Drought Mechanisms and Predictability

DTF research projects have had a long-term focus on improving our understanding of various hydrological and coupled processes (land, ocean, and atmosphere) and how these contribute to the development of drought, and specifically in determining the potential to predict drought. In particular, there has been considerable focus on the more recent 2010-12 period of intense droughts over the United States to explore sources of predictability that can contribute to forecast skill and to learn about predictability limits (in the case of 2012). The key findings are:

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the roles of SSTs in the 2011 and 2012 US droughts, Wang et al. (2014) found that other oceans (Indian and Atlantic) can play an important role in either enhancing or suppressing the role of the Pacific.

  • • We now have a better appreciation of the role of internal atmospheric variability in producing some of the most extreme droughts, limiting the predictability of such events (e.g., the 2012 upper Great Plains drought) on seasonal and longer time scales.
  • • Hoerling et al. (2014) also indicated that the 2012 upper Great Plains drought event might be linked to a regime shift toward a warmer and drier summer in the Great Plains as part of natural decadal variability.
  • • Research has improved our understanding of the role of land surface processes/feedbacks during drought and the potential benefits of higher-resolution precipitation information for streamflow forecasts. Koster et al. (2014) demonstrated that high-resolution precipitation forecasts will only be effective in improving (large-scale) stream- flow forecasts in areas with limited evaporation from land surfaces. Dirmeyer et al. (2014) found that changes in local and remote surface evaporation sources of moisture supplying precipitation over land are more of a factor during droughts than in wet periods over much of the globe.

soil moisture, and runoff) available for the official USDM product to take into consideration. A significant challenge is to objectively integrate these myriad inputs into the USDM in a reproducible fashion, especially without undermining consistency with current popular USDM products. There is also a critical need to create a quantitative and structured pathway for developing and testing new monitoring-related research products in the operational USDM process, including benchmarking them against current operational versions.

 
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