• Balmaseda, M. A., K. Mogensen, and A. T. Weaver, 2013: Evaluation of the ECMWF ocean reanalysis system ORAS4. Quart. J. Roy. Meteor. Soc., 139, 11321161, https://doi.org/10.1002/qj.2063.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., D. Mocko, J. O. Roads, and A. Ruane, 2009: A multimodel analysis for the Coordinated Enhanced Observing Period (CEOP). J. Hydrometeor., 10, 912934, https://doi.org/10.1175/2009JHM1090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cinquini, L., and Coauthors, 2014: The Earth System Grid Federation: An open infrastructure for access to distributed geospatial data. Future Gener. Comput. Syst., 36, 400417, https://doi.org/10.1016/j.future.2013.07.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Compo, G. P., and Coauthors, 2011: The Twentieth Century Reanalysis Project. Quart. J. Roy. Meteor. Soc., 137, 128, https://doi.org/10.1002/qj.776.

  • Davis, S. M., and Coauthors, 2017: Assessment of upper tropospheric and stratospheric water vapor and ozone in reanalyses as part of S-RIP. Atmos. Chem. Phys., 17, 1274312778, https://doi.org/10.5194/acp-17-12743-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dole, R., M. Hoerling, and S. Schubert, Eds., 2008: Reanalysis of historical climate data for key atmospheric features: Implications for attribution of causes of observed change. U.S. Climate Change Science Program and the Subcommittee on Global Change Research Rep., National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, 156 pp., www.globalchange.gov/browse/reports/sap-13-reanalysis-historical-climate-data-key-atmospheric-features-implications.

  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gregow, H., 2014: Procedure for comparing reanalyses, and comparing reanalyses to assimilated observations and CDRs. Community Research and Development Information Service Deliverable D5.53, 45 pp., www.coreclimax.eu/sites/coreclimax.itc.nl/files/documents/Deliverables/WP_Reports/Deliverable-D553-CORECLIMAX.pdf.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kobayashi, S., and Coauthors, 2014: The JRA-55 Reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 548, https://doi.org/10.2151/jmsj.2015-001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Köhl, A., 2015: Evaluation of the GECCO2 ocean synthesis: Transports of volume, heat and freshwater in the Atlantic. Quart. J. Roy. Meteor. Soc., 141, 166181, https://doi.org/10.1002/qj.2347.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • New, M., D. Lister, M. Hulme, and I. Makin, 2002: A high-resolution data set of surface climate over global land areas. Climate Res., 21, 125, https://doi.org/10.3354/cr021001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Onogi, K., and Coauthors, 2007: The JRA-25 Reanalysis. J. Meteor. Soc. Japan, 85, 369432, https://doi.org/10.2151/jmsj.85.369.

  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, https://doi.org/10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, https://doi.org/10.1175/2010BAMS3001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, C. A., G. P. Compo, and D. K. Hooper, 2014: Web-based Reanalysis Intercomparison Tools (WRIT) for analysis and comparison of reanalyses and other datasets. Bull. Amer. Meteor. Soc., 95, 16711678, https://doi.org/10.1175/BAMS-D-13-00192.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Storto, A., S. Masina, and A. Navarra, 2016: Evaluation of the CMCC eddy-permitting global ocean physical reanalysis system (C-GLORS, 1982-2012) and its assimilation components. Quart. J. Roy. Meteor. Soc., 142, 738758, https://doi.org/10.1002/qj.2673.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. a. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toyoda, T., Y. Fujii, T. Yasuda, N. Usui, K. Ogawa, T. Kuragano, H. Tsujino, and M. Kamachi, 2016: Data assimilation of sea ice concentration into a global ocean–sea ice model with corrections for atmospheric forcing and ocean temperature fields. J. Oceanogr., 72, 235262, https://doi.org/10.1007/s10872-015-0326-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, A., and X. Zeng, 2013: Development of global hourly 0.5° land surface air temperature datasets. J. Climate, 26, 76767691, https://doi.org/10.1175/JCLI-D-12-00682.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, D., and Coauthors, 2013: Ultrascale visualization of climate data. Computer, 46, 6876, https://doi.org/10.1109/MC.2013.119.

  • Zhang, S., M. J. Harrison, A. Rosati, and A. Wittenberg, 2007: System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon. Wea. Rev., 135, 35413564, https://doi.org/10.1175/MWR3466.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zuo, H., M. A. Balmaseda, and K. Mogensen, 2015: The new eddy-permitting ORAP5 ocean reanalysis: Description, evaluation and uncertainties in climate signals. Climate Dyn., 49, 791811, https://doi.org/10.1007/s00382-015-2675-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • View in gallery

    A schematic view of the processing necessary to obtain, convert, and reformat the reanalysis data to the CMIP standards.

  • View in gallery

    Zonal-average precipitation anomaly (mm day−1) from 1980 to 2016 for five reanalyses. Results are compared to those for GPCP.

  • View in gallery

    The Jan 1998 ocean ensemble salinity (psu) at 5-m depth: (a) mean and (b) standard deviation.

  • View in gallery

    The 6-hourly data from MERRA-2, CFSR, ERA-Interim, and JRA-55 precipitation (mm day−1) for the month of Jan 2017. The data represent the average of ±1° around 38°N, 122°W (San Francisco).

  • View in gallery

    Screenshot of the CREATE-V interactive tool interface to display multiple reanalyses side by side (https://cds-cv.nccs.nasa.gov/CREATE-V/).

  • View in gallery

    Using the CREATE-V visualization tool to zoom in on a region and select a point, producing the monthly anomaly and the average seasonal cycle of the selected location.

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Enabling Reanalysis Research Using the Collaborative Reanalysis Technical Environment (CREATE)

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  • 1 NASA Goddard Spaceflight Center/NASA Center for Climate Simulation, Greenbelt, Maryland
  • | 2 Global Modeling and Assimilation Office, NASA Goddard Spaceflight Center, Greenbelt, Maryland
  • | 3 NASA Goddard Spaceflight Center/NASA Center for Climate Simulation, Greenbelt, Maryland
  • | 4 NASA Headquarters, Washington, D.C.
  • | 5 Lawrence Livermore National Laboratory, Livermore, California
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Abstract

This paper describes the repackaging and consistent distribution of the world’s major atmospheric and oceanic reanalyses. It also presents examples of the usefulness of examining multiple reanalyses. This service will make it much easier for anybody using reanalysis to access multiple datasets using an approach similar to that of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Experienced users as well as students will find the standardized formatted data convenient to use.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR: Gerald L. Potter, gerald.potter@nasa.gov

Abstract

This paper describes the repackaging and consistent distribution of the world’s major atmospheric and oceanic reanalyses. It also presents examples of the usefulness of examining multiple reanalyses. This service will make it much easier for anybody using reanalysis to access multiple datasets using an approach similar to that of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Experienced users as well as students will find the standardized formatted data convenient to use.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR: Gerald L. Potter, gerald.potter@nasa.gov

Modern atmospheric and oceanic reanalyses are valuable assets for atmospheric research and climate monitoring (Kalnay et al. 1996). Now that most reanalysis records are more than 36 years long, the data have become more useful for climate modeling research (Dole et al. 2008). For investigators who need to use multiple reanalyses, a common challenge is that the data are distributed at various sites and often in different formats. The NASA CREATE system provides access to the data in one location in a standard format (one variable per file and standardized metadata in the CMIP5 style; see Table 1 for a list of the key acronyms used in this paper). The collection includes monthly and 6-hourly data from the seven major atmospheric reanalyses: CFSR (Saha et al. 2010), ERA-Interim (Dee et al. 2011), MERRA (Rienecker et al. 2011), MERRA-2 (Gelaro et al. 2017), JRA-25 (Onogi et al. 2007), JRA-55 (Kobayashi et al. 2014), and 20CRv2c (Compo et al. 2011). An ancillary portion of CREATE includes eight ocean reanalyses: NCEP CFSR, CMCC C-GLORSv5 (Storto et al. 2016), ECMWF ORAS4 (Balmaseda et al. 2013), ECMWF ORAP5.0 (Zuo et al. 2015), University of Hamburg GECCO2 (Köhl 2015), GFDL ECDA (Zhang et al. 2007), NOAA GODAS (Saha et al. 2010), and MOVE/MRI.COM-G2i (Toyoda et al. 2016). The ocean state variables were similarly reformatted but were then also regridded onto a common horizontal and vertical grid. This approach facilitated the calculation of an ensemble average and spread that is also published alongside the native gridded data. A third reanalysis product is a global hourly 0.5° land surface air temperature dataset constructed by Wang and Zeng (2013). All three datasets are distributed through the ESGF in a format consistent with the CMIP style described by Cinquini et al. (2014).

Table 1.

List of key acronyms used in this paper.

Table 1.

BACKGROUND.

The CMIP3/CMIP5 effort was extremely successful in providing the climate research community with accessible data and provides a template for how comparing climate models benefits climate prediction (Taylor et al. 2012). CREATE provides the community with an easily accessible multiple-reanalysis dataset patterned after the success of CMIP5. Once the reanalyses have been standardized and placed in a common location, it is relatively easy to compare them or select the one most suited for a study. One of the important benefits of reanalysis intercomparison is the ability to reveal uncertainty for fields where the various reanalyses disagree and to provide confidence in those fields where they agree.

We have chosen to process a common subset of monthly and 6-hourly reanalysis output similar to the variables chosen for the standard output of climate modeling in the CMIP5 experiments. Tables 24 show the list of variables selected. The 3D fields are published on pressure levels that include the CMIP5 standard levels as well as on the native horizontal grid. This standardized processing requires that the variable names be changed to the CMIP5 (CF compliant) names and that the dimension bounds be set. The processing also provides a standardized set of metadata to allow the ESGF software queries to perform searches across all the reanalyses.

Table 2.

The standard CF long names of the atmospheric variables published in CREATE on ESGF. The data are monthly averages. An asterisk indicates that the data are also 6 hourly.

Table 2.
Table 3.

The standard CF long names of the land surface variables published in CREATE on ESGF. The data are monthly averages.

Table 3.
Table 4.

The standard CF long names of the ocean variables published in CREATE on ESGF. The data are monthly averages.

Table 4.

PROCESSING: ATMOSPHERIC REANALYSES.

Figure 1 is a schematic summary of the processing necessary to standardize each reanalysis to a common format. Except for NASA’s MERRA and MERRA-2, each reanalysis required a different data retrieval method. In addition, ERA-Interim, JRA-25, JRA-55, and CFSR all required conversion from GRIB to NetCDF format. None of these processing steps in themselves were particularly difficult, but each required a unique set of procedures. Processing of the 6-hourly data was similar to the monthly data, but it was necessary to process the data into monthly chunks to allow the file size to be manageable for download. Once the data were organized, it was subjected to the Climate Model Output Rewriter (CMOR), where units, axis labeling, and standardized metadata were modified to conform to the CMIP5 standards (Williams et al. 2013).

Fig. 1.
Fig. 1.

A schematic view of the processing necessary to obtain, convert, and reformat the reanalysis data to the CMIP standards.

Citation: Bulletin of the American Meteorological Society 99, 4; 10.1175/BAMS-D-17-0174.1

This processing is now largely automated, allowing newly processed reanalyses to be added to CREATE shortly after the data are made available by the reanalysis centers.

Bosilovich et al. (2009) demonstrated that a number of errors in individual analyses are reduced when combined as an ensemble average. We have recently calculated the ensemble mean of the individual reanalyses and the standard deviation for each month of key fields from the atmospheric reanalyses and published this as the MRE in the CREATE project in ESGF. A preliminary view of the MRE suggests a similar result for a few selected variables in most of the reanalyses used to determine the ensemble mean. To perform the ensemble mean, it was necessary to regrid each reanalysis onto a standard horizontal and vertical grid. These data are also included as part of the CREATE ESGF site.

OCEAN REANALYSES.

The ocean reanalysis intercomparison work is a collaboration with the CLIVAR GSOP. The objective was to develop ensemble averages and spread products for monthly, three-dimensional potential temperature, salinity, and zonal and meridional velocities from eight ocean reanalyses.

The monthly fields from each ocean reanalysis were horizontally regridded and vertically interpolated to a common 1° × 1° latitude–longitude grid and common World Ocean Atlas 2009 depths. These data, along with the native grids, are also available via the CREATE ESGF site and adhere to the same common CF standards as the atmospheric reanalyses. In addition to the regridded and native gridded data, we have provided the ensemble mean and standard deviation for each variable as additional products.

LAND SURFACE AIR TEMPERATURE.

The reanalysis-based hourly land surface air temperature dataset (Wang and Zeng 2013) was also reformatted to adhere to the CMIP5 standards and was then published in the CREATE ESGF repository. The primary purpose of creating this dataset was to produce a bias-corrected hourly land 0.5° × 0.5° surface air temperature product using both reanalysis and the Climate Research Unit high-resolution surface air temperature dataset (New et al. 2002).

SCIENTIFIC USE CASES.

One of the primary benefits for gathering the reanalyses onto one site, and formatting them similarly, is the ease with which a user can both access the data and analyze it with a single workflow. Since the data are organized by variable, it is a simple task to retrieve the same variable from several reanalyses and extract all or part of the time series. The data can be regridded onto a common grid if desired, and for the three-dimensional data, all the atmospheric reanalyses include the common CMIP5 pressure levels.

One of the motivations for comparing reanalyses is to determine what variables are the most reliable estimates of the real world. For example, Fig. 2 shows the zonally averaged precipitation anomaly for five of the CREATE atmospheric reanalyses for the period 1980–2012 compared with GPCP. All the reanalyses suggest an increase in tropical precipitation sometime after 2000, whereas GPCP does not show this as clearly. Figure 2 shows a large variation in decadal variability and more reliable short-term variation, as indicated by the relatively consistent signal of the 1997/98 El Niño (Dole et al. 2008).

Fig. 2.
Fig. 2.

Zonal-average precipitation anomaly (mm day−1) from 1980 to 2016 for five reanalyses. Results are compared to those for GPCP.

Citation: Bulletin of the American Meteorological Society 99, 4; 10.1175/BAMS-D-17-0174.1

Another example that demonstrates possible tests of reliability is the ensemble average and spread of the eight ocean reanalyses’ salinity at 5 m for January 1998, as shown in Fig. 3. The spread among the ensembles in the central Pacific near the 1998 El Niño maximum indicates some disagreement among the ensemble members.

Fig. 3.
Fig. 3.

The Jan 1998 ocean ensemble salinity (psu) at 5-m depth: (a) mean and (b) standard deviation.

Citation: Bulletin of the American Meteorological Society 99, 4; 10.1175/BAMS-D-17-0174.1

EXAMPLE OF 6-HOURLY HISTORY DATA.

The 6-hourly data files are very large, with a single month requiring 4 GB and 37 years requiring 1.7 TB. To study a single event, it is possible to select a time and region subset using the THREDDS OPeNDAP server either from the NASA Center for Climate Simulation (NCCS) THREDDS data server or from ESGF (additional information can be found online at https://cds.nccs.nasa.gov/data/faq/). None of the reanalysis data centers provides this type of data service. For example, it is simple to download a subset of the precipitation for a series of atmospheric rivers that deluged California in January 2017. Figure 4 shows the average of four grid points surrounding the latitude and longitude of San Francisco, California. In general, the four reanalyses shown captured the precipitation during these events but there are some differences in structure during the peak times.

Fig. 4.
Fig. 4.

The 6-hourly data from MERRA-2, CFSR, ERA-Interim, and JRA-55 precipitation (mm day−1) for the month of Jan 2017. The data represent the average of ±1° around 38°N, 122°W (San Francisco).

Citation: Bulletin of the American Meteorological Society 99, 4; 10.1175/BAMS-D-17-0174.1

DATA ACCESS.

Downloading reanalysis data.

A convenient way to download the CREATE data is to access the ESGF/CoG site. It is first necessary to register with ESGF at www.earthsystemcog.org/projects/cog/doc/esgf/faq/login. Once the credentials have been obtained, then the user can log in and go to https://esgf.nccs.nasa.gov/projects/create-ip/.

For those unfamiliar with the ESGF/CoG, a tutorial is available at www.earthsystemcog.org/projects/cog/tutorials_web. For additional help, there is a search tool available to assist with downloading variables (click on the question mark to the left of the “Search” button at https://esgf.nccs.nasa.gov/search/create-ip/).

After data are selected and placed in the cart, there are several options for downloading, including HTTP, WGET, and OPeNDAP. For large datasets, such as 6-hourly atmospheric temperature, the OPeNDAP option is often the most convenient. For OPeNDAP-enabled analysis or visualization tools that are unable to work with ESGF’s credentialed access, a second option is to use the NCCS/THREDDS server (https://dataserver.nccs.nasa.gov/thredds/catalog/bypass/CREATE-IP/reanalysis/catalog.html).

Visualization.

Another option is to view the data before downloading. The CREATE-V website (https://cds-cv.nccs.nasa.gov/CREATE-V/) has implemented a quick-look comparison capability that allows a user to display atmospheric data (monthly or 6 hourly) or ocean data (monthly).

Numerous variables are available for display with options to select the date, level, color map, and scale. Figures 5 and 6 show screenshots of the CREATE-V interface, displaying multiple reanalyses and the ability to produce anomalies of the selected variable by picking a location on the map. The anomalies are calculated on the fly using a prototype of a climate data analytics tool in development at the NCCS. Developing this interface was considerably simplified by standardizing the data. Improvements are planned for this site, including calculating the differences and anomalies and simple contour-interval selection. This tool complements the WRIT tool developed at NOAA (Smith et al. 2014) and the KNMI’s suite of tools (https://climexp.knmi.nl/start.cgi?id=someone@somewhere).

Fig. 5.
Fig. 5.

Screenshot of the CREATE-V interactive tool interface to display multiple reanalyses side by side (https://cds-cv.nccs.nasa.gov/CREATE-V/).

Citation: Bulletin of the American Meteorological Society 99, 4; 10.1175/BAMS-D-17-0174.1

Fig. 6.
Fig. 6.

Using the CREATE-V visualization tool to zoom in on a region and select a point, producing the monthly anomaly and the average seasonal cycle of the selected location.

Citation: Bulletin of the American Meteorological Society 99, 4; 10.1175/BAMS-D-17-0174.1

Example using Jupyter Notebooks to perform analytics.

It is also possible to perform calculations and create plots without directly downloading the data. An example of such access is demonstrated in the sample Jupyter Notebook available via NASA’s website (https://esgf.nccs.nasa.gov/projects/create-ip/use_cases). This example can be run on computers running Linux or MacOS X after installing Anaconda (www.continuum.io/) and following the instructions available at https://github.com/UV-CDAT/uvcdat/wiki/Install-using-Anaconda. Data are accessed through the NASA/NCCS THREDDS server mentioned above. The notebook contains a very simple Python script that opens and extracts surface temperature from two different reanalyses, calculates the monthly global mean, and plots a graph for the reanalysis period. Those familiar with Python may want to modify the script or add other reanalyses.

UPDATES.

For studies of recent events using reanalysis data, we have automated the processing of the atmospheric data. As each center completes their processing of a new month, the NCCS runs automatic processing scripts to download and reformat the data and make them available through ESGF and THREDDS shortly thereafter. This allows for studying events such as El Niño and the unusually intense precipitation along the U.S. West Coast during the winter and spring of 2016/17. The selected data are published at monthly and 6-hourly intervals (Table 1). The 20CRv2c, JRA-25, and MERRA results are not automatically updated because all three reanalyses ended in or before 2016.

CURRENT SCIENCE APPLICATIONS FOR CREATE.

Recently, the SPARC S-RIP team performed a comprehensive assessment of upper-tropospheric and stratospheric water vapor and ozone in reanalyses (Davis et al. 2017). The work makes use of the atmospheric CREATE data in determining the utility of stratospheric water vapor from reanalyses.

FUTURE PLANS.

We hope to include the CERA20C and ERA5. We also hope to include more value-added products such as the resultant wind velocity.

We are considering the inclusion of the reanalysis increments. The reanalysis centers typically do not release these fields, but they would contribute significantly to our understanding of the reanalyses error estimates. The difficulty with using these test statistics is that the reanalysis centers do not use the same standards for generating the fields (Gregow 2014).

SUMMARY.

This paper summarizes the CREATE project. The primary goals of CREATE are to serve the CMIP and reanalysis science community with easy access to the major atmospheric and ocean reanalysis products to facilitate comparison and to aid in the evaluation of the various reanalysis efforts operating today. One of the primary benefits of using CREATE for accessing reanalysis data is the time saved in the processing. For most reanalysis datasets, accessing many variables from the original site and reformatting the data for a consistent workflow requires a time commitment on the order of weeks. Using CREATE to access the data reduces the time required to prepare the data for analysis to a few hours. CREATE is closely coupled to reanalysis.org and is coordinated with the WCRP TIRA team.

ACKNOWLEDGMENTS

The authors would like to acknowledge support from the NASA Science Mission Directorate, Earth Science Division; the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center; the Goddard Earth Sciences, Technology, and Research cooperative agreement between NASA and the Universities Space Research Association; the NASA MAP program; and the U.S. Department of Energy Office of Science/Office of Biological and Environmental Research under Contract DE-AC52-07NA27344 at Lawrence Livermore National Laboratory. We want to thank Mark McInerney for his support during the early stages of this project. Others who contributed to the project that we want to thank are Julian Peters for CREATE-V software development, Tom Maxwell for analytics development, Savannah Finch for testing, Yingshuo Shen for ESGF data publication, and Neh Patel for help with web support. We would also like to thank the reviewers for their valuable comments.

FOR FURTHER READING

  • Balmaseda, M. A., K. Mogensen, and A. T. Weaver, 2013: Evaluation of the ECMWF ocean reanalysis system ORAS4. Quart. J. Roy. Meteor. Soc., 139, 11321161, https://doi.org/10.1002/qj.2063.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., D. Mocko, J. O. Roads, and A. Ruane, 2009: A multimodel analysis for the Coordinated Enhanced Observing Period (CEOP). J. Hydrometeor., 10, 912934, https://doi.org/10.1175/2009JHM1090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cinquini, L., and Coauthors, 2014: The Earth System Grid Federation: An open infrastructure for access to distributed geospatial data. Future Gener. Comput. Syst., 36, 400417, https://doi.org/10.1016/j.future.2013.07.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Compo, G. P., and Coauthors, 2011: The Twentieth Century Reanalysis Project. Quart. J. Roy. Meteor. Soc., 137, 128, https://doi.org/10.1002/qj.776.

  • Davis, S. M., and Coauthors, 2017: Assessment of upper tropospheric and stratospheric water vapor and ozone in reanalyses as part of S-RIP. Atmos. Chem. Phys., 17, 1274312778, https://doi.org/10.5194/acp-17-12743-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dole, R., M. Hoerling, and S. Schubert, Eds., 2008: Reanalysis of historical climate data for key atmospheric features: Implications for attribution of causes of observed change. U.S. Climate Change Science Program and the Subcommittee on Global Change Research Rep., National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, 156 pp., www.globalchange.gov/browse/reports/sap-13-reanalysis-historical-climate-data-key-atmospheric-features-implications.

  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gregow, H., 2014: Procedure for comparing reanalyses, and comparing reanalyses to assimilated observations and CDRs. Community Research and Development Information Service Deliverable D5.53, 45 pp., www.coreclimax.eu/sites/coreclimax.itc.nl/files/documents/Deliverables/WP_Reports/Deliverable-D553-CORECLIMAX.pdf.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kobayashi, S., and Coauthors, 2014: The JRA-55 Reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 548, https://doi.org/10.2151/jmsj.2015-001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Köhl, A., 2015: Evaluation of the GECCO2 ocean synthesis: Transports of volume, heat and freshwater in the Atlantic. Quart. J. Roy. Meteor. Soc., 141, 166181, https://doi.org/10.1002/qj.2347.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • New, M., D. Lister, M. Hulme, and I. Makin, 2002: A high-resolution data set of surface climate over global land areas. Climate Res., 21, 125, https://doi.org/10.3354/cr021001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Onogi, K., and Coauthors, 2007: The JRA-25 Reanalysis. J. Meteor. Soc. Japan, 85, 369432, https://doi.org/10.2151/jmsj.85.369.

  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, https://doi.org/10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, https://doi.org/10.1175/2010BAMS3001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, C. A., G. P. Compo, and D. K. Hooper, 2014: Web-based Reanalysis Intercomparison Tools (WRIT) for analysis and comparison of reanalyses and other datasets. Bull. Amer. Meteor. Soc., 95, 16711678, https://doi.org/10.1175/BAMS-D-13-00192.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Storto, A., S. Masina, and A. Navarra, 2016: Evaluation of the CMCC eddy-permitting global ocean physical reanalysis system (C-GLORS, 1982-2012) and its assimilation components. Quart. J. Roy. Meteor. Soc., 142, 738758, https://doi.org/10.1002/qj.2673.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. a. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toyoda, T., Y. Fujii, T. Yasuda, N. Usui, K. Ogawa, T. Kuragano, H. Tsujino, and M. Kamachi, 2016: Data assimilation of sea ice concentration into a global ocean–sea ice model with corrections for atmospheric forcing and ocean temperature fields. J. Oceanogr., 72, 235262, https://doi.org/10.1007/s10872-015-0326-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, A., and X. Zeng, 2013: Development of global hourly 0.5° land surface air temperature datasets. J. Climate, 26, 76767691, https://doi.org/10.1175/JCLI-D-12-00682.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, D., and Coauthors, 2013: Ultrascale visualization of climate data. Computer, 46, 6876, https://doi.org/10.1109/MC.2013.119.

  • Zhang, S., M. J. Harrison, A. Rosati, and A. Wittenberg, 2007: System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon. Wea. Rev., 135, 35413564, https://doi.org/10.1175/MWR3466.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zuo, H., M. A. Balmaseda, and K. Mogensen, 2015: The new eddy-permitting ORAP5 ocean reanalysis: Description, evaluation and uncertainties in climate signals. Climate Dyn., 49, 791811, https://doi.org/10.1007/s00382-015-2675-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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