CliMA Land: A next-generation land surface model that bridges vegetation processes and remote sensing

by Yujie Wang and Renato Braghiere: Climate model predictions of future land carbon sink strength show significant discrepancies. To enhance predictive accuracy and reduce inter-model disagreements, it is crucial to improve the representation of vegetation processes and calibrate the models using more observational data. However, the limitations of computational resources in the past have hindered the integration of new theories and advances into traditional climate models, which often rely on statistical models to parameterize vegetation processes instead of mechanistic and physiological models (such as stomatal control models). Additionally, the preference for faster models has limited the incorporation of complex features (e.g.,…
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GriddingMachine: A new database and software for sharing global datasets

Researchers are spending way too much time finding, reading, and processing public data. The ever increasing amount of data, various data formats, and different data layouts are increasing the time spent on handling data—before getting ready for scientific analysis. While the intention of sharing data is to facilitate their broad use and promote research, the increasing fragmentation makes it harder to find and access the data. Taking my personal experience as an example, I spent months to identify, download, and standardize the global datasets we use with the CliMA Land model, which came in a plethora of formats (e.g., NetCDF,…
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