The Effects of Climate Downscaling Technique and Observational Dataset on Modeled Ecological Responses: Supporting Data Tables

These data have been prepared as a supplement to Pourmokhtarian et al. (2016; full citation below), where complete
details on methods can be found.
We evaluated three downscaling methods: the delta method (or the change factor method); monthly quantile
mapping (Bias Correction-Spatial Disaggregation, or BCSD); and daily quantile regression (Asynchronous Regional
Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models
(AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios
on two sets of observations (1/8th degree resolution grid vs. individual weather station) to generate the
high-resolution climate input for the forest biogeochemical model PnET-BGC (8 ensembles of 6 runs). This dataset consists of three files - 1) a zip archive file of all raw daily downscaled AOGCMs (csv format; years 1960-2099;
delta method 2012-2099 only) which were
used as input for PnET-BGC model, 2) a zip archive file of all PnET-BGC output files for each model run (csv format; years 1000-2100), and 3) a pdf document
file that describes the content of the input and output files.
Data were also used from the following Hubbard Brook longterm datasests:
Daily Streamflow Watershed 6 (http://dx.doi.org/10.6073/pasta/23d7b5feb24156908fe552f492f774e9 )
Chemistry of Streamwater at the Hubbard Brook Experimental Forest, Watershed 6 (http://dx.doi.org/10.6073/pasta/2ec152b0ab1d4e64aa40f4aa9bc492ac )
Daily Precipitation Watershed 6 (http://dx.doi.org/10.6073/pasta/54133475a47d98472eb6389035753c33)
Daily Maximum/Minimum Temperature Data (http://dx.doi.org/10.6073/pasta/343016c156eaac9bb7cb6c5d6fc04d2f)
Daily Solar Radiation Data (http://dx.doi.org/10.6073/pasta/2b74a8bda3eaa49e2caf1d19dafb23af)