Improving statistical projections of ocean dynamic sea-level change using pattern recognition techniques

Malagón-Santos, Víctor; Slangen, Aimée B. A.; Hermans, Tim H. J.; Dangendorf, Sönke; Marcos, Marta; Maher, Nicola

doi:https://doi.org/10.5194/os-19-499-2023

Articles | Volume 19, issue 2

https://doi.org/10.5194/os-19-499-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/os-19-499-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 2

Research article

|

21 Apr 2023

Research article |

| 21 Apr 2023

Improving statistical projections of ocean dynamic sea-level change using pattern recognition techniques

Víctor Malagón-Santos, Aimée B. A. Slangen, Tim H. J. Hermans, Sönke Dangendorf, Marta Marcos, and Nicola Maher

Supplement

https://doi.org/10.5194/os-19-499-2023-supplement

Data sets

MPI Grand Ensemble - ESGF ESGF https://esgf-data.dkrz.de/projects/mpi-ge/

CMIP5 data search CMIP5 https://esgf-node.llnl.gov/search/cmip5/

Short summary

Climate change will alter heat and freshwater fluxes as well as ocean circulation, driving local changes in sea level. This sea-level change component is known as ocean dynamic sea level (DSL), and it is usually projected using computationally expensive global climate models. Statistical models are a cheaper alternative for projecting DSL but may contain significant errors. Here, we partly remove those errors (driven by internal climate variability) by using pattern recognition techniques.