In Part Seven we looked at some early GCM work – late 80’s to mid 90’s. In Part Eight we looked at some papers from the “Noughties” – atmospheric GCMs with prescribed ocean temperatures and some intermediate complexity models.
All of these papers were attempting to do the most fundamental of ice age inception – perennial snow cover at high latitudes. Perennial snow cover may lead to permanent ice sheets – but it may not. This requires an ice sheet model which handles the complexities of how ice sheets grow, collapse, slide and transfer heat.
Given the computational limitations of models even running a model to produce (or not) the basics of perennial snow cover has not been a trivial exercise, but a full atmospheric ocean GCM with an ice sheet model run for 130,000 years was not a possibility.
In this article we will look at a very recent paper, where fully coupled GCMs are used. “Fully coupled” means an atmospheric model and an ocean model working in tandem – transferring heat, moisture and momentum.
Smith & Gregory (2012)
The problem:
It is generally accepted that the timing of glacials is linked to variations in solar insolation that result from the Earth’s orbit around the sun (Hays et al. 1976; Huybers and Wunsch 2005). These solar radiative anomalies must have been amplified by feedback processes within the climate system, including changes in atmospheric greenhouse gas (GHG) concentrations (Archer et al. 2000) and ice-sheet growth (Clark et al. 1999), and whilst hypotheses abound as to the details of these feedbacks, none is without its detractors and we cannot yet claim to know how the Earth system produced the climate we see recorded in numerous proxy records. This is of more than purely intellectual interest: a full understanding of the carbon cycle during a glacial cycle, or the details of how regional sea-level changed as the ice-sheets waxed and waned would be of great use in accurately predicting the future climatic effects of anthropogenic CO2 emissions, as we might expect many of the same fundamental feedbacks to be at play in both scenarios..
..The multi-millennial timescales involved in modelling even a single glacial cycle present an enormous challenge to comprehensive Earth system models based on coupled atmosphere–ocean general circulation models (AOGCMs). Due to the computational expense involved, AOGCMs are usually limited to runs of a few hundred years at most, and their use in paleoclimate studies has generally been through short, ‘‘snapshot’’ runs of specific periods of interest.
Transient simulations of glacial cycles have hitherto only been run with models where important climate processes such as clouds or atmospheric moisture transports are more crudely parameterised than in an AOGCM or omitted entirely. The heavy restrictions on the feedbacks involved in such models limit what we can learn of the evolution of the climate from them, particularly in paleoclimate states that may be significantly different from the better-known modern climates which the models are formulated to reproduce. Simulating past climate states in AOGCMs and comparing the results to climate reconstructions based on proxies also allows us to test the models’ sensitivities to climate forcings and build confidence in their predictions of future climate.
[Emphasis added. And likewise for all bold text in future citations].
Their model:
For these simulations we use FAMOUS (FAst Met. Office and UK universities Simulator), a low resolution version of the Hadley Centre Coupled Model (HadCM3) AOGCM. FAMOUS has approximately half the spatial resolution of HadCM3, which reduces the computational cost of the model by a factor of 10.
[For more on the model, see note 1]
Their plan:
Here we present the first AOGCM transient simulations of the whole of the last glacial cycle. We have reduced the computational expense of these simulations by using FAMOUS, an AOGCM with a relatively low spatial resolution, and by accelerating the boundary conditions that we apply by a factor of ten, such that the 120,000 year cycle occurs in 12,000 years. We investigate how the influences of orbital variations in solar irradiance, GHGs and northern hemisphere ice-sheets combine to affect the evolution of the climate.
There is a problem with the speeding up process – the oceans respond on completely different timescales from the atmosphere. Some ocean processes take place over thousands of years, so whether or not the acceleration approach produces a real climate is open to discussion.
Their approach:
The aim of this study is to investigate the physical climate of the atmosphere and ocean through the last glacial cycle. Along with changes in solar insolation that result from variations in the Earth’s orbit around the sun, we treat northern hemisphere ice-sheets and changes in the GHG composition of the atmosphere as external forcing factors of the climate system which we specify as boundary conditions, either alone or in combination. Changes in solar activity, Antarctic ice, surface vegetation, or sea- level and meltwater fluxes implied by the evolving ice- sheets are not included in these simulations. Our experimental setup is thus somewhat simplified, with certain potential climate feedbacks excluded. Although partly a matter of necessity due to missing or poorly modelled processes in this version of FAMOUS, this simplification allows us to more clearly see the influence of the specified forcings, as well as ensuring that the simulations stay close to the real climate.
Let’s understand the key points of this modeling exercise:
- A full GCM is used, but at reduced spatial resolution
- The forcings are speeded up by a factor of 10 over their real life versions
- Two of the critical forcings applied are actually feedbacks that need to be specified to make the model work – that is, the model is not able to calculate these critical feedbacks (CO2 concentration and ice sheet extent)
- Five different simulations were run to see the effect of different factors:
- Orbital forcing only applied (ORB)
- GHG only forcing applied (GHG)
- Ice sheet extent only applied (ICE)
- All of the above with 2 different ice sheet reconstructions (ALL-ZH & ALL-5G – note that ALL-ZH has the same ice sheet reconstruction as ICE, while ALL-5G has a different one)
Here are the modeled temperature results compared against actual (Black) for Antarctica and Greenland:
Figure 1
Lots of interesting things to note here.
When we look at Antarctica we see that orbital forcing alone and Northern hemisphere ice sheets alone do little or nothing to model past temperatures. But GHG concentrations by themselves as a forcing provide a modeled temperature that is broadly similar to the last 120kyrs – apart from higher frequency temperature variations, something we return to later. When we add the NH ice sheets we get an even better match. I’m surprised that the ice sheets don’t have more impact given that amount of solar radiation they reflect.
Both GHGs and ice sheets can be seen as positive feedbacks in reality (although in this model they are specified), and for the southern polar region GHGs have a much bigger effect.
Looking at Greenland, we see that orbital forcing once again has little effect on its own, while GHGs and ice sheets alone have similar effects but individually are a long way off the actual climate. Combining into all forcings, we see a reasonable match with actual temperatures with one sheet reconstruction and not so great a match for the other. This implies – for other models that try to model dynamic ice sheets (rather than specify) the accuracy may be critical for modeling success.
We again see that higher frequency temperature variations are not modeled at all well, and even some lower frequency variations – for example the period from 110 kyr to 85 kyr has some important missing variability (in the model).
The authors note:
The EPICA data [Antarctica] shows that, relative to their respective longer term trends, temperature fell more rapidly than CO2 during this period [120 – 110 kyrs], but in our experiments simulated Antarctic temperatures drop in line with CO2. This suggests that there is an important missing feedback in our model, or that our model is perhaps over-sensitive to CO2, and under-sensitive to one of the other forcing factors. Tests of the model where the forcings were not artificially accelerated rule out the possibility of the acceleration being a factor.
Abrupt Climate Change
What about the higher frequency temperature signals? The Greenland data has a much larger magnitude than Antarctica for this frequency, but neither are really reproduced in the model.
The other striking difference between the model and the NGRIP reconstruction is the model’s lack of the abrupt, millennial scale events of large amplitude in the ice-core data. It is thought that periodic surges of meltwater from the northern hemisphere ice-sheets and subsequent disruption of oceanic heat transports are involved in these events (Bond et al. 1993; Blunier et al. 1998), and the lack of ice-sheet meltwater runoff in our model is probably a large part of the reason why we do not simulate them.
The authors then discuss this a little more as the story is not at all settled and conclude:
Taken together, the lack of both millennial scale warm events in the south and abrupt events in the north strongly imply a missing feedback of some importance in our model.
CO2 Feedback
The processes by which sufficient quantities of carbon are drawn down into the glacial ocean to produce the atmospheric CO2 concentrations seen in ice-core records are not well understood, and have to date not been successfully modelled by a realistic coupled model. FAMOUS, as used in this study, does have a simple marine biogeochemistry model, although it does not respond to the forcings in these simulations in a way that would imply an increased uptake of carbon. A further FAMOUS simulation with interactive atmospheric CO2 did not produce any significant changes in atmospheric CO2 during the early glacial when forced with orbital variations and a growing northern hemisphere ice-sheet.
Accurately modelling a glacial cycle with interactive carbon chemistry requires a significant increase in our understanding of the processes involved, not simply the inclusion of a little extra complexity to the current model.
Conclusion
This is a very interesting paper, highlighting some successes, computational limitations, poorly understand feedbacks and missing feedbacks in climate models.
The fact that 120 kyrs of climate history has been simulated with a full GCM is great to see.
The lack of abrupt climate change in the simulation, the failure to track the fast rate of temperature fall at the start of ice age inception and the lack of ability to model key feedbacks all indicate that climate models – at least as far as the ice ages are concerned – are at a rudimentary stage.
(This doesn’t mean they aren’t hugely sophisticated, it just means climate is a little bit tricky).
Articles in the Series
Part One – An introduction
Part Two – Lorenz – one point of view from the exceptional E.N. Lorenz
Part Three – Hays, Imbrie & Shackleton – how everyone got onto the Milankovitch theory
Part Four – Understanding Orbits, Seasons and Stuff – how the wobbles and movements of the earth’s orbit affect incoming solar radiation
Part Five – Obliquity & Precession Changes – and in a bit more detail
Part Six – “Hypotheses Abound” – lots of different theories that confusingly go by the same name
Part Seven – GCM I – early work with climate models to try and get “perennial snow cover” at high latitudes to start an ice age around 116,000 years ago
Part Seven and a Half – Mindmap – my mind map at that time, with many of the papers I have been reviewing and categorizing plus key extracts from those papers
Part Eight – GCM II – more recent work from the “noughties” – GCM results plus EMIC (earth models of intermediate complexity) again trying to produce perennial snow cover
Part Ten – GCM IV – very recent work from 2012, a high resolution GCM called CCSM4, producing glacial inception at 115 kyrs
Pop Quiz: End of An Ice Age – a chance for people to test their ideas about whether solar insolation is the factor that ended the last ice age
Eleven – End of the Last Ice age – latest data showing relationship between Southern Hemisphere temperatures, global temperatures and CO2
Twelve – GCM V – Ice Age Termination – very recent work from He et al 2013, using a high resolution GCM (CCSM3) to analyze the end of the last ice age and the complex link between Antarctic and Greenland
Thirteen – Terminator II – looking at the date of Termination II, the end of the penultimate ice age – and implications for the cause of Termination II
Fourteen – Concepts & HD Data – getting a conceptual feel for the impacts of obliquity and precession, and some ice age datasets in high resolution
Fifteen – Roe vs Huybers – reviewing In Defence of Milankovitch, by Gerard Roe
Sixteen – Roe vs Huybers II – remapping a deep ocean core dataset and updating the previous article
Seventeen – Proxies under Water I – explaining the isotopic proxies and what they actually measure
Eighteen – “Probably Nonlinearity” of Unknown Origin – what is believed and what is put forward as evidence for the theory that ice age terminations were caused by orbital changes
Nineteen – Ice Sheet Models I – looking at the state of ice sheet models
References
The last glacial cycle: transient simulations with an AOGCM, Smith & Gregory, Climate Dynamics (2012)
Notes
Note 1: FAMOUS
The ocean component is based on the rigid-lid Cox-Bryan model (Pacanowski et al. 1990), and is run at a resolution of 2.5° latitude by 3.75° longitude, with 20 vertical levels. The atmosphere is based on the primitive equations, with a resolution of 5° latitude by 7.5° longitude with 11 vertical levels (see Table 1).
Version XDBUA of FAMOUS (simply FAMOUS hereafter, see Smith et al. (2008) for full details) has a preindustrial control climate that is reasonably similar to that of HadCM3, although FAMOUS has a high latitude cold bias in the northern hemisphere during winter of about 5°C with respect to HadCM3 (averaged north of 40°N), and a consequent overestimate of winter sea-ice extent in the North Atlantic.
The global climate sensitivity of FAMOUS to increases in atmospheric CO2 is, however, similar to that of HadCM3.
FAMOUS incorporates a number of differences from HadCM3 intended to improve its climate simulation—for example, Iceland has been removed (Jones 2003) to encourage more northward ocean heat transport in the Atlantic. Smith and Gregory (2009) demonstrate that the sensitivity of the Atlantic meridional overturning circulation (AMOC) to perturbations in this version of FAMOUS is in the middle of the range when compared to many other coupled climate models. The model used in this study differs from XDBUA FAMOUS in that two technical bugs in the code have been fixed. Latent and sensible heat fluxes from the ocean were mistakenly interchanged in part of the coupling routine, and snow falling on sea-ice at coastal points was lost from the model. Correction of these errors results in an additional surface cold bias of a degree or so around high latitude coastal areas with respect to XDBUA, but no major changes to the model climatology. In addition, the basic land topography used in these runs was interpolated from the modern values in the ICE-5G dataset (Peltier 2004), which differs somewhat from the US Navy-derived topography used in Smith et al. (2008) and HadCM3.
IMO, this is a really interesting, detailed post. I really dig it. Thanks.
Almost no change in global Albedo then.
Despite all that extra continental glacier and sea ice and desert/grassland/tundra and decline in forested areas which reflects a huge amount of solar energy compared to interglacial conditions.
Cloud Albedo is also supposed to go up as temperatures decline – an extra -0.75 W/m2/K.
Its impossible to model the last glacial timeline when the ice-albedo and land-surface-albedo forcing and cloud albedo forcing is downplayed so much compared to what must have actually happened.
Bill,
I expect there is a significant change in global albedo in the model, but other factors act as negative feedbacks. In the next article we will see a paper which breaks out the various positive and negative feedbacks found in their model (attempting to produce perennial snow cover at 115kyrs). What is fascinating is that the amplification of the lower insolation in summer at high latitudes is amplified by snow/ice albedo and then reduced by other factors.
It is remarkable just how small the orbital forcing term is and how uncorrelated it is to the 100,000y glacial cycle. The main signal is the 41,000 y change in orbital tilt which does show up as a perturbation in the temperature data. The orbital forcing also seems to be larger over the southern hemisphere rather than the northern hemisphere. You would expect the ice albedo feedback to orbital forcing to be much larger in the northern hemisphere but this is not actually modelled, and instead is an input parameter. The change in ice albedo apparently has no effect whatsoever in Antarctica. I wonder if that also applies to the tropics and S. hemisphere? This seems strange to me. There should be an effect on MOC heat transfer through the oceans and for example the position of jet streams.
In the end the model relies on CO2 forcing (taken as an input) to get a reasonable fit. However we know that the CO2 curve is taken from same the Ice core temperature data, so it is not surprising that the model can follows the 100y cycle. HADCM3 also has one of the highest CO2 sensitivities in CMIP5.
It also seems likelt that 5-G simply has an ice extent distribution which is tuned to agree with the Greenland temperature data.
Still this is a valiant attempt to use a GCM to model the last glacial cycle and the authors are to be applauded for trying. I still have the feeling though that we are still missing something fundamental to properly explain ice ages.
In a sense we might perhaps say that the calculation tells on a rough consistency of the calculated temperatures with those inferred from proxies, when other major factors are forced to their ice age values, rather than presents an explanation of ice ages.
That’s certainly better than nothing, but something very essential is still missing.
Clive,
In another paper we will see how insolation variation appears to have such a dominance, to the point I have emailed the lead author with a request to explain it.
This other paper is:
Investigating the evolution of major Northern Hemisphere ice sheets during the last glacial-interglacial cycle
Bonelli, S., S. Charbit, M. Kageyama, M. N. Woillez, G. Ramstein, C. Dumas, and A. Quiquet
Climate of the Past (2009)
A thought. As temperature decreases, could ocean current changes occur at some point that may increase nutrient up-welling, thus resulting in greatly increase ocean plant life over large areas, thus reducing atmospheric CO2?
John Phillips,
I used to think this was a simple question that I hadn’t got around to delving into. Now I believe it is one of the many major questions in understanding the ice ages. Due to its importance I am sure we will review the theories and evidence later in this series.
This paper published last August in Nature claims to have solved the mystery:
Insolation-driven 100,000-year glacial cycles and hysteresis of ice-sheet volume, Ayako Abe-Ouchi et al. doi:10.1038/nature12374 (Maureen Raymo is one of the authors). They have a model whereby as the N. American glacier grows it eventually extends too far south for it’s own stability and then rapidly collapses during the next warm spell caused by orbital forcing. Once melting starts it rapidly collapses within a few thousand years ending an ice age.
Shawn Marshall comments on the Abe-Ouchi et al paper in the News and views section of that same issue of Nature. He writes some positive comments:
but also presents some doubts:
My first reaction is that the density of ice is much lower than the density of the Earth crust and mantle. Thus added ice should always lead to a surface at higher altitude even after the deformation of the crust and mantle is complete. The dynamics may lead to an initial ice sheet that reaches a higher altitude than is maintained under later conditions, but many factors must cooperate to make this explanation work. In particular the gradual lowering of the surface under the pressure of ice must not be compensated by formation of more ice. The issues mentioned by Shawn Marshall in his last paragraph seem to be essential for deciding whether the overall picture of Abo-Ouchi et al may be correct.
Those without access to Nature can get some idea of the work from the video and supplementary information linked to at the bottom of this page.
In the quote from Marshall where he says “Moreover, Abe-Ouchi and colleagues’ findings do not explain the transition that took place 900,000 years ago, when the world moved from 41-kyr to 100-kyr glacial cycles” I wonder if a periodic or other forcing is needed to explain some of these variations or whether a dynamical argument in the spirit of A. M. Hogg, GRL, VOL. 35, L01701, doi:10.1029/2007GL032071, 2008, “Glacial cycles and carbon dioxide: A conceptual model”, suffices?
hypergeometric,
Interesting paper.
There are many other papers in this category, one I have seen most often cited is:
A sea ice climate switch mechanism for the 100-kyr glacial cycles, Gildor and Tziperman (2001)
Some others:
Stochastically driven climatic fluctuations in the sea ice, ocean temperature, CO2 feedback system, Saltzman (1982)
Discontinuous auto-oscillations of the ocean thermohaline circulation and internal variability of the climate system, Kagan, Maslova (1994)
Coherence resonance and ice ages, Jon D. Pelletier (2003)
Some interesting points arise from the paper you cited and others in the category:
1. It’s fairly easy to come up with systems that oscillate in and out of ice ages, with parameters that are in the bounds of reasonable physics/chemistry/biology.
2. In the cases like Hogg 2008 where the “initial perturbation/forcing” is orbital changes, given that we are talking of very small changes, why doesn’t the paper evaluate the impact of the much larger “naturally occurring” climate changes we see in the proxy record?
In Hogg 2008 the temperature variation induced by eccentricity is given as 0.1’C.
What happens when you impose a 1’C or 2’C century-long perturbation into one of these systems?
SoD,
I would say that it would depend a lot on where you were in the cycle and the direction of the perturbation. The Eemian interglacial, by the ice-core and sea level record, was warmer than this one at its peak. Yet it still didn’t last.
The interesting question is not how glacial periods start, it’s why interglacial periods don’t last very long compared to glacial periods. The sawtooth pattern of the temperature record strongly indicates, to me at least, that warm periods just aren’t stable with the current planetary geography. The fact that GCM’s don’t simulate this well is most likely a problem with the GCM’s and our understanding of how the climate of the planet actually functions.
DeWitt (and audience),
Just to clarify, the models of Hogg, Salzman and (unreferenced) by Walsh, McGehee, and Widiasih are *not* GCMs. They are much simpler models described as coupled *dynamical* *systems*. Their simplicity is intended to help convey understanding. While, technically, GCMs might also be dynamical systems, they are intended to be as faithful as possible to climatology, whereas these are intended to explore the space of possible climates and interrelationships among parameters. I exp[lain this because an audience may not be able to follow this from what’s been written here.
From the perspective of a dynamicist, “how the climate of a planet actually functions” is of less importance than understanding the space of *all* possible climate futures from a given starting point in a state space. Now, surely, this may help improve mechanics of GCMs at some point, because simplifications may be possible, but it is not the primary purpose of Hogg-like simple conceptual models.
The limiting factor in most GCMs is the resolution of the grid they are run upon. That relates directly to available computing power, and, while these are run on some very big systems, the biggest systems have not yet been applied to the problem. Limited applications of the biggest ones, typically those used for nuclear weapons engineering, to climate-related problems like melt-back of glacier calving surfaces has shown far better fidelity with observation than the coarser models. So there’s a lot of hope there.
hypergeometric,
OK, bad terminology. But I believe my point is still valid. The underlying assumption of whatever you call the models being used is that the warm interglacial climate is stable unless forced to cool somehow. I don’t see how you can look at the various proxy records for the last few million years or the Vostok ice core record and draw that conclusion. The current base state is glacial and has in the past always decayed back to that state after transient excursions into interglacial conditions. Since the separation of Antarctica from South America and more recently the closure of the Isthmus of Panama, the Atlantic Ocean has been acting as a giant heat pump cooling the planet. I sympathize with Ruddiman’s view that the only reason that the planet hasn’t already started to descend back into glacial conditions is that humans have altered the climate starting with the invention of large scale agriculture.
[Moderator’s note – requested edit carried out]
Should add, now that I have the “space”, that dynamicists explore state spaces for features like bifurcations which, while they are very interesting from an understanding an academic perspective, may well be more important from a practical perspective, given the dramatic changes in climate they imply. Still, this is a centuries-long process and, to the degree we can’t get policymakers or public to care much about 2100, worrying about 2200+, even if they should, seems to be just unhappiness incarnate.
Simpler models of the climate produce a number of bifurcations:
– Snowball earth vs ice free earth
– No MOC with typically colder higher latitude E. Atlantic temperatures vs an MOC with typically warmer equivalents
– Glacial inception vs none dependent on high latitude insolation
I’m sure there’s lots more – generally systems with positive feedbacks but some overall constraints do this quite easily. Another non-physics example, but relevant for climate, occurs in the biological space with predators vs predatee (invented word for the “to be eaten” species).
GCMs generally seem to have more trouble re-creating bifurcations that are found in simple systems and intermediate complexity systems (like EMICs), but it’s a subject I have little understanding of.
All kind of complex dynamics requires that there’s is not too much dissipation relative to other dynamical factors and memory at spatial and temporal scales relevant to the particular form of dynamics being considered. It’s conceivable that present GCMs have so much effective dissipation on the relevant scales that their dynamics becomes too much a combination of changes driven by forcings and noise.
That may be true both for the phenomena that control glacial cycles and for multidecadal oscillations. At the same time they may contain red noise that causes variability in the results at a level that may look superficially similar to the dynamics of the real Earth system, but based on totally wrong dynamic couplings.
@DeWittt, regarding “I sympathize with Ruddiman’s view that the only reason that the planet hasn’t already started to descend back into glacial conditions is that humans have altered the climate starting with the invention of large scale agriculture.” I also am sympathetic with that view, now having read Ruddiman and your comment, but I just don’t know where the line was crossed or where we otherwise would have dipped into a glacial without the forcing.
Ruddiman’s hypothesis led naturally to the immediate criticism that the human population was too low for supporting releases necessary for the hypothesis.
Joos has been studying carbon cycle for long and is also the first author of the review article used heavily by IPCC AR5. He was one of the early critics. Another interesting paper that considers Ruddiman’s hypothesis highly suspect at least for CO2 is a lengthier 2006 text by Olofsson.
Ruddiman has defended his work in this 2009 paper.. The discrepancies between the estimates are large. Intuitively it’s much easier for me to think that Ruddiman’s hypothesis cannot be correct, but I haven’t looked carefully on much of this material.
AR5 reports on the issue in Chapter 6.2.2.1 as follows (2 paragraphs out of 7):
Pekka,
My reading of Ruddiman is not that humans caused CO2 and CH4 to increase a lot, it’s that they should have decreased based on the pattern from previous interglacials, and in fact were decreasing from their peaks until ~6.000 ya. See Figure 6 here.
Also, Ruddiman does not include any possible contribution from slash and burn agriculture.
According to Ruddiman CO2 concentration increased by about 20 ppm over the 8000 years to 1800, but would have declined by almost 20 ppm without the human contribution. Joos estimated that such an effect would have required a release of about 700 GtC over that period taking into account the natural uptake over such a long period.
Olofsson concluded that the actual releases from land use changes have been less than 10% of the required amount, other estimates have indicated that the discrepancy would not necessarily be quite that large, but a common belief seems to be that the actual releases have been by a factor of three or more smaller than the hypothesis of Ruddiman implies. As my quote from AR5 tells
Kaplan et al give an estimate that could support Ruddiman – and Ruddiman is one of the coauthors of that study. if the uptake mechanisms are in addition weaker than the present best estimates indicate. AR5 and the 2013 background review paper by Joos et al tell about large remaining uncertainties in the uptake of CO2.
The paper of Kaplan et al is in a special issue (August 2011, Vol 21, No 5, Holocene Special Issue: The early-Anthropocene hypothesis Edited by: William F. Ruddiman, Michael C. Crucifix and Frank A. Oldfield) of the Journal The Holocene. Ruddiman is the first name on the list of editors of that issue.
The issue contains also the article: Ruddiman, Kutzbach and Vavrus: Can natural or anthropogenic explanations of late-Holocene CO2 and CH4 increases be falsified? The abstract of that article reads:
I haven’t looked in more detail in these articles, but my impression is still that most scientists are highly skeptic on Ruddiman’s hypothesis, while it cannot be definitely dismissed.
@Pekka,
Regarding “I haven’t looked in more detail in these articles, but my impression is still that most scientists are highly skeptic on Ruddiman’s hypothesis, while it cannot be definitely dismissed.”
Thanks Pekka!
[…] about dynamical systems modeling of climate, and stumbled across a train of papers. This train is apparently well-known. The two papers and presentation I’ll mention here […]
in light of the very recent paper on models vs. reality and clouds, could the glacial climates in reality make more clouds outside the ice sheet areas thus keeping the rest of the surface a bit warmer? the insolation is presumed to be constant so it would steepen the slope from tropics to the edge of the ice sheet, have I got this wrong somehow?
DeWitt,
This is an interesting hypothesis, and following it we might then expect that the local impact of lower insolation at higher latitudes to be a factor that can perturb the climate out of its unstable state into its stable state.
In which case, the even more difficult question becomes – how is it possible for local higher insolation at high latitudes to push the climate out of its very stable ice age state into an inter-glacial? And the additional factor to consider is the amount of solar radiation now reflected at the Last Glacial Maximum due to the large ice sheets covering significant land areas.
There are more questions related to the climate during the last ice age, and why the last ice age ended when it did, which we will see in future articles.
SoD,
Obviously there is another mechanism in play that is not being modeled. The Roe paper shows that the rate of ice volume change appears to track high latitude insolation changes except at glacial to interglacial transition. Then the reduction and following increase in ice volume is much larger than modeled by orbital changes alone.
The obvious suspect would be CO2. You need some sort of CO2 storage mechanism that becomes less stable over time so that it can be triggered to begin release by a relatively small change in temperature, such as a Dansgaard-Oeschger event, and amplify it. Once the reservoir empties, atmospheric CO2 removal starts again and the climate cools without having to invoke changes in insolation at high latitudes. That likely helps too, but it’s not at all clear that it’s the primary driver.
I haven’t done it, but I’ll be surprised if when you plot ice core CO2 and temperature vs time, you don’t find that CO2 changes much less with temperature during D-O events during the glacial period than at the glacial to interglacial transition.
Less likely, IMO, is some variation on Richard Muller’s orbital plane dust theory.
The Roe paper is interesting, the problem with assessing it is the low resolution graphic that compresses the last 100kyrs into something that is hard to make out – it looks like some critical periods are out of phase rather than in phase.
Also I don’t know how the HW04 series stacks up against recent developments in dating from EPICA.
In fact, assessing the graphs in most ice age papers is a problem. Jonathon Levine told me that was actually the reason he wrote his Matlab program – so he could produce his own high resolution time series and evaluate all these different theories, instead of trying to use a ruler on a small graphic that covers 800k years.
I have tried to reproduce the Roe result. The graph linked below shows my calculation of insolation at the North Pole plotted together with the rate of change of ice volume.
The correlation is not perfect
More summer insolation clearly melts more ice – That is the conclusion.
The cause of the 100,000y cycle remains unexplained. The problem with Muller’s theory is that the change in inclination of the Earth’s orbit to the solar ecliptic plane does not correlate with Ice Ages. However the change in ellipticity does correlate.
I came up with an explanation involving dust clouds – see: The real cause of Ice Ages
I then went looking for evidence in the TSI data but I couldn’t find significant evidence of significant dust clouds in the data. However I did find a beautiful lunar signal in the data. The moon-earth system orbits the C.of mass 4000km outside the Earth’s core. This causes a monthly oscillation in the Earth-Sun distance which shows up in TSI.
I have another question about the Roe paper, that’s been bubbling away at the back of my mind.
If the relationship is so strong between summer insolation 65’N and d(Ice volume)/dt then why in the proxy record does the 100kyr time period show up strongly (when analyzed in the frequency domain)?
This period doesn’t show up in the 65’N insolation.
Here was my plot, shown in Part Five – Obliquity & Precession Changes:
That’s an extremely good question. It’s exactly why Muller proposed his dust hypothesis. As I pointed out above, though, dV/dt α S_65N(t) appears to hold only between the major transitions. Something else is going on. Until we know what that is, it’s not at all clear that a reliable model can be constructed and that results from current models may not even be particularly instructive.
A sudden change in a quasi-periodic oscillation frequency can be expected from a system that exhibits long term persistence. The trigger in that case can be very small. It can, in fact, be just noise. However, it’s likely, IMO, that the dissipation built in to current climate models is so high that even if you could run them long enough, it’s not clear that they would exhibit spectral power distributions that in any way mimic the real climate at very low frequencies. And that’s not to mention the massively different time scales of the oceans and the atmosphere.
I’m afraid that, like string theory in Physics, a lot of people are spending a lot of time and money on a dead end.
Clive,
What data source did you use for ice volume?
Sorry – the source is LR04 – 57 globally distributed benthic d18O records.
Looking at the curves of Clive it’s difficult to avoid concluding that:
1) Strong enough insolation at North Pole leads always to a large drop in ice volume (unless the volume is already near the minimum level).
2) The ice volume grows steadily in absence of high insolation maximum up to a level where a weaker insolation maximum is enough to turn the volume to rapid decline. In this case some kind of state shift is involved as the decline from a high value ends always with a very low volume.
3) The lowest ice volumes are reached only after a high maximum volume. When the volume starts to grow from such a minimum the rate of growth is initially very high. Thus both highest and lowest volumes lead to a state shift that ends them. We might have three states:
– gradual growth of volume intercepted by high insolation maximums without a shift out of this state
– rapid decline of volume from very high to very low
– initial rapid growth of volume after the deep minimum.
clivebest,
Minor point in your dust cloud post, it’s 3He that’s correlated with cosmic dust. 3H (not H3) or tritium isn’t stable and has a half life of 12.32 years.
I don’t think you can use δ18O from benthic foraminifera directly as a proxy for ice volume either. See this reference, for example.
DeWitt Payne,
Thanks – Yes indeed it should have been 3He – sorry for typo.
I agree that δ18O are not a direct proxy for ice volume but they display the same dependence as ice core from Greenland and Antarctica. They also demonstrate that glacial cycles were global effects.
Clive,
So that data is from A Pliocene-Pleistocene stack of 57 globally distributed benthic D18O records, Lorraine E. Lisiecki & Maureen E. Raymo (2005)?
This paper’s approach, along with most proxy dating, doesn’t have a dating mechanism, so takes as the best starting point that the peaks and troughs are aligned with insolation curves from high latitude. That said, I’m still at a beginner stage with the historical records so there might be some independent verification that makes it justified.
I just downloaded the NGRIP data which is ice core data from N. Greenland and goes back to 123kyrs – the advantage there is that firstly individual years can be seen in the record, secondly when individual years can no longer be detected the length vs age relationship is constrained by some fairly basic physics.
Here is an extract from the paper cited above for the benthic records:
I’m sure it’s clear why the coherence of the insolation at high latitudes with a proxy record tuned to the insolation at high latitudes will be higher than the coherence of insolation with the actual record.
So it’s necessary to get a better understanding of the data sources, how they were constructed and the assumptions both in dating and in the relationship of the measured parameter (the ‘proxy’) to the actual data.
Yes I am using the 57 stack of δ180 cores.
I agree that the Ice core data has much more accurate and detailed time measurements.
Despite this The LR04 data has two other big advantages :
1. They show that the effects are global and not concentrated at the poles
2. Even if the absolute time scale is calibrated against the ice core it thyen extends backwards 5 million years. This is how we know 100,000y glaciations only began 800,000y ago. Why?
The method for establishing the age of the core is described in section 5.2 of the paper. There is an equation:
Rate of change of ice volume = [constant for nonlinearity] * ([insolation at 65’N on 21 June] – ice volume) / time constant
The method finds the best result for age vs depth from that equation with penalties the more the age vs depth departs from linearity.
The “constants” are moved around as well in an ad hoc process:
I’m not claiming that this isn’t all justified, there is a substantial body of work, described in papers from the 1980s through to today to get to grips with.
But it does help to understand that a set of cores which are best fit to rate of change of ice volume will correspond pretty well to rate of change of ice volume.
This experimental calibration is interesting. I bet it could be updated, and updated in a way which would seem less ad hod.
SoD,
If the variation in δ180 were random and not correlated in any way with insolation changes at high latitudes, no rational amount of tuning would give coherency over 5.3My. And sedimentation rate would seem to be a fairly strong constraint.
I suspect that you will find that there has been at least some unstable isotope ratio dating of the cores as well. The U/Th ratio has been used for dating corals and foraminifera.
It would be interesting to see if anyone has done testing with random time series to see if, like hockey sticks from red noise, you can find a signal that isn’t actually there.
DeWitt,
That’s not my hypothesis – no correlation of ice volume with insolation at high latitudes.
I think I agree with your statement above.
Anyway, I don’t have a specific hypothesis, more a number of them that need to be evaluated. One hypothesis is that insolation at high latitudes affects ice sheet growth but doesn’t start and end an ice age. If that hypothesis is true, then the ‘orbital tuning’ of proxy dates will obscure the actual causes and effects. A similar but different hypothesis is that low insolation at high latitudes starts an ice age and something completely different ends it.
Verified dating of key transitions, not derived from assuming the Milankovitch hypothesis, is a necessary piece of the jigsaw puzzle.
There is a fascinating paper to review soon which seems to so totally miss the point with the end of the last ice age and the orbital hypothesis that I wonder if I have missed something completely obvious. And there is another paper with a different approach that I am similarly amazed about. Once again I start to wonder if I am missing something so clear that was spelt out 30 years ago and of course no one bothers to repeat it. Then I remember that almost every paper I look at has a review which says that the actual cause and effect are still a bit of a mystery..
More on this soon.
Clive,
The two reasons you give for using the benthic aggregated data are completely valid.
All of the available data needs to be used and your question is an important one.
(Sorry … on “ad ho_”. My Kindle spelling corrector apparently doesn’t like Latin.)
@DeWitt, I need more details before I have any idea of what I’m doing, but I was thinking of using a Bayesian approach, so the complete characterization of uncertainty should be captured in the posterior for each variable.
For people interested, I downloaded the Excel file for NGRIP.
The related paper is High-resolution record of Northern Hemisphere climate extending into the last interglacial period, North Greenland Ice Core Project members, Nature (2004). The file is in the Supplemental Info for the paper on the Nature website.
Because WordPress doesn’t support “attached media” as Excel, I had to change the extension to .doc, so download, change it to a .xls and it should open fine in Excel.
NGRIP ice-core data
The website noted in the Excel file for updates didn’t work when I tried it, so I emailed the person listed in the file (one of the co-authors of the paper) in case there is any updated data.
Abstract for the paper:
thank you SoD, I looked for the Toba eruption from the Antarctica sulfate record some time ago, the spike was so large it muddled the record for a while. Here it could be partly responsible for the first deep plunge in the deepest glacial climate. I don’t easily get why there’s a spike after that, One could assume that it took 10000 years (70000-60000) to planet to get a grip after that, but this would mean the dating of the supereruption is a bit off. Then there’s the Oruanui eruption (commonly dated to 26500bp after which there’s also a gap in the spikes. Probably this is all well known in the geologist circles so I won’t speculate more.
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