Sentences with phrase «feedback sensitivity parameter»

It's also dependent on the magnitude of the no - feedback sensitivity parameter (call it λo to be consistent with several literature sources).

Not exact matches

Climate is not different, as can be seen in the fact that a broad range of cloud feedbacks (compensated by other parameters...) or a range of combined aerosol / CO2 sensitivities is able to fit the temperature of the past century.

So the reference system climate sensitivity parameter is based on a negative feedback due to Stefan's law.

New paper mixing «climate feedback parameter» with climate sensitivity... «climate feedback parameter was estimated to 5.5 ± 0.6 W m − 2 K − 1» «Another issue to be considered in future work should be that the large value of the climate feedback parameter according to this work disagrees with much of the literature on climate sensitivity (Knutti and Hegerl, 2008; Randall et al., 2007; Huber et al., 2011).

As Roe says in the paper «However, it should be borne in mind that, in so choosing, the feedback factor becomes dependent on the reference - system sensitivity parameter».

They find a climate feedback parameter of 2.3 ± 1.4 W m — 2 °C — 1, which corresponds to a 5 to 95 % ECS range of 1.0 °C to 4.1 °C if using a prior distribution that puts more emphasis on lower sensitivities as discussed above, and a wider range if the prior distribution is reformulated so that it is uniform in sensitivity (Table 9.3).

Some of these papers also used other priors for climate sensitivity as alternatives, typically either informative «expert» priors, priors uniform in the climate feedback parameter (1 / S) or in one case a uniform in TCR prior.

These models all suggest potentially serious limitations for this kind of study: UVic does not simulate the atmospheric feedbacks that determine climate sensitivity in more realistic models, but rather fixes the atmospheric part of the climate sensitivity as a prescribed model parameter (surface albedo, however, is internally computed).

Climate is not different, as can be seen in the fact that a broad range of cloud feedbacks (compensated by other parameters...) or a range of combined aerosol / CO2 sensitivities is able to fit the temperature of the past century.

The climate feedback parameter is also defined in the IPCC glossary, and equation (1) is just an algebraic transformation of the mathematical definition of the climate sensitivity parameter given there.

Further to earlier post, the attached curve shows various estimates of (2xCO2) climate sensitivity plotted against the feedback parameter.

The different curves are generated by varying the feedback parameter (climate sensitivity) in the EBM.

To obtain a likelihood function by estimating the climate feedback parameter and then to present it as a likelihood function in climate sensitivity, a reciprocal parameter, alongside other likelihoods that may have been derived in the sensitivity parameter space, seems to me misleading.

One empirical analysis of the type of F+G 06 does not tell that the climate feedback parameter Y is 2.3 ± 1.4 W m ^ -2 K ^ -1 with 95 % certaintyor that the equilibrium climate sensitivity is in the corresponding range 1.0 — 4.1 K. Those limits are obtained only, when the additional assumption of uniform prior in Y is made.

The Figure 9.20 climate sensitivity PDF that is effectively based on a uniform prior in the climate feedback parameter Y is that for Gregory 02.

The egregious and misleading stuff from the warmists IMO consists of a) overstating the quality of the physics in their models and the confidence we should have that they are correct; b) treating the ad hoc parameter of «feedback» or «sensitivity» as something they can set on heuristic grounds, and then optimizing the other parameters of their models around it.

There is also doubt about the value of the Planck climate - sensitivity parameter, which also can not be measured but is crucial because not only the original warming caused by CO2 before feedbacks but also, separately, the feedbacks themselves are dependent upon it.

The lack of a unique natural measure in the space of continuous parameters like climate sensitivity S or feedback strength Y or any of the infinite number of equivalent functions is an essential problem with the present amount of empirical data.

Moreover the recent decline of the yearly increments d (CO2) / dt acknowledged by Francey et al (2013)(figure 17 - F) and even by James Hansen who say that the Chinese coal emissions have been immensely beneficial to the plants that are now bigger grow faster and eat more CO2 due to the fertilisation of the air (references in note 19) cast some doubts on those compartment models with many adjustable parameters, models proved to be blatantly wrong by observations as said very politely by Wang et al.: (Xuhui Wang et al: A two-fold increase of carbon cycle sensitivity to tropical temperature variations, Nature, 2014) «Thus, the problems present models have in reproducing the observed response of the carbon cycle to climate variability on interannual timescales may call into question their ability to predict the future evolution of the carbon cycle and its feedbacks to climate»

For the «no - feedback climate sensitivity» no empirical evidence can ever be given because the concept is an artificial theoretical parameter by construction.

[*] You had said: «is based purely on observational evidence, with no dependence on any climate model simulations... to obtain a direct measure of the overall climate response or feedback parameter... Measuring radiative flux imbalances provides a direct measure of Y, and hence of S, unlike other ways of diagnosing climate sensitivity.»

Some people may prefer to say that no - feedback climate sensitivity is estimated, I used calculated, as that fits well with the fact that it's a value defined true some formulas rather than by specifying a real physical parameter to be estimated.

Yes, I agree that the no - feedback sensitivity to a doubling of CO2 is a theoretically calculated parameter that can never be measured in the real world.

Accordingly, the forcing estimation method relies upon a model exhibiting a fairly linear climate response, and hence having a climate feedback parameter (and an effective climate sensitivity) that does not vary with time (in addition to having a temperature response that is proportional to forcing).

Using feedback parameters from Fig. 8.14, it can be estimated that in the presence of water vapor, lapse rate and surface albedo feedbacks, but in the absence of cloud feedbacks, current GCMs would predict a climate sensitivity (± 1 standard deviation) of roughly 1.9 °C ± 0.15 °C (ignoring spread from radiative forcing differences).

Some of these papers also used other priors for climate sensitivity as alternatives, typically either informative «expert» priors, priors uniform in the climate feedback parameter (1 / S) or in one case a uniform in TCR prior.

ΔTλ is, at its simplest, the product of three factors: the sum ΔF2x of all anthropogenic - era radiativeforcingsat CO2 doubling; the base or «no - feedbacks» climate sensitivity parameter κ; and the feedback multiplier f, such that the final or «with - feedbacks» climate sensitivity parameter λ = κf.

[20] Gregory, J. M., and T. Andrews, 2016: Variation in climate sensitivity and feedback parameters during the historical period.

A forcing dF is input by multiplication to the final or «with - feedbacks» climate sensitivity parameter λ = κf, yielding the output dT = dFλ = dFκf.

The base climate sensitivity parameter κis the most influential of the three factors of ΔTλ: for the final or «with - feedbacks» climate sensitivity parameter λ is the product of κand the feedback factor f, which is itselfdependent not only on the sum b of all climate - relevant temperature feedbacks but also on κ.Yet κ has received limited attention in the literature.

[8] I estimate GISS - E2 - R's effective climate sensitivity applicable to the historical period as 1.9 °C and its ERF F2xCO2 as 4.5 Wm − 2, implying a climate feedback parameter of 2.37 Wm − 2 K − 1, based on a standard Gregory plot regression of (ΔF − ΔN) on ΔT for 35 years following an abrupt quadrupling of CO2 concentration.

I don't know precisely what Wyant's values for other feedbacks are, but he explicitly gives us his sensitivity parameter: lambda = 0.41 K / Wm -2, which gives us a climate sensitivity of 1.5 K. And anyway, in later papers, Wyant argues that SPs grossly exaggerate the negative cloud feedback.