedbacks are explicitly entered into GCM’s algorithms; they are just the result of analyzing their outputs. So, we have to look for the point in the process where the AGW-assumption of “no natural forcings” i.e., ΔSWIN= 0, has its impact and thus, “sneaks” into these GCM-simulations. To my understanding, that can only happen during the tuning process to generate a climate that runs over a long period with a constant behavior. Once such stability is created, that AGW-characteristic of ΔFSW = 0, is an integral part of this particular climate as internal dependencies are tuned to it. Then, adding extra GHG’s to that tuned atmosphere to calculate its climate reactions, could very well deliver these exaggerated warmings.
But such a stable and constant climate has never existed. History has shown strong natural fluctuations over and over again. Even during my own, human time scale, the unexplained Global Cooling of the 1950-1975 period has shown that nothing is constant in our climate. GCM-algorithms based on proper physics are probably not bad at all, except may be for the modelling of clouds. Their initial conditions to run them however, might be fundamentally wrong and distorting their output.
I cannot come up with any other explanation, and if valid, this can easily be solved by tuning to e.g., these CERES data or other “known” climate (re-analysis) data from the recent past.
However, the real problem created with this analysis is, that forecasting with GCM’s has become a useless and meaningless exercise as long as we cannot reliably forecast natural changes in SWIN. For the anthropogenic part it’s pretty clear: with a growth to a maximum CO2-level of 560 ppm, even under a realistic ‘business as usual’ scenario [11], there is certainly no more than about 0.4oC to go.
Ad Huijser, October 2022
Added after completion: In a series of posts https://wattsupwiththat.com/2022/10/21/scatterplot-sensitivity/ , Willis Eschenbach recently published a number of scatterplots from 1×1 degree gridded CERES data. From these data, average climate sensitivities are calculated for solar radiation of 1/λSW = 0.16 K/W/m2, and for the greenhouse effect 1/λGHG = 0.58 K/W/m2, respectively (negative feedback signs are left out for simplicity). These values are derived by assigning surface temperatures to either pure solar (∆FGHG = 0), or the pure GHG cause (∆FSW = 0). By taking however, the relative contribution of the forcings by solar ∆FSW and GHG’s ∆FGHG with a ratio of 2.2 as derived from eq.8 in this work into account, the average climate sensitivity for all forcings can be calculated as:
1/λ = (2.2 x 1/λSW + 1 x 1/λGHG)/3.2 = 0.29 K/W/m2,
close enough to the 0.3 K/W/m2 of the inverse Planck feedback parameter, to conclude that also in Eschenbach’s analyses this Planck feedback parameter is the climate-change determining factor.
References:
- See for a summary, https://klimaatgek.nl/wordpress/2020/12/01/de-zon-en-de-opwarming-van-nederland/#more-6953 (In Dutch but on-site translation by Google-translate available)
- https://www.knmi.nl/kennis-en-datacentrum/achtergrond/knmi-14-klimaatscenario-s
- A. Huijser (2021), https://www.clepair.net/clouds-AdHuijser.pdf
- A. Huijser (2022), https://wattsupwiththat.com/2022/02/21/outside-the-black-box/
- W. Eschenbach (2022), https://wattsupwiththat.com/2022/09/08/the-ceres-data/
- https://data.giss.nasa.gov/modelforce/
- https://www.metoffice.gov.uk/hadobs/hadcrut5/
- W.A. van Wijngaarden and W. Happer (2021), Relative Potency of Greenhouse Molecules, https://arxiv.org/abs/2103.16465v1
- IPCC_AR6_WGI_Full_Report, A.4.4.
- https://earthobservatory.nasa.gov/features/OceanCooling
- https://www.climategate.nl/2022/09/pfff-gelukkig-nog-maar-ongeveer-06-graden-c-te-gaan/
- https://andymaypetrophysicist.com/2022/09/22/the-winter-gatekeeper-hypothesis-vii-a-summary-and-some-questions/
- S. C. Sherwood, et al. (2020). An assessment of Earth’s climate sensitivity using multiple lines of evidence, Reviews of Geophysics, 58, https://doi.org/10.1029/2019RG000678
All the technical jargon is just to confuse – all you need to know is that CO2 in the atmosphere from man’s industrial and transportation activity is so tiny it is barely measurable (something like one one hundredth of one percent) which common sense would tell you couldn’t possibly have any affect on the earth’s temperature or climate – what a giant scam
Homoeopathic Climate Change, doncha know…
Beg to differ just a bit. The no feedbacks ECS case for a doubling of CO2 actually yields about 1.1-1.2C. Monckton’s equation and his inputs yields 1.16C, unnecessary precision.
I can live with that. The only down side from doubling CO2 is the need to use the hedge trimmers and strimmer more frequently. On the plus side the lush growth means there is more steak available to provide the energy needed to cope. 🙂
Are you basing your ECS claim about CO2 on a single variable closed system laboratory experiment — CO2 infrared spectroscopy, using water vapor-free air? If not, how could you possibly know the CO2 ECS with such precision. And why no feedbacks, when you know a warmer troposphere will hold more water vapor?
Because there is no evidence that water is only a positive feedback, see WE’s and others observational studies.
He was being sarcastic.
BUT he does post on both sides of the issue.
Physics frequently falsifies common sense. For example the sun neither rises nor sets. We spin.
Er, no. Relativity.
It simply depends on where you place your coordinates’ axes..
You look at it as the Earth spinning while orbiting the Sun because it’s easier to understand what is going on, not because it’s true.
Interestingly, two Popes in Copernicus’s time liked his system because of its elegance. But the same sort of thinking kept people persisting with circular orbits.
The planet is measurably greening, as CO2 concentration increases.
Beneficial, then.
(something like one one hundredth of one percent)
Total BS
Manmade CO2 is 32,5% of total CO2
The only person confused is you.
For ease of calculation CO2 is 400 ppm, Or .0004% of atmosphere. Of that .0004% you say 1/3 is human induced. So .00013% In atmosphere is human induced. William may have over stated his case.
400 parts per 1,000,000 = 4 parts per 10,000 = .04%
Hi William, you may be correct about CO2’s influence on climate, but where did you get your figure of the human component being so low? Other climate physicists talk about 4% -6%, can you supply a reference for your data please.?
Manmade CO2 emissions account for 32.5% of total atmospheric CO2 — please give us a break with the 4% to 6% nonsense
BS.
Wiliam,
Human emissions are currently about 10% both in the atmosphere as in the ocean surface layer.
That is proven by the 13C/12C ratio.
All together, the increase is over 30%, all caused by the human contribution, but about 2/3 of the original human CO2 molecules are replaced by natural CO2 molecules from other reservoirs, mainly the deep oceans. Replaced as individual molecules, not removed as total mass…
Ferdinand,
My understanding is that deep water has a rather low C13/C12 ratio.
If the upwelling increases the atmospheric/surface sea C13/C12 would go down. Correct?
The same would happen to the C14/C12 ratio.
I am not at all saying that this is the case, but in my mind it is an alternative explanation.
Jonas,
You are right for the 14C/12C ratio and that is the reason that the decline of the 14C peak from the 1945-1960 nuclear bomb tests was much faster than for any excess 12CO2 addition.
For the 13/12C ratio it would be the opposite:
the ratio in the ocean surface is between +1 and +5 per mil (due to active plant life in the ocean surface) and for the deep oceans around zero per mil.
There is a shift in ratio when CO2 leaves the ocean and enters the atmosphere of about -10 per mil and +2 per mil when it is absorbed again. Thus when oceanic release and absorption are in equilibrium, the total shift is around -8 per mil.
The pre-industrial ratio over 10,000 years was -6.4 +/- 0.2 per mil in the atmosphere. Even a glacial – interglacial change and back didn’t give more than a few tenths of a per mil change in the 13C/12C ratio of CO2 in the ice cores.
Near all inorganics (oceans, carbonate rocks, volcanic CO2) are around zero per mil, while near all organics (fossil and current) are below -20 per mil.
Thus ocean releases tend to increase the per mil of the current atmosphere, while forest destruction and burning fossil fuels decrease the per mil.
As the biosphere today is gaining mass (the earth is greening), all decline in 13C/12C ratio is from human emissions…
Ok, thanks for your answer.
I am inclined to agree on your statement that human emissions are the main reason for the atmospheric increase. A difficult statement from my side, since I have been sceptical to this hypothesis for a long time.
I think there still are some uncertainties about the C13/C12 global measurements ? My understanding is that there is a rather big variation in data (a bit contradictory to your figure).
If human emission is the main source, which I consider to be a realistic theory, then comes the question about the absorption rate.
Todays models (Bern/IPCC) does only deal with the “short term” equilibrium. The “long term” equilibrium includes dissolution of Caliumcarbonate.
Question if this really is “long term”. In the original paper by Bolin/Eriksson they claimed that the long term equilibrium is in the order of 1000 years. Their view was that when pH was reduced, caliumcarbonate were dissolved at the sea floor (4000 meters down). They stated that the circulation between surface and sea floor is on a 1000-year scale.
My thinking is that calciumcarbonate can dissolve in much shallower water. That would significantly effect the so called “Revelle factor”. Dissolution of calciumcarbonate in shallow water will significantly change the value of the Revell factor, and subsequently increase the absorption of human emissions.
Had some problems with the new login procedure…
Doesn’t seem that there is much difference in the δ13C data globally , except that there is a much larger seasonal fluctuation near ground in the NH (Barrow), less at height (Mauna Loa) than in the SH (South Pole). See attached graph.
The δ13C level is more prone to year by year variability, because changes in vegetation release/uptake have a higher influence on δ13C levels than on CO2 levels…
There is a lag between between Mauna Loa and Barrow and a larger lag between the SH and the NH stations, which points to a strong negative δ13C level source in the NH, where 90% of human emissions occur…
Forget the IPCC’s Bern (and other) models. They assume a saturation of all reservoirs, while that is only the case for the ocean surface, but not for vegetation (most plants have their maximum growth at 1000-1500 ppmv), neither for the deep oceans.
If all human CO2 since 1850 ultimately would get into the deep oceans, that would give an increase of some 1% of total CO2 at that depth, or when in equilibrium with the atmosphere an increase of 3 ppmv above the Henry’s law equilibrium for the current average ocean surface temperature of 295 ppmv.
No problem at all for the calcification organisms which evolved in much higher CO2 levels (Cretaceous: 1000-2000 ppmv) with much lower pH and brought us the magnificent carbonate rocks of Dover and many other places on earth…
The deep ocean – atmosphere circulation indeed takes some 1000 years in the deep before returning to the surface near the equator. That makes that what returns is all “old” CO2/isotope composition…
Sorry but far too long and far far from the basics; for the basics lie in the thermodynamics of the Hydrological Cycle (the water cycle) where it halts the so called Greenhouse Effect (GHE) when the atmosphere becomes saturated with water at about 4.2%.
This means that so long as the Planet has adequate supplies of water, there is NO POSSIBILITY of runaway global warming.
Just forget about this obsession with CO2 and its Emissions; for at the basics level it is just tiny bit player in the overall scene and the human bit is even tinier.
IMO The scientific Community needs to get out from under the Politician’s heels and the obsession with radiation attempting to explain everything and return to basic thermodynamics.
How can we “forget about this obsession with CO2” when the crazy obsessive climate disordered have control of government, science and business??? Even the unions have been quiet in spite of the huge attack on jobs in the mining, oil, transportation, and manufacturing sectors.
People will literally be freezing to death this winter and people are just standing around with their finger up their nose (along with the other hand glued to some work of art….)
As a retired Chartered Engineer, I have at least basic understanding of the theoretical arguments.
But, in my opinion, it is easiest and most important to recognise a fundamental fact.
One side of the debate is dominated by people who are the opposite of hubristic, who do their very best to set out facts, formulae, approaches to “problems” both real and imaginary. Happy to discuss, debate, share data, who may occasionally in honest error but care about ordinary people and the environment.
Then, on the other side we have what might be described as the Michael Mann approach.
Which approach is most likely to represent common sense and truth?
The approach NOT eagerly endorsed by 97% of gormless, venal politicians, of course.
Come back, Guy Fawkes, all is forgiven!
The 97% is a fake number
I estimate about 99.9% of scientists believe there is a greenhouse effect and CO2 is part of it. There is no agreement on the exact effect of CO2, (plus any feedbacks, if there are any), just a wide range of wild guesses.
The surveys that reach a 97% conclusion are worded so that almost everyone becomes part of the 97%. The surveys then falsely interpret the 97% to be agreement with the IPCC. In fact, the only 97% agreement is that AGW exists, but no 97% agreement on how much AGW exists, and definitely no 97% agreement with IPCC wild guesses of future CAGW.
Well i did not understand very little of that and i doubt whether any other readers will either. Needless to say that we are all on the same page.
it wasn’t well written. Could someone explain it in more easily understood language please,
OK IF CO2 and amplification of it were the dominant cause of modern climate change THEN the correlation between CO2 rise and temperature would be extremely tight.
It isn’t.
Not so
If CO2 caused 51% of the warming and other variables caused 49%, you could say CO2 was the dominant variable. The correlation would be positive but not perfect.
Wait for HadCRUT10
Just because Earth absorbs more energy, does not mean it is the sun. For instance, what if we have an increase of high altitude ice clouds on the expense of lower water clouds? Ice clouds have a lower albedo than water clouds, absorbing more radiation. Also they have a stronger GHE. And such ice clouds reduce the solar input into the natural water cycle below, thereby making the atmosphere drier and of course causing the reduction in low clouds.
Of course that is not just an “if”, rather all these things are observed. Again I have to point to the presentation of Charles Long providing a lot of good hints..
Thanks for this link E. Dr. Long’s presentation is a good follow-up for this great post by Ad Huijser.
There are three basic ways to show almost all climate models (here, CMIP6) are wrong:
So they MUST run hot. Energy budget ECS methods say by about a factor of 2.
And there is a simple way to explain why this is so. Due to CFL computational constraints, they have to be parameterized to best hindcast 30 years (a formal CMIP requirement). Parameterization automatically drags in the attribution issue (natural variation, as this post also notes), which the climate modelers assume away as near zero. Yet as IPCC AR4 SPM WG1 figure 4 itself showed, that assumption is provably false by comparing ~1920-1945 and ~1975-2000. The former warming is indistinguishable from the latter, yet per IPCC could NOT have been caused by an increase in CO2. See my two previous posts on this for illustrative details.
Actually I would love to see you write an article on that, with overlapping graphs. It would be great if other warming periods could be included even if they are by proxy, or limited in geographic coverage.
Already did. Published here long ago. Look up guest post ‘Why Climate Models Run Hot’. Even has the IPCC SPM fig 4 as an illustration.
https://wattsupwiththat.com/2017/07/06/why-climate-models-run-hot/
Rud – “they have to be parameterized to best hindcast 30 years”
That is the nub of the problem, I think. We have instrumental records going back to 1659, and we can see from that data that there’s a cycle of around 60 years’ length involved. Thus at a minimum, the models should be able to hindcast the last 60 years, and better if we can hindcast a multiple of that period.
Yep, putting in that requirement to be able to replicate the experimentally-measured temperatures going back for say 120 years would make it difficult for the models to pass, but projecting forwards based on a fraction of the period of a periodic variation will produce wrong predictions. For example, if we projected sea-level rise based on a 10-hour series of measurements, we’d predict a range between total inundation or the ocean totally disappearing depending on precisely when our 10-hour period occurs relative to the local tides.
The fall in “average global temperature (GAT)” between ~1945 and ~1975 you mention led to predictions of a coming glaciation at the time, and I have not put those predictions into the memory hole as today’s Climate Scientists would like us to. The current “pause” in rising temperature (see Lord Moncton’s analyses) should have been expected based on that ~60 year cycle, and also from now onwards a period of falling GAT. That’s without identifying the cause of that cycle, just seeing that it exists from the data available. Looking longer into the past, there’s also a cycle length of around 1000 years, with a few hundred years warmer in every ~1000 years. These cycles fairly obviously must have natural causes.
Looking at too short a time-period in the past, and then extrapolating that too far into the future, is unlikely to give better predictions than tossing a coin.
“Yep, putting in that requirement to be able to replicate the experimentally-measured temperatures going back for say 120 years would make it difficult for the models to pass, but projecting forwards based on a fraction of the period of a periodic variation will produce wrong predictions.”
“Looking at too short a time-period in the past, and then extrapolating that too far into the future, is unlikely to give better predictions than tossing a coin.”
You’ll never convince the climate alarmists of these simple facts. The temp has been going up for the past 40 year and so it will continue going up forever. Thus there’s no use in trying to identify a pause, pauses are statistically insignificant compared to the longer linear regression.
“T_S form HadCrut5 [7], the greenhouse gas (GHG) forcing FGHG from NASA/GISS [6] and calculate the climate sensitivity as ̶ 1/λ = ∆T_S/∆F_GHG (see eq.3 further on) over the last century. In order to use this sensitivity as a good proxy to the Equilibrium Climate Sensitivity ECS, long periods of 15 years were applied for determining the average slopes in T_S(t) and F_GHG(t).”
A common mistake at WUWT. Climate sensitivity is not a derivative of a time varying curve. If you look at the definition, it is a ratio of equilibrium ∆T as a response to a sustained step change ∆F. It is not the slope of the time varying curve. And certainly 15 years is not an appropriate interval. Transient CS is defined, as a more measurable quantity, but even that is the change at the end of 70 years of a prescribed rising sequence in F.
You would not expect such a derivative to be a sensible measure of sensitivity. You’d expect T to be proportional to the integral of the rate of heating F. If you apply a constant flame to a kettle, you don’t, in the short term, get a response T. You get a steadily rising T. If it settles down to an equilibrium value, you can sensibly call that ratio a sensitivity. But you won’t get it from time derivatives.
Nick,
For clarity, can you please explain the difference between a “time varying curve” and “sustained step change” in the sense that a sustained change of an “Equilibrium deltaT” is a physical event, not just an adoptred definition used to simplify some models.
What physical evidence exists that an equilibrium can be reached and indeed is reached?
My view, which could be wrong, is that he uncertainty in TOA energy balance, if you rely on that, is too large to see if equilibrium is reached.
Geoff S
Geoff,
ECS is based on a step change (once only change) in forcing, which eventually produces an equilibrium change in temperature. It is a unique number.
But if you divide any old temperature change over time by a forcing, you don’t have a unique number. Again think of heating a kettle. With constant heat, the initial rapid rise in T tapers off, either because of boiling or because it doesn’t even get to boiling. What is the sensitivity ∆T/∆F? A time-varying quantity, though it will eventually converge. For short times, it would be near zero.
Can equilibrium be reached? What else? Go on heating for ever? There could be turbulence, but basically heat is diffusive, and you’ll reach equilibrium when it has all diffused. To put it another way, it requires a continual supply of energy to sustain disequilibrium.
You have no idea about heat do you? How many “bodies” have you just described? How many gradients have you just described with your flame and pot?
As I said earlier, you add joules into a system, any system, and you will immediately see gradients as heat diffuses throughout the system. Those gradients will converge to an equilibrium temperature among the bodies in the system and it will be based on a time function.
You talk like a “step function” will allow you to determine a transient response that allows you to determine something. The problem is that the output response must be broken down into the component parts. If all your assumption is based on CO2 ‘s response, then you will be missing many, many other parts of the system.
You have no concept of heat transfer do you? Have you ever taken courses on thermodynamics that require calculus? I’ll be not.
“You have no concept of heat transfer do you? Have you ever taken courses on thermodynamics that require calculus? I’ll be not.”
Here are a few of my published papers on heat transfer.
You didn’t answer my question!
Since you apparently believe you have some background in thermodynamics then you should know what you have proposed is wrong. Gradients are 3 dimensional and do vary both in time and in conductance’s until equilibrium is reached.
I have attached a single page from A Heat Transfer Textbook by John H. Lienhard IV and John H. Lienhard V that begins the analysis of conduction.
You will notice that time is an important variable in the equation as are coordinates of x, y, z.
Radiative heat flux is Joules/second. Again, time is intimately involved in energy transfer. Gradients are built from this.
Too many people look a S-B or Planck’s equations and never realize that they are only single points in time. Time is part of heat transfer and is used in integrating fluxes to determine heat/energy transfer.
Your papers appear to be primarily concerned with fluid flow. For instance “Numerical simulation of forced convection near a heated plate”. What does that have to do with temperature conduction, etc in a gas, i.e. the atmosphere?
Apparently you do know some calculus. It’s not obvious you can apply your knowledge.
Integrals must be evaluated between two points. That means that you *will* get a ΔT for a ΔF. Thus there *will*be a ΔT/ΔF. In the limit that goes to dT/dF, i.e. the slope of the temperature change or the ECS value. What makes you think that has to be a constant and can’t change?
“It’s not obvious you can apply your knowledge.“
This search string brings up more of my papers that are concerned with engineering applications of heat transfer.
None of which have anything to do with the subject at hand. Nice attempt at the argumentative fallacy of Argument to Authority!
The E in ECS takes 400 years, so is absolutely of no interest for policy makers. It is just a useful scaring tool for environmentalists that we are all doomed.
Which we are not.
You’ve never studied thermodynamics have you? Just exactly what do you think a gradient of warming/cooling is? It is a time based derivative of energy exchange. Why do you think you had to have 13 hours of calculus and more hours of differential equations before taking thermodynamics courses? If time derivatives weren’t part of thermodynamics there would be no need.
You are so full of crap. If you add joules to a pot, you damn sure will have gradients derived from conduction, convection, and radiation from the very first moment, take my word for it. Does the concept of entropy mean anything to you?
“Climate sensitivity is not a derivative of a time varying curve”
It would be a plot of noise if it were. Since I’ve never seen such a plot here, your first sentence is wrong.
If you are trying to say that it’s worse than what the data shows, you have a slight problem with proving it.
“ Since I’ve never seen such a plot here, your first sentence is wrong.”
Eq 5 in the head post explicitly expresses sensitivity as a ratio of time derivatives. And Fig 1 implements that, with the derivatives approximated with 15 year differences.
And yes, it doesn’t mean much.
The point was you don’t, despite it often being said, measure a derivative. You measure finite changes.
If you are trying to say plot T(n+30) – T(n+15) / Fn – F(n+15), you will find that it still shows natural variation are larger than effects of emissions.
Ad Huijser,
thanks for the very detailed explanation.
You say
‘ Personally, I do believe that most scientists behind climate models do, and have always done, their utmost to simulate Earth’ climate to the best of their knowledge.’
My impression from most people that post detailed and analytical articles here regarding the climate models is that the ‘to the best of their knowledge’ is a stretch.
Second, how do you square, align or otherwise relate your hypothesis with the winter gatekeeper hypothesis?
R
JBP
Butt but but….the United Nations says CO2 is at a 2 million year high….Oh, the humanity….we can hardly breathe.
“Oh, the humanity….we can hardly breathe.”
That isn’t because of CO2 – it is the dust cloud from the IPCC bs
“…the dust cloud from the IPCC bs”
_____________________________
Good one (-:
Boy is this long and technical. However the initial statement that the IPCC assumes all the warming is due to GHGs is wrong. First we are talking about the CMIP models under the direction of the UN WCRP, not the IPCC, which only does literature reviews.
More importantly these models use about a dozen anthro forcings, many of which are not GHGs. Most are listed in figure 2 of the AR6 WG1 SPM so hardly a secret. I think the CMIP6 guidance even specifies the magnitude of each forcing to be used, or something like that.
UN WCRP
________
Unworldly Crap
Warming is mainly from greenhouse gases
Let’s not exaggerate
figure 2 of the ar6 wg1 spm – Bing images
Larger figure 2 image:
image-7.png (979×715) (wordpress.com)
Richard,
In 1871 there was a scientific expedition by ship up and down the Queensland coast, taking in almost all of the Great Barrier Reef. Sea temperatrures were measured many times a day, both heading north and later south.
Colleage Dr Bill Johnston has analysed the temperatures and shown they are much the same as those of today.
This kinda throws doubt on your chart that shows warming from the olden days to now. Geoff S
www. BomWatch.com.au
I think this is important work. It wasn’t as hard to follow as some other work, the math and formulas lose me more than anything. However I was really struck by one sentence:
“On the other hand, these high sensitivities, nor these accompanying large feedbacks are explicitly entered into GCM’s algorithms; they are just the result of analyzing their outputs”
Is it true that sensitivities and and feedbacks aren’t a part of the GCM algorithms? This needs some clarification, I don’t understand how they could not be part of the algorithm? I think sensitivity and feedback is pretty much everything.
I find it very embarrassing when someone is presenting these highly complex mathematical formulas to back up their theories, all the while they can’t even use basic units correctly. Kilowatts per square meter are written correctly as kW/m², and not K/W/m²! Hard to believe, that someone so versed in complex mathematics doesn’t know that forward slash (/) means division. Kilo (k) is a multiplier (thousand times) so it is downright wrong to divide multiplier by the unit, not to mention, that the author writes capital “K”, instead of lower case “k”.
” … that forward slash (/) means division. “
We learn that early on, not when starting to study complex mathematics.
On the other hand, I had to put the date on a ballot today and the form wanted
the American method, namely: day/month/year. Thus: 11/07/2022 which equals 7.7716546e-4 🙂
HA Ha ha and if you enter =11/7/2022 in Excel you get: 1/0/1900
But if you enter 11; 7; 2022 and =A1/A2/A3 in cells A1 thru A4 respectively you get: 0.0007772
The Date Function in Microsoft’s Excel drives me crazy.
The American method is Month/Day/Year.
Thus 11/07/22 is November 7th of 2022.
Day/Month/Year is European.
I think you mean month/day/year.
K/W/m is degrees K per Watt per sq m.
The little ‘2’ disappeared. Try K/W/m2.
K/W/m^2?
On your desk top personal computer (PC)
press and hold the [Alt] key and type in “0178” to get ²
You’re welcome.
Thx. Actually, I copied & pasted from the comment I was responding to, and didn’t notice the missing ‘2’ till too late.
0.3 K/W/m2
____________
Right off the bat, I stopped reading and just scanned because
Kelvins per Watts per square meter
didn’t make any sense.
0.1 K/W/sqm means you get 0.1 K per 1 W/sqm. If you think about it long enough, you can make yourself believe that it’s logical.
Not if you know any physics, you can’t 🙂 W/m^2 depends on temperature differences, so writing “sensitivity” as K/W/m^2 is backwards… (yes, temperature will eventually change as a result of power/energy transfer, until equilibrium or steady state is reached, but the power depends on the temperature to begin with, not vice versa, so a better unit would be W/m^2/K, which is basically S-B again)
I stopped reading after I spotted three punctuation errors. Who could believe a science article with so many punctuation errors? There were also too many upper-case B’s in the title.
Only as used by by systems that do not provide definitive multiplication, division, exponential, derivative, etc. symbols.
The era of using the typewriter or systems derived from typewriter (DVORAK) keyboards.
The “forward slash” is a clumsy method as many systems use the forward slash as a separator or a delimiter.
Simply stated, a forward slash’s use as a division symbol is an implicit assumption, not a hard fact.
My COBOL and FORTRAN teachers demanded that we use ‘explicit’ symbols. Use of implicit symbols would get a zero.
Ad Huijser estimates sensitivity to cloud change at about 0.1 K/%cc. This tallies with my paper‘s “Linear 1983-2017 change is -7.3% cloud (NB. percent, not percentage points), 0.7 deg C temperature” [Figure 1 caption]. We need a lot more papers and articles addressing all the influences of clouds on climate, as that is where many of the solutions may be found.
Incidentally, sensitivity to cloud change is negative, as is made clear in Ad Huijser’s article, so sensitivity to cloud change should be given as about -0.1 K/%cc.
HadCrut 5 is adjusted temperature data and has little relationship to reality. No serious analysis should use this data. End of story.
Bingo! And didn’t they misplace the box with the original records and as near as I can tell never really looked for it.
I agree with others, this post was certainly not “back to the basics”. Back to the basics would/could state:
The ability of CO2 to absorb outgoing longwave IR is logarithmic and limited to certain wavelengths that are already mostly saturated,
Water vapor is a much greater “greenhouse gas” than CO2, and varies much more with every peak and trough in Earth’s temperature, yet still never leads to runaway warming or cooling,
In the ice core samples, there’s clear evidence that changes in CO2 concentrations in the atmosphere come after the temperature changes, not before, just like your soda doesn’t fizz as much when it’s colder,
There are many other factors affecting climate on short and long term periods. Albedo (land use changes/deforestation comes to mind), SO2 levels (which we’ve worked to mitigate), volcanic dust and aerosols (no real big eruptions since Pinatubo), ENSO, the PDO, and so on.
BTW, even though I took AP calculus in high school, I HATE math…
In the ice core samples,
Ice core samples have nothing to do with manmade CO2 emissions.
But if the ice core samples are to be believed how much of the increase in CO2 is down to the warming since the coldest part of the LIA?
You gave a very precise figure for our contribution earlier, do human and warming additions make up the whole increase since measurements began?
We do not have a precise number for global warming since the cold 1690s decade. Climate reconstructions suggest at least +2 degrees C. The three Central England weather stations show about +3 degree C. To be conservative let’s assume +2 degrees C. We know +0.7 degree C. is the warming since 1940, that could be caused by manmade CO2. There was little manmade CO2 before 1940. Therefore, of the +2 degrees C, warming since the 1690s, about +1.3 degrees C. must have had natural causes.
Concerning CO2:
The entire increase must be manmade because nature is a net CO2 absorber. The roughly estimated CO2 level in 1850 is 280ppm. The current CO2 level is measured at 415ppm. The difference is an increase of +135ppm (415ppm minus 280ppm). The increase of +135ppm is a 32.5% increase from 280ppm and is entirely manmade. Actual manmade CO2 emissions in that period were at least +200ppm, but after nature absorbed some of that manmade CO2, the net increase was about +135ppm.
Thank you
Supplementary, despite warming for 300+ years there has been no increase in atmospheric CO2 from natural sources. I’ve read in many places on many occasions that CO2 lags warming by around 400years. So is there going to be an increase in naturally produced CO2 by the end of the century despite any reduction due to Net Zero by many nations?
Dissolved CO2 escapes from the oceans as they get warmer. The +1 degree C. global warming since 1850 (rough estimate) could have caused 25ppm of CO2 to escape from the ocean into the atmosphere, of which perhaps half was absorbed by biomass. The result is a net increase of about +10ppm to +15ppm.
But the oceans are still absorbing CO2. So if they warm by +1 degree C., that only means the oceans about 25ppm CO2 less than before.
I don’t know why this Henry’s Law effect would take an average of 800 years, based on the Vostok ice cores. Maybe someone else could explain that.
So the oceans don’t outgas CO2 as they warm naturally from the LIA. HMMMM……
Yes, they do, but very modest: 12-16 ppmv/K
There is a formula from Takahashi, based on hundred thousands of sea surface samples that gives the change in pCO2 (the partial pressure of CO2 with the above atmosphere from the seawater when in equilibrium) with temperature, according to Henry’s law:
∂ln pCO2/∂T=0.0423/K
When the pCO2 of the atmosphere is above the level in the oceans, the CO2 flux is from atmosphere into the oceans and vv.
For the current (weighted) average ocean surface temperature, the equilibrium CO2 level in the atmosphere would be around 295 ppmv. That is a dynamic equilibrium: some 40 GtC as CO2 is released near the equator and absorbed near the poles in the sinking waters that sink into the deep to return hundreds of years later…
Feely e.a. have compiled a lot of sea surface measurements which overview can be found at:
https://www.pmel.noaa.gov/pubs/outstand/feel2331/mean.shtml
and several previous pages…
The difference between the MWP and LIA was less than 10 ppmv. If the current times are not warmer than the MWP, the increase also would be not more than 10 ppmv:
Johne,
CO2 lags temperature on very long time scale (800,000 years in ice cores) and on the scale of seasons and year by year (El Niño, Pinatubo,…). The latter two even in opposite direction.
CO2 leads temperature over the past 170 year, far beyond the temperature influence per Henry’s law (12-16 ppmv since the LIA)…
It is good to see you are still around, Ferdinand. Your arguments convinced me years ago when I started learning about climate. For that, I am very grateful to you. Too bad there is less science and more politics in WUWT these days. The world is becoming more divisive just when it needs most to be united. The climate kerfuffle will fix itself when the promised warming or sea level rise doesn’t happen.
Javier,
Thanks a lot for your kind words…
I was less active in last decade, because of the gradual increasing illness of my wife (an auto-immune disease) and she passed away begin this year.
Fortunately, our two daughters are of much relief…
Indeed it is a pity that nowadays there is too much politics on WUWT these days and less science… Will try to give more comments where science gets first…
“sophisticated climate models tell us something different, so your simplistic model must be wrong”.
And so the sophistry in their models is deliberate – is that what institutional meteorology is telling us?
Climate models begin with a conclusion demanded by “management” — CAGW. Nothing else mattesr for scientists who want a paycheck.
There is merit in showing how IPCC and CMIP neglect natural climate change through their assumptions, but this basic model cannot be right:
What is clear is that since 2005 solar activity has been below average. If below-average solar activity produces significant warming we would be toast by now.
Solar activity only changes with about 0.1% in energy output over a cycle, but even so, the change in de UV output is about 10%.
UV changes have a huge impact on the ozone layer, the temperature difference between equator and poles in the lower stratosphere and hence the jet streams strength and position and cloud/rain patterns.
Besides that, solar activity also has an impact on the strength of cosmic rays, which also influence cloud cover,
So, a small initial change can have a huge effect…
I know, Ferdinand. My climate book is full of information about how solar changes affect climate. However, a decrease in solar activity below its long-term average cannot be responsible for further warming the surface. If that was the case the planet would have overheated long ago.
Agreed!
Normally one would expect a cooling with lower sun activity…
My impression is that (deep) ocean currents play a dominant role, like the PDO, AMO for “short” changes of around 60 year wavelength and other for the 1000-1200 year “wave” between warmer (Minoïn, Roman, Medieval and current) times and colder (LIA) in-between times…
“sophisticated (climate) models tell us something different, so your simplistic model must be wrong”. That statement describes most of what is wrong with our technologically driven, credential focused endeavor called science. All fields are suffering from the same thinking.
“the global warming as measured since 1980, is for about 2/3rd the result of an increase in incoming solar power and can only for 1/3rd be attributed to an increase in GHG’s, at max.”
My first thought: “Could the author know this, or is he speculating? The obvious conclusion is that he is speculating.
To know the 2/3 and 1/3 claimed by the author, he would have to know the effect of every climate change variable. He does not know that. No one knows that. Therefore, any claim to know how much CO2 (or any other variable) affects the global average temperature HAS TO BE SPECULATION, NOT A FACT. Following is my own list of likely climate change variables. You can’t “know” what any one variable does unless you know what all of them do.
The following variables are likely to influence Earth’s climate:
1) Earth’s orbital and
orientation variations
2) Changes in ocean circulation
Including ENSO and others
3) Solar activity and irradiance,
including clouds, volcanic and manmade aerosols, plus possible effects of cosmic rays and extraterrestrial dust
4) Greenhouse gas emissions
5) Land use changes
(cities growing, logging, crop irrigation, etc.)
6) Unknown causes of variations of a
complex, non-linear system
7) Unpredictable natural and
manmade catastrophes
8) Climate measurement errors
(unintentional or deliberate)
9) Interactions and feedbacks,
involving two or more variables.
Yes, we need more evidence to confirm or deny any dependence. One of these might be the correlation between the CO2 concentration and the global temperature. Considering only the last 10’000 years up to 1650, we rather get an anti-correlation! See at: Tom van Leeuwen “Temperature versus CO2 – the big picture”, at https://holoceneclimate.com/temperature-versus-co2-the-big-picture.html. Other sources support this relationship: the CO2 rises and the temperature falls and vice versa.
I am joining another reader’s (Leo Smith) comment: the warming cannot be caused mainly by CO2, because historical data show CO2 concentration uncorrelated with the warming.
Yup.Me too Martin,with paleoclimatology as a back up.
Bad logic. It’s like saying that polonium won ‘t kill you because historical data shows that deaths are not correlated with polonium ingestion.
AGW says that digging up and burning C will cause warming. It’s no use looking for past correlations there. It hasn’t been done before.
The CO2 climate sensitivity has been reported in a wide uncertainty range between 0 and 10 degrees Celsius per doubling CO2. Make your own choice. I personally believe it is below 2 degrees. It is like religion. Nobody knows. So I prefer to rely on simple visual relationships. The diagram above, between 10000 years and 1650 years ago is one of these.
The polonium example does not work because nobody has to eat it.
Correction: look at the data between 10000 years ago and the year 1650. After that the crazy rise started and it is not only the CO2, as Richard Greene summarized.
That is an argument from authority fallacy. It is also a fake and disingenuous argument since “sophisticated” climate models do NOT model cloud formation from basic principals but inject “parameters” invented by the modellers and tweaked to get the desired or “expected” outcomes.
These parameter ( and there are scores of them ) can be adjusted until they get results which
fit their expectations or political bias. There is NOTHING “sophisticated” about this other than their skill in manipulating the outcome.
There is no reasonable argument that this process is superior or more accurate than a more simple observational process.
Yes!
This is from a researcher with intimate knowledge of the climate models. His name is Mototaka Nakamura. His book is available at no charge on Kindle. His assessement of how clouds are handled is damning.
中村 元隆. 気候科学者の告白 地球温暖化説は未検証の仮説: Confessions of a climate scientist The global warming hypothesis is an unproven hypothesis (Kindle Locations 2249-2250). Kindle Edition.
Those “parameters” are, at best, GUESSES.
“There is no reasonable argument that this process is superior or more accurate than a more simple observational process.”
Yep.
I applaud the effort which has gone into this but it is non physical !
You cannot do a radiation based analysis based on some fictitious physical quantity like “average temperature”. Temperature is NOT and extensive property of matter. You may NOT average temperature of different media ( land and water ). Your ∆TS is a fictional quantity.
Yes MOST climate “science” does this too and it’s bunk. But that does not matter because they can rig the results by tweaking. You need to do it correctly.
Energy balance requires an energy analysis and temperature is not a proxy for energy across all media.
I discussed this in more depth in an article on Judith Curry’s site:
https://judithcurry.com/2016/02/10/are-land-sea-temperature-averages-meaningful/
YES! YES!