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Results and Discussion

Simulated Baseline Climate (1976-2005)

The simulated 2-m air temperature climate over India is in relatively good agreement with the reference climate. The summer monsoon seasonal mean differences are generally smaller than 1 °C (Fig. 1), although statistically significant differences above 2°C exist in parts of north India and the adjoining Himalayan mountain region. The six-member ensemble mean (Fig. 1b) is closer to observed temperature over the central India than the individual simulations for the summer monsoon season (Fig. 1c-f). This is due to the cancellation of the biases of opposite signs in the models when added over this region. It may be noted that the two RCMs viz. CCLM4(MPI) and REMO (MPI) driven with the same CMIP5 AOGCM show different temperature bias patterns, particularly over the western parts of India. The REMO (MPI) was reported to have a larger annual mean cold bias over the entire west Asia when compared with the driving CMIP5 MPI-ESM-LR (Teichmann et al. 2013). It is also found from the comparison of the spatial pattern of temperature in two versions of the RegCM4 driven with the GFDL-ESM2 M that the latest version of this RCM (RegCM445; Fig. 1 f) has to some extent reduced the larger cold bias in the earlier version of the same model (RegCM411; Fig. 1h). Compared to the APHRODITE dataset, precipitation in CORDEX South Asia RCMs six-member ensemble mean is slightly underestimated during summer in the central and Indo-Gangetic plains over India, while significant overestimation is found over parts of south peninsula (Fig. 2b). However, the ensemble mean is closer to reference precipitation than most individual simulations for the summer monsoon season (Fig. 2c-f). This is because the biases of opposite signs in the models over parts of

a Summer monsoon (JJAS) season mean precipitation (mm day ; APHRODITE) for 1976-2005 and biases of precipitation (%) in the CORDEX South Asia simulations driven by CMIP5 AOGCM historical experiments

Fig. 2 a Summer monsoon (JJAS) season mean precipitation (mm day- ; APHRODITE) for 1976-2005 and biases of precipitation (%) in the CORDEX South Asia simulations driven by CMIP5 AOGCM historical experiments: b multi-model ensemble mean (ENSM) and c-h six different CORDEX RCMs listed in Table 1. Stippling denotes areas where the 30-year mean differences are not statistically significant at the 1 % level using Student’s t test south India tend to cancel each other when added, cf. LMDZ4 (IPSL) and RCA4 (ICHEC) in Fig. 2. The summer monsoon season wet bias seen in REMO (MPI) over the central and southern parts of India (Fig. 2g) was also found for the simulated annual mean precipitation with this RCM, while the driving MPI-ESM-LR had a dry bias over this region (Teichmann et al. 2013). The relatively lesser wet biases over south India in the latest version of RegCM4 (Fig. 2f) compared to the earlier version of this model (Fig. 2h) have also to some extent contributed to the improved spatial pattern of precipitation in the six-member ensemble mean.

Table 2 summarizes the climatological skill of 2-m temperature in the CORDEX historical experiments and the corresponding driving CMIP5 AOGCMs in simulating summer monsoon (JJAS), post-monsoon (OND) seasonal and annual means for the 30-year period 1976-2005 over the South Asia land areas (60-100°E, 5-35° N). In general, it is found that the RCMs and their driving AOGCMs tend to underestimate the spatial mean and overestimate the standard deviation over this large domain. The pattern correlation with APHRODITE is found to be relatively improved for all the RCMs in comparison with their driving AOGCMs in both seasons and annually. The spatial variability simulated by individual models relative to APHRODITE is assessed using a Taylor diagram (Taylor 2001; Fig. 3a). It is seen that the individual RCMs and their driving AOGCMs consistently yield higher Taylor skill in simulating the annual mean 2-m temperature distribution over land areas in South Asia. These models tend to overestimate the spatial mean precipitation for both seasons and annually in this 30-year period over this region (Table 3). The RCMs consistently show higher magnitude of spatial standard

Table 2 Performance of 2-m temperature (oC) climatology (1976-2005) averaged over land grid points in South Asia (60-100°E, 5-35°N)

Experiment

name

Spatial mean

Standard deviation

Pattern correlation

JJAS

OND

Annual

JJAS

OND

Annual

JJAS

OND

Annual

APHRODITE

23.4

14.8

18.4

8.7

11.8

10.9

-

-

-

LMDZ4

(IPSL)

  • 22.0
  • (22.0)
  • 12.1
  • (11.0)
  • 16.1
  • (15.2)
  • 10.3
  • (10.8)
  • 13.2
  • (13.9)
  • 12.5
  • (13.1)
  • 0.98
  • (0.96)
  • 0.99
  • (0.98)
  • 0.99
  • (0.98)

RCA4

(ICHEC)

  • 20.1
  • (18.8)
  • 9.2
  • (10.7)
  • 13.7
  • (14.3)
  • 10.8
  • (9.1)
  • 14.2
  • (12.4)
  • 13.3
  • (11.6)
  • 0.99
  • (0.97)
  • 0.99
  • (0.98)
  • 0.99
  • (0.98)

CCLM4

(MPI)

  • 21.9
  • (22.5)
  • 12.8
  • (11.4)
  • 16.6
  • (16.6)
  • 10.3
  • (10.0)
  • 13.7
  • (11.8)
  • 13.0
  • (11.6)
  • 0.98
  • (0.94)
  • 0.99
  • (0.97)
  • 0.99
  • (0.96)

RegCM445

(GFDL)

  • 19.0
  • (23.2)
  • 9.8
  • (13.4)
  • 13.1
  • (17.5)
  • 11.2
  • (10.6)
  • 13.4
  • (12.6)
  • 12.9
  • (12.2)
  • 0.97
  • (0.96)
  • 0.98
  • (0.97)
  • 0.98
  • (0.97)

REMO (MPI)

  • 21.5
  • (22.5)
  • 10.2
  • (11.4)
  • 15.1
  • (16.6)
  • 10.4
  • (10.0)
  • 13.2
  • (11.8)
  • 12.6
  • (11.6)
  • 0.99
  • (0.94)
  • 0.99
  • (0.97)
  • 0.99
  • (0.96)

RegCM411

(GFDL)

  • 18.5
  • (23.2)
  • 8.2
  • (13.4)
  • 12.0
  • (17.5)
  • 10.2
  • (10.6)
  • 13.1
  • (12.6)
  • 12.5
  • (12.2)
  • 0.97
  • (0.96)
  • 0.99
  • (0.97)
  • 0.98
  • (0.97)

The spatial skill for the six different CORDEX RCMs listed in Table 1 are compared with (in parenthesis) the corresponding CMIP5 AOGCM historical experiment used to drive the RCMs. The bold text shows the improved performance of the RCM relative to its driving AOGCM

Taylor diagram for the annual mean a 2-m air temperature

Fig. 3 Taylor diagram for the annual mean a 2-m air temperature (°C) and b precipitation (mm day-1) climatology (1976-2005) averaged over land grid points in South Asia (60-100°E, 5- 35°N). The radial coordinate shows the standard deviation of the spatial pattern, normalized by the observed standard deviation. The azimuthal variable shows the correlation of the modelled spatial pattern with the observed spatial pattern. The distance between the reference (REF) dataset (APHRODITE) and individual points corresponds to root-mean-square error (RMSE). The diagram shows the skill for the six different CORDEX RCMs listed in Table 1 and for the four CMIP5 model historical experiments used to drive the CORDEX South Asia RCMs

Table 3 Performance of precipitation (mm d 1) climatology (1976-2005) averaged over land grid points in South Asia (60-100°E, 5-35°N)

Experiment

name

Spatial

mean

Standard deviation

Pattern correlation

JJAS

OND

Annual

JJAS

OND

Annual

JJAS

OND

Annual

APHRODITE

4.7

1.0

2.2

4.2

1.3

1.8

-

-

-

LMDZ4

(IPSL)

  • 5.2
  • (3.1)
  • 1.6
  • (1.4)
  • 2.7
  • (1.7)
  • 5.6
  • (2.8)
  • 1.9
  • (1.2)
  • 2.7
  • (1.3)
  • 0.61
  • (0.62)
  • 0.78
  • (0.71)
  • 0.61
  • (0.61)

RCA4

(ICHEC)

  • 5.2
  • (5.7)
  • 1.5
  • (1.4)
  • 2.7
  • (2.8)
  • 5.4
  • (3.0)
  • 1.8
  • (1.4)
  • 2.6
  • (1.4)
  • 0.66
  • (0.76)
  • 0.81
  • (0.91)
  • 0.68
  • (0.79)

CCLM4

(MPI)

  • 4.7
  • (5.5)
  • 1.4
  • (1.6)
  • 2.5
  • (2.6)
  • 4.8
  • (3.7)
  • 1.3
  • (1.4)
  • 2.4
  • (1.7)
  • 0.80
  • (0.63)
  • 0.72
  • (0.73)
  • 0.78
  • (0.60)

RegCM445

(GFDL)

  • 5.2
  • (5.0)
  • 1.8
  • (1.2)
  • 3.2
  • (2.4)
  • 3.1
  • (4.2)
  • 2.0
  • (1.1)
  • 2.3
  • (2.0)
  • 0.54
  • (0.56)
  • 0.07
  • (0.70)
  • 0.54
  • (0.53)

REMO (MPI)

  • 6.0
  • (5.5)
  • 2.0
  • (1.6)
  • 3.2
  • (2.6)
  • 6.5
  • (3.7)
  • 2.7
  • (1.4)
  • 3.3
  • (1.7)
  • 0.65
  • (0.63)
  • 0.74
  • (0.73)
  • 0.66
  • (0.60)

RegCM411

(GFDL)

  • 5.1
  • (5.0)
  • 1.8
  • (1.2)
  • 3.0
  • (2.4)
  • 6.0
  • (4.2)
  • 1.8
  • (1.1)
  • 2.7
  • (2.0)
  • 0.46
  • (0.56)
  • 0.69
  • (0.70)
  • 0.67
  • (0.53)

The spatial skill for the six different CORDEX RCMs listed in Table 1 are compared with (in parenthesis) the corresponding CMIP5 AOGCM historical experiment used to drive the RCMs. The bold text shows the improved performance of the RCM relative to its driving AOGCM deviation for precipitation than APHRODITE observations suggesting that the high-resolution downscaling has overestimated the spatial variability of precipitation over this region. The simulated spatial patterns relative to APHRODITE (Table 3) vary among the RCMs and are found to be better than the driving AOGCMs in both seasons only for few individual RCMs viz. LMDZ4 (IPSL), CCLM4 (MPI) and REMO (MPI). The Taylor diagram for the annual mean precipitation distribution over land areas in South Asia (Fig. 3b) shows the large spread in the Taylor skill between the individual RCMs and their driving AOGCMs.

Figure 4 presents the monthly 2-m air temperature (left panels) and the precipitation (right panels) annual cycle for the period 1976-2005 simulated in the individual RCMs and their driving AOGCMs for the central, south-west and

Mean seasonal cycle for the period 1976-2005 of

Fig. 4 Mean seasonal cycle for the period 1976-2005 of (left panels) 2-m air temperature (°C) and (right panels) precipitation rate (mm day-1) for the six different CORDEX RCMs (thin lines) listed in Table 1 and for the four CMIP5 models (dashed lines) used to drive the RCMs used in the CORDEX South Asia historical experiments. The observed values based on APHRODITE (thick line) are used as reference. The analysis used the land grid points in the sub-regions a, d Central India (CLI; 20-25°N, 78-82°E), b, e South-West India (SWI; 20-25°N, 78-82°E), and c, f South-East India (SEI; 20-25°N, 78-82°E) south-east sub-regions over India. All the models simulate the phase of the seasonality in temperature well than the amplitude relative to APHRODITE observations in the three sub-regions. The individual model skill in simulating the seasonal cycle of temperature is summarized for the three sub-regions in Table 4. The root-mean-square error (RMSE) normalized with the APHRODITE annual range in temperature reveals that three RCMs viz. LMDZ4(IPSL), RCA4(ICHEC) and CCLM4(MPI) are able to outperform their driving AOGCMs in simulating the amplitude of seasonality in temperature over central India. The correlation coefficient between the model simulated and APHRODITE annual cycle of temperature further confirms that these RCMs improve not only the amplitude but also the phase of the temperature seasonality compared to their driving AOGCMs over central India. However, the RCMs are in general not able to improve the amplitude or the phase of the annual cycle of temperature over the hilly regions in south-west India and the drier regions in south-east India. Despite large inter-model variations found in the simulated precipitation seasonality, some RCMs appear to agree relatively closer with APHRODITE than their driving AOGCMs at least in capturing the phase of the seasonality over the three sub-regions (Fig. 4; right panels). However, the individual model skill summarized in Table 5 shows that only LMDZ4 (IPSL) is able to show an added value compared to its driving AOGCM in simulating the amplitude and phase of the seasonality in precipitation for all three sub-regions over India.

Table 4 Performance of 2-m temperature (°C) monthly annual cycle climatology (1976-2005) averaged over land grid points in three sub-regions: Central India (CLI; 20-25°N, 78-82°E), South-West India (SWI; 20-25°N, 78-82°E) and, (c) South-East India (SEI; 20-25°N, 78-82°E)

Experiment

name

Normalized RMSE

Correlation coefficient

CLI

SWI

SEI

CLI

SWI

SEI

LMDZ4 (IPSL)

  • 0.11
  • (0.16)
  • 0.22
  • (0.21)
  • 0.39
  • (0.31)
  • 0.97
  • (0.93)
  • 0.97
  • (0.83)
  • 0.91
  • (0.92)

RCA4 (ICHEC)

  • 0.23
  • (0.25)
  • 0.36
  • (0.37)
  • 0.53
  • (0.49)
  • 0.99
  • (0.98)
  • 0.91
  • (0.95)
  • 0.86
  • (0.98)

CCLM4 (MPI)

  • 0.08
  • (0.13)
  • 0.18
  • (0.16)
  • 0.17
  • (0.44)
  • 0.99
  • (0.97)
  • 0.93
  • (0.98)
  • 0.98
  • (0.98)

RegCM445

(GFDL)

  • 0.26
  • (0.11)
  • 0.22
  • (0.15)
  • 0.35
  • (0.14)
  • 0.92
  • (0.99)
  • 0.95
  • (0.99)
  • 0.94
  • (0.97)

REMO (MPI)

  • 0.21
  • (0.13)
  • 0.41
  • (0.16)
  • 0.38
  • (0.44)
  • 0.99
  • (0.97)
  • 0.96
  • (0.98)
  • 0.96
  • (0.98)

RegCM411

(GFDL)

  • 0.40
  • (0.11)
  • 0.58
  • (0.15)
  • 0.58
  • (0.14)
  • 0.95
  • (0.99)
  • 0.93
  • (0.99)
  • 0.98
  • (0.97)

The root-mean-square error (RMSE) normalized with the APHRODITE annual range and the correlation coefficient between the simulated and APHRODITE annual cycle for the six different CORDEX RCMs listed in Table 1 are compared with (in parenthesis) the corresponding CMIP5 model historical experiment used to drive the RCMs. The bold text shows the improved performance of the RCM relative to its driving AOGCM

Table 5 Performance of precipitation (mm day monthly annual cycle climatology (19762005) averaged over land grid points in three sub-regions: Central India (CLI; 20-25°N, 78-82°E), South-West India (SWI; 20-25°N, 78-82°E) and, (c) South-East India (SEI; 20-25°N, 78-82°E)

Experiment

name

Normalized RMSE

Correlation coefficient

CLI

SWI

SEI

CLI

SWI

SEI

LMDZ4 (IPSL)

  • 0.81
  • (1.09)
  • 0.66
  • (0.99)
  • 0.72
  • (0.71)
  • 0.93
  • (0.82)
  • 0.90
  • (0.63)
  • 0.86
  • (0.74)

RCA4 (ICHEC)

  • 0.65
  • (0.37)
  • 1.23
  • (0.37)
  • 0.99
  • (0.29)
  • 0.96
  • (0.94)
  • 0.53
  • (0.98)
  • 0.63
  • (0.93)

CCLM4 (MPI)

  • 0.66
  • (0.30)
  • 0.71
  • (0.29)
  • 0.51
  • (1.10)
  • 0.96
  • (0.97)
  • 0.82
  • (0.91)
  • 0.96
  • (0.76)

RegCM445

(GFDL)

  • 0.84
  • (0.44)
  • 0.54
  • (0.35)
  • 1.22
  • (0.22)
  • 0.95
  • (0.99)
  • 0.96
  • (0.99)
  • 0.25
  • (0.90)

REMO (MPI)

  • 0.35
  • (0.30)
  • 0.33
  • (0.29)
  • 0.45
  • (1.10)
  • 0.98
  • (0.97)
  • 0.98
  • (0.91)
  • 0.92
  • (0.76)

RegCM411

(GFDL)

  • 0.97
  • (0.44)
  • 1.21
  • (0.35)
  • 1.88
  • (0.22)
  • 0.96
  • (0.99)
  • 0.98
  • (0.99)
  • 0.54
  • (0.90)

The root-mean-square error (RMSE) normalized with the APHRODITE annual mean value and the correlation coefficient between the simulated and APHRODITE annual cycle for the six different CORDEX RCMs listed in Table 1 are compared with (in parenthesis) the corresponding CMIP5 model historical experiment used to drive the RCMs. The bold text shows the improved performance of the RCM relative to its driving AOGCM

 
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