Eisenman et al examine the 40W/m^2 spread in downwelling long wave (or infra-red) in a large sample of models.
Figure 1 from Eisenman et al. Cloudiness and downwelling longwave in GCMs.
Jakobsson et al find that the change in July 65degN insolation that lead to near ice free conditions in the Arctic around 6 to 8k years ago was around 40W/m^2. This gives an indication of the significance of the 40W/m^2 spread of IR radiation in GCMs, especially when you consider that July insolation isn't being applied year round, as the spread in IR is.
Using two mathematical models, less complex than GCMs, Eisenman et al find that this spread in IR implies a substantial range in equilibrium sea ice thickness. They note that most of the ice in the North American sector (off Greenland and the CAA) has a residence time of at least 5 to 10 years, this was based on research in 2004. I'm confident it is much less now, from Maslanik's work, probably a bit over 4 years. This long residence time gave the ice ample time to reach equilibrium thickness of an average of about 3m. This is borne out by PIOMAS gridded thickness data. The reduced complexity model runs from Eisenman et al find that the IR spread implies a spread of equilibrium thickness from 1 to 10m, far in excess of the observed spread of equilibrium thickness.
So what do the modellers do about this problem? As Eisenman et al state: "A frequently used approach in GCM sea ice components is to tune the parameters associated with the ice surface albedo." Tuning is a common feature of all complex models where the problem being studied has patchy data with which to analyse it. Patchy data can imply that some aspects of the model physics are not properly constrained by observations. So adjusting internal parameters to make the overall model output fit more closely with a parameter that is a result of the model is done where those internal parameters are not closely constrained by observations. I should stress here that the 'result' of the model I'm talking about should be chosen such that it depends on a wide spread of model parameters including the ones not properly constrained. PIOMAS has been tuned in a similar manner, with model output being matched against a subset of the DRA submarine data, a subset that is not then included in subsequent validation intercomparisons.
Eisenman et al find that in many models the adjustment used is to adjust surface albedo. In my previous post I've covered research into the substantial albedo impacts of the transition to a largely young ice pack, and what this means for energy gain. The adjustments in albedo that Eisenman et al discuss are small, less than 10%. Is there an equally large spread of downwelling long wave (DLW) in CMIP5? I don't know, I've been unable to find anything on this in the literature.
Eisenman's main point is that the projections made by models need to be considered with care. This doesn't mean that the models are of no use, as I've discussed several times before, models show behaviour of the atmosphere that plays a role in recent ice loss. Furthermore they're crucial for diagnosis and attribution of AGW and its consequent impacts, even within the Arctic where their performance with regards sea ice is arguably poor.
More interesting to me is the implication of DLW and ice thickness. Eisenman use two basic models of less complexity than GCMs, but their work also implies that GCMs show the relationship between DLW and sea ice thickness. This implies that while other factors, such as changes in ice export through the Fram Strait may have contributed to thinning, increased DLW would be continually driving the equilibrium thickness downwards. Eisenman et al note that the changes of DLW involved could be too small to detect using current instruments. Need I mention Bitz & Roe again?
CO2 increases drive a continual increase in the amount of downwelling IR. However it is not as simple as pointing the finger at CO2 and letting the matter rest there. Francis & Hunter 2006 find that changes in DLW play a significant role in ice loss during spring. The authors state in the opening of the paper.
Limited data have hampered attempts to identify which culprits are to blame [for sea ice loss], but new satellite-derived information provides insight into the drivers of change. A clear message emerges. The location of the summer ice edge is strongly correlated to variability in longwave (infrared) energy emitted by the atmosphere (downward longwave flux; DLF), particularly during the most recent decade when losses have been most rapid. Increasing DLF, in turn, appears to be driven by more clouds and water vapor in spring over the Arctic.They find that anomalies in surface insolation are negatively correlated with ice retreat, meaning that variation in insolation isn't the driver of the trend in ice area loss. However DLW is consistently positively correlated, supporting the idea that it plays a role in the trend of ice loss. I had thought before when reading Francis & Hunter that perhaps cause & effect were getting mixed up. They show significant contribution of DLW from around mid June (the earliest period considered) to the sea ice minimum over much of the Arctic, so maybe the correlation is due to open water venting more water vapour and warming the atmosphere. However upon re-reading it's occurred to me that I was thinking with a post 2007 mindset, over regions considered there was much more ice cover, especially towards June. In any case, just because more water vapour is a consequence of reduced sea ice, that doesn't mean sea ice can't be further affected by the resultant increase in DLW, as Eisenman et al shows.
Francis & Hunter also note that:
Increased temperatures, cloud amount, and water vapor—consistent with global-scale anthropogenic effects—enhance the atmosphere’s infrared emission, which reduces the thickness as well as the extent of the thinner ice cover.So the increase in DLW is mainly due to water vapour feedback on temperature and increased influx of more humid sub-arctic air. It remains at the global scale that CO2 has its small but persistent effect. But CO2 driven AGW remains the base driver for the loss of sea ice in the Arctic, see here, whatever intermediate processes come to play.
PS. While looking for CMIP5 work on DLW I came across this:
Notz D. , null : " Sea-ice extent provides a limited metric of model performance" , Journal of Geophysical Research , SUBMITTEDAs it's only submitted, I can't get access, but from the title, and having read the author's previous work, it looks like it has the potential to be very interesting indeed.
Eisenman et al, 2007, "On the reliability of simulated Arctic sea ice in global climate models."
Francis & Hunter, 2007, "New Insight Into the Disappearing Arctic Sea Ice."
Jakobsson et al, 2011, "New insights on Arctic Quaternary climate variability from palaeo-records and numerical modelling"