**Chapter 3. Results of coupled climate model**

**3.5 South Asia**

In South Asia, it is a good case to discuss the irrigation effect on heat waves

because of the high temperature of the dry season and the wet season, from April to

May and July to August, respectively. In addition, the irrigation area in Indo-Gangetic

Plain is also larger compared to other places. Therefore, we discuss this place more

comprehensively to figure out the effect of irrigation on wet-bulb temperature.

First of all, we do the same analysis on the change in the wet seasons. In the

nutshell, during the wet seasons, the wet-bulb temperature increases by 0.5 (K) in figure

17c because of the change in specific humidity. But why the specific humidity doesn’t

go up when the irrigation fraction increases in figure 17b?

To figure out the reason, we distinguish the irrigation effect and other effects in the

map. Figure 18 shows the specific humidity change induced by other forcings. The

contour represents the irrigation fraction which illustrates that Northeast India is a

highly irrigated area. Interestingly, the specific humidity decreases in high and increases

in low irrigated areas. The mechanism comes from the irrigation cooling effect. When

the irrigation makes the surface temperature goes down, that means the air parcel cannot

hold as much water vapor according to the c-c equation. On the other hand, global

warming makes the temperature in other places rise. In summer, the monsoon brings

lots of moisture into India, so the higher temperature place can hold much more water

vapor. Therefore, the increasing moisture from irrigation and the decreasing moisture

from the cooling effect cancel out. That’s why the specific humidity in figure 17b stays

still as the irrigated fraction increases.

For the dry season in figure 19, we analyze the Tw change in South Asia from

April to May. We can see that the Tw change over 1 (K) as the irrigated change fraction

equals 0.3, which is much higher than the wet season. The reason also comes from the

increased specific humidity, which almost results from the irrigation. In this part, it is

important to understand that the range of the humidity can change a lot in the dry season

and it is from the irrigation expansion. On the other hand, the 10m high humidity is low

so the gradient of humidity increases, which makes the evaporation work effectively.

Although it effectively cools down the surface temperature, the cooling effect is little

compared to the moistening effect. Therefore, the heat wave in the dry season might be

dangerous for humans because the air parcel can have much more water vapor and

make the atmosphere humid leading to a higher Tw.

Figure 20 shows the irrigation effect of one month's average in South Asia. The

irrigation effect is the simulation from irr minus control in table 1. The criteria is RH

lower than one standard deviation. Note that the red dots represent temperature change

is the dominant factor. That is to say, the absolute value of the temperature difference is

larger than the mixing ratio.

According to our calculation, when the mixing ratio change is small compared to

the temperature change, the Tw change and T change are linear. The regression slope of

the red dots is 0.20 which is close to the theoretical value of 0.125. On the other hand, if

the mixing ratio change is over about 0.0003 (kg/kg), the moistening effect dominates

the region. This means the wet-bulb temperature change is positive no matter how the

dry-bulb temperature change is induced by the irrigation effect, so most of the black

dots are situated in the first and second quadrants in the left diagram due to the

moistening effect. Note that the red-to-black ratio is low so the possibility of

temperature change dominating in the dry condition is low. In addition, this is evidence

that the Tw is more sensitive to mixing ratio change.

Same as above, the red region represents the temperature change dominating the

area over the wet conditions (Figure 21). Comparing figure 20 and figure 21, the red

dots of dry-bulb temperature in dry condition skew to the negative value. However, in

wet conditions, the red dots of dry-bulb temperature do not skew to the negative value,

which means the temperature cooling effect does not exist in this scenario. This

phenomenon can be seen in figure 22a which is the probability density function of

temperature change. The definition of dry and wet conditions is the same as in figures

20 and 21. To confirm the significance, we set the hypothesis as below:

𝐻_{"}: 𝜇_{0'1} ≥ 0

𝐻_{&}: 𝜇_{0'1} < 0

( 16 )

The one-tail test tells us that the z-value is low enough to reject the null hypothesis, and

the p-value is lower than 0.01. Therefore, the irrigation cooling effect affects the

temperature in dry conditions significantly. The mechanism might come from the

gradient of humidity between the soil and the atmosphere increasing so evaporation

works effectively compared to wet conditions.

To find the possibility of the temperature change dominating region, we do the

possibility density function of wet and dry conditions and the result is shown in figure

22b. First of all, the probability of wet conditions of mixing ratio changes close to 0

(kg/kg) is higher than in dry conditions. Also, we do the one-tailed z-test to verify the

red line skews to the positive value. The hypothesis is as below:

𝐻_{"}: 𝜇_{0'1} ≤ 𝜇_{}

$2-𝐻_{&}: 𝜇_{0'1} > 𝜇_{}

$2-( 17 )

The z-value equals 5.0661 and the p-value is lower than 0.01, which means we have

99% confidence to reject the null hypothesis and the mean value of mixing ratio change

is significantly skewed to a positive value.

In addition, the variance is also important when we investigate the moistening

effect. Here, we do the one-tailed F-test to verify whether the variance of dry conditions

is larger than wet conditions. The hypothesis is as below:

𝐻_{"}: 𝜎_{0'1}^{(} ≤ 𝜎_{$2-}^{(}

𝐻_{&}: 𝜎_{0'1}^{(} > 𝜎_{$2-}^{(} .

( 18 )

The F value is 6.9501 and the critical value is 1.0956, which means we have 99% of

confidence in rejecting the null hypothesis. In this analysis, we can conclude that the

distribution of dry conditions is wider than wet conditions. The probability of suffering

from an extreme event is higher than in wet conditions because of the skewed and wide

distribution. Therefore, the ratio of dots on the left-hand side of figure 20 in the first and

second quadrants to the third and fourth quadrants is larger than that in figure 21. Also,

some wet-bulb changes can reach up to 6 (K) in figure 20, which results from the

extreme value of the change of mixing ratio.

As the discussion above, it is interesting to investigate the mechanism of

evaporation influenced by the gradient of soil and air moisture. Here, we explore the

difference in latent heat flux change. As shown in figure 22c, the dry condition curve

skews to the positive value. This means the net effect of irrigation for dry conditions can

bring more evaporation, especially from April to May (as appendix A2). Note that the

appendix figures point out the dry seasons and wet seasons in South Asia. The number

in the parentheses denotes the number of dry or wet events from 1981 to 2010. From

April to May, the climate is hot and dry, which means the background relative humidity

is low. If we add water to the soil, the gradient of moisture goes up and makes

evaporation effective. On the other hand, we find that there is no apparent difference in

other seasons. In summary, the cooling effect of irrigation on dry-bulb temperature is

sensitive due to the effective evaporation. This is an important conclusion to discuss the

characteristic of temperature in each condition.