Environmental Issues

Update: Nature has just published a thoughtful commentary on the report

The Inter-Academy Council report on the processes and governance of the IPCC is now available. It appears mostly sensible and has a lot of useful things to say about improving IPCC processes – from suggesting a new Executive to be able to speak for IPCC in-between reports, a new communications strategy, better consistency among working groups and ideas for how to reduce the burden on lead authors in responding to rapidly increasing review comments.

As the report itself notes, the process leading to each of the previous IPCC reports has been informed from issues that arose in previous assessments, and that will obviously also be true for the upcoming fifth Assessment report (AR5). The suggestions made here will mostly strengthen the credibility of the next IPCC, particularly working groups 2 and 3, though whether it will make the conclusions less contentious is unclear. Judging from the contrarian spin some are putting on this report, the answer is likely to be no.

James Cameron, director of the award-winning film "Avatar," is featured in a series of new NASA public service announcements that describe the many contributions of NASA's Earth science program to environmental awareness and exploration of our home planet.

We’ve been a little preoccupied recently, but there are some recent developments in the field of do-it-yourself climate science that are worth noting.

First off, the NOAA/BAMS “State of the Climate 2009” report arrived in mailboxes this week (it has been available online since July though). Each year this gets better and more useful for people tracking what is going on. And this year they have created a data portal for all the data appearing in the graphs, including a lot of data previously unavailable online. Well worth a visit.

Second, many of you will be aware that the UK Met Office is embarking on a bottom-up renovation of the surface temperature data sets including daily data and more extensive sources than have previously been available. Their website is surfacetemperatures.org, and they are canvassing input from the public until Sept 1 on their brand new blog. In related news, Ron Broberg has made a great deal of progress on a project to use the much more extensive daily weather report data into a useful climate record. Something that the pros have been meaning to do for a while….

Third, we are a little late to the latest hockey stick party, but sometimes taking your time makes sense. Most of the reaction to the new McShane and Wyner paper so far has been more a demonstration of wishful thinking, rather than any careful examination of the paper or results (with some notable exceptions). Much of the technical discussion has not been very well informed for instance. However, the paper commendably comes with extensive supplementary info and code for all the figures and analysis (it’s not the final version though, so caveat lector). Most of it is in R which, while not the easiest to read language ever devised by mankind, is quite easy to run and mess around with (download it here).

The M&W paper introduces a number of new methods to do reconstructions and assess uncertainties, that haven’t previously been used in the climate literature. That’s not a bad thing of course, but it remains to be seen whether they are an improvement – and those tests have yet to be done. One set of their reconstructions uses the ‘Lasso’ algorithm, while the other reconstruction methods use variations on a principal component (PC) decomposition and simple ordinary least squares (OLS) regressions among the PCs (varying the number of PCs retained in the proxies or the target temperatures). The Lasso method is used a lot in the first part of the paper, but their fig. 14 doesn’t show clearly the actual Lasso reconstructions (though they are included in the background grey lines). So, as an example of the easy things one can look at, here is what the Lasso reconstructions actually gave:

‘Lasso’ methods in red and green over the same grey line background (using the 1000 AD network).

It’s also easy to test a few sensitivities. People seem inordinately fond of obsessing over the Tiljander proxies (a set of four lake sediment records from Finland that have indications of non-climatic disturbances in recent centuries – two of which are used in M&W). So what happens if you leave them out?

No Tiljander (solid), original (dashed), loess smooth for clarity, for the three highlighted ‘OLS’ curves in the original figure).

… not much, but it’s curious that for the green curves (which show the OLS 10PC method used later in the Bayesian analysis) the reconstructed medieval period gets colder!

There’s lots more that can be done here (and almost certainly will be) though it will take a little time. In the meantime, consider the irony of the critics embracing a paper that contains the line “our model gives a 80% chance that [the last decade] was the warmest in the past thousand years”….

Global plant productivity that once was on the rise with warming temperatures and a lengthened growing season is now on the decline because of regional drought according to a new study of NASA satellite data.

NASA has awarded $7.7 million in cooperative agreements to 17 organizations across the United States to enhance learning through the use of NASA's Earth science resources.

NASA will host LAUNCH: Health, a global forum focusing on health issues, at the agency's Kennedy Space Center in Florida from Oct. 30-31.

A response from Justin Wood, writing to me from Australia after my previous post (cited with permission below), has prompted me to write a follow-up on the story of the greenhouse effect (GHE).

I wonder if you’ve seen this terrible description of the greenhouse effect on a UNFCCC background page? http://unfccc.int/essential_background/feeling_the_heat/items/2903.php
It actually says that incoming solar energy is ‘reflected’ by the planet’s surface ‘in the form of a calmer, more slow-moving type of energy called infrared radiation. … Infrared radiation is carried slowly aloft by air currents, and its eventual escape into space is delayed by greenhouse gases’ (emphasis added).

Given your recent excellent explanation of the real physics on RC, I thought you might be interested! It’s downright disturbing that this silliness comes from such an important source; and I’ve found it repeated all over the place. (On that RC post, I would humbly suggest that the section on stratospheric cooling could helpfully be expanded to make that clearer?)

I won’t discuss the stratospheric cooling now, but rather try to place recent events (including floods in Niger), which involve the hydrological cycle and atmospheric circulation, into the framework from my previous post ‘A simple recipe for GHE‘.

Again, it can be useful to stop and contemplate whether a simple conceptual framework can provide greater understanding of climate model predictions and the observations we make on the climate system. I think that there are not too many simple descriptions, as Wood pointed out, that are convincing in terms of physics.

Can we use such simple conceptual explanations for events such as the recent spate of extreme rainfall and heat waves then? I want to stress, as we did when discussing tropical cyclones, that single events do not constitute evidence of a climate change. Since climate can be defined as ‘typical weather pattern’ (or weather statistics), then climate change can be that extremes become more or less typical, and such change must start with a few events. This touches the difference between weather and climate, and each of these events can be considered as weather. But there is a connection between these weather events and results obtained from climate models.

There are fascinating as well as disconcerting sides to the fact that global climate models reported in the IPCC AR4 suggest warming in the upper troposphere in the tropics (Figure 1 below). I regard these traits as important clues that may help unveil the secrets of the troposphere; The key into this mystery involves energy conservation, planetary energy balance, and the planetary energy input taking place at its surface while its heat loss mainly occurs at higher levels, as discussed in ‘A simple recipe for GHE‘.

This story is about surface fluxes, a fuzzy connection between energy flow and circulation of water, and physical constraints pin-pointing the solutions. In other words, the hydrological cycle associated with moisture transport is tied to the energy flow associated with moist convection.

IPCC AR4 Figure 9.1: zonal mean atmospheric temperature change from 1890-1999 (deg C/100yrs). Panel (f) shows modelled response to all forcings.

Another simple mental picture
I will yet again try to present a simplified physical picture: Our climate includes energy transport both from the equatorial region to the poles as well as a vertical flow from the surface to the height from which it can escape freely into outer space. The story behind mid-to-upper tropospheric warming strongly involves the vertical energy flow, which will be the focus of the discussion. In very simple terms, the laws of physics say there has to be a flow of energy from the planet’s surface, where energy is deposited, to the heights from where the heat loss takes place (see schematic below).

A schematic illustration showing the surface acting as an energy source while the energy sink is found higher aloft. The flow of energy between these two levels is key to understand the effect of the GHE on the hydrological cycle.

The vertical energy flow can take several forms: radiative, latent, and sensible heat. The radiative energy transfer has a character of diffusion (photon diffusion), and the more opaque the atmosphere, due to increased GHG concentrations, the slower the effective radiative energy transfer. A similar situation is believed to take place in the outer layer of the Sun, in the opaque convective zone, where convection is the main mode of energy transfer (which by the way subsequently play a role in solar activity).

If this were the whole story, then an increase in GHG concentrations would imply a deficit between the rate of energy gained at the surface and heat loss from the upper atmosphere due to hypothetically lowered energy transfer between the two levels: The emission temperature would decline as a result of net heat loss high up, and surface temperature would increase as a result of net gain in energy on the ground.

One consequence of a deficit in the vertical energy flow would be different heating and cooling rates at different heights that subsequently would alter the atmosphere’s vertical structure (lapse rate). The planetary heat loss would drop if the emission temperature were to drop, and the planet would no longer be in energy balance, resulting in energy accumulation. However, planets will eventually reach new equilibrium states where the heat-loss balances the energy input.

Other forms for heat flow between the two levels are expected to compensate for the reduction in radiative energy transfer (despite greater temperature differences) if the planetary energy input and heat loss are to balance. One such candidate is convection, carrying both latent and sensible heat and where the energy transfer takes place in form of heat-carrying vertical motion. Indeed, warming below and cooling aloft give rise to more unstable conditions that favours convection.

Convection is a common factor for many types of clouds, which need moist air to form. Usually the moisture is transported aloft through convection (photo:Rasmus).

Higher temperatures near the surface also cause increased evaporation according to a physical law known as ‘the Clapeyron-Clausius equation‘. Evaporation requires energy so that heat, which otherwise would go to increase temperatures, is instead used to transform water to water vapour (phase change). Differences in the molecular weights of N2 and H2O means that moist air is lighter than dry air. Thus, increased evaporation favours convection, which transports both energy – as sensible (higher temperature) and latent (vapour) heat – and moisture. This is seen occurring naturally, especially in association with warm ocean surface in connection with the El Nino Southern Oscillation. Convection can therefore compensate for reduced radiative transfer if its mean vertical extent reaches the height of the planetary heat loss. Convection also is one of the factors that determines the thickness of the tropopause (Wikipedia on Troposphere: “The word troposphere derives from the Greek: tropos for “turning” or “mixing,” reflecting the fact that turbulent mixing plays an important role in the troposphere’s structure and behavior.”).

Moist convection results in cloud formation: water vapour condenses and form cloud drops. The condensation releases heat and hence increase the temperatures, which subsequently has an effect on the black body radiation. Hence, cloud formation plays a crucial role for the planetary heat loss – in addition to affecting the planetary albedo.

The reason why Figure 9.1 in IPCC AR4 is disconcerting is that the temperature anomaly in the upper tropical atmosphere bears the signature of increased moist convective activity, which means that the hydrological cycle probably gets perturbed by increased GHG forcings, hence affecting rainfall patterns.

There have been some misunderstanding regarding the enhanced warming in the upper troposphere – mistakenly taken as being inconsistent with the climate models, or taken as the “finger print” of GHE, rather than as a plausible consequence predicted for an enhanced GHE due to the perturbation of the hydrological cycle (the “finger print”-misconception assumes that the models are perfect).

Changes in the convective activity also have other repercussions. Air just doesn’t pile up, but if is rises in some places, it means that there is sinking air elsewhere. A typical example of this is the Hadley cell, where the circulation involves rising air near equator associated with low sea level pressure and downward motion poleward of this region – an arid region known as the subtropics with high sea level pressure. A change in convection on a planetary scale, due to compensating a reduction in the vertical radiative energy transport, hence may have a bearing on drought and flooding events – and this is what the global climate models seem to suggest. If a shift in the hydrological cycle were to lower the response in the global mean temperature, there may be a poisonous sting in such a negative feedback: changes in the precipitation patterns.

When GHG concentrations change, there is also a disruption in the vertical energy flow so that the planetary energy balance is perturbed. This is the frequently cited extra forcing estimated at the top of the atmosphere (TOA), and this is where some of the assumptions made above don’t quite hold (the picture is correct for a planet in equilibrium, but during a transition the planet is no longer in an equilibrium) and extra energy is taken up by warming of the oceans and surface.

As a physicist, the key to understanding the relationship between GHE and the hydrological cycle – and indeed the troposphere – is in embedded in the question of what happens with the energy flow between the two levels where the planet receives its energy and where it leaves the planet. For more numbers and details, I’d recommend a number of posts previously published here on RC (here, here, here, here, and here).

NASA will host media in Fort Lauderdale, Fla., and Houston on Tuesday, Aug. 31, for a behind-the-scenes look at the agency's major airborne campaign studying Atlantic Ocean and Gulf of Mexico hurricanes.

During a meeting Tuesday at NASA Headquarters in Washington, NASA Administrator Charles Bolden and Israel Space Agency Director General Zvi Kaplan signed a joint statement of intent to expand the agencies' cooperation in civil space activities.

WRI is working with Google to make our data related to climate change more approachable and interactive than ever.

Google's Public Data Explorer is a new tool that makes large data sets easier to understand and explore. Users can reimagine data sets from a growing list of providers (like the U.S. Census, Eurostat, the World Bank, and, now, WRI's Climate Analysis Indicators Tool - CAIT) as interactive charts and maps that illustrate data relationships and trends over time. These new data visualizations can be embedded in other websites and easily shared via email or social networks.

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Guest commentary by Barry R. Bickmore, Brigham Young University

If you look around the websites dedicated to debunking mainstream climate science, it is very common to find Lord Christopher Monckton, 3rd Viscount of Brenchley, cited profusely. Indeed, he has twice testified about climate change before committees of the U.S. Congress, even though he has no formal scientific training. But if he has no training, why has he become so influential among climate change contrarians? After examining a number of his claims, I have concluded that he is influential because he delivers “silver bullets,” i.e., clear, concise, and persuasive arguments. The trouble is his compelling arguments are often constructed using fabricated facts. In other words, he makes it up. (Click here to see a number of examples by John Abraham, here for a few by myself, and here for some by Tim Lambert).

Here I’m going to examine some graphs that Lord Monckton commonly uses to show that the IPCC has incorrectly predicted the recent evolution of global atmospheric CO2 concentration and mean temperature. A number of scientists have already pointed out that Monckton’s plots of “IPCC predictions” don’t correspond to anything the IPCC ever predicted. For example, see comments by Gavin Schmidt (Monckton’s response here,) John Nielsen-Gammon (Monckton’s response here,) and Lucia Liljegren. Monckton is still happily updating and using the same graphs of fabricated data, so why am I bothering to re-open the case?

My aim is to more thoroughly examine how Lord Monckton came up with the data on his graphs, compare it to what the IPCC actually has said, and show exactly where he went wrong, leaving no excuse for anyone to take him seriously about this issue.

Atmospheric CO2 Concentration

By now, everyone who pays any attention knows that CO2 is an important greenhouse gas, and that the recent increase in global average temperature is thought to have been largely due to humans pumping massive amounts of greenhouse gases (especially CO2) into the atmosphere. The IPCC projects future changes in temperature, etc., based on projections of human greenhouse gas emissions. But what if those projections of greenhouse gas emissions are wildly overstated? Lord Monckton often uses graphs like those in Figs. 1 and 2 to illustrate his claim that “Carbon dioxide is accumulating in the air at less than half the rate the UN had imagined.”

Figure 1. Graph of mean atmospheric CO2 concentrations contrasted with Monckton’s version of the IPCC’s “predicted” values over the period from 2000-2100. He wrongly identifies the concentrations as “anomalies.” Taken from the Feb. 2009 edition of Lord Monckton’s “Monthly CO2 Report.”

Figure 2. Graph of mean atmospheric CO2 concentrations contrasted with Monckton’s version of the IPCC’s “predicted” values over the period from Jan. 2000 through Jan. 2009. Taken from the Feb. 2009 edition of Lord Monckton’s “Monthly CO2 Report.”

It should be noted that Lord Monckton faithfully reproduces the global mean sea surface CO2 concentration taken from NOAA, and the light blue trend line he draws through the data appears to be legitimate. Unfortunately, nearly everything else about the graphs is nonsense. Consider the following points that detail the various fantasies Monckton has incorporated into these two graphics.

Fantasy #1.
Lord Monckton claims the light blue areas on his graphs (Figs. 1 and 2) represent the IPCC’s predictions of atmospheric CO2 concentrations.

Reality #1.
The IPCC doesn’t make predictions of future atmospheric CO2 concentrations. And even if we ferret out what Lord Monckton actually means by this claim, he still plotted the data incorrectly.

The IPCC doesn’t really make predictions of how atmospheric CO2 will evolve over time. Rather, the IPCC has produced various “emissions scenarios” that represent estimates of how greenhouse gas emissions might evolve if humans follow various paths of economic development and population growth. The IPCC’s report on emissions scenarios states, “Scenarios are images of the future, or alternative futures. They are neither predictions nor forecasts. Rather, each scenario is one alternative image of how the future might unfold.” Lord Monckton explained via e-mail that he based the IPCC prediction curves “on the IPCC’s A2 scenario,which comes closest to actual global CO2 emissions at present” (2). In his “Monthly CO2 Report” he added, “The IPCC’s estimates of growth in atmospheric CO2 concentration are excessive. They assume CO2 concentration will rise exponentially from today’s 385 parts per million to reach 730 to 1020 ppm, central estimate 836 ppm, by 2100,” which is consistent with the A2 scenario. In other words, Monckton has picked one of several scenarios used by the IPCC and misrepresented it as a prediction. This is patently dishonest.

Monckton’s misrepresentation of the IPCC doesn’t end here, however, because he has also botched the details of the A2 scenario. The IPCC emissions scenarios are run through models of the Carbon Cycle to estimate how much of the emitted CO2 might end up in the atmosphere. A representative (i.e., “middle-of-the-road”) atmospheric CO2 concentration curve is then extracted from the Carbon Cycle model output, and fed into the climate models (AOGCMs) the IPCC uses to project possible future climate states. Figure 3 is a graph from the most recent IPCC report that shows the Carbon Cycle model output for the A2 emissions scenario. The red lines are the output from the model runs, and the black line is the “representative” CO2 concentration curve used as input to the climate models. I digitized this graph, as well, and found that the year 2100 values were the same as those cited by Monckton. (Monckton calls the model input the “central estimate.” )

Figure 3. Plot of atmospheric CO2 concentrations projected from 2000-2100 for the A2 emissions scenario, after the emissions were run through an ensemble of Carbon Cycle models. The red lines indicate model output, whereas the black line represents the “representative” response that the IPCC used as input into its ensemble of climate models (AOGCMs). Taken from Fig. 10.20a of IPCC AR4 WG1.

Now consider Figure 4, where I have plotted the A2 model input (black line in Fig. 3), along with the outer bounds of the projected atmospheric CO2 concentrations (outer red lines in Fig. 3). However, I have also plotted Monckton’s Fantasy IPCC predictions in the figure. The first thing to notice here is how badly Monckton’s central tendency fits the actual A2 model input everywhere in between the endpoints. Monckton’s central tendency ALWAYS overestimates the model input except at the endpoints. Furthermore, the lower bound of Monckton’s Fantasy Projections also overestimates the A2 model input before about the year 2030. What appears to have happened is that Lord Monckton chose the correct endpoints at 2100, picked a single endpoint around the year 2000-2002, and then made up some random exponential equations to connect the dots with NO REGARD for whether his lines had anything to do with what the IPCC actually had anywhere between.

Figure 4. Here the black lines represent the actual A2 input to the IPCC climate models (solid) and the upper and lower bounds of the projected CO2 concentrations obtained by running the A2 emissions scenario through an ensemble of Carbon Cycle models. This data was digitized from the graph in Fig. 3, but a table of model input concentrations of CO2 resulting from the different emissions scenarios can be found here. The red lines represent Monckton’s version of the IPCC’s “predicted” CO2 concentrations. The solid red line is his “central tendency”, while the dotted lines are his upper and lower bounds. Monckton’s data was digitized from the graph in Fig. 1.

John Nielsen-Gammon also pointed some of this out, but Lord Monckton responded:,

[Nielsen-Gammon] says my bounds for the 21st-century evolution of CO2 concentration are not aligned with those of the UN. Except for a very small discrepancy between my curves and two outliers among the models used by the UN, my bounds encompass the output of the UN’s models respectably, as the blogger’s own overlay diagram illustrates. Furthermore, allowing for aspect-ratio adjustment, my graph of the UN’s projections is identical to a second graph produced by the UN itself for scenario A2 that also appears to exclude the two outliers.

It is fair enough to point out that Fig. 10.26 in IPCC AR4 WG1 has a plot of the projected A2 CO2 concentrations that seems to leave out the outliers. However, Monckton’s rendition is still not an honest representation of anything the IPCC ever published. I can prove this by blowing up the 2000-2010 portion of the graph in Fig. 4. I have done this in Fig. 5, where I have also plotted the actual mean annual global CO2 concentrations for that period. The clear implication of this graph is that even if the A2 scenario did predict atmospheric CO2 evolution (and it doesn’t,) it would actually be a good prediction, so far. In Figures 1 and 2, Lord has simply fabricated data to make it seem like the A2 scenario is wrong.

Figure 5. This is a blow-up of the graph in Fig. 4 for the years 2000-2010. I have also added the annual global mean atmospheric CO2 concentrations (blue line), obtained from NOAA.

Fantasy #2.
Monckton claims that “for seven years, CO2 concentration has been rising in a straight line towards just 575 ppmv by 2100. This alone halves the IPCC’s temperature projections. Since 1980 temperature has risen at only 2.5 °F (1.5 °C) per century." In other words, he fit a straight line to the 2002-2009 data and extrapolated to the year 2100, at which time the trend predicts a CO2 concentration of 575 ppm. (See the light blue line in Fig. 1.)

Reality #2.
It is impossible to distinguish a linear trend from an exponential trend like the one used for the A2 model input over such a short time period.

I pointed out to Lord Monckton that it’s often very hard to tell an exponential from a linear trend over a short time period, e.g., the 7-year period shown in Fig. 2. He replied,

I am, of course, familiar with the fact that, over a sufficiently short period (such as a decade of monthly records), a curve that is exponential (such as the IPCC predicts the CO2 concentration curve to be) may appear linear. However, there are numerous standard statistical tests that can be applied to monotonic or near-monotonic datasets, such as the CO2 concentration dataset, to establish whether exponentiality is being maintained in reality. The simplest and most direct of these is the one that I applied to the data before daring to draw the conclusion that CO2 concentration change over the past decade has degenerated towards mere linearity. One merely calculates the least-squares linear-regression trend over successively longer periods to see whether the slope of the trend progressively increases (as it must if the curve is genuinely exponential) or whether, instead, it progressively declines towards linearity (as it actually does). One can also calculate the trends over successive periods of, say, ten years, with start-points separated by one year. On both these tests, the CO2 concentration change has been flattening out appreciably. Nor can this decay from exponentiality towards linearity be attributed solely to the recent worldwide recession: for it had become evident long before the recession began.

In other words, the slope keeps getting larger in an exponential trend, but stays the same in a linear trend. Monckton is right that you can do that sort of statistical test, but Tamino actually applied Monckton’s test to the Mauna Loa observatory CO2 data since about 1968 and found that the 10-year slope in the data has been pretty continuously rising, including over the last several years. Furthermore, look at the graph in Fig. 5, and note that the solid black line representing the A2 climate model input looks quite linear over that time period, but looks exponential over the longer timeframe in Fig. 4. I went to the trouble of fitting a linear trend line to the A2 model input line from 2002-2009 and obtained a correlation coefficient (R2) of 0.99967. Since a perfectly linear trend would have R2 = 1, I suggest that it would be impossible to distinguish a linear from an exponential trend like that followed by the A2 scenario in real, “noisy” data over such a short time period.

Temperature Projections

Atmospheric CO2 concentration wouldn’t be treated as such a big deal if it didn’t affect temperature; so of course Lord Monckton has tried to show that the Fantasy IPCC “predictions” of CO2 concentration he made up translate into overly high temperature predictions. This is what he has done in the graph shown in Fig. 6.

Figure 6. Lord Monckton’s plot of global temperature anomalies over the period January 2002 to January 2009. The red line is a linear trend line Monckton fit to the data, and the pink/white field represents his Fantasy IPCC temperature predictions. I have no idea what his base period is. Taken from the Feb. 2009 edition of Lord Monckton’s “Monthly CO2 Report.”.

FANTASY #3. Lord Monckton uses graphs like that in Fig. 6 to support his claim that the climate models (AOGCMs) the IPCC uses to project future temperatures are wildly inaccurate.

REALITY #3.
Monckton didn’t actually get his Fantasy IPCC predictions of temperature evolution from AOGCM runs. Instead, he inappropriately fed his Fantasy IPCC predictions of CO2 concentration into equations meant to describe the EQUILIBRIUM model response to different CO2 concentrations.

Monckton indicated to me (5) that he obtained his graph of IPCC temperature predictions by running his Fantasy CO2 predictions (loosely based on the A2 emissions scenario) through the IPCC’s standard equation for converting CO2 concentration to temperature change, which can be found here.

The problem is that the equation mentioned is meant to describe equilibrium model response, rather than the transient response over time. In other words, they take the standard AOGCMs, input a certain stabilized CO2 concentration, and run the models until the climate output stabilizes around some new equilibrium. But it takes some time for the model systems to reach the new equilibrium state, because some of the feedbacks in the system (e.g., heat absorption as the ocean circulates) operate on fairly long timescales. Therefore, it is absolutely inappropriate to use the IPCC’s equation to describe anything to do with time evolution of the climate system. When I brought this up to Lord Monckton, he replied that he knows the difference between equilibrium and transient states, but he figures the equilibrium calculation comes close enough. But since the IPCC HAS published time-series (rather than just equilibrium) model output for the A2 scenario (see Fig. 7,) why wouldn’t he just use that?

Figure 7. Ensemble AOGCM output for the A2 emissions scenario, taken from Fig. 10.5 of IPCC AR4 WG1.

The answer is that if Lord Monckton had used the time-series model output, he would have had to admit that the IPCC temperature projections are still right in the ballpark. In Fig. 8, I have digitized the outer bounds of the model runs in Fig. 7, and also plotted the HadCRUT3 global annual mean temperature anomaly over the same period. The bottom line is that Monckton has put the wrong data into the wrong equation, and (surprise!) he got the wrong answer.


Figure 8. The blue and green lines represent the upper and lower bounds of the global average temperature anomaly from AOGCM output for the A2 emissions scenario during the 2002-2010 period. The black line represents the HadCRUT3 global temperature anomalies for that timeframe, normalized to the same base period.

Summary

I have shown here that in order to discredit the IPCC, Lord Monckton produced his graphs of atmospheric CO2 concentration and global mean temperature anomaly in the following manner:

1. He confused a hypothetical scenario with a prediction.
2. He falsely reported the data from the hypothetical scenario he was confusing with a prediction.
3. He plugged his false data into the wrong equation to obtain false predictions of time-series temperature evolution.
4. He messed up the statistical analyses of the real data.

These errors compound into a rather stunning display of complete incompetence. But since all, or at least nearly all, of this has been pointed out to Monckton in the past, there’s just no scientifically valid excuse for this. He’s just making it up.

Guest commentary by William R. L. Anderegg, Jim Prall, Jacob Harold, Stephen H. Schneider

Note: Before Stephen Schneider’s untimely passing, he and his co-authors were working on a response to the conversation sparked by their recent paper in the Proceedings of the National Academy of Sciences on climate change expertise. One of Dr. Schneider’s final interviews also addresses and discusses many of the issues covered here.

We accept and rely upon the judgment and opinions of experts in many areas of our lives. We seek out lawyers with specific expertise relevant to the situation; we trust the pronouncement of well-trained airplane mechanics that the plane is fit to fly. Indeed, the more technical the subject area, the more we rely on experts. Very few of us have the technical ability or time to read all of the primary literature on each cancer treatment’s biology, outcome probabilities, side-effects, interactions with other treatments, and thus we follow the advice of oncologists. We trust the aggregate knowledge of experts – what do 97% of oncologists think about this cancer treatment – more than that of any single expert. And we recognize the importance of relevant expertise – the opinion of vocal cardiologists matters much less in picking a cancer treatment than does that of oncologists.

Our paper Expert Credibility in Climate Change is predicated on this idea. It presents a broad picture of the landscape of expertise in climate science as a way to synthesize expert opinion for the broader discourse. It is, of course, only a first contribution and, as such, we hope motivates discussion and future research. We encourage follow-up peer-reviewed research, as this is the mark of scientific progress. Nonetheless, some researchers have offered thoughtful critiques about our study and others have grossly mischaracterized the work. Thus, here we provide responses to salient comments raised.

Definition of groups: The first of four broad comments about our study examines the relevancy of our two studied groups – those Convinced of the Evidence that much of the warming of the last half century is due in large part to human emissions of greenhouse gases, as assessed by the IPCC, which we term “CE,” and those who are Unconvinced of the Evidence (“UE”). Some have claimed that such groups do not adequately capture the complexity of expert opinion and therefore lose meaning. To be sure, anthropogenic climate change (ACC) is an immensely multi-faceted and complex area and expert opinion mirrors this complexity. Nonetheless, society uses simplifications of complex opinion landscapes all the time (e.g. Democrat versus Republican for political views) that don’t “lose their meaning” by ignoring the complexity of nuanced differences on specific topics within these broad groups.

The central questions at hand are: are these groups (1) clearly defined, (2) different in views of ACC, (3) reasonably discrete, and (4) in the main mutually exclusive? Our definition of groups, based entirely in the case of the UE group on their self-selected, voluntarily signed statements and petitions expressing various versions of skepticism about ACC, is clearly defined in the published paper. The strongest evidence indicating that our CE and UE groups satisfy the second and third criteria is that only three of 1,372 researchers fell into both groups—and in two of those cases, the researcher unwittingly added themselves to a statement they did not in fact support. Thus, if only one researcher of 1,372, or 0.07%, legitimately falls into both of our groups, this suggests that the two groups both differ starkly and are discrete. Any statistical analysis would be only trivially altered by having three redundant members of the cohort. Furthermore, the CE and UE groups are coherent, as around 35% of signers in each group also signed another statement in that set.

Another researcher suggests that his views have been “misclassified” by our inclusion of older public statements, as he signed a 1992 statement. Using a sweeping set of public statements that cover a broad time period to define the UE group allows us to compile an extensive (e.g. make an effort to be as comprehensive as possible) dataset and to categorize a researcher’s opinion objectively. However, were we to reclassify this researcher, it would only strengthen our results as then none of the top fifty researchers (rather than one researcher, or 2%) would fall in the UE camp.

Others have contended that the only experts we should have analyzed were those researchers involved specifically in detection and attribution of human-caused climate change. Importantly, much of the most convincing evidence for ACC comes from our understanding of the underlying physics of the greenhouse effect, illuminated long before the first detection/attribution studies, and these studies provide only one statistical line of evidence. The study could have been done in this manner but let us follow that logic to its conclusion. Applying this stricter criterion to the CE list does cause it to dwindle substantially…but applying it to the UE list causes it to approach close to zero researchers. To our knowledge, there are virtually no UE researchers by this logic who publish research on detection and attribution. Following this logic one would have to conclude that the UE group has functionally no credibility as experts on ACC. We would, however, argue that even this premise is suspect, as ecologists in IPCC have done detection and attribution studies using plants and animals (e.g. Root et al. 2005). Finally, applying a criterion such as this would require subjective judgments of a researcher’s focus area. Our study quite purposefully avoids making such subjective determinations and uses only objective lists of researchers who are self-defined. They were not chosen by our assessment as to which groups they may or may not belong in.

Some have taken issue with our inclusion of IPCC AR4 WGI authors in with the CE group, in that the IPCC Reports are explicitly policy-neutral while the four other CE policy statements/petitions are policy prescriptive. However, we believe our definition of the CE group is scientifically sound. Do IPCC AR4 WGI authors subscribe to the basic tenets of ACC? We acknowledge that this is an assumption, but we believe it is very reasonable one, given the strength of the ultimate findings of the IPCC AR4 WGI report. We classify the AR4 WGI authors as CE because they authored a report in which they show that the evidence is convincing. Naturally, authors may not agree with everything in the report, but those who disagreed with the most fundamental conclusions of the report would likely have stepped down and not signed their names. The presence of only one of 619 WGI contributors on a UE statement or petition, compared to 117 that signed a CE statement, provides further evidence to support this assumption. Furthermore, repeating our analysis relying only on those who signed at least one of the four CE letters/petitions and not on IPCC authorship yields similar results to those published.

No grouping of scientists is perfect. We contend that ours is clear, meaningful, defensible, and scientifically sound. More importantly, it is based on the public behavior of the scientists involved, and not our subjective assignments based on our reading of individuals’ works. We believe it is far more objective for us to use choices by scientists (over which we have no influence) for our data instead of our subjective assessment of their opinions.

Scientists not counted: What about those scientists who have not been involved with the IPCC or signed a public statement? What is their opinion? Would this influence our finding that 97% of the leading researchers we studied endorse the broad consensus regarding ACC expressed in IPCC’s AR4? We openly acknowledge in the paper that this is a “credibility” study and only captures those researchers who have expressed their opinions explicitly by signing letters/petitions or by signing their names as authors of the IPCC AR4 WGI report. Some employers explicitly preclude their employees from signing public statements of this sort, and some individuals may self-limit in the same way on principle apart from employer rules. However, the undeclared are not necessarily undecided. Two recent studies tackle the same question with direct survey methods and arrive at the same conclusion as reached in our study. First, Doran and Kendall-Zimmerman (2009) surveyed 3,146 AGU members and found that 97% of actively publishing climate researchers believe that “human activity is a significant factor in changing mean global temperatures.” A recently published study, Rosenberg et al (2010), finds similar levels of support when surveying authors who have published during 1995-2004 in peer-reviewed journals highlighting climate research. Yes, our study cannot answer for – and does not claim to – those who have not publically expressed their opinions or worked with the IPCC, but other studies have and their results indicate that our findings that an overwhelming percentage of publishing scientists agree with the consensus are robust. Perfection is not possible in such analyses, but we believe that the level of agreement across studies indicates a high degree of robustness.

Publication bias: A frequent response to our paper’s analysis consists of attributing the patterns we found to a systematic, potentially conspiratorial suppression of peer-reviewed research from the UE group. As of yet, this is a totally unsupported assertion backed by no data, and appears untenable given the vast range of journals which publish climate-related studies. Notably, our publication and citation figures were taken from Google Scholar, which is one of the broadest academic databases and includes in its indexing journals openly receptive to papers taking a different view from the mainstream on climate. Furthermore, recently published analysis (Anderegg 2010) examines the PhD and research focus of a subsample of the UE group, compared to data collected by Rosenberg et al. 2010 for a portion of the climate science community publishing in peer-reviewed journals. If the two groups had similar background credentials and expertise (PhD topic and research focus – both non-publishing metrics), it might indicate a suppression of the UE group’s research. They don’t. The background credentials of the UE group differ starkly from that of the “mainstream” community. Thirty percent of the UE group sample either do not have a documented PhD or do not have a PhD in the natural sciences, as compared to an estimated 5% of the sample from Rosenberg et al; and nearly half of the remaining sample have a research focus in geology (see the interview by Schneider as well).

“Blacklist”: The idea that our grouping of researchers comprises some sort of “blacklist” is the most absurd and tragic misframing of our study. Our response is two-fold:

1. Our study did not create any list. We simply compiled lists that were publicly available and created by people who voluntarily self-identified with the pronouncements on the statements/letters. We did not single out researchers, add researchers, drop researchers; we have only compiled individuals from a number of prominent and public lists and petitions that they themselves signed, and then used standard social science procedure to objectively test their relative credibility in the field of climate science.
2. No names were used in our study nor listed in any attachments. We were very aware of the pressure that would be on us to provide the raw data used in our study. In fact, many journalists we spoke with beforehand asked for the list of names and for specific names, which we did not provide. We decided to compromise by posting only the links to the source documents – the ‘raw data’ in effect (the broader website is not the paper data), where interested parties can examine the publically available statements and petitions themselves. It is ironic that many of those now complaining about the list of names are generally the same people that have claimed that scientists do not release their data. Implying that our list is comparable to that created by Mark Morano when he worked for Senator Inhofe is decidedly unconvincing and irresponsible, given that he selected individuals based on his subjective reading and misreading of their work. See here for a full discussion of this problematic claim or read Schneider’s interview above.

In sum, the various comments and mischaracterizations discussed above do not in any way undermine the robust findings of our study. Furthermore, the vast majority of comments pertain to how the study could have been done differently. To the authors of such comments, we offer two words – do so! That’s the hallmark of science. We look forward to your scientific contributions – if and when they are peer-reviewed and published – and will be open to any such studies. In our study we were subjected to two rounds of reviews by three social scientists and in addition comments from the PNAS Board, causing us to prepare three drafts in response to those valuable peer comments that greatly improved the paper. We hope that this response further advances the conversation.

References
Anderegg, W.R.L. (2010) Moving Beyond Scientific Agreement. Climatic Change, 101 (3) 331-337.
Doran PT, Zimmerman MK (2009) Examining the Scientific Consensus on Climate Change. Eos Trans. AGU 90.
Root, T.L. et al. (2005) Human-modified temperatures induce species changes: Joint attribution. PNAS May 24, 2005 vol. 102 no. 21 7465-7469
Rosenberg, S. et al (2010) Climate Change: A Profile of U.S. Climate Scientists’ Perspectives. Climatic Change, 101 (3) 311-329.

NASA will hold a media teleconference on Thursday, Aug. 5, at 3 p.m. EDT to discuss its upcoming airborne research campaign into hurricane behavior.

Global warming is turning 35! Not only has the current spate of global warming been going on for about 35 years now, but also the term “global warming” will have its 35th anniversary next week. On 8 August 1975, Wally Broecker published his paper “Are we on the brink of a pronounced global warming?” in the journal Science. That appears to be the first use of the term “global warming” in the scientific literature (at least it’s the first of over 10,000 papers for this search term according to the ISI database of journal articles).

In this paper, Broecker correctly predicted “that the present cooling trend will, within a decade or so, give way to a pronounced warming induced by carbon dioxide”, and that “by early in the next century [carbon dioxide] will have driven the mean planetary temperature beyond the limits experienced during the last 1000 years”. He predicted an overall 20th Century global warming of 0.8ºC due to CO2 and worried about the consequences for agriculture and sea level.

Global temperature up to June 2010 according to the NASA GISS data. Grey line is the 12-month running average, red dots are annual-mean values. The thick red line is a non-linear trend line. Broecker of course did not have these data available, not even up to 1975, since this global compilation was only put together in the late 1970s (Hansen et al. 1981). He had to rely on more limited meteorological data.

To those who even today claim that global warming is not predictable, the anniversary of Broecker’s paper is a reminder that global warming was actually predicted before it became evident in the global temperature records over a decade later (when Jim Hansen in 1988 famously stated that “global warming is here”).

Broecker is one of the great climatologists of the 20th Century: few would match his record of 400 scientific papers, a full sixty of which have over 100 citations each! Interestingly, his “global warming” paper is not amongst those highly-cited ones, with “only” 79 citations to date. Broecker is most famous for his extensive work on paleoclimate and ocean geochemistry.

It is very instructive to see how Broecker arrived at his predictions back in 1975 – not least because even today, many lay people incorrectly assume that we attribute global warming to CO2 basically because temperature and CO2 levels have both gone up and thus correlate. Broecker came to his prediction at a time when CO2 had been going up but temperatures had been going down for decades – but Broecker (like most other climate scientists at the time, and today) understood the basic physics of the issue.

Basically his prediction involved just three simple steps that in essence are still used today.

Step 1: Predict future emissions

Broecker simply assumed a growth in fossil fuel CO2 emissions of 3% per year from 1975 onwards. With that, he arrived at cumulative fossil CO2 emissions of 1.67 trillion tons by the year 2010 (see his Table 1). Not bad: the actual emissions turned out to be about 1.3 trillion tons (Canadell et al, PNAS 2007 – estimate extended to 2010 by me).

A shortcoming, from the modern point of view, is that Broecker did not include other anthropogenic greenhouse gases or aerosol particles in his calculations. He does however discuss aerosols, which he calls “dust”. In fact, the first sentence of the abstract (quoted above) in full starts with an if-statement:

If man-made dust is unimportant as a major cause of climate change, then a strong case can be made that the present cooling trend will, within a decade or so, give way to a pronounced warming induced by carbon dioxide.

That is a nod to the discussion about aerosol-induced cooling in the early 1970s. Broecker rightly writes:

It is difficult to determine the significance of the next most important climatic effect induced by man, “dust”, because of uncertainties with regard to the amount, the optical properties and the distribution of man-made particles,

citing a number of papers by Steve Schneider and others. Because he cannot quantify it, he leaves out this effect. Here luck was on Broecker’s side: the warming by other greenhouse gases and the cooling by aerosols largely cancel today, so considering only CO2 leads to almost the same radiative forcing as considering all anthropogenic effects on climate (see IPCC AR4, Fig. SPM.2).

Table 1 of Broecker (1975)

Step 2: Predict future concentrations

To go from the amount of CO2 emitted to the actual increase in the atmosphere, one needs to know what fraction of the emissions remains in the air: the “airborne fraction”. Broecker simply assumed, based on past data of emissions and CO2 concentrations (Keeling’s Mauna Loa curve), that the airborne fraction is a constant 50%. I.e., about half of our fossil fuel emissions accumulates in the atmosphere. That is still a good assumption today, if you look at the observed CO2 increase as fraction of fossil fuel emissions. Broecker calculated that about 35% of the emissions is taken up by the ocean and the other 15% by the biosphere (again not far from modern values, see Canadell et al.). On this basis he argued that if the ocean is the main sink, the airborne fraction would remain almost constant for the decades to come (his calculations extend to the year 2010).

Thus, with a 3% increase in emissions per year and 50% of that remaining airborne, it is easy to compute the increase in CO2 concentrations. He obtains an increase from 295 to 403 ppm from 1900 to 2010. The actual value in 2010 is 390 ppm, a little lower than Broecker estimated because his forecast cumulative emissions were a little too high.

Step 3: Compute the global temperature response

Now we come to the temperature response to increased CO2 concentration. Broecker writes:

The response of the global temperature to the atmospheric CO2 content is not linear. As the CO2 content of the atmosphere rises, the absorption of infrared radiation will “saturate” over an ever greater portion of the band. Rasool and Schneider point out that the temperature increases as the logarithm of the atmospheric CO2 concentration.

Based on this logarithmic relationship (still valid today) Broecker assumes a climate sensitivity of 0.3ºC warming for each 10% increase in CO2 concentration, which amounts to 2.2ºC warming for CO2 doubling. This is based on early calculations by Manabe and Wetherald. Broecker writes:

Although surprises may yet be in store for us when larger computers and better knowledge of cloud physics allow the next stage of modeling to be accomplished, the magnitude of the CO2 effect has probably been pinned down to within a factor of 2 to 4.

The AR4 gives the uncertainty range of climate sensitivity as 2-4.5ºC warming for CO2 doubling, so there still is about a factor of 2 uncertainty and Broecker used a value near the very low end of this uncertainty range. Modern estimates are not only based on model calculations but also on paleoclimatic and modern data; the AR4 lists 13 studies that constrain climate sensitivity in its table 9.3.

In Broecker’s paper the warming calculated with the help of climate sensitivity happens instantaneously. Today we know that the climate system responds with a time lag due to ocean thermal inertia. By neglecting this, Broecker overestimated the warming at any given time; accounting for thermal inertia would have reduced his warming estimate by about a third (see AR4 Fig. SPM.5). But again he was lucky: picking ~2ºC rather than the more likely ~3ºC climate sensitivity compensates roughly for this, so his 20th-Century warming of 0.8ºC is almost spot on (the actual estimate being closer to 0.7ºC, see Fig. above). (A modern version of this back-of-envelope warming calculation is found e.g. in our book Our Threatened Oceans, p.82.)

Natural Variability

Broecker was not the first to predict CO2-induced warming. In 1965, an expert report to US President Lyndon B. Johnson had warned: “By the year 2000, the increase in carbon dioxide will be close to 25%. This may be sufficient to produce measurable and perhaps marked changes in climate.” And in 1972, a more specific prediction similar to Broecker’s was published by the eminent atmospheric scientist J.S. Sawyer in Nature (for a history in a nutshell, see my newspaper column here).

The innovation of Broecker’s article – apart from introducing the term “global warming” – was in combining estimates of CO2 warming with natural variability. His main thesis was that a natural climatic cooling

has, over the last three decades, more than compensated for the warming effect produced by the CO2 [....] The present natural cooling will, however, bottom out during the next decade or so. Once this happens, the CO2 effect will tend to become a significant factor and by the first decade of the next century we may experience global temperatures warmer than any in the last 1000 years.

The latter turned out to be correct. The idea that the small cooling from the 1940s to 1970s is due to natural variability still cannot be ruled out, although more likely this is a smaller part of the explanation and the cooling is primarily due to the “dust” neglected by Broecker, i.e. due to the rise of anthropogenic aerosol pollution (Taylor and Penner, 1994). However, the way Broecker estimated and even predicted natural variability has not stood the test of time. He used data from the Camp Century ice core in Greenland, arguing that these “may give a picture of the natural fluctuations in global temperature over the last 1000 years”. Ironically, Broecker’s own later work on Atlantic ocean circulation changes showed that Greenland is likely even less representative of global temperature changes than most other places on Earth, it being strongly affected by variability in ocean heat transport (see our recent post on the Younger Dryas, or Broecker’s latest book The Great Ocean Conveyor). However, Broecker was right to conclude that the buildup of CO2 would sooner or later overwhelm such natural climate variations.

Overall, Broecker’s paper (together with that of Sawyer) shows that valid predictions of global warming were published in the 1970s in the top journals Science and Nature, and warming has been proceeding almost exactly as predicted for at least 35 years now. Some important aspects were not understood back then, like the role of greenhouse gases other than CO2, of aerosol particles and of ocean heat storage. That the predictions were almost spot-on involved an element of luck, since the neglected processes do not all affect the result in the same direction but partly cancel. Nevertheless, the basic fact that rising CO2 would cause a “pronounced global warming”, as Broecker put it, was well understood in the 1970s. In a 1979 TV interview, Steve Schneider rightly described this as a consensus amongst experts, with controversy remaining about the exact magnitude and effects.

Reference
BROECKER WS, 1975: CLIMATIC CHANGE – ARE WE ON BRINK OF A PRONOUNCED GLOBAL WARMING?
SCIENCE Volume 189, Pages 460-463.

Guest Commentary by Dirk Notz, MPI Hamburg

It’s almost routine by now: Every summer, many of those interested in climate change check again and again the latest data on sea-ice evolution in the Arctic. Such data are for example available on a daily basis from the US National Snow and Ice Data Center. And again and again in early summer the question arises whether the most recent trend in sea-ice extent might lead to a new record minimum, with a sea-ice cover that will be smaller than that in the record summer of 2007.

However, before looking at the possible future evolution of Arctic sea ice in more detail, it might be a good idea to briefly re-capitulate some events of the previous winter, because some of those are quite relevant for the current state of the sea-ice cover. The winter 2009/2010 will be remembered by many people in Europe (and not only there) as particularly cold, with lots of snow and ice. Not least because of the sustained cold, some began to wonder if global warming indeed was real.

Such questioning of global warming based on a regional cold period of course neglects the crucial difference between weather and climate, with the former being the only thing that we as individuals will ever be able to experience first hand. A single regional cold spell has not a lot to do with climate – let alone with global climate. This becomes quite obvious if one instead considers the mean temperature of the entire globe during the last 12 months: this period was, according to the GISS data, the warmest 12-month period since the beginning of the records 130 years ago. Regarding sea ice, it was particularly important that temperatures in parts of the Arctic were well above average for most of the winter. This was directly experienced by some members of our working group during a field experiment at the West Coast of Greenland.

Fig. 1: Temperature anomaly at 1000 hPa during the first half of January 2010 with respect to the period 1968-1996. Warm anomalies in the Arctic and cold anomalies in Northern Europe and parts of North America are clearly visible.

The initial plan of this field experiment was to study the growth and decay of sea ice in great detail throughout an entire winter. In particular, we wanted to focus on the evolution of very young sea ice that had just formed from open water. Therefore, we wanted to start our measurements just before initial ice formation, which usually takes place in mid-November, at least according to past experience of the local Greenlandic population. Hence, we traveled to our measuring site close to the Greenlandic settlement of Upernavik in early November to put out our measuring buoys. We were hoping that ice formation would start shortly after we had put out the instruments such that they were protected from storms and waves. However, with temperatures that were often more than 10°C above the long-term mean, sea ice was nowhere to be seen. Even in January, there were days on end with above 0°C temperature and heavy rain fall. Finally, in February a stable ice cover formed, which of course remained relatively thin and which hence had melted completely by mid May.

The fact that it was sometimes warmer at our measurement site at the West Coast of Greenland than it was in Central Europe at the same time surprised us quite a bit. However, some recent studies indicate that such a distribution of relatively high temperature in parts of the Arctic and relatively low temperature in Northern and Central Europe and parts of the US might become somewhat more wide-spread in the future. While the Arctic has always shown large internal variability that lead to large-scale shifts in weather patterns, in the future the ongoing retreat of Arctic sea ice might cause those weather patterns to occur more often that allow for Northerly winds to bring cold air from the Arctic to the mid-latitudes. Hence, it is quite possible that because of the retreat of Arctic sea ice, some smaller parts of the Northern Hemisphere will experience pronounced cold spells during winter every now and then. The mean temperature of the Northern Hemisphere will nevertheless increase further, and the export of cold air from the Arctic of course leads to warm anomalies there.

Fig.2: Evolution of Arctic sea-ice extent from September 2009 until mid May 2010. The blue line denotes the mean extent from 1979 until 2000, while the shaded region denotes the variability during that time (± 2 standard deviations)

But let’s return to the evolution of Arctic sea ice. Because of relatively high temperatures, Arctic sea-ice extent remained well below the long-term mean for most of the preceding winter. However, in March temperatures suddenly dropped for a couple of weeks, in particular in parts of the Barents Sea and in parts of the Beaufort Sea. This in turn lead to the formation of a thin ice cover in these regions, which caused a marked increase in observed sea-ice extent. For the measurement of this extent, it doesn’t matter at all how thick the ice is: any ice, however thin, contributes to sea-ice extent. Therefore, only considering a possible “recovery” of just the extent of Arctic sea ice always remains somewhat superficial, since sea-ice extent contains no information on the thickness of the ice. A much more useful measure for the state of Arctic sea ice is therefore the total sea-ice volume. However, for its estimation one additionally requires information on the overall distribution of ice thickness, which we have not been able to measure routinely in the past. While this will hopefully change in the future because of the successful launch of the Cryosat 2 satellite a couple of weeks ago, at the moment we unfortunately must rely on judging the current state of the Arctic sea-ice cover mostly by its extent.

Fig.3: Evolution of Arctic sea-ice extent since April 2010 in comparison to 2007 and 2009. The blue line denotes the mean extent from 1979 until 2000, while the shaded region denotes the variability during that time (± 2 standard deviations)

Because of the very low thickness of much of the Arctic sea ice, it wasn’t too surprising that at the end of the winter, sea-ice extent decreased rapidly. This rapid loss lead up to the lowest June sea-ice extent since the beginning of reliable observations. After this rapid loss of the very thin ice that had formed late in winter, the retreat slowed down substantially but the ice extent remained well below the long-term mean. Currently, the ice covers an area that is slightly larger than the extent in late July of the record year 2007. However, this does not really allow for any reliable projections regarding the future evolution of Arctic sea ice in the weeks to come.

The reason for this is mostly that sea ice in the Arctic has become very thin. Hence, in contrast to the much thicker ice of past decades, the ice now reacts very quickly and very sensitively to the weather patterns that are predominant during a certain summer. This currently limits the predictability of sea-ice extent significantly. For example, in 2007 a relatively stable high-pressure system formed above the Beaufort sea, towards the north of North America, leading to rapid melting of sea ice there. If again such stable high pressure system forms in the Arctic throughout the coming weeks, we might well experience a sea-ice minimum that is below the record minimum as observed in 2007. However, if the summer should turn out to be colder than during the previous years, a sea-ice minimum similar to that observed in 2009 would not be too surprising. Hence, at the moment all that remains is to wait – and to check again and again the latest data of Arctic sea-ice extent.

Fig.4: Arctic sea-ice extent on June 28July 20, 2010. The orange line denotes the mean extent on June 28July 20 from 1979 until 2000.

Dirk Notz is head of the research group “Sea ice in the Earth System” at the Max-Planck-Institute for Meteorology in Hamburg.

The original version of this article was published in German at KlimaLounge

References:

Honda, M., J. Inoue, and S. Yamane (2009), Influence of low Arctic sea-ice minima on anomalously cold Eurasian winters, Geophys. Res. Lett., 36, L08707, doi:10.1029/2008GL037079.

Notz, D. The future of ice sheets and sea ice: Between reversible retreat and unstoppable loss. Proc. Nat. Ac. Sci. 106(49), 20590–20595, doi:10.1073/pnas.0902356106 (2009).

Polyakov, I. V., and M. A. Johnson (2000), Arctic decadal and interdecadal variability, Geophys. Res. Lett., 27(24), 4097–4100.

Credits:
Figure 1: NOAA ESRL Physics Science division

Figures 2-4: Data: NSIDC, Graphics: D. Notz.

Guest commentary by Tamino

Update: Another review of the book has been published by Alistair McIntosh in the Scottish Review of Books (scroll down about 25% through the page to find McIintosh’s review)

Update #2 (8/19/10): The Guardian has now weighed in as well.

If you don’t know much about climate science, or about the details of the controversy over the “hockey stick,” then A. W. Montford’s book The Hockey Stick Illusion: Climategate and the Corruption of Science might persuade you that not only the hockey stick, but all of modern climate science, is a fraud perpetrated by a massive conspiracy of climate scientists and politicians, in order to guarantee an unending supply of research funding and political power. That idea gets planted early, in the 6th paragraph of chapter 1.

The chief focus is the original hockey stick, a reconstruction of past temperature for the northern hemisphere covering the last 600 years by Mike Mann, Ray Bradley, and Malcolm Hughes (1998, Nature, 392, 779, doi:10.1038/33859, available here), hereafter called “MBH98″ (the reconstruction was later extended back to a thousand years by Mann et al, 1999, or “MBH99″ ). The reconstruction was based on proxy data, most of which are not direct temperature measurements but may be indicative of temperature. To piece together past temperature, MBH98 estimated the relationships between the proxies and observed temperatures in the 20th century, checked the validity of the relationships using observed temperatures in the latter half of the 19th century, then used the relationships to estimate temperatures as far back as 1400. The reconstruction all the way back to the year 1400 used 22 proxy data series, although some of the 22 were combinations of larger numbers of proxy series by a method known as “principal components analysis” (hereafter called “PCA”–see here). For later centuries, even more proxy series were used. The result was that temperatures had risen rapidly in the 20th century compared to the preceding 5 centuries. The sharp “blade” of 20th-century rise compared to the flat “handle” of the 15-19th centuries was reminiscent of a “hockey stick” — giving rise to the name describing temperature history.

But if you do know something about climate science and the politically motivated controversy around it, you might be able to see that reality is the opposite of the way Montford paints it. In fact Montford goes so far over the top that if you’re a knowledgeable and thoughtful reader, it eventually dawns on you that the real goal of those whose story Montford tells is not to understand past climate, it’s to destroy the hockey stick by any means necessary.

Montford’s hero is Steve McIntyre, portrayed as a tireless, selfless, unimpeachable seeker of truth whose only character flaw is that he’s just too polite. McIntyre, so the story goes, is looking for answers from only the purest motives but uncovers a web of deceit designed to affirm foregone conclusions whether they’re so or not — that humankind is creating dangerous climate change, the likes of which hasn’t been seen for at least a thousand or two years. McIntyre and his collaborator Ross McKitrick made it their mission to get rid of anything resembling a hockey stick in the MBH98 (and any other) reconstruction of past temperature.

Principal Components

For instance: one of the proxy series used as far back as the year 1400 was NOAMERPC1, the 1st “principal component” (PC1) used to represent patterns in a series of 70 tree-ring data sets from North America; this proxy series strongly resembles a hockey stick. McIntyre & McKitrick (hereafter called “MM”) claimed that the PCA used by MBH98 wasn’t valid because they had used a different “centering” convention than is customary. It’s customary to subtract the average value from each data series as the first step of computing PCA, but MBH98 had subtracted the average value during the 20th century. When MM applied PCA to the North American tree-ring series but centered the data in the usual way, then retained 2 PC series just as MBH98 had, lo and behold — the hockey-stick-shaped PC wasn’t among them! One hockey stick gone.

Or so they claimed. In fact the hockey-stick shaped PC was still there, but it was no longer the strongest PC (PC1), it was now only 4th-strongest (PC4). This raises the question, how many PCs should be included from such an analysis? MBH98 had originally included two PC series from this analysis because that’s the number indicated by a standard “selection rule” for PC analysis (read about it here).

MM used the standard centering convention, but applied no selection rule — they just imitated MBH98 by including 2 PC series, and since the hockey stick wasn’t one of those 2, that was good enough for them. But applying the standard selection rules to the PCA analysis of MM indicates that you should include five PC series, and the hockey-stick shaped PC is among them (at #4). Whether you use the MBH98 non-standard centering, or standard centering, the hockey-stick shaped PC must still be included in the analysis.

It was also pointed out (by Peter Huybers) that MM hadn’t applied “standard” PCA either. They used a standard centering but hadn’t normalized the data series. The 2 PC series that were #1 and #2 in the analysis of MBH98 became #2 and #1 with normalized PCA, and both should unquestionably be included by standard selection rules. Again, whether you use MBH non-standard centering, MM standard centering without normalization, or fully “standard” centering and normalization, the hockey-stick shaped PC must still be included in the analysis.

In reply, MM complained that the MBH98 PC1 (the hockey-stick shaped one) wasn’t PC1 in the completely standard analysis, that normalization wasn’t required for the analysis, and that “Preisendorfer’s rule N” (the selection rule used by MBH98) wasn’t the “industry standard” MBH claimed it to be. Montford even goes so far as to rattle off a list of potential selection rules referred to in the scientific literature, to give the impression that the MBH98 choice isn’t “automatic,” but the salient point which emerges from such a list is that MM never used any selection rules — at least, none that are published in the literature.

The truth is that whichever version of PCA you use, the hockey-stick shaped PC is one of the statistically significant patterns. There’s a reason for that: the hockey-stick shaped pattern is in the data, and it’s not just noise it’s signal. Montford’s book makes it obvious that MM actually do have a selection rule of their own devising: if it looks like a hockey stick, get rid of it.

The PCA dispute is a prime example of a recurring McIntyre/Montford theme: that the hockey stick depends critically on some element or factor, and when that’s taken away the whole structure collapses. The implication that the hockey stick depends on the centering convention used in the MBH98 PCA analysis makes a very persuasive “Aha — gotcha!” argument. Too bad it’s just not true.

Different, yes. Completely, no.

As another example, Montford makes the claim that if you eliminate just two of the proxies used for the MBH98 reconstruction since 1400, the Stahle and NOAMER PC1 series, “you got a completely different result — the Medieval Warm Period magically reappeared and suddenly the modern warming didn’t look quite so frightening.” That argument is sure to sell to those who haven’t done so. But I have. I computed my own reconstructions by multiple regression, first using all 22 proxy series in the original MBH98 analysis, then excluding the Stahle and NOAMER PC1 series. Here’s the result with all 22 proxies (the thick line is a 10-year moving average):

Here it is with just 20 proxies:

Finally, here are the 10-year moving average for both cases, and for the instrumental record:

Certainly the result is different — how could it not be, using different data? — but calling it “completely different” is just plain wrong. Yes, the pre-20th century is warmer with the 15th century a wee bit warmer still — but again, how could it not be when eliminating two hand-picked proxy series for the sole purpose of denying the unprecedented nature of modern warming? Yet even allowing this cherry-picking of proxies is still not enough to accomplish McIntyre’s purpose; preceding centuries still don’t come close to the late-20th century warming. In spite of Montford’s claims, it’s still a hockey stick.

Beyond Reason

Another of McIntyre’s targets was the Gaspe series, referred to in the MBH98 data as “treeline-11.” It just might be the most hockey-stick shaped proxy of all. This particular series doesn’t extend all the way back to the year 1400, it doesn’t start until 1404, so MBH98 had extended the series back four years by persistence — taking the earliest value and repeating it for the preceding four years. This is not at all an unusual practice, and — let’s face facts folks — extending 4 years out of a nearly 600-year record on one out of 22 proxies isn’t going to change things much. But McIntyre objected that the entire Gaspe series had to be eliminated because it didn’t extend all the way back to 1400. This argument is downright ludicrous — what it really tells us is that McIntyre & McKitrick are less interested in reconstructing past temperature than in killing anything that looks like a hockey stick.

McIntyre also objected that other series had been filled in by persistence, not on the early end but on the late end, to bring them up to the year 1980 (the last year of the MBH98 reconstruction). Again, this is not a reasonable argument. Mann responded by simply computing the reconstruction you get if you start at 1404 and end at 1972 so you don’t have to do any infilling at all. The result: a hockey stick.

Again, we have another example of Montford implying that some single element is both faulty and crucial. Without nonstandard PCA the hockey stick falls apart! Without the Stahle and NOAMER PC1 data series the hockey stick falls apart! Without the Gaspe series the hockey stick falls apart! Without bristlecone pine tree rings the hockey stick falls apart! It’s all very persuasive, especially to the conspiracy-minded, but the truth is that the hockey stick depends on none of these elements. You get a hockey stick with standard PCA, in fact you get a hockey stick using no PCA at all. Remove the NOAMER PC1 and Stahle series, you’re left with a hockey stick. Remove the Gaspe series, it’s still a hockey stick.

As a great deal of other research has shown, you can even reconstruct past temperature without bristlecone pine tree rings, or without any tree ring data at all, resulting in: a hockey stick. It also shows, consistently, that nobody is trying to “get rid of the medieval warm period” or “flatten out the little ice age” since those are features of all reconstructions of the last 1000 to 2000 years. What paleoclimate researchers are trying to do is make objective estimates of how warm and how cold those past centuries were. The consistent answer is, not as warm as the last century and not nearly as warm as right now.

The hockey stick is so thoroughly imprinted on the actual data that what’s truly impressive is how many things you have to get rid of to eliminate it. There’s a scientific term for results which are so strong and so resistant to changes in data and methods: robust.

Cynical Indeed

Montford doesn’t just criticize hockey-stick shaped proxies, he bends over backwards to level every criticism conceivable. For instance, one of the proxy series was estimated summer temperature in central England taken from an earlier study by Bradley and Jones (1993, the Holocene, 3, 367-376). It’s true that a better choice for central England would have been the central England temperature time series (CETR), which is an instrumental record covering the full year rather than just summertime. The CETR also shows a stronger hockey-stick shape than the central England series used by MBH98, in part because it includes earlier data (from the late 17th century) than the Bradley and Jones dataset. Yet Montford sees fit to criticize their choice, saying “Cynical observers might, however, have noticed that the late seventeenth century numbers for CETR were distinctly cold, so the effect of this truncation may well have been to flatten out the little ice age.”

In effect, even when MBH98 used data which weakens the difference between modern warmth and preceding centuries, they’re criticized for it. Cynical indeed.

Face-Palm

The willingness of Montford and McIntyre to level any criticism which might discredit the hockey stick just might reach is zenith in a criticism which Montford repeats, but is so nonsensical that one can hardly resist the proverbial “face-palm.” Montford more than once complains that hockey-stick shaped proxies dominate climate reconstructions — unfairly, he implies — because they correlate well to temperature.

Duh.

Guilty

Criticism of MBH98 isn’t restricted to claims of incorrect data and analysis, Montford and McIntyre also see deliberate deception everywhere they look. This is almost comically illustrated by Montford’s comments about an email from Malcolm Hughes to Mike Mann (emphasis added by Montford):

Mike — the only one of the new S.American chronologies I just sent you that already appears in the ITRDB sets you already have is [ARGE030]. You should remove this from the two ITRDB data sets, as the new version should be different (and better for our purposes).
Cheers,
Malcolm

Here’s what Montford has to say:

It was possible that there was an innocent explanation for the use of the expression “better for our purposes”, but McIntyre can hardly be blamed for wondering exactly what “purposes” the Hockey Stick authors were pursuing. A cynic might be concerned that the phrase actually had something to do with “getting rid of the Medieval Warm Period”. And if Hughes meant “more reliable”, why hadn’t he just said so?

This is nothing more than quote-mining, in order to interpret an entirely innocent turn of phrase in the most nefarious way possible. It says a great deal more about the motives and honesty of Montford and McIntyre, than about Mann, Bradley, and Hughes. The idea that MM’s so-called “correction” of MBH98 “restored the MWP” constitutes a particularly popular meme in contrarian circles, despite the fact that it is quite self-evidently nonsense: MBH98 only went back to AD 1400, while the MWP, by nearly all definitions found in the professional literature, ended at least a century earlier! Such internal contradictions in logic appear to be no impediment, however, to Montford and his ilk.

Conspiracies Everywhere

Montford also goes to great lengths to accuse a host of researchers, bloggers, and others of attempting to suppress the truth and issue personal attacks on McIntyre. The “enemies list” includes RealClimate itself, claimed to be a politically motivated mouthpiece for “Environmental Media Services,” described as a “pivotal organization in the green movement” run by David Fenton, called “one of the most influential PR people of the 20th century.” Also implicated are William Connolley for criticizing McIntyre on sci.environment and James Annan for criticizing McIntyre and McKitrick. In a telling episode of conspiracy theorizing, we are told that their “ideas had been picked up and propagated across the left-wing blogosphere.” Further conspirators, we are informed, include Brad DeLong and Tim Lambert. And of course one mustn’t omit the principal voice of RealClimate, Gavin Schmidt.

Perhaps I should feel personally honored to be included on Montford’s list of co-conspirators, because yours truly is also mentioned. According to Montford’s typical sloppy research I have styled myself as “Mann’s Bulldog.” I’ve never done so, although I find such an appellation flattering; I just hope Jim Hansen doesn’t feel slighted by the mistaken reference.

The conspiracy doesn’t end with the hockey team, climate researchers, and bloggers. It includes the editorial staff of any journal which didn’t bend over to accommodate McIntyre, including Nature and GRL which are accused of interfering with, delaying, and obstructing McIntyre’s publications.

Spy Story

The book concludes with speculation about the underhanded meaning of the emails stolen from the Climate Research Unit (CRU) in the U.K. It’s really just the same quote-mining and misinterpretation we’ve heard from many quarters of the so-called “skeptics.” Although the book came out very shortly after the CRU hack, with hardly sufficient time to investigate the truth, the temptation to use the emails for propaganda purposes was irresistible. Montford indulges in every damning speculation he can get his hands on.

Since that time, investigation has been conducted, both into the conduct of the researchers at CRU (especially Phil Jones) and Mike Mann (the leader of the “hockey team”). Certainly some unkind words were said in private emails, but the result of both investigations is clear: climate researchers have been cleared of any wrongdoing in their research and scientific conduct. Thank goodness some of those who bought in to the false accusations, like Andy Revkin and George Monbiot, have seen fit actually to apologize for doing so. Perhaps they realize that one can’t get at the truth simply by reading people’s private emails.

Montford certainly spins a tale of suspense, conflict, and lively action, intertwining conspiracy and covert skullduggery, politics and big money, into a narrative worthy of the best spy thrillers. I’m not qualified to compare Montford’s writing skill to that of such a widely-read author as, say, Michael Crichton, but I do know they share this in common: they’re both skilled fiction writers.

The only corruption of science in the “hockey stick” is in the minds of McIntyre and Montford. They were looking for corruption, and they found it. Someone looking for actual science would have found it as well.

Scientists have produced a first-of-its kind map of the height of the world's forests by combining data from three NASA satellites. The map will help scientists build an inventory of how much carbon the world's forests store and how fast that carbon cycles through ecosystems and back into the atmosphere.

We were greatly saddened to learn that our revered colleague Stephen Schneider passed away this morning.

We are posting a personal account by Ben Santer of Steve’s amazing accomplishments and contributions. Ben’s account provides a glimpse into what made Steve so special, and why he will be so deeply missed:

Today the world lost a great man. Professor Stephen Schneider – a climate scientist at Stanford University – passed away while on travel in the United Kingdom.

Stephen Schneider did more than any other individual on the planet to help us realize that human actions have led to global-scale changes in Earth’s climate. Steve was instrumental in focusing scientific, political, and public attention on one of the major challenges facing humanity – the problem of human-caused climate change.

Some climate scientists have exceptional talents in pure research. They love to figure out the inner workings of the climate system. Others have strengths in communicating complex scientific issues to non-specialists. It is rare to find scientists who combine these talents.

Steve Schneider was just such a man.

Steve had the rare gift of being able to explain the complexities of climate science in plain English. He could always find the right story, the right metaphor, the right way of distilling difficult ideas and concepts down to their essence. Through his books, his extensive public speaking, and his many interactions with the media, Steve did for climate science what Carl Sagan did for astronomy.

But Steve was not only the world’s pre-eminent popularizer of climate science. He also made remarkable contributions to our scientific understanding of the nature and causes of climate change. He performed pioneering research on the effects of aerosol particles on climate. This work eventually led to investigation of how planetary cooling might be caused by the aerosol particles arising from large-scale fires generated by a nuclear war. This clear scientific warning of the possible climatic consequences of nuclear war may have nudged our species onto a different – and hopefully more sustainable – pathway.

Steve was also a pioneer in the development and application of the numerical models we now use to study climate change. He and his collaborators employed both simple and complex computer models in early studies of the role of clouds in climate change, and in research on the climatic effects of massive volcanic eruptions. He was one of the first scientists to address what we now call the “signal detection problem” – the problem of determining where we might expect to see the first clear evidence of a human effect on global climate.

After spending many years at the National Center for Atmospheric Research in Boulder, Steve moved to Stanford in 1996. At Stanford, Steve and his wife Terry Root led ground-breaking research on the impacts of human-caused climate change on the distribution and abundance of plant and animal species. More recently, Steve kept intellectual company with some of the world’s leading experts on the economics of climate change, and attempted to estimate the cost of stabilizing our planet’s climate. Until his untimely death, he continued to produce cutting-edge scientific research on such diverse topics as abrupt climate change, policy options for mitigating and adapting to climate change, and whether we can usefully identify levels of planetary temperature increase beyond which we risk “dangerous anthropogenic interference” with the climate system.

Steve Schneider helped the world understand that the burning of fossil fuels had altered the chemistry of Earth’s atmosphere, and that this change in atmospheric composition had led to a discernible human influence on our planet’s climate. He worked tirelessly to bring this message to the attention of fellow scientists, policymakers, and the general public. His voice was clear and consistent, despite serious illness, and despite encountering vocal opposition by powerful forces – individuals who seek to make policy on the basis of wishful thinking and disinformation rather than sound science.

Steve Schneider epitomized scientific courage. He was fearless. The pathway he chose – to be a scientific leader, to be a leader in science communication, and to fully embrace the interdisciplinary nature of the climate change problem – was not an easy pathway. Yet without the courage of leaders like Stephen Schneider, the world would not be on the threshold of agreeing to radically change the way we use energy. We would not be on the verge of a global treaty to limit the emissions of greenhouse gases.

It was a rare privilege to call Steve Schneider my colleague and friend. It was a privilege to listen to Steve jamming on his beloved 12-string guitar; to sing Bob Dylan songs with him. It was a privilege to share laughter, and good food, and a good glass of red wine. It was a privilege to hear his love of science, and his deep passion for it.

We honor the memory of Steve Schneider by continuing to fight for the things he fought for – by continuing to seek clear understanding of the causes and impacts of climate change. We honor Steve by recognizing that communication is a vital part of our job. We honor Steve by taking the time to explain our research findings in plain English. By telling others what we do, why we do it, and why they should care about it. We honor Steve by raising our voices, and by speaking out when powerful “forces of unreason” seek to misrepresent our science. We honor Steve Schneider by caring about the strange and beautiful planet on which we live, by protecting its climate, and by ensuring that our policymakers do not fall asleep at the wheel.

Ben Santer

Guest Commentary by Chris Colose

One of the most intriguing and well-studied climatic events in the past is the Younger Dryas (YD), a rather abrupt climate change between ~12.9 and 11.6 thousand years ago. As the world was slowly warming and ice was retreating from the last glaciation, the YD effectively halted the transition to today’s relatively warm, interglacial conditions in many parts of the world. This event is associated with cold and dry conditions increasing with latitude in the North, temperature and precipitation influences on tropical and boreal wetlands, Siberian-like winters in much of the North Atlantic, weakening of monsoon intensity, and southward displacement of tropical rainfall patterns. RealClimate has previously discussed the YD (here and here) however there have been a number of developments in recent years which deserve further attention, particularly with respect to the spatial characteristics and causes of the YD.

The YD is often discussed in the same context as the ‘Dansgaard-Oeschger’ events seen in the ice cores during full glacial conditions, and the ‘Heinrich events’ of layers of ice-rafted debris in North Atlantic ocean sediments. Indeed, some people occasionally refer to the YD as Heinrich event 0, but this implies that the YD cooling was caused by an ice-rafting event (probably untrue) and should be avoided.  The YD occurred last of several prominent and abrupt deglacial events including Heinrich Event 1 (~17.5 to 16 ka) which is an event contained within the Older Dryas (18 to 14.7 ka), followed by the Bølling-Allerød warm period (~14.7 to 12.9 ka) whose end then marks the start of the YD. The end of the YD can be said to be the start of the Holocene. It has been proposed that the warmings before and after the YD can be viewed as Dansgaard-Oeschger events with the YD just a regular cold (i.e. stadial) phase in between (Rahmstorf 2002, 2003). In Antarctica (~15 to 13 ka), the most featured event is that as the Younger Dryas begins, warming is occurring in Antarctica.  The cold period in Antarctica that precedes the Younger Dryas is referred to as the Antarctic Cold Reversal (ACR) (see figure, from Shakun and Carlson, 2010) and was once thought to be in phase with the YD.  They are neither directly in phase nor anti-phased with one another (see e.g. Steig and Alley, 2002).

Fig. 1. Deglacial ice core time series and insolation. (a) GISP2 δ18O (black step plot) (Blunier and Brook, 2001). (b) Byrd δ18O (grey step plot) (Blunier and Brook, 2001). (c) Insolation (Incoming Solar Radiation) for 60ºN on June 21 (black line) and for 60ºS on December 21 (dashed black line) (Berger and Loutre, 1991). The timing of the Younger Dryas (YD), Bølling/Allerød (B/A), Heinrich Event 1 (H1), Oldest Dryas (OD) and Antarctic Cold Reversal
(ACR) are denoted.

Unlike changes in global temperature (such as modern day global warming) which can be understood as a result of perturbations to the planetary energy balance, the millennial-scale climate changes during the last glaciation are viewed primarily from the lens of internal dynamics, including ice retreat and re-organizations of ocean circulation. They are not dominated by changes in global mean temperature but rather changes in temperature distribution, explained by changes in oceanic or atmospheric heat transport. In particular, proxies of deepwater formation show large reductions in the Atlantic meridional overturning circulation (AMOC) coincident with the start of the YD. This suggested weakening of overturning circulation provides immense explanatory power for the onset of the YD although no consensus has emerged concerning the trigger of the AMOC reduction. There are some radiative changes associated with millennial-scale climate change induced by the ice-albedo effect, extra dust loading out of Asia during cold snaps, as well as greenhouse gas feedbacks– although they are relatively small. However, pinning down the exact sequence of causes and effects is rather difficult since precise chronologies and global-scale reconstructions are difficult to come by prior to the Holocene.

A new study though ( Shakun and Carlson, 2010) has compiled over 100 high-resolution proxy records to characterize the timing and extent of the Last Glacial Maximum (LGM) and the deglacial evolution into the Holocene, including the shorter-lived Younger Dryas. Several of the key features of the study include:

1. The global mean cooling of the LGM relative to the peak of our current interglacial is approximately 5ºC as a minimum value. It is likely larger than this since many of the records are from the ocean which are typically less sensitive to temperature change than landmasses, and further, adiabatic cooling of marine air advected over land masses would result from the ~120 m reduction in sea level. The cooling is global in scale and largest at high latitudes, as expected from polar amplification.
2. In contrast, during the YD, there is much more spatial heterogeneity as the North became colder and drier (increasing with latitude) while the South became warmer and wetter in the opposite sense. The global mean cooling during the YD is only ~0.6ºC .  The tropics cooled by 2.5ºC (with an error of about a degree in either direction) at the LGM, yet exhibited very little temperature change during the YD. Thus, while the YD was a global scale climate change event with widespread signatures, it was not a widespread global cooling event.
   
   

   
   Fig. 2. Magnitude of the glacial-interglacial temperature change relative to absolute latitude. (Shakun and Carlson 2010)Fig. 3. Magnitude of the Younger Dryas temperature change. Map of the Younger Dryas temperature anomaly (a). Circle denotes the size of the temperature change. Blue is cooling, red warming (Shakun and Carlson 2010).

3. The timing of the LGM and peak interglacial is synchronized between hemispheres on orbital timescales, which the authors attribute primarily to the global radiative forcing provided by CO2. As has been noted in the past, the CO2 lags the onset of deglaciation in most records, as this is paced by summer insolation changes. However the CO2 still acts as the dominant temperature-change influence throughout the deglacial period and provides an effective means to communicate temperature anomalies to the tropics. On the other hand, the YD exhibits the well-known bipolar see-saw effect which involves a reduction in northward heat transport, which warms the South. The see-saw is best expressed in the mid to high latitudes, although the see-saw model is a poor descriptor for the tropical variability.

The see-saw effect during millennial-scale climate changes has been confirmed before (also discussed at RealClimate in the context of the somewhat similar Dansgaard-Oeshger events) and is consistent with modeling efforts of the climate evolution during the last deglaciation, including Liu et al., 2009 (discussed here) who show that current state-of-the-art models can simulate the magnitude of abrupt climate changes well.

So what caused the reduction in the AMOC?

The most prevalent concept for slowing AMOC involves a reduction in the surface water density at the ocean surface via adding freshwater into the ocean. The preferred location is primarily the North Atlantic, which is a key point for deep ocean convection. The original idea for this to cause a YD-event was proposed in 1976 by Johnson and McClure, and involved the opening of eastern Lake Agassiz outlet via northward retreat of the Laurentide Ice Sheet out of Lake Superior. This re-routed drainage from the Mississippi to the St. Lawrence River.

There is a difference between the diversion of continental runoff from the Mississippi River (routing) and the relatively fast pro-glacial lake drainage to a new level (flooding). In contrast to the Johnson and McClure paper, many recent studies have focused on short-lived floods, although the re-routing mechanism might be a necessary, and in fact primary ingredient (Carlson et al., 2007; Carlson and Clark, 2008) in accord with modeling studies which require a persistent forcing to substantially alter AMOC (Meissner and Clark, 2006).

Evidence of a specific flood water pathway at the right time has proven to be elusive. No clear evidence exists for a flood event into the Atlantic, though evidence discussed by Murton et al. (2010) for an Arctic pathway has recently emerged.

There has also been interest in the prospect of a comet impact during the YD triggering a flood (e.g., Firestone et al., 2007 discussed previously at RC) although subsequent work has suggested that their results are not robust (Surovell et al., 2009), and it is likely that the impacts a comet would have on atmospheric chemistry, particularly the formation of nitrate and ammonium, is inconsistent with observations in ice core records (Melott et al., 2010). Further, the problem with a comet impact still remains — how could it generate a continuous freshwater forcing? Because of dicey evidence and no predictive ability, the comet hypothesis has not gained much favour.

Recently another hypothesis has been put forward: The Younger Dryas, instead of being a freak occurrence, is instead a key (and normal) part of the deglaciation process. This was most clearly expressed in a new paper by Broecker et al (2010) ( including George Denton and Richard Alley). Their main point is that a catastrophic flood or comet would only serve as a trigger for an event that was already primed to happen. Evidence for this comes primarily for the existence of YD-like events during previous deglaciations, notably from Chinese stalagmite data (Cheng et al., 2009) who looked at monsoon patterns in the past. In particular, a YD-like event shows up during Termination III (~ 245 ka) and possibly Termination IV, which share similar characteristics to the YD. The finding of many events with characteristics like the YD further provides evidence against the necessity of comet-impact hypothesis. However, this concept doesn’t negate the need to understand the mechanisms for the YD or its potential predecessors. Whether it was primed to happen or not, what actually happened and how is still of great interest.

Broecker et al (2010) cite Lowell et al (2005) and Fisher et al (2008) to justify their reason for why the flood hypothesis is unappealing, but further work done by Carlson et al. (2007), Carlson and Clark (2008), and Carlson et al. (2009) provides newer support for the re-routing hypothesis. Furthermore, while Broecker et al. emphasize the lack of evidence for a catastrophic event, if the slower re-routing hypothesis is correct, then the lack of evidence for a sudden flood is irrelevant. This may very well be the mechanism that is common to previous deglacial events.

The existence of events similar to the YD in the more distant past has been proposed before (Carlson, 2008). By analyzing paleo-methane concentrations, Carlson (2008) also noted that events similar to the YD happened during T III and possibly earlier deglaciations (see figure, from Broecker et al 2010).

Fig. 4. Major events surrounding Termination III. (A) shows Vostok temperature deviation (purple) and CH4 (blue) records (Suwa and Bender, 2008). (B) shows EPICA/Dome C (EDC) δD (orange) and CH4 (blue) records (Loulergue et al., 2008). (C) is the Vostok CO2 record (Petit et al., 1999). (D) is the absolute-dated Asian Monsoon record from Sanbao Cave, China (Cheng et al., 2006). (E) and (F) show IRD and inferred seawater δ18O records from marine core ODP-980 (McManus et al., 1999). Both Vostok and EDC timescale were shifted in order to correlate the abrupt jump of the last portion of CH4 in ice cores to the abrupt monsoon jump in panel (D) (Cheng et al., 2009). The ODP-980 records are on original timescales. Two Weak Monsoon Intervals (WMI) are marked by yellow background. Termination III events, analogous to the YD, B/A, ACR and MI are labeled: YD III, B/A III, ACR III and MI-III. Figure is simplified from that in Cheng et al. (2009). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

As a conclusion, over the last couple of years, there has now been growing evidence that an event similar to the YD is not “unique” but instead is a common theme across various deglacial events; this provides evidence against the necessity for a “catastrophic trigger,” and while it may be the case that a comet or some other catastrophe occurs at each termination, that seems improbable.

References
Broecker, W.S., Denton G.H., Edwards L.R., Cheng H., Alley R.B., Putnam A.E., 2010. Putting the Younger Dryas cold event into context. Quaternary Science Reviews , 29, 1078-1081
Carlson, A.E., 2008. Why there was not a Younger Dryas-like event during the Penultimate Deglaciation: Quaternary Science Reviews, v. 27, p. 882-887
Carlson, A.E., and Clark, P.U., 2008. Rapid climate change and Arctic Ocean freshening: Comment: Geology, v. 36, p. e177
Carlson, A.E., Clark, P.U., and Hostetler, S.W., 2009. Comment: Radiocarbon deglaciation chronology of the Thunder Bay, Ontario area and implications for ice sheet retreat patterns: Quaternary Science Reviews, v. 28
Cheng, H., Edwards, R.L., Broecker, W.S., Denton, G.H., Kong, X., Wang, Y., Zhang, R., and Wang, X., 2009. Ice Age Terminations: Science, v. 326, p. 248–252
Firestone, R.B., et al., 2007. Evidence for an extraterrestrial impact 12,900 years ago that contributed to the megafaunal extinctions and the Younger Dryas cooling: Proceedings of the National Academy of Sciences of the United States of America, v. 104, p. 16016–16021

Fisher, T.G., Yansa, C.H., Lowell, T.V., Lepper, K., Hajdas, I., Ashworth, A., 2008. The chronology, climate, and confusion of the Moorehead Phase of glacial Lake Agassiz: new results from the Ojata Beach, North Dakota. Quaternary Science Reviews, 27, 1124–1135

Johnson, R.G., McClure, B.T., 1976. A model for Northern Hemisphere continental ice sheet variation. Quaternary Research 6, 325–353
Liu, Z., Otto-Bliesner, B., He, F., Brady, E., Thomas, R., Clark, P.U., Carlson, A.E., Lynch-Stieglitz, J., Curry, W., Brook, E., Erickson, D., Jacob, R., Kutzbach, J., and Chen, J., 2009. Transient Climate Simulation of Last Deglaciation with a New Mechanism for Bølling-Allerød Warming: Science, v. 325, p. 310-314
Lowell, T.V., Waterson, N., Fisher, T., Loope, H., Glover, K., Comer, G., Hajdas, I., Denton, G., Schaefer, J., Rinterknecht, V., Broecker, W., and Teller, J., 2005, Testing the Lake Agassiz meltwater trigger for the Younger Dryas: EOS (Transactions, American Geophysical Union), v. 86, p. 365–373
Meissner, K.J., and Clark, P.U., 2006. Impact of floods versus routing events on the thermohaline circulation: Geophysical Research Letters, v. 33, L15704
Melott, A.L., Thomas, B.C., Dreschhoff, G., and Johnson, C.K., 2010. Cometary airbursts and atmospheric chemistry: Tunguska and a candidate Younger Dryas event, Geology, v. 38, 355–358
Murton J.B., Bateman M.D., Dallimore S.R., Teller J.T., Yang Z., 2010. Identification of Younger Dryas outburst flood path from Lake Agassiz to the Arctic Ocean, Nature, 464, 740-743
Shakun, J.D., and Carlson, A.E., 2010. A Global Perspective On Last Glacial Maximum to Holocene Climate Change: Quaternary Science Reviews.
Steig, E.J., and Alley, R.B. Phase relationships between Antarctic and Greenland climate records. Annals of Glaciology 35: 451-456 (2002).

Rasmus’ recent post on the greenhouse effect raised some interesting points concerning the technical level at which posts or other public communications should be written. This was a relatively technical article as these things go, eschewing the very basic ‘the greenhouse effect is like a blanket’ but not really approaching the level of a technical paper on the subject (no line-by-line calculations for instance). Nonetheless, there were complaints that was too much to be absorbed by the lay public, counter-arguments that making it too simple was patronising, as well as complaints that the discussions were not technical enough (for instance in explaining stratospheric cooling). In these discussions there are clearly the outlines of a common debate, and perhaps a way forward in the future.

An anecdote is maybe relevant. I was on a panel with a long-time science writer from New York Times and we were discussing the information content in science columns versus sports columns (the latter having far more because the writers see no need to waste space to explain the rules, introduce the players, or even explicitly state what the actual sport is!). The NYT writer explained that she always pitched her stories at exactly the same level – (paraphrasing) the interested, but educated, person who did not need the details but wanted the big picture. Indeed, she went so far as to say that was the only relevant mode of public communication on science issues. I took issue with this (of course), because I think this ‘mainstream media’ mode of communication leaves a lot of people very unsatisfied and indeed, RealClimate is in part a response to that.

Both these examples suggest that there is a very widespread feeling that there is only one level at which public communications must be conducted (though people often disagree with what that is). But this is rather a pointless argument to be having. Particularly in the new landscape of disaggregated media, the idea that there is only one anything seems completely anachronistic. It might have been ok when the daily paper was the only information source that some people had and its audience could be assumed to be relatively homogeneous, but these things are certainly no longer true (if indeed they ever were).

Instead, I think we should be explicitly thinking about information levels and explicitly catering to different audiences with different needs and capabilities. One metaphor that might work well is that of an alpine ski hill. There we have (in the US for instance) green runs for beginners wanting a gentle introduction and where hopefully nothing too bad can happen. Blue runs where the technical level is a little more ambitious and a little more care needs to be taken. Black expert runs for those who know what they are doing and are doing it well, and finally, double black diamond runs for the true masters. No-one accuses ski resorts of being patronising when they have green runs interspersed with the more difficult ones, and neither do they get accused of elitism when one peak has only black runs going down (as I recall all too painfully on my first ski outing). People self-segregate and generally find their way to the level at which the feel comfortable – whether they want a easy or challenging ride – and there is nothing stopping them varying the levels as their mood or inclination takes them.

I think this is exactly what we need in science communication. Explanations and stories unapologetically pitched at all sorts of different levels (and not just at a fictional ‘Mr or Ms. Average Newspaper Reader’) actually already happens in many environments (though not in newspapers, TV or institutional websites), however, where the analogy breaks down is that there is no signage. There is no Google icon that tells you whether the link is a green level explanation or an experts-only-you-will-get-hurt-if-you-don’t-know-what-you-are-doing technical discussion. There is no Wikipedia sliding scale to direct you to the information level appropriate to your level of competence or background knowledge.

Thus we often find that beginners are confused or turned off by inappropriate (for them) complexity, and old hands demanding something more challenging, and people in the middle despairing that we aren’t reaching the ‘right’ people with whatever level we adopt.

So how should we move forward? Can we institute a some kind of information level meta-tagging that would eventually be recognised by Google? (does that even matter)? Does such a system exist already?