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| Titel |
Using reactive transport codes to provide mechanistic biogeochemistry representations in land surface models: A proof of concept with CLM-PFLOTRAN |
| VerfasserIn |
Guoping Tang, Fengming Yuan, Gautam Bisht, Glenn Hammond, Peter Lichtner, Jitendra Kumar, Richard Mills, Xiaofeng Xu, Ben Andre, Scott Painter, Peter Thornton |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250127328
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| Publikation (Nr.) |
 EGU/EGU2016-7191.pdf |
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| Zusammenfassung |
| We explore coupling to a~configurable subsurface reactive transport
code as a~flexible and extensible approach to biogeochemistry in land
surface models.
A~reaction network with
the CLM-CN decomposition, nitrification, denitrification, and plant
uptake similar to CLM4.5 is used as an example. We implement the reactions in the
open-source PFLOTRAN code, coupled with the Community Land Model
(CLM), and test at Arctic, temperate, and tropical sites. To make the
reaction network designed for use in explicit time stepping in CLM
compatible with the implicit time stepping used in PFLOTRAN, the Monod
substrate rate-limiting function with a~residual concentration is used
to represent the limitation of nitrogen availability on plant uptake
and immobilization. Switching from explicit to implicit methods increases
numerical rigor but introduces computational challenges. Our objective is to
achieve accurate, efficient, and robust
numerical solutions to demonstrate the feasibility of CLM-PFLOTRAN soil biogeochemistry.
Our results suggest that care needs to be taken to use scaling, clipping,
or log transformation to avoid negative concentrations during the
Newton iterations.
With a~tight relative update tolerance to avoid
false convergence, an accurate solution can be achieved with about
50\,{\%} more computing time than CLM in point mode site simulations
using either the scaling or clipping methods. The log transformation
method takes 60--100\,{\%} more computing time than CLM. The
computing time increases slightly for clipping and scaling; it
increases substantially for log transformation for half saturation
decrease from $10^{-3}$ to $10^{-9}$\,\unit{mol\,m^{-3}}$, which
normally results in decreasing nitrogen concentrations. The frequent
occurrence of very low concentrations (e.g. below nanomolar) can
increase the computing time for clipping or scaling by about 20\,{\%};
computing time can be doubled for log transformation. Caution needs to
be taken in choosing the appropriate scaling factor because a~small
value caused by a~negative update to a~small concentration may
diminish the update and result in false convergence even with very
tight relative update tolerance. As some biogeochemical processes
(e.g., methane and nitrous oxide production and consumption) involve
very low half saturation and threshold concentrations, this work
provides insights for addressing nonphysical negativity issues and
facilitates the representation of a~mechanistic biogeochemical
description in earth system models to reduce climate prediction
uncertainty. |
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