This report summarizes the results and methodologies that have been obtained by individual members of the NDMC network throughout the last decade. It describes the requirements of data reduction for long- term analysis of airglow-based trend estimates, as they were identified within NDMC network since it founding in 2007.
Potential long-term trends are best to detect in the upper atmosphere, more precise in the mesopause, due to the smaller capacity of the rarified air. This reduces external influences, e.g., solar cycle variability, affecting the trend estimate. One of NDMC’s primary objectives is the identification and quantification of climate changes in the mesopause region.
The major challenges that have to be faced with airglow data before a trend estimate can be derived are seasonality and solar forcing (solar cycle). Two different approaches to deal with this are either, complicated, multiple linear regressions, and treating (sub-) annual variations separately per season or month without actual decomposing the data series. Examples illustrate how difficult it is to estimate a trend, using either of the two methods, whose form is not known (linear or not) in a system, namely the atmosphere, which is not fully understood. Discrimination from solar or larger scale dynamical influences is currently not possible due to the length of the data series.
C Schmidt, S Wust, M Bittner, PSM Smets. TR 5.3 Summary of methodology and temperature series