1. Presented by: Javed Akhter1 Contributors: Lalu Das 2and Monami Dutta2
How well do the CMIP5 Models simulate Summer Monsoon Rainfall over the Core Monsoon Region of India?
Presented by: Javed Akhter1
Contributors: Lalu Das 2and Monami Dutta2
1Department of Physics, Jadavpur University
2Department of Agricultural Meteorology and Physics,
Bidhan Chandra Krishi Viswavidyalaya

2. To compare the skills of different multi-model ensemble approaches.
Objectives
To investigate the performances of CMIP5 models in simulating spatio-temporal pattern of intra-seasonal monsoon precipitation over Core Monsoon Zone of India.
To compare the skills of different multi-model ensemble approaches.
To judge whether the bias correction can provides added values for model performances.
Observational Data: IMD Gridded Data (0.25x0.25 degree)
GCM Data : Historical simulations of 45 GCMs from CMIP5 archive. (
Domain: E, N, within which the monsoon trough/ CTCZ normally fluctuates.[Mandke et al. (2007)]
Time Period used:

3. Thresholds for different indices
Performances of GCMs to simulate Regional Averaged Precipitation
Statistical Indices used for model evaluation:
Agreement Indices- Nash-Sutcliffe efficiency (NSE), d-index
Error Indices-ratio of RMSE to the standard deviation of the (RSR) and Percentage Bias(PB)
For the error indices (RSR & PB),threshold is calculated as the 1st quartile of all the model values.
For the agreement indices (NSE & d), threshold is calculated as the 3rd quartile of all the model values.
Threshold for monsoon is calculated as the average of the threshold values of four months.
Models crossing threshold values for each index gets score 1; otherwise zero.
Thresholds for different indices
NSE d
RSR PB
June
-3.446
0.392
2.085
±40.800
July
-2.284
0.363
1.792
±25.300
August
-2.302
0.385
1.797
±19.500
September
-0.885
0.413
1.358
±16.000
Monsoon
-2.229
0.388
1.758
±25.400
Better Perform-ance of Models

4. Performances of GCMs to simulate Regional Averaged Precipitation

5. Performances of GCMs to simulate Regional Averaged Precipitation

6. Performances of GCMs to simulate Regional Averaged Precipitation

7. Performances of GCMs to simulate Regional Averaged Precipitation

8. Performances of GCMs to simulate Regional Averaged Precipitation
Model
June
July
August
September
Overall
ACCESS1-0 1
ACCESS1.3
bcc-csm1-1-m 2
bcc-csm1-1 3
BNU-ESM 4 15
CanCM4
CanESM2
CCSM4 14
CESM1-BGC 13
CESM1-CAM5 9
CMCC-CESM 8
CMCC-CM
CMCC-CMS
CNRM-CM5-2
CNRM-CM5
CSIRO-Mk3-6-0
EC-EARTH 10
FGOALS_g2
FIO-ESM 5
GFDL-CM2p1 11
GFDL-CM3 12
GFDL-ESM2G 6
GFDL-ESM2M
Model performance Scores:

9. Performances of GCMs to simulate Regional Averaged Precipitation
Model
June
July
August
September
Overall
GISS-E2-H-CC
GISS-E2-H
GISS-E2-R-CC
GISS-E2-R
HadCM3 1 3 4
HadGEM2-AO
HadGEM2-CC
HadGEM2-ES
inmcm4 8
IPSL-CM5A-LR 2
IPSL-CM5A-MR
IPSL-CM5B-LR
MIROC-ESM-CHEM 10
MIROC-ESM 11
MIROC4h 7
MIROC5 5
MPI-ESM-LR
MPI-ESM-MR
MRI-CGCM3
MRI-ESM1
NorESM1-M
NorESM1-ME
Model performance Scores:

10. Multi-Model Ensemble:
Multi-model ensembles constructed in 3 different ways:
i)Taking mean of all models(MM-all)
ii) Taking mean of 7 better models(MM7)
iii)Using multiple regression of 7 better models(MM7-reg)
JUNE
NSE d
RSR PB
MM-all
-1.88
0.44
1.68
-45.3
MM7
-0.36
0.56
1.15
-23.1
MM7-reg
-0.22
0.35
1.09
0.5
Best Model(bcc-csm1-1)
-1.14
0.53
1.45
-28.2
JULY
NSE d
RSR PB
MM-all
-4.29
0.37
2.27
-44.5
MM7
0.08
0.41
0.95
-1.8
MM7-reg
-0.41
0.11
1.17
0.5
Best Model(CESM1-BGC)
-0.44
0.53
1.19
1.2

11. Multi-Model Ensemble:
AUGUST
NSE d
RSR PB
MM-all
-4.55
0.39
2.33
-39.8
MM7
-0.12
1.04
-1.9
MM7-reg
-0.33
0.23
1.14
-0.2
Best Model(CESM1-BGC)
-0.58
0.56
1.24
6.8
SEPTEMBER
NSE d
RSR PB
MM-all
-0.53
0.43
1.22
-27.4
MM7
0.004
0.30
0.99
0.0
MM7-reg
-0.15
0.27
1.06
1.5
Best Model(CESM1-CAM5)
-0.26
0.55
1.11
12.7

12. Effect of Bias Correction:
Quantile mapping used -PTF(Parametric transformations), DIST(Distribution derived transformations), QUANT(Empirical Quantiles), RQUANT(Robust Empirical Quantiles)
Bias corrected values verified using leave-one-out-cross-validation(LOOCV).
June
NSE d
RSR PB
INTPL
-6.74
0.33
2.75
-80.50
QUANT
-1.49
0.26
1.56
0.30
RQUANT
-1.47
0.27
1.55
0.00
PTF
-7.41
0.29
2.87
-49.40
DIST
-1.63
1.60
-0.50
One example:HadCM3
July
NSE d
RSR PB
INTPL
-3.71
0.35
2.15
-30.00
QUANT
-1.28
0.29
1.49
-0.30
RQUANT
-1.22
0.30
1.47
-0.20
PTF
-3.20
2.03
-9.60
DIST
-1.31
0.32
1.50
0.10

13. Effect of Bias Correction:
August
NSE d
RSR PB
INTPL
-4.17
0.38
2.25
-29.60
QUANT
-1.24
0.40
1.48
0.10
RQUANT
-1.21
1.47
0.20
PTF
-3.28
0.37
2.05
-4.70
DIST
-1.09
1.43
0.00
One example:HadCM3
September
NSE d
RSR PB
INTPL
-1.94
0.44
1.70
-46.70
QUANT
-0.72
0.50
1.30
0.60
RQUANT
-0.69
1.28
PTF
-1.71
0.48
1.63
-14.80
DIST
-0.67
0.49

14. Performances of GCMs to simulate Spatial Pattern of Precipitation

15. Performances of GCMs to simulate Spatial Pattern of Precipitation

16. Performances of GCMs to simulate Spatial Pattern of Precipitation
June
Observed
Raw-GCM
BC-GCM

17. Performances of GCMs to simulate Spatial Pattern of Precipitation
July
Observed
Raw-GCM
BC-GCM

18. Performances of GCMs to simulate Spatial Pattern of Precipitation
August
Observed
Raw-GCM
BC-GCM

19. Performances of GCMs to simulate Spatial Pattern of Precipitation
September
Observed
Raw-GCM
BC-GCM

20. Conclusions:
BNU-ESM,CCSM4,CESM1-BGC,GFDL-CM3 are better compared to rest of the models.
Models perform relatively better in September than other months.
Multi-model ensemble using all models is not so skillful compared to ensemble comprising smaller subset of better models.
Bias correction provides additional improvement to the model performances.

21. References:
Taylor KE (2001) Summarizing multiple aspects of model performance in single diagram. J. Geophys. Res., 106, 7183–7192
Rajeevan M, Bhate J, Kale J, and Lal B (2006) High resolution daily gridded rainfall data for the Indian region: analysis of break and active monsoon spells. Curr. Sci., 91, 296–306.
Mandke S, Sahai AK, Shinde MA, Susmitha Joseph and Chattopadhyay R (2007) Simulated changes in active/break spells during the Indian summer monsoon due to enhanced CO2 concentrations: Assessment from selected coupled atmosphere–ocean global climate models; Int. J. Climatol. 27 ,837–859
Das L, Annan JD, Hargreaves JC, Emori S (2012) Improvements over three generations of climate model simulations for eastern India, Climate Research, Vol. 51: 201–216.
Hua Chen, Chong-Yu Xu, Shenglian Guo (2012) Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. Journal of Hydrology , , 36-45

22. Thank you