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  • 1
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    Berlin Dahlem precipitation time-series 2001-2013 (2017)
    PANGAEA
    In:  Supplement to: Ritschel, Christoph; Ulbrich, Uwe; Névir, Peter; Rust, Henning (2017): Precipitation extremes on multiple timescales - Bartlett-Lewis rectangular pulse model and intensity-duration-frequency curves. Hydrology and Earth System Sciences, 21(12), 6501-6517, https://doi.org/10.5194/hess-21-6501-2017
    Details
    Publication Date: 2023-01-13
    Description: For several hydrological modelling tasks, precipitation time-series with a high (i.e. sub-daily) resolution are indispensable. This data is, however, not always available and thus model simulations are used to compensate. A canonical class of stochastic models for sub-daily precipitation are Poisson-cluster processes, with the Bartlett-Lewis rectangular pulse model (BLRPM) as a prominent representative. The BLRPM has been shown to well reproduce certain characteristics found in observations. Our focus is on intensity-duration-frequency relationship (IDF), which are of particular interest in risk assessment. Based on a high resolution precipitation time-series (5-min) from Berlin-Dahlem, BLRPM parameters are estimated and IDF curves are obtained on the one hand directly from the observations and on the other hand from BLRPM simulations. Comparing the resulting IDF curves suggests that the BLRPM is able to reproduce main features of IDF statistics across several durations but cannot capture singular events (here an event of magnitude 5 times larger than the second larges event). Here, IDF curves are estimated based on a parametric model for the duration dependence of the scale parameter in the General Extreme Value distribution; this allows to obtain a consistent set of curves over all durations. We use the BLRPM to investigate the validity of this approach based on simulated long time series.
    Keywords: Berlin, Germany; Berlin-Dahlem_BotGarden; DATE/TIME; ORDINAL NUMBER; Precipitation; Tipping bucket; Weather station/meteorological observation; WST
    Type: Dataset
    Format: text/tab-separated-values, 113952 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 2
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    Earth system model results by the MPI-ESM-LR of the MiKlip Decadal climate prediction experiment improved by ocean ensemble dispersion filtering, links to NetCDF files (2017)
    PANGAEA
    In:  Supplement to: Kadow, Christopher; Illing, Sebastian; Kröner, Igor; Ulbrich, Uwe; Cubasch, Ulrich (2017): Decadal climate predictions improved by ocean ensemble dispersion filtering. Journal of Advances in Modeling Earth Systems, https://doi.org/10.1002/2016MS000787
    Details
    Publication Date: 2023-01-13
    Description: Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two timescales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state towards the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering towards the ensemble mean.
    Keywords: File content; File format; File name; File size; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 20 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
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