Mixed Layer Depth Research Articles

Effects of Oceanographic Conditions on Fishery Distribution: A Case Study of Chub Mackerel (Scomber japonicus) in Northeastern Taiwan

Changes in oceanographic conditions can affect species distribution in marine habitats. Global climate change may negatively influence the oceanographic factor–species distribution relationship. Here, we assessed the influence of oceanographic conditions on chub mackerel (Scomber japonicus) distribution in northeastern Taiwan by constructing and using a habitat ensemble model incorporating chub mackerel fishery, climatic oscillation, and oceanography data. Our results indicated that the chub mackerel catch was mainly influenced by the Western Pacific Oscillation. Moreover, sea-surface height and mixed-layer depth exerted the most and least significant effects on chub mackerel distribution, respectively. The chub mackerel catch rate peaked in the study area with a sea-surface temperature of 29°C, sea-surface chlorophyll of 0.25 mg/m3, sea-surface salinity of 33.7 psu, and SSH of 0.575 m. Chub mackerel was the most widely distributed in the area between 25°N, 120.5°E and 26.2°N, 121.5°E. Our findings can be used to develop critical adaptation plans for managing chub mackerel fisheries in the northeastern waters of Taiwan. Considering changing climate conditions globally, the incorporation of this knowledge into managerial strategies may aid decision-makers in protecting not only other ocean fisheries but also individuals dependent on them.

Prediction of the Relative Resource Abundance of the Argentine Shortfin Squid Illex argentinus in the High Sea in the Southwest Atlantic Based on a Deep Learning Model

To analyze the impact of the marine environment on the relative abundance of Illex argentinus (high and low categories) in the southwest Atlantic, this study collected logbook data from Chinese pelagic trawlers from December 2014 to June 2024, including vessel position data and oceanographic variables such as sea surface temperature, 50 m and 100 m water temperature, sea surface salinity, sea surface height, chlorophyll-a concentration, and mixed layer depth. Vessel positions were used to enhance the logbook data quality, allowing an analysis of the annual trends in the resource center of this squid at a spatial resolution of 0.1° × 0.1° and a temporal resolution of ten days. The findings showed that the resource center is primarily located around 42° S in the north and between 45° S and 47° S in the south, with a trend of northward movement during the study period. Additionally, we constructed two ensemble learning models based on decision trees—AdaBoost and PSO-RF—aiming to identify the most critical environmental factors that affect its resource abundance; we found that the optimal model was the PSO-RF model with max_depth of 5 and n_estimators of 46. The importance analysis revealed that sea surface temperature, mixed layer depth, sea surface height, sea surface salinity, and 50 m water temperature are critical environmental factors affecting this species’ resources. Given that deep learning models generally have shorter running times and higher accuracy than other models, we developed a CNN-Attention model based on the five most important input factors. This model achieved an accuracy of 73.6% in forecasting this squid for 2024, predicting that the population would first appear near the Argentine exclusive economic zone around mid-December 2023 and gradually move east and south thereafter. The predictions of the model, validated through log data, maintained over 70% accuracy during most periods at a time scale of ten days. The successful construction of the resource abundance forecasting model and its accuracy improvements can help enterprises save fuel and time costs associated with blind searches for target species. Moreover, this research contributes to improving resource utilization efficiency and reducing fishing duration, thereby aiding in lowering carbon emissions from pelagic trawling activities, offering valuable insights for the sustainable development of this species’ resources.

Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model

Abstract. We evaluate the vertical turbulent-kinetic-energy (TKE) mixing scheme of the NEMO-SI3 ocean–sea-ice model in sea-ice-covered regions of the Arctic Ocean. Specifically, we assess the parameters involved in TKE mixed-layer-penetration (MLP) parameterization. This ad hoc parameterization aims to capture processes that impact the ocean surface boundary layer, such as near-inertial oscillations, ocean swells, and waves, which are often not well represented in the default TKE scheme. We evaluate this parameterization for the first time in three regions of the Arctic Ocean: the Makarov, Eurasian, and Canada basins. We demonstrate the strong effect of the scaling parameter that accounts for the presence of sea ice. Our results confirm that TKE MLP must be scaled down below sea ice to avoid unrealistically deep mixed layers. The other parameters evaluated are the percentage of energy penetrating below the mixed layer and the length scale of its decay with depth. All these parameters affect mixed-layer depth and its seasonal cycle, surface temperature, and salinity, as well as underlying stratification. Shallow mixed layers are associated with stronger stratification and fresh surface anomalies, and deeper mixed layers correspond to weaker stratification and salty surface anomalies. Notably, we observe significant impacts on sea-ice thickness across the Arctic Ocean in two scenarios: when the scaling parameter due to sea ice is absent and when the TKE mixed-layer-penetration process vanishes. In the former case, we observe an increase of several meters in mixed-layer depth, along with a reduction in sea-ice thickness ranging from 30 to 40 cm, reflecting the impact of stronger mixing. Conversely, in the latter case, we notice that a shallower mixed layer is accompanied by a moderate increase in sea-ice thickness, ranging from 10 to 20 cm, as expected from weaker mixing. Additionally, interannual variability suggests that experiments incorporating a scaling parameter based on sea-ice concentration display an increased mixed-layer depth during periods of reduced sea ice, which is consistent with observed trends. These findings underscore the influence of enhanced ocean mixing, through specific parameterizations, on the physical properties of the upper ocean and sea ice.

Planktonic to sessile: drivers of spatial and temporal variability across barnacle life stages and indirect effects of the Pacific Marine Heatwave

ABSTRACT Barnacles are a foundation species in intertidal habitats. During the Pacific Marine Heatwave (PMH), intertidal barnacle cover increased in the northern Gulf of Alaska (GoA); however, the role of pelagic larval supply in this increase was unknown. Using long-term monitoring data on intertidal benthic (percent cover) and pelagic larval populations (nauplii and cyprid concentrations), we examined potential environmental drivers (temperature, chlorophyll-a, mixed layer depth) of larval concentration and whether including larval concentration at regional and annual scales improved intertidal barnacle percent cover models in two study regions in the GoA. In both regions, larval concentrations were slightly higher following the PMH. Percent cover models were improved by including cyprid concentrations (but not nauplii), and the effect strength varied by site and tidal elevation. This indicates that larval concentration contributes as a bottom–up driver of benthic barnacle abundance. There is little evidence of a direct effect of the PMH on either life stage. Instead, our results may illustrate the positive feedback between life stages, where higher adult benthic abundance increased larval concentrations, which then supplied more new recruits to the benthos. As heatwaves continue to occur, integrating various data types can provide insights into factors influencing both benthic and pelagic communities.

Analyses of sea surface chlorophyll a trends and variability from 1998 to 2020 in the German Bight (North Sea)

Abstract. Satellite remote sensing of ocean colour properties allows observation of the ocean with high temporal and spatial coverage, facilitating the better assessment of changes in marine primary production. Ocean productivity is often assessed using satellite-derived chlorophyll a concentrations, a commonly used proxy for phytoplankton concentration. We used the Copernicus GlobColour remote sensing chlorophyll a surface concentration to investigate seasonal and non-seasonal variability, temporal trends, and changes in spring bloom chlorophyll a magnitude. Complementary, we analysed the chlorophyll a relationship with sea surface temperature and mixed-layer depth in the German Bight from 1998 to 2020. Empirical orthogonal functions were employed in order to investigate dominant spatial and temporal patterns (modes) related to the main processes of chlorophyll a variability. Multi covariance analysis was used to extract the dominant structures that maximize the covariance between chlorophyll a and sea surface temperature mixed-layer depth fields. High levels of chlorophyll a were found near the coast, showing a decreasing gradient towards offshore waters. A significant chlorophyll a positive trend was observed close to the Elbe estuary and adjacent area, while 55 % of the German Bight was characterized by a significant chlorophyll a negative trend. The chlorophyll a non-seasonal variability showed that the first four modes explained around 45 %, with the first and second modes related to inter- and intra-annual variability, respectively, observed in the temporal principal components spectral analyses. Monthly chlorophyll a concentration anomalies co-varied by 45 % with sea surface temperature anomalies and 23 % with mixed-layer depth anomalies. The monthly averages of chlorophyll a anomaly fields were suitable to investigate long-term trends and variability. The rising water temperature, combined with its indirect effects on other variables, can partially explain the observed trends in chlorophyll a.

Dimethyl sulfide (DMS) climatologies, fluxes, and trends – Part 1: Differences between seawater DMS estimations

Abstract. Dimethyl sulfide (DMS) is a naturally emitted trace gas that can affect the Earth's radiative budget by changing cloud albedo. Most atmospheric models that represent aerosol processes depend on regional or global distributions of seawater DMS concentrations and sea–air flux parameterizations to estimate its emissions. In this study, we analyse the differences between three estimations of seawater DMS, one of which is an observation-based interpolation method following Hulswar et al. (2022) (hereafter referred to as H22) and two of which are proxy-based parameterization methods following Galí et al. (2018) (hereafter referred to as G18) and Wang et al. (2020a) (hereafter referred to as W20). The interpolation-based method depends on the distribution of observations and the methods used to fill data between observations, while the parameterization-based methods rely on establishing a relationship between DMS and environmental parameters such as chlorophyll a, mixed-layer depth, nutrients, sea surface temperature, etc., which can then be used to predict DMS concentrations. On average, the interpolation-based methods show higher DMS values compared to the parameterization-based methods. Even though the interpolation method shows higher values than the parameterization-based methods, it fails to capture mesoscale variability. The regression-based parameterization method (G18) shows the lowest values compared to other estimations, especially in the Southern Ocean, which is the high-DMS region in austral summer. The parameterization-based methods suggest positive long-term trends in seawater DMS (3.82±0.79 % per decade for G18 and 2.13±0.32 % per decade for W20). Since large differences, often more than 100 %, are observed between the different estimations of seawater DMS, the derived sea–air fluxes and, hence, the impact of DMS on the radiative budget are sensitive to the estimate used.

Diverse Responses of Upper Ocean Temperatures to Chlorophyll‐Induced Solar Absorption Across Different Coastal Upwelling Regions

AbstractChlorophyll in phytoplankton absorbs solar radiation (SR) and affects the thermal structure and dynamics within upwelling regions. However, research on this process across global‐scale coastal upwelling systems is still lacking. Here, we use a coupled ocean‐biogeochemical model to investigate differing responses to chlorophyll‐induced solar absorption between Pacific and Atlantic coastal upwelling regions. Chlorophyll‐induced solar absorption leads to colder Pacific coastal upwelling but warmer Atlantic coastal upwelling. In the Pacific, the shading effect of the surface chlorophyll maximum leads to colder subsurface water, which is then upwelled, contributing to cooling. The more stratified upper ocean leads to shallower mixed layer depth, intensifying offshore transport and upwelling. In the Atlantic, the absorption of SR by the subsurface chlorophyll maximum causes warmer and weaker upwelling. The processes described, in turn, trigger positive feedback to ocean biogeochemistry and potentially interact with climate dynamics, underscoring the necessity to incorporate them into Earth system models.

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.

The Rapid Response of Southern Ocean Biological Productivity to Changes in Background Small Scale Turbulence

AbstractBackground subsurface vertical mixing rates in the Southern Ocean (SO) are known to vary by an order of magnitude temporally and spatially, due to variability in their generating mechanisms, which include winds and shear instabilities at the surface, and the interaction of tides and lee waves with rough bottom topography. There is great uncertainty in the parameterization of this mixing in coarse resolution Earth System Models (ESM), and in the impact that this has on SO biological productivity on sub decadal timescales. Using a data assimilating biogeochemical ocean model we show that SO phytoplankton productivity is highly sensitive to differences in background diapycnal mixing over short timescales. Changes in the background vertical mixing rates alter key biogeochemical and physical conditions. The greatest changes to the distribution of physical and biogeochemical tracers occur in regions with very strong tracer vertical gradients. A combination of reduced nutrient limitation and reduced light limitation causes a strong increase in SO phytoplankton productivity with higher background mixing. This leads to increased summer carbon export but reduced wintertime export over the mixed layer depth, which could alter the strength of the SO biological carbon pump and atmospheric concentrations on centennial to millennial timescales. This study demonstrates the importance of accurately representing diapycnal mixing in ESM to predict SO biogeochemical dynamics and their broader climatic implications.

Modelling global mesozooplankton biomass using machine learning

Mesozooplankton are a crucial link between primary producers and higher trophic levels and play a vital role in marine food webs, biological carbon pumps, and sustaining fishery resources. However, the global distribution of mesozooplankton biomass and the relevant controlling mechanisms remain elusive. We compared four machine learning algorithms (Boosted Regression Trees, Random Forest, Artificial Neural Network, and Support Vector Machine) to model the spatiotemporal distributions of global mesozooplankton biomass. These algorithms were trained on a compiled dataset of published mesozooplankton biomass observations with corresponding environmental predictors from contemporaneous satellite observations (temperature, chlorophyll, salinity, and mixed layer depth). We found that Random Forest achieved the best predictive accuracy with R2 and RMSE (Root Mean Standard Error) of 0.57 and 0.39, respectively. Also, the global distribution of mesozooplankton biomass predicted by the Random Forest model was more consistent with the observational data than other models. We used the Random Forest model to create a global map of mesozooplankton biomass which serves as a reference for validating process-based ecosystem models. The model outputs confirm that environmental factors, especially surface Chl a, a proxy for prey availability, significantly correlate with the spatiotemporal distribution of mesozooplankton biomass. The scaling relationship between the mesozooplankton biomass and Chl a can be used as an emergent constraint for model validation and development. Moreover, our model predicts that the global mesozooplankton biomass will decrease by 3% by the end of this century under the “business-as-usual” scenarios, potentially reducing fishery production and carbon sequestration. Our study contributes to predicting global mesozooplankton biomass and provides deep insights into the underlying environmental impacts on the distribution of mesozooplankton biomass.

Long‐Term Variability of Phytoplankton Primary Production in the Ulleung Basin, East Sea/Japan Sea Using Ocean Color Remote Sensing

AbstractIn recent years, significant changes in environmental conditions and marine ecosystems have been observed in the East Sea/Japan Sea. This study investigates the long‐term environmental dynamics and phytoplankton responses in the Ulleung Basin, situated in the southwestern East Sea/Japan Sea, utilizing satellite and in situ data from 2002 to 2021. Over this period, there was a noticeable increase in sea surface temperature (SST) (r = 0.5739, p < 0.01), accompanied by decreasing mixed layer depth (MLD) and chlorophyll‐a (Chl‐a) concentration (r = −0.6193 and −0.6721, respectively; p < 0.01). Nutrient concentrations within the upper 50 m significantly declined for nitrate and phosphate. A reduction in the N:P ratio indicated a shift from phosphorus‐limited to nitrogen‐limited environment. Moreover, primary production (PP) demonstrated a decreasing trend (r = −0.5840, p < 0.01), coinciding with an increase in small phytoplankton contribution (r = 0.6399, p < 0.01). Rising SST potentially altered the water column's vertical structure, hindering nutrient entrainment from the deep ocean. Consequently, this nutrient limitation may increase small phytoplankton contribution, resulting in a decline in total PP. Under the IPCC's SSP5‐8.5 scenario, small phytoplankton contribution in the Ulleung Basin is projected to rise by over 10%, resulting in a 29% average PP decrease by 2100. This suggests a diminishing energy supply to the food web in a warming ocean, impacting higher trophic levels and major fishery resources. These findings emphasize the critical need for understanding and monitoring these environmental shifts for effective fisheries management and marine ecosystem conservation.

Submesoscale Processes in the Northern Red Sea: Insights From Underwater Glider Observations

AbstractThe critical role of oceanic submesoscale currents in promoting energy cascade and modulating biogeochemical processes as well as the heat budget in the upper ocean has gained wide recognition. Although high‐resolution numerical simulations have enabled qualitative investigation of the spatiotemporal variability of submesoscale processes in the north Red Sea (NRS), observational evidence remains scarce. This study investigated the submesoscale processes in the NRS through field observations of underwater gliders. High‐resolution glider and satellite observation data sets reveal the spatiotemporal variation characteristics of submesoscale fronts and deepen mixed layer depth during winter. Diagnosis of potential vorticity (PV) and classifications of submesoscale instabilities demonstrate conducive conditions for the mixed layer baroclinic, gravitational, and symmetric instability. The significant negative PV induced by atmospheric cooling associated with robust fronts promotes the development of submesoscale processes. Combining the Omega equation with biogeochemical observations suggests that coherent pathways via submesoscale processes lead to the vertical transport of biomass and oxygen patches, supplying nutrients into the euphotic layer and ventilating hypoxic waters at depths. These results demonstrate the fundamental role of submesoscale processes in the ocean dynamics of the NRS.

Ocean Alkalinity Enhancement in Deep Water Formation Regions Under Low and High Emission Pathways

AbstractOcean Alkalinity Enhancement (OAE) is an ocean‐based Carbon Dioxide Removal (CDR) method to mitigate climate change. Studies to characterize regional differences in OAE efficiencies and biogeochemical effects are still sparse. As subduction regions play a pivotal role for anthropogenic carbon uptake and centennial storage, we here evaluate OAE efficiencies in the subduction regions of the Southern Ocean, the Northwest Atlantic, and the Norwegian‐Barents Sea region. Using the ocean biogeochemistry model FESOM2.1‐REcoM3, we simulate continuous OAE globally and in the subduction regions under high (SSP3‐7.0) and low (SSP1‐2.6) emission scenarios. The OAE efficiency calculated by two different metrics is higher (by 8%–30%) for SSP3‐7.0 than for SSP1‐2.6 due to a lower buffer factor in a high‐ world. All subduction regions show a CDR potential (0.23–0.31; PgC uptake per Pg alkaline material) consistent with global OAE for both emission scenarios. Calculating the efficiency as the ratio of excess dissolved inorganic carbon (DIC) to excess alkalinity shows that the Southern Ocean and the Northwest Atlantic are as efficient as the global ocean (0.79–0.85), while the Norwegian‐Barents Sea region has a lower efficiency (0.65–0.75). The subduction regions store a fraction of excess carbon below 1 km that is 1.9 times higher than the global ocean. The excess surface alkalinity and thus uptake and storage follow the mixed‐layer depth seasonality, with the majority of the excess flux occurring in summer at shallow mixed layer depths. This study therefore highlights that subduction regions can be efficient for OAE if optimal deployment strategies are developed.

Using artificial intelligence algorithms in the investigation of mixed layer depth seasonal changes in the Barents Sea

The study aims at discovering features of seasonal changes in the mixed layer depth (MLD) of the Barents Sea in 1993–2020.Charts of the distribution of the MLD in the Barents Sea in 1993–2020 provided by the Copernicus Marine Service were used as the material of the study.Methods of the study: cluster analysis, machine learning, neuronal networks, the nearest neighbor method (kNN).Results. Classification of the data sets of the MLD distribution according to their seasonal features was carried out based on the modelling using AI algorithms and machine learning. It was concluded that winter is specified by two classes (increased/decreased values of the layer thickness). The third class includes spring and autumn when distributions of the MLD are close to one another, and the fourth class comprises summer (June-September) when the MLD grows very slowly.Practical relevance. The results will contribute to a better understanding of the hydrophysical processes of the Barents Sea and can further be used as series of independent variables to study the Barents Sea ecosystem and to estimate a stock and a catch forecast of commercial aquatic organisms.

Small topographical variations controlling trace maker community: Combining palaeo- and neoichnological data at the Porcupine Abyssal Plain

Ichnological research has generally assumed that abyssal plains are dominated by quiescent, homogenous environmental conditions. Thus, deep-sea trace fossil assemblage changes have been usually linked to significant spatial and temporal environmental variations. Here, we conducted a comparative ichnological analysis between a small abyssal hill (50 m elevation) and the surrounding abyssal plain; this modest bathymetric variation is known to generate substantial environmental heterogeneity for the benthic fauna community of the Porcupine Abyssal Plain (c. 4850 m depth), Northeast Atlantic. Based on X-ray data from a 5 × 5 core grid emplaced in two box cores, we compared hill and plain bioturbational sedimentary structures, including trace fossil assemblages (e.g., ichnotaxonomy) and biodeformational structures (e.g., mixed-layer depth). We observed that topographically-enhanced near-bottom currents over the hill likely produce significant changes in depositional dynamics and sediment properties (e.g., grain size, organic matter content and degradation), and control specificities of bioturbational sedimentary structures (e.g., trace fossils, mixed layer attributes such as thickness, mottled background, discrete traces). Palaeoichnological data suggested that the abyssal plain had experienced consistent conditions during the last thousands of years while the abyssal hill recorded improving environmental conditions for the trace maker community. Our results highlight the complexity of the deep-sea environment, demonstrating that small changes in bioturbated sedimentary assemblages appear even within the same box core (m-scale), and that substantial changes can occur due to environmental heterogeneity (e.g., subtle topographic variations) at the local scale (km-scale). Considering the vast global extent of abyssal hill terrain, we suggest that their influence on the bioturbational sedimentary record may be significantly under-appreciated and require more attention in palaeoenvironmental reconstructions.

Mechanisms regulating trophic transfer in the Humboldt Upwelling System differ across time scales

Abstract The Humboldt Upwelling System hosts a highly productive ecosystem with central importance for global fisheries, yet with strong seasonal and interannual variability in the planktonic base of the food chain ultimately affecting fish yield. Understanding the variability in energy transfer within the plankton community in the contemporary climate can provide valuable insights for future projections of planktonic dynamics. Therefore, we use a regional physical-biogeochemical ocean model simulation (CROCO-BioEBUS) from 1990 to 2010 to investigate the underlying mechanisms of seasonal and interannual variability of the trophic transfer. Our model simulations suggest that, on an interannual scale, variations in trophic transfer are governed by variations in the offshore surface flow that modulate the plankton cross-shore distribution. Weak offshore surface flow, as simulated during the El Niño period, allows the zooplankton to stay relatively close to the shore, leading to more efficient grazing and trophic transfer compared to years with strong offshore flow. This mechanism differs from the seasonal one, where the mixed layer depth is the primary driver of variations in plankton dynamics, including trophic transfer. Our results highlight that mechanisms controlling plankton trophic transfer differ across time scales, and thus stress that extrapolating solely from seasonal findings to understand long-term trophic transfer changes in the context of climate change may be insufficient.

Response of Upper Ocean to Parameterized Schemes of Wave Breaking under Typhoon Condition

The study of upper ocean mixing processes, including their dynamics and thermodynamics, has been a primary focus for oceanographers and meteorologists. Wave breaking in deep water is believed to play a significant role in these processes, affecting air–sea interactions and contributing to the energy dissipation of surface waves. This, in turn, enhances the transfer of gas, heat, and mass at the ocean surface. In this paper, we use the FVCOM-SWAVE coupled wave and current model, which is based on the MY-2.5 turbulent closure model, to examine the response of upper ocean turbulent kinetic energy (TKE) and temperature to various wave breaking parametric schemes. We propose a new parametric scheme for wave breaking energy at the sea surface, which is based on the correlation between breaking wave parameter RB and whitecap coverage. The impact of this new wave breaking parametric scheme on the upper ocean under typhoon conditions is analyzed by comparing it with the original parametric scheme that is primarily influenced by wave age. The wave field simulated by SWAVE was verified using Jason-3 satellite altimeter data, confirming the effectiveness of the simulation. The simulation results for upper ocean temperature were also validated using OISST data and Argo float observational data. Our findings indicate that, under the influence of Typhoon Nanmadol, both parametric schemes can transfer the energy of sea surface wave breaking into the seawater. The new wave breaking parameter RB scheme effectively enhances turbulent mixing at the ocean surface, leading to a decrease in sea surface temperature (SST) and an increase in mixed layer depth (MLD). This further improves upon the issue of uneven mixing of seawater at the air–sea interface in the MY-2.5 turbulent closure model. However, it is important to note that wave breaking under typhoon conditions is only one aspect of wave impact on ocean disturbances. Therefore, further research is needed to fully understand the impact of waves on upper ocean mixing, including the consideration of other wave mechanisms.

Chile Niño/Niña in the coupled model intercomparison project phases 5 and 6

The north and central coast of Chile is influenced by El Niño-Southern Oscillation (ENSO) through oceanic and atmospheric teleconnections. However, it also experiences episodic oceanic warmings off central Chile (30°S) lasting a few months that are not necessarily associated with ENSO. These episodes, called “Chile Niño” events, besides their ecological and socio-economical impacts, have also the potential to influence tropical Pacific variability. Here, we investigate how realistically the models in the Coupled Model Intercomparison Project (CMIP, Phases 5 and 6) simulate Chile Niño/Niña (CN) events, and quantify their changes under anthropogenic forcing. Despite limitations of the global models in simulating realistically coastal upwelling dynamics, we show that they simulate reasonably well the observed spatial pattern, amplitude and seasonal evolution of CN events. They however fail to properly represent the positive skewness from observations. The analysis of a sub-group of models (36) that simulate ENSO realistically reveals that CN events increase in amplitude and variance in the future climate with no changes in their frequency of occurence. This is interpreted as resulting from compensating effects amongst changes in remote drivers and local feedbacks. In particular, ENSO variance increases while that of the South Pacific Oscillation decreases. Conversely, we found that while the Wind-Evaporation-SST feedback tends to increase and the coupling between mixed-layer depth and SST weakens, favoring the development of CN events, the thermocline and wind-SST feedbacks decrease. However, only the change in the thermocline feedback is correlated to changes in CN variance amongst the models, suggesting a dominant role of local oceanic stratification changes in constraining the sensitivity of CN to global warming.

Species Distribution Models for Mesopelagic Mesozooplankton Community

ABSTRACTAimWe aimed to enhance our understanding of the distribution of mesopelagic mesozooplankton (MM) using species distribution models, assess the performance of various modelling techniques, identify key environmental predictors for MM distribution and compute their habitat suitability indices.LocationOur study focused on the mesopelagic zone globally, with data analysed from different oceans.TaxonOur focus was primarily on mesopelagic mesozooplankton, gathering data on 861 different species from the Mesopelagic Mesozooplankton and Micronekton (MMM) Database.MethodsWe used an ensemble of species distribution models, applying 10 different modelling algorithms and three multi‐model ensemble approaches. We explored two important factors that can affect model performance: subsampling and the choice of background points. We also estimated the relative importance of various environmental conditions such as mixed layer depth, temperature, salinity, net primary productivity, euphotic zone depth and dissolved nitrate concentration on the distribution of these species.ResultsEuphotic zone depth, salinity and dissolved nitrate concentration were identified as the most important variables for explaining the distribution of mesopelagic mesozooplankton. The ensemble modelling results were robust in areas with abundant observational records, but high uncertainty was observed in data‐limited regions. We found a patchy habitat suitability map for zooplankton when modelled within their native range, largely due to uneven sampling. Unrestricted range models yielded smoother patterns but could inaccurately project species in areas where they do not occur.Main ConclusionsOur study highlights the need for increased sampling effort in data‐limited regions to improve the accuracy of mesopelagic species distribution models. Despite some inaccuracies, unrestricted range models, assuming ecological equivalence (where different species occupying a similar ecological niche in different geographical regions or different ecosystems exhibit similar adaptations and behaviours), provide a reasonable comparison for habitat suitability maps and model performance. It also confirms the significant impact of certain environmental conditions on mesozooplankton distribution.