Open AccessNearest Neighbor–Genetic Algorithm for Downscaling of Climate Change Data from GCMs
Author(s) -
Soojun Kim,
Jaewon Kwak,
Hung Soo Kim,
Younghun Jung,
Gilho Kim
Publication year - 2016
Publication title -
journal of applied meteorology and climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.079
H-Index - 134
eISSN - 1558-8432
pISSN - 1558-8424
DOI - 10.1175/jamc-d-15-0100.1
Subject(s) - downscaling , climatology , gcm transcription factors , climate change , precipitation , coupled model intercomparison project , general circulation model , environmental science , common spatial pattern , climate model , series (stratigraphy) , meteorology , geology , mathematics , geography , statistics , paleontology , oceanography
The spatial and temporal resolution of readily available climate change projections from general circulation models (GCM) has limited applicability. Consequently, several downscaling methods have been developed. These methods predominantly focus on a single meteorological series at specific sites. Spatial and temporal correlation of the precipitation and temperature fields is important for hydrologic applications. This research uses a nearest neighbor–genetic algorithm (NN–GA) method to analyze the Namhan River basin in the Korean Peninsula. Using the simulation results of the CNRM-CM for the RCP 8.5 climate change scenario, archived in the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the GCM projections are downscaled through the NN–GA. The NN–GA simulations reproduce the features of the observed series in terms of site statistics as well as across variables and sites.