ORCID Profile
0000-0003-3032-7875
Current Organisation
Colorado State University
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Publisher: American Geophysical Union (AGU)
Date: 07-01-2006
DOI: 10.1029/2004GB002439
Publisher: American Geophysical Union (AGU)
Date: 26-05-2007
DOI: 10.1029/2006JD007659
Publisher: American Geophysical Union (AGU)
Date: 22-07-2006
DOI: 10.1029/2006JD007428
Publisher: American Geophysical Union (AGU)
Date: 12-1996
DOI: 10.1029/96GB01892
Publisher: American Geophysical Union (AGU)
Date: 24-01-2004
DOI: 10.1029/2003GB002111
Publisher: American Geophysical Union (AGU)
Date: 03-2008
DOI: 10.1029/2007JG000562
Publisher: American Geophysical Union (AGU)
Date: 08-2021
DOI: 10.1029/2021MS002555
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.
Publisher: American Geophysical Union (AGU)
Date: 03-2006
DOI: 10.1029/2005GL025403
Publisher: American Geophysical Union (AGU)
Date: 08-2008
DOI: 10.1029/2007GB003050
Publisher: Springer Science and Business Media LLC
Date: 02-2002
DOI: 10.1038/415626A
Publisher: Stockholm University Press
Date: 04-1999
Publisher: American Geophysical Union (AGU)
Date: 26-11-2008
DOI: 10.1029/2007GB003081
Publisher: American Meteorological Society
Date: 08-2003
Abstract: The Common Land Model (CLM) was developed for community use by a grassroots collaboration of scientists who have an interest in making a general land model available for public use and further development. The major model characteristics include enough unevenly spaced layers to adequately represent soil temperature and soil moisture, and a multilayer parameterization of snow processes an explicit treatment of the mass of liquid water and ice water and their phase change within the snow and soil system a runoff parameterization following the TOPMODEL concept a canopy photo synthesis-conductance model that describes the simultaneous transfer of CO2 and water vapor into and out of vegetation and a tiled treatment of the subgrid fraction of energy and water balance. CLM has been extensively evaluated in offline mode and coupling runs with the NCAR Community Climate Model (CCM3). The results of two offline runs, presented as ex les, are compared with observations and with the simulation of three other land models [the Biosphere-Atmosphere Transfer Scheme (BATS), Bonan's Land Surface Model (LSM), and the 1994 version of the Chinese Academy of Sciences Institute of Atmospheric Physics LSM (IAP94)].
Location: United States of America
No related grants have been discovered for Scott Denning.