Challenge and directions for improving GCM simulations of

the monsoon

Julia Slingo and Andrew Turner

Asian and Australian Monsoons are dominated by the effects of convection organised on a wide range of space and time scales (diurnal cycle, tropical cyclones, monsoon depressions, MJO, BSISO, convectively coupled equatorial waves…)

Increasing evidence of multi-scale interactions involving: Coupling between dynamics and physics on wide range of scales within components of the climate system Coupling on wide range of scales between components of the climate system

Increasing evidence that multi-scale interactions affect: Mean state of the climate system Low frequency variability of the climate system

Challenge 1: Multi-scale Processes

From THORPEX/WCRP Workshop on Organised Convection and the MJO

Scale interactions are fundamental to the tropical climate system

Challenge 2: Air-sea interaction and coupling with the ocean

• Increasing evidence that many aspects of monsoon variability involve air-sea interaction and coupled processes: Implies that atmosphere-only models may not be appropriate for monsoon studies.

• Indian Ocean may play a much more significant role than previously thought: Implies the need for more detailed evaluation of Indian Ocean in coupled models.

• Diurnal cycle in the ocean mixed layer may be important for the mean state and for intraseasonal variability: Implies that higher vertical resolution in the upper ocean may be needed.

High-frequency, observed SST forcing and the intraseasonal

oscillation Objective

To determine the influence of high frequency SSTs on intraseasonal monsoon variability.

SST forcing dataset

Feb. 2005–2006 reanalysis from the Met Office GHRSST project.

Assimilates satellites (e.g., TRMM) and in situ buoys.

Available as daily analyses at 1/20° spatial resolution.

Substantial intraseasonal (30-70 day) variability during the monsoon.

Standard deviation of 30-70 day SSTs for June – September.

Line contours give percentage of variability explained.

High-frequency, observed SST forcing and the intraseasonal

oscillation

HadAM3 ensembles

“Daily” ensemble forced by daily GHRSST SST product.

“Monthly” ensemble forced by monthly mean GHRSST (following AMIP II method)

N144 (1.125°x0.875°) and 30 vertical levels, beginning 1 Feb.

30 ensemble members

Difference between the ensembles shows the influence of sub-monthly SSTs.

Seasonal-mean rainfall

Sub-monthly SST variability projects onto the ensemble-mean, seasonal-mean rainfall.

Differences are small but statistically significant.

Difference in ensemble-mean,JJAS-mean rainfall, taken as

the Daily ensemble mean minusthe Monthly ensemble mean.

Intraseasonal variability

Significant increase in 30-70 day variability in Bay of Bengal and Arabian Sea.

Spatial pattern of increases is broadly consistent with regions of high 30-70 day variability in GHRSST SSTs.

No coherent northward-propagating signal from the equatorial Ocean to the Indian peninsula – lack of coupling?

High-frequency, observed SST forcing and the intraseasonal

oscillation

Difference in ensemble-meanstandard deviation in 30-70 day

filtered JJAS rainfall.

Intraseasonal variability

Daily ensemble contains much stronger power at intraseasonal (30-50 day) periods.

Sub-monthly SST variability can increase the variability of rainfall at much longer timescales.

High-frequency, observed SST forcing and the intraseasonal

oscillation

Daily Ensemble

Monthly Ensemble

Ensemble-mean 1D wavelet transforms of Bay of Bengal rainfall

Spatial variability of intraseasonal modes

HadCM3 HadCM3FAERA-40

10-2

0day

30-6

0day

The spatial pattern of explained variance is better simulated in HadCMFA, especially in the 30-60 day band.

Percentage variance explained by each band of total intraseasonal variance of U850 wind anomalies:

Temporal variability of intraseasonal modes

HadCM3 HadCM3FAERA-40

10-2

0day

30-6

0day

Northward propagating modes on 30-60 day timescales show no improvement in HadCM3FA.

Lag-regressions of U850 against reference timeseries (85-90E, 5-10N), showing westward (10-20) and northward (30-60) propagation

Mixed layer depth anomaly active and break composites

HadCM3 HadCM3FA

Active

Break

Mixed layer model studies of the diurnal cycle: Sensitivity to vertical resolution

1m resolution (CTR) gives good simulation of diurnal and intraseasonal variability

10m resolution of most ocean models will not resolve diurnal variability of SST

Intraseasonal variability is ~0.4°C less than CTR

Implies 40% underestimate of the strength of air-sea coupling

Bernie et al. 2005

Diurnal Coupling with the Ocean: Impact on the annual mean

climate

HadAM3 coupled to OPA with high vertical ocean resolution – 1 meter in near surface layer:

HDC: Hourly coupling

HDM: Daily coupling

Dan Bernie, Eric Guilyardi, Gurvan Madec, Steve Woolnough & Julia Slingo

DJF MAM

JJA SON

Amplitude of SST diurnal cycle in HadOPA (L300)

Dan Bernie, Eric Guilyardi, Gurvan Madec, Steve Woolnough & Julia Slingo

Note large seasonality in the amplitude of the diurnal cycle for the northern Indian Ocean. Is this a crucial component of the pre-monsoon high SSTs?

A very interesting talk

Challenge 3: Influence of basic state errors on monsoon variability

The effect of heat flux adjustments

The effect of heat flux adjustments

More in session 4….

HadCM2

1994

HadCM3

1998

HadGEM1

2004

HiGEM

2005

NUGAM

2006

Atmosphere ~300km

19 levels

~300km

19 levels

~150km

38 levels

~90km

38 levels

~60km

38 levels

Ocean 2.50 x 3.750

20 levels

1.250 x 1.250

20 levels

10 x 10 (1/30)

40 levels

1/30 x 1/30

40 levels

(1/30 x 1/30)

(40 levels)

Flux

Adjustment?

Yes No No No (No)

Computing 1 4 40 400 Earth Simulator

Recent developments in UK Climate Models

Challenge 4: Sensitivity to resolution

HiGEM

HadGAM

HiGAM

HadGEM

JJA precipitation minus CMAP

Page 20© Crown copyright 2006

Tropical Precipitation

Errors JJA 2004

Dry Wet

Probability density function of central relative vorticity for tropical cyclones

135 km model

90 km model

60 km model

Distribution shifted to higher intensities as resolution is increased

Observed hurricanes/typhoons seen to have vorticities (spin) between 10-70 x10-5 s-1

x10-5

• Atmosphere-only model fails to simulate MJO • HiGEM is a significant improvement on HadCM3 (and HadGEM1)

Atmosphere Only