Abstract
Abstract. Climate impact assessments require information about climate change at regional and ideally also local scales. In dendroecological studies, this information has traditionally been obtained using statistical methods, which preclude the linkage of local climate changes to large-scale drivers in a process-based way. As part of recent efforts to investigate the impact of climate change on forest ecosystems in Bavaria, Germany, we developed a high-resolution atmospheric modelling dataset, BAYWRF, for this region over the thirty-year period of September 1987 to August 2018. The atmospheric model employed in this study, the Weather Research and Forecasting (WRF) model, was configured with two nested domains of 7.5 and 1.5 km grid spacing centred over Bavaria and forced at the outer lateral boundaries by ERA5 reanalysis data. Using an extensive network of observational data, we evaluate (i) the impact of using grid analysis nudging for a single-year simulation of the period of September 2017 to August 2018 and (ii) the full BAYWRF dataset generated using nudging. The evaluation shows that the model represents variability in near-surface meteorological conditions generally well, although there are both seasonal and spatial biases in the dataset that interested users should take into account. BAYWRF provides a unique and valuable tool for investigating climate change in Bavaria with high interdisciplinary relevance. Data from the finest-resolution WRF domain are available for download at daily temporal resolution from a public repository at the Open Science Framework (Collier, 2020; https://doi.org/10.17605/OSF.IO/AQ58B).
Highlights
The forcing of climate change in modern times is clearly of global nature, and many important scientific problems can be understood at the global scale as well (e.g. Held and Soden, 2006)
We presented a climatological kilometre-scale simulation with the atmospheric model Weather Research and Forecasting (WRF) over Bavaria for the period of September 1987 to August 2018
Comparison of simulations for the period of September 2017 to August 2018 with and without grid-analysis nudging against extensive meteorological measurements across Bavaria showed that nudging decreased the mean deviations and increased the coefficient of determination at the majority of sites for most evaluated atmospheric variables, in particular precipitation
Summary
The forcing of climate change in modern times is clearly of global nature, and many important scientific problems can be understood at the global scale as well (e.g. Held and Soden, 2006). Must understand the effects at regional and even local scales in order to develop appropriate adaptation and mitigation measures. The traditional approach is to compute a calibration function between local or regional tree-ring parameters and climatic variables. Such a statistical relationship would try to utilize local station data (which are generally sparse), gridded observations (which tend to be of coarse resolution) or indices of large-scale climate dynamics (which describe coupled atmosphere–ocean modes) as the climatic influence
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