期刊
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
卷 13, 期 8, 页码 -出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2021MS002555
关键词
Amazon; gross primary productivity (GPP); model benchmarking; seasonality; tropical rainforest; variability
资金
- National Aeronautics and Space Administration (NASA) LBA investigation [CD-32]
- NASA LBA-DMIP project [NNX09AL52G]
- Department of Energy [DE-SC0014438]
- National Aeronautics and Space Administration [NNX14AI52G]
- NASA [113489, NNX09AL52G] Funding Source: Federal RePORTER
- U.S. Department of Energy (DOE) [DE-SC0014438] Funding Source: U.S. Department of Energy (DOE)
Estimates of Amazon rainforest gross primary productivity (GPP) vary significantly among different models, which adds uncertainty to future climate predictions. Models with stronger seasonal swings in GPP have stronger responses to climate drivers than observed in actual data.
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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