Pulse Brain · Growing Health Evidence Index
Peer-reviewed

A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach

Daniel Broullón, Fı́z F. Pérez, A. Velo, Mario Hoppema, Are Olsen, Taro Takahashi, Robert M. Key, Toste Tanhua, J. Magdalena Santana‐Casiano, Alex Kozyr

Earth system science data · 2020

Read source ↗ All evidence

Summary

Abstract. Anthropogenic emissions of CO2 to the atmosphere have modified the carbon cycle for more than 2 centuries. As the ocean stores most of the carbon on our planet, there is an important task in unraveling the natural and anthropogenic processes that drive the carbon cycle at different spatial and temporal scales. We contribute to this by designing a global monthly climatology of total dissolved inorganic carbon (TCO2), which offers a robust basis in carbon cycle modeling but also for other studies related to this cycle. A feedforward neural network (dubbed NNGv2LDEO) was configured to extract from the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2.2019) and the Lamont–Doherty Earth Observatory (LDEO) datasets the relations between TCO2 and a set of variables related to

Source type
Peer-reviewed study
DOI
10.5194/essd-12-1725-2020
Catalogue ID
BFmoakvhu2-n1tzop
Pulse AI · ask about this record

Dig deeper with Pulse AI.

Pulse AI has read the whole catalogue. Ask about this record, its theme, or how the findings apply to UK farming and policy — every answer cites the underlying studies.