My Research

Constructing a realistic model including the cryptic sulfur cycle in Chesapeake Bay

Previous biogeochemical models for Chesapeake Bay have oversimplified representations of the impacts of sulfur cycling. In this project, we implemented a previously published biogeochemical code (BioRedoxCNPS) developed for open-ocean waters that includes “cryptic” microbial sulfur cycling within the ChesROMS physical model of the Chesapeake Bay.

Schematic of the merged biogeochemical code develped as the new baseline model for the Bay

However, translating the model to the Bay turns out not to be that straightforward. Model comparisons show that particle sinking velocities, burial, organic matter, nitrification and light attenuation all have significant impacts on simulation results. Further sensitivity studies are conducted, and we propose a new baseline model for the Bay, which includes DOM-dependent absorption, a higher O2:N stoichiometry and easier nitrification. Our new model incorporates cryptic sulfur cycling and demonstrates comparable skill in predicting oxygen as ChesROMS_ECB, but also has improved simulation of nitrogen species compared to both the original codes.

Studying the role of Colored Dissolved Organic Matter (CDOM) in coastal ocean hypoxia

Representation of CDOM absorption in coastal environments remains rudimentary in models that nonetheless show skill in simulating these environments. This includes one of the leading models of the Chesapeake, which represents the impacts of CDOM in terms of a salinity-dependent absorption. However, more explicit representation of CDM/Chl absorption has been found to improve physical simulations, and such implicit representation of CDOM/CDM absorption limits our ability to examine its separate impact on water clarity and quality.

In this project, we perform a series of experiments examining the role of Colored Dissolved Organic Matter (CDOM) in coastal ocean hypoxia. Our findings suggest that the impact of CDOM on hypoxia is region-specific and multifaceted. There are complex and region-specific interactions between environmental factors caused by CDOM and hypoxia dynamics in the Bay.

Left-hand panel: Change in number of days with O2 <100 mmol/m3 due to setting rDOC to zero. Right-hand panel: Change in number of days with O2 <62.5 mmol/m3 due to setting rDOC to zero

Distributional characteristic and driver sensitivity study of global particulate inorganic carbon versus particulate organic carbon

Holder and Gnanadesikan (2023) have shown that Earth System Models (ESMs) qualitatively simulate the relationships between phytoplankton carbon and environmental parameters known to influence phytoplankton we see in observations. However, the question of whether ESMs accurately capture the underlying mechanisms governing the distribution and quantity of particulate inorganic carbon (PIC) versus particulate organic carbon (POC) in the ocean remains a critical knowledge gap.

Global distribution of PIC (upper row) and POC (lower row) in January (left column) and July (right column) from the Moderate Resolution Imaging Spectroradiometer (MODIS) averaged over all days during the entire measuring period (2002-2022).

We are conducting further investigations into distributional characteristics and sensitivities of PIC versus POC to environmental drives using Radom Forest on both observational data and ESM outputs. The goal of this research is to help the modeling community make more accurate predictions of the role of different types of phytoplankton under climate change by incorporating more precise constraints on biogeochemical processes.