Abstract. Climate models provide the required input data for global or regional climate impact analysis in temporally aggregated form, often in daily resolution to save space on data servers. Today, many impact models work with daily data; however, sub-daily climate information is becoming increasingly important for more and more models from different sectors, such as the agricultural, water, and energy sectors. Therefore, the open-source Teddy tool (temporal disaggregation of daily climate model data) has been developed to disaggregate (temporally downscale) daily climate data to sub-daily hourly values. Here, we describe and validate the temporal disaggregation, which is based on the choice of daily climate analogues. In this study, we apply the Teddy tool to disaggregate bias-corrected climate model data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We choose to disaggregate temperature, precipitation, humidity, longwave radiation,
shortwave radiation, surface pressure, and wind speed. As a reference, globally available bias-corrected hourly reanalysis WFDE5 (WATCH Forcing Data methodology applied to ERA5) data from 1980–2019 are used to take specific local and seasonal features of the empirical diurnal profiles into account. For a given location and day within the climate model data, the Teddy tool screens the reference data set to find the most similar meteorological day based on rank statistics. The diurnal profile of the reference data is then applied on the climate model. The physical dependency between variables is preserved, since the diurnal profile of all variables is taken from the same, most similar meteorological day of the historical reanalysis dataset. Mass and energy are strictly preserved by the Teddy tool to exactly reproduce the daily values from the climate models. For evaluation, we aggregate the hourly WFDE5 data to daily values and apply the Teddy tool for disaggregation. Thereby, we compare the original hourly data with the data disaggregated by Teddy. We perform a sensitivity analysis of different time window sizes used for finding the most similar
meteorological day in the past. In addition, we perform a cross-validation
and autocorrelation analysis for 30 globally distributed samples around the
world that represent different climate zones. The validation shows that Teddy is able to reproduce historical diurnal courses with high correlations >0.9 for all variables, except for wind speed (>0.75) and precipitation (>0.5). We discuss the limitations of the method regarding the reproduction of precipitation extremes, interday connectivity, and disaggregation of end-of-century projections with strong
warming. Depending on the use case, sub-daily data provided by the Teddy tool could make climate impact assessments more robust and reliable.
