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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
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