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

Abstract 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...

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Autores principales:	Sarah M. Gallup, Ian T. Baker, John L. Gallup, Natalia Restrepo‐Coupe, Katherine D. Haynes, Nicholas M. Geyer, A. Scott Denning
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2021
Materias:	Amazon gross primary productivity (GPP) model benchmarking seasonality tropical rainforest variability Physical geography GB3-5030 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/04b3225c9d3146d89ba6bec3b4eb5e05
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spelling	oai:doaj.org-article:04b3225c9d3146d89ba6bec3b4eb5e052021-11-12T07:13:23ZAccurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?1942-246610.1029/2021MS002555https://doaj.org/article/04b3225c9d3146d89ba6bec3b4eb5e052021-08-01T00:00:00Zhttps://doi.org/10.1029/2021MS002555https://doaj.org/toc/1942-2466Abstract 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.Sarah M. GallupIan T. BakerJohn L. GallupNatalia Restrepo‐CoupeKatherine D. HaynesNicholas M. GeyerA. Scott DenningAmerican Geophysical Union (AGU)articleAmazongross primary productivity (GPP)model benchmarkingseasonalitytropical rainforestvariabilityPhysical geographyGB3-5030OceanographyGC1-1581ENJournal of Advances in Modeling Earth Systems, Vol 13, Iss 8, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Amazon gross primary productivity (GPP) model benchmarking seasonality tropical rainforest variability Physical geography GB3-5030 Oceanography GC1-1581
spellingShingle	Amazon gross primary productivity (GPP) model benchmarking seasonality tropical rainforest variability Physical geography GB3-5030 Oceanography GC1-1581 Sarah M. Gallup Ian T. Baker John L. Gallup Natalia Restrepo‐Coupe Katherine D. Haynes Nicholas M. Geyer A. Scott Denning Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?
description	Abstract 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.
format	article
author	Sarah M. Gallup Ian T. Baker John L. Gallup Natalia Restrepo‐Coupe Katherine D. Haynes Nicholas M. Geyer A. Scott Denning
author_facet	Sarah M. Gallup Ian T. Baker John L. Gallup Natalia Restrepo‐Coupe Katherine D. Haynes Nicholas M. Geyer A. Scott Denning
author_sort	Sarah M. Gallup
title	Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?
title_short	Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?
title_full	Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?
title_fullStr	Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?
title_full_unstemmed	Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?
title_sort	accurate simulation of both sensitivity and variability for amazonian photosynthesis: is it too much to ask?
publisher	American Geophysical Union (AGU)
publishDate	2021
url	https://doaj.org/article/04b3225c9d3146d89ba6bec3b4eb5e05
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Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?

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