The application of COSMIC data to global change research
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The constellation observing system for meteorology, ionosphere, and climate (COSMIC) is well-suited to climate research, especially as it pertains to climate modeling. It presents a challenge to climate models, which are currently tuned to match climate mean states, by providing precisely calibrated data which can be analyzed according to two methods that are insensitive to standard model tuning. Those two methods are climate signal detection and second-moment statistics, both of which consider the most useful climate model to be the one w hich provides the best predictions rather than the one which best recreates the current climate. In this paper we discuss these two new, alternative approaches to improving climate models and how COSMIC occultation data can be analyzed in this context. Climate signal detection is usually applied to determine what trends in a climate data set can be described by external effects, such as increasing greenhouse gas concentrations, sulfur dioxide aerosols, etc. Here we show that it is actually a method to test climate models. By examining climate trends and anomalies as revealed by COSMIC data, we can test whether climate models reproduce those trends and anomalies. We describe in detail how trends and anomalies can be extracted from COSMIC occultation data and the relevance it should have to climate models. The fluctuation dissipation theorem, as applied to the climate, shows that a second-moment analysis of a climate model's output will reveal more about its physical soundness than does the mean states it produces. While this theorem shows how a Green's function for climate change can be derived from the second-moments of the climate system, it is best applied by comparing like second-moments in data and in model output. This method of testing models is most likely to reveal which parameterizations of convection and moisture dispersion are most appropriate. The two methods of improving climate models are discussed in the context of COSMIC, showing how the occultation data can be processed to apply each of them.