Predictability of midsummer great plains low-level jet and associated precipitation

Kelsey M. Malloy, Ben P. Kirtman

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Warm-season precipitation in the U.S. ‘‘Corn Belt,’’ the Great Plains, and the Midwest greatly influences agricultural production and is subject to high interannual and intraseasonal variability. Unfortunately, current seasonal and subseasonal forecasts for summer precipitation have relatively low skill. Therefore, there are ongoing efforts to understand hydroclimate variability targeted at improving predictions, particularly through its primary transporter of moisture: the Great Plains low-level jet (LLJ). This study uses the Community Climate System Model, version 4 (CCSM4), July forecasts, made as part of the North American Multi-Model Ensemble (NMME), to assess skill in reproducing the monthly Great Plains LLJ and associated precipitation. Generally, the CCSM4 forecasts capture the climatological jet but have problems representing the observed variability beyond two weeks. In addition, there are predictors associated with the large-scale variability identified through linear regression analysis, shifts in kernel density estimators, and case study analysis that suggest potential for improving confidence in forecasts. In this study, a strengthened Caribbean LLJ, negative Pacific–North American (PNA) teleconnection, El Niño, and a negative Atlantic multidecadal oscillation each have a relatively strong and consistent relationship with a strengthened Great Plains LLJ. The circulation predictors, the Caribbean LLJ and PNA, present the greatest ‘‘forecast of opportunity’’ for considering and assigning confidence in monthly forecasts.

Original languageEnglish (US)
Pages (from-to)215-235
Number of pages21
JournalWeather and Forecasting
Issue number1
StatePublished - Feb 2020


  • Climate prediction
  • Hydrometeorology
  • Interannual variability
  • Model evaluation/performance
  • Probability forecasts/models/distribution
  • Regression analysis

ASJC Scopus subject areas

  • Atmospheric Science


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