With all of the emphasis that is often placed on hemispheric or global mean temperature trends during the past millennium, and the context they provide for interpreting modern warming trends, one thing is often lost in the discussion: space matters as much as time. Indeed, it is likely that the regional patterns of past climate changes, rather than simple hemispheric or global mean temperature trends, will best inform our understanding of the dynamical mechanisms involved. Since much of the uncertainty in future projections relates to regional climate change impacts, it makes particular sense to focus on those changes in the past that involve regional changes and the underlying mechanisms behind them.
For instance, melting of the cryosphere (and consequent rises in sea level), subtle shifts in drought and rainfall patterns, and extreme events, are all regional effects that could be important threats to ecosystems and our environment. Such changes are often associated with phenomena like ENSO or the North Atlantic Oscillation. Yet there remain large uncertainties about how such mechanisms will respond to anthropogenic climate change.
There are a number of potential ways forward to improve our understanding. A first step is to look directly at the time-series of specific systems (like the ENSO index or the ocean temperatures in the North Atlantic) and try to extend them as far back as possible using proxy data. This gives more information on what the natural variations in these phenomena look like, and thus a better idea of how big a forced response would need to be before it could be reliably detected. Secondly, we can look to see if there is a relationship between various natural drivers of climate change (volcanic eruptions, solar variability or orbital forcing say) and any characteristics of these phenomena – amplitude, frequency or duration. Do volcanic eruptions appear to affect El Niño for instance?
For phenomena that need annual or decadal resolution data to be resolved, the last millennium or so is an obvious (and only) time period to be looking at for it is only for that period that there is sufficient paleo-data coverage of high enough temporal resolution. Other periods – such as the mid-Holocene 6000 years ago – are also useful, but the results are more long-term in nature (there is also a discussion of the value of different periods for reducing future projection uncertainty in this recent paper).
There are a number of different approaches to looking at reconstructions in recent centuries – some rely on high density regional networks (as seen in this recent paper by Guiot et al concerning European temperature trends for which they mostly used pollen data) and some rely on wider networks of more diverse proxies which aim to capture longer-range correlations to specific phenomena (such as the recent Mann et al (2009) paper).
When this is done, people usually find that while it was relatively cool in global mean temperatures from the 1400s to the 1800s known as the “Little Ice Age” and relatively mild in the 900s to 1300s interval ( sometimes termed the “Medieval Warm Period”). But the spatial reconstructions reveal, however, why such global terms can be quite misleading, and why alternative phrases such as the “Medieval Climate Anomaly” are being increasingly favored by the community. This latter terminology recognizes that while the interval displayed significant climate anomalies, they varied greatly, even in sign, from region to region. Many of the more profound climate anomalies, moreover, involve variables other than temperature, such as drought, rainfall, and atmospheric circulation. Though the medieval period is seen to be modestly warmer globally in comparison with the later centuries of the Little Ice Age (the peak global mean warmth is likely comparable to mid, but not late, 20th century warmth), some key regions appear to have in fact been colder, while other regions appear to have been warmer. Southern Greenland, for example, appears within uncertainties to have been as warm as today. However, much of the tropical Pacific was unusually cold, suggestive of the La Niña phase of the ENSO phenomenon (a similar conclusion was reached by Trouet et al (2009)). Thus even though some locations may have been as warm or warmer than today, the hemispheric mean appears not to have been.
Why does this matter? It matters because there are plenty of factors that can affect the overall mean temperature (solar variability, volcanoes, greenhouse gases, internal variability etc.) and so it is hard, given the uncertainties in the solar or volcanic reconstructions to precisely attribute the paleo changes in the global or hemispheric mean to these factors. But if we can look at more complex fingerprints of the changes, it might be possible to be more quantitative in those attributions since the spatial fingerprints of the different factors are easier to distinguish. Furthermore, if we can clearly tie the regional patterns to the different forcings, we might be able to use that information to inform regional projections under future conditions.
Thus we can basically say that the warmer conditions of the Medieval era were tied to higher solar output and few volcanic eruptions and the cooler conditions of the Little Ice Age resulted from lower solar output and more frequent volcanic eruptions. But these drivers appear to have had an equally important, though more subtle, influence on regional temperature patterns through their impact on climate phenomena such as ENSO and the North Atlantic Oscillation. The modest increase in solar output during Medieval times appears to have favored the tendency for the positive phase of the NAO, associated with a more northerly jet stream over the North Atlantic. This brought relatively greater warmth in winter to the North Atlantic and Eurasia. A tendency toward the opposite negative NAO phase helps to explain the enhanced winter cooling over a large part of Eurasia during the later Little Ice Age period.
There is some model support for these patterns (see also instance Shindell et al, 2001) when the models include interactive ozone photochemistry to produce this dynamical response to solar forcing, but it is not captured in a simulation of the NCAR CSM coupled model which lacks those processes. Neither model simulation reproduces the apparent La Niña pattern seen in the Medieval temperature reconstructions:
Figure 1: Spatial pattern of mean temperature difference between the MCA and LIA periods (defined at the intervals AD 950-1250 CE and 1400-1700 CE respectively) compared with simulations of two different climate models forced with estimated differences in natural (volcanic and solar) radiative forcing between the two periods (Mann et al, 2009).
Other model simulations, however, using a climate model that exhibits a particular tropical Pacific mechanism, do reproduce such a response. In such models, the tropical Pacific counter-intuitively tends to the cold La Niña phase during periods of increased heating, such as provided by the increase in solar output and low volcanism of the Medieval era. If this response holds for the future, something that is still vigorously debated, it could imply a more La Niña-like response in the future. Most of the state-of-the-art climate models, e.g. those used in the IPCC Fourth Assessment, by contrast, suggest the opposite–a more El Niño-like future climate. The credibility of the models with regard to this phenomenon is not high, however, and lots more work is going to be needed (both on paleo-reconstructions and model improvements) before we can be confident in the future projections of changes in ENSO-like dynamics and mean state.
“Spreading is what invasive species do.”
But why hadn’t they spread into those areas before? It’s not like they were only created a year ago.
Septic Matthew@446,
There is no AGW theory. There is a theory of Earth’s climate that explains the bulk of evidence going back hundreds of millions of years, that illuminates the dynamics of current climate and that is both self-consistent and based on basic physics. It is an unfortunate, but inevitable consequence of this theory that the 38% increase in atmospheric CO2 is bound to raise temperatures by roughly 3 degrees per doubling.
If you want to understand the science, forget about “AGW theory”. It is a straw man constructed by denialists. Learn the theory of Earth’s climate and you will see why it is so successful and why the overwhelming majority of scientists are convinced beyond doubt of the reality of anthropogenic causation of the current warming epoch. Doing anything less is just pudknocking.
Septic Matthew says …
Or knowledge of plant physiology (since kudzu is a plant).
Invasive species frequently become extremely expensive pests, and as such become among the most heavily-studied species on the planet.
The same is true of kudzu. It’s known the northern range of this species in the eastern US is temperature-limited. It’s not a guess.
On the other hand … you’re guessing. Which requires “great belief”, faith in your guess, or faith in the professionals who’ve studied this species and have determined their ecological needs?
SepticMatthew has done better in the past. This is some kind of regression, or unveiling. Only he can say.
Ray Ladbury wrote: “… the overwhelming majority of scientists are convinced beyond doubt of the reality of anthropogenic causation of the current warming epoch.”
To which the predictable “septical” response is: sure, anthropogenic causation is a reality — but the overwhelming majority of scientists are not convinced beyond doubt that the worst imaginable outcomes of AGW are certain to occur, therefore there is no reason to do anything about it (e.g. anything that would diminish ExxonMobil’s one hundred million dollars per day in profit).
CFU,
This began with a comment by Frank Giger way back at #73, which launched a discussion about whether one could say the drought was caused by GW or not. At #93, the Seager paper was brought up. At #97 you came in asking: “Can you say it would have been as bad if it weren’t warming?” Before long (#147), you were taking Frank to task for failing to remember that the discussion had really been about your question all along.
So I’ll concede your #428. You have indeed consistently talked about man-made warming making drought worse (= making stuff drier). As if that were somehow quite different from causing drought (= making stuff too dry). And yet, at the same time, as if it were a trenchant rebuttal to those saying man-made warming had not been shown to cause the drought.
I admit I never really got the distinction.
> When a position is logical, it’s hard NOT to use an argument that shows
> the illogical extreme.
I think that’s my cue to sink back, exhausted, onto the cartons of yoghurt.
From Tamino a couple of years ago:
http://tamino.wordpress.com/2008/05/30/drought-in-australia/
Also of interest:
http://news.bbc.co.uk/2/hi/science/nature/3191174.stm
http://www.springerlink.com/content/j7188j6389327284/
Enough of the references I found:
http://assets.panda.org/downloads/wwf2002drought_ut3s.pdf
Ofcourse I would like to see more stats methods. Do you have stat references CFU?
It seems that insolution was furthest south about 2000 years ago (minimum of orbitl forcing in the far north). It also seems that norhern hemisphere temperatures have been declining, on average, over those same two millennia; see the hockeystick but also other resconstructions, as illustrated by
http://en.wikipedia.org/wiki/File:Holocene_Temperature_Variations.png
What about the nest two millennia under the (alternate universe) assumption of no substantial anthropogenic influences? Would temperatures be going (slowly) up anyway? Further down due to longer phase lag to the orbital forcing?
Crucifix & Rougier (2009), Figure 9, suggests that, on avaerage, temperatures would continue to climb for about 10 ky in that alternate universe. This suggests that phase lag isn’t overly large given that orbital forcing is not increasing and will do so for about 10 ky. So possibly even without excess CO2 temperatures now would be slowly increasing and continue to do so. That would mean a phase lag of almost 2 ky and as we will eventually see that has some profound consequences for various conceptual models of long term climate.
But first, lets see if we can find a (linear) trend in the instrumental record. Tol, R.S.J. and A.F. de Vos (1998), ‘A Bayesian Statistical Analysis of the Enhanced Greenhouse Effect’, Climatic Change, 38, 87-112, determine one, much less important than increasing CO2 concentrations. I modified
https://www.realclimate.org/index.php/archives/2010/03/unforced-variations-3/comment-page-12/#comment-168530
to include a linear trend over the 13 decades of the GISTEMP instrument based global temperature product. The best fitting trend is but 0.063K/Century so most of the observed increase is attributed to CO2; moreover the fit is essentially no better than the simplier model linked above and parsimony, in the form of AIC, recommends the simplier model. But for our current purposes, neither Tol & de Vos nor I find a negative trend which suggests that the long term trend would indeed be upwards in the alternate universe.
This is very far from the so-called hockeystick. The presumed uptrend in this alternate universe is of about the same size, or less, than the downtrend seen in the initial portion of the hockeystick. Clearly something extraordinary, excess CO2 in superabundance, explains the blade uptrend of the hockey stick; not the issue here.
Attempting to explain the consequences (for long term models) of such a short pahse lag, only around 2 ky, will require some formulas and is best done in a separate comment after a bit more thought about it.
I previsously posted about at least three different long term climate conceptual models; two of these use modes, Paillard uses three modes and Tziperman et al. use but two. Assume 2 or 3 modes but we are currently in the interglacial mode so we only have the linear differential equation part to consider. Both of those papers appear to have a characteristic time of about 1/a = 20 ky, so we will use that, for now, with time units of 1 ky. I’ll simplify to use around 20 ky for the precession period and around 40 ky for the obliquity period in the orbital forcings; being precise about these values is not required.
While all those papers treat ice volume as the variable, here I will use temperature instead, and rather crudely assume that high temperature is linear proportion to lower ice volume. I want to, once again, introduce Laplace transforms (s-plane) as more convenient for linear system analysis. So the temperature is denoted by the pair
(Y(s), y(t))
following the convention that the transform is given on the left and the corresponding time domain function is given on the right. The raditive orbital forcing, the input, is denoted
(R(s),r(t))
and the system transfer function by
(W(s),w(t))
where
Y(s) = W(s)R(s)
and then we simply look up the time domain answer, y(t), in a table of Laplace transform — function paris.
The linear system models of the two papers mentioned are of the form W(s) = 1/(s+a) thought of as a reservoir (of ice) which relaxes along a decaying expotential exp(-at) to an equilibrium value when perturbed by a unit impulse input R(s) = 1. Then for sinusoidal input R(s) = 1/(s^2+k^2) the temperature varies as a sinusoidal of the same frequency at a phase lag given by
arctan(k/a).
For the response to obliquity we then have
arctan(2.pi(1/40)/(1/20)) = arctan(pi) ~ 72 arcdegrees being around 7 ky of lag
while for precession
arctan(2.pi(1/20)/(1/20)) = arctan(2.pi) = 81 arcdegrees being around 4 ky of lag. Both, from my previous comment, seem far too long.
That probably makes little difference for the analysis done in the published papers but simply doesn’t agree with those analyses, mentioned in the prior post, which certainly indicate a phase lag of at most 3 ky. Something is missing, except in Crucifix & Rougier (2009); carbon dioxide.
So we need a system transfer function which explicitly includes the positive feedback induced by CO2 (+ water vapor + clouds + …). This must wait until tomorrow.
David, I must say I have been enjoying your posts as o late.
Ray Ladbury (435), I am overwhelmed and somewhat shocked.
If you plot a graph of CO2 concentration along the X-axis and forcing in W/m^2 along the Y-axis you get a curve. (That’s Algebra-1, btw.) That curve has a slope, though probably variable depending on the scale. That slope is called the differential of the function. (Some may prefer “derivative”.) In case I’m going too fast — That slope is called the differential. It measures the rate of change of forcing against the changing concentration. At certain X values, that’s what I claimed is not completely known.
What’s Happer got to do with it?!?!
All of that other crap you accuse me of saying or implying is, well, crap.
Septic only dissenters like Lindzen refers to it as “AGW theory.”
Also an interesting read in regards to regional precipitation:
Extremes (2010) 13:219–239
DOI 10.1007/s10687-009-0098-2
A comparison study of extreme precipitation
from six different regional climate models
via spatial hierarchical modeling
Erin M. Schliep ·Daniel Cooley·Stephan R. Sain ·
Jennifer A. Hoeting.
Abstract:
“We analyze output from six regional climate models (RCMs) via a spatial
Bayesian hierarchical model. The primary advantage of this approach is that the statistical
model naturally borrows strength across locations via a spatial model on the
parameters of the generalized extreme value distribution. This is especially important
in this application as the RCM output we analyze have extensive spatial coverage, but
have a relatively short temporal record for characterizing extreme behavior. The hierarchical
model we employ is also designed to be computationally efficient as we
analyze RCM output for nearly 12000 locations. The aim of this analysis is to compare
the extreme precipitation as generated by these RCMs. Our results show that,
although the RCMs produce similar spatial patterns for the 100-year return level,
their characterizations of extreme precipitation are quite different. Additionally, we
examine the spatial behavior of the extreme value index and find differing spatial
patterns for the point estimates for the RCMs. However, these differences may not be
significant given the uncertainty associated with estimating this parameter.”
Extremes (2010) 13:205–217
DOI 10.1007/s10687-010-0105-7
Sources of uncertainty in the extreme value statistics
of climate data
Michael Wehner
Received: 1 April 2009 / Revised: 27 January 2010 /
Accepted: 11 February 2010 / Published online: 25 February 2010
c The Author(s) 2010. This article is published with open access at Springerlink.com
Abstract:
“We investigate three sources of uncertainty in the calculation of extreme
value statistics for observed and modeled climate data. Inter-model differences in
formulation, unforced internal variability and choice of statistical model all contribute
to uncertainty. Using fits to the GEV distribution to obtain 20 year return
values, we quantify these uncertainties for the annual maximum daily mean surface
air temperatures of pre-industrial control runs from 15 climate models in the CMIP3
dataset.
Keywords Extreme temperature · Return value · Uncertainty · Climate models
AMS 2000 Subject Classification 62G32.”
I would give the links, but these come from my University Pro-quest account. Not too difficult to get the whole of the publications with University access or a Springer link account, but if You cannot, I can cut and paste the results if requested.
These are heavily statistically based as is necessary for this subject matter.
And finally:
“Climate changenext term impacts on previous termcrop yield, cropnext term water productivity and food security – A review
Alert
This article is not included in your organization’s subscription. However, you may be able to access this article under your organization’s agreement with Elsevier.
Yinhong Kanga, Shahbaz Khanb, Corresponding Author Contact Information, E-mail The Corresponding Author and Xiaoyi Maa
aDepartment of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
bDivision of Water Sciences, UNESCO, 1 Rue Miollis, 75 732 Paris Cedex 15, SP, France
Received 20 May 2009;
revised 8 August 2009;
accepted 14 August 2009.
Available online 31 October 2009.
Abstract
This paper provides a comprehensive review of literature related to the assessment of previous term climate change next term impacts on previous term crop next term productivity using previous term climate,next term water and previous term crop yield next term models. The existing studies present that previous term climate change next term models with higher spatial resolution can be a way forward for future previous term climate next term projections. Meanwhile, stochastic projections of more than one previous term climate next term model are necessary for providing insights into model uncertainties as well as to develop risk management strategies. It is projected that water availability will previous term increase next term in some parts of the world, which will have its own effect on water use efficiency and water allocation. previous term Crop next term production can previous term increase next term if irrigated areas are expanded or irrigation is intensified, but these may previous term increase next term the rate of environmental degradation. Since previous term climate change next term impacts on soil water balance will lead to previous term changesnext term of soil evaporation and plant transpiration, consequently, the previous term crop next term growth period may shorten in the future impacting on water productivity. previous termCrop yieldsnext term affected by previous termclimate changenext term are projected to be different in various areas, in some areas previous term crop yields next term will previous term increase,next term and for other areas it will decrease depending on the latitude of the area and irrigation application. Existing modeling results show that an previous term increase next term in precipitation will previous term increase crop yield,next term and what is more, previous term crop yield next term is more sensitive to the precipitation than temperature. If water availability is reduced in the future, soils of high water holding capacity will be better to reduce the impact of drought while maintaining previous term crop yield.next term With the temperature increasing and precipitation fluctuations, water availability and previous term crop next term production are likely to decrease in the future. If the irrigated areas are expanded, the total previous term crop next term production will previous term increase;next term however, food and environmental quality may degrade.
Keywords: previous term Climate change next term impacts; previous term Crop yield next term; Food security; Water productivity; Water use efficiency.”
Progress in Natural Science
Volume 19, Issue 12, 10 December 2009, Pages 1665-1674.
The points of all of this is simple: the issue is complex. I do not know for certain any specific drought and its duration or extent in relation to AGW though I certainly know some of these droughts must be made worse by AGW in duration and extent in time. I also know that flooding can and IS made worse by AGW though I cannot say with certainty which ones precisely or to what extent.
Prior soil conditions as pointed out by Luke on The Tamino link I provided affects sensitivity, as does irrigation access and methods as pointed out the last set of references I quoted.
Floods in an arid drought ridden demographic is NOT a good thing either, as they can rob soil of necessary nutrients, destroy crops and other plant life, and contaminate drinking water supplies.
Some drought prone regions Do and will end up cooling a bit due to climate change, but that is not necessarily always a good thing either. Although some crops have, do and will grow faster due to elevated C02 and temperatures, the limiting of nitrate conversion to protein is one issue, and another in the articles I posted is what precipitation conditions were present prior to the regional temperature changes in question.
Droughts are difficult to analyze in the long term in my humble opinion. Historically there are some really long, killer droughts prior to the 20th century in susceptible areas. This does NOT mean that AGW is not contributing such occurrences now, nor is AGW not a serious matter.
What IS clear is as good as GCM’s have become, as talented and hard working the climatologists are, (for the most part, minus the “usual suspects, in the RC vernacular)as much as the majority of the IPCC report is valid and reliable, and as much about the physics we do know, there are quite significant uncertainties in the descriptive data, inferential statistical analysis, and thus prediction methods.
I do not want to have to say this last thing again: I do NOT deny that we must face potential serious consequences of AGW as well as CURRENT issues as well. What I do not like is when one aspect is hyper focused on at the exclusion of other major concerns or an individual claims certainty in a particular claim than there is.
I also appreciate RC’s patience overall in these heated discussions.
I have also found Michael Mann’s papers of great benefit too.
Jacob, that one was easy: it was statistically anomalous. Rather like the heatwave in 2003 in Europe.
But practically every drought was made worse by AGW, even if they don’t seem to be out of the ordinary.
After all, the warming didn’t know that the Australian drought “needed” to be so deep and concentrate all its’ changes into that one event.
If you go waaaay back, you’ll see my explanation of a 4% increase in severity of an event that has a random attribution of severity and how you’d need ~400 events to show that it was increasing by 4%.
SEUS drought was one of those 400.
You cannot show this by statistical analysis, but you can infer it from modelling by leaving out ~1/4 of the CO2 and running a hindcast model over and over again.
Nobody has ever done that as far as I know and published the results, though the test was done to show that anthropogenic changes had to be included to make a climate that reflects the current one.
And the point is that AGW is costing us every time there’s something that can be exacerbated by warming temperatures. Not just the big events, even the little ones.
“I admit I never really got the distinction.”
That was my frustration: I knew you weren’t getting that and that once you did, you’d either come up with a wrinkle that I hadn’t considered, or accepted and modified my proposition to fit in with what you knew.
Heck, maybe someone out there would have shown that the coastal extreme of that event was actually made less severe because of increased evaporation from the nearby coast (with wind patterns to show this).
It’s not a particularly difficult concept, but so many people were arguing what they thought I meant and didn’t change their assumption.
Quite annoying.
“This began with a comment by Frank Giger way back at #73, ”
And frank also turned it from the specific case (that event) into the generic which then led to everyone not getting my point.
And Frank also going “you can’t EVER attribute one single event to AGW, that is SO WRONG” which you then seemed to be picking up.
Which he was hoping would happen by morphing the statement from the specific to the general. Notice that he’s kept silent recently.
“What I do not like is when one aspect is hyper focused on at the exclusion of other major concerns or an individual claims certainty in a particular claim than there is.”
Who is doing that?
I’m focusing on the discussion on one thing, other people are focusing on other things.
Rod B.,
Precision is important. So is ensuring that your language affects the physics. Your posts have been so vague that it isn’t even possible to say whether they are right or wrong. That is worse than being wrong! Wrong can be corrected. Bullshit can’t. We know CO2 sensitivity quite well. We know it is very unlikely to be above 4.5 degrees per doubling and extremely unlikely to be below 2 degrees per doubling. That is a pretty tight constraint.
There are many things we don’t understand about climate. They do not invalidate what we do understand.
Bill Ruddimann (#365),
I’m generally wary of arguments which pit “natural” against “anthropogenic”. It’s philosophically unsound and your argument reads as an “excluded middle” fallacy.
Sensitivity is a convenient abstraction which is not going to apply equally in all contexts, especially not if the variations in global temperature under examination are small to begin with. Is it not conceivable that the kind of regional climate changes hypothesized in Mike’s orginal post are going to have an impact on the carbon cycle quite aside from any impact on the global temperature average? Increased rainfall on arid regions adjacent to forests could promote tree growth for instance. Such a conjecture does not imply that you are wrong of course, only that your carbon cycle sensitivity argument is insufficient. I for one am interested to read more about the research reagarding any impact the genocide of native Americans might have had on the climate.
[edit – for once can people argue about something interesting rather than picking over pointless semantic confusions?]
“It measures the rate of change of forcing against the changing concentration. At certain X values, that’s what I claimed is not completely known.”
Where did you say “the rate of change of forcing … is not completely known”???
“the forcing equation for CO2 as it goes from say 350ppm to 700ppm because of anthropogenic emissions has never been observed”
Doesn’t say that.
Neither does “My assertions of the “less than irrefutable” are based on the degree or the differential of the functions.”
Nor “theoretically if CO2 did absorb all of the radiation in its absorption band, then no matter how much CO2 was at higher altitudes it would have no radiation to absorb”
So where did you say “it is not completely known”? And what is “it”? CO2/temperature sensitivity?
We wait with baited breath…
PPS wasn’t another poster pissed off at how there was concentration on one topic? Well when I try, what happens? [edits], that’s what.
[cry us a river. edits occur because you’re continually engaging in pointless, self-defensive bickering and/or insulting others. be happy anything gets through and get over yourself.]
At any rate most of is agree we should continue to lower GHG gases, preserve more forests, plant more trees, save dying out animal species, and in the meantime adopt technology that helps us adapt to the changing climate… yes? Maybe we should concentrate on where we agree and discuss what might be done about slowing the increasing GHG emissions and conserving natural habitats/crops. I mean this specifically in relation to those regions most heavily affected by weather extremes and climate induced damage, whatever the synergy of causes.
> baited breath
http://karenspoetryspot.blogspot.com/2008/01/cruel-clever-cat-by-geoffrey-taylor.html
476: I always thought that meant you were waiting with a worm on your tongue.
#463 Rod B (Black was it?)
I have to join Ray in his concern. In many ways, it is safe to say, ambiguity is the root of all evil. Especially true in science.
Your continued habit of not including the constraints and the relative certainty associated with the values in your considerations, devalues your opinion/perspective.
In other words your continuing to be play in the noise while ignoring the signal does not make your opinion look intelligent, in fact quite the opposite.
This translates to your presented lack of relevant contextual reasoning on the issue as wholly or largely inadequate.
Plus, I’m not a maths guy, but I trust Ray a hell of a lot more than you on these subjects. Your track record has not exactly been a shining example of reasonability, in my opinion.
—
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Learn the Issue & Sign the Petition
Ah, CFU:
“And Frank also going “you can’t EVER attribute one single event to AGW, that is SO WRONG” which you then seemed to be picking up.
Which he was hoping would happen by morphing the statement from the specific to the general. Notice that he’s kept silent recently.”
One can’t say any one weather event is proof (positive or negative) of AGW. Period. Weather happens.
Today was well within seasonal norms for my area. Is this a result of AGW? How? Was it supposed to rain today if not for AGW? Prove it.
What you miss, CFU, is that I’m not saying climate change isn’t happening, or that we’re influencing it.
My point – which stands – is that it does little service to anyone to point at particular weather events (including very short term ones over several months) as proof or disproof of climate change. Credibility goes out of the window.
I’ve pretty much bailed from this thread because you’re just all worked up and obstinantly refusing to actually comprehend what’s being written.
So far we have taken the system transfer function response to orbital forcing R(s) to be due to slow changes in an ice reservoir. But suppose the relaxation is fast, on the order of the deep ocean characteristic time so that a = 1 ky^(1-). Then the phase lag for obliquity is only
arctan(2.pi/40) ~ 9 arcdegrees so around 1 ky of phase lag
and for precession but
arctan(2.pi/20) ~ 12 arcdegrees being a phase lag of 0.667 ky.
Perhaps these are too small as we seem to have closer to 2 ky of phase lag to the recent minimum in r(t).
Whatever seems an appropriate characteristic time for the temperature response to r(t) we still ought to take the positive feedback due to CO2 into account. As is well understood this is nonlinear in the concentration but as the variations in concentrations we need to consider are small, a linear approximation is adequate for starters anyway. These variations are small because we are only considering nonanthropogenic variations over an interglacial, the Holocene. So the minimum is maybe as low as 260 ppm and from Crucifix & Rougeier (2009) a absolute maximum of 310 ppm certainly suffices. We’ll just rough in a linear approximation over this range.
We take the ocean as the CO2 reservoir so the feedbak factor is
H(s) = h/(s+b)
with b (for now) being 1 ky^(-1). The forward gain we alredy have considered but now write as
G(s) = 1/(s+a).
With positive feedback the system transfer function is now
W(s) = G(s)/(1 – G(s)H(s))
and using the auxiliary variables
c = (a+b)/2
d^2 = h – ab + c^2
one rearranges the transfer function as
W(s) = (s+b)/[(s+c)^2 + d^2]
and noting that the inverse Laplace transform of 1/[(s+c)^2+d^2] is
(1/d)exp(-at)sinh(bt)
proceed to find the inverse Laplace transform of W(s). Before (or after) doing so, note that h, the feedback constant, is bounded above by ab for stability. We have some reason to think it is about half that maximum value, so hereinafter we use
h = (1/2)ab.
Irrespective of the value chosen for h, the inverse Laplace transform of W(s) is
W(t) = (1/2d)[(b-c+d)exp(-(c-d)t) + (c+d-b)exp(-(c+d)t)]
which can be thought of as the relaxation of two reservoirs, one with characteristic time 1/(c-d) and the other with characteristic time 1/(c+d). This came as quite a surprise to me. (I’ve never considered such a complicated system with positive feedback before.) By its very form it has some rather amazing properties as a, b (and h) are varied. In particular, if c-d is small the left term has a very long characteristic time; one way to make it small is to increase h towards the maximum. This shows that the positive feedback is prolonging the response to a unit impulse function; similarly in response to a sinusoid the phase lag as close to pi/2. For example with a = 1/40 and b = 1 we have
W(t) = 0.969exp(-0.019t) + 0.031exp(-1.031t)
and so the dominant term has a characteristic time of about 53 ky!
So adding in the postive feedback of CO2 just mad the pahse lag even worse, although it is certainly proper to include. All the clues should now be in place and in my next comment I’ll explain a solution altough I do hope you’ll work it out for yourself in the nonce.
Correction: the inverse Laplace transform of 1/[(s+c)^2+d^2] is
(1/d)exp(-ct)sinh(dt)
Another correction, apologies to all.
For example with a = 1/20 and b = 1 we have
W(t) = 0.969exp(-0.019t) + 0.031exp(-1.031t)
and so the dominant term has a characteristic time of about 53 ky!
Rod B saays: “if you plot a graph of CO2 concentration along the X-axis and forcing in W/m^2 along the Y-axis you get a curve. (That’s Algebra-1, btw.)”
I’m beginning to understand your difficulties. The fact that such a plot produces a curve is not algebra. It is physics. Nothing in all of mathematics implies that the forcing is a single valued function of [CO2]. The fact that one obtains a curve rather than a random cloud of points is a consequence of physics, not mathematics.
For 1/a the characteristic response time of ice to the orbital forcing, r(t), and 1/b the characteristic time for CO2 to respond to temperature, we are assuming that temperature goes up as ice wanes and vice versa. The values of a and b have to be estimated by some means, but irrespective of the values using the auxiliary variables
c = (a+b)/2
d^2 = h – ab + c^2
we the system transfer function
W(t) = (1/2d)[(b-c+d)exp(-(c-d)t) + (c+d-b)exp(-(c+d)t)]
in which we set the feedback constant h to h = (1/2)ab, i.e., half maximum as determined from other studies.
We have seen that using 1/a on the order of tens of kiloyears (with b=l) eads to impossibly long lag times. So lets try 1/a = 1 ky (with b=1) as the ice characteristic time during an interglacial (this might be different after a mode switch). Then
W(t) = 0.5exp(-0.5t) + 0.5exp(-1.707t)
so the left term has a characteristic time of 2 ky while the right term’s characteristic time is 0.586 ky. These seem about right. For obliquity forcing, r(t) = sin(wt) with w = 2.pi/40, we have for the left term a pahse lag of p_l = 17.5(2.pi/360) radians and for the right term p_r = 5.5(2.pi/360) radians in
W(t) ~ sin(wt – p_l) + sin(wt – p_r)
which is a sine wave with a phase lag of about 1.3 ky, close enough to see that setting 1/a to about 2 ky (with b = 1) ought to do it.
We can simplify by ignoring CO2 to just use W(s) = 1/(s+a) = 1/(s+1/3) to obtain an obliquity phase lag of about 1.7 ky and a precession phase lag of about 2.4 ky.
I take all this to demonstrate that the characteristic time for ice volume response to orbital forcing during an interglacial to be around 1–3 ky, being in better accord with the past two millennia than the much longer characteristic times used in the long range climate conceptual models I have read about.
Something else learned from studying the past millenia or so.
Rod B sir please, you are just embarrassing yourself.
Looks like business as usual really steepens the emissions curve.
Global CO2 emissions to jump 43% by 2035 — EIA
http://www.eenews.net/eenewspm/2010/05/25/5/
(05/25/2010) (subscription)
Katherine Ling, E&E reporter
Energy-related carbon dioxide emissions will rise 43 percent worldwide by 2035 under current policies, the U.S. Energy Information Administration said in an analysis released today.
Emissions will rise from 29.7 billion metric tons in 2007 to 42.4 billion metric tons in 2035, says the report summary, the first effort by EIA to forecast emissions till 2035. The agency will release the full report later this year.
Growing economies and populations will drive emission increases, with world energy consumption expected to increase 49 percent from 2007 to 2035, EIA said.
Developing countries are projected to increase energy demand by 84 percent and double their share of the world’s carbon emissions, contributing two-thirds of the global total by 2035, EIA said. Developed countries’ energy consumption will expand 14 percent while they maintain levels of carbon emissions, the agency said. Industrialized countries’ emissions made up about half the world’s total in 2007 but are expected to represent a third of global emissions in 2035.
Fossil fuels will meet more than 75 percent of total energy needs in 2035, EIA said. World oil production will increase to 110.6 million barrels per day and reach $133 per barrel in 2035, under the agency’s reference case.
But oil will make up 30 percent of world energy consumption in 2035, compared to 35 percent in 2007, the agency said. It is also about 10 percent less than what EIA had predicted about five years ago, Guy Caruso, senior adviser at Center for Strategic & International Studies and former EIA administrator, said at a press conference in Washington, D.C.
EIA’s high oil-price scenario reaches $210 per barrel in 2035 — accounting for possible lower production from key exporters — and the low oil-price scenario stays around $51 per barrel.
The natural gas industry needs to increase production in 2035 by 46 percent to meet the projected growth in demand, but EIA predicts “well supplied” markets will keep prices down.
Howard Gruenspecht, deputy EIA administrator, said he expects oil prices to remain about three times higher than gas prices in North America because of shale gas, tight gas and coalbed methane supply in the United States and Canada.
Global coal consumption is projected to continue rising, especially in China, growing at an average annual rate of 1.6 percent, the report says. Coal will still account for 43 percent of the world’s electric generation by 2035, EIA said.
The report cites “improved prospects” for nuclear power, backed by policy changes in Europe and overall higher capacity utilization rates — on top of more than 8 percent growth per year in new power plants in China and India. EIA’s forecast for nuclear generation in 2030 is 9 percent higher than last year’s projections and reaches a total of 4.5 trillion kilowatt-hours in 2035. But nuclear still decreases its supply in the overall global electricity portfolio, accounting for 14 percent in 2007 and 13 percent in 2035, the report says.
EIA forecasts renewable energy as the fastest-growing source of world power supply, “but from a relatively small base,” growing from 10 percent to 14 percent of supplies. Renewables will make up 23 percent of electricity supply in 2035 but with 80 percent of that coming from hydropower and wind power.
“Except for those two sources, most renewable generation technologies are not economically competitive with fossil fuels over the projection period,” the report says.
Asked how to square EIA’s forecast with the goal of some scientists and environmentalists for cutting greenhouse gas emissions 80 percent by 2050, Gruenspecht replied, “Policymakers have a lot of work to do.”
452 Ray Ladbury: There is no AGW theory.
If you insist. I’ll try to call it AGW for short.
To me it is at least as theoretical as General Relativity or the Krebs Cycle, each of which is a part of a larger system.
453, dhogaza:The same is true of kudzu. It’s known the northern range of this species in the eastern US is temperature-limited. It’s not a guess.
That’s only “known” if species are immutable. When species experience random variation, their extents are not limited by physics. It is not a guess that SIV is limited to a subset of Africa, yet since the 1930s (or maybe since the 1950s), its variant HIV has spread worldwide.
Ray Ladbury (470), I simply and clearly (I thought) said one of my biggest areas of skepticism is the forcing math and physics of the doubling of CO2 in the domain of 350ppm to 700ppm or at some higher doubling values. What is vague about that? Is this not precise enough for you? How about 396ppm to 792ppm?? What is bullshit about this? (You claim my physics/math assertion is not correct but that’s not what you were calling bullshit.)
PS on reflection the blame ought to be distributed ; maybe my point wasn’t originally as clearly stated in this thread as I thought. Though it had been before (to wit my broken record comment); maybe I assumed too much.
Still, the non-understanding of “differential” is astounding to me. But, what the hey. It’s the kind of debate that the moderators would prefer die.
CFU #466, let me get this straight: We’ve been going in circles for eight pages or so over whether AGW is making an as yet *undetectable* contribution to worse droughts? Good grief.
“One can’t say any one weather event is proof (positive or negative) of AGW. Period. Weather happens.”
Indeed it does. And so is winning the 100m by a whole second. It’s not PROOF they’ve cheated.
You never answered me in #175, did you.
“What you miss, CFU, is that I’m not saying climate change isn’t happening, or that we’re influencing it.”
What you miss is that if we’re not changing any single event, we cannot change the climate. Your position is completely untenable.
“My point…is that it does little service to anyone to point at particular weather events … as proof or disproof of climate change. Credibility goes out the window”
Good job I’ve never said that. Neither has anyone else except to strawman a “You’re wrong” out of it.
You never answered #175 and you’ve never explained how we can change the climate without changing every event.
You bailed because you couldn’t answer #175 and you are unable to accept that there is change even in a single event because without changing the single events you don’t change the trend.
Is this because you want to continue to say that AGW *will* be a problem, just isn’t one now?
Rod 463,
A differential is NOT the same as a derivative. You need to reread a calculus textbook.
David,
I feel your pain. For long expositions on technical subjects, I’ve started to write them out in a text editor first, and check them over, before posting to RC. But it’s still easy to click “Say It!” and get some asinine error in there…
#488–Sorry, Matthew, your appeal to genetic variation or change is a fail, and you’d have known that if you’d read the links posted.
It is known that kudu is temperature limited, since the researchers actually measured tissue damage at various (cold) temperatures, using different populations of kudzu from different areas. There’s basically no reproduction for kudzu chilled to -20C for 4 hours. The data on geographic spread are consistent with that -20C thermocline.
They didn’t just guess.
“A differential is NOT the same as a derivative.”
I called the Humpty-Dumpty defence right, didn’t I.
Maybe an intern is posting for Rod B.
“We’ve been going in circles for eight pages or so over whether AGW is making an as yet *undetectable* contribution to worse droughts”
No, YOU have been arguing that I’ve said that we can attribute a drought to AGW.
I’ve been arguing I haven’t been arguing that and restating what I *have* been arguing until I find the shape of words that fits in your head.
And it is a detectable contribution. It just isn’t utile as a statistical proof of the strength of climate change on its own because nobody has done the work yet to see what the inputs are for that weather event.
That you continue to get my argument wrong is a sheesh moment.
“We’ve been going in circles for eight pages or so over whether AGW is making an as yet *undetectable* contribution to worse droughts”
No, YOU have been arguing that I’ve said that we can attribute a drought to AGW.
I’ve been arguing I haven’t been arguing that and restating what I *have* been arguing until I find the shape of words that fits in your head.
And it is a detectable contribution. It just isn’t utile as a statistical proof of the strength of climate change on its own because nobody has done the work yet to see what the inputs are for that weather event.
And that you make that unsupported statement (2W/m2 IS detectable as an energy source) is why we’ve spent 8 pages.
Like Frank, I’ve pretty much bailed on the “drought debate” as unproductive. However, here’s what I’m hearing:
CFU: every weather event must logically be affected by AGW. Specifically, every drought event must logically be worsened.
(My thought, FWIW: This can’t be refuted as possibly true, yet it seems to rest on an unproven assumption that AGW is effective *always* and *everywhere*–an assumption dubious in light of the fact that to date, at least, there are still places that don’t (yet) show a warming trend. Of course, they might have been cooler still in the (hypothetical) no-AGW case, but the point is we don’t know. Hence, CFU’s assumption remains just that.)
Frank: Plausible peer-reviewed research (ie., Seager et al) says the SEUS drought can’t be attributed to AGW. More generally, you can’t attribute any single “weather” event to AGW.
(My thought, FWIW: First point seems fair enough; I have some reservations about Seagar et al, as expressed above (with all due humility, I hope), but the bottom line does seem to be that there is no strong evidence to support a specific attribution. The second point seems to be a reach, however; for instance, the 2003 heat wave has, in some fashion at least, been partially attributed: http://www.nature.com/nature/journal/v432/n7017/full/nature03089.html.)
CM: Frank’s point #1 above. More generally, over-attributing is scientifically unsupported and plays into several popular denialist memes–such as the “religious cult” meme, the “alarmist” meme, and the “AGW-causes-everything-mockery” meme. (My examples, not anything CM said.)
(My thought FWIW: CM is probably right about this. CFU’s position is not really susceptible of refutation, but, like a hypothesis that’s unfalsifiable, it seems unlikely to advance the state of our knowledge: what are we supposed to do as a consequence of CFU’s points? (Though I did like the statistical point about the 400 events, etc.) As a practical advocate on news sites, I do frequently get accused of the various “alarmist” faults I mentioned, and others too, so CM’s concern seems well-founded to me. We shouldn’t attribute where there isn’t specific evidence to say so, and if we use events (eg., Katrina) as examples, we should be clear about what we’re doing.)
Rod B.,
It may be somewhat harsh of me to expect precision from a layman. However, I do think that your tendency toward vague expression contributes to you difficulty in understanding the theory. First, I think that you get wrapped around the axle when it comes to the specific mathematical expressions without quite understanding the physics. My recommendation is to start with the physics.
Start by understanding WHY absorption does not saturate with concentration. Look at how the wings of the distribution broaden. At the same time, we know that absorption is not going to increase linearly–because the light in the middle of the band gets absorbed very quickly. Start with that and then ask yourself what sorts of mathematical functions reflect this behavior. You will find that the logarithmic dependence is the most reasonable fit. It also makes sense given the rough power law shape of the wings of the distribution.
When in doubt, start with the physics.