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.