arXiv subjects

Mohammad M. Amirian

Publications and source records attributed to Mohammad M. Amirian.

7 recordsLinked to original sources

arXiv2026

Neptune: An AI model for Global Ocean Subseasonal Prediction

Subseasonal-to-seasonal (S2S) forecasting is societally critical, supporting decision-making in sectors ranging from water and agricultural management to disaster risk reduction, energy planning, and insurance. Achieving reliable predictions at these timescales requires representing the ocean and its dynamics, but traditional physics-based Ocean General Circulation Models (OGCMs), are computationally expensive and difficult to develop and improve because of the code complexity. In this work, we propose Neptune, an end-to-end data-driven framework for global ocean and sea-ice components emulation tailored for S2S timescales, up to 60 days. Neptune combines Convolutional Neural Networks (CNNs) and Spherical Fourier Neural Operators (SFNOs) to effectively capture local features and global cross-scale interactions, thereby obtaining a coherent representation of the ocean state. Forced by prescribed daily atmospheric fields, Neptune emulates ocean state variables, from temperature and salinity, to zonal and meridional currents, from sea surface height to sea ice thickness and concentration, with daily outputs at the ocean surface and through the water column. Specifically, we propose two variants of Neptune, Neptune-1 and Neptune-025, capable of emulating the ocean state at 1° and 0.25° resolution, respectively. Evaluated against a suite of metrics, including statistics (RMSE, CRPS and ACC), physical coherency (Ocean Heat Content, Eddy Kinetic Energy and Ice Brier Score) and climate indices (ENSO and Z20 metric, IOD), Neptune successfully reproduces the spatio-temporal evolution of the oceanic fields up to 60 days, and is stable over long timescales. Neptune provides compelling evidence that end-to-end data-driven ocean emulators can become a powerful component of next-generation S2S forecasting systems, emulating ocean state at high spatio-temporal resolution.

physics.ao-ph↗

arXiv2026

Resolving satellite-in situ mismatches in Net Primary Production using high-frequency in situ bio-optical observations in the subpolar Northwest Atlantic

Net primary productivity (NPP) plays a central role in biological carbon pump (BCP) and global carbon cycling, but NPP estimates in high-latitude regions remain highly uncertain despite their disproportional contribution to the global carbon sink. Previous studies identified discrepancies among satellite and in situ-derived NPP estimates, but the drivers of disagreement remain poorly resolved due to limited high-frequency observations. Here, we present continuous depth-resolved estimates of in situ NPP in the subpolar Northwest Atlantic (56°N), reconstructed from daily bio-optical profiles collected between May and October 2016, and a 40-years dataset of photosynthesis parameters derived from ^{14}C photosynthesis-irradiance (P-I) incubation experiments. Comparing with two satellite-based models showed satellite models overestimated NPP by factors of 2.5 to 4 from the study period. Statistical analyses identified the parameterization of the P-I relationship as the primary source of disagreement between satellite and in situ NPP estimates. Given the importance of NPP to estimates of the biological carbon pump, our findings highlight the need for regionally appropriate P-I parameterization in NPP models, particularly in high-latitude regions.

q-bio.QM↗

arXiv2025

piCurve: an R package for modeling photosynthesis-irradiance curves

Photosynthesis-irradiance (PI) curves are foundational for quantifying primary production, parameterizing ecosystem and biogeochemical models, and interpreting physiological acclimation to light. Despite their broad use, researchers lack a unified, reproducible toolkit to fit, compare, and diagnose the many PI formulations that have accumulated over the last century. We introduce piCurve, an R package that standardizes the modeling of PI relationships, with a library of widely used light-limited, light-saturated, and photoinhibited formulations and a consistent statistical framework for estimation and comparison. With the total of 24 PI models, piCurve supports mean squared error (MSE) and maximum likelihood estimation (MLE), provides uncertainty quantification via information matrix (Hessian), and includes automated, data-informed initialization to improve convergence. Utilities classify PI data into light-limited, light-saturated, and photoinhibited regions, while plotting and 'tidy' helpers streamline workflow and reporting. Together, these features enable reproducible analyses and fair model comparisons, including for curves exhibiting a plateau followed by photoinhibition.

stat.AP↗

arXiv2025

Parameterization of Photoinhibition For Phytoplankton

Mathematical models of photosynthesis-irradiance relationships in phytoplankton are used to compute integrated water-column photosynthetic rates and predict primary production. Models typically ignore an important phenomenon observed in most experiments: photosynthetic rate remains constant over a range of irradiances before declining due to photoinhibition. Here we develop an approach that captures both photoinhibition and this plateau. We test six new models of photoinhibition and ten more photoinhibition models from the literature against a database of 1808 photosynthesis-irradiance curves exhibiting photoinhibition. The best model improves adjusted R2 by 6-37% and reduces RMSE by 15-70% compared to existing models. In the best model, photoinhibition is phenomenologically described by multiplication by a saturating function of the reciprocal of irradiance, simplifies to the widely-used Jassby & Platt photosynthesis-irradiance curve in the absence of photoinhibition, and only requires one new parameter. This photoinhibition parameter identifies the onset of photoinhibition and is the rate of decrease in photosynthetic rate at that irradiance. Simulations show that while parameter values vary up to 40% across existing models, particularly with and without photoinhibition, our representation of photoinhibition does not affect the interpretation or numerical values of parameters compared to the corresponding model without photoinhibition.

q-bio.OT↗

arXiv2022

Extending the Monod Model of Microbial Growth with Memory

Monod's model describes the growth of microorganisms using a hyperbolic function of extracellular resource concentration. Under fluctuating or limited resource concentrations this model performs poorly against experimental data, motivating the more complex Droop model with a time-varying internal storage pool. We extend the Monod model to incorporate memory of past conditions, adding a single parameter motivated by a fractional calculus analysis. We show how to interpret the memory element in a biological context and describe its connection to a resource storage pool. Under nitrogen starvation at non-equilibrium conditions, we validate the model with simulations and empirical data obtained from lab cultures of diatoms (T. pseudonana and T. weissflogii) and prasinophytes (Micromonas sp. and O. tauri), globally influential phytoplankton taxa. Using statistical analysis, we show that our Monod-memory model estimates the growth rate, cell density, and resource concentration as well as the Droop model while requiring one less state variable. Our simple model may improve descriptions of phytoplankton dynamics in complex earth system models at a lower computational cost than is presently achievable.

q-bio.PE↗

arXiv2022

Stability Analysis of Fractional Order Memristor Synapse-coupled Hopfield Neural Network with Ring Structure

A memristor is a nonlinear two-terminal electrical element that incorporates memory features and nanoscale properties, enabling us to design very high-density artificial neural networks. To enhance the memory property, we should use mathematical frameworks like fractional calculus, which is capable of doing so. Here, we first present a fractional-order memristor synapse-coupling Hopfield neural network on two neurons and then extend the model to a neural network with a ring structure that consists of n sub-network neurons, increasing the synchronization in the network. Necessary and sufficient conditions for the stability of equilibrium points are investigated, highlighting the dependency of the stability on the fractional-order value and the number of neurons. Numerical simulations and bifurcation analysis, along with Lyapunov exponents, are given in the two-neuron case that substantiates the theoretical findings, suggesting possible routes towards chaos when the fractional order of the system increases. In the n-neuron case also, it is revealed that the stability depends on the structure and number of sub-networks.

math.DS↗

arXiv2020

Memory and mutualism in species sustainability: a time-fractional Lotka-Volterra model with harvesting

We first present a predator-prey model for two species and then extend the model to three species where the two predator species engage in mutualistic predation. Constant effort harvesting and the impact of by-catch issue are also incorporated. Necessary sufficient conditions for the existence and stability of positive equilibrium points are examined. It is shown that harvesting is sustainable, and the memory concept of the fractional derivative damps out oscillations in the population numbers so that the system as a whole settles on an equilibrium quicker than it would with integer time derivatives. Finally, some possible physical explanations are given for the obtained results. It is shown that the stability requires the memory concept in the model.

math.DS↗