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Evaluation and analysis of a dynamic terrestrial ecosystem model under preindustrial conditions at the global scale
Author(s) -
Friend Andrew D.,
White Andrew
Publication year - 2000
Publication title -
global biogeochemical cycles
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.512
H-Index - 187
eISSN - 1944-9224
pISSN - 0886-6236
DOI - 10.1029/1999gb900085
Subject(s) - environmental science , primary production , tundra , terrestrial ecosystem , vegetation (pathology) , evergreen , biomass (ecology) , productivity , ecosystem , larch , soil carbon , carbon sequestration , atmospheric sciences , carbon cycle , soil water , ecology , soil science , carbon dioxide , geology , medicine , economics , biology , macroeconomics , pathology
The ability of a mechanistically based dynamic terrestrial ecosystem model, Hybrid v4.1, to predict the global distribution of vegetation, primary productivity, biomass carbon, and soil carbon under preindustrial conditions of climate, atmospheric CO 2 , and nitrogen deposition is evaluated. This model predicts the dynamic global distribution of eight Plant Functional Types (PFTs) by treating the interactions between individual trees, an herbaceous layer, and their physical environment at independent points on the land surface. Carbon, water, and nitrogen flows are simulated on a daily, or subdaily, basis resulting in dynamic predictions of productivity, biomass, and plant and soil carbon and nitrogen contents. Hybrid v4.1 successfully predicts the major global patterns of preindustrial vegetation, primary productivity, biomass carbon, and soil carbon. When not subject to competition, single PFTs have much broader distributions across climatic gradients than when allowed to compete with one another, demonstrating the importance of competition in determining vegetation distribution. Trade‐offs between the avoidance of frost and drought damage, growing season length, and foliage nitrogen allocation determine the relative performance of tree PFTs along climatic gradients. Six areas of disagreement between prediction and reality are noted: (1) African savanna, (2) South American grassland, (3) an area of desert in Amazonia, (4) Southern Chinese evergreen forest, (5) Siberian larch forest, and (6) tundra. These discrepancies provide useful information for future model development.