Literature DB >> 32747850

A Modeling Study of the Causes and Predictability of the Spring 2011 Extreme US Weather Activity.

Siegfried Schubert1, Yehui Chang1, Hailan Wang1, Randal Koster1, Max Suarez1.   

Abstract

This study examines the causes and predictability of the spring 2011 U.S. extreme weather using the Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalyses and Goddard Earth Observing System, version 5 (GEOS-5) Atmospheric General Circulation Model simulations. The focus is on assessing the impact on precipitation of sea surface temperature (SST) anomalies, land conditions, and large-scale atmospheric modes of variability. A key result is that the April record-breaking precipitation in the Ohio River Valley was primarily the result of the unforced development of a positive North Atlantic Oscillation (NAO)-like mode of variability with unusually large amplitude, limiting the predictability of the precipitation in that region at one month leads. SST forcing (La Nina conditions) contributed to the broader continental scale pattern of precipitation anomalies, producing drying in the southern plains and weak wet anomalies in the northeast, while the impact of realistic initial North American land conditions was to enhance precipitation in the upper Midwest and produce deficits in the southeast. It was further found that: The March 1 atmospheric initial condition was the primary source of the ensemble mean precipitation response over the eastern U.S. in April (well beyond the limit of weather predictability) suggesting an influence on the initial state of the previous SST forcing and/or tropospheric/stratospheric coupling linked to an unusually persistent and cold polar vortex.Stationary wave model experiments suggest that the SST-forced base state for April enhanced the amplitude of the NAO response compared to that of the climatological state, though the impact is modest and can be of either sign.

Entities:  

Year:  2016        PMID: 32747850      PMCID: PMC7398347          DOI: 10.1175/JCLI-D-15-0673.1

Source DB:  PubMed          Journal:  J Clim        ISSN: 0894-8755            Impact factor:   5.148


  2 in total

1.  Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation.

Authors:  Abby Stevens; Rebecca Willett; Antonios Mamalakis; Efi Foufoula-Georgiou; Alejandro Tejedor; James T Randerson; Padhraic Smyth; Stephen Wright
Journal:  J Clim       Date:  2021-01-01       Impact factor: 5.148

2.  Hotspots of Predictability: Identifying Regions of High Precipitation Predictability at Seasonal Timescales From Limited Time Series Observations.

Authors:  Antonios Mamalakis; Amir AghaKouchak; James T Randerson; Efi Foufoula-Georgiou
Journal:  Water Resour Res       Date:  2022-05-24       Impact factor: 6.159

  2 in total

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