A machine learning approach to estimate surface ocean pCO(2) from satellite measurements

摘要

Surface seawater partial pressure of CO2 (pCO(2)) is a critical parameter in the quantification of air-sea CO2 flux, which further plays an important role in quantifying the global carbon budget and understanding ocean acidification. Yet, the remote estimation of pCO(2) in coastal waters (under influences of multiple processes) has been difficult due to complex relationships between environmental variables and surface pCO(2). To date there is no unified model to remotely estimate surface pCO(2) in oceanic regions that are dominated by different oceanic processes. In our study area, the Gulf of Mexico (GOM), this challenge is addressed through the evaluation of different approaches, including multi-linear regression (MLR), multi-nonlinear regression (MNR), principle component regression (PCR), decision tree, supporting vector machines (SVMs), multilayer perceptron neural network (MPNN), and random forest based regression ensemble (RFRE). After modeling, validation, and extensive tests using independent cruise datasets, the RFRE model proved to be the best approach. The RFRE model was trained using data comprised of extensive pCO(2) datasets (collected over 16 years by many groups) and MODIS (Moderate Resolution Imaging Spectroradiometer) estimated sea surface temperature (SST), sea surface salinity (SSS), surface chlorophyll concentration (Chl), and diffuse attenuation of downwelling irradiance (Kd). This RFRE-basedpCO(2) model allows for the estimation of surface pCO(2) from satellites with a spatial resolution of 1 km. It showed an overall performance of a root mean square difference (RMSD) of 9.1 mu atm, with a coefficient of determination (R-2) of 0.95, a mean bias (MB) of - 0.03 mu atm, a mean ratio (MR) of 1.00, an unbiased percentage difference (UPD) of 0.07%, and a mean ratio difference (MRD) of 0.12% for pCO(2) ranging between 145 and 550 mu atm. The model, with its original parameterization, has been tested with independent datasets collected over the entir