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A Data-Driven Approach to Investigating Climate Change
People have always talked about the weather, but these days weather and climate have become a major topic for discussion. Extreme weather events throughout the country and the world lead news stories and many people are concerned with what is causing these events and how the climate may be changing.
Assistant Professor Peter Snyder (Soil, Water, and Climate) is using MSI to support his work examining how climate change will affect extreme precipitation and severe weather in the central U.S. over the next century. The group uses the Weather Research and Forecasting (WRF) model, which is ideal for examining future changes in drought both because simulations with extremely high spatial and temporal resolution can effectively resolve the physical processes that drive precipitation processes and the model can efficiently represent synoptic-scale dynamical motion (e.g., blocking highs) that contribute to initiation and persistence of drought. This model is especially valuable for simulating events in the central region of the U.S., where severe weather and extremes in precipitation happen often. This region is the home of major agricultural production, so changes in climate and increases in drought may have significant effects on food security.
In a recent paper, Professor Snyder and members of his research group investigated whether dynamical downscaling could improve the WRF’s simulation capabilities for precipitation extremes. Their results showed a positive affect on the model’s results, which could mean that it would be easier to make predictions about rainfall. The researchers were able to use models that performed well to predict how rainfall might change over the Midwest in different climate-change scenarios. This work also provided some indications about the mechanisms that contribute to the intensification of heavy precipitation events. The paper appeared in November 2013 in the Journal of Geophysical Research - Atmospheres (Harding, Keith J., Peter K. Snyder, and Stefan Liess. 2013. Use of dynamical downscaling to improve the simulation of central US warm season precipitation in CMIP5 models. Journal of Geophysical Research: Atmospheres 118 (22) (NOV 27): 12522-36). Professor Snyder and his research group are continuing their research at MSI, performing additional simulations using different datasets. These simulations will provide insights into how droughts in the Midwest might change in the future and how they might impact water resources and food security.
The Snyder group is also participating in a study of the phenomenon of “urban heat islands,” which is a situation where cities have higher temperatures that the surrounding areas. The study, called Islands in the Sun, is a four-year project funded by the University of Minnesota Institute on the Environment and the College of Food, Agricultural, and Natural Resource Sciences. An article about this study recently appeared in the Minnesota Daily (Kristopher Teague, “Can’t take the heat? Get out of the city,” Minnesota Daily, May 6, 2014, online, downloaded May 7, 2014.)
Image description: The 1979–2005 June-July-August (a and b) precipitation (mm), (c and d) 850 hPa wind speed (m s−1). Figures a and c show the average of all observational (or reanalysis) datasets for each variable. Figure b shows the multimodel ensemble (MME) mean minus observations, while Figure d shows the MME mean. Image and description adapted from Harding, KJ, et al., Journal of Geophysical Research: Atmospheres, 118(22):12522-12636 (2013). © American Geophysical Union.
posted on June 18, 2014