BATS-1D-VAR v1.0: A One-Dimensional Variational Data Assimilative Biogeochemical Model of the Bermuda Atlantic Time-Series Study (BATS) Site

The BATS-1D-VAR (v1.0) model is a one-dimensional (1-D) marine ecosystem model designed for data-assimilative biogeochemical simulations at the Bermuda Atlantic Time-series Study (BATS) site. It incorporates a variational adjoint method to optimize model parameters by minimizing the mismatch between simulated and observed ocean biogeochemical variables. This optimization is achieved through an iterative process that integrates forward and backward model simulations, where the backward model is a tangent linear adjoint version of the forward model. By assimilating observational data, BATS-1D-VAR (v1.0) enhances predictive accuracy in representing key ecosystem processes such as net community production (NCP), bacterial respiration, and organic carbon cycling under both present and future climate conditions.

The model consists of four key components: (1) a forward model driven by physical forcings, initial conditions, and boundary conditions; (2) a cost function that quantifies misfits between model simulations and assimilated observations; (3) a tangent linear adjoint model that computes the gradient of the cost function with respect to model parameters; and (4) an optimization algorithm (M1QN3 3.1) that adjusts parameter values iteratively to minimize the cost function. Through this data assimilation framework, BATS-1D-VAR (v1.0) refines model parameters to produce an optimized numerical solution that improves representations of bacterial metabolic processes, dissolved organic matter (DOM) cycling, and vertical carbon fluxes. This approach enables a more mechanistic understanding of how microbial communities influence carbon cycling and ecosystem metabolism in the Sargasso Sea under both baseline and projected climate scenarios.