Real Climate (11 klimatforskare från USA)

And so it goes – another year, another annual data point. As has become a habit (2009, 2010), here is a brief overview and update of some of the most relevant model/data comparisons. We include the standard comparisons of surface temperatures, sea ice and ocean heat content to the AR4 and 1988 Hansen et al simulations.

First, a graph showing the annual mean anomalies from the IPCC AR4 models plotted against the surface temperature records from the HadCRUT3v, 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.

The La Niña event that emerged in 2011 definitely cooled the year a global sense relative to 2010, although there were extensive regional warm extremes. Differences between the observational records are mostly related to interpolations in the Arctic (but we will check back once the new HadCRUT4 data are released). Checking up on our predictions from last year, I forecast that 2011 would be cooler than 2010 (because of the emerging La Niña), but would still rank in the top 10. This was true looking at GISTEMP (2011 was #9), but not quite in HadCRUT3v (#12) or NCDC (#11). However, this was the warmest year that started off (DJF) with a La Niña (previous La Niña years by this index were 2008, 2001, 2000 and 1999 using a 5 month minimum for a specific event) in the GISTEMP record, and the second warmest (after 2001) in the HadCRUT3v and NCDC indices. Given current indications of only mild La Niña conditions, 2012 will likely be a warmer year than 2011, so again another top 10 year, but not a record breaker – that will have to wait until the next El Niño.

People sometimes claim that “no models” can match the short term trends seen in the data. This is not true. For instance, the range of trends in the models for 1998-2011 are [-0.07,0.49] ºC/dec, with MRI-CGCM (run5) the laggard in the pack, running colder than observations.

In interpreting this information, please note the following (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.
• The AR4 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 HadCRUT3v 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.

Foster and Rahmstorf (2011) showed nicely that if you account for some of the obvious factors affecting the global mean temperature (such as El Niños/La Niñas, volcanoes etc.) there is a strong and continuing trend upwards. An update to that analysis using the latest data is available here – and shows the same continuing trend:

There will soon be a few variations on these results. Notably, we are still awaiting the update of the HadCRUT (HadCRUT4) product to incorporate the new HadSST3 dataset and the upcoming CRUTEM4 data which incorporates more high latitude data (Jones et al, 2012). These two changes will impact the 1940s-1950s temperatures, the earliest parts of the record, the last decade, and will likely affect the annual rankings (and yes, I know that this is not particularly significant, but people seem to care).

Ocean Heat Content

Figure 2 is the comparison of the ocean heat content (OHC) changes in the models compared to the latest data from NODC. As before, I don’t have the post-2003 AR4 model output, so I have extrapolated the ensemble mean to 2012 (understanding that this is not ideal). New this year, are the OHC changes down to 2000m, as well as the usual top-700m record, which NODC has started to produce. For better comparisons, I have plotted the ocean model results from 0-750m and for the whole ocean. All curves are baselined to the period 1975-1989.

I’ve left off the data from the Lyman et al (2010) paper for clarity, but note that there is some structural uncertainty in the OHC observations. Similarly, different models have different changes, and the other GISS model from AR4 (GISS-EH) had slightly less heat uptake than the model shown here.

As can be seen the long term trends in the models match those in the data, but the short-term fluctuations are both noisy and imprecise. As an aside, there are a number of comparisons floating around using only the post 2003 data to compare to the models. These are often baselined in such a way as to exaggerate the model data discrepancy (basically by picking a near-maximum and then drawing the linear trend in the models from that peak). This falls into the common trap of assuming that short term trends are predictive of long-term trends – they just aren’t (There is a nice explanation of the error here).

Summer sea ice changes

Sea ice changes this year were dramatic, with the Arctic September minimum reaching record (or near record) values (depending on the data product). Updating the Stroeve et al, 2007 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 may not be true for the CMIP5 simulations – but we’ll have a specific post on that another time.

Hansen et al, 1988

Finally, we update the Hansen et al (1988) 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 exactly the same as the different scenarios used. 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).

The trends for the period 1984 to 2011 (the 1984 date chosen because that is when these projections started), scenario B has a trend of 0.28+/-0.05ºC/dec (95% uncertainties, no correction for auto-correlation). For the GISTEMP and HadCRUT3, the trends are 0.18+/-0.05 and 0.17+/-0.04ºC/dec. For reference, the trends in the AR4 models for the same period have a range 0.21+/-0.16 ºC/dec (95%).

As we stated before, the Hansen et al ‘B’ projection is running warm compared to the real world (exactly how much warmer is unclear). 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, the use of this comparison to refine estimates of climate sensitivity should be done cautiously, as the result is strongly dependent on the magnitude of the assumed forcing, which is itself uncertain. Recently there have been some updates to those forcings, and so my previous attempts need to be re-examined in the light of that data and the uncertainties (particular in the aerosol component). However, this is a complicated issue, and requires more space than I really have here to discuss, so look for this in an upcoming post.

Overall, given the latest set of data points, we can conclude (once again) that global warming continues.

1. G. Foster, and S. Rahmstorf, "Global temperature evolution 1979–2010", Environmental Research Letters, vol. 6, 2011, pp. 044022-. DOI.
2. P.D. Jones, D.H. Lister, T.J. Osborn, C. Harpham, M. Salmon, and C.P. Morice, "Hemispheric and large-scale land surface air temperature variations: An extensive revision and an update to 2010", Journal of Geophysical Research. DOI.
3. J.C. Hargreaves, "Skill and uncertainty in climate models", Wiley Interdisciplinary Reviews: Climate Change, vol. 1, 2010, pp. 556-564. DOI.

My co-authors and I have just published an article in Nature Geoscience (advance online publication here; associated press release here) which seeks to explain certain enigmatic features of tree-ring reconstructions of Northern Hemisphere (NH) temperatures of the past millennium. Most notable is the virtual absence of cooling in the tree-ring reconstructions during what ice core and other evidence suggest is the most explosive volcanic eruption of the past millennium–the AD 1258 eruption. Other evidence suggests wide-spread global climate impacts of this eruption [see e.g. the review by Emile-Geay et al (2008)]. We argue that this–and other missing episodes of volcanic cooling, are likely an artifact of biological growth effects, which lead to a substantial underestimation of the largest volcanic cooling events in trees growing near treeline. We speculate that this underestimation may also have led to overly low estimates of climate sensitivity in some past studies attempting to constrain climate model sensitivity parameters with proxy-reconstructed temperature changes.

Tree rings are used as proxies for climate because trees create unique rings each year that often reflect the weather conditions that influenced the growing season that year. For reconstructing past temperatures, dendroclimatologists typically seek trees growing at the boreal or alpine treeline, since temperature is most likely to be the limiting climate variable in that environment. But this choice may also prove problematic under certain conditions. Because the trees at these locations are so close to the threshold for growth, if the temperature drops just a couple of degrees during the growing season, there will be little or no growth and therefore a loss of sensitivity to any further cooling. In extreme cases, there may be no growth ring at all. And if no ring was formed in a given year, that creates a further complication, introducing an error in the chronology established by counting rings back in time.

We compared simulated temperature of the past millennium derived by driving theoretical climate models with estimated natural (volcanic+solar) and anthropogenic forcings for the past millennium. We employed two different climate model simulations: (1) the simulation of the NCAR CSM 1.4 coupled atmosphere-ocean General Circulation Model (GCM) analyzed by Ammann et al (2007) and (2) simulations of a simple Energy Balance Model (EBM). While the GCM provides a more comprehensive and arguably realistic description of the climate system, the computational simplicity of the EBM lends itself to extensive sensitivity tests. As the target for our comparison, we used a state-of-the-art tree-ring based Northern Hemisphere (NH) mean temperature reconstruction of D’Arrigo et al (2006). The reconstruction was based on a composite of tree ring annual ring width series from boreal and alpine treeline sites across the northern hemisphere, and made use of a very conservative (“RCS”) tree-ring standardization procedure designed to preserve as much low-frequency climatic information as possible.

Interestingly, the long-term variations indicated by the model simulations compared remarkably well with those documented by the tree-ring reconstruction, showing no obvious sign of the potential biases in the estimated low-frequency temperature variations that have been the focus of much previous work (see e.g. this previous RealClimate review). Instead, the one glaring inconsistency was in the high-frequency variations, specifically, the cooling response to the largest few tropical eruptions, AD 1258/1259, 1452/1453 and the 1809+1815 double pulse of eruptions, which is sharpy reduced in the reconstruction relative to the model predictions. Indeed, this was found to be true for any of several different published volcanic forcing series for the past millennium, regardless of the precise geometric scaling used to estimate radiative forcing from volcanic optical depth, and regardless of the precise climate sensitivity assumed.

Following the AD 1258 eruption, the climate model simulations predict a drop of 2C, but the tree ring-based reconstruction shows only about a 0.5C cooling. Equally vexing, the cooling in the reconstruction occurs several years late relative to what is predicted by the model. The other large eruptions showed similar discrepancies. An analysis using synthetic proxy data with spatial sampling density and proxy signal-to-noise ratios equivalent to those of the D’Arrigo et al (2006) tree-ring network suggest that these discrepancies cannot be explained in terms of either the spatial sampling/extent or the intrinsic “noisiness” of the network of proxy records.

However, using a tree growth model that accounts for the temperature growth thresholding effects discussed above, combined with the complicating effects of chronological errors due to potential missing growth rings, explains the observed features remarkably well.

Show in the above figure (Figure 2d from the article) is the D’Arrigo et al tree-ring based NH reconstruction (blue) along with the climate model (NCAR CSM 1.4) simulated NH mean temperatures (red) and the “simulated tree-ring” NH temperature series based on driving the biological growth model with the climate model simulated temperatures (green). The two insets focus on the response to the AD 1258 and AD 1809+1815 volcanic eruption sequences. The attenuation of the response is produced primarily by the loss of sensitivity to further cooling for eruptions that place growing season temperatures close to the lower threshold for growth. The smearing and delay of the cooling, however, arises from another effect: when growing season lengths approach zero, we assume that no growth ring will be detectable for that year. That means that an age model error of 1 year will be introduced in the chronology counting back in time. As multiple large eruptions are encountered further back in time, these age model errors accumulate. This factor would lead to a precise chronological error, rather than smearing of the chronology, if all treeline sites experienced the same cooling. However, stochastic weather variations will lead to differing amounts of cooling for synoptically distinct regions. That means that in any given year, some regions might fall below the “no ring” threshold, while other regions do not. That means that different chronological errors accumulate in synoptically-distinct regions of the Northern Hemisphere. In forming a hemispheric composite, these errors thus lead to a smearing out of the signal back in time as slightly different age model errors accumulate in the different regions contributing to the composite.

Including this effect, our model accounts not only for the level of attenuation of the signal, but the delayed and smeared out cooling as well. This is particularly striking in comparing the behavior following both the AD 1258 and AD 1809 eruptions (compare the green and blue curves in the insets of the figure). Our model, for example, predicts the magnitude of the reduction of cooling following the eruptions and the delay in the apparent cooling evidence in the tree-ring record (i.e. in AD 1262 rather than AD 1258). We have also included a minor additional effect in these simulations. While volcanic aerosols cause surface cooling due to decreased shortwave radiation at the surface, they also lead to increased indirect, scattered light at the surface. Plant growth benefits from indirect sunlight, and past studies show that e.g. a Pinatubo-sized eruption (roughly -2W/m^2 radiative forcing) can result in a 30% increase in carbon assimilation by plants. This effect turns out to be relatively small because it is proportional in nature, and thus results in a very small absolute increase when growth is suppressed i the first place by limited growing seasons. However, not including this effect results in a slightly less good reproduction (purple dashed curves in the two insets of the figure) of the observed behavior.

As noted earlier, our main conclusions are insensitive to the precise details of the forcing estimates used, the volcanic scaling assumptions made, and the precise assumed climate sensitivity. They were also insensitive to the details of the biological tree growth model over a reasonable range of model assumptions. The conclusion that tree-ring temperature reconstructions might suffer from age model errors due to missing rings is bound to be controversial. A few points are worth making here. First of all, our conclusion is quite specific to temperature-sensitive trees at treeline, and it does not imply more general problems in the larger discipline of dendrochronology. Secondly, the conclusion at this stage simply a hypothesis, a hypothesis that can account for these key enigmatic features in the actual tree-ring hemisphere temperature reconstruction: the attenuation, and the increasing (back in time) delay and temporal smearing of the cooling response to past volcanic forcing. Were an equally successful and more parsimonious hypothesis to be provided for these observations, I would be the first to concede and defer to this alternative explanation.

One argument against the specific conclusion of missing growth rings is that trees are carefully cross-dated when forming regional chronologies, and this precludes the possibility of chronological errors. That, however, assumes that there are at least some trees within a particular region that will not suffer a missing ring during the years where our model predicts it. Yet our prediction is that all trees within a region of synoptic or lesser scale where growing season temperatures lie below the growth threshold will experience a missing ring. Thus, cross-dating within that region, regardless of how careful, cannot resolve the lost chronological information. It is my hope that dendroclimatologists will reassess raw chronologies more carefully and critically assess the extent to which the predicted features might indeed be present in the underlying tree-ring data. Again, this paper presents a hypothesis for explaining some enigmatic features of existing tree-ring temperature reconstructions. It is hardly the last word on the matter.

Finally it is worth discussing the potential wider implication of these findings. Climate scientists use the past response of the climate to natural factors like volcanoes to better understand how sensitive Earth’s climate might be to the human impact of increasing greenhouse gas concentrations, e.g. to estimate the equilibrium sensitivity of the climate to CO2 doubling i.e. the warming expected for an increase in radiative forcing equivalent to doubling of CO2 concentrations. Hegerl et al (2006) for example used comparisons during the pre-industrial of EBM simulations and proxy temperature reconstructions based entirely or partially on tree-ring data to estimate the equilibrium 2xCO2 climate sensitivity, arguing for a substantially lower 5%-95% range of 1.5–6.2C than found in several previous studies. The primary radiative forcing during the pre-industrial period, however, is that provided by volcanic forcing. Our findings therefore suggest that such studies, because of the underestimate of the response to volcanic forcing in the underlying data, may well have underestimated the true climate sensitivity.

It will be interesting to see if accounting for the potential biases identified in this study leads to an upward revision in the estimated sensitivity range. Our study, in this regard, once again only puts forward a hypothesis. It will be up to other researchers, in further work, to assess the validity and potential implications of this hypothesis.

Guest Commentary by Eugenie Scott, National Center for Science Education

Imagine you’re a middle-school science teacher, and you get to the section of the course where you’re to talk about climate change. You mention the “C” words, and two students walk out of the class.

Or you mention global warming and a hand shoots up.

“Mrs. Brown! My dad says global warming is a hoax!”

Or you come to school one morning and the principal wants to see you because a parent of one of your students has accused you of political bias because you taught what scientists agree about: that the Earth is getting warmer, and human actions have had an important role in this warming.

Or you pick up the newspaper and see that your state legislature is considering a bill that declares that accepted sciences like global warming (and evolution, of course) are “controversial issues” that require “alternatives” to be taught.

Incidents like these have happened in one or more states, and they are likely to continue to happen. Teachers are encountering pushback from many directions as they try to teach global warming and other climate science topics.

The importance of climate change education is, to the RealClimate community, a no-brainer. Numerous professional science organizations, from the American Chemical Society to the American Geophysical Union to the Geological Society of America have stressed the imperative of climate science being an integral part of science education.

So What’s a Teacher to Do?

Long a defender of the teaching of evolution, the National Center for Science Education has recently launched an initiative to support and defend the teaching of climate change science.

The “support” part has challenges all its own. Unlike evolution, which easily fits into biology and other life science courses, climate science spans multiple disciplines and can fall through disciplinary cracks in biology, chemistry and physics, or appear briefly in more specialized disciplines like ecology or Earth sciences. Moreover, climate science is complex and often non-intuitive, and students (and all too often teachers) stumble over misinformation and misconceptions that are hard to overcome. Many educational institutions are wrestling with how to support climate science in the K-12 curriculum.

But the “defend” part is where NCSE will make a unique contribution. Our experience over the decades helping teachers and school boards resolve the problems that have arisen over the teaching of evolution should stand us in good stead in helping them deal with this newer “controversial science”. Of course, there are many perspectives affecting the objections to climate science education, and each requires its own response.

Some of the denial is literal (It’s not happening! The science is bad!), some of it may be interpretive (it’s maybe happening but people aren’t to blame), and some of it stems more from the implications of climate change (it’s happening and maybe humans are responsible, but someone else is to blame and/or there’s nothing I can do about it). We’re going to help teachers understand where pressure against climate science education comes from, as the first step in helping them construct a response. From the evolution education controversy we learned long ago that one does not solve these problems merely by piling on more or better science: the underlying, motivating issues must be addressed. The science is essential, but not sufficient.

Climate change education should be an integral part of science education. Students should graduate from high school and certainly college with at least a basic understanding of the foundational concepts of climate science so they can understand human activities and how they are impacting climate and other aspects of the earth system.

This is no small task, and obviously NCSE as a relatively small non-profit can only do so much. We need your help.

We have been successful because we marshal allies, like scientists, teachers, parents, and other citizens, at the grassroots. NCSE’s success over recent decades in defending the teaching of evolution has been due in large measure to scientists and others who are willing to support good science education locally and at the state level. We also need scientists to provide us with their scientific expertise.

If you are a climate scientist, please give us your contact information so we can consult with you. Also, your contact information will be helpful to us if something occurs in your region or state where we need a scientist to write a letter, testify before a committee, support a teacher, or help in some other way.

Of course, an obvious way you can help is to join NCSE, but even if you don’t, your expertise will be helpful to us.

Visit our website, and contact our new Programs and Policy Director, Mark McCaffrey, who will be helping spearhead the new initiative, to let us know you support our effort. Teachers will thank you.

This month’s open thread. Current topics are focused on the laughingly bad Daily Mail article by David Rose, the fallout from the Wall Street Journal’s latest regurgitation of why no-one should ever do anything ever. And perhaps someone might want to audit some of David Whitehouse’s arithmetic and reading comprehension…

Or anything else. Within reason.

Back in 2007, the IPCC AR4 SPM stated that:

“Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations.”

This is a clear statement that I think is very well supported and correctly reflects the opinion of most climate scientists on the subject (and was re-affirmed in two recent papers (Jones and Stott, 2011;, Huber and Knutti, 2011)). It isn’t an isolated conclusion from a single study, but comes from an assessment of the changing patterns of surface and tropospheric warming, stratospheric cooling, ocean heat content changes, land-ocean contrasts, etc. that collectively demonstrate that there are detectable changes occurring which we can attempt to attribute to one or more physical causes.

Yet, in a paper just out in BAMS (Curry and Webster, 2011) this statement is apparently evidence that IPCC is unable to deal with uncertainty. Furthermore, Judith Curry has reiterated on her blog that the term ‘most’ is imprecise and undefined. For instance:

Apart from the undefined meaning of “most” in AR4 (which was subsequently clarified by the IPCC), the range 50.1-95% is rather imprecise in the context of attribution.

However, Curry’s argument is far from convincing, nor is it well formed (why is there a cap at 95%?). Nor was it convincing when I discussed the issue with her in the comments at Collide-a-Scape last year where she made similar points. Since the C&W paper basically repeats that argument (as has also been noticed by Gabi Hegerl et al who have a comment on the paper (Hegerl et al.)), it is perhaps worth addressing these specific issues again.

Let’s start with what the statement actually means. “Most” is an unambiguous adjective (meaning more than half), and ‘very likely’ in IPCC-speak means that the statement is being made with between 90 to 99% confidence (i.e. for every 10 such statements, the scientists expect 9 or more to pan out). Given that some people have found this confusing, it may help somewhat if the contents of the statement are visualised:

Figure 1: Two schematic distributions of possible ‘anthropogenic GHG contributions’ to the warming over the last 50 years. Note that in each case, despite a difference in the mean and variance, the probability of being below 50, is exactly 0.1 (i.e. a 10% likelihood).

The figure shows two Gaussian distributions, both of which have the probability of x being less than 50 at 0.1. i.e. P(x<50)=0.1. If either of them had been the distribution of the estimated increase in global temperatures due to anthropogenic greenhouse gas increases relative to the observed increase, the IPCC statement would have been almost exactly correct (i.e. if x=100*trend_caused_by_GHG/actual_trend). These distributions show a number of key issues that need to be appreciated. First, the actual increase of temperatures purely due to the rise in GHGs is not precisely known (and therefore there is a distribution of potential values). Note that we are presuming that there is a single ‘true’ answer, so the distribution is a measure of our ignorance, not a claim that the answer itself is a random variable.

Second, the IPCC statement is not a declaration about what the most likely value of ‘x’ is. It states merely that P(x> 50%) is at least 0.9. In the two figures, one has the mean value of x at 80%, while the other has the mean value at 100%. Both fit the IPCC statement equally well. Some people have interpreted the IPCC statement confusing the likelihood of the statement with the actual relative trend (i.e. that the 90% refers to the expected attribution), but that would be a big misreading of the text.

Third, there is certainly a potential for the increase in temperatures due to anthropogenic GHG changes to be greater than the observed trend because we know that there have been both natural (volcanic and solar) and human-caused (reflective aerosols, land use change) factors that are expected to have lead to cooling over the post-1950 period (therefore there is no cut off at 95% of the actual trend). The actual trend will be a function of the warming factors, balanced by the cooling factors. And of the warming factors, the well-mixed greenhouse gas (CO2, CH4, N2O, CFCs) changes are the dominant term (about 75% of the increase in warming factors from 1950, the rest is related to black carbon effects, ozone etc.).

Fourth, the statement clearly encompasses many different estimates of what the actual trends are being driven by and is not therefore a particularly strong conclusion. Myles Allen (Allen, 2011) points out that during the drafting, the text was changed from ‘contributed substantially’ to ‘most’, and focused on greenhouse gases rather than the total anthropogenic effect specifically in order to have a more quantitative conclusion and more justifiable statement.

Now let’s put some real numbers in here. Attribution is fundamentally a modelling task, and the principal models that can be used are the coupled GCMs – at least to start with. What do they estimate the warming trend from the well-mixed GHGs to have been over the last 50 years? The figure below shows this for some of the GISS CMIP5 models (more model data can be downloaded from CMIP5 portal):

The 50 year trends (here, from 1956 to 2005, 5 ensemble members), are 0.84ºC (range [0.79,0.92]) for just greenhouse gas forcing. and 0.67ºC (range [0.54,0.76]) for the all-forcings case (in CMIP3, the envelope of the all-forcing trends is [0.4,1.3], or equivalently 0.74 +/- 0.22ºC (1 sigma spread) using 55 individual model simulations – the wider spread reflecting structural variations in the models and forcings). As in the more recent model simulations, the GISS CMIP3 50 year trends using only well-mixed GHG forcings is around 0.1ºC more than the ‘all-forcing’ case (data here).

The actual observed trend depends a little on the dataset used, but is around 0.6 +/- 0.05ºC (1 sigma uncertainty in the OLS fit). If we then estimate the percentage (as illustrated above), assuming a 0.2ºC sigma in the model spread, ‘x’ is roughly 140% +/- 35% (1 sigma). If we interpreted that range as a Gaussian distribution (not really a good idea, but simple enough for illustration), we’d estimate that P(x<50%) would be less than 1% (even less likely than the IPCC AR4 statement allowed for).

There are good reasons why the IPCC assessed that the probability was not as low as suggested by the models or any individual attribution paper. Specifically, the overall assessment must take into account potential structural uncertainties that don’t come into the straight model analysis. For instance, the models may systematically be overestimating the GHG-driven trend, they may be underestimating the internal variability, and they may be undersampling the structural uncertainty in making models themselves. The first kind of error would cause an overestimate in the mean of the distribution, while the other factors would cause an underestimate in the variance of the trends – all would increase P(x < 50%). On the other hand, the net forcing is almost certainly less than the effect of anthropogenic GHGs alone and so that biases the mean of the ‘all-forcings’ trends low, and some of the spread in the trends is related to different models having different forcings (biasing the spread wide). These elements can be quantified during the attribution (using fingerprint scaling, monte-carlo emulators etc.), but when they are all taken into account, the difference is less than one might think (it turns out that structural uncertainty likely isn’t being underestimated and the internal variability in models comfortably spans the range inferred in the real world (Yokohata et al., 2011; Santer et al., 2011)).

Curry and Webster specifically bring up two issues that, they claim, lessen the confidence one should have in the IPCC statement: that the history of solar forcing is uncertain in scale, and that aerosol forcings have a huge error bar. These two statements are true as far as they go – the scale of solar forcing is not tightly constrained prior to about 1960, and the total aerosol forcing and it’s variation in time is uncertain. But C&W’s specific complaint is that the attribution studies used in AR4 used solar forcing that was too large compared to more recent studies. However, reducing any warming trend associated with solar actually makes the attribution statement more likely which somewhat undercuts their point.

With respect to aerosols, the key thing to remember that regardless of the magnitude of the change, the sign of the forcing is almost certainly negative (i.e. the net aerosol effect has been one of cooling). The dominant anthropogenic aerosols are sulphates (derived from the SO2 emitted during the burning of sulphur-containing fossil fuels), which are reflective, and hence cooling. Other aerosols (black carbon, organic carbon, nitrates) are more uncertain, but have a net effect that is smaller.

Now, the statement in AR4 specifically states that the effect of greenhouse gases is more than half of the observed trend, which is actually independent of the effects of aerosols. But with the high probability of aerosols being a net cooling, this increases the ratio of the GHG-driven trends to the actual forced trend.

The final issue is whether the internal variability of the system on multi-decadal timescales has been properly characterised. For instance, it is possible that all the models grossly underestimate the internal variability, in which case any expected trend due to GHGs would be drowned out in the noise. But there is no positive evidence for this at all – as Hegerl et al point out, the estimates of multi-decadal variability in the models and observational records all overlap within their (substantial) uncertainties (arising from the shortness of the record, and the difficulty in estimating internal variability in the presence of multiple forcings). So while it is conceivable be that there is a bias, it is currently undetectable, which implies it can’t be that large.

In summary then, the IPCC AR4 statement was a fair, even conservative, assessment. There is an unfortunate tendency to reify the particular statements made by IPCC, since there were clearly other correct statements that could have been made. For instance, it might well have been worthwhile to add a statement about the likely range of the anthropogenic trends (i.e 80-120% of the actual trend or similar), so that a better picture of the appropriate distribution could be given (see Huber and Knutti (2011) for examples). But claims that the statement was unsupported, or that it demonstrated that IPCC was ignoring uncertainty are simply untenable.

The next iteration (IPCC AR5) is now underway, but given the early results of the CMIP5 models (which are on the whole very similar, as discussed at fall AGU), and more recent literature on this issue (see refs below), I see no reasons in the recent literature why the conclusions in AR5 will be much different. But if anyone still finds the assessment confusing, they have an opportunity to make their points via the IPCC review process, and the resulting conclusions will likely be clearer because of them.

1. G.S. Jones, and P.A. Stott, "Sensitivity of the attribution of near surface temperature warming to the choice of observational dataset", Geophysical Research Letters, vol. 38, 2011. DOI.
2. M. Huber, and R. Knutti, "Anthropogenic and natural warming inferred from changes in Earth’s energy balance", Nature Geoscience, vol. 5, 2011, pp. 31-36. DOI.
3. J.A. Curry, and P.J. Webster, "Climate Science and the Uncertainty Monster", Bulletin of the American Meteorological Society, vol. 92, 2011, pp. 1667-1682. DOI.
4. G. Hegerl, P. Stott, S. Solomon, and F. Zwiers, "Comment on “Climate Science and the Uncertainty Monster” J. A. Curry and P. J. Webster", Bulletin of the American Meteorological Society, vol. 92, 2011, pp. 1683-1685. DOI.
5. M. Allen, "In defense of the traditional null hypothesis: remarks on the Trenberth and Curry WIREs opinion articles", Wiley Interdisciplinary Reviews: Climate Change, vol. 2, 2011, pp. 931-934. DOI.
6. T. Yokohata, J.D. Annan, M. Collins, C.S. Jackson, M. Tobis, M.J. Webb, and J.C. Hargreaves, "Reliability of multi-model and structurally different single-model ensembles", Climate Dynamics. DOI.
7. B.D. Santer, C. Mears, C. Doutriaux, P. Caldwell, P.J. Gleckler, T.M.L. Wigley, S. Solomon, N.P. Gillett, D. Ivanova, T.R. Karl, J.R. Lanzante, G.A. Meehl, P.A. Stott, K.E. Taylor, P.W. Thorne, M.F. Wehner, and F.J. Wentz, "Separating signal and noise in atmospheric temperature changes: The importance of timescale", Journal of Geophysical Research, vol. 116, 2011. DOI.

A group of researchers at Carnegie Mellon University is trying to get a better understanding of the views of earth scientists regarding various climate change topics. They have set up an ongoing poll to do this, called Vision Prize. It’s a short (10 question) poll, covering topics like the rate of CO2 increase, predicted future temperatures, sea ice and sea level states, and hurricane frequencies. Early participants can designate a $20 donation from the group to a charity of their choice, upon completion. Please take a few minutes to help them out if qualified.

Update January 27: There is also another recent dog-based animations from Victoria (southeast Australia) explaining some of the key drivers of our climate and how some are changing.

A TV series that ran on Norwegian TV (NRK) last year included a simple and fun cartoon that demonstrates some important concepts relative to weather and climate:

In the animation, the man’s path can be considered as analogous to a directional climatic change, while the path traced by his dog’s whimsical movements represent weather fluctuations, as constrained by the man’s path, the leash, and the dog’s moment-by-moment decisions of what seems important to investigate in his small world. What might the leash length represent? The man’s momentary pause? The dog’s exact route relative to concepts of random variation? The messages in this animation are similar to the recent results of Grant Foster and Stefan Rahmstorf in ERL (see post here).

We’d also like to praise the TV-series ‘Siffer‘, hosted by an enthusiastic statistician explaining how most things in our world relate to mathematics. The series covers a range of subjects, for instance gambling theory, the Tragedy of the Commons, anecdotes about mathematical riddles, medical statistics, and construction design; it even answers why champagne from a large bottle tastes better than that from a smaller one. There is also an episode devoted to weather forecasting and climate.

Success in understanding our universe often depends on how the ‘story’ about it is told, and a big part of that often involves how mental images are presented. Mathematics and statistics can describe nature in great detail and “elegance”, but they are often difficult and inaccessible to the average person. Conversely, the man-and-dog animation is intuitive and easy to comprehend. Similarly, Hans Rosling’s Fun with Stats provides some very nice demonstrations of how to convey meaning via the creative display of numbers.

Almost 3000 non-science major undergraduates at the University of Chicago have taken PHSC13400, Global Warming: Understanding the Forecast, since Ray Pierrehumbert and I (David Archer) first developed it back in 1995. Since the publication of the textbook for the class in 2005 (and a much-cleaned-up 2nd edition now shipping), enrollment has gone through the roof, it’s all I’ve been able to teach the last few years, trying to keep up with demand. I hear it is the largest class on campus, with 4-500 students a year out of an annual class of only around 1400. Now the content of this class is being served to the internet world at large: Open Climate 101.

You can watch video lectures followed by quizzes to challenge and hopefully stimulate your understanding, and work your way through tutorials with interactive models and simple mathematical ideas. Actually all that stuff has been available for a long time, online or in the textbook, but now it’s packaged into an interactive assessing system, which admittedly lacks the personality and finesse of our graduate student teaching assistants, but I hope it’ll get the job done. You can work at your own pace, on your own time. You don’t get University of Chicago credit, but it’s free, and if you get to the end of it you can download a certificate of accomplishment with your name and a verification code, signed by me. I hope people find it useful.

I’ve put together an easy-to-play-with online model of methane in the atmosphere. I’m going to use it for teaching along with the rest of the Understanding the Forecast webmodels, but it was designed to be relevant to the issue of abrupt new methane burps as we’ve been ruminating about lately on Realclimate.

The model runs in three stages: a pre-anthropogenic steady state which ends in the model year
-50, addition of a new chronic source for 50 years (from human activity),then a spike beginning at model year 0 (supposed to be today) and running for 100 years into the model future. Here are results from the “worst case scenario” in the last post (whether you believe it is the true worst case or not): 200 Gton C over 100 years.

Looks like we got the factor of 10 methane increase about right.

Source and sink of methane in the model.

The lifetime of methane in the atmosphere, used to calculate the methane sink in any time step, is parameterized as a function of concentration following Schmidt and Shindell (2003).

The atmospheric lifetime of methane, used to calculate the sink flux.

The radiative forcing is parameterized from output from the NCAR model, scaled by an efficacy factor of 1.4 from Hansen et al, (2005). The radiative forcing is compared with Business-as-usual CO2 radiative forcing with the model year 0 corresponding roughly to year 2010, and with CO2 rising at 0.65% per year. The methane radiative forcing before year 0 is not time-realistic because the real human sources did not switch on instantaneously 50 years ago, but you can compare the future evolution of radiative forcing from CO2 and methane, from year 0 onwards.

The radiative forcings of CO2 and methane compared. The scenario is more-or-less comparable to 750 ppm CO2, as we thought.

The CO2 concentration used to generate the last figure.

Timing is everything

Four simulations with the same amount of carbon released as methane in the “spike”, on different time scales for the release.

10 Gton C release in 1 year — the spike.

Same spike but not as sharp: 10 Gton over 20 years.

Same 10 Gton but spread over 50 years.

100 years.

Enjoy. Go and get your swamp gas on, and give the poor model planet your worst. Bwahahahahaha!

1. G.A. Schmidt, "Atmospheric composition, radiative forcing, and climate change as a consequence of a massive methane release from gas hydrates", Paleoceanography, vol. 18, 2003. DOI.
2. J. Hansen, "Efficacy of climate forcings", Journal of Geophysical Research, vol. 110, 2005. DOI.

Let’s suppose that the Arctic started to degas methane 100 times faster than it is today. I just made that number up trying to come up with a blow-the-doors-off surprise, something like the ozone hole. We ran the numbers to get an idea of how the climate impact of an Arctic Methane Nasty Surprise would stack up to that from Business-as-Usual rising CO2

Walter et al (2007) says that Arctic lakes are 10% of natural global emissions, or about 5% of total emissions. I believe that was considered to be remarkably high at the time but let’s take it as a given, and representing the Arctic as a whole. If the number of lakes or their bubbling intensity suddenly increased by a factor of 100, and it persisted this way for 100 years, it would come to about 200 Gton of carbon emission, which is on the same scale as our entire fossil fuel emission so far (300 Gton C), or roughly the amount of traditional reserves of natural gas (although I’m not sure where estimates are since fracking) or petroleum. It would be a whopper of a surprise.

Scaling Walter’s Arctic lake emission rates up by a factor of 100 would increase the overall emission rate, natural and anthropogenic, by about a factor of 5 from where it is today. The weak leverage is because the high latitudes are a small source today relative to tropical wetlands and anthropogenic sources, so they have to grow a lot before they make much difference to the sum of all sources.

The steady-state methane concentration in the air scales nearly linearly with the emission rate. Actually, the concentration goes up somewhat faster than a constant times the emission rate, because the lifetime in the atmosphere gets longer (IPCC TAR). Let’s err on the side of flamboyance (great word in this context) and say the concentration of methane in the air goes up by a factor of 10 for the duration of the extra methane emission (meaning that the lifetime doubles).

Using the modtran model on line I get a radiative forcing from 10 * atmospheric methane of 3.4 Watts/m2 (the difference in the instantaneous IR flux out, labeled Iout, between cases with and without 10x methane). Using the TAR estimates of radiative forcing gives 2.7 Watts/m2.

But methane is a reactive gas and its presence leads to other greenhouse forcings, like the water vapor it decomposes into. Hansen estimates the “efficacy” of methane radiative forcing to be 1.4 (Hansen et al, 2005, Shindell et al, 2009), so that puts us to 4 or even 5 Watts/m2.

This is about twice the radiative forcing today from all anthropogenic greenhouse gases today, or (again according to Modtran) it would translate to an equivalent CO2 at today’s methane concentration of about 750 ppm. That seems significant, for sure.

Or, trying to “correct” for the different lifetimes of the gases using Global Warming Potentials, over a 100-year time horizon (which still way under-represents the lifetime of the CO2), you get that the methane would be equivalent to increasing CO2 to about 500 ppm, lower than 750 because the CO2 forcing lasts longer than the methane, which the GWP calculation tries in its own myopic way to account for.

But the methane worst case does not suddenly spell the extinction of human life on Earth. It does not lead to a runaway greenhouse. The worst-case methane scenario stands comparable to what CO2 can do. What CO2 will do, under business-as-usual, not in a wild blow-the-doors-off unpleasant surprise, but just in the absence of any pleasant surprises (like emission controls). At worst comparable to CO2 except that CO2 lasts essentially forever.

1. K.M. Walter, L.C. Smith, and F. Stuart Chapin, "Methane bubbling from northern lakes: present and future contributions to the global methane budget", Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 365, 2007, pp. 1657-1676. DOI.
2. J. Hansen, "Efficacy of climate forcings", Journal of Geophysical Research, vol. 110, 2005. DOI.
3. D.T. Shindell, G. Faluvegi, D.M. Koch, G.A. Schmidt, N. Unger, and S.E. Bauer, "Improved Attribution of Climate Forcing to Emissions", Science, vol. 326, 2009, pp. 716-718. DOI.

Methane is a powerful greenhouse gas, but it also has an awesome power to really get people worked up, compared to other equally frightening pieces of the climate story.

What methane are we talking about?

The largest methane pools that people are talking about are in sediments of the ocean, frozen into hydrate or clathrate deposits (Archer, 2007). The total amount of methane as ocean hydrates is poorly constrained but could rival the rest of the fossil fuels combined. Most of this is unattractive to extract for fuel, and mostly so deep in the sediment column that it would take thousands of years for anthropogenic warming to reach them. The Arctic is special in that the water column is colder than the global average, and so hydrate can be found as shallow as 200 meters water depth.

On land, there is lots of methane in the thawing Arctic, exploding lakes and what not. This methane is probably produced by decomposition of thawing organic matter. Methane could only freeze into hydrate at depths below a few hundred meters in the soil, and then only at “lithostatic pressure” rather than “hydrostatic”, meaning that the hydrate would have to be sealed from the atmosphere by some impermeable layer. The great gas reservoirs in Siberia are thought to be in part frozen, but evidence for hydrate within the permafrost soils is pretty thin (Dallimore and Collett,1995)

Is methane escaping due to global warming?

There have been observations of bubbles emanating from the sea floor in the Arctic (Shakhova, 2010; Shakhova et al., 2005) and off Norway (Westbrook, 2009). The Norwegian bubble plume coincides with the edge of the hydrate stability zone, where a bit of warming could push the surface sediments from stable to unstable. A model of the hydrates (Reagan, 2009) produces a bubble plume similar to what’s observed, in response to the observed rate of ocean water warming over the past 30 years, but with this warming rate extrapolated further back in time over the past 100 years. The response time of their model is several centuries, so pre-loading the early warming like they did makes it difficult to even guess how much of the response they model could be attributed to human-induced climate change, even if we knew how much of the last 30 years of ocean warming in that location came from human activity.

Lakes provide an escape path for the methane by creating “thaw bulbs” in the underlying soil, and lakes are everywhere appearing and disappearing in the Arctic as the permafrost melts. (Whether you get CO2 or a mixture of CO2 plus methane depends critically on water, so lakes are important for that reason also.)

Methane bubbles captured in freezing lake ice in Alaska

So far there hasn’t been strong evidence presented for detection enhanced methane fluxes due to anthropogenic warming yet. Yet it is certainly believable for the coming century however, which brings us to the next question:

What effect would a methane release have on climate?

The climate impact of releasing methane depends on whether it is released all at once, faster than its lifetime in the atmosphere (about a decade) or in an ongoing, sustained release that lasts for longer than that.

When methane is released chronically, over decades, the concentration in the atmosphere will rise to a new equilibrium value. It won’t keep rising indefinitely, like CO2 would, because methane degrades while CO2 essentially just accumulates. Methane degrades into CO2, in fact, so in simulations I did (Archer and Buffett, 2005) the radiative forcing from the elevated methane concentration throughout a long release was about matched by the radiative forcing from the extra CO2 accumulating in the atmosphere from the methane as a carbon source. In the figure below, the dashed lines are from a simulation of a fossil fuel CO2 release, and the solid lines are the same model but with an added methane hydrate feedback. The radiative forcing from the methane combines the CH4 itself which only persists during the time of the methane release, plus the added CO2 in the atmosphere, which persists throughout the simulation of 100,000 years.

The possibility of a catastrophic release is of course what gives methane its power over the imagination (of journalists in particular it seems). A submarine landslide might release a Gigaton of carbon as methane (Archer, 2007), but the radiative effect of that would be small, about equal in magnitude (but opposite in sign) to the radiative forcing from a volcanic eruption. Detectable perhaps but probably not the end of humankind as a species.

What could happen to methane in the Arctic?

The methane bubbles coming from the Siberian shelf are part of a system that takes centuries to respond to changes in temperature. The methane from the Arctic lakes is also potentially part of a new, enhanced, chronic methane release to the atmosphere. Neither of them could release a catastrophic amount of methane (hundreds of Gtons) within a short time frame (a few years or less). There isn’t some huge bubble of methane waiting to erupt as soon as its roof melts.

And so far, the sources of methane from high latitudes are small, relative to the big player, which is wetlands in warmer climes. It is very difficult to know whether the bubbles are a brand-new methane source caused by global warming, or a response to warming that has happened over the past 100 years, or whether plumes like this happen all the time. In any event, it doesn’t matter very much unless they get 10 or 100 times larger, because high-latitude sources are small compared to the tropics.

Methane as past killing agent?

Mass extinctions like the end-Permean and the PETM do typically leave tantalizing spikes in the carbon isotopic records preserved in limestones and organic carbon. Methane has an isotopic signature, so any methane hijinks would be recorded in the carbon isotopic record, but so would changes in the size of the living biosphere, soil carbon pools such as peat, and dissolved organic carbon in the ocean. The end-Permean extinction is particularly mysterious, and my impression is that the killing mechanism for that is still up for grabs. Methane is also one of the usual suspects for the PETM, which consisted of about 100,000 years of isotopically light carbon, which is thought to be due to release of some biologically-produced carbon source, similar to the way that fossil fuel CO2 is lightening the carbon isotopes of the atmosphere today, in concert with really warm temperatures. I personally believe that the combination of the carbon isotopes and the paleotemperatures pretty much rules out methane as the original carbon source (Pagani et al., 2006), although Gavin draws an opposite conclusion, which we may hash out in some future post. In any case, the 100,000-year duration of the warming means that the greenhouse agent through most of the event was CO2, not methane.

Could there be a methane runaway feedback?.

The “runaway greenhouse effect” that planetary scientists and climatologists usually call by that name involves water vapor. A runaway greenhouse effect involving methane release (such as invoked here) is conceptually possible, but to get a spike of methane concentration in the air it would have to released more quickly than the 10-year lifetime of methane in the atmosphere. Otherwise what you’re talking about is elevated methane concentrations, reflecting the increased source, plus the radiative forcing of that accumulating CO2. It wouldn’t be a methane runaway greenhouse effect, it would be more akin to any other carbon release as CO2 to the atmosphere. This sounds like semantics, but it puts the methane system into the context of the CO2 system, where it belongs and where we can scale it.

So maybe by the end of the century in some reasonable scenario, perhaps 2000 Gton C could be released by human activity under some sort of business-as-usual scenario, and another 1000 Gton C could come from soil and methane hydrate release, as a worst case. We set up a model of the methane runaway greenhouse effect scenario, in which the methane hydrate inventory in the ocean responds to changing ocean temperature on some time scale, and the temperature responds to greenhouse gas concentrations in the air with another time scale (of about a millennium) (Archer and Buffett, 2005). If the hydrates released too much carbon, say two carbons from hydrates for every one carbon from fossil fuels, on a time scale that was too fast (say 1000 years instead of 10,000 years), the system could run away in the CO2 greenhouse mode described above. It wouldn’t matter too much if the carbon reached the atmosphere as methane or if it just oxidized to CO2 in the ocean and then partially degassed into the atmosphere a few centuries later.

The fact that the ice core records do not seem full of methane spikes due to high-latitude sources makes it seem like the real world is not as sensitive as we were able to set the model up to be. This is where my guess about a worst-case 1000 Gton from hydrates after 2000 Gton C from fossil fuels in the last paragraph comes from.

On the other hand, the deep ocean could ultimately (after a thousand years or so) warm up by several degrees in a business-as-usual scenario, which would make it warmer than it has been in millions of years. Since it takes millions of years to grow the hydrates, they have had time to grow in response to Earth’s relative cold of the past 10 million years or so. Also, the climate forcing from CO2 release is stronger now than it was millions of years ago when CO2 levels were higher, because of the band saturation effect of CO2 as a greenhouse gas. In short, if there was ever a good time to provoke a hydrate meltdown it would be now. But “now” in a geological sense, over thousands of years in the future, not really “now” in a human sense. The methane hydrates in the ocean, in cahoots with permafrost peats (which never get enough respect), could be a significant multiplier of the long tail of the CO2, but will probably not be a huge player in climate change in the coming century.

Could methane be a point of no return?

Actually, releasing CO2 is a point of no return if anything is. The only way back to a natural climate in anything like our lifetimes would be to anthropogenically extract CO2 from the atmosphere. The CO2 that has been absorbed into the oceans would degas back to the atmosphere to some extent, so we’d have to clean that up too. And if hydrates or peats contributed some extra carbon into the mix, that would also have to be part of the bargain, like paying interest on a loan.

Conclusion

It’s the CO2, friend.

1. D. Archer, "Methane hydrate stability and anthropogenic climate change", Biogeosciences, vol. 4, 2007, pp. 521-544. DOI.
2. N. Shakhova, I. Semiletov, I. Leifer, A. Salyuk, P. Rekant, and D. Kosmach, "Geochemical and geophysical evidence of methane release over the East Siberian Arctic Shelf", Journal of Geophysical Research, vol. 115, 2010. DOI.
3. N. Shakhova, "The distribution of methane on the Siberian Arctic shelves: Implications for the marine methane cycle", Geophysical Research Letters, vol. 32, 2005. DOI.
4. G.K. Westbrook, K.E. Thatcher, E.J. Rohling, A.M. Piotrowski, H. Pälike, A.H. Osborne, E.G. Nisbet, T.A. Minshull, M. Lanoisellé, R.H. James, V. Hühnerbach, D. Green, R.E. Fisher, A.J. Crocker, A. Chabert, C. Bolton, A. Beszczynska-Möller, C. Berndt, and A. Aquilina, "Escape of methane gas from the seabed along the West Spitsbergen continental margin", Geophysical Research Letters, vol. 36, 2009. DOI.
5. M.T. Reagan, and G.J. Moridis, "Large-scale simulation of methane hydrate dissociation along the West Spitsbergen Margin", Geophysical Research Letters, vol. 36, 2009. DOI.
6. D. Archer, "Time-dependent response of the global ocean clathrate reservoir to climatic and anthropogenic forcing", Geochemistry Geophysics Geosystems, vol. 6, 2005. DOI.
7. M. Pagani, K. Caldeira, D. Archer, and J.C. Zachos, "ATMOSPHERE: An Ancient Carbon Mystery", Science, vol. 314, 2006, pp. 1556-1557. DOI.

First open thread of 2012, so perhaps some discussion of the highlights and lowlights of 2011 are in order? Top 5 lists welcome…

Two slightly off-center topics that Realclimate has covered in the past have recently come up again. The first is an analysis of Freakonomics by statisticians Andrew Gelman and Kaiser Fung in American Scientist, while the second is a recent reimagining of Washington crossing the Delaware.

The Gelman and Fung piece goes through a number of the errors found in the Freakonomics ouevre (from Steves Levitt and Dubner), and specifically references Ray Pierrehumbert’s critique of the climate change chapter in SuperFreakonomics (also discussed here). The whole piece is worth reading for how trust in their networks might be undermining Levitt and Dubner’s ability to critically view the ‘contrary’ positions they highlight.

“Washington crossing the Delaware” in 1776 was featured on RealClimate in a piece on ‘Art and Climate‘ back in 2006. We had noted that the icebergs seen in the classic scene (painted in 1851) did not resemble anything that you would likely see on the Delaware:

Well it seems that the re-commission of this scene was driven by the need to fix a number of historical inaccuracies in the classic Emanuel Leutze painting, not least the use of an anachronous flag, a row-boat instead of a ferry etc. but, in addition, the nature of the river ice!

The ferry cuts through thick layers of ice, which Mr. Künstler says corresponds with the photographs he obtained of the actual way the Delaware freezes.

People will clearly differ on the artistic merits of the resulting picture:

but the improvement in climatological veracity is to be praised. Maybe Levitt and Dubner will take note.

Happy Holidays.

There was a really interesting article in Physics Today this past October on the parallels between the slow acceptance of the idea of anthropogenic climate change and of the idea that the earth circles the sun.

Author Steven Sherwood writes that:

“Many who are unwilling to accept the full brunt of greenhouse warming have embraced a more comforting compromise reminiscent of the Tychonic system*: that CO2 has some role in climate but its importance is being exaggerated. But accepting a nonzero warming effect puts one on a slippery slope: Once acknowledged, the effect must be quantified, and every legitimate method for doing so yields a significant magnitude. As the evidence sinks in, we can expect a continued, if slow, drift to full acceptance. It took both Copernicanism and greenhouse warming roughly a century to go from initial proposal to broad acceptance by the relevant scientific communities. It remains to be seen how long it will take greenhouse warming to achieve a clear public consensus; one hopes it will not take another century.”

A really important point is that what Sherwood is talking about here is not about acceptance of anthropogenic greenhouse warming within the scientific community — that acceptance has already happened — but amongst the general public. Of course, the analogy with Copernicus is still a good one, because it did take some time for understanding of the greenhouse idea to really take hold within the scientific community. Indeed, it has only been in the last year that the American Physical Society (APS) has considered climate change a central-enough topic to deem it worthy to start a climate change ‘topical group’. (The APS topical groups are formal, structured discussion groups that have to be approved by APS. Note for those that might think politics might have played a role in slowing things down: this most certainly isn’t the case. The APS leadership is simply very conservative about what is deemed central enough to Physics to approve a topical discussion group; for example, a colleague of mine spent several years trying to convince APS to support such a group on Quantum Information. That climate change is now an APS focus group topic makes a strong statement, but not a political one: it simply reflects the maturity of the field. Members of APS that are interested in climate should consider joining; there are bound to be some very interesting discussions in areas such as radiative transfer and atmospheric dynamics.)

Sherwood’s article deserves to be widely read. It is freely available on the Physics Today web site.

For more on the history of of the development of the greenhouse idea within the physics community, our own Ray Pierrehumbert’s article in Physics Today (pdf) is also a very worthwhile read.

*Tycho accepted the evidence that the other planets orbit the sun, but tried to come up with a way to still keep the sun orbiting the earth.

Guest commentary by Mark Boslough*

The Third Santa Fe Conference on Global and Regional Climate Change was held during Halloween week. It was most notable for the breadth of opinion — and the span of credibility — of its speakers. I have long complained about the lack of willingness of most contrarians to attend and present their arguments at mainstream scientific conferences. After three years of convening climate-related sessions at AGU, I have yet to receive an abstract that argues against anthropogenic global warming. Such presentations can usually only be seen at conferences held by the Heartland Institute. There isn’t much chance of a mainstream scientist attending a meeting organized by a political think tank known for its anti-science activism, so opportunities for interaction between the groups are rare.

The conference was the third in a series (the first was held in Halifax ten years ago) that actively solicits participation from conventional scientists as well as those on the fringes. Organized by the Center for Nonlinear Studies of the Los Alamos National Laboratory, with co-sponsorship from the International Arctic Research Center, Brookhaven, and Oak Ridge National Laboratory, the meeting has sufficient credibility to attract speakers like Richard Peltier and Gerald North, while also providing the podium to Christopher Monckton and Don Easterbrook. Travel grants from LANL were provided to assist some of the speakers.

It quickly became apparent that the meeting would be run with a firm, no-nonsense approach to confrontation. In my original abstract, I used the term “contrarian,” which I have always considered to be a polite, non-judgmental descriptive term. Petr Chylek, LANL Laboratory Fellow and chair of the conference program committee responded by telling me, “I would like to ask you for some revision. The designations like ‘contrarians, skeptics, deniers, etc.’ may be offensive to some scientists present. Perhaps you can re-write your abstract and your presentation without using such words.” Fair enough, given the potential for contentiousness. Later, a generalized request went to all speakers: “Please, do not use any demeaning labels like deniers, contrarians, warmers, alarmists, … Please, stick to science. Stay away from personal attacks on other scientists present or not.”

I was disappointed, however, that the poster abstract I submitted with Lloyd Keigwin (WHOI), “Misrepresentations of Sargasso Sea Temperatures by Global Warming Doubters,” was rejected. This abstract was essentially the same material we presented at last year’s GSA meeting in Denver, and revealed the fact that a graph in Lloyd’s 1996 Science paper had been redrawn for the paper “Environmental Effects of Increased Atmospheric Carbon Dioxide” by Arthur Robinson and coauthors. Some of the original data on Lloyd’s graph had been removed and replaced by fabricated data, apparently in an attempt to argue that temperatures are lower now than the 3000-year average. The doctored version of the graph has been used repeatedly in opinion pieces and was reprinted by Fred Singer in the NIPCC report. It is arguably one of the most widely reproduced graphs in contrarian literature, and in one form was sent out to tens of thousands of scientists to solicit signatures for the so-called “Oregon petition”.

Petr Chylek, explaining his reason for rejection, said, “This Conference is not a suitable forum for type of presentations described in submitted abstract. We would accept a paper that spoke to the science, the measurements, the interpretation, but not simply an attempted refutation of someone else’s assertions (especially when made in unpublished reports and blog site).” Of course, I’m not sure that a correction by the author of a graph that has been improperly reproduced in the primary contrarian literature is not the same thing as an “attempted refutation”.

The first day of the conference was buzzing with news of Richard Muller’s announcement of the Berkeley Earth Surface Temperature (BEST) results. Just a week earlier, he had published an op-ed in the Wall Street Journal, titled, “The Case Against Global-Warming Skepticism (There were good reasons for doubt, until now)”. Then, only one day before the conference, David Rose of the Daily Mail broke a supposed “scandal”: “Scientist who said climate change sceptics had been proved wrong accused of hiding truth by colleague”. Muller’s coauthor, Judith Curry, was quoted saying, “There is no scientific basis for saying that warming hasn’t stopped. To say that there is detracts from the credibility of the data, which is very unfortunate.” This story was picked up by Fox News and the narrative that spread throughout the blogosphere was that “Curry has turned on Muller.”

Reading about climate change in the mainstream media — let alone on blogs — can be like looking at reality in a funhouse mirror. When Muller got up to discuss the BEST results on Tuesday morning, the first thing he did was point out that the title of the WSJ piece did not come from him. His original title was “Cooling the Global Warming Debate.” But since his name was under the title he didn’t write, it was automatically attributed to him, as a direct quote. In fact he said, he had been misquoted more times since this was published than he had in the rest of his life. The Daily Mail/Fox News story seemed just as distorted. If Curry and Muller had a major scientific disagreement, wouldn’t a scientific conference be the appropriate place for the debate? If they were at loggerheads over the fundamental question of whether “global warming hasn’t stopped” wouldn’t one of them have mentioned it? They each gave two presentations, and this never came up in public or in any conversation I was aware of.

The conference was remarkably well run. For the most part, participants were well behaved and adhered to Petr Chylek’s strict rules—avoiding inflammatory terms, and steering away from personal attacks and interruptions. The one exception was Judith Curry, who apparently did not get the memo. She gave a banquet presentation entitled, “The Uncertainty Monster at the Climate Science-Policy Interface”. My impression was that her presentation was intended to be more of a vehicle to criticize her adversaries than to talk about uncertainty.

Her most personal attack was against Michael Mann, who she used to illustrate “uncertainty hiding” by showing a caricature of him standing next to the “uncertainty monster” holding a hockey stick and hidden by a sheet, with the cartoon-Mann saying “what uncertainty?” Next to the cartoon was and image of the cover of the book “The Hockey Stick Illusion: Climategate and the Corruption of Science” illustrated with the multiproxy time series that Mann and his coauthors made famous. Ironically, Mann’s carefully plotted uncertainty bands were not visible on the presentation graphic, just as they were not reproduced in Fred Singer’s NIPCC report. “What uncertainty?” indeed!

Curry described her transition from a scientist who felt that it was the responsible thing to do to support the IPCC conclusions to someone who is “about 50% a denier”. She attributed this change to “climategate” and the reaction she received due to her initial comments about it. She was the only speaker who ignored the policy against the word “denier.” She used the banned “d-word” repeatedly for effect when setting up a straw-man argument against what she called “IPCC/UNFCCC ideology” — a term she coined to label notions such as “anthropogenic climate change is real” and “deniers are attacking climate science and scientists”. She assured the audience that she didn’t think there were any “IPCC ideologues” at the conference but she had heard rumors that some were invited and had declined. She called out Kevin Trenberth as a supposed example of such an ideologue (again rejecting the policy against personal attack).

Among her straw-man arguments was her dismissal of standard risk-reduction methodology for low-probability high-consequence events as a mere “precautionary principle” (the same principle that nuclear weapons engineers are taught when they told to always ask “what can go horribly wrong?”). One colleague later remarked that her approach to uncertainty quantification reminded him of an English major who had just finished reading Kuhn’s “The Structure of Scientific Revolutions.”

I met most of the conference participants during the course of the week, and had cordial conversations with all those with whom I disagreed. One thing I have long suspected was strongly reinforced: there is no common scientific understanding amongst contrarians. Many of them are just as critical of one another’s ideas as they are of conventional science. William Gray stood up after many of the presentations on solar influence to declare that solar variability is not important. It’s even less important than CO2, he said. It’s ocean variability that is the cause of most climate change. Petr Chylek stood up after Fred Singer’s presentation (in which Singer presented old uncorrected UAH MSU data that suggested cooling) and said emphatically, “Denying the warming makes no sense!”.

I spent a lot of time talking to Christopher Monckton, who may have been the only non-scientist to give a presentation. He has no understanding of science or the scientific method, and when I asked him about scientific prediction, he called it a “fool’s errand”. He has a strong authoritarian approach to those with whom he disagrees, and his conspiracy theories run deep and dark. He names specific names and calls IPCC contributors “malevolent”. I asked him to share the very worst hacked email he could remember. The only specific example he gave was the one in which someone referred to him as a “charlatan”.

Several of us had beers at the Marble Brewery overlooking the Santa Fe plaza on Thursday evening, where Monckton recounted his efforts to get the police involved in an investigation of one IPCC lead author who (he says) committed criminal fraud associated with a graph in the IPCC report. (His own adventures in graphical misrepresentation are of course completely unproblematic).

The main lesson I took away from the conference was this: there is no consistent contrarian science, and there is no defining contrarian ideology or motivation. Some are sincere. Others are angry at their lack of funding. Some appear to be envious of the IPCC scientists’ success, and others have found a niche that gets them attention they would not otherwise get. Only a few appear to be motivated by politics. No single label applies to them, and I found myself referring to them as “contrarians/skeptics/deniers/enablers/provocateurs/publicity-seekers”.

The one common thread I found among them was the fervent belief that “Climategate” was a conspiracy and that the IPCC is rigged. This faith-based belief seems to be unshakable, and is the antithesis of true skepticism. Those I met were uniformly cynical about the honesty and motivations of mainstream scientists. If I were forced to use a single label, I would be inclined to call them “science cynics”.

*These comments reflect the personal opinion of the author and should not be taken to reflect the opinions of his employer or his funding agencies.

It is not every day that I come across a scientific publication that so totally goes against my perception of what science is all about. Humlum et al., 2011 present a study in the journal Global and Planetary Change, claiming that most of the temperature changes that we have seen so far are due to natural cycles.

They claim to present a new technique to identify the character of natural climate variations, and from this, to produce a testable forecast of future climate. They project that

the observed late 20th century warming in Svalbard is not going to continue for the next 20–25 years. Instead the period of warming may be followed by variable, but generally not higher temperatures for at least the next 20–25 years.

However, their claims of novelty are overblown, and their projection is demonstrably unsound.

First, the claim of presenting “a new technique to identify the character of natural climate variations” is odd, as the techniques Humlum et al. use — Fourier transforms and wavelet analysis — have have been around for a long time. It is commonplace to apply them to climate data.

Longyearbyen, at Svalbard

Using these methods, the authors conclude that “the investigated Svalbard and Greenland temperature records show high natural variability and exhibit long-term persistence, although on different time scales”. No kidding! Again, it is not really a surprise that local records have high levels of variability, and the “long-term persistent” character of climate records has been reported before and is even seen in climate models.

The most problematic aspect of the paper concerns the Greenland temperature from GISP2 and their claim that they can “produce testable forecasts of future climate” from extending their statistical fit.

Of course, these forecasts are testable – we just have to wait for the data to come in. But why should extending these fits produce a good forecast? It is well known that one can fit a series of observations to arbitrary accuracy without having any predictability at all. One technique to demonstrate credibility is by assessing how well the statistical model does on data that was not used in the calibration. In this case, the authors have produced a testable forecast of the past climate by leaving out the period between the end of the last ice age and up to 4000 years before present. This becomes apparent if you extend their fit to the part of data that they left out (figure below).

I extended their analysis back to the end of the last ice age. The figure here shows my replication of part of their results, and I’ve posted the R-script for making the plot. The full red line shows their fit (“model results”) and the dashed red lines show two different attempts to extend their model to older data.

For the initial attempt, keeping their trend obviously caused a divergence. So in the second attempt, I removed the trend to give them a better chance of making a good hind cast. Again, the fit is no longer quite as remarkable as presented in their paper.

Clearly, their hypothesis of 3 dominant periodicities no longer works when extending the data period. So why did they not show the part of the data that break with the pattern established for the 4000 last years? According to the paper, they

chose to focus on the most recent 4000 years of the GISP2 series, as the main thrust of our investigation is on climatic variations in the recent past and their potential for forecasting the near future.

One could of course attempt to rescue the fit by proposing that some other missing factor is responsible for the earlier divergence. But this would be entirely arbitrary. Choosing to ignore the well known (anthropogenic) factors affecting current climate, on the other hand, is not arbitrary at all.

Humlum et al. also suggest, on the basis of a coincidence between one of their cycles (8.7 years) and a periodicity in the Earth-Moon orbital distance (8.85 years), that the Moon plays a role for climate change (seriously!):

We hypothesise that this may bring about the emergence of relatively warm or cold water masses from time to time in certain parts of oceans, in concert with these cyclic orbit variations of the Moon, or that these variations may cause small changes in ocean currents transporting heat towards high latitudes, e.g. in the North Atlantic.

How wonderfully definitive. They, however, admit that their

main focus is the identification of natural cyclic variations, and only secondary the attribution of physical reasons for these.

So, if the curve-fitting points to periodicities that are anywhere near any of the frequencies that can be associated with a celestial object, then that’s apparently sufficient. You can get quite a range of periodicities if you consider all the planets in our solar system, their resonances and harmonics, see any of Scafetta’s recent papers for more examples. And of course, if there are parts of the data that do not match the periodicity you believe to be there, you can just throw it away to make the cycles fit. Quite easy really.

In short, Humlum et al’s results are similar to those I discussed concerning meaningless numerical exercises, and their efforts really bring out the points I made in my previous post: arbitrarily splitting a time series up into parts generally does not allow one to learn anything.

1. O. Humlum, J. Solheim, and K. Stordahl, "Identifying natural contributions to late Holocene climate change", Global and Planetary Change, vol. 79, 2011, pp. 145-156. DOI.

(Day 1)(Day 2)(Days 3&4)

After 5 days, there is a definite slowdown in energy, desire to ask questions and attendance. But there were still a lot of good talks to be seen. Perhaps most relevant here were a few sessions talking about initial results from the CMIP5 models and the data with which they are being assessed. Overall, most comparisons to the CMIP3 models showed that despite substantial improvements in resolution, completeness, and scope, the CMIP5 models do not show any dramatic differences at the broad-scale diagnostics (global means etc.).

This is not particularly surprising, since it is expected that the importance of the new simulations will be seen in the differences between model types (i.e. including carbon cycles, atmospheric chemistry etc.), or in new kinds of diagnostics from say, the initialized decadal predictions, that weren’t available before.

Looking back at the whole meeting (20,000+ scientists, dozens of simultaneous sessions), it is perhaps worth noting the reasons why such meetings are so important. Obviously, no-one can see everything that is relevant to their research, or talk to everyone they might want to, but there is a lot that can be seen and absorbed much more efficiently than would be possible at home. The social aspect of conferences is also important – beer is an essential lubricant for geophysicists it seems. More important than the sessions are often the chance encounters on the escalators or corridors. Many people get to meet in person who only ever emailed – and this includes other bloggers as well as scientists. We met Eli Rabett, John Cook (Skeptical Science), Zeke Hausfather, Kate @ ClimateSight, Steve Easterbrook, and many others who are only known by their screen names and comments. Many of the scientists whose work has been discussed here recently were also present – Andreas Schmittner, Robert Rohde (of BEST), Jim Hansen, Ben Santer, Roy Spencer, along with many, many first timers whose work will become more prominent. The palpable sense of excitement at the directions the science is taking is very much driven by the bright ideas and new approaches being generated by the younger scientists – including undergraduates and graduate students. And it is the serendipitous encounters with these new voices that are the most unanticipated (and unplanned) benefits of these meetings. This doesn’t happen with Skype unfortunately.

We know that we didn’t see everything we wanted to, so if any other attendees are reading this, we encourage them to point out in the comments any particular highpoints they came across – especially if the talks were part of those broadcast, or if the poster is available on-line.

(Day 1)(Day 2)

Sorry for the slow blogging, but with the AGU fun run starting at 6.15am, and the Awards ending at around ~10pm, and the actual science portion of the day squeezed in the middle, little time was available on Wednesday for reporting. Thursday seemed equally busy. So today you get two days in one.

One session on Wednesday that was really quite good was the session on Earth System Sensitivity. We’ve discussed this before (notably in discussing Hansen’s Target CO2 paper). The main idea is that the sensitivity of the climate system to a radiative forcing is not going to be constrained to effect only the factors included in GCM in 1979. That is, other feedbacks come into play – vegetation, ice sheets, aerosols, CH4 etc. will all change as a function a warming (or cooling), which are not included in the standard climate sensitivity definition. Talks by Eelco Rohling, Dan Lunt, and Jim Hansen all made excellent points on how one should think about constraints on ESS from paleo-climate records. The periods considered were mainly the Pleistocene ice age cycles, the LGM and the Pliocene, but Paul Valdes provided some interesting modeling that also included the Oligocene, the Turonian, the Maastrichtian and Eocene, indicating the importance of the base continental configuration, ice sheet position, and ocean circulation for sensitivity. Vegetation feedbacks were invariably reported as an amplifying feedback – which is interesting because that encompasses both ‘fast’ and ‘slow’ feedbacks.

Wednesday night was the awards, and as we reported, one of us (Gavin) was presented with the inaugural prize for Climate Communication. He will be posting a specific piece on this honor in a couple of days.

Thursday, there was a keynote (video available here) from Ben Santer at the Stephen Schneider event who persuasively argued that in doing the science necessary to refute baseless claims made in the media and in front of Congress, actual progress can be made beyond simply demonstrating that the original claim was made up. Specifically, he addressed a claim made by Will Happer, a Princeton professor, that no models demonstrate decadal variability in trends (which was not the case), and explored in depth the signal to noise ratio in determining climate trends much more comprehensively than had been done previously.

In sessions, there were a lot of papers on new approaches to estimating the climate of the common era (since 0 AD) – many of them using Bayesian methods of one sort or another. Hugues Goosse gave an interesting talk on paleo-data assimilation. A poster session had some first results from the CMIP5 models – including some intriguing results from Ben Booth looking at the Hadley Centre simulations of the role of aerosols in forcing multi-decadal variability in the North Atlantic.

Many of the lectures earlier this week are now available on demand. We hear that the Charney lecture from Graeme Stephens was particularly good.

(Day 5 and wrap up)

(Day 1)

Tuesday

There were two interesting themes in the solar sessions this morning. The first was a really positive story about how instrumental differences between rival (and highly competitive) teams can get resolved. This refers to the calibration of measurements of the Total Solar Irradiance (TSI). As is relatively well known, the different satellite instruments over the last 30 or so years have shown a good coherence of variability – especially the solar cycle, but have differed markedly on the absolute value of the TSI (see the figure). In particular, four currently flying instruments (SORCE, ACRIM3, VIRGO and PREMOS) had offsets as large as 5W/m2. However, the development of a test-facility at NASA Langley the
University of Colorado, Laboratory for Atmospheric and Space Physics in Boulder
Colorado – an effort led by Greg Kopp’s group – has allowed people to test their instruments in a vacuum, with light levels comparable to the solar irradiance, and have the results compared to really high precision measurements. This was a tremendous technical challenge, but as Kopp stated, getting everyone on board was perhaps a larger social challenge.

The facility has enabled the different instrument teams to calibrate their instruments, and check for uncorrected errors, like excessive scattering and diffusive light contamination in the measurement chambers. In doing so, Richard Wilson of the ACRIM group reported that they found higher levels of scattering than they had anticipated, which was leading to slightly excessive readings. Combined with a full implementation of an annually varying temperature correction, their latest processed data product has reduced the discrepancy with the TIM instrument from over 5 W/m2 to less than 0.5 W/m2 – a huge improvement. The new PREMOS instrument onboard Picard, a french satellite, was also tested before launch last year, and they improved their calibration as well – and the data that they reported was also very close to the SORCE/TIM data: around 1361 W/m2 at solar minimum.

The errors uncovered and the uncertainties reduced as a function of this process was a great testament to the desire of everyone concerned to work towards finding the right answer – despite initial assumptions about who may have had the best design. The answer is that space borne instrumentation is hard to do, and thinking of everything that might go wrong is a real challenge.

The other theme was the discussion of the spectral irradiance changes – specifically by how much the UV changes over a solar cycle are larger in magnitude than the changes in the total irradiance. The SIM/SOLSTICE instruments on SORCE have reported much larger UV changes than previous estimates, and this has been widely questioned (see here for a previous discussion). The reason for the unease is that the UV instruments have a very large degradation of their signal over time, and the residual trends are quite sensitive to the large corrections that need to be made. Jerry Harder discussed those corrections and defended the SIM published data, while another speaker made clear how anomalous that data was. Meanwhile, some climate modellers are already using the SIM data to see whether that improves the model simulations of ozone and temperature responses in the stratosphere. However, the ‘observed’ data on this is itself somewhat uncertain – for instance, comparing the SAGE results (reported in Gray et al, 2011) with the SABER results (Merkel et al, 2011), shows a big difference in how large the ozone response is. So this remains a bit of a stumper.

The afternoon sessions on water isotopes in precipitation was quite exciting because of the number of people looking at innovative proxy archives, including cave records of 18O in calcite, or deuterium in leaf waxes, which are extending the coverage (in time and space) of this variable. Even more notable, was the number of these presentations that combined their data work with interpretations driven by GCM models that include isotope tracers that allow for more nuanced conclusions. This is an approach that was pioneered decades ago, but has taken a while to really get used routinely.

(Days 3&4)(Day 5 and wrap up)

1. L.J. Gray, J. Beer, M. Geller, J.D. Haigh, M. Lockwood, K. Matthes, U. Cubasch, D. Fleitmann, G. Harrison, L. Hood, J. Luterbacher, G.A. Meehl, D. Shindell, B. van Geel, and W. White, "SOLAR INFLUENCES ON CLIMATE", Reviews of Geophysics, vol. 48, 2010. DOI.
2. A.W. Merkel, J.W. Harder, D.R. Marsh, A.K. Smith, J.M. Fontenla, and T.N. Woods, "The impact of solar spectral irradiance variability on middle atmospheric ozone", Geophysical Research Letters, vol. 38, 2011. DOI.

There are two interesting pieces of news on the global temperature evolution.

First, today a paper by Grant Foster and Stefan Rahmstorf was published by Environmental Research Letters, providing a new analysis of the five available global (land+ocean) temperature time series. Foster and Rahmstorf tease out and remove the short-term variability due to ENSO, solar cycles and volcanic eruptions and find that after this adjustment all five time series match much more closely than before (see graph). That’s because the variability differs between the series, for example El Niño events show up about twice as strongly in the satellite data as compared to the surface temperatures. In all five adjusted series, 2009 and 2010 are the two warmest years on record. For details have a look over at Tamino’s Open Mind.

Meanwhile, the World Meteorological Organisation has recently come out with a Provisional Statement on the Status of the Global Climate for 2011. In addition to a discussion of some of the extreme events of 2011 this also comes with a first estimate of the 2011 global temperature, see their graph below:

They find – pretty much in line with the Foster and Rahmstorf analysis – that La Niña conditions have made 2011 a relatively cool year – relatively, because they predict it will still rank amongst the 10 hottest years on record. They further predict it will be the warmest La Niña year on record (those are the blue years in the bar graph above).

1. G. Foster, and S. Rahmstorf, "Global temperature evolution 1979–2010", Environmental Research Letters, vol. 6, 2011, pp. 044022-. DOI.