Grasslands cover about 30% of global land area and provide staple carbohydrate products for humans as well as an important food source for animals. Grasslands in the U.S., especially in the Great Plains, are intensively used for agriculture and grazing. There are two major photosynthesis pathways of grass species (i.e., C4 and C3) that play different roles in grassland function, ecology, and biogeography. C4 species are typically more efficient water users and often adapted to warmer and dryer conditions compared to most C3 species, but both species are sensitive to climate change. The widely distributed C4 and C3 grasses across different climate zones can influence terrestrial gross primary productivity (GPP) and ecosystem structure and function at a large scale. Ecosystem models (e.g., ELM) are an effective tool to predict grassland photosynthesis at multiple spatial (e.g., site, regional, or global) and temporal scales (e.g., hourly, daily, monthly, yearly, multi-yearly, and century). Under the supervision of Dr. Rongting Xu and Dr. Christopher Still, the MEP student will conduct three broad tasks: 1) examine photosynthesis of C4- or C3-dominated grasslands at ten different grassland sites; 2) become familiar with and analyze inputs and outputs of the ecosystem model-ELM; 3) compare ELM modeling results against grassland observations using statistical indices. Data analysis and model-observation comparison conducted by the student will largely improve the accuracy of ELM estimates of photosynthesis at different U.S. grasslands. Meanwhile, there is a good chance that the student will be involved in one of our potential manuscripts that focuses on estimating grassland photosynthesis and organic carbon storage across the contiguous U.S. The project will be conducted 100% remotely, with no required field or laboratory work. Since the project is remote and purely data analysis, the student will need consistent access to a computer with an internet connection.