Using Gaussian Process Regression to disentangle marine carbonate system trends and variability

Substantial natural variability can obscure the detection of anthropogenic long-term trends in the marine carbonate system (e.g., ocean acidification). Yet the magnitude of the trends and variability remains poorly constrained due to limited marine carbonate system observations. Here, we use a Bayesian machine-learning approach based on Gaussian Process Regression (GPR) to decompose total variability of ocean acidification-related variables into seasonal, interannual and long-term components. The method is first applied to three decades of observations from the Line P carbon program, the longest marine carbonate system timeseries in the Northeast Pacific (1990-2019), typically taking samples three times per year. We found that over the period from 1990 to 2019, the local oceanic uptake of anthropogenic carbon dioxide from the atmosphere was the main driver of long-term changes in the marine carbonate system, including acidification. The seasonal cycle of dissolved inorganic carbon and the aragonite saturation state (both indicators of ocean acidification) was the dominant contributor to total variability in the top 60-70 m of the water column, with a mean surface seasonal amplitude of 35 ± 3 µmol kg−1 and 0.31 ± 0.04, respectively. In this depth range, the magnitude of the interannual variability was at least half of the seasonal variability for most variables. We then apply GPR to output from a global ocean biogeochemical model subsampled as per availability of observations, to assess the observational effort required to detect future ocean carbon trends, with a particular focus on detecting signals related to potential marine carbon dioxide removal interventions.