Hajoon Song

Mesoscale ocean fronts enhance carbon export due to gravitational sinking and subduction

Significance Transport of organic carbon from the sunlit surface ocean to deeper depths drives net oceanic uptake of CO2 from the atmosphere. However, mechanisms that control this carbon export remain poorly constrained, limiting our ability to model and predict future changes in this globally important process. We show that the flux of sinking particles (typically considered the dominant form of downward transport of organic carbon) is twice as high at a frontal system, relative to surrounding waters or to nonfrontal conditions. Furthermore, downward transport by subduction leads to additional carbon export at the front that is similar in magnitude to the sinking flux. Such enhanced C export at episodic and mesoscale features needs to be incorporated into biogeochemical forecast models. Enhanced vertical carbon transport (gravitational sinking and subduction) at mesoscale ocean fronts may explain the demonstrated imbalance of new production and sinking particle export in coastal upwelling ecosystems. Based on flux assessments from 238U:234Th disequilibrium and sediment traps, we found 2 to 3 times higher rates of gravitational particle export near a deep-water front (305 mg C⋅m−2⋅d−1) compared with adjacent water or to mean (nonfrontal) regional conditions. Elevated particle flux at the front was mechanistically linked to Fe-stressed diatoms and high mesozooplankton fecal pellet production. Using a data assimilative regional ocean model fit to measured conditions, we estimate that an additional ∼225 mg C⋅m−2⋅d−1 was exported as subduction of particle-rich water at the front, highlighting a transport mechanism that is not captured by sediment traps and is poorly quantified by most models and in situ measurements. Mesoscale fronts may be responsible for over a quarter of total organic carbon sequestration in the California Current and other coastal upwelling ecosystems.

An Adaptive Approach to Mitigate Background Covariance Limitations in the Ensemble Kalman Filter

Abstract A new approach is proposed to address the background covariance limitations arising from undersampled ensembles and unaccounted model errors in the ensemble Kalman filter (EnKF). The method enhances the representativeness of the EnKF ensemble by augmenting it with new members chosen adaptively to add missing information that prevents the EnKF from fully fitting the data to the ensemble. The vectors to be added are obtained by back projecting the residuals of the observation misfits from the EnKF analysis step onto the state space. The back projection is done using an optimal interpolation (OI) scheme based on an estimated covariance of the subspace missing from the ensemble. In the experiments reported here, the OI uses a preselected stationary background covariance matrix, as in the hybrid EnKF–three-dimensional variational data assimilation (3DVAR) approach, but the resulting correction is included as a new ensemble member instead of being added to all existing ensemble members. The adaptive ap...

Mesoscale modulation of air-sea CO2 flux in Drake Passage

We investigate the role of mesoscale eddies in modulating air-sea CO2 flux and associated biogeochemical fields in Drake Passage using in situ observations and an eddy-resolving numerical model. Both observations and model show a negative correlation between temperature and partial pressure of CO2 (pCO2) anomalies at the sea surface in austral summer, indicating that warm/cold anticyclonic/cyclonic eddies take up more/less CO2. In austral winter, in contrast, relationships are reversed: warm/cold anticyclonic/cyclonic eddies are characterized by a positive/negative pCO2 anomaly and more/less CO2 outgassing. It is argued that DIC-driven effects on pCO2 are greater than temperature effects in austral summer, leading to a negative correlation. In austral winter, however, the reverse is true. An eddy-centric analysis of the model solution reveals that nitrate and iron respond differently to the same vertical mixing: vertical mixing has a greater impact on iron because its normalized vertical gradient at the base of the surface mixed layer is an order of magnitude greater than that of nitrate. This article is protected by copyright. All rights reserved.

Anomalous chlorofluorocarbon uptake by mesoscale eddies in the Drake Passage region

The role of mesoscale eddies in the uptake of anthropogenic chlorofluorocarbon-11 (CFC-11) gas is investigated with a 1/20° eddy-resolving numerical ocean model of a region of the Southern Ocean. With a relatively fast air-sea equilibrium time scale (about a month), the air-sea CFC-11 flux quickly responds to the changes in the mixed layer CFC-11 partial pressure (pCFC-11). At the mesoscale, significant correlations are observed between pCFC-11 anomaly, anomalies in sea surface temperature (SST), net heat flux, and mixed layer depth. An eddy-centric analysis of the simulated CFC-11 field suggests that anticyclonic warm-core eddies generate negative pCFC-11 anomalies and cyclonic cold-core eddies generate positive anomalies of pCFC-11. Surface pCFC-11 is modulated by mixed layer dynamics in addition to CFC-11 air-sea fluxes. A negative cross correlation between mixed layer depth and surface pCFC-11 anomalies is linked to higher CFC-11 uptake in anticyclones and lower CFC-11 uptake in cyclones, especially in winter. An almost exact asymmetry in the air-sea CFC-11 flux between cyclones and anticyclones is found.

An Adjoint-Based Adaptive Ensemble Kalman Filter

A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters.

Linear versus Nonlinear Filtering with Scale-Selective Corrections for Balanced Dynamics in a Simple Atmospheric Model

ThispaperinvestigatestheroleofthelinearanalysisstepoftheensembleKalmanfilters(EnKF)indisrupting the balanced dynamics in a simple atmospheric model and compares it to a fully nonlinear particle-based filter (PF). The filters have a very similar forecast step but the analysis step of the PF solves the full Bayesian filtering problem while the EnKF analysis only applies to Gaussian distributions. The EnKF is compared to two flavors of the particle filter with different sampling strategies, the sequential importance resampling filter (SIRF) and the sequential kernel resampling filter (SKRF). The model admits a chaotic vortical mode coupled to a comparatively fastgravity wave mode.It can also beconfiguredeithertoevolve ona so-calledslowmanifold, where the fast motion is suppressed, or such that the fast-varying variables are diagnosed from the slow-varying variablesas slaved modes. Identical twinexperiments showthatEnKFand PF capture the variablesonthe slow manifold well as the dynamics is very stable. PFs, especially the SKRF, capture slaved modes better than the EnKF, implying that a full Bayesian analysis estimates the nonlinear model variables better. The PFs perform significantlybetterinthefullycouplednonlinearmodelwherefastandslowvariablesmodulateeachother.This suggeststhattheanalysisstepinthePFsmaintainsthebalanceinbothvariablesmuchbetterthantheEnKF.Itis also shown that increasing the ensemble size generally improves the performance ofthe PFs but has less impact on the EnKF after a sufficient number of members have been used.

Seasonal Variation in the Correlation Between Anomalies of Sea Level and Chlorophyll in the Antarctic Circumpolar Current

The Antarctic Circumpolar Current (ACC) has highly energetic mesoscale phenomena, but their impacts on phytoplankton biomass, productivity, and biogeochemical cycling are not understood well. We analyze satellite observations and an eddy‐rich ocean model to show that they drive chlorophyll anomalies of opposite sign in winter versus summer. In winter, deeper mixed layers in positive sea surface height (SSH) anomalies reduce light availability, leading to anomalously low chlorophyll concentrations. In summer with abundant light, however, positive SSH anomalies show elevated chlorophyll concentration due to higher iron level, and an iron budget analysis reveals that anomalously strong vertical mixing enhances iron supply to the mixed layer. Features with negative SSH anomalies exhibit the opposite tendencies: higher chlorophyll concentration in winter and lower in summer. Our results suggest that mesoscale modulation of iron supply, light availability and vertical mixing plays an important role in causing systematic variations in primary productivity over the seasonal cycle.