13 March 2007 4th C20C Workshop 1
Interannual Variability of Atmospheric Circulation in C20C models
Simon Grainger1, Carsten Frederiksen1 and Xiagou Zheng2
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 2007 4th C20C Workshop 2
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 2007 4th C20C Workshop 3
Theory
rrrrx symsyysym εδβ
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 2007 4th C20C Workshop 4
Components of variability
(o = seasonal mean)
Rowell et al. (1995) separate external and internal components
rrVrVrxV syosyysyo εδβ
Cannot separate , and monthly anomalies, but can for the interannual variability of seasonal mean
rrVrVrxV syysyosyo δβε
Zheng and Frederiksen (1999) separate intra-seasonal component
♦ and hence can deduce slow-internal component V(sy)
13 March 2007 4th C20C Workshop 5
Estimating Intraseasonal Variability
Zheng and Frederiksen (2004) estimated intraseasonal variance as a function of monthly differences using moment estimation
rxfrV symsyo ε (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 2007 4th C20C Workshop 6
Total Variability – DJF 1951-2000NCEP BOM (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
13 March 2007 4th C20C Workshop 7
Intraseasonal Variability – DJF 1951-2000NCEP BOM (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
13 March 2007 4th C20C Workshop 8
rx
rr
syo
syy
V
Vlity Predictabi
Potential Predictability (%) – DJF 1951-2000NCEP BOM (S=10) CSIRO (S=10)
COLA (S=10) GSFC (S=14) UKMO (S=12)
13 March 2007 4th C20C Workshop 9
Potential Predictability (%) – JJA 1951-2000NCEP BOM (S=10) CSIRO (S=10)
COLA (S=10) GSFC (S=14) UKMO (S=12)
13 March 2007 4th C20C Workshop 10
NCEP Covariability – NH DJF 1949-2002
Total Slow Intraseasonal
13 March 2007 4th C20C Workshop 11
Slow PC Regression – NH DJF 1951-2000BOM C(xoyo,xyo) C(y,xyo) C(y,y+sy)
NAO -0.069 -0.096 -0.117
PNA 0.756 0.779 0.861
W. Pacific 0.322 0.398 0.520
E. Atlantic 0.244 0.339 0.477
TNH 0.194 0.204 0.277
CSIRO C(xoyo,xyo) C(y,xyo) C(y,y+sy)
NAO 0.001 0.002 0.003
PNA 0.698 0.717 0.792
W. Pacific 0.175 0.183 0.239
E. Atlantic 0.192 0.265 0.374
TNH 0.432 0.473 0.639
COLA C(xoyo,xyo) C(y,xyo) C(y,y+sy)
NAO 0.097 0.135 0.164
PNA 0.617 0.642 0.709
W. Pacific 0.228 0.269 0.351
E. Atlantic 0.197 0.249 0.351
TNH 0.179 0.198 0.268
GSFC C(xoyo,xyo) C(y,xyo) C(y,y+sy)
NAO 0.379 0.446 0.545
PNA 0.784 0.794 0.878
W. Pacific 0.178 0.191 0.250
E. Atlantic 0.503 0.636 0.895
TNH 0.273 0.300 0.405
UKMO C(xoyo,xyo) C(y,xyo) C(y,y+sy)
NAO 0.241 0.308 0.376
PNA 0.803 0.828 0.915
W. Pacific 0.207 0.225 0.294
E. Atlantic 0.210 0.448 0.631
TNH 0.253 0.302 0.408
13 March 2007 4th C20C Workshop 12
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
SlowUEOF-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 2007 4th C20C Workshop 13
Slow PC Regression – SH JJA 1951-2000BOM C(xoyo,xyo) C(y,xyo) C(y,y+sy)
High Latitude 0.366 0.560 0.656
ENSO Warm 0.588 0.643 0.766
ENSO Cold 0.590 0.631 0.785
SP Wave 0.302 0.349 0.432
CSIRO C(xoyo,xyo) C(y,xyo) C(y,y+sy)
High Latitude 0.341 0.440 0.516
ENSO Warm 0.590 0.657 0.782
ENSO Cold 0.574 0.647 0.806
SP Wave 0.338 0.370 0.458
COLA C(xoyo,xyo) C(y,xyo) C(y,y+sy)
High Latitude 0.197 0.235 0.275
ENSO Warm 0.516 0.540 0.643
ENSO Cold 0.473 0.519 0.646
SP Wave 0.314 0.331 0.409
GSFC C(xoyo,xyo) C(y,xyo) C(y,y+sy)
High Latitude 0.287 0.319 0.374
ENSO Warm 0.606 0.663 0.790
ENSO Cold 0.528 0.553 0.689
SP Wave 0.124 0.131 0.162
UKMO C(xoyo,xyo) C(y,xyo) C(y,y+sy)
High Latitude 0.212 0.266 0.312
ENSO Warm 0.559 0.606 0.722
ENSO Cold 0.526 0.560 0.697
SP Wave 0.238 0.254 0.314
13 March 2007 4th C20C Workshop 14
COLA Variability – DJF 1951-2000Slow 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
13 March 2007 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
13 March 2007 4th C20C Workshop 16
Australian Potential Predictability (%)
DJF MAM JJA SONTmax
Precip
Tmin
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