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the (1167), and (281), that (246), for (195), this (150), from (127), tcr (93), #likely (89), forcing (88), models (83), #warming (82), with (81), than (79), are (78), cmip5 (76), model (68), not (68), 2013 (65), would (65), 2014 (60), more (60), have (59), ecs (58), using (55), which (55), range (52), observed (48), but (48), temperature (47), there (47), these (47), one (47), here (44), forcings (43), estimates (42), over (42), only (41), bias (40), between (39), period (39), mean (37), sensitivity (36), rcp6 (36), response (36), confidence (35), efficacy (33), high (32), end (32), global (30), century (30), about (30), rcp4 (30), may (29), above (29), can (28), see (28), discrepancy (28), projections (27), temperatures (27), was (27), transient (27), shindell (27), climate (26), most (26), relative (26), seems (26), method (26), evidence (25), aerosol (25), equilibrium (24), value (24), otto (24), difference (24), will (24), 2012 (23), runs (23), all (23), other (23), what (23), 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o2 are now used by the anything but sensitivity abs group which seems to argue that the difference between modeled and actual temperatures may be due to anything besides oversensitivity in cmip5 models and while many of these explanations likely have merit i have not yet seen somebody try to quantify all of the various contributions together in this post i attempt perhaps too ambitiously to quantify likely contributions from coverage bias in observed temperatures el nino southern oscillation enso post 2005 forcing discrepancies volcanic solar anthropogenic aerosols and co2 the pacific decadal oscillation pdo and finally the implications for transient climate sensitivity since the start of the hiatus is not well defined i will consider 4 different start years all ending in 2013 1998 is often used because of the large el nino that year which minimizes the magnitude of the trend starting in that year on the other hand the start of the 21 st century is sometimes considered as well moreover i will use hadcrutv4 as the temperature record since this is the more cited to represent the hiatus which will show a larger discrepancy at the beginning than giss but will also show a larger influence from the coverage bias the general approach here is to consider that if the cmip5 multi model mean for rcp4 5 is unbiased what percentage of the discrepancy can we attribute to the various factors only at the end do we look into how the model sensitivity may need to be adjusted note that each of the steps below are cumulative building off of previous adjustments given that here is the discrepancy we start with start year hadcrut4 k century rcp4 5 mmm k century 1998 0 44 2 27 1999 0 75 2 16 2000 0 44 2 01 2001 0 08 1 92 code and data my script and data for this post can all be downloaded in the zip package here regarding the source of all data source of raw temperature data is hadcrutv4 coverage bias adjusted temperature data is from cowtan and way 2013 hybrid with uah cmip5 multi model mean for rcp4 5 comes from climate explorer multivariate enso index mei comes from noaa by way of climate explorer total solar irradiance tsi reconstruction comes from sorce stratospheric aerosol optical thickness comes from sato et al 1993 by way of giss cmip5 multi model mean for natural only comes from my previous survey pdo index comes from the jisao at the university of washington by way of climate explorer step 1 coverage bias for the first step we represent the contribution from coverage bias using the results from cowtan and way 2013 this is one of two options with the other being to mask the output from models and compare it to hadcrut4 the drawback of using cw13 is that we are potentially introducing spurious warming by extrapolating temperatures over the arctic the drawback of masking however is that if indeed the arctic is warming faster in reality than it is in the multi model mean then we are missing that contribution ultimately i chose to use cw13 in this post because it is a bit easier and because it likely represents an upper bound on the coverage bias contribution i may examine the implications of using masked output in a future post the above graph is baselined over 1979 1997 prior to the start of the hiatus which highlights the discrepancy that occurs during the hiatus start year s1 hadcrut4 coverage adj cw13 k century rcp4 5 mmm k century 1998 1 10 2 27 1999 1 38 2 16 2000 1 05 2 01 2001 0 54 1 92 step 2 enso adjustment the enso adjustment here is simply done using multiple linear regressions similar to lean and rind 2008 or foster and rahmstorf 2011 except using the exponential decay fit for other forcings as described here while i have noted several problems with the lr08 and fr11 approach with respect to solar and volcanic attribution which i mention in the next step i also found that enso variations are high enough frequency so as to be generally unaffected by other limitations in the structural fit of the regression model step 3 volcanic and solar forcing updates for multi model mean the next step in this process is a bit more challenging we want to see to what degree updated solar and volcanic forcings would have decreased the multi model mean trend over the hiatus period but it is quite a task to have all the groups re run their cmip5 models with these updated forcings moreover as i mentioned above and in previous posts and my discussion paper simply using linear regressions does not adequately capture the influence of solar and volcanic forcings instead here i use a two layer model from geoffrey et al 2013 to serve as an emulator for the multi model mean fitting it to the mean of those natural only forcing runs over the period this is a sample of the best fit which seems to adequately capture the fast response at least even if it may be unable to capture the response over longer periods but we only care about the updates from 2005 2013 and here are the updates to the solar and volcanic forcings updates in red for the volcanic forcing we have cmip5 volcanic aerosols returning to background levels after 2005 for the solar forcing we have cmip5 using a naïve recurring 11 year solar cycle as shown here after 2005 the multi model mean is then adjusted by the difference between our emulated volcanic and solar temperatures from the cmip5 forcings and the observed forcings the result is seen below over the hiatus period the effect of the updated solar and volcanic forcings reduces the multi model mean trend by between 13 and 20 depending on the start year updated anthropogenic forcings with regards to the question of how updated greenhouse gas and aerosols forcings may have contributed to the discrepancy over the hiatus period it is not easy to get an exact number but based on evidence of concentrations and emissions that i ve seen there does not seem to be a significant deviation between the rcp4 5 scenario from 2005 2013 and what we ve observed this is unsurprising as the projected trajectories for all of the rcp scenarios 2 6 4 5 6 0 8 5 don t substantially deviate until after this period for instance the rcp4 5 scenario assumes the co2 concentration goes from 376 8 ppm in 2004 to 395 6 ppm in 2013 meanwhile the measured annual co2 concentration has gone from 377 5 ppm in 2004 to 396 5 ppm in 2013 by my back of the envelopment calculation this means we have actually experienced an increase in forcing of 0 002 w m 2 more than in the rcp4 5 scenario which is a magnitude away from being relevant here for aerosols murphy 2013 suggests little change in forcing from 2000 2012 the bulk of the hiatus period examined klimont et al 2013 find a reduction in global and chinese sulfur dioxide emissions since 2005 compared to the steady emission used in rcp4 5 from 2005 2013 meaning that updating this forcing would actually increase the discrepancy between the mmm and observed temperatures however it seems safer to simply assume that mismatches between projected and actual greenhouse gas and aerosol emissions have contributed a likely maximum of 0 to the observed discrepancy over the hiatus and it is quite possible that they have contributed a negative amount that is using the observed forcing would increase the discrepancy step 4 5 pdo influence and tcr adjustment trying to tease out the natural variability influence on the hiatus is quite challenging however most work seems to point to the variability in the pacific trenberth and fasullo 2013 suggest the switch to the negative phase of the pdo is responsible causing changing surface wind patterns and sequestering more heat in the deep ocean matthew england presents a similar argument tying in his recent study to that of kosaka and xie 2013 over at real climate in general the idea is that the phase of the pdo affects the rate of surface warming if we assume that the pdo index properly captures the state of the pdo and that the rate of warming is proportional to the pdo index after some lag we should be able to integrate the pdo index to capture the form of the influence on global temperatures unfortunately because of the low frequency of this oscillation significant aliasing may occur between the pdo and anthropogenic component if we regress this form directly against observed temperatures there are thus two approaches i took here first we can regress the remaining difference between the mmm adjusted for updated forcings and the enso adjusted cw13 which should indicate how much of this residual discrepancy can be explained by the pdo in this test the result of the regression was insignificant the coefficient was in the wrong direction implying that the negative phase produced warming and r 2 0 04 this is because as trenberth et al 2013 note the positive phase was in full force from 1975 1998 contributing to surface warming but the mmm matches too well the rate of observed surface warming from 1979 1998 leaving no room for the natural contribution from the pdo to me it seems that if you are going to leave room for the pdo to explain a portion of the recent hiatus it means that models probably overestimated the anthropogenic component of the warming during that previous positive phase of the pdo thus for my second approach i again use the enso adjusted cw13 as my dependent variable in the regression but in addition to using the integrated pdoi as one explanatory variable i include the adjusted mmm temperatures as a second variable this will thus find the best scaling of the mmm temperature along with the coefficient for the pdo after using this method we indeed find the correct direction for the influence of the pdo according to this regression the warm phase of the pdo contributed about 0 1 k to the warming from 1979 2000 or about 1 3 of the warming over that period since shifting to the cool phase at the turn of the 21 st century it has contributed about 0 04 k cooling to the hiatus this suggests a somewhat smaller influence than england et al 2014 finds for the mmm coefficient we get a value of 0 73 this would imply that the transient climate sensitivity is biased 37 too high in the multi model mean since the average transient climate sensitivity for cmip5 is 1 8 k this coefficient suggests that the tcr should be adjusted to 1 3 k this value corresponds to those found in other observationally based estimates most notably otto et al 2013 when we put everything together and perform the tcr adjustment to the cmip5 multi model mean as well we get the following result conclusion using the above methodology the table below shows the estimated contribution by each factor to the modeled vs observational temperature discrepancy during the hiatus note that these rows don t necessarily add up to 100 since the end result is not a perfect match start year step1 coverage bias step2 enso step3 volc solar forcings step4 pdo surface winds ocean uptake step5 tcr bias 1998 36 10 17 2 28 1999 45 0 23 6 35 2000 39 9 23 7 28 2001 31 16 20 5 21 according to this method the coverage bias is responsible for the greatest discrepancy over this period this is likely contingent upon using cowtan and way 2013 rather than simply masking the cmip5 mmm output and using hadcrut4 rather than giss moreover 65 79 of the temperature discrepancy between models and observations during the hiatus may be attributed to something other than a bias in model sensitivity nonetheless this residual warm bias in the multi model mean does seem to exist such that the new best estimate for tcr should be closer to 1 3k obviously there are a number of uncertainties regarding this analysis and many of these uncertainties may be compounded at each step regardless it seems pretty clear that while the hiatus does not mean that surface warming from greenhouse gases has ceased given the other factors that may be counteracting such warming in the observed surface temperatures there is still likely some warm bias in the cmip5 modeled tcr contributing to the discrepancy comments 8 october 17 2013 how well do the ipcc s statements about the 2 c target for rcp4 5 and rcp6 0 scenarios reflect the evidence filed under uncategorized troyca 7 57 pm 1 introduction in the ipcc ar5 summary for policy makers spm there are few statements that are likely to garner more attention than those related to projected warming for this century under various scenarios in particular given the prominence placed on the 2 degrees celsius target i would argue that section e 1 is of great importance for policy makers in the top box we read global surface temperature change for the end of the 21st century is likely to exceed 1 5 c relative to 1850 to 1900 for all rcp scenarios except rcp2 6 it is likely to exceed 2 c for rcp6 0 and rcp8 5 and more likely than not to exceed 2 c for rcp4 5 my bold note that rcp4 5 involves a continual increase of global co2 emissions up until 2040 whereas rcp6 0 shows a large increase in emissions until 2060 see below from figure 6 of van vuuren et al 2011 it is obviously of interest to know the likelihood of staying under the 2 c since pre industrial target this century without reducing global emissions note that reducing emissions is not the same as reducing the rate of emissions increase for another 30 50 years figure 6 van vuuren et al 2011 the statement is repeated in a bullet point below e 1 along with the reference to where we can find more information in the heart of the report relative to the average from year 1850 to 1900 global surface temperature change by the end of the 21st century is projected to likely exceed 1 5 c for rcp4 5 rcp6 0 and rcp8 5 high confidence warming is likely to exceed 2 c for rcp6 0 and rcp8 5 high confidence more likely than not to exceed 2 c for rcp4 5 high confidence but unlikely to exceed 2 c for rcp2 6 medium confidence warming is unlikely to exceed 4 c for rcp2 6 rcp4 5 and rcp6 0 high confidence and is about as likely as not to exceed 4 c for rcp8 5 medium confidence 12 4 my bold based on my reading i think the current state of evidence makes it difficult to agree with the qualitative expressions of probability given for rcp4 5 more likely than not and rcp6 0 likely regarding the 2 degrees target as well as the high confidence given which i will explain in more depth below my interest in this was sparked recently when using a two layer model with prescribed tcr and effective sensitivities to trace the warming up to the year 2100 somewhat surprisingly i found that for many realistic tcr scenarios the simulated earth warmed less than 2 c by the end of the century i began to do a scan of the recently released ar5 to find the justification for the scenarios mentioned above 2 probabilistic statements and confidence first let s discuss what the spm means by more likely than not to exceed 2 c for rcp4 5 high confidence based on my reading of the uncertainty guidance i believe this should mean there is more than a 50 chance of reaching 2 c by the end of the century more likely than not and that there is plenty of e...
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