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)
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].
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]
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.
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:
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.
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.
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
The last glacial cycle: transient simulations with an AOGCM, Smith & Gregory, Climate Dynamics (2012)
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.