Why are observationally-based estimates of the cloud feedback more uncertain than model-based estimates?
Dessler (2010) quantified the cloud feedback by relating changes in outgoing radiation to surface temperature anomalies. He concluded that clouds trap 0.54 ± 0.74 watts of additional energy as the surface warms.
Dessler also compared his results to eight different models. All of these models had very small error bars in comparison with his findings. http://geotest.tamu.edu/userfiles/216/dessler10b.pdf
Why could this be?
bob3262011-06-04T23:50:22Z
Favorite Answer
Well, you must remember that with observations and models we're talking about two very different things. There's no reason for the uncertainties to be the same in both. Model based climate sensitivity estimates may be more certain than empirical based estimates, but that doesn't mean the models are overconfident or wrong as long as the error bars overlap. Models also predict a current energy imbalance of something like 0.65 +/- 0.15 W/m2 (I'd have to look at Hansen 2011 again for the exact figure), but current satellite based measurements aren't accurate to less than +/- several W/m2 and cannot rule out an imbalance of 0.
In the real world there is only one cloud feedback -- no error bars. Our cloud observational systems have limits in measuring a particular quantity due to instrumentation sensitivity and changing instrumentation, satellite viewing geometry, spatial coverage, and temporal resolution. Uncertainties in cloud modeling come from the differences in the way cloud microphysics/motions/processes, etc. are treated.
Large intermodel differences increase the ensemble uncertainty relative to uncertainty from individual models, however. Indeed, as Chem Flunky mentions, the cloud feedback is incredibly complex, with different mechanisms behind the formation of different cloud types in different latitudes and different altitudes, each of which have different radiative contributions. Most of these issues are subgrid scale processes which GCMs cannot resolve, confusing things further. Certain modeling techniques such as superparameterizations, in which each grid is replaced with a simplified cloud-resolving model, are improving things, but they issues as well and are computationally intensive. Marked improvement should be seen within the next decade.
The experimental data is sparse and hence the result has a lot of uncertainty. Dessler did the best he could with the available data. With more years of data, the error bars might shrink and observation will provide a better test of the models. If the error bars in 50 years are comparable to the model error bars, then the models include all of the major inputs. If the error bars remain large, then nature is more complex than the models.
For starters, your penis will proceed to advance for some greater years, till you're 18 or so. you're neither small nor large. Your length places you interior the accepted and fully common selection for somebody your age. those female you sext with are purely that, females. they have little or no life adventure, and are generally so heavily inspired by skill of the media, and so busy attempting to act cool that they think of the bigger the penis the greater effectual. in relatively certainty it rather is not lots length that makes intercourse enjoyable for a girl, however the girth of the penis (thickness). A vagina in basic terms has nerve endings contained in the decrease a million/third, and those nerves respond greater to a stretching sensation. a protracted penis could properly be fairly uncomfortable or perhaps painful, exceptionally specifically positions the place penetration would be deep, and the penis can finally end up bumping against the cervix. additionally, in my view, it rather is not the dimensions of the wand, however the wizard in the back of it. i could desire a shorter fatter penis as antagonistic to a protracted skinny one!
Perhaps the number of data points plays a role? Figure 2 shows quite a few more points for the model output than observed data, which might constrain the bounds better. I'm not terribly familiar with statistics (I really ought to become so), but I would think a trend wouldn't be as strong if it is based off of less data.
Cloud feedbacks are complicated, and we don't fully understand them yet.
And, frankly, it's entirely possible that those 8 different models were overconfident, or just wrong. I don't think anyone's claiming that we completely understand every nuance of the Earth's climate.