Journal Article

FZJ-2020-03037

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Internal variability versus multi‐physics uncertainty in a regional climate model

Lavin‐Gullon, A. (Corresponding author) ; Fernandez, J. ; Bastin, S. ; Cardoso, R. M. ; Fita, L. ; Giannaros, T. M. ; Görgen, K.FZJ* ; Gutierrez, J. M. ; Kartsios, S. ; Katragkou, E. ; Lorenz, T. ; Milovac, J. ; Soares, P. M. M. ; Sobolowski, S. ; Warrach‐Sagi, K.

2021
Wiley Chichester [u.a.]

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Please use a persistent id in citations: http://hdl.handle.net/2128/26856  doi:10.1002/joc.6717

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 1 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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Contributing Institute(s):

1. Agrosphäre (IBG-3)
2. JARA - HPC (JARA-HPC)

Research Program(s):

1. 2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217) (POF4-217)
2. Convection-permitting regional climate modelling: Contribution to WCRP CORDEX Flagship Pilot Study ensemble over Europe and joint analysis of water cycle processes and properties (jjsc39_20190501) (jjsc39_20190501)

Appears in the scientific report 2021

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Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DEAL Wiley ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Web of Science Core Collection

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