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Challenge and directions for improving GCM simulations of the monsoon Julia Slingo and Andrew Turner.

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Presentation on theme: "Challenge and directions for improving GCM simulations of the monsoon Julia Slingo and Andrew Turner."— Presentation transcript:

1 Challenge and directions for improving GCM simulations of the monsoon Julia Slingo and Andrew Turner

2 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

3 From THORPEX/WCRP Workshop on Organised Convection and the MJO Scale interactions are fundamental to the tropical climate system

4 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.

5 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.

6 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 minus the Monthly ensemble mean.

7 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-mean standard deviation in 30-70 day filtered JJAS rainfall.

8 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

9 Spatial variability of intraseasonal modes HadCM3HadCM3FAERA-40 10-20day 30-60day 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:

10 Temporal variability of intraseasonal modes HadCM3HadCM3FAERA-40 10-20day 30-60day 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

11 Mixed layer depth anomaly active and break composites HadCM3HadCM3FA Active Break

12 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

13 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

14 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?

15 A very interesting talk Challenge 3: Influence of basic state errors on monsoon variability

16 The effect of heat flux adjustments

17 More in session 4….

18 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 2.5 0 x 3.75 0 20 levels 1.25 0 x 1.25 0 20 levels 1 0 x 1 0 (1/3 0 ) 40 levels 1/3 0 x 1/3 0 40 levels (1/3 0 x 1/3 0 ) (40 levels) Flux Adjustment? YesNo (No) Computing1440400Earth Simulator Recent developments in UK Climate Models Challenge 4: Sensitivity to resolution

19 HiGEM HadGAM HiGAM HadGEM JJA precipitation minus CMAP

20 Page 20© Crown copyright 2006 Tropical Precipitation Errors JJA 2004 Dry Wet

21 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

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

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