Posted by Jeff Condon on May 18, 2011
After Willis Eschenbach’s post on the linearity of climate model results, I’ve realized there is a need to explain why some of us find that a significant conclusion. Nick Stokes made the correct point that with enough parameters you can fit equations to most anything. My reply was that if the parameters are all linear the fit is far less likely. A lot of this is Greek to English blog readers.I really did intend to quit blogging due to time constraints and I am avoiding necessary work but this is my relaxation time. Don’t tell my wife- she doesn’t read often. However, I cannot spend enough time to work through the equations of different aspects of models to demonstrate why I disagree with those who claim that these simple linear fits Willis demonstrated are expected or even that they should be expected. What I can do is provide a few directions for the interested and technically inclined such that others can work it out for themselves. Lets start with statements and answers. First, climate models are claimed to be based purely on physics. This is true except that our physics knowledge is limited requires a few assumptions.
Climate models/scientists are claimed by uninformed to agree with each other. This is demonstrably false.
Climate models demonstrate warming due to CO2. This is true.
Climate models are useless junk. This is false although GIGO applies.
My point in the amazing match to Willis’s fit was that climate model results are way way too linear. Nobody expects convection instability to react linearly to heating — at least to my knowledge. Nobody would expect ocean temps, aerosols, cloud formation, condensation, ocean currents, ice melt, to simply increase linearly to forcings — right?
Maybe they do but it is news to me.
So Nick stokes pointed out that I’m too broad in my statements calling models linear. He’s right, they aren’t linear. Linear being an equal percent increase in response to any given input. Yet the non-linear components are so damned small that the whole global climate model can be represented by a linear equation with a few terms and near zero error. If you have any math wits, that is something interesting. Nick knows this IMO but likes to work the crowd.
I’ll narrow this down with a few words from the CAM documentation. CAM is a fine climate model with mainstream focus and excellent documentation.
As an example here is a discussion of the parametrization of the non-convective cloud processes.
The intro is below:
The parametrization of non-convective cloud processes in CAM 3.0 is described in Rasch and Kristjánsson  and Zhang et al. . The original formulation is introduced in Rasch and Kristjánsson . Revisions to the parameterization to deal more realistically with the treatment of the condensation and evaporation under forcing by large scale processes and changing cloud fraction are described in Zhang et al. . The equations used in the formulation are discussed here. The papers contain a more thorough description of the formulation and a discussion of the impact on the model simulation.
The formulation for cloud condensate combines a representation for condensation and evaporation with a bulk microphysical parametrization closer to that used in cloud resolving models. The parametrization replaces the diagnosed liquid water path of CCM3 with evolution equations for two additional predicted variables: liquid and ice phase condensate. At one point during each time step, these are combined into a total condensate and partitioned according to temperature (as described in section 4.5.3), but elsewhere function as independent quantities. They are affected by both resolved (e.g. advective) and unresolved (e.g. convective, turbulent) processes. Condensate can evaporate back into the environment or be converted to a precipitating form depending upon its in-cloud value and the forcing by other atmospheric processes. The precipitate may be a mixture of rain and snow, and is treated in diagnostic form, i.e. its time derivative has been neglected.
The parametrization calculates the condensation rate more consistently with the change in fractional cloudiness and in-cloud condensate than the previous CCM3 formulation. Changes in water vapor and heat in a grid volume are treated consistently with changes to cloud fraction and in-cloud condensate. Condensate can form prior to the onset of grid-box saturation and can require a significant length of time to convert (via the cloud microphysics) to a precipitable form. Thus a substantially wider range of variation in condensate amount than in the CCM3 is possible.
The new parametrization adds significantly to the flexibility in the model and to the range of scientific problems that can be studied. This type of scheme is needed for quantitative treatment of scavenging of atmospheric trace constituents and cloud aqueous and surface chemistry. The addition of a more realistic condensate parametrization closely links the radiative properties of the clouds and their formation and dissipation. These processes must be treated for many problems of interest today (e.g. anthropogenic aerosol-climate interactions).
The parametrization has two components: 1) a macroscale component that describes the exchange of water substance between the condensate and the vapor phase and the associated temperature change arising from that phase change Zhang et al. ; and 2) a bulk microphysical component that controls the conversion from condensate to precipitate . These components are discussed in the following two sections.
I have bolded two sections of the discussion. Before continuing it took less than 5 minutes to find an example in CAM which supports my contentions in previous threads I have alluded to the fact that models are stiff due to assumption and recently I’ve made the point as to why they are functionally linear.
First bold above states that the cloud values are not dependent on local Navier Stokes flow, saturation pressures or by any pure physics but rather are defined by expected cloud formation in certain conditions as defined by some papers. Nothing wrong with that except that you have to understand the papers themselves, the adaptation of the cloud formation and the eventual effect on the model. Black box in other words.
The second bold is very interesting in that the scientists are recognizing that climate model grids are so coarse that in previous models the entire grid block needed to achieve cloud formation conditions before clouds were even considered. How crude is that? I’m sure that graphs of climate model clouds demonstrated that such a large scale grid event was unlikely. In this improved model, the clouds can form before true cloud formation conditions exist. The point is to achieve greater ‘flexibility’ in the models without actually calculating the formation on a small enough grid scale to use actual physics to determine if condensation has occurred.
Is this method physically wrong — NO!
Is this method prone to potential error. HELL YES!
So for those ready to chuck the model to the curb, I don’t agree. For those ready to trust the model for anything other than a toy to be verified, I also don’t agree. The scientists wonders, how do we determine if the model is correct? Which assumptions are correct. In my opinion a true scientist wouldn’t trust a thing from climate models until the whole model is disassembled and verified section by section, engineering style. This is not what climate scientists have given us, instead we are lectured on the benefits of socialist society, reduced consumption, massive government and bovine scatology energy production. Not good folks.
If the study truly existed for a model or group of models and it were properly verified, I remain very open minded on the topic. It is not up to me whether the standard math in this model is correct, it is up to physics. We have no say. Pure linearity of response to forcings is not a good sign IMO.
Despite Neven’s hopes, I’m not stupid and I am not ignorant, I simply am not convinced. If those who say I should be convinced can answer my questions rather than censor them, they have a much better shot. This is where climate science fails – and dramatically.
Read the climate model link. Read to other sections — there are MANY examples like the above. Nick’s statement that models are simple Navier Stokes solutions are incomplete to the point that they are not correct. My (and others) point that Willis’s study of the linearity of climate models is shocking should resonate with the mathematically inclined and not in a good way.
Read and study. I believe 100% that climate models will be accurate enough for prediction and planning in the future. There is no question in my mind. Today, the process is corrupted by agenda, tomorrow it will change and like Galileo, the Earth will eventually orbit the Sun.