Spatiotemporal reconstruction of global ocean surface pCO2 based on optimized random forest

Huisheng Wu; Lejie Wang; Xiaochun Ling; Long Cui; Ruixue Sun; Na Jiang

doi:10.1016/j.scitotenv.2023.169209

Spatiotemporal reconstruction of global ocean surface pCO₂ based on optimized random forest

Sci Total Environ. 2024 Feb 20:912:169209. doi: 10.1016/j.scitotenv.2023.169209. Epub 2023 Dec 11.

Authors

Huisheng Wu¹, Lejie Wang², Xiaochun Ling³, Long Cui⁴, Ruixue Sun⁵, Na Jiang³

Affiliations

¹ College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong 266580, China. Electronic address: wuhuisheng@upc.edu.cn.
² College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong 266580, China. Electronic address: z22160024@s.upc.edu.cn.
³ Shandong Provincial Institute of Land Surveying and Mapping, Jinan, Shandong 250102, China.
⁴ College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong 266580, China. Electronic address: z22160008@s.upc.edu.cn.
⁵ College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong 266580, China.

PMID: 38092211
DOI: 10.1016/j.scitotenv.2023.169209

Abstract

The partial pressure of ocean surface CO₂ (pCO₂) plays an important role in quantifying the carbon budget and assessing ocean acidification. For such a vast and complex ocean system as the global ocean, most current research practices tend to study the ocean into regions. In order to reveal the overall characteristics of the global ocean and avoid mutual influence between zones, a holistic research method was used to detect the correlation of twelve predictive factors, including chlorophyll concentration (Chlor_a), diffuse attenuation coefficient at 490 nm (Kd_490), density ocean mixed layer thickness (Mlotst), eastward velocity (East), northward velocity (North), salinity (Sal), temperature (Temp), dissolved iron (Fe), dissolved silicate (Si), nitrate (NO₃), potential of hydrogen (pH), phosphate (PO₄), at the global ocean scale. Based on measured data from the Global Surface pCO₂ (LDEO) database, combined with National Aeronautics and Space Administration (NASA) Ocean Color satellite data and Copernicus Ocean reanalysis data, an improved optimized random forest (ORF) method is proposed for the overall reconstruction of global ocean surface pCO₂, and compared with various machine learning methods. The results indicate that the ORF method is the most accurate in overall modeling at the global ocean scale (mean absolute error of 6.27μatm, root mean square error of 15.34μatm, R² of 0.92). Based on independent observations from the LDEO dataset and time series observation stations, the ORF model was further validated, and the global ocean surface pCO₂ distribution map of 0.25° × 0.25° for 2010 to 2019 was reconstructed, which is of significance for the global ocean carbon cycle and carbon assessment.

Keywords: Geographic detector; Global ocean; Machine learning; Ocean surface pCO(2); Random forests; Satellite remote sensing.