The authors are grateful for the many people who have managed and operated flux towers in the Amazon, developed the models assessed, and participated in MsTMIP. Ideas shared freely at Cross Validated (Stackexchange) helped with myriad details of this study. The authors thank Forrest Hoffman, Mike Ryan, Editor Eleanor Blyth, and two anonymous reviewers for comments that substantially improved the manuscript. EC data assimilation and analysis was funded by the National Aeronautics and Space Administration (NASA) LBA investigation CD-32, and NASA LBA-DMIP project (NNX09AL52G). The authors thank the Department of Energy (DE-SC0014438) and the National Aeronautics and Space Administration (NNX14AI52G) for funding and support.
Climatic changes -- Research, Greenhouse gases -- Research
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.
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Gallup, S. M., Baker, I. T., Gallup, J. L., Restrepo‐Coupe, N., Haynes, K. D., Geyer, N. M., & Denning, A. S. (2021). Accurate simulation of both sensitivity and variability for Amazonian photosynthesis: Is it too much to ask?. Journal of advances in modeling earth systems, 13(8), e2021MS002555.