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Statistical significance of trends in monthly heavy precipitation over the US.
- Source :
- Climate Dynamics; Apr2012, Vol. 38 Issue 7/8, p1375-1387, 13p, 1 Chart, 9 Graphs, 1 Map
- Publication Year :
- 2012
-
Abstract
- Trends in monthly heavy precipitation, defined by a return period of one year, are assessed for statistical significance in observations and Global Climate Model (GCM) simulations over the contiguous United States using Monte Carlo non-parametric and parametric bootstrapping techniques. The results from the two Monte Carlo approaches are found to be similar to each other, and also to the traditional non-parametric Kendall's τ test, implying the robustness of the approach. Two different observational data-sets are employed to test for trends in monthly heavy precipitation and are found to exhibit consistent results. Both data-sets demonstrate upward trends, one of which is found to be statistically significant at the 95% confidence level. Upward trends similar to observations are observed in some climate model simulations of the twentieth century, but their statistical significance is marginal. For projections of the twenty-first century, a statistically significant upwards trend is observed in most of the climate models analyzed. The change in the simulated precipitation variance appears to be more important in the twenty-first century projections than changes in the mean precipitation. Stochastic fluctuations of the climate-system are found to be dominate monthly heavy precipitation as some GCM simulations show a downwards trend even in the twenty-first century projections when the greenhouse gas forcings are strong. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09307575
- Volume :
- 38
- Issue :
- 7/8
- Database :
- Complementary Index
- Journal :
- Climate Dynamics
- Publication Type :
- Academic Journal
- Accession number :
- 73888729
- Full Text :
- https://doi.org/10.1007/s00382-011-1091-4