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  • 1
    In: Tellus B: Chemical and Physical Meteorology, Stockholm University Press, Vol. 70, No. 1 ( 2018-01-01), p. 1450589-
    Type of Medium: Online Resource
    ISSN: 1600-0889 , 0280-6509
    RVK:
    RVK:
    Language: Unknown
    Publisher: Stockholm University Press
    Publication Date: 2018
    detail.hit.zdb_id: 2026992-4
    detail.hit.zdb_id: 246061-0
    SSG: 16,13
    Location Call Number Limitation Availability
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  • 2
    Online Resource
    Online Resource
    Wiley ; 2019
    In:  International Journal of Climatology Vol. 39, No. 4 ( 2019-03-30), p. 2324-2335
    In: International Journal of Climatology, Wiley, Vol. 39, No. 4 ( 2019-03-30), p. 2324-2335
    Abstract: This study examines potential benefits of performance‐based multi‐model ensembles (MMEs) in projecting the impacts of climate change on extreme precipitation indices over East Asia (EA) using the data from 19 GCMs in the coupled model intercomparison project 5 (CMIP5). The Taylor skill score is adopted as the measure of the model skills in simulating the spatial and interannual variability of the selected extreme precipitation indices over four EA regions. The overall rank based on the total skill score ( TSC ) is used to construct two skill‐based MMEs, MME of high‐skill, MMH (MME of low‐skill, MML) that include the top (bottom) seven models, in addition to the simple ensemble of all 19 GCMs (ENS). Inter‐GCM consistency is measured using the signal‐to‐noise ratio ( SNR ). In the present‐day period, MMH yields higher skill scores than MML and ENS for almost all extreme precipitation indices as well as regions. Regional variations in biases, inter‐model consistency, and TSC are large. The inter‐model consistency is highest for Northern China and Manchuria and is lowest for Southern China. The most notable differences in the key properties of climate change signals from the three MMEs among the three ensembles are that the climate change signals from MMH and ENS exceed the 90% significance level in much larger areas than those from MML. However, the differences in the climate change signals between MMH and MML are generally below the 90% significance level. The SNR of the projected climate change signals shows that MMH yields more consistent climate change signals than ENS/MML. Both the SNR differences and the area in which statistical significance exceed the 90% level suggest that constructing climate change signals from a group of higher‐skill models may yield more reliable projections than constructing MMEs from the entire models or a group of lower‐skilled models.
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 1491204-1
    SSG: 14
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  • 3
    Online Resource
    Online Resource
    American Meteorological Society ; 2019
    In:  Monthly Weather Review Vol. 147, No. 3 ( 2019-03-01), p. 971-985
    In: Monthly Weather Review, American Meteorological Society, Vol. 147, No. 3 ( 2019-03-01), p. 971-985
    Abstract: Although a terrain-following vertical coordinate is well suited for the application of surface boundary conditions, it is well known that the influences of the terrain on the coordinate surfaces can contribute to increase numerical errors, particularly over steep topography. To reduce these errors, a hybrid sigma–pressure coordinate is formulated in the Weather Research and Forecasting (WRF) Model, and its effects are illustrated for both an idealized test case and a real-data forecast for upper-level turbulence. The idealized test case confirms that with the basic sigma coordinate, significant upper-level disturbances can be produced due to numerical errors that arise as the advection of strong horizontal flow is computed along coordinate surfaces that are perturbed by smaller-scale terrain influences. With the hybrid coordinate, this artificial noise is largely eliminated as the mid- and upper-level coordinate surfaces correspond much more closely to constant pressure surfaces. In real-data simulations for upper-level turbulence forecasting, the WRF Model using the basic sigma coordinate tends to overpredict the strength of upper-air turbulence over mountainous regions because of numerical errors arising as a strong upper-level jet is advected along irregular coordinate surfaces. With the hybrid coordinate, these errors are reduced, resulting in an improved forecast of upper-level turbulence. Analysis of kinetic energy spectra for these simulations confirms that artificial amplitudes in the smaller scales at upper levels that arise with the basic sigma coordinate are effectively removed when the hybrid coordinate is used.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    RVK:
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2019
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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