13 March 20074th C20C Workshop1 Interannual Variability of Atmospheric Circulation in C20C models Simon Grainger 1, Carsten Frederiksen 1 and Xiagou Zheng.

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Presentation transcript:

13 March 20074th C20C Workshop1 Interannual Variability of Atmospheric Circulation in C20C models Simon Grainger 1, Carsten Frederiksen 1 and Xiagou Zheng 2 1. Bureau of Meteorology, Melbourne, Australia 2. National Institute of Water and Atmospheric Research, Wellington, New Zealand Acknowledgments: C20C Modelling groups, David Straus

13 March 20074th C20C Workshop2 Motivation  What are the distributions of the components of variability?  How well do models reproduce observed variability?  What are the sources of these patterns?  How does the interannual variability change over time? In observed data? In models – including different forcing scenarios? To investigate the properties of the interannual variability of seasonal mean climate data

13 March 20074th C20C Workshop3 Theory  x = monthly anomaly of climate variable   = external forcings (eg SST) assumed to be constant over a season   = slowly varying internal dynamics internal to the atmosphere  and  are potentially predictable at long range (> 1 season)   = intraseasonal component weather events that are not predictable at long range (eg blocking)  and  given by variability between ensemble members (m = 1,2,3 months, y = 1,Y years, s = 1,S members, r = points)

13 March 20074th C20C Workshop4 Components of variability (o = seasonal mean)  Rowell et al. (1995)  separate external and internal components Cannot separate ,  and  monthly anomalies, but can for the interannual variability of seasonal mean  Zheng and Frederiksen (1999)  separate intra-seasonal component ♦ and hence can deduce slow-internal component V(  sy )

13 March 20074th C20C Workshop5 Estimating Intraseasonal Variability Zheng and Frederiksen (2004) estimated intraseasonal variance as a function of monthly differences using moment estimation (m = 1,2,3) Assumes that:  x can be modelled by a first-order autoregressive process Implies that intermonthly correlations can be constrained  Variances V(  sym ) are stationary across the season Reasonable assumption for summer and winter

13 March 20074th C20C Workshop6 Total Variability – DJF NCEPBOM (S=10)CSIRO (S=10) COLA (S=10)GSFC (S=14)UKMO (S=12)

13 March 20074th C20C Workshop7 Intraseasonal Variability – DJF NCEPBOM (S=10)CSIRO (S=10) COLA (S=10)GSFC (S=14)UKMO (S=12)

13 March 20074th C20C Workshop8 Potential Predictability (%) – DJF NCEPBOM (S=10)CSIRO (S=10) COLA (S=10)GSFC (S=14)UKMO (S=12)

13 March 20074th C20C Workshop9 Potential Predictability (%) – JJA NCEPBOM (S=10)CSIRO (S=10) COLA (S=10)GSFC (S=14)UKMO (S=12)

13 March 20074th C20C Workshop10 NCEP Covariability – NH DJF TotalSlowIntraseasonal

13 March 20074th C20C Workshop11 Slow PC Regression – NH DJF BOM C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) NAO PNA W. Pacific E. Atlantic TNH CSIRO C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) NAO PNA W. Pacific E. Atlantic TNH COLA C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) NAO PNA W. Pacific E. Atlantic TNH GSFC C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) NAO PNA W. Pacific E. Atlantic TNH UKMO C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) NAO PNA W. Pacific E. Atlantic TNH

13 March 20074th C20C Workshop12 ENSO Composites NCEP Covariability – SH JJA Slow UEOF-S1 (32.0%)UEOF-S2 (14.7%) UEOF-S3 (8.7%)UEOF-S4 (7.9%) UEOF-I1 (23.1%)UEOF-I2 (16.6%) UEOF-I3 (10.2%)UEOF-I4 (8.3%) Intraseasonal

13 March 20074th C20C Workshop13 Slow PC Regression – SH JJA BOM C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) High Latitude ENSO Warm ENSO Cold SP Wave CSIRO C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) High Latitude ENSO Warm ENSO Cold SP Wave COLA C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) High Latitude ENSO Warm ENSO Cold SP Wave GSFC C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) High Latitude ENSO Warm ENSO Cold SP Wave UKMO C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) High Latitude ENSO Warm ENSO Cold SP Wave

13 March 20074th C20C Workshop14 COLA Variability – DJF Slow V(  y +  sy )Slow External V(  y )Slow Internal V(  sy )

13 March 20074th C20C Workshop15 Conclusions  C20C models are generally able to reproduce most of the large-scale observed grid point variability Although subtle differences at smaller scales are likely to be important  C20C Intraseasonal covariability modes resemble observed, although relative importance changes  For NH DJF, C20C models reproduce the PNA, but do not generally reproduce other observed modes of slow covariability Particularly not the NAO  For SH JJA, C20C models reproduce both ENSO modes, but not necessarily other slow modes  In some C20C models, separation of slow variability components reproduces expected internal modes

13 March 20074th C20C Workshop16 Australian Potential Predictability (%) DJFMAMJJASON T max Precip T min