Reliable estimates of historical and current biogeochemistry are essential for understanding past ecosystem variability and predicting future changes. Efforts to translate improved physical ocean state estimates into improved biogeochemical estimates, however, are hindered by high biogeochemical sensitivity to transient momentum imbalances that arise during physical data assimilation. Most notably, the breakdown of geostrophic constraints on data assimilation in equatorial regions can lead to spurious upwelling, resulting in excessive equatorial productivity and biogeochemical fluxes. This hampers efforts to understand and predict the biogeochemical consequences of El Niño and La Niña. We develop a strategy to robustly integrate an ocean biogeochemical model with an ensemble coupled‐climate data assimilation system used for seasonal to decadal global climate prediction. Addressing spurious vertical velocities requires two steps. First, we find that tightening constraints on atmospheric data assimilation maintains a better equatorial wind stress and pressure gradient balance. This reduces spurious vertical velocities, but those remaining still produce substantial biogeochemical biases. The remainder is addressed by imposing stricter fidelity to model dynamics over data constraints near the equator. We determine an optimal choice of model‐data weights that removed spurious biogeochemical signals while benefitting from off‐equatorial constraints that still substantially improve equatorial physical ocean simulations. Compared to the unconstrained control run, the optimally constrained model reduces equatorial biogeochemical biases and markedly improves the equatorial subsurface nitrate concentrations and hypoxic area. The pragmatic approach described herein offers a means of advancing earth system prediction in parallel with continued data assimilation advances aimed at fully considering equatorial data constraints.

Ocean chlorophyll concentration, a proxy for phytoplankton, is strongly influenced by internal ocean dynamics such as those associated with El Niño–Southern Oscillation (ENSO). Observations show that ocean chlorophyll responses to ENSO generally lead sea surface temperature (SST) responses in the equatorial Pacific. A long‐term global Earth system model simulation incorporating marine biogeochemical processes also exhibits a preceding chlorophyll response. In contrast to simulated SST anomalies, which significantly lag the wind‐driven subsurface heat response to ENSO, chlorophyll anomalies respond rapidly. Iron was found to be the key factor connecting the simulated surface chlorophyll anomalies to the subsurface ocean response. Westerly wind bursts decrease central Pacific chlorophyll by reducing iron supply through wind‐driven thermocline deepening but increase western Pacific chlorophyll by enhancing the influx of coastal iron from the maritime continent. Our results mechanistically support the potential for chlorophyll‐based indices to inform seasonal ENSO forecasts beyond previously identified SST‐based indices.

Subseasonal to decadal ocean forecasting can make significant contributions to achieving effective management of living marine resources in a changing ocean. Most applications rely on indirect proxies, however, often measured at the ocean surface and lacking a direct mechanistic link to the dynamics of marine populations. Here, we take advantage of three‐dimensional, dynamical reconstructions and forecasts of ocean biogeochemistry based on a global Earth system model to hindcast and assess the capacity to anticipate fluctuations in the dynamics of bigeye tuna (Thunnus obesus Lowe) in the Pacific Ocean during the last six decades. We reconstructed spatial patterns in catch per unit effort (CPUE) through the combination of physiological indices capturing both habitat preferences and physiological tolerance limits in bigeye tuna. Our analyses revealed a sequence of four distinct regimes characterized by changes in the zonal distribution and average CPUE of bigeye tuna in the Pacific Ocean. Habitat models accounting for basin‐wide fluctuations in the thermal structure and oxygen concentration throughout the water column captured interannual fluctuations in CPUE and regime switches that models based solely on surface information were unable to reproduce. Decade‐long forecast experiments further suggested that forecasts of three‐dimensional biogeochemical information might enable anticipation of fluctuations in bigeye tuna several years ahead. Synthesis and applications. Together, our results reveal the impact of variability of biogeochemical conditions in the ocean interior on the dynamics of bigeye tuna on the Pacific Ocean, raising concerns about the future impact of ocean warming and deoxygenation. The results also lend support to incorporating subsurface biogeochemical information into ecological forecasts to implement efficient dynamic management strategies and promote the sustainable use of marine living resources.