New research funded by CPO’s Modeling, Analysis, Predictions, and Projections Program focuses on drought in the central U.S./Great Plains region and evaluates why summer droughts occur in the Southern Great Plains during some La Niña years but not in others, and how several drought indicators may promote drought preparedness during future flash drought (droughts that intensify rapidly) events.
Droughts are one of the most common and devastating natural disasters in the U.S., with the potential to cause substantial damage to the economy (including agriculture, hydropower, tourism, and recreation), environment, and human health.
Scientists work to understand the underlying mechanisms of drought in order to provide earlier warning of drought and promote mitigation, such as soil and water conservation methods like crop rotation. La Niña, characterized by cooler than normal sea-surface temperatures (SST) in the central and eastern tropical Pacific, tends to favor drought in the Great Plains. However, the phenomenon is not always a good predictor of droughts in the region, especially during the summer.
Research led by Bing Pu (University of Texas at Austin) analyzed SST patterns, circulation conditions, and the difference in precipitation between dry and non-dry summers during La Niña years from 1950-2013 in the Southern Great Plains to assess what might account for the differences in precipitation. They found that the relationship between La Niña and drought in the Southern Great Plains region is only significant in the winter.
The occurrence of a dry or non-dry summer during La Niña years in the Southern Great Plains depends on a combination of SST anomalies and atmospheric internal variabilities that result in favorable or unfavorable conditions for drought development.
Access the studies below:
Pu et al.: http://onlinelibrary.wiley.com/doi/10.1002/2015JD023508/epdf
Otkin et al: http://ac.els-cdn.com/S0168192315300265/1-s2.0-S0168192315300265-main.pdf
Two new MAPP-funded studies provide new insight into drought understanding and prediction in the Central U.S.
