Positive cloud feedback is the key to Climate Alarmism, but the science behind it is questionable.
Note how the alarmists cannot respond to this important issue, other than with insane rants and
conspiracies.

http://www.drroyspencer.com/2009/12/...limate-system/

December 6, 2009, 08:19:36 | Roy W. Spencer, Ph. D.

In a little over a week I will be giving an invited paper at the Fall meeting of the American
Geophysical Union (AGU) in San Francisco, in a special session devoted to feedbacks in the climate
system. If you don't already know, feedbacks are what will determine whether anthropogenic global
warming is strong or weak, with cloud feedbacks being the most uncertain of all.

In the 12 minutes I have for my presentation, I hope to convince as many scientists as possible the
futility of previous attempts to estimate cloud feedbacks in the climate system. And unless we can
measure cloud feedbacks in nature, we can not test the feedbacks operating in computerized climate
models.

WHAT ARE FEEDBACKS?

To review, the main feedback issue is this: In response to the small direct warming effect of more
CO2 in the atmosphere, will clouds change in ways that amplify the warming (e.g. a cloud reduction
letting more sunlight in, which would be a positive feedback), or decrease the warming (e.g. a
cloud increase causing less sunlight to be absorbed by the Earth, which would be a negative
feedback)?

In the former case, we could be heading for a global warming catastrophe. In the latter case,
manmade global warming might be barely measurable (and previous warming would be mostly the result
of some natural cause). All climate models tracked by the IPCC now have positive cloud feedbacks,
by varying amounts, which partly explains why the IPCC expects anthropogenic global warming to be
so strong.

Obviously, we need to know what feedbacks operate in the climate system.

ESTIMATING FEEDBACKS: AN UNSOLVED PROBLEM

I am now quite convinced that most, if not all, previous estimates of feedback from our satellite
observations of natural climate variability are in error. Furthermore, this error is usually in the
direction of positive feedback, which will then give the illusion of a 'sensitive' climate system.
More on that later.

The goal seems simple enough: to measure cloud feedbacks, we need to determine how much clouds
change in response to a temperature change. But most researchers do not realize that this is not
possible without accounting for causation in the opposite direction, i.e., the extent to which
temperature changes are a response to cloud changes.

As I will demonstrate in my AGU talk on December 16, for all practical purposes it is not possible
(at least not yet) to measure cloud feedbacks because the two directions of causation are
intermingled in nature. As a result, it is not possible with current methods to measure feedbacks
in response to a radiative forcing event such as a change in cloud cover, or even a major volcanic
eruption, such as that from the 1991 eruption of Mt. Pinatubo.

The reason is that the size of the radiative forcing of a temperature change overwhelms the size of
the radiative feedback upon that temperature change, and our satellite measurements can not tell
the difference. There are only two special situations where it can be done: (1) the theoretical
case of an instantaneously imposed, and then constant amount of radiative forcing.which never
happens in the real world; and (2) the real world case where temperature changes are caused
non-radiatively. While I will not go into the evidence here, satellite observations suggest that
cloud feedbacks in the latter case are strongly negative.

Now, if you have an accurate estimate of the radiative forcing of temperature change, accurate
estimates of radiative feedback can be made. But we do not have good estimates of this forcing
during natural climate variations. Only in climate model simulations where a known amount of
radiative forcing is imposed upon the model can this be done. (In another method, if you try to
estimate feedback by measuring how fast the ocean responds, you also run into problems because your
answer depends upon how fast and how deep in the ocean you assume the temperature change will
extend.)

EXAMPLE 1: FEEDBACKS FROM THE CHANGE IN SEASONS

Once one realizes that clouds causing a temperature change (forcing) corrupts our estimates of
temperature causing a cloud change (feedback), it becomes apparent that many of the previous
attempts to estimate feedback will not work.

For instance, many researchers think that you can estimate feedbacks from the seasonal cycle in
average solar illumination of the Earth and the resulting temperature response. There is about a 7%
peak-to-peak variation in the amount of solar energy reaching the Earth during the year, with a
maximum occurring in March and September, and the minimum in June. So, one would think we could
measure by how much this change in solar heating causes a change in temperature.

The trouble is that global circulation patterns also change dramatically with the seasons, mostly
due to the large difference in land masses between the Northern and Southern Hemispheres. Since
cloud formation is affected by a variety of circulation induced effects (fronts, temperature
inversions, etc.), the cloud cover and thus the natural shading of the Earth by clouds also changes
with the seasons, through these seasonal circulation changes.

These non-temperature effects on cloud cover will confound the estimation of feedbacks, because
their magnitude is considerably larger than the magnitude of the feedbacks. If the Earth was 100%
covered by ocean that had a constant depth everywhere, then it might be possible to estimate
feedbacks in this way.but not in the real world.

EXAMPLE 2: FEEDBACKS FROM EL NINO & LA NINA

Researchers have also made feedback estimates from the anomalously warm conditions that exist
during El Nino, and the cool conditions during La Nina. But this runs into a similar problem as
estimating feedbacks from the change in seasons: there are substantial variations in global
circulation patterns between El Nino and La Nina, especially in the tropics. These circulation
changes can induce cloud changes - wholly apart from temperature-induced changes - and there is no
known way to separate the circulation-induced cloud changes (forcing) from the feedback-induced
changes.

THE ERRORS WHICH RESULT FROM PREVIOUS FEEDBACK ESTIMATES

So, how do these problems impact our estimates of feedback? Except under certain circumstances,
they will always cause a bias toward positive feedback. The reason is that radiative forcing and
radiative feedback always work in opposition to each other. (Here I am speaking of the net feedback
parameter, which also contains the increase in loss of infrared radiation by the Earth in direct
response to warming).

Since our satellites measure the two effects combined, if you assume only feedback is being
measured when both feedback and forcing are occurring, then you will underestimate the feedback
parameter, which is a bias in the direction of positive feedback.

THE IMPACT ON CLIMATE MODEL VALIDATION

I can predict that the climate modelers will claim that we really do not need to know the direction
of causation.we can just measure the temperature/cloud relationships in nature, and then adjust the
models until they produce the same temperature/cloud relationships.

While this might sound reasonable, it turns out that the radiative signature of forcing is much
larger than that of feedback. As a result, one can get pretty good agreement between models and
observations even when the model feedbacks are greatly in error. Another way of saying this is that
you can get good agreement between the model behavior and observations whether the cloud feedbacks
are positive OR negative. This is another fact I will be demonstrating on December 16.

WHERE DO WE GO FROM HERE?

My first task is to convince both observationalists and modelers that much of what they previously
believed about atmospheric feedbacks operating in the real world can be tossed out the window.
Obviously, this will be no small task when so many climate experts assume that nothing important
could have been overlooked after 20 years and billions of dollars of climate research.

But even if I can get a number of mainstream climate scientists to understand that we still do not
know whether cloud feedbacks are positive or negative, it is not obvious how to fix the problem. As
I suggested a couple of blog postings ago, maybe we should quit trying to test whether a climate
model that produces 3 deg. C of warming in response to a doubling of carbon dioxide is "true", and
instead test to see if we can falsify a climate model which only produces 0.5 deg. C of warming. As
someone recently pointed out in an email to me, a climate model IS a hypothesis, and in science a
hypothesis can only be falsified - not proved true.

From what I have seen from my analysis of output from 18 of the IPCC's climate models, I'll bet
that we can not falsify such a model with our current observations of the climate system. I suspect
that the climate modeling groups have only publicized models that produce the amount of warming
they believe "looks about right", or "looks reasonable". Through group-think (or maybe the
political leanings of, and pressure from, the IPCC leadership?), they might well have tossed out
any model experiments which produced very little warming.

In any event, I believe that the scientific community's confidence that climate change is now
mostly human-caused is seriously misplaced. It is time for an independent review of climate
modeling, with experts from other physical (and even engineering) disciplines where computer models
are widely used. The importance of the issue demands nothing less.

Furthermore, the computer codes for the climate models now being used by the IPCC should be made
available to other researchers for independent testing and experimentation. The Data Quality Act
for U.S.-supported models already requires this, but this law is being largely ignored.

As a (simple) modeler and computer programmer myself, I know that the modeling groups will protest
that the models are far too complex and finely tuned to let amateurs play with them. But that's
part of the problem. If the models are that complex and fragile, should we be basing multi-trillion
dollar policy decisions on them?