I have just recently posted this on the forum at globalwarmingskeptics.info also.
Summary for Policy Makers: Recent research strongly suggests that the effect of human emissions of greenhouse gases on climate is smaller than climate models suggest. Because of this, it no longer makes sense to speak of an urgent need for action on global warming. Policy makers should act cautiously, and only take “no regrets” steps to climate mitigation/adaptation.
Abstract: The IPCC says that climate sensitivity is between 1.5 and 4.5 degrees Celsius for a doubling of CO2, suggesting potentially catastrophic warming if current emission trends continue. However, much recent research suggests that this range is much too high. Herein, I discuss how this discrepancy can be reconciled.
The UN’s Intergovernmental Panel on Climate Change and others have argued that the temperature response of the Earth’s climate to changes in radiative forcing likely falls within the range of about .4 to 1.2 degrees Celsius per Watt per meter squared (1.5 to 4.5 degrees Celsius for 2XCO2). Since these values are all greater than the theoretical grey body value of .3 degrees per W/m2 (1.2 2XCO2), this implies that the climate models with sensitivities in this range are dominated by positive feedbacks. Various lines of evidence have been cited as confirming this general range (Wigley et al. 2005, Hoffert and Covey 1992). On the other hand, others have cited observational evidence indicating that net climate feedbacks should be negative or near zero (Douglass et al. 2004, Douglass and Knox 2005, Douglass et al. 2006, Douglass and Christy 2008, Kärner 2002, Kärner 2005, Kärner 2007a, Kärner 2007b, Lindzen and Giannitsis 1998, Lindzen and Giannitsis 2002). How can this disparity be reconciled? The purpose of this paper is to propose a set of hypotheses which, when considered together, may eliminate this discrepancy.
First, we may wish to consider recent, industrial era climate change. The figure below shows the most recent official estimates of radiative forcings over this period.
Figure 1, Canonical estimates of industrial era climate forcings, IPCC AR4
It is claimed that a comparison between model output with these forcings as input and observed climate change obtains a good agreement, supporting model climate sensitivities. This is clearly absurd. I encourage you to do the math yourself and discover that by taking extreme negative or small values for each anthropogenic forcing, the net result is in fact negative. Clearly these forcings are too uncertain to support such a claim. However, it may be worth examining this argument further. As Lindzen (2007) notes, aerosol forcing is crucial to this argument, providing an effect to cancel what would otherwise be excessive greenhouse warming. However, as Anderson et al. (2003) note, aerosol forcing is extremely uncertain, and often an “inverse” method is employed to more easily calculate the forcing by tuning to optimize agreement between models and observations-however, as they also note, using such resulting agreement to test models creates a circular argument-indeed, according to Kiehl (2007) the sensitivity of a model that “backcasts” 20th century climate change is strongly correlated with the aerosol forcing input (as an aside, there is an over looked natural aerosol effect-that of dust blow of the Earth into the atmosphere by wind-recent studies (Foltz et al. 2008, Evan et al. 2009) have concluded that the effect of decreasing dust blown off the Sahara Desert over the Atlantic Ocean could have significantly contributed to the regional warming there). A recent consideration of aerosol climate forcing leads to the conclusion that climate sensitivity is between .29 and .49 degrees Celsius per W/m2 (1 to 1.8 2XCO2) (Chylek et al. 2007) and aerosols appear to be clearing out of the air (Mishchenko et al. 2007). It should also be noted that aerosols do not uniformly lead to cooling (Ramanathan et al. 2007). Other anthropogenic climate forcings may be underestimated, as Ramanathan and Carmichael (2008) report new estimates of climate forcing by black carbon soot place it at as much as 60% of the climate forcing of CO2 (much greater than the estimates in Figure 1). Estimates of forcing due to solar irradiance do not consider the effects of UV/Ozone interactions (Shindell et al. 1999) or effects of cosmic rays on clouds (Marsh and Svensmark 2000, Svensmark 2007, Svensmark et al. 2007) mainly because these remain somewhat theoretical, although Shaviv (2008) argues there is strong evidence that some strong amplifying mechanism exists, whether one of those suggested here or an unknown mechanism. However given the revelation that solar forcing may have been the dominant twentieth century climate forcing (Scafetta and West 2007), solar effects may need to be reexamined, especially if there have been positive trends in solar activity in the late twentieth century (Ahluwalia 1997, Willson and Mordivinov 2003, Scafetta and Wilson 2009) when warming is usually attributed to anthropogenic forcing. We shall return to the subject of solar forcing later. One may also question the assumption that models are accurately reproducing the climate systems natural internal variability (that is, we might suggest that warming has arisen at least partly or mainly from an internally generated oscillation of the climate system). It is quite clear that many aspects of observed climate change are not reproduced by models. For example, winter surface temperature changes in the Arctic over the last half century do not agree well with models, as shown below.
Figure 2, Winter surface temperature changes in the Arctic over the last half century. Source:
http://people.iarc.uaf.edu/~sakasofu/pd … _LIA_R.pdf
This failure may result from a failure to accurately represent internal oscillations, perhaps in this case the Arctic Oscillation. Interestingly, Graverson et al. (2008) argue that the vertical structure of Arctic summer warming matches the warming trends expected from the variability in the heat exchange between the low latitude and the high latitudes, rather than greenhouse warming. Circulation changes, then, may be precisely what is at work here (else where, Compo and Sardeshmukh 2008 found that continental warming could understood to be forced by SST changes, although they left what caused those changes open). Tsonsis et al. (2007) were able to explain a great deal of twentieth century climate variability statistically with various such indices. This is hardly the only failure of models on a regional scale; Koutsoyiannis et al. (2008) report numerous such instances. Some examples include the Central US warming “hole” (Kunkel et al. 2006), the fact that the California Valley has warmed much faster than the Sierras (Christy et al. 2006), decrease in the diurnal temperature range being greater in observations than predicted by models (Braganza et al. 2004), The sign of trends in sea level pressure in the Indian ocean (Copsey et al. 2006), and the long term cooling in the Southeastern US (John Christy, personal communication).
It also appears that models fail to reproduce the apparent magnitude of the Medieval Warm Period as found in various paleoclimate records (Alley 2000, Andreev et al. 2007, Dahl-Jensen et al. 1999, Ge et al. 2003, Goni et al. 2004, Holmgren et al. 2001, Keigwin 1996, Loehle and McCulloch 2008, Luckman and Wilson 2005, Lund and Curry 2006, Mangini et al. 2005, Newton et al. 2006, Pla and Catalan 2005, Rein et al. 2005, Richey et al. 2007, Tyson et al. 2000, Weckstrom et al. 2006, Zabenskie and Gajewski 2007). All of this appears to confirm suggestions by Cohn and Lins (2005) that natural climate variations could be quite large, making attribution of changes difficult. However it has been suggested that the general tendency of models to produce large tropical tropospheric warming compared to surface warming (Douglass et al. 2007) can be used to illustrate that the greenhouse effect has caused only a third of the warming (owing to the relatively low warming rates so far observed, as illustrated below.) (Lindzen 2007).
Figure 3, Observed (black) and predicted (22 models) tropical temperature trends by pressure level from 1979-2004. Data from Douglass et al. 2007. Note RAOBCORE is v1.2. The rational is explained here.
It may also be the case that land surface temperature trends are exaggerated due to land use changes and urbanization around thermometer sites, among other things (de Laat and Maurellis 2004, de Laat and Maurellis 2006, McKitrick and Michaels 2004, McKitrick and Michaels 2007, Pielke et al. 2007) and there is no reason to assume that the sea surface records are really any better (Singer 2005). If this is the case, then even treatments of climate sensitivity like that of Schwartz (2007) may be biased, although it is not obvious by how much. Whether the use of a global mean temperature is appropriate at all may even be questioned (Essex et al. 2007) but it is not the aim of this paper to delve into this issue.
Another commonly cited line of evidence involves paleoclimate. Hoffert and Covey (1992) argue that climate change since the last ice age is of a magnitude comparable to that expected fom the known forcings and the model sensitivities. However, as Lindzen and Pan (1994) point out, Milankovitch forcing is not homogenous, and will alter equator to pole heat fluxes, which, in the presence of a strong negative feedback in the tropics, would lead to large mean temperature changes. At any rate, Chylek and Lohmann (2008) offer a new, lower estimate based upon the same climate change.
Tying this all together, it may seem that we have gotten an estimate of climate sensitivity which we may reasonably place at .4 degrees Celsius per W/m2 (1.5 2XCO2). Yet this is still not quite to the point that negative feedback is dominating. At this point, it is help to revisit solar forcing, particularly cosmic ray effects. Shaviv (2005) estimates that, considering cosmic ray effects would reduce older estimates (like that of Hoffert and Covey (1992)) down to between .26 and .44 degrees Celsius per W/m2 (.96 to 1.6 2XCO2) Combining this with other cited effects/recalculations would reduce our estimate at last below the gray body value. It is difficult to estimate exactly, however approximately .18 degrees per W/m2 (.6 2XCO2) would be my first estimate.
If that is in fact the correct sensitivity, why do models get the wrong value? Actually, it should hardly be surprising. Climate sensitivity ultimately depends greatly on how water vapor and clouds react to warming, but models have never been good at simulating clouds. Below is one image which illustrates this well, from Gates et al. (1999).
Figure 4, Models hindcast of total cloud cover by latitude and the observed distribution.
It is likely that this is the main error of models, although there could easily be significant errors in other feedbacks-Minschwaner and Dessler (2004) for instance, found that models overestimate the tropical water vapor feedback by 15%, while radiosonde reanalyses, for all there problems, show a possible negative water vapor feedback (Paltridge et al. 2009). But still, a really strong negative feedback appears necessary. One good candidate would be the “adaptive infrared iris” effect of Lindzen et al. (2001), further evidence for which has recently been found by Spencer et al. (2007). Indeed, the negative feedback estimated by Lindzen et al. (2001) was enough to reduce model estimates down to closer to the value estimated here. It is also worth noting that some attempts to assess climate feedback from observations may have a positive bias as a result of mixing cause and effect (Spencer and Braswell 2008). The controversy spawned by the iris findings spark many criticisms, which the originators responded (Lindzen et al. 2002, Lindzen and Hou 2002, Chou and Lindzen, 2002, Chou and Lindzen 2005). It should be noted that the results of this paper do not hinge on any of these feedbacks actually existing, but are based on considerations of paleoclimate and recent temperature variation and associated forcings.
All of this has important implications for the policy debate about what to do about human impacts on climate. If the sensitivity of models is way too high, then the predictions of catastrophe are no longer likely to come true. The urgency for action would then be reduced, and human climate impacts could be minimized in the most expedient way possible, minimizing negative economic costs of mitigation. Policy makers should therefore avoid taking actions they might regret based on increasingly unlikely catastrophic scenarios.
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