This is a README file to indicate the locations of codes, data sets, and other supporting materials. This dataset is associated with the following publication: Spero , T., C. Nolte , J.H. Bowden, M.S. Mallard, and J. Herwehe. The Impact of Incongruous Lake Temperatures on Regional Climate Extremes Downscaled from the CMIP5 Archive Using the WRF Model. Journal of Climate. American Meteorological Society, Boston, MA, USA, 29(2): 839-853, (2016).

This dataset contains monthly aggregates of 2D near-surface fields from the WRF model simulations labeled "default" (using WRF default approach to setting lake surface temperatures for downscaling) and "clmlst" (which uses lake surface temperatures from CLM to supplement those provided with CMIP5 archive of CESM). This dataset is associated with the following publication: Spero , T., C. Nolte , J.H. Bowden, M.S. Mallard, and J. Herwehe. The Impact of Incongruous Lake Temperatures on Regional Climate Extremes Downscaled from the CMIP5 Archive Using the WRF Model. Journal of Climate. American Meteorological Society, Boston, MA, USA, 29(2): 839-853, (2016).

This dataset contains monthly aggregates of 2D near-surface fields from the WRF model simulations labeled "default" (using WRF default approach to setting lake surface temperatures for downscaling) and "clmlst" (which uses lake surface temperatures from CLM to supplement those provided with CMIP5 archive of CESM). This dataset is associated with the following publication: Spero , T., C. Nolte , J.H. Bowden, M.S. Mallard, and J. Herwehe. The Impact of Incongruous Lake Temperatures on Regional Climate Extremes Downscaled from the CMIP5 Archive Using the WRF Model. Journal of Climate. American Meteorological Society, Boston, MA, USA, 29(2): 839-853, (2016).

Model input and simulation output files. This dataset is associated with the following publication: Butcher, J., T. Zi, M. Schmidt, T. Johnson, D. Nover, and C. Clark. Critical Lake Temperature Response to Climate Change across the United States. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 12(11): 1-16, (2017).

Model input and simulation output files. This dataset is associated with the following publication: Butcher, J., T. Zi, M. Schmidt, T. Johnson, D. Nover, and C. Clark. Critical Lake Temperature Response to Climate Change across the United States. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 12(11): 1-16, (2017).

Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 7 sets of SD-processed CMIP5 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using four GCMs from the CMIP5 archive (CMCC-CM, HadGEM2-CC, inmcm4, MRI-ESM1), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP5 GCMs simulated response under the representative concentration pathways (RCPs) 4.5 and 8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The resulting SD-processed projections are on a 1 km by 1 km grid covering the EAR in south central Texas (100.75 degress E to 97.5 degrees E, 28.75 degrees N to 30.50 degrees N). Both historical baseline files (1980-2005) and future projections (2006-2100) are provided. Applied researchers may explore aspects of potential changes in the EAR using these high resolution projections, including as inputs to additional modelling (e.g. hydrology modeling, crop modeling, etc.). This collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the EAR. These climate projection data products are provided as is without any warranty and no agreement to support subsequent projects based on this dataset, beyond providing the data to public domain.