Land Carbon Cycle Modeling
GBP 180.00
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| ISBN13: | 9781032698496 |
| ISBN10: | 1032698497 |
| Binding: | Hardback |
| No. of pages: | 312 pages |
| Size: | 280x210 mm |
| Language: | English |
| Illustrations: | 24 Illustrations, black & white; 172 Illustrations, color; 2 Halftones, black & white; 47 Halftones, color; 22 Line drawings, black & white; 125 Line drawings, color; 24 Tables, black & white |
| 700 |
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Natural sciences in general, history of science, philosophy of science (charity campaign)
Hydrobiology (charity campaign)
Botany, plant sciences (charity campaign)
Zoology (charity campaign)
Agriculture in general (charity campaign)
Environmental sciences (charity campaign)
Agribusiness (charity campaign)
Earth sciences in general (charity campaign)
Geology (charity campaign)
Environmental protection (charity campaign)
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This new edition includes 7 new chapters on machine learning and its applications to carbon cycle research, on principles underlying carbon dioxide removal from the atmosphere, a contemporary active research and management issue, and on community infrastructure for 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
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