Abstract
All model intercomparison projects (MIPs) have shown large uncertainties in prediction of carbon sequestration among models and poor model-data matches. Although great efforts have been made, it is still difficult to identify causes of model uncertainty. This chapter offers a unified diagnostic system, which is also called a 1-3-5 scheme of diagnostics, for uncertainty analysis in carbon cycle modeling. The number 1 stands for one formula to unify the land carbon models, the number 3 is for one three-dimensional (3D) space to evaluate all model outputs, and the number 5 is for five traceable components to pinpoint uncertainty sources via traceability analysis. Once the uncertainty sources are pinpointed, the model uncertainty can be shrunk to zero by standardizing the traceable components.
| Original language | English (US) |
|---|---|
| Title of host publication | Land Carbon Cycle Modeling |
| Subtitle of host publication | Matrix Approach, Data Assimilation, Ecological Forecasting, and Machine Learning, Second Edition |
| Publisher | CRC Press |
| Pages | 57-62 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781040026298 |
| ISBN (Print) | 9781032698496 |
| DOIs | |
| State | Published - Jan 1 2024 |
| Externally published | Yes |
ASJC Scopus subject areas
- General Business, Management and Accounting
- General Agricultural and Biological Sciences
- General Earth and Planetary Sciences
- General Environmental Science
- General Engineering