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ortionately cooled by aerosols relative to the sh these are the most directly relevant and yet using the simple method in these cases produces underestimates of 6 and 2 which would hardly change the results of a paper like otto et al 2013 on the other hand the shindell 2014 correction causes overestimates of 40 and 34 e g a shift from a 1 3k most likely value from otto to the 1 7k reported by s14 if we look at the model that the simple method largely underestimates gfdl cm3 we see that the 0 84 nh sh in that model is the farthest away from the one we ve observed in the real world suggesting it is likely the least relevant in fact i would argue that the amplification of the nh sh ratio in the historicalghg simulation relative to the historical simulation for a model could be used to better estimate the tcr bias calculated using that model this is because the difference in the nh sh ratio in the historicalghg simulation and that of the historical simulation implicitly combines the actual aerosol forcing and the enhancement of this forcing rather than trying to estimate these highly uncertain values separately which is even more directly relevant to the degree of tcr bias indeed if we look here there appears to be excellent correlation impressive eh now i will mention that i believe things to be less pretty than the r 2 0 92 value shown above this is primarily because the 3 models bundled in shindell et al 2014 actually have very different rates of global warming as well as the nh sh ratios so i m not sure it makes sense to bundle them but have done so here for consistency with s14 one problem with using my method here and applying it to the real world is that we don t know what the nh sh warming ratio would be for the real world in the ghg only scenario however given the high value of 1 48 observed for the real world all forcing case i suspect that the difference between this ratio and the ghg only scenario can t be that large unless models have seriously underestimated the historicalghg warming ratios moreover i would argue that the value is likely to be better constrained from models than the value of e which depends on the much more uncertain aerosol properties regardless the average model warming ratio for nh sh for the historicalghg simulations in the above models is 1 54 if you plug in the observed value of 1 48 for the historical observed nh sh ratio in the real world and use the linear regression from above the estimated tcr bias amplification factor is 0 96 this would suggest using the simple method slightly overestimates tcr but by an extremely small fraction conclusion while shindell 2014 uses several gcm results to argue that traditional methods to calculate tcr lead to an underestimate testing these methods against the outputs of those same gcms seems to suggest the simple e 1 0 methods perform better than the s14 corrected method furthermore when we consider the actual observed nh sh warming ratio it also seems to suggest that the tcr bias in the traditional simple method is either very small or non existent data and code comments 18 march 11 2014 how sensitive are the otto et al tcr and ecs estimates to the temperature and ocean heat datasets filed under uncategorized troyca 10 59 pm there seems to have been some interest in the sensitivity of otto et al 2013 lately for instance see piers forster s comments about hadcrut4 in otto or trenberth and fasullo 2014 which notes that using oras 4 for the ohc dataset raises the estimate of ecs from 2 0 to 2 5 k anyhow i ran a few of the tests and thought i would share the results otto et al 2013 uses five different intervals over which the differences of temperature forcing and toa imbalance for the ecs estimate are calculated 1 2000s base 2 1990s base 3 1980s base 4 1970s base and 5 the 40 year interval from 1970 2009 the base period used is 1860 1879 here are the results when using the best or cowtan and way 2013 global temperature datasets which infill under sampled regions to get more global coverage to calculate tcr alongside hadcrut4 δf w m 2 hadcrut4 δt k hadcrut4 tcr k cw13 δt k cw13 tcr k best δt k best tcr k 2000s base 1 94 0 75 1 33 0 81 1 44 0 81 1 44 1990s base 1 25 0 57 1 57 0 6 1 65 0 6 1 65 1980s base 0 99 0 39 1 36 0 43 1 49 0 44 1 53 1970s base 0 75 0 22 1 01 0 26 1 19 0 27 1 24 2009 1970 1 28 0 53 1 42 0 56 1 51 0 54 1 45 in most situations it appears that the difference is relatively small adding 0 1k of tcr when using either cowtan and way 2013 or best global temperatures for ecs i used cowtan and way 2013 for the temperature dataset and three different ocean heat content datasets 1 lyman and johnson 2014 2 oras 4 the reanalysis product introduced in balmaseda et al 2013 and 3 levitus et al 2012 for 1 and 2 i digitized the values from their respective papers since the calculation of ecs is slightly more complicated i have included additional steps along the way ohu represents the rate of ocean heat uptake as calculated over the period from the observed ocean heat content and δq represents the change in toa imbalance over the interval i assume 90 of the toa imbalance goes into the ocean and per otto et al that the imbalance of the base period was 0 08 w m 2 i also include a most recent period calculation which occurs if we measure the ohc after the bulk of the argo deployment took place for lj14 they present this value from 2004 2011 for l12 they don t present annual estimates until 2005 so i ve used 2005 2013 for b13 i simply used 2004 2010 here are the results δf w m 2 cw13 δt k lj14 ohu w m 2 lj14 δq w m 2 lj14 ecs k b13 ohu w m 2 b13 δq w m 2 b13 ecs k l12 ohu w m 2 l12 δq w m 2 l12 ecs k 2000s base 1 94 0 81 0 50 0 48 1 90 0 85 0 86 2 59 0 48 0 45 1 87 1990s base 1 25 0 60 0 25 0 20 1 96 0 22 0 32 1 31 0 25 0 20 1 96 1980s base 0 99 0 43 0 26 0 37 1 09 0 49 0 46 2 81 0 13 0 06 1 60 1970s base 0 75 0 26 0 32 0 28 1 89 0 16 0 10 1 37 0 49 0 46 3 13 2009 1970 1 28 0 56 0 20 1 78 0 77 3 75 0 01 1 49 post_argo 2004 or 2005 base 1 94 0 85 0 29 0 24 1 72 0 51 0 49 2 01 0 54 0 52 2 06 as you can see the bulk of the estimates still seem to suggest an ecs 2k in line with the otto et al 2013 calculations for lj14 the results are actually pretty tightly constrained apart from the low ball 1980s estimate between 1 7 and 2 0 k using oras 4 b13 does increase the ecs estimate when using the 2000s only but it also largely decreases when using the 1990s or 1970s when using only post argo data it is pretty much in line with the others suggesting an ecs 2k l12 is also fairly tightly constrained apart from the highball estimate of the 1970s overall using oras 4 produces the largest error margins and interval specific sensitivity however it is worth noting that using the post argo ohc data seems to largely remove the dependence on which ohc dataset is chosen producing best estimates between 1 7 and 2 1 for ecs data for this post available here comments 2 february 28 2014 initial thoughts on the schmidt et al commentary in nature filed under uncategorized troyca 7 20 pm thanks to commenter gpiton who on my last post about attributing the pause alerted me to the commentary by schmidt et al 2014 in nature hereafter sst14 in the commentary the authors attempt to see how the properties of the cmip5 ensemble and mean might change if updated forcings were used by running a simpler model and find that the results are closer to the observed temperatures thus primarily attributing the temperature trend discrepancy to incorrect forcings overall i don t think there is anything wrong with the general approach as i did something very similar in my last post however i do think that some of the assumptions about the cooler forcings in the correction are more favorable to the models than others might choose and a conclusion could easily be misinterpreted this is my list of comments that were i a reviewer i would submit comment 1 the sentence we see no indication however that transient climate response is systematically overestimated in the cmip5 climate models as has been speculated or that decadal variability across the ensemble of models is systematically underestimated although at least some individual models probably fall short in this respect is vague enough to perhaps be technically true while at the same time giving the incorrect imo impression they have found that models are correctly simulating tcr or decadal variability it may be technically true in that they find no indication of bias in tcr or internal variability due to the residual prediction uncertainty but this is one of those absence of evidence is not evidence of absence scenarios where even if the models were biased high in their tcr there would be no indication by this definition by the description in the commentary the adjustments only remove about 60 65 of the discrepancy the rest of the discrepancy may be related to non enso noise but it also may be related to tcr bias and would be what we expect to see if for example the true tcr was 1 3k as in otto et al 2013 vs the cmip5 mean of 1 8k obviously the reference to otto et al 2013 might be mistaken by some to suggest an answer refutation to that study which used a longer period to diagnose tcr in order to reduce the noise but clearly this would be wrong had i been a reviewer i would have suggested changing the wording the residual discrepancy may be consistent with an overestimate of the transient climate response in cmip5 models otto et al 2013 or an underestimate of decadal variability but it is also consistent with internal noise unrelated to enso and we thus cannot neither rule out nor confirm any of the explanations in this analysis certainly has a different feel but it essentially communicates the same information and is much less likely to be misinterpreted by the reader comment 2 regarding the overall picture of updated forcings it is worth pointing out that ipcc ar5 chapter 9 box 9 2 p 770 describes a largely different opinion erf effective radiative forcing for the periods 1984 1998 and 1951 2011 the cmip5 ensemble mean erf trend deviates from the ar5 best estimate erf trend by only 0 01 w m 2 per decade box 9 2 figure 1e f after 1998 however some contributions to a decreasing erf trend are missing in the cmip5 models such as the increasing stratospheric aerosol loading after 2000 and the unusually low solar minimum in 2009 nonetheless over 1998 2011 the cmip5 ensemble mean erf trend is lower than the ar5 best estimate erf trend by 0 03 w m 2 per decade box 9 2 figure 1d furthermore global mean aod in the cmip5 models shows little trend over 1998 2012 similar to the observations figure 9 29 although the forcing uncertainties are substantial there are no apparent incorrect or missing global mean forcings in the cmip5 models over the last 15 years that could explain the model observations difference during the warming hiatus my emphasis essentially the authors of this chapter find a discrepancy of 1 5 0 03 0 045 w m 2 over the hiatus whereas sst14 use a discrepancy of around 0 3 w m 2 which is nearly 7 times larger and there does not appear to have been new revelations about these forcings since the contributions to the report were locked down the report references the increasing stratospheric aerosol loading after 2000 and unusually low solar minimum in 2009 mentioned in the commentary regarding the anthropogenic aerosols both the shindell et al 2013 and bellouin et al 2011 papers referenced by sst14 for the nitrate and indirect aerosol estimates are also referenced in that ar5 chapter and shindell was a contributing author to the chapter this is not to say that the ipcc is necessarily right in this matter but it does suggest that not everyone agrees with the magnitude of the forcing difference used in sst14 comment 3 regarding the solar forcing update per box 1 sst14 note we multiplied the difference in total solar irradiance forcing by an estimated factor of 2 based on a preliminary analysis of solar only transient simulations to account for the increased response over a basic energy balance calculation when whole atmosphere chemistry mechanisms are included i would certainly want to see more justification for doubling the solar forcing discrepancy this choice alone accounts for about 15 of the 1998 2012 forcing discrepancy used if i understand correctly they are saying that they found that the transient response to the solar forcing is approximately double the response to other forcings in their preliminary analysis but this higher sensitivity to a solar forcing would seem to be an interesting result in its own right and i would want to know more about this analysis and what simulations were used was this observed in one some most or all of the cmip5 models after all if adjusting the cmip5 model mean it would be important to know that this was a general property shared across most of the cmip5 models comment 4 for anthropogenic tropospheric aerosols two adjustments are made one for the nitrate aerosol forcing and the second for the aerosol indirect effect sst14 notes that only two models include the nitrate aerosol forcing whereas half contain the aerosol indirect effect and so the ensemble and mean are adjusted for these but it is not clear to me if the individual runs for each of the cmip5 models are adjusted and thereby no adjustments are made to runs from models that include the effect and the mean recalculated or if simply the mean is adjusted the line if the ensemble mean were adjusted using results from a simple impulse response model with our updated information on external drivers makes me think the latter but if it is this latter case clearly this is incorrect if half of the models already include the effect you would be overcorrecting by a factor of 2 if you adjusted the mean by this full amount perhaps the 0 06 is halved before the adjustment is actually made but it is not specified comment 5 regarding the indirect aerosol forcing belloin et al 2011 is used as the reference which uses the hadgem2 es model it is worth noting the caveats the first indirect forcing in hadgem2 es which can be diagnosed to the first order as the difference between total and direct forcing jones et al 2001 might overestimate that effect by considering aerosols as externally mixed whereas aerosols are internally mixed to some extent by comparing with satellite retrievals quaas et al 2009 suggest that hadgem2 is among the climate models that overestimate the increase in cloud droplet number concentration with aerosol optical depth and would therefore simulate too strong a first indirect effect moreover i am not quite sure the origin of the 0 06 w m 2 belloin et al 2011 suggest an indirect effect from nitrates that is 40 the strength of the direct effect so if only nitrate aerosols increased over the post 2000 period i would expect an indirect effect of 0 01 w m 2 it seems to me that this must include the much larger effect of sulfate aerosols which le...
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