Title |
Downscaled rainfall projections in south Florida using self-organizing maps
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Published in |
Science of the Total Environment, April 2018
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DOI | 10.1016/j.scitotenv.2018.04.144 |
Pubmed ID | |
Authors |
Palash Sinha, Michael E. Mann, Jose D. Fuentes, Alfonso Mejia, Liang Ning, Weiyi Sun, Tao He, Jayantha Obeysekera |
Abstract |
We make future projections of seasonal precipitation characteristics in southern Florida using a statistical downscaling approach based on Self Organized Maps. Our approach is applied separately to each three-month season: September-November; December-February; March-May; and June-August. We make use of 19 different simulations from the Coupled Model Inter-comparison Project, phase 5 (CMIP5) and generate an ensemble of 1500 independent daily precipitation surrogates for each model simulation, yielding a grand ensemble of 28,500 total realizations for each season. The center and moments (25%ile and 75%ile) of this distribution are used to characterize most likely scenarios and their associated uncertainties. This approach is applied to 30-year windows of daily mean precipitation for both the CMIP5 historical simulations (1976-2005) and the CMIP5 future (RCP 4.5) projections. For the latter case, we examine both the "near future" (2021-2050) and "far future" (2071-2100) periods for three scenarios (RCP2.6, RCP4.5, and RCP8.5). |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 10 | 50% |
United Kingdom | 2 | 10% |
Australia | 2 | 10% |
Netherlands | 1 | 5% |
Unknown | 5 | 25% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 16 | 80% |
Scientists | 3 | 15% |
Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 27 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 6 | 22% |
Researcher | 3 | 11% |
Student > Doctoral Student | 2 | 7% |
Student > Postgraduate | 2 | 7% |
Student > Bachelor | 1 | 4% |
Other | 5 | 19% |
Unknown | 8 | 30% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 8 | 30% |
Environmental Science | 6 | 22% |
Earth and Planetary Sciences | 2 | 7% |
Mathematics | 1 | 4% |
Unspecified | 1 | 4% |
Other | 2 | 7% |
Unknown | 7 | 26% |