Ocean Acidification

The sea surface total alkalinity (AT) and total dissolved inorganic carbon (CT) are two essential carbonate variables to understand the marine carbon cycle, yet it has been a big challenge to retrieve AT and CT from space for the Yellow and East China Seas (YECS) owing to they are affected coupling by physical and biogeochemical processes and the heterogeneous coastal environments. To address these challenges, we developed multilayer perceptron neural network (MPNN)‐based AT and CT models with the field measured environmental variables as the model predictors, and obtained a root mean square difference (RMSD) of 27.05 μmol/kg and coefficient of determination (R²) of 0.91 for AT (N = 1,520) and a RMSD was 28.31 μmol/kg and R² was 0.88 for CT (N = 513). Further, the MPNN‐based model showed much promise in remotely retrieving surface AT and CT with the spatial resolution of ∼1 km in the YECS with a RMSD of 26.59 μmol/kg, R² of 0.76 for AT and a RMSD of 37.14 μmol/kg, R² of 0.79 for CT. Applying the MPNN‐based model to the Moderate Resolution Imaging Spectroradiometer (MODIS) products, retrieved the monthly distributions of AT and CT over the past 20 years for the first time, demonstrated strong linkages to water masses circulations, upwelling and biological processes with seasonal cycles. Also, the interannual variations of AT and CT had significant relationships with the environmental proxies, as well as climate indices (North Pacific Gyre Oscillation). This work advances understanding of coastal carbon cycling and offers a valuable tool for large‐scale, high spatial‐temporal resolution monitoring of carbon dynamics.

Plain Language Summary

Understanding the coastal carbon cycle is critical for addressing climate change, but continuously measuring key carbonate variables total alkalinity (A_T) and total dissolved inorganic carbon (C_T) in dynamic coastal regions like the Yellow and East China Seas (YECS) has been challenging. To overcome this challenge, we developed a machine learning based A_T and C_T models based on extensive field data set with uncertainties of 27.05 μmol/kg for A_T and 28.31 μmol/kg for C_T, demonstrated that the environmental variables, such as sea surface temperature, salinity and chlorophyll a allow for retrieving A_T and C_T. Further validation showed that the model has much promise in retrieving surface A_T and C_T from space. Applying the model to satellite products, this work provides the monthly distributions and interannual dynamics of A_T and C_T in the YECS over the past 20 years for the first time. We found the physical and biogeochemical processes shape the monthly distribution of A_T and C_T in the YECS, and its interannual variations are strongly correlated with environmental proxies, as well as climate forcings. The integration of satellite-derived products and machine learning based approach enables cost-effective, high-resolution monitoring of coastal carbonate system dynamics.

Key Points

• A machine learning-based model was developed to retrieve the surface carbonate variables from space for the Yellow and East China Seas
• The multilayer perceptron neural network-based model has high tolerance to these uncertainties in satellite-derived environmental proxies
• The interannual and seasonal variations of carbonate variables are affected by coupling physical and biogeochemical processes

The Bering Sea shelf supports a highly productive marine ecosystem that is vulnerable to ocean acidification (OA) due to the cold, carbon-rich waters. Previous observational evidence suggests that bottom waters on the shelf are already seasonally undersaturated with respect to aragonite (i.e. Ω_arag<1) and that OA will continue to increase the spatial extent, duration, and intensity of these conditions. Here, we use a regional ocean biogeochemical model to simulate changes in ocean carbon chemistry for the Bering Sea shelf from 1970–2022. Over this timeframe, model results suggest that surface Ω_arag decreases by −0.043 per decade and surface pH by −0.014 per decade, comparable to observed global rates of OA. However, bottom water pH decreases at twice the rate of surface pH, while bottom [H⁺] decreases at nearly 3 times the rate of surface [H⁺]. This amplified bottom water acidification has emerged over the past 25 years and is likely driven by a combination of anthropogenic carbon accumulation and increasing primary productivity and subsurface respiration and remineralization. Due to this enhanced bottom water acidification, the spatial extent of bottom waters with Ω_arag<1 has greatly expanded over the past 2 decades, along with pH conditions harmful to red king crab. Interannual variability in surface and bottom Ω_arag, pH, and [H⁺] has also increased over the past 2 decades, resulting in part from the increased physical climate variability. We also find that the Bering Sea shelf is a net annual carbon sink of 1.1–7.9 Tg C yr⁻¹, with the range resulting from the difference in the two different atmospheric forcing reanalysis products used. Seasonally, the shelf is a significant carbon sink from April–October but a somewhat weaker carbon source from November–March.

Abstract

This study presents the first dynamically downscaled projections of ocean acidification (OA) for the Main Hawaiian Islands using coupled Regional Ocean Modeling System and Carbon, Ocean Biogeochemistry, and Lower Trophics models integrated with Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs from the Community Earth System Model 2. We analyze three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP3-7.0) and introduce a climate novelty metric to assess the extent to which future OA conditions exceed historical variability by comparing the magnitude of projected changes to past variability. Our results indicate unprecedented levels of OA within the next three decades across all scenarios, with aragonite saturation state (Ω_A), pH, and substrate-to-inhibitor ratio (bicarbonate to free hydrogen ions [HCO₃⁻]/[H⁺]) projected to decline significantly. By 2100, under SSP3-7.0, Ω_A novelty could exceed reference variability by a factor of 12. Spatial analysis reveals heterogeneous OA impacts, with windward coastlines consistently exhibiting higher novelty levels. Importantly, we find contrasting spatial patterns of OA indices due to varying sensitivities to temperature and dissolved inorganic carbon, resulting in higher Ω_A novelty in northern areas and higher pH and substrate-to-inhibitor ratio novelty in southern regions.

Key Points

• Three climate scenarios (SSP1-2.6, SSP2-4.5, and SSP3-7.0) show distinct implications for ocean acidification in the main Hawaiian Islands
• Aragonite saturation, pH, and substrate (bicarbonate) to inhibitor (free hydrogen) ratio lead to distinct spatial patterns
• Future conditions under SSP3-7.0 are projected to exceed historical variability with strong spatial differences along Hawaiian coasts

Plain Language Summary

Our oceans are acidifying as they absorb carbon dioxide from the atmosphere. This change threatens coral reefs, which are vital ecosystems supporting marine life and providing coastal protection. In this study, we used advanced computer models to project how ocean chemistry around the main Hawaiian Islands might change over the 21st century under different scenarios based on how much carbon dioxide we continue to emit. Our results show that ocean acidification is expected to increase significantly across all scenarios, but the extent and timing of these changes vary. In the high-emission scenario, ocean chemistry will become dramatically different from what we have seen historically, potentially posing challenges to coral reefs and their ability to adapt. Even in the low-emission scenario, some changes are inevitable, but they are less extreme and occur more gradually. We introduced a concept called “novelty” to measure how future ocean conditions might deviate from what coral reefs have experienced in recent history, and discovered that different parts of the Hawaiian Islands may experience acidification differently. This research helps us understand the future challenges facing Hawaiian coral reefs and provides information for researchers, conservationists and policymakers for preserving these critical ecosystems for future generations.

This study investigated processes responsible for the acidification of the mixed layer water at station K2 (N, E) in the western subarctic North Pacific by analyzing physical and biogeochemical variables during 1850–2014 in a Geophysical Fluid Dynamics Laboratory Earth system model of Coupled Model Intercomparison Project Phase 6 under historical carbon dioxide () emissions. Analysis revealed an accelerated pH decline at a rate of −0.0015 , which began around 1960. An increase in dissolved inorganic carbon (DIC) is found to be the principal factor of the pH decline. DIC budget analysis of the mixed layer identified the primary physical process accelerating the DIC increase during 1960–2014: enhanced vertical entrainment of the DIC-rich deep water into the mixed layer during autumn–winter (rate of 3.1 μ). This enhancement is attributed to the equatorial Pacific frequent high sea surface temperature condition due to global warming and El Niño events through the model’s atmospheric teleconnection.  gas exchange at the sea surface contributes secondarily to the DIC increase (0.16 μ). Meanwhile, horizontal advection (−0.6 μ) and biological consumption (−2.6 μ) partially counteract DIC input. These physical and biological processes collectively resulted in a simulated DIC increase of 0.043 μ (i.e., 0.52 μ), consistent with the observed increase rate.