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Interannual Variability of Atmospheric Circulation in C20C models. Simon Grainger 1 , Carsten Frederiksen 1 and Xiagou Zheng 2 Bureau of Meteorology, Melbourne, Australia National Institute of Water and Atmospheric Research, Wellington, New Zealand
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Interannual Variability of Atmospheric Circulation in C20C models Simon Grainger1, Carsten Frederiksen1 and Xiagou Zheng2 Bureau of Meteorology, Melbourne, Australia National Institute of Water and Atmospheric Research, Wellington, New Zealand Acknowledgments: C20C Modelling groups, David Straus 4th C20C Workshop
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? Motivation To investigate the properties of the interannual variability of seasonal mean climate data 4th C20C Workshop
Theory (m = 1,2,3 months, y = 1,Y years, s = 1,S members, r = points) • 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 4th C20C Workshop
Rowell et al. (1995) • separate external and internal components (o = seasonal mean) • Zheng and Frederiksen (1999) • separate intra-seasonal component • and hence can deduce slow-internal component V(sy) Components of variability Cannot separate , and monthly anomalies, but can for the interannual variability of seasonal mean 4th C20C Workshop
Zheng and Frederiksen (2004) estimated intraseasonal variance as a function of monthly differences using moment estimation Estimating Intraseasonal Variability (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 4th C20C Workshop
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 Total Variability – DJF 1951-2000 NCEP BOM (S=10) CSIRO (S=10) COLA (S=10) GSFC (S=14) UKMO (S=12) 4th C20C Workshop
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 Intraseasonal Variability – DJF 1951-2000 NCEP BOM (S=10) CSIRO (S=10) COLA (S=10) GSFC (S=14) UKMO (S=12) 4th C20C Workshop
NCEP BOM (S=10) CSIRO (S=10) COLA (S=10) GSFC (S=14) UKMO (S=12) Potential Predictability (%) – DJF 1951-2000 4th C20C Workshop
Potential Predictability (%) – JJA 1951-2000 NCEP BOM (S=10) CSIRO (S=10) COLA (S=10) GSFC (S=14) UKMO (S=12) 4th C20C Workshop
NCEP Covariability – NH DJF 1949-2002 Total Slow Intraseasonal 4th C20C Workshop
Slow PC Regression – NH DJF 1951-2000 4th C20C Workshop
Intraseasonal ENSO Composites 1957-1998 UEOF-I1 (23.1%) UEOF-I2 (16.6%) UEOF-I3 (10.2%) UEOF-I4 (8.3%) -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 NCEP Covariability – SH JJA 1951-2000 Slow UEOF-S1 (32.0%) UEOF-S2 (14.7%) UEOF-S3 (8.7%) UEOF-S4 (7.9%) 4th C20C Workshop
Slow PC Regression – SH JJA 1951-2000 4th C20C Workshop
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 -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 COLA Variability – DJF 1951-2000 Slow V(y + sy) Slow External V(y) Slow Internal V(sy) 4th C20C Workshop
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 4th C20C Workshop
Australian Potential Predictability (%) Tmax DJF MAM JJA SON Tmin Precip 4th C20C Workshop