The diurnal cycles of near-surface shear and stratification, also known as diurnal jet and diurnal warm layer (DWL), are ubiquitous in the tropical oceans, affecting the heat and momentum budget of the ocean surface layer, air-sea interactions, and vertical mixing. Here, we analyse the presence and descent of near-surface diurnal shear and stratification in the upper 20 m of the equatorial Atlantic as a function of wind speed using ocean current velocity and hydrographic data taken during two trans-Atlantic cruises along the equator in autumn 2019 and spring 2022, data from three types of surface drifters, and data from PIRATA moorings along the equator. The observations during two seasons with similar wind speeds but varying net surface heat fluxes reveal similar diurnal jets with an amplitude of about 0.11 m s-1 and similar DWLs when averaging along the equator. We find that higher wind speeds lead to earlier diurnal peaks, deeper penetration depths, and faster descent rates of DWL and diurnal jet. While the diurnal amplitude of shear is maximum for intermediate wind speeds, the diurnal amplitude of stratification is maximum for minimal wind speeds. The presented wind dependence of the descent rates of DWL and diurnal jet is consistent with the earlier onset of deep-cycle turbulence for higher wind speeds. The DWL and the diurnal jet not only trigger deep-cycle turbulence but are also observed to modify the wind power input and thus the amount of energy available for mixing.

Four underwater glider missions were carried out to sample the physical and bio-optical properties inside a Loop Current Eddy (LCE) in the Gulf of Mexico (GoM), to investigate whether the winter deepening of the mixed-layer and erosion of the nitracline stimulates phytoplankton growth. Recent coupled physical-biogeochemical numerical models support this mechanism, but observations using Lagrangian floats suggest that there is no seasonal cycle on integrated phytoplankton biomass. Here, data collected by underwater gliders during a full seasonal cycle and inside the LCE Poseidon support the occurrence of a seasonal cycle, which is consistent with nutrient entrainment into the euphotic zone. The changes in fluorescence emission per chlorophyll-a unit and its implications for interpreting bio-optical variability were also assessed. Linear regressions between in vivo chlorophyll-a fluorescence and satellite chlorophyll-a concentration show the largest (smallest) slopes during winter (summer), suggesting a shift in the phytoplankton community along the year. Although the glider dataset is convolved by temporal and spatial variability, and chlorophyll-a fluorescence is affected by several factors, the concomitant enhancement of particle backscattering coefficient and chlorophyll-a observed during winter supports the occurrence of a seasonal cycle in phytoplankton biomass. Deep winter convection inside the core of the LCE, can promote fertilization through vertical diffusion of nutrients. Poseidon was an extraordinary, large, and strong, LCE that prompted phytoplankton blooms in winter highlighting their relevance for primary production and in general for biogeochemical processes.

The ocean oxygen (O2) inventory has declined in recent decades but the estimates of O2 trend is uncertain due to its sparse and irregular sampling. A refined estimate of deoxygenation rate is developed for the North Atlantic basin using machine learning techniques and biogeochemical Argo array. The source data includes 159 thousand historical shipboard (bottle and CTD-O2) profiles from 1965 to 2020 and 17 thousand Argo O2 profiles after 2005. Neural network and random forest algorithms were trained using 80% of this data using different hyperparameters and predictor variable sets. From a total of 240 trained algorithms, 12 high performing algorithms were selected based on their ability to accurately predict the 20% of oxygen data withheld from training. The final product includes gridded monthly O2 ensembles with similar skills (mean bias < 1mol/kg and R2 > 0.95). The reconstruction of basin-scale oxygen inventory shows a moderate increase before 1980 and steep decline after 1990 in agreement with a previous estimate using an optimal interpolation method. However, significant differences exist between reconstructions trained with only shipboard data and with both shipboard and Argo data. The gridded oxygen datasets using only shipboard measurements resulted in a wide spread of deoxygenation trends (0.8-2.7% per decade) during 1990-2010. When both shipboard and Argo were used, the resulting deoxygenation trends converged within a smaller spread (1.4-2.0% per decade). This study demonstrates the importance of new biogeochemical Argo arrays in combination with applications of machine learning techniques.

Western boundaries have been suggested as mesoscale eddy graveyards, using a diagnostic of the eddy kinetic energy (EKE) flux divergence based on sea surface height (η). The graveyard’s paradigm relies on the approximation of geostrophy — required by the use of η — and other approximations that support long baroclinic Rossby waves as the dominant contribution to the EKE flux divergence. However, a recent study showed an opposite paradigm in the Agulhas Current region using an unapproximated EKE flux divergence. Here, we assess the validity of the approximations used to derive the η-based EKE flux divergence using a regional numerical simulation of the Agulhas Current. The EKE flux divergence consists of the eddy pressure work (EPW) and the EKE advection (AEKE). We show that geostrophy is valid for inferring AEKE, but that all approximations are invalid for inferring EPW. A scale analysis shows that at mesoscale (L > O(30)km), EPW is dominated by coupled geostrophic-ageostrophic EKE flux and that Rossby waves effect is weak. There is also a hitherto neglected topographic contribution, which can be locally dominant. AEKE is dominated by the geostrophic EKE flux, which makes a substantial contribution (54%) to the net regional mesoscale EKE source represented by the EKE flux divergence. Other contributions, including topographic and ageostrophic effects, are also significant. Our results support the use of η to infer a qualitative estimate of the EKE flux divergence in the Agulhas Current region. However, they invalidate the approximations on mesoscale eddy dynamics that underlie the graveyard’s paradigm.

We investigate the impact of ocean data assimilation using the Ensemble Adjustment Kalman Filter (EAKF) from the Data Assimilation Research Testbed (DART) on the oceanic and atmospheric states of the Red Sea. Our study extends the ocean data assimilation experiment performed by Sanikommu et al. (2020) by utilizing the SKRIPS model coupling the MITgcm ocean model and the Weather Research and Forecasting (WRF) atmosphere model. Using a 50-member ensemble, we assimilate satellite-derived sea surface temperature and height and in-situ temperature and salinity profiles every three days for one year, starting January 01 2011. Atmospheric data are not assimilated in the experiments. To improve the ensemble realism, perturbations are added to the WRF model using several physics options and the stochastic kinetic energy backscatter (SKEB) scheme. Compared with the control experiments using uncoupled MITgcm with ECMWF ensemble forcing, the EAKF ensemble mean oceanic states from the coupled model are better or insignificantly worse (root-mean-square-errors are 30% to -2% smaller), especially when the atmospheric model uncertainties are accounted for with stochastic perturbations. We hypothesize that the ensemble spreads of the air–sea fluxes are better represented in the downscaled WRF ensembles when uncertainties are well accounted for, leading to improved representation of the ensemble oceanic states in EAKF. Although the feedback from ocean to atmosphere is included in this two-way regional coupled configuration, we find no significant effect of ocean data assimilation on the latent heat flux and 10-m wind speed, suggesting the improved skill is from downscaling the ensemble atmospheric forcings.

The ocean is responsible for taking up approximately 25% of anthropogenic CO2 emissions and stores > 50 times more carbon than the atmosphere. Biological processes in the ocean play a key role, maintaining atmospheric CO2 levels 200 ppm lower than they would otherwise be. The ocean’s ability to take up and store CO2 is sensitive to climate change, however the key biological processes that contribute to ocean carbon storage are uncertain, as are their response and feedbacks to climate change. As a result, biogeochemical models vary widely in their representation of relevant processes, driving large uncertainties in the projections of future ocean carbon storage. This review identifies key biological processes that affect how carbon storage may change in the future in three thematic areas: biological contributions to alkalinity, net primary production, and interior respiration. We undertook a review of the existing literature to identify processes with high importance in influencing the future biologically-mediated storage of carbon in the ocean, and prioritised processes on the basis of both an expert assessment and a community survey. Highly ranked processes in both the expert assessment and survey were: for alkalinity – high level understanding of calcium carbonate production; for primary production – resource limitation of growth, zooplankton processes and phytoplankton loss processes; for respiration – microbial solubilisation, particle characteristics and particle type. The analysis presented here is designed to support future field or laboratory experiments targeting new process understanding, and modelling efforts aimed at undertaking biogeochemical model development.

Most of the primary productivity in the ocean comes from phytoplankton, and is impacted, among other things, by the amount of nutrients available, as well as by temperature. The Late Miocene and Pliocene were marked by global aridification, linked to the emergence of the large deserts, likely increasing the input of dust and thus nutrients into the ocean. There was also a global decrease in temperature during this period, linked to a decline in atmospheric CO2 concentration. The objective of this study is to quantify the impact of dust and pCO2 levels on primary productivity in the oceans under Late Miocene boundary conditions. New simulations were performed with the coupled ocean-atmosphere model IPSL-CM5A2 and its marine biogeochemistry component PISCES with a Late Miocene paleogeography. Our results show that an increase in dust input produces a quasi-generalized increase in primary productivity, associated with a decrease in nutrient limitation. This increase in productivity also leads to nutrient deficits in some areas. The decrease in pCO2 levels and the associated lower water temperatures lead to a reduction in primary productivity. This decrease is mainly due to a reduction in the supply of nutrients resulting from less intense remineralization. In addition, our results show that change in carbon export resulting from change in dust input and temperature are highly heterogeneous spatially. Simulations combined with sedimentary data suggesting a link between aridification, cooling and the Biogenic Bloom of the Late Miocene and Pliocene.

Satellite-observed microwave radiances provide information on both surface and atmosphere. For operational weather forecasting, information on atmospheric temperature, humidity, cloud and precipitation is directly inferred using all-sky radiance data assimilation. In contrast, information on the surface state, such as sea surface temperature (SST) and sea ice fraction, is typically provided through third-party retrieval products. Scientifically, this is a sub-optimal use of the observations, and practically it has disadvantages such as time delays of more than 48 hours. A better solution is to jointly estimate the surface and atmospheric state from the radiance observations. This has not been possible until now due to incomplete knowledge of the surface state and the radiative transfer that links this to the observed radiances. A new approach based on an empirical state and empirical sea ice surface emissivity model is used here to add sea ice state estimation, including sea ice concentration (SIC), to the European Centre for Medium-range Weather Forecasts atmospheric data assimilation system. The sea ice state is estimated using augmented control variables at the observation locations. The resulting SIC estimates are of good quality and they highlight apparent defects in the existing OCEAN5 sea ice analysis. The SIC estimates can also be used to track giant icebergs, which may provide a novel maritime application for passive microwave radiances. Further, the SIC estimates should be suitable for onward use in coupled ocean-atmosphere data assimilation. There is also increased coverage of microwave observations in the proximity of sea ice, leading to improved atmospheric forecasts out to day 4 in the Southern Ocean.

A document by Jakob Christiaanse. Click on the document to view its contents.

A document by William W. Chadwick. Click on the document to view its contents.

Deficiencies in upper ocean vertical mixing parameterizations contribute to tropical upper ocean biases in global coupled general circulation models, affecting their simulated ocean heat uptake and ENSO variability. To better understand these deficiencies, we develop a suite of ocean model experiments including both idealized single column models and realistic global simulations. The vertical mixing parameterizations are first evaluated using large eddy simulations as a baseline to assess uncertainties and evaluate their implied turbulent mixing. Global models are then developed following NOAA/GFDLâ\euro™s 0.25$\degree$ nominal ocean horizontal grid spacing OM4 (uncoupled ocean) configuration of the MOM6 ocean model, with various modifications that target improvements to biases in the original model. We identify a variety of enhancements to the existing mixing schemes that are evaluated using observational constraints from TAO moorings and Argo floats. In particular, we find that we can improve the diurnal variability of mixing in OM4 via modifications to its mixing scheme, and that we can improve the net mixing in the upper thermocline by reducing the background vertical viscosity, allowing for more realistic, less diffuse currents. The improved OM4 model better represents the mixing and its diurnal deep-cycle variability, leading to more realistic time-mean tropical thermocline structure, mixed layer depths, SSTs, and a better Pacific Equatorial Undercurrent.

Data assimilation (DA) plays a pivotal role in diverse applications, ranging from climate predictions and weather forecasts to trajectory planning for autonomous vehicles. A prime example is the widely used ensemble Kalman filter (EnKF), which relies on linear updates to minimize variance among the ensemble of forecast states. Recent advancements have seen the emergence of deep learning approaches in this domain, primarily within a supervised learning framework. However, the adaptability of such models to untrained scenarios remains a challenge. In this study, we introduce a novel DA strategy that utilizes reinforcement learning (RL) to apply state corrections using full or partial observations of the state variables. Our investigation focuses on demonstrating this approach to the chaotic Lorenz ’63 system, where the agent’s objective is to minimize the root-mean-squared error between the observations and corresponding forecast states. Consequently, the agent develops a correction strategy, enhancing model forecasts based on available system state observations. Our strategy employs a stochastic action policy, enabling a Monte Carlo-based DA framework that relies on randomly sampling the policy to generate an ensemble of assimilated realizations. Results demonstrate that the developed RL algorithm performs favorably when compared to the EnKF. Additionally, we illustrate the agent’s capability to assimilate non-Gaussian data, addressing a significant limitation of the EnKF.

The threshold of motion of bioclastic sediments is the fundamental aspect for understanding of sediment dynamics in coral reef systems while there are currently few studies on its prediction. We conducted laboratory experiments, and showed that the threshold of motion of coral skeletal grains is more appropriately characterized by the nominal diameter and particle density that is defined as the density of grains with its skeletal void filled by the fluid. Distinctions in threshold of motion of observed coral particles and other bioclastic sediments arise from the influences of grain density and shape, resulting in a notable departure from existing empirical thresholds based on quartz sand. We then propose a new formula for estimating critical shear velocity of bioclastic sediments by introducing a grain shape parameter. The new comprehensive criterion improves the understanding of threshold of motion of bioclastic sediments with highly heterogeneous properties under steady unidirectional flow.

Arctic sea ice is retreating, thinning, and exhibiting increased mobility. In the Beaufort Gyre (BG), liquid freshwater content (FWC) has increased by 40\% in the last two decades, with sea ice melting being a primary contributor. This study utilizes satellite observations of sea surface salinity (SSS) and sea ice concentration, along with model-based sea ice thickness from 2011 to 2019. The aim is to investigate the sea ice-SSS relationship at different scales in the Arctic and understand the sea-ice meltwater dynamics in the BG. Our findings reveal a strong synchrony and positive correlation between sea ice area and SSS in the Arctic Ocean. In September, when the BG exhibits the largest ice-free ocean surface, a noticeable release of freshwater from sea ice melting occurs, a phenomenon not accurately reproduced by the models. The SMOS (Soil Moisture and Ocean Salinity) mission proves valuable in detecting meltwater lenses (MWL) originating from sea ice melting. These MWLs exhibit mean SSS ranging from 19 psu at the begining of sea ice retreat to 25 psu before sea ice formation. Wind-driven anticyclonic eddies can trap MWLs, preserving the freshest SSS imprints on the sea surface for up to 10 days. Furthermore, events of sea surface salinification following sea ice formation suggest that SMOS SSS might be capturing information on brine rejection. The daily evolution of sea ice-SSS within the MWLs demonstrates a tight correlation between both variables after sea ice melting and just before sea ice formation, indicating a transient period in between.

Ice shelves flex in response to surface ocean waves, which imposes stresses and strains on the shelves that promote iceberg calving. Previous modelling studies of ice shelf responses to ocean waves have focussed on highly idealised geometries with uniform ice thickness and flat seabeds. This study leverages on a recently developed mathematical model that incorporates spatially varying geometries, combined with measured ice shelf thickness and seabed profiles, to conduct a statistical assessment of how fifteen Antarctic ice shelves respond to ocean waves over a broad range of relevant wave periods, from swell to infragravity waves to very long period waves. The results show the most extreme responses at a given wave period are generated by features in the ice shelves and/or seabed geometries, depending on the wave regime. Relationships are determined between the median ice shelf response and the median shelf front thickness or the median cavity depth. The findings provide further evidence of the role of ocean waves in large-scale calving events for certain ice shelves (particularly the Wilkins), indicate a possible role of ocean waves in calving events for other shelves (Larsen C and Conger), and the relationships determined provide a method to assess how ice shelf responses are evolving with climate change and project future scenarios.

Oceanic Rossby waves play a crucial role in shaping the physical and biological dynamics of both open and coastal oceans, especially within the tropical band spanning between the 10°S and 10°N parallels. Yet, the extent to which Rossby waves can transmit and impact the hydrography and ecosystem of semi-enclosed seas like the South China Sea (SCS) remains unclear. This study aims to investigate the transmission of Rossby waves through the Philippine archipelago, using satellite altimeter-derived sea level anomaly (SLA) and coastal tide gauge records. Our findings reveal that westward-propagating Rossby waves in the tropical Pacific Ocean with a wave speed of ~0.64 m s−1 first entered the Celebes Sea, and then passed through the Sibutu Passage into the Sulu Sea from April to December 2017. Subsequently, the waves propagated along the northeast coast of Sabah and the east coast of Palawan before exiting through the Mindoro Strait to the central SCS. Additionally, a β-refracted Rossby wave with wave speed of 0.28 m s−1 also penetrated the archipelago but at a latitude further north ~10°N from July to November via Surigao Strait and propagated toward the south and north of Palawan, ultimately reaching the west coast of Palawan in the eastern central SCS. This study verifies that the transmission of Rossby waves originating from the east of the Philippines could induce intraseasonal sea level oscillations off Palawan, which could subsequently propagate westward across the central SCS as identified in previous field observations.

The biological carbon pump is a key controller of how much carbon is stored within the global ocean. This pathway is influenced by food web interactions between zooplankton and their prey. In global biogeochemical models, Holling Type functional responses are frequently used to represent grazing interactions. How these responses are parameterised greatly influences biomass and subsequent carbon export estimates. The half-saturation constant, or k value, is central to the Holling functional response. Empirical studies show k can vary over three orders of magnitude, however, this variation is poorly represented in global models. This study derives zooplankton grazing dynamics from remote sensing products of phytoplankton biomass, resulting in global distribution maps of the grazing parameter k. The impact of these spatially varying k values on model skill and carbon export flux estimates is then considered. This study finds large spatial variation in k values across the global ocean, with distinct distributions for micro- and mesozooplankton. High half-saturation constants, which drive slower grazing, are generally associated with areas of high productivity. Grazing rate parameterisation is found to be critical in reproducing satellite-derived distributions of nanophytoplankton biomass, highlighting the importance of top-down drivers for this size class. Spatially varying grazing dynamics decrease mean total carbon export by >17% compared to globally homogeneous dynamics, with increases in faecal pellet export and decreases in export from algal aggregates. This study highlights the importance of grazing dynamics to both community structure and carbon export, with implications for modelling marine carbon sequestration under future climate scenarios.

Sea ice mediates the exchange of momentum, heat, and moisture between the atmosphere and the ocean. Cyclones produce strong gradients in the wind field, imparting stress into the ice and causing the ice to deform. In turn, increased sea ice drift speeds and rapid changes in drift direction during the passage of a cyclone may result in enhanced momentum flux into the upper ocean.  During the year-long MOSAiC expedition, an array of drifting buoys was deployed surrounding the R/V Polarstern, enabling the characterization of sea ice motion and deformation across a range of spatial scales. In addition, autonomous sensors at a subset of sites measured the atmospheric and oceanic structure and vertical fluxes. Here, we examine a strong cyclone that impacted the MOSAiC site during January and February, 2020, while the MOSAiC site was near the North Pole. The cyclone track intersected the MOSAiC buoy array, providing an opportunity to examine spatial variability in sea ice motion during the storm in unprecedented detail. A key feature of the storm was the formation of a low-level jet (LLJ), first in the warm sector of the storm, then growing to eventually encircle the central low. The highest rates of ice motion and deformation coincide with effects of LLJ transitions. Analysis of deformation using the Green’s theorem approach indicates divergence and cyclonic vorticity as the LLJ enters the region, and convergence and anticyclonic vorticity as the LLJ leaves; maximum shear strain rate is enhanced throughout the LLJ’s passage. While the vorticity signal is particularly clear, floe structure and internal ice stresses result in high spatial variability in the magnitude of divergence and shear strain rates, especially at smaller scales. Increased current speed and shear in the upper layer of the ocean during the passage of the LLJ resulted from ice drag forcing the ocean mixed layer current. The results suggest an important role for cyclone-forced ocean mixing in pack ice during the Arctic winter.

• A physics-informed neural network trained without ground truths can provide accurate initial fields for numerical prediction. • The system's kinetic features are embedded into the model through our four-dimensional variational form loss function. • We show on Lorenz96 that the proposed method can be used directly for accurate data assimilation at a low computational cost.

The International Arctic Buoy Programme (IABP) maintains fundamental in situ components of the Arctic Observing Network. Automated Drifting Stations (ADS) consisting of sea ice, meteorological, and oceanographic buoys are collectively deployed at many sites with webcams to help understand the intricate and complex interactions between sea ice, the atmosphere, and the ocean.While passive microwave satellites provide substantial information about the Arctic, remote sensing still has resolution limitations despite broad spatial coverage. Climate modeling and atmospheric reanalysis help surmount these limitations, but traditional observational methods of in situ data collection still have many advantages. Buoys and webcams can monitor Arctic sea ice changes above and below, allowing for more direct observations of localized ice floes when deployed in close proximity.Using data from webcams in the Arctic, we have stitched together images into time-lapse animations that provide insight into physical sea ice processes. Coupled with buoy data, we compare physical measurements (like temperature) with webcam observations (like cloud cover) to explain trends and anomalies. For example, isothermal periods in the buoy temperature data match time-lapse images with cloudy skies, while the opposite is also true: high variability correlates with sunny skies. Hence, these instruments allow for the verification of Arctic observations both visually and statistically.Although significant challenges like camera lifetimes and temporal resolution still persist, we argue that buoys and time-lapse videos can help validate satellite data and offer cheaper solutions to collecting vital information that increases our understanding of geophysical processes. We’ve compiled these datasets and present case studies showing the use of time-lapse videos to help monitor and understand the interplay and processes of the Arctic environment.

Meridional eddy transport across the Antarctic Circumpolar Current is an essential component of the global meridional overturning circulation and the transport of climate relevant tracers. Challenges in comparing model and observational estimates of the transport arise from varying methodologies describing ‘eddy’ processes. We reconcile the approach used in shipboard surveys of eddies, complemented by satellite eddy tracking, with Reynolds decomposition applied to model outputs. This allows us to estimate the fraction of total meridional tracer transport attributed to coherent eddies in a global 0.1$^\circ$ ocean model. The model realistically simulates observed eddy kinetic energy and three-dimensional characteristics, particularly in representing an observed cyclonic eddy near 150 \degrees E, a hotspot for poleward heat flux. Annual meridional transports due to coherent eddies crossing the Subantarctic Front are estimated by vertically and radially integrating the tracer contents of all eddies. Notably, only cyclonic eddies moving equatorward across the Subantarctic Front contribute to the coherent eddy transport, with no anticyclonic eddies found to cross the front poleward in this region. Applying Reynolds decomposition, our study reveals predominantly poleward meridional transports due to all transient processes in a standing meander, particularly between the northern and southern branches of the Subantarctic Front. Coherent, long-lived eddies tracked from satellite data contribute less than 20\% to transient poleward heat transport, and equatorward nitrate transport in the model. Furthermore, we demonstrate that the integrated surface elevation of mesoscale eddies serves as a reliable proxy for inferring subsurface eddy content.

A document by Dhruv Balwada. Click on the document to view its contents.

A document by Mochamad Furqon Azis Ismail. Click on the document to view its contents.

The intra-seasonal CO2 flux (FCO2) variability across the Southern Ocean is poorly understood due to sparse observations at the required temporal and spatial scales. Twinned Waveglider-Seaglider experiments were used to investigate how storms influence FCO2 through both the gas transfer velocity (kw) and the air-sea gradient in partial pressure of CO2 (ΔpCO2) in the sub-Antarctic zone. Winter-spring storms caused ΔpCO2 to weaken (by 15-55 μatm) due to mixing/entrainment and weaker stratification. This response in ΔpCO2 was in phase with kw resulting in a counteractive weakening in FCO2 (by 6.6 - 26.5% per storm), despite the wind-driven increase in kw. Stronger stratification during summer explained the weaker sensitivity of ΔpCO2 to storms, instead its thermal drivers dominated the ΔpCO2 variability. These results highlight the importance of observing synoptic-scale variability in ΔpCO2, the absence of which may propagate significant biases to the mean annual FCO2 estimates from large-scale observing programmes and reconstructions.