Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO2 Flux and its Inter-Annual Variability

Mahmud, Kashif; Scott, Russell L.; Biederman, Joel A.; Litvak, Marcy E.; Kolb, Thomas; Meyers, Tilden P.; Krishnan, Praveena; Bastrikov, Vladislav; MacBean, Natasha

doi:10.1029/2021jg006400

Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO₂ Flux and its Inter-Annual Variability

Journal Article · Wed Oct 06 00:00:00 EDT 2021 · Journal of Geophysical Research. Biogeosciences

DOI:https://doi.org/10.1029/2021jg006400· OSTI ID:1978552

^[1]; ^[2]; ^[2]; ^[3]; ^[4]; Meyers, Tilden P. ^[5]; ^[6]; ^[7]; ^[8]

Indiana Univ., Bloomington, IN (United States); OSTI
US Dept. of Agriculture (USDA), Tucson, AZ (United States). Agricultural Research Service (ARS)
Univ. of New Mexico, Albuquerque, NM (United States)
Northern Arizona Univ., Flagstaff, AZ (United States)
National Oceanic and Atmospheric Administration (NOAA), Oak Ridge, TN (United States). ARL Atmospheric Turbulence and Diffusion Division
National Oceanic and Atmospheric Administration (NOAA), Oak Ridge, TN (United States). ARL Atmospheric Turbulence and Diffusion Division; Oak Ridge Associated Universities (ORAU), Oak Ridge, TN (United States)
Univ. Paris-Saclay, Gif-sur-Yvette (France)
Indiana Univ., Bloomington, IN (United States)

Drylands occupy ~40% of the land surface and are thought to dominate global carbon (C) cycle inter-annual variability (IAV). Therefore, it is imperative that global terrestrial biosphere models (TBMs), which form the land component of IPCC earth system models, are able to accurately simulate dryland vegetation and biogeochemical processes. However, compared to more mesic ecosystems, TBMs have not been widely tested or optimized using in situ dryland CO₂ fluxes. Here, for this work, we address this gap using a Bayesian data assimilation system and 89 site-years of daily net ecosystem exchange (NEE) data from 12 southwest US Ameriflux sites to optimize the C cycle parameters of the ORCHIDEE TBM. The sites span high elevation forest ecosystems, which are a mean sink of C, and low elevation shrub and grass ecosystems that are either a mean C sink or “pivot” between an annual C sink and source. We find that using the default (prior) model parameters drastically underestimates both the mean annual NEE at the forested mean C sink sites and the NEE IAV across all sites. Our analysis demonstrated that optimizing phenology parameters are particularly useful in improving the model's ability to capture both the magnitude and sign of the NEE IAV. At the forest sites, optimizing C allocation, respiration, and biomass and soil C turnover parameters reduces the underestimate in simulated mean annual NEE. Our study demonstrates that all TBMs need to be calibrated for dryland ecosystems before they are used to determine dryland contributions to global C cycle variability and long-term carbon-climate feedbacks.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of New Mexico, Albuquerque, NM (United States)

Sponsoring Organization:: National Science Foundation (NSF); Science Foundation Arizona; USDA; USDOE Office of Science (SC)

Grant/Contract Number:: FG02-08ER46506

OSTI ID:: 1978552

Journal Information:: Journal of Geophysical Research. Biogeosciences, Journal Name: Journal of Geophysical Research. Biogeosciences Journal Issue: 10 Vol. 126; ISSN 2169-8953

Publisher:: American Geophysical UnionCopyright Statement

Country of Publication:: United States

Language:: English

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Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological "common sense" in a model–data fusion framework Bloom, A. A.; Williams, M. Biogeosciences, Vol. 12, Issue 5 https://doi.org/10.5194/bg-12-1299-2015	journal	January 2015
Environmental drivers of drought deciduous phenology in the Community Land Model Dahlin, K. M.; Fisher, R. A.; Lawrence, P. J. Biogeosciences, Vol. 12, Issue 16 https://doi.org/10.5194/bg-12-5061-2015	journal	January 2015
Using satellite data to improve the leaf phenology of a global terrestrial biosphere model MacBean, N.; Maignan, F.; Peylin, P. Biogeosciences, Vol. 12, Issue 23 https://doi.org/10.5194/bg-12-7185-2015	journal	January 2015
Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe De Kauwe, M. G.; Zhou, S. -X.; Medlyn, B. E. Biogeosciences, Vol. 12, Issue 24 https://doi.org/10.5194/bg-12-7503-2015	journal	January 2015
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The improvement of soil thermodynamics and its effects on land surface meteorology in the IPSL climate model Wang, F.; Cheruy, F.; Dufresne, J. -L. Geoscientific Model Development, Vol. 9, Issue 1 https://doi.org/10.5194/gmd-9-363-2016	journal	January 2016
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ORCHIDEE model outputs files and codes associated with the manuscript "Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO2 Flux and its Inter-Annual Variability" by Mahmud et al. (2021). Mahmud, Kashif figshare https://doi.org/10.6084/m9.figshare.14327489	dataset	January 2021
Evaluation of continental carbon cycle simulations with North American flux tower observations Raczka, Brett M.; Davis, Kenneth J.; Huntzinger, Deborah Figshare https://doi.org/10.6084/m9.figshare.c.3309804.v1	collection	January 2016

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59 BASIC BIOLOGICAL SCIENCES
carbon cycle
data assimilation
drylands
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Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO2 Flux and its Inter-Annual Variability

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Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO₂ Flux and its Inter-Annual Variability