Transfered from our old blog.
by Anthony Cox and Joanne Nova.
3 R.S. Knox and D.H. Douglass – The missing heat is not in the ocean.
The dominant explanation for where Trenberth’s missing warming or heat is that it is in the ocean. This missing heat is the difference between the climate effects, particularly change in global average temperature, which global warming predicted we would have and the much lower change in global average temperature we have had. In 2009 modeling von Shuckmann et al15 seemed to have found this missing heat at depths of 2000 metres in the ocean. One immediate problem for von Shuckmann et al is found in the NOAA graph in Figure 3. This graph is based on data for ocean heat content to depths of 700 metres which show no warming from 2003:Figure 3:
[http://www.nodc.noaa.gov/OC5/3M_HEAT_CONTENT/index.html]
The problem this shows for von Shuckmann et al [and other papers which also use modeling to ‘find’ deep-ocean warming16] is; how could the ocean depths be warming when the ocean top was cooling?
A second problem was raised in 2 papers by the team of Ablain17 and Cazenave18; they showed that not only was the rate of sea level rise decreasing but the steric part of the sea level rise, which is based on ocean heat content, was also decreasing from 2006.
The third contradiction to von Shuckmann et al and the missing heat is in Knox and Douglass’s paper19. Knox & Douglass are both imminent atmospheric physicists and have already written a number of papers dealing with ocean based climatic events and the connection between the ocean radiative rate of change [Fohc] and the radiative rate of change at the top of the atmosphere [Ftoa].
In their latest paper Knox & Douglass showed that not only was ocean heat content declining but that the Fohc was negative, which meant more radiative energy was leaving the ocean than being stored:
Figure 4 From Knox & Douglass page 1. Fohc left scale.
Figure 1. Ocean heat content from Argo (left scale: blue, original data; red, filtered) and ocean surface temperatures (right scale, green). Conversion of the OHC slope to W/m2 is made by multiplying by 0.62, yielding –0.161 W/m2.
Knox & Douglass’s findings about ocean heat content were based on empirical measurements and are consistent with studies by Willis, Loehle, and Pielke, and NOAA data [see Figure 3].
Knox & Douglass conclude that because “90% of the variable heat content resides in the upper ocean” the Fohc can accurately infer the Ftoa. Therefore if Fohc is negative then Ftoa is as well. A negative Ftoa is contrary to Trenberth’s claims of missing heat being stored most likely in the oceans. Without missing heat the models have greatly overestimated the effect of global warming.
4 Miskolczi – The optical depth of the atmosphere hasn’t changed
Figure 5 [from Miskolczi 2010]
Ferenc Miskolczi was a NASA atmospheric physicist whose 2 papers in 200720 and 201021 were both peer reviewed and have never been refuted. These papers draw on data and calculations made by Miskolczi in a 2004 paper co-authored by NASA physicist Martin Mlynczak. Miskolczi 200422 shows that radiation leaving the Earth, outgoing long-wave radiation, is based on zonal and global averages of real atmospheric conditions as shown in the atmospheric optical thickness.
Miskolczi 2007 and 2010 measure “the true greenhouse-gas optical thickness” [Abstract, Miskolczi 2010]. This is made up of two parts which are depicted in Figure 4.
a. τA — is defined as “the total IR flux optical depth” [page 5 Miskolczi 2007]. This is a measure of the total amount of infra-red or long-wave radiation which is absorbed between the surface and the top of the atmosphere.
b. A — is the flux absorbance [page 3 Miskolczi 2010] and is a measure of what wavelengths of long-wave radiation are being absorbed and transmitted in the atmosphere by 11 greenhouse gases [page 7, Miskolczi 2004].
Together τA and A are the optical depth of the atmosphere The optical depth is a kind of proxy measure of the greenhouse effect. Global warming says that more CO2 will increase the optical depth. Miskolczi showed that available empirical measurements of the optical depth are consistent with no change in 61 years. This means that even though CO2 has increased over the 61 years of measurement and increased the optical depth slightly, “variations in water vapor column amounts” [Figure 11, Miskolczi 2010] have decreased the optical depth by a similar amount. Paltridge et al.23 have confirmed a decrease in water vapor for this period.
If the optical depth has not increased overall, it suggests the slight warming of the 20th C has not been due to an increase in the greenhouse effect.
In addition Miskolczi also finds no positive feedback from water vapor on atmospheric long-wave radiation absorption, which negates what the models have predicted; this lack of positive feedback has been confirmed by the missing ‘Tropical hot spot’ [see section 6].
5 McShane and Wyner24 – The Hockeystick is broken
Figure 6. McShane&Wyner, page 36
Blakeley McShane and Abraham Wyner attempted to replicate Michael Mann’s infamous hockeystick using Mann’s own data. The hockeystick first appeared in Mann’s 1998 paper and has been a centre-piece of global warming evidence ever since. The hockeystick is important because it supposedly shows recent warming is exceptional and “unprecedented”. The hockeystick is based on dendro-climatic proxies or tree-rings which supposedly provide evidence for past temperatures.
Mann’s hockeystick shows basically flat temperature until the 20th C and then a sudden and rapid increase.Mann’s data was highly problematic. Mann had used the wrong type of tree, and at times, hardly any samples. Some of the tree-ring records even show the opposite “temperature” trend to what thermometers show suggesting those trees don’t make a good or accurate alternative to thermometers.
McShane &Wyner tried to create the same graph from the same data, but, as Figure 5 above shows, could not. They conclude:
“Using our model, we calculate that there is a 36% posterior probability that 1998 was the warmest year over the past thousand. If we consider rolling decades, 1997-2006 is the warmest on record; our model gives an 80% chance that it was the warmest in the past thousand years. Finally, if we look at rolling thirty-year blocks, the posterior probability that the last thirty years (again, the warmest on record) were the warmest over the past thousand is 38%.”[page 37]
So, even using Mann’s dubious data and employing a variety of statistical methods, McShane & Wyner’s model suggests that there is only an 80% chance that one recent decade was the warmest of the last 1000 years, and 1998 is most likely not the warmest year [64% against] and the last 30 year period, is also unlikely to have been the warmest [62% against]. In other words, the type of weather we have now has all occurred before, and in the not too distant past when CO2 was supposedly low.
The paper correctly describes the importance of the hockeystick not only to global warming but also Green policies:
“the effort of world governments to pass legislation to cut carbon to pre-industrial levels cannot proceed without the consent of the governed and historical reconstructions from paleoclimatological models [ie hockeysticks] have indeed proven persuasive and effective at winning the hearts and minds of the populace.” [page 2]
It would seem the hearts and minds have been won with false promises.
In recognition of the importance of McShane & Wyner’s paper it was published as an edition discussion piece in Annals of Applied Statistics25 As well as the original paper 15 discussion papers were included in the edition.
Two salient points emerge from this discussion. The first is noted in McIntyre and McKitrick’s comment where they say:
McShane & Wyner’s results are, in a sense, a best case as they assume that the quality of the data set is satisfactory [page 4]
In fact, as noted, the data was not satisfactory. The significance of this is that the ‘science’ of the hockeystick is the data; the data is the proxy for the climatic processes which are analysed in McShane & Wyner’s statistical overview.
This statistical analysis is the second point and it is in this respect that McShane & Wyner are unassailable because they have anticipated every complaint and objection to their critique of Mann’s statistical justification for the hockeystick. As this stage therefore their view on the hockeystick is definitive.
6 McKitrick, McIntyre, Herman26 – The hot spot is really missing
Figure 7. Based on Figures 2 and 3, page 13 of McKitrick et al.
If the IPCC models are right about the feedbacks, we would see a hot spot 10km above the tropics. Global warming theory says this should happen because more water will have been evaporated to this part of the atmosphere and would have caused rapid warming. Observations as shown in Figure 7 contradict this . Thus the main, most powerful factor in the climate models turns out to not match the real world.
Douglass et al 27 pointed out the glaring discrepancy of the missing hot spot in 2007. However Douglass et al did not adequately distinguish model variability in terms of single model or ensemble model outputs. Nor did Douglass et al adjust the data for autocorrelation which meant the data did not have satisfactory confidence levels or error bars.
As a result Santer et al [2008] 28 claimed Douglass got it wrong, and that the data and the models did agree. But Santer et al used a truncated set of data ending in 1999 to achieve the model and data correlation.
Christy et al [2010] 29 responded to Santer et al by developing a scaling ratio comparing the atmospheric trend to the surface trend. Christy et al showed the models predicted a scaling ratio of 1.4 ±0.8 [i.e. the atmosphere should warm 40% faster than the surface]. In reality the observations showed a scaling ratio of 0.8 ± 0.3 [i.e. the atmosphere was not warming as fast as the surface].
McKitrick et al [2010] also use the extended data and addressed the data adjustment issues but used a greater range of statistical analysis. They found that the model predictions are about 4 times higher and outside the error bars of the weather balloons and satellites measurements [see Figure 7].
McKitrick et al’s findings have been replicated by Fu et al 30 who also find a discrepancy between the models and observations about Troposphere warming, although not to the same extent as McKitrick et al do. However, in a follow-up paper, McKitrick 31 not only confirms that the predictions of warming by the models have been exaggerated but also shows the small amount of recent warming was due to a natural climate shift in 1977. This climate shift has been noted by many other researchers 32 and means global warming is playing an even smaller role then predicted by the models.As noted in section 4, the absence of a tropical hot spot vindicates Miskolczi because either the optical depth is not changing or, if it is, it means that extra water vapor and CO2, which would change the optical depth, are not heating in the way predicted by AGW.
7 Anagnostopoulos, G. G., Koutsoyiannis, D., Christofides, A., Efstratiadis, A. & Mamassis, N. The only thing certain is the models are wrong.
If McKitrick et al shows that the IPCC global computer models can’t model the present and therefore the future, Professor Demetrius Koutsoyiannis and his team show those models can’t even model the past.
Koutsoyiannis is one of the world’s leading hydrologists and an expert on Hurst and stochastic effects. Hurst or Long Term Persistence refers to the uncertainty and random qualities present in all complex natural systems. Koutsoyiannis argues that global warming modeling does not take into account this uncertainty.
In his 2008 paper Koutsoyiannis33 compared the model predictions from 1990 to 2008 and whether those models could retrospectively match the actual temperature over the past 100 years. This test of retrospectivity is called hindcasting. If a model has valid assumptions about the climatic effect of variables such as greenhouse gases, particularly CO2, then the model should be able to match past known data.
Koutsoyiannis’s 2008 paper has not had a peer reviewed rebuttal but was subject to a critique at Real Climate by Gavin Schmidt.34 Schmidt’s criticism was 4-fold; that Koutsoyiannis uses a regional comparison, few models, real temperatures not anomalies and too short a time period.Each of Schmidt’s criticisms was either wrong or anticipated by Koutsoyiannis. The period from 1990-2008 was the period in which IPCC modeling had occurred; the IPCC had argued that regional effects from global warming would occur; model ensembles were used by Koutsoyiannis; and since the full 100 year temperature and rainfall data was used in intra-annual and 30 year periods by Koutsoyiannis anomalies were irrelevant.
In 2008 Koutsoyiannis found that while the models had some success with the monthly data all the models were “irrelevant with reality” at the 30 year climate scale.
Koutsoyiannis’s 201035 paper “is a continuation and expansion of Koutsoyiannis 2008”. The differences are that (a) Koutsoyiannis 2008 had tested only eight points, whereas 2010 tests 55 points for each variable; (b) 2010 examines more variables in addition to mean temperature and precipitation; and (c) 2010 compares at a large scale in addition to point scale. The large, continental scale in this case is the contiguous US.
Again Koutsoyiannis 2010 found that the models did not hindcast successfully with real data from all the 55 world regions not matching what the models produced. The models were even worse in hindcasting against the real data for the US continent.
So that is 3 strikes for global warming models; they could not predict the future in 1990; they cannot predict the present and they could not replicate or match the past.
Conclusion
The global warming models amplify CO2’s effect by 3 – 7 fold, but no matter how you measure it [outgoing long wave radiation, cloud changes, optical depth, historical temperatures, vertical heating patterns in the atmosphere] the real measurements contradict the models and their assumptions about the feedbacks appear to be unconnected with real data. It follows that the global warming predictions about climate sensitivity to a doubling of CO2 are exaggerated by at least 3C.
Figure 8 Climate Sensitivity Comparison
The Hansen36 point of 1.2ºC in Figure 8 is a non-feedback calculation for the temperature increase from a doubling of CO2. While that non-feedback figure is essentially meaningless in the real world it is a convenient half-way house between the climate sensitivity estimates of the IPCC and the models which assume positive feedback and the empirical measurements of the papers discussed in this article which consider the actual measured feedbacks to increases in CO2.
The climate sensitivity estimates of the discussed papers establish two points which are fundamentally opposite to global warming. The first is that a large portion of the temperature response to 2X CO2 has already occurred. CO2 atmospheric concentrations have risen approximately 40% since 1900. Any temperature increase due to the increase in CO2 during this period would have already occurred.
The second point and as a corollary to the first is that there is no delay or lag in temperature response as a proxy for climate sensitivity. The IPCC makes a distinction between transient climate sensitivity and equilibrium climate sensitivity with transient climate sensitivity being less and on a shorter term than equilibrium sensitivity [see AR4, WG 1, TS.6.4.2]. These papers strongly suggest that there is no such distinction between transient and equilibrium sensitivity and that any CO2 temperature response is not delayed. This aspect of climate sensitivity has been independently confirmed in the Beenstock and Reingewertz analysis.37 Beenstock finds that any effect CO2 increase has on temperature is temporary and not related to the absolute level of CO2.
The global warming predictions are contradicted by past, present and future data. Feynman’s maxim applies and the vast funding which is now being directed to ‘solving’ global warming should be redirected to hypothesis which are consistent with empirical data and confirmed by observable evidence.
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