Understanding and modeling climate impacts on ecosystem dynamics with FLUXNET data and artificial intelligence
Abstract
Since preindustrial era, the radiative energy balance of the earth system has been largely perturbed by anthropogenic activities such as CO2 emissions from fossil fuel burning. As a net effect, global temperature increasingly warms up and will further increase in the future if CO2 concentration in the atmosphere keeps going up. Plants sequestrate a large amount of atmospheric CO2 via photosynthesis, thus greatly mediate the global warming. In this study, we aim to model the temporal dynamics of photosynthesis for various different vegetation types and further understand controlling factors of photosynthesis machinery. Our results showed that the photosynthesis and its interactions with climate drivers, such as temperature, precipitatin, radiation, and vapor pressure deficit, has an internal system memory about 14 days. Thus, the predictive model could be best trained with historical data of the past two weeks and could best predict future temporal evolution of photosynthesis in the following two weeks. Our leave-one-out experiment also showed that temperature and solar radiation dramatically control grassland and forest photosynthesis activity.
- Publication:
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IOP Conference Series: Earth and Environmental Science
- Pub Date:
- April 2019
- DOI:
- 10.1088/1755-1315/257/1/012005
- Bibcode:
- 2019E&ES..257a2005Z