edu.ucar.opensky::articles:26038 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T19:40:55.409937 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d78g8qm7 https://doi.org/10.22033/ESGF/CMIP6.1317 https Related Dataset #1 CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP https://doi.org/10.22033/ESGF/CMIP6.1532 https Related Dataset #2 IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP https://doi.org/10.22033/ESGF/CMIP6.1567 https Related Dataset #3 MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP https://doi.org/10.22033/ESGF/CMIP6.2056 https Related Dataset #4 CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP https://doi.org/10.22033/ESGF/CMIP6.2242 https Related Dataset #5 NIMS-KMA KACE1.0-G model output prepared for CMIP6 ScenarioMIP https://doi.org/10.22033/ESGF/CMIP6.2285 https Related Dataset #6 CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP https://doi.org/10.22033/ESGF/CMIP6.2291 https Related Dataset #7 CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP https://doi.org/10.22033/ESGF/CMIP6.251 https Related Dataset #8 EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP https://doi.org/10.22033/ESGF/CMIP6.3673 https Related Dataset #9 CCCma CanESM5 model output prepared for CMIP6 CMIP piControl https://doi.org/10.22033/ESGF/CMIP6.4311 https Related Dataset #10 CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP piControl https://doi.org/10.22033/ESGF/CMIP6.4312 https Related Dataset #11 CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP piControl https://doi.org/10.22033/ESGF/CMIP6.5251 https Related Dataset #12 IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP piControl https://doi.org/10.22033/ESGF/CMIP6.5710 https Related Dataset #13 MIROC MIROC-ES2L model output prepared for CMIP6 CMIP piControl https://doi.org/10.22033/ESGF/CMIP6.5711 https Related Dataset #14 MIROC MIROC6 model output prepared for CMIP6 CMIP piControl https://doi.org/10.22033/ESGF/CMIP6.604 https Related Dataset #15 NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP https://doi.org/10.22033/ESGF/CMIP6.6298 https Related Dataset #16 MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP piControl https://doi.org/10.22033/ESGF/CMIP6.6675 https Related Dataset #17 MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP piControl https://doi.org/10.22033/ESGF/CMIP6.727 https Related Dataset #18 EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP https://doi.org/10.22033/ESGF/CMIP6.793 https Related Dataset #19 MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP https://doi.org/10.22033/ESGF/CMIP6.8217 https Related Dataset #20 NCC NorESM2-LM model output prepared for CMIP6 CMIP piControl https://doi.org/10.22033/ESGF/CMIP6.8425 https Related Dataset #21 NIMS-KMA KACE1.0-G model output prepared for CMIP6 CMIP piControl https://doi.org/10.22033/ESGF/CMIP6.898 https Related Dataset #22 MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP https://doi.org/10.22033/ESGF/CMIP6.936 https Related Dataset #23 MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP https://doi.org/10.3929/ethz-b-000589377 https Related Dataset #24 Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change - code and data Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change 2023-01-26T00:00:00Z publication https://n2t.org/ark:/85065/d78g8qm7 https://doi.org/10.5194/esd-14-81-2023 de Vries, I. E. author Sippel, S. author Pendergrass, Angeline author Knutti, R. author UCAR/NCAR - Library publisher Detection and attribution (D&A) of forced precipitation change are challenging due to internal variability, limited spatial, and temporal coverage of observational records and model uncertainty. These factors result in a low signal-to-noise ratio of potential regional and even global trends. Here, we use a statistical method – ridge regression – to create physically interpretable fingerprints for the detection of forced changes in mean and extreme precipitation with a high signal-to-noise ratio. The fingerprints are constructed using Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model output masked to match coverage of three gridded precipitation observational datasets – GHCNDEX, HadEX3, and GPCC – and are then applied to these observational datasets to assess the degree of forced change detectable in the real-world climate in the period 1951–2020. We show that the signature of forced change is detected in all three observational datasets for global metrics of mean and extreme precipitation. Forced changes are still detectable from changes in the spatial patterns of precipitation even if the global mean trend is removed from the data. This shows the detection of forced change in mean and extreme precipitation beyond a global mean trend is robust and increases confidence in the detection method's power as well as in climate models' ability to capture the relevant processes that contribute to large-scale patterns of change. We also find, however, that detectability depends on the observational dataset used. Not only coverage differences but also observational uncertainty contribute to dataset disagreement, exemplified by the times of emergence of forced change from internal variability ranging from 1998 to 2004 among datasets. Furthermore, different choices for the period over which the forced trend is computed result in different levels of agreement between observations and model projections. These sensitivities may explain apparent contradictions in recent studies on whether models under- or overestimate the observed forced increase in mean and extreme precipitation. Lastly, the detection fingerprints are found to rely primarily on the signal in the extratropical Northern Hemisphere, which is at least partly due to observational coverage but potentially also due to the presence of a more robust signal in the Northern Hemisphere in general. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. None eng geoscientificInformation