(As part of Rabett Run's Gerlich and Tscheuschner project, Eli has started drafting parts of a response, which we will gift wrap in Bozo paper and send to some unsuspecting journal, but certainly arXiv. This comes again from Chris Colose Way to go Chris. The Editorial Board expresses its thanks=:> Suggestions for changes and additions are welcome. It's a nice summary of the state of climate modeling. Perhaps this would go well in the Conclusion, or should it go in the Introduction)
FW?IW the idiocy du jour is that thermal energy is not heat. Thermal energy is heat. Joule showed that about 150 years ago
GCM’s are often referred to as General Circulation Models, which replicate from first principles the statistical description of the large-scale motions of the atmosphere and ocean. In modern times, where circulation is only one component in modeling exercises, GCM’s are more broadly defined as Global Climate Models.
Climate models range in complexity from basic energy-balance models where solutions can be worked out by hand, to very sophisticated models that make use of some of the fastest and most powerful computers available. There is a broad range of physics and parameterizations included in GCM’s. Processes must conserve energy, momentum, and mass for example. Most GCM’s make use of primitive equations (USCCP 2008) which is a simplified form of the equations of motion. Use is made of the fact that the atmosphere is thin in comparison to its horizontal extent. Small terms in the momentum equations are generally neglected.
Modern GCM’s have evolved tremendously over the decades following increased computing power and our understanding of the processes relevant to global climate. Improvements include increases in atmospheric resolution, height of the model top, sea ice dynamics, representation of atmospheric chemistry, improved cloud microphysical schemes, modeling of the terrestrial biosphere and vegetation interactions with climate, among other things (Schmidt et al 2006; Randall et al 2007). Many realistic factors of global climate emerge from the fundamental physics including ocean and atmospheric “modes” and oscillations, displacement of storm tracks and jet streams, heat transport mechanisms, and climate feedbacks as a response to warming (USCCP 2008). How well a model performs depends on what climate variable you are interested in (e.g., temperature, precipitation, sea level rise, humidity patterns), the statistics (e.g., trends, extremes, variability), as well as the spatial and temporal scales of interest (Knutti 2008a). Further, various models perform better for different questions than other models. Perhaps if Gerlich and Tscheuschner (2009) made their model criticisms too specific, they know it would be that much easier to invalidate.
Detection involves the processes whereby a change in climate can be identified against the background noise of natural variability, and Attribution allows one to assign causes to that change with some level of confidence. The ability to hindcast the time-evolution of the 20th century climate change (e.g. Meehl et al 2004) as well as realistically past climates (e.g. the Last Glacial Maximum) with standard radiative forcing and feedback concepts gives confidence in our understanding of the essential features governing global climate (Randall et al 2007; USCCP 2008). For example, the NASA GISS climate model was used to make a prediction of the global cooling that followed the 1991 Mt. Pinatubo volcanic eruption (Hansen et al 1992). The predicted global cooling as well as the recovery back to the ongoing global warming was well simulated. Successful climate prediction involves understanding how radiative transfer is affected with changes in the solar luminosity, planetary albedo, or changes in atmospheric chemistry. This is because the radiative balance of the planet serves to define the basic boundary conditions which constrain the global climate.
However, formal attribution involves comparing spatio-temporal patterns between observations and models, not the ability to simulate the amplitude of temperature change to a set of forcings (Knutti 2008b). There are many “fingerprints” of greenhouse-gas induced warming which include corresponding changes in the emission spectrum of longwave radiation (Harries et al 2001), stratospheric cooling, and decreases in the diurnal temperature gradient. These things have been both modeled and observed (Hegerl et al 2007). Anthropogenic causation as been detected in the world’s ocean heat content trends (Barnett et al 2001), atmospheric moisture content (Santer et al 2007), in the world’s biosphere (Rosenzweig et al 2008) and continues to provide a more consistent explanation of continental to global scale climate change than natural forcing alone. Despite their assertions, Gerlich and Tscheuschner (2009) have failed to show that this science is incorrect or in contradiction to known physics.
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