Ocean Mixed Layer Research Articles

A dynamically adaptive Langmuir turbulence parameterization scheme for variable wind wave conditions: Model application

Langmuir circulations and turbulence (LT) are crucial in the upper ocean mixed layer, significantly affecting the air-sea exchange of momentum, heat, and mass. The development of an appropriate LT parameterization scheme is vital for ocean modeling. This study employed the Large-eddy Simulation (LES) and the Physics-informed Neural Network (PINN) to optimize the KC04 Langmuir turbulence scheme by dynamically adjusting E6 as a key parameter determined by winds and waves. The LES simulations under different wind wave states indicated the PINN-inferred values for E6. Modelling results from GOTM in OCSPapa station demonstrated that the optimized scheme outperformed the original KC04 scheme in simulating the vertical eddy diffusivity and temperature, with an ∼6.24% annual reduction in the root mean square error (RMSE) for the temperature and an ∼8.23% reduction in the RMSE during autumn. Furthermore, the optimized scheme resulted in a thicker mixed layer, reaching 4.9 m. This enhanced LT parameterization scheme exhibited the improved robustness for variable spatiotemporal resolutions, significantly improving the modeling accuracy.

Responses of the Southern Ocean mixed layer depth to the eastern and central Pacific El Niño events during austral winter

Responses of the Southern Ocean mixed layer depth to the eastern and central Pacific El Niño events during austral winter

A Framework for Assessing Ocean Mixed Layer Depth Evolution

AbstractThe ocean surface mixed layer plays a crucial role as an entry or exit point for heat, salt, momentum, and nutrients from the surface to the deep ocean. In this study, we introduce a framework to assess the evolution of the mixed layer depth (MLD) for realistic forcings and preconditioning conditions. Our approach involves a physically‐based parameter space defined by three dimensionless numbers: λs representing the relative contribution of the buoyancy flux and the wind stress at the air‐sea interface, Rh the Richardson number which characterizes the stability of the water column relative to the wind shear, and f/Nh which characterizes the importance of the Earth's rotation (ratio of the Coriolis frequency f and the pycnocline stratification Nh). Four MLD evolution regimes (“restratification,” “stable,” “deepening,” and “strong deepening”) are defined based on the values of the normalized temporal evolution of the MLD. We evaluate the 3D parameter space in the context of 1D simulations and we find that considering only the two dimensions (λs, Rh) is the best choice of 2D projection of this 3D parameter space. We then demonstrate the utility of this two‐dimensional λs − Rh parameter space to compare 3D realistic ocean simulations: we discuss the impact of the horizontal resolution (1°, 1/12°, or 1/60°) and the Gent‐McWilliams parameterization on MLD evolution regimes. Finally, a proof of concept of using observational data as a truth indicates how the parameter space could be used for model calibration.

Prokaryotic morphological features and maintenance activities governed by seasonal productivity conditions.

Prokaryotic maintenance respiration and associated metabolic activities constitute a considerable proportion of the total respiration of carbon to CO2 in the ocean's mixed layer. However, seasonal influences on prokaryotic maintenance activities in terms of morphological and metabolic adaptations at low (winter) and high productivity (summer) are still unclear. To address this, we examined the natural prokaryotic communities at the mesocosm scale to analyse the differences in their morphological features and gene expression at low and high maintenance respiration, experimentally manipulated with the specific growth rate. Here, we showed that morphological features including membrane blebbing, membrane vesicles and cell‒cell connections occurred under high productivity. Metabolic adaptations associated with maintenance activities were observed under low productivity. Several Kyoto Encyclopedia of Genes and Genomes categories related to signal transduction, energy metabolism, and translational machinery supported maintenance activities under simulated winter conditions. Differential abundances of genes related to transporters, osmoregulation, nitrogen metabolism, ribosome biogenesis, and cold stress were observed. Our results demonstrate how specific growth rate in different seasons can influence resource allocation at the levels of morphological features and metabolic adaptations. This motivates further study of morphological features and their ecological role during high productivity, while investigations of metabolic adaptations during low productivity can advance our knowledge about maintenance activities.

Conceptual Models for Exploring Sea-Surface Temperature Variability Vis-à Long-Range Weather Forecasting

This paper analyzes the ability of three conceptual stochastic models (one-box, two-box, and diffusion models) to reproduce essential features of sea surface temperature variability on intra-annual time scales. The variability of sea surface temperature, which is particularly influenced by feedback mechanisms in ocean surface–atmosphere coupling processes, is characterized by power spectral density, commonly used to analyze the response of dynamical systems to random forcing. The models are aimed at studying local effects of ocean–atmosphere interactions. Comparing observed and theoretical power spectra shows that in dynamically inactive ocean regions (e.g., north-eastern part of the Pacific Ocean), sea surface temperature variability can be described by linear stochastic models such as one-box and two-box models. In regions of the world ocean (e.g., north-western Pacific Ocean, subtropics of the North Atlantic, the Southern Ocean), in which the observed sea surface temperature spectra on the intra-annual time scales do not obey the ν−2 law (where ν is a regular frequency), the formation mechanisms of sea surface anomalies are mainly determined by ocean circulation rather than by local ocean–atmosphere interactions. The diffusion model can be used for simulating sea surface temperature anomalies in such areas of the global ocean. The models examined are not able to reproduce the variability of sea surface temperature over the entire frequency range for two primary reasons; first, because the object of study, the ocean surface mixed layer, changes during the year, and second, due to the difference in the physics of processes involved at different time scales.

Correction: Oceanic bottom mixed layer in the Clarion-Clipperton Zone: potential influence on deep-seabed mining plume dispersal

Correction: Oceanic bottom mixed layer in the Clarion-Clipperton Zone: potential influence on deep-seabed mining plume dispersal

Impact of ocean mixed layer depth on tropical cyclone characteristics: a numerical investigation

Impact of ocean mixed layer depth on tropical cyclone characteristics: a numerical investigation

Suitability of the CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization

Abstract. The Los Alamos Community Ice CodE (CICE) sea ice model is being tested in standalone mode to identify biases that limit its suitability for seasonal prediction, where it is driven by atmospheric forcings from the NCEP Climate Forecast System Reanalysis (CFSR) and a built-in mixed-layer ocean model in CICE. The initial conditions for the sea ice and mixed-layer ocean are also from CFSR in the control experiments. The simulated sea ice extent agrees well with observations during the warm season at all lead times up to 12 months, in both the Arctic and the Antarctic. This suggests that CICE is able to provide useful sea ice edge information for seasonal prediction. However, the model's initial conditions have ice that is too thick in the Beaufort Sea, resulting in excessive ice extent in the Arctic at 6-month lead forecasts and errors in ice volume at all lead times when compared to available observations. To address this limitation, additional CS2_IC experiments were conducted, where the Arctic ice thickness was initialized using CryoSat-2 satellite observations while keeping all other initial fields the same as in the control experiments. This reduced the positive bias in the ice thickness in the initial conditions, leading to improvements in both the simulated ice edge and the ice thickness at the seasonal timescale. This indicates that CICE has the potential to improve its seasonal forecast skill and provide more accurate predictions of sea ice extent and thickness when initialized with a more realistic sea ice thickness. This study highlights that the suitability of CICE for seasonal prediction depends on various factors, including initial conditions such as sea ice thickness, in addition to sea ice coverage, as well as oceanic and atmospheric conditions.

Evaluating Existing Ocean Glider Sampling Strategies for Submesoscale Dynamics

Abstract Mixing in the upper ocean is important for biological production and the transfer of heat and carbon between the atmosphere and deep ocean, properties commonly targeted by observational campaigns using ocean gliders. We assess the reliability of ocean gliders to obtain a robust statistical representation of submesoscale variability in the ocean mixed layer of the Weddell Sea. A 1/48° regional simulation of the Southern Ocean is sampled with virtual “bow-tie” glider deployments, which are then compared against the reference model output. Sampling biases of lateral buoyancy gradients associated with the arbitrary alignment between glider paths and fronts are formally quantified, and the magnitude of the biases is comparable to observational estimates, with a mean error of 52%. The sampling bias leaves errors in the retrieved distribution of buoyancy gradients largely insensitive to deployment length and the deployment of additional gliders. Notable sensitivity to these choices emerges when the biases are removed by sampling perpendicular to fronts at all times. Detecting seasonal change in the magnitude of buoyancy gradients is sensitive to the glider-orientation sampling bias but the change in variance is not. We evaluate the impact of reducing the number of dives and climbs in an observational campaign and find that small reductions in the number of dive–climb pairs have a limited effect on the results. Lastly, examining the sensitivity of the sampling bias to path orientation indicates that the bias is not dependent on the direction of travel in our deep ocean study site. Significance Statement Recent observational campaigns have focused on using autonomous vehicles to better understand processes responsible for mixing in the surface region of the ocean. There exists uncertainty around how effective these missions are at returning reliable and representative information. This study seeks to quantify the performance of existing strategies in observing mixing processes, and we confirm that strategies are biased to underestimate indicators of mixing. Furthermore, compensating for the bias by increasing the number of resources or changing the manner in which resources are used has limited reward. Our findings are important for decision-making during the planning phase of an observational campaign and display that further innovations are required to account for the sampling bias.

Polar Lows and Their Effects on Sea Ice and the Upper Ocean in the Iceland, Greenland, and Labrador Seas

AbstractUsing two case studies, we analyze the effects of explicitly resolving polar lows in a global climate model (ICON‐Sapphire) with a high resolution of 2.5 km on the upper ocean and sea ice. We aim to understand the mechanism of how polar lows form in a global coupled model and how they interact with the upper ocean and sea ice. When polar lows form at the sea ice edge, they induce marine cold air outbreaks that lead to large heat loss from the ocean. This heat loss contributes to dense water formation in the Iceland and Greenland Seas, which replenishes the climatically important Denmark Strait Overflow Water (DSOW). The high wind speeds of polar lows open leads and polynyas in the sea ice cover, such as the Sirius Water Polynya in northeastern Greenland. Heat loss in polynyas is compensated for by the formation of new ice, and the rejected brine densifies the water on the Greenland shelf. In the Labrador Sea, polar lows intensify cold air outbreaks from the sea ice and rapidly deepen the ocean mixed layer. Resolving polar lows and kinematic features in the sea ice improves the realism of climate models, in particular the surface heat loss and the dense water formation in (sub)polar oceans.

Influence of Radiation Stress on Upper-Layer Ocean Temperature under Geostrophic Condition

Wave-induced radiation stress (RS), as a primary driver of ocean currents influenced by waves, plays an important role in the response of upper ocean temperatures under typhoons. Previous studies have mainly focused on wave-generated currents and coastal currents in nearshore areas. This paper incorporates the geostrophic effect into the wave-induced radiation stress of wave-current interaction, and the effect of waves on the changes in upper ocean temperature (including sea surface temperature (SST) and mixed layer temperature) under typhoon Nanmadol (2022) is studied. The FVCOM-SWAVE model is used to conduct a preliminary numerical study in the western Pacific Ocean. The RS with the geostrophic effect increased the horizontal and vertical components, leading to an enhancement in turbulent mixing and a decrease in SST by up to 1.0 °C to 1.4 °C, which is closer to the SST obtained by OISST remote sensing fusion observation data. In the strong divergence domain, the direction of the vortex flow exhibits a more pronounced turn to the right, accompanied by an increase in water velocity. The vertical temperature profile of the ocean shows that the water below is perturbed by the RS component of the geostrophic effect, and the depth of the mixed layer increases by about 2 m, which is closer to the depth of the mixed layer observed by the Argo floats, indirectly enhancing the vertical mass transport of the ocean. In general, this shows that RS, which takes into account geostrophic effects, enhances the effect of waves on the water below, indirectly leading to lower temperatures in the upper ocean, and the simulated results align more closely with the observed data, offering valuable insights for enhancing marine numerical forecasting accuracy.

The Inter‐Model Uncertainty of Projected Precipitation Change in Northern China: The Modulating Role of North Atlantic Sea Surface Temperature

AbstractPrecipitation changes in northern China are projected to increase in the Coupled Model Inter‐comparison Project Phase 6 (CMIP6) multi‐model ensemble. However, these projections are accompanied by notable inter‐model uncertainty, and the sources of this uncertainty remain largely unexplored. By analyzing 30 CMIP6 models, this research explores the source of inter‐model uncertainty in projected precipitation change and reveals the fundamental mechanism driving uncertainty spread. Following the empirical orthogonal function of inter‐model projected precipitation change, the leading mode displays a seesaw spatial pattern between northwest and north China. This phenomenon predominantly stems from the inter‐model divergence of projected sea surface temperature (SST) warming in the North Atlantic. Further scrutinizing the ocean mixed layer heat budget, we discover that the combined effect of surface sensible heat flux, net surface shortwave radiation flux, and ocean heat transport convergence influences heat flux and SST change of North Atlantic. The multi‐model projections indicate that localized increases in solar radiation and heat convergence warm sea surface, raising SST and initiating convective motion. This convective motion subsequently transforms the 200 hPa teleconnection wave train, leading to an anti‐phase pattern over northern China. This wave pattern modulates total cloud cover percentage, influences surface upward latent heat flux, and adjusts the top of atmosphere outgoing longwave radiation, collectively resulting in the seesaw pattern. Our study underscores the pivotal role of inter‐model disparities in North Atlantic SST warming projection, which is a primary driver of precipitation uncertainty in northern China. These insights offer an essential foundation for refining and diminishing inter‐model uncertainty.

Evaluation of multi-season convection-permitting atmosphere – mixed-layer ocean simulations of the Maritime Continent

Abstract. A multi-season convection-permitting regional climate simulation of the Maritime Continent (MC) using the Met Office Unified Model (MetUM) with 2.2 km grid spacing is presented and evaluated. The simulations pioneer the use of atmosphere–ocean coupling with the multi-column K profile parametrisation (KPP) mixed-layer ocean model in atmospheric convection-permitting climate simulations. Comparisons are made against a convection-parametrised simulation in which it is nested and which in turn derives boundary conditions from the ERA5 reanalysis. This paper describes the configuration, performance of the mean state and variability in the two simulations compared against observational datasets. The models have both minor sea surface temperature (SST) and wet precipitation biases. The diurnal cycle, representation of equatorial waves, and relationship between SST and precipitation are all improved in the convection-permitting model compared to the convection-parametrised model. The Madden–Julian oscillation (MJO) is present in both models with a faster-than-observed propagation speed. However, it is unclear whether fidelity of the MJO simulation is inherent to the model or whether it predominantly arises from the forcing at the boundaries.

Trend and interannual variability of summer marine heatwaves in the tropical Indian ocean: Patterns, mixed layer heat budget, and seasonal prediction

Marine heatwaves (MHWs) are extreme sea surface temperature (SST) events in all ocean basins, with far-reaching impacts on marine ecosystems and socio-economy. The leading patterns, trend, and interannual variability of summer MHWs in the tropical Indian Ocean (TIO) are investigated in this study. The first empirical orthogonal function (EOF) mode of frequency of MHWs exhibits a monopole pattern over the entire basin. This mode is highly associated with the concurrent Indian Ocean Basin warming, indicating a remarkable trend over the past four decades. The linear trend in MHW properties largely relates to increased summer Indian Ocean mean SST. The second EOF mode exhibits a zonal dipole with the MHW numbers increasing in the west and decreasing in the east. On the interannual timescale, the first two EOF modes are remotely affected by antecedent and concurrent El Niño events, respectively. The ocean mixed layer budget is utilized for examining the formation of different summer MHW patterns. During the preceding spring, the surface heat flux is important for the development of MHWs, while the ocean advections play a secondary role in the South Indian Ocean for the MHW monopole. Once the SST anomaly rises in summer, the ocean advections play a dominant role in maintaining the SST. Last, we assess the prediction skill of summer TIO MHWs by performing a bilinear seasonal statistical prediction model. Our results suggest the frequency of summer MHWs in the TIO could be predicted one season in advance. This study has great implications for understanding and predicting ocean extreme events in the TIO.

Vertical Localization for Strongly Coupled Data Assimilation: Experiments in a Global Coupled Atmosphere‐Ocean Model

AbstractStrongly coupled data assimilation allows observations of one Earth system component (e.g., the ocean) to directly update another component (e.g., the atmosphere). The majority of the information transfer in strongly coupled atmosphere‐ocean systems is passed through vertical correlations between atmospheric boundary layer and ocean mixed layer fields. In this work we use correlations from a global, coupled model to study vertical observation‐space localization techniques for strongly coupled data assimilation. We generate target correlations using a bootstrapping approach from a single 24 hr forecast from a realistic global, weakly coupled atmosphere‐ocean cycling system with an 80‐member ensemble, which is the ensemble size currently used by the NOAA operational global data assimilation system. We compare data assimilation methods with different localization schemes using single‐update, offline experiments. We develop a new strategy for optimal observation space localization, called Empirical Optimal R‐localization (EORL), to give an upper bound on the improvement we can expect with any localization scheme. We then evaluate Gaspari‐Cohn localization, which is a commonly used parametric localization function and review its performance with respect to the optimal localization scheme. We investigate how the performance of these localization strategies changes with increasing ensemble sizes. Our results show that strongly coupled data assimilation has the potential to be an improvement over weakly coupled data assimilation when large ensembles are used. We also show that the Gaspari‐Cohn localization function does not appear to be a particularly good choice for cross‐fluid vertical localization.

Sources of low-frequency variability in observed Antarctic sea ice

Abstract. Antarctic sea ice has exhibited significant variability over the satellite record, including a period of prolonged and gradual expansion, as well as a period of sudden decline. A number of mechanisms have been proposed to explain this variability, but how each mechanism manifests spatially and temporally remains poorly understood. Here, we use a statistical method called low-frequency component analysis to analyze the spatiotemporal structure of observed Antarctic sea ice concentration variability. The identified patterns reveal distinct modes of low-frequency sea ice variability. The leading mode, which accounts for the large-scale, gradual expansion of sea ice, is associated with the Interdecadal Pacific Oscillation and resembles the observed sea surface temperature trend pattern that climate models have trouble reproducing. The second mode is associated with the central Pacific El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode and accounts for most of the sea ice variability in the Ross Sea. The third mode is associated with the eastern Pacific ENSO and Amundsen Sea Low and accounts for most of the pan-Antarctic sea ice variability and almost all of the sea ice variability in the Weddell Sea. The third mode is also related to periods of abrupt Antarctic sea ice decline that are associated with a weakening of the circumpolar westerlies, which favors surface warming through a shoaling of the ocean mixed layer and decreased northward Ekman heat transport. Broadly, these results suggest that climate model biases in long-term Antarctic sea ice and large-scale sea surface temperature trends are related to each other and that eastern Pacific ENSO variability is a key ingredient for abrupt Antarctic sea ice changes.

Influence of the solar penetration depth and heat-fluxes on the sea surface temperature using an ocean mixed layer model

Solar penetration depth is a major factor in modulating the Sea Surface Temperature (SST) and hence influences the air-sea interface processes. This study investigates the influence of solar penetration depth and heat-fluxes on the SST at three RAMA (Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction) buoy locations (along 90°E at 15°N, 12°N, and 8°N) in the Bay of Bengal (BoB) using the One-Dimensional Price-Weller-Pinkel (1D-PWP) model. The results show that SST has strong dependence on the solar penetration depths with the shortwave component of solar insolation outweighing its longwave component. We found that spatio-temporally varying penetration depths refine the simulated SST, when compared against constant penetration depths. These spatio-temporally varying penetration depths (ranging from 10 to 75 m for the shortwave component; with higher values during monsoon) can be attributed to spatio-temporally changing biological parameters, wind, precipitation, and suspended particulate matter. Upon examination, an antiphase relationship between the penetration depths and total chlorophyll, total primary production, & total phytoplankton was found. Upon including the effect of horizontal advection and vertical entrainment in the 1D-PWP model results, the SST predictions improved albeit by a small amount. Furthermore, the effects of increased heat-fluxes, under the different Representative Concentration Pathway (RCP) equivalent scenarios, coupled with varying penetration depths were estimated. It was found that for the spatio-temporally varying penetration depths, the mean (maximum) increment was 0.52 °C (0.92 °C) under the highest emission scenario, i.e., RCP8.5, across all the locations. The results of the study can potentially be used further for 3D ocean modeling studies, and the characterization of air-sea interaction during monsoon and cyclones in the BoB.

Langmuir turbulence in suspended kelp farms

This study investigates the influence of suspended kelp farms on ocean mixed layer hydrodynamics in the presence of currents and waves. We use the large eddy simulation method, where the wave effect is incorporated by solving the wave-averaged equations. Distinct Langmuir circulation patterns are generated within various suspended farm configurations, including horizontally uniform kelp blocks and spaced kelp rows. Intensified turbulence arises from the farm-generated Langmuir circulation, as opposed to the standard Langmuir turbulence observed without a farm. The creation of Langmuir circulation within the farm is attributed to two primary factors depending on farm configuration: (i) enhanced vertical shear due to kelp frond area density variability, and (ii) enhanced lateral shear due to canopy discontinuity at lateral edges of spaced rows. Both enhanced vertical and lateral shear of streamwise velocity, representing the lateral and vertical vorticity components, respectively, can be tilted into downstream vorticity to create Langmuir circulation. This vorticity tilting is driven by the Craik–Leibovich vortex force associated with the Stokes drift of surface gravity waves. In addition to the farm-generated Langmuir turbulence, canopy shear layer turbulence is created at the farm bottom edge due to drag discontinuity. The intensity of different types of turbulence depends on both kelp frond area density and the geometric configuration of the farm. The farm-generated turbulence has substantial consequences for nutrient supply and kelp growth. These findings also underscore the significance of the presence of obstacle structures in modifying ocean mixed layer characteristics.

Seasonal variability and long-term winter shoaling of the upper mixed layer in the southern Baltic Sea

The upper ocean mixed layer plays a crucial role in regulating the exchange between the ocean and the atmosphere. The Mixed Layer Depth (MLD) is a key parameter affecting the air-sea exchanges of momentum and heat and determining the upper ocean temperature. Numerous previous studies have investigated MLD variability in the global ocean or regional seas but no such studies were carried in the Baltic Sea. In this study, we present the first observational assessment of the MLD and its properties in the Southern Baltic Sea including quantification of its seasonal and long-term changes and identification of the multi-year winter shoaling. We calculated monthly maps of MLD in the southern Baltic Sea using a large number of historical CTD profiles collected in 1995–2021 from a combination of different data sets. To test the robustness of the results we compared the MLDs calculated using different threshold methods. Throughout the southern Baltic Sea, across its three basins, a distinct seasonality is evident in the MLD, with values varying from 12 m in July to 60 m in December–March. During winter the water column is well mixed down to the upper halocline depth and the MLD reaches about 45 m in the Bornholm Basin, 50 m in the Slupsk Furrow, and 60 m in the Gdansk Basin. The observed global warming and decadal changes in the salty inflows from the North Sea to the Baltic have had an impact on stratification by increasing water densities in the intermediate and deep layers. Consequently, density gradients have strengthened with depth while the upper ocean mixing has weakened during the winter season. The results reveal a significant winter shoaling of the mixed layer by 4 m per decade, driven by the increased stratification due to rising temperatures and salinity. These changes could have significant impacts on the dynamics and productivity of marine ecosystems.

Slow response of surface water temperature to fast atmospheric variability reveals mixing heterogeneity in a deep lake

Slow and long-term variations of sea surface temperature anomalies have been interpreted as a red-noise response of the ocean surface mixed layer to fast and random atmospheric perturbations. How fast the atmospheric noise is damped depends on the mixed layer depth. In this work we apply this theory to determine the relevant spatial and temporal scales of surface layer thermal inertia in lakes. We fit a first order auto-regressive model to the satellite-derived Lake Surface Water Temperature (LSWT) anomalies in Lake Garda, Italy. The fit provides a time scale, from which we determine the mixed layer depth. The obtained result shows a clear spatial pattern resembling the morphological features of the lake, with larger values (7.18± 0.3 m) in the deeper northwestern basin, and smaller values (3.18 ± 0.24 m) in the southern shallower basin. Such variations are confirmed by in-situ measurements in three monitoring points in the lake and connect to the first Empirical Orthogonal Function of satellite-derived LSWT and chlorophyll-a concentration. Evidence from our case study open a new perspective for interpreting lake-atmosphere interactions and confirm that remotely sensed variables, typically associated with properties of the surface layers, also carry information on the relevant spatial and temporal scales of mixed-layer processes.