Internal variability versus multi-physics uncertainty in a regional climate model
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Identificadores
URI: http://hdl.handle.net/10902/21134DOI: 10.1002/joc.6717
ISSN: 0899-8418
ISSN: 1097-0088
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Lavín Gullón, Álvaro


Fecha
2021-01-18Derechos
©John Wiley & Sons - This is the peer reviewed version of the following article: Lavin-Gullon A,Fernandez J, Bastin S, et al. Internal variability versus multi-physics uncertainty in a regionalclimate model. Int J Climatol. 2021;41 (Suppl. 1):E656-E671, which has been published in final form at https://doi.org/10.1002/joc.6717. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving
Publicado en
Int. J Climatol. 2021; 41 (Suppl. 1): E656-E671
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John Wiley and Sons Ltd
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Palabras clave
Internal variability
Regional climate models
Uncertainty
Physical parameterizations
Ensemble
Resumen/Abstract
In a recent study, Coppola et al. assessed the ability of an ensemble of convection-permitting models (CPM) to simulate deep convection using three case studies. The ensemble exhibited strong discrepancies between models, which were attributed to various factors. In order to shed some light on the issue, we quantify in this article the uncertainty associated to different physical parameterizations from that of using different initial conditions, often referred to as the internal variability. For this purpose, we establish a framework to quantify both signals and we compare them for upper atmospheric circulation and near-surface variables. The analysis is carried out in the context of the CORDEX Flagship Pilot Study on Convective phenomena at high resolution over Europe and the Mediterranean, in which the intermediate RCM WRF simulations that serve to drive the CPM are run several times with different parameterizations. For atmospheric circulation (geopotential height), the sensitivity induced by multi?physics and the internal variability show comparable magnitudes and a similar spatial distribution pattern. For 2-m temperature and 10-m wind, the simulations with different parameterizations show larger differences than those launched with different initial conditions. The systematic effect over one year shows distinct patterns for the multi-physics and the internal variability. Therefore, the general lesson of this study is that internal variability should be analysed in order to properly distinguish the impact of other sources of uncertainty, especially for short-term sensitivity simulations.
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