First off, it’s important to note that the paper has only appeared in CPD, it still has to pass review. However, I’m going to comment on the results for two reasons. Mundanely, I have a sliver of free time now, and I don’t know that the same will be true after the paper’s (presumed) eventual publication. More importantly, however, I think it’s safe to say that its results will be misinterpreted to the same or even a greater extent than Schmittner et al., 2011 (hereafter S11) was. The mainstream press largely ignored some potential reasons to be skeptical of that paper’s results (discussed by RealClimate and Skeptical Science among others, as well as by one of the paper’s authors in an interview with me at Planet 3.0). And of course the denialist echo chamber distorted the results ludicrously, going so far as to erase an entire portion demonstrating them to be consistent with the larger body of evidence on climate sensitivity (e.g. Knutti and Hegerl, 2008) and inconvenient to dismissals of the danger posed by unchecked GHG emissions.
With the throat clearing out of the way, here’s how things stand. Fyke and Eby (2012) offered some criticisms of S11. They objected to some of the proxy data used, and more importantly, pointed out that the model used (a version of the UVic model, which is more akin to simplified EMICs than GCMs) simply couldn’t produce realistic behaviors of key atmospheric processes which caused it to underestimate ECS:
[T]o explore the potentially large dependence of Schmittner et al.’s results on the choice of climate model, we carried out a new model simulation with the most recent version of the UVic ESCM in which the atmospheric latitudinal profile of heat diffusion varies in response to the global average atmospheric temperature anomaly (the “Mod” simulation in Fig. 2). This functionality gives a new model with much improved fit to both Antarctic and Arctic LGM temperatures as recorded by ice cores, yet still retains an excellent fit to low-latitude temperatures. Notably, and most importantly, we found that this model ranks very well with respect to the relative RMSE test, but with a much higher ECS (3.6°C) than similarly ranked models in (1). As suggested in (1), the lack of dust forcing in our LGM model may lower the equivalent ECS by ~0.3°C, but this is still well above the median ECS estimate of 2.3°C in (1).
Fyke and Eby’s revised LGM-derived ECS was quite similar to other LGM-based studies, such as Holden, et al. (2010). Criticism that the UVic model used had an atmospheric component that was perhaps insufficient to fully capture the climate state at the LGM was echoed in the RealClimate discussion as well as by coauthor Nate Urban in our interview.
Schmittner, et al. (2012) responded to Fyke and Eby by largely disagreeing with their discarding of some proxy records, but conceding that their model choice may well have led to underestimating ECS and uncertainty in their reconstruction:
This tentatively supports the conclusion in (1) that structural model uncertainties (in particular, formulations of atmospheric heat transport) may have led to systematic underestimation of ECS2xC in (2). Further study with new ensemble model experiments, including the modified heat flux formulation and LGM dust forcing, are necessary to quantify the effect of heat flux uncertainties on the best ECS2xC estimate.
Schmittner, et al. go on to suggest that further modeling be done to try to better test the effects of using more realistic models with their approach.
Several groups are doing that, or something very similar. One is Tamsin Edwards, who has teased her experiment but not revealed its results (yet). Another is Jules Hargreaves and James Annan, who discussed S11 and also teased their experiment some months back but likewise did not discuss their results.
Which brings us to today (or, technically, Wednesday). Annan and Hargreaves, 2012 (hereafter AH12) has been submitted to Climate of the Past – Discussion, and their results are now available. They used almost exactly the same proxy data as S11, but used a different model (in fact, an ensemble of the GCMs used in the PMIP2 project) and methodology to constrain the difference in climate between the present and the LGM. Their results share some similarities to S11 but also contain some differences.
AH12 use pseudo-proxy data to validate their reconstruction. Their fit to the proxy data is improved relative to S11 (correlation of 0.73 vs S11’s 0.53).
One of the criticisms of S11 was that it found an LGM globally-averaged surface temperature that seemed awfully warm (areas where proxy data were available averaged a mere~2°C colder than more modern temperatures) relative to other estimates, which show an LGM nearly three times that cold (e.g. von Deimling et al., 2006). This warmer LGM was necessarily responsible for much of the difference in their ECS value vs. the “canonical” estimate of 3°C. The authors attributed much of this difference to the use of warmer MARGO SST data vs. older (and cooler) data, but that explanation might appear somewhat insufficient, as the PMIP2 models that best fit the MARGO data themselves had ECS estimates closer to 3°C (Otto-Bliesner et al., 2009). Another odd result of S11 was the large discrepancy between their land only and ocean only results.
AH12 find an overall cooling at the LGM of ~4°C. Their land only and ocean only data are somewhat different, but are much closer than S11’s and are consistent within their uncertainties:
In some ways, this represents a validation of S11: it’s certainly warmer than previous estimates, and the warm SSTs do arise from the MARGO data rather than some problem with S11. In other ways, however, it’s a contradiction of S11 and a validation of consensus estimates: the IPCC AR4’s estimate for LGM cooling was 4-7°C, consistent with AH12 but not S11.
AH12’s LGM-derived ECS is where I anticipate the greatest amount of well-meaning misunderstanding as well as outright misrepresentation. Why? Because it’s low: 1.7°C (1.2-2.4°C).
One of the criticisms of S11 I raised with Nate Urban in our interview was the problem of the asymmetry of climate sensitivity during different climatic states- i.e. climate sensitivity itself may be smaller at colder times than it is during warmer times. So hypothetically a perfect estimate of equilibrium sensitivity derived from data from the LGM might be significantly lower than a perfect estimate of ECS in a doubled-CO2 future due to the non-linearity of certain feedacks. While this asymmetry is by no means an unquestionably real phenomenon, there are some very good reasons to suspect it to be true (e.g. Crucifix, 2006; Hargreaves et al., 2007; Yoshimori et al., 2011). In fact, the authors of the MARGO SST data used by S11 themselves go out of their way to warn against mistaking an LGM-derived ECS as being comparable to 2xCO2 ECS for precisely this reason (Waelbroeck et al., 2009).
AH12 note this explicitly:
However, such a simplistic estimate is far from robust, as it ignores any asymmetry or nonlinearity which is thought to exist in the response to different forcings… The ratio between temperature anomalies obtained under LGM and doubled CO2 conditions found in previous modelling studies varies from 1.3… to over 2…
Therefore, a more apples-to-apples comparison (taking into consideration the asymmetry issue) of their findings to a doubling of CO2 might look more like 2.8°C, with a range of 1.56-4.8°C.
[All I’ve done is apply the average of asymmetry values (1.3-2) cited by AH12 to their central value of 1.7°C, while applying the low and high end asymmetry values to their lower and upper 95% CI values respectively. This is obviously meant to be illustrative of the difference taking asymmetry into account makes for 2xCO2 vs. LGM values rather than a rigorous quantitative exploration.]
This puts the 2xCO2 ECS inline with consensus estimates such as the IPCC AR4 GCM-only estimate of 3±1.5°C. Interestingly, some of the S11 authors, using the same UVic model but with instrumental rather than LGM paleo data, found broadly similar results for ECS (Olson et al., 2012).
I’m not claiming to show what AH12 “really” says about ECS, but rather making a general point that often gets overlooked in discussions of ECS estimates derived from colder climates. And it’s certainly possible that my not-even-back-of-the-envelope extrapolation of their LGM ECS into a 2xCO2 ECS is horribly misguided for some reason that I am as of yet unaware- but I’ve inquired, and will dutifully revise this post if there is.
More than anything, this is a place-marker in the event that the typical denialist spin cranks up as it has over papers in the past.
- Annan, J. D., and J. C. Hargreaves (2012), A new global reconstruction of temperature changes at the Last Glacial Maximum, Climate of the Past Discussions, 8(5), 5029–5051, doi:10.5194/cpd-8-5029-2012.
Clark, P. U., A. S. Dyke, J. D. Shakun, A. E. Carlson, J. Clark, B. Wohlfarth, J. X. Mitrovica, S. W. Hostetler, and A. M. McCabe (2009), The Last Glacial Maximum, Science, 325(5941), 710–714, doi:10.1126/science.1172873.
- Crucifix, M. (2006), Does the Last Glacial Maximum constrain climate sensitivity?, Geophys. Res. Lett., 33(18), L18701, doi:10.1029/2006GL027137.
Fyke, J., and M. Eby (2012), Comment on “Climate Sensitivity Estimated from Temperature Reconstructions of the Last Glacial Maximum,” Science, 337(6100), 1294–1294, doi:10.1126/science.1221371.
- Hargreaves, J. C., A. Abe-Ouchi, and J. D. Annan (2007), Linking glacial and future climates through an ensemble of GCM simulations, Clim. Past, 3(1), 77–87, doi:10.5194/cp-3-77-2007.
- Holden, P., N. Edwards, K. Oliver, T. Lenton, and R. Wilkinson (2010), A probabilistic calibration of climate sensitivity and terrestrial carbon change in GENIE-1, Climate Dynamics, 35(5), 785–806, doi:10.1007/s00382-009-0630-8.
Knutti, R., and G. C. Hegerl (2008), The equilibrium sensitivity of the Earth’s temperature to radiation changes, Nature Geoscience, 1(11), 735–743, doi:10.1038/ngeo337.
- Olson, R., R. Sriver, M. Goes, N. M. Urban, H. D. Matthews, M. Haran, and K. Keller (2012), A climate sensitivity estimate using Bayesian fusion of instrumental observations and an Earth System model, J. Geophys. Res., 117(D4), D04103, doi:10.1029/2011JD016620.
- Otto-Bliesner, B. et al. (2009), A comparison of PMIP2 model simulations and the MARGO proxy reconstruction for tropical sea surface temperatures at last glacial maximum, Climate Dynamics, 32(6), 799–815, doi:10.1007/s00382-008-0509-0.
- Schmittner, A., N. M. Urban, J. D. Shakun, N. M. Mahowald, P. U. Clark, P. J. Bartlein, A. C. Mix, and A. Rosell-Melé (2011), Climate Sensitivity Estimated from Temperature Reconstructions of the Last Glacial Maximum, Science, 334(6061), 1385–1388, doi:10.1126/science.1203513.
- Schmittner, A., N. M. Urban, J. D. Shakun, N. M. Mahowald, P. U. Clark, P. J. Bartlein, A. C. Mix, and A. Rosell-Melé (2012), Response to Comment on “Climate Sensitivity Estimated from Temperature Reconstructions of the Last Glacial Maximum,” Science, 337(6100), 1294–1294, doi:10.1126/science.1221634.
- von Deimling, T. S., A. Ganopolski, H. Held, and S. Rahmstorf (2006), How cold was the Last Glacial Maximum?, Geophys. Res. Lett., 33(14), L14709, doi:10.1029/2006GL026484.
- Waelbroeck, C. et al. (2009), Constraints on the magnitude and patterns of ocean cooling at the Last Glacial Maximum, Nature Geoscience, 2(2), 127–132, doi:10.1038/ngeo411.
- Yoshimori, M., J. C. Hargreaves, J. D. Annan, T. Yokohata, and A. Abe-Ouchi (2011), Dependency of Feedbacks on Forcing and Climate State in Physics Parameter Ensembles, Journal of Climate, 24(24), 6440–6455, doi:10.1175/2011JCLI3954.1.