How much additional carbon dioxide will be released to, or removed from, the atmosphere, by the oceans and the biosphere in response to global warming over the next century? That is an important question, and David Frank and his Swiss coworkers at WSL have just published an interesting new approach to answering it. They empirically estimate the distribution of gamma, the temperature-induced carbon dioxide feedback to the climate system, given the current state of the knowledge of reconstructed temperature, and carbon dioxide concentration, over the last millennium. It is a macro-scale approach to constraining this parameter; it does not attempt to refine our knowledge about carbon dioxide flux pathways, rates or mechanisms. Regardless of general approach or specific results, I like studies like this. They bring together results from actually or potentially disparate data inputs and methods, which can be hard to keep track of, into a systematic framework. By organizing, they help to clarify, and for that there is much to be said.

Gamma has units in ppmv per ºC. It is thus the inverse of climate sensitivity, where CO2 is the forcing and T is the response. Carbon dioxide can, of course, act as both a forcing and a (relatively slow) feedback; slow at least when compared to faster feedbacks like water vapor and cloud changes. Estimates of the traditional climate sensitivity, e.g. Charney et al., (1979) are thus not affected by the study. Estimates of more broadly defined sensitivities that include slower feedbacks, (e.g. Lunt et al. (2010), Pagani et al. (2010)), could be however.

Existing estimates of gamma come primarily from analyses of coupled climate-carbon cycle (C4) models (analyzed in Friedlingstein et al., 2006), and a small number of empirical studies. The latter are based on a limited set of assumptions regarding historic temperatures and appropriate methods, while the models display a wide range of sensitivities depending on assumptions inherent to each. Values of gamma are typically positive in these studies (i.e. increased T => increased CO2).

To estimate gamma, the authors use an experimental (“ensemble”) calibration approach, by analyzing the time courses of reconstructed Northern Hemisphere T estimates, and ice core CO2 levels, from 1050 to 1800, AD. This period represents a time when both high resolution T and CO2 estimates exist, and in which the confounding effects of other possible causes of CO2 fluxes are minimized, especially the massive anthropogenic input since 1800. That input could completely swamp the temperature signal; the authors’ choice is thus designed to maximize the likelihood of detecting the T signal on CO2. The T estimates are taken from the recalibration of nine proxy-based studies from the last decade, and the CO2 from 3 Antarctic ice cores. Northern Hemisphere T estimates are used because their proxy sample sizes (largely dendro-based) are far higher than in the Southern Hemisphere. However, the results are considered globally applicable, due to the very strong correlation between hemispheric and global T values in the instrumental record (their Figure S3, r = 0.96, HadCRUT basis), and also of ice core and global mean atmospheric CO2.

The authors systematically varied both the proxy T data sources and methodologicalvariables that influence gamma, and then examined the distribution of the nearly 230,000 resulting values. The varying data sources include the nine T reconstructions (Fig 1), while the varying methods include things like the statistical smoothing method, and the time intervals used to both calibrate the proxy T record against the instrumental record, and to estimate gamma.

Figure 1. The nine temperature reconstructions (a), and 3 ice core CO2 records (b), used in the study.

Some other variables were fixed, most notably the calibration method relating the proxy and instrumental temperatures (via equalization of the mean and variance for each, over the chosen calibration interval). The authors note that this approach is not only among the mathematically simplest, but also among the best at retaining the full variance (Lee et al, 2008), and hence the amplitude, of the historic T record. This is important, given the inherent uncertainty in obtaining a T signal, even with the above-mentioned considerations regarding the analysis period chosen. They chose the time lag, ranging up to +/- 80 years, which maximized the correlation between T and CO2. This was to account for the inherent uncertainty in the time scale, and even the direction of causation, of the various physical processes involved. They also estimated the results that would be produced from 10 C4 models analyzed by Friedlingstein (2006), over the same range of temperatures (but shorter time periods).

So what did they find?

In the highlighted result of the work, the authors estimate the mean and median of gamma to be 10.2 and 7.7 ppm/ºC respectively, but, as indicated by the difference in the two, with a long tail to the right (Fig. 2). The previous empirical estimates, by contrast, come in much higher–about 40 ppm/degree. The choice of the proxy reconstruction used, and the target time period analyzed, had the largest effect on the estimates. The estimates from the ten C4 models, were higher on average; it is about twice as likely that the empirical estimates fall in the model estimates? lower quartile as in the upper. Still, six of the ten models evaluated produced results very close to the empirical estimates, and the models’ range of estimates does not exclude those from the empirical methods.

Figure 2. Distribution of gamma. Red values are from 1050-1550, blue from 1550-1800.

Are these results cause for optimism regarding the future? Well the problem with knowing the future, to flip the famous Niels Bohr quote, is that it involves prediction.

The question is hard to answer. Empirically oriented studies are inherently limited in applicability to the range of conditions they evaluate. As most of the source reconstructions used in the study show, there is no time period between 1050 and 1800, including the medieval times, which equals the global temperature state we are now in; most of it is not even close. We are in a no-analogue state with respect to mechanistic, global-scale understanding of the inter-relationship of the carbon cycle and temperature, at least for the last two or three million years. And no-analogue states are generally not a real comfortable place to be, either scientifically or societally.

Still, based on these low estimates of gamma, the authors suggest that surprises over the next century may be unlikely. The estimates are supported by the fact that more than half of the C4-based (model) results were quite close (within a couple of ppm) to the median values obtained from the empirical analysis, although the authors clearly state that the shorter time periods that the models were originally run over makes apples to apples comparisons with the empirical results tenuous. Still, this result may be evidence that the carbon cycle component of these models have, individually or collectively, captured the essential physics and biology needed to make them useful for predictions into the multi-decadal future. Also, some pre-1800, temperature independent CO2 fluxes could have contributed to the observed CO2 variation in the ice cores, which would tend to exaggerate the empirically-estimated values. The authors did attempt to control for the effects of land use change, but noted that modeled land use estimates going back 1000 years are inherently uncertain. Choosing the time lag that maximizes the T to CO2 correlation could also bias the estimates high.

On the other hand, arguments could also be made that the estimates are low. Figure 2 shows that the authors also performed their empirical analyses within two sub-intervals (1050-1550, and 1550-1800). Not only did the mean and variance differ significantly between the two (mean/s.d. of 4.3/3.5 versus 16.1/12.5 respectively), but the R squared values of the many regressions were generally much higher in the late period than in the early (their Figure S6). Given that the proxy sample size for all temperature reconstructions generally drops fairly drastically over the past millennium, especially before their 1550 dividing line, it seems at least reasonably plausible that the estimates from the later interval are more realistic. The long tail–the possibility of much higher values of gamma–also comes mainly from the later time interval, so values of gamma from say 20 to 60 ppm/ºC (e.g. Cox and Jones, 2008) certainly cannot be excluded.

But this wrangling over likely values may well be somewhat moot, given the real world situation. Even if the mean estimates as high as say 20 ppm/ºC are more realistic, this feedback rate still does not compare to the rate of increase in CO2 resulting from fossil fuel burning, which at recent rates would exceed that amount in between one and two decades.

I found some other results of this study interesting. One such involved the analysis of time lags. The authors found that in 98.5% of their regressions, CO2 lagged temperature. There will undoubtedly be those who interpret this as evidence that CO2 cannot be a driver of temperature, a common misinterpretation of the ice core record. Rather, these results from the past millennium support the usual interpretation of the ice core record over the later Pleistocene, in which CO2 acts as a feedback to temperature changes initiated by orbital forcings (see e.g. the recent paper by Ganopolski and Roche (2009)).

The study also points up the need, once again, to further constrain the carbon cycle budget. The fact that a pre-1800 time period had to be used to try to detect a signal indicates that this type of analysis is not likely to be sensitive enough to figure out how, or even if, gamma is changing in the future. The only way around that problem is via tighter constraints on the various pools and fluxes of the carbon cycle, especially those related to the terrestrial component. There is much work to be done there.

References

Pagani, M, Z. Liu, J. LaRiviere, and A.C.Ravelo. High Earth-system climate sensitivity determined from Pliocene carbon dioxide concentrations. Nature Geosci., 3, 27-30

First of all, this is a paper about internal variability of the climate system in the last decade, not on additional factors that drive climate. Second, this is a discussion about stratospheric water vapour (10 to 15 km above the surface), not water vapour in general. Stratospheric water vapour comes from two sources – the uplift of tropospheric water through the very cold tropical tropopause (both as vapour and as condensate), and the oxidation of methane in the upper stratosphere (CH4+2O2 –> CO2 + 2H2O NB: this is just a schematic, the actual chemical pathways are more complicated). There isn’t very much of it (between 3 and 6 ppmv), and so small changes (~0.5 ppmv) are noticeable.

The decreases seen in this study are in the lower stratosphere and are likely dominated by a change in the flux of water through the tropopause. A change in stratospheric water vapour because of the increase in methane over the industrial period would be a forcing of the climate (and is one of the indirect effects of methane we discussed last year), but a change in the tropopause flux is a response to other factors in the climate system. These might include El Nino/La Nina events, increases in Asian aerosols, or solar impacts on near-tropopause ozone – but this is not addressed in the paper and will take a little more work to figure out.

Update: This last paragraph was probably not as clear as it should be. If the lower stratospheric water vapour (LSWV) is relaxing back to some norm after the 1997/1998 El Nino, then what we are seeing would be internal variability in the system which might have some implications for feedbacks to increasing GHGs, and my estimate of that would be that this would be an amplifying feedback (warmer SSTs leading to more LSWV). If we are seeing changes to the tropopause temperatures as an indirect impact from increased Asian aerosol emissions or solar-driven ozone changes, then this might be better thought of as impacting the efficacy of those forcings rather than implying some sensitivity change.

The study includes an estimate of the effect of the observed stratospheric water decadal decrease by calculating the radiation flux with and without the change, and comparing this to the increase in CO2 forcing over the same period. This implicitly assumes that the change can be regarded as a forcing. However, whether that is an appropriate calculation or not needs some careful consideration. Finally, no-one has yet looked at whether climate models (which have plenty of decadal variability too) have phenomena that resemble these observations that might provide some insight into the causes.

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.

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.

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.

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.

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.

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.

A recent paper by Lindzen and Choi in GRL (2009) (LC09) purported to demonstrate that climate had a strong negative feedback and that climate models are quite wrong in their relationships between changes in surface temperature and corresponding changes in outgoing radiation escaping to space. This publication has been subject to a considerable amount of hype, for instance apparently “[LC09] has absolutely, convincingly, and irrefutably proven the theory of Anthropogenic Global Warming to be completely false.” and “we now know that the effect of CO2 on temperature is small, we know why it is small, and we know that it is having very little effect on the climate”. Not surprisingly, LC09 has also been highly publicized in various contrarian circles.

Our initial reading of their article had us independently asking, how we could have missed such explicit evidence of the cloud feedback as shown in LC09? Why would such a significant finding have gone undiscovered when these feedbacks are widely studied and recognised as central to the projections of climate change? We discovered these common concerns at a meeting last year and then teamed up to address these questions.

With the hype surrounding the manuscript, one would think that the article provides a sound, rock solid basis for a reduced climate sensitivity. However, our examination of the study’s methods demonstrates that this is not the case. In an article in press (Trenberth et al. 2010 (sub. requ.), hereafter TFOW), we show that LC09 is gravely flawed and its results are wrong on multiple fronts. These are the major issues we found:

• The LC09 results are not robust.

  A goal of LC09 was to quantify the cloud feedback by examining variability in top-of-atmosphere (TOA) radiative fluxes in the tropics as it relates to variability in mean sea surface temperature (SST). To do this they examine only tropical data. In general, they find that during periods of higher-than-normal SST, the radiation emitted and reflected to space by the earth goes up as well, cooling the Earth and amounting to an overall negative climate feedback. To show this, they select intervals of warming and cooling (in a time series of monthly averaged values) and compare fluxes at their endpoints (see Figure). They didn’t provide an objective criterion for selecting these endpoints and in some instances (see their Fig. 1), the selection of these intervals actually appears to be quite odd.

  
  Fig. 1: Warming (red) and cooling (blue) intervals of tropical SST (20°N – 20°S) used by LC09 (solid circles) and an alternative selection proposed derived from an objective approach (open circles) (TFOW, 2010).
  

  The result one obtains in estimating the feedback by this method turns out to be heavily dependent on the endpoints chosen. [edit] In TFOW we show that the apparent relationship is reduced to zero if one chooses to displace the endpoints selected in LC09 by a month or less. So with this method the perceived feedback can be whatever one wishes it to be, and the result obtained by LC09 is actually very unlikely. This is not then really indicative of a robust cloud feedback.

• LC09 misinterpret air-sea interactions in the tropics

  The main changes in tropical SST and radiative fluxes at TOA are associated with El Niño-Southern Oscillation (ENSO) and are not necessarily indicative of forced variability in a closed system. ENSO events cause strong and robust exchanges of energy between the ocean and atmosphere, and tropics and subtropics. Yet LC09 treat the tropical atmosphere as a closed and deterministic system in which variations in clouds are driven solely by SST. In fact, the system is known to be considerably more complex and changes in the flow of energy arise from ocean heat exchange through evaporation, latent heat release in precipitation, and redistribution of that heat through atmospheric winds. These changes can be an order of magnitude larger than variability in TOA fluxes, and their effects are teleconnected globally. It is therefore not possible to quantify the cloud feedback with a purely local analysis.

• More robust methods show no discrepancies between models and observations

  In TFOW, we compute correlations and regressions between tropical SSTs and top-of-atmosphere (TOA) longwave, shortwave and net radiation using a variety of methods. LC09 found the observed behavior to be opposite from that of 11 atmospheric models forced by the same SSTs and conclude that the models display much higher climate sensitivity than is inferred from ERBE. However, in our analysis comparing these relationships with models, we are unable to find any systematic model bias. More importantly, the nature of these relationships in models bears no relationship to simulated sensitivity. That is, the metric developed by LC09 is entirely ineffective as a proxy for simulated sensitivity.

• LC09 have compared observations to models prescribed with incomplete forcings

  The AMIP configuration in the model simulations used by LC09 have incomplete forcings. The AMIP protocol started off a test only of how an atmospheric model reacts to changes in ocean temperatures, and so models often only use the ocean temperature change when doing these kinds of experiments. However, over the period of this comparison, many elements – greenhouse gases, aerosols, the sun and specifically, volcanoes changed the radiative fluxes, and this needs to be taken into account. Some models did this in these experiments, but not all of them.

  For instance, the dominant source of variability in the reflected solar flux arises from aerosols associated with the eruption of Mount Pinatubo in June of 1991 yet all but 2 model simulations examined by LC09 omit such forcings entirely. Other radiative species are absent from the models altogether. It is thus obviously inappropriate to expect such model simulations to replicate observed variability in TOA fluxes.

• LC09 incorrectly compute the climate sensitivity

  By not allowing for the black body radiation (the Planck function) in their feedback parameter, LC09 underestimate climate sensitivity. Using the correct equations, LC09 should obtain a feedback parameter and climate sensitivity of -0.125 and 0.82 K, respectively, rather than their values of -1.1 and 0.5 K.  In contrast, TFOW results yield a positive feedback parameter and greater sensitivity estimate, though we also caution that this approach is not a valid technique for estimating sensitivity, as a closed and therefore global domain is essential (though not by itself sufficient). Lastly, LC09 fail to account for variability in forcings in estimating sensitivity.

While climate models are known to struggle with many aspects of tropical climate, especially in regards to its coupled variability, the problems claimed by LC09 are not among them. Forster and Gregory [2006] and Murphy et al. [2009] address changes in the energy budget with surface temperatures for a much larger domain and present a much more complete and defensible analysis and discussion of issues. They demonstrate that recent observed variability indeed supports a positive shortwave cloud feedback. So the feedbacks from processes other than the Planck function response are clearly positive in both observations and models, in contrast to LC09’s conclusions. Moreover, it is not appropriate to use only tropical SSTs and TOA radiation for feedback analysis as the transports into the extratropics are substantial. Any feedback analysis must also recognize changes in ocean heat storage and atmospheric energy transport into and out of the tropics which are especially large during ENSO events. While the tropics play an important role in determining climate sensitivity, simplistic and arbitrary analyses of tropical variability can be grossly misleading.

References

Forster, P. M. F., and J. M. Gregory (2006), The climate sensitivity and its components diagnosed from Earth Radiation Budget Data, J. Clim., 19, 39–52
Lindzen, R. S., and Y.-S. Choi (2009), On the determination of climate feedbacks from ERBE data, Geophys. Res. Lett., 36, L16705, doi:10.1029/2009GL039628.
Murphy, D. M., S. Solomon, R. W. Portmann, K. H. Rosenlof, P. M. Forster , and T. Wong (2009), An observationally based energy balance for the Earth since 1950, J. Geophys. Res., 114, D17107, doi:10.1029/2009JD012105.
Trenberth, K. E., J. T. Fasullo, Chris O’Dell, and T. Wong, (2010): Relationships between tropical sea surface temperature and top-of-atmosphere radiation. Geophys. Res. Lett., 37, doi:10.1029/2009GL042314, in press.

To understand if Plass should get full credit, we need to see his workings. These are mainly outlined in two more technical papers in Tellus and QJRMS earlier that year. In today’s parlance, Plass calculated the change in top-of-the-atmosphere (TOA) radiative fluxes given a doubling (or a halving) of CO2 while everything else stayed the same. He then took that number and using someone else’s estimate of the sensitivity of the TOA radiation to the surface temperature, he calculated the temperature change that would be necessary to compensate. Converting from the units he used, the radiative flux values for a doubling of CO2 were 8.3 W/m2 and 5.8 W/m2 for clear-sky (no clouds) and averagely cloudy conditions (all-sky) respectively (and slightly larger and of opposite sign for a halving). The sensitivity of the TOA flux to surface temperature he used was around 2.3 W/m2 per ºC (equivalent to a temperature sensitivity of 0.4 ºC/(W/m2)). However, this is a ‘no-feedback’ estimate (allowing only the surface temperature to change with a constant lapse rate, but with no changes to water vapour, albedo or clouds).

Today, our current best guess for the forcing due to 2xCO2 is around 4 W/m2, and the ‘no-feedback’ sensitivity is around 0.3 ºC/(W/m2), giving an expected no-feedback temperature change of about 1.2 ºC, a factor of 3 smaller than the number Plass quoted, though since our number is for ‘all sky’ conditions, it would be a little better to compare it to his averagely cloudy number 2.5 ºC (so a factor of two higher). Note that Plass was a little casual in how he described his numbers and the ‘clear sky’ designation for the 3.6ºC number was not always made clear. However, Plass was well aware that the ‘no-feedback’ case was unrealistic and estimated that the water vapour, cloud and ice-albedo feedbacks would be amplifying, although he was not able to quantify them.

Moving now to the rate of change of CO2 in the atmosphere, Plass made a very good estimate as to how much human emissions of CO2 were increasing. His estimate was (again, in modern units) that then-current emissions were 1.5 GtC based on earlier estimates from Callendar, which actually was an underestimate. Our current best estimate for the anthropogenic emissions in 1956 is about 2.2 GtC. Given the increasing nature of the emissions, Plass then estimated that concentrations would rise about 30% by the end of the 20th Century. This however needs an estimate of how much of the emissions would be absorbed by the oceans and biosphere. Here, Plass has another impressive insight that the ocean chemistry would prevent quick uptake of the human CO2, a concept that wasn’t fully worked out until Revelle and Suess’s paper in 1957 (though possibly he may have been aware of some informal communications earlier). Plass actually assumed that none of the CO2 would be taken up in the short term. So his 30% growth estimate (the actual rise was 36%) was derived from an underestimate in emissions (and emissions growth) combined with an overestimate of the ‘airborne fraction’ (which is roughly 40% of total emissions).

Finally, his estimate of temperature rise of about 1ºC by the end of century follows from the two previous numbers, along with two further assumptions – that the climate is always close to equilibrium with the forcings and that of course, there aren’t any other factors changing. The first assumption affected by the substantial lag in the system because of the thermal inertia of the oceans, and of course, there are many more factors driving climate change over the 20th C. Plass can of course be forgiven for not knowing about the greenhouse impact of rises in CH4, N2O and CFCs (not realised until 1974), or the role of aerosol emissions (1970s), and indeed, he was fortunate that the net effect of all non-CO2 drivers is close to zero (though with significant uncertainties).

So Plass was correct about all of the big issues, but lucky that, in his quantitative estimates, the errors went both ways and end up pretty much cancelling out.

Eli has described this using Isaiah Berlin’s Hedgehog and the Fox metaphor – Plass being the Hedgehog who knows one big thing, and for whom the details are more incidental. I think this is a reasonable take, as long as it is realised that Hedgehogs are not always right, even though in this case he was.

The Fox in this case was another big name in atmospheric physics, Lewis Kaplan. He published a counter to Plass’s 1956 work in Tellus in 1960 (vol. 12, p204-208), and there was a “spirited” exchange of letters in 1961 (vol. 13, p296-302) (references for those of you with libraries – for some reason, none of the old Tellus volumes are online). His calculation used a different methodology, more up-to-date spectra but was different enough in approach and specifics to make a fair apples-to-apples comparison between the results hard to do. Nonetheless, Kaplan declared that “Plass’ estimate of a temperature drop of 3.8ºC due to a halving of [CO2] appears to be too high by a factor of two or three” and that “it would seem, then, that CO2 variations could not play a role in the ice-age cycle unless the changes were by an order of magnitude”.

The subsequent comment and reply are actually very reminiscent of recent disputes in climate science. Plass complains that not enough information was provided to replicate the analysis, that Kaplan used unjustified precision, that he wasn’t comparing like-with-like (all-sky with clear-sky), that he made unjustified technical assumptions, and that his overall conclusion was ‘misleading’ because of the neglected feedbacks (that neither of them had quantified). Kaplan responds that of course there is enough information to check his workings (in another paper), that it was Plass’ fault he compared the all-sky and clear-sky numbers, and that he has exaggerated the impact of the technical criticisms. Notably, Kaplan did not respond on the issue of feedbacks.

Looking over the exchange with a 50 year perspective, a number of things stand out. First, Kaplan does seem to have been closer to modern values in his calculation – Plass was out by a factor of two for the all-sky no-feedback case. I’m not really familiar enough with the details to be be able to tell why (perhaps someone can enlighten us in the comments). However, Kaplan was wrong about everything that has ended up mattering – CO2 does play a big role in ice age cycles (with a magnitude of change close to what Plass anticipated) and its growth today is climatically significant. Significantly, I can find no trace in the literature of any resolution of the technical issues raised in the letters. Resolution in Plass’ favour of the big questions came with further independent efforts as computers got fast enough to do the more complicated feedback problem, better observations, better spectral data and better paleo-climate information (particularly from the ice cores). In some sense, resolution of their technical differences would have been moot because that wasn’t the real issue. Of course, that would have been difficult to see at the time.

So, to summarize, Plass did have some key insights and in many respects was well ahead of his time. But he was also lucky.

Update: Stay tuned, it looks like there is another little wrinkle to this story…

Knorr (2009): Case in point, Knorr (GRL, 2009) is a study about how much of the human emissions are staying the atmosphere (around 40%) and whether that is detectably changing over time. It does not undermine the fact that CO2 is rising. The confusion in the denialosphere is based on a misunderstanding between ‘airborne fraction of CO2 emissions’ (not changing very much) and ‘CO2 fraction in the air’ (changing very rapidly), led in no small part by a misleading headline (subsequently fixed) on the ScienceDaily news item Update: MT/AH point out the headline came from an AGU press release (Sigh…). SkepticalScience has a good discussion of the details including some other recent work by Le Quéré and colleagues.

Update: Some comments on the John Coleman/KUSI/Joe D’Aleo/E. M. Smith accusations about the temperature records. Their claim is apparently that coastal station absolute temperatures are being used to estimate the current absolute temperatures in mountain regions and that the anomalies there are warm because the coast is warmer than the mountain. This is simply wrong. What is actually done is that temperature anomalies are calculated locally from local baselines, and these anomalies can be interpolated over quite large distances. This is perfectly fine and checkable by looking at the pairwise correlations at the monthly stations between different stations (London-Paris or New York-Cleveland or LA-San Francisco). The second thread in their ‘accusation’ is that the agencies are deleting records, but this just underscores their lack of understanding of where the GHCN data set actually comes from. This is thoroughly discussed in Peterson and Vose (1997) which indicates where the data came from and which data streams give real time updates. The principle one is the CLIMAT updates of monthly mean temperature via the WMO network of reports. These are distributed by the Nat. Met. Services who have decided which stations they choose to produce monthly mean data for (and how it is calculated) and is absolutely nothing to do with NCDC or NASA.

Further Update: NCDC has a good description of their procedures now available, and Zeke Hausfather has a very good explanation of the real issues on the Yale Forum.