2022
DOI: 10.1007/s00382-022-06560-2
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Spin-up time and internal variability analysis for overlapping time slices in a regional climate model

Abstract: Long-term regional climate simulations are computationally very costly. One way to improve their computational efficiency is to split them into overlapping time slices, which can then be run in parallel. Although this procedure reduces the cost, sufficient spin-up must be left at the start of each slice. In any case, discontinuities will occur due to internal variability where two different slices join. In this study, we explore the relative role of spin-up time and internal variability in the discontinuities … Show more

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Cited by 3 publications
(3 citation statements)
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“…The overlapping periods (2018-2020 and 2068-2070) were compared (not shown). No significant climatological differences were found several month after simulation start, in accordance with the findings from Lavin-Gullon et al (2022).…”
Section: The Cosmo-clm Ensemblesupporting
confidence: 90%
“…The overlapping periods (2018-2020 and 2068-2070) were compared (not shown). No significant climatological differences were found several month after simulation start, in accordance with the findings from Lavin-Gullon et al (2022).…”
Section: The Cosmo-clm Ensemblesupporting
confidence: 90%
“…The overlapping periods (2018-2020 and 2068-2070) were compared (not shown). No relevant differences were found sev-eral months after simulation start, in accordance with the findings from Lavin-Gullon et al (2023).…”
Section: The Cosmo-clm Ensemblesupporting
confidence: 89%
“…The magnitudes of these inland T2 differences are overall small, making it hard to clearly distinguish effects from ROMS coupling and WRF internal variability. Lavin-Gullon et al (2022) show that the magnitude of internal variations might not be small for slow responding model components. However, simulations in this study all use spectral nudging that would act to limit internal variability of the regional model (Alexandru et al, 2009).…”
Section: Figure 12mentioning
confidence: 85%