Peter
From the literature the mechanism seems to be that the trees die from salt water in the roots and rot aerobically (bacteria) into methane and CO2 (initially air). The remains are broken down by wave action and consumed by worms etc. The organic parts of the soils as well.
As nothing is left, these huge forests appear to have gone into thin air – the Drowned Forest Effect seems to have been overlooked, so nobody really has any idea on what went into air, the sea or new forests as the ice retreated and present land warmed. I have seen estimates of carbon in new forest growth. In time, either the Drowned Forest Effect will be of interest to the community or it will be dismissed as rubbish. The numbers point to a real effect, as does the higher patm (T) on the exposed lands (pls see the reply to Steve).
Comment on Sea levels, atmospheric pressure and land temperature during glacial maxima by Alan Cannell
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Comment on Sea levels, atmospheric pressure and land temperature during glacial maxima by Alan Cannell
It looks like the Drowned Forest Effect has been overlooked and may be worth raising. As replied to Peter, I have no idea where this carbon went (nobody has) and how it would fit into the very short-term carbon cycle. A hunch would be that there is a lag between the drowned carbon and the full grown new forest carbon, thus a climate optimum at the end of the sea-level rise – which seems to fit the data.
Nice MSc thesis for someone!
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by Javier
There’s plenty of evidence for cold ACEs during the Holocene that is well reflected in the literature:
There’s some uncertainty about their magnitude and exact timing, and a profound lack of knowledge about their causes, since GHGs were not involved.
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by Robert I. Ellison
Solar variability is too small to have much of a direct effect. This is very evident your narratives notwithstanding.
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by Robert I. Ellison
This is just one of the lines of evidence presented. They all show warming and cooling obviously.
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by Robert I. Ellison
It is quite obvious that there is abrupt warming and cooling.
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by Robert I. Ellison
Sorry abrupt cooling and warming…. lol…
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by Javier
Solar variability is too small
How ignorant. UV, solar wind, solar magnetism, and some effects on Earth, like cosmic rays, stratospheric temperature, the size of Earth’s atmosphere, and ozone, show much larger variability than TSI with the solar cycle.
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by Javier
Yet you have not mentioned a single abrupt warming effect during the Holocene that has been studied in the literature. Your capacity to ignore the elephant in the room is really big.
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by Robert I. Ellison
The evidence comes from oxygen isotopes in ice cores, ice rafting debris, sediment cores and temperature sensitive organisms, etc. The usual suspects.
And abrupt climate change is how climate changes. Javier’s wild tangential enthusiasms notwithstanding – abrupt change is the result of tremendous energy cascading through powerful subsystems. Dynamical complexity at the core of the Earth system.
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by Robert I. Ellison
I feel sure I said direct effects. UV may vary more than other solar frequencies but the power in this band is minuscule.
I have written frequently about polar annular modes and the gyre hypothesis. Solar variability – including UV and consequent ozone chemistry – modulates polar surface pressure, the penetration of storm fronts into lower latitudes and circulation in all the world’s ocean. e.g. –
https://www.nature.com/articles/ncomms8535
http://iopscience.iop.org/article/10.1088/1748-9326/5/2/024001/meta
http://iopscience.iop.org/article/10.1088/1748-9326/6/3/034004/meta
https://www.mdpi.com/2225-1154/3/4/833
What is needed – as Palle et al 2007 said in the quote provided earlier – is an internal amplifying mechanism that modulates Earth albedo.
At the core of the Earth system is dynamical complexity – hard to explain to those for whom it has not clicked.
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.”
Javier ties himself in knots trying to explain things in ways that can provide no explanation. And he resists the one idea that can. I repeated this earlier.
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.
But apparently both this committee of illustrious scientists and I have leaped to poorly based conclusions.
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by Richard Arrett
Steve Mosher said “its funny how CERTAIN ya’ll are about time periods in which there is no actual data, or precious little to speak of.”
I do agree with this.
But I also think many are CERTAIN about the future.
Which is more funny? Being certain about the past or being certain about the future. My bias says it is more funny to be certain you know what will happen in the future than to think you knew what happened in the past – but YMMV.
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by verytallguy
“its funny how CERTAIN ya’ll are about time periods in which there is no actual data, or precious little to speak of.”
Certain, indeed, of the opposite of what the precious little data actually shows.
From the epistemic overconfidence thread, natch.
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Comment on National Climate Assessment: A crisis of epistemic overconfidence by sifttheashes
Low cost/high benefit strategies include things like promoting the use of cover crops by farmers, effective use of biodigesters (particularly for farming applications, where manure from growth operations is used on site to fuel barn heating and other local needs) and other low tech solutions, most of which aren’t “sexy” enough to attract the attention of advocates, who prefer high tech solutions like solar and modern wind power.
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by frankclimate
Steven, I’m just a little disappointed and a few other persons too I think. My mainpost was about the forced part/ internal variability (IV) of the GMST 1950…2016 and I tried to find this IV only from observations with some success IMO. A few disputants questioned the C&W record which is not a sacrilege in my wiew. However, I poited out why I selected this dataset ( others don’t disagree in a meaningful quantity) and the possible error is small for this time span. All the other here introduced (solar at most) records have much bigger uncertainties! And all these disputants missed to constrain the influences on the selected periode. In the end I found a TCR of about 1.3°C/doubling CO2 which is ( as Nic Lewis and Judy pointed out) on the lower end of the possible range, also with allowing of some ( in this time frame) further influences ( but not to observe due to the shortness of the reference time window) which would reduce this measure further on. And in the end the discussion is about some hypothetical impacts of “unicorns” on the GMST. If this goes on the “skeptics” will lose albeit the “skeptics” have the observations on their side. It’s a tragedy. ( with appolgies to the BeeGees)
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by matthewrmarler
frankclimate: And in the end the discussion is about some hypothetical impacts of “unicorns” on the GMST.
You are more credible without the “unicorn” canard, so to speak. Yes the mechanisms are hypothetical but they are not impossible and they are worth investigating.
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by frankclimate
Matthew: perhaps it’s a missunderstanding.. I meant with “unicorns” some effects not ot measure (in a physical way up to now) influences on the GMST. You can also call it: unknown unknowns. However, if one can’t measure it and is not informed about the uncertainties NOW, it won’t help.
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by dpy6629
I agree with Frank that the solar thing is a distraction from the main post.
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by matthewrmarler
frankclimate: However, if one can’t measure it and is not informed about the uncertainties NOW, it won’t help.
Consider the fluctuations displayed in RIE’s Temperature Curve Last Fifteen Thousand Years: Would it help to know what caused the fluctuations? Whether the causes have persisted or recurred? Whether they were active since 1950? Whether they were global in extent? The effects can’t be estimated from short time series of data, so they can not be included in your modeling. That does not mean that they are not active and important. If they are, then your CO2 sensitivity estimate is an over-estimate by a currently inestimable amount.
Javier emphasizes the possibility of quasiperiodic changes in forcing. RIE emphasizes the possibility that the fluctuations result from the natural dynamics of a chaotic system with (near) constant forcing. Mosher (seemingly) denies the relevance of thinking about them.
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Comment on Reconstructing a dataset of observed global temperatures 1950-2016 from human and natural influences by frankclimate
Matthew: “hat does not mean that they are not active and important. If they are, then your CO2 sensitivity estimate is an over-estimate by a currently inestimable amount.” i agree. It would reduce the TCR more than I can estimate without those influences. BUT: The estimated TCR is also without these ” unicorn” inluences well below the best estimate from CMIP5 on the ground of real and reproducible data. Is it helpful to point to NOT reproducible lower sesitivities or is it better to take what we know for sure? I tried to point out what is known, not what could be estimated. Sorry for this.
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