ORCID Profile
0000-0002-8962-1406
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Copernicus GmbH
Date: 30-10-2020
Abstract: Abstract. Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool for quantifying the forced response and internal variability in various components of the climate system. However, there is no consensus on the ideal or even sufficient ensemble size for a large ensemble. Here, we introduce an objective method to estimate the required ensemble size that can be applied to any given application and demonstrate its use on the ex les of global mean near-surface air temperature, local temperature and precipitation, and variability in the El Niño–Southern Oscillation (ENSO) region and central United States for the Max Planck Institute Grand Ensemble (MPI-GE). Estimating the required ensemble size is relevant not only for designing or choosing a large ensemble but also for designing targeted sensitivity experiments with a model. Where possible, we base our estimate of the required ensemble size on the pre-industrial control simulation, which is available for every model. We show that more ensemble members are needed to quantify variability than the forced response, with the largest ensemble sizes needed to detect changes in internal variability itself. Finally, we highlight that the required ensemble size depends on both the acceptable error to the user and the studied quantity.
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-9811
Abstract: & & In a collaborative effort, data management specialists at the German Climate Computing Centre (Deutsches Klimarechenzentrum, DKRZ) and researchers at the Max Planck Institute for Meteorology (MPI-M) are joining forces to achieve long-term and effective global availability of a high-volume flagship climate model dataset: the MPI-M Grand Ensemble (MPI-GE, Maher et al. 2019& sup& & /sup& ), which is the largest ensemble of a single state-of-the-art comprehensive climate model (MPI-ESM1.1-LR) currently available. The MPI-GE has formed the basis for a number of scientific publications over the past 4 years& sup& & /sup& . However, the wealth of data available from the MPI-GE simulations was essentially invisible to potential data users outside of DKRZ and MPI-M.& & & & In this contribution, we showcase the adopted strategy, experiences made and the current status of FAIR long-term preservation of the MPI-GE dataset in the World Data Center for Climate (WDCC), hosted at DKRZ. The importance of synergistic cooperation between domain-expert data providers and knowledgeable repository staff will be highlighted.& & & & Recognising the demand for MPI-GE data access outside of its native environment, the development of a strategy to make MPI-GE data globally available began in mid 2018. A two-stage dissemination reservation process was decided upon.& & & & In a first step, MPI-GE data would be published and made globally available via the Earth System Grid Federation (ESGF) infrastructure. Second, the ESGF-published data would be transferred to DKRZ& #8217 s long-term and FAIR archiving service WDCC. Datasets preserved in the WDCC can be made accessible via ESGF - global access via the established system would thus still be ensured.& & & & To date, the first stage of the above process is completed and data are available via the ESGF& sup& & /sup& . Data published in the ESGF has to comply with strict data standards in order to ensure efficient data retrieval and interoperability of the dataset. Standardization of the MPI-GE data required selection of an applicable data standard (CMIP5 in this case) and an appropriate variable subset, adaptation and application of fit-for-purpose DKRZ-supplied post-processing software and of course the post-processing of the data itself. All steps required dedicated communication and collaboration between DKRZ and MPI-M staff and required significant time resources. Currently, some 87 TB, comprised of more than 55 000 records, of standardized MPI-GE data are available for search and download from the ESGF. About three to four thousand records with an accumulated volume of several hundred GB are downloaded by ESGF users each month.& & & & The long-term archival of the standardized MPI-GE data using DKRZ& #8217 s WDCC-service is planned to begin within the first half of 2020. All preparatory work done so far, especially the data standardization, significantly reduces the effort and resources required for achieving FAIR MPI-GE data preservation in the WDCC.& & & & & sup& & /sup& Maher, N. et al. ( 2019). J. Adv. Model Earth Sy., 11, 2050& #8211 2069. 0.1029/2019MS001639& & & & & sup& & /sup& www.mpimet.mpg.de/en/grand-ensemble ublications/& & & & & sup& & /sup& esgf-data.dkrz.de rojects/mpi-ge/& & & & & br& & br& & &
Publisher: American Geophysical Union (AGU)
Date: 07-2019
DOI: 10.1029/2019MS001639
Publisher: Copernicus GmbH
Date: 29-11-2019
DOI: 10.5194/ESD-2019-70
Abstract: Abstract. Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool to quantify the forced response and internal variability in various components of the climate system. However, there is no consensus on the ideal or even sufficient ensemble size for a large ensemble. Here, we introduce an objective method to estimate the required ensemble size that can be applied to any given application and demonstrate its use on the ex les of global mean surface temperature, local surface temperature and precipitation and variability in the ENSO region and central America. Where possible, we base our estimate of the required ensemble size on the pre-industrial control simulation, which is available for every model. First, we determine how much of an available ensemble size is interpretable without a substantial impact of res ling ensemble members. Then, we show that more ensemble members are needed to quantify variability than the forced response, with the largest ensemble sizes needed to detect changes in internal variability itself. Finally, we highlight that the required ensemble size depends on both the acceptable error to the user and the studied quantity.
Location: United Kingdom of Great Britain and Northern Ireland
Location: Switzerland
No related grants have been discovered for Dirk Olonscheck.