During periods when the moon is full, the howling of dogs increase and when no moon appears, dogs can hardly be heard.
Our conclusion - howling dogs cause full moons.
That analogy is certainly absurd, but current arguments regarding Climate Change follow the same logic. Neither the "chicken littles" nor the "deniers" have proven their case with sound scientific proof. Each relies on howling dog type of correlations. That is why the debate continues.
The mathematical models are the basis of all the Chicken Little claims. The quality of these models can best be demonstrated by considering the accuracy of weather forecasts. Although weather and climate are not the same, the modeling techniques and issues are similar. Both topics are extremely complex and non-linear with large numbers variables. The most serious model deficiency is that they do not include feedback mechanism, which are highly non-linear. Tiny errors are frequently amplified (exaggerated) by many orders leading to "butterfly effect," well-known in weather modeling and equally applicable to climate models.
Climate modeling consist of differential equations without precise solutions. Answers require adjustable para-meters that force a match to current and historical data. The results are hardly without bias. They are "plugged" (tweaked) to give the desired conclusions. If the objective is to show that CO2 (or sunspots) drives climate change, that will happen.
DRIVERS OF CLIMATE CHANGE
The argument of the "chicken littles" is summarized by the following IPCC statement:
Most of the observed increase in globally-averaged temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic GHG concentrations. This is an advance since the TAR’s conclusion that “most of the observed warming over the last 50 years is likely to have been due to the increase in GHG concentrations” . . . IPCC Fourth Assessment Report
The "deniers" argue that all Climate Change is natural and a direct result in changes in solar radiation and position or it inclination of the earth in its orbit around the sun. If any anthropogenic effect exists, it is negligible.
Some of the issues with Global Climate Models are obvious in the graph from the left which appears in the IPCC Assessment. For more details about the sources of referenced models, check the Wiki link below the graph.
Graph Observations
1) 2 to 1 differences in the predictions between models in only 100 years.
2) All models give the same temperature anomaly in 2000, because they were FORCED via parameterization.
3) Absence of comparisons to historical data and from what we can see significant variation and unrealistic monotonic behaviour in that period.
4) ALL anomalies are monotonic and increase in the future. The absence of structure emphasizes the linear nature (absence of feedback) of modeling. The absence of any cooling dips (even in history) suggests the potential of systemic bias to show only a temperature increase.
5) These predictions include NO feedback mechanisms or potential variations solar radiation.
Accuracy of models that predict global warming
According to the IPCC, climate scientists point out that the models have flaws, such as albedo errors and effects of clouds, and that external factors not taken into consideration could change their conclusion. Clouds cool the surface by reflecting sunlight back into space and warm it by increasing the amount of infrared radiation emitted from the atmosphere to the surface. The 2001 IPCC report highlighted the possible changes in cloud cover as one of the dominant uncertainties in predicting future climate change.
In 2000, a comparison between measurements and dozens of GCM simulations of ENSO-driven tropical precipitation, water vapor, temperature, and outgoing longwave radiation found similarity between measurements and simulation of most factors. However the simulated change in precipitation was about one-fourth less than what was observed. Errors in simulated precipitation imply errors in other processes, such as errors in the evaporation rate that provides moisture to create precipitation.
The model mean exhibits good agreement with observations, while individual models often exhibit worse agreement with observations. Many of the non-flux adjusted models suffered from unrealistic climate drift up to about 1°C/century in global mean surface temperature.
All models have shortcomings in their simulations of the present day climate of the stratosphere, which might limit the accuracy of predictions of future climate change. Coupled climate models do not simulate with reasonable accuracy clouds and some related hydrological processes.
Problems in the simulation of clouds and upper tropospheric humidity, remain worrisome
because the associated processes account for most of the uncertainty in
climate model simulations of anthropogenic change.
The Bottom-Line in the above is - Models predicting human-impact
on Global Warming may NOT be accurate.
