Mesoscale Systems Research Articles

The Arrhenius law prefactor in permalloy mesoscale systems

The Arrhenius equation was used to describe the dynamics of two-state switching in mesoscale, ferromagnetic particles. Using square permalloy dots as an idealized two-state switching system, measurements of the prefactor of the Arrhenius law changed by 26 decades over barrier heights from 30 to 700 meV. Measurements of the prefactor ratios for a two well system revealed significant deviations from the common interpretation of the Arrhenius law. The anomalous Arrhenius prefactors and the prefactor ratios can be fitted to a modified model that includes entropic contributions to two-state transitions. Similar considerations are likely for the application of the Arrhenius law to other mesoscale systems.

Secondary Gravity Wave Propagation in Tropical Thermospheric Region: Role of Varying Kinematic Viscosity

AbstractThe current study has investigated the thunderstorm induced atmospheric gravity waves (AGWs) over Indian region based on the perturbation signatures in ionospheric total electron content (TEC) measurement. Robust traveling ionospheric disturbance (TID) signature has been identified along the east side of the thunderstorm affected area. Neutral wind was found to have a favorable impact in this aspect for a certain time duration of the day by modulating the vertical wavelength. The role of temperature was analyzed in terms of kinematic viscosity which is a crucial component, especially over tropical region, for wave dissipation and reflection along its propagation path. Ray tracing algorithm is also applied with varying kinematic viscosity and thermal diffusivity for retrieval of possible ray paths and source location of observed waves. A statistical investigation has been carried out to identify the dissipation altitude of observed waves along the ray paths. It has been found that all waves dissipated at almost a constant altitude for a specific kinematic viscosity and above this altitude vertical wavelength was found to decrease. The ray paths interacted at a common point which was located at about 125 km altitude and was very close to the region of maximum lightning activity. It can also be noted that the observed phase velocities can't be achieved by a wave below the turbopause. It indicates that the observed waves were excited from a secondary source and not directly connected to convective system. The study provides an in‐depth analysis of mesoscale system induced gravity wave propagation and dissipation over tropical region.

Data-driven discovery of dynamics from time-resolved coherent scattering

Coherent X-ray scattering (CXS) techniques are capable of interrogating dynamics of nano- to mesoscale materials systems at time scales spanning several orders of magnitude. However, obtaining accurate theoretical descriptions of complex dynamics is often limited by one or more factors—the ability to visualize dynamics in real space, computational cost of high-fidelity simulations, and effectiveness of approximate or phenomenological models. In this work, we develop a data-driven framework to uncover mechanistic models of dynamics directly from time-resolved CXS measurements without solving the phase reconstruction problem for the entire time series of diffraction patterns. Our approach uses neural differential equations to parameterize unknown real-space dynamics and implements a computational scattering forward model to relate real-space predictions to reciprocal-space observations. This method is shown to recover the dynamics of several computational model systems under various simulated conditions of measurement resolution and noise. Moreover, the trained model enables estimation of long-term dynamics well beyond the maximum observation time, which can be used to inform and refine experimental parameters in practice. Finally, we demonstrate an experimental proof-of-concept by applying our framework to recover the probe trajectory from a ptychographic scan. Our proposed framework bridges the wide existing gap between approximate models and complex data.

Characteristics and Driving Mechanisms of Coastal Wind Speed during the Typhoon Season: A Case Study of Typhoon Lekima

The development and utilization of wind energy is of great significance to the sustainable development of China’s economy and the realization of the “dual carbon” goal. Under typhoon conditions, the randomness and volatility of wind speed significantly impact the energy efficiency and design of wind turbines. This paper analyzed the changes in wind speed and direction using the BFAST method and Hurst index based on data collected at 10 m, 30 m, 50 m, and 70 m heights from a wind power tower in Yancheng, Jiangsu Province. Furthermore, the paper examined the causes of wind speed and direction changes using wind speed near the typhoon center, distance from the typhoon center to the wind tower, topographic data, and mesoscale system wind direction data. The conclusions drawn are as follows: (i) Using the BEAST method, change points were identified at 10 m, 30 m, 50 m, and 70 m heights, with 5, 5, 6, and 6 change points respectively. The change points at 10 m, 30 m, and 50 m occurred around node 325, while the change time at 70 m was inconsistent with other heights. Hurst index results indicated stronger inconsistency at 70 m altitude compared to other altitudes. (ii) By analyzing the wind direction sequence at 10 m, 30 m, 50 m, and 70 m, it was found that the wind direction changes follow the sequence Southeast (SE)—East (E)—Southeast (SE)—Southwest (SW)—West (W)—Northwest (NW). Notably, the trend of wind direction at 70 m significantly differed from other altitudes during the wind speed strengthening and weakening stages. (iii) Wind speed at 10 m and 70 m altitudes responded differently to the distance from the typhoon center and the wind near the typhoon center. The correlation between wind speed and the distance to the typhoon center was stronger at 10 m than at 70 m. The surface type and the mesoscale system’s wind direction also influenced the wind speed and direction. This study provides methods and theoretical support for analyzing short-term wind speed changes during typhoons, offering reliable support for selecting wind power forecast indicators and designing wind turbines under extreme gale weather conditions.

How Well Does the DOE Global Storm Resolving Model Simulate Clouds and Precipitation Over the Amazon?

AbstractThis study assesses a 40‐day 3.25‐km global simulation of the Simple Cloud‐Resolving E3SM Model (SCREAMv0) using high‐resolution ground‐based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal timing of boundary layer clouds yet underestimates the boundary layer cloud fraction and mid‐level congestus. SCREAMv0 well replicates the precipitation diurnal cycle, however it exhibits biases in the precipitation cluster size distribution compared to scanning radar observations. Specifically, SCREAMv0 overproduces clusters smaller than 128 km, and does not form enough large clusters. Such biases suggest an inhibition of convective upscale growth, preventing isolated deep convective clusters from evolving into larger mesoscale systems. This model bias is partially attributed to the misrepresentation of land‐atmosphere coupling. This study highlights the potential use of high‐resolution ground‐based observations to diagnose convective processes in global storm resolving model simulations, identify key model deficiencies, and guide future process‐oriented model sensitivity tests and detailed analyses.

Supercrystal Engineering of Nanoarrows Enabled by Tailored Concavity.

Self-assembly of nanoparticles into supercrystals represents a powerful approach to create unique and complex superstructures with fascinating properties and novel functions, but the complexity in spatial configuration, and the tunability in lattice structure are still quite limited compared to the crystals formed by atoms and molecules. Herein, shallowly concave gold nanoarrows with a unique concave-convex geometry are synthesized and employed as novel building blocks for shape-directed self-assembly of a wealth of complex 3D supercrystals with unprecedented configurations. The obtained diverse superstructures including six Interlocking-type supercrystals and three Packing-type supercrystals exhibit four types of Bravais lattices (i.e., tP, oI, tI, and oF) and six types of crystallographic space groups (i.e., Pmmm, I222, Pnnm, Ibam, I4/mmm, and Fmmm), which have not been documented in the mesoscale self-assembled systems. It has been revealed that the relative yield of different supercrystal structures is mainly determined by the packing density and deformability of the supercrystals, which are closely related to the tailored concavity of the nanoparticles and is affected by the particle concentration, thus allowing for programmable self-assembly into specific supercrystals through particle shape modulation. The concavity-enabled supercrystal engineering may open a new avenue toward unconventional nanoparticle superstructures with expanded complexity, tunability, and functionality.

Transient temperature dynamics of reservoirs connected through an open quantum system.

The dynamics of open quantum systems connected with several reservoirs attract great attention due to their importance in quantum optics, biology, quantum thermodynamics, transport phenomena, etc. In many problems, the Born approximation is applicable, which implies that the influence of the open quantum system on the reservoirs can be neglected. However, in the case of long-time dynamics or mesoscopic reservoirs, the reverse influence can be crucial. In this paper, we investigate the transient dynamics of several bosonic reservoirs connected through an open quantum system. We use an adiabatic approach to study the temporal dynamics of temperatures of the reservoirs during relaxation to thermodynamic equilibrium. We show that there are various types of temperature dynamics that strongly depend on the values of dissipative rates and initial temperatures. We demonstrate that temperatures of the reservoirs, including the hottest and coldest ones, can exhibit nonmonotonic behavior. Moreover, there are moments of time during which the reservoir with an initially intermediate temperature becomes the hottest or coldest reservoir. The obtained results pave the way for managing energy flows in mesoscale and nanoscale systems.

Implications of WRF model resolutions on resolving rainfall variability with topography over East Africa

There is an increasing need to improve the accuracy of extreme weather forecasts for life-saving applications and in support of various socioeconomic sectors in East Africa, a region with remarkable mesoscale systems due to its complex topography defined by sharp gradients in elevation, inland water bodies, and landuse conversions. This study sought to investigate the impacts of the Weather Research and Forecasting (WRF) model spatial resolution on resolving rainfall variability with topography utilizing nested domains at 12 and 2.4 km resolutions. The model was driven by the National Centers for Environmental Prediction (NCEP)-Global Data Assimilation System (GDAS) Global Forecast System (GFS) final (FNL) reanalysis to simulate the weather patterns over East Africa from 3rd April 2018 to 30th April 2018, which were evaluated against several freely available gridded weather datasets alongside rainfall data from the Kenya Meteorological Department (KMD) stations. The reference datasets and the model outputs revealed that the highlands had more rainfall events and higher maximum daily rainfall intensity compared to the surrounding lowlands, attributed to orographic lifting enhancing convection. Rainfall was inversely proportional to altitude from 500 m to 1,100 m above sea level (ASL) for both coarse and fine resolutions. The convection-permitting setup was superior in three aspects: resolving the inverse altitude-rainfall relationship for altitudes beyond 3000 m ASL, simulating heavy rainfall events over the lowlands, and resolution of the diurnal cycle of low-level wind. Although the coarse resolution setup reasonably simulated rainfall over large mountains, only the convection-permitting configuration could accurately resolve rainfall variability over contrasting topographical features. The study notes that high-resolution modeling systems and topography-sensitive bias correction techniques are critical for improving the quality of operational weather forecasts in East Africa.

A study of extreme rainfall events and urban flooding over Hyderabad, October 2020

The present study analyses and describes the evolution of the Mesoscale Convective Complex (MCC) and its atmospheric conditions during Extreme rainfall event in Hyderabad, on 13th October 2020. This extreme weather event was a mesoscale event embedded in a synoptic-scale system. During the second week of October 2020, a depression formed over the west-central Bay of Bengal (BoB) and travelled north-westwards through peninsular India, causing heavy rains in Andhra Pradesh and Telangana states of India on October 13-14. On October 13, many parts of Hyderabad and Cyberabad received more than 300 mm of rain within 24 hrs. Satellite imagery suggests that this mesoscale system constituted a unique set of structured convection those reported in MCC. This MCC has a cloud shield with a continuous low IR temperature of less than - 33 °C over an area of more than 100000 km2 and a cloud shield with a continuous low IR temperature of less than -54 °C over an area of more than 50000 km2 over Hyderabad with a life cycle of about 9 hours. This MCC featured multi cellular characteristics, showing that there was significant low-level moisture in its environment, as well as a mix of vigorous updrafts, implying significant rainfall rates over Hyderabad. The synoptic features suggest that with high precipitable water, the long axis of low-level moisture convergence at 0850 hPa and large horizontal vorticity at 0925 hPa were oriented parallel to the system's mean wind flow. In this case, a clusters of thunderstorms arose in the area of moisture convergence which prolonged the duration of extremely heavy rainfall. The high rain rate, relatively sluggish storm motion, and prolonged back-building over the same locations for several hours are likely to blame for the heavy rainfall accumulations that were observed. The hydrological conditions compounded the effects of the torrential rain, resulting in a natural hazard.

Diurnal cycle of precipitation over the tropics and central United States: intercomparison of general circulation models

AbstractDiurnal precipitation is a fundamental mode of variability that climate models have difficulty in accurately simulating. Here the diurnal cycle of precipitation (DCP) in participating climate models from the Global Energy and Water Exchanges' DCP project is evaluated over the tropics and central United States. Common model biases such as excessive precipitation over the tropics, too frequent light‐to‐moderate rain, and the failure to capture propagating convection in the central United States still exist. Over the central United States, the issues of too weak rainfall intensity in climate runs is well improved in their hindcast runs with initial conditions from numerical weather prediction analyses. But the improvement is minimal over the central Amazon. Incorporating the role of the large‐scale environment in convective triggering processes helps resolve the phase‐locking issue in many models where precipitation often incorrectly peaks near noon due to maximum insolation over land. Allowing air parcels to be lifted above the boundary layer improves the simulation of nocturnal precipitation which is often associated with the propagation of mesoscale systems. Including convective memory in cumulus parameterizations acts to suppress light‐to‐moderate rain and promote intense rainfall; however, it also weakens the diurnal variability. Simply increasing model resolution (with cumulus parameterizations still used) cannot fully resolve the biases of low‐resolution climate models in DCP. The hierarchy modeling framework from this study is useful for identifying the missing physics in models and testing new development of model convective processes over different convective regimes.

Interdecadal change of Tibetan Plateau vortices during the past 4 decades and its possible mechanism

The Tibetan Plateau vortices (TPVs) are mesoscale weather systems active at the near-surface of the Tibetan Plateau (TP), which are one of the major precipitation-producing systems over the TP and its surrounding areas. TPVs mainly occur in the warm season from May to September. In this paper, we investigated the interdecadal change of TPVs in the warm seasons of 1979–2020 with the six widely used reanalysis datasets. A significant change of the TPVs frequency appears around the mid-1990s, associated with less TPVs during 1979–1995 and more TPVs during 1996–2020, which is constant among the multiple reanalysis datasets. The abrupt change of TPVs is caused by a transition of the Atlantic Multi-decadal Oscillation (AMO) from a cold phase to a warm phase in the mid-1990s. The shift of AMO leads to a silk-road pattern-like wave train and a spatially asymmetric change of tropospheric temperature. It modifies the intensity of the subtropical westerly jet and the TP heating, leading to the interdecadal change of TPV activities.

Comparison Link Function from Summer Rainfall Network in Amazon Basin

The Amazon Basin is the largest rainforest in the world, and studying the rainfall in this region is crucial for understanding the functioning of the entire rainforest ecosystem and its role in regulating the regional and global climate. This work is part of the application of complex networks, which refer to a network modeled by graphs and are characterized by their high versatility, as well as the extraction of key information from the system under study. The main objective of this article is to examine the precipitation system in the Amazon basin during the austral summer. The networks are defined by nodes and connections, where each node represents a precipitation time series, while the connections can be represented by different similarity functions. For this study, three rainfall networks were created, which differ based on the correlation function used (Pearson, Spearman, and Kendall). By comparing these networks, we can identify the most effective method for analyzing the data and gain a better understanding of rainfall’s spatial structure, thereby enhancing our knowledge of its impact on different Amazon basin regions. The results reveal the presence of three important regions in the Amazon basin. Two areas were identified in the northeast and northwest, showing incursions of warm and humid winds from the oceans and favoring the occurrence of large mesoscale systems, such as squall lines. Additionally, the eastern part of the central Andes may indicate an outflow region from the basin with winds directed toward subtropical latitudes. The networks showed a high level of activity and participation in the center of the Amazon basin and east of the Andes. Regarding information transmission, the betweenness centrality identified the main pathways within a basin, and some of these are directly related to certain rivers, such as the Amazon, Purus, and Madeira. Indicating the relationship between rainfall and the presence of water bodies. Finally, it suggests that the Spearman and Kendall correlation produced the most promising results. Although they showed similar spatial patterns, the major difference was found in the identification of communities, this is due to the meridional differences in the network’s response. Overall, these findings highlight the importance of carefully selecting appropriate techniques and methods when analyzing complex networks.

Modeling photothermal effects in high power optical resonators used for coherent levitation

Radiation pressure can be used to enable optomechanical control and manipulation of the quantum state of a mechanical oscillator. Optomechanical interaction can also be mediated by photothermal effects which, although frequently overlooked, may compete with radiation pressure interaction. Understanding of how these phenomena affect the coherent exchange of information between optical and mechanical degrees of freedom is often underdeveloped, particularly in mesoscale high-power systems where photothermal effects can fully dominate the interaction. Here we report an effective theoretical model to predict and successfully reconstruct the dynamics of a unique optomechanical system: a cavity-enhanced setup for macroscopic optical levitation, where a free-standing mirror acts as the optomechanical oscillator. We decompose the photothermal interaction into two opposing light-induced effects, photothermal expansion, and thermo-optic effects. We then reconstruct a heuristic model that links the intracavity field to four types of cavity length changes caused by acoustic (), centre of mass (), photothermal () and thermo-optic () displacements. This offers refined predictions with a higher degree of agreement with experimental results. Our work provides a means to precisely model the photothermal effect of high power optomechanical systems, as well as for developing more precise photothermal modeling of photonics systems for precision sensing and quantum measurements.

Significant catalyst savings and reaction kinetics enhancement for catalytic micro-combustors via non-uniform catalyst segmentation

A recent prioritization of energy-efficient and sustainable technologies has fueled an ongoing demand for improving the heat sources necessary to drive thermally dependent processes in portable and small-scale devices. Previous efforts have explored hydrocarbon-based catalytic combustion (and its kinetics and fuel usage efficiency) for reliable heating in microscale devices (TPVs, TEGs, etc.). However, for mesoscale systems that require a larger length scale combustor, fuel management and the ability to maintain consistent heat flux across the combustor become challenging due to kinetics and mass diffusion limitations, which are extrinsic to microscale combustors. In this work, we present the design and experimental evaluation of a compact parallel plate catalytic micro-combustor with non-uniform, segmented catalytic zones that allow for direct control of heating, spatially. Specifically, we performed a comparative FEM study of various catalyst segmentation patterns to further optimize catalyst bed spacing for decreased fuel consumption and improved combustion reaction kinetics. Due to these enhancements, the proposed patterning scheme can achieve similar temperature profiles to those of a fully-coated surface while using 77.8% less catalyst and reducing extreme thermal gradients during the early transient combustion phase. Finally, we demonstrated the capabilities of the proposed catalytic combustor by testing the combustor in a controlled environment.

Distribution and Trend of Wind Power Input to Near‐Inertial Motions in the Southern Ocean

AbstractWind power input to near‐inertial motions is an important energy source for generating diapycnal mixing in the ocean. However, the distribution and long‐term trend of this input over the Southern Ocean have yet to be quantified. In this study, we investigate the near‐inertial wind power input (WPIi) to the Southern Ocean using a global eddy‐permitting coupled ocean‐sea ice model forced by a high‐resolution atmospheric reanalysis product. Our results reveal a zonally asymmetric distribution of WPIi in the Southern Ocean, with the strongest input in the South Indian Ocean and the weakest in the South Pacific. The integrated WPIi between 30°S and 60°S exhibits a significant positive trend over the past four decades due to the intensification of mesoscale weather systems. The surface mixed‐layer depth is found to modulate the spatial pattern and trend of WPIi by altering the surface near‐inertial currents.

Performance Evaluation and Optimization of the Weather Research and Forecasting (WRF) Model Based on Kunpeng 920

The Weather Research and Forecasting (WRF) model is a mesoscale numerical weather prediction system, which is widely used in major high-performance server platforms. This study focuses on the performance evaluation and optimization of WRF on Huawei’s self-developed kunpeng 920 processor platform, aiming to improve the operational efficiency of WRF. The results of the study show that the scalability of WRF on kunpeng 920 processor is well performed; the performance of WRF on kunpeng 920 processor is improved by 32.6% after invoking the Fast Math Library and Domain Decomposition Core Tile Division optimization. In terms of IO, the main optimizations are parallel IO and asynchronous IO. Eventually, the single output time of WRF is reduced from 37.28 s in serial IO mode to 0.14 s in asynchronous IO mode, and the overall running time is reduced from 1078.80 s to 807.94 s.

A Multicloud Model for Coastal Convection

Coastal convection is often organized into multiple mesoscale systems that propagate in either direction across the coastline (i.e., landward and oceanward). These systems interact non-trivially with synoptic and intraseasonal disturbances such as convectively coupled waves and the Madden–Julian oscillation. Despite numerous theoretical and observational efforts to understand coastal convection, global climate models still fail to represent it adequately, mainly because of limitations in spatial resolution and shortcomings in the underlying cumulus parameterization schemes. Here, we use a simplified climate model of intermediate complexity to simulate coastal convection under the influence of the diurnal cycle of solar heating. Convection is parameterized via a stochastic multicloud model (SMCM), which mimics the subgrid dynamics of organized convection due to interactions (through the environment) between the cloud types that characterize organized tropical convection. Numerical results demonstrate that the model is able to capture the key modes of coastal convection variability, such as the diurnal cycle of convection and the accompanying sea and land breeze reversals, the slowly propagating mesoscale convective systems that move from land to ocean and vice-versa, and numerous moisture-coupled gravity wave modes. The physical features of the simulated modes, such as their propagation speeds, the timing of rainfall peaks, the penetration of the sea and land breezes, and how they are affected by the latitudinal variation in the Coriolis force, are generally consistent with existing theoretical and observational studies.

A phase-field lattice model (PFLM) for fracture problem: Theory and application in composite materials

In the present work, a phase-field lattice model (PFLM) is proposed to model fracture problems. The element deletion process and oversimplified failure criterion of the classical lattice model not only lead to a strong mesh sensitivity but also limit the method’s application to various materials and fracture modes. Hence, a smeared form of crack is introduced into the lattice model to deal with these problems. The model exploits discontinuous discrete methods to model the propagation of three-dimensional cracks by characterizing the crack with a phase-field variable. Moreover, by regarding the crack propagation process as a multi-field problem composed of a displacement field and a phase-field, a flexible and robust algorithm is established, in which the crack path can be obtained directly by resolving the governing equations. Numerical simulations were performed and compared with the experimental results and other numerical models. It is shown that the PFLM can capture the main features of the fracture for both brittle and quasi-brittle materials. To further demonstrate the performance of the model, a mesoscale system of cement composite generated by computed tomography scanning technology was examined. The result demonstrates that the fracture of composite materials can also be well predicted.

Evaluating Contour Band Depth as a Method for Understanding Ensemble Uncertainty

Abstract Estimating and predicting the state of the atmosphere is a probabilistic problem for which an ensemble modeling approach often is taken to represent uncertainty in the system. Common methods for examining uncertainty and assessing performance for ensembles emphasize pointwise statistics or marginal distributions. However, these methods lose specific information about individual ensemble members. This paper explores contour band depth (cBD), a method of analyzing uncertainty in terms of contours of scalar fields. cBD is fully nonparametric and induces an ordering on ensemble members that leads to box-and-whisker-plot-type visualizations of uncertainty for two-dimensional data. By applying cBD to synthetic ensembles, we demonstrate that it provides enhanced information about the spatial structure of ensemble uncertainty. We also find that the usefulness of the cBD analysis depends on the presence of multiple modes and multiple scales in the ensemble of contours. Finally, we apply cBD to compare various convection-permitting forecasts from different ensemble prediction systems and find that the value it provides in real-world applications compared to standard analysis methods exhibits clear limitations. In some cases, contour boxplots can provide deeper insight into differences in spatial characteristics between the different ensemble forecasts. Nevertheless, identification of outliers using cBD is not always intuitive, and the method can be especially challenging to implement for flow that exhibits multiple spatial scales (e.g., discrete convective cells embedded within a mesoscale weather system). Significance Statement Predictions of Earth’s atmosphere inherently come with some degree of uncertainty owing to incomplete observations and the chaotic nature of the system. Understanding that uncertainty is critical when drawing scientific conclusions or making policy decisions from model predictions. In this study, we explore a method for describing model uncertainty when the quantities of interest are well represented by contours. The method yields a quantitative visualization of uncertainty in both the location and the shape of contours to an extent that is not possible with standard uncertainty quantification methods and may eventually prove useful for the development of more robust techniques for evaluating and validating numerical weather models.

Phytoplankton diversity explained by connectivity across a mesoscale frontal system in the open ocean

Phytoplankton community composition is important in establishing ecosystem structure and function. Intuitively, we recognize that water movements must be important for modifying spatial gradients and plankton diversity. However, identifying boundaries and exchange between habitats in the open ocean is not straightforward. Here, we use the abundance of nine phytoplankton species closely sampled in a mesoscale frontal system in the northeastern North Sea as a proxy for community composition and explore the relationship between phytoplankton biogeography and transport patterns. Subsurface community distributions could be related to modeled patterns in water movement. A methodology for analyzing pelagic diversity that includes a representation of plankton community composition and an Eulerian connectivity tracer was developed, and the relative importance of connectivity and geographical distance for phytoplankton species composition analyzed. The connectivity tracer identifies timescales and dispersal barriers in the open ocean. Connectivity was found to be superior in explaining pelagic plankton diversity and found to be a prerequisite for understanding the pelagic phytoplankton composition. This approach is a valuable tool for establishing the link between ocean transports, ecosystem structure and biodiversity and for informing the placement of marine protected areas.