Earth and Environmental Science

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The San Joaquin Valley is a region with an economy dominated by agriculture that experiences some of the worst air quality in the U.S. because of extreme summertime smog and wintertime conditions for fine particle formation. The region is thus expected to be heavily impacted by climate change in the coming decades. In recent years, Fresno State faculty have developed the capability to study air quality and greenhouse gas emissions in Central California, particularly with respect to impacts by, and on, agriculture. This work has been supported by substantial funding from agencies including the National Science Foundation, the United States Department of Agriculture, and the National Oceanic and Atmospheric Administration. These grants have primarily supported projects carried out by undergraduate research students, and have included stipends and support for student professional development opportunities as major components of the budget.

A significant missing institutional research component has been in the capacity to fully explore the impacts of various climate change scenarios on emissions and air quality. TheComputational Science Center has now enabled undergraduate students to carry out simulations using regional air quality models such as CAM, and to explore the value of computationally complex regional climate models with associated downscaling of model output. The CSC is used to evaluate the impacts of new emissions inventories, revised atmospheric chemistry reaction mechanisms and climate impacts on levels of air pollution.

The CSC allows the following capabilities for undergraduate instruction and research:

  1. Regional air quality and meteorology forecasting using WRF-Chem for the San Joaquin Valley. This data could be made available to the community online through web pages and Google Earth.
  2. Hindcast air quality modeling in support of San Joaquin Valley air quality management strategies.
  3. Regional climate modeling for San Joaquin Valley using the NCAR Community Atmospheric Model (CAM) scenarios with dynamic downscaling using WRF-Chem.
  4. Regional air quality and climate interaction modeling estimating future emissions and air quality and how climate change increases or decreases air quality episodes.
  5. Renewable energy potential assessment using regional meteorological models to determine areas with best wind and solar capacity.
  6. Renewable energy power forecasting for planning of energy dispatch.

As computational capacities increase, there is a need to train students in regional meteorological modeling. Human capital in this respect is lagging. Training students in air quality and regional climate modeling along with addressing important policy and science questions allows students to be competitive for jobs and consulting.