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Dynamical prediction of terrestrial ecosystems and the global carbon cycle: A 25‐year hindcast experiment
Author(s) -
Zeng Ning,
Yoon JinHo,
Vintzileos Augustin,
Collatz G. James,
Kalnay Eugenia,
Mariotti Annarita,
Kumar Arun,
Busalacchi Antonio,
Lord Stephen
Publication year - 2008
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 - 10.1029/2008gb003183
Subject(s) - hindcast , climatology , predictability , environmental science , terrestrial ecosystem , anomaly (physics) , subtropics , carbon cycle , vegetation (pathology) , forcing (mathematics) , ecosystem , atmospheric sciences , ecology , geology , biology , medicine , physics , condensed matter physics , quantum mechanics , pathology
Using a 25‐year hindcast experiment, we explore the possibility of seasonal‐interannual prediction of terrestrial ecosystems and the global carbon cycle. This has been achieved using a prototype forecasting system in which the dynamic vegetation and terrestrial carbon cycle model VEGAS was forced with 15‐member ensemble climate predictions generated by the NOAA/NCEP coupled climate forecasting system (CFS) for the period 1981–2005, with lead times up to 9 months. The results show that the predictability is dominated by the ENSO signal with its major influence on the tropical and subtropical regions, including South America, Indonesia, southern Africa, eastern Australia, western United States, and central Asia. There is also important non‐ENSO related predictability such as that associated with midlatitude drought. Comparison of the dynamical prediction results with benchmark statistical prediction methods such as anomaly persistence and damping show that the dynamical method performs significantly better. The hindcasted ecosystem variables and carbon flux show significantly slower decrease in skill at longer lead time compared to the climate forcing variables, partly because of the memories in land and vegetation processes that filter out the higher‐frequency noise and sustain the signal.