IA 3: Global High-Resolution Climate Reconstruction

In order to represent processes in the atmosphere, in the ocean or ashore, climate models need data – for instance of wind speed or temperatures. The more precise data are, the more realistic are any projected results. The project Global High-Resolution Climate Reconstructions creates a global data record for meteorological processes, aiming to win data streams with high temporal and spatial resolution. In addition, they are to be particulary homogeneous, i.e. showing as few gaps or leaps as possible. The re-analysing data of the American National Center for Environmental Prediction, covering the years between 1948 and 2013, serve as the basis, operating the so-called ECHAM6, a high-resolution global atmospheric model.

A spectral nudging procedure is carried out in order to show processes within different layers of the atmosphere in high resolution, featuring smallest details. Broad information of re-analysing data are being imprinted on the model fields, starting at a level of 750 hectopascal. In order to secure high quality, the simulated data are then being compared with actual observations. The new data results are being used to operate ocean or wave models. Consequently, this model offers high-precision evaluations of upcoming coastal currents or storm floods.


The project “Global High-Resolution Climate Reconstructions” is part of the “Integrated Activities” of the Cluster of Excellence “Integrated Climate System Analysis and Prediction” (CliSAP) at the University of Hamburg. The goal of this project is to generate a global homogenous long-term data-set at high space and time resolution for scientific studies. To realize this aim, we use 6 hourly NCEP 1 (1948 – 2013) reanalysis data at T62L28 resolution to drive a high-resolution state-of-the-art global atmosphere model.

The chosen model ECHAM6 (T255L95) will be run using the so-called spectral nudging-technique. Different nudging variations in ECHAM6 (developed in collaboration with the MPI-M) will be tested. With the nudging process, the large scale information (e.g. selected waves of the horizontal wind U and V) of the reanalysis data will be impressed onto the simulated ECHAM model fields at levels above 750 hPa. By dynamical downscaling, we gain a surplus in resolution and can thus reproduce meteorological phenomena of small spatial extension.

To assess the quality of the simulated data they will undergo a comparison with observational data using suitable statistical methods. The resulting data-set will be used to investigate intensive storms like polar lows, medicanes or tropical cyclones. It is intended to use this data-set as forcing data for diverse ocean or wave models to examine e.g. the behavior of coast currents and storm surges.

Temperature at the lowest model level for 30.01.2004, 00UTC. Left panel: NCEP1-reanalysis, middle panel: difference between ECHAM6 with spectral nudging and NCEP1, right panel: difference between ECHAM6 without spectral nudging and NCEP1.

Latest IA3 Publications

  • Schubert-Frisius, M., Feser, F., von Storch, H., & Rast, S. (2017). Optimal spectral nudging for global dynamical downscaling. Monthly Weather Review, 145, 909-927. doi:10.1175/MWR-D-16-0036.1.
  • Prein, A., Langhans, W., Fosser, G., Andrew, F., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., van Lipzip, N., & Leung, L. R. (2015). A review on convection permitting climate modeling: demonstrations, prospects, and challenges. Reviews of Geophysics, 53(2), 323-361. doi:10.1002/2014RG000475.
  • Feser, F., Barcikowska, M., Haeseler, S., Levebvre, C., Schubert-Frisius, M., Stendel, M., von Storch, H., & Zahn, M. (2015). Hurricane Gonzalo and its Extratropical Transition to a Strong European Storm. Bulletin of the American Meteorological Society, 96(2012), 51-55. doi:10.1175/BAMS-D-15-00122.1.
  • Feser, F., Rockel, B., von Storch, H., Winterfeldt, J., & Zahn, M. (2011). Regional climate models add value to global model data, A Review and Selected Examples. BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 92(9), 1181-1192. doi:10.1175/2011BAMS3061.1.
  • Rybski, D., Bunde, A., & von Storch, H. (2008). Long-term memory in 1000-year simulated temperature records. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 113(D2): D02106. doi:10.1029/2007JD008568.