Full Implementation of Matrix Approach to Biogeochemistry Module of CLM5

Xingjie Lu, Zhenggang Du, Yuanyuan Huang, David Lawrence, Erik Kluzek, Nathan Collier, Danica Lombardozzi, Negin Sobhani, Edward A.G. Schuur, Yiqi Luo

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Earth system models (ESMs) have been rapidly developed in recent decades to advance our understanding of climate change-carbon cycle feedback. However, those models are massive in coding, require expensive computational resources, and have difficulty in diagnosing their performance. It is highly desirable to develop ESMs with modularity and effective diagnostics. Toward these goals, we implemented a matrix approach to the Community Land Model version 5 (CLM5) to represent carbon and nitrogen cycles. Specifically, we reorganized 18 balance equations each for carbon and nitrogen cycles among the 18 vegetation pools in the original CLM5 into two matrix equations. Similarly, 140 balance equations each for carbon and nitrogen cycles among the 140 soil pools were reorganized into two additional matrix equations. The vegetation carbon and nitrogen matrix equations are connected to soil matrix equations via litterfall. The matrix equations fully reproduce simulations of carbon and nitrogen dynamics by the original model. The computational cost for forwarding simulation of the CLM5 matrix model was 26% more expensive than the original model, largely due to calculation of additional diagnostic variables, but the spin-up computational cost was significantly saved. We showed a case study on modeled soil carbon storage under two forcing data sets to illustrate the diagnostic capability that the matrix approach uniquely offers to understand simulation results of global carbon and nitrogen dynamics. The successful implementation of the matrix approach to CLM5, one of the most complex land models, demonstrates that most, if not all, the biogeochemical models can be reorganized into the matrix form to gain high modularity, effective diagnostics, and accelerated spin-up.

Original languageEnglish
Article numbere2020MS002105
JournalJournal of Advances in Modeling Earth Systems
Volume12
Issue number11
DOIs
StatePublished - Nov 2020

Funding

This research has been supported by the National Key Research and Development Program of China under grants 2017YFA0604300, 2017YFA0604600 and 2016YFB0200801, U.S. Department of Energy grants DE‐SC0006982, 4000161830, U.S. National Science Foundation (NSF) grants DEB 1655499 and 2017884, subcontract 4000158404 from Oak Ridge National Laboratory (ORNL) to Northern Arizona University, and the Natural Science Foundation of China under grants 41575072, 41575092, 41730962, and U1811464. We thank William Wieder for the discussion on the model implementation, and we thank Olson Keith for conducting initial test of the computational cost. We thank two anonymous reviewers' insightful suggestions. This research has been supported by the National Key Research and Development Program of China under grants 2017YFA0604300, 2017YFA0604600 and 2016YFB0200801, U.S. Department of Energy grants DE-SC0006982, 4000161830, U.S. National Science Foundation (NSF) grants DEB 1655499 and 2017884, subcontract 4000158404 from Oak Ridge National Laboratory (ORNL) to Northern Arizona University, and the Natural Science Foundation of China under grants 41575072, 41575092, 41730962, and U1811464. We thank William Wieder for the discussion on the model implementation, and we thank Olson Keith for conducting initial test of the computational cost. We thank two anonymous reviewers' insightful suggestions.

FundersFunder number
U.S. National Science Foundation
William Wieder
National Science Foundation1636476, 1655499, 4000158404, 2017884, DEB 1655499
U.S. Department of EnergyDE‐SC0006982, 4000161830
Oak Ridge National Laboratory
Northern Arizona University
National Natural Science Foundation of China41730962, U1811464, 41575072, 41575092
National Key Research and Development Program of China2016YFB0200801, 2017YFA0604600, 2017YFA0604300

    Keywords

    • biogeochemistry modeling
    • diagnostic
    • matrix approach
    • model structure
    • modularity
    • traceability analysis

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