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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 on theme: "13 March 20074th C20C Workshop1 Interannual Variability of Atmospheric Circulation in C20C models Simon Grainger 1, Carsten Frederiksen 1 and Xiagou Zheng."— Presentation transcript:

1 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

2 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

3 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)

4 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 )

5 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

6 13 March 20074th C20C Workshop6 Total Variability – DJF 1951-2000 NCEPBOM (S=10)CSIRO (S=10) COLA (S=10)GSFC (S=14)UKMO (S=12) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70

7 13 March 20074th C20C Workshop7 Intraseasonal Variability – DJF 1951-2000 NCEPBOM (S=10)CSIRO (S=10) COLA (S=10)GSFC (S=14)UKMO (S=12) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70

8 13 March 20074th C20C Workshop8 Potential Predictability (%) – DJF 1951-2000 NCEPBOM (S=10)CSIRO (S=10) COLA (S=10)GSFC (S=14)UKMO (S=12)

9 13 March 20074th C20C Workshop9 Potential Predictability (%) – JJA 1951-2000 NCEPBOM (S=10)CSIRO (S=10) COLA (S=10)GSFC (S=14)UKMO (S=12)

10 13 March 20074th C20C Workshop10 NCEP Covariability – NH DJF 1949-2002 TotalSlowIntraseasonal

11 13 March 20074th C20C Workshop11 Slow PC Regression – NH DJF 1951-2000 BOM C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) NAO-0.069-0.096-0.117 PNA0.7560.7790.861 W. Pacific0.3220.3980.520 E. Atlantic0.2440.3390.477 TNH0.1940.2040.277 CSIRO C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) NAO0.0010.0020.003 PNA0.6980.7170.792 W. Pacific0.1750.1830.239 E. Atlantic0.1920.2650.374 TNH0.4320.4730.639 COLA C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) NAO0.0970.1350.164 PNA0.6170.6420.709 W. Pacific0.2280.2690.351 E. Atlantic0.1970.2490.351 TNH0.1790.1980.268 GSFC C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) NAO0.3790.4460.545 PNA0.7840.7940.878 W. Pacific0.1780.1910.250 E. Atlantic0.5030.6360.895 TNH0.2730.3000.405 UKMO C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) NAO0.2410.3080.376 PNA0.8030.8280.915 W. Pacific0.2070.2250.294 E. Atlantic0.2100.4480.631 TNH0.2530.3020.408

12 13 March 20074th C20C Workshop12 ENSO Composites 1957-1998 NCEP Covariability – SH JJA 1951-2000 -0.12 -0.10 -0.08 -0.06 -0.04 -0.02 0.0 0.02 0.04 0.06 0.08 0.10 0.12 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 13 March 20074th C20C Workshop13 Slow PC Regression – SH JJA 1951-2000 BOM C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) High Latitude0.3660.5600.656 ENSO Warm0.5880.6430.766 ENSO Cold0.5900.6310.785 SP Wave0.3020.3490.432 CSIRO C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) High Latitude0.3410.4400.516 ENSO Warm0.5900.6570.782 ENSO Cold0.5740.6470.806 SP Wave0.3380.3700.458 COLA C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) High Latitude0.1970.2350.275 ENSO Warm0.5160.5400.643 ENSO Cold0.4730.5190.646 SP Wave0.3140.3310.409 GSFC C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) High Latitude0.2870.3190.374 ENSO Warm0.6060.6630.790 ENSO Cold0.5280.5530.689 SP Wave0.1240.1310.162 UKMO C(x oyo,x yo )C(  y,x yo )C(  y,  y +  sy ) High Latitude0.2120.2660.312 ENSO Warm0.5590.6060.722 ENSO Cold0.5260.5600.697 SP Wave0.2380.2540.314

14 13 March 20074th C20C Workshop14 COLA Variability – DJF 1951-2000 Slow V(  y +  sy )Slow External V(  y )Slow Internal V(  sy ) -0.12 -0.10 -0.08 -0.06 -0.04 -0.02 0.0 0.02 0.04 0.06 0.08 0.10 0.12 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70

15 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

16 13 March 20074th C20C Workshop16 Australian Potential Predictability (%) DJFMAMJJASON T max Precip T min

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