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Even optimistic climate scenarios predict catastrophic consequences for coral reef ecosystems by 2100. Understanding how reef connectivity, biodiversity and resilience are shaped by climate variability would improve chances to establish sustainable management practices. In this regard, ecoregionalization and connectivity are pivotal to designating effective marine protected areas. Here, machine learning algorithms and physical intuition are applied to sea surface temperature anomaly data over a twenty-four-year period to extract ecoregions and assess connectivity and bleaching recovery potential in the Coral Triangle and surrounding oceans. Furthermore, the impacts of the El Niño Southern Oscillation (ENSO) on biodiversity and resilience are quantified. We find that resilience is higher for reefs north of the Equator and that the extraordinary biodiversity of the Coral Triangle is dynamic in time and space, and benefits from ENSO. The large-scale exchange of genetic material is enhanced between the Indian Ocean and the Coral Triangle during La Niña years, and between the Coral Triangle and the central Pacific in neutral conditions. Through machine learning the outstanding biodiversity of the Coral Triangle, its evolution and the increase of species richness are contextualized through geological times, while offering new hope for monitoring its future.

 

The ocean circulation is modulated by meandering currents and eddies. Forecasting their evolution is a key target of operational models, but their forecast skill remains limited. We propose a machine learning approach that improves the output of an ocean circulation model by learning and predicting its systematic biases. This method can be applied a priori to any region, and is tested in the Gulf of Mexico, where the Loop Current (LC) and the large anticyclonic eddies that detach from it are major forecasting targets. The LC dynamics are recurrent and lie on a low‐dimensional dynamical attractor. Building upon the information gained analyzing this low dimensional attractor, we improve the representation of sea surface anomalies in model outputs through information from satellite altimeter data using a Sequence‐to‐Sequence model, which is a special class of Recurrent Neural Network. Building upon the HYCOM‐NCODA analysis system, we deliver a correction to the forecast at the observation resolution. For at least 15 days the proposed method learns to forecast the systematic bias in the HYCOM‐NCODA, outperforming persistence, and improving the forecast. This data‐driven approach is fast and can be implemented as an added step to any dynamical hindcasting or forecasting model. It offers an interesting avenue for further developing hybrid modeling tools. In these tools, fundamental physical conservations are preserved through the integration of partial differential equations which obey them. In addition, the method highlights specific deficiencies of the hindcast system that deserve further investigation in the future.

 

Increased Net Primary Productivity (NPP) around small islands have been documented worldwide. Despite having been known for decades, the interactions between physical and biogeochemical processes behind this phenomenon – that takes the name of Island Mass Effect –remain unclear. In this paper we review the physical processes involved while proposing a method to identify the prevailing mechanisms by analyzing their imprint on NPP and Sea Surface Temperature (SST). These mechanisms can be quite different, but all enhance vertical exchanges, increasing the input of nutrients in the euphotic layer and favoring biological productivity. Nutrient-rich deeper waters are brought up to the surface through upwelling and mixing, leaving a cold imprint on the SST as well. Here we analyze satellite data of SST and NPP around small islands and archipelagos to catalog the physical mechanisms that favor the Island Mass Effect, with the aid of oceanic and atmospheric reanalysis. The multiplicity of these processes and the convolution of their interactions highlight the complexity of the physical forcing on the biomass production and the uniqueness of each island. However, analysis from 19 small islands throughout the tropics shows that two kinds of SST patterns emerge, depending on the size and altitude of the island. Around islands with considerable elevation and greatest diameters, cold/warm anomalies, most likely corresponding to upwelling/downwelling zones, emerge. This signal can be mainly ascribed to oceanic and atmospheric forcing. Around small islands, on the other hand, warm anomalies do not appear and only local cooling, associated with current-island interactions, is found. In the vicinity of a single island, more than one process responsible for the increased nutrient input into the euphotic layer might coexist, the prevailing one varying along the year and depending on the strength and direction of the incoming atmospheric and oceanic flow. 

The Gulf of Mexico is a very productive and economically important system where riverine runoff acts as a linkage between the continental shelf and the open ocean, providing nutrients in addition to freshwater. This work investigates the three-dimensional transport and pathway structure of this river runoff offshore the continental shelf using ensembles of numerical simulations with different configurations regarding grid resolution (mesoscale resolving and submesoscale permitting) and river setup using suites of 5-months long integrations covering nearly 3 years. The riverine forcing is applied only at the surface over an area around the river mouth, a strategy often adopted in numerical studies, or as a meridional flux with a vertical extension. The simulated flow captures the southward offshore transport of river runoff driven by its interaction with the largest mesoscale circulations in the basin, the Loop Current and Loop Current eddies. This pathway is strong and well-document during summer but also active and relevant in winter, despite a less obvious surface signature. The most intense transport occurs primarily at the peripheries of the Loop Current and the detached eddies, and the freshwater is subducted as deep as 600 m around the mesoscale anticyclonic eddies. Submesoscale motions strengthen slightly the spread of freshwater plumes in summer but their contribution is negligible, if not negative, in winter. Differences in the freshwater distribution and transport volume among runs are small and generally less than 10% among ensembles, with overall slightly higher volume of freshwater transported off-shore and at depth in submesoscale permitting runs that include a velocity flux in their riverine input representation. 

Diatoms are among the most efficient autotrophic organisms for oceanic primary production and carbon sequestration. Yet, the spatial distributions of these planktonic organisms remain puzzling and the underlying physical processes poorly known, especially in oligotrophic open waters. Here we investigate what dynamical conditions are conducive to episodic diatom blooms in oceanic deserts based on Lagrangian diagnosis and satellite‐derived phytoplankton functional types and currents. The coherence of the flow is diagnosed in space and time simultaneously through the Lagrangian coherence rate (LCR) to identify which dynamical structures favor diatom growth. Observations evidence that flow structures with high LCR (40 days or longer) in areas with elevated eddy kinetic energy and vorticity sustain high diatom concentrations in the sunlit layers. Our findings show that the integration of Eulerian kinematic variables into a Lagrangian frame reveals new dynamical aspects of geophysical turbulence and unveil their biological impacts.

 

Submesoscale circulations influence momentum, buoyancy and transport of biological tracers and pollutants within the upper turbulent layer. How much and how far into the water column this influence extends remain open questions in most of the global ocean. This work evaluates the behavior of neutrally buoyant particles advected in simulations of the northern Gulf of Mexico by analyzing the trajectories of Lagrangian particles released multiple times at the ocean surface and below the mixed layer. The relative role of meso- and submesoscale dynamics is quantified by comparing results in submesoscale permitting and mesoscale resolving simulations. Submesoscale circulations are responsible for greater vertical transport across fixed depth ranges and also across the mixed layer, both into it and away from it, in all seasons. The significance of the submesoscale-induced transport, however, is far greater in winter. In this season, a kernel density estimation and a detailed vertical mixing analysis are performed. It is found that in the large mesoscale Loop Current eddy, upwelling into the mixed layer is the major contributor to the vertical fluxes, despite its clockwise circulation. This is opposite to the behavior simulated in the mesoscale resolving case. In the “submesoscale soup,” away from the large mesoscale structures such as the Loop Current and its detached eddies, upwelling into the mixed layer is distributed more uniformly than downwelling motions from the surface across the base of the mixed layer. Maps of vertical diffusivity indicate that there is an order of magnitude difference among simulations. In the submesoscale permitting case values are distributed around 10 –3 m 2 s –1 in the upper water column in winter, in agreement with recent indirect estimates off the Chilean coast. Diffusivities are greater in the eastern portion of the Gulf, where the submesoscale circulations are more intense due to sustained density gradients supplied by the warmer and saltier Loop Current. 

Jupiter’s atmosphere is one of the most turbulent places in the solar system. Whereas observations of lightning and thunderstorms point to moist convection as a small-scale energy source for Jupiter’s large-scale vortices and zonal jets, this has never been demonstrated due to the coarse resolution of pre-Juno measurements. The Juno spacecraft discovered that Jovian high latitudes host a cluster of large cyclones with diameter of around 5,000 km, each associated with intermediate- (roughly between 500 and 1,600 km) and smaller-scale vortices and filaments of around 100 km. Here, we analyse infrared images from Juno with a high resolution of 10 km. We unveil a dynamical regime associated with a significant energy source of convective origin that peaks at 100 km scales and in which energy gets subsequently transferred upscale to the large circumpolar and polar cyclones. Although this energy route has never been observed on another planet, it is surprisingly consistent with idealized studies of rapidly rotating Rayleigh–Bénard convection, lending theoretical support to our analyses. This energy route is expected to enhance the heat transfer from Jupiter’s hot interior to its troposphere and may also be relevant to the Earth’s atmosphere, helping us better understand the dynamics of our own planet.

 

Earth System Models project a decline of dissolved oxygen in the oceans due to climate warming. Observational studies suggest that the ratio of O₂inventory to ocean heat content is several fold larger than what can be explained by solubility alone, but the ratio remains poorly understood. In this work, models of different complexity are used to understand the factors controlling the air‐sea O₂flux to heat flux ratio (O₂/heat flux ratio) during deep convection. Our theoretical analysis based on a one‐dimensional convective adjustment model indicates that the vertical stratification and distribution of oxygen before the convective mixing determines the upper bound for the O₂/heat flux ratio. Two competing rates, the mean entrainment rate of deeper waters into the mixed layer and the rate of air‐sea gas exchange, determine how much the actual ratio departs from the upper bound. The theoretical predictions are tested against the outputs of a regional ocean model. The model sensitivity experiments broadly agree with the theoretical predictions. Our results suggest that the relative vertical gradients of temperature and oxygen at sites of deep water formation are an important local metric to quantify the marginal changes between years with high and lower heat loss.

 

Near the ocean surface, river plumes influence stratification, buoyancy and transport of tracers, nutrients and pollutants. The extent to which river plumes influence the overall circulation, however, is generally poorly constrained. This work focuses on the South China Sea (SCS) and quantifies the dynamical impacts of the Mekong River plume, which is bound to significantly change in strength and seasonality in the next 20 years if the construction of over hundred dams moves ahead as planned. The dynamic impact of the freshwater fluxes on the SCS circulation are quantified by comparing submesoscale permitting and mesoscale resolving simulations with and without riverine input between 2011 and 2016. In the summer and early fall, when the Mekong discharge is at its peak, the greater stratification causes a residual mesoscale circulation through enhanced baroclinic instability. The residual circulation is shaped as an eddy train of positive and negative vorticity. Submesoscale fronts are responsible for transporting the freshwater offshore, shifting eastward the development of the residual mesoscale circulation, and further strengthening the residual eddy train in the submesoscale permitting case. Overall, the northward transport near the surface is intensified in the presence of riverine input. The significance of the mesoscale‐induced and submesoscale‐induced transport associated with the river plume is especially important in the second half of the summer monsoon season, when primary productivity has a secondary maximum. Circulation changes, and therefore productivity changes, should be anticipated if human activities modify the intensity and seasonality of the Mekong River plume.

 

The diurnal cycling of submesoscale circulations in vorticity, divergence, and strain is investigated using drifter data collected as part of the Lagrangian Submesoscale Experiment (LASER) experiment, which took place in the northern Gulf of Mexico during winter 2016, and ROMS simulations at different resolutions and degree of realism. The first observational evidence of a submesoscale diurnal cycle is presented. The cycling is detected in the LASER data during periods of weak winds, whereas the signal is obscured during strong wind events. Results from ROMS in the most realistic setup and in sensitivity runs with idealized wind patterns demonstrate that wind bursts disrupt the submesoscale diurnal cycle, independently of the time of day at which they happen. The observed and simulated submesoscale diurnal cycle supports the existence of a shift of approximately 1–3 h between the occurrence of divergence and vorticity maxima, broadly in agreement with theoretical predictions. The amplitude of the modeled signal, on the other hand, always underestimates the observed one, suggesting that even a horizontal resolution of 500 m is insufficient to capture the strength of the observed variability in submesoscale circulations. The paper also presents an evaluation of how well the diurnal cycle can be detected as function of the number of Lagrangian particles. If more than 2000 particle triplets are considered, the diurnal cycle is well captured, but for a number of triplets comparable to that of the LASER analysis, the reconstructed diurnal cycling displays high levels of noise both in the model and in the observations.