Abstract
Future changes in the frequency of environmental conditions conducive for convective storm days ("CE days") are determined for the northeastern United States (NEUS) during the warm seasons (April-September) of the twenty-first century. Statistical relationships between historical runs of seven models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and radar-classified convective storm days are developed using linear discriminant analysis (LDA), and these relationships are then applied to analyze changes in the convective environment under the high-emissions representative concentration pathway 8.5 (RCP8.5) scenario over the period 2006-99. The 1996-2007 warm seasons are used to train the LDA thresholds using convective precipitation from two reanalysis datasets and radar data, and the 1979-95 and 2008-10 warm seasons are used to verify these thresholds. For the CMIP5 historical period (1979-2005), the frequency of warm season CE days averaged across the CMIP5 models is slightly greater than that derived using reanalysis data, although both methods indicate a slight increasing trend through the historical period. Between 2006 and 2099, warm season CE day frequency is predicted to increase substantially at an average rate of 4-5 days decade-1 (50%-80% increase over the entire period). These changes are mostly attributed to a predicted 30%-40% increase in midlevel precipitable water between the historical period and the last few decades of the twenty-first century. Consistent with previous studies, there is decreasing deep-layer vertical wind shear as a result of a weakening horizontal temperature gradient, but this is outweighed by increases in instability led by the moisture increases.
| Original language | English |
|---|---|
| Pages (from-to) | 4327-4345 |
| Number of pages | 19 |
| Journal | Journal of Climate |
| Volume | 29 |
| Issue number | 12 |
| DOIs | |
| State | Published - Jun 1 2016 |
Keywords
- Climate prediction
- Climate variability
- Convective storms
- Forecasting
- General circulation models
- Models and modeling
- Physical meteorology and climatology
- Variability
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