Jim Biard, Laura Stevens, Liqiang Sun, NCSU/NCICS
LOCA Scenarios for the Fourth National Climate Assessment (https://scenarios.globalchange.gov/loca-viewer/)
November 02, 2018

The accompanying files contain data derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5), downscaled using Localized Constructed Analogs (LOCA), for a subset of climate variables. LOCA uses statistical techniques to correct global climate model data for biases and downscale those data to a 1/16th degree spatial resolution.

Data are available in netCDF format. There are three directories containing LOCA data for each variable for both the historical period (1950-2005) and future (2006-2099 or 2006-2100, model dependent) periods under the RCP4.5 and 8.5 scenarios. Within each directory there is one netCDF file for each of the 32 models.

LOCA derived climate variables included here are:
* tmax90F: Annual number of days > 90F (days)

The 32 CMIP5 models are:
ACCESS1-0, ACCESS1-3, bcc-csm1-1, bcc-csm1-1-m, CanESM2, CCSM4, CESM1-BGC, CESM1-CAM5, CMCC-CM, CMCC-CMS, CNRM-CM5, CSIRO-Mk3-6-0, EC EARTH, FGOALS-g2, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, GISS-E2-H-p1, GISS-E2-R-p1, HadGEM2-AO, HadGEM2-CC, HadGEM2-ES, inmcm4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC5, MIROC-ESM-CHEM, MIROC-ESM, MPI-ESM-LR, MPI-ESM-MR, MRI-CGCM3, NorESM1-M.

Ensemble averages for each grid cell should be calculated using a vector of model weights, using the values in "LOCA_weights.csv". These weights have been determined using the weighting strategy of Sanderson et al. (2017):
Sanderson, B. M., Wehner, M., and Knutti, R.: Skill and independence weighting for multi-model assessments, Geosci. Model Dev., 10, 2379-2395, https://doi.org/10.5194/gmd-10-2379-2017, 2017.

More information on CMIP5 can be found at: https://cmip.llnl.gov/cmip5/
More information on the LOCA dataset can be found at: http://loca.ucsd.edu/

Dataset citations:
Pierce, D.W., D.R. Cayan, and B.L. Thrasher, 2014: Statistical downscaling using Localized Constructed Analogs (LOCA), J. Hydrometeorology, 15, 2558-2585. doi:10.1175/JHM-D-14-0082.1
Pierce, D.W., D.R. Cayan, E.P. Maurer, J.T. Abatzoglou, and K.C. Hegewisch, 2015: Improved bias correction techniques for hydrological simulations of climate change. J. Hydrometeorology, 16, 2421-2442. doi:10.1175/JHM-D-14-0236.1

For additional information on this derived dataset, please contact:
Laura Stevens
North Carolina State University (NCSU)
North Carolina Institute for Climate Studies (NCICS) 
151 Patton Ave, Asheville, NC 28801
laura.stevens@noaa.gov
(828) 257-3006