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  • Data Drought in the Humid T...
    Frankenberg, C.; Bar‐On, Y. M.; Yin, Y.; Wennberg, P. O.; Jacob, D. J.; Michalak, A. M.

    Geophysical research letters, 28 April 2024, Volume: 51, Issue: 8
    Journal Article

    Diagnosing land‐atmosphere fluxes of carbon‐dioxide (CO2) and methane (CH4) is essential for evaluating carbon‐climate feedbacks. Greenhouse gas satellite missions aim to fill data gaps in regions like the humid tropics but obtain very few valid measurements due to cloud contamination. We examined data yields from the Orbiting Carbon Observatory alongside Sentinel‐2 cloud statistics. We find that the main contribution to low data yields are frequent shallow cumulus clouds. In the Amazon, the success rate in obtaining valid measurements vary from 0.1% to 1.0%. By far the lowest yields occur in the wet season, consistent with Sentinel‐2 cloud patterns. We find that increasing the spatial resolution of observations to ∼200 m would increase yields by 2–3 orders of magnitude and allow regular measurements in the wet season. Thus, the key to effective tropical greenhouse gas observations lies in regularly acquiring high‐spatial resolution data. Plain Language Summary Our research looks at how well satellites are able to observe greenhouse gases such as carbon dioxide and methane in tropical areas, which is important for understanding climate change. We find that these satellites often cannot make good measurements in places like the Amazon rainforest due to clouds. By using space‐based instruments that can peek in between clouds (requiring about ∼200 m spatial resolution), we would get much more frequent information, even during the rainy season. Our study shows that high‐spatial resolution is needed to regularly observe greenhouse gases in the tropics. Key Points Data yields of current remotely sensed greenhouse gas (GHG) missions in the humid tropics are often below 1% Shallow cumulus clouds cause most of the low data yields, esp. in the wet season Finer spatial resolution (∼200 m) can overcome the data sparsity in the tropics