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The ocean mixed layer model (OMLM) is improved using the large-eddy simulation (LES) and the inverse estimation method. A comparison of OMLM (Noh model) and LES results reveals that underestimation of the turbulent kinetic energy (TKE) flux in the OMLM causes a negative bias of the mixed layer depth (MLD) during convection, when the wind stress is weak or the latitude is high. It is further found that the entrainment layer thickness is underestimated. The effects of alternative approaches of parameterizations in the OMLM, such as nonlocal mixing, length scales, Prandtl number, and TKE flux, are examined with an aim to reduce the bias. Simultaneous optimizations of empirical constants in the various versions of Noh model with different parameterization options are then carried out via an iterative Green’s function approach with LES data as constraining data. An improved OMLM is obtained, which reflects various new features, including the enhanced TKE flux, and the new model is found to improve the performance in all cases, namely, wind-mixing, surface heating, and surface cooling cases. The effect of the OMLM grid resolution on the optimal empirical constants is also investigated. Significance Statement This work illustrates a novel approach to improve the parameterization of vertical mixing in the upper ocean, which plays an important role in climate and ocean models. The approach utilizes the data from realistic turbulence simulation, called large-eddy simulation, as proxy observation data for upper ocean turbulence to analyze the parameterization, and the statistical method, called inverse estimation, to obtain the optimized empirical constants used in the parameterization. The same approach can be applied to improve other turbulence parameterization, and the new vertical mixing parameterization can be applied to improve climate and ocean models.

Extended medium-range prediction targets a period of up to 30 days, which is a longer period than medium-range (up to 15 days) and shorter than seasonal (up to 3 months) forecast. The atmospheric response to the initial condition significantly impacts predictability in medium-range prediction while ocean response which is a slower change compared to the atmosphere is also an important factor in extended medium-range prediction. Thus, it is important to consider not only initial forcing but also air-sea interaction containing ocean response in extended medium-range prediction. The interactions in the earth system model can be considered among the atmosphere, ocean, sea-ice, and ocean wave by coupling of each components. The Korean Integrated Model (KIM) system, which is a global atmospheric forecast model, was developed by the Korea Institute of Atmospheric Prediction Systems. Recently, the ocean and sea-ice model components have been coupled with the KIM atmosphere system, and continuous efforts are being made to improve its performance. The air-sea interaction in an atmosphere-ocean coupled system can be considered by exchanging the variables that require interaction between components with a coupler. The bulk type exchange method basically transfers state variables such as temperature, pressure, and wind, which are used to get flux variables that contain interacting information among the atmosphere, ocean, and sea-ice. The bulk method is simple but the energy budget at the interface among the model components may become inconsistent due to the use of different formulas during calculation of the flux variables. In this study, exchange variables are changed by replacing atmospheric state variables with flux and momentum variables, which are the final form used in the ocean model. It is found that the corrected flux and momentum of the ocean surface resulting from the flux type exchange method change the ocean structure, particularly over the low latitude region. The atmosphere reacts to the changed ocean state, affecting not only the lower atmosphere but also the upper atmosphere. The results show that the flux type variable exchange method has advantages for considering air-sea interaction, which would improve the performance of extended medium-range weather forecast compared to the bulk type exchange method.Keywords: extended medium-range forecast, coupled model, air-sea interaction, bulk type methodAcknowledgementThis work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2022-01210.

The so-called frontal-scale air-sea interaction describing the atmospheric responses to oceanic fronts has been mainly discussed in the context of interactions between sea surface temperature and surface winds. The ocean current also influences the surface winds, which can significantly affect the atmosphere, especially in regions of energetic ocean currents and mesoscale activities as in the western boundary current systems. This study uses an atmosphere-ocean coupled model to analyze how the Kuroshio Current affects the momentum and turbulent heat fluxes and the atmospheric boundary layer and how these responses feed back to the ocean and atmosphere in this region. The ocean current coupling influences the path of Kuroshio extension and the eddy activities by mechanical and thermal current feedbacks. Mechanical current feedback reduces momentum flux and damps eddy kinetic energy (EKE) by reducing wind work as expected. On the other hand, the thermal current feedback associated with turbulent heat fluxes injects EKE by baroclinic energy conversion. Overall, the shift of Kuroshio Current and the change of eddy activities impact the region of strong turbulent heat release to the atmosphere, which can eventually trigger changes in weather systems.

Hadley circulation (HC) is a planetary-scale overturning circulation in the tropics that transports momentum, heat, and moisture poleward. In this study, we evaluate the strength and extent of the HC in the historical and future climate simulations of the Korean Meteorological Administration (KMA) Advanced Community Earth system model (K-ACE), which was recently developed by the National Institute of Meteorological Sciences of Korea. Compared with a reanalysis product, the overall structure of the HC is reasonably reproduced by the K-ACE. At the same time, it is also found that the Northern Hemisphere HC in the K-ACE is shifted southward by a few degrees, while the strength of the Southern Hemisphere (SH) HC is under-represented by approximately 20%. These biases in the strength and extent of the HC can be explained by biases in the eddy momentum flux and precipitation in the tropics. In the future climate simulations under the Shared Socioeconomic Pathway 5-Representative Concentration Pathway 8.5 scenario, the HCs in the K-ACE show a weakening and widening trend in both hemispheres, which is consistent with the projections of many Coupled Model Intercomparison Project Phase 6 models. A notable feature of the K-ACE is the widening of the SH HC, which takes place at a rate that is about double the multi-model mean. Climate models that share the component models with the K-ACE, such as UKESM, HadGEM3-GC31-LL, and ACCESS-CM2/ESM1, also show enhanced poleward expansion of the HC in the SH. This strong expansion is shown to be dominated by the expansion of the regional HC over the Pacific.

The observational record shows a substantial 40-yr upward trend in summertime westerly winds over the Southern Ocean, as characterized by the southern annular mode (SAM) index. Enhanced summertime westerly winds have been linked to cold summertime sea surface temperature (SST) anomalies. Previous studies have suggested that Ekman transport or upwelling is responsible for this seasonal cooling. Here, another process is presented in which enhanced vertical mixing, driven by summertime wind anomalies, moves heat downward, cooling the sea surface and simultaneously warming the subsurface waters. The anomalously cold SSTs draw heat from the atmosphere into the ocean, leading to increased depth-integrated ocean heat content. The subsurface heat is returned to the surface mixed layer during the autumn and winter as the mixed layer deepens, leading to anomalously warm SSTs and potentially reducing sea ice cover. Observational analyses and numerical experiments support our proposed mechanism, showing that enhanced vertical mixing produces subsurface warming and cools the surface mixed layer. Nevertheless, the dominant driver of surface cooling remains uncertain; the relative importance of advective and mixing contributions to the surface cooling is model dependent. Modeling results suggest that sea ice volume is more sensitive to summertime winds than sea ice extent, implying that enhanced summertime westerly winds may lead to thinner sea ice in the following winter, if not lesser ice extent. Thus, strong summertime winds could precondition the sea ice cover for a rapid retreat in the following melt season.

We diagnose the ocean’s residual overturning circulation of the Arabian Gulf in a high-resolution model and interpret it in terms ofwater-mass transformation processes mediated by air-sea buoyancy fluxes and interior mixing. We attempt to rationalise the complex three-dimensional flow in terms of the superposition of a zonal (roughly along-axis) and meridional (transverse) overturning pattern. Rates of overturning and the seasonal cycle of air-sea fluxes sustaining them are quantified and ranked in order of importance. Air-sea fluxes dominate the budget so that, at zero order, the magnitude and sense of the overturning circulation can be inferred from air-sea fluxes, with interior mixing playing a lesser role. We find that wintertime latent heat fluxes dominate the water-mass transformation rate in the interior waters of the Gulf leading to a diapycnal volume flux directed toward higher densities. In the zonal overturning cell, fluid is drawn in from the Sea of Oman through the Strait of Hormuz, transformed and exits the Strait near the southern and bottom boundaries. Along the southern margin of the Gulf, evaporation plays an important role in the meridional overturning pattern inducing sinking there.

The predictability of the sea surface temperature (SST) in seasonal forecast systems is crucial for accurate seasonal predictions. In this study, we evaluate the prediction of SST in the Global Seasonal forecast system version 5 (GloSea5) hindcast with particular interest over the western North Pacific (WNP) in which the SST can modify atmospheric convection and the East Asian weather. GloSea5 has a cold SST bias in the WNP that grows over at least 7 months. The bias originates from the surface heat flux in which the latent heat flux bias shows the biggest contribution. We identify the overestimated cloud in the first few days after initialization that causes insufficient shortwave radiation and negative bias of the surface net heat flux. Uncoupled ocean model experiments infer that the ocean model is unlikely the primary source of the SST bias.

We report the significant impact of near-inertial waves (NIWs) on vertical mixing and air-sea carbon dioxide (CO2) fluxes in the Southern Ocean using a biogeochemical model coupled to an eddy-rich ocean circulation model. The effects of high-frequency processes are quantified by comparing the fully coupled solution (ONLINE) to two offline simulations based on 5-day-averaged output of the ONLINE simulation: one that uses vertical mixing archived from the ONLINE model (CTRL) and another in which vertical mixing is recomputed from the 5-day average hydrodynamic fields (5dAVG). In this latter simulation, processes with temporal variabilities of a few days including NIWs are excluded in the biogeochemical simulation. Suppression of these processes reduces vertical shear and vertical mixing in the upper ocean, leading to decreased supply of carbon-rich water from below, less CO2 outgassing in austral winter, and more uptake in summer. The net change amounts up to one third of the seasonal variability in Southern Ocean CO2 flux. Our results clearly demonstrate the importance of resolving high-frequency processes such as NIWs to better estimate the carbon cycle in numerical model simulations. ©2019. American Geophysical Union. All Rights Reserved., NSF MOBY project (OCE-1048926), NSF MOBY project (OCE-1048897), National Research Foundation of Korea (NRF) (grant no. NRF-2019R1C1C1003663), Yonsei University Research Fund (2018-22-0053)

Anthropogenic influences have led to a strengthening and poleward shift of westerly winds over the Southern Ocean, especially during austral summer. We use observations, an idealized eddy-resolving ocean sea ice channel model, and a global coupled model to explore the Southern Ocean response to a step change in westerly winds. Previous work hypothesized a two time scale response for sea surface temperature. Initially, Ekman transport cools the surface before sustained upwelling causes warming on decadal time scales. The fast response is robust across our models and the observations: We find Ekman-driven cooling in the mixed layer, mixing-driven warming below the mixed layer, and a small upwelling-driven warming at the temperature inversion. The long-term response is inaccessible from observations. Neither of our models shows a persistent upwelling anomaly, or long-term, upwelling-driven subsurface warming. Mesoscale eddies act to oppose the anomalous wind-driven upwelling, through a process known as eddy compensation, thereby preventing long-term warming. ©2019

Estimating interannual variability in carbon export is a key goal of many marine biogeochemical studies. However, due to variations in export mechanisms between regions, generalized models used to estimate global patterns in export often fail when used for intra-regional analysis. We present here a region-specific model of export production for the California Current Ecosystem (CCE) parameterized using intensive Lagrangian process studies conducted during El Nino-Southern Oscillation (ENSO) warm and neutral phases by the CCE Long-Term Ecological Research (LTER) program. We find that, contrary to expectations from prominent global algorithms, export efficiency (e-ratio = export / primary productivity) is positively correlated with temperature and negatively correlated with net primary productivity (NPP). We attribute these results to the substantial horizontal advection found within the region, and verify this assumption by using a Lagrangian particle tracking model to estimate water mass age. We further suggest that sinking particles in the CCE are comprised of a recently-produced, rapidly-sinking component (likely mesozooplankton fecal pellets) and a longer-lived, slowly-sinking component that is likely advected long distances prior to export. We determine a new algorithm for estimating particle export in the CCE from NPP (Export = 0.08 · NPP + 72 mg C m-2 d-1). We apply this algorithm to a two-decade long time series of NPP in the CCE to estimate spatial and interannual variability across multiple ENSO phases. Reduced export during the warm anomaly of 2014-2015 and El Nino 2015-2016 resulted primarily from decreased export in the coastal upwelling region of the CCE; the oligotrophic offshore region exhibited comparatively low seasonal and interannual variability in flux. The model resolves intra-regional patterns of in situ export measurements, and provides a valuable contrast to global export models.

The seasonal ice zone around both the Arctic and the Antarctic coasts is typically characterized by warm and salty waters underlying a cold and fresh layer that insulates sea-ice floating at the surface from vertical heat fluxes. Here, we explore how a mesoscale eddy field rubbing against ice at the surface can, through Ekman-induced vertical motion, bring warm waters up to the surface and partially melt the ice. We dub this the “Eddy-Ice-Pumping” (EIP) mechanism. When sea-ice is relatively motionless, underlying mesoscale eddies experience a surface drag that generates Ekman upwelling in anticyclones and downwelling in cyclones. An eddy composite analysis of a Southern Ocean eddying channel model, capturing the interaction of the mesoscale with sea-ice, shows that within the compact ice zone, the mixed layer depth (MLD) is shallow in anticyclones (∼20 m) due to sea-ice melt and deep in cyclones (∼50–200m) due to brine rejection. “EIP” warms the core of anticyclones without significantly affecting the temperature of cyclones, producing a net upward vertical heat flux that reduces the mean sea-ice thickness by 10% and shoals the MLD by 60% over the course of winter and spring. In the following months, the sea-ice thickness recovers with an overshoot, due to strong negative feedbacks associated with atmospheric cooling and salt stratification. Consequently, the effect of “EIP” does not accumulate over the years, but modulates the seasonal cycle of ice within the compact ice zone.

The Antarctic Circumpolar Current 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., National Science Foundation (U.S.) (OCE-1048926)

Marine heatwaves (MHWs), which are characterized by extremely warm water, can harm the marine ecosystem and fishing industry; improving the prediction of such events could reduce their harmful impact. In this study, we examined MHWs occurring in the North Pacific in winter/early spring, and their relationship with North Pacific subtropical mode water (STMW), based on the data analysis and numerical experiments. The time-lagged correlation between the cumulative intensity of MHWs and volume of STMW in March of each year suggests that STMW can modulate MHWs for up to three years after its formation. A patch of statistically significant negative correlation initially appeared in the formation region of the STMW but was found to the east of it near the Transition Zone Chlorophyll Front (TZCF) after one year. This patch stagnated near this remote site in the second winter and early spring. Passive tracer experiments using a numerical model indicate that the STMW, formed near the Kuroshio Extension in March, moves to the east underneath the mixed layer and is entrained to the surface in the following winter while altering the properties of STMW. The STMW reemerges in the second winter, after stagnating under the mixed layer near the TZCF. This suggests that the reemergence of STMW can suppress MHWs in the North Pacific during winter and early spring by reducing the sea surface temperature; if the volume of STMW is anomalously low, there is a greater likelihood of the occurrence of MHWs near the TZCF in the following two winters and early springs. Our results indicate that understanding STMW formation is crucial for predicting MHWs in the North Pacific Ocean during winter and early spring.

Particles and aggregates created in the surface layers of the ocean are transported not only by gravity, but also by the horizontal and vertical advection of the surrounding water. Subduction, in particular, can transport organic matter from the surface ocean to the mesopelagic in a manner that is not likely to be detected by typical in situ carbon export measurements (e.g., sediment traps and 238U-234Th disequilibrium). To assess the importance of subduction to the biological pump, we combined in situ sediment trap, thorium, primary productivity, and particulate organic carbon (POC) measurements with a data-assimilative physical circulation model and a Lagrangian particle tracking model. We develop a simple parameterization of two alternative particle sinking processes (Phytoplankton-Fecal Pellet [PFP] and Aggregation) using results from 13 extensively sampled water parcels in the California Current Ecosystem. Both parameterizations suggested that subduction is an important, at times dominant, mechanism of POC vertical export in the region (median 44% and 23% contribution to total POC export for PFP and Aggregate parameterizations at the 100-m depth horizon). The percentage contribution of subduction was highly variable across water parcels (ranging from 7% to 90%), with subduction typically more important in offshore, oligotrophic regions. On average the fate of particles that are passively transported out of the surface layer by advection is different from that of particles that sink across the 100-m depth horizon. Subducted particles were predominantly remineralized shallower than 150 m, while approximately 50% of gravitationally exported POC was remineralized at depths > 500 m.

Biogeochemical numerical models coupled to physical ocean circulation models are commonly combined with data assimilation in order to improve the models’ state or parameter estimates. Yet much still needs to be learned about important aspects of biogeochemical data assimilation, such as the effect of model complexity and the importance of more realistic model formulations on assimilation results. In this study, 4D-Var-based state estimation is applied to two biogeochemical ocean models: a simple NPZD model with 4 biogeochemical variables (including 1 phytoplankton, 1 zooplankton) and the more complex NEMURO model, containing 11 biogeochemical variables (including 2 phytoplankton, 3 zooplankton). Both models are coupled to a 3-dimensional physical ocean circulation model of the U.S. west coast based on the Regional Ocean Modelling System (ROMS). Chlorophyll satellite observations and physical observations are assimilated into the model, yielding substantial improvements in state estimates for the observed physical and biogeochemical variables in both model formulations. In comparison to the simpler NPZD model, NEMURO shows a better overall fit to the observations. The assimilation also results in small improvements for simulated nitrate concentrations in both models and no apparent degradation of the output for other unobserved variables. The forecasting skill of the biogeochemical models is strongly linked to model performance without data assimilation: for both models, the improved fit obtained through assimilation degrades at similar relative rates, but drops to different absolute levels. Despite the better performance of NEMURO in our experiments, the choice of model and desired level of complexity should depend on the model application and the data available for assimilation.

A quadratic formulation for an incremental lognormal 4-dimensional variational assimilation method (incremental L4DVar) is introduced for assimilation of biogeochemical observations into a 3-dimensional ocean circulation model. L4DVar assumes that errors in the model state are lognormally rather than Gaussian distributed, and implicitly ensures that state estimates are positive definite, making this approach attractive for biogeochemical variables. The method is made practical for a realistic implementation having a large state vector through linear assumptions that render the cost function quadratic and allow application of existing minimization techniques. A simple nutrient-phytoplankton-zooplankton-detritus (NPZD) model is coupled to the Regional Ocean Modeling System (ROMS) and configured for the California Current System. Quadratic incremental L4DVar is evaluated in a twin model framework in which biological fields only are in error and compared to G4DVar which assumes Gaussian distributed errors. Five-day assimilation cycles are used and statistics from four years of model integration analyzed. The quadratic incremental L4DVar results in smaller root-mean-squared errors and better statistical agreement with reference states than G4DVar while maintaining a positive state vector. The additional computational cost and implementation effort are trivial compared to the G4DVar system, making quadratic incremental L4DVar a practical and beneficial option for realistic biogeochemical state estimation in the ocean.

A fully coupled physical and biogeochemical ocean data assimilation system is tested in a realistic configuration of the California Current System using the Regional Ocean Modeling System. In situ measurements for sea surface temperature and salinity as well as satellite observations for temperature, sea level and chlorophyll are used for the year 2000. Initial conditions of the combined physical and biogeochemical state are adjusted at the start of each 3-day assimilation cycle. Data assimilation results in substantial reduction of root-mean-square error (RMSE) over unconstrained model output. RMSE for physical variables is slightly lower when assimilating only physical variables than when assimilating both physical variables and surface chlorophyll. Surface chlorophyll RMSE is lowest when assimilating both physical variables and surface chlorophyll. Estimates of subsurface, nitrate and chlorophyll show modest improvements over the unconstrained model run relative to independent, unassimilated in situ data. Assimilation adjustments to the biogeochemical initial conditions are investigated within different regions of the California Current System. The incremental, lognormal 4-dimensional data assimilation method tested here represents a viable approach to coupled physical biogeochemical state estimation at practical computational cost.

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.

Possible nutrient sources and delivery mechanisms for the highly productive Patagonian shelf in the southwest Atlantic are identified. Using a passive tracer adjoint sensitivity experiment, we identify three source waters: waters local to the Patagonian shelf, coastal waters near the Chilean coast and the subsurface waters in the southeast Pacific. We perform a series of forward simulations of a biogeochemical model to investigate the impact of nutrient perturbations in these source regions to productivity on the Patagonian shelf. Positive nitrate perturbations from local waters have an immediate impact elevating productivity. Iron perturbations local to the shelf, however, do not change productivity because the shelf region is limited by nitrate. Additional nutrient supply from the other source regions leads to increases in productivity. We find that positive nutrient perturbations in subsurface waters in the southeast Pacific result in the largest boost of productivity over the shelf. These source waters are rich in nutrients and upwelled from the depth where light levels are so low that they cannot be consumed. Finally, we identify wintertime intense vertical mixing as the key process which draws nutrients from below 300–500 m to the surface before being delivered to the shelf.

This study examines the effect of surface current in the bulk formula for the wind stress, referred to as the relative wind (RW) effect, on the energetics of the geostrophic circulation and the upper ocean stratification in the Bay of Bengal (BoB) during the summer monsoon seasons. When the RW effect is taken into account in the high-resolution SCOAR (WRF-ROMS) regional coupled model simulation and compared to the run without such a consideration, the kinetic energy both in the mean (MKE) and eddy (EKE) is reduced by more than a factor of two. The most significant reduction in the kinetic energy is found along the path of the northward East India Coastal Current (EICC) and to the south of its separated latitude. The energetics calculations and spectral analysis reveal that this significant damping of EKE is primarily due to reduced eddy wind work principally at wavelengths close to the first baroclinic Rossby deformation radius, indicating the modulation of the wind work by geostrophic mesoscale eddy fields. Moreover, the mixed layer depth (MLD) is significantly shoaled south of the separated EICC latitude, the area dominated by anticyclonic eddy activity. The shallower mixed layer and enhanced stratification with the RW effect are attributed to doming of the isopycnals by the anomalous upward Ekman velocity, which itself is generated by the interaction of anticyclonic mesoscale surface current and the prevailing southwesterly monsoonal wind. Overall, the geostrophic circulation and upper ocean stratification along the EICC and south of its separated latitude exhibit the most significant dynamical response. This result implies that this southwestern part of the BoB is a hot spot for the momentum exchange between the surface circulation and the monsoonal winds, thus a potential area for focused field measurements for the ocean circulation energetics and air-sea interaction.

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.

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.

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.

This paper investigates the role of the linear analysis step of the ensemble Kalman filters (EnKF) in disrupting 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 fast gravity wave mode. It can also be configured either to evolve on a so-called slow manifold, where the fast motion is suppressed, or such that the fast-varying variables are diagnosed from the slow-varying variables as slaved modes. Identical twin experiments show that EnKF and PF capture the variables on the 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 significantly better in the fully coupled nonlinear model where fast and slow variables modulate each other. This suggests that the analysis step in the PFs maintains the balance in both variables much better than the EnKF. It is also shown that increasing the ensemble size generally improves the performance of the PFs but has less impact on the EnKF after a sufficient number of members have been used.

A data assimilation method for positive-definite variables is investigated and applied to a 1-Dimensional (1D) advection–diffusion model and a 3-variable nutrient–phytoplankton–zooplankton (NPZ) model. Conventional data assimilation methods that assume Gaussian distributed errors are problematic for most biogeochemical models because they do not constrain posterior estimates for concentration-based variables to be positive-definite. We apply the approach outlined by Fletcher (2010) that formulates the 4-dimensional variational (4DVAR) assimilation problem assuming lognormally distributed errors. This approach is sensible because many biogeochemical variables are better represented by lognormal than by Gaussian statistics, and it ensures positive-definite state variables. We introduce the incremental formulation of lognormal 4DVAR (L4DVAR) and consider two solutions – incremental mode (imode) and incremental median (imedian) which approximate the mode and the median of different posterior probability density functions. In a simple 0D test case, the two solutions performed similarly with small observation and background uncertainty, but the imedian solution resulted in smaller geometric bias and root-mean-squared error as uncertainty increased. Both solutions of incremental L4DVAR using a 1D linear advection–diffusion model and a nonlinear NPZ model reduce misfit between the model and observations significantly in various assimilation settings and yield a positive-definite adjusted state. We report also on the success of the incremental L4DVAR approach when model error is introduced.

In the California Current System (CCS), upwelling is one of the most important features that enrich the coastal ecosystem. It is highly dependent on both wind stress and wind stress curl, because they contribute to the upwelling system through Ekman transport away from the coast and Ekman pumping as a result of the surface divergence, respectively. Various wind stress products are known to contain sharply different patterns of wind stress, and well-resolved wind forcing products have been shown to drive stronger upwelling due to their better-resolved wind stress curl in previous studies. However, sensitivities of upwelling to changes in wind stress patterns, and each of their control to the source waters and paths of the upwelling cells, are not yet well known for the CCS. Here we study these effects using the Regional Ocean Modeling System (ROMS) and its adjoint model under idealized wind stress forcing patterns representing three widely-used products in addition to a constant wind stress field (no curl): the NCEP/NCAR Reanalysis, the QuikSCAT satellite observations, and the Regional Spectral Model (RSM) downscaling. Changes in currents and isopycnal patterns during the upwelling season are first studied in ROMS under the four different wind stress fields. The model simulations show that the locations of the core of the equatorward flow and the gradient of the cross-shore isopycnals are controlled by the wind stress curl field. The core of the equatorward flow is found under negative wind stress curl, and a deeper upwelling cell is found as the gradient from positive and negative wind stress curl increases. Source waters for the upwelling in each of the four wind stress patterns are investigated using the ROMS adjoint model. The simulations follow a passive tracer backward in time and track the source waters for upwelling in two key areas of interest: inshore and offshore of the Point Sur region of California. The upwelling source waters depend strongly on the depth of the upwelling cell and the alongshore current location. We further relate these results to recent studies of the observed trends in upwelling favorable winds and consequent wind stress curl changes in the CCS.

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 approach is tested with the Lorenz-96 model. The hybrid EnKF–3DVAR is used as a benchmark to evaluate the performance of the adaptive approach. Assimilation experiments suggest that the new adaptive scheme significantly improves the EnKF behavior when it suffers from small size ensembles and neglected model errors. It was further found to be competitive with the hybrid EnKF–3DVAR approach, depending on ensemble size and data coverage.

[1] The Pacific sardine (Sardinops sagax) showed significant differences in spawning habitat area, spawning habitat quality and availability of survivor habitat as the Pacific Ocean went through the La Nina state in April 2002 to a weak El Nino in April 2003. During another El Nino/Southern Oscillation transition period in 2006–2007 when the El Nino state retreated and the La Nina returned, a similar pattern in spawning habitat quality was seen. The coupling between the atmospheric forcing, the physical ocean states and the properties of the sardine egg spawning are investigated using dynamically consistent data-assimilation fits of the available physical oceanographic observations during these months. Fits were executed using the Regional Ocean Modeling System four-dimensional variational assimilation platform along with adjoint model runs using a passive tracer to deduce source waters for the areas of interest. Analysis using the data-assimilation model runs reveals that unusually strong equatorward wind-forcing drives offshore transport during the La Nina conditions, which extends the spawning habitat for sardine further offshore. A statistical model of sardine spawning habitat shows better habitat quality during the El Nino conditions, which is associated with higher egg densities and corresponded to higher daily egg production. Concentration of eggs is also increased by convergence of water. The results of the source waters analysis using the adjoint data assimilation model support the idea that offshore transport extends the spawning habitat, and show that higher levels of nutrient are brought into the spawning habitat with high concentration of sardine eggs.