Global Regionalisation of NCEP re-analysis

CliSAP product

of a climate reconstruction of the period January 1948 to April 2015 with high spatial-temporal resolution.

Example: Low pressure system “Vicinette” which caused the February 1962 storm surge along the coastline of northern Germany and in Hamburg.



This global spatially and temporally high resolution hindcast simulation data is only available via the DKRZ and/or the CERA data base; access is therefore free but requires a user account at DKRZ:


General information about data access from DKRZ can be found here:


Because of the very large data volume we recommend to generally get into contact with Hannes Thiemann from DKRZ before the data download.


Climate models require data, for instance wind speed and air temperature, in order to correctly represent processes in the atmosphere, the ocean or over land. The more accurate such data are the more realistic the climate projections can be. One of the difficulties to obtain such data is their often sparse spatial coverage. This difficulty can be mitigated by using so-called re-analysis data which are, however, of a quite coarse spatial resolution.

In the project "Globale hochaufgelöste Klimarekonstruktionen" one such coarse resolution re-analysis: the NCEP1 re-analysis (NCEP=National Center for Environmental Prediction) data set was used to generate a particularly homogeneous, high-quality set of meteorological parameter data streams with high spatial-temporal resolution for the period January 1948 to April 2015. This was achieved by forcing the ECHAM6 model, which is a high-resolution global atmospheric model, with NCEP1 data as described below.

NCEP1 meteorological data were assimilated into ECHAM6 down to the 750 hPa pressure level employing spectral nudging. Spectral nudging has the advantage that various physical processes can be described and realized at various altitude levels with high spatial-temporal resolution. A high quality of the ECAHM6 model output was secured by extensive inter-comparison of the model results with independent observations.

The intention is to also use this new high resolution data set of the complete set of relevant meteorological parameters to force ocean and ocean surface wave models. This will permit to evaluate the representation of ocean coastal currents in ocean models with high precision and to better predict storm surges.

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In its CMIP5 version 6.1.00 the ECHAM6 model saved 8 data streams with a total of 270 data sets. In the following we list the data streams with the names under which the data files can be found in the CERA data base - as an example for Dec. 01, 1992. At the end you find the total number of variables stored in each data stream. The data stream ending with "_echam" contains the typical and most used meteorological parameters. Each name is linked to the corresponding code list (pdf-file). These code lists contain in four columns the following information: Code number (column 1), number of model levels (column 2), alphanumeric abbreviation for the variable (column 3), and a short description of the variable together with its unit (column 4).

echam6_t255l95_sn_ncep1_199212.01_co2         (11 variables)
echam6_t255l95_sn_ncep1_199212.01_echam    (127 variables)
echam6_t255l95_sn_ncep1_199212.01_jsbach    (19 variables)
echam6_t255l95_sn_ncep1_199212.01_nudg      (12 variables)
echam6_t255l95_sn_ncep1_199212.01_veg         (66 variables, 12 hourly)
echam6_t255l95_sn_ncep1_199212.01_vphysc   (8 variables)
echam6_t255l95_sn_ncep1_199212.01_surf        (9 variables)
echam6_t255l95_sn_ncep1_199212.01_land       (18 variables)

Further it is possible to extract additional ECHAM6 variables, e.g. vertical velocity, ..., etc. by using the co-called AFTERBURNER (CDOs). Information about how to do this can be found starting at page 166 of the CDO documentation Version 1.7.0, October 2015, which can be accessed here: https://code.zmaw.de/projects/cdo/embedded/cdo.pdf.

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Coverage, temporal and spatial resolution

Period and temporal resolution:

  • 01-01-1948 to 30-04-2015
  • hourly

Coverage and spatial resolution:

  • Global
  • Spatial resolution: 0.46875° x 0.46875° (about 52 km x 52 km), Gaussian grid
  • Geographical longitude: 0°E to 359.531°E
  • Geographical latitude: 89.6416°N to 89.6416°S
  • Dimension: 768 (longitude) x 384 (latitude)
  • Spectral resolution: about 80 km
  • Spectral coefficients: 255 x 256
  • Altitude: Hybrid system - the model-levels are situated more parallel to the topography for the lower model levels while they are situated parallel to the pressure levels for the higher model levels; number of model levels: 95; altitude of uppermost model level: about 80 km


  • Grib, which can be converted into netCDF

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Data quality

First evaluations of this data set produced with ECHAM6 focused on a consistency check with a number of independent data sets at different spatial resolutions from observations: DWD, EOBs, CRU; and re-analyses: ERA-Interim and NCEP.

First analyses confirm the expected added value of the ECHAM6 hindcast simulation results compared to re-analysis data on almost all spatial-temporal scales - except regions over the tropical oceans.

The number of investigations carried out with this data set to test its skill for climate re-construction is still quite small. Therefore we recommend to use it carefully. The German Climate Computing Centre (DKRZ) carried out a quality assessment with the focus on the physical consistency of the hindcast simulations and based on the positive outcome provided the DOI.

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Data production
Martina Schubert-Frisius
HZG, Geesthacht/Hamburg
email: martina.schubert-frisius (at) hzg.de

Questions related to ECHAM6
Sebastian Rast
Max-Planck Institute for Meteorology, Hamburg
email: sebastian.rast (at) mpimet.mpg.de

Data administration
Hannes Thiemann
DKRZ, Hamburg
email: data (at) dkrz.de

Stefan Kern
ICDC, CEN, Universität Hamburg
email: stefan.kern (at) uni-hamburg.de

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General information about the ECHAM6 model:

Publications about the model, forcing data and spectral nudging:

  • Yoshimura, K. and M. Kanamitsu, 2008: Dynamical Global Downscaling of Global Reanalysis. Mon. Wea. Rev., 136, 2983-2998.
  • Kim, J.-E. and S.-Y. Hong, 2012: A Global Atmospheric Analysis Dataset Downscaled from the NCEP-DOE Reanalysis. J. Climate, 25, 2527-2534.
  • von Storch, H., H. Langenberg, and F. Feser, 2000: A Spectral Nudging Technique for Dynamical Downscaling Purposes. Mon. Wea. Rev., 128, 3664-3673.
  • Stevens, B., M. Giorgetta, M., Esch, T., Mauritsen, T., Crueger, T., S. Rast, ... and E. Roeckner, 2013: Atmospheric component of the MPI‐M Earth System Model: ECHAM6. Journal of Advances in Modeling Earth Systems, 5(2), 146-172.
  • Kalnay et al., 1996: The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, 437-470.
  • Dee D. P., et. al., 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553–597.
  • Feser, F., M. Barcikowska, S. Haeseler, C. Lefebvre, M. Schubert-Frisius, M. Stendel, H. von Storch, and M. Zahn, 2015: Hurricane Gonzalo and its extratropical transition to a strong European storm. Special Supplement "Explaining extreme events of 2014 from a climate perspective" to Bull. Amer. Meteorol. Soc., 96, 51-55.
  • Schubert-Frisius, M., F. Feser, H. von Storch, and S. Rast, 2016: Optimal spectral nudging for global dynamic downscaling, Mon. Wea. Rev., 145(3), doi:10.1175/MWR-D-16-0036.1

Data citation

The data should be cited as follows:

Schubert-Frisius, M., F. Feser, and H. v. Storch, Global High Resolution Climate Reconstruction with ECHAM6 using the spectral nudging technique, run by Helmholtz-Zentrum Geesthacht, WDCC at DKRZ, Hamburg, Germany, doi:10.1594/WDCC/CLISAP_MPI-ESM-XR_t255l95

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