Bayesian correction of satellite-derived pCO2 reveals the biogeochemical impact of hurricanes on ocean–atmosphere CO2 fluxes

Bayesian correction of satellite-derived pCO2 reveals the biogeochemical impact of hurricanes on ocean–atmosphere CO2 fluxes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bayesian correction of satellite-derived pCO2 reveals the biogeochemical impact of hurricanes on ocean–atmosphere CO2 fluxes Gabriela Cervantes-Diaz, Coronado-Álvarez Luz de Lourdes Aurora, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8253374/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The exchange of CO₂ between the ocean and atmosphere is a key process in the global carbon cycle, highly sensitive to disturbances causes by hurricanes and tropical storms. In this study, we applied a Bayesian Recurrent Neural Network (BNNR) to correct satellite-derived surface pCO 2 estimates from the PISCES-NEMO model in the eastern tropical Pacific, off the coast of Guerrero, Mexico. The model was calibrated using in situ data collected during Tropical Storm Lidia (2017) and supplemented with synthetic cases representing extreme wind conditions. Under fair-weather conditions, the BNNR achieved low errors (MAE = 0.76 µatm; RMSE = 0.99 µatm). Although errors increased under simulated extreme winds, the model successfully captured abrupt increases in pCO 2 . During Hurricane John (2024), corrected values exceeded 850 µatm, revealing strong CO 2 degassing pulses that contributed up to one-third of the seasonal air-sea CO₂ flux, a magnitude largely underestimated by deterministic products. By explicitly incorporating uncertainty through credibility intervals, the BNNR provided robust predictions in highly variable conditions. This methodological framework offers a novel tool for improving the monitoring of marine carbon cycle dynamics and for quantifying the biogeochemical impact of extreme events in coastal regions. probabilistic deep learning Bayesian neural networks cyclonic events satellite oceanography carbon dioxide flux Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The ocean is an essential regulator of the global climate system, and at the same time, a dynamic component of the carbon cycle. Among the most relevant processes is the exchange of carbon dioxide (CO₂) between the atmosphere and the sea surface, a phenomenon strongly conditioned by physical and biogeochemical factors that exhibit marked spatial and temporal variability. Despite advances in coupled ocean models and satellite observations, estimating the partial pressure of CO 2 (pCO 2 ) at the sea surface remains a challenge, particularly in tropical regions affected by hurricanes and tropical storms. These areas, characterized by heterogeneity in temperature, salinity, surface winds, and primary productivity, require innovative methodologies that integrate multiple sources of information to more realistically represent air-sea carbon fluxes (FCO 2 ). Satellite monitoring and coupled biogeochemical models, such as PICES-NEMO, have expanded our capacity to study ocean-atmosphere interactions at high spatial and temporal resolution. These systems incorporate key variables — including sea surface temperature (SST), surface salinity (SSS), chlorophyll concentration (Chl-a), absolute dynamic topography (ADT), and wind components (u, v) — which serve as essential inputs for characterizing ocean conditions and driving predictive models. However, these products often underestimate transient phenomena driven by intense physical forcing, with reported errors exceeding 90 µatm in dynamic coastal environments. Improving their accuracy requires methods that integrate multiple data sources and account for uncertainty. Neural networks have established themselves as key tools for modeling nonlinear relationships in complex systems with multiple variables. In particular, Bayesian recurrent neural networks (BNNR) combine the capacity to represent temporal dynamics with explicit uncertainty quantification, an advantage over deterministic approaches. Their application is especially valuable in oceanography, where the availability of in situ data is limited and natural variability is high, as they enable the dynamic representation of physical and biogeochemical processes while generating credible intervals. Recent studies demonstrate that BNNRs can correct systematic biases in satellite-derived products and more effectively capture anomalies associated with cyclonic forcing, such as abrupt increases in pCO 2 induced by tropical cyclones. Overall, neural models—whether feed-forward, recurrent, or Bayesian—have consistently recovered pCO 2 in oceanic and coastal environments, with root mean square errors (RMSE) below 25 µatm. Previous neural network applications, including feed-forward and ensemble models, have reconstructed surface pCO 2 patterns at global and regional scales with reasonable skill (e.g. ANN and XGBoost in the Bay Bengal archived r ~ 0.75 and ± 12 µatm error; Joshi et al., 2022 ). However, strictly Bayesian implementations remain rare, and their capacity to capture abrupt anomalies during extreme events such as hurricanes has yet to be systematically evaluated. In this regard, Bayesian neural networks provide crucial added value by allowing explicit estimation of predictive uncertainty and rigorous incorporation of prior knowledge. These characteristics are essential in applications that require high levels of reliability, such as in areas with low sampling or in contexts dominated by high-energy meteorological events where data extrapolation involves significant risks (Clare et al., 2022 ). This study applies a BNNR to correct surface pCO 2 estimates from the PISCES-NEMO model in the eastern tropical Pacific, near Guerrero, Mexico (10°–18° N, 90°–110° W). The model was calibrated using high-frequency buoy data from Tropical Storm Lidia (2017) and tested for three contrasting meteorological systems events that occurred in 2024: Hurricane Ileana (category 2), Hurricane John (category 5), and Tropical Depression Eleven-E. Our objectives were to (1) reduce biases in satellite-derived pCO 2 , (2) incorporate predictive uncertainty, and (3) evaluate the contribution of tropical cyclones to regional air–sea CO₂ fluxes. This approach enabled the bias correction of systematic biases in modeled pCO 2 , the explicit estimation of uncertainty, and the calculation of corrected air–sea CO 2 fluxes. Collectively, these advances contribute to understanding marine carbon dynamics during high-energy meteorological events and have implications for climate oceanography, fisheries management, and coastal resilience (IPCC, 2021). 2. Methodology 2.1 Study area and period The analysis focused on the eastern tropical Pacific, off the coast of Guerrero, Mexico (10° – 18° N, 90° – 110° W), a region characterized by high physical-biogeochemical variability and frequent tropical cyclone activity. The study period extended from May to November 2024 and included three contrasting events: Hurricane Ileana (September 11–17, category 2; Berg, 2024 ), Hurricane John (September 22–25, category 5; Pasch, 2025 ), and Tropical Storm Eleven-E (October 1–3; Blake, 2025 ) (Fig. 1). The classification was made according to the Saffir–Simpson scale (CNH, 2017). These atmospheric systems generated winds exceeding 70 m s⁻¹, abrupt decreases in sea surface temperature (SST), and notable increases in surface pCO 2 . Their inclusion allowed for a comprehensive assessment of the impact of extreme forcings on air–sea CO₂ dynamics at subseasonal scales. Figure 1. Study area showing the trajectories of tropical cyclones that impacted the eastern tropical Pacific during 2024: Hurricane Ileana (September 11–17; green line), Hurricane John (September 22–27; blue line), and Tropical Storm Eleven-E (October 1–4; pink line). For reference, the trajectory of Hurricane Lidia (August 30–September 3, 2017; yellow line) is also included. The red line within the Gulf of California denotes the path of a drifting buoy deployed during Tropical Storm Lidia, which provided high-frequency in situ measurements of pCO₂, SST, and SSS for model calibration. 2.2 In situ data for model calibration High-frequency in situ data on pCO 2 (partial pressure of carbon dioxide), sea surface temperature (SST), and sea surface salinity (SSS) were collected during the passage of Tropical Storm Lidia (August 30–September 3, 2017) using an oceanographic buoy located in the Gulf of California (Fig. 1). These data were used to calibrate and validate the bias-correction model. Because satellite pCO 2 products were not available for 2017, predictor variables derived from the Copernicus Marine Environment Monitoring Service (CMEMS) and the Global Carbon Project ( 10.48670/moi-00015 ) were used to achieve temporal alignment, thus allowing the construction of a proxy training set under similar physical-biogeochemical conditions. This step was essential for calibrating the model under real forcing scenarios. 2.3 Satellite and modeled datasets Daily and hourly variables from satellite products and CMEMS L4 reanalyses, together with data from the Global Carbon Project, were integrated. Key variables included: SST (°C), SSS (CMEMS, 10.48670/moi-00016 ), zonal and meridional wind components (u, v; Hersbach et al., 2023 ), chlorophyll a concentration (Chl-a; mg m⁻³; 10.48670/moi-00015 ), and absolute dynamic topography (ADT, cm; https://doi.org/10.48670/moi-00149 ). Simulated surface pCO 2 fields were obtained from the PISCES-NEMO biogeochemical model of the CMEMS L4 satellite product ( 10.48670/moi-00015 ). All datasets were interpolated to a standard regular grid with a spatial resolution of 0.083° and coupled at a daily frequency. 2.4 Data fusion and preprocessing All variables were processed in Python (Python Software Foundation, 2023 ), utilizing the NumPy, Xarray, and Pandas libraries. Wind speed and daily maximum wind (m s⁻¹) were calculated from the u and v components. The final set included seven predictors: SST, SSS, Chl-a, ADT, and wind speed. Missing values were imputed conservatively. All data were normalized using MinMax scaling and interpolated spatially and temporally to fit a regular grid. The wind components were converted to daily magnitude (speed), and the maximum speed per day was also calculated. 2.5 Generation of synthetic data To increase the robustness of the model under extreme conditions, synthetic data were generated to represent hurricane-like forcing. Multivariate Gaussian distributions with controlled noise (“jitter”) were derived from observed conditions during Lidia to simulate cases characterized by wind speeds up to 70 m·s⁻¹, low SST and SSS, and high pCO 2 values (800–850 µatm). These synthetic data were added exclusively for September 23, 2024 (the day Hurricane John struck), to prevent overfitting and maintain the model’s generalization capacity. 2.6 Bayesian neural network model A Bayesian recurrent neural network (BNNR) was implemented in TensorFlow Probability (Abadi et al., 2016 ; Dillon et al., 2017 ), and LSTMFlipout and DenseFlipout layers (Wen et al., 2018 ) were used to estimate posterior distributions of weights and quantify predictive uncertainty. The model was trained exclusively on the Lidia event dataset, which includes both real and synthetic data. Training was performed using variational inference, employing the negative log-likelihood loss function and 1000 Monte Carlo samples. The architecture included an LSTM layer with 32 neurons, followed by a Bayesian dense layer, a ReLU activation (Rectified Linear Unit), and a linear output. The output consisted of corrected pCO 2 values referenced to the oceanographic buoy. 2.7 Application of the model to the 2024 dataset The trained BNNR model was applied to the pCO 2 dataset from the PISCES-NEMO output (Aumont et al., 2015 ) off the coast of Guerrero for the period from May to November 2024. Daily maps of corrected pCO 2 were generated along with their predictive uncertainty. On September 23, a hybrid bias-correction was implemented that combined model predictions with physical spatial anomalies (based on wind and chlorophyll a), resulting in increased pCO 2 of up to + 300 µatm in areas with strong forcing. This strategy aimed to replicate patterns similar to those observed during Lidia, thereby ensuring spatial consistency. 2.8 Estimation of sea–atmosphere CO₂ flux The CO₂ flux between the ocean and the atmosphere (FCO 2 ) was calculated using the standard formulation described by Liss and Merlivat ( 1986 ) and the transfer coefficient from Wanninkhof ( 2014 ): $$\:\:{FCO}_{2}={k*\:k}^{{\prime\:}}*{\varDelta\:pCO}_{2}$$ Where k is the gas transfer rate (quadratic function of wind speed) and k′ is the solubility coefficient of CO₂, calculated based on temperature and salinity according to Weiss ( 1974 ), and ΔpCO 2 = pCO 2 ocean - pCO 2 air. A constant average pCO 2 atmospheric value of ~ 422 µatm was assumed for the study period (Lan et al., 2024 ). Flows were calculated in mmol C m⁻² d⁻¹ per grid cell and integrated spatially and temporally. 2.9 Model evaluation and event attribution Model performance was evaluated by comparing corrected and uncorrected pCO 2 fields using root mean square error (RMSE), mean absolute error (MAE), and satellite bias reduction. Event-specific analyses quantified the relative contribution of each event (Ileana, John, Eleven-E) to the seasonal total of air–sea CO₂ flux (FCO 2 ). This methodology quantifies carbon flux variability induced by high-energy meteorological events in a dynamic region, despite limited information on biogeochemical changes resulting from meteorological events. 3. Results 3.1 Physical characteristics during meteorological events Between May and November 2024, three tropical systems were recorded off the coast of Guerrero: tropical storms Ileana and Eleven-E, and category 5 hurricane John (CNH, 2024). Among them, John produced the strongest effects, with wind speeds exceeding 70 m·s⁻¹, a rapid surface cooling of approximately 1.5°C between September 22 and 27 (Fig. 2a), and a simultaneous increase in surface chlorophyll a (Fig. 3). The SSS exhibited short-lived positive increases during both John and Ileana, whereas Eleven-E generated disturbances of lesser magnitude and spatial extent (Fig. 2b). The T–S diagram revealed the presence of Tropical Surface Water (TSW) and a weak signal of Gulf of California Water (GCW) (Fig. 2c), consistent with the classification proposed by Fiedler and Lavín ( 2017 ). Figure 2. Temporal evolution of sea surface temperature (SST; panel a) and sea surface salinity (SSS; panel b), showing spatial and temporal variability (mean ± standard deviation) between May and November 2024. A temperature–salinity (T–S) diagram (panel c) illustrates the surface distribution of water masses, identifying Tropical Surface Water (TSW) and Gulf of California Water (GCW), classified according to the boundaries defined by Fiedler and Lavín ( 2017 ). 3.2 Calibration of the BNNR model The BNNR was trained with pCO 2 data collected in the Gulf of California during Storm Lidia (2017), when the oceanographic buoy drifted through the storm`s influence from August 30 to September 3 (trajectories in Fig. 1). Although Lidia's track remained outside the Gulf, its influence was strong enough to cause the buoy, and drive it northward (Fig. 1, S1). This event provided a reference series of pCO 2 , SST, and SSS under extreme forcing conditions, which enabled the calibration of the Bayesian model using high-frequency time series. After aligning buoy data with 2023 satellite predictors, the model was trained using variational inference and a negative log-likelihood loss. Calibration showed strong agreement with observed pCO 2 (MAE: 0.76 µatm, RMSE: 0.99 µatm) under normal conditions. When synthetic extreme-wind data were incorporated, errors increased (MAE: 23.4 µatm, RMSE: 28.7 µatm), reflecting higher variability during intense events but without degrading the model’s ability to capture rapid pCO 2 increases. The Monte Carlo-derived uncertainty bands encompassed observed values within 95% credibility intervals, confirming the model’s robustness and generalizability to similar events. 3.3 Integration of Satellite Data and Predictor Variables Satellite and reanalyzed variables were integrated to construct a spatially coherent prediction framework. Predictors included SST, SSS, chlorophyll a, Absolute Dynamic Topography (ADT), and wind components (u, v), from which wind speed and daily maximum wind speed were derived. All data were interpolated to a regular grid of 0.083° and daily frequency. Preprocessing was performed using Python (Xarray, Pandas, and Scipy libraries), and null values were removed. Missing data were conservatively imputed. Predictor variables were scaled with MinMaxScaler. This process generated a regular grid (0.083°) of more than 200 days, providing a consistent framework for model application. The SST and SSS distributions confirmed the coexistence of tropical and local water masses, with high temporal variability. 3.4 Correction of pCO₂ modeled by PISCES-NEMO After calibration, the BNNR model was applied to the Guerrero 2024 dataset, which showed that the model systematically corrected the PISCES-NEMO bias (Fig. 3b). The network generated daily predictions of corrected pCO 2 and associated uncertainty. Compared to the original PISCES-NEMO model values (Fig. 3a, c), the corrected predictions (Fig. 3b) showed significant increases during meteorological events, especially on September 23 (Hurricane John), with local increases of up to ~ 300 µatm in coastal areas, reaching values above 850 µatm (Fig. 3d). On days without high-energy meteorological events, the differences between corrected and original pCO 2 were less than ± 50 µatm, demonstrating the model’s specific sensitivity to physical forcing without inducing systematic artifacts (Fig. 3). Figure 3. Surface distribution maps of partial pressure of CO₂ (pCO₂): satellite-estimated values (panel a), corrected estimates produced by the Bayesian neural network (panel b), and the difference between both products (satellite minus corrected; panel c). Maps correspond to the average of the days with the greatest variation (September 22–25). Panel d shows a temporal comparison between satellite-estimated (blue line) and corrected (red line) pCO₂, with mean ± standard deviation across the study area 3.5 Physical adjustment during Hurricane John To represent the extreme intensity of Hurricane John more accurately, an additional physical adjustment was applied to the BNNR output for September 23. This adjustment incorporated observed satellite anomalies in wind speed (greater than 50 m·s⁻¹) and chlorophyll a (Fig. 4), resulting in spatially increased corrected pCO 2 in affected regions (Fig. 3b, d). The strategy replicated the spatial pattern observed during Lidia (2017) and improved the physical consistency of predictions, particularly in areas with elevated physical forcing and increased chlorophyll a (Fig. 4). Figure 4. Time series of chlorophyll a (Chl-a) concentration, showing the mean and standard deviation of spatial and temporal variability between May and November 2024. 3.6 Spatial and Temporal Patterns of Corrected pCO₂ The model generated daily maps of corrected pCO 2 along with their respective uncertainty bands. The areas most affected by cyclones showed increases of more than 200 µatm compared with uncorrected values, with the extreme values were concentrated in the coastal strip and upwelling regions. Predictive uncertainty was lower on regular days (± 30 µatm) and increased during events (up to ± 80 µatm), reflecting greater variability and physical complexity during extreme conditions. 3.7 Comparison of pCO₂ between events The magnitude of the impact was heterogeneous among the three events. Hurricane John presented the highest corrected pCO 2 values, with local maxima above 850 µatm and differences of up to + 300 µatm relative to the uncorrected product. At the same time, the associated CO₂ fluxes (FCO 2 ) reached their highest values, with a notable intensification from September 22 to 25 (Fig. 3b). Hurricane Ileana showed an intermediate increase, with pCO₂ differences of around 150–200 µatm, reaching maximum values close to 700 µatm (Fig. S2). The increase in fluxes was also noticeable, although of lesser magnitude and spatial extent compared to John. Tropical Depression Eleven-E generated the most minor variations, with pCO 2 increases of less than 100 µatm (Figs. S4). Its effects were limited to a narrower coastal strip and were short-lived (October 1–4). The differences in spatial extent were also evident: John affected virtually the entire coastal strip off Guerrero, Ileana showed a more localized pattern, and Eleven-E had a restricted impact. 3.8 Variability of air–sea CO₂ fluxes (FCO₂) Corrected CO₂ fluxes using the BNNR (Fig. 5b, d) revealed large degassing pulses, particularly during Hurricane John, with values up to twice those of the original PISCES-NEMO estimates (Fig. 5a). In contrast, the satellite product showed more uniform and moderate fluxes (Fig. 5a), indicating that deterministic models may underestimate CO₂ exchange during cyclonic forcing. The integrated time series confirmed that John produced the largest degassing pulse, Ileana an intermediate event, and Eleven-E a minimal impact (Figs. 5, S3, S5, respectively). Overall, tropical cyclones contributed up to one-third of the seasonal atmospheric carbon flux, underscoring their significant role in regional carbon variability. Figure 5. Surface distribution maps of air–sea CO₂ fluxes (FCO₂): satellite-estimated (panel a), corrected (panel b), and the difference between both products (panel c). Maps represent the average of the most variable days (September 22–25). Panel d compares satellite-estimated (blue line) and corrected (red line) FCO₂ time series, with mean ± standard deviation for May–November 2024 3.9 Physical-biogeochemical conditions during cyclones The environmental variables associated with the three events confirmed the strong interaction between atmospheric forcing and oceanographic structure. Sea surface temperature (SST) decreased by more than 4°C during Hurricane John (Fig. 6b), consistent with the vertical mixing processes induced by intense winds. In parallel, surface salinity (SSS; Fig. 6a) showed localized positive deviations, possibly linked to subsurface intrusion or lateral transport. Chlorophyll a (Chl-a; Fig. 6c) recorded notable increases in coastal areas, indicating a rapid biological response to upwelling and nutrient redistribution. The overlap of these three variables reinforces that the intensification of pCO₂ (Fig. 3b) and CO₂ fluxes (Fig. 5b) during John was coupled to a physical-biogeochemical feedback system, in which cooling, saline intrusion, and primary productivity combined to amplify the air–sea exchange signal. Figure 6. Maps of environmental variables averaged for September 22–25, 2024, during Hurricane John: (a) sea surface temperature (SST, °C), (b) sea surface salinity (SSS, units), and (c) chlorophyll a concentration (Chl-a, mg m⁻³). These maps highlight the physical-biogeochemical feedbacks driving the observed anomalies 3.10 Model sensitivity to predictors Sensitivity analysis of the BNNR identified the relative importance of each predictor in correcting pCO 2 . Maximum daily wind speed accounted for approximately 35–40% of the explained variance, followed by sea surface temperature (SST) at 25–30%, sea surface salinity (SSS) at 15–20%, and, to a lesser extent, chlorophyll a and absolute dynamic topography (ADT) at less than 10%. This hierarchy indicates that atmospheric forcing is the primary modulator of pCO 2 anomalies during cyclones, while temperature and salinity primarily influence the air–sea gradient under baseline conditions. We used SHAP (Shapley Additive exPlanations) values to quantify the contribution of each predictor (wind speed, SST, SSS, and chlorophyll a) to the BNNR pCO 2 predictions. The SHAP analysis confirmed that increases in wind speed above 40 m·s⁻¹ are associated with sudden increases in pCO 2 (> 200 µatm whereas decreases in SST greater than 2°C explain between 20–30% of the variability in the days following the passage of cyclones. Sensitivity to SSS was more limited, indicating that its role is concentrated in localized subsurface intrusion events. Taken together, these results validate that the BNNR architecture explicitly captures the nonlinear relationship between physical forcings and surface biogeochemical dynamics. 4. Discussion A Bayesian recurrent neural network (BNNR) was implemented to correct satellite-derived pCO 2 estimates from the PISCES-NEMO model in the tropical Pacific off Mexico, focusing on extreme meteorological events, such as hurricanes and tropical storms. The bias-correction utilized in situ reference data from Tropical Storm Lidia (2017) and was applied to satellite and modeled data from May to November 2024, encompassing three tropical cyclones. This approach reduced systematic bias in the PISCES-NEMO biogeochemical model and, to our knowledge, for the first time in this region, incorporated explicit uncertainty into pCO₂ estimates. The integration of real and synthetic data facilitated the representation of extreme conditions, enhancing model performance during hurricanes. The following section presents the main findings on model performance, corrected pCO₂ dynamics, air–sea CO₂ flux (FCO₂) variability, and the biogeochemical implications of these intense events in this tropical region. 4.1 Performance of the Bayesian correction model The BNNR effectively corrected biases in the PISCES-NEMO pCO 2 estimates, achieving very low errors under normal conditions and capturing sharp increases during cyclones. The BNNR’s ability to reduce systematic errors in pCO 2 estimates is comparable to other machine learning approaches applied to the ocean (e.g., optimized Random Forest, RMSE ≈ 15 µatm; Wu et al., 2024 ). Unlike deterministic methods, it provides credibility intervals, a critical feature in the context of high variability. Recent studies in simplified carbon and climate modeling have highlighted the value of Bayesian frameworks for representing uncertainty and assimilation of new observations (Bossy et al., 2022 ). The results suggest that the Bayesian model can complement global products by better adapting to local conditions and extreme events. This probabilistic framework is consistent with recent advances in climate and biogeochemical modeling, where representing uncertainty is key for decision-making. The results presented here contrast with previous applications of deterministic neural networks, such as those by Bittig et al. ( 2018 ) and Denvil-Sommer et al. ( 2019 ), as they explicitly incorporate uncertainty into daily pCO 2 predictions in a highly dynamic tropical region. 4.2 Model sensitivity analysis Sensitivity analysis identified maximum wind speed as the dominant predictor in pCO 2 correction, consistent with previous studies highlighting the role of mechanical forcing in subsurface ventilation and the intensification of air–sea fluxes (Yu et al., 2020 ; Wang et al., 2020 ). The relatively high contribution of SST corroborates that surface cooling after cyclonic events acts as a secondary modulator of the air–sea gradient, consistent with observations in the eastern Pacific and South China Sea (Sun et al., 2020; Wu et al., 2025). The lower sensitivity attributed to SSS and chlorophyll a reflects their role as contextual modulators rather than primary triggers of pCO 2 increase. However, their incursion ensured physical-biogeochemical consistency and avoid spurious predictions, which is consistent with the findings of Somavilla et al. ( 2016 ) on the importance of including physical redundancies in ocean prediction models. Taken together, these results reaffirm that the BNNR not only reduces the systematic bias of PISCES-NEMO but also provides an explicit disaggregation of the relative weight of each forcing factor. This level of interpretability constitutes an advance over deterministic or “black box” models and provides a useful framework for assessing the differential impact of future cyclones in climate change scenarios (IPCC, 2021). 4.3 Impact of extreme events on surface pCO₂ Cyclones generated short-lived but intense pCO 2 pulses, exceeding 300 µatm in less than 24 hours, which coincides with observations in other tropical basins (Landschützer et al., 2016 ; Wang et al., 2020 ). These increases were linked to surface cooling, vertical mixing, and the ventilation of carbon-rich subsurface waters. Similar phenomena have been reported in other tropical basins, where typhoons and hurricanes trigger abrupt increases in pCO 2 and air–sea CO₂ fluxes (Yu et al., 2020 ). The heterogeneity between events was explained by the combination of intensity, trajectory, translation speed, and previous state of the system, which modulated the magnitude and spatial extent of the anomalies (Yu et al., 2020 ). In particular, John (cat. 5) produced the most pronounced and extensive changes, Ileana produced an intermediate response, and Eleven-E had a limited impact in magnitude and duration, in line with the dynamic control of mechanical forcing on local ventilation/upwelling. Comparable responses of pCO 2 to cyclonic forcing have been reported in other tropical basins. For example, in the Bay of Bengal, neural network and gradient boosting models driven by SST and SSS captured anomalies of ± 12 µatm associated with monsoon-driven variability (Joshi et al., 2022 ). Similarly, in the northern South China Sea, typhoons produced abrupt increases in surface pCO 2 and degassing fluxes exceeding background variability, consistent with the role of extreme wind forcing (Yu et al., 2020 ). Positioning our results within this broader context highlights that the Bayesian bias-correction approach developed here is not limited to Guerrero, but represents a transferable strategy for other cyclone-prone tropical regions. 4.4 Biogeochemical implications in FCO₂ Using uncertainty-corrected pCO 2 and FCO 2 revealed short-lived but large degassing pulses, which contributed up to ~ 30% of the seasonal flux. This pattern supports the role of cyclones as intensifiers of air–sea CO₂ exchange in tropical regions (Resplandy et al., 2019 ; Riebesell et al., 2021 ), highlighting the importance of capturing cyclonic forcing to close seasonal balances. The colocation of FCO₂ maxima with SST minima and chlorophyll peaks suggests a physical-biological coupling: vertical mixing and subsurface intrusion raise pCO 2 and the air–sea gradient. At the same time, nutrient injection triggers a rapid trophic response that can modulate the return to background conditions. The combination of satellite diagnostics and Bayesian bias-correction enabled the quantification of this coupling with daily resolution, a feat difficult to achieve with deterministic products or sporadic in situ sampling (Liss & Merlivat, 1986 ; Wanninkhof, 2014 ; Weiss, 1974 ). 4.5 Contribution of the Bayesian approach to oceanographic monitoring The added value of the Bayesian approach can be summarized in three key aspects: Incorporation of uncertainty : Credibility intervals enable the robustness of predictions to be assessed, a crucial property in decision-making for climate and fisheries management. This type of probabilistic representation has been highlighted in recent ocean and climate modeling studies, where uncertainty estimation is crucial for assessing carbon fluxes and their large-scale implications (Bossy et al., 2022 ). Temporal flexibility : the LSTM architecture ensures consistency in time series, avoiding spurious oscillations during periods without events. The usefulness of recurrent architectures for capturing the temporal dynamics of marine processes has been validated in reconstructions of pCO₂ and other biogeochemical variables, showing clear improvements over conventional deterministic networks (Wu et al., 2025). Increased sensitivity : the inclusion of redundant predictors, such as mean wind and maximum wind, allowed the model to respond differentially to atmospheric forcing, a feature that “black box” algorithms, like SVM or XGBoost, tend to lose by not preserving physical consistency. Recent studies confirm that integrating redundant forcings enhances predictive capacity in dynamic regions by more accurately capturing the nonlinear responses of complex ocean processes (Somavilla et al., 2016 ; Wu et al., 2024 ). A distinctive strength of the Bayesian framework is its capacity to generate credibility intervals that support both scientific interpretation and management decisions. In practice, these intervals provide quantitative bounds on the reliability of pCO 2 and FCO₂ predictions, enabling decision-makers to distinguish between robust signals—such as degassing pulses clearly outside the uncertainty range—and conditions where the overlap of intervals advises caution. This probabilistic framing enhances the usability of bias-corrected estimates in operational contexts, for instance, early warning systems or carbon accounting schemes, where confidence in anomaly detection is as important as the mean prediction itself. 4.6 Limitations and future directions This study has two main limitations: calibration relied on a single reference event (Lidia 2017), which may not capture the full range of conditions in the Mexican Pacific. Additionally, the synthetic data rely on statistical assumptions that require further validation with additional in situ observations from oceanographic campaigns or buoys. Another limitation is that the input data resolution of 0.083° may miss submesoscale processes relevant during cyclones. Future research should: Incorporate multiple calibration events to strengthen training. Evaluate hybrid architectures that explicitly integrate physical processes, for example, coupling with simplified biogeochemical models (Pathfinder, PISCES-NEMO; Aumont et al., 2015 ). Extend the methodology to climate change scenarios, considering projections of more intense cyclones in the eastern Pacific (IPCC, 2021). 5. Conclusions A Bayesian recurrent neural network was successfully applied to correct satellite-derived pCO 2 in the tropical Pacific off Guerrero, Mexico, during extreme meteorological events. The model reduced systematic biases, incorporated predictive uncertainty, and revealed short-lived but significant CO 2 degassing pulses, particularly during Hurricane John (2024). These pulses accounted for nearly one-third of the seasonal flux, underscoring the importance of cyclones in regional carbon dynamics. This study demonstrates that Bayesian approaches provide a powerful and reliable framework for monitoring air–sea CO₂ exchange in highly variable environments. Beyond improving the accuracy of satellite products, they enhance our capacity to evaluate the biogeochemical impacts of extreme events. Declarations Acknowledgment We thank the Secretariat of Science, Humanities, Innovation, and Technology (SECIHTI) for awarding a postdoctoral fellowship to the first author (EPM-2022(3)). We also acknowledge Dr. T.L.E.C. for providing the Gulf of California oceanographic buoy data, which was essential for model calibration. Funding This work was supported by multiple institutions. Dr. T. Leticia Espinosa Carreón received institutional support from CIDIIR-IPN , which provided financial, administrative, and technical assistance, including buoy deployment, supplies, and travel for equipment maintenance. Dr. Luz de Lourdes Aurora Coronado Álvarez was supported by the Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI) through a postdoctoral fellowship awarded to the first author (CVU 353474). Dr. Gabriela Y. Cervantes Díaz received institutional support from the Universidad Autónoma de Baja California (UABC) , which provided financial and administrative assistance, as well as coverage of publication expenses through an institutional agreement with the journal. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. The authors further declare that they have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Luz de Lourdes Aurora Coronado Álvarez , Gabriela Y. Cervantes Díaz , and T. Leticia Espinosa Carreón . The first draft of the manuscript was written by Luz de Lourdes Aurora Coronado Álvarez , and all authors commented on and revised previous versions. All authors read and approved the final manuscript. Data Availability The datasets generated and/or analyzed during the current study are not publicly available because they form part of an ongoing research project, but they are available from the corresponding author upon reasonable request. References Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X (2016) TensorFlow: A system for large-scale machine learning. 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Sci Total Environ 922:170735. https://doi.org/10.1016/j.scitotenv.2024.170735 Yu P, Wang ZA, Churchill J, Zheng M, Pan J, Bai Y, Liang C (2020) Effects of typhoons on surface seawater pCO₂ and air–sea CO₂ fluxes in the northern South China Sea. Journal of Geophysical Research: Oceans, 125 (8), e2020JC016258. https://doi.org/10.1029/2020JC016258 Supplementary Files Supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":1224395,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area showing the trajectories of tropical cyclones that impacted the eastern tropical Pacific during 2024: Hurricane Ileana (September 11–17; green line), Hurricane John (September 22–27; blue line), and Tropical Storm Eleven-E (October 1–4; pink line). For reference, the trajectory of Hurricane Lidia (August 30–September 3, 2017; yellow line) is also included. The red line within the Gulf of California denotes the path of a drifting buoy deployed during Tropical Storm Lidia, which provided high-frequency in situ measurements of pCO₂, SST, and SSS for model calibration.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8253374/v1/eac35d80a4a32d3d7bc5cf35.png"},{"id":97689027,"identity":"eb401415-cce3-4ffe-a53a-7f104e153338","added_by":"auto","created_at":"2025-12-08 10:42:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":586243,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal evolution of sea surface temperature (SST; panel a) and sea surface salinity (SSS; panel b), showing spatial and temporal variability (mean ± standard deviation) between May and November 2024. A temperature–salinity (T–S) diagram (panel c) illustrates the surface distribution of water masses, identifying Tropical Surface Water (TSW) and Gulf of California Water (GCW), classified according to the boundaries defined by Fiedler and Lavín (2017).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8253374/v1/a4b96af841e7ef0d688d69bc.png"},{"id":97689028,"identity":"2857c67c-1046-420a-9c46-cd47fb3f3db4","added_by":"auto","created_at":"2025-12-08 10:42:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1637410,"visible":true,"origin":"","legend":"\u003cp\u003eSurface distribution maps of partial pressure of CO₂ (pCO₂): satellite-estimated values (panel a), corrected estimates produced by the Bayesian neural network (panel b), and the difference between both products (satellite minus corrected; panel c). Maps correspond to the average of the days with the greatest variation (September 22–25). Panel d shows a temporal comparison between satellite-estimated (blue line) and corrected (red line) pCO₂, with mean ± standard deviation across the study area\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8253374/v1/0b17573dbc366796a48fc76d.png"},{"id":97689050,"identity":"91307e3b-e42f-4fbd-9708-6e84675aca32","added_by":"auto","created_at":"2025-12-08 10:42:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":213941,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of chlorophyll a (Chl-a) concentration, showing the mean and standard deviation of spatial and temporal variability between May and November 2024.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8253374/v1/9e8c76cd65f5850193757a0e.png"},{"id":97689031,"identity":"a64e6c62-9453-412b-a85d-d0915ffbcbc0","added_by":"auto","created_at":"2025-12-08 10:42:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1468719,"visible":true,"origin":"","legend":"\u003cp\u003eSurface distribution maps of air–sea CO₂ fluxes (FCO₂): satellite-estimated (panel a), corrected (panel b), and the difference between both products (panel c). Maps represent the average of the most variable days (September 22–25). Panel d compares satellite-estimated (blue line) and corrected (red line) FCO₂ time series, with mean ± standard deviation for May–November 2024\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8253374/v1/9140f51efeeddd96cb512ab4.png"},{"id":97893024,"identity":"33a0c1f0-2451-4fbc-b889-1f7d1c779ac0","added_by":"auto","created_at":"2025-12-10 15:25:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1219446,"visible":true,"origin":"","legend":"\u003cp\u003eMaps of environmental variables averaged for September 22–25, 2024, during Hurricane John: (a) sea surface temperature (SST, °C), (b) sea surface salinity (SSS, units), and (c) chlorophyll a concentration (Chl-a, mg m⁻³). These maps highlight the physical-biogeochemical feedbacks driving the observed anomalies\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8253374/v1/3968495fdb8166168cb24a34.png"},{"id":105034907,"identity":"45949c35-5c6b-4534-b5fe-ee5f76c1cd6b","added_by":"auto","created_at":"2026-03-20 07:24:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7654857,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8253374/v1/ccb18aeb-690e-4e9b-9119-1d4489e14b0d.pdf"},{"id":97689048,"identity":"edeb0134-c779-41f4-9778-f5ceb4d813f1","added_by":"auto","created_at":"2025-12-08 10:42:16","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":3359585,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8253374/v1/40ff64009a9f69cb6603f05a.docx"}],"financialInterests":"","formattedTitle":"Bayesian correction of satellite-derived pCO2 reveals the biogeochemical impact of hurricanes on ocean–atmosphere CO2 fluxes","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe ocean is an essential regulator of the global climate system, and at the same time, a dynamic component of the carbon cycle. Among the most relevant processes is the exchange of carbon dioxide (CO₂) between the atmosphere and the sea surface, a phenomenon strongly conditioned by physical and biogeochemical factors that exhibit marked spatial and temporal variability. Despite advances in coupled ocean models and satellite observations, estimating the partial pressure of CO\u003csub\u003e2\u003c/sub\u003e (pCO\u003csub\u003e2\u003c/sub\u003e) at the sea surface remains a challenge, particularly in tropical regions affected by hurricanes and tropical storms. These areas, characterized by heterogeneity in temperature, salinity, surface winds, and primary productivity, require innovative methodologies that integrate multiple sources of information to more realistically represent air-sea carbon fluxes (FCO\u003csub\u003e2\u003c/sub\u003e).\u003c/p\u003e\u003cp\u003eSatellite monitoring and coupled biogeochemical models, such as PICES-NEMO, have expanded our capacity to study ocean-atmosphere interactions at high spatial and temporal resolution. These systems incorporate key variables \u0026mdash; including sea surface temperature (SST), surface salinity (SSS), chlorophyll concentration (Chl-a), absolute dynamic topography (ADT), and wind components (u, v) \u0026mdash; which serve as essential inputs for characterizing ocean conditions and driving predictive models. However, these products often underestimate transient phenomena driven by intense physical forcing, with reported errors exceeding 90 \u0026micro;atm in dynamic coastal environments. Improving their accuracy requires methods that integrate multiple data sources and account for uncertainty.\u003c/p\u003e\u003cp\u003eNeural networks have established themselves as key tools for modeling nonlinear relationships in complex systems with multiple variables. In particular, Bayesian recurrent neural networks (BNNR) combine the capacity to represent temporal dynamics with explicit uncertainty quantification, an advantage over deterministic approaches. Their application is especially valuable in oceanography, where the availability of in situ data is limited and natural variability is high, as they enable the dynamic representation of physical and biogeochemical processes while generating credible intervals. Recent studies demonstrate that BNNRs can correct systematic biases in satellite-derived products and more effectively capture anomalies associated with cyclonic forcing, such as abrupt increases in pCO\u003csub\u003e2\u003c/sub\u003e induced by tropical cyclones. Overall, neural models\u0026mdash;whether feed-forward, recurrent, or Bayesian\u0026mdash;have consistently recovered pCO\u003csub\u003e2\u003c/sub\u003e in oceanic and coastal environments, with root mean square errors (RMSE) below 25 \u0026micro;atm. Previous neural network applications, including feed-forward and ensemble models, have reconstructed surface pCO\u003csub\u003e2\u003c/sub\u003e patterns at global and regional scales with reasonable skill (e.g. ANN and XGBoost in the Bay Bengal archived r\u0026thinsp;~\u0026thinsp;0.75 and \u0026plusmn;\u0026thinsp;12 \u0026micro;atm error; Joshi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, strictly Bayesian implementations remain rare, and their capacity to capture abrupt anomalies during extreme events such as hurricanes has yet to be systematically evaluated.\u003c/p\u003e\u003cp\u003eIn this regard, Bayesian neural networks provide crucial added value by allowing explicit estimation of predictive uncertainty and rigorous incorporation of prior knowledge. These characteristics are essential in applications that require high levels of reliability, such as in areas with low sampling or in contexts dominated by high-energy meteorological events where data extrapolation involves significant risks (Clare et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study applies a BNNR to correct surface pCO\u003csub\u003e2\u003c/sub\u003e estimates from the PISCES-NEMO model in the eastern tropical Pacific, near Guerrero, Mexico (10\u0026deg;\u0026ndash;18\u0026deg; N, 90\u0026deg;\u0026ndash;110\u0026deg; W). The model was calibrated using high-frequency buoy data from Tropical Storm Lidia (2017) and tested for three contrasting meteorological systems events that occurred in 2024: Hurricane Ileana (category 2), Hurricane John (category 5), and Tropical Depression Eleven-E. Our objectives were to (1) reduce biases in satellite-derived pCO\u003csub\u003e2\u003c/sub\u003e, (2) incorporate predictive uncertainty, and (3) evaluate the contribution of tropical cyclones to regional air\u0026ndash;sea CO₂ fluxes. This approach enabled the bias correction of systematic biases in modeled pCO\u003csub\u003e2\u003c/sub\u003e, the explicit estimation of uncertainty, and the calculation of corrected air\u0026ndash;sea CO\u003csub\u003e2\u003c/sub\u003e fluxes. Collectively, these advances contribute to understanding marine carbon dynamics during high-energy meteorological events and have implications for climate oceanography, fisheries management, and coastal resilience (IPCC, 2021).\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study area and period\u003c/h2\u003e\u003cp\u003eThe analysis focused on the eastern tropical Pacific, off the coast of Guerrero, Mexico (10\u0026deg; \u0026ndash; 18\u0026deg; N, 90\u0026deg; \u0026ndash; 110\u0026deg; W), a region characterized by high physical-biogeochemical variability and frequent tropical cyclone activity. The study period extended from May to November 2024 and included three contrasting events: Hurricane Ileana (September 11\u0026ndash;17, category 2; Berg, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Hurricane John (September 22\u0026ndash;25, category 5; Pasch, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and Tropical Storm Eleven-E (October 1\u0026ndash;3; Blake, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) (Fig.\u0026nbsp;1). The classification was made according to the Saffir\u0026ndash;Simpson scale (CNH, 2017). These atmospheric systems generated winds exceeding 70 m s⁻\u0026sup1;, abrupt decreases in sea surface temperature (SST), and notable increases in surface pCO\u003csub\u003e2\u003c/sub\u003e. Their inclusion allowed for a comprehensive assessment of the impact of extreme forcings on air\u0026ndash;sea CO₂ dynamics at subseasonal scales.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 1. Study area showing the trajectories of tropical cyclones that impacted the eastern tropical Pacific during 2024: Hurricane Ileana (September 11\u0026ndash;17; green line), Hurricane John (September 22\u0026ndash;27; blue line), and Tropical Storm Eleven-E (October 1\u0026ndash;4; pink line). For reference, the trajectory of Hurricane Lidia (August 30\u0026ndash;September 3, 2017; yellow line) is also included. The red line within the Gulf of California denotes the path of a drifting buoy deployed during Tropical Storm Lidia, which provided high-frequency in situ measurements of pCO₂, SST, and SSS for model calibration.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 In situ data for model calibration\u003c/h2\u003e\u003cp\u003eHigh-frequency in situ data on pCO\u003csub\u003e2\u003c/sub\u003e (partial pressure of carbon dioxide), sea surface temperature (SST), and sea surface salinity (SSS) were collected during the passage of Tropical Storm Lidia (August 30\u0026ndash;September 3, 2017) using an oceanographic buoy located in the Gulf of California (Fig.\u0026nbsp;1). These data were used to calibrate and validate the bias-correction model. Because satellite pCO\u003csub\u003e2\u003c/sub\u003e products were not available for 2017, predictor variables derived from the Copernicus Marine Environment Monitoring Service (CMEMS) and the Global Carbon Project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.48670/moi-00015\u003c/span\u003e\u003cspan address=\"10.48670/moi-00015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to achieve temporal alignment, thus allowing the construction of a proxy training set under similar physical-biogeochemical conditions. This step was essential for calibrating the model under real forcing scenarios.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Satellite and modeled datasets\u003c/h2\u003e\u003cp\u003eDaily and hourly variables from satellite products and CMEMS L4 reanalyses, together with data from the Global Carbon Project, were integrated. Key variables included: SST (\u0026deg;C), SSS (CMEMS, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.48670/moi-00016\u003c/span\u003e\u003cspan address=\"10.48670/moi-00016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), zonal and meridional wind components (u, v; Hersbach et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), chlorophyll a concentration (Chl-a; mg m⁻\u0026sup3;; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.48670/moi-00015\u003c/span\u003e\u003cspan address=\"10.48670/moi-00015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and absolute dynamic topography (ADT, cm; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48670/moi-00149\u003c/span\u003e\u003cspan address=\"10.48670/moi-00149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Simulated surface pCO\u003csub\u003e2\u003c/sub\u003e fields were obtained from the PISCES-NEMO biogeochemical model of the CMEMS L4 satellite product (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.48670/moi-00015\u003c/span\u003e\u003cspan address=\"10.48670/moi-00015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All datasets were interpolated to a standard regular grid with a spatial resolution of 0.083\u0026deg; and coupled at a daily frequency.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data fusion and preprocessing\u003c/h2\u003e\u003cp\u003eAll variables were processed in Python (Python Software Foundation, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), utilizing the NumPy, Xarray, and Pandas libraries. Wind speed and daily maximum wind (m s⁻\u0026sup1;) were calculated from the u and v components. The final set included seven predictors: SST, SSS, Chl-a, ADT, and wind speed. Missing values were imputed conservatively. All data were normalized using MinMax scaling and interpolated spatially and temporally to fit a regular grid. The wind components were converted to daily magnitude (speed), and the maximum speed per day was also calculated.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Generation of synthetic data\u003c/h2\u003e\u003cp\u003eTo increase the robustness of the model under extreme conditions, synthetic data were generated to represent hurricane-like forcing. Multivariate Gaussian distributions with controlled noise (\u0026ldquo;jitter\u0026rdquo;) were derived from observed conditions during Lidia to simulate cases characterized by wind speeds up to 70 m\u0026middot;s⁻\u0026sup1;, low SST and SSS, and high pCO\u003csub\u003e2\u003c/sub\u003e values (800\u0026ndash;850 \u0026micro;atm). These synthetic data were added exclusively for September 23, 2024 (the day Hurricane John struck), to prevent overfitting and maintain the model\u0026rsquo;s generalization capacity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Bayesian neural network model\u003c/h2\u003e\u003cp\u003eA Bayesian recurrent neural network (BNNR) was implemented in TensorFlow Probability (Abadi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Dillon et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and LSTMFlipout and DenseFlipout layers (Wen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) were used to estimate posterior distributions of weights and quantify predictive uncertainty. The model was trained exclusively on the Lidia event dataset, which includes both real and synthetic data. Training was performed using variational inference, employing the negative log-likelihood loss function and 1000 Monte Carlo samples. The architecture included an LSTM layer with 32 neurons, followed by a Bayesian dense layer, a ReLU activation (Rectified Linear Unit), and a linear output. The output consisted of corrected pCO\u003csub\u003e2\u003c/sub\u003e values referenced to the oceanographic buoy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Application of the model to the 2024 dataset\u003c/h2\u003e\u003cp\u003eThe trained BNNR model was applied to the pCO\u003csub\u003e2\u003c/sub\u003e dataset from the PISCES-NEMO output (Aumont et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) off the coast of Guerrero for the period from May to November 2024. Daily maps of corrected pCO\u003csub\u003e2\u003c/sub\u003e were generated along with their predictive uncertainty. On September 23, a hybrid bias-correction was implemented that combined model predictions with physical spatial anomalies (based on wind and chlorophyll a), resulting in increased pCO\u003csub\u003e2\u003c/sub\u003e of up to +\u0026thinsp;300 \u0026micro;atm in areas with strong forcing. This strategy aimed to replicate patterns similar to those observed during Lidia, thereby ensuring spatial consistency.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Estimation of sea\u0026ndash;atmosphere CO₂ flux\u003c/h2\u003e\u003cp\u003eThe CO₂ flux between the ocean and the atmosphere (FCO\u003csub\u003e2\u003c/sub\u003e) was calculated using the standard formulation described by Liss and Merlivat (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) and the transfer coefficient from Wanninkhof (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\:{FCO}_{2}={k*\\:k}^{{\\prime\\:}}*{\\varDelta\\:pCO}_{2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cem\u003ek\u003c/em\u003e is the gas transfer rate (quadratic function of wind speed) and \u003cem\u003ek\u0026prime;\u003c/em\u003e is the solubility coefficient of CO₂, calculated based on temperature and salinity according to Weiss (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1974\u003c/span\u003e), and ΔpCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;pCO\u003csub\u003e2\u003c/sub\u003e ocean - pCO\u003csub\u003e2\u003c/sub\u003e air. A constant average pCO\u003csub\u003e2\u003c/sub\u003e atmospheric value of ~\u0026thinsp;422 \u0026micro;atm was assumed for the study period (Lan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Flows were calculated in mmol C m⁻\u0026sup2; d⁻\u0026sup1; per grid cell and integrated spatially and temporally.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Model evaluation and event attribution\u003c/h2\u003e\u003cp\u003eModel performance was evaluated by comparing corrected and uncorrected pCO\u003csub\u003e2\u003c/sub\u003e fields using root mean square error (RMSE), mean absolute error (MAE), and satellite bias reduction. Event-specific analyses quantified the relative contribution of each event (Ileana, John, Eleven-E) to the seasonal total of air\u0026ndash;sea CO₂ flux (FCO\u003csub\u003e2\u003c/sub\u003e). This methodology quantifies carbon flux variability induced by high-energy meteorological events in a dynamic region, despite limited information on biogeochemical changes resulting from meteorological events.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Physical characteristics during meteorological events\u003c/h2\u003e\u003cp\u003eBetween May and November 2024, three tropical systems were recorded off the coast of Guerrero: tropical storms Ileana and Eleven-E, and category 5 hurricane John (CNH, 2024). Among them, John produced the strongest effects, with wind speeds exceeding 70 m\u0026middot;s⁻\u0026sup1;, a rapid surface cooling of approximately 1.5\u0026deg;C between September 22 and 27 (Fig.\u0026nbsp;2a), and a simultaneous increase in surface chlorophyll a (Fig.\u0026nbsp;3). The SSS exhibited short-lived positive increases during both John and Ileana, whereas Eleven-E generated disturbances of lesser magnitude and spatial extent (Fig.\u0026nbsp;2b). The T\u0026ndash;S diagram revealed the presence of Tropical Surface Water (TSW) and a weak signal of Gulf of California Water (GCW) (Fig.\u0026nbsp;2c), consistent with the classification proposed by Fiedler and Lav\u0026iacute;n (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 2. Temporal evolution of sea surface temperature (SST; panel a) and sea surface salinity (SSS; panel b), showing spatial and temporal variability (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) between May and November 2024. A temperature\u0026ndash;salinity (T\u0026ndash;S) diagram (panel c) illustrates the surface distribution of water masses, identifying Tropical Surface Water (TSW) and Gulf of California Water (GCW), classified according to the boundaries defined by\u003c/b\u003e Fiedler and Lav\u0026iacute;n (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Calibration of the BNNR model\u003c/h2\u003e\u003cp\u003eThe BNNR was trained with pCO\u003csub\u003e2\u003c/sub\u003e data collected in the Gulf of California during Storm Lidia (2017), when the oceanographic buoy drifted through the storm`s influence from August 30 to September 3 (trajectories in Fig.\u0026nbsp;1). Although Lidia's track remained outside the Gulf, its influence was strong enough to cause the buoy, and drive it northward (Fig.\u0026nbsp;1, S1). This event provided a reference series of pCO\u003csub\u003e2\u003c/sub\u003e, SST, and SSS under extreme forcing conditions, which enabled the calibration of the Bayesian model using high-frequency time series.\u003c/p\u003e\u003cp\u003eAfter aligning buoy data with 2023 satellite predictors, the model was trained using variational inference and a negative log-likelihood loss. Calibration showed strong agreement with observed pCO\u003csub\u003e2\u003c/sub\u003e (MAE: 0.76 \u0026micro;atm, RMSE: 0.99 \u0026micro;atm) under normal conditions. When synthetic extreme-wind data were incorporated, errors increased (MAE: 23.4 \u0026micro;atm, RMSE: 28.7 \u0026micro;atm), reflecting higher variability during intense events but without degrading the model\u0026rsquo;s ability to capture rapid pCO\u003csub\u003e2\u003c/sub\u003e increases. The Monte Carlo-derived uncertainty bands encompassed observed values within 95% credibility intervals, confirming the model\u0026rsquo;s robustness and generalizability to similar events.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Integration of Satellite Data and Predictor Variables\u003c/h2\u003e\u003cp\u003eSatellite and reanalyzed variables were integrated to construct a spatially coherent prediction framework.\u003c/p\u003e\u003cp\u003ePredictors included SST, SSS, chlorophyll a, Absolute Dynamic Topography (ADT), and wind components (u, v), from which wind speed and daily maximum wind speed were derived. All data were interpolated to a regular grid of 0.083\u0026deg; and daily frequency.\u003c/p\u003e\u003cp\u003ePreprocessing was performed using Python (Xarray, Pandas, and Scipy libraries), and null values were removed. Missing data were conservatively imputed. Predictor variables were scaled with MinMaxScaler. This process generated a regular grid (0.083\u0026deg;) of more than 200 days, providing a consistent framework for model application. The SST and SSS distributions confirmed the coexistence of tropical and local water masses, with high temporal variability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Correction of pCO₂ modeled by PISCES-NEMO\u003c/h2\u003e\u003cp\u003eAfter calibration, the BNNR model was applied to the Guerrero 2024 dataset, which showed that the model systematically corrected the PISCES-NEMO bias (Fig.\u0026nbsp;3b). The network generated daily predictions of corrected pCO\u003csub\u003e2\u003c/sub\u003e and associated uncertainty. Compared to the original PISCES-NEMO model values (Fig.\u0026nbsp;3a, c), the corrected predictions (Fig.\u0026nbsp;3b) showed significant increases during meteorological events, especially on September 23 (Hurricane John), with local increases of up to ~\u0026thinsp;300 \u0026micro;atm in coastal areas, reaching values above 850 \u0026micro;atm (Fig.\u0026nbsp;3d). On days without high-energy meteorological events, the differences between corrected and original pCO\u003csub\u003e2\u003c/sub\u003e were less than \u0026plusmn;\u0026thinsp;50 \u0026micro;atm, demonstrating the model\u0026rsquo;s specific sensitivity to physical forcing without inducing systematic artifacts (Fig.\u0026nbsp;3).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 3. Surface distribution maps of partial pressure of CO₂ (pCO₂): satellite-estimated values (panel a), corrected estimates produced by the Bayesian neural network (panel b), and the difference between both products (satellite minus corrected; panel c). Maps correspond to the average of the days with the greatest variation (September 22\u0026ndash;25). Panel d shows a temporal comparison between satellite-estimated (blue line) and corrected (red line) pCO₂, with mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation across the study area\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Physical adjustment during Hurricane John\u003c/h2\u003e\u003cp\u003eTo represent the extreme intensity of Hurricane John more accurately, an additional physical adjustment was applied to the BNNR output for September 23. This adjustment incorporated observed satellite anomalies in wind speed (greater than 50 m\u0026middot;s⁻\u0026sup1;) and chlorophyll a (Fig.\u0026nbsp;4), resulting in spatially increased corrected pCO\u003csub\u003e2\u003c/sub\u003e in affected regions (Fig.\u0026nbsp;3b, d). The strategy replicated the spatial pattern observed during Lidia (2017) and improved the physical consistency of predictions, particularly in areas with elevated physical forcing and increased chlorophyll a (Fig.\u0026nbsp;4).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 4. Time series of chlorophyll a (Chl-a) concentration, showing the mean and standard deviation of spatial and temporal variability between May and November 2024.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.6 Spatial and Temporal Patterns of Corrected pCO₂\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThe model generated daily maps of corrected pCO\u003csub\u003e2\u003c/sub\u003e along with their respective uncertainty bands. The areas most affected by cyclones showed increases of more than 200 \u0026micro;atm compared with uncorrected values, with the extreme values were concentrated in the coastal strip and upwelling regions. Predictive uncertainty was lower on regular days (\u0026plusmn;\u0026thinsp;30 \u0026micro;atm) and increased during events (up to \u0026plusmn;\u0026thinsp;80 \u0026micro;atm), reflecting greater variability and physical complexity during extreme conditions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Comparison of pCO₂ between events\u003c/h2\u003e\u003cp\u003eThe magnitude of the impact was heterogeneous among the three events. Hurricane John presented the highest corrected pCO\u003csub\u003e2\u003c/sub\u003e values, with local maxima above 850 \u0026micro;atm and differences of up to +\u0026thinsp;300 \u0026micro;atm relative to the uncorrected product. At the same time, the associated CO₂ fluxes (FCO\u003csub\u003e2\u003c/sub\u003e) reached their highest values, with a notable intensification from September 22 to 25 (Fig.\u0026nbsp;3b). Hurricane Ileana showed an intermediate increase, with pCO₂ differences of around 150\u0026ndash;200 \u0026micro;atm, reaching maximum values close to 700 \u0026micro;atm (Fig. S2). The increase in fluxes was also noticeable, although of lesser magnitude and spatial extent compared to John.\u003c/p\u003e\u003cp\u003eTropical Depression Eleven-E generated the most minor variations, with pCO\u003csub\u003e2\u003c/sub\u003e increases of less than 100 \u0026micro;atm (Figs. S4). Its effects were limited to a narrower coastal strip and were short-lived (October 1\u0026ndash;4). The differences in spatial extent were also evident: John affected virtually the entire coastal strip off Guerrero, Ileana showed a more localized pattern, and Eleven-E had a restricted impact.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Variability of air\u0026ndash;sea CO₂ fluxes (FCO₂)\u003c/h2\u003e\u003cp\u003eCorrected CO₂ fluxes using the BNNR (Fig.\u0026nbsp;5b, d) revealed large degassing pulses, particularly during Hurricane John, with values up to twice those of the original PISCES-NEMO estimates (Fig.\u0026nbsp;5a). In contrast, the satellite product showed more uniform and moderate fluxes (Fig.\u0026nbsp;5a), indicating that deterministic models may underestimate CO₂ exchange during cyclonic forcing. The integrated time series confirmed that John produced the largest degassing pulse, Ileana an intermediate event, and Eleven-E a minimal impact (Figs.\u0026nbsp;5, S3, S5, respectively). Overall, tropical cyclones contributed up to one-third of the seasonal atmospheric carbon flux, underscoring their significant role in regional carbon variability.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 5. Surface distribution maps of air\u0026ndash;sea CO₂ fluxes (FCO₂): satellite-estimated (panel a), corrected (panel b), and the difference between both products (panel c). Maps represent the average of the most variable days (September 22\u0026ndash;25). Panel d compares satellite-estimated (blue line) and corrected (red line) FCO₂ time series, with mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for May\u0026ndash;November 2024\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.9 Physical-biogeochemical conditions during cyclones\u003c/h2\u003e\u003cp\u003eThe environmental variables associated with the three events confirmed the strong interaction between atmospheric forcing and oceanographic structure. Sea surface temperature (SST) decreased by more than 4\u0026deg;C during Hurricane John (Fig.\u0026nbsp;6b), consistent with the vertical mixing processes induced by intense winds. In parallel, surface salinity (SSS; Fig.\u0026nbsp;6a) showed localized positive deviations, possibly linked to subsurface intrusion or lateral transport. Chlorophyll a (Chl-a; Fig.\u0026nbsp;6c) recorded notable increases in coastal areas, indicating a rapid biological response to upwelling and nutrient redistribution. The overlap of these three variables reinforces that the intensification of pCO₂ (Fig.\u0026nbsp;3b) and CO₂ fluxes (Fig.\u0026nbsp;5b) during John was coupled to a physical-biogeochemical feedback system, in which cooling, saline intrusion, and primary productivity combined to amplify the air\u0026ndash;sea exchange signal.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 6. Maps of environmental variables averaged for September 22\u0026ndash;25, 2024, during Hurricane John: (a) sea surface temperature (SST, \u0026deg;C), (b) sea surface salinity (SSS, units), and (c) chlorophyll a concentration (Chl-a, mg m⁻\u0026sup3;). These maps highlight the physical-biogeochemical feedbacks driving the observed anomalies\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.10 Model sensitivity to predictors\u003c/h2\u003e\u003cp\u003eSensitivity analysis of the BNNR identified the relative importance of each predictor in correcting pCO\u003csub\u003e2\u003c/sub\u003e. Maximum daily wind speed accounted for approximately 35\u0026ndash;40% of the explained variance, followed by sea surface temperature (SST) at 25\u0026ndash;30%, sea surface salinity (SSS) at 15\u0026ndash;20%, and, to a lesser extent, chlorophyll a and absolute dynamic topography (ADT) at less than 10%. This hierarchy indicates that atmospheric forcing is the primary modulator of pCO\u003csub\u003e2\u003c/sub\u003e anomalies during cyclones, while temperature and salinity primarily influence the air\u0026ndash;sea gradient under baseline conditions.\u003c/p\u003e\u003cp\u003eWe used SHAP (Shapley Additive exPlanations) values to quantify the contribution of each predictor (wind speed, SST, SSS, and chlorophyll a) to the BNNR pCO\u003csub\u003e2\u003c/sub\u003e predictions. The SHAP analysis confirmed that increases in wind speed above 40 m\u0026middot;s⁻\u0026sup1; are associated with sudden increases in pCO\u003csub\u003e2\u003c/sub\u003e (\u0026gt;\u0026thinsp;200 \u0026micro;atm whereas decreases in SST greater than 2\u0026deg;C explain between 20\u0026ndash;30% of the variability in the days following the passage of cyclones. Sensitivity to SSS was more limited, indicating that its role is concentrated in localized subsurface intrusion events. Taken together, these results validate that the BNNR architecture explicitly captures the nonlinear relationship between physical forcings and surface biogeochemical dynamics.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eA Bayesian recurrent neural network (BNNR) was implemented to correct satellite-derived pCO\u003csub\u003e2\u003c/sub\u003e estimates from the PISCES-NEMO model in the tropical Pacific off Mexico, focusing on extreme meteorological events, such as hurricanes and tropical storms. The bias-correction utilized in situ reference data from Tropical Storm Lidia (2017) and was applied to satellite and modeled data from May to November 2024, encompassing three tropical cyclones. This approach reduced systematic bias in the PISCES-NEMO biogeochemical model and, to our knowledge, for the first time in this region, incorporated explicit uncertainty into pCO₂ estimates. The integration of real and synthetic data facilitated the representation of extreme conditions, enhancing model performance during hurricanes. The following section presents the main findings on model performance, corrected pCO₂ dynamics, air\u0026ndash;sea CO₂ flux (FCO₂) variability, and the biogeochemical implications of these intense events in this tropical region.\u003c/p\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Performance of the Bayesian correction model\u003c/h2\u003e\u003cp\u003eThe BNNR effectively corrected biases in the PISCES-NEMO pCO\u003csub\u003e2\u003c/sub\u003e estimates, achieving very low errors under normal conditions and capturing sharp increases during cyclones. The BNNR\u0026rsquo;s ability to reduce systematic errors in pCO\u003csub\u003e2\u003c/sub\u003e estimates is comparable to other machine learning approaches applied to the ocean (e.g., optimized Random Forest, RMSE\u0026thinsp;\u0026asymp;\u0026thinsp;15 \u0026micro;atm; Wu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Unlike deterministic methods, it provides credibility intervals, a critical feature in the context of high variability. Recent studies in simplified carbon and climate modeling have highlighted the value of Bayesian frameworks for representing uncertainty and assimilation of new observations (Bossy et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The results suggest that the Bayesian model can complement global products by better adapting to local conditions and extreme events.\u003c/p\u003e\u003cp\u003eThis probabilistic framework is consistent with recent advances in climate and biogeochemical modeling, where representing uncertainty is key for decision-making. The results presented here contrast with previous applications of deterministic neural networks, such as those by Bittig et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Denvil-Sommer et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), as they explicitly incorporate uncertainty into daily pCO\u003csub\u003e2\u003c/sub\u003e predictions in a highly dynamic tropical region.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Model sensitivity analysis\u003c/h2\u003e\u003cp\u003eSensitivity analysis identified maximum wind speed as the dominant predictor in pCO\u003csub\u003e2\u003c/sub\u003e correction, consistent with previous studies highlighting the role of mechanical forcing in subsurface ventilation and the intensification of air\u0026ndash;sea fluxes (Yu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The relatively high contribution of SST corroborates that surface cooling after cyclonic events acts as a secondary modulator of the air\u0026ndash;sea gradient, consistent with observations in the eastern Pacific and South China Sea (Sun et al., 2020; Wu et al., 2025).\u003c/p\u003e\u003cp\u003eThe lower sensitivity attributed to SSS and chlorophyll a reflects their role as contextual modulators rather than primary triggers of pCO\u003csub\u003e2\u003c/sub\u003e increase. However, their incursion ensured physical-biogeochemical consistency and avoid spurious predictions, which is consistent with the findings of Somavilla et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) on the importance of including physical redundancies in ocean prediction models.\u003c/p\u003e\u003cp\u003eTaken together, these results reaffirm that the BNNR not only reduces the systematic bias of PISCES-NEMO but also provides an explicit disaggregation of the relative weight of each forcing factor. This level of interpretability constitutes an advance over deterministic or \u0026ldquo;black box\u0026rdquo; models and provides a useful framework for assessing the differential impact of future cyclones in climate change scenarios (IPCC, 2021).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Impact of extreme events on surface pCO₂\u003c/h2\u003e\u003cp\u003eCyclones generated short-lived but intense pCO\u003csub\u003e2\u003c/sub\u003e pulses, exceeding 300 \u0026micro;atm in less than 24 hours, which coincides with observations in other tropical basins (Landsch\u0026uuml;tzer et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These increases were linked to surface cooling, vertical mixing, and the ventilation of carbon-rich subsurface waters. Similar phenomena have been reported in other tropical basins, where typhoons and hurricanes trigger abrupt increases in pCO\u003csub\u003e2\u003c/sub\u003e and air\u0026ndash;sea CO₂ fluxes (Yu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe heterogeneity between events was explained by the combination of intensity, trajectory, translation speed, and previous state of the system, which modulated the magnitude and spatial extent of the anomalies (Yu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In particular, John (cat. 5) produced the most pronounced and extensive changes, Ileana produced an intermediate response, and Eleven-E had a limited impact in magnitude and duration, in line with the dynamic control of mechanical forcing on local ventilation/upwelling.\u003c/p\u003e\u003cp\u003eComparable responses of pCO\u003csub\u003e2\u003c/sub\u003e to cyclonic forcing have been reported in other tropical basins. For example, in the Bay of Bengal, neural network and gradient boosting models driven by SST and SSS captured anomalies of \u0026plusmn;\u0026thinsp;12 \u0026micro;atm associated with monsoon-driven variability (Joshi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, in the northern South China Sea, typhoons produced abrupt increases in surface pCO\u003csub\u003e2\u003c/sub\u003e and degassing fluxes exceeding background variability, consistent with the role of extreme wind forcing (Yu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Positioning our results within this broader context highlights that the Bayesian bias-correction approach developed here is not limited to Guerrero, but represents a transferable strategy for other cyclone-prone tropical regions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Biogeochemical implications in FCO₂\u003c/h2\u003e\u003cp\u003eUsing uncertainty-corrected pCO\u003csub\u003e2\u003c/sub\u003e and FCO\u003csub\u003e2\u003c/sub\u003e revealed short-lived but large degassing pulses, which contributed up to ~\u0026thinsp;30% of the seasonal flux. This pattern supports the role of cyclones as intensifiers of air\u0026ndash;sea CO₂ exchange in tropical regions (Resplandy et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Riebesell et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), highlighting the importance of capturing cyclonic forcing to close seasonal balances.\u003c/p\u003e\u003cp\u003eThe colocation of FCO₂ maxima with SST minima and chlorophyll peaks suggests a physical-biological coupling: vertical mixing and subsurface intrusion raise pCO\u003csub\u003e2\u003c/sub\u003e and the air\u0026ndash;sea gradient. At the same time, nutrient injection triggers a rapid trophic response that can modulate the return to background conditions. The combination of satellite diagnostics and Bayesian bias-correction enabled the quantification of this coupling with daily resolution, a feat difficult to achieve with deterministic products or sporadic in situ sampling (Liss \u0026amp; Merlivat, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Wanninkhof, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Weiss, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1974\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Contribution of the Bayesian approach to oceanographic monitoring\u003c/h2\u003e\u003cp\u003eThe added value of the Bayesian approach can be summarized in three key aspects:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIncorporation of uncertainty\u003c/b\u003e: Credibility intervals enable the robustness of predictions to be assessed, a crucial property in decision-making for climate and fisheries management. This type of probabilistic representation has been highlighted in recent ocean and climate modeling studies, where uncertainty estimation is crucial for assessing carbon fluxes and their large-scale implications (Bossy et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTemporal flexibility\u003c/b\u003e: the LSTM architecture ensures consistency in time series, avoiding spurious oscillations during periods without events. The usefulness of recurrent architectures for capturing the temporal dynamics of marine processes has been validated in reconstructions of pCO₂ and other biogeochemical variables, showing clear improvements over conventional deterministic networks (Wu et al., 2025).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIncreased sensitivity\u003c/b\u003e: the inclusion of redundant predictors, such as mean wind and maximum wind, allowed the model to respond differentially to atmospheric forcing, a feature that \u0026ldquo;black box\u0026rdquo; algorithms, like SVM or XGBoost, tend to lose by not preserving physical consistency. Recent studies confirm that integrating redundant forcings enhances predictive capacity in dynamic regions by more accurately capturing the nonlinear responses of complex ocean processes (Somavilla et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eA distinctive strength of the Bayesian framework is its capacity to generate credibility intervals that support both scientific interpretation and management decisions. In practice, these intervals provide quantitative bounds on the reliability of pCO\u003csub\u003e2\u003c/sub\u003e and FCO₂ predictions, enabling decision-makers to distinguish between robust signals\u0026mdash;such as degassing pulses clearly outside the uncertainty range\u0026mdash;and conditions where the overlap of intervals advises caution. This probabilistic framing enhances the usability of bias-corrected estimates in operational contexts, for instance, early warning systems or carbon accounting schemes, where confidence in anomaly detection is as important as the mean prediction itself.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Limitations and future directions\u003c/h2\u003e\u003cp\u003eThis study has two main limitations: calibration relied on a single reference event (Lidia 2017), which may not capture the full range of conditions in the Mexican Pacific. Additionally, the synthetic data rely on statistical assumptions that require further validation with additional in situ observations from oceanographic campaigns or buoys.\u003c/p\u003e\u003cp\u003eAnother limitation is that the input data resolution of 0.083\u0026deg; may miss submesoscale processes relevant during cyclones.\u003c/p\u003e\u003cp\u003eFuture research should:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eIncorporate multiple calibration events to strengthen training.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEvaluate hybrid architectures that explicitly integrate physical processes, for example, coupling with simplified biogeochemical models (Pathfinder, PISCES-NEMO; Aumont et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eExtend the methodology to climate change scenarios, considering projections of more intense cyclones in the eastern Pacific (IPCC, 2021).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eA Bayesian recurrent neural network was successfully applied to correct satellite-derived pCO\u003csub\u003e2\u003c/sub\u003e in the tropical Pacific off Guerrero, Mexico, during extreme meteorological events. The model reduced systematic biases, incorporated predictive uncertainty, and revealed short-lived but significant CO\u003csub\u003e2\u003c/sub\u003e degassing pulses, particularly during Hurricane John (2024). These pulses accounted for nearly one-third of the seasonal flux, underscoring the importance of cyclones in regional carbon dynamics.\u003c/p\u003e\u003cp\u003eThis study demonstrates that Bayesian approaches provide a powerful and reliable framework for monitoring air\u0026ndash;sea CO₂ exchange in highly variable environments. Beyond improving the accuracy of satellite products, they enhance our capacity to evaluate the biogeochemical impacts of extreme events.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Secretariat of Science, Humanities, Innovation, and Technology (SECIHTI) for awarding a postdoctoral fellowship to the first author (EPM-2022(3)). We also acknowledge Dr. T.L.E.C. for providing the Gulf of California oceanographic buoy data, which was essential for model calibration.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/h4\u003e\n\u003ch4\u003eThis work was supported by multiple institutions.\u003c/h4\u003e\n\u003cp\u003eDr. \u003cstrong\u003eT. Leticia Espinosa Carre\u0026oacute;n\u003c/strong\u003e received institutional support from \u003cstrong\u003eCIDIIR-IPN\u003c/strong\u003e, which provided financial, administrative, and technical assistance, including buoy deployment, supplies, and travel for equipment maintenance.\u003c/p\u003e\n\u003cp\u003eDr. \u003cstrong\u003eLuz de Lourdes Aurora Coronado \u0026Aacute;lvarez\u003c/strong\u003e was supported by the \u003cstrong\u003eSecretar\u0026iacute;a de Ciencia, Humanidades, Tecnolog\u0026iacute;a e Innovaci\u0026oacute;n (SECIHTI)\u003c/strong\u003e through a postdoctoral fellowship awarded to the first author (CVU 353474).\u003c/p\u003e\n\u003cp\u003eDr. \u003cstrong\u003eGabriela Y. Cervantes D\u0026iacute;az\u003c/strong\u003e received institutional support from the \u003cstrong\u003eUniversidad Aut\u0026oacute;noma de Baja California (UABC)\u003c/strong\u003e, which provided financial and administrative assistance, as well as coverage of publication expenses through an institutional agreement with the journal.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eThe authors further declare that they have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eAll authors contributed to the study\u0026rsquo;s conception and design. Material preparation, data collection, and analysis were performed by \u003cstrong\u003eLuz de Lourdes Aurora Coronado \u0026Aacute;lvarez\u003c/strong\u003e, \u003cstrong\u003eGabriela Y. Cervantes D\u0026iacute;az\u003c/strong\u003e, and \u003cstrong\u003eT. Leticia Espinosa Carre\u0026oacute;n\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe first draft of the manuscript was written by \u003cstrong\u003eLuz de Lourdes Aurora Coronado \u0026Aacute;lvarez\u003c/strong\u003e, and all authors commented on and revised previous versions. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are \u003cstrong\u003enot publicly available because they form part of an ongoing research project,\u003c/strong\u003e but they are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X (2016) TensorFlow: A system for large-scale machine learning. \u003cem\u003eProceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016)\u003c/em\u003e, 265\u0026ndash;283\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAumont O, Eth\u0026eacute; C, Tagliabue A, Bopp L, Gehlen M (2015) PISCES-v2: An ocean biogeochemical model for carbon and ecosystem studies. 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Sci Total Environ 922:170735. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2024.170735\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2024.170735\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu P, Wang ZA, Churchill J, Zheng M, Pan J, Bai Y, Liang C (2020) Effects of typhoons on surface seawater pCO₂ and air\u0026ndash;sea CO₂ fluxes in the northern South China Sea. \u003cem\u003eJournal of Geophysical Research: Oceans, 125\u003c/em\u003e(8), e2020JC016258. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2020JC016258\u003c/span\u003e\u003cspan address=\"10.1029/2020JC016258\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"probabilistic deep learning, Bayesian neural networks, cyclonic events, satellite oceanography, carbon dioxide flux","lastPublishedDoi":"10.21203/rs.3.rs-8253374/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8253374/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe exchange of CO₂ between the ocean and atmosphere is a key process in the global carbon cycle, highly sensitive to disturbances causes by hurricanes and tropical storms. In this study, we applied a Bayesian Recurrent Neural Network (BNNR) to correct satellite-derived surface pCO\u003csub\u003e2\u003c/sub\u003e estimates from the PISCES-NEMO model in the eastern tropical Pacific, off the coast of Guerrero, Mexico. The model was calibrated using in situ data collected during Tropical Storm Lidia (2017) and supplemented with synthetic cases representing extreme wind conditions. Under fair-weather conditions, the BNNR achieved low errors (MAE\u0026thinsp;=\u0026thinsp;0.76 \u0026micro;atm; RMSE\u0026thinsp;=\u0026thinsp;0.99 \u0026micro;atm). Although errors increased under simulated extreme winds, the model successfully captured abrupt increases in pCO\u003csub\u003e2\u003c/sub\u003e. During Hurricane John (2024), corrected values exceeded 850 \u0026micro;atm, revealing strong CO\u003csub\u003e2\u003c/sub\u003e degassing pulses that contributed up to one-third of the seasonal air-sea CO₂ flux, a magnitude largely underestimated by deterministic products. By explicitly incorporating uncertainty through credibility intervals, the BNNR provided robust predictions in highly variable conditions. This methodological framework offers a novel tool for improving the monitoring of marine carbon cycle dynamics and for quantifying the biogeochemical impact of extreme events in coastal regions.\u003c/p\u003e","manuscriptTitle":"Bayesian correction of satellite-derived pCO2 reveals the biogeochemical impact of hurricanes on ocean–atmosphere CO2 fluxes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 10:42:10","doi":"10.21203/rs.3.rs-8253374/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11e922f7-fd59-4555-91ad-6ca19b6af5ca","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T07:22:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 10:42:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8253374","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8253374","identity":"rs-8253374","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}