Downscaled rainfall projections in south Florida using self-organizing maps

Palash Sinha; Michael E Mann; Jose D Fuentes; Alfonso Mejia; Liang Ning; Weiyi Sun; Tao He; Jayantha Obeysekera

doi:10.1016/j.scitotenv.2018.04.144

Downscaled rainfall projections in south Florida using self-organizing maps

Sci Total Environ. 2018 Sep 1:635:1110-1123. doi: 10.1016/j.scitotenv.2018.04.144. Epub 2018 Apr 24.

Authors

Palash Sinha¹, Michael E Mann¹, Jose D Fuentes², Alfonso Mejia¹, Liang Ning³, Weiyi Sun³, Tao He³, Jayantha Obeysekera⁴

Affiliations

¹ The Pennsylvania State University, University Park, PA, USA.
² The Pennsylvania State University, University Park, PA, USA. Electronic address: jdfuentes@psu.edu.
³ School of Geography Science, Nanjing Normal University, Nanjing, China.
⁴ South Florida Water Management District, West Palm Beach, FL, USA.

PMID: 29710566
DOI: 10.1016/j.scitotenv.2018.04.144

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).

Keywords: Climate; Downscaling; Drought; Hydrology; Rainfall.