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\begin{document}

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



\author[1,1,1]{Kashif Mahmud}%
\author[2,2,2]{Joel Biederman}%
\author[3,3,3]{Russ Scott}%
\author[4,4,4]{Marcy Litvak}%
\author[5,5,5]{Thomas Kolb}%
\author[6,6,6]{Tilden Meyers}%
\author[6,6,6]{Praveena Krishnan}%
\author[7,7,7]{Vladislav Bastrikov}%
\author[1,1,1]{Natasha MacBean}%
\affil[1]{Indiana University}%
\affil[2]{USDA-ARS Southwest Watershed Research Center}%
\affil[3]{United States Department of Agriculture, Agricultural Research Service, Tucson, AZ 85719, USA}%
\affil[4]{University of New Mexico}%
\affil[5]{Northern Arizona University}%
\affil[6]{5NOAA/ARL Atmospheric Turbulence and Diffusion Division}%
\affil[7]{Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL}%


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  \date{May 24, 2024}


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\begin{abstract}
Drylands occupy \textasciitilde{}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, 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.%
\end{abstract}%



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