An improved parameterization of leaf area index (LAI) seasonality in the Canadian Land Surface Scheme (CLASS) and Canadian Terrestrial Ecosystem Model (CTEM) modelling framework
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Published:2018-11-19
Issue:22
Volume:15
Page:6885-6907
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ISSN:1726-4189
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Container-title:Biogeosciences
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language:en
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Short-container-title:Biogeosciences
Author:
Asaadi Ali, Arora Vivek K., Melton Joe R.ORCID, Bartlett PaulORCID
Abstract
Abstract. Leaf area index (LAI) and its seasonal dynamics are key determinants of
vegetation productivity in nature and as represented in terrestrial biosphere
models seeking to understand land surface atmosphere flux dynamics and its
response to climate change. Non-structural carbohydrates (NSCs) and their
seasonal variability are known to play a crucial role in seasonal variation
in leaf phenology and growth and functioning of plants. The carbon stored in
NSC pools provides a buffer during times when supply and demand of carbon are
asynchronous. An example of this role is illustrated when NSCs from previous
years are used to initiate leaf onset at the arrival of favourable weather
conditions. In this study, we incorporate NSC pools and associated
parameterizations of new processes in the modelling framework of the Canadian
Land Surface Scheme-Canadian Terrestrial Ecosystem Model (CLASS–CTEM) with
an aim to improve the seasonality of simulated LAI. The performance of these
new parameterizations is evaluated by comparing simulated LAI and
atmosphere–land CO2 fluxes to their observation-based estimates,
at three sites characterized by broadleaf cold deciduous trees selected from
the FLUXNET database. Results show an improvement in leaf onset and offset
times with about 2 weeks shift towards earlier times during the year in
better agreement with observations. These improvements in simulated LAI help
to improve the simulated seasonal cycle of gross primary productivity (GPP)
and as a result simulated net ecosystem productivity (NEP) as well.
Publisher
Copernicus GmbH
Subject
Earth-Surface Processes,Ecology, Evolution, Behavior and Systematics
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