| 02 Sep 2026
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.
;;;;;;;;;; General comment
In this manuscript authors present methods (CMM and L-CMM) to investigate the drivers of the oceanic surface pCO2. This is applied in the North-West Pacific subtropical region using SOCAT and CMES data-sets for the period 2000 to 2020. Authors conclude that “SST, Si, PO43-, O2, TCO2 and TALK serve as core dominant drivers of pCO2 variation”. I guess such result is known since decades (e.g. Takahashi et al 1993, 2002). What is new here and how this helps interpreting the processes that drive the ocean CO2 cycle (and air-sea CO2 fluxes) ? As the results confirm many previous studies and because authors have in hand the global SOCAT data-set, I suggest the authors extend their analysis to the global ocean, e.g. applying the methods to all biomes (Fay and McKinley, 2014)
In its present form the paper is not suitable for publication and need major revision. Figures need to be revised and I think some figures are missing. A list of the data-sets used should be presented in a table with associated references. I’ve listed below specific comments and questions.
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Specific comments
C-01, Line 24: Authors write: ”Against the backdrop of global climate change, the ocean, as one of the largest active carbon sinks, plays a critical buffering role in the climate system by regulating atmospheric CO₂ concentrations (Tjiputra et al., 2025)”. Is Tjiputra et al., (2025) who used models relevant reference for this note. I think Friedlingstein et al, (2025) should also be refer.
C-02: Section “2.1.1 Overview of the Study Area”. A map of the region with a scheme of circulation would be useful for readers not familiar with this region. Such map could include the mean air-sea CO2 fluxes as color code.
C-03, Line 73: Authors write: ”We focused on the subtropical waters of the northwestern Pacific (28° N–40° N, 140° E–180° E). As one of the world’s major ocean carbon sinks(Chen et al., 2025), this region exhibits substantial air-sea CO₂ exchange fluxes and is shaped by complex ocean dynamic processes, making it an ideal study area for identifying causal relationships among key components and driving factors of the ocean carbon sink.” Why focusing in the NW SBT Pacific ? In this region, it is well known that pCO2 is mainly driven by SST (e.g. Inoue et al, 1999; Takahashi et al, 2002). I think authors should list previous studies and processes identified and quantified) as main drivers of pCO2 at seasonal to multi-decadal scales.
C-04, Line 77: Authors write: ”We selected sea surface partial pressure of carbon dioxide (pCO₂) as the core parameter characterizing the ocean carbon sink, and identified its potential driving factors”. Please, explain why you prefer to select pCO2 rather than DIC that is a core parameter of the CO2 cycle (recall that pCO2 is the result of DIC, ALK and SST).
C-05, Line 79: Why pH is listed in the driven variables ?
C-06, Line 91: Authors write: ”The sea surface pCO₂ observations were retrieved from the Surface Ocean CO₂ Atlas (SOCAT) (Bakker et al., 2016). ” Please specify the SOCAT version and present a map of the SOCAT data you used. Specify the SOCAT data selected (both Cruises and fCO2 flags). As SOCAT data are for fCO2, did you convert the data to pCO2 ?
C-07, Line 98: Authors write: ”The data spanned from 2000 to 2020 at a monthly resolution”. Why this period is selected ? Because of available data ? Other reasons (e.g. Post 1998 ENSO ?).
C-08, Line 100: (b) Daily-scale Data. Original SOCAT data are also at local/daily scales. Why not using the original SOCAT data to get high frequency scale.
C-09: Figure 1 Caption: Sentences repeated.
C-10, Line 214: Authors write: ”Among these, pH and SST exhibited the highest cross-mapping skill with pCO₂, with ρ values reaching 0.9467 (pCO₂ xmap pH) and 0.8064 (pCO₂ xmap SST), respectively, indicating a tight coupling relationship between these factors and pCO₂”. I probably understand the high skill for SST in the SBT region but I don’t understand the link with pH. By the way, what are the pH data used ? I guess this is not clearly listed in the manuscript. Are the pH data independent from pCO2 ? Is it measured or calculated pH ?
C-11: Figure 2: Difficult to read (somehow fuzzy).
C-12: Figure 3: Difficult to read (somehow fuzzy).
C-13, Line 310: Authors write: ”For example, the ρ value of pH was 0.5969 at Lc = 0; after incorporating the optimal lag of 23 days, ρ increased to 0.7748, with an improvement of approximately 23 %. For MLOTST, ρ was 0.7271 at Lc = 0; after incorporating the optimal lag of 31 days, ρ rose to 0.9205, representing a 21 % improvement.” I don’t understand why pH would directly drive pCO2 (instead of DIC and or TALK); what do we learn here on the processes governing the CO2 cycle and air-sea CO2 fluxes ?
C-14, Line 335: Authors write: ”Chl, pH and Vwind showed lower ρ values at the daily-scale (Lc = 0) than at the monthly-scale, which may be attributed to insufficient data volume”. To better understand this description authors should indicate somewhere the number of data used. I think a table with the list of parameters and reference of data-sets is needed.
;;;;;;;;; References added in this review not listed in the MS
Fay, A. R., and McKinley, G. A.: Global open-ocean biomes: Mean and temporal variability. Earth System Science Data, 6(2), 273–284. https://doi.org/10.5194/essd-6-273-2014, 2014
Friedlingstein, P., et al: Global Carbon Budget 2024, Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, 2025.
Inoue, H.Y., Ishii, M., Matsueda, H., Saito, S., Midorikawa, T. and Nemoto, K. (1999) ‘MRI measurements of partial pressure of CO2 in surface waters of the Pacific during 1968 to 1970: re-evaluation and comparison of data with those of the 1980s and 1990s’, Tellus B: Chemical and Physical Meteorology, 51(4), p. 830–848. Available at: https://doi.org/10.3402/tellusb.v51i4.16492.
Takahashi, T., Olafsson, J., Goddard, J. G., Chipman, D. W., and Sutherland, S. C.: Seasonal variation of CO2 and nutrients in the high-latitude surface oceans: A comparative study, Global Biogeochem. Cycles, 7, 843–878, https://doi.org/10.1029/93GB02263, 1993.
;;;;;;;;;;;;;;;;;;;;;;;;;;;; end review