EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Pacific CO2 fluxes pattern analysis through SST clustering

Pradeebane Vaittinada Ayar and Jerry Tjiputra
Pradeebane Vaittinada Ayar and Jerry Tjiputra
  • NORCE, Climate, Bergen, Norway (

Elucidating the coherent spatio-temporal patterns of historical ocean CO2 fluxes is an essential step to understand the dominant drivers of their variability and predict how they may be altered by future climate change. Here, we applied an unsupervised classification of SST to tease out and assess the spatial and temporal variability of marine CO2 uptake in the Pacific basin. The classification is performed using a Gaussian Mixture Model (GMM) that decomposes the Probability Density Function of a dataset into a weighted sum of Gaussian distribution. Classification is performed on monthly SST anomalies from the JRA-55 reanalysis and CMIP6 historical simulations. The associated patterns of CO2 fluxes anomalies in both observations and models are evaluated for consistencies. Our objective is to determine the ability of the GMM-based clustering method, applied on surface temperature, to retrieve relevant physical mechanisms that predominantly explain the observed spatial and temporal CO2 fluxes patterns. The evolution of these clustering-based patterns, as projected by the models, under future scenario will also be presented.

How to cite: Vaittinada Ayar, P. and Tjiputra, J.: Pacific CO2 fluxes pattern analysis through SST clustering, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8286,, 2021.


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