Tero Mielonen ^1* Alexander Marshak ² Yongxiang Hu ³

• ¹ Finnish Meteorological Institute, Helsinki, Finland
• ² Goddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, Maryland, United States
• ³ Langley Research Center, National Aeronautics and Space Administration, Hampton, Virginia, United States

It can be argued that enough is already known about the physical basis of climate change and therefore, researchers should move their focus on supporting climate action with the data they produce. However, not everything is known -at least not accurately enough. Satellite data sets have sampling issues and biases that still need to be resolved (e.g., Chung et al. (2024), Magruder et al. (2024)). Moreover, the modelling community is moving forward to regional analyses with high-resolution models, thus more detailed observations are needed for the development and validation of these new models. In addition, remote sensing data records are approaching climate-relevant time scales and offering invaluable information on the changes already caused by climate change. With novel algorithms and machine learning methods even more could be learned from these long data series. Furthermore, people's behavior and their emissions are constantly changing, thus accurate monitoring is needed also in the future. Especially, as the global average temperature will rise above 1.5 degrees Celsius which will take us to uncharted territory with an increasing possibility for abrupt changes and crossing of tipping points. Consequently, new and refined satellite retrievals will likely lead to new understanding and better support for climate action. This new and improved information will also be a crucial part of the seventh IPCC Assessment Report which is planned to be released in 2029.To address the above-mentioned needs, this Research Topic includes six original research articles. All of them present novel methods and/or new atmospheric satellite products which will be beneficial for the scientific community and society at large. 2024) also used data from the EPIC instrument on-board the DSCOVR satellite. They analyzed the Earth's reflectance and produced temporally and conditionally averaged reflectance images on a fixed grid separated by surface types (land or ocean), and atmospheric conditions (cloudy or clear). These kind of reflectance maps can offer insights into climate science and diagnostic, as well as educational tools for the public. 2024) present a novel method for characterizing atmospheric aerosol with polarimetry and shadow hiding. The combination of these approaches reveals information on the size, shape and chemical composition of the aerosol. With this method they were able to identify the contributions of water-ice, mineral-dust and carbonaceous aerosols in their observations. The possibility to characterize the type of the aerosol is a valuable addition to atmospheric aerosol observations. As the summaries of the articles in this Research Topic show, there is still room for improvement in our current satellite algorithms. However, the articles also provide potential solutions to the shortcomings of the operational algorithms, and essential knowledge for the development of atmospheric models. Satellite observations are vital for model evaluation, but it is good to keep in mind, that models can also be used to understand and evaluate the satellite observations. For example, Kokkola et al. (2024) used an ensemble of large eddy simulations to show that the satellite-based estimate of the relationship between cloud droplet number and cloud water is biased due to combination of spatial variability in cloud properties and instrumental noise. Nevertheless, the analysis implied that satellite data can provide robust results if the cloud cases are carefully selected from similar meteorological conditions. This example underscores how combining global satellite observations with models leads to more accurate insights and better answers to climate-related questions, ultimately helping us make sustainable decisions related to climate action.