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At least 415 records · Page 23

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DOE OSTI2023

Kohn–Sham accuracy from orbital-free density functional theory via Δ-machine learning

Here, we present a Δ-machine learning model for obtaining Kohn–Sham accuracy from orbital-free density functional theory (DFT) calculations. In particular, we employ a machine-learned force field (MLFF) scheme based on the kernel method to capture the difference between Kohn–Sham and orbital-free DFT energies/forces. We implement this model in the context of on-the-fly molecular dynamics simulations and study its accuracy, performance, and sensitivity to parameters for representative systems. We find that the formalism not only improves the accuracy of Thomas–Fermi–von Weizsäcker orbital-free energies and forces by more than two orders of magnitude but is also more accurate than MLFFs based solely on Kohn–Sham DFT while being more efficient and less sensitive to model parameters. We apply the framework to study the structure of molten Al 0.88 Si 0.12 , the results suggesting no aggregation of Si atoms, in agreement with a previous Kohn–Sham study performed at an order of magnitude smaller length and time scales.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Flow of long chain hydrocarbons through carbon nanotubes (CNTs)

The pressure-driven flow of long-chain hydrocarbons in nanosized pores is important in energy, environmental, biological, and pharmaceutical applications. This paper examines the flow of hexane, heptane, and decane in carbon nanotubes (CNTs) of pore diameters 1–8 nm using molecular dynamic simulations. Enhancement of water flow in CNTs in comparison to rates predicted by continuum models has been well established in the literature. Our work was intended to observe if molecular dynamic simulations of hydrocarbon flow in CNTs produced similar enhancements. We used the OPLS-AA force field to simulate the hydrocarbons and the CNTs. Our simulations predicted the bulk densities of the hydrocarbons to be within 3% of the literature values. Molecular sizes and shapes of the hydrocarbon molecules compared to the pore size create interesting density patterns for smaller sized CNTs. We observed moderate flow enhancements for all the hydrocarbons (1–100) flowing through small-sized CNTs. For very small CNTs the larger hydrocarbons were forced to flow in a cork-screw fashion. As a result of this flow orientation, the larger molecules flowed as effectively (similar enhancements) as the smaller hydrocarbons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1982

Effects of rotation and magnetic field on the onset of convective instability in a liquid layer due to buoyancy and surface tension

Thermocapillary stability characteristics of a horizontal liquid layer heated from below rotating about a vertical axis and subjected to a uniform vertical magnetic field are analyzed under a variety of thermal and electromagnetic boundary conditions. Results based on analytical solutions to the pertinent eigenvalue problems are discussed in the light of earlier work on special cases of the more general problem considered here to show in particular the effects of the heat transfer, nonzero curvature and gravity waves at the two-fluid interface. Although the expected stabilizing action of the Coriolis and Lorentz force fields in this configuration are in evidence the optimal choice of an appropriate range for the relevant parameters is shown to be critically dependent on the interfacial effects mentioned above.

Sarma, G. S. R.↗

NASA NTRS1990

Fluid behavior in microgravity environment

The instability of liquid and gas interface can be induced by the presence of longitudinal and lateral accelerations, vehicle vibration, and rotational fields of spacecraft in a microgravity environment. In a spacecraft design, the requirements of settled propellant are different for tank pressurization, engine restart, venting, or propellent transfer. In this paper, the dynamical behavior of liquid propellant, fluid reorientation, and propellent resettling have been carried out through the execution of a CRAY X-MP super computer to simulate fluid management in a microgravity environment. Characteristics of slosh waves excited by the restoring force field of gravity jitters have also been investigated.

Hung, R. J.↗

NASA NTRS2000

On the Origin of Monsoon

The notion that the continental-scale land-sea contrast is the main reason that monsoon circulation exists has been a long-held belief. The purpose of this paper is to point out that this notion should be substantially modified. The central idea of this notion states that in summer, radiative heating of the continent, say Asia, gives rise to a continental-scale thermal low and surrounding the thermal low in its southeast direction the low level wind flows in from south-west. This low-level inflow creates a convergence of moisture, which maintains the cumulus convection. And in winter, radiative cooling of continent gives rise to a thermal high and to its southeast the low-level wind is from northeast. The mechanism in this interpretation does undoubtedly exist. However, this mechanism, though believed to be the main driving force of monsoon, has not been tested in numerical experiments. There has been an increasing recognition in the recent years that monsoon is inextricably tied to the heating in the intertropical convergence zone (ITCZ). We propose that the main cause of monsoon is ITCZ's being substantially away from the equator. A brief qualitative explanation of why the ITCZ can be a source of monsoon circulation can be offered based on the circulation field forced by the ITCZ heating. The existence of the ITCZ's does not always have to rely on land-sea contrast on the continental scale. This is hinted in the fact that in February the ITCZ close to Australia (and its associated monsoon circulation) covers a longitudinal range several times as long as that of Australia and thus cannot possibly be caused mainly by the land-sea contrast associated with Australia. Yet, this cannot be used as a proof that the ITCZ in the Asian summer monsoon is not mainly due to land-sea contrast. One of the purposes of this work is to provide a convincing proof. In this work the role of land-sea contrast in the origin of monsoon is examined through numerical simulation with the Goddard general circulation model. The Asian and Australian monsoon circulations are obtained in a four-year integration and then the integration is repeated with Asia, the maritime continent, and Australia replaced by ocean. The sea surface temperature (SST) at each affected grid is specified as the SST at the first grid to the east that is an ocean grid in the first experiment. The latter integration shows that the monsoon circulation pattern over where south Asia and Australia were and the surrounding region has largely remained. The results discount land-sea contrast as the main cause of Asian monsoon. A third experiment is the same as the first except that the topography of Asia, the maritime continent, and Australia is reduced to zero. This experiment reveals that the difference between the first two experiments is due more to the removal of topography than to the removal of land-sea contrast. August precipitation is shown averaged over the last three years of each of the three experiments. They show that the Asian monsoon rainy region is largely intact in the second experiment and the difference between the second and the third experiment is mainly in the longitudinal location of the maximum precipitation. Additionally, in Asian and Australian winter monsoons land-sea contrast also plays only a modifying role. Although land-sea contrast plays only a modifying role in Asian and Australian (and Central American including Mexican) monsoons, it is the main reason that ITCZ (and thus monsoon) exists in Africa and South America. Thus, monsoons can be classified into two groups depending on whether land-sea contrast plays a major role.

Chao, Winston C.↗

NASA NTRS2014

Plasmonic Force Propulsion Revolutionizes Nano/Picosatellite Capability

This project investigated a new type of small spacecraft propulsion for attitude control, specifically proximity and precision pointing control. Plasmonic force propulsion uses solar light focused on deep-subwavelength nanostructures to excite strong optical forces that accelerate and expel nanoparticle propellant. The goal of the project was to assess the feasibility of plasmonic force propulsion for nano/pico-satellite applications by evaluating key mission parameters for a nano/pico-satellite using plasmonic force propulsion in a NASA-relevant mission context. We achieved this goal and objective by evaluating plasmonic force propulsion within a NASA mission that required attitude control and precision pointing of a small satellite. We numerically simulated plasmonic force fields with asymmetric/gradient geometry and relevant solar light constraints, predicted nanoparticle velocity, mass flow rate, and resulting propulsion performance (thrust, specific impulse), and evaluated spacecraft position control resolution and pointing precision. Additionally we compared the precision pointing capabilities of plasmonic propulsion, as well as the mass, volume, and power requirements, with other state-of-the-art control techniques, such as reaction wheels and colloid/electrospray electric propulsion. The results are very exciting. Plasmonic force propulsion can significantly enhance the state-of-the-art in small spacecraft position and attitude control by 1-2 orders of magnitude. This is most succinctly shown in the figure below, which compares proximity and attitude control of plasmonic force propulsion (PFP) with other state-of-the-art thruster systems (μCAT, VAT, electrospray). Additionally this figure also shows the proximity and attitude control required for different existing (James Webb Space Telescope, Hubble) and future (LISA and Stellar Imager) NASA missions. While some of these NASA missions are not small spacecraft missions, the requirements serve to illustrate the fact that more precise proximity and attitude control will be required for future NASA science missions. Stellar imager is a proposed NASA missions that requires an extremely high pointing precision of 0.1 milliarcseconds (2.7x10(exp -7) deg.) for an ultraviolet telescope that has over 200× the resolution of the Hubble Space Telescope, is able to take images showing details on the surfaces of other stars, consists of 20-30 small "mirror sats" flying in formation to produce a giant mirror, and requires each mirror-sat to be placed with nanometer precision and control its attitude with milliarcsecond precision.

Rovey, Joshua L.↗

DOE OSTI2023

CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling

Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modelling. Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab initio molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study technologically relevant phenomena. Here we present the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph neural network-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. CHGNet is pretrained on the energies, forces, stresses and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory calculations of more than 1.5 million inorganic structures. The explicit inclusion of magnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons, enhancing its capability to describe both atomic and electronic degrees of freedom. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in Li x MnO 2 , the finite temperature phase diagram for Li x FePO 4 and Li diffusion in garnet conductors. We highlight the significance of charge information for capturing appropriate chemistry and provide insights into ionic systems with additional electronic degrees of freedom that cannot be observed by previous MLIPs.

36 MATERIALS SCIENCE↗

NASA NTRS2001

Carbon Nanotubes in Water: MD Simulations of Internal and External Flow, Self Organization

We have developed computational tools, based on particle codes, for molecular dynamics (MD) simulation of carbon nanotubes (CNT) in aqueous environments. The interaction of CNTs with water is envisioned as a prototype for the design of engineering nano-devices, such as artificial sterocillia and molecular biosensors. Large scale simulations involving thousands of water molecules are possible due to our efficient parallel MD code that takes long range electrostatic interactions into account. Since CNTs can be considered as rolled up sheets of graphite, we expect the CNT-water interaction to be similar to the interaction of graphite with water. However, there are fundamental differences between considering graphite and CNTs, since the curvature of CNTs affects their chemical activity and also since capillary effects play an important role for both dynamic and static behaviour of materials inside CNTs. In recent studies Gordillo and Marti described the hydrogen bond structure as well as time dependent properties of water confined in CNTs. We are presenting results from the development of force fields describing the interaction of CNTs and water based on ab-initio quantum mechanical calculations. Furthermore, our results include both water flows external to CNTs and the behaviour of water nanodroplets inside heated CNTs. In the first case (external flows) the hydrophobic behaviour of CNTs is quantified and we analyze structural properties of water in the vicinity of CNTs with diagnostics such as hydrogen bond distribution, water dipole orientation and radial distribution functions. The presence of water leads to attractive forces between CNTs as a result of their hydrophobicity. Through extensive simulations we quantify these attractive forces in terms of the number and separation of the CNT. Results of our simulations involving arrays of CNTs indicate that these exhibit a hydrophobic behaviour that leads to self-organising structures capable of trapping water clusters. In the second case (internal flows) we study the behaviour of water droplets confined inside CNTs. Constant temperature simulations allow us to capture structural properties such as the contact angles and density profiles of the equilibrated drops. By heating and subsequently cooling of the CNT, we are able to measure the evaporation and the condensation rate of the entrapped water.

Jaffe, Richard L.↗

DOE OSTI2024

Molecular Dynamics Study of the Effect of Grafting Density on Ion Diffusivity in a MARTINI Coarse-Grained Strong Polyelectrolyte Brush

Because surface-grafted polyelectrolyte brushes (PEBs) are responsive to external stimuli, such as electric fields and ionic strength, PEBs are attractive for applications ranging from drug delivery to separation technologies. Essential to PEB utilization is understanding how critical parameters like grafting density (σ) impact the PEB structure and the dynamics of the PEB and counterions. To study the effect of σ on PEB and the counterion structure and dynamics, we fine-tune a coarse-grained model that retains the chemical specificity of a strong polyelectrolyte, poly[(2-(methacryloyloxy)ethyl) trimethylammonium chloride] (PMETAC), using the MARTINI force field. Using “salt-free” conditions where the counterion concentration balances the charge on the brush, we build coarse-grained (CG) molecular dynamics simulations for MARTINI PMETAC brushes (N = 150 monomers; M W = 31.2 kg/mol) at experimentally relevant values of σ = 0.05, 0.10, 0.20, and 0.40 chains/nm 2 . Using 5 μs simulations, we investigate the effects of grafting density on the PEB structure, ion dissociation dynamics, polymer mobility, and counterion diffusivity. Results show that competition between electrostatic interactions, steric hindrance, and polymer mobility controls counterion diffusivity. Finally, the interplay of these factors leads to diffusivity that depends non-monotonically on σ, with counterion diffusivity peaking at an intermediate σ = 0.10 chains/nm 2 .

36 MATERIALS SCIENCE↗

DOE OSTI2025

Modeling the Behavior of Complex Aqueous Electrolytes Using Machine Learning Interatomic Potentials: The Case of Sodium Sulfate

Understanding the structure and thermodynamics of solvated ions is essential for advancing applications in electrochemistry, water treatment, and energy storage. While ab initio molecular dynamics methods are highly accurate, they are limited by short accessible time and length scales whereas classical force fields struggle with accuracy. Herein, we explore the structure and thermodynamics of complex monovalent-divalent ion pairs using Na 2 SO 4 (aq) as a case study by applying a machine learning interatomic potential (MLIP) trained on density functional theory (DFT) data. Our MLIP-based approach reproduces key bulk properties such as density and radial distribution functions of water. We provide the hydration structure of the sodium and sulfate ions in the 0.1–2 M concentration range and the one-dimensional and two-dimensional potentials of mean force for the sodium–sulfate ion pairing at the low concentration limit (0.1 M), which are inaccessible to DFT. At low concentrations, the sulfate ion is strongly solvated, leading to the stabilization of solvent-separated ion pairs over contact ion pairs. Minimum energy pathway analysis revealed that coordinating two sodium ions with a sulfate ion is a multistep process whereby the sodium ions coordinate to the sulfate ion sequentially. Finally, we demonstrate that MLIPs allow the study of solvated ions beyond simple monovalent pairs with DFT-level accuracy in their low concentration limit (0.1 M) via statistically converged properties from ns-long simulations.

anions↗

DOE OSTI2020

Detecting Fractional Chern Insulators in Optical Lattices Through Quantized Displacement

The realization of interacting topological states of matter such as fractional Chern insulators (FCIs) in cold atom systems has recently come within experimental reach due to the engineering of optical lattices with synthetic gauge fields providing the required topological band structures. However, detecting their occurrence might prove difficult since transport measurements akin to those in solid state systems are challenging to perform in cold atom setups and alternatives have to be found. Here, we show that for a ν = 1/2 FCI state realized in the lowest band of a Harper-Hofstadter model of interacting bosons confined by a harmonic trapping potential, the fractionally quantized Hall conductivity σ xy can be accurately determined by the displacement of the atomic cloud under the action of a constant force which provides a suitable experimentally measurable signal for detecting the topological nature of the state. Using matrix-product state algorithms, we show that, in both cylinder and square geometries, the movement of the particle cloud in time under the application of a constant force field on top of the confining potential is proportional to σ xy for an extended range of field strengths.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

NENCI-2021. I. A large benchmark database of non-equilibrium non-covalent interactions emphasizing close intermolecular contacts

In this work, we present NENCI-2021, a benchmark database of ~8000 Non-Equilibirum Non-Covalent Interaction energies for a large and diverse selection of intermolecular complexes of biological and chemical relevance. To meet the growing demand for large and high-quality quantum mechanical data in the chemical sciences, NENCI-2021 starts with the 101 molecular dimers in the widely used S66 and S101 databases and extends the scope of these works by (i) including 40 cation–π and anion–π complexes, a fundamentally important class of non-covalent interactions that are found throughout nature and pose a substantial challenge to theory, and (ii) systematically sampling all 141 intermolecular potential energy surfaces (PESs) by simultaneously varying the intermolecular distance and intermolecular angle in each dimer. Designed with an emphasis on close contacts, the complexes in NENCI-2021 were generated by sampling seven intermolecular distances along each PES (ranging from 0.7× to 1.1× the equilibrium separation) and nine intermolecular angles per distance (five for each ion–π complex), yielding an extensive database of 7763 benchmark intermolecular interaction energies (E int ) obtained at the coupled-cluster with singles, doubles, and perturbative triples/complete basis set [CCSD(T)/CBS] level of theory. The E int values in NENCI-2021 span a total of 225.3 kcal/mol, ranging from -38.5 to +186.8 kcal/mol, with a mean (median) E int value of -1.06 kcal/mol (-2.39 kcal/mol). In addition, a wide range of intermolecular atom-pair distances are also present in NENCI-2021, where close intermolecular contacts involving atoms that are located within the so-called van der Waals envelope are prevalent—these interactions, in particular, pose an enormous challenge for molecular modeling and are observed in many important chemical and biological systems. A detailed symmetry-adapted perturbation theory (SAPT)- based energy decomposition analysis also confirms the diverse and comprehensive nature of the intermolecular binding motifs present in NENCI-2021, which now includes a significant number of primarily induction-bound dimers (e.g., cation–π complexes). NENCI-2021 thus spans all regions of the SAPT ternary diagram, thereby warranting a new four-category classification scheme that includes complexes primarily bound by electrostatics (3499), induction (700), dispersion (1372), or mixtures thereof (2192). A critical error analysis performed on a representative set of intermolecular complexes in NENCI-2021 demonstrates that the E int values provided herein have an average error of ±0.1 kcal/mol, even for complexes with strongly repulsive E int values, and maximum errors of ±0.2–0.3 kcal/mol (i.e., ~±1.0 kJ/mol) for the most challenging cases. For these reasons, we expect that NENCI-2021 will play an important role in the testing, training, and development of next-generation classical and polarizable force fields, density functional theory approximations, wavefunction theory methods, and machine learning based intra- and inter-molecular potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Stress field and interaction forces between dislocations and precipitate distributions

A computational method is developed for calculation of the stress field and interaction forces between dislocations and precipitates of arbitrary shape and distribution. The internal stress generated by precipitates due to coherency strain is implemented within the discrete dislocation dynamics (DDD) framework. The s-version finite element method (s-FEM), which models a precipitate of arbitrary shape using a local mesh is used to calculate coherency stress fields. The method facilitates meshing precipitate volumes of arbitrary geometry, and multiple local meshes can be superimposed at various positions of the global mesh. Accuracy and convergence conditions of the method are established. For a single precipiate, the method is shown to be 3.5 times faster than a standard FEM calculation for the same accuracy, and the gain in speed increases with the number of precipitates. The CRSS of spherical precipitates is found to be higher than disk-shaped precipitates of the same volume fraction.

42 ENGINEERING↗

DOE OSTI2024

Simple and Accurate One-Body Energy and Dipole Moment Surfaces for Water and Beyond

Water is often the testing ground for new, advanced force fields. While advanced functional forms for intermolecular interactions have been integral to the development of accurate water models, less attention has been paid to a transferable model for intramolecular valence terms. In this work, we present a one-body energy and dipole moment surface model, named 1B-UCB, that is simple yet accurate and can be feasibly adapted for both standard and advanced potentials. 1B-UCB for water is comparable in accuracy to those with much more complex functional forms, despite having drastically fewer parameters. The parametrization protocol has been implemented as part of the Q-Force automated workflow and requires only a quantum mechanical Hessian calculation as reference data, hence allowing it to be easily extended to a variety of molecular systems beyond water, which we demonstrate on a selection of small molecules with different symmetries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Test samples and infrastructure for accelerator magnet developments

LHC still does not operate at full energy due to training features in magnets. Higher field practical superconductors (Nb 3 Sn) show much worse training behavior than NbTi. Most magnet performance issues relate to quenching, and it is this phenomenon and its dependencies that need to be understood for us to succeed addressing them in magnets. For pragmatic and scientific reasons underlying mechanisms are hard to investigate in real magnets. Available data suggest that emulating local conditions at quench location may be enough to arrive at a good understanding of what drives observed behavior. "Local" conditions imply emulation by small samples of cables/"stacks" powered within a facility with controllable conditions, including external magnetic and force fields. This paper argues about the necessity of this approach, while reminding current understanding and directions of quench studies, and pointing out inadequacy of available techniques. Improved facilities and change of perspective are required for consistent and affordable development.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

36 MATERIALS SCIENCE↗

DOE OSTI2024

Geometry-complete perceptron networks for 3D molecular graphs

Abstract Motivation The field of geometric deep learning has recently had a profound impact on several scientific domains such as protein structure prediction and design, leading to methodological advancements within and outside of the realm of traditional machine learning. Within this spirit, in this work, we introduce GCPNet, a new chirality-aware SE(3)-equivariant graph neural network designed for representation learning of 3D biomolecular graphs. We show that GCPNet, unlike previous representation learning methods for 3D biomolecules, is widely applicable to a variety of invariant or equivariant node-level, edge-level, and graph-level tasks on biomolecular structures while being able to (1) learn important chiral properties of 3D molecules and (2) detect external force fields. Results Across four distinct molecular-geometric tasks, we demonstrate that GCPNet’s predictions (1) for protein–ligand binding affinity achieve a statistically significant correlation of 0.608, more than 5%, greater than current state-of-the-art methods; (2) for protein structure ranking achieve statistically significant target-local and dataset-global correlations of 0.616 and 0.871, respectively; (3) for Newtownian many-body systems modeling achieve a task-averaged mean squared error less than 0.01, more than 15% better than current methods; and (4) for molecular chirality recognition achieve a state-of-the-art prediction accuracy of 98.7%, better than any other machine learning method to date. Availability and implementation The source code, data, and instructions to train new models or reproduce our results are freely available at https://github.com/BioinfoMachineLearning/GCPNet.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2000

Significant Findings: Tracking the SeaWiFS Record with a Coupled Physical/Biogeochemical/Radiative Model of the Global Oceans

The Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) has observed 2.5 years of routine global chlorophyll observations from space. The mission was launched into a record El Nino event, which eventually gave way to one of the most intensive and longest-lasting La Nina events ever recorded. The SeaWiFS chlorophyll record captured the response of ocean phytoplankton to these significant events in the tropical Indo-Pacific basins, but also indicated significant interannual variability unrelated to the El Nino/La Nina events. This included large variability in the North Atlantic and Pacific basins, in the North Central and equatorial Atlantic, and milder patterns in the North Central Pacific. This SeaWiFS record was tracked with a coupled physical/biogeochemical/radiative model of the global oceans using near-real-time forcing data such as wind stresses, sea surface temperatures, and sea ice. This provided an opportunity to offer physically and biogeochemically meaningful explanations of the variability observed in the SeaWiFS data set, since the causal mechanisms and interrelationships of the model are completely understood. The coupled model was able to represent the seasonal distributions of chlorophyll during the SeaWiFS era, and was capable of differentiating among the widely different processes and dynamics occurring in the global oceans. The model was also reasonably successful in representing the interannual signal, especially when it was large, such as, the El Nino and La Nina events in the tropical Pacific and Indian Oceans. The model provided different phytoplankton group responses for the different events in these regions: diatoms were predominant in the tropical Pacific during the La Nina but other groups were predominant during El Nino. The opposite condition occurred in the tropical Indian Ocean. Both situations were due to the different responses of the basins to El Nino. The interannual variability in the North Atlantic, which was exhibited in SeaWiFS data as a decline in the spring/summer bloom in 1999 relative to 1998, resulted in the model from a more slowly shoaling mixed layer, allowing herbivore populations to keep pace with increasing phytoplankton populations. However, several aspects of the interannual cycle were not well-represented by the model. Explanations ranged from inherent model deficiencies, to monthly averaging of forcing fields, to biases in SeaWiFS atmospheric correction procedures.

Watson, Gregg W.↗