Physical and biogeochemical mechanisms of internal carbon cycling in Lake Michigan

Abstract

The lakewide seasonal carbon cycle of Lake Michigan is poorly quantified and lacks the mechanistic links necessary to determine impacts upon it from eutrophication, invasive species, and climate change. A first step toward a full appreciation of Lake Michigan's carbon cycle is to quantify the dominant mechanisms of its internal carbon cycle. To achieve this, we use the MIT general circulation model configured to the bathymetry of Lake Michigan and coupled to an ecosystem model to simulate the seasonal cycle of productivity, temperature, circulation, and the partial pressure of CO 2 in water (pCO 2 ). This biogeochemistry is designed to be appropriate for the prequagga mussel state of the lake. The primary mechanism behind the seasonal cycle of primary productivity is lake physics. The offshore spring phytoplankton bloom begins following a reduction in deep vertical mixing and ends with the depletion of nutrients via thermal stratification. The exception is the western shoreline, where summer winds drive coastal upwelling, providing hypolimnetic nutrients and generating significant productivity. Surface pCO 2 is controlled by the net effect from temperature on solubility, and is modulated by biological uptake of dissolved inorganic carbon (DIC) and isothermal mixing of DIC-rich water in winter. Temperature tends to have the greatest seasonal impact in nearshore regions, while local DIC has the greatest impact in offshore regions. Lakewide, the model suggests that carbon is absorbed from the atmosphere during the spring bloom and released to the atmosphere during winter mixing and when summer surface temperatures are at their maximum.

Assessment of a metaviromic dataset generated from nearshore Lake Michigan

“…The nearshore waters of a large lake differ in composition, physicochemically, from offshore waters (Yurista et al 2012;Pilcher et al 2015), so it follows that bacterial and phage community composition will vary from position to position and over time: on both large and small scales. However, capturing and successfully interrogating the changes in phage diversity temporally and spatially would require an exhaustive sampling and deep-sequencing effort.…”

The Potential for CO2-Induced Acidification in Freshwater: A Great Lakes Case Study

“…The MITgcm (Marshall et al, 1997) was configured for Lake Superior (Bennington et al, 2010). Within this eddy-resolving physical model (MITgcm.Superior), a biogeochemical-carbon module including complete carbon chemistry, air-water CO 2 fluxes, a lower food web module, and biogeochemical inputs from rivers (Bennington et al, 2012) (Pilcher et al, 2015).…”

Modeled sensitivity of Lake Michigan productivity and zooplankton to changing nutrient concentrations and quagga mussels

“…We use a 3‐D hydrodynamic model (Figure ) coupled to an intermediate complexity ecosystem model, previously utilized for Lake Michigan (MITgcm.Michigan) [ Pilcher et al ., ; Pilcher , ]. Briefly, the hydrodynamic model uses atmospheric forcing from the North American Regional Reanalysis Project (NARR) over a 2007–2010 timeframe, with lake ice imposed from observations [ Mesinger et al ., ; U.S. National Ice Center , ].…”

“…The model compares favorably to observations of surface water temperature from two offshore buoys, with annual root‐mean‐square error (RMSE) values of 1.92°C and 2.13°C. The model also captures spatial variability in surface temperature, including the seasonal development of coastal upwelling along the western shoreline [ Pilcher et al ., ]. However, the model does contain a consistent warm bias, which results from a warm bias in the NARR forcing product and the use of prescribed rather than simulated lake ice coverage [ Bennington et al ., ; Rowe et al ., ].…”

“…However, the model does contain a consistent warm bias, which results from a warm bias in the NARR forcing product and the use of prescribed rather than simulated lake ice coverage [ Bennington et al ., ; Rowe et al ., ]. This results in the model stratifying approximately 1 month earlier than observed [ Pilcher et al ., ].…”