Open AccessFine‐resolution climate projections enhance regional climate change impact studies
Author(s) -
Maurer Edwin P.,
Brekke Levi,
Pruitt Tom,
Duffy Philip B.
Publication year - 2007
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 - 10.1029/2007eo470006
Subject(s) - downscaling , climate model , climate change , general circulation model , environmental science , greenhouse gas , probabilistic logic , climatology , scale (ratio) , meteorology , grid , representative concentration pathways , computer science , geography , precipitation , cartography , ecology , geodesy , artificial intelligence , geology , biology
A new data set enhances the abilities of researchers and decision‐makers to assess possible future climates, explore societal impacts, and approach policy responses from a risk‐based perspective. The data set, which consists of a library of 112 fine‐resolution climate projections, based on 16 climate models and three greenhouse gas emissions scenarios, is now publicly available. Monthly climate projections from 1950 to 2099 were downscaled to a spatial resolution of ⅛° (about 140 square kilometers per grid cell) covering the conterminous United States and portions of Canada and Mexico. For the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, climate modeling groups produced hundreds of simulations of past and future climates. The colocation of these simulations in a single archive (at the Program for Climate Model Diagnosis and Intercomparison at Lawrence Livermore National Laboratory (LLNL), established to facilitate assessment of general circulation models, or GCMs) and the conversion of all results to a common data format have made probabilistic, multi‐model projections and impact assessments practical. A remaining issue is that the spatial scale of climate model output is typically too coarse for regional impact studies. Multiple downscaling approaches exist for deriving regional climate from coarse‐resolution model output; these approaches are typically applied on an ad hoc basis to a particular region.