Time for the 2012 updates!
As has become a habit (2009, 2010, 2011), here is a brief overview and update of some of the most discussed model/observation comparisons, updated to include 2012. I include comparisons of surface temperatures, sea ice and ocean heat content to the CMIP3 and Hansen et al (1988) simulations.
First, a graph showing the annual mean anomalies from the CMIP3 models plotted against the surface temperature records from the HadCRUT4, NCDC and GISTEMP products (it really doesn’t matter which). Everything has been baselined to 1980-1999 (as in the 2007 IPCC report) and the envelope in grey encloses 95% of the model runs.
Correction (02/11/12): Graph updated using calendar year mean HadCRUT4 data instead of meteorological year mean.
The La Niña event that persisted into 2012 (as with 2011) produced a cooler year in a global sense than 2010, although there were extensive regional warm extremes (particularly in the US). Differences between the observational records are less than they have been in previous years mainly because of the upgrade from HadCRUT3 to HadCRUT4 which has more high latitude coverage. The differences that remain are mostly related to interpolations in the Arctic. Checking up on the predictions from last year, I forecast that 2012 would be warmer than 2011 and so a top ten year, but still cooler than 2010 (because of the remnant La Niña). This was true looking at all indices (GISTEMP has 2012 at #9, HadCRUT4, #10, and NCDC, #10).
This was the 2nd warmest year that started off (DJF) with a La Niña (previous La Niña years by this index were 2008, 2006, 2001, 2000 and 1999 using a 5 month minimum for a specific event) in all three indices (after 2006). Note that 2006 has recently been reclassified as a La Niña in the latest version of this index (it wasn’t one last year!); under the previous version, 2012 would have been the warmest La Niña year.
Given current near ENSO-neutral conditions, 2013 will almost certainly be a warmer year than 2012, so again another top 10 year. It is conceivable that it could be a record breaker (the Met Office has forecast that this is likely, as has John Nielsen-Gammon), but I am more wary, and predict that it is only likely to be a top 5 year (i.e. > 50% probability). I think a new record will have to wait for a true El Niño year – but note this is forecasting by eye, rather than statistics.
People sometimes claim that “no models” can match the short term trends seen in the data. This is still not true. For instance, the range of trends in the models for cherry-picked period of 1998-2012 go from -0.09 to 0.46ºC/dec, with MRI-CGCM (run3 and run5) the laggards in the pack, running colder than the observations (0.04–0.07 ± 0.1ºC/dec) – but as discussed before, this has very little to do with anything.
In interpreting this information, please note the following (mostly repeated from previous years):
- Short term (15 years or less) trends in global temperature are not usefully predictable as a function of current forcings. This means you can’t use such short periods to ‘prove’ that global warming has or hasn’t stopped, or that we are really cooling despite this being the warmest decade in centuries. We discussed this more extensively here.
- The CMIP3 model simulations were an ‘ensemble of opportunity’ and vary substantially among themselves with the forcings imposed, the magnitude of the internal variability and of course, the sensitivity. Thus while they do span a large range of possible situations, the average of these simulations is not ‘truth’.
- The model simulations use observed forcings up until 2000 (or 2003 in a couple of cases) and use a business-as-usual scenario subsequently (A1B). The models are not tuned to temperature trends pre-2000.
- Differences between the temperature anomaly products is related to: different selections of input data, different methods for assessing urban heating effects, and (most important) different methodologies for estimating temperatures in data-poor regions like the Arctic. GISTEMP assumes that the Arctic is warming as fast as the stations around the Arctic, while HadCRUT4 and NCDC assume the Arctic is warming as fast as the global mean. The former assumption is more in line with the sea ice results and independent measures from buoys and the reanalysis products.
- Model-data comparisons are best when the metric being compared is calculated the same way in both the models and data. In the comparisons here, that isn’t quite true (mainly related to spatial coverage), and so this adds a little extra structural uncertainty to any conclusions one might draw.
Given the importance of ENSO to the year to year variability, removing this effect can help reveal the underlying trends. The update to the Foster and Rahmstorf (2011) study using the the latest data (courtesy of Tamino) (and a couple of minor changes to procedure) shows the same continuing trend:
Similarly, Rahmstorf et al. (2012) showed that these adjusted data agree well with the projections of the IPCC 3rd (2001) and 4th (2007) assessment reports.
Ocean Heat Content
Figure 3 is the comparison of the upper level (top 700m) ocean heat content (OHC) changes in the models compared to the latest data from NODC and PMEL (Lyman et al (2010) ,doi). I only plot the models up to 2003 (since I don’t have the later output). All curves are baselined to the period 1975-1989.
This comparison is less than ideal for a number of reasons. It doesn’t show the structural uncertainty in the models (different models have different changes, and the other GISS model from CMIP3 (GISS-EH) had slightly less heat uptake than the model shown here). Neither can we assess the importance of the apparent reduction in trend in top 700m OHC growth in the 2000s (since we don’t have a long time series of the deeper OHC numbers). If the models were to be simply extrapolated, they would lie above the observations, but given the slight reduction in solar, uncertain changes in aerosols or deeper OHC over this period, I am no longer comfortable with such a simple extrapolation. Analysis of the CMIP5 models (which will come at some point!) will be a better apples-to-apples comparison since they go up to 2012 with ‘observed’ forcings. Nonetheless, the long term trends in the models match those in the data, but the short-term fluctuations are both noisy and imprecise.
Summer sea ice changes
Sea ice changes this year were again very dramatic, with the Arctic September minimum destroying the previous records in all the data products. Updating the Stroeve et al. (2007)(pdf) analysis (courtesy of Marika Holland) using the NSIDC data we can see that the Arctic continues to melt faster than any of the AR4/CMIP3 models predicted. This is no longer so true for the CMIP5 models, but those comparisons will need to wait for another day (Stroeve et al, 2012).
Hansen et al, 1988
Finally, we update the Hansen et al (1988) (doi) comparisons. Note that the old GISS model had a climate sensitivity that was a little higher (4.2ºC for a doubling of CO2) than the best estimate (~3ºC) and as stated in previous years, the actual forcings that occurred are not the same as those used in the different scenarios. We noted in 2007, that Scenario B was running a little high compared with the forcings growth (by about 10%) using estimated forcings up to 2003 (Scenario A was significantly higher, and Scenario C was lower), and we see no need to amend that conclusion now.
Correction (02/11/12): Graph updated using calendar year mean HadCRUT4 data instead of meteorological year mean.
The trends for the period 1984 to 2012 (the 1984 date chosen because that is when these projections started), scenario B has a trend of 0.29+/-0.04ºC/dec (95% uncertainties, no correction for auto-correlation). For the GISTEMP and HadCRUT4, the trends are 0.18 and 0.17+/-0.04ºC/dec respectively. For reference, the trends in the CMIP3 models for the same period have a range 0.21+/-0.16 ºC/dec (95%).
As discussed in Hargreaves (2010), while this simulation was not perfect, it has shown skill in that it has out-performed any reasonable naive hypothesis that people put forward in 1988 (the most obvious being a forecast of no-change). However, concluding much more than this requires an assessment of how far off the forcings were in scenario B. That needs a good estimate of the aerosol trends, and these remain uncertain. This should be explored more thoroughly, and I will try and get to that at some point.
Summary
The conclusion is the same as in each of the past few years; the models are on the low side of some changes, and on the high side of others, but despite short-term ups and downs, global warming continues much as predicted.
References
- G. Foster, and S. Rahmstorf, "Global temperature evolution 1979–2010", Environmental Research Letters, vol. 6, pp. 044022, 2011. http://dx.doi.org/10.1088/1748-9326/6/4/044022
- S. Rahmstorf, G. Foster, and A. Cazenave, "Comparing climate projections to observations up to 2011", Environmental Research Letters, vol. 7, pp. 044035, 2012. http://dx.doi.org/10.1088/1748-9326/7/4/044035
- J.M. Lyman, S.A. Good, V.V. Gouretski, M. Ishii, G.C. Johnson, M.D. Palmer, D.M. Smith, and J.K. Willis, "Robust warming of the global upper ocean", Nature, vol. 465, pp. 334-337, 2010. http://dx.doi.org/10.1038/nature09043
- J. Stroeve, M.M. Holland, W. Meier, T. Scambos, and M. Serreze, "Arctic sea ice decline: Faster than forecast", Geophysical Research Letters, vol. 34, 2007. http://dx.doi.org/10.1029/2007GL029703
- J.C. Stroeve, V. Kattsov, A. Barrett, M. Serreze, T. Pavlova, M. Holland, and W.N. Meier, "Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations", Geophysical Research Letters, vol. 39, 2012. http://dx.doi.org/10.1029/2012GL052676
- J. Hansen, I. Fung, A. Lacis, D. Rind, S. Lebedeff, R. Ruedy, G. Russell, and P. Stone, "Global climate changes as forecast by Goddard Institute for Space Studies three‐dimensional model", Journal of Geophysical Research: Atmospheres, vol. 93, pp. 9341-9364, 1988. http://dx.doi.org/10.1029/JD093iD08p09341
- J.C. Hargreaves, "Skill and uncertainty in climate models", WIREs Climate Change, vol. 1, pp. 556-564, 2010. http://dx.doi.org/10.1002/wcc.58
Regarding Fig.1 and Fig. 3: both show a change of behavior after 2000. For the raw temperature data it has been noted but for the OHC (especially NODC) the change to much lower growth (if any) has not been so widely commented.
The OHC, as cumulative phenomenon, is not so much dependent on the short term variability. Is there any explanation (rather than denial or data manipulation) of the origin of the post-2000 hiatus in BOTH datasets?
ENSO clearly affects global temps; is there a view on Pacific Decadal Oscillation? Seems to be a flattening of the global temperature trace whenever PDO is in a major negative phase. OK, I know, a mere two point correlation: 1945-1976 & ~2005-present… (compare https://en.wikipedia.org/wiki/File:PDO.svg and e.g. https://www.realclimate.org/images/hadcrut_diff.jpg)
PAber (#51)
While not closed, given that there are depths below 2000 m, one might notice that the energy gain in the depth range 0-2000 m is steady so the rate of gain in the range 700 to 2000 m must have grown in the period you point to. Thus, disregarding the lowest depths, a change in the rate of mixing between the 0 to 700 m layer and the 700 to 2000 m layer during that period might explain what you noticed in fig. 3. It might also contribute to what you noticed in fig. 1. Look at the third figure (Comparison of Global Heat Content 0-700 meters layer vs. 0-2000 meters layer) here: http://www.nodc.noaa.gov/OC5/3M_HEAT_CONTENT/
There are measurements indicating that wind strength and thus wave action are increasing globally http://www.sciencemag.org/content/332/6028/451 which might influence mixing as might increased evaporation which can change the vertical density profile in salt water. http://journals.ametsoc.org/doi/abs/10.1175/2010JCLI3377.1
This is not an explanation, just a few guesses at what might be occurring. That the 0-2000 m level of the oceans continues to gain thermal energy at a steady rate does indicate that warming continues at a steady rate though.
PAber, If you are saying there was a “change” in 2000, then it consisted of a huge jump upward followed by a return to the previous trend. It looks to me as though there may be a response to the 1998 El Nino and then a return to trend. In any case the time segment is too short for any meaningful statement.
“Model-data comparisons are best when the metric being compared is calculated the same way in both the models and data. In the comparisons here, that isn’t quite true (mainly related to spatial coverage), and so this adds a little extra structural uncertainty to any conclusions one might draw.”
Has that really not been done for surface temperature? WOuld it be as simple as masking out the data where it is missing in the observations, or would you want to do something more complicated?
[Response: That would be a good start. Ed Hawkins has done some work on that i.e. http://www.climate-lab-book.ac.uk/2012/on-comparing-models-and-observations/ – gavin]
“The model simulations use observed forcings up until 2000 (or 2003 in a couple of cases) and use a business-as-usual scenario subsequently (A1B). The models are not tuned to temperature trends pre-2000.”
Should this be “temperature trends post-2000”? Or do the models use observed forcings pre-2000 and observed temperature post-2000?
[Response: That would be strange. The models don’t use observed temperatures ever. – gavin]
Conerning post 50. Goldstein was asking about the impact of carbon fuels, and there the net effect of aerosols is about nil, but with a high degree of uncertainty.
http://onlinelibrary.wiley.com/doi/10.1002/jgrd.50171/pdf
That paper also claims that the warming effects of soot are greater than previously thought.
I don’t think much of the common theory that polution in China is, in the net, slowing temperature growth because it reflects radiation.
[Response: Read the papers you cite.The net effect of BC producing activities is close to zero because of the amount of organic carbon that is emitted alongside. And it should probably go without saying that what you think of a theory is not determinative of whether it is in fact true. – gavin]
PAber says @ 51
Is there any explanation (rather than denial or data manipulation) of the origin of the post-2000 hiatus in BOTH datasets?
These discussions always remind of a paper by Tsonis and Swanson (author of a RC article about warming interrupted) in which they describe a new regime starting ~2000 in which the deep oceans would warm and the SAT would be flat – for a fewish decades.
Gavin, thanks for the pointer to Ed Hawkins, and poking around there, this is fun:
What happens if you spin the Earth backwards?
Concerning post 57. No. The report says the net short-term forcing from carbon fuels IS about nil (p. 134), which means that pollution may not have the negative forcing impact widely assumed. There is little evidence that the cooling effect of radiation reflection due to aerosols is much greater than the direct and indirect warming effects of soot.
[Response: You are jumping to conclusions. This does not include the effect of sulphates from power station burning of coal (which has little to no BC). The net effect of aerosols is strongly negative, even including the latest estimate of BC impacts. – gavin]
Gavin responded to post 57, saying that the nil impact is due to the fact that the estimate includes the effect of organic carbon. But the report gives the results without the effects of organic carbon, and that is a POSITIVE forcing, again with a large margin of error. That is not a reason for saying that the estimate of a nil effect, rather than the commonly believed negative forcing, is due to the inclusion of organic carbon in the estimate.
As I said, I don’t see much evidence for a net negative forcing due to pollution, and Gavin’s response does not change that.
The report’s calculations, leading to an estimate of net nil effect, only includes short term gases. It does not include the impact of CO2 emitted at the same time as pollution is created. Pollution is short-term, since it would disappear soon if emissions stopped, and the short-term effect of CO2 is relatively small. The long term net impact of pollution, if one includes the production of CO2 in the process that produces the pollution, would obviously be strongly positive.
GlenFergus @52 — In my simple two box climate model the Akaike Information Criterion indicates that the PDO should be ignored as the cost of the additional parameter is not worth the tiny gain in explained variance.
PAber (#51),
I notice that the trend in wind speed may be more muted than the paper I linked to indicates. http://www.sciencemag.org/content/334/6058/905.2.full
T Marvell (#60),
The wording is a little confusing, but if you look at section 3.4.2 you’ll see: “Emissions from coal-fired power plants, which emit much less BC because of their better combustion efficiency, are not included here.”
Fig. 10.2 also puts it in context. The green line at the bottom shows all aerosol forcings which is most likely negative. Looks like they favor Sophie’s pick on aerosol forcing.
T. Marvell — “supercritical” coal plants run very hot and burn carbon very efficiently. We can be glad they require metallurgy to build generators that will operate reliably without corroding at such high temperatures. “Thirty years from now” fusion could produce similarly high temperatures — and be swapped in as the heat source, allowing continuing use of those high temp generating systems.
(Even standard coal burning plants run hotter than that other heat source. This makes replacing the heat source a matter for planning carefully — but do it over there not here).
What you left out of your analysis for Hansen et al is the fact that Scenarios A,B, and C are for different increases of CO2 atmospheric concentrations over the time for the curves. While we have exceeded Hansen et al CO2 atmospheric concentration for scenario A, the observed temps are following the scenario C curve which was for very low increase in atmospheric concentration of CO2. In fact, we have emitted more CO2 into the atmosphere than Hansen et al’s Scenario A.
[Response: It was not ‘left out’ at all. The link to our first discussion on this had exactly what was in the scenarios and the fact that it is the net forcing that counts – not CO2 alone. And on that measure both scenario A and (though to a lesser extent) B overshot the actual forcing. But your claim is not even true for CO2 alone: 2012 was 396 in scenario A, and 393 in scenario B, 368 in scenario C. The CO2eq forcings are significantly higher. – gavin]
#33 Mark
Joint attribution involves both attribution of observed changes to regional climate change and attribution of a measurable proportion of either regional climate change or the associated observed changes in the system to anthropogenic causes, beyond natural variability. This process involves statistically linking climate change simulations from climate models with the observed responses in the natural or managed system. Confidence in joint attribution statements must be lower than the confidence in either of the individual attribution steps alone, due to the combination of two separate statistical assessments.
Re: #35 Tom Scharf
Just to add to Martin’s point. Gavin replied the first time you made the comment.
here
The earlier version is slightly more comprehensible except that it is still not clear whether you know about the log law relating forcing to the greenhouse gas concentration. Thus
implies that you might be thinking that a superlinear CO2 vs time function implies the same for the predicted global warming.
In any case you need to be more specific about this topic. What are the starting and end points you are considering when invoking the ‘predicted but non observed’ acceleration?
Your five year criterion is also non quantitative just where does it have to fall on these graphs?
Here is an interesting use of the Argo float network: http://www.agu.org/pubs/crossref/pip/2012GL053196.shtml
In it, a seasonal signal in ocean mixing has been detected. I wonder how easy it would be to pull a Fasullo one https://www.realclimate.org/index.php/archives/2013/01/on-sensitivity-part-ii-constraining-cloud-feedback-without-cloud-observations/ and turn that seasonal signal into a calibration for global changes in ocean mixing related to changing wind fields?
In response to 62, ahh, I see it Gavin thanks for pointing me too it.
#67 Geoff
I’m arguing from the high level trending. If the current rates of temperature increase (0.8C / century) and sea level (3 mm / year) are maintained, then we obvious aren’t going to reach the IPCC estimates of ~3C and 3 feet SLR. Clearly the rate of increase needs to increase to reach that target (i.e. acceleration).
I assume everyone watches these trends very closely, and looks for this acceleration “fingerprint” as a sign that positive feedbacks are actually happening.
One can argue that a long term accelerating trend sure look linear in short term pieces, but we are 30 years out now from 1980 and I’m not seeing this fingerprint.
One can also argue that confounding factors such as ENSO are suppressing the emergence of this trend. Maybe. We will know more in 10 or 20 years.
As for me, I’m watching the trends. I think they are the most important evidence there is of establishing the theory of positive feedbacks from CO2, and conversely of falsifying it. We’ve emitted a lot of carbon in the past 30 years.
Many thanks Chris.
So if you are hindcasting, could you prompt the model when to produce an el Nino &c? If so, will it tend to match the observed temperature fluctuations better? If not, might not the heat transfers values need tweaking?
If it is found that the output is more accurate with better timed ocean current changes in hindcasting, could you, in forecasts, again prompt the model with best guess estimates of what the ocean currents are going to do, based on the historical records? Would this not produce a more accurate output?
I realise that as models will evolve to produce their own correctly timed ocean changes, but until that happens, the contrarians are going to continue to give us earache every time the models deviate from the observations.
If I am just totally off the wall here, just point me in the direction of a good introduction to modelling. I have read Paul Edwards “A vast Machine” and Michael Mann’s Climate Wars, but given the intense hatred leveled against models by the contrarians, I have tended to steer clear of the subject in my 4-year running battle with them on the blogosphere.
Recently though in debate with lukewarmers, I find they simply discount all models, insist purely on empirical studies, which typically give ECS ranges of 1.2 – 2.4C. Why do models give the 2-4C range? Is it simply because the empirical studies are measuring only the transitional CS?
I have posted about models here: http://greenerblog.blogspot.com/2012/11/climate-models-are-they-any-good.html and here: http://greenerblog.blogspot.com/2012/11/climate-models-are-they-scientific-and.html
Rather than compare observations to computer models, I’ve compared observations to existing trends. The result is here:
http://tamino.wordpress.com/2013/02/12/2012-updates-to-trend-observation-comparisons/
[Response: Nice complement! – gavin]
About Foster and Rahmstorf:
http://troyca.wordpress.com/2013/01/25/could-the-multiple-regression-approach-detect-a-recent-pause-in-global-warming/
[Response: I will let Stefan weigh in on your statistics should he choose to do so, but what on earth are you talking about with your statements about a “recovery from Pinatubo”. That was in 1992, and the sulfate in the atmosphere last just a few years. No one I’m aware of has suggested any sort of “recovery from Pinatubo argument.–eric]
@73, Armando
Unfortunately one of my comments related to the limitations and applicability of multiple regression did not make through the moderation.
Fortunately, Armando did much better – provided actual calculations. Thanks.
In short: multiple regression is a statistical, not physical modelling tool. And if, as Foster and Rahmsdorf do, one looks for the best fit coefficients among 4 independent variables: linear anthropogenic warming ternd, MEI, volcanic and solar, then the subtraction of the MEI, volcanic and solar parts leaves us with … linear (or linear+noise to be precise) trend. Linear in, linear out. No physics.
Using a different set of assumptions (as Armando did – using a different form of the underlying trend) may lead to different coefficients and different form of the “underlying signal”, i.e. signal with volcanic, solar and MEI parts removed).
Because Foster and Rahmsdorf used linear form of one of the global temperature trend, it is no wonder that they arrived at linear end result for this variable – as shown in Fig.2. In my opinion it is much better to rely on the raw data instead.
Lastly, I repeat my point that while solar and volcanic activities are not influenced by the global trends in temperature, the ENSO or MEI may be. Growing global temperature may change the characteristics of ENSO, for all we know these changes may be nonlinear. There is little data on the detailed links between the one and the other. And unrecognized relationships between the “independent” variables in multiple regression are one of the widely known problems of such method.
Eric,
I don’t think it’s far-fetched to assume there should be a long-tail warming influence in the quiet period since Pinatubo, following a relatively active volcanic period between 1960 and 1995. If you look at the AR4 natural model runs the temperature at the end of the record is about 0.15ºC lower than mid-century. This can’t have much to do with a negligable solar trend so must be mostly the result of the cumulative volcanic cooling. It therefore makes sense that there would be a gradual warming influence during a subsequent quiet period.
However, in the relatively short period since Pinatubo the analysis will be confused by a “rebound” effect that James Hansen noted in the recent Energy Imbalance paper (I think). This is where you get a warming overshoot, probably in late-90s/early-2000s, from the fast recovery. It also ignores a gentle negative forcing from stratospheric aerosol influence that has been identified over 2000-2010, which would counteract any tendancy for long-tail warming.
Richard Lawson,
Recently though in debate with lukewarmers, I find they simply discount all models, insist purely on empirical studies, which typically give ECS ranges of 1.2 – 2.4C. Why do models give the 2-4C range? Is it simply because the empirical studies are measuring only the transitional CS?
Climate sensitivity isn’t something that can be directly observed, so it’s important to note that all the empirical studies rely on models which the authors believe are representative of how the climate system works in relation to ECS or Transient Climate Response (TCR).
One key assumption made by the majority of models used in empirical studies is that there should be a linear relationship between net forcing and temperature response, so that net forcing from diverse sources over the historical period can be directly translated to a 2xCO2 forcing. In some brief analyses of CMIP5 model simulations this assumption doesn’t appear to bear fruit – the temperature response to historical GHG-only forcing does not scale at all linearly with temperature response to all-forcing. It’s therefore not clear to me that an ECS of 1.2 – 2.4C for a scaled historical forcing is incompatible with an ECS of 2 -4C for 2xCO2 forcing.
Eric wrote: “No one I’m aware of has suggested any sort of “recovery from Pinatubo argument.–eric”
Well, no-one serious, or at least, not exactly.
However, at least one follower of John Christy has been pushing a Pinatubo/El Nino argument in the great, wide world of Blog Science:
–SPECIAL 33-YEAR GLOBAL TEMPERATURE REPORT, NOVEMBER 2011
EARTH SYSTEM SCIENCE CENTER, THE UNIVERSITY OF ALABAMA IN HUNTSVILLE
http://vortex.nsstc.uah.edu/climate/2011/November/Nov2011GTR.pdf
This fairly casual description has been reified by said acolyte into, well, basically, an attributional claim that (you guessed it) the UAH record is inconsistent with AGW.
Of course, it’s a problematic description. For one thing, if the Pinatubo eruption ’tilts’ the later record, it certainly also ’tilts’ the former record, helping to account for the “little or no warming for the first 19 years.” And for another, the quasi-attribution of “clear net warming” to the ’98 El Nino fails to account for the fact that temperatures immediately following that El Nino were lower than those immediately preceding.
I know, it’s not the same thing Armando was saying, though there’s a certain parallelism.
Armando’s post, along with most of the oeuvre of the denialati seems to insist on looking at only a tiny portion of the evidence, cherrypicking time periods, datasets and analysis methods and then declaring victory. Meanwhile, ice continues to melt, storms continue to worsen, drought intensifies, springs come earlier, fall frosts later and on and on. It would appear that anymore to be a denialist is to focus more and more on less and less until your perspective becomes a singularity.
What Ray Ladbury said @~78
We still loan too much credibility to fake skeptics. It is hard to respond to outright dishonesty when cleverly masked, and hard to plumb the depths of magical thinking exploited by politics masking itself as science. Wasting scientists’ valuable time is one of the unhappy offshoots of all this, pushing us towards a climate cliff.
@ Ray Ladbury #78
Just to be on the factual side: at the moment ice (in the Arctic) continues to form, quite fast in fact. Who is cherrypicking of September data (not annual averages or March data) with big, big news headline making stories?
Hi Eric #73:
“I will let Stefan weigh in on your statistics should he choose to do so, but what on earth are you talking about with your statements about a “recovery from Pinatubo”. That was in 1992, and the sulfate in the atmosphere last just a few years. No one I’m aware of has suggested any sort of “recovery from Pinatubo argument.”
Consider it from an energy balance perspective. You have about -3 W/m^2 near-instantaneous forcing perturbation applied at the time of the eruption, and though there is nowhere near the time for the system to reach a new equilibrium, you’re looking at a radiative response of around 0.4-0.6 W/m^2 (short-term climate feedback x surface temperature change). When the sulfate in the atmosphere disappears, your forcing returns to what it was before the eruption, but you now have the bulk of this imbalance stemming from the radiative response still remaining. This imbalance is similar in magnitude to what we currently see today, and no realistic value for mixed-layer heat capacity or ocean heat uptake is going to get you from an 0.4 W/m^2 imbalance to near equilibrium after 5-7 months (the AOD lags used in F&R). Indeed, any simple energy balance model is going to show warming due to the Pinatubo recovery well into the first decade of the 21st century…see Fig22b, column 2 of Hansen et al. (2011), for example, which shows > 0.1 K increase in temperatures due to Pinatubo post-2000.
As Paul S alluded to, perhaps the energy balance models are too simple, and there is something more akin to a rebound effect where the temperature response overshoots in ~2001. This is difficult to diagnose given the CMIP5 models troubles in simulating the volcanic response (Driscoll et al., 2012). However, either way, it seems pretty unambigious that the warming response from the Pinatubo recovery persists until at least around 2000. The F&R method only diagnoses significant warming from Pinatubo until 5-7 months after the AOD is near zero, or ~1995. This means that you’re likely getting a mis-attribution of substantial warming from 1996 at least into the early 2000s, potentially later.
I would be interested to hear from Stefan on how successful their method was at diagnosing the volcanic response in AOGCMs.
#78 and #79 Ray and Susan –
What on earth are you talking about? I’ve used the same time periods, datasets, and methodology of F&R to test their methodology. As you can see in the very next realclimate post, I co-authored a paper that found that there is no significant contribution from UHI in the post-1930s USHCN homogenized dataset. How does that make me a “fake skeptic”?
The sulfate from volcanic eruptions is short-lived, but they have a signature in the oceans that lasts for considerably longer…
Re: #74 (PAber)
This is not just mistaken, it’s naive enough to call your objectivity into question.
Troy in CA, say again. I don’t see where I or Susan mentioned you at all. However, I think your conclusion about the duration of volcanic effects is way off. It is certainly not reflected in the data–even for very large eruptions.
Pinatubo effect graphic from a comment at Skeptical Science
Just for the record, this is what I was supporting; a worldwide problem:
This kind of thing started out being unpleasant and peculiar in its absent relationship to the kind of truth-seeking that is normal in science, but as the years go by is now mad bad and dangerous as the evidence piles up and starts to pour into the lives of the many who inhabit this planet. My problem is that we fail to stem the tide of waste washing on the shores of knowledge – as the seas rise, the truth seems to be eroding as well.
Just took a peek at the borehole, and found the regular suspects lined up, no surprises. PAber a regular feature …
tamino,
Having worked with Troy on our UHI paper, I can say that he is pretty objective and open minded. His approach (attempting to replicate the original method and testing how well it works on synthetic data) is generally how one would go about evaluating the results of a paper, and doesn’t deserve to be dismissed out-of-hand.
Ray, you did mention Troy, since you called out Armando’s post. Armando merely pointed you to Troy Master’s analysis.
PAber #80
Yep. I’ve heard this facetiously referred to as “winter”.
You don’t want to wait until the winter ice is gone before admitting to the existence of a problem ;-)
Re: #87 (Zeke Hausfather)
Perhaps Troy is open-minded but just had a brain fart, or perhaps the idea that “the subtraction of the MEI, volcanic and solar parts leaves us with … linear (or linear+noise to be precise) trend” originates with PAber (to whom I responded) rather than Troy.
But the fact remains that the idea is sufficiently naive to cast doubt on one’s objectivity.
With respect to the Pinatubo ‘tail’, I don’t think this is an accurate characterisation of what is happening in Troy’s analysis. Rather, Pinatubo occurs in a climate that has not yet recovered from previous volcanic eruptions, and the post Pinatubo rise is better characterised as the recovery from the initially cold temperatures, rather than Pinatubo per se. This tail is longer and deeper than you see in GCMs. For reference, the figure below shows the response to volcanoes only in Troy’s EBM and the GISS-E2-H 5-member Ensemble mean. – gavin
The SkS comment–it’s the last, #37–is helpful in clarifying Troy’s point, and grounds it in the literature.
Are we in a longer than usual period of volcanic inactivity now?
tamino,
Mia culpa. Apologies for jumping to conclusions.
Re: #94 (Zeke Hausfather)
Although I think that PAber’s statement is plainly mistaken, looking at Troy’s analysis I’d say it deserves some attention. I don’t necessarily agree with it, but there may be some insight to be gained. I might post on the subject on my blog.
Thanks Gavin (#91)! As you may have guessed from our e-mail correspondence, that was exactly the thing I wanted to investigate (along with the other volcanic-only GISS-E runs). As you indicate, the EBM does not respond as starkly to the spikes, as it only has a constant value for that radiative restoration strength. However, it looks like that ensemble mean from GISS-E2-H, if we are to take that as the forced temperature response to volcanic activity, indicates a large contribution from this activity to the post-1995 trend, as well as the early 21st century temperature trend. I think this is quite an interesting topic to dive further into.
JCH (#85) – yup, that is part of the same figure I mentioned. However, the “effect” there is not referring to the temperature response, but rather deals with the TOA imbalance…if you look in column 2 of that same line in that figure, you will find the effect on temperature, where the trend from 1995 through the early 21st century is distinctly positive.
Zeke, thanks for the vote of confidence! As Tamino indicated and you mentioned, however, I do not believe he was replying to my comments/post, but someone else.
Gavin,
It’s interesting the end of that time series is significantly warmer than the beginning even though there was a volcanically-quiet period of 40-odd years prior to 1963. Can you tell whether the warmth at the end is a spike which cools afterwards, or something longer-lasting?
[Response: My graph should have only gone to 2005 – after that it is a single run. So, some part of the apparent peak is probably noise. I have another set of volcano-only runs that are being processed now and when that’s done, I’ll update. – gavin]
Troy, I was responding more to the tone of Armando’s post. I am skeptical about a prolonged post-Pinatubo effect, but I don’t think your contention is that this was responsible for the majority of warming.
Re: #83 @tamino
In post 74 I wrote:
“… if, as Foster and Rahmsdorf do, one looks for the best fit coefficients among 4 independent variables: linear anthropogenic warming ternd, MEI, volcanic and solar, then the subtraction of the MEI, volcanic and solar parts leaves us with … linear (or linear+noise to be precise) trend”
To which you replied:
“This is not just mistaken, it’s naive enough to call your objectivity into question.”
I ask where is the mistake? The multiple regression procedure is exactly as described. Moreover, to quote the original F&R paper:
“Figure 4 shows the adjusted data sets (with the influence of
MEI, AOD and TSI, as well as the residual annual cycle
removed) for monthly data. Two facts are evident. First, the agreement between the different data sets, even between surface and LT data, is excellent. Second, the global warming signal (which is still present in the adjusted data because the linear time trend is not removed) is far clearer and more consistent.”
Linear trend is not removed … so it is still present and visible. Should the assumed time trend be different (say, long period sinusoidal), the coefficients for the MEI, AOD and TSI fits would differ. Without physical mechanisms to show the actual links the whole process of statistical fitting is very vulnerable to initial assumptions.
Does this make me – as Susan is keen to write – ” a regular suspect” of ?ourageous denialism?
PAber,
With all due respect, if the temperature trend were not linear, then the resulting trend would not be linear. This is not an assumption–it is IN THE DATA.
The whole point of F&R 2011 is that if you take a simple model and account for 3 influences known to be operating and important in the climate, then the residual shows clearly the remaining dominant influence–anthropogenic warming. Were the simple model use wrong, it is exceedingly unlikely that agreement between 4 independent datasets was improved. Were there another exceptionally important factor that did not vary linearly, you would not expect the agreement to improve significantly. So, it seems to me that the only reasonable criticism of this work is that it neglected a forcing that trended in a linear manner similar to anthropogenic forcing. I know of no such forcing. Do you?
Looking back at your comment, perhaps you did not express yourself clearly, because as it reads, it is either wrong or “not even wrong”.