I have posted on this a zillion times over here, and most of you are up to speed on this, but I posted this for my Coyote Blog readers and thought it would be good to repost over here.
Take all the psuedo-quasi-scientific stuff you read in the media about global warming. Of all that mess, it turns out there is really only one scientific question that really matters on the topic of man-made global warming: Feedback.
While the climate models are complex, and the actual climate even, err, complexer, we can shortcut the reaction of global temperatures to CO2 to a single figure called climate sensitivity. How many degrees of warming should the world expect for each doubling of CO2 concentrations (the relationship is logarithmic, so that is why sensitivity is based on doublings, rather than absolute increases — an increase of CO2 from 280 to 290 ppm should have a higher impact on temperatures than the increase from, say, 380 to 390 ppm).
The IPCC reached a climate sensitivity to CO2 of about 3C per doubling. More popular (at least in the media) catastrophic forecasts range from 5C on up to about any number you can imagine, way past any range one might consider reasonable.
But here is the key fact — Most folks, including the IPCC, believe the warming sensitivity from CO2 alone (before feedbacks) is around 1C or a bit higher (arch-alarmist Michael Mann did the research the IPCC relied on for this figure). All the rest of the sensitivity between this 1C and 3C or 5C or whatever the forecast is comes from feedbacks (e.g. hotter weather melts ice, which causes less sunlight to be reflected, which warms the world more). Feedbacks, by the way can be negative as well, acting to reduce the warming effect. In fact, most feedbacks in our physical world are negative, but alarmist climate scientists tend to assume very high positive feedbacks.
What this means is that 70-80% or more of the warming in catastrophic warming forecasts comes from feedback, not CO2 acting alone. If it turns out that feedbacks are not wildly positive, or even are negative, then the climate sensitivity is 1C or less, and we likely will see little warming over the next century due to man.
This means that the only really important question in the manmade global warming debate is the sign and magnitude of feedbacks. And how much of this have you seen in the media? About zero? Nearly 100% of what you see in the media is not only so much bullshit (like whether global warming is causing the cold weather this year) but it is also irrelevant. Entirely tangential to the core question. Its all so much magician handwaving trying to hide what is going on, or in this case not going on, with the other hand.
To this end, Dr. Roy Spencer has a nice update. Parts are a bit dense, but the first half explains this feedback question in layman’s terms. The second half shows some attempts to quantify feedback. His message is basically that no one knows even the sign and much less the magnitude of feedback, but the empirical data we are starting to see (which has admitted flaws) points to negative rather than positive feedback, at least in the short term. His analysis looks at the change in radiative heat transfer in and out of the earth as measured by satellites around transient peaks in ocean temperatures (oceans are the world’s temperature flywheel — most of the Earth’s surface heat content is in the oceans).
Read it all, but this is an interesting note:
In fact, NO ONE HAS YET FOUND A WAY WITH OBSERVATIONAL DATA TO TEST CLIMATE MODEL SENSITIVITY. This means we have no idea which of the climate models projections are more likely to come true.
This dirty little secret of the climate modeling community is seldom mentioned outside the community. Don’t tell anyone I told you.
This is why climate researchers talk about probable ranges of climate sensitivity. Whatever that means!…there is no statistical probability involved with one-of-a-kind events like global warming!
There is HUGE uncertainty on this issue. And I will continue to contend that this uncertainty is a DIRECT RESULT of researchers not distinguishing between cause and effect when analyzing data.