In collaboration with the CLIVAR Global Synthesis and Observations Panel (GSOP) we are processing and publishing the state variables from eight ocean reanalyses, from 1980 to 2010. Here, the data are regridded to a common 1° x 1° horizontal grid and vertically interpolated to the World Ocean Atlas 09 (WOA09) depths and prepared in a format compatible with the CMIP5 archive as part of CREATE. In addition, an ensemble average and spread are generated and also made available.
The wide variety of grids used in ocean reanalysis has proven to be challenging and we highlight the methods used in horizontal regridding and vertically interpolating the monthly fields to a common 1x1 degree latitude-longitude grid and common depths. We will also highlight some of the differences and similarities among the various reanalyses.
We have also prepared a more expansive selection of variables from the major atmospheric reanalyses for the CREATE project. This work includes the ECMWF ERA-Interim, NASA/GMAO MERRA and MERRA2, NOAA/NCEP CFSR, NOAA/ESRL 20CR and 20CRv2, JMA JRA25, and JRA55. Each dataset is reformatted similarly to the models in the CMIP5 archive. By repackaging the reanalysis data into a common structure and format, it simplifies access, subsetting, and reanalysis comparison. Both monthly average data and a selection of high frequency data (6-hour) relevant to investigations such as the 2016 El Niño are provided. Much of the processing workflow has been automated and new data appear on a regular basis.
For a quick view of the data available for the atmospheric and oceanic reanalyses, we have developed a web based visualization tool (CREATE-V) that allows the user to simultaneously view four reanalyses to facilitate comparison. The addition of a backend analytics engine, based on UV-CDAT and Scala, provides the ability to generate a time series and anomaly for any given location on a map. The system enables scientists to identify data of interest and visualize, subset and compare data without the need to download large volumes of data for local visualization.