1 / 16

Interannual Variability of Atmospheric Circulation in C20C models

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

xue
Télécharger la présentation

Interannual Variability of Atmospheric Circulation in C20C models

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. NCEP BOM (S=10) CSIRO (S=10) COLA (S=10) GSFC (S=14) UKMO (S=12) Potential Predictability (%) – DJF 1951-2000 4th C20C Workshop

  9. Potential Predictability (%) – JJA 1951-2000 NCEP BOM (S=10) CSIRO (S=10) COLA (S=10) GSFC (S=14) UKMO (S=12) 4th C20C Workshop

  10. NCEP Covariability – NH DJF 1949-2002 Total Slow Intraseasonal 4th C20C Workshop

  11. Slow PC Regression – NH DJF 1951-2000 4th C20C Workshop

  12. 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

  13. Slow PC Regression – SH JJA 1951-2000 4th C20C Workshop

  14. 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

  15. 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

  16. Australian Potential Predictability (%) Tmax DJF MAM JJA SON Tmin Precip 4th C20C Workshop

More Related