TY - JOUR
T1 - Predictability of midsummer great plains low-level jet and associated precipitation
AU - Malloy, Kelsey M.
AU - Kirtman, Ben P.
N1 - Funding Information:
Acknowledgments. The authors acknowledge support from NOAA NA15OAR4320064, NA16OAR4310141, NA16OAR4310149, and NA18OAR4310293. The University of Miami Center for Computational Science (CCS) provided the computational resources to complete the numerical experiments, and the authors are most grateful for the use of the Community Climate System Model from NCAR. Last, the authors want to thank Dr. Gary Lackmann and two anonymous reviewers for their thoughtful and thorough feedback on the manuscript, which greatly improved its structure and content.
PY - 2020/2
Y1 - 2020/2
N2 - 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.
AB - 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.
KW - Climate prediction
KW - Hydrometeorology
KW - Interannual variability
KW - Model evaluation/performance
KW - Probability forecasts/models/distribution
KW - Regression analysis
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U2 - 10.1175/WAF-D-19-0103.1
DO - 10.1175/WAF-D-19-0103.1
M3 - Article
AN - SCOPUS:85081624716
VL - 35
SP - 215
EP - 235
JO - Weather and Forecasting
JF - Weather and Forecasting
SN - 0882-8156
IS - 1
ER -