On Climate Sensitivity, Models, etc.

[Itscoldinhere]

Reproduced here again (it was on the old forum...) is my essay on Climate Response, for your enjoyment. The basic point is-if one looks at the latest research, catastrophic CO2 warming seems highly unlikely (although some warming would probably still happen) thus eliminating the entire impetus for "action" on AGW.

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.

[Itscoldinhere]

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[Derek]

;D Thank you Itscoldinhere  for posting your essay here.
This is one of the articles I miss from the old forum,
I hope many other contributors at the old forum follow your example.

[Sunsettommy]

Glad to see you here Itscoldinhere.

I thought CO2 follows temperature change by 6-9 months?

If so,what warming could it add when it is always lagging?

[Itscoldinhere]

[quote author=sunsettommy link=topic=108.msg699#msg699 date=1248097043]
Glad to see you here Itscoldinhere.

I thought CO2 follows temperature change by 6-9 months?

If so,what warming could it add when it is always lagging?
[/quote]

Well, it would then act as a positive feedback (assuming that the forcing calculations are correct, which some would dispute ) The main point is that CO2 does not cause much warming because the system is robust to perturbation, that is, dominated by negative feedbacks which act to resist to much change.

I had something on CO2 lags somewhere, so maybe I'll dig that out.

[blouis79]

Waterloo complained about the misuse of the label climate "sensitivity".
http://climateaudit.org/2010/01/18/curry...ent-216675

IPCC started with "CO2 doubling climate sensitivity", which is fallacious and misleading.

Now IPCC has (AR4 Ch2) "climate sensitivity parameter" for computing surface temperature change from radiative forcing.
(ΔTs): ΔTs = λRF, where λ is the climate sensitivity parameter

What they really mean is something like λ = "surface temperature radiative forcing index".

What is missing from that is that some effects cause negative radiative forcing. The net long-term forcing effect of a stable system should be zero. The "surface temperature radiative forcing index" should be able to be robustly derived from analysing mountains of daily data. The arguments about the exact number for "climate sensitivity" may reflect unaccounted negative feedback effects over the long term.

IPCC AR4 8.6.3.2 Clouds

Quote:By reflecting solar radiation back to space (the albedo
effect of clouds) and by trapping infrared radiation emitted by
the surface and the lower troposphere (the greenhouse effect
of clouds), clouds exert two competing effects on the Earth’s
radiation budget.
[...] In response to
global warming, the cooling effect of clouds on climate might be
enhanced or weakened, thereby producing a radiative feedback
to climate warming (Randall et al., 2006; NRC, 2003; Zhang,
2004; Stephens, 2005; Bony et al., 2006).

In many climate models, details in the representation of
clouds can substantially affect the model estimates of cloud
feedback and climate sensitivity (e.g., Senior and Mitchell,
1993; Le Treut et al., 1994; Yao and Del Genio, 2002; Zhang,
2004; Stainforth et al., 2005; Yokohata et al., 2005). Moreover,
the spread of climate sensitivity estimates among current
models arises primarily from inter-model differences in cloud
feedbacks (Colman, 2003a; Soden and Held, 2006; Webb et al.,
2006; Section 8.6.2, Figure 8.14). Therefore, cloud feedbacks
remain the largest source of uncertainty in climate sensitivity
estimates.

George White has analysed the climate system using annual seasonal cycles. demonstrating negative feedbacks.
http://www.palisad.com/co2/eb/eb.html
This makes a lot of sense to me. I can't see why they can't model daily cycles either. (I'm not sure that his fundamental assumption of "conservation of energy" on earth is correct though.)
http://www.palisad.com/co2/slides/img56.html
....but his site is an interesting read
http://www.palisad.com/co2/

The climate modelling community maintain that they have to use complex computer models because they can't do "normal" science experiments to test their hypotheses. I think that's an enormous cop out.

Nature does experiments every day including ranges of radiative forcing between day and night and ranges of temperature way beyond the mean global temperature. Every day of weather provides a huge amount of data which can be used for testing model behaviour.

Even IPCC scientists said that climate models should be exercised on testing weather.
http://environmentalresearchweb.org/cws/...nion/35820

Quote:• Climate models need to be exercised for weather prediction; there are necessary but not sufficient things that can best be tested in this framework, which is just beginning to be exploited.

but warmists disagree - "climate is simple, weather is complex".
http://www.skepticalscience.com/weather-...ctions.htm

What White has enumerated is consistent with what makes intuitive sense – ie negative feedbacks exist and are significant. It was interesting reading IPCC AR4 Ch8 on climate models. AR4,(p591): “Most AOGCMs no longer use flux adjustments, which were previously required to maintain a stable climate.”

This means that the climate models lack sufficient modeling of negative feedbacks that stablize the real climate on earth. The fact that flux adjustments are no longer required reflects that the models are improving and that further modeling of negative feedback effects are likely to further stabilize the models.

IPCC AR4 Ch 8:
8.6.3.2.4 Conclusion on cloud feedbacks

Quote:Despite some advances in the understanding of the physical
processes that control the cloud response to climate change
and in the evaluation of some components of cloud feedbacks
in current models, it is not yet possible to assess which of
the model estimates of cloud feedback is the most reliable.
However, progress has been made in the identification of the
cloud types, the dynamical regimes and the regions of the globe
responsible for the large spread of cloud feedback estimates
among current models. This is likely to foster more specifi c
observational analyses and model evaluations that will improve
future assessments of climate change cloud feedbacks.

I’d like to see simple grey body climate model at top of atmosphere with positive and negative feedbacks and responses to cosmic rays, solar activity and geothermal activity.

All the attempts to model what goes on inside the grey box are inaccurate (still getting better with every iteration) and are a major distraction to the reality on earth – poor input data, urban/airport heat islands, 1200km radius temperature averaging and a relatively stable climate.

IPCC knows the models have not been tested appropriately. IPCC AR4 8.6.4 How to Assess Our Relative Confidence in Feedbacks Simulated by Different Models?

Quote:A number of diagnostic tests have been proposed since the
TAR (see Section 8.6.3), but few of them have been applied to
a majority of the models currently in use. Moreover, it is not yet
clear which tests are critical for constraining future projections.
Consequently, a set of model metrics that might be used to
narrow the range of plausible climate change feedbacks and
climate sensitivity has yet to be developed.

The biosphere response to warming appears to be completely wrong. Climate models assume the biosphere carbon sink *reduces* rather than increases in response to warming. IPCC AR4 Ch8 8.2.3.1 p605: “Friedlingstein et al. (2006) found that in all models examined, the sink decreases in the future as the climate warms.” and “However, it is not clear how well current climate models can capture the impact of future warming on the terrestrial carbon balance. A systematic evaluation of AOGCMs with the carbon cycle represented would help increase confidence in the contribution of the terrestrial surface resulting from future warming.”

I thought it was well established that plants grow better with natural increases in CO2, increasing the carbon sink.

Until climate modellers can model a stable climate system, there will be no confidence that models reflect the real world.

(http://climateprediction.net/ is interesting.)

[Sunsettommy]

(07-20-2009 10:46 AM)Itscoldinhere Wrote:  [quote author=sunsettommy link=topic=108.msg699#msg699 date=1248097043]
Glad to see you here Itscoldinhere.

I thought CO2 follows temperature change by 6-9 months?

If so,what warming could it add when it is always lagging?

Itscoldinhere:

Quote:Well, it would then act as a positive feedback (assuming that the forcing calculations are correct, which some would dispute ) The main point is that CO2 does not cause much warming because the system is robust to perturbation, that is, dominated by negative feedbacks which act to resist to much change.

I had something on CO2 lags somewhere, so maybe I'll dig that out.

There have been discussions on the 6-9 month lag at WUWT and elsewhere,that showed the obvious lag.Since we know there never has been a run away warming trend in the past,the short lag must not be causing it.

I see the role of Water Vapor as being a Negative feedback because of it's enormous capacity to carry upward from the surface the heat energy,and release it in the upper atmosphere.CO2 can not do that because it has no "carry the heat upward" capacity,it simply absorbs and release individual IR photons,creating a slight delay.

Yes since there is so little OUTGOING IR being absorbed by CO2,it has no capability to throw the climatic system out place enough for the effect to be positively measured.

[blouis79]

IPCC AR4 references above should be qualified as IPCC AR4 WG1 ("The Physical Science Basis")

[blouis79]

IGBP report 58 is a bit more emphatic on aerosols and clouds - p22 of report
http://www.igbp.net/page.php?pid=222

Quote:SHORTCOMING IDENTIFIED in ASSESSABLE MATERIAL FOR
IPCC AR4:
Uncertainty in aerosol-cloud interaction and associated indirect
radiative effects
SURVEY RESPONSES:
Major reasons:
• Lack of understanding of fundamental processes
• Insufficient model parameterizations
• Lack of observations and data quality
Negative consequences:
• Large uncertainty in GCMs and in estimating climate sensitivity
• Uncertainty in prediction of regional precipitation
Possible solutions:
• Ground-based, balloon-based and aircraft-borne column
measurements, and collocating measures of clouds, aerosols
and soil moisture from satellites such as CALIPSO
• Improved process research (eventually including ice clouds), on
a more global scale than GEWEX does now; high-resolution
regional modelling (e.g., in low shallow clouds) and better
representation in GCMs
Linked issues:
• Volcanic forcing uncertainty, solar variability inadequately
addressed

Page 23 is similarly emphatic on clouds specifically:

Quote:SHORTCOMING IDENTIFIED in ASSESSABLE MATERIAL FOR
IPCC AR4:
Models differ considerably in their estimates of the strength of
different feedbacks in the climate system; the response of clouds
to global climate change is particularly uncertain
SURVEY RESPONSES:
Major reasons:
• Lack of understanding of fundamental processes
• Inadequate model parameterization of processes; model
resolution issues (e.g., when accommodating poorlyparameterized
small-scale processes)
• Response of tropical low clouds particularly uncertain
Negative consequences:
• Not understanding feedbacks a key problem of climate models
Possible solutions:
• Better observations of clouds, e.g., using CloudSat
• Constrain radiative forcing
• Link cloud-resolving models to AOGCM
• Improve parameterization of convection processes
• Reduce uncertainties in cloud feedbacks, e.g., through
collaborative efforts between cloud feedback model
intercomparison project (CFMIP) and the GEWEX cloud system
study (GCSS)
• Develop a proven set of model metrics (before including them in
a future IPCC assessment), e.g., through working groups,
comparing for feedbacks, to be validated from observations; use
perturbed parameter ensembles to indicate sensitivity and
spread feedbacks
Linked issue:
• What will be the use of model metrics, particularly with respect to
future climate change assessments?
• Identify where the issue of model performance is (1) scale, i.e.,
likely to be addressed by simply increasing resolution, (2)
parameterization, e.g., convective processes (3) more physical
process understanding/data is required before (2) can be
achieved, e.g., soil moisture and land use feedbacks

[Terry Oldberg]

The existence of a climate sensitivity is not a fact but rather is an expression of the discredited philosophical position which is called "mechanistic reductionism." Under the incarnation of this position in climatology, any increase in the concentration of a greenhouse gas "forces" the surface temperature to rise as the turning of a gear forces the adjoining gear to turn. The mechanism of the "forcing" is the so-called "greenhouse effect."