SIParCS 2023 - Robin Armstrong

Optimization of an Ocean Biogeochemistry Model

Recorded Talk

Ocean ecosystems sustain marine fisheries and mediate fluxes of carbon that are important for maintaining the ocean’s vast inventory of carbon dioxide. Earth system models represent these ocean ecosystems using numerical models based on empirical constraints from ocean observations and understanding of fundamental biogeochemical processes. However, these models include many parameters specifying, e.g., interactions among trophic levels and between physical and biological processes. In many cases, the “correct” values of these parameters are poorly understood, and these uncertainties translate into errors in model representations of ocean ecosystems and make it difficult to compare simulations to real-world observations and make useful ocean forecasts. In this project, the student will help develop a parameter optimization framework for the Marine Biogeochemistry Library (MARBL), which is the ocean ecosystem model coupled to the Community Earth System Model, using the Data Assimilation Research Testbed (DART). DART is a sequential ensemble data assimilation tool that has been heavily tested and used for weather forecasting, ocean prediction, climate projections, flood prediction, parameter estimation, and other applications. Here we will exploit simplified ocean model configurations to enable rapid prototyping and iteration and consider optimizing a collection of these across a set of testbed sites sampling different oceanographic settings. The student will join a team of researchers with diverse expertise in oceanography and data assimilation; the student will have the opportunity to learn about ocean biogeochemistry and ecology, Earth system modeling, data assimilation, and high performance computing.

Mentors: Moha Gharamti, Dan Amrhein (CGD), Matt Long (CGD)

Slides and poster