Predictability and feedbacks of the ocean-soil-plant-atmosphere water cycle: deep learning water conductance in Earth System Model | Zendy

Alexandre A. Renchon | Zendy; Roser Matamala | Zendy; Miquel A. GonzàlezMeler | Zendy; Zoë G. Cardon | Zendy; Sébastien Lacube | Zendy; Julie Jastrow | Zendy; Beth Drewniak | Zendy; Jules Cacho | Zendy; James Franke | Zendy

Open Access

Predictability and feedbacks of the ocean-soil-plant-atmosphere water cycle: deep learning water conductance in Earth System Model

Author(s) -

Alexandre A. Renchon,

Roser Matamala,

Miquel A. GonzàlezMeler,

Zoë G. Cardon,

Sébastien Lacube,

Julie Jastrow,

Beth Drewniak,

Jules Cacho,

James Franke

Publication year - 2021

Publication title -

osti oai (u.s. department of energy office of scientific and technical information)

Language(s) - English

Resource type - Reports

DOI - 10.2172/1769763

Subject(s) - predictability , environmental science , water cycle , data assimilation , stomatal conductance , earth system science , precipitation , water content , land cover , climatology , vegetation (pathology) , atmosphere (unit) , meteorology , atmospheric sciences , land use , geology , oceanography , ecology , geography , photosynthesis , medicine , physics , botany , geotechnical engineering , pathology , quantum mechanics , biology

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