I just found this great article (nepotism moi? ;)! http://lookinwonderment.blogspot.com/2010/01/sanitation-case-study-for-our-anti.html Maybe we need a 'sanitation' approach to climate change. Less hot air, more unsung heroes?

I was talking about risk last night with a couple of friends. I was asked why climate change is a risk to individuals, nations and the world. Here's why. The following distribution describes the estimated chance of different warmings if we give up producing CO2 and other greenhouse gases now (actually if we stopped in 2005). You will see that we have a more than evens chance already of melting the greenland ice sheet (7m of sea level rise, enough to reach the shores of Cambridge). Over the next few years we will reach the same chance of destroying the amazon rain forest (it's even possible that if climate change was compounded by drought, a vast fire might erupt). Big risks.



















from: http://www-ramanathan.ucsd.edu/dai/Ramanathan-Feng-PNAS-2008.pdf

The commentary "Stop Worrying Start Panicking?" on this is here:
http://www-ramanathan.ucsd.edu/dai/Schellnhuber-PNAS-2008.pdf

The other thing I've been thinking about is the basic evidence about why the planet is cooking (in particular, how we determine the radiative forcing of different gases). I've found this article which appears quite good: http://www-ramanathan.ucsd.edu/RamAmbio.pdf

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Following the Copenhagen international climate negotiations in December, many people are asking themselves what happens now. Was the Copenhagen conference a failure? What lessons can be drawn from the outcome of the conference? Next time, what should we do more of, what should we do less of?

01:01:10 minutes (31.22 MB)

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Postgraduate students are invited to apply for a new British Council programme aimed at helping businesses in the UK and China fight climate change. Students will be selected from the Tyndall Centre partner Universities and the Chinese Universities of Fudan, Peking, Tongji and Tsinghua.

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Like all human endeavours, the IPCC is not perfect. Despite the enormous efforts devoted to producing its reports with the multiple levels of peer review, some errors will sneak through. Most of these will be minor and inconsequential, but sometimes they might be more substantive. As many people are aware (and as John Nieslen-Gammon outlined in a post last month and Rick Piltz goes over today), there is a statement in the second volume of the IPCC (WG2), concerning the rate at which Himalayan glaciers are receding that is not correct and not properly referenced.

The statement, in a chapter on climate impacts in Asia, was that the likelihood of the Himalayan glaciers “disappearing by the year 2035″ was “very high” if the Earth keeps warming at the current rate (WG 2, Ch. 10, p493), and was referenced to a World Wildlife Fund 2005 report. Examining the drafts and comments (available here), indicates that the statement was barely commented in the reviews, and that the WWF (2005) reference seems to have been a last minute addition (it does not appear in the First- or Second- Order Drafts). This claim did not make it into the summary for policy makers, nor the overall synthesis report, and so cannot be described as a ‘central claim’ of the IPCC. However, the statement has had some press attention since the report particularly in the Indian press, at least according to Google News, even though it was not familiar to us before last month.

It is therefore obvious that this error should be corrected (via some kind of corrigendum to the WG2 report perhaps), but it is important to realise that this doesn’t mean that Himalayan glaciers are doing just fine. They aren’t, and there may be serious consequences for water resources as the retreat continues. See also this review paper (Ren et al, 2006) on a subset of these glaciers.

East Rongbuk glacier just below Mt. Everest has lost 3-400 ft of ice in this area since 1921.

More generally, peer-review works to make the IPCC reports credible because many different eyes with different perspectives and knowledge look over the same text. This tends to make the resulting product reflect more than just the opinion of a single author. In this case, it appears that not enough people with relevant experience saw this text, or if they saw it, did not comment publicly. This might be related to the fact that this text was in the Working Group 2 report on impacts, which does not get the same amount of attention from the physical science community than does the higher profile WG 1 report (which is what people associated with RC generally look at). In WG1, the statements about continued glacier retreat are much more general and the rules on citation of non-peer reviewed literature was much more closely adhered to. However, in general, the science of climate impacts is less clear than the physical basis for climate change, and the literature is thinner, so there is necessarily more ambiguity in WG 2 statements.

In future reports (and the organisation for AR5 in 2013 is now underway), extra efforts will be needed to make sure that the links between WG1 and the other two reports are stronger, and that the physical science community should be encouraged to be more active in the other groups.

In summary, the measure of an organisation is not determined by the mere existence of errors, but in how it deals with them when they crop up. The current discussion about Himalayan glaciers is therefore a good opportunity for the IPCC to further improve their procedures and think more about what the IPCC should be doing in the times between the main reports.

Update: This backgrounder presented by Karkel et al AGU this December is the best summary of the current state of the Himalayas and the various sources of misinformation that are floating around. It covers this issue, the Raina report and the recent Lau et al paper.

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Researchers in the Tyndall Centre are playing an important role in leading a new €7 million EU-wide Network of Excellence, which aims to provide a focus for international research on the use of evidence, science and assessment tools in policy-making.

Professor Andrew Jordan and Dr John Turnpenny are leading a critical area of work on understanding the needs of those producing and using policy analysis tools such as cost benefit analysis and computer models.

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This is Hansen et al’s end of year summary for 2009 (with a couple of minor edits).

If It’s That Warm, How Come It’s So Damned Cold? 

 
by James Hansen, Reto Ruedy, Makiko Sato, and Ken Lo
 
The past year, 2009, tied as the second warmest year in the 130 years of global instrumental temperature records, in the surface temperature analysis of the NASA Goddard Institute for Space Studies (GISS). The Southern Hemisphere set a record as the warmest year for that half of the world. Global mean temperature, as shown in Figure 1a, was 0.57°C (1.0°F) warmer than climatology (the 1951-1980 base period). Southern Hemisphere mean temperature, as shown in Figure 1b, was 0.49°C (0.88°F) warmer than in the period of climatology.

Figure 1. (a) GISS analysis of global surface temperature change. Green vertical bar is estimated 95 percent confidence range (two standard deviations) for annual temperature change. (b) Hemispheric temperature change in GISS analysis. (Base period is 1951-1980. This base period is fixed consistently in GISS temperature analysis papers – see References. Base period 1961-1990 is used for comparison with published HadCRUT analyses in Figures 3 and 4.)

The global record warm year, in the period of near-global instrumental measurements (since the late 1800s), was 2005. Sometimes it is asserted that 1998 was the warmest year. The origin of this confusion is discussed below. There is a high degree of interannual (year‐to‐year) and decadal variability in both global and hemispheric temperatures. Underlying this variability, however, is a long‐term warming trend that has become strong and persistent over the past three decades. The long‐term trends are more apparent when temperature is averaged over several years. The 60‐month (5‐year) and 132 month (11‐year) running mean temperatures are shown in Figure 2 for the globe and the hemispheres. The 5‐year mean is sufficient to reduce the effect of the El Niño – La Niña cycles of tropical climate. The 11‐year mean minimizes the effect of solar variability – the brightness of the sun varies by a measurable amount over the sunspot cycle, which is typically of 10‐12 year duration.

Figure 2. 60‐month (5‐year) and 132 month (11‐year) running mean temperatures in the GISS analysis of (a) global and (b) hemispheric surface temperature change. (Base period is 1951‐1980.)

There is a contradiction between the observed continued warming trend and popular perceptions about climate trends. Frequent statements include: “There has been global cooling over the past decade.” “Global warming stopped in 1998.” “1998 is the warmest year in the record.” Such statements have been repeated so often that most of the public seems to accept them as being true. However, based on our data, such statements are not correct. The origin of this contradiction probably lies in part in differences between the GISS and HadCRUT temperature analyses (HadCRUT is the joint Hadley Centre/University of East Anglia Climatic Research Unit temperature analysis). Indeed, HadCRUT finds 1998 to be the warmest year in their record. In addition, popular belief that the world is cooling is reinforced by cold weather anomalies in the United States in the summer of 2009 and cold anomalies in much of the Northern Hemisphere in December 2009. Here we first show the main reason for the difference between the GISS and HadCRUT analyses. Then we examine the 2009 regional temperature anomalies in the context of global temperatures.

Figure 3. Temperature anomalies in 1998 (left column) and 2005 (right column). Top row is GISS analysis, middle row is HadCRUT analysis, and bottom row is the GISS analysis masked to the same area and resolution as the HadCRUT analysis. [Base period is 1961‐1990.]

Figure 3 shows maps of GISS and HadCRUT 1998 and 2005 temperature anomalies relative to base period 1961‐1990 (the base period used by HadCRUT). The temperature anomalies are at a 5 degree‐by‐5 degree resolution for the GISS data to match that in the HadCRUT analysis. In the lower two maps we display the GISS data masked to the same area and resolution as the HadCRUT analysis. The “masked” GISS data let us quantify the extent to which the difference between the GISS and HadCRUT analyses is due to the data interpolation and extrapolation that occurs in the GISS analysis. The GISS analysis assigns a temperature anomaly to many gridboxes that do not contain measurement data, specifically all gridboxes located within 1200 km of one or more stations that do have defined temperature anomalies.

The rationale for this aspect of the GISS analysis is based on the fact that temperature anomaly patterns tend to be large scale. For example, if it is an unusually cold winter in New York, it is probably unusually cold in Philadelphia too. This fact suggests that it may be better to assign a temperature anomaly based on the nearest stations for a gridbox that contains no observing stations, rather than excluding that gridbox from the global analysis. Tests of this assumption are described in our papers referenced below.

Figure 4. Global surface temperature anomalies relative to 1961‐1990 base period for three cases: HadCRUT, GISS, and GISS anomalies limited to the HadCRUT area. [To obtain consistent time series for the HadCRUT and GISS global means, monthly results were averaged over regions with defined temperature anomalies within four latitude zones (90N‐25N, 25N‐Equator, Equator‐25S, 25S‐90S); the global average then weights these zones by the true area of the full zones, and the annual means are based on those monthly global means.]

Figure 4 shows time series of global temperature for the GISS and HadCRUT analyses, as well as for the GISS analysis masked to the HadCRUT data region. This figure reveals that the differences that have developed between the GISS and HadCRUT global temperatures during the past few decades are due primarily to the extension of the GISS analysis into regions that are excluded from the HadCRUT analysis. The GISS and HadCRUT results are similar during this period, when the analyses are limited to exactly the same area. The GISS analysis also finds 1998 as the warmest year, if analysis is limited to the masked area. The question then becomes: how valid are the extrapolations and interpolation in the GISS analysis? If the temperature anomaly scale is adjusted such that the global mean anomaly is zero, the patterns of warm and cool regions have realistic‐looking meteorological patterns, providing qualitative support for the data extensions. However, we would like a quantitative measure of the uncertainty in our estimate of the global temperature anomaly caused by the fact that the spatial distribution of measurements is incomplete. One way to estimate that uncertainty, or possible error, can be obtained via use of the complete time series of global surface temperature data generated by a global climate model that has been demonstrated to have realistic spatial and temporal variability of surface temperature. We can sample this data set at only the locations where measurement stations exist, use this sub‐sample of data to estimate global temperature change with the GISS analysis method, and compare the result with the “perfect” knowledge of global temperature provided by the data at all gridpoints.

	1880‐1900	1900‐1950	1960‐2008
Meteorological Stations	0.2	0.15	0.08
Land‐Ocean Index	0.08	0.05	0.05

Table 1. Two‐sigma error estimate versus period for meteorological stations and land‐ocean index.

Table 1 shows the derived error due to incomplete coverage of stations. As expected, the error was larger at early dates when station coverage was poorer. Also the error is much larger when data are available only from meteorological stations, without ship or satellite measurements for ocean areas. In recent decades the 2‐sigma uncertainty (95 percent confidence of being within that range, ~2‐3 percent chance of being outside that range in a specific direction) has been about 0.05°C. The incomplete coverage of stations is the primary cause of uncertainty in comparing nearby years, for which the effect of more systematic errors such as urban warming is small.

Additional sources of error become important when comparing temperature anomalies separated by longer periods. The most well‐known source of long‐term error is “urban warming”, human‐made local warming caused by energy use and alterations of the natural environment. Various other errors affecting the estimates of long‐term temperature change are described comprehensively in a large number of papers by Tom Karl and his associates at the NOAA National Climate Data Center. The GISS temperature analysis corrects for urban effects by adjusting the long‐term trends of urban stations to be consistent with the trends at nearby rural stations, with urban locations identified either by population or satellite‐observed night lights. In a paper in preparation we demonstrate that the population and night light approaches yield similar results on global average. The additional error caused by factors other than incomplete spatial coverage is estimated to be of the order of 0.1°C on time scales of several decades to a century, this estimate necessarily being partly subjective. The estimated total uncertainty in global mean temperature anomaly with land and ocean data included thus is similar to the error estimate in the first line of Table 1, i.e., the error due to limited spatial coverage when only meteorological stations are included.

Now let’s consider whether we can specify a rank among the recent global annual temperatures, i.e., which year is warmest, second warmest, etc. Figure 1a shows 2009 as the second warmest year, but it is so close to 1998, 2002, 2003, 2006, and 2007 that we must declare these years as being in a virtual tie as the second warmest year. The maximum difference among these in the GISS analysis is ~0.03°C (2009 being the warmest among those years and 2006 the coolest). This range is approximately equal to our 1‐sigma uncertainty of ~0.025°C, which is the reason for stating that these five years are tied for second warmest.

The year 2005 is 0.061°C warmer than 1998 in our analysis. So how certain are we that 2005 was warmer than 1998? Given the standard deviation of ~0.025°C for the estimated error, we can estimate the probability that 1998 was warmer than 2005 as follows. The chance that 1998 is 0.025°C warmer than our estimated value is about (1 – 0.68)/2 = 0.16. The chance that 2005 is 0.025°C cooler than our estimate is also 0.16. The probability of both of these is ~0.03 (3 percent). Integrating over the tail of the distribution and accounting for the 2005‐1998 temperature difference being 0.61°C alters the estimate in opposite directions. For the moment let us just say that the chance that 1998 is warmer than 2005, given our temperature analysis, is at most no more than about 10 percent. Therefore, we can say with a reasonable degree of confidence that 2005 is the warmest year in the period of instrumental data.

Figure 5. (a) global map of December 2009 anomaly, (b) global map of Jun‐Jul‐Aug 2009 anomaly. #4 and #2 indicate that December 2009 and JJA are the 4th and 2nd warmest globally for those periods.

What about the claim that the Earth’s surface has been cooling over the past decade? That issue can be addressed with a far higher degree of confidence, because the error due to incomplete spatial coverage of measurements becomes much smaller when averaged over several years. The 2‐sigma error in the 5‐year running‐mean temperature anomaly shown in Figure 2, is about a factor of two smaller than the annual mean uncertainty, thus 0.02‐0.03°C. Given that the change of 5‐year‐mean global temperature anomaly is about 0.2°C over the past decade, we can conclude that the world has become warmer over the past decade, not cooler.

Why are some people so readily convinced of a false conclusion, that the world is really experiencing a cooling trend? That gullibility probably has a lot to do with regional short‐term temperature fluctuations, which are an order of magnitude larger than global average annual anomalies. Yet many lay people do understand the distinction between regional short‐term anomalies and global trends. For example, here is comment posted by “frogbandit” at 8:38p.m. 1/6/2010 on City Bright blog:

“I wonder about the people who use cold weather to say that the globe is cooling. It forgets that global warming has a global component and that its a trend, not an everyday thing. I hear people down in the lower 48 say its really cold this winter. That ain’t true so far up here in Alaska. Bethel, Alaska, had a brown Christmas. Here in Anchorage, the temperature today is 31[ºF]. I can’t say based on the fact Anchorage and Bethel are warm so far this winter that we have global warming. That would be a really dumb argument to think my weather pattern is being experienced even in the rest of the United States, much less globally.”

What frogbandit is saying is illustrated by the global map of temperature anomalies in December 2009 (Figure 5a). There were strong negative temperature anomalies at middle latitudes in the Northern Hemisphere, as great as ‐8°C in Siberia, averaged over the month. But the temperature anomaly in the Arctic was as great as +7°C. The cold December perhaps reaffirmed an impression gained by Americans from the unusually cool 2009 summer. There was a large region in the United States and Canada in June‐July‐August with a negative temperature anomaly greater than 1°C, the largest negative anomaly on the planet.

Figure 6. Arctic Oscillation (AO) Index. Positive values of the AO index indicate high low pressure in the polar region and thus a tendency for strong zonal winds that minimize cold air outbreaks to middle latitudes. Blue dots are monthly means and the red curve is the 60‐month (5‐year) running mean.

How do these large regional temperature anomalies stack up against an expectation of, and the reality of, global warming? How unusual are these regional negative fluctuations? Do they have any relationship to global warming? Do they contradict global warming?

It is obvious that in December 2009 there was an unusual exchange of polar and mid‐latitude air in the Northern Hemisphere. Arctic air rushed into both North America and Eurasia, and, of course, it was replaced in the polar region by air from middle latitudes. The degree to which Arctic air penetrates into middle latitudes is related to the Arctic Oscillation (AO) index, which is defined by surface atmospheric pressure patterns and is plotted in Figure 6. When the AO index is positive surface pressure is high low in the polar region. This helps the middle latitude jet stream to blow strongly and consistently from west to east, thus keeping cold Arctic air locked in the polar region. When the AO index is negative there tends to be low high pressure in the polar region, weaker zonal winds, and greater movement of frigid polar air into middle latitudes.

Figure 6 shows that December 2009 was the most extreme negative Arctic Oscillation since the 1970s. Although there were ten cases between the early 1960s and mid 1980s with an AO index more extreme than ‐2.5, there were no such extreme cases since then until last month. It is no wonder that the public has become accustomed to the absence of extreme blasts of cold air.

Figure 7. Temperature anomaly from GISS analysis and AO index from NOAA National Weather Service Climate Prediction Center. United States mean refers to the 48 contiguous states.

Figure 7 shows the AO index with greater temporal resolution for two 5‐year periods. It is obvious that there is a high degree of correlation of the AO index with temperature in the United States, with any possible lag between index and temperature anomaly less than the monthly temporal resolution. Large negative anomalies, when they occur, are usually in a winter month. Note that the January 1977 temperature anomaly, mainly located in the Eastern United States, was considerably stronger than the December 2009 anomaly. [There is nothing magic about a 31 day window that coincides with a calendar month, and it could be misleading. It may be more informative to look at a 30‐day running mean and at the Dec‐Jan‐Feb means for the AO index and temperature anomalies.]

The AO index is not so much an explanation for climate anomaly patterns as it is a simple statement of the situation. However, John (Mike) Wallace and colleagues have been able to use the AO description to aid consideration of how the patterns may change as greenhouse gases increase. A number of papers, by Wallace, David Thompson, and others, as well as by Drew Shindell and others at GISS, have pointed out that increasing carbon dioxide causes the stratosphere to cool, in turn causing on average a stronger jet stream and thus a tendency for a more positive Arctic Oscillation. Overall, Figure 6 shows a tendency in the expected sense. The AO is not the only factor that might alter the frequency of Arctic cold air outbreaks. For example, what is the effect of reduced Arctic sea ice on weather patterns? There is not enough empirical evidence since the rapid ice melt of 2007. We conclude only that December 2009 was a highly anomalous month and that its unusual AO can be described as the “cause” of the extreme December weather.

We do not find a basis for expecting frequent repeat occurrences. On the contrary. Figure 6 does show that month‐to‐month fluctuations of the AO are much larger than its long term trend. But temperature change can be caused by greenhouse gases and global warming independent of Arctic Oscillation dynamical effects.

Figure 8. Global maps 4 season temperature anomalies for ~2009. (Note that Dec is December 2008. Base period is 1951‐1980.)

Figure 9. Global maps 4 season temperature anomaly trends for period 1950‐2009.

So let’s look at recent regional temperature anomalies and temperature trends. Figure 8 shows seasonal temperature anomalies for the past year and Figure 9 shows seasonal temperature change since 1950 based on local linear trends. The temperature scales are identical in Figures 8 and 9. The outstanding characteristic in comparing these two figures is that the magnitude of the 60 year change is similar to the magnitude of seasonal anomalies. What this is telling us is that the climate dice are already strongly loaded. The perceptive person who has been around since the 1950s should be able to notice that seasonal mean temperatures are usually greater than they were in the 1950s, although there are still occasional cold seasons.

The magnitude of monthly temperature anomalies is typically 1.5 to 2 times greater than the magnitude of seasonal anomalies. So it is not yet quite so easy to see global warming if one’s figure of merit is monthly mean temperature. And, of course, daily weather fluctuations are much larger than the impact of the global warming trend. The bottom line is this: there is no global cooling trend. For the time being, until humanity brings its greenhouse gas emissions under control, we can expect each decade to be warmer than the preceding one. Weather fluctuations certainly exceed local temperature changes over the past half century. But the perceptive person should be able to see that climate is warming on decadal time scales.

This information needs to be combined with the conclusion that global warming of 1‐2°C has enormous implications for humanity. But that discussion is beyond the scope of this note.

References:
Hansen, J.E., and S. Lebedeff, 1987: Global trends of measured surface air temperature. J. Geophys. Res., 92, 13345‐13372.
Hansen, J., R. Ruedy, J. Glascoe, and Mki. Sato, 1999: GISS analysis of surface temperature change. J. Geophys. Res., 104, 30997‐31022.
Hansen, J.E., R. Ruedy, Mki. Sato, M. Imhoff, W. Lawrence, D. Easterling, T. Peterson, and T. Karl, 2001: A closer look at United States and global surface temperature change. J. Geophys. Res., 106, 23947‐23963.
Hansen, J., Mki. Sato, R. Ruedy, K. Lo, D.W. Lea, and M. Medina‐Elizade, 2006: Global temperature change. Proc. Natl. Acad. Sci., 103, 14288‐14293.

A panel discussion with Professor Diana Liverman, Professor Yadvinder Malhi, journalist Mark Lynas, and BBC correspondent James Painter. Oxford University Centre for the Environment, Friday 15 Jan 2010 at  4pm.

Applications are invited for the above post at The Tyndall Centre for Climate Change Research at the University of Manchester.

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RealClimate is run by a rather loosely organized volunteer consortium of people with day jobs that in and of themselves can be quite consuming of attention. And so it came to pass that the first I learned about Gavin’s interest in the work of Plass was — by reading RealClimate! In fact, David Archer and I have a book due to appear this year from Wiley/Blackwell (The Warming Papers), which is a collection of historic papers on global warming, together with interpretive essays by David and myself. Needless to say, we pay a lot of attention to the seminal work by Plass in this book. His 1956 QJRMS technical paper on radiative transfer, which is largely the basis of his more popular writings on global warming, was one of the papers we chose to reprint in our collection. In reading historic papers, it is easy to fall into the trap of assuming that investigators of the past are working on the basis of the same underlying set of assumptions in common use today. Through a very close reading of the paper, David and I noticed something about the way Plass estimated surface temperature increase, that Gavin and all previous commentators on Plass — including Kaplan himself — seem to have overlooked.

These days, it is fairly common knowledge that determination of surface temperature change requires simultaneous satisfaction of the top-of-atmosphere energy budget and surface energy budget, and that in most circumstances it is the top-of-atmosphere budget that plays by far the leading role. This is one of the many things that Arrhenius got spot-on right in his conceptual framework for computing surface temperature. His computation explicitly takes both balance requirements into account, though substantial inaccuracies were introduced because the onerous computations involved in solving the model pretty much restricted him to a one-layer representation of the atmosphere. Later workers improved on Arrhenius by introducing multiple layers and more accurate spectroscopy, but did not always note the importance of satisfying the top-of-atmosphere balance. I think it seems natural to most people to assume that if one is interested in surface temperature, the surface budget must be the most important thing to look at. Plass, for all his brilliance in computing the radiative effects of CO2, was one of the ones who was led astray by this fallacy.

Since discussions of radiative forcing today are almost invariably based on top-of-atmosphere budgets (or at least top-of- troposphere budgets, which are almost the same thing), it is natural for the modern reader to assume that when a paper quotes a radiative forcing, it must be a top-of-atmosphere forcing. This is what Gavin assumed, but a close reading of the 1956 QJRMS paper shows that this is not, in fact, what Plass was talking about. In that paper, Plass does not get around to turning his voluminous radiative calculations into a surface temperature change until nearly the last page of the paper, and when he does, he spends barely a page explaining the reasoning.

The radiative forcing Plass quotes is actually the increase in downward infrared radiation to the surface, which you get if you double CO2 while holding the atmospheric temperature fixed . This back-radiation increases because increasing the concentration of a greenhouse gas makes the atmosphere a more efficient emitter of infrared radiation, at least up to the point where the lowest bits of the atmosphere emit so well that they essentially have become a blackbody, whereafter the emission to the ground can no longer increase unless the air temperature changes. For Earthlike conditions, the emission from CO2 is nowhere near saturated in this sense (see this post ) , so Plass was entirely correct in inferring an increase in the back-radiation, at least for a relatively dry atmosphere. Adding CO2 to the atmosphere is a bit like turning up the dial on a heat lamp you are lying underneath.

It is in the final stages of the calculation that Plass went wrong. He assumed that the surface would get rid of the extra infrared radiation it was receiving by heating up until it was able to radiate away the excess. This reasoning ignores the fact that radiation is not the only means of exchanging heat between the atmosphere and the surface. There are also turbulent exchanges, including evaporation, and these would tend to limit the surface warming to values far less than the values Plass estimated. Further, when the lower atmosphere is warm and moist, such as in the tropics, the great infrared opacity of the large quantity of water vapor tends to limit the direct effect of CO2 on back-radiation into the surface, which further limits the surface warming if the air temperature is held fixed as Plass did. To be fair, Plass does include a sentence implying that he was concerned about the portion of the retained flux that exited through the top of the atmosphere, but even if one gives the most generous interpretation to what might have been meant by this statement, there is no way to make a consistent calculation out of it, given the use of the surface back-radiation as radiative forcing.

The way the greenhouse effect really works is that adding CO2 reduces the infrared out the top of the atmosphere, which means the planet receives more solar energy than it is getting rid of as infrared out the top. The only way to bring the system back into balance is for the whole troposphere to warm up. It is the corresponding warming of the low level air that drags the surface temperature along with it — an effect left entirely out of Plass’ calculation.

A more quantitative discussion of the way all this works can be found in The Warming Papers, and a yet more advanced discussion of such things can be found in Chapter 6 of my book Principles of Planetary Climate (which at long last has been shipped off to Cambridge University press, animula vagula blandula)

In point of fact, Plass did compute the top-of-atmosphere radiative forcing due to doubling or halving the concentration of CO2. The result is plainly shown in the rightmost graph of his Figure 7, where he shows the vertical profile of upward and downward flux for three different CO2 concentrations. Reading the values from the top of the graph, I get that Plass computes a 3.2 Watt per square meter reduction in the outgoing radiation for a doubling of CO2. This is really quite close to the modern value. Plass does not mention this number, or its importance, anywhere in the text, however. Still, it would be fair to give Plass the credit for the first calculation of top-of-atmosphere radiative forcing using correct modern radiative physics. Though he did not make good use of the calculation himself, the methods he introduced are largely the same as those used by Manabe and Wetherald in 1967, who were the first to put together correct spectroscopy with a correct framework for computing surface temperature, adding in accurate water vapor spectroscopy and the effects of convection along the way.

Thus, while Plass made seminal contributions to radiative transfer, his actual estimate of surface temperature increase cannot be regarded as an improvement over Arrhenius. Plass had better spectroscopy than Arrhenius, but a framework that would not give the right answer no matter how good the radiative transfer was. The point of all this historical deconstruction is not to poke fun at Plass or detract from his contributions. Theories do not spring from scientists full-formed like Athena from the head of Zeuss. Science often proceeds through a series of errors and corrections, and those who move the ball forward are in the thick of this process even if they have made some mistakes. The point is that our current understanding of global warming rests on the shoulders of some of the greatest giants of physics of the past century or more, and myriad lesser but still substantial intellects as well.

So, when push comes to shove, was Plass a Hedgehog or a Fox? The answer is: a bit of both. With regard to computing the radiative fluxes due to CO2, Plass was a true hedgehog — he knew that one thing really, really well, and that had a lasting impact on our science. But in his Tellus article, he also showed himself to be quite a fox, in that by knowing (and explaining) many independent lines of thinking, he helped to revive attention to the wide-ranging importance of CO2 in climate. You could say he was not enough of a fox to have also absorbed the lesson of the importance of top-of-atmosphere balance, known already to Arrhenius. But also, you could say that if you’re going to be a hedgehog and pick one thing to be the central organizing principle of your world view, it had better be a pretty darn universally important thing to know. If you’re going to be a climate hedgehog, the constraint imposed by top-of-atmosphere radiation balance would be a pretty good place to hang your hat.

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I said on Friday that I didn’t think that Lindzen and Choi (2009) was obviously nonsense. Well, a number of people have disagreed with me, and in doing so, have presented some of the back story on the how the response was handled. I think this deserves to be more widely known in the hope that it will generate some discussion in the community for how such situations might be dealt with in the future.

From Chris O’Dell:

Given the large number of comments on the peer-review process in general and in the LC09 case in particular, it is probably worthwhile to give a bit more backstory to our Trenberth et al. paper. On my first reading of LC09, I was quite amazed and thought if the results were true, it would be incredible (and, in fact, a good thing!) and hence warranted independent checking. Very simple attempts to reproduce the LC09 numbers simply didn’t work out and revealed some flaws in their process. To find out more, I contacted Dr. Takmeng Wong at NASA Langley, a member of the CERES and ERBE science teams (and major player in the ERBE data set) and found out to my surprise that no one on these teams was a reviewer of LC09. Dr. Wong was doing his own verification of LC09 and so we decided to team up.

After some further checking, I came across a paper very similar to LC09 but written 3 years earlier – Forster & Gregory (2006) , hereafter FG06. FG06, however, came to essentially opposite conclusions from LC09, namely that the data implied an overall positive feedback to the earth’s climate system, though the results were somewhat uncertain for various reasons as described in the paper (they attempted a proper error analysis). The big question of course was, how is it that LC09 did not even bother to reference FG06, let alone explain the major differences in their results? Maybe Lindzen & Choi didn’t know about the existence of FG06, but certainly at least one reviewer should have. And if they also didn’t, well then, a very poor choice of reviewers was made.

This became clear when Dr. Wong presented a joint analysis he & I made at the CERES science team meeting held in Fort Collins, Colorado in November. At this meeting, Drs. Trenberth and Fasullo approached us and said they had done much the same thing as we had, and had already submitted a paper to GRL, specifically a comment paper on LC09. This comment was rejected out of hand by GRL, with essentially no reason given. With some more inquiry, it was discovered that:

1. The reviews of LC09 were “extremely favorable”
2. GRL doesn’t like comments and is thinking of doing away with them altogether.
3. GRL wouldn’t accept comments on LC09 (and certainly not multiple comments), and instead it was recommended that the four of us submit a stand-alone paper rather than a comment on LC09.

We all felt strongly that we simply wanted to publish a comment directly on LC09, but gave in to GRL and submitted a stand-alone paper. This is why, for instance, LC09 is not directly referenced in our paper abstract. The implication of statement (1) above is that LC09 basically skated through the peer-review process unchanged, and the selected reviewers had no problems with the paper. This, and for GRL to summarily reject all comments on LC09 appears extremely sketchy.

In my opinion, there is a case to be made on the peer-review process being flawed, at least for certain papers. Many commenters say the system isn’t perfect, but it in general works. I would counter that it certainly could be better. For AGU journals, authors are invited to give a list of proposed reviewers for their paper. When the editor is lazy or tight on time or whatever, they may just use the suggested reviewers, whether or not those reviewers are appropriate for the paper in question. Also, when a comment on a paper is submitted, the comment goes to the editor that accepted the original paper – a clear conflict of interest.

So yes, the system may work most of the time, but LC09 is a clear example that it doesn’t work all of the time. I’m not saying LC09 should have been rejected or wasn’t ultimately worthy of publication, but reviewers should have required major modifications before it was accepted for publication.

To me this raises a number of questions. Why are the editors at GRL apparently not following the published editorial policy on comments? The current policy might not be ideal, and perhaps should be changed, but surely not by fiat, and surely not without announcing that policy change? This particular example has ended up divorcing the response from the original paper and clearly makes it harder to follow the development of this analysis in the literature. Additionally, in cases where there appears to have been lapses in peer-review (for whatever reason), is there not an argument for having a different editor deal with the comment/response? Perhaps a new online journal which independently publishes peer-reviewed comments and responses is called for?

Everyone involved in the peer-review process knows full well the difficulty in finding suitable reviewers who have the time and inclination to do a good review. The pressures on editors both to be seen to be fair, and to actually be fair to the authors (and the readers!) are strong, and occasionally things will go wrong. The measure of such a system is not whether it is perfect, but whether it deals appropriately and quickly with problems when they (inevitably) arise.

NB. Comments on how to improve the situation are welcome, but please avoid simply criticising papers that you personally think shouldn’t have been published in the form they were.

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The first published response to Lindzen and Choi (2009) (LC09) has just appeared “in press” (subscription) at GRL. LC09 purported to determine climate sensitivity by examining the response of radiative fluxes at the Top-of-the-Atmosphere (TOA) to ocean temperature changes in the tropics. Their conclusion was that sensitivity was very small, in obvious contradiction to the models.

In their commentary, Trenberth, Fasullo, O’Dell and Wong examine some of the assumptions that were used in LC09’s analysis. In their guest commentary, they go over some of the technical details, and conclude, somewhat forcefully, that the LC09 results were not robust and do not provide any insight into the magnitudes of climate feedbacks.

Coincidentally, there is a related paper (Chung, Yeomans and Soden) also in press (sub. req.) at GRL which also compares the feedbacks in the models to the satellite radiative flux measurements and also comes to the conclusion that the models aren’t doing that badly. They conclude that

In spite of well-known biases of tropospheric temperature and humidity in climate models, comparisons indicate that the intermodel range in the rate of clear-sky radiative damping are small despite large intermodel variability in the mean clear-sky OLR. Moreover, the model-simulated rates of radiative damping are consistent with those obtained from satellite observations and are indicative of a strong positive correlation between temperature and water vapor variations over a broad range of spatiotemporal scales.

It will take a little time to assess the issues that have been raised (and these papers are unlikely to be the last word), but it is worth making a couple of points about the process. First off, LC09 was not a nonsense paper – that is, it didn’t have completely obvious flaws that should have been caught by peer review (unlike say, McLean et al, 2009 or Douglass et al, 2008). Even if it now turns out that the analysis was not robust, it was not that the analysis was not worth trying, and the work being done to re-examine these questions is a useful contributions to the literature – even if the conclusion is that this approach to the analysis is flawed.

More generally, this episode underlines the danger in reading too much into single papers. For papers that appear to go against the mainstream (in either direction), the likelihood is that the conclusions will not stand up for long, but sometimes it takes a while for this to be clear. Research at the cutting edge – where you are pushing the limits of the data or the theory – is like that. If the answers were obvious, we wouldn’t need to do research.

Update: More commentary at DotEarth including a response from Lindzen.