Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask? Article Swipe
Sarah M. Gallup
,
Ian Baker
,
John L. Gallup
,
Natalia Restrepo‐Coupé
,
Katherine Haynes
,
N. M. Geyer
,
Scott Denning
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1029/2021ms002555
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1029/2021ms002555
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.
Related Topics
Concepts
Environmental science
Amazon rainforest
Variance (accounting)
Climate change
Covariance
Primary production
Sensitivity (control systems)
Meteorology
Climate model
Climatology
Precipitation
Eddy covariance
Atmospheric sciences
Econometrics
Statistics
Ecosystem
Mathematics
Geography
Ecology
Physics
Geology
Accounting
Biology
Electronic engineering
Business
Engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1029/2021ms002555
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2021MS002555
- OA Status
- gold
- Cited By
- 7
- References
- 186
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3082628956
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3082628956Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1029/2021ms002555Digital Object Identifier
- Title
-
Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-07-31Full publication date if available
- Authors
-
Sarah M. Gallup, Ian Baker, John L. Gallup, Natalia Restrepo‐Coupé, Katherine Haynes, N. M. Geyer, Scott DenningList of authors in order
- Landing page
-
https://doi.org/10.1029/2021ms002555Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2021MS002555Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2021MS002555Direct OA link when available
- Concepts
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Environmental science, Amazon rainforest, Variance (accounting), Climate change, Covariance, Primary production, Sensitivity (control systems), Meteorology, Climate model, Climatology, Precipitation, Eddy covariance, Atmospheric sciences, Econometrics, Statistics, Ecosystem, Mathematics, Geography, Ecology, Physics, Geology, Accounting, Biology, Electronic engineering, Business, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2023: 1, 2022: 2, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
186Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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