Resolving spatially varying exoplanet features from single-point light curves is essential for determining whether Earth-like worlds harbor geological features and/or climate systems that influence habitability. To evaluate the feasibility and requirements of this spatial-feature resolving problem, we present an analysis of multi-wavelength single-point light curves of Earth, where it plays the role of a proxy exoplanet. Here, ∼10,000 Deep Space Climate Observatory/Earth Polychromatic Imaging Camera frames collected over a two-year period were integrated over the Earth’s disk to yield a spectrally dependent point source and analyzed using singular value decomposition. We found that, between the two dominant principal components (PCs), the second PC contains surface-related features of the planet, while the first PC mainly includes cloud information. We present the first two-dimensional (2D) surface map of Earth reconstructed from light curve observations without any assumptions of its spectral properties. This study serves as a baseline for reconstructing the surface features of Earth-like exoplanets in the future.

We develop a new retrieval scheme for obtaining two-dimensional surface maps of exoplanets from scattered light curves. In our scheme, the combination of the L1-norm and total squared variation, which is one of the techniques used in sparse modeling, is adopted to find the optimal map. We apply the new method to simulated scattered light curves of the Earth, and find that the new method provides a better spatial resolution of the reconstructed map than those using Tikhonov regularization. We also apply the new method to observed scattered light curves of the Earth obtained during the two-year Deep Space Climate Observatory/Earth Polychromatic Imaging Camera observations presented by Fan et al. The method with Tikhonov regularization enables us to resolve North America, Africa, Eurasia, and Antarctica. In addition to that, the sparse modeling identifies South America and Australia, although it fails to find Antarctica, maybe due to low observational weights on the poles. Besides, the proposed method is capable of retrieving maps from noise-injected light curves of a hypothetical Earthlike exoplanet at 5 pc with a noise level expected from coronagraphic images from a 8 m space telescope. We find that the sparse modeling resolves Australia, Afro-Eurasia, North America, and South America using 2 yr observation with a time interval of one month. Our study shows that the combination of sparse modeling and multiepoch observation with 1 day or 5 days per month can be used to identify main features of an Earth analog in future direct-imaging missions such as the Large UV/Optical/IR Surveyor.

In this paper, the non‐orographic gravity waves (GW) parameterization of the Mars Planetary Climate Model (PCM) previously implemented by Gilli et al. (2020, https://doi.org/10.1029/2018JE005873 ) is revisited and extended to the exobase (∼250 km). The simulations performed with the new scheme correct some known biases in the modeled thermal tide amplitudes and polar warming, improving the agreement with Mars Climate Sounder (MCS) observed thermal structures and tides below ∼100 km. Additionally, we find that the simulated densities above 150 km are compatible with NGIMS (Neutral Gas and Ion Mass Spectrometer) measured abundances. Large drag depositions ranging up to >∼950 m s ⁻¹ sol ⁻¹ are induced at altitude of 90–170 km due to the wave saturation (breaking) and depletion, leading to winds damped to magnitudes of ∼150–225 and ∼80 m s ⁻¹ in the zonal and meridional directions, respectively. Resulting temperature variations are ∼±10–30 K or 5%–10% at most latitudes except in the polar regions (where they can reach ∼±30–60 K). The results indicate that non‐orographic GW play a significant role in the dynamics of the middle‐upper atmosphere of Mars via the induced transfer of momentum and energy from the lower atmosphere.

Energy has been propelling the development of human civilization for millennia. Humanity presently stands at Type 0.7276 on the Kardashev Scale, which was proposed to quantify the relationship between energy consumption and the development of civilizations. However, current predictions of human civilization remain underdeveloped and energy consumption models are oversimplified. In order to improve the precision of the prediction, we use machine learning models random forest and autoregressive integrated moving average to simulate and predict energy consumption on a global scale and the position of humanity on the Kardashev Scale through 2060. The result suggests that global energy consumption is expected to reach ~ 887 EJ in 2060, and humanity will become a Type 0.7449 civilization. Additionally, the potential energy segmentation changes before 2060 and the influence of the advent of nuclear fusion are discussed. We conclude that if energy strategies and technologies remain in the present course, it may take human civilization millennia to become a Type 1 civilization. The machine learning tool we develop significantly improves the previous projection of the Kardashev Scale, which is critical in the context of civilization development.

Thermal tides in the Martian atmosphere are analyzed using temperature profiles retrieved from nadir observations obtained by the TIRVIM Fourier‐spectrometer, part of the Atmospheric Chemistry Suite onboard the ExoMars Trace Gas Orbiter. The data is selected near the northern summer solstice at solar longitude (L_S) 75°–105° of Martian Year 35. The observations have a full local time coverage, which enables analyses of daily temperature anomalies. The observed zonal mean temperature is lower by 4–6 K at ∼100 Pa, but higher toward the summer pole, compared to the Laboratoire de Météorologie Dynamique (LMD) Mars General Circulation Model (GCM). Wave mode decomposition shows dominant diurnal tide and important semi‐diurnal tide and diurnal Kelvin wave, with maximal amplitudes of 5, 3, and 2.5 K, respectively, from tens to hundreds of Pa. The results generally agree well with the LMD Mars GCM, but with noticeable earlier phases of diurnal (∼1 hr) and semi‐diurnal (∼3 hr) tides.