Mathematical Reconstruction of Land Carbon Models From Their Numerical Output: Computing Soil Radiocarbon From 12C Dynamics

Abstract Radiocarbon (14C) is a powerful tracer of the global carbon cycle that is commonly used to assess carbon cycling rates in various Earth system reservoirs and as a benchmark to assess model performance. Therefore, it has been recommended that Earth System Models (ESMs) participating in the C...

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Main Authors: Holger Metzler, Qing Zhu, William Riley, Alison Hoyt, Markus Müller, Carlos A. Sierra
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2020-01-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
Online Access:https://doi.org/10.1029/2019MS001776
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spelling doaj-3f158c6cafb146d191106297604d95552021-04-08T18:46:33ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662020-01-01121n/an/a10.1029/2019MS001776Mathematical Reconstruction of Land Carbon Models From Their Numerical Output: Computing Soil Radiocarbon From 12C DynamicsHolger Metzler0Qing Zhu1William Riley2Alison Hoyt3Markus Müller4Carlos A. Sierra5Max Planck Institute for Biogeochemistry Jena GermanyClimate and Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA USAClimate and Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA USAMax Planck Institute for Biogeochemistry Jena GermanyMax Planck Institute for Biogeochemistry Jena GermanyMax Planck Institute for Biogeochemistry Jena GermanyAbstract Radiocarbon (14C) is a powerful tracer of the global carbon cycle that is commonly used to assess carbon cycling rates in various Earth system reservoirs and as a benchmark to assess model performance. Therefore, it has been recommended that Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 report predicted radiocarbon values for relevant carbon pools. However, a detailed representation of radiocarbon dynamics may be an impractical burden on model developers. Here, we present an alternative approach to compute radiocarbon values from the numerical output of an ESM that does not explicitly represent these dynamics. The approach requires computed 12C stocks and fluxes among all carbon pools for a particular simulation of the model. From this output, a time‐dependent linear compartmental system is computed with its respective state‐transition matrix. Using transient atmospheric 14C values as inputs, the state‐transition matrix is then applied to compute radiocarbon values for each pool, the average value for the entire system, and component fluxes. We demonstrate the approach with ELMv1‐ECA, the land component of an ESM model that explicitly represents 12C, and 14C in 7 soil pools and 10 vertical layers. Results from our proposed method are highly accurate (relative error <0.01%) compared with the ELMv1‐ECA 12C and 14C predictions, demonstrating the potential to use this approach in CMIP6 and other model simulations that do not explicitly represent 14C.https://doi.org/10.1029/2019MS001776carbon cycle modelscompartmental systemsradiocarbonmodel diagnosticsdynamical systemsEarth system models
collection DOAJ
language English
format Article
sources DOAJ
author Holger Metzler
Qing Zhu
William Riley
Alison Hoyt
Markus Müller
Carlos A. Sierra
spellingShingle Holger Metzler
Qing Zhu
William Riley
Alison Hoyt
Markus Müller
Carlos A. Sierra
Mathematical Reconstruction of Land Carbon Models From Their Numerical Output: Computing Soil Radiocarbon From 12C Dynamics
Journal of Advances in Modeling Earth Systems
carbon cycle models
compartmental systems
radiocarbon
model diagnostics
dynamical systems
Earth system models
author_facet Holger Metzler
Qing Zhu
William Riley
Alison Hoyt
Markus Müller
Carlos A. Sierra
author_sort Holger Metzler
title Mathematical Reconstruction of Land Carbon Models From Their Numerical Output: Computing Soil Radiocarbon From 12C Dynamics
title_short Mathematical Reconstruction of Land Carbon Models From Their Numerical Output: Computing Soil Radiocarbon From 12C Dynamics
title_full Mathematical Reconstruction of Land Carbon Models From Their Numerical Output: Computing Soil Radiocarbon From 12C Dynamics
title_fullStr Mathematical Reconstruction of Land Carbon Models From Their Numerical Output: Computing Soil Radiocarbon From 12C Dynamics
title_full_unstemmed Mathematical Reconstruction of Land Carbon Models From Their Numerical Output: Computing Soil Radiocarbon From 12C Dynamics
title_sort mathematical reconstruction of land carbon models from their numerical output: computing soil radiocarbon from 12c dynamics
publisher American Geophysical Union (AGU)
series Journal of Advances in Modeling Earth Systems
issn 1942-2466
publishDate 2020-01-01
description Abstract Radiocarbon (14C) is a powerful tracer of the global carbon cycle that is commonly used to assess carbon cycling rates in various Earth system reservoirs and as a benchmark to assess model performance. Therefore, it has been recommended that Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 report predicted radiocarbon values for relevant carbon pools. However, a detailed representation of radiocarbon dynamics may be an impractical burden on model developers. Here, we present an alternative approach to compute radiocarbon values from the numerical output of an ESM that does not explicitly represent these dynamics. The approach requires computed 12C stocks and fluxes among all carbon pools for a particular simulation of the model. From this output, a time‐dependent linear compartmental system is computed with its respective state‐transition matrix. Using transient atmospheric 14C values as inputs, the state‐transition matrix is then applied to compute radiocarbon values for each pool, the average value for the entire system, and component fluxes. We demonstrate the approach with ELMv1‐ECA, the land component of an ESM model that explicitly represents 12C, and 14C in 7 soil pools and 10 vertical layers. Results from our proposed method are highly accurate (relative error <0.01%) compared with the ELMv1‐ECA 12C and 14C predictions, demonstrating the potential to use this approach in CMIP6 and other model simulations that do not explicitly represent 14C.
topic carbon cycle models
compartmental systems
radiocarbon
model diagnostics
dynamical systems
Earth system models
url https://doi.org/10.1029/2019MS001776
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