Spatio-temporal Statistical Framework for the Evaluation of Global Change in the Ionosphere

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This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Increased temperatures at the earth's surface and in the troposphere are a well-known consequence of increases in atmospheric greenhouse gases. But the expected consequence of greenhouse gas increases in the upper atmosphere, including the stratosphere, mesosphere and thermosphere, is cooling rather than warming. The cooling of the upper atmosphere results in thermal contraction, which should be accompanied by a lowering of the ionospheric layer in which the density of free electrons reaches its peak value (the F layer). While the lowering of ionospheric layers is predicted by established theory and is known to occur in computer simulations, lowering trends and their impacts on ionospheric properties have yet to be convincingly documented in observations. Ionospheric trend detection is hampered by the sparseness of ionsospheric data as well as the large amount of background ionospheric variability, much of which is associated with solar cycles and geomagnetic effects.This project will develop a statistical methodology for the evaluation of global changes in ionospheric parameters over the last five decades, for which sufficient globally distributed and well-organized data are now available. The modeling framework developed in the project will take into account the spatial distribution of ionospheric measurements, and will use more realistic assumptions on the distribution of measurement errors than those used in previous studies. The determination of trends over five decades, together with rigorous estimates of their uncertainty, will provide a statistical basis for improving all present day ionospheric models and reducing their residual error.Research conducted in this project contributes to basic upper-atmospheric science by attempting to confirm theoretical predictions of ionospheric trends. More generally, the project will develop new statistical tools for detecting trends and other signals in datasets in which the observations are sparse and contain variability on a variety of fast and slow time scales. Such datasets are the norm for many hard-to-observe geophysical and ecological variables like soil moisture, snow depth, and net primary productivity. In addition, research in this project will have practical value, as the state of the ionosphere has a significant impact on space-based communication networks. The analysis of ionsopheric observations will guide attempts to improve ionospheric models, resulting in improved forecasts of communication-relevant space weather.