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

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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