The local response to the NAO in a RegCM 30-year run Roxana Bojariu and Liliana Velea National Institute of Meteorology Bucharest, Romania

Large scale NAO features

Data Simulated data: –air surface temperature, precipitation and sea level pressure from the control run of RegCM forced by the HadCM 3 run (with observed SSTs and sea-ice for the interval ). –Resolution 50 km –119 grids in longitude and 98 in latitude (PRUDENCE domain) –Winter (December to February) Observed data: –air surface temperature and precipitation from CRU ( ) –air surface and temperature from 59 stations over the Romanian territory ( )

Analysis methodology Canonical correlation analysis (CCA): 1.The CCA selects a pair of spatial patterns of two variables such that their time evolution is optimally correlated (Preisendorfer 1988; Zorita et al. 1992; Bretherton, 1992; Kharin 1994; Von Storch 1995). 2.Before canonical correlation analysis, the original data are usually projected onto their Empirical Orthogonal Functions (EOFs), retaining only a limited number of them in order to minimize the noise. 3.The CCA patterns are normalized such that the coefficients have standard deviation units, so the patterns represent typical anomalies in their specific units.

1 st CCA CRU Data Air surface temperature (˚C) and SLP (hPa) anomalies Precipitation (mm/day) and SLP (hPa) anomalies

Hurrell’s NAO index (black) and the time series (green) associated with the 1 st CCA of SLP and T (CRU data) r=0.70

CCA patterns of air surface temperature CRU r=0.93 v slp =40% v T =38% RegCM r=0.95 v slp =50% v T =32%

CCA patterns of precipitation CRU r=0.93 v slp =42% v p =26% RegCM r=0.98 v slp =43% v p =31%

The local response to NAO type variability over Romanian territory RegCM: 1 st CCA SLP/T Observations: difference of high and low NAO composites

Conclusions The data simulated by RegCM capture features of the local response to NAO type circulation. In this context, the downscaling of climate change scenarios becomes more reliable for European area. Follow up The analysis of other sources of variability for the European regions (e.g. Eastern Atlantic pattern). The analysis of other fields (e.g. snow cover) The analysis of climate change scenarios.