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This study analyzes a method of constructing a homogeneous, high-resolution global atmospheric hindcast. The method is the spectral nudging technique, which was applied to a state-of-the-art general circulation model (ECHAM6, T255L95). Large spatial scales of the global climate model prognostic variables were spectrally nudged toward a reanalysis dataset (NCEP-1, T62L28) for the past few decades. The main idea is the addition of dynamically consistent regional weather details to the coarse-grid NCEP-1 reanalysis. A large number of sensitivity experiments was performed, using different nudging e-folding times, vertical profiles, wavenumbers, and variables. Comparisons with observations and several reanalyses showed a high dependency on the variations of the nudging configuration. At the global scale, the accordance is very high for extratropical regions and lower in the tropics. A wavenumber truncation of 30, a relatively short e-folding time of 50 min, and a plateaushaped nudging profile applied only to divergence and vorticity generally yielded the best results. This is one of the first global spectral nudging hindcast studies and the first applying an altitude-dependent profile to selected prognostic variables. The method can be applied to reconstructing the history of extreme events such as intense storms within the context of ongoing climate change.

This paper demonstrates two important aspects of regional dynamical downscaling of multidecadal atmospheric reanalysis. First, that in this way skillful regional descriptions of multidecadal climate variability may be constructed in regions with little or no local data. Second, that the concept of large‐scale constraining allows global downscaling, so that global reanalyses may be completed by additions of consistent detail in all regions of the world. Global reanalyses suffer from inhomogeneities. However, their large‐scale componenst are mostly homogeneous; Therefore, the concept of downscaling may be applied to homogeneously complement the large‐scale state of the reanalyses with regional detail—wherever the condition of homogeneity of the description of large scales is fulfilled. Technically, this can be done by dynamical downscaling using a regional or global climate model, which's large scales are constrained by spectral nudging. This approach has been developed and tested for the region of Europe, and a skillful representation of regional weather risks—in particular marine risks—was identified. We have run this system in regions with reduced or absent local data coverage, such as Central Siberia, the Bohai and Yellow Sea, Southwestern Africa, and the South Atlantic. Also, a global simulation was computed, which adds regional features to prescribed global dynamics. Our cases demonstrate that spatially detailed reconstructions of the climate state and its change in the recent three to six decades add useful supplementary information to existing observational data for midlatitude and subtropical regions of the world.

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