More inconvenient truth about those peskily inaccurate climate models

Here's a question. If billions of dollars were spent developing financial forecasting models so we could tell what the markets would be like in 50 years' time and they had almost no ability to predict what happened in the past then should politicians bet trillions of dollars of the world's economy on their accuracy?

The climate change debate degenerated into a propaganda war long ago. Lacking a sound scientific basis for making outrageous claims of planetary catastrophe, those organisations promoting the most dire consequences of increasing CO2 emissions did what all ideology-based movements do - ratcheted up the level of propaganda to drown out opposition, paying particular attention to 'playing the man and not the ball'.

The proof of this particular climate pudding was definitely shown to be in the eating when the IPCC released its Fourth Assessment Report, which included reduced predictions about the next one hundred years including a sea level increase of 18-59cm (7-23 inches), the lower end being below what actually happened in the twentieth century. In a moment of pure comedy gold, activist organisations, previously in lock-step with the IPCC, suddenly turned on the organisation accusing it of not being alarmist enough.

The climate propaganda machine had previously been able to point to the so-called science of the IPCC to validate their claims. Deprived of this validation, enviro-fascists have made a complete spectacle of themselves by making even more hysterical claims that require us to, basically, stop using energy now, now, now. Of course, by 'us' they mean we in the rich West. They impose no such requirements on India, China or Brazil - which pretty much gives away their socialist, regressive agenda.

Climate science is based on a number (I think it's around 26) of recognised climate models from around the world. The IPCC ranges tend to represent the low and high ranges provided across all of the models.

I remind readers of a couple of steps in the Scientific Method:

Predict: Use the hypothesis to predict the results of new observations or measurements. Often, advanced mathematical and statistical hypothesis testing techniques are used to design experiments that attempt to effectively test the plausibility of hypotheses.

Verify: Perform experiments to test those predictions. Attempting to experimentally falsify hypotheses is thought by many to be a better choice of term here.

The fact is that there's a huge elephant in the room for climate scientists, namely that these models have a diabolical predictive record. No wonder they have to make up new statistical methods in order to 'validate' their methodologies.

It's always worth keeping an eye on what Roger Pielke Sr is writing over at Climate Science. He's old enough to not have to worry about criticism from self-interested climate scientists and dishonest, play-the-man climate propagandists.

Climate Science has already weblogged on the claim in the 2007 IPCC WG1 report that,

"Projecting changes in climate due to changes in greenhouse gases 50 years from now is a very different and much more easily solved problem than forecasting weather patterns just weeks from now. To put it another way, long-term variations brought about by changes in the composition of the atmosphere are much more predictable than individual weather events." [from page 105]

One of the criticisms of climate science is that it's like trying to predict the weather 50 or 100 years out. If they can't get it right next week what chance in 50 years? The IPCC deals with this issue by making the clearly preposterous claim above. A clear analogy would be a financial forecaster telling you what the market will be doing in 50 or 100 years when he's only right about next week a small percentage of the time.

This weblog provides a short summary of why such a claim is absurd.

First, all climate and weather models include two components; a dynamic core (which involves advection, the pressure gradient force, and the gravitational acceleration) and parameterized or prescribed) physical, chemical and biological processes. Only the dynamic core is basic physics. All parameterizations are engineering code which means they include tunable components.

Weather prediction models parameterize long- and short-wave radiative flux divergence, stable clouds and precipitation, deep cumulus clouds, turbulence, and air-sea and air-land fluxes. The state variables in weather model are the three components of velocity, temperature, pressure, density of air, and the three phases of water (and sometimes other gaseous and aerosol components). A detailed discussion of this type of model is given, for example, in Pielke, R.A., Sr., 2002: Mesoscale meteorological modeling. 2nd Edition, Academic Press, San Diego, CA, 676 pp. [Table of Contents]

The state variables are initialized from real world observations such as from radiosonde and satellite data. If the weather model is a regional model, it obtains information through lateral boundary conditions. The dynamic core of the weather model, therefore, is constrained by the real-world initial conditions and lateral boundary conditions. Most of the surface boundary conditions are prescribed. This includes, for instance, sea surface temperature, sea ice coverage, vegetation, and snow cover. Only certain quantities, such as soil moisture and land surface temperature may be permitted to change in response to the land-air fluxes. When the initial conditions of the weather model are "forgotten", the parameterizations must skillfully predict the evolution of the state variables from that time forward, which is the reason that the weather prediction accuracy degrades and becomes of no value after a certain time period (e.g. see).

A climate model, in contrast, must model more processes than in a weather model (such as biogeochemistry of vegetation on land and plants in the ocean; sea ice dynamics; aerosol processes; ocean circulation; ground freezing and thawing; snow accumulation and melt and sublimation, etc. - see). For some of these climate processes (which involve physics, biology and chemistry) they are modeled, as with a weather model, by a dynamical core and by parameterizations. These include sea ice dynamics and ocean circulation, which both have advection, pressure gradient and gravitational parts, as well as the parameterization of other effects (such as turbulence, phase changes of water). Some of the climate processes, such as biogeochemistry and biogeography have no dynamical core, and are completely parameterized models.

Thus, a climate model involves more parameterizations with their tunable components than for a weather model, as well as additional new state variables (such as salinity, ice, snow, vegetation type and its root depth etc) for which initial conditions are required for all of these variables.

The climate model also has no real world constraint such as supplied by real-world initial conditions (and for a regional model lateral boundary conditions). This real-world data constrains its predictions. Instead, the state variables required for the dynamic core of each component of the climate model (i.e. the state variables for the atmosphere, land, ocean and continental ice) must be generated from the parameterizations!

The claim by the IPCC that an imposed climate forcing (such as added atmospheric concentrations of CO2) can work through the parameterizations involved in the atmospheric, land, ocean and continental ice sheet components of the climate model to create skillful global and regional forecasts decades from now is a remarkable statement. That the IPCC states that this is a "much more easily solved problem than forecasting weather patterns just weeks from now" is clearly a ridiculous scientific claim. As compared with a weather model, with a multi-decadal climate model prediction there are more state variables, more parameterizations, and a lack of constraint from real-world observed values of the state variables.

Climate modelling is non-trivial stuff. It's clear that none of the models are anywhere near getting it right in spite of the billions of dollars that have been spent on them.

Backwards, socialist environmentalists might want us to spend trillions on these models but that's because they 1) don't understand where money comes from; 2) don't understand what motivates people; and 3) paradoxically, don't understand the environment (or socialism's catastrophic impact on it).

One day people will look back at the money sent down the climate model hole, look at the health debacle in Africa and wonder what the heck we were thinking.