the local response to the nao in a regcm 30-year run
DESCRIPTION
The local response to the NAO in a RegCM 30-year run. Roxana Bojariu and Liliana Velea National Institute of Meteorology Bucharest, Romania E-mail:[email protected]. Large scale NAO features. Data. Simulated data: - PowerPoint PPT PresentationTRANSCRIPT
The local response to the NAO in a The local response to the NAO in a RegCM 30-year runRegCM 30-year run
Roxana Bojariu and Liliana VeleaRoxana Bojariu and Liliana VeleaNational Institute of MeteorologyNational Institute of Meteorology
Bucharest, RomaniaBucharest, RomaniaE-mail:[email protected]:[email protected]
Large scale NAO featuresLarge scale NAO features
DataData• 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 1960-1990).
– 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 (1960-1990)– air surface and temperature from 59 stations over the Romanian
territory (1960-2000)
Analysis methodologyAnalysis 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.
11stst CCA CRU Data 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) Hurrell’s NAO index (black) and the time series (green) associated with the 1associated with the 1stst CCA of SLP and T (CRU data) CCA of SLP and T (CRU data)
r=0.70
CCA patterns of air surface temperatureCCA patterns of air surface temperature
CRU r=0.93 vslp=40% vT=38% RegCM r=0.95 vslp=50% vT=32%
CCA patterns of precipitationCCA patterns of precipitation
CRU r=0.93 vslp=42% vp=26% RegCM r=0.98 vslp=43% vp=31%
The local response to NAO type variability over Romanian The local response to NAO type variability over Romanian territoryterritory
RegCM: 1st CCA SLP/T Observations: difference of high and low NAO composites
Conclusions 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.