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Using models to guide field experiments: a priori predictions for the CO 2 response of a nutrient‐ and water‐limited native Eucalypt woodland
Author(s) -
Medlyn Belinda E.,
De Kauwe Martin G.,
Zaehle Sönke,
Walker Anthony P.,
Duursma Remko A.,
Luus Kristina,
Mishurov Mikhail,
Pak Bernard,
Smith Benjamin,
Wang YingPing,
Yang Xiaojuan,
Crous Kristine Y.,
Drake John E.,
Gimeno Teresa E.,
Macdonald Catriona A.,
Norby Richard J.,
Power Sally A.,
Tjoelker Mark G.,
Ellsworth David S.
Publication year - 2016
Publication title -
global change biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.146
H-Index - 255
eISSN - 1365-2486
pISSN - 1354-1013
DOI - 10.1111/gcb.13268
Subject(s) - environmental science , nutrient , primary production , woodland , eucalyptus , ecosystem , computer science , ecology , biology
The response of terrestrial ecosystems to rising atmospheric CO 2 concentration (C a ), particularly under nutrient‐limited conditions, is a major uncertainty in Earth System models. The Eucalyptus Free‐Air CO 2 Enrichment (Euc FACE ) experiment, recently established in a nutrient‐ and water‐limited woodland presents a unique opportunity to address this uncertainty, but can best do so if key model uncertainties have been identified in advance. We applied seven vegetation models, which have previously been comprehensively assessed against earlier forest FACE experiments, to simulate a priori possible outcomes from Euc FACE . Our goals were to provide quantitative projections against which to evaluate data as they are collected, and to identify key measurements that should be made in the experiment to allow discrimination among alternative model assumptions in a postexperiment model intercomparison. Simulated responses of annual net primary productivity ( NPP ) to elevated C a ranged from 0.5 to 25% across models. The simulated reduction of NPP during a low‐rainfall year also varied widely, from 24 to 70%. Key processes where assumptions caused disagreement among models included nutrient limitations to growth; feedbacks to nutrient uptake; autotrophic respiration; and the impact of low soil moisture availability on plant processes. Knowledge of the causes of variation among models is now guiding data collection in the experiment, with the expectation that the experimental data can optimally inform future model improvements.