SLAMS‐2.0: A Stochastic, Lagrangian Particle‐Resolving Modeling Framework for Investigating the Biological Carbon Pump

Abstract Sinking biogenic particles drive the ocean biological carbon pump (BCP), yet the processes governing their formation, transformation, and fate remain poorly constrained and are difficult to represent with existing modeling frameworks. Traditional approaches either resolve particle populations as bulk distributions or simulate individual particles at high computational cost, limiting their ability to capture both particle‐scale processes and emergent particulate fluxes. Here we present SLAMS‐2.0, a stochastic, particle‐resolving Lagrangian framework that bridges particle‐based and population‐based approaches by explicitly simulating individual, heterogeneous particles and their probabilistic interactions while efficiently capturing the evolving particle population across the full size spectrum through a “superparticle” formulation. The model combines this approach with observation‐informed BCP process representations, allowing particle composition, microstructure, and interactions to emerge dynamically from first‐principles fractal scaling and stochastic encounter dynamics. SLAMS‐2.0 is evaluated at six open‐ocean time‐series sites spanning contrasting biogeographical regimes against a wide range of observations, including particulate organic carbon (POC) and biogenic mineral fluxes, particle abundances, and size distributions. We show that carbon packaging, porosity, mineral ballast, and aggregation efficiency—rather than particle size or sinking velocity alone—govern POC flux transfer to depth, with large, porous, ballast‐rich aggregates driving deep‐ocean POC flux. SLAMS‐2.0 provides a framework for investigating how particle physicochemical attributes and interactions shape emergent carbon fluxes, with applications to both observational process studies and the development of improved biogeochemical model representations.