Open AccessA geostatistical approach to downscaling climate forecasts
Author(s) -
Schultz Colin
Publication year - 2013
Publication title -
eos, transactions american geophysical union
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.316
H-Index - 86
eISSN - 2324-9250
pISSN - 0096-3941
DOI - 10.1002/2013eo130016
Subject(s) - downscaling , climatology , general circulation model , scale (ratio) , climate change , geostatistics , climate model , environmental science , computer science , spatial ecology , kriging , meteorology , econometrics , spatial variability , geography , geology , statistics , mathematics , cartography , machine learning , ecology , oceanography , biology
Though global general circulation models are the tool of choice for forecasting the effects of climate change, their spatial resolutions are too broad for the needs of regional planners. To provide locally relevant information, modelers typically employ one of two techniques: producing a new forecast using a regional dynamic model or statistically downscaling the projections of the larger model. As a subset of the statistical approach, Jha et al . propose a geostatistical technique to translate climate‐modeling results to a smaller spatial scale.