The great lakes basin (GLB) has been a regional hotspot of climate change impacts, including lake warming, declining ICE cover, and increased lake-effect snowfall. Modeling studies have demonstrated…

This federal contract award to Michigan Technological University (Michigan Tech) is for research on modeling and simulating lake-atmosphere interactions in the Great Lakes Basin to improve understanding of lake-effect snowstorms. The $327,923.43 grant from the National Aeronautics and Space Administration (NASA) will fund the development of a two-way coupled lake-atmosphere-land model (NU-WRF/FVCOM) to generate more accurate projections of lake-effect snow. The research aims to leverage recent advances in data collection and modeling tools to better represent the complex dynamics of these extreme cold-season events, which have significant socioeconomic impacts in the region but have received limited attention. This award is not associated with a set-aside program and represents the university's extensive capabilities in conducting climate research and providing technical services to federal agencies.

The great lakes basin (GLB) has been a regional hotspot of climate change impacts, including lake warming, declining ICE cover, and increased lake-effect snowfall. Modeling studies have demonstrated the substantial contribution of lake-atmosphere interactions and uncertainty in lake-effect snowfall projections. Despite the vast socio-economic impacts of lake-effect snowstorms (LES), these extreme events have received minimal attention from ipcc and national climate assessment. The insufficient investigation of projected changes in these cold season extremes is partly due to the general lack of appropriate modeling tools that properly represent the great lakes and lake-atmosphere interactions. There have been recent advances in the representation of lake-atmosphere interactions among regional climate models through 2-way coupling with 1D lake models, permitting the simulation of broad-scale lake-effect snow features. However, such lake models are inappropriate for deep lakes, leading to large biases in lake surface temperature (LST), ICE cover, and evaporation. XUE ET AL. Employed offline and 2-way coupling of the 3D finite volume community ocean model (fvcom) to WRF and REGCM4 and demonstrated the benefits of incorporating 3D models to represent great lakes hydrodynamics, including dramatic bias reductions and accurate turbulent flux representation, with implications for LES. Recent advances in data collection, including over-lake flux measurements through the great lakes evaporation network (glen) and ontario winter lake- effect systems field campaign, along with improved spaceborne cold season cloud/precipitation detection by cloudsat and global precipitation measurement (GPM) platforms, have deepened the understanding of lake-atmosphere interactions, LES dynamics, and shallow cumuliform snow microphysics. We proposed to evaluate and advance the representation of lake-atmosphere interactions and heavy LES in the GLB by NU-WRF. Nuwrf permits 2 crude lake treatments: remotely-sensed lsts are provided as boundary conditions or the atmosphere is 2-way coupled to the 1D flake model. In addition to exploring lsts, turbulent fluxes, clouds, and LES in these configurations, we will enhance the representation of 3D lake processes and lake-atmosphere interconnections by developing a 2-way coupled lake-atmosphere-land model, NU-WRF/fvcom, which permits the development of dynamically downscaled LES projections. Historical NU-WRF runs will be produced for the aforementioned lake treatments and evaluated against observations to quantify added value to over-lake turbulent fluxes and LES frequency/intensity from incorporating 3D lake processes and advanced lake-atmosphere coupling. Glen measurements will be used to evaluate turbulent flux responses to ICE cover and temporal distribution of evaporation events. For a set of observed LES, we will create a NU-WRF/fvcom ensemble, exploring the impact of microphysical, boundary layer, convective, and radiation schemes; vertical resolution; and grid spacing on morphologies of lake-induced mesoscale circulations. By varying parameterizations and performing runs with imposed lake temperature anomalies, the sensitivity of spatio-temporal patterns of lakeeffect snowfall and resulting snowfall biases can be attributed to LST or microphysics biases. Evaluating the representation of heavy LES in NU-WRF will be grounded in cloudsat/GPM products. We will evaluate cloudsat/GPM snowfall retrievals against station observations. In addition to evaluating NU-WRF against cloudsat/GPM vertical radar reflectivity transects for case studies, we will examine the spectrum of snow events through density functions of radar reflectivity-based estimates of snowfall intensity/frequency and cloud attributes, allowing for the evaluation of the model s microphysics. Radar simulators will be applied to model output to compare radar reflectivity distributions and characteristics between simulated and observed LES datasets.