tag:blogger.com,1999:blog-8216971263350849959.post4965383881725832117..comments2023-06-03T08:31:31.097+02:00Comments on Die Klimazwiebel: Hans von Storch and Eduardo Zorita: on our paper on stagnation and trendseduardohttp://www.blogger.com/profile/17725131974182980651noreply@blogger.comBlogger53125tag:blogger.com,1999:blog-8216971263350849959.post-10238912188753667342013-09-15T01:27:36.299+02:002013-09-15T01:27:36.299+02:00It is undeniable fact that the cumulative comment ...It is undeniable fact that the cumulative comment count of this blog remains stagnant since nine days now!<br /><br />This is extremely unlikely according to to our ensemble of of blog comment simulations in which only 2 in a hundred show a similar behavior. The paper was rejected by Nature, but this incidence still points not only at a possible sudden death of Klimazwiebel but puts in question hitherto undoubted results, on which our models are based, about the character of cumulative anything.<br /><br /> hvwnoreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-70817431183965370682013-09-03T13:19:03.244+02:002013-09-03T13:19:03.244+02:00The stratosphere has not been cooling since 1995 s...The stratosphere has not been cooling since 1995 so no need to find an explanation for that at all! Stratospheric cooling in fact was the official IPCC "fingerprint" for AGW and in any other field the hypothesis would have been rejected rather than the wrong-footed "experts" being allowed to suggest a slew of contradictory and unphysical excuses for a "warming masked by cooling". Whither Occams razor?<br /><br />Explaining the brief, minor and beneficial heating period of the 20th century is less useful than explaining historical cooling periods. What caused the recovery from the ice ages when CO2 was at it's maximum levels? What caused the little ice age? What caused the drop after the 1940's, what causes the current plateau? As it happens the only plausible theories available for all of these are amplified solar forcing. CO2 cannot explain cooling at all. And since what causes the cooling likely also causes the heating then CO2 is not required to explain the 20th century. <br /><br />Solar forcing was the dominant consensus theory for centuries. It is also still perfectly valid for both the Arctic and the US48 temperature datasets; the only ones with little likely influence from urban heat islands.<br /><br />And if I was to explain that the current plateau should really have started in the 60's when sunspots levelled out but aerosol reduction from the clean air act caused a temporary cooling then you'd rightly say that I just made that up. Yet that is the current, accepted reasoning for the inability of the CO2 hypothesis to explain the post 40's drop in temperature. This juxtaposition demonstrates the facile logic that is allowed only if you are a catastrophist.JamesGnoreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-29724919544284473562013-09-03T00:19:29.503+02:002013-09-03T00:19:29.503+02:00Lucia,
the number of realizations enetsr in the e...Lucia,<br /><br />the number of realizations enetsr in the estimation of the empirical dustribution of trends under the null hypothesis<br /><br />in the supp. info:<br />the deviation in the j-th trend for model i that is induced by internal variability. Since<br />the model i ensemble is generally small, the deviations are smaller than would be<br />representative of an infinitely large replication of runs for model i, and so to<br />compensate for that loss of variance, multiply the difference M ij − M i. by<br />[ Ni /( N i − 1 )]1 / 2 .<br /><br /><br />So it is not a direct model weighting, but the number of realizations is taken into account indirectlyeduardohttps://www.blogger.com/profile/17725131974182980651noreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-57244168341175685072013-08-30T05:27:54.328+02:002013-08-30T05:27:54.328+02:00eduardo--
I looked at the supplemental in fyfe and...eduardo--<br />I looked at the supplemental in fyfe and saw they don't weight by realizations. My discussion is addressing hvw concern. I pointed out that I get the same thing weighting by model rather than realization. <br /><br />I didn't plow through the details in fyfe enough to know know what happens in the case where the distribution of model runs about the model mean is normal and the distribution of model mean is normal and how that compares to what I did. I just skimmed. That gives me the gist but I often have to sit down and think through limits to fully understand how methods relate. <br /><br />I think hvw's concern when criticizing weighting by runs might be your paper where things are weighted by run/realization.<br /><br />But as I noted: I get more or less the same results with different weightings and using a different method. So as a practical matter, I don't think the choice is making much difference. And also, I'm not claiming one weighting is necessarily better than the other especially given that we really are <i>not</i> able to pull models randomly from a set of 'all possible models'. luciahttp://rankexploits.com/musingsnoreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-3844807721181079512013-08-30T01:58:51.260+02:002013-08-30T01:58:51.260+02:00Lucia,
Fyfe, Gillet and Zwiers construct an empir...Lucia,<br /><br />Fyfe, Gillet and Zwiers construct an empirical distribution of the difference of trends ( model minus observations ) based on bootstrapping (see their supplementary information). They do take into account the different number of realizations but their scheme implies a much smoother weighting than just weighting by the number of realizations eduardohttps://www.blogger.com/profile/17725131974182980651noreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-25050438078339979662013-08-29T23:04:18.214+02:002013-08-29T23:04:18.214+02:00eduardo,
I agree with you that weighting is not ne...eduardo,<br />I agree with you that weighting is not necessarily better. On other hand, it's not necessarily worse either. To some extent, weighting by runs vs. weighting by model are just different ways and getting similar results both ways merely shows a degree of robustness. That is: the result isn't emerging merely because of a somewhat arbitrary choice. <br /><br />For example, while you example explains the difficulty with treating two things that <i>claim</i> to be different models as different when they are the same, a similar issue would hold if the 50 run model and the 2 run model really <i>were</i> different with different parameterizations or solution methodology, but we weighted by runs. In this case, each model does provide an independent estimate of the variance runs about a mean given that set of parameterizations. Meanwhile the difference in the mean between the two runs gives an estimate of the effect of structural uncertainty.<br /><br />Addressing your example where the same model is run 50 times and called "A" and then run 2 times and called "B", computing the variance by combining the two models wouldn't result in a great deal of bias in the computed variance which will tend to be the same as if we pool all 52 and simply compute over the full 52. My understanding is the difficulty is merely that we will get a less precise value estimate. And while the variance computed this way will be unbiased, the standard deviation will tend to have a low bias arising form the small sample size of 2 runs. So, weighting by model would, in this instance, be a suboptimal use of the data, but not truly horrible. Meanwhile if they had been different models, weighting by runs could result in model A's variance swamping the analysis. <br /><br />I elected to do by model because <br />(a) I wanted to look at individual models anyway to see how their means and variances looked relative to observations, <br />(b) back when the AR4 was published, the multi-model mean highlighted in graphs and tables was obtained by first computing model means and then averaging over model means. So, my graphs mimic that methodology and <br />c) computing a pooled variance from individual models gives a cleaner estimate of typical internal variability stripped of the variance that springs from the structural uncertainty and (c) cannot be done by computing the variance in trends over runs without first separating into models. As such, the variance weighted by model is a better model based guide to variability arising from uncertainty in initial conditions. (Assuming models get 'weather right, of course.) <br /><br /><br />I did by the way agree with the comment in your manuscript that the ensemble is not really a random sampling of putative 'model space' (while replicate runs in an individual model may be.)luciahttp://rankexploits.com/musingsnoreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-3657186541318764432013-08-29T22:13:10.518+02:002013-08-29T22:13:10.518+02:00#41,44
I am not sure that the models should be wei...#41,44<br />I am not sure that the models should be weighted by the number of realizations they provide. This would assume that the models are independent, which has been shown not to be true. Let us assumed we have 50 realizations with model M. Perhaps 2 of this realizations have been done on another computer, or with another compiler, or someone changed a comma in the FORTRAN code. Formally, these two realizations belong to a different model, and yet in reality, they are almost the same model. If we weight compute the variance separately and combine them, these two realizations would be unduly overweighted.<br />We have two sources of variability for the trends: The structural (model) variability, and the internal variability. By weighting the ensemble, we are implying that the first is more important. Why ?<br />This question was indirectly addressed in the manuscript (supp. info). The ensemble is not a random sampling of a putative `model space`. Actually, we do not know what the ensemble represents, and so weighting the ensemble is not per se better.eduardohttps://www.blogger.com/profile/17725131974182980651noreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-77024841895541864822013-08-29T20:10:21.165+02:002013-08-29T20:10:21.165+02:00Hans von Storch,
I don't know if your response...Hans von Storch,<br />I don't know if your response was addressed to me.<br /><br />Like you, I doubt we can ran models according to quality. I think <i>hypothetically</i> it could be done. But -- as you say, why prefer ability to mimic ENSO vs. MJO? Hypothetically if one model was sufficiently bad at everything we could throw that one away. <br /><br />On the 'ENSO part', the only reason I think it's worth examining whether ENSO is an explanation is that when presented data showing the current observations are skirting or outside the range of the models, some people always immediately suggest it is ENSO as hwp did just above. Since methods of explaining ENSO for earth observations exist, when some suggest 'it's just ENSO', it can be worth looking into that issue and seeing whether correction application does change the result. <br /><br /><i>namely that we do not need to touch on the quality of the forcing nor on the quality of the response to forcing?</i><br />I actually favor these two as the more likely reasons because I <b>don't</b> think the main reason for the discrepancy is ENSO. <br /><br /><i>models usually describe the full variability in the 20th century well,</i><br />Do models describe it well "well"? And how well? Collectively the variability of 10 year trends in the models used in the AR4 was <i>exceeds</i> the variability of 10 year earth trends in the 20th century by between 2% to 30% depending on whether the comparison is made between models and HadCrut3, NCDC or GISTemp.<br /><br />The collectively model variability in 10 year trends exceed that of the earth variability despite the fact that (a) the earth includes measurement errors on top of other variability and (b) some of the the AR4 models did <i>not</i> include volcanic or solar forcings. The effect of each factor individually should tend to make variability in observations of earth trends <i>larger</i> than in models-- and yet earth trends are, if anything, somewhat smaller. (The amount depends on whether one chooses HadCrut3, NCDC or GISTemp for the comparison.)<br /><br /><i>then the additional/missing natural variability must have been accounted for by forced variability</i><br />In fact, with some of the models in the AR4, we can see large variability. But if tabulated, the excess might be overlooked -- because those model runs contained no volcanic forcings. And so while the very large spikes in earth temperature frequently coincided with volcanic eruptions, those in the model simply occur due to that models internal variability. For example, see echam5:<br /><a href="http://rankexploits.com/musings/wp-content/uploads/2009/06/figure2_echamp.jpg" rel="nofollow">http://rankexploits.com/musings/wp-content/uploads/2009/06/figure2_echamp.jpg</a><br /><br />So, what we have here is a model whose variability of 10 year trends might not look so poor when variability of 10 year trends in single runs over the 20th century are tabulated and compared to that of earth trends, but which, to some extent, achieved that goal precisely be leaving out volcanic forcings which are thought to have caused a portion of the variability in 10 year trends for the earth.<br /><br />Certainly there are other models whose variability seems possibly too small. But if we make the comparison in the aggregate, the variability of 10 year trends in individual models seems more likely on the high side than the low side. <br /><br /><i>We need time and patience to deal with these issues</i><br />I agree with this. Unfortunately, with only one earth one can't go to the lab collect replicate earth observations which would be very useful if we could have them.luciahttp://rankexploits.com/musingsnoreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-28830043578175362482013-08-29T18:26:47.585+02:002013-08-29T18:26:47.585+02:00I do not think that one can order models according...I do not think that one can order models according to skill or quality. Depends all on the metric, and there is no way of choosing a "best" metric. Why should ENSO be more important than extreme rainfall in Asia, than the MJO, or the formation of blockings, just to mention a few.<br /><br />Also, when jumping on the ENSO part, you have made a choice among the three (or four) explanations for the inconsistency of observed recent trend vs A1B/RCP4.5 trends - you say: it is the natural variability. But how do you know it is not a a lack of external forcing, or a possibly slight overestimating of the GHG response? Maybe we even had only bad luck, and this stagnation is a two in hundred rare event?<br /><br />Does the natural variability explanation - which I personally find attractive - have a specific political utility, namely that we do not need to touch on the quality of the forcing nor on the quality of the response to forcing?<br /><br />By the way, when the natural variability is not ok, and the models usually describe the full variability in the 20th century well, then the additional/missing natural variability must have been accounted for by forced variability. If too little natural, then the responses is overestimated, if it is too large, then it is underestimated.<br /><br />We need time and patience to deal with these issues and should not jump on the most convenient explanation why our scenarios fail in describing the recent (and quite possibly soon ending) stagnation.Hans von Storchhttps://www.blogger.com/profile/08778028673130006646noreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-1281042525252177052013-08-29T15:46:14.943+02:002013-08-29T15:46:14.943+02:00hwp,
lucia, thanks for the info. Your approach to ...hwp,<br /><i>lucia, thanks for the info. Your approach to dealing with such an unbalanced ensemble sounds like an improvement.</i><br />I don't know if it is an improvement--but it has the potential for addressing whether the estimate of the variance in trends is over-dominated by models with smaller variances in trends which some like Ed Hawkins suspect to be the case. <br /><br />In this regard: it is worth noting that if we examine <i>residuals</i> from the linear trends relative to what we see for the earth, on average the models have <i>too much</i> natural variability, not too little. Mind you: this test is dominated by variability at timescales <i>less than</i> the trend length and also, some models have less small scale variability that the observation. And also: the test is ambiguous (high model residuals could arise from excessive internal variability in models or from failure to correctly model volcanic eruptions). Nevertheless, the test can be done, where in contrasts tests to compare variability at long time scales to earth variability have such low power as to be practically impossible). And this test which has the advantage of being 'doable' does not point to individual models having <i>too little</i> internal variability on average.<br /><br /><br /><br /><i>I wonder whether something can be learned by sorting the models under consideration by their performance in capturing ENSO.</i><br />I was planning to apply an enso adjustment to the models which must be done if one is going to compare ENSO corrected observations to model outcomes. I grabbed the required model data, but haven't done it yet. (I've got to get of my duff and do it.)<br /><br />If the models do simulate enso propery, this should narrow the variance in trends for models. As we have had La Nina's recently, it ought to move the earth trends more positive. How the two will pan out together I don't know-- but I anticipate to be similar to what's in the Fyfe paper. <br /> <br />FWIW: I prefer the comparisons without ENSO as more useful for a variety of reasons including the fact that once one considers ENSO, one has a variety of choices to try to remove ENSO, and many choices means that one might hunt for the method that gives answer the analyst 'prefers'. <br /><br /><i>Another thought: If we assume (or better hope) that modelled global temperature variability doesn't change much with the system's position on a warming trend</i><br />That's an important issue. But this assumption that the variance in 'n' year trends is identical over all periods is testable using the exact same methods we can use to test whether the variance in trends from different models differ from each other. So the assumption seems warranted (or at least is not inconsistent with the model-data available.)<br /><br />We can compute the variability in trends across runs of identical models over matched periods and test if this variability changes over time. (The other test is to see if variability differs across models). <br /><br />I have done so in the past with the AR4 models and there is no particular evidence the natural variability in trends increased (or decreased) over the 20th or modeled 21st centuries or that the variance differs from period to period. <br /><br />In contrast, the same method used to detect whether variances differ across models confirms they do differ. The variance in trends is larger in some models than others. I need to repeat this and formalize it. (I think I mostly did 10 year trends too. ) I haven't done this check for the AR5 models mostly because I have a number of items on the 'to check' list. <br /><br />I'd write more but blogger limits characters. Plus, I need to go actually implement the ENSO correction. :)luciahttp://rankexploits.com/musingsnoreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-21032014849539657222013-08-29T11:56:10.375+02:002013-08-29T11:56:10.375+02:00After rolling two dice severel times does it make ...After rolling two dice severel times does it make sense to question the dice model after havong obtained two sixes?wflammehttps://www.blogger.com/profile/18260929727390446009noreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-32510121521296677302013-08-29T09:21:51.111+02:002013-08-29T09:21:51.111+02:00hvw - the new study published by nature on a possi...hvw - the new study published by nature on a possible link to ENSO is certainly encouraging, but it is typical how things are negotiated - somebody suggests one solution - which explains what happens, but it dos not help to sort our question, what is wrong with the scenario simulations - but only one. There may be others, and before declaring that our problem is solved we must be able to exclude other "solutions". Hans von Storchhttps://www.blogger.com/profile/08778028673130006646noreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-14488998553493800502013-08-29T09:15:36.563+02:002013-08-29T09:15:36.563+02:00lucia, thanks for the info. Your approach to deali...lucia, thanks for the info. Your approach to dealing with such an unbalanced ensemble sounds like an improvement.<br /><br />A new study (http://www.nature.com/nature/journal/vaop/ncurrent/full/nature12534.html) seems to point to a link between ENSO-related SST patterns and the currently observed small global temperature trend.<br /><br />I wonder whether something can be learned by sorting the models under consideration by their performance in capturing ENSO.<br /><br />Another thought: If we assume (or better hope) that modelled global temperature variability doesn't change much with the system's position on a warming trend (and you and HvS and EZ apparently do that by considering the distribution of n-year trends stationary in a 55 year interval), then it might be worthwhile to examine the AMIP runs with respect to their decadal variability. But someone already did this, I suppose ...<br /> <br /><br />hvwnoreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-61446997943423572522013-08-29T05:29:42.147+02:002013-08-29T05:29:42.147+02:00hvw said
Doesn't that give undue weight to the...hvw said<br /><i>Doesn't that give undue weight to the models with many runs? In other words, would different realizations of the same model not be expected to show a similar, model-specific variability?</i><br />They do. I've done this a different way <a href="http://rankexploits.com/musings/2013/ar5-trend-comparison/" rel="nofollow">combining the distitributions by model</a>. I've counted each entry at the climate explorer as a model to estimate a typical variability for a model but based the estimate on models with more than 1 run in the projection. (My method requires repeat runs from a model to estimate the variability due to initial conditions only.) <br /><br />If you examine my figure in that post you'll see the variability of trends differs from model to model as does the mean trend. <br /><br />The results are similar to VonStorch and Zoritas.<br /><br />I haven't organized the code to collect together some models listed in several cases (e.g. E2-H_p1, _p2, _p3 better be considered 1 estimate of the variability; if this is done, likely E2-R and MPI-ESM should be similarly grouped. )luciahttp://rankexploits.com/musingsnoreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-69336907742595188082013-08-28T16:17:14.529+02:002013-08-28T16:17:14.529+02:00Paul, we mostly interested in the ability of scena...Paul, we mostly interested in the ability of scenario simulations in describing the present stagnation, not in explaining the stagnation. That is quite different.<br />What I find difficult with the "other" paper that it is again an a-posteriors explanation (like cold European winters caused by less Arctic sea ice in the preceding fall) and just one. There are in principle others, and we would need to do some work to disentangle the plausibility of different explanations. <br />Hans von Storchhttps://www.blogger.com/profile/08778028673130006646noreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-12108232109976051872013-08-28T16:11:16.777+02:002013-08-28T16:11:16.777+02:00It must be rather frustrating for you to have your...It must be rather frustrating for you to have your paper rejected by Nature and then see today a paper published in Nature saying more or less the same thing<br />"Overestimated global warming over the past 20 years".<br /><br />I wonder what is the difference between the two papers, apart from the names of the authors? <br /><br />Paul Matthewshttps://www.blogger.com/profile/13612822196780702827noreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-50898903407354953862013-08-27T16:14:19.492+02:002013-08-27T16:14:19.492+02:00@HvS: Vielen Dank für Ihre Antwort.
_Flin_@HvS: Vielen Dank für Ihre Antwort.<br /><br />_Flin_Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-25825345767542103892013-08-26T18:50:33.236+02:002013-08-26T18:50:33.236+02:00Thank you, Dr. Zorita!Thank you, Dr. Zorita!MikeRhttps://www.blogger.com/profile/00127456522803816485noreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-75190623935451677672013-08-26T18:00:53.298+02:002013-08-26T18:00:53.298+02:00@30
Mike,
in some sense, estimations of the clima...@30<br /><br />Mike,<br />in some sense, estimations of the climate sensitivity based on Bayesian methods are based on what you are proposing. They essentially are weighted averages, the weights being a measure of how close a model is to past observations. <br />However, this becomes quickly a more fundamental question: if I am a pilot and one among three on board computers disagrees with the other two, I would not build an average among three. I would try to understand why this happens. On the other hand, it may very well happen that the model one would reject because it fails to reproduce the temperature trends, is the one that produces a better annual cycle of, say precipitation. <br /><br />I would essentially agree with you that one goal should be to disregard the worst models, but there are different opinions on this. In the end, the question boils down to 'what does an ensemble of models represent, when at most only one can be right ?'<br /><br /><a href="http://www.iac.ethz.ch/people/knuttir/papers/knutti10cc.pdf" rel="nofollow">The end of model democracy </a><br /><br /><a href="https://www.sciencedirect.com/science/article/pii/S1355219810000468" rel="nofollow">Predicting weather and climate: Uncertainty, ensembles and probability</a>eduardohttps://www.blogger.com/profile/17725131974182980651noreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-71280072382589468302013-08-26T17:39:48.000+02:002013-08-26T17:39:48.000+02:00@33
dear hwv
thank you very much again. We will c...@33<br />dear hwv<br /><br />thank you very much again. We will check out what we have missed.<br /><br />Related to this, it is a pity that the CMIP5 site is so cumbersome. I think it is a missed opportunity to increase the transparency. I guess that it is is a difficult task though.eduardohttps://www.blogger.com/profile/17725131974182980651noreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-39326182389782175482013-08-26T16:48:48.925+02:002013-08-26T16:48:48.925+02:00Dear Eduardo,
you are absolutely right that the n...Dear Eduardo,<br /><br />you are absolutely right that the number of filenames I listed are useless. Not because they refer to different timesteps (it's all monthly) but because files are split into different intervals. If you like to compare with what you got from Climate Explorer (would that be regarded as an authoritative source anyways?) here are the number of realizations per model that I can see and from what you have about 74%:<br /><br />ACCESS1-0 : 1<br />ACCESS1-3 : 1<br />bcc-csm1-1 : 1<br />bcc-csm1-1-m : 1<br />BNU-ESM : 1<br />CanCM4 : 10<br />CanESM2 : 5<br />CCSM4 : 6<br />CESM1-BGC : 1<br />CESM1-CAM5 : 3<br />CESM1-CAM5-1-FV2 : 1<br />CESM1-WACCM : 3<br />CMCC-CM : 1<br />CMCC-CMS : 1<br />CNRM-CM5 : 1<br />CSIRO-Mk3-6-0 : 10<br />EC-EARTH : 10<br />FGOALS-g2 : 1<br />FIO-ESM : 3<br />GFDL-CM2p1 : 10<br />GFDL-CM3 : 1<br />GFDL-ESM2G : 1<br />GFDL-ESM2M : 1<br />GISS-E2-H : 15<br />GISS-E2-H-CC : 1<br />GISS-E2-R : 17<br />GISS-E2-R-CC : 1<br />HadCM3 : 10<br />HadGEM2-AO : 1<br />HadGEM2-CC : 1<br />HadGEM2-ES : 4<br />inmcm4 : 1<br />IPSL-CM5A-LR : 4<br />IPSL-CM5A-MR : 1<br />IPSL-CM5B-LR : 1<br />MIROC4h : 3<br />MIROC5 : 3<br />MIROC-ESM : 1<br />MIROC-ESM-CHEM : 1<br />MPI-ESM-LR : 3<br />MPI-ESM-MR : 3<br />MRI-CGCM3 : 1<br />NorESM1-M : 1<br />NorESM1-ME : 1<br /><b>Total: 148</b><br /><br />That brings be to another question: Apparently you are using multiple realization for a model, if available. Doesn't that give undue weight to the models with many runs? In other words, would different realizations of the same model not be expected to show a similar, model-specific variability?<br />hvwnoreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-22587070747561997372013-08-26T16:14:35.567+02:002013-08-26T16:14:35.567+02:00Andreas/28.
Unsere Beobachtung hebt ab auf den Gra...Andreas/28.<br />Unsere Beobachtung hebt ab auf den Grad an Realismus, <b>den die Szenarienrechnungen</b> zeigen. <br /><br />Diese Szenarienrechnungen werden in fast allen Abschätzungen der Wirkung von Klimaänderungen verwendet. Die genannten "internen" Faktoren sollten von den Modellen in den Szenarien dargestellt werden; vielleicht gelingt dies nur unzureichend. Es kann auch sein, daß andere Wirkfaktoren unzureichend beschrieben sind, was heißen würde, daß relevante Faktoren unberücksichtigt bleiben. Alles schöne Herausforderungen für weitere Forschung.Hans von Storchhttps://www.blogger.com/profile/08778028673130006646noreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-56713283369085351722013-08-26T15:41:01.225+02:002013-08-26T15:41:01.225+02:00Dear hwv,
thank you for your input. The number af...Dear hwv,<br /><br />thank you for your input. The number after the semicolon, as you said, represents the number of files, which may refer to 6-hourly, daily and months means for different sub-periods altogether. For instance, for model GFDL-CM3 you indicate 59 number of files. The CMIP5 site at Lawrence Livermore Nat. Lab. includes just 1 realization of model GFDL-CM3 for scenario rcp4.5 ( I just checked this).<br /><br />In my previous comment - maybe you overlooked it- I indicated that we have repeat the analysis downloading the global means from the Climate E Explorer for a total of 109 simulations, and the results are the same as in our manuscripteduardohttps://www.blogger.com/profile/17725131974182980651noreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-89687555288972633452013-08-26T15:34:52.499+02:002013-08-26T15:34:52.499+02:00Drs. Storch and Zorita, I'm wondering why the ...Drs. Storch and Zorita, I'm wondering why the ensemble of models is the right metric to be using. Would it be a good idea to identify which models failed, and are rejected, and which are not rejected (yet)? Why not get rid of the ones that didn't work, and proceed with what remains?MikeRhttps://www.blogger.com/profile/00127456522803816485noreply@blogger.comtag:blogger.com,1999:blog-8216971263350849959.post-4264860117383285912013-08-26T15:03:24.914+02:002013-08-26T15:03:24.914+02:00Dear Eduardo,
I am well aware that the relationsh...Dear Eduardo,<br /><br />I am well aware that the relationship between the ESG database and actually existing and usable files is not perfect. It's a pain. However, I am sitting in front of actual files of tas for rcp45 from 44 models. Given that an unavoidable weak point of such an analysis is that you are restricted to an "ensemble of opportunity", I believe it is highly desirable to make sure to use anything that is available. If I were a reviewer, I'd be bitching big time if you presented only a subset and state that the incompleteness is "not relevant here".<br /><br />----------------------------<br />ACCESS1-0 : 1<br />ACCESS1-3 : 1<br />bcc-csm1-1 : 2<br />bcc-csm1-1-m : 1<br />BNU-ESM : 1<br />CanCM4 : 10<br />CanESM2 : 7<br />CCSM4 : 7<br />CESM1-BGC : 1<br />CESM1-CAM5 : 4<br />CESM1-CAM5-1-FV2 : 1<br />CESM1-WACCM : 3<br />CMCC-CM : 10<br />CMCC-CMS : 10<br />CNRM-CM5 : 6<br />CSIRO-Mk3-6-0 : 13<br />EC-EARTH : 312<br />FGOALS-g2 : 27<br />FIO-ESM : 3<br />GFDL-CM2p1 : 70<br />GFDL-CM3 : 59<br />GFDL-ESM2G : 19<br />GFDL-ESM2M : 39<br />GISS-E2-H : 90<br />GISS-E2-H-CC : 2<br />GISS-E2-R : 188<br />GISS-E2-R-CC : 4<br />HadCM3 : 20<br />HadGEM2-AO : 1<br />HadGEM2-CC : 5<br />HadGEM2-ES : 28<br />inmcm4 : 1<br />IPSL-CM5A-LR : 4<br />IPSL-CM5A-MR : 2<br />IPSL-CM5B-LR : 1<br />MIROC4h : 9<br />MIROC5 : 3<br />MIROC-ESM : 2<br />MIROC-ESM-CHEM : 1<br />MPI-ESM-LR : 4<br />MPI-ESM-MR : 3<br />MRI-CGCM3 : 1<br />NorESM1-M : 2<br />NorESM1-ME : 1<br />---------------------------<br />numbers after the colon are number of files. That includes those referring to the period after 2060 though. Let me know if you want more, tracking-ids for example.<br /><br />That said, I do not strongly believe that the results change significantly if you include all models.<br />The paper is nice and clear and somebody has to do this first step. Otherwise I agree with Andreas below that this cannot be all we've got to offer. This is an exploratory result which doesn't provide a robust answer to the question about the likelihood of this 15y trend happening conditional on the models having no collective error that would lead to the underestimation of variability on that timescale. More research is needed :).<br /> <br />hvwnoreply@blogger.com