Dr. Roy Spencer has updated his paper and started a blog on his new web site. Be sure to see this blog post (get out your reading glasses first). The paper “Satellite and Climate Model Evidence Against Substantial Manmade Climate Change (supercedes “Has the Climate Sensitivity Holy Grail Been Found?") can be found here. Excerpts follow Roy’s photo.

Three IPCC climate models, recent NASA Aqua satellite data, and a simple 3-layer climate model are used together to demonstrate that the IPCC climate models are far too sensitive, resulting in their prediction of too much global warming in response to anthropogenic greenhouse gas emissions. The models’ high sensitivity is probably the result of a confusion between forcing and feedback (cause and effect) when researchers have interpreted cloud and temperature variations in the real climate system. (What follows is a brief summary of research we will be submitting to Journal of Climate in January 2009 for publication. I challenge any climate researcher to come up with an alternative explanation for the evidence presented below. I would love to hear. My e-mail address is at the bottom of the page.)

Since computerized climate models are the main source of concern over manmade global warming, it is imperative that they be tested against real measurements of the climate system. The amount of warming these models predict for the future in response to rising concentrations of carbon dioxide in the atmosphere is anywhere from moderate to catastrophic. Why is this?

It is well known that most of that warming is NOT due to the direct warming effect of the CO2 by itself, which is relatively weak. It is instead due to indirect effects (positive feedbacks) that amplify the small amount of direct warming from the CO2. The most important warmth-amplifying feedbacks in climate models are clouds and water vapor.

Cloud feedbacks are generally considered to be the most uncertain of feedbacks, although all twenty climate models tracked by the Intergovernmental Panel on Climate Change (IPCC) now suggest cloud feedbacks are positive (warmth-amplifying) rather than negative (warmth-reducing). The only question in the minds of most modelers is just how strong those positive feedbacks really are in nature. This article deals with how feedbacks are estimated from satellite observations of natural climate variability…and describes a critical error in interpretation which has been made in the process.

Instead of the currently popular practice of building immensely complex and expensive climate models and then making only simple comparisons to satellite data, I have done just the opposite: Examine the satellite data in great detail, and then build the simplest model that can explain the observed behavior of the climate system.

The resulting picture that emerges is of an IN-sensitive climate system, dominated by negative feedback. And it appears that the reason why most climate models are instead VERY sensitive is due to the illusion of a sensitive climate system that can arise when one is not careful about the physical interpretation of how clouds operate in terms of cause and effect (forcing and feedback).

There is nothing inherently wrong with a model-centric approach to climate researc as long as the modeler continues to use the observations to guide the model development over time. Unfortunately, as Richard Lindzen at MIT has pointed out, the fact that modelers use the term “model validation” rather than “model testing” belies their inherent preference of theory over observations. The allure of models is strong: they are clean, with well-defined equations and mathematical precision. Observations of the real climate system are dirty, incomplete, and prone to measurement error.

The comparisons modelers make between their models and satellite data are typically rather crude and cursory. They are not sufficiently detailed to really say anything of substance about feedbacks - in either the models or the satellite data - and yet it is the feedbacks that will determine how serious the manmade global warming problem will be.

And as I have tried to demonstrate here, the main reason for the current inadequacy of such methods of comparison between models and observations is the contaminating effect of clouds causing temperatures to change (forcing) when trying to estimate how temperatures cause clouds to change (feedback). This not a new issue, as it has been addressed by Forster and Gregory (2006, applied to satellite measurements) and Forster and Taylor (2006, applied to climate model output). I have merely demonstrated that the same contamination occurs from internal fluctuations in clouds in the climate system. Read full paper here.