Comparisons of Climate Indices Based on Several Modeled and Observed U.S. Temperature Data Products Using a Metadata-Preserving, Modular R Coding Framework

The tracking of the data provenance and lineage ("breadcrumb trail") is a critical element to be considered when calculating climate indices from different sources as the data components, inputs, processes and outputs are necessary to provide context and allow for interoperability. In addition, having information about which data sources are producing which output data is of extreme importance. With different data sources and the need to produce output that has accurate metadata sufficient to allow one to reproduce the processing, we need to be mindful that the output of one diagnostics-generating program can become the input of another. Accordingly, when developing a software system to generate and intercompare multiple, large climate information data sources, traceability and reproducibility of analyses needs to be considered when developing a system to generate and intercompare multiple, large climate information data sources. Here we illustrate procedures used by the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluids Dynamics Laboratory (GFDL) Statistical Downscaling Team when computing temperature-related climate indices. First, we standardize input data from various sources into netCDF forms with metadata conforming to Climate and Forecasting (CF) (Hassell et al. 2017) standards with our own local extensions. From there we use the comparisons between computed between gridded observational data products (e.g., Livneh, nClimGrid, PRISM, Daymet, reanalysis (e.g., ERA-5), select station data, and downscaled data to illustrate differences in the calculation of different climate indices relevant to the 5th National Climate Assessment Report (e.g., differences in days > 95F, Climdex indices, date of last spring freeze) and importance of metadata preservation.

References: Hassell, D., Gregory, J., Blower, J., Lawrence, B. N., and Taylor, K. E.: A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1), Geosci. Model Dev., 10, 4619–4646, https://doi.org/10.5194/gmd-10-4619-2017, 2017.