Land Carbon Cycle Modeling : Matrix Approach, Data Assimilation, Ecological Forecasting, and Machine Learning (Hardcover, 2 ed)

Unit 1: Fundamentals of carbon cycle modeling.?

Chapter 1: Theoretical foundation of the land carbon cycle and matrix approach.?Yiqi Luo.?

Chapter 2: Introduction to modeling.?Benjamin Smith.?

Chapter 3: Flow diagrams and balance equations of land carbon models.?Yuanyuan Huang.?

Chapter 4: Practice 1, Developing carbon flow diagrams and balance equations.?Yuanyuan Huang.?

Unit 2:?Matrix representation of carbon balance.?

Chapter 5: Developing matrix representation of land carbon models.?Yuanyuan Huang.?

Chapter 6: Coupled carbon-nitrogen matrix models.?Zheng Shi and Xingjie Lu.?

Chapter 7: Compartmental systems.?Carlos Sierra.?

Chapter 8: Practice 2, Matrix representation of carbon balance equations and coding.?Yuanyuan Huang.?

Unit 3:?Carbon cycle diagnostics for uncertainty analysis.?

Chapter 9: Unified diagnostic system for uncertainty analysis.?Yiqi Luo.?

Chapter 10: Matrix phosphorus model and data assimilation.?Enqing Hou.?

Chapter 11: Principles underlying carbon dioxide removals from the atmosphere. Yiqi Luo

Chapter 12: Practice 3, Diagnostic variables in matrix models.?Xingjie Lu.

?Unit 4:?Semi-analytic spin-up (SASU).?

Chapter 13: Non-autonomous ODE system solver and stability analysis.?Ying Wang.?

Chapter 14: Semi-Analytic Spin-Up (SASU) of coupled carbon-nitrogen cycle models.?Xingjie Lu and Jianyang Xia.?

Chapter 15: Time characteristics of compartmental systems.?Carlos Sierra.?

Chapter 16: Practice 4, Efficiency and convergence of semi-analytic spin-up (SASU) in TECO.?Xingjie Lu.?

Unit 5:?Traceability and benchmark analysis.?

Chapter 17: Overview of traceability analysis.?Jianyang Xia.?

Chapter 18: Applications of the transient traceability framework.?Lifen Jiang.?

Chapter 19: Benchmark analysis.?Yiqi Luo & Forrest M. Hoffman.?

Chapter 20: Practice 5, Traceability analysis for evaluating terrestrial carbon cycle models.?Jianyang Xia & Jian Zhou.?

Unit 6:?Introduction to data assimilation.?

Chapter 21: Data assimilation: Introduction, procedure, and applications.?Yiqi Luo.?

Chapter 22: Bayesian statistics and Markov chain Monte Carlo method in data assimilation.?Feng Tao.?

Chapter 23: Application of data assimilation to soil incubation data.?Junyi Liang & Jiang Jiang.?

Chapter 24: Practice 6, The seven-step procedure for data assimilation.?Xin Huang.?

Unit 7:?Data assimilation with field measurements and satellite data.?

Chapter 25: Model-data integration at the SPRUCE experiment.?Daniel Ricciuto.?

Chapter 26: Application of data assimilation to a peatland methane study.?Shuang Ma.?

Chapter 27: Global data assimilation using earth observation ? the CARDAMOM approach.?Mathew Williams.?

Chapter 28: Practice 7, Data assimilation at the SPRUCE site.?Shuang Ma.?

Unit 8:?Ecological forecasting with EcoPAD.?

Chapter 29: Introduction to ecological forecasting.?Yiqi Luo.?

Chapter 30: Ecological Platform for Assimilating Data (EcoPAD) for ecological forecasting.?Yuanyuan Huang.?

Chapter 31: Community cyberinfrastructure for ecological forecasting. Xin Huang & Lifen Jiang

Chapter 32: Practice 8, Ecological forecasting at the SPRUCE site.?Jiang Jiang.?

Unit 9: Machine learning and its applications to carbon cycle research

Chapter 33: Introduction to machine learning and its applications to carbon cycle research.?Yuanyuan Huang.?

Chapter 34: Estimation of terrestrial gross primary productivity

using Long Short-Term Memory network. Yao Zhang.?

Chapter 35: Machine learning to predict and explain complex carbon cycle interactions, Julia Green

Chapter 36: Practice 9, Applications of machine learning to predict soil organic carbon content.?Feng Tao and Kostia Viatkin.?

Unit 10:?Process-based machine learning and data-driven modeling (PRODA).?

Chapter 37: Introduction to machine learning and neural networks.?Toby Dylan Hocking.?

Chapter 38: PROcess-guided deep learning and DAta-driven modeling (PRODA).?Feng Tao & Yiqi Luo.?

Chapter 39: Hybrid modeling in earth system science, Yu Zhou

Chapter 40: Practice 10, Deep learning to optimize parametrization of CLM5.?Feng Tao.?

Appendices.?

Appendix 1: Matrix algebra in land carbon cycle modeling.?Ye Chen.?

Appendix 2: Introduction to programming in Python.?Xin Huang.?

Appendix 3: CarboTrain user guide.?Jian Zhou