ALBERT

Description: Context. The first opportunity to detect indications for life outside of the Solar System may be provided already within the next decade with upcoming missions such as the James Webb Space Telescope (JWST), the European Extremely Large Telescope (E-ELT) and the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) mission, searching for atmospheric biosignatures on planets in the habitable zone of cool K- and M-stars. Nevertheless, their harsh stellar radiation and particle environment could lead to photochemical loss of atmospheric biosignatures. Aims. We aim to study the influence of cosmic rays on exoplanetary atmospheric biosignatures and the radiation environment considering feedbacks between energetic particle precipitation, climate, atmospheric ionization, neutral and ion chemistry, and secondary particle generation. Methods. We describe newly combined state-of-the-art modeling tools to study the impact of the radiation and particle environment, in particular of cosmic rays, on atmospheric particle interaction, atmospheric chemistry, and the climate-chemistry coupling in a self-consistent model suite. To this end, models like the Atmospheric Radiation Interaction Simulator (AtRIS), the Exoplanetary Terrestrial Ion Chemistry model (ExoTIC), and the updated coupled climate-chemistry model are combined. Results. In addition to comparing our results to Earth-bound measurements, we investigate the ozone production and -loss cycles as well as the atmospheric radiation dose profiles during quiescent solar periods and during the strong solar energetic particle event of February 23, 1956. Further, the scenario-dependent terrestrial transit spectra, as seen by the NIR-Spec infrared spectrometer onboard the JWST, are modeled. Amongst others, we find that the comparatively weak solar event drastically increases the spectral signal of HNO3, while significantly suppressing the spectral feature of ozone. Because of the slow recovery after such events, the latter indicates that ozone might not be a good biomarker for planets orbiting stars with high flaring rates.

Description: We present a climatology of the diurnal variation of short-lived atmospheric compounds, such as ClO, BrO, HO2, and HOCl, as well as longer-lived species: O3, the hydrogen chloride isotopes H35Cl and H37Cl, and HNO3. Measurements were taken by the Superconducting Submillimeter-wave Limb-Emission Sounder (SMILES). This spectrally resolving radiometer, with very low observation noise and altitude range from the lower stratosphere to the lower thermosphere (20–100km), was measuring vertical profiles of absorption spectra along a non-sun-synchronous orbit, thus observing at all local times. We used the retrieved volume mixing ratio profiles to compile climatologies that are a function of pressure, a horizontal coordinate (latitude or equivalent latitude), and a temporal coordinate (solar zenith angle or local solar time). The main product presented are climatologies with a high resolution of the temporal coordinate (diurnal variation climatologies). In addition, we provide climatologies with a high resolution of the horizontal coordinate (zonal climatologies).The diurnal variation climatologies are based on data periods of 2 months and the zonal climatologies on monthly data periods. Consideration of the SMILES time-space sampling patterns with respect to the averaging coordinates is a key issue for climatology creation, especially in case of diurnal variation climatologies. Biases induced by inhomogeneous sampling are minimized by carefully choosing the size of averaging bins. The sampling biases of the diurnal variation climatology of ClO and BrO are investigated in a comparison of homogeneously sampled model data versus SMILES-sampled model data from the stratospheric Lagrangian chemistry and transport model ATLAS. In most cases, the relative sampling error is in the range of 0–20%. The strongest impact of sampling biases is found where the species' temporal gradients are strongest (mostly at sunrise and sunset), with a relative error of 60–100%. The SMILES climatology data sets are available via the SMILES data distribution home page.

Description: 〈jats:title〉Abstract〈/jats:title〉 〈jats:p〉In climate modeling, the stratospheric ozone layer is typically only considered in a highly simplified form due to computational constraints. For climate projections, it would be of advantage to include the mutual interactions between stratospheric ozone, temperature, and atmospheric dynamics to accurately represent radiative forcing. The overarching goal of our research is to replace the ozone layer in climate models with a machine-learned neural representation of the stratospheric ozone chemistry that allows for a particularly fast, but accurate and stable simulation. We created a benchmark data set from pairs of input and output variables that we stored from simulations of the ATLAS Chemistry and Transport Model. We analyzed several variants of multilayer perceptrons suitable for physical problems to learn a neural representation of a function that predicts 24-h ozone tendencies based on input variables. We performed a comprehensive hyperparameter optimization of the multilayer perceptron using Bayesian search and Hyperband early stopping. We validated our model by replacing the full chemistry module of ATLAS and comparing computation time, accuracy, and stability. We found that our model had a computation time that was a factor of 700 faster than the full chemistry module. The accuracy of our model compares favorably to the full chemistry module within a 2-year simulation run, also outperforms a previous polynomial approach for fast ozone chemistry, and reproduces seasonality well in both hemispheres. In conclusion, the neural representation of stratospheric ozone chemistry in simulation resulted in an ozone layer that showed a high accuracy, significant speed-up, and stability in a long-term simulation.〈/jats:p〉