Training courses on the matrix approach to modeling land carbon and nitrogen cycles; 2018-2021: Flagstaff, AZ

点击翻译按钮获取中文摘要

This project will train early career scientists and develop the U.S. workforce. The training will occur through a series of short courses on the topic of land carbon and nitrogen cycles. Carbon and nitrogen are both important for human well-being and ecosystem function. Land ecosystems absorb approximately 30% of current carbon dioxide emissions by humans. We need further details about how this happens. To improve models that can provide these details, scientists have recently developed a new technique called the matrix approach. The matrix approach has been recognized as a way to unify all the land carbon cycle models. There are additional benefits from this approach that involve understanding uncertainty, reducing computation time, and merging data into the models. The approach has the potential to improve land carbon and nitrogen models to better predict future changes in ecosystem services. The courses will train a new generation of ecosystem modelers to achieve simplicity in coding, cleaner and more efficient models, faster results, and a better bridge between model complexity and understandable results. This project will run an annual short training course for the next three years. The topic of the course is modeling land carbon and nitrogen cycles. The specific approach is called the matrix approach. The course will focus on five skills to be trained: (1) drawing the carbon flow diagram of a model; (2) writing carbon balance equations of a model; (3) developing a matrix model out of carbon balance equations; (4) adding diagnostic variables; and (5) adding semi-analytic spin-up algorithms. The hands-on training will be combined with lectures on a variety of topics. The topics will be: theories of land carbon cycle dynamics, data assimilation, matrix operation, differential equation solvers, and model structure databases. The training course will divide the trainees into several working groups, each with one instructor to ensure individual help. The course will be improved to become more effective and efficient over time as feedback from trainees is incorporated.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.