Identifying Drivers of Sea Surface pCO₂ via the Lag-Convergent Cross Mapping Model

Abstract. In the context of global climate change, analyzing the driving factors of key ocean carbon cycle parameters is crucial for accurately quantifying ocean carbon sink capacity. The Convergent Cross-Mapping (CCM) method provides an effective approach for causal inference in nonlinear systems, yet it cannot characterize the ubiquitous time-lag effects within ocean carbon cycle systems, which readily results in underestimated causal strength and misidentified causal relationships. To address this limitation, this study incorporates the causal time-lag parameter (Lc) into the CCM framework as an optimized variable, and proposes a Lag-Convergent Cross Mapping (L-CCM) approach that accounts for optimal causal time lags, thereby constructing a time-lag embedded causal inference model. Taking the subtropical Northwest Pacific as the study domain, this study combines monthly causal screening and daily quantitative time-lag analysis to identify 18 potential drivers of sea surface pCO₂. Results show that L-CCM detects optimal causal time lags, under which the average causal strength of all drivers increased by 10.32 %. Strong causal drivers of sea surface pCO₂ include sea surface temperature (SST), sea surface salinity (SSS), chlorophyll a concentration (Chl), pH, etc., whereas weak causal factors cover surface zonal and meridional currents (Uo, Vo), zonal and meridional geostrophic currents (Ugos, Vgos). This method effectively reduces underestimation and misidentification of causal links, offers a reliable framework for ocean carbon cycle causal analysis, and holds promise for wider applications.