ABSTRACT

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; and doing real- or near-time ecological forecasting for decision support. This book strives to balance theoretical considerations, technical details, and applications of ecosystem modeling for research, assessment, and crucial decision making.

Key Features

  • Helps readers understand, implement, and criticize land carbon cycle models
  • Offers a new theoretical framework to understand transient dynamics of 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, for model evaluation and improvement

Related Titles

Isabel Ferrera, ed. Climate Change and the Oceanic Carbon Cycle: Variables and Consequences

(ISBN 978-1-774-63669-5)

Lal, R. et al., eds. Soil Processes and the Carbon Cycle (ISBN 978-0-8493-7441-8)

Windham-Myers, L., et al., eds. A Blue Carbon Primer: The State of Coastal Wetland Carbon

Science, Practice and Policy (ISBN 978-0-367-89352-1)

part Unit One|34 pages

Fundamentals of Carbon Cycle Modeling

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chapter Two|9 pages

Introduction to Modeling

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chapter Four|4 pages

Practice 1

Carbon Flow Diagram and Carbon Balance Equations
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part Unit Two|35 pages

Matrix Representation of Carbon Balance

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chapter Eight|5 pages

Practice 2

Matrix Representation of Carbon Balance Equations and Coding
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part Unit Three|29 pages

Carbon Cycle Diagnostics for Uncertainty Analysis

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chapter Ten|7 pages

Sensitivity Analysis with Matrix Equations

A Case Study with ORCHIDEE
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chapter Twelve|5 pages

Practice 3

Diagnostic Variables in Matrix Models
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part Unit Four|35 pages

Semi-Analytic Spin-Up (SASU)

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chapter Sixteen|7 pages

Practice 4

Efficiency and Convergence of Semi-Analytic Spin-Up (SASU) in TECO
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part Unit Five|34 pages

Traceability and Benchmark Analysis

chapter Seventeen|8 pages

Overview of Traceability Analysis

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chapter Nineteen|6 pages

Benchmark Analysis

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chapter Twenty|8 pages

Practice 5

Traceability Analysis for Evaluating Terrestrial Carbon Cycle Models
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part Unit Six|36 pages

Introduction to Data Assimilation

chapter Twenty-One|8 pages

Data Assimilation

Introduction, Procedure, and Applications
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chapter Twenty-Four|10 pages

Practice 6

The Seven-step Procedure for Data Assimilation
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part Unit Seven|35 pages

Data Assimilation with Field Measurements and Satellite Data

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chapter Twenty-Seven|11 pages

Global Carbon Cycle Data Assimilation Using Earth Observation

The CARDAMOM Approach
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chapter Twenty-Eight|5 pages

Practice 7

Data Assimilation at the SPRUCE Site
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part Unit Eight|41 pages

Value of Data to Constrain Models and Their Predictions

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chapter Thirty-Two|11 pages

Practice 8

Information Contents of Land Carbon Pool and Flux Measurements to Constrain a Land Carbon Model
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part Unit Nine|22 pages

Ecological Forecasting with EcoPAD

chapter Thirty-Three|6 pages

Introduction to Ecological Forecasting

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chapter Thirty-Five|6 pages

Practice 9

Ecological Forecasting at the SPRUCE Site
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part Unit Ten|30 pages

Process-based Machine Learning and Data-driven Modeling (PRODA)

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chapter Thirty-Eight|8 pages

Practice 10

Deep Learning to Optimize Parameterization of CLM5
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chapter Appendix 1|6 pages

Matrix Algebra in Land Carbon Cycle Modeling

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chapter Appendix 2|9 pages

Introduction to Programming in Python

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chapter Appendix 3|10 pages

CarboTrain User Guide

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