Environment Canada Environnement Canada Effects of elevated CO 2 on modelled ENSO variability Bill Merryfield Canadian Centre for Climate Modelling and Analysis Meteorological Service of Canada Service météorologique du Canada How does elevated CO 2 influence ampitude, pattern, and frequency content of ENSO variability in IPCC AR4 coupled simulations? Issue For low CO 2 climate consider pre-industrial control (picntrl) simulations. For high CO 2 climate consider first 100 years following stabilization at doubled CO 2 in 1%to2x simulations. Select centennial time series of monthly averaged data, with mean annual cycles and linear trends removed, from models having ≥ 200 years of picntrl output and ≥ 100 years of post- stabilization 1%to2x output available *. Compute empirical orthogonal functions (EOFs) and principal component (PC) time series of surface skin temperature (ts) for each centennial time series in region 120  E-90  W, 10  S-10  N, excluding cells containing >1% land. Compute PC1 power spectra smoothed with 24-month Parzen window Methodology Legend *exceptions: For GISS_ER and IAP use last 20 years of 1%/yr and first 80 years of x2 CO2 stabilization to represent high CO2 climate For NCAR_PCM1 use last 4 years of 1%/yr and first 96 years of x2 CO2 stabilization to represent high CO2 climate For NCAR_CCSM3_0 use years of sresb1 runs (stabilized at slightly < 2x CO2) to represent high CO2 climate Obtain means and standard deviations  from available sample, e.g. 5 centuries for a 500-year control run, 3 centuries for ensemble of 3 2x CO2 runs. For pre-industrial vs 2x CO 2 cases compare: Analysis A Mean PC1, PC2, and total SST variances. To assess significance of 2x CO 2 minus pre-industrial differences, express in units of standard deviation of pre-industrial sample. B Spatial patterns of EOF1 C Variance-preserving PC1 power spectra Equatorial Pacific SST variances A Raw variances Normalized Upper: variances of 1st and 2nd principal compone- nts, and total variance of equatorial Pacific SST. Lower: same data, normalized by pre-industrial mean total variance to facilitate comparison PC1 Spectra C CC CC Pre-industrial 2x CO 2 OBS (GISST ) EOF1 spatial patterns B CCSR MEDRES CCCMA CNRM IAP GFDL CM2.0 GFDL CM2.1 GISS EH GISS ER MRI MPI ECHAM5 NCAR CCSM3.0 NCAR PCM1 Period (yr) Total SST variance PC1 variance PC2 variance Pre-industrial 2x CO 2 means  1  error bars GISST ( ) Pre-industrial EOF1 pattern, standard normalization (RMS=1) 2x CO2 EOF1 pattern, normalized by 2X CO2 PC1 RMS Pre-industrial PC1 RMS ( ) Period (yr) X CO2 PC1 power spectrum Pre-industrial PC1 power spectrum (mean,  1  ) Difference (2X CO2 minus pre-industrial) Conclusions Some IPCC AR4 models exhibit statistically significant changes in ENSO amplitude under CO 2 doubling. However, the signs of these changes are not consistent across models: 4 models (CCSR, CNRM, GISS ER, IAP) exhibit > 2  decreases in PC1 variance 3 models (GFDL CM2.1, MPI, MRI) exhibit > 2  increases in PC1 variance 5 models (CCCMA, GFDL CM2.0, GISS EH, NCAR CCSM3.0, NCAR PCM1) exhibit smaller changes in PC1 amplitude under CO 2 doubling, i.e. that are within the range of natural variability of PC1 amplitude on centennial time scales in a pre-industrial climate. ~ ~ Changes (2X CO2 minus pre-industrial) in equatorial Pacific SST variances in units of , the standard deviation of PC1 amplitude on centennial time scales in the pre-industrial control runs. 1  - wide Gaussians having the same area are plotted for comparison. Total SST variance PC1 variance Number of models 2X CO2 minus pre-industrial differences (  )