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Artículo

Ocean biogeochemistry control on the marine emissions of brominated very short‐lived ozone‐depleting substances: A machine‐learning approach

Wang, Siyuan; Kinnison, Douglas E.; Montzka, Stephen A.; Apel, Eric C.; Hornbrook, Rebecca S.; Hills, Alan J.; Blake, Donald R.; Barletta, Barbara; Meinardi, Simone; Sweeney, Colm; Moore, Fred; Long, Matthew; Saiz-lopez, Alfonso; Fernandez, Rafael PedroIcon ; Tilmes, Simone; Emmons, Louisa K.; Lamarque, Jean-François
Fecha de publicación: 11/2019
Editorial: American Geophysical Union (AGU)
Revista: Journal of Geophysical Research: Atmospheres
ISSN: 2169-8996
e-ISSN: 2169-897X
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Otras Ciencias de la Computación e Información

Resumen

Halogenated very short lived substances (VSLS) affect the ozone budget in the atmosphere. Brominated VSLS are naturally emitted from the ocean, and current oceanic emission inventories vary dramatically. We present a new global oceanic emission inventory of Br‐VSLS (bromoform and dibromomethane), considering the physical forcing in the ocean and the atmosphere, as well as the ocean<br />biogeochemistry control. A data‐oriented machine‐learning emulator was developed to couple the air‐sea exchange with the ocean biogeochemistry. The predicted surface seawater concentrations and the surface atmospheric mixing ratios of Br‐VSLS are evaluated with long‐term, global‐scale observations; and the predicted vertical distributions of Br‐VSLS are compared to the global airborne observations in both boreal summer and winter. The global marine emissions of bromoform and dibromomethane are estimated to be 385 and 54 Gg Br per year, respectively. The new oceanic emission inventory of Br‐VSLS is more skillful than the widely used top‐down approaches for representing the seasonal/spatial variations and the<br />annual means of atmospheric concentrations. The new approach  improves the model predictability for the coupled Earth system model and can be used as a basis for investigating the past and future ocean emissions and feedbacks under climate change. This model framework can be used to calculate the bidirectional oceanic fluxes for other compounds of interest.
Palabras clave: MACHINE LEARNING APPROACH , HALÓGENOS VSL , CAM-CHEM , EMISIONES OCEÁNICAS
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info:eu-repo/semantics/embargoedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/110384
URL: https://onlinelibrary.wiley.com/doi/abs/10.1029/2019JD031288
DOI: http://dx.doi.org/10.1029/2019JD031288
Colecciones
Articulos(ICB)
Articulos de INSTITUTO INTERDISCIPLINARIO DE CIENCIAS BASICAS
Citación
Wang, Siyuan; Kinnison, Douglas E.; Montzka, Stephen A.; Apel, Eric C.; Hornbrook, Rebecca S.; et al.; Ocean biogeochemistry control on the marine emissions of brominated very short‐lived ozone‐depleting substances: A machine‐learning approach; American Geophysical Union (AGU); Journal of Geophysical Research: Atmospheres; 124; 12; 11-2019; 319-339
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