by Alberto Zaragoza Comendador
How sensitive is the Earth’s climate to greenhouse gases? Speaking about carbon dioxide in particular, how much would air temperatures increase if we doubled atmospheric concentrations of said gas?
This question lies at the heart of climate science. It is to climate what GDP is to economics – the central concept. So central that it’s very difficult to have a coherent discussion of climate issues if one does not know about sensitivity. But there is a crucial difference between these measures: the layman is somewhat familiar with GDP but not at all familiar with climate sensitivity.
Okay, many or most people cannot tell you exactly what GDP is. But many others will give you a crude, approximately correct definition. Furthermore, even those who cannot define it intuitively get the implications of both slow and fast GDP growth, and could tell you at least its order of magnitude (i.e. it happens at rates of 1% or 3% a year; not 0.1% or 30%).
As for climate sensitivity, I can report anecdotal experience from the Madrid area: not a single person that I’ve talked to has a clue what it is. I don’t mean that they fail to provide the technical definition – they don’t even know what it’s about. Ask about sensibilidad climática and people will think you’re talking about how humans react to temperatures, not how the atmosphere reacts to greenhouse gases.
It’s a pity because, at its core, climate sensitivity is an easy concept. And the way it’s calculated is easier than GDPs. You just need to grasp the interplay between:
- Temperatures: duh
- Forcing: it would be easier if we just called it “impact” or something to that effect, but still, not that hard. The more forcing, the more warming.
- Ocean heat uptake and the corresponding energy imbalance: perhaps the least intuitive part of the calculation. Nevertheless, it’s only necessary in order to estimate equilibrium climate sensitivity (ECS); for the transient climate response (TCR) all you need are temperatures and forcing.
People even talk about climate sensitivity without realizing it. For instance, one common argument among climate skeptics is that emissions of CO2 were small prior to 1950, and thus the warming that took place before that year could not have been due to man-made CO2 emissions. But what people making this argument mean, even without putting it that way, is that if CO2 drove the early 20th century warming then climate sensitivity must have been high. And yet, the evolution of temperatures since 1950 suggests a lower climate sensitivity. Skeptics making this argument are implying that it makes no sense for climate sensitivity to have been much lower since 1950 than before, and therefore something else must have been driving warming before 1950.
The problem with this kind of arguments (from all sides of the debate) is not that they use numbers, but that they’re not numerical enough. Or perhaps I should say rigorous enough. Now, I don’t have anything in particular against the early-20th-century-warming argument; I find it to be good example because it’s common. There are three issues plaguing this kind of argument:
- What matters for warming is not CO2 emissions or concentrations, but radiative forcing. This may seem obvious but even some sophisticated authors don’t actually to look at forcing – here’s a recent example.
- Proponents of the argument usually don’t even try to quantify the non-CO2 forcings. Okay, there are probably natural forcings (e.g. clouds) that we cannot quantify because there are no records until the last couple decades. But that doesn’t mean you shouldn’t account for the known forcings! Methane has a warming effect, aerosols have a cooling effect, etc. You have to take these into account if you want to know exactly why the climate did what it did.
- You then have to compare the evolution of these two inputs (forcing and temperatures) in order to arrive at an output, which is the amount of warming per unit of forcing. This ratio is essentially the TCR. Most proponents of the early-20th-century-warming argument cannot calculate a TCR because they don’t use radiative forcing as an input, and some don’t really check temperatures either (they just eyeball a temperature chart).
Nevertheless, it’s easy to understand why people would eyeball temperature charts, and easier to understand why they don’t look at radiative forcing. Even though the websites for temperature records are available to anyone with an internet connection, they require some sacrifice in terms of learning to navigate the data; if you don’t know that Wood for Trees exists, figuring out the exact temperature change between two points in time can be difficult.
As for radiative forcing, the “official” sources (e.g. the IPCC AR5 report) are updated every five or six years; if you want an estimate between those, you have to check the scientific literature. And unlike the Met Office or GISS, there isn’t any organization regularly pushing out press releases on what the latest forcing levels are. There is also the issue of aggregating the dozen or so different forcings into a single time series – one more obstacle for anyone who wants to calculate sensitivity.
Wouldn’t it be great if an app could check all this? You tell the app what years you’re interested in, and the app gives you:
- Temperatures or, more accurately, temperature anomalies
- Forcing levels. Not just for CO2, but for the aggregate of all forcings we more or less know about.
- Energy imbalance – derived from a closely related measure, ocean heat uptake
The app could also tell you what is the difference in these measures between two periods; that is to say, the app could inform the user that forcing between two points in time increased by A, temperatures increased by B, and energy imbalance increased by C. Going even further, the app could then spit out estimates of TCR and ECS.
Well, such an app now exists.
Clisense: an app that does all the climate sensitivity math for you
If you google “climate sensitivity calculator” you’ll find several websites that use this term. However, they don’t actually do what I mentioned in the above section. This one, for example, simply shows how much temperatures will go up depending on climate sensitivity and CO2 concentrations (which are prescribed by the user); it’s actually a temperature calculator.
So, to the best of my knowledge, Clisense is the first app that allows the user to estimate climate sensitivity. The user selects two periods, which can actually be single years if you so wish (just select the same year as both the start and the end of a given period). The app will then estimate TCR and ECS while showing each step of the calculation. This allows users greater insight into just how we “know” that ECS is this low or that high.
Additionally, the app asks the user to prescribe:
- How much of the Earth’s energy imbalance is made up by ocean heat uptake. The IPCC’s AR5 report estimated 93% of planetary heat uptake was oceanic, and that is the default value used by Clisense, but this percentage is not totally certain.
- How “efficacious” the different forcings are. For the most part there is no reason to believe some forcings have greater efficacy than others, but the app allows users to play with the numbers and see how estimates vary. For instance, how would our estimates of sensitivity change if aerosols had greater efficacy than CO2?
The numbers will be meaningless to the average online Joe, so I also made this explanatory website. Having two websites is not the most elegant solution, but custom domains on Shiny Apps go for $300 a month so it will have to do.
Only as good as the data that goes in
To be clear, Clisense estimates climate sensitivity according to a variety of inputs. If our data on temperatures, forcing, and/or energy imbalance were significantly wrong, then the app’s estimates would also be wrong. Of course developing better estimates of all three inputs is one of the main goals in climate science, but the app cannot “guarantee” that the data is correct.
For temperature, I used HadCRUT as it’s the record I’m most familiar with. Going forward, at a minimum I would like to add the Cowtan & Way version, which shows greater warming.
For forcings, I took last year’s Lewis & Curry paper, which has data up to 2016. Since that paper also used HadCRUT for one of its set of results (for the other it used Cowtan & Way), it’s possible to replicate at least one of the paper’s TCR values. Clisense uses only arithmetic for now; I don’t calculate any confidence interval, I don’t use any Bayesian prior, etc. But the result Clisense gives is the same as in Lewis & Curry, which proves the paper’s estimates aren’t biased down by its use of Bayesian priors, as some online commentators had argued.
For ocean heat uptake, the source is Zanna et al 2019. This paper was really key for the app as it’s the only source I know of that offers yearly estimates going back to the 19th century; other ocean temperature records only go back to 1950 or so. Besides, Zanna et al offers a full-depth estimate of ocean heat uptake; using other datasets often involves adding or subtracting different sources to arrive at a complete estimate.
Zanna et al is the data source used in the app that I have greatest reservations about, because it shows a rate of ocean heat uptake of 0.3 or 0.4 w/m2 going all the way back to 1930. If you run the numbers with an initial period like 1930-1950 and a final period like 2007-2016, there is virtually no increase the rate of ocean heat uptake. This is hard to believe, as man-made forcing increased very strongly between 1930 and 2016. And the result is that is that for periods like those the ECS estimate is only marginally higher than that for TCR – or even lower, depending on the exact combination of periods. It’s not clear if the paper’s figures are too high for the first half of the 20th century or too low for the recent decades, but in either case the result would be to bias down ECS estimates.
It must also be said the actual ocean heat uptake data is not available; Clisense uses my digitization of the paper’s plots. In the future, I want to add other sources of data on ocean heat uptake, although that will probably mean the year range will be restricted.
Finally, a note of caution. The app, like the scientific literature, uses volcanoes and solar radiation as the only natural forcings. In other words, the app makes the assumption that other natural factors don’t matter. This is obviously absurd for short periods of time, due to oscillations like El Niño. That’s why users are asked to select not two years but two periods: so that natural variability evens out. However, just because one selects two periods, one cannot be sure that natural variability has been completely removed. If some natural factor (e.g. reduced cloud cover) has effectively acted as a positive forcing over decade or multi-decade timescales, then ECS estimates will be biased high, because we’re assuming all the warming was caused by man-made forcing when in fact part was caused by natural forcing. The reverse is true if natural variability has acted to cool the climate. I prefer not to go further down that hole, as the research is still very tentative.
The way forward
Let me emphasize that Clisense is a project developed in my spare time. I receive no funding – in fact it costs me money, both for the Shiny app and the WordPress site. If my personal situation were to change, I might find myself without sufficient time and energy to keep improving the app. My goal is to add a ton of features – I just cannot guarantee I will.
If you have comments, questions or feedback of any form, I encourage you to share them with me by writing to alberto.zaragoza.comendador at gmail.com.
“Global-scale multidecadal variability missing in state-of-the-art climate models -https://www.nature.com/articles/s41612-018-0044-6
But then so too are they in Lewis and Curry. And then it is the nature of perpetual change that hints at potential sensitivities.
https://watertechbyrie.files.wordpress.com/2014/06/unstable-ebm-fig-2-jpg1.jpg
Michael Ghil’s model shows that climate sensitivity (γ) is variable. It is the change in temperature (ΔT) divided by the change in the control variable (Δμ) – the tangent to the curve as shown above. Sensitivity increases moving down the upper curve to the left towards the bifurcation and becomes arbitrarily large at the instability. The problem in a chaotic climate then becomes not one of quantifying climate sensitivity in a smoothly evolving climate but of predicting the onset of abrupt climate shifts and their implications for climate and society. The problem of abrupt climate change on multi-decadal scales is of the most immediate significance.
Please supply a link to the model. If I understand the graph correctly, we are safely in the upper stable region, and lowering the temperature might be very dangerous.
A simple set of equations intended to illustrate a principle.
http://research.atmos.ucla.edu/tcd/PREPRINTS/Ghil-A_Met_Soc_refs-rev'd_vf-black_only.pdf
It should not be over interpreted as a representation of the vastly more complex and dynamic Earth system.
http://research.atmos.ucla.edu/tcd/PREPRINTS/Ghil-A_Met_Soc_refs-rev'd_vf-black_only.pdf
“Sensitive dependence nonetheless does exist in the climate system, as well as in climate models — albeit in a very different sense from the one claimed in the linear work under scrutiny — and we illustrate it using a classical energy balance model (EBM) with nonlinear feedbacks. EBMs exhibit two saddle-node bifurcations, more recently called “tipping points”, which give rise to three distinct steady-state climates, two of which are stable. Such bistable behavior is, furthermore, supported by results from more realistic, nonequilibrium climate models. In a truly nonlinear setting, indeterminacy in the size of the response is observed only in the vicinity of tipping points. We show, in fact, that small disturbances cannot result in a large-amplitude response, unless the system is at or near such a point. We discuss briefly how the distance to the bifurcation may be related to the strength of Earth’s ice-albedo feedback.” https://www.researchgate.net/publication/45903640_Another_Look_at_Climate_Sensitivity
Weird chart that has nothing to do with climate. Warm times end with colder, colder times end with warmer, evolving changing cycles that are always bounded when warm thawed oceans provide more snowfall and it gets colder hundreds or thousands of years later. Cold periods with frozen oceans and lack of evaporation and snowfall end when the sequestered ice is depleted and retreats There is no chaos in these normal, natural and necessary cycles. Chaotic climate is only in the minds of people who do not really understand the internal natural process of ice cycles. if you understand climate it is normal, if you do not study and understand it is chaotic. Sometimes you post some stuff that makes some sense, chaotic charts do not qualify as anything that makes any sense.
The complex and dynamic Earth system – biosphere, atmosphere, hydrosphere, cryrosphere and their interactions exhibit properties of clustering og similar sized events and abrupt regime shifts. It is all in the data.
https://watertechbyrie.files.wordpress.com/2019/03/last-glacial-transition.jpg
Such behavior is entirely physical – as it must be.
“Figure 6: Deglacial Time Series.
(a) top-to-bottom: Greenland Ice Sheet Project (GISP2) δ18O (Grootes et al. 1993), average CdW from Florida Current (24 °N, 83 °W, 751 m; Came et al. 2008) and from the deep western North Atlantic (33.7 °N, 57.6 °W, 4450 m, Boyle & Keigwin 1987). (b) top-to-bottom: Greenland Ice Sheet Project (GISP2) δ18O (Grootes et al. 1993), δ13C from the Florida Current (16.9°N; 16.2°W, 648 m, Lynch-Stieglitz et al. 2011), and from the deep eastern North Atlantic (37.8°N, 10.2 °W, 3166m, Skinner & Shackleton 2004). Time scales for the ice core records are from Blunier & Brooks 2001). Generally high CdW and low δ13C in the deep Atlantic (bottom panels) indicate a relatively small contribution of NADW during the Heinrich Stadial and Younger Dryas. Low CdW values in the shallow North Atlantic suggest reduced northward flow of AAIW at the same time. The Heinrich Stadial (HS1), the Younger Dryas (YD), and the intervening warm period, the Bølling-Allerød (BA) are identified by shading. Note there is some debate about the timing of the Heinrich Stadial.” https://www.nature.com/scitable/knowledge/library/deep-atlantic-circulation-during-the-last-glacial-25858002
This is internal variation from multiple causes at just one scale. It evolves with physical changes in the sub-systems. So if you would like to call it dynamical complexity or emergent phenomenon – it is the same thing. But it is far from simple.
On the lower branch a doubling, 1.0 to 2.0, does not raise the global temperature very much. Earth is still very cold. On the upper branch, a doubling, 1.0 to 2.0, takes the global temperature close to hothouse conditions. Which means going up the upper branch is more often affected by events that cause additional warming and less often affected by events that cause cooling. If it were otherwise, the curve of the upper branch would not be steeper.
But if you pray, maybe it will be otherwise. Pray a lot.
The US National Academy of Sciences (NAS) defined abrupt climate change as a new climate paradigm as long ago as 2002. A paradigm in the scientific sense is a theory that explains observations. A new science paradigm is one that better explains data – in this case climate data – than the old theory. The new theory says that climate change occurs as discrete jumps in the system. Climate is more like a kaleidoscope – shake it up and a new pattern emerges – than a control knob with a linear gain.
This idea is the most modern – and powerful – in climate science and has profound implications for the evolution of climate this century and beyond. A mechanical analogy might set the scene.
https://watertechbyrie.files.wordpress.com/2014/06/unstable-mechanical-analogy-fig-1-jpg1.jpg
Figure 1: Simple mechanical analogy (Source: NAS Committee on Abrupt Climate Change, 2002)
Many simple systems exhibit abrupt change. The balance above consists of a curved track on a fulcrum. The arms are curved so that there are two stable states where a ball may rest. ‘A ball is placed on the track and is free to roll until it reaches its point of rest. This system has three equilibria denoted (a), (b) and (c) in the top row of the figure. The middle equilibrium (b) is unstable: if the ball is displaced ever so slightly to one side or another, the displacement will accelerate until the system is in a state far from its original position. In contrast, if the ball in state (a) or (c) is displaced, the balance will merely rock a bit back and forth, and the ball will roll slightly within its cup until friction restores it to its original equilibrium.’(1)
In (a1) the arms are displaced but not sufficiently to cause the ball to cross the balance to the other side. In (a2) the balance is displaced with sufficient force to cause the ball to move to a new equilibrium state on the other arm. There is a third possibility in that the balance is hit with enough force to cause the ball to leave the track, roll off the table and under the sofa.
There are tipping points in the Earth system.
Whether to colder.
https://www.researchgate.net/publication/324452795_Observed_fingerprint_of_a_weakening_Atlantic_Ocean_overturning_circulation
Or to hothouse Earth.
https://www.nature.com/articles/s41561-019-0310-1
Is beyond my ken. As much as doing more than examining the auguries is beyond JCH’s.
Sun comes up. Tropical ocean starts to heat up. When it passes a certain threshold, a fully formed cumulus cloud field forms. This field rejects a large amount of the incoming sunlight back to space.
If temperatures continue to rise, thunderstorms form, cooling the surface in a variety of way and greatly reducing the solar warming.
Please point out to us where these everyday occurrences, which have a HUGE effect on the temperature, are calculated in your “Clisense” app.
See here, here, here, and here for further details of some of the very important phenomena you’ve entirely overlooked in your simplistic calculator.
Best regards,
w.
Ask him for his code
QUANTIFY your claims in a way that can be put into a testable model
add your speculations to his work
build understanding that can be shared ( like the fricken math)
before you die
Steven, I have written at least a hundred posts on the subjects I touched on above, in which I have QUANTIFIED my claim in a variety of fields. I have posted the math and a host of observational data to back up my claims and theories, endlessly. I have described in detail all of these things.
But you know all of that. So go try your science-shaming on someone else. It doesn’t work on me.
w.
Following Mr. Mosher’s recommendations, Orville and Wilbur would have never flown.
“Following Mr. Mosher’s recommendations, Orville and Wilbur would have never flown.”
The Wright brothers succeed precisely because they meticulously tested models and quantified the results.
https://www.grc.nasa.gov/www/k-12/airplane/wrights/results.html
Yep; they didn’t rely on unvalidated models to launch something off a cliff early on. UN IPCC climate models are not sufficient for the purpose of fundamentally altering our society, economy and energy systems. All of the CliSci junk studies and posturing is just putting lipstick on that pig.
None of our young congress critters and marching children seem to realize that mobs go crazy, you can’t control them. Nancy is relearning (too late) the lesson of the French Revolution; don’t start something that will end up eating you! The madness of mobs is one of the reasons our nation’s founders came up with the Electoral College.
When the public learns of the costs in dollars and personal freedom of the GND (based on unvalidated models), they will rise in righteous wrath.
willis there isnt a single person on this planet outside of yourself
who could replicate your ideas Or carry on your work.
take this
“Sun comes up. Tropical ocean starts to heat up. When it passes a certain threshold, a fully formed cumulus cloud field forms. This field rejects a large amount of the incoming sunlight back to space.”
None of this is put down into math or code and shared adequately
how is tropical defined, how tight is the threshhold, how large is the cumulus field? how long does it take, what are the variations and limits,
how much sunlight does it reject? always the same amount? etc ect
ya ya ya there are a few posts. no text book, no comprehensive theory.
Nothing anyone could pick up and teach in a classroom.
when you die no one will carry on your work or even be able to explain it.
folks may link to dead posts with closed comments.
example: where are bob carters students? who carries on in warrick hughes shoes? John Daly? what did he leave for others to build on?
tick tock
“Following Mr. Mosher’s recommendations, Orville and Wilbur would have never flown.”
not really, but the wright brothers are a good example: They described what they did so that others could follow. The field progressed.
how many folks have picked up willis’ ideas and flown for themselves or advanced his science?
zero.
Here’s a challenge. Read willis’ papers build a similar calculator to show all the variables and how they work.
tick tock
The brothers didn’t turn into flight activists, demanding the world shift to air transportation to the exclusion of other modes of transportation.
Steven Mosher: take this
“Sun comes up. Tropical ocean starts to heat up. When it passes a certain threshold, a fully formed cumulus cloud field forms. This field rejects a large amount of the incoming sunlight back to space.”
None of this is put down into math or code and shared adequately
The phenomena have been observed by millions of people, all over the world. It doesn’t just occur in the tropics, but in the US in the land between the Rockies and the Appalachians. Nobody has quantified the effects. Some relevant equations have been described in standard texts, such as Thermal Physics of the Atmosphere, by Maarten Ambaum (read the sections on CAPE), but the effects on reducing incoming sunlight have not been quantified. Romps et al (the paper on cloud-to-ground lightning strikes) made a stab at relating temp and rainfall to lightning ground strike frequency, but not on the effects of lightning clouds reducing incoming sunlight.
This is a well-known “known unknown”.
Are you telling us that you have never witnessed thunderclouds building up? That you think the descriptions of the phenomena are irrelevant without code?
Willis Eschenbach has in fact quantified some of the phenomena, as when he showed that above average am temperatures are followed by below average pm temperatures in the TAO/TRITON data set. The locations of the buoys, the data, and his code have been described. It is a shame that established climate scientists and their graduate students are ignoring the phenomena and his work on them.
To be sure, most “climate scientists,” established or not, operate without much serious scientific analysis of the phenomena they describe. There is nothing, however, in Willis’ numerous posts that elevates his rather commonplace observations to a more lofty, rigorous level. Far from being “ignored,” his largely qualitative descriptions have been known to meteorologists and other geoscientists for generations.
Yes, huge natural response counters warming, from any cause, powered by more evaporation, convection up and forming of precipitation and cooler ice or water falling down, with powerful IR out from the tops of the clouds. The convection of the moisture filled air from the surface to where it forms clouds is carried to above most of the CO2. No mention of this in consensus alarmism. This is why I like Willis, he tells this frequently. Huge amounts of cooling of earth come from processes that have nothing to do with radiation of energy from the surface to space. All their greenhouse study’s deal with stuff that really does not matter.
Very Cool
and you used Shiny!
Shiny is a godsend. It’s almost surreal that you can build a web app while knowing nothing of web programming.
Re the Willis comment above: as the article said, there are natural phenomena we cannot quantify. It’s hard enough to know what the clouds are doing now (with CERES etc), and why; in the timescales used by climate sensitivity it’s essentially impossible because there is no data. We don’t know if cloud fraction increased between 1900 and 1950, or if decreased, or remained stable. So the app has to be used with a those of salt, but the same goes for any study that looks at the historical records to infer climate sensitivity.
That said, a future version may allow the user to set his own “natural forcing” for different periods, whether positive or negative, and see how the sensitivity estimates vary as a result. The challenge is how to fit that, and a bunch of other things I want to add, without making the interface incomprehensible.
Yes Shiny is pretty awesome. I once build a shiny app to get all the temperature datasets and basically replace woodsfortrees.
I think when Nic published one of this first papers I suggested this sort of calculator, but you took it way beyond what I was envisioning.
really nice work.
Present a special session at “International Conference on Coastal Ecosystem and Management” to share your views with others in Amsterdam, The Netherlands on September 16-17, 2019.
For abstract submission, please visit:
meetingsint.com/conferences/coastalzone/abstract-submission
coastalzone@insightsummits.com
+65-31080483
Contact Name: Joel Adams
I don’t read blogs – and I can’t read Willis. A word here or there. So I may be missing something. Emergent behavior? Has this been taken taken to the logical conclusion? Clouds and homey anecdotes mostly.
In this app – like Lewis and Curry – and I really cannot spare the interest to get across the details – is based on temperature and energy. In this way – surface temperature should integrate all changes in the Earth system and sensitivity calculable if only the energy estimates are right.
But there is something else I am missing in Willis. Thought experiments. Not the lack of them but the lack of any empirical basis. Compare Einstein’s train with Willis’ refrigerator. The train experiment was based on a the invariance of the speed of light in different inertial frames. The refrigerator seems based on the idea that more evaporation produces more shading. But where is the evidence?
Cloud over warm oceans?
https://watertechbyrie.files.wordpress.com/2018/03/clement-figure-3-e1528927033848.jpg
http://science.sciencemag.org/content/325/5939/460
Starting from this data – one can conclude that cloud is a positive feedback.
Robert I. Ellison | March 19, 2019 at 2:42 am
And despite that, you have the unprecedented arrogance to not only discuss but to dismiss my work?
For shame. READ IT FIRST BEFORE COMMENTING.
w.
Willis, the last couple of days have reminded me why I quit trying to communicate with Robert I. Ellison some time ago. To my profound regret I tried again and was frustrated, again, with his illogical utterances. You obviously see the problems with his approach to argumentation.
Oh Willis – I read enough in your comment and links to understand that positive cloud feedbacks to SST – in both theory and observation – are far from your ideas. Surely you got that as context?
Robert I. Ellison | March 19, 2019 at 8:22 pm |
“Got that as context”? Since you didn’t say one word about it in your comment to me, was I supposed to read your mind?
Next, I have no idea what your comment means. I have discussed AND MAPPED both positive and negative cloud feedbacks to SST. But of course, you’re trying to analyze my work without having actually read it, so I suppose you wouldn’t know that …
w.
“Hoo-ha! The Trenberth crowd finally found out what Bob Tisdale has been telling us for years
” https://judithcurry.com/2019/03/17/week-in-review-science-edition-97/#comment-890421
“It was a little more than 10 years ago that I published my first blog posts on the obvious upward steps in the sea surface temperatures of a large portion of the global oceans…upward steps that are caused by El Niño events…upward steps that lead to sunlight-fueled, naturally occurring global warming.” Bob Tisdale
https://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-18-0607.1
It was more than 10 years ago that I published my first multi-decadal ENSO piece. And almost as long since my blog here on multi-decadal cloud variability.
https://judithcurry.com/2011/02/09/decadal-variability-of-clouds/
Ahhh… the long lost days of blog innocence.
And Bob is right. Unlike David – who doesn’t know enough seemingly to decipher just what the hell Bob is saying. Hell it is sunlight fueled warming over the past few decades – largely because of positive cloud/sst feedback in the Pacific. Very approximate and simple calcs – and $5 – will get you a cup of coffee. Only data matters.
https://watertechbyrie.files.wordpress.com/2014/12/wong2006figure7-e1542128275460.png
https://journals.ametsoc.org/doi/10.1175/JCLI3838.1
Ta da!!!
Some people – David – should learn discretion.
If Willis wants to point to his positive cloud feedback – I will stand corrected. But it seems starkly at odds with his comments here. As I quite clearly said. I can only go on what I read.
I am not here to research the Willis archives.
Then STFU about Willis’ work.
Robert I Ellison: But it seems starkly at odds with his comments here.
What contradicts what? Exact quotes, please!
“Sun comes up. Tropical ocean starts to heat up. When it passes a certain threshold, a fully formed cumulus cloud field forms. This field rejects a large amount of the incoming sunlight back to space.”
I presume as the oceans warm that this is the very tired old skeptic meme of a negative cloud feedback.
Is Matthew unable to decide what comment I refer to? Who are these pipsqueak pretenders? Put up some evidence that shows that Clement, Loeb, Koren and Bob Tisdale – for starters – are wrong or as Dave so eloquently puts it – stfu.
Robert I Ellison: I presume as the oceans warm that this is the very tired old skeptic meme of a negative cloud feedback.
Was that in fact the statement of WE that you claimed contradicted the rest of his work? What exactly is the contradiction?
You call thundercloud buildup a “tired meme”, but I call it a “known unknown”. Fifteen minutes or so of extra cloud cover in the summer noon and afternoon time would cancel a considerable portion of any 4 W/m^2 of extra downwelling LWIR. Would it be produced by an extra 1C of mean surface temperature? Nobody has published reliable empirical study, but Romps et al is a start.
What else would it be?
I believe I claimed that the evidence contradicted the assertion of a negative sst/cloud feedback..
https://watertechbyrie.files.wordpress.com/2014/06/clement-et-al-e1512080464744.png
But at any rate – lighting strikes over the continental US is the wrong metric and the wrong place to look.
Robert I Ellison: and I can’t read Willis. A word here or there.
…
I read enough in your comment and links to understand that positive cloud feedbacks to SST
Make up your mind. Did you read anything in particular by WE that you would care to comment on? Be sure to quote it exactly so all of us readers know what you are referring to.
Emergent phenomena!!! Has he taken it to its logical conclusion? And are you just farting around with passive aggressive trivialities as usual?
Robert I Ellison: And are you just farting around with passive aggressive trivialities as usual?
You wrote a blatant contradiction.
Making it personal rather than discuss the actual science on positive cloud feedback briefly mentioned and linked to is the usual red herring.
As I said – I read the comment and scanned Willis’ links.
The following uncontested facts indicate 1) that CO2 is not in control of climate at this time, at these levels, and 2) that we are not in control of CO2.
First, there is the natural experiment of 1929-1931, when human production of CO2 went down rapidly by 30%. Global CO2 did not decline, and temperatures kept rising until 1942. Thus, no effect and tremendous cost. During the WWII years and post-war rebuilding, CO2 quite unaccountably stabilized and temperatures declined despite the production of huge amounts of CO2. Declined only slightly, but enough to evoke predictions of the Coming Ice Age – see the covers of Newsweek and Time and Science News in the early ’70s. Thus, no effect and tremendous cost. And that was world CO2, not just Oregon.
Next, there has never been a temperature reversal in the last 500 million years preceded by a CO2 change. Nor, more recently, did CO2 change precede the emergence from the Last Glacial Maximum, the temperature plunge into the Younger Dryas, the rapid rise to the Holocene Optimum (with its 280 ppm CO2) and the gradual decline since then, punctuated by Minoan, Roman, Medieval and current Warmings, not to ignore the Little Ice Age. In the Eemian, 120,000 years ago, global temperature was 2C higher and sea level was 6 meters higher and CO2 was 280 ppm.
Not just control but the very notion of causality demands that the cause precede the effect in time. That is not seen here. The confusion of correlation with causation is common, but remediable.
Third, as Arrhenius noted, the CO2 GHG effect declines exponentially. The first 20 ppm of CO2 produces 50% of the GHG effect and it declines exponentially after that. We are in the fifth half-life of that decline. So if CO2 doubles to 800ppm, its GHG effect will increase by 1.4%, totally submerged in the other eight influences on global climate.
Understandably, we are horrified by the prospect of change. Perhaps the Neanderthals were too. But let’s remember that 30% of the agriculture increase since 1950 has been attributed to CO2. Satellite pictures demonstrate the greening of the earth. CO2 is also virtually the only GHG in the stratosphere capable of radiating IR out to space.
So CO2 warms us, and cools us, and feeds us. Let’s concentrate on something we can do well – adapt – rather than virtue signalling to no effect.
I think the CO2 GHG effect increases logarithmically. That is not the same as ‘declines exponentially’. (Nor does the rate of warming decline exponentially).
On the logarithmic model there is no limit to the amount of warming you can get, though you need to keep doubling the amount of CO2 to get the same amount of warming (which is why climate sensitivity is often expressed as degrees of warming per doubling of CO2)
“Ocean heat uptake and the corresponding energy imbalance: perhaps the least intuitive part of the calculation. Nevertheless, it’s only necessary in order to estimate equilibrium climate sensitivity (ECS); for the transient climate response (TCR) all you need are temperatures and forcing.”
You need low cloud cover and water vapour feedbacks, which along with ocean heat uptake, are counter-intuitive. Ocean heat content does nothing or even cools during a cold Atlantic Multidecadal Oscillation phase, and increases during a warm AMO phase. During a warm AMO, low cloud cover declines, hence OHC increases, and lower troposphere water vapour increases, aided by increased surface wind speeds over the oceans. And the AMO is normally warm during centennial solar minima, and colder when the solar wind is stronger.
Very useful contribution. TY. I look forward to exercising it. And yes, ocean heat uptake uncertainty is the weak link; by design, it will take ARGO another decade to provide a first solid observational estimate of the ‘93%’.
Alberto
Thanks for your great start in making climate sensitivity calculations accessible.
Recommend reviewing and incorporating Svensmark’s review of solar impacts. He finds that feedbacks can amplify solar/cosmic impacts about to 10x larger than just direct Total Solar Insolation. I encourage you to provide these factors with conventional defaults on up to Svensmark’s ranges. See:
Henrik Svensmark Force Majeure: The Sun’s Role in Climate Change
https://www.thegwpf.org/content/uploads/2019/03/SvensmarkSolar2019-1.pdf
Executive summary
Alberto
Please add a specific item of cloud cover and feedback. Roy Spencer notes that about a 3% change in cloud cover could explain the recent warming:
My Global Warming Skepticism for Dummies
https://www.drroyspencer.com/my-global-warming-skepticism-for-dummies/
The forcing from 1950 to 2016 is about 2w/m2. While the cloud’s cooling effect is about 50w/m2, their net effect is 20-25w/m2. I suppose Spencer’s 3% is a rough approximation if you use only the negative forcing, but for net cloud forcing it’s more like 8-10%.
Thanks for the post and the app. Great work.
Messing around with the app a bit this AM I see that varying ocean heat uptake between .85 and 1.0 changes calculated ECS between 1.38 and 1.41 for the starting conditions in the app I accessed. I am trying to understand then why ocean heat uptake is a key parameter in this.
The effect is a bit easier to see if you select only the most recent years as the second period, because those years have a higher rate of ocean heat uptake. For example, choose 2011-2016 and set the ratio 1 (the first period remains the default, 1873-1900). The results for first and second period are respectively 0.06w/m2 and 0.43w/m2.
By setting the ratio to 1, which is to say 100%, you’re telling the app that ALL the planet’s energy gain took place in the ocean. Under this scenario, ocean heat uptake = energy imbalance. 0.06w/m2 and 0.43w/m2 are the rates of ocean heat uptake according to the Zanna paper, and that’s what Clisense reports.
Now set the ratio instead to 0.85. What you’re telling the app now is that, out of all planetary energy gain, “only” 85% took place in the ocean. Hence the figures from Zanna are divided by 0.85: the first period goes from 0.06 to 0.07w/m2, and the second period goes from 0.43 to 0.50w/m2 (due to independent rounding there may be a difference of 0.01w/m2 here and there).
The increase in energy imbalance between both periods , which is what counts for the calculation of sensitivity, was 0.37w/m2 when the ratio was set to 1 (0.43 minus 0.06). With the ratio set to 0.85, this increase becomes 0.5 – 0.07 = 0.43w/m2.
In other words, there can be an “additional” 0.06w/m2, depending on your assumption of how much of the Earth’s energy imbalance is made up by ocean heat uptake. That’s why the estimated climate sensitivity is higher when you set the ratio to the lowest possible level: you’re telling the app that there is a greater energy imbalance, and a greater energy imbalance requires more atmospheric warming (the imbalance will only disappear when infrared emissions increase, and that will only happen when air temperatures increase).
Not to beat my own drum but this page of the explanatory website says it better than I can express in a comment: https://cli-sense.com/what-is-climate-sensitivity/
With the Zanna paper this ratio doesn’t matter much because the paper has relatively low rates of heat uptake for the last decades. But for other datasets that show heat uptake of 0.6 or 0.7w/m2 the effect would be noticeable.
I don’t want to spam the thread so I’ll also use this comment to thank everybody for your comments. I’ve already got some feedback via email too, and I hope to add stuff the app little by little.
Alberto, thanks for your reply.
When I go through the steps you suggest, I do indeed get the energy imbalances you cite. However, ECS ranges between 1.44 at .85 for ocean uptake and 1.4 for a value of 1.0. Is ECS really so insensitive to ocean heat uptake? If so, why does it matter if ARGO confirms 93% or comes up with some other value?
Mark,
For the Zanna heat record, ECS is indeed very insensitive, as you say. Varying ocean heat uptake as a percentage of total energy imbalance between 85% and 100% only gives you an additional 0.06 (increase) or 0.07 (total) watts per square meter. Since forcing between the periods involved increased by well over 2w/m2, it doesn’t matter much which percentage you choose.
If the app used other datasets, which show ocean heat uptake of about 0.7w/m2 for the last decade, the impact would be greater. The point is that ARGO cannot confirm the 93%; ARGO and other sources can only confirm the absolute ocean heat uptake (in terms of w/m2). Whether those w/m2 are 85%, 93%, etc. of the total energy imbalance is unknown, so that’s what I let users decide.
Alberto Zaragoza Comendador: “As for climate sensitivity, I can report anecdotal experience from the Madrid area: not a single person that I’ve talked to has a clue what it is. I don’t mean that they fail to provide the technical definition – they don’t even know what it’s about.”
The politics of climate change and the science of climate change as practiced by today’s mainstream climate scientists, and by today’s climate change activists, share a common thread.
As long as the thirty-year running average of GMT is even slightly positive — or even flat for that matter — the climate models which assume CO2 is the earth’s climate control knob have been verified for all practical purposes, politically and scientifically.
It was in early 2018 that Javier presented a detailed cyclic analysis of the earth’s temperature patterns. I asked Javier the question of when, according to his analysis, the thirty-year running average of GMT would turn definitively down, and then stay in a statistically significant downward trend for another thirty years.
Javier’s answer was that the GMT’s permanent inflection point occurs in about the year 2200.
IMHO, no one has yet presented credible evidence that a permanent inflection point in the earth’s long-term GMT trend line is on the near-term horizon. The earth has been warming for more than one-hundred fifty years, and it’s likely to keep on warming for another hundred years at the least.
If Javier’s cyclic trend analysis is correct, and a permanent downturn happens in about the year 2200, this means we have roughly another two hundred years of debate before the question of the climate’s sensitivity to higher concentrations of CO2 is definitively settled to everyone’s satisfaction.
By that time, peak oil will already have passed and most of the world’s energy will be coming from nuclear power, making moot the question of the earth’s climate sensitivity to the presence of CO2.
Javier is wrong, Grand Solar Minima series occur every 863 years, and I can show exactly why. The synodic cycles of Earth-Venus-Jupiter-Uranus order sunspot cycles and centennial minima through a cycle of 1726.62 years and 16 centennial minima. Twice during this series the alignments are displaced and produce a grand minimum series, at the half cycles. For example the Antique little ice age from 350 AD is followed by the LIA 863 years later from the early 1200’s. Seven 1726.62 year cycles before 1215 was 10871 BC, and grand minimum series at the start of the Younger Dryas. No doubt they will be related to DO events too. The Eddy cycle doesn’t exist, and possibly 3*863 could be confused with a ~2450 year Bray cycle. I have three astronomically related lines of evidence that the next two centennial minima will be the longest pair for 3500 years, and can map their start and duration precisely.
Ulric, I respectfully ask the same question of you that I asked of Javier in early 2018: When, according to your own analysis, will the thirty-year running average of GMT turn definitively down and then stay in a statistically significant downward trend for another thirty years?
The earth has not experienced a good old-fashioned cooling of the type you are asking about since the turn of 19th century. It can’t. Why? Because ACO2 is the control knob of the current climate. So the knob has been cranked up to ~404. If the AMOC were to entirely shut down, you would get a brief period of what you are describing, which would be followed by a rapid, springback warming.
That would be defined by the AMO envelope. I am predicting the next cold AMO phase starting from the mid 2030’s, with the coldest AMO anomaly in the mid 2040’s accompanied by multi-year La Nina, much like the mid 1970’s. The 1975-1995 period is not necessarily the perfect analogue for the 2044-2064 period, it could be a similar warming rate or slightly faster. So no downward trend for 30 years as such.
JCH, there is no sign of CO2 being in control of the AMOC and AMO, but the AMO is definitely a controller of CO2.
https://www.researchgate.net/publication/258807285_Atlantic_Ocean_CO2_uptake_reduced_by_weakening_of_the_Meridional_Overturning_Circulation
“Only as good as the data that goes in”
True, true… faulty sighting of official thermometers and UHI ’cause’ increasing ‘official’ warming irrespective of whether or not CO2 as a percent of the atmosphere is increasing. We also know that the temperature inside a greenhouse doesn’t rise because CO2 gas is added– the temperature rises for the same reason it rises inside a car with the windows rolled up.
“Only as good as the data that goes in”
No! Not better than the data that goes in but if the insides are wrong what comes out cannot be as good as the data that goes in. Put good data into climate models and you still always get bad results. No model output resembles real data.
“It [CO2] is to climate what GDP is to economics.”
Humanity’s CO2’s is to climate what a vuvuzela is to soccer– “…it should be recognized that the basis for a climate that is highly sensitive to added greenhouse gasses is solely the computer models. The relation of this sensitivity to catastrophe, moreover, does not even emerge from the models, but rather from the fervid imagination of climate activists.” ~Richard Lindzen (at EIKE)
Thank you Alberto.
While I am somewhat sceptical of the mainstream claims of climate science – because we do not fundamentally understand climate change – this does not change the fact that we have massively increased the proportion of GHGs in the atmosphere, that the GHE is real and therefore that the potential parameters of ECS and TCR over time are of great interest.
Anything that increases the ability of the lay person to understand get involved in the climate conversation is a huge boon; your efforts are very much appreciated.
Unfortunately the claim “The more forcing, the more warming” is false. CO2 might double and the temperature go down, even dropping into the next ice age. Does that make sensitivity negative? No, it just shows that temperature is not controlled by CO2 level so there is no such thing as sensitivity in the real world.
Sensitivity may be a valid abstraction, like how a feather would fall in a vacuum, telling us what would happen if nothing else happened, but that tells us nothing about our world.
Unfortunately, sensitivity is often taken as a prediction, not an abstraction, even by many scientists. This is a huge mistake. In short, sensitivity is not fundamental to climate science. If anything it is a misleading distraction.
Regarding this: “That’s why users are asked to select not two years but two periods: so that natural variability evens out.”
There is no reason to believe that natural variability “evens out” at any scale. That it does not is even provably true if climate is chaotic.
Natural variability is less pronounced over decadal than yearly timescales. This is obvious just looking at any chart, though it can also be proven by statistics. What I say (in the sentence right after the one you quoted) is that you cannot be sure natural variability has been completely removed just because you use longer timescales.
None of what you say has anything to do with natural variability “evening out.” As for decadal scales, so-called abrupt events are thought to have seen temperature changes of several degrees in a few decades.
More importantly, for all we know there is no CO2 induced warming in the entire modern record, yet you claim to measure what may not even exist. See my little https://www.cfact.org/2018/01/02/no-co2-warming-for-the-last-40-years/.
I think you should make clear that your app implements certain contentious assumptions, especially the existence of CO2 caused warming.
natural variability evens out means nothing. You can average anything and all you get is the average, If you have no good reason it matters it means nothing. if you have a reason but it really is a stupid reason, you have nothing. Have the person doing the averaging to make up something that at least seems to be worthwhile. They are not even close.
Steven Mosher | March 19, 2019 at 10:44 am |
Steven, that is an ELEVATOR SPEECH about my hypothesis. OF COURSE it doesn’t contain numbers, math, or details.
But in my individual posts, I go over the numbers in great detail. Heck, look at my very first post on this question on WUWT, where I quantify and specify a whole lot what you have requested … and that was just my first post out of hundreds that did the same.
Heck, take a look at the post I linked to above, “How Thunderstorms Beat The Heat“. In it, I calculate the amount of heat removed solely by evaporation by the thunderstorms all around the earth, on a 1°x1° gridcell basis, and I explained exactly the math I used to do it.
And in fact, I’m known for posting up the math and the data so folks can do the analysis themselves.
So your claim that “none of this is put down into math” is just a sad attempt to get people to believe something that is totally untrue.
Finally, perhaps it is true that YOU could not “replicate [my] ideas Or carry on [my] work”. However … claiming as you did that your own personal inability applies to every individual on the planet is … well … as Spock would say, “illogical” …
However, that’s just science … and here in the real world, my very best regards to you.
w.
Willis, I do like your elevator speech. you only deal with tropical cooling and it is the majority of the cooling of earth. You ignore polar ice cycles and the cooling that ice causes that causes ice cycles in polar regions. my elevator speech covers the ice cycles, which provide most of the cooling beyond the tropical regions.
About 2000 years ago, there was a Roman Warm Period and then it got cold. About 1000 years ago, there was a Medieval Warm Period and then it got cold. That was the Little Ice Age. When Oceans are warm, Polar Oceans thaw, snowfall increases and rebuilds ice on Greenland, Antarctic and Mountain Glaciers. Ice builds, spreads and makes earth cold again. Snowfall decreases and the Sun removes ice every year until it gets warm again. It is warm again now because it is supposed to be warm now. It is a natural cycle and we did not cause it. CO2 just makes green things grow better, while using less water. The alarmists scare us so they can tax and control us.
Scarcely! All that is done is to convert the gridded satellite rainfall data into putative “evaporation” data under the wholly unrealistic premise that the two distinct variables are simply related and are geographically coincident. This claim exemplifies how far removed from genuine science such unenlightened number-crunching exercises really are.
john321s: All that is done is to convert the gridded satellite rainfall data into putative “evaporation” data under the wholly unrealistic premise that the two distinct variables are simply related and are geographically coincident.
The hypothesis that rainfall equals evaporation has been used by others in the peer-reviewed literature, for example in the lightning strike paper by Romps et al. The assumption that rainfall in a region during a time span equals evaporation in that same region in that same time span is of course suspect, and can only be true of large enough regions and large enough time spans to account for water vapor transport by wind: for example, the whole earth for a whole year. The weakness of that assumption is one of the weaknesses of the Romps et al study, as pointed out, for example, by Willis Eschenbach in his critique of the paper that he posted at WUWT.
Aside: If over a time span global rainfall has increased by 6% (c.f. the review by O’Gorman et al), then global evapotranspiration has to have increased by approximately 6% as well. How much increased power has to be radiated to the earth surface to power that much increased evapotranspiration-rainfall has not been reported in the peer-reviewed literature, to my knowledge. I posted my attempt here a few years ago.
john321s | March 22, 2019 at 5:57 pm
John, if you have better numbers, or some theoretical reason to think that I’m wrong that rainfall = evaporation, now would be the time to bring them up.
Steven falsely said I didn’t give numbers. I gave numbers. If you don’t like the numbers, it will take more than handwaving to make them vanish. When you post up better data, let me know. Until then, sorry, you’re just mumbling about sour grapes.
w.
“Stratocumuli swathe enormous regions of Earth’s surface and exhibit a great variety of structure on a wide range of spatial scales (Fig. 1). They cover approximately one-fifth of Earth’s surface in the annual mean (23% of the ocean surface and 12% of the land surface), making them the dominant cloud type by area covered (Warren et al. 1986, 1988; Hahn and Warren 2007). Stratocumuli strongly reflect incoming solar radiation (Chen et al. 2000) and exert only a small effect on outgoing longwave radiation, with the result being a strong negative net radiative effect that markedly affects Earth’s radiative balance (e.g., Stephens and Greenwald 1991; Hartmann et al. 1992). Only small changes in the coverage and thickness of stratocumulus clouds are required to produce a radiative effect comparable to those associated with increasing greenhouse gases (Hartmann and Short 1980; Randall et al. 1984; Slingo 1990). Understanding why, where, when, and how stratocumuli form, and being able to quantify their properties, therefore constitutes a fundamental problem in the atmospheric sciences.”
https://journals.ametsoc.org/na101/home/literatum/publisher/ams/journals/content/mwre/2012/15200493-140.8/mwr-d-11-00121.1/20120803/images/medium/mwr-d-11-00121.1-f2.gif
https://journals.ametsoc.org/doi/full/10.1175/MWR-D-11-00121.1
Conservation of mass ensures that precipitation equals evaporation but it is unclear to me what useful lesson can be drawn from that.
The physics of marine strato-cumulus are bistable and nonlinear.
e.g. https://aip.scitation.org/doi/10.1063/1.4973593
Convective cells persist for longer over cooler oceans before raining out from the center. A positive albedo feedback to SST.
Robert I Ellison: Stratocumuli strongly reflect incoming solar radiation (Chen et al. 2000) and exert only a small effect on outgoing longwave radiation, with the result being a strong negative net radiative effect that markedly affects Earth’s radiative balance (e.g., Stephens and Greenwald 1991; Hartmann et al. 1992). Only small changes in the coverage and thickness of stratocumulus clouds are required to produce a radiative effect comparable to those associated with increasing greenhouse gases (Hartmann and Short 1980; Randall et al. 1984; Slingo 1990).
Hence the question: How would a 1C increase in mean surface ocean temp affect stratocumulus cloud cover?
And your point is?
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017MS001096
Nowhere does Willis’ actually calculate the claimed “amount of heat removed solely by evaporation by the thunderstorms all around the earth, on a 1°x1° gridcell basis.” The results shown in Figure 1 of his WUWT post are predicated upon nothing more than the mistaken premise that the global balance of evaporation and precipitation holds on a local gridcell basis, namely:
What he actually shows is merely satellite-sensed rainfall amount times 80.
In reality, surface evaporation is by no means an exclusively thunderstorm phenomenon, nor does the water vapor condense and precipitate only locally. On the contrary, what’s evaporated anywhere over oceans or land today may precipitate a week later and a thousand kilometers away. The transport of vapor by the ever-blowing winds around the globe and the manifold factors that trigger precipitation cannot totally ignored. It’s only a scientifically unenlightened rush to “give numbers” that produces the grossly misleading results whose criticism Willis wants to dismiss here as “mumbling about sour grapes.”
Robert I Ellison: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017MS001096
Thank you for the link.
Absolute 0 is 0’ Kelvin, -273’ Centigrade, and -459’ Fahrenheit. At the height of the Ice Age the oceans were 400’ lower than present.
Water reflects Radiant heat from the sun out to outer space.
The average surface temperature of the earth is about 63’F
The average temperature of the sun is (10000’F -7300’F) / 2 +7300’F = 8650’F. This will vary +/- a few hundred degrees over the centuries.
A simple explanation of the Ice Age.
The surface area of the earth is about 196,900,000 square miles and if I assume the average surface temperature is a constant 63’F it is radiating a fixed amount of heat to the black sky every 24 hours.
The sun is radiating heat to the earth’s surface and the surface area of the earth receiving that heat is the area of the great circle perpendicular to a line between the two centers. That surface area is about 49,360 square miles.
According to my reading of the Antarctic Ice core the Radiant heat hitting the earth every 24 hours is greater than that radiating out to the black sky every 24 hours. Because the sun is an active star the average surface temperature will vary over the centuries up or down a few hundred degrees, but will always be more than radiated out by the 63’F earth.
About 18,000 years ago the new Ice Age began because the surface area of earth covered by water was large enough that it reflected enough radiant heat out by the 63’F surface than that retained from the sun. At this point Nature began removing heat from the oceans and adding it to the atmosphere. It does this by evaporating water from the oceans, taking it to the poles and dropping it in the form of frozen water.
About 18,000 years ago the new Ice Age began
You should check the data, about 18000 years ago the last major ice age ended and warming started. That was when the oceans started warming.
Get the basic facts right first.
Becoming more sympathetic to the Pat Franks argument that the physical error propagation in models is too large for any GCM to make meaningful predictions more than 10 years in the future, at least until we understand the climate system quite a bit better than today.
https://www.skeptic.com/reading_room/a-climate-of-belief/
When I was involved with economic modelling to assess, for example, the potential impacts of alternative policies,we never looked beyond ten years. In part, because discount rates meant that anything beyond ten years had little impact, but more importantly because of the great uncertainties regarding a longer period (and even within ten years). And we never assumed certainty, we didn’t make quantitative forecasts as opposed to comparisons: we would say that modelling suggest that over ten years policy A would be likely have X benefits, against Y for policy B. So you’ld tend to prefer the policy with the greatest net benefits.
More broadly, I’ve long argued that the future will always surprise us,the best policies are those which give us the greatest capacity to deal with whatever unknown future befalls. Trying to base policies on what might happen to climate by 2100 seems to me to be absurd.
“Lorenz was able to show that even for a simple set of nonlinear equations (1.1), the evolution of the solution could be changed by minute perturbations to the initial conditions, in other words, beyond a certain forecast lead time, there is no longer a single, deterministic solution and hence all forecasts must be treated as probabilistic.”
https://royalsocietypublishing.org/doi/full/10.1098/rsta.2011.0161
Pat Franks is from alone in recognizing the not so secret life of models that is sensitive dependence on initial conditions.
What can go wrong is shown in a schematic of a decadal probabilistic forecast – a single model showing multiple trajectories from imprecisely known inputs as a result of sensitive dependence. Even before wondering if the system is adequately represented and if the parametizations are correct.
https://royalsocietypublishing.org/cms/attachment/94141948-e1c2-4dc4-ac3e-82ca271c2911/rsta20110161f08.jpg
CMIP opportunistic ensembles dispense with all this uncertainty. A solution is picked out of many 1000’s of possible solutions over far too long a period because it looks good (McWilliams, 2007) and emailed to the IPCC – where it and many other qualitatively selected solutions (McWilliams 2007) are averaged and what not. The word scam is thrown around too easily – but this seems to qualify.
But there are lots of real world solutions. There are ways to a bright future for the planet, its peoples and its wild places – but these need to be consciously designed in a broad context of economics and democracy, population, development, technical innovation, land use and the environment.
We might even solve global warming. Electricity is 25% of the problem of greenhouse gas emissions. A multi-gas and aerosol strategy is required for a comprehensive solution – decreases in carbon dioxide, CFC, nitrous oxides, methane, black carbon and sulfate emissions. Ongoing decreases in carbon intensity and increases in efficiency and productivity. And technical innovation across sectors – energy, transport, industry, residential and agriculture and forestry.
Some of the answer is under our feet. Rattan Lal – himself a scientific treasure – estimates that some 500 Gigatonne (GtC) carbon has been lost from terrestrial systems. ‘Soil is like a bank account – we must replace what we have removed.”
Increased agricultural productivity, increased downstream processing and access to markets build local economies and global wealth. Economic growth provides resources for solving problems – conserving and restoring ecosystems, better sanitation and safer water, better health and education, updating the diesel fleet and other productive assets to emit less black carbon and reduce health and environmental impacts, developing better and cheaper ways of producing electricity, replacing cooking with wood and dung with better ways of preparing food thus avoiding respiratory disease and again reducing black carbon emissions. A global program of agricultural soils restoration is the foundation for balancing the human ecology.
“It isn’t enough to repair the damage our progress has brought. It is also not enough to manage our risks and be more shock-resistant. Now is not only the time to course correct and be more resilient. It is a time to imagine what we can generate for the world. Not only can we work to minimize our footprint but we can also create positive handprints. It is time to strive for a world that thrives.” Jean Russell
It can be surprising how much uncertainty lurks even in the seemingly most predictable and staid of phenomena… I did not realize until reading Cixin Liu’s book that the orbits of the planets are not predictable below a certain scale, and we are only able to estimate them outside of that scale using various simplifying assumptions (essentially treating them as a 2D planar system in which the Sun is totally unaffected by the planets). https://en.wikipedia.org/wiki/N-body_problem
https://en.wikipedia.org/wiki/Three-body_problem#Restricted_three-body_problem
I quickly glanced through the comments discussing this topic on climate sensitivity (originally coined for a doubling of CO2. Unfortunately I can’t remember which IPCC report it was where the IPCC did admit that increasing concentration of atmospheric CO2 is not able to increase the “required” forcing on its own, i.e. to increase “global” temperature. Therefore the IPCC desinged a trick: They insist that a small increase in CO2 causes the forcing to slightly warm the ocean surface thus increasing evaporation of water and IT IS THIS ADDITIONAL WATER VAPOR NOW FUNCTIONING AS A FEEDBACK IS THE CAUSE OF WARMING. Ain’t this just beautiful!
I wonder why nobody here has noticed this very questionable explanation to
this hoax, in other words to the so-called man-made catstrophic global warming hype.
Regards and thanks Willis for your tehory on the water thermostat!
Boris
Boris: What you describe is not considered a hoax. If the planet behaved like a simple graybody with a surface temperature of 288 K and emissivity of 0.615, it would emit 3.3 W/m2 more LWR to space for each degK it warmed. (A blackbody at 255 K would emit 3.7 W/m2/K. More sophisticated models that account for the variation in temperature say 3.2 W/m2/K.) So a radiative imbalance of 3.7 W/m2 from doubled CO2 (a forcing) could be eliminated by warming of 1.1 K.
W = eoT^4 dW/dT = 4eoT^3 = 3.3 W/m2/K
However, as the planet warms, there should be more water vapor in the atmosphere (a GHG that slows down radiative cooling to space), more warming in the upper atmosphere than at the surface (enhancing radiative cooling to space compared with a graybody), a reduction in reflection of SWR by ice and snow, and probably changes in the emission of LWR and reflection of SWR by clouds. These are called feedbacks – changes in emission of LWR and reflection of SWR CAUSED by changes in (surface) temperature. They are reported in units of W/m2/K. If we knew how much more LWR our planet emitted and SWR it reflected per degK of warming, then we would know how much warming to expect from doubled CO2 (ie climate sensitivity). The best estimates today are 1 or 2 W/m2/K, not the 3.3 W/m2/K expected for a graybody.
There is NO doubt that feedbacks exist. They can be observed from space by satellites during the large change in temperature associated with the season. The paper linked below clearly show that our planet emits about 2.2 W/m2/K (not 3.3 W/m2/K) more LWR as it warms with the seasons. (The dotted line in Figure 1 has a slope of 3.2 W/m2/K). The change in reflection of SWR is not linear with temperature. Seasonal warming (warming in the NH combined with cooling in the SH) isn’t an ideal model for global warming, but it proves that feedbacks do exist in both the LWR and SWR channels from both clear and cloudy skies. Global warming is occurring too slowly to accurately measure changes the changes in LWR and SWR associated with this process.
https://www.pnas.org/content/110/19/7568
The paper also shows that climate models do a lousy job – and mutually inconsistent job of reproducing what we observe from space.
Franktoo, thanks for your comment, however my point is that water vapor itself should be called a forcing and not as a feedback following a slight increase in CO2. Water vapor is anyhow the most important “greenhouse gas”. However, water vapor has an even greater role in adjusting global climate/weather through thermal energy exchange between the three phases: gas, liquid and solid.
Boris: You can, if you want, try to treat water vapor as a forcing. You could assume modern agriculture or dams or something else is increasing or decreasing water vapor. The online Modtran calculator can increase water vapor at all altitudes by 7% (say for 1 degK of warming) can calculate that OLR transmitted through a clear tropical sky drops by 1.6 W/m2. And you could do the same sophisticated calculations that are done for CO2, getting a global forcing value by looking at the change in radiative cooling in many representative regions with and without clouds.
http://climatemodels.uchicago.edu/modtran/
What are the advantages and weaknesses of this approach? The problem with treating water vapor like a forcing is that it is constantly changing in response to changing temperature – it isn’t a constant forcing. The average water molecule remains in the air for nine days between evaporation and precipitation; five days in the tropics. Water vapor responds to changes temperature orders of magnitude faster than temperature can be changed by water vapor.
If you look at the PNAS paper I linked above, OLR emitted through clear skies to space varies linearly with temperature at -2.2 W/m2/K, not -3.2 W/m2/K because increasing water vapor acts as a GHG (WV feedback) and because water vapor lowers the lapse rate producing more warming in the upper atmosphere than at the surface (lapse rate feedback). The change in temperature is driven by massive changes in incoming SWR (hundreds of W/m2) and the difference in heat capacity between the two hemispheres. Water vapor and its effect are the only feedbacks operating on LWR emitted through clear skies. The direction of causation should be clear – changes in water vapor as caused by changes in temperature which are caused by changes in incoming SWR.
So the traditional approach is to treat the changes in radiative cooling and lapse rate produced by changes in water vapor as feedbacks (in W/m2/K) which basically get multiplied by warming (change in K) when being mathematically combined with forcing (W/m2). You can accomplish the same thing by treating water vapor as a forcing and calculating the water vapor forcing for different temperatures. If you believe that the local long-term average amount of water vapor varies for some reason besides temperature (saturation water vapor pressure is a function of temperature), then you might be correct that water vapor can’t be treated as a feedback.
Is water vapor the most important GHG? It increases about 7% per K of warming, carbon dioxide is going to increase roughly 100%. Forcing is usually proportional to the log of the percent change, not the absolute amount of the change. At the surface, we have 400 ppm CO2 and 1000-3000 ppm H2O, but at the tropopause H2O has dropped to 3 ppm. Absorption and emission cause OLR to change with altitude up to about 25 km, several times higher than the altitude where CO2 becomes the dominant GHG in terms of ppm. Finally, if water vapor truly dominated, water vapor feedback would produce a runaway greenhouse effect. I think an appropriate statement is that they are comparably important.
If you really want to understand the science behind some of the phenomena you and other skeptics often bring up, I recommend ScienceofDoom.com. Many think the host is a supporter of the consensus, because he is a passionate defender of established physics. However, established physics doesn’t tell us how much more OLR the planet radiates to space and SWR it reflects back to space as it warms. This is sometimes known as the climate feedback parameter (measured in W/m2/K). This is what determines how much warming a given forcing (W/m2) will produce at equilibrium. If you are satisfied believing what you already believe (ie with confirmation bias), then SoD will be frustrating. Good luck.
Franktoo, thanks for your additional info. The MODTRAN site and software is excellent to explain outgoing and incoming radiation; I used to play around with it over a decade or two ago and really learned a lot.
My point in addressing IPCC’s trick as I call it, is that a slight increase in CO2 radiative forcing, which as iPCC admits is not by itself able to cause the rise in the recorded air temperature. So by insisting that the very slight increase in radiative forcing (by CO2) leads to substantial increase in evaporation and it is this assumed increase in water vapor (the most potent greenhouse gas) that IPCC uses as a scape goat to explain any observed increases in air temperature.
To use this I strongle object, because water surface evaporation is mainly amplified by wind “forcing”, much more than by temperature.
Agree?
regards from Boris
Boris: I respectfully don’t agree. Water vapor is changed by temperature much faster than temperature can be changed by water vapor acting as a forcing. So we can account water vapor as a feedback (W/m2/K) that is correctly account for (in terms of W/m2) by temperature.
Yes, increasing wind and temperature do increase the rate of evaporation. Evaporation is part of the SURFACE energy balance. There is also a TOA energy balance, which is simpler because only radiation is involved. No matter what happens with wind, it is impossible for an increase in evaporation due to surface warming to put 7%/K more latent heat (5.6 W/m2/K) into the bottom of the atmosphere if only 2.2 W/m2/K of OLR is exiting the top of the atmosphere. (The latter is what CERES shows for seasonal warming, but whatever the correct value is, the surface flux and TOA flux must increase with temperature in parallel.) Climate models predict an increase in relative humidity over the oceans and decrease in wind will suppress the expected 7%/K increase in latent heat flux and precipitation (to about 2%/K). This happens because vertical convection is slows. There is no reason to believe that AOGCMs correctly model this process, but – in the long run – heat can’t escape from the surface of the planet any faster than it leaves the TOA as thermal IR. Heat can’t be “pushed” up from below, because convection requires an unstable lapse rate and that requires radiative cooling from the upper troposphere. (It took me a long time to grasp this point. (Willis’s thermostat hypothesis ignores this reality.)
I’ll be happy to agree with you about the gross distortions that are created by presenting the warming effects of rising GHGs as simple absorption (trapping) of heat. And the distortions produced by their CO2-centric view of climate. And by “amplification”. Since you appreciate Modtran, we probably agree about radiative forcing (except by water vapor), which might be called settled science. The other thing we need to know is how much additional OLR is emitted by the planet and SWR is reflected by the planet per degK of surface warming. In other words, we need to know the climate feedback parameter (W/m2/K). That isn’t settled science. And the climate feedback parameter must be the same at all altitudes down to the surface, where latent heat is the dominant factor. Net upward radiation (OLR – DLR) doesn’t change much with surface temperature. (You can explore that phenomena with Modtran). Even worse, the IPCC disguises their ignorance about the climate feedback parameter (W/m2/K) by discussing its reciprocal (K/(W/m2)) and converting W/m2 to doublings of CO2 to get ECS (K/doubling). So, instead of two independent phenomena – forcing (W/m2) and feedback (W/m2/K) – we have ECS where one particular forcing (doubled CO2) obscures a fundamental property of our climate system – how the radiative balance at the TOA changes with surface temperature. Mathematically the radiative imbalance, Ri, is given by:
Ri = (S/4)*(1-a) – eoT^4
dRi/dTs = -(S/4)*(da/dTs) – 4eoT^3 – (oT^4)*(de/dTs)
dRi/dTs is the climate feedback parameter. -(S/4)*(da/dTs) is the sum of all SWR feedbacks. -4eoT^3 is Planck feedback (-3.3 W/m2/K for a graybody model, -3.2 W/m2/K if you believe climate models) and -(oT^4)*(de/dTs) is the sum of all LWR feedbacks except Planck feedback.
When you think of the problem in terms of a climate feedback parameter – a fundamental property of the planet – there is no need for any nonsense about amplification (including Monckton’s nonsense). These is no need for a no-feedbacks – except for Planck feedback – climate sensitivity, because it is trivial to see from space that -(oT^4)*(de/dTs) modifies 4eoT^3 significantly. There is no need for ECS – an intangible that will be approached in a century. All of the major responses to warming (feedbacks) are complete in a few months. The slow feedbacks that are missing (ice caps, vegetation, and outgassing of CO2) aren’t fully represented in ECS either.
Franktoo,
I can’t help feeling that you are drawing overconfident conclusions from the seasonal data. The authors of the paper you reference re-state a very important point which has been brought up many times before, but which I respectfully suggest you don’t seem to have taken on board, at least in your confident conclusions. The authors note:-
“One can argue whether the strength of the feedback inferred from the annual variation is relevant to global warming. Nevertheless, it can provide a powerful constraint against which every climate model should be validated.”
Quite. The warming pattern associated with the seasonal fluctuation is radically different from the relatively-more-uniform warming associated with a GHG-induced change. The spatially-averaged seasonal fluctuation in temperature, which amounts to just a few degrees of change, comes from the averaging of huge, out-of-phase fluctuations in temperature between the two hemispheres, with amplitudes ranging from over 30K at the poles (after diurnal averaging) to a few degrees in the tropics. There is little reason to believe that the ratio of an aggregate (and averaged) radiative flux to an averaged temperature change for this pattern of warming gives anything close to a realistic estimate for the same ratio calculated from the more uniform “gentler” pattern of warming expected from GHG-induced warming. Indeed, AOGCM experiments (see Andrews 2014 and sequel papers) with prescribed SST suggest that much smaller variations in the pattern of warming (than the difference between the seasonal variation and GHG-heating) can induce a change in averaged net total feedback by a factor of around two.
Quite separate from this vexed problem of spatial weighting of low feedback latitudes and temporal averaging, there is a second reason to doubt the utility/reliability of the seasonal-based estimate of OLR response at 2.2 W/m2/K. There is an orbitally-induced annual cycle and a semiannual cycle of change in atmospheric angular momentum, well established and measured in the LOD record, which has a marked effect on prevailing winds and cloud distribution. Sailors have known about trade wind variation for many centuries. This non-radiative external forcing means that the changes in TOA fluxes during the seasonal cycle are not solely dependent on the change in temperature (distribution). They are responding as well to an externally driven change in meridonial currents and heat fluxes in both atmosphere and oceans which is not accounted for.
A more direct way to estimate the aggregate OLR response to slow average temperature change is to compare INTERANNUAL estimates of OLR and temperature changes using the satellite datasets. This approach generally shows a far larger OLR response to temperature change than the 2.2 W/m2/K evident in the seasonal or intra-annual data.
Here is a useful recent paper by Dewitte et al: – https://www.researchgate.net/publication/327874661_Decadal_Changes_of_Earth's_Outgoing_Longwave_Radiation
From the abstract…
“In this paper, decadal changes of the Outgoing Longwave
Radiation (OLR) as measured by the Clouds and Earth’s Radiant Energy System from 2000 to 2018, the Earth Radiation Budget Experiment from 1985 to 1998, and the High-resolution Infrared Radiation
Sounder from 1985 to 2018 are analysed. The OLR has been rising since 1985, and correlates well with the rising global temperature. An observational estimate of the derivative of the OLR with respect to
temperature of 2.93 +/−0.3 W/m2/K is obtained.”
Dewittes’ calculation is based on the temperature change in the Nasa GISS dataset which is perhaps biased high, and his uncertainty range is solely reflecting the uncertainty in linear model fit. IMO, therefore, it is overconfident to suggest that we even have certainty about the sign of temperature feedback in the longwave.
Kribaez: You are right: I do place too much emphasis on the analysis of seasonal warming by Tsushima and Manabe. Seasonal warming is an average of warming in the NH of about 10 K and cooling in the SH of about 3 K. This is not “global” warming and could be misleading if the differences in geography are important – and they are. There is relatively little seasonal snow cover in the SH compared with the NH and the changes in reflected SWR through clear skies reflects that difference. There is also little seasonal temperature change in the tropics, so the seasonal change in OLR is probably dominated by the extra-tropical response. Lindzen and Choi and Stevens and Mauritsen (Iris Effect) show LWR feedback in the tropics is 4-5 W/m2/K; and Dewitte and Clerbaux show a global average of 2.9 W/m2/K.
However, Tsushima and Manabe’s 2013 paper on seasonal feedback is my favorite climate science paper for several reasons. 1) The temperature change and resulting changes observed from space are unambiguous, reproducible and linear (for LWR). 2) My goal was to prove to Boris that our planet doesn’t behave like a simple graybody – ie that feedbacks exist. There is no doubt that our planet emits about 2.2 W/m2/K more LWR (not 3.2 W/m2/K) as it warms seasonally. Reflection of SWR from clouds also changes with the seasons. The evidence that positive feedback through clear skies (WV+LR) exists is unambiguous. 3) Observations show no positive LWR cloud feedback, but AOGCMs do. Even if LWR cloud feedback is positive in the underweighted tropics, AOGCMs still produce positive LWR cloud feedback that we don’t observe. 4) The paper clearly shows that AOGCMs can’t reproduce the feedbacks we observe during seasonal warming and therefore can’t be trusted with global warming. The predictions of AOGCMs are also mutually inconsistent.
Thanks very much for taking the time to write a thoughtful reply.
Franktoo,
Thanks for listening and my apologies for the delayed response. Can I just gently re-emphasise that the Dewitte and Clerbaux central estimate of 2.93 W/m2/K derives from GISS. If Hadcrut4 had been used, then this value becomes 3.55 W/m2/K. A satellite TLT series would yield an even higher value. This calls into question whether we can say WITH ANY CERTAINTY that the OLR response to gentle heating is less than Planck, equal to Planck or greater than Planck.
My main point is that you cannot deduce a positive WV and LR feedback from the seasonal data. The comparison of the 2.2 OLR gradient against 3.3 is quite simply illegitimate, because the 3.3 value is not the correct benchmark for this comparison.
Some years ago I set up a simple 17-band latitude model to test the numerical effects of the averaging process during the seasonal cycle. The grid properties included assignment of a latitude-dependent feedback term to calculate OLR from surface temperature. Each latitude band received as input an estimate of temperature variation over a one-year period – crude but not unrealistic with respect to amplitude and phasing. Absolute temperature was tuned to 288K annual average. Globally averaged temperature variation was tuned to between 3 and 4K amplitude. The feedback terms were based on a profile from tropics to poles, with different profiles for each hemisphere, and were then manually adjusted so that a one degree uniform temperature rise yielded an OLR response of 3.3 W/m2/K.
A crossplot of OLR against temperature for the seasonal temperature variation revealed a squashed elliptical distribution of points with a derivative ALWAYS significantly less than 3.3. I was unable to reproduce the 2.2 value observed – my values typically came out between 2.5 and 2.9. However, I do not know whether this inability was due to model inadequacy or data inadequacy – probably both. More importantly, the significant difference between the (exact) OLR response for uniform heating in the model and my various estimates of the OLR-temperature gradient from the seasonal temperature data was sufficient to infer that it is dangerously unsafe to try to infer feedbacks by naive comparison of the seasonal data with the uniform heating response of a single body with constant assumed properties.
While I can easily accept that the WV and LR feedback may be positive, I cannot accept that conclusion from the Ramanathan approach. It is simply not a valid comparison.
After reviewing the range of possible values for dOLR/dTs, Kribaez concludes: “This calls into question whether we can say WITH ANY CERTAINTY that the OLR response to gentle heating is less than Planck, equal to Planck or greater than Planck.”
Despite your summary, the seasonal cycle stillclearly proves that the planet doesn’t behave like a simple graybody in all circumstance; that feedbacks exist and Boris must include them in any sensible model for our planet’s behavior. And that includes the SWR feedbacks to seasonal warming, which apparently aren’t complete within one month and have some lagged components.
Consider the equation below for the climate feedback parameter. There is certainly a decrease in emissivity due to rising water vapor, an increase in emissivity because there will likely be more warming higher in the atmosphere than at the surface, and changes in the altitude and nature of clouds. According to climate models, they vary regionally. If by chance the global sum of these LWR feedbacks is near zero or has a confidence interval that includes zero, we still need to include a de/dTs term in our analysis of the problem.
Ri = (S/4)*(1-a) – eoT^4
dRi/dTs = -(S/4)*(da/dTs) – 4eoT^3 – (oT^4)*(de/dTs)
And if I want to get really picky, we need to recognize that their are fast and slow feedbacks. If forcings remained the same, a millennium from now our planet’s climate feedback parameter will have changed due to melting of the ice caps and outgassing of CO2 from the oceans. If de/dTs is effectively zero today, outgassed CO2 will change that. (The consensus chooses to ignore that these are slow feedbacks and call them forcing.)
“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.” https://www.mdpi.com/2225-1154/6/3/62
There is an old skeptic meme that increased evaporation cools the planet with more cloud. It is wrong. In both surface and satellite observations.
https://watertechbyrie.files.wordpress.com/2014/06/clement-et-al-e1512080464744.png
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016GL069961
It has to do with the nature of convection in a fluid heated from below – oceans warming atmosphere.
https://eoimages.gsfc.nasa.gov/images/imagerecords/43000/43795/clperu_amo_2010107_cu.jpg
Closed cells with a higher domain albedo persist for longer over cooler oceans before raining out from the center to form open cells. Rain and cloud is a nonlinear bistable phenomenon.
e.g. https://www.seas.harvard.edu/climate/eli/reprints/Koren-Tziperman-Feingold-2017.pdf
Because it involves low cloud cover the SW effects are dominant. As in the Loeb et al CERES study quoted. There the changes are primarily the result of warming in the eastern Pacific feeding back into cloud cover and energy dynamics causing most of post hiatus warming. The Pacific charge/discharge oscillator is thus a mechanism for large scale, perpetual change
in energy inputs to the system over decades to millennia. And is not just a matter of energy moving back and forth between ocean and atmosphere. This system shifts abruptly between states.
https://www.esrl.noaa.gov/psd/enso/mei/ts.gif
A shift to more frequent and intense El Nino – and global warming in both oceans and atmosphere – after 1976 and a change after 1998 with first a hiatus and then a post-hiatus. It makes a nonsense of this app – and linear climate sensitivity more generally – with its assumption of natural variation cancelling out over only years.
Although a significant factor in global climate on the scale of decades to millennia – the Pacific Ocean modes are part of a global coupled climate system that is variable at many scales in time and space.
In the words of Michael Ghil (2013) the ‘global climate system is composed of a number of subsystems – atmosphere, biosphere, cryosphere, hydrosphere and lithosphere – each of which has distinct characteristic times, from days and weeks to centuries and millennia. Each subsystem, moreover, has its own internal variability, all other things being constant, over a fairly broad range of time scales. These ranges overlap between one subsystem and another. The interactions between the subsystems thus give rise to climate variability on all time scales.’
The theory suggests that the system is pushed by greenhouse gas changes and warming – as well as solar intensity and Earth orbital eccentricities – past a threshold at which stage the components start to interact chaotically in multiple and changing negative and positive feedbacks – as tremendous energies cascade through powerful subsystems. Some of these changes have a regularity within broad limits and the planet responds with a broad regularity in changes of ice, cloud, Atlantic thermohaline circulation and ocean and atmospheric circulation.
Dynamic climate sensitivity implies the potential for a small push to initiate a large shift. Climate in this theory of abrupt change is an emergent property of the shift in global energies as the system settles down into a new climate state. The traditional definition of climate sensitivity as a temperature response to changes in CO2 makes sense only in periods between climate shifts – as climate changes at shifts are internally generated. Climate evolution is discontinuous at the scale of decades and longer.
In the way of true science – it suggests at least decadal predictability. The Pacific Ocean state shifts at 20 to 30 years intervals. The flip side is that – beyond the next decade – the evolution of the global mean surface temperature may hold surprises on either warm or cold ends of the spectrum (Swanson and Tsonis, 2009).
The eastern Pacific cooled from ~1983 to ~2014, a period of 31 years.It shifted to a positive phase which could last anywhere 0 to ~25 more years.
This, a semblance of a negative PDO pattern, has not been seen since the fall of 2013:
https://www.ospo.noaa.gov/data/sst/anomaly/2012/anomnight.9.6.2012.gif
That the eastern Pacific cooled is confirmed by data. It is in peer-reviewed papers. More than one. Ditto for the positive phase of the PDO.
You are the one who makes things up.
The eastern and central Pacific were in a warm phase from 1977 to 1999.
https://watertechbyrie.files.wordpress.com/2019/02/tpi-sst.png
And some people have speculated that the system shifted to a warm epoch post 2015. We shall see.
Warm states add to global warming and cool states counter warming. The energy dynamic includes cloud cover anti-correlated with sea surface temperature. With respect to AGW – it matters in that early 20th century warming – and much of late century warming – had another cause.
States vary – along with Australian rainfall – on millennial scales.
https://watertechbyrie.files.wordpress.com/2018/02/ipo-proxy-vance.png
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014GL062447
The gyre hypothesis suggests that this is a solar amplification mechanism.
https://digitalcommons.uri.edu/gsofacpubs/140/
Residual warming in the modern era is not all that dire. And the question is how much of that will be lost as the sun dims and more meridional polar modes kick in this century?
https://watertechbyrie.files.wordpress.com/2018/12/cowtan-trend.png
And still hand waving at supposed studies showing otherwise means not a damn thing.
1977 to 1999 is not the same as ~1983 to ~21014:
https://i.imgur.com/atx0sm0.png
The issue is what is the trend in SW reflection from ~1983 to ~2014. It’s not one that would warm the global surface.
It is not speculation that the PDO has flipped positive. It has flipped; that train has left the station. The pure PDO, one with ENSO removed, has clearly flipped to a positive phase. This is inconvenient.
https://i.imgur.com/hDogytg.png
Well – SW reflected in the tropics from 1979 to the end of the century seems to have decreased by about 2.1W/m2.
https://watertechbyrie.files.wordpress.com/2014/12/wong-et-al-2006-e1510341656183.png
But it is not a switch that is off or on. The Pacific state tends to cluster about a mean for an extended period and then shift. There is always variance about the mean. The NCEI PDO has been largely negative since July 2017. As I have shown you previously. And you may arbitrarily reject this data but such is meaningless twaddle. As is your WFD graph.
https://www.ncdc.noaa.gov/teleconnections/pdo/
But what would it mean if it has flipped again to a warm mode? Another extended period where natural variability added to warming?
In the longer term – as the sun dims – will there be a centennial scale resurgence of La Nina frequency and intensity?
https://watertechbyrie.files.wordpress.com/2014/06/vance2012-antartica-law-dome-ice-core-salt-content-e1540939103404.jpg
https://journals.ametsoc.org/doi/10.1175/JCLI-D-12-00003.1
Oh and yet 1977 to 1999 is shown as the being IPO positive – coincident with the positive PDO phase. Whatever data source is consulted.
JCH: “It is not speculation that the PDO has flipped positive. It has flipped; that train has left the station.”
It’s arriving the station again? Last data: 2019.02.
https://i.imgur.com/22k6m5m.jpg
`The shift in mean between periods can be seen – with the transition – as generally agreed – around the turn of the millennium.
Dear Judy, I have a comment in moderation and I swear that there is nothing included that would break the rules of this blog ( albeit I contradicted JCH somewhat :-) ).
Tsonis – what matters is when there is a change in direction, up or down, of the trend. The big lumps of positive or negative that you focus have sent you, and others, up the wrong tree. There is a change of direction in that trend that lasted 30 years.
Tsonis/others of that ilk ignored that change in direction of a major ocean oscillation and suffered the embarrassment of that mistake.
Mantua’s PDO:
https://i.imgur.com/4dXiKHr.png
https://i.imgur.com/jDxOR3V.png
Franktoo, there are papers indicating the duration of the PDO phases will get shorter with AGW.
This is the NOAA PDO for October and November of 2018:
https://i.imgur.com/FrAaUyC.png
This is the sea surface anomaly map for October 29, 2018:
https://www.ospo.noaa.gov/data/sst/anomaly/2018/anomnight.10.29.2018.gif
Can you say that is close to the pattern for the negative phase of the PDO? Because I can say it is not close to the pattern for the positive phase of the PDO. It’s not even remotely close. The NOAA PDO say negative -.75 . Mantua’s PDO for October 2018 is +.09.
It’s neutral.
Tsonis and every other atmospheric physicist and oceanographer.on the planet. Even in his own conveniently marked up chart.
https://i.imgur.com/hDogytg.png
The PDO was groundbreaking work. Can you see the pattern everyone else does? You can read about it in the text.
http://research.jisao.washington.edu/pdo/pdo_tsplot_jan2017.png
http://research.jisao.washington.edu/pdo/
The NCEI PDO is more recent – it shifts the zero point. This is when cold upwelling commences in the physical process. The temperature based IPO tripole index is more recent still.
Whether the surface of the eastern and central Pacific is cool or warm would seem to be of interest. Surely if it were not the case there would be one scientist at least saying it isn’t.
The Mantua numbers from January 2014 through February 2019 include only three months with a negative PDO index: March 2018 at -.05′; June 2018 at -.04; November 2018 at -.05. Those are small numbers.
The other 59 months, including the first two months of 2019, are positive.
It is currently best described as PDO neutral. It is not negative.
Frankly – who gives a rat’s arse why you keep going over the misguided point. Like a wind up monkey with a drum. I linked the JISAO site – and discussed. Here again is the more recent NCEI PDO.
https://www.ncdc.noaa.gov/teleconnections/pdo/
I don’t care which is more right. They differ seemingly only in the zero point.
But things are cooler post post-hiatus.
Because you keep insisting it is negative, which it is not; in fact, the index numbers for Dec 18, Jan 19, and Feb 19 are a lot higher that the 11 months prior. Still neutral, but barely.
Numbers from the guy who came up with the PDO.
Which may explain why the GMST has been going up as of late, which March currently looking like the GISS number will be well above 1.0 ℃.
Well – what I said inter alia was the NCEI verion is negativr.
https://www.ncdc.noaa.gov/teleconnections/pdo/
I agree, it’s a useful tool. However, it’s not so different from the approach of my post in January https://judithcurry.com/2019/01/03/reconstructing-a-dataset-of-observed-global-temperatures-1950-2016-from-human-and-natural-influences/ : When using the forcing data ( here from L/C18) and the observed delta T from this forcing..one implies that the delta T has it’s source in the ( at most) anthropogenic forcing, in other words: It comes very mostly from man made influences. This is the case when using this approach. From some commenter I got some harsh reponses BUT the issue is objective and also included in the described App.
Until you come to realize that we are now loosing more heat to black sky radiation than we retain from the sun daily the rest of this discusion is useless. Weather is just nature mantaining a constant average temperature of the earth’s surface. RADIANT HEAT IS REFLECTED BY WATER!!!
In the CERES record there is less reflected shortwave and more emitted IR. With sw dominant.
https://watertechbyrie.files.wordpress.com/2019/01/ceres_ebaf-toa_ed4.0_anom_toa_longwave_flux-all-sky_march-2000tojuly-2018-1-1-e1552498942640.png
https://watertechbyrie.files.wordpress.com/2018/12/CERES_EBAF-TOA_Ed2.8_anom_TOA_Shortwave_Flux-All-Sky_March-2000toFebruary-2017-1-e1545585067853.png
And the planet is warming. Mostly in the tropics.
https://www.climate4you.com/images/ArgoGlobalSummaryGraph.gif
Do you men that IR is reflected by oceans? If so you are still wrong.
RT @NatureNews: More than 800 signatories call for the entire concept of statistical significance to be abandoned. go.nature.com/2TTNRRZ
Special issue of The American Statistician discussing the replacement of “statistical significance”: https://tandfonline.com/toc/utas20/current?nav=tocList
Open Access
To me this is less useful than banning “hate speech”; you’ll notice that there is no agreement on what can replace the concepts of “signal detection” or “signifying a possibly true effect”. In my experience (not that great) too many people forget or ignore that a decision based on even good statistical analysis may be in error because of the known and unknown sources of randomness in the data and the phenomenon being measured. Consider Ioannides’ evidence that the replication rate in good medical journals is “only” 40%: what should it be, given that research focuses on what is not yet known? It can’t be 100%.
oops, in medical and neurohysiological research the replication rate is 60%. In experimental psychology the replication rate is 40%.
Sorry for the mistake.
Hi, Thanks for your views, which my limited experience finds acceptable.
The study you mention (https://www.mdpi.com/2225-1154/6/3/62) is interesting, but what is the reliability of the measurement result of “0.83 ± 0.41 Wm−2 reduction” at TOA. How is that measured with an accuracy of 2 decimals or is it just the result of calculations. Is this a kin to global temperature anomalies given with two decimals although monthly averages of actual point measurements never really represent the reality. This considering the idea of an average global temperature, I hope you accept as fantasy. Insisting that a large number of measurements will actually even off all errors and verify the belief that the end result, that is the average, is correct. This especially after noting the many adjustments are being made to new and old temperature data.
Another point, which has to do with the role given CO2 as the driver of global climate. My definite view is that CO2 has only a very minor role and that water vapor is the real driver. PLEASE explain your view on CO2 versus water vapor in my example how IPCC uses forcing and feedback to explain warming where the radiative forcing by CO2 is multiplied by water vapor feedback???
regards
Boris
The data is the most precise that 21st century technology can provide.
But the world has been warming – and a warmer atmosphere holds more water vapor – a fundamental physical law.
http://www.drroyspencer.com/wp-content/uploads/UAH_LT_1979_thru_February_2019_v6-900×519.jpg
Global scale multidecadal variability missing in state of the art models? There may not be a Shiny app but there is a Youtube video.
https://www.youtube.com/watch?v=7VbgzCahx8o
https://www.nature.com/articles/s41612-018-0044-6
Models have other problems. “Sensitive dependence and structural instability are humbling twin properties for chaotic dynamical systems, indicating limits about which kinds of questions are theoretically answerable.” James McWilliams
This app is misleadingly simple. Too much is missing – for reasons of a lack of knowledge of natural systems – a need for definitive answers in the face of complexity – a mathematical conceit in doing what can be done rather rather than admit that the tools are inadequate – or simply the drunk’s dilemma in searching for their keys under a streetlamp.
“Climate is ultimately complex. Complexity begs for reductionism. With reductionism, a puzzle is studied by way of its pieces. While this approach illuminates the climate system’s components, climate’s full picture remains elusive. Understanding the pieces does not ensure understanding the collection of pieces.” Marcia Wyatt
The collection of pieces shows emergent behavior that is at least as fundamental to the future evolution of climate as this impossible to convincingly calculate sensitivity. Climate is most sensitive near tipping points.
Alberto, you and Javier both deserve the espanol congeniality award for your pleasant posts. This was a delightful read, so rare in these days of polarized climate debate. (truth be told, there are a lot of congenial posts out there, but yours and Javier’s stand out to me the most)…
i wonder if your ocean uptake wouldn’t be better served by a simple calculation of temperature above an equilibrium state temperature than Zanna (which you seem to have little confidence in). If we assume that an ocean at an equilibrium state gives us a full allotment of warming, just like land, then all that needs be done is to find out what percentage of warming is being syphoned away at current pause temperatures. (and any temperature in between the equilibrium state temp and the pause temperature would be given a fraction of that) This has been a fiercely debated point here at Climate, etc on those very rare occasions when it has been discussed. The consensus view (here) being that the ocean is not in a state of imbalance. None the less, it doesn’t seem that you’re well served using Zanna. i myself am just starting to delve into the idea. It was being pushed here ad nauseum by a commenter who went by the name of Jim D, but he never got around to quantifying it. i’ve never had a deep conviction that the IPCC has a handle on this either. Indeed if they had, one would think that they would be crediting warming a century ago to a lack of ocean heat uptake (and thus anthropogenic in origin) which they don’t.
Thanks again for a wonderful post, and refreshing read…
Thank you. I’m afraid I don’t really follow how one would calculate ocean heat uptake in the case you mention; if the ocean is not in a state of imbalance, then by definition the net ocean heat uptake is zero (i.e. it is neither gaining nor losing heat).
if the ocean is not in a state of imbalance, then by definition the net ocean heat uptake is zero (i.e. it is neither gaining nor losing heat).
Exactamundo(!) Now you can see where the controversy lies. There are those who believe that the ocean is not in a state of imbalance. Hence, to them, the oceans are not warming. Dr Curry once mentioned that solar physicists tend to fall into this category. i, myself, once ran the idea of ocean warming by Dr Svalgaard and, sure enough, he does not believe that the oceans are warming. Then there are others, like our little Jimmy D, who do believe that the oceans are in a state of imbalance, hence they are warming. My comment to you (above) is suggesting that Jim may be correct and, thus, there may be a better way to calculate ocean heat uptake than Zanna. Dr Spencer has mentioned that the oceans have a top down temperature gradient. Change any part of that gradient and a new gradient must be established. If the ocean, say, back in 1900 was more or less in a state of equilibrium, then there would be no uptake. One hundred years later, the ocean could be said to be out of equilibrium (at current pause sea surface temps). So, in theory, we can calculate ocean uptake in 1900, which would be zero, the 2000s (or the pause), and all points in between. This is the point that i’m stuck at because, as i said, Jim never got around to quantifying it. And this is an argument that is so rare that i wouldn’t even know where to find out. How much warmer would SSTs be were it not for ocean uptake? That’s the million dollar question and if it doesn’t have a quantifiable answer, then how do we gauge ocean uptake’s impact on surface temps? Maybe we don’t. (and if that’s the case, then we can’t determine ECS either)…
If we assume that an ocean at an equilibrium state gives us a full allotment of warming, just like land,…
Alberto, this statement in my original comment may have caused some confusion here. It would be better phrased as If we assume that an ocean at an equlibrium state gives us a full allotment of surface warming, just like land,…
afonzarelli
The 2-meter temperature record and anomaly from 1900 and including the satellite data identify a near regular undulation temperature trend. Dr Curry has wisely questioned the 1900 to 1950 temperature rise without a CO2 signature. Ocean temperatures are not in constant balance, or maintain a constant temperature. The 2-meter temperature is a direct measure of this imbalance, that is why the also undulate. You are wise to follow those instincts.
JimD is correct, but can not document his belief as there is insufficient rational discussion, therefore data and knowledge because the dominant current belief is that the ocean is constant. Dr Curry confirmed in the tropical cyclone posts that there are larger area’s of ocean at higher SST since 2000, but there was a declining number of TC and they formed in the same locations, with a larger percentage of Cat. 4 and 5. There are logical reasons for this.
The primary method of ocean energy / heat transfer to the atmosphere is via convection, (please correct me if I am wrong). To my knowledge there is no consideration of, let alone data that attempts to quantify or qualify what percentage of the energy presented at the ocean / atmosphere convective interface, is in fact removed. It is assumed to be 100%. How does that efficiency magically increase during El Nino years to remove all energy presented, when the wind speed approaching the convective interface remains near constant year round. During the period May to September in the tropics both humidity and quantity of water in the atmosphere progressively increases, as does rainfall, which returns a percentage of the energy to the ocean. Additionally ocean currents are to be considered and that energy remaining in the ocean from in-efficient convection or returned as rain mixes and circulates.
To my knowledge the only barrier considered for earth to space energy retention is in the atmosphere, namely CO2 and therefore this is causing the the ocean to warm. NO. First it has to get out of the ocean.
Further, when the energy as vapor is presented in volume and in an uneven delivery to the atmosphere, atmospheric dynamics determine where that heat goes, influencing the anomaly.
afonzarelli, like you I am new to this subject in atmospheric science, however influential other areas of atmospheric science of interest. I manufacture large systems that use heat and convection to separate dissolved water from fluids and regularly replicate the seasonal variability of temperature and convective volume similar to what occurs in the tropical convection zone with identical outcomes. Higher humidity and rainfall. During the July to September period in particular the tropical convective process of transport can not accommodate the volume presented.
I look forward to polite discussion.
Regards, Martin
The global first order differential energy equation can be written as the change in heat in oceans is approximately equal to energy in less energy out at the top of the atmosphere (TOA).
Δ(ocean heat) ≈ Ein – Eout
92% is the proportion of global heat content stored in the oceans. The energy imbalance is said to evolve with a lag between very small instantaneous increases in downwelling radiation and the re-establishment of an energy balance at the surface of oceans. A reduction of the energy gradient across the surface causes heat to accumulate in the oceans until upwelling radiation again equal downwelling. Over the period of the lag an energy imbalance accumulates at TOA as heat is transported deeper into oceans geologically slowly on eddies created by the collapse of surface gravity waves. The energy reality is very different to this grossly simplified conceptual framework.
The current orbital eccentricity:
https://watertechbyrie.files.wordpress.com/2018/05/earths-orbit.png
Drives annual variability in energy in:
https://watertechbyrie.files.wordpress.com/2018/05/earths-orbit.png
That is translated to depth on ocean turbulence:
https://www.climate4you.com/images/ArgoWorldOceanSince200401%2065N-65S.gif
Because of greenhouse gas warming the oceans may stay a little warmer in the annual cycle of warming and cooling.
Outgoing energy has the same annual variability as incoming energy of course. But if we look at anomalies – we see something else.
Warming in SW:
https://watertechbyrie.files.wordpress.com/2018/05/ceres_ebaf-toa_ed4-0_anom_toa_shortwave_flux-all-sky_march-2000tonovember-20171.png?w=768
Cooling in IR:
https://watertechbyrie.files.wordpress.com/2018/05/ceres_ebaf-toa_ed4-0_anom_toa_longwave_flux-all-sky_march-2000tonovember-2017.png?w=768
This is cloud change mostly in the tropical and sub-tropical Pacific. SW is dominant in sunlight fueled ocean warming.
Incoming energy:
https://watertechbyrie.files.wordpress.com/2018/08/ceres_ebaf-toa_ed4-0_incoming_solar_flux_march-2000tomarch-2018.png?w=768
I guess I am incorrect. I thought the molicules of water on the top of the ocean,radiate heat to the black sky.They just lose energy by conduction and conduction. The older you get, the more you learn.
“The global-mean temperature trends associated with GSW are as large as 0.3 °C per 40 years, and so are capable of doubling, nullifying or even reversing the forced global warming trends on that timescale.”
https://www.nature.com/articles/s41612-018-0044-6
GSW is the global stadium – a signal propagated across the globally coupled Earth system flow field. Climate is a fluid flow problem. You don’t have to understand Navier-Stokes equations or Rayleigh-Benard convection. It can be seen in a near real time representation of the atmosphere. Note the 2 cyclones off Northern Australia.
https://www.nature.com/articles/s41612-018-0044-6
You can see the governing mode if you stare at a mountain stream for long enough. “There will be vortexes of different sizes at different places at different times. But if you observe patiently, you will notice that there are places where there almost always are vortexes and they almost always have similar sizes – these are the quasi standing waves of the spatio-temporal chaos governing the river. If you perturb the flow, many quasi standing waves may disappear. Or very few. It depends.” Tomas Milanovic
It is turbulent flow and must be chaotic across time and space as tremendous energy cascades though powerful Earth sub-systems establishing ‘quasi standing waves’ – synchronous chaotic oscillators in the GSW – across the planet.
e.g. https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/Plot/
At times the global system adds to warming from anthropogenic greenhouse gases – and sometimes it counters anthopogenic warming. With an energy signature both observationally and theoretically centered on the tropical and sub-tropical Pacific. It will likely shift again soon if it hasn’t already. In response to a perturbance in the flow. This is the most fundamental of Earth system dynamics.
https://watertechbyrie.files.wordpress.com/2018/12/cowtan-trend.png
The quandary for this century is how much of last centuries residual warming was solar amplification by a factor of 10 or more in the terrestrial system . As has been suggested for a long time now. But then sensitivity is greatest near tipping points and the future is another country.
Now this was meant to be a separate thread.
https://judithcurry.com/2019/03/18/climate-sensitivity-calculator-app/#comment-890619
URGENT ALERT FOR ANYONE USING FIREFOX AS THEIR BROUSER.
FIREFOX HAS BEEN HACKED !
OT HAS OF IT’S OWN WISH INTRODUCED NEW SOFTWARE WHICH MAKE IT VIRTUALLY IMPOSSIBLE TO USE
IF YOU WANT TO USE FIREFOX, DO NOT ALLOW YOUR FIREFOX BROUSER TO UPGRADE TO THE CURRENT ONE.
OTHERWISE YOU WILL NEED TO USE CHROME WHICH IS WHAT I AM USING RIGHT NOW.
PLEASE COPY & PASTE THIS ELSEWHERE
BILL IN OZ
Clisense now lets users choose between HadCRUT and Cowtan & Way.
There are a couple mini-issues:
-For C&W, I’m using the had4_krig_annual_v2_0_0.txt file; I think this is the only reconstruction they have that goes all the way back to 1850. They state that it uses the same baseline as HadCRUT, i.e. 1961-1990. But the anomaly I get for that period is 0.0055 (in Clisense this gets rounded up to 0.01). It’s not much, but by definition the anomaly in the baseline period should be zero.
-When trying to replicate the results in Lewis & Curry, I get almost the same results, but not exactly. For 1930-50 and 2007-2016, the TCR I get is 1.22 and 1.28 for HadCRUT and C&W, respectively; the results in the paper (table 3) are 1.20 and 1.27.
The issue with TCR may be independent rounding in Clisense giving a marginally wrong answer, or perhaps the efficacy of black carbon on snow. As for the latter, I set it by default to 3, whereas Lewis & Curry set it “probabilistically” to 2-4; I have no idea if that’s equivalent to 3 or some other number.
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Okay, looking at table 2 in LC18:
+The difference in TCR for Cowtan & Way arises exclusively from the difference in forcing. For the period between 1930-50 and 2007-16, it’s 1.93w/m2 in Clisense and 1.94w/m2 in LC18. I don’t know why this difference exists, but it’s a very small issue.
+The difference in TCR for HadCRUT arises both from the tiny difference in forcing mentioned above, and the fact that the increase in temperatures between both periods is 0.61K in LC18 but 0.62K in Clisense. This may be due to changes in the HadCRUT record since the study was published (I’m using a file downloaded in February of this year).
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