Land Carbon Cycle Modeling: Matrix Approach, Data Assimilation, & Ecological Forecasting

Carbon moves through the atmosphere, through the oceans, onto land, and into ecosystems. This cycling has a large effect on climate - changing geographic patterns of rainfall and the frequency of extreme weather - and is altered as the use of fossil fuels adds carbon to the cycle. The dynamics of this global carbon cycling are largely predicted over broad spatial scales and long periods of time by Earth system models. This book addresses the crucial question of how to assess, evaluate, and estimate the potential impact of the additional carbon to the land carbon cycle. The contributors describe a set of new approaches to land carbon cycle modeling for better exploring ecological questions regarding changes in carbon cycling; employing data assimilation techniques for model improvement; doing real- or near-time ecological forecasting for decision support; and combining newly available machine learning techniques with process-based models to improve prediction of the land carbon cycle under climate change. This new edition includes seven new chapters: machine learning and its applications to carbon cycle research (five chapters); principles underlying carbon dioxide removal from the atmosphere, contemporary active research and management issues (one chapter); and community infrastructure for ecological forecasting (one chapter).

Key Features

• Helps readers understand, implement, and criticize land carbon cycle models
• Offers a new theoretical framework to understand transient dynamics of the land carbon cycle
• Describes a suite of modeling skills - matrix approach to represent land carbon, nitrogen, and phosphorus cycles; data assimilation and machine learning to improve parameterization; and workflow systems to facilitate ecological forecasting
• Introduces a new set of techniques, such as semi-analytic spin-up (SASU), unified diagnostic system with a 1-3-5 scheme, traceability analysis, and benchmark analysis, and PROcess-guided machine learning and DAta-driven modeling (PRODA) for model evaluation and improvement
• Reorganized from the first edition with seven new chapters added
• Strives to balance theoretical considerations, technical details, and applications of ecosystem modeling for research, assessment, and crucial decision-making

Yiqi Luo is Liberty Hyde Bailey Professor at Cornell University, USA. He obtained his PhD degree from the University of California, Davis in 1991 and did postdoctoral research at UCLA and Stanford University from 1991 to 1994 before he worked at the Desert Research Institute as Assistant and Associate Research Professor from 1994 to 1998, the University of Oklahoma as Associate, Full, and George Lynn Cross Professor from 1999 to 2017, and Northern Arizona University as Full and Regents Professor. Professor Luo has studied land carbon cycling using empirical and modeling approaches for more than 30 years. His research program has been focused on addressing two key issues: (1) how global change alters the structure and functions of terrestrial ecosystems, and (2) how terrestrial ecosystems regulate climate change. To address these issues, Dr. Luo’s laboratory has conducted field global change experiments; developed terrestrial ecosystem models; synthesized extensive data sets using meta-analysis methods; integrated data and models with data assimilation techniques; and carried out theoretical and computational analysis. Particularly, his research team has recently developed a matrix approach to land carbon cycle modeling; applied data assimilation techniques to ecological research; and pioneered in ecological forecasting. Previously he has published two books, 30 book chapters, and more than 500 papers in peer-reviewed journals. He was a Highly Cited Researcher recognized by the Web of Science Group, Clarivate Analytics in 2018-2022. He was elected fellow of the American Association for the Advancement of Science (AAAS) in 2013; the American Geophysical Union (AGU) in 2016; and the Ecological Society of America (ESA) in 2018. This book, Land Carbon Cycle Modeling: Matrix Approach, Data Assimilation, Ecological Forecasting, & Machine Learning, evolved from an international training course, New Advances in Land Carbon Cycle Modeling. The training course has been held six times from 2018 to 2023. The materials in the book have been partly or fully used by approximately 600 attendees of the training course.

Benjamin Smith is a Professor of Ecosystem Science, based in Sydney, Australia where he is Research Director of Western Sydney University’s Hawkesbury Institute for the Environment, a leading center for global change ecosystem research and innovation. Following undergraduate studies in biology at the University of Tasmania, Australia, Ben relocated to Dunedin, New Zealand, where he obtained his PhD from the University of Otago in 1996. Following postdoctoral posts in Sweden and Germany, he obtained tenure at Lund University in Sweden, transitioning to his current role at Western Sydney University in 2018. Benjamin Smith is known as a pioneer in the dynamic global vegetation modeling field. The original developer of the widely used LPJ-GUESS ecosystem model, he continues to lead a multi-institutional consortium of developers serving its international user community. As a Visiting Scientist with CSIRO Oceans & Atmosphere Flagship, he contributed to the implementation of vegetation demography, disturbance and wildfire dynamics in the Australian Community Land Surface Model, CABLE. He is interested in the role of the biosphere in regional and global climate dynamics, using earth system models to examine feedback of ecosystems and land surface changes to the atmosphere and climate. He led the development of the first published regional earth system model, RCA-GUESS, and is active in the pan European consortium developing the global EC-EARTH ESM. An author of several influential papers in the global change modeling and assessment fields, Ben was recognized in the Clarivate Highly Cited Researcher list from 2019-2021.