The Great Global Warming Blunder: How Mother Nature Fooled the World’s Top Climate Scientists iby Roy Spencer was given to me for review by the publisher.
The trick Spencer says Mother Nature played on the world’s top climate scientists was to pull the cotton over their eyes. Cotton, I say, as in clouds. Spencer says other climatologists don’t understand clouds the way he does. Everybody has noticed that, at times, there have been fewer clouds hanging about. Spencer’s special understanding impels him to claim that fewer clouds cause the higher temperatures we have also seen. The other fellows insist that higher temperatures drove the clouds away. Who is right?
Let the battle commence!
We can’t just consider clouds, but must also investigate various other forces that might change the climate. However, there are nothing but minor skirmishes over forcing. All agree that, on average, more CO2, and other similar gases, pumped into the atmosphere means warmer weather. But how much warmer? If climate models are run at twice the pre-industrial levels of CO2, the direct warming effect is predicted to be only about 1 degree C. “And since atmospheric convection typically causes more warming at high altitudes than near the surface, the surface warming can amount to only 0.5 C.” Half a degree? A pittance! So why fret?
Because positive feedback might take that half degree and ramp it up into two, three, even four or more degrees at which point we’d face…well, we’d face something all right. Anybody paying attention to press reports might guess this something will be an environmental apocalypse, but never mind that. It’s feedback where the real fighting occurs.
Spencer spends a couple of chapters laying out the plan of his attack, first drawing the differences between forcing and feedback, writing for an audience who have had no experience in such matters. The examples are fine, but can be skipped by anybody who is looking for the heavy artillery, which is in Chapters 5 and 6.
Feedback and Forcing
All climate models - doing what they are designed to - predict the atmosphere will warm. But how much of the warming predicted by models have we seen so far? If anybody gives you a number which he swears to, don’t believe him. The manner and the places at which we measure temperature have changed and changed again, and are changing more even now. Even the weather satellites in “fixed” orbits have a nasty habit of wandering from their appointed paths. Turns out the uncertainty in the measurements from all these disparate sources is larger than the suspected change in temperature. Yet it is still the satellites from which we derive our most reliable data.
From satellites we can measure both temperature and cloud cover, and we can estimate the various forcings and feedbacks affecting the climate system. One possible positive feedback says that as the temperature warms, low-level clouds decrease, which in turn lets in more sunlight, which causes more warming, which…well, you get the idea. Is this feedback genuine? There have been observations of fewer clouds, but the feedback could have worked in a negative direction, too. Fewer clouds could have let in more sun, which caused heating which led to fewer clouds, and so on.
But how do researchers know that “warmer temperatures caused a decrease in cloud cover, rather than the decrease in cloud cover causing warmer temperatures?” They do not: it is merely assumed. If the feedback is positive, we might have some worrying to do; but if they feedback is negative, we’ll have to find another subject over which to fret.
Spencer and a colleague decided to check which direction the feedback worked by examining the data - and not relying on a model. Plotting the radiative energy imbalance against the observed temperature change is one way to estimate the direction and magnitude of feedback. But only just over seven years of reliable data exist from the CERES satellite, which is not a lot. This means our certainty, no matter what is discovered, cannot be high. Spencer does not emphasize this, but neither do the folks on the other side. Over-certainty is rampant in this field.
Figure 14 in the book shows that, very roughly, when the energy imbalance (due to forcings) is positive, the temperature increases; likewise when the imbalance is negative, temperature decreases. But this relationship is noisy. So noisy that in more than a third of cases when the imbalance is positive, temperaturedecreases, and when the imbalance is negative, temperatureincreases. It is from this very highly variable relationship that feedback is estimated.
Spencer re-examines his data and notes that “month-to-month line segments are preferentially aligned along a” different slope than the regression line fitted to the raw measurements. The line fitted to the raw measurements implies positive feedbacks are important. The line fitted to the month-to-month line segments say that negative feedbacks are.
This strategy is unusual, so I ran a simple experiment to investigate it. I first generated random points with the approximate normal distributions of the temperature change, and then simulated the regression line given in his picture with non-correlated residuals (with slope 2.5; the parameters chosen by eye; I stress the exact values do not matter). I then computed the ordinary regression line and also found how the “month-to-month line segments are preferentially aligned.” The simulated regression line - the true line in this case - indicates positive feedback. The month-to-month lines segments will have a larger slope, which indicates negative feedback.