Using a nested single-model large ensemble to assess the internal variability of the North Atlantic Oscillation and its climatic implications for Central Europe

• Main Authors: Böhnisch, Andrea, Ludwig, Ralf, Leduc, Martin
• Format: Text
• Language: English
• Published: 2019
• Subjects: North Atlantic; North Atlantic oscillation
• Online Access: https://doi.org/10.5194/esd-2019-58; https://esd.copernicus.org/preprints/esd-2019-58/

Central European weather and climate is closely related to atmospheric mass advection triggered by the North Atlantic Oscillation (NAO) which is a relevant index for quantifying natural variability on multi-annual time scales. It remains unclear, though, how large-scale circulation variability affects local climate characteristics when downscaled using a regional climate model. In this study, 50 members of a single-model initial-condition large ensemble (LE) ( http://www.climex-project.org/ ) are analyzed for a climate–NAO relationship, especially its inter-member spread and its transfer from the driving model CanESM2 into the driven model CRCM5. The NAO pressure dipole is quantified in the CanESM2-LE by an extended station-based index; responses of mean surface air temperature and total precipitation to changes in the index value are determined for a Central European domain (CEUR) in both the CanESM2-LE and CRCM5-LE. NAO–response relationships are expressed via Pearson correlation coefficients (strength) and the change per unit index change for historical (1981–2010) and future (2070–2099) winters. Results show that (a) statistically robust NAO patterns are found in the CanESM2-LE under current forcing conditions and (b) impulses from the NAO in the CanESM2-LE produce correct responses in the high-resolution CRCM5-LE. Relationships weaken in the future period, but the amplitude of their inter-member spread shows no significant change. Among others, the results strengthen the validity of the climate module in the ClimEx model chain for further impact modelling and stress the importance of single-model ensembles for evaluating internal variability.