Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?

Autor: Gallup SM; Graduate Degree Program in Ecology Colorado State University Fort Collins CO USA., Baker IT; Department of Atmospheric Science Colorado State University Fort Collins CO USA., Gallup JL; Department of Economics Portland State University Portland OR USA., Restrepo-Coupe N; Department of Ecology and Evolutionary Biology University of Arizona Tucson AZ USA.; School of Life Sciences University of Technology Sydney Ultimo NSW Australia., Haynes KD; Department of Atmospheric Science Colorado State University Fort Collins CO USA., Geyer NM; Department of Atmospheric Science Colorado State University Fort Collins CO USA., Denning AS; Graduate Degree Program in Ecology Colorado State University Fort Collins CO USA.; Department of Atmospheric Science Colorado State University Fort Collins CO USA.
Jazyk: angličtina
Zdroj: Journal of advances in modeling earth systems [J Adv Model Earth Syst] 2021 Aug; Vol. 13 (8), pp. e2021MS002555. Date of Electronic Publication: 2021 Aug 25.
DOI: 10.1029/2021MS002555
Abstrakt: Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade-off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade-off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.
Competing Interests: The authors declare no conflicts of interest relevant to this study.
(© 2021. The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union.)
Databáze: MEDLINE
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