Coupling of solar and geomagnetic activity with the spatial distribution of trends in green-house gases in the upper atmosphere

热层-电离层（T-I）区域的结构和组成受到来自太阳的极紫外通量的强烈影响。该区域变化的另一个外部驱动因素是地球磁场，它将带电粒子引导到大气中，在那里它们将能量储存起来（空间上围绕两极）。然而，最近的数据表明，几个内部驱动过程可以在很短的时间（天）内支配T-I的变化，但也可以在较长的时间尺度（年）内支配。长期强迫的一个主要作用被认为是中间层和低层热层（MLT）中CO2的稳定增加，这导致辐射冷却的增加和随后的大气收缩。然而，其他温室气体也被发现对T-I变率有强烈的调节作用，如臭氧和NO（短期）。我们的主要目标是研究MLT温室气体长期趋势的空间分布如何与T-I长期变率耦合。为此，我们计划同时使用地面数据和卫星数据，包括CO2、O3、NO、H2O、中性密度和电子密度。结合以下空间实验的数据：CHAMP、GRACE、SWARM、COSMIC、GOMOS、ACE-FTS、MLS、SABER、MIPAS、HALOE和AIM，我们计划在近2个太阳周期内获得近全球覆盖范围。将利用这些数据集建立全球气候学，并推导出长期趋势及其在时间和空间上的相关性，以及与T-I指数的相关性。我们将研究可变性中可能的时间滞后，并限制哪些动力学和化学途径可能负责驱动T-I对这些变化的响应。此外，我们计划首次从全球气候学中计算真正的冷却/加热速率，并研究这些区域与T-I区域之间的关系。这些也可以直接用于全球环流模式，而不是由测量的体积排放率得出的能量冷却率。

The structure and composition of the thermosphere-ionosphere (T-I) region is known to be strongly influenced by the EUV flux emanating from the Sun. The other external driver of variability of this region is the Earth's magnetic field which directs charged particles into the atmosphere where they deposit their energy (spatially around poles). Nevertheless, recent data show that several internal driving processes can dominated the T-I variability on very short (days), but also longer time scales (years). A major role in long term forcing is thought to be played by the steady increase of CO2 in the mesosphere and lower thermosphere (MLT), which leads to increasing radiative cooling and subsequent atmospheric contraction. Nevertheless other green-house gases were also found to strongly modulate T-I variability, such as ozone, and NO (on shorter term). Our main goal in this work is to investigate how the spatial distribution of long-term trends of MLT green-house gases couples with T-I long term variability. To this end we plan to use both, ground-based as well as satellite data of CO2, O3, NO, H2O, neutral and electron densities. Combining the data from the following space experiments: CHAMP, GRACE, SWARM, COSMIC, GOMOS, ACE-FTS, MLS, SABER, MIPAS, HALOE, and AIM we plan to obtain nearly a global coverage over a period of nearly 2 solar cycles. The global climatologies will be build using these datasets and derive the long term trend and their correlations in time and space and with T-I indices. We will investigate the possible time lags in variability and constrain what dynamical and chemical pathways may be responsible in driving the T-I response to these changes. In addition, we plan for the first time to calculate true cooling/heating rates from the global climatologies and investigate how these and T-I regions correlate. These can also later be used directly in the global circulation models, as oppose to the energetics cooling rates derived from measured volume emission rates.