The statement Pielke considers “misinformation” is a single sentence from a recent posting:

Some aspects of climate change are progressing faster than was expected a few years ago - such as rising sea levels, the increase of heat stored in the ocean and the shrinking Arctic sea ice.

First of all, we are surprised that Pielke levelled such strong allegations against RealClimate, since the statement above merely summarises some key findings of the Synthesis Report of the Copenhagen Climate Congress, which we discussed last month. This is a peer-reviewed document authored by 12 leading scientists and “based on the 16 plenary talks given at the Congress as well as input of over 80 chairs and co-chairs of the 58 parallel sessions held at the Congress.” If Pielke disagrees with the findings of these scientists, you’d have thought he’d take it up with them rather than aiming shrill accusations at us. But in any case let us look at the three items of alleged misinformation:

1. Sea level. The Synthesis Report shows the graph below and concludes:

Since 2007, reports comparing the IPCC projections of 1990 with observations show that some climate indicators are changing near the upper end of the range indicated by the projections or, as in the case of sea level rise (Figure 1), at even greater rates than indicated by IPCC projections.

This graph is an update of Rahmstorf et al., Science 2007, with data through to the end of 2008. (Note the comparison is with IPCC TAR projections, but since AR4 projections are within 10% of the TAR models this makes little difference.)

Pielke claims this is “NOT TRUE” (capitals and bold font are his), stating “sea level has actually flattened since 2006” and pointing to this graph. This graph shows a sea level trend over the full satellite period (starting 1993) of 3.2 +/- 0.4 mm/year and is very similar to an independent French analysis of those very same satellite data shown in the Synthesis Report (blue lines above). The best estimate of the IPCC models for the same time period is 1.9 mm/year (coloured dashed lines in the middle of the grey uncertainty range). Hence the conclusion of the Synthesis Report is entirely correct.

The “flattening of sea level since 2006” that Pielke refers to is beside the point and deceptive for several reasons (note too that Anthony Watts has extended this even further to declare that sea level from 2006 to present is actually “flat”!). First of all, trends over such a short sub-interval of a few years vary greatly due to short-term natural variations, and one could get any result one likes by cherry-picking a suitable interval (as Pielke and Lomborg both have). The absurdity of this approach is see by picking an even more recent trend, say starting in June 2007, which gives 5.3+/-2.2 mm/yr! Secondly, this short-term trend (1.6 +/- 0.9 mm/yr) is not even robust across data sets – the French analysis shown above has a trend since the beginning of 2006 of 2.9 mm/year, very similar to the long-term trend. Third, the image Pielke links to shows the data without the inverted barometer correction – the brief marked peak in late 2005, which makes the visual trend (always a poor choice of statistical methodology) almost flat since then, disappears when this effect is accounted for. This means the 2005 peak was simply due to air pressure fluctuations and has nothing to do with climatic ocean volume changes. The trend from 2006 in the data with the inverse barometer adjustment is 2.1 +/- 0.8 mm/yr.

2. Ocean heat content. The Synthesis Report states:

Current estimates indicate that ocean warming is about 50% greater than had been previously reported by the IPCC.

This is a conclusion of a revised analysis of ocean heat content data by Domingues et al., Nature 2008, and it applies to the period 1961-2003 also analysed in the IPCC report. Pielke claims this is “NOT TRUE” and counters with the claim: “There has been no statistically significant warming of the upper ocean since 2003.” But again this is not relevant to the point the Synthesis Report actually makes and again, Pielke is referring to a 5-year period which is too short to obtain statistically robust trends in the presence of short-term variability and data accuracy problems (the interannual variability for instance differs greatly between different ocean heat content data sets):

For good reasons, the Synthesis Report discusses a time span that is sufficiently long to allow meaningful comparisons. But in any case, the trend in from 2003 to 2008 in the Levitus data (the Domingues et al data does not extend past 2003), is still positive but with an uncertainty (both in the trend calculation and systematically) that makes it impossible to state whether there has been a significant change.

3. Arctic Sea Ice. The Synthesis Report states:

One of the most dramatic developments since the last IPCC Report is the rapid reduction in the area of Arctic sea ice in summer. In 2007, the minimum area covered decreased by about 2 million square kilometres as compared to previous years. In 2008, the decrease was almost as dramatic.

This decline is clearly faster than expected by models, as the following graph indicates.

Pielke’s claim that this is “NOT TRUE” is merely based on the statement that “since 2008, the anomalies have actually decreased.”

Yes, same thing again: Pielke’s argument is beside the point, since the Synthesis Report is explicitly talking about the summer sea ice minimum reached each September in the Arctic, and we don’t even know yet what its value will be for 2009. And Pielke is again referring to a time span (“since 2008”!) that is far too short to have much to do with climatic trends.

We thus have to conclude that there are no grounds whatsoever for Pielke’s wild allegations against us and implicitly the Synthesis Report authors. The final sentence of his post ironically speaks for itself:

Media and policymakers who blindly accept these claims are either naive or are deliberately slanting the science to promote their particular advocacy position.

Indeed.

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First off the authors of the submission; Alan Carlin is an economist and John Davidson is an ex-member of the Carter administration Council of Environmental Quality. Neither are climate scientists. That’s not necessarily a problem - perhaps they have mastered multiple fields? - but it is likely an indication that the analysis is not going to be very technical (and so it will prove). Curiously, while the authors work for the NCEE (National Center for Environmental Economics), part of the EPA, they appear to have rather closely collaborated with one Ken Gregory (his inline comments appear at multiple points in the draft). Ken Gregory if you don’t know is a leading light of the Friends of Science - a astroturf anti-climate science lobbying group based in Alberta. Indeed, parts of the Carlin and Davidson report appear to be lifted directly from Ken’s rambling magnum opus on the FoS site. However, despite this odd pedigree, the scientific points could still be valid.

Their main points are nicely summarised thus: a) the science is so rapidly evolving that IPCC (2007) and CCSP (2009) reports are already out of date, b) the globe is cooling!, c) the consensus on hurricane/global warming connections has moved from uncertain to ambiguous, d) Greenland is not losing mass, no sirree…, e) the recession will save us!, f) water vapour feedback is negative!, and g) Scafetta and West’s statistical fit of temperature to an obsolete solar forcing curve means that all other detection and attribution work is wrong. From this “evidence”, they then claim that all variations in climate are internal variability, except for the warming trend which is caused by the sun, oh and by the way the globe is cooling.

Devastating eh?

One can see a number of basic flaws here; the complete lack of appreciation of the importance of natural variability on short time scales, the common but erroneous belief that any attribution of past climate change to solar or other forcing means that CO2 has no radiative effect, and a hopeless lack of familiarity of the basic science of detection and attribution.

But it gets worse, what solid peer reviewed science do they cite for support? A heavily-criticised blog posting showing that there are bi-decadal periods in climate data and that this proves it was the sun wot done it. The work of an award-winning astrologer (one Theodor Landscheidt, who also thought that the rise of Hitler and Stalin were due to cosmic cycles), a classic Courtillot paper we’ve discussed before, the aforementioned FoS web page, another web page run by Doug Hoyt, a paper by Garth Paltridge reporting on artifacts in the NCEP reanalysis of water vapour that are in contradiction to every other reanalysis, direct observations and satellite data, a complete reprint of another un-peer reviewed paper by William Gray, a nonsense paper by Miskolczi etc. etc. I’m not quite sure how this is supposed to compete with the four rounds of international scientific and governmental review of the IPCC or the rounds of review of the CCSP reports….

They don’t even notice the contradictions in their own cites. For instance, they show a figure that demonstrates that galactic cosmic ray and solar trends are non-existent from 1957 on, and yet cheerfully quote Scafetta and West who claim that almost all of the recent trend is solar driven! They claim that climate sensitivity is very small while failing to realise that this implies that solar variability can’t have any effect either. They claim that GCM simulations produced trends over the twentieth century of 1.6 to 3.74ºC - which is simply (and bizarrely) wrong (though with all due respect, that one seems to come directly from Mr. Gregory). Even more curious, Carlin appears to be a big fan of geo-engineering, but how this squares with his apparent belief that we know nothing about what drives climate, is puzzling. A sine qua non of geo-engineering is that we need models to be able to predict what is likely to happen, and if you think they are all wrong, how could you have any faith that you could effectively manage a geo-engineering approach?

Finally, they end up with the oddest claim in the submission: That because human welfare has increased over the twentieth century at a time when CO2 was increasing, this somehow implies that no amount of CO2 increases can ever cause a danger to human society. This is just boneheadly stupid.

So in summary, what we have is a ragbag collection of un-peer reviewed web pages, an unhealthy dose of sunstroke, a dash of astrology and more cherries than you can poke a cocktail stick at. Seriously, if that’s the best they can do, the EPA’s ruling is on pretty safe ground.

If I were the authors, I’d suppress this myself, and then go for a long hike on the Appalachian Trail….

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The climate congress was organised by a “star alliance” of research universities: Copenhagen, Yale, Berkeley, Oxford, Cambridge, Tokyo, Beijing - to name a few. The Synthesis Report is the most important update of climate science since the 2007 IPCC report.

So what does it say? Our regular readers will hardly be surprised by the key findings from physical climate science, most of which we have already discussed here. Some aspects of climate change are progressing faster than was expected a few years ago - such as rising sea levels, the increase of heat stored in the ocean and the shrinking Arctic sea ice. “The updated estimates of the future global mean sea level rise are about double the IPCC projections from 2007″, says the new report. And it points out that any warming caused will be virtually irreversible for at least a thousand years - because of the long residence time of CO2 in the atmosphere.

Perhaps more interestingly, the congress also brought together economists and social scientists researching the consequences of climate change and analysing possible solutions. Here, the report emphasizes once again that a warming beyond 2ºC is a dangerous thing:

Temperature rises above 2ºC will be difficult for contemporary societies to cope with, and are likely to cause major societal and environmental disruptions through the rest of the century and beyond.

(Incidentally, by now 124 nations have officially declared their support for the goal of limiting warming to 2ºC or less, including the EU - but unfortunately not yet the US.)

Some media representatives got confused over whether this 2ºC-guardrail can still be met. The report’s answer is a clear yes - if rapid and decisive action is taken:

The conclusion from both the IPCC and later analyses is simple - immediate and dramatic emission reductions of all greenhouse gases are needed if the 2ºC guardrail is to be respected.

Cause of the confusion was apparently that the report finds that it is inevitable by now that greenhouse gas concentrations in the atmosphere will overshoot the future stabilization level that would keep us below 2ºC warming. But this overshooting of greenhouse gas concentrations need not lead temperatures to overshoot the 2ºC mark, provided it is only temporary. It is like a pot of water on the stove - assume we set it to a small flame which will make the temperature in the pot gradually rise up to 70ºC and then no further. Currently, the water is at 40ºC. When I turn up the flame for a minute and then back down, this does not mean the water temperature will exceed 70ºC, due to the inertia in the system. So it is with climate - the inertia here is in the heat capacity of the oceans.

From a natural science perspective, nothing stops us from limiting warming to 2ºC. Even from an economic and technological point of view this is entirely feasible, as the report clearly shows. The ball is squarely in the field of politics, where in December in Copenhagen the crucial decisions must be taken. The synthesis report puts it like this: Inaction is inexcusable.

Related links

Press release of PIK about the release of the synthesis report

Copenhagen Climate Congress - with webcasts of the plenary lectures (link on bottom right - my talk is in the opening session part 2, just after IPCC chairman Pachauri)

Nobel Laureate Meeting in London - a high caliber gathering in May that agreed on a remarkable memorandum which calls for immediate policy intervention: “We know what needs to be done. We can not wait until it is too late.” The new U.S. Energy Secretary Steven Chu participated over the full three days in the scientific discussions - how many politicians would have done that?

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There was an interesting AP story this week about possible changes in wind speed over the continental US. The study (by Pryor et al (sub.)), put together a lot of observational data, reanalyses (from the weather forecasting models) and regional models, and concluded that there was some evidence for a decrease in wind speeds, particularly in the Eastern US. However, although this trend appeared in the observational data, it isn’t seen in all the reanalyses or regional models, leaving open a possibility that the trend is an artifact of some sort (instrumental changes, urbanization etc.). If the effect is real though, one would want to see whether it could be tied to anything else (such as forcing from greenhouse gas or aerosol increases), and indeed, whether it had any implications for wind-generated electricity, water evaporation etc.

Amusingly, both of us were quoted in the story as having ostensibly conflicting views. Mike was quoted as finding the evidence for a trend reasonably convincing, while Gavin was quoted as being unconvinced of the evidence for an anthropogenic climate change signal (note that the two statements are not in fact mutually inconsistent). As one should expect in any news story, these single lines don’t really do justice to the longlonger interviews both of us gave the reporter Seth Borenstein. So what is the bigger context?

First some background. It’s important to note that ‘windiness’ is not a globally uniform field, and that changes will occur in different regions for very different reasons. Also, note that mean wind speed is not the same as storminess^*.

Winds in the mid-latitudes are a function of the jet stream and of the ‘baroclinic instability’ that we see as low-pressure systems. In the tropics, winds locally depend strongly on convective activity and on a larger scale, the Hadley circulation. In monsoonal regions (West Africa, India etc.), winds are a function of the temperature contrasts over land and sea during the warm seasons. Winds can be affected by the ozone hole in the Southern Ocean, a change in the orbit of the Earth in the tropics, or by the presence or absence of an ice sheet. So the concept of winds changing in a general sense is not unusual or unexpected. However, because of the many distinct influences you wouldn’t expect all winds to increase or decrease together.

In the free atmosphere off the equator, wind is essentially ‘geostrophic’ which means that it’s driven by the (predominantly north-south) gradients in air pressure, and follows contours of constant pressure (’isobars’). Near the surface, friction slows the winds, and causes them to cross the isobars from high to low pressure (hence we get ‘convergence’ in the center of surface low pressure regions). Nonetheless, changes in surface winds will follow approximately from the associated change in the surface pressure field.

The business-as-usual projections show a general poleward shift of the current subtropical surface high pressure belt into the mid-latitudes, especially during summer (a poleward shift of the descending branch of the so-called “Hadley Cell”). The high pressure belt is a region of low pressure gradient, and hence low wind. A northward shift displaces the region of maximum westerly surface winds poleward, from the U.S. into, say, southern Canada. A decrease in the mean strength of the surface westerlies over the U.S. would therefore appear to be consistent with projected changes in large-scale circulation. However, it’s not that simple. The average wind speed at these latitudes depends as much on the day-to-day variance (driven primarily by mid-latitude storms) as it does on the mean strength of the climatological westerly surface winds. The gradient in temperature between subtropics and pole tends to decrease with global warming (due to the ‘polar amplification’ of warming) and this, in turn, diminishes the “baroclinicity” of the atmosphere, and thus, the degree of storminess. So both a decrease in baroclinicity and a poleward shift in the extratropical band of westerly surface winds would therefore seem to work in the direction of decreasing wind in mid-latitudes.

But even this reasoning is somewhat questionable, as wind anomalies over a region as small as the U.S. are unlikely to be representative of the trend for the entire latitude band on the whole. Factors such as El Nino, and the “Northern Annular Mode” have an important role on wind patterns over the U.S., and changes in the behavior of these phenomena could easily overwhelm the average trend for the mid-latitude band. So in short, the observations of decreasing wind speeds over the U.S. are in a rough sense consistent with these ideas, but given the uncertainties in factors that are important in determining wind patterns over the scale of the U.S. continent, it’s hard to say precisely what would be expected.

Figure 1. The trends in the station winds and in the N. American reanalysis (from fig.4 in Pryor et al.)

In the specific case of the GISS-ER model, we can easily see what the model suggests. The picture below gives the annual mean wind speed change for a business-as-usual scenario out to 2100 (we picked this just because the changes are large, but a picture for simulated trends over the last 50 years is similar).

The first thing to note is that the expected changes are complex. There is a clear increase in the Southern Oceans (related to changing temperature trends in the lower stratosphere associated with both the ozone hole and greenhouse gas increases). There is also a change near the equator associated with increases in convective activity and a shift in the Hadley Cell. Note also that changes over land are very small, and in particular, over the US no significant changes are seen. The situation might be different in different models (or different seasons, or in the day-to-day variance), and so one wouldn’t want to read to much into this single figure, but it makes clear that a change in US windiness is not a strong ‘a priori’ expectation from global warming. This doesn’t of course shed any light on whether the observed trends are real, but it does speak to the attribution part of the discussion.

Indeed, you would need a careful detection/attribution analysis to see if the observed changes in wind speeds are consistent with the multi-model climate change projections. This has been done for surface temperature, precipitation, and sea level pressure changes, and there is no obvious reason it can’t be done for wind speeds if the data holds up.

Regardless of the cause of the indicated decline, is this likely to have a direct impact on wind power generation? There is a study by Archer and Jacobson that explores the potential for wind power over the US, and the results can be seen in this graph:

Wind speed class 3 (usable for power generation) and above (dark blue, green, yellow, red and black dots) are not that widespread, and are concentrated over the plains and offshore. Comparison to the trend map in the Pryor et al study (figure 1 above) shows only a limited overlap, so even if all these sites were being used, it’s not clear the trends would hamper wind-power generation much. However, this is highly speculative and will need to be looked at much more carefully in future.

Whether the wind of change is truly blowing through this continent remains to be seen…

^{Note that an apparent quote from David Deming that the possibility of decreased wind speed over the Eastern US is somehow in contradiction with the possibility of increased tropical storm intensity in the tropical Atlantic is embarrassing in the inappropriateness of the comparison.}

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But, to be frank, there has been another reason. When we started this blog, there was a lot of ground to cover - how climate models worked, the difference between short term noise and long term signal, how the carbon cycle worked, connections between climate change and air quality, aerosol effects, the relevance of paleo-climate, the nature of rapid climate change etc. These things were/are fun to talk about and it was/is easy for us to share our enthusiasm for the science and, more importantly, the scientific process.

However, recently there has been more of a sense that the issues being discussed (in the media or online) have a bit of a groundhog day quality to them. The same nonsense, the same logical fallacies, the same confusions - all seem to be endlessly repeated. The same strawmen are being constructed and demolished as if they were part of a make-work scheme for the building industry attached to the stimulus proposal. Indeed, the enthusiastic recycling of talking points long thought to have been dead and buried has been given a huge boost by the publication of a new book by Ian Plimer who seems to have been collecting them for years. Given the number of simply made-up ‘facts’ in that tome, one soon realises that the concept of an objective reality against which one should measure claims and judge arguments is not something that is universally shared. This is troubling - and although there is certainly a role for some to point out the incoherence of such arguments (which in that case Tim Lambert and Ian Enting are doing very well), it isn’t something that requires much in the way of physical understanding or scientific background. (As an aside this is a good video description of the now-classic Dunning and Kruger papers on how the people who are most wrong are the least able to perceive it).

The Onion had a great piece last week that encapsulates the trajectory of these discussions very well. This will of course be familiar to anyone who has followed a comment thread too far into the weeds, and is one of the main reasons why people with actual, constructive things to add to a discourse get discouraged from wading into wikipedia, blogs or the media. One has to hope that there is the possibility of progress before one engages.

However there is still cause to engage - not out of the hope that the people who make idiotic statements can be educated - but because bystanders deserve to know where better information can be found. Still, it can sometimes be hard to find the enthusiasm. A case in point is a 100+ comment thread criticising my recent book in which it was clear that not a single critic had read a word of it (you can find the thread easily enough if you need to - it’s too stupid to link to). Not only had no-one read it, none of the commenters even seemed to think they needed to - most found it easier to imagine what was contained within and criticise that instead. It is vaguely amusing in a somewhat uncomfortable way.

Communicating with people who won’t open the book, read the blog post or watch the program because they already ‘know’ what must be in it, is tough and probably not worth one’s time. But communication in general is worthwhile and finding ways to get even a few people to turn the page and allow themselves to be engaged by what is actually a fantastic human and scientific story, is something worth a lot of our time.

Along those lines, Randy Olson (a scientist-turned-filmmaker-and-author) has a new book coming out called “Don’t Be Such a Scientist: Talking Substance in an Age of Style” which could potentially be a useful addition to that discussion. There is a nice post over at Chris Mooney’s blog here, though read Bob Grumbine’s comments as well. (For those of you unfamiliar the Bob’s name, he was one of the stalwarts of the Usenet sci.environment discussions back in the ‘old’ days, along with Michael Tobis, Eli Rabett and our own William Connolley. He too has his own blog now).

All of this is really just an introduction to these questions: What is it that you feel needs more explaining? What interesting bits of the science would you like to know more about? Is there really anything new under the contrarian sun that needs addressing? Let us know in the comments and we’ll take a look. Thanks.

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For starters, consider the following simple example. Suppose we have measured two variables over some time period (say, temperature and humidity, call them x and y). We manage to get 10 observations of x and y simultaneously, but unfortunately one of our instruments breaks and we wind up with 10 additional measurements of x only, but none of y. Fortunately, we know that x and y are physically related to one another, so our hope is that we can use the paired observations of x and y to estimate what y should have been, had we been able to measure it throughout the experiment. We plot the variables y vs. x and try fitting some function to the data. If we get the function right, we ought to be able to estimate what y is for any arbitrary value of x. The obvious thing one might try, given the apparent curve to the data, is to use a 2nd-order polynomial (that is, a parabola):

Looks pretty good right?

Well, .. no. Actually, in for this particular example, we should have just used a straight line. This becomes obvious after we fix the broken instrument and manage to increase the size of the data set:

Of course, our estimate of the best fit line to the data is itself uncertain, and when we include more data, we wind up with a slightly different result (shown by the dashed green line, below). But we are a lot closer than we would have been using the parabola, which diverges radically from the data at large values of x:

Of course, the parabola looked liked it might be a better fit — after all, it is closer to more of the data points, which really do seem to be curving upwards. And we couldn’t have known in advance, could we? Well, we could have increased our chances of being right, by using some basic statistics (a chi-squared test for example). The results would have shown us that there were not enough degrees of freedom in the data to justify the use of the higher-order curve (which reduces the degrees of freedom by from 8 to 7 in this example). Choosing the parabola in this case is a classic example of overfitting.

The basic lesson here is that one should avoid using more parameters than necessary when fitting a function (or functions) to a set of data. Doing otherwise, more often than not, leads to large extrapolation errors.

In using PCA for statistical climate reconstruction, avoiding overfitting means — in particular — carefully determining the right number of PCs to retain. Just as in the simple example above, there are two different — but both important — tests to apply here, one a priori and one a posteriori.

First, we are interested in distinguishing those PCs that may be related to the true ‘modes’ of variability in the data. In our case, we are interested in those PCs that represent variations in Antarctica temperature that are related to, for example, changes in the circumpolar wind strength, variations in sea ice distribution, etc. While linear methods like PCA are inherently just an approximation to the real modes in the climate system, in general the first few PCs — which, by construction capture that variability that occurs over large spatial scales — do bear a strong relationship with the underlying physics. At the same time, we want to avoid those PCs that are unlikely to represent physically meaningful variability, either because they are simply noise (whether instrumental noise or true random variability in the climate) or because they represent variations of only local significance. This is not to say that local or ‘noisy’ parts of the system aren’t important, but this kind of variability is unlikely to be well represented by a network of predictor variables that is even sparser than the original data field from which the PCs are obtained.

The standard approach to determining which PCs represent to retain is to use the criterion of North et al. (1982), which provides an estimate of the uncertainty in the eigenvalues of the linear decomposition of the data into its time varying PCs and spatial eigenvectors (or EOFs). Most of the higher order PCs will have indistinguishable variance. In the case where the lower order PCs (which explain the majority of the variance) also have indistinguishable variance, caution needs to be applied in choosing which to retain, because PCA will usually scramble the underlying spatial (and temporal) structure these PCs represent. One can easily demonstrate this by creating an artificial spatio-temporal data set from simple time functions (say, a bunch of sines and cosines), plus random noise. PCA will only extract the original functions if they have significantly different amplitudes.

The figure below shows the eigenvalue spectrum — including the uncertainties — for both the satellite data from the main temperature reconstruction and the occupied weather station data used in Steig et al., 2009.

It’s apparent that in the satellite data (our predictand data set), there are three eigenvalues that lie well above the rest. One could argue for retaining #4 as well, though it does slightly overlap with #5. Retaining more than 4 requires retaining at least 6, and at most 7, to avoid having to retain all the rest (due to their overlapping error bars). With the weather station data (our predictor data set), one could justify choosing to retain 4 by the same criteria, or at most 7. Together, this suggests that in the combined data sets, a maximum of 7 PCs should be retained, and as few as 3. Retaining just 3 is a very reasonable choice, given the significant drop off in variance explained in the satellite data after this point: remember, we are trying to avoid including PCs that simply represent noise. For simple filtering applications (image processing for example), the risk of retaining too much noise is small. For extrapolation in time – the climate reconstruction problem — it is critical that the PCs approximate the dynamics in the system. In that application, retaining fewer PCs — and in particular only those that are distinguished by a large break in slope (i.e. PCs 3 or 4 in the actual data, above) is the choice least likely to unnecessarily inflate possible noise.

In short, we a priori would not want to retain more than 7, and as few as 3 PCs is clearly justifiable. We would most certainly not want to retain 9, 11, or 25 since doing so is almost certain to result in extrapolation errors. Which leads us to our a posteriori test: how well do the various reconstructions do, as a function of the number of retained PCs?

As in the simple x,y example above, we now want to take a look at how our extrapolation compares with reality. To do this, we withhold some of the data, calibrate the model (a separate calculation for each number of PCs we want to consider), and then compare the resulting reconstruction with the withheld data. Such calibration/verification tests, were, of course, done in our paper and is the major thing that distinguished our paper from previous work.

As shown in the figure below, there is not much change to the goodness-of-fit (as measured in this case by the correlation coefficient of the withheld and reconstructed data in the split calibration/verification tests), whether one uses 3, 4, 5, or 6 PCs. There is a significant (and continuous) drop off in skill, however, if one uses more than 7. We can therefore eliminate using more than 7 PCs by this a posteriori test. And since little was gained by using more than 3 or 4, parsimony would indicate using no more.

Now the alert reader may point out that the reconstruction skill (as calculated with simple PCA and correlation scores at least) seems to be the greatest in West Antarctica when we use 4 PCs, rather than 3 as in the paper. However, as shown in the first figure below, choosing more than 3 PCs results in the same or even larger trends (especially in West Antarctica) than does using 3 PCs. Of course, we can get smaller trends if we use just two PCS, or more than 10, but this can’t be justified under either a priori or a posteriori criteria — no more so than using 13, or 25 can.* The result is the same whether we use a simple PCA (as I’ve mostly restricted myself to here, for simplicity of discussion), or the RegEM algorithm (we used both methods in the paper):

In summary: our choice to retain 3 PCs was not only a reasonable a priori choice, but it produces comparable or smaller trends to other reasonable a priori choices. Using 4, 6 or 7produces larger trends in West Antarctica, and little change in East Antarctica. Using more than 7 (at least up to 12) increases the trend in both areas. So much for the claim, reported on several web sites, that we purposefully chose 3 PCs to maximize the estimated warming trend!

All of this talk about statistics, of course, can distract one from the fact that the key finding in our paper — warming in West Antarctica, akin to that on the Peninsula — is obvious in the raw data:

Raw trends in temperature — in different versions of the monthly cloud-masked temperature data (a) Comiso (decadal trends 1982-1999), (b) Monaghan et al. (1982-1999) c) Steig et al. (1982-1999), (d) Steig et al., (1982-2006).

It is, furthermore, exactly what one would expect from the atmospheric dynamics. Low-pressure storms rarely penetrate into the high polar plateau of East Antarctica, and temperatures there are radiation dominated, not-transported dominated as they are in West Antarctica and on the Peninsula. This is why those general circulation models that properly simulate the observed atmospheric circulation and sea ice changes — increasing around most of East Antarctica, but decreasing off the coast of West Antarctica and the Peninsula — also match the pattern and magnitude of the temperature trends we observe:

Figure 3b from Goosse et al., 2009, showing temperature trends (1960-2000) simulated by an intermediate complexity coupled ocean-atmosphere model that uses data-assimilation to constrain the model to match observed surface temperature variations at surface weather stations. No satellite temperature data are used. Yellow areas are warming at 0.1 to 0.2 degrees/decade in the simulation and light orange 0.2 to 0.3 degrees/decade.

A final point is that it is actually rather bizarre that so much effort has been spent in trying to find fault with our Antarctic temperature paper. It appears this is a result of the persistent belief that by embarrassing specific scientists, the entire edifice of ‘global warming’ will fall. This is remarkably naive, as we have discussed before. The irony here is that our study was largely focused on regional climate change that may well be largely due to natural variability, as we clearly state in the paper. This seems to have escaped much of the blogosphere, however.

*While I was working on this post, someone called “Ryan O” posted a long discussion claiming that he gets better verification skill than in our paper using 13 PCs. This is curious, since it contradicts my finding that using so many PCs substantially degrades reconstruction skill. It appears that what has been done is first to adjust the satellite data so that it better matches the ground data, and then to do the reconstruction calculations. This doesn’t make any sense: it amounts to pre-optimizing the validation data (which are supposed to be independent), violating the very point of ‘verification’ altogether. This is not to say that some adjustment of the satellite data is unwarranted, but it can’t be done this way if one wants to use the data for verification. (And the verification results one gets this way certainly cannot be compared against the verification results based on untuned satellite data.)

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Several studies have shown that hurricane activity is generally reduced during years when there is a thick aerosol haze over the subtropical Atlantic. The haze is comprised mainly of soil particles, stripped by wind erosion from the barren ground over the Sahara and Sahel. These particles are lifted into the atmosphere and carried by the Trade winds as far as the Caribbean and Amazon basin. Plumes of dust streaming off the African coast are easily recognized in satellite imagery, and were even described by Charles Darwin during his voyage on the Beagle.

The amount of dust crossing the Atlantic has been measured at Barbados since the mid 1960s (aptly by Prospero and colleagues). These measurements show a threefold increase in dust between the original part of the record and the mid 1980s at the peak of the Sahel drought, when the region was unusually vulnerable to wind erosion. African dust crosses the tropical Atlantic within the Saharan Air Layer (SAL), an elevated duct of air between about 2 and 5 km in altitude. Because of its continental origin, this air is not only dusty but extremely dry.

Figure 2: Monthly mean dust concentration measured at Barbados. Arrows mark years with large El Niño events, which are irrelevant here (Prospero and Lamb, 2003).

There is an observed anti-correlation between dustiness and tropical cyclone days in the Atlantic (Evan et al, 2006). This anti-correlation might indicate the a direct influence of dust on hurricanes, or a connection between the dry air the dust resides in and hurricanes, or might even be related to a much larger scale pattern which controls both hurricanes and dustiness. If there is a connection, one hypothesis is that entrainment of dry SAL air rapidly strangles a developing cyclone because of the low humidity that accompanies the dusty air, while the dust itself has no direct effect. An alternative hypothesis is that the reduction in sunlight beneath the dust layer cools the ocean surface, whose temperature is a well-known predictor of hurricane activity (at least at the basin scale). Thus it is plausible that decadal variations in dustiness could contribute to decadal variations in hurricane activity, but how big might such an effect be?

A recent article in Science by Evan et al. (2009) is one of the few attempts to quantify the contribution of both dust and volcanic aerosols to the observed warming within the tropical Atlantic. The authors infer the amount of total aerosol using the Advanced Very High-Resolution Radiometer (AVHRR) satellite instrument and screen for locations where dust is present (they note that other aerosols might be mixed with the dust, but neglect this overlap). They also assume that dust has no effect where there are clouds. However, where the SAL extends over low marine clouds, the dust (since it is darker than cloud) might have an opposing effect to that seen in clear sky regions, although this is hard to quantify. They then calculate the contribution by dust and volcanic aerosols to observed changes in sea surface temperature (SST) during the satellite record between 1982 and 2007. During this period, the aerosol amount varied with dust export from Africa, but also from major eruptions by two volcanoes (El Chichon in 1982 and Pinatubo in 1991), each of which left a reflective layer of sulfate droplets in the lower stratosphere for a couple of years.

Evan et al. calculate that between 1982 and 2007 the ocean surface warmed by 0.25°C/decade in the main region of Atlantic hurricane genesis (15-­65°W and 0­-30°N). For comparison, they calculate a warming trend of 0.18°C/decade due to a reduction of dust and volcanic aerosols. That decreasing aerosols account for two-thirds of the observed warming might suggest that other factors like the increase in greenhouse gas concentrations (combined with anthropogenic aerosol changes) made a relatively modest net contribution to the warming (and by implication to observed trends in hurricane activity). For the natural aerosols, they calculate that stratospheric aerosols made roughly twice the contribution of dust over this period.

So how did they do this calculation? Firstly, they use a relatively simple model to relate SST to the reduction in net radiation into the ocean surface, prior to any climatic response. This forcing is calculated using the total aerosol amount inferred from the AVHRR data. Variations in SST due to variations in heat transport by ocean currents or diffusion into the thermocline are neglected while contributions by changes in evaporation, turbulent transfer, and surface radiation are estimated as being proportional to the anomalous air-sea temperature difference. Cooling of the ocean by aerosols must therefore be offset by a reduction in heat lost from the ocean to the atmosphere.

They note a key simplification is their neglect of any change to the surface air temperature when calculating anomalous air-sea temperature difference. This would require an atmospheric model along with a consideration of aerosol forcing at the top of the atmosphere (TOA). There is a strong relationship between surface air temperature and TOA forcing (at least at large spatial scales). As a consequence, the ocean-atmosphere flux depends upon not only forcing at the surface but the forcing at the TOA. By neglecting the effect of the changes in surface air temperature upon SST, Evan et al. may be underestimating the impact of the aerosols on their calculated trend. This is especially important for volcanic aerosols, whose TOA forcing is large and comparable to the surface forcing, as opposed to absorbing aerosols like dust where the surface forcing is larger than at TOA. However, balancing this effect is the neglect of heat diffusion into the thermocline which would reduce the ocean cooling. It is not a priori obvious which effect is more important, especially since the atmosphere can balance the forcing by adjusting lateral heat transport, which would also influence the anomalous surface air temperature.

Another way to test the importance of atmospheric changes would be to calculate both the TOA and surface forcing using the satellite measurements, and then impose this transient forcing in a general circulation model that calculates both the atmosphere and ocean response. That too would have problems, given that the models are not perfect, but it would be a useful check on the order of magnitude of the inferred effects. Indeed, assessments of the causes of tropical Atlantic trends using the IPCC AR4 models (Santer et al, 2006) come up with a much larger component due to anthropogenic effects, though those models did not include dust forcing changes.

Using their methodology, Evan et al. find that a decline in total aerosols contributed around two-thirds of the observed warming in NH tropical Atlantic SST between 1982 and 2007. Most of this is due to the two major volcanic eruptions (El Chichon and Pinatubo) that cooled the ocean early on in this period (and so lead to a warming once they were no longer present). However, the attributed aerosol trend would have been smaller had the satellite record extended a decade earlier. The estimated contribution of dust changes to the observed trend is small, roughly one-quarter of the total trend.

Whatever its impact upon SST, dust might impact other factors contributing to cyclone intensity (Emanuel, 1995), in particular, the reduction of the air-sea heat flux and temperatures in the upper troposphere. Unfortunately, global models don’t quite have the resolution to explicitly calculate all these effects.

Ultimately, the effect of dust upon hurricanes is important because, like ocean temperatures, African dust export is expected to change during the 21st century in response to global warming and changes in African rainfall. One study shows that dust production is expected to decrease (Mahowald and Luo, 2003), though given the diversity of Sahel rainfall projections and the preliminary state of vegetation models, this is not necessarily going to be a universal response.

The calculation by Evan et al. is an interesting first step to quantifying the effect of dust changes on SST, but there plenty of issues left to investigate.

Footnote: For some presumably poetic reason, the Bard neglected to note that the Main Development Region is more like 25,000 furlongs across and the Sahara is about 2 billion acres.

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The top 5 do start discussing how to manage the transition. They realise that the continued growth in catches - driven by improved technology and increasing effort - is not sustainable, and make a plan to reduce their catch by 80% over a number of years. But there is opposition - manufacturers of fishing boats, tackle and fish processing plants are worried that this would imply less sales for them in the short term. Strangely, they don’t seem worried that a complete collapse of the fishery would mean no sales at all - preferring to think that the science can’t possibly be correct and that everything will be fine. These manufacturers set up a number of organisations to advocate against any decreases in catch sizes - with catchy names like the Fisherfolk for Sound Science, and Friends of Fish. They then hire people who own an Excel spreadsheet program do “science” for them - and why not? They live after all in a free society.

After spending much energy and money on trying to undermine the science - with claims that the pond is much deeper than it looks, that the fish are just hiding, that the records of fish catches were contaminated by being done near a supermarket - the continued declining stocks and smaller and smaller fish make it harder and harder to sound convincing. So, in a switch of tactics so fast it would impress Najinsky, the manufacturers’ lobby suddenly decides to accept all that science and declares that the ‘fish are hiding’ crowd are just fringe elements. No, they said, we want to help with this transition, but …. we need to be sure that the plans will make sense. So they ask their spreadsheet-wielding “advocacy scientists” to calculate exactly what would happen if the top 5 (and only the top 5) did cut their catches by 80%, but meanwhile everyone else kept increasing their catch at the current (unsustainable rate). Well, the answers were shocking - the total catch would be initially still be 84% of what it is now and would soon catch up with current levels. In fact, the exact same techniques that were used to project the fishery collapse imply that this would only delay the collapse by a few years! and what would be the point of that?

The fact that the other top fishermen are discussing very similar cuts and that the fisherfolk council was trying to coordinate these actions to minimise the problems that might emerge, are of course ignored and the cry goes out that nothing can be done. In reality of course, the correct lesson to draw is that everything must be done.

In case you think that no-one would be so stupid as to think this kind of analysis has any validity, I would ask that you look up the history of the Newfoundland cod fishery. It is indeed a tragedy.

And the connection to climate? Here.

I’ll finish with a quotation attributed to Edmund Burke, one the founders of the original conservative movement:

“Nobody made a greater mistake than he who did nothing because he could do only a little.”

See here for a much better picture of what coordinated action could achieve.

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This issue is a very familiar one when it comes to long-time series from satellite data. Each individual satellite only lasts a few years, and so a 30 year time series needs to be stitched together from a series of satellites. Each of those instruments might have a different calibration, and may have non-climatic drifts associated with instrument degradation, or orbital effects. Thus it can often be the case that there is a degree of ambiguity in putting together the series. This issue is at least part of the difference between the RSS and UAH tropospheric temperature trends, and in the CERES/ERBE analyses discussed recently.

The differences between PMOD and ACRIM have already been discussed by the SkepticalScientist and Tamino, so here is just an update in the light of the two recent papers. The important issue here is the so-called ‘ACRIM-gap’, the time between the ACRIM-I instrument ceased and when the ACRIM-II observations started (mid-1989 to late 1991), and how the data from these two instruments are combined using other overlapping observations. Note that the ‘ACRIM’ name for the Willson et al time-series simply implies that it was put together by some people on the ACRIM science team, not that they use different satellite data.

The focus on these papers is what the ‘ACRIM gap’ implies for TSI levels during the solar minimum at solar cycles 21 and 22. Whereas PMOD suggests that the TSI levels during these minima are similar, ACRIM suggests that the TSI level is higher during the minimum of cycle 22. SW08 even claim that there has been a positive ‘minima trend‘.

LF08 conclude that the PMOD is more realistic, since the change in the TSI levels during the solar minima, suggested by ACRIM, is inconsistent with the known relationship between TSI and galactic cosmic rays (GCR). It is well-known that the GCR flux is generally low when the level of solar activity is high, because the solar magnetic fields are more extensive and these shield the solar system against GCR (charged particles). However the two effects don’t always go in lockstep, so this is suggestive rather than conclusive.

It is also clear from the instrumental data that the TSI tends to increase with the solar activity level - at least over the solar cycle. LF08 argue that if the ACRIM ‘minimum trend’ is correct, this will mean that past reconstruction of TSI based on e.g. sunspots are incorrect, and a lot of studies on the past climate variations would be wrong. This does not mean that the ACRIM data are useless, but that there are uncertainties regarding the relationship TSI-levels, solar activity for different time scales.

I found insufficient detailed description in SW09 of the methodology used in their analysis to be able to judge the real merit of their work. The paper provides a link to auxiliary material that does not work. However, the figures in the paper don’t really convince when I don’t know how they were made.

Furthermore, I found the SW09 a bit confusing, as it gives the impression that the PMOD composite relies on ERBS/ERBE data during the ACRIM-gap (“The PMOD team uses the sparse ERBS/ERBE data base to ‘bridge’ the ACRIM gap, conforming the higher cadence Nimbus 7/ERB to it by making adjustments due to …”). However the information in LF08 says PMOD used HF from Nimbus 7 (ERB).

The PMOD analysis involves an adjustment to correct for a glitch in the ERB data (orientation changes and/or switching off), but SW09 claims - without providing convincing arguments - that this correction cannot be justified.

The ACRIM composite does not account for a jump in the ‘ACRIM-gap’ due to instrumental changes. SW09 show a comparison between different analyses and Krivova et al. (2007) modeled TSI, but later acknowledge that the latter modeled TSI disagrees with measurements on decadal time scales. Furthermore, when the TSI is not adjusted over the ‘ACRIM-gap’, there is the apparent inconsistency between TSI and GCR.

Update: My conclusion is that the LF08 paper is far more convincing than the SW09 in terms of whether the TSI data should be adjusted over the ‘ACRIM-gap’. But the this is probably not the final word on the matter.

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The letter he has written to the NY Times public editor, with its liberal sprinkling of his usual pomposity, has at its heart the following graph:

Among other issues, it is quite amusing that Monckton apparently thinks that;

• trends from January 2002 are relevant to a complaint about a story discussing a 1995 report,
• someone might be fooled by the cherry-picked January 2002 start date,
• no-one would notice that he has just made up the IPCC projection curves

The last is even more amusing because he was caught out making stuff up on a slightly different figure just a few weeks ago.

To see the extent of this chicanery, one needs only plot the actual IPCC projections against the observations. This can be done a number of ways, firstly, plotting the observational data and the models used by IPCC with a common baseline of 1980-1999 temperatures (as done in the 2007 report) (Note that the model output is for the annual mean, monthly variance would be larger):

These show clearly that 2002-2009 is way too short a period for the trends to be meaningful and that Monckton’s estimate of what the IPCC projects for the current period is woefully wrong. Not just wrong, fake.

Even if one assumes that the baseline should be the year 2002 making no allowance for internal variability (which makes no sense whatsoever), you would get the following graph:

- still nothing like Monckton showed. Instead, he appears to have derived his ‘projections’ by drawing a line from 2002 to a selection of real projections in 2100 and ignoring the fact that the actual projections accelerate as time goes on, and thus strongly over-estimating the projected changes that are expected now (see here).

Lest this be thought a mere aberration or a slip of his quill, it turns out he has previously faked the data on projections of CO₂ as well. This graph is from a recent presentation of his, compared to the actual projections:

How can this be described except as fake?

Apart from this nonsense, is there anything to Monckton’s complaint about Revkin’s story? Sadly no. Once one cuts out the paranoid hints about dark conspiracies between “prejudiced campaigners”, Al Gore and the New York Times editors, the only point he appear to make is that this passage from the scientific advice somehow redeems the industry lobbyists who ignored it:

The scientific basis for the Greenhouse Effect and the potential for a human impact on climate is based on well-established scientific fact, and should not be denied. While, in theory, human activities have the potential to result in net cooling, a concern about 25 years ago, the current balance between greenhouse gas emissions and the emissions of particulates and particulate-formers is such that essentially all of today’s concern is about net warming. However, as will be discussed below, it is still not possible to accurately predict the magnitude (if any), timing or impact of climate change as a result of the increase in greenhouse gas concentrations. Also, because of the complex, possibly chaotic, nature of the climate system, it may never be possible to accurately predict future climate or to estimate the impact of increased greenhouse gas concentrations.

This is a curious claim, since the passage is pretty much mainstream. For instance, in the IPCC Second Assessment Report (1995) (p528):

Complex systems often allow deterministic predictability of some characteristics … yet do not permit skilful forecasts of other phenomena …

or even more clearly in IPCC TAR (2001):

In climate research and modeling, we should recognize that we are dealing with a coupled non-linear chaotic system, and therefore that the long-term prediction of future climate states is not possible. The most we can expect to achieve is the prediction of the probability distribution of the system’s future possible states….

Much more central to the point Revkin was making was the deletion of the sections dealing with how weak the standard contrarian arguments were - arguments that GCC publications continued to use for years afterward (and indeed arguments that Monckton is still using) (see this amendment to the original story).

Monckton’s ironic piece de resistance though is the fact that he entitled his letter “Deliberate Misrepresentation” - and this is possibly the only true statement in it.

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