Abstract

The Indian summer monsoon rainfall is dominated by oceanic sources of moisture. However, land surface processes also have a significant role in the generation of precipitation within the Indian subcontinent. Evapotranspiration over a region supplies moisture to the atmosphere, which may lead to precipitation in the same region. This is known as recycled precipitation. The role of evapotranspiration as an additional source of moisture to precipitation has been investigated in earlier studies at continental scales; however, the amount of monsoon precipitation generated from evapotranspiration has not been quantified at the daily scale for the Indian subcontinent. To examine the role of land surface hydrology in regional precipitation and to quantify recycled precipitation, the dynamic recycling model at a daily scale with NCEP Climate Forecast System Reanalysis (CFSR) data for the period of 1980–2010 is used. A high precipitation recycling ratio, that is, the ratio of recycled precipitation to total precipitation, is found at the end of the monsoon (September). As the monsoon progresses in India, enhanced soil moisture and vegetation cover lead to increased evapotranspiration and recycled precipitation. The recycling ratio is highest (around 25%) in northeastern India, which has high vegetation cover leading to high evapotranspiration. Recycled precipitation over central and northeastern India in September is responsible for delaying the withdrawal of the summer monsoon over these regions. A trend analysis of recycled precipitation shows a statistically significant decreasing trend in northeastern India.

1. Introduction

The Indian summer monsoon rainfall (ISMR) during June–September (JJAS) is part of the Asian monsoon (Rajeevan et al. 2010) and is the major source of annual precipitation over the Indian subcontinent. A large population relying on agriculture is highly dependent on the seasonal characteristics of ISMR. Though ISMR is dominated by an oceanic source, evapotranspiration from terrestrial sources is also an important contributor to precipitation. The development of low pressure over subcontinental land during premonsoon months (i.e., April and May), owing to the land–sea thermal contrast between Indian land regions and the Indian Ocean, results in advection of evaporated oceanic moisture toward the Indian region (Krishnamurthy and Kinter 2003). Although the land–sea thermal contrast is one of the major mechanisms influencing summer monsoon precipitation characteristics, several land surface processes, such as soil moisture, evapotranspiration, and topography and land use, are also associated with seasonal monsoon precipitation dynamics (Meehl 1994; Yasunari 2007; Bellon 2010; Saha et al. 2012). The spatial and temporal variability of rainfall is often regulated by the strength of land surface feedbacks (Shukla and Mintz 1982; Yasunari 2007). For example, evapotranspiration over a region provides additional moisture to the atmosphere, which leads to precipitation in the same region, and this is known as recycled precipitation. Significant variations in the strength of these feedbacks through evapotranspiration can affect regional precipitation characteristics during the monsoon season. Therefore, understanding and modeling of the feedback processes from the land surface to the atmosphere are of major importance for the study of any regional climate system. Here, we model precipitation recycling in the Indian subcontinent during a monsoon to understand the feedback processes with the land surface and their strength and variability.

Land surface interacts with the adjoining atmosphere by exchanging energy, mass, and momentum (Dominguez and Kumar 2008), and this significantly affects the characteristics of regional climate system (Dirmeyer and Shukla 1993; Higgins and Gochis 2007; Watts et al. 2007). Moreover, recent studies (Wang 2006, and references therein; Yasunari 2007; Bellon 2010; Saha et al. 2012) have also revealed that the geophysical processes associated with ISMR originate from land, ocean, and atmospheric interactions. Unlike ocean–atmosphere interactions through sea surface temperature (SST) variations, the role of land–atmosphere interaction in ISMR at the regional scale has not been extensively investigated and quantified.

To get a holistic view of the regional precipitation process during summer monsoon, the quantification of precipitation generated by local evapotranspiration is required. The impact of precipitation recycling on total precipitation has been identified through a variety of approaches using analytical models that are derived from the principle of conservation of atmospheric water vapor (Budyko 1974; Lettau et al. 1979; Brubaker et al. 1993; Eltahir and Bras 1996; Burde and Zangvil 2001; Dominguez et al. 2006). Eltahir and Bras (1996) studied the potential impacts of local land surface processes on the regional water cycle over the Amazon basin. The study explained that the amount of precipitable water contained in the atmospheric control volume over any particular land region is mainly composed of two components: (i) the advected component that includes both oceanic as well as remote terrestrial sources and (ii) the internal component that includes local terrestrial sources. The partitioning of precipitable water, based on geographic locations of their evaporative sources, can give detailed information about the role of local land surface characteristics in regional precipitation (Eltahir and Bras 1994).

In the context of ISMR, literature shows the possibility of impacts of land surface hydrology on ISMR through feedback mechanisms (Meehl 1994; Yasunari 2007; Asharaf et al. 2011; Tuinenburg et al. 2012; Saha et al. 2012). Bosilovich and Schubert (2002) identified local and remote sources of moisture used in summer monsoon precipitation over North America and India by implementing three-dimensional water vapor tracers (WVTs) in general circulation model (GCM) simulations. It was observed that the southern and western Indian Ocean significantly contribute to the monsoon precipitation over the Indian subcontinent, with the highest amount in June and lowest amount in August. Gimeno et al. (2010) used a three-dimensional Lagrangian transport model, Flexible Particle Dispersion Model (FLEXPART; Stohl and James 2004, 2005), to identify the oceanic moisture sources for the continental precipitation. Six different oceanic sources (i.e., Indian Ocean, Arabian Sea, Zanzibar Current, Agulhas Current, tropical South Africa, and Red Sea) were identified for the Indian monsoon rainfall during June–August (JJA). Gimeno et al. (2012) studied the oceanic and terrestrial sources of continental precipitation. The authors investigated the source and sink regions of atmospheric water vapor using an analytical box model, numerical water vapor tracers, and isotopes. Different terrestrial and oceanic regions across the globe were identified as sources or sinks. The source of water vapor for monsoon precipitation over the Indian subcontinent is mainly from local recycling over the continent and moisture inflow from neighboring oceanic sources. However, the impact of local terrestrial sources is observed to be less as compared to the oceanic sources. Van der Ent et al. (2010) used a water accounting model to study the importance of terrestrial evaporation through moisture recycling. The study demonstrated the importance of terrestrial moisture sources (i.e., continental evaporation) in the monsoon precipitation, with a maximum continental moisture recycling ratio (~20%–40%), which is observed during July. Tuinenburg et al. (2012) studied the moisture recycling for the Ganges River basin using the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim) data corresponding to the period of 1990–2009. A moisture recycling ratio of almost 5% was observed during the monsoon season for this basin. Asharaf et al. (2011) performed a perturbation-based simulation using the Consortium for Small-Scale Modelling in Climate Mode (COSMO-CLM), where a nonhydrostatic regional climate model was forced with ERA-Interim data to investigate the soil moisture–precipitation feedback over the Indian region. Both positive and negative feedbacks were observed during the summer monsoon period. The existence of a dominant negative feedback mechanism over western and northern regions was the most important conclusion of their study. The study was based on numerical experiments of atmospheric circulations under simulated conditions with or without perturbation of soil moisture and evaporation from land surface. The quantification of the feedbacks of land surface processes on ISMR at daily time scales was not addressed by that study.

The main objective of the present work is to investigate and quantify the role of precipitation recycling in ISMR and the associated intraseasonal rainfall variability. ISMR has huge variability in both spatial and temporal scales (Gadgil and Joseph 2003; Krishnamurthy and Shukla 2007; Rajeevan et al. 2010). The subseasonal rainfall variability during the monsoon is associated with active (wet spell) and break (dry spell) phases with a duration of about 3–8 days (Lawrence and Webster 2001; Gadgil 2003; Rajeevan et al. 2010). The changes in land surface energy exchanges during these periods can induce significant variability in the monsoon rainfall characteristics over a region. Hence, the time scale to be used for recycling studies of ISMR should be shorter than a week. Most of the earlier recycling models (e.g., Budyko 1974; Brubaker et al. 1993; Eltahir and Bras 1994) are based on the common assumption that the change in the storage of atmospheric water vapor is small and hence can be neglected. This assumption is valid for monthly or longer time scales, and this restricts the applicability of these models to a shorter time scale such as daily or weekly. The importance of moisture storage at the daily scale in precipitation recycling analysis was addressed by Dominguez et al. (2006) through the development of a dynamic recycling model (DRM). This approach facilitated the recycling analysis at various time scales ranging from daily to seasonal. The dynamic recycling model is derived from the equation of conservation of atmospheric water vapor with the assumption of a well-mixed atmosphere. The dynamic recycling model is a simple and computationally efficient approach for analyzing the potential impact of regional evapotranspiration on regional precipitation. The model is best suited for the present study analyzing daily recycled precipitation for Indian monsoon. Here, we use DRM at a spatial resolution of 0.5° × 0.5° and daily temporal resolution. The dataset from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR; Saha et al. 2010) corresponding to the period of 1980–2010 is used in the present study. The entire area of Indian subcontinent is first subdivided into 11 different rectangular zones (Fig. 1) to study the proportion of the precipitation within each zone, originating from the evapotranspiration of the same zone. The analysis is performed at the daily scale for JJAS. The analysis is also performed considering the subcontinent as one region to study the impacts of the entire subcontinental land evapotranspiration on ISMR. Trends and intraseasonal variations of the computed recycled precipitation are studied to understand their association with the temporal variability of ISMR.

Fig. 1.

Zonation of India for modeling precipitation recycling. The Indian subcontinent is divided into 11 equal-sized zones in such a way that each zone covers the max land region. Zones 6 and 7 cover the northeastern part, whereas zone 3 covers the northwestern part of the subcontinent.

Fig. 1.

Zonation of India for modeling precipitation recycling. The Indian subcontinent is divided into 11 equal-sized zones in such a way that each zone covers the max land region. Zones 6 and 7 cover the northeastern part, whereas zone 3 covers the northwestern part of the subcontinent.

This paper is organized as follows. Section 2 provides detailed information about the datasets used in the present study and illustrates a detailed methodology of dynamic recycling model used for ISMR. Section 3 discusses the important results obtained from this analysis. Finally, a summary and the conclusions of the major results of this study are presented in section 4.

2. Data and methodology

a. CFSR

The Indian subcontinent has large topographic and climatic variations with six different climate subtypes (Peel et al. 2007). Monsoon precipitation characteristics over India show significant spatial and temporal variability (Gadgil 2003). A high-resolution reanalysis dataset is thus required to study the impacts of land surface feedbacks on regional precipitation. In the present analysis, we use NCEP CFSR, version ds093.0, for the time period 1980–2010 at 0.5° × 0.5° spatial resolution. The spatial resolution of other reanalysis data, such as NCEP reanalysis (2.5° × 2.5°), 15-yr ECMWF Re-Analysis (ERA-15; T106, 2.5° × 2.5°), 40-yr ECMWF Re-Analysis (ERA-40; 1.125° × 1.125° and 2.5° × 2.5°), Japanese 25-year Reanalysis Project (JRA-25; 1.125° × 1.125° and 2.5° × 2.5°), and National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA; 0.5° × 0.667°), are coarse (Dee et al. 2014), and hence, we do not use them for the present study. CFSR is a third-generation reanalysis product that provides the best estimate of the land–ocean–atmosphere coupled domain at high resolution and for a long duration (Saha et al. 2010). The CFSR atmospheric model considers observed variations in carbon dioxide (CO2), together with changes in aerosols, other trace gases, and solar variations. The model also considers inputs from all available conventional and satellite observations. The output data from the Noah land surface model in terms of mean vegetation percentage and vegetation type are also included in NCEP CFSR. The dataset is available at 6-hourly, monthly, and selected hourly time scales. Table 1 shows the detailed description of NCEP CFSR variables used in the study. We consider precipitation and evapotranspiration at the surface level. Zonal and meridional wind and specific humidity are vertically integrated at 1000–300 mb pressure levels. The 6-hourly CFSR data are averaged to daily mean values and are used at 0.5° × 0.5° spatial resolutions. The variables that are not at 0.5° × 0.5° spatial resolutions are brought to this resolution using bilinear interpolation.

Table 1.

Description of NCEP CFSR data used in the present study.

Description of NCEP CFSR data used in the present study.
Description of NCEP CFSR data used in the present study.

b. Study area

We first subdivide the Indian subcontinent study region into 11 different rectangular boxes (hereafter referred as zones), as presented in Fig. 1.These 11 zones are different from the regions specified by the Indian Meteorological Department (IMD) or global climatic zones based on latitudes. We keep areas of all the zones nearly the same to avoid the impact of the size of the area on the estimation of recycling ratio (Dominguez et al. 2006). Since the Indian subcontinent has a long coastline, the zones are created in such a way that each of the zones cover maximum land area and retain uniform climate properties to the maximum extent possible. Table 2 shows the percentage area of different climate subtypes, as specified by IMD, under each zone. We also apply the same recycling model separately by considering the entire subcontinent as one zone.

Table 2.

Percentage area under different climate subtypes for each zone [classification source: Peel et al. (2007)].

Percentage area under different climate subtypes for each zone [classification source: Peel et al. (2007)].
Percentage area under different climate subtypes for each zone [classification source: Peel et al. (2007)].

c. Precipitation recycling

The total amount of water vapor present in a zone’s atmospheric column (i.e., precipitable water) has two sources: advective water vapor from a different zone and evapotranspiration from the same zone. The total precipitation P is the sum of advective Pa and recycled Pr components of water vapor:

 
formula

The locally evaporated and advective water vapors are assumed to be well mixed in the atmospheric column, and therefore,

 
formula

Figure 2 illustrates various fluxes associated with precipitation processes in a region. The upward evaporative flux is represented by E. The precipitation over the region is composed of recycled and advective precipitation. The inflow and outflow of advective moisture is represented by Fin and Fout, respectively.

Fig. 2.

Schematic representation of zonal atmospheric fluxes for a single zone. The area of individual grid is represented by ΔA.

Fig. 2.

Schematic representation of zonal atmospheric fluxes for a single zone. The area of individual grid is represented by ΔA.

The relative contributions of advective and recycled water vapor to precipitation depend on the location, season, and size of the zone (or region) considered. The local recycling ratio denoted by Rl is given by ratio of recycled precipitation to the total precipitation at a particular grid (here, the CFSR grids with spatial resolution 0.5° × 0.5°). For instance, in a uniformly spaced gridded zone (or region), the recycling ratio Rln in a particular grid n is given as

 
formula

Here, Pn and Prn are the total precipitation and recycled precipitation, respectively. Following the grid-based approach similar to Eltahir and Bras (1994), the regional recycling ratio, a representative value of precipitation recycling within a zone (composed of n = 1, 2, …, N, number of grids) of size [NA)] is given by

 
formula

The time period during which water vapor stays in a zone provides important information about the potential role of precipitation recycling in the precipitation process. The length of this period is known as the moisture residence time. We use a numerical scheme, following the method used by Merrill et al. (1986), to trace the path traversed by the water vapor within the zone’s geographical boundary before precipitation.

d. Dynamic recycling model

The vertically integrated moisture balance equation for total atmospheric water vapor as well as locally evaporated water vapor are expressed as (Dominguez et al. 2006)

 
formula
 
formula
 
formula

The amount of total water vapor and locally evaporated water vapor present in the atmospheric column is represented by w and wr, respectively. The moisture-weighted zonal and meridional winds are represented by um and υm, respectively. The local recycling ratio at a certain grid is estimated as

 
formula

Here, represent the evapotranspiration, precipitable water, and local recycling ratio in a grid. The regional recycling ratio Rz is calculated for each zone by using Eq. (4). Table 3 shows a list of derived variables with their derivation equation. The recycling ratio expressed in Eq. (8) is analytically derived and, hence, computationally efficient [see model derivation in Dominguez et al. (2006) for detailed explanation]. The recycling ratio increases with the increase of moisture residence time over the region. The estimate of local recycling ratio at any grid shows the precipitation contribution of land evapotranspiration from the zone to which the grid point belongs. The recycling ratio computed with Eq. (8) is used to assess the regional recycling ratio and recycled precipitation [Eqs. (3) and (4)]. Here, we calculate the recycling ratio for two cases, first by dividing the subcontinent into 11 different zones and then by considering the entire Indian subcontinent as one zone. The first case provides information about the contribution of local/zonal evapotranspiration toward precipitation, whereas the second case computes the contribution of entire subcontinental evapotranspiration.

Table 3.

A list of derived variables with their derivation equation. [See model derivation in Dominguez et al. (2006) for detailed explanation.]

A list of derived variables with their derivation equation. [See model derivation in Dominguez et al. (2006) for detailed explanation.]
A list of derived variables with their derivation equation. [See model derivation in Dominguez et al. (2006) for detailed explanation.]

3. Results and discussion

Here, we present the variability of precipitation recycling estimates and their impact on monsoon precipitation for different months and zones. First, we discuss the recycling estimates computed with DRM by considering 11 different zones that cover the Indian subcontinent.

a. Spatial and temporal variation of recycling

To study the spatial and temporal variability of recycling estimates, the climatological mean of regional recycled precipitation and regional recycling ratio for different zones are presented in Fig. 3. Here, day 1 corresponds to 1 June, the first day of JJAS. The regional recycled precipitation increases almost in all the zones after the onset of the monsoon and decreases toward the end of the monsoon because of the reduction of precipitable water in the atmosphere. We observe high regional recycled precipitation in zones 5 (maximum 1.5 mm day−1), 6 (maximum 1.5 mm day−1), and 7 (maximum 2.5 mm day−1). The seasonal variation of regional recycled precipitation in zone 7 is relatively low compared to the other zones, indicating that the regional source of moisture (i.e., local recycling) during monsoon season in this zone is stable. High regional recycling ratios are observed in zones 2, 5, 6, and 7 during JJAS. Higher values of regional recycling ratio in zones 5 (maximum 0.2), 6 (maximum 0.16), and 7 (maximum 0.17) are due to the strong land surface feedback through evapotranspiration, whereas the same in zone 2 (maximum 0.25) is due to low precipitable water. The regional recycling ratio also increases after monsoon onset, with the highest value during September.

Fig. 3.

Climatological mean of (top) recycled precipitation and (bottom) recycling ratio, over different zones during JJAS. The peak of recycling ratios is observed during September.

Fig. 3.

Climatological mean of (top) recycled precipitation and (bottom) recycling ratio, over different zones during JJAS. The peak of recycling ratios is observed during September.

To study the spatial variability of recycling estimates across different zones, we compare them (Fig. 4) for summer monsoon months. We find high spatial variability of the recycling estimates, with the highest amount observed in the northeastern part of the subcontinent and the lowest amount in northwestern India. High values of recycled precipitation in zones 6 and 7 (i.e., northeastern India) indicate the possible influence of land surface feedback on regional precipitation (Fig. 4). On the contrary, such dominant influence of precipitation recycling is not observed in the remaining zones (Fig. 4). The precipitation over the Western Ghats (zones 8 and 11), which receives high monsoon rainfall throughout the summer monsoon season, is also influenced by precipitation recycling, but this is not as prominent as in northeastern India. This is probably because of the proximity of these zones (i.e., zones 8 and 11) to the Arabian Sea and the orography of Western Ghats.

Fig. 4.

(top) Recycled precipitation (mm day−1), (middle) recycling ratio, and (bottom) total precipitation (mm day−1) over different zones during the summer monsoon months (JJAS).

Fig. 4.

(top) Recycled precipitation (mm day−1), (middle) recycling ratio, and (bottom) total precipitation (mm day−1) over different zones during the summer monsoon months (JJAS).

Consistently high values of local recycling ratio (>0.15) as well as high recycled precipitation (~3 mm day−1; Fig. 4) are observed over almost the entire northeastern zones (zones 6 and 7) during JJAS. These results demonstrate the influence of local land surface processes in regional precipitation for these zones. Although the local recycling ratios during September show higher values in central (~0.15) as well as northern (~0.25) zones, the amounts of recycled precipitation are comparatively low in these zones (Fig. 4). Low recycled precipitation in central India (zone 4) is due to the dominance of advection of locally evaporated water from other zones. The northern zones (i.e., zones 1 and 2) receive relatively low recycled precipitation (Fig. 4); however, the recycling ratio is high because of low precipitable water (Fig. 5), which is the denominator in Eq. (8). The recycled precipitation in the northwestern zone is very low, and hence, the recycling ratio is also low. Even the low value of total precipitable water is not sufficient to improve the ratio for this region. The overall high evapotranspiration rate (Fig. 5) over northeastern India (zones 6 and 7), in comparison to the other zones, during the entire summer monsoon is responsible for high recycling.

Fig. 5.

(top) Evapotranspiration (mm day−1), (middle) precipitable water (mm), and (bottom) moisture residence time (mm day−1) in the Indian subcontinent during summer monsoon months (JJAS). The overall high values of evapotranspiration over the northeastern region during JJAS contribute to the higher recycling ratio over this region.

Fig. 5.

(top) Evapotranspiration (mm day−1), (middle) precipitable water (mm), and (bottom) moisture residence time (mm day−1) in the Indian subcontinent during summer monsoon months (JJAS). The overall high values of evapotranspiration over the northeastern region during JJAS contribute to the higher recycling ratio over this region.

The advective moisture from the oceanic sources is responsible for the monsoon initiation, but the local moisture through evapotranspiration may enhance the total amount of precipitation. The peak of total precipitation is observed during July and August, followed by a reduction in September. However, the local recycling ratio shows a slightly different pattern of intraseasonal variations among different monsoon months (Fig. 4). The local recycling ratio increases as monsoon season progresses over the Indian subcontinent, with the highest amount in September due to increased evapotranspiration (Fig. 5). The high recycling ratio (>0.10 in Fig. 4) over most of the Indian region during September indicates that local evapotranspiration is an important factor in sustaining the monsoon. The relatively low recycling ratio during June and July (when compared with September) over the northeastern zone (Fig. 4) of the Indian subcontinent is because of a stronger influence of oceanic sources with a high amount of advective moisture.

Figure 6 shows the estimates of regional recycled precipitation and recycling ratio in different zones for summer monsoon months. High values of regional recycled precipitation (~200–400 mm day−1) as well as regional recycling ratio (~10%–15%) are observed over zones 5–7. The low recycling ratio over zones 8–11 (southwestern, southeastern, and southern regions) are due to the dominance of oceanic sources (Arabian Sea and Bay of Bengal) of moisture in the precipitation process. The total recycled precipitation over the western and northern parts of the Indian subcontinent (i.e., zones 1–3) is relatively low in comparison to the other zones (Fig. 6). However, the recycling ratios are on the higher side.

Fig. 6.

Interzonal variation of (top) recycled precipitation and (bottom) regional recycling ratio during the summer monsoon. Zone 7 has the highest recycled precipitation in northeastern India. Higher recycling ratios are observed during September in all the zones except 1 and 2.

Fig. 6.

Interzonal variation of (top) recycled precipitation and (bottom) regional recycling ratio during the summer monsoon. Zone 7 has the highest recycled precipitation in northeastern India. Higher recycling ratios are observed during September in all the zones except 1 and 2.

The results for the interior of India, that is, zones 4–7, do not fully represent the impacts of the entire subcontinental evapotranspiration. The above-mentioned results for a specific zone only show the impacts of evapotranspiration from the same zone and not the evapotranspiration from the entire subcontinent. This motivates us to apply DRM to the entire subcontinent as a single zone.

b. Impact of the subcontinent’s evapotranspiration on precipitation recycling

The contribution of the entire subcontinent’s evapotranspiration to the monsoon precipitation is studied by applying DRM and considering the entire subcontinent as a single zone. The results are compared with those obtained earlier that considered the entire subcontinent as 11 different zones. We observe a similar pattern of intraseasonal variability in recycled precipitation amount, with an increase during the later half (August and September) of the monsoon (Fig. 7). Overall, we find a higher amount of recycled precipitation with this present approach, specifically in central India and the Gangetic Plain. However, significant differences are not observed in northeastern India.

Fig. 7.

As in Fig. 4, but for application of DRM considering the entire subcontinent as one zone.

Fig. 7.

As in Fig. 4, but for application of DRM considering the entire subcontinent as one zone.

Analyzing the path followed by atmospheric moisture (water vapor) prior to precipitation is important for understanding the role of monsoon circulation on precipitation. Figure 8 shows the mean moisture flux vectors during the monsoon season (i.e., JJAS) over the entire subcontinent. The increased moisture circulation over central India and the Gangetic Plain promotes the use of water vapor from subcontinental evapotranspiration in the precipitation process. This explains the occurrences of high recycled precipitation over the Gangetic Plain during August and September (Fig. 7). Small differences between recycled precipitation in northeastern India obtained from zonal (Fig. 4) and subcontinental (Fig. 7) analyses suggest that the higher amount of moisture resulting from evapotranspiration comes from the same zone, which is not true for Gangetic Plain. For southwestern India, the source of moisture is the Arabian Sea, and hence, the recycled precipitation is very low. On the western coast of India, low recycled precipitation and high total precipitation result in a very low recycling ratio. The direction of moisture flux vectors over northeastern India changes as the monsoon season progresses. We find that during June and July, it originates from the Bay of Bengal and propagates toward the continental interior, whereas during September it originates from the western parts of Southeast Asia. Hence, during September a larger amount of land area is being traversed by the moisture flux prior to the actual precipitation event.

Fig. 8.

Moisture flux vectors during JJAS over the Indian subcontinent. Local moisture contributes significantly to precipitation over central and northeastern India.

Fig. 8.

Moisture flux vectors during JJAS over the Indian subcontinent. Local moisture contributes significantly to precipitation over central and northeastern India.

c. Precipitation recycling and monsoon withdrawal

The precipitable water available in the atmosphere is relatively low during September as compared to June, July, and August (Fig. 5). However, a higher amount of evapotranspiration is observed over the entire subcontinent during September (Fig. 5), which supplies additional moisture to the atmosphere and hence provides additional strength to the weakened monsoon. To investigate the impact of precipitation recycling on monsoon withdrawal, we calculate the withdrawal dates of the southwest monsoon separately by considering total precipitation (actual) and advective precipitation independently. Here, we define monsoon withdrawal as the cessation of rainfall activity over the area for a continuous 5 days after 1 September. Figure 9 shows the mean difference in days, that is, prolongation, between advective precipitation withdrawal and total precipitation withdrawal. Here, the method for computing withdrawal date has the limitation that it does not consider important factors and indicators such as wind shear, vertically integrated moisture transport, outgoing longwave radiation, etc. The result (Fig. 9) shows that precipitation recycling is responsible for delaying of southwest monsoon withdrawal over the northeastern (~3–5 days) and eastern regions (~2–3 days). Hence, it prolongs the monsoon over these regions for a few more days. We do not find the impact of precipitation recycling in delaying the monsoon withdrawal for other regions.

Fig. 9.

Impacts of precipitation recycling on summer monsoon withdrawal. Precipitation recycling delays the monsoon withdrawal in northeastern and eastern India.

Fig. 9.

Impacts of precipitation recycling on summer monsoon withdrawal. Precipitation recycling delays the monsoon withdrawal in northeastern and eastern India.

d. Trends of recycled precipitation

We study the trends of recycling estimates at the 95% significance level. Figure 10 shows the trends of recycled precipitation, moisture residence time, and total precipitation. We observe increasing trends for June and July recycled precipitation at the 95% significance level in zones 5 and 6. This shows that the impacts of land surface feedbacks on monsoon rainfall are becoming more prominent in these zones. We find a decreasing trend of recycled precipitation in zone 7 despite the absence of any trend in total precipitation, which indicates the weakening of land surface feedback in northeastern India. Decreasing trends of recycled precipitation, total precipitation, and moisture residence time (Fig. 10) are observed during August over the central region. The recycled precipitation and moisture residence time (Fig. 10) in the central zone (i.e., zone 4) during September also have a decreasing trend, but the same is not present in total precipitation.

Fig. 10.

Trends of (top) recycled precipitation, (middle) moisture residence time, and (bottom) total precipitation in India during the summer monsoon months. The trends are computed at the 95% significance level. Decreasing trends of recycled precipitation are observed over northeastern India during JJAS.

Fig. 10.

Trends of (top) recycled precipitation, (middle) moisture residence time, and (bottom) total precipitation in India during the summer monsoon months. The trends are computed at the 95% significance level. Decreasing trends of recycled precipitation are observed over northeastern India during JJAS.

e. Precipitation recycling during the strongest and weakest monsoon years

To understand the sensitivity of the strength of monsoon on recycled precipitation, we analyze the results for the strongest and weakest monsoon years from the selected duration of 1980–2010. Figure 11 shows the variation in monsoon rainfall for different years. We observe that 1987 is the weakest monsoon year and 1988 is the strongest. Figure 12 shows the local recycling ratio and recycled precipitation for September during these two years. We observe a higher local recycling ratio over the entire subcontinent during September of the weakest monsoon year in comparison with the strongest monsoon year. It is important to note that the year 1987 was an El Niño year, as described by the National Weather Service Climate Prediction Center (Climate Prediction Center 2014), as well as a drought year, as classified by the IMD. The year 1988 was a La Niña year with above-normal summer monsoon rainfall. Here, we select September for comparison because of high recycling estimates during this month. A relatively high amount of recycled precipitation is observed during the strongest monsoon year. Although, the amount of recycled precipitation is low during the weakest monsoon, the recycling ratio is on the higher side.

Fig. 11.

JJAS total recycled precipitation and total precipitation for the period 1980–2010. Highest and lowest monsoon rainfalls are observed during 1988 and 1987, respectively.

Fig. 11.

JJAS total recycled precipitation and total precipitation for the period 1980–2010. Highest and lowest monsoon rainfalls are observed during 1988 and 1987, respectively.

Fig. 12.

(a),(b) Recycling ratio and (c),(d) recycled precipitation in India during September of the weakest (1987) and strongest monsoon (1988) years. A higher amount of recycled precipitation is observed during the strongest monsoon year over all of India.

Fig. 12.

(a),(b) Recycling ratio and (c),(d) recycled precipitation in India during September of the weakest (1987) and strongest monsoon (1988) years. A higher amount of recycled precipitation is observed during the strongest monsoon year over all of India.

The results obtained in the present study show a similar pattern, when compared with the results presented in earlier literature for the United States. Dirmeyer and Brubaker (1999) investigated the source of moisture for precipitation over the Mississippi River basin during 1988 (drought) and 1993 (flood) using a quasi-isentropic back trajectories scheme. It was observed that terrestrial moisture plays a significant role in precipitation with contributions of 41% (1988) and 33% (1993). Bosilovich et al. (2003) showed the importance of local moisture sources over the United States using water vapor tracers. The wettest monsoon observed has more continental evaporative sources than a drier monsoon. Bosilovich and Schubert (2001) investigated precipitation recycling over the central United States during the wettest and driest years, using the bulk recycling model of Eltahir and Bras (1994). The authors indicated that the wettest period (flood during 1993) is associated with a relatively low recycling ratio (maximum 30%) as compared to the dry period (maximum 60% during the drought of 1988). The present study shows a similar pattern of recycling ratio over the Indian subcontinent (Fig. 12), where weaker oceanic moisture transport in the weakest monsoon year 1987 is associated with a high recycling ratio and vice versa. Dominguez and Kumar (2008) used DRM and showed that the feedback through evapotranspiration during the low rainfall period provides the extra moisture for the precipitation over the U.S. plains ecoregion, and hence, precipitation recycling provides the stability to the seasonal rainfall. Similarly, evapotranspiration over the Indian subcontinent during a low rainfall period (i.e., September) is responsible for a higher recycling ratio, and thus, it strengthens the monsoon rainfall (Fig. 7).

4. Conclusions

We analyze the impact of local and subcontinental evapotranspiration on regional precipitation characteristics. The results show the potential impacts of land surface feedbacks through evapotranspiration on regional precipitation variability. This analysis reveals that precipitation recycling is an integral part of monsoon rainfall. The increase in soil wetness after the onset of monsoon increases evapotranspiration in the summer monsoon season, which in turn is responsible for a higher recycling ratio. We find that the significant amount of precipitation comes from precipitation recycling in the Gangetic Plain and northeastern India. However, southern, western, and southeastern parts receive less recycled precipitation, owing to dominance of oceanic moisture and high advective moisture transport. The high regional recycling ratios for almost all years over zones 6 and 7 (eastern and northeastern India) suggest a strong role of land surface in the generation of precipitation over that zone. The recycling ratio as well as recycled precipitation over northeastern India is found to be consistently higher (during JJAS) as compared to other zones, with the highest value observed during September. The increased recycled precipitation during September provides additional strength to the weakened monsoon, and it is also responsible for delaying the monsoon withdrawal over the eastern and northeastern zones.

The role of precipitation recycling in providing strength to the overall rainfall, as discussed by Dirmeyer and Brubaker (1999), Bosilovich and Schubert (2001), Bosilovich et al. (2003), and Dominguez and Kumar (2008) over the central U.S. region and Dominguez et al. (2008) over the North American monsoon region, is also observed over the Indian subcontinent. Furthermore, the weakest monsoon is observed to have a high recycling ratio when compared with strongest monsoon, consistent with prior studies. The high recycling ratio during the weakest period is due to the low availability of precipitable water.

The DRM used in this study is based on the assumption of well-mixed atmospheric conditions. This may not be always true for the Indian monsoon, and this is a possible limitation of the present study. Lettau et al. (1979), Burde (2006), and Burde et al. (2006) addressed the issue of incomplete vertical mixing of water vapor in the atmosphere by using a dimensionless parameter K [Eqs. (3.5) and (3.13) in Burde (2006)]. The estimation of parameter K involves usage of the ratios Pm/P and wm/w, which are generally obtained from a GCM WVT experiment. The involvement of a GCM WVT experiment output may increase the accuracy of the recycling estimate, but they are computationally expensive.

The DRM, like any other two-dimensional approximation model, neglects the effect of the vertical distribution of moisture. The inhomogeneities in wind are normally generated because of the presence of directional wind shear, and as a result, there can be incomplete or weak mixing of water vapor. Goessling and Reick (2012) showed the importance of incomplete vertical mixing due to directional wind shear in vertical levels. The study showed that the two-dimensional approximation models are less accurate over tropical regions than extratropical regions, where the effect of directional shear is at its maximum.

Van der Ent et al. (2013) also showed that the assumption of well-mixed water vapor has the largest influence on the results over the tropics, when compared using three different tracking methods, namely, regional climate model (RCM) tagging, water accounting model (WAM), and three-dimensional trajectories analysis. RCM tagging, which includes all of the atmospheric processes, gives more accurate results when compared with the other two approaches that are based on the well-mixed assumption. However, the study also concluded that the effect due to incomplete mixing of water vapor is not significant over the Indian region. The modification of the well-mixed atmosphere assumption by allowing the effect due to “fast recycling” (Lettau et al. 1979, page 231) can further improve the accuracy; hence, a future attempt can be made to improve the analysis.

The topography of the region may also play an important role in enhancing the recycling ratio and recycled precipitation. The Himalayan mountain ranges are possibly responsible for moisture circulation within the subcontinent resulting in a high recycling ratio in the central and northeastern regions. Such a hypothesis may be tested with model-based analysis. It should be noted that, while computing the moisture flux, we have considered the pressure levels from 1000 to 300 mb. However, for the Himalayan mountainous region, the elevation is too high as compared to 1000 mb. This is a major limitation of the present analysis, and consideration of orography in DRM may be considered as an area of future research.

The recycling ratio for mountainous regions (western coast and northeastern India), with a high precipitation amount and spatial variability, may be sensitive to the spatial resolution, and coarse-resolution data may fail to capture orographic effects. However, precipitation on the western coast (Western Ghats mountainous region) primarily results from oceanic sources, and hence, a major difference in recycled precipitation may not be expected with changes in spatial resolution. The effect of resolution may be prominent in the computed recycled precipitation of the northeastern region because of high orography and recycling ratio, and the study of such sensitivity may be considered as a potential future research area.

Acknowledgments

The authors sincerely acknowledge Francina Dominguez, University of Arizona, for her suggestions on this work. The first author thanks Beas Barik for her help in editing the manuscript.

REFERENCES

REFERENCES
Asharaf
,
S.
,
A.
Dobler
, and
B.
Ahrens
,
2011
:
Soil moisture initialization effects in the Indian monsoon system
.
Adv. Sci. Res.
,
6
,
161
165
, doi:.
Bellon
,
G.
,
2010
:
Monsoon intraseasonal oscillation and land–atmosphere interaction in an idealized model
.
Climate Dyn.
, 37, 1081–1096, doi:.
Bosilovich
,
M. G.
, and
S. D.
Schubert
,
2001
: Precipitation recycling over the central United States diagnosed from the GEOS-1 Data Assimilation System. J. Hydrometeor.,2, 26–35, doi:.
Bosilovich
,
M. G.
, and
S. D.
Schubert
,
2002
:
Water vapor tracers as diagnostics of the regional hydrologic cycle
.
J. Hydrometeor.
,
3
,
149
165
, doi:.
Bosilovich
,
M. G.
,
Y. C.
Sud
,
S. D.
Schubert
, and
G. K.
Walker
,
2003
:
Numerical simulation of the large-scale North American monsoon water sources
.
J. Geophys. Res.
,
108
,
8614
, doi:.
Brubaker
,
K. L.
,
D.
Entekhabi
, and
P. S.
Eagleson
,
1993
:
Estimation of continental precipitation recycling
.
J. Climate
,
6
,
1077
1089
, doi:.
Budyko
,
M. I.
,
1974
: Climate and life. Academic Press, 508 pp.
Burde
,
G. I.
,
2006
:
Bulk recycling models with incomplete vertical mixing. Part I: Conceptual framework and models
.
J. Climate
,
19
,
1461
1472
, doi:.
Burde
,
G. I.
, and
A.
Zangvil
,
2001
:
The estimation of regional precipitation recycling. Part II: A new recycling model
.
J. Climate
,
14
,
2509
2527
, doi:.
Burde
,
G. I.
,
C.
Gandush
, and
Y.
Bayarjargal
,
2006
:
Bulk recycling models with incomplete vertical mixing. Part II: Precipitation recycling in the Amazon basin
.
J. Climate
,
19
,
1473
1489
, doi:.
Climate Prediction Center
, cited
2014
: Historical El Niño/La Niña episodes (1950–present). [Available online at www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml.]
Dee
,
D.
, and Coauthors
, cited
2014
: The Climate Data Guide: Atmospheric reanalysis: Overview and comparison tables. [Available online at https://climatedataguide.ucar.edu/climate-data/atmospheric-reanalysis-overview-comparison-tables.]
Dirmeyer
,
P. A.
, and
J.
Shukla
,
1993
: Observational and modeling studies of the influence of soil moisture anomalies on the atmospheric circulation. Predictions of Interannual Climate Variations, J. Shukla, Ed., NATO Series: I, Vol. 6, Springer-Verlag, 1–23.
Dirmeyer
,
P. A.
, and
K. L.
Brubaker
,
1999
:
Contrasting evaporative moisture sources during the drought of 1988 and the flood of 1993
.
J. Geophys. Res.
,
104
,
19 383
19 398
, doi:.
Dominguez
,
F.
, and
P.
Kumar
,
2008
:
Precipitation recycling variability and ecoclimatological stability—A study using NARR data. Part I: Central U.S. plains ecoregion
.
J. Climate
,
21
,
5165
5186
, doi:.
Dominguez
,
F.
,
P.
Kumar
,
X.
Liang
, and
M.
Ting
,
2006
:
Impact of atmospheric moisture storage on precipitation recycling
.
J. Climate
,
19
,
1513
1530
, doi:.
Dominguez
,
F.
,
P.
Kumar
, and
E. R.
Vivoni
,
2008
:
Precipitation recycling variability and ecoclimatological stability—A study using NARR data. Part II: North American monsoon region
.
J. Climate
,
21
,
5187
5203
, doi:.
Eltahir
,
E. A. B.
, and
L. B.
Bras
,
1994
:
Precipitation recycling in the Amazon basin
.
Quart. J. Roy. Meteor. Soc.
,
120
,
861
880
, doi:.
Eltahir
,
E. A. B.
, and
L. B.
Bras
,
1996
:
Precipitation recycling
.
Rev. Geophys.
,
34
,
367
378
, doi:.
Environmental Modeling Center
,
2010
: NCEP Climate Forecast System Reanalysis (CFSR) selected hourly time-series products, January 1979 to December 2010. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, CO. [Available online at http://rda.ucar.edu/datasets/ds093.1.]
Gadgil
,
S.
,
2003
:
Indian monsoon and its variability
.
Annu. Rev. Earth Planet. Sci.
,
31
,
429
467
, doi:.
Gadgil
,
S.
, and
P. V.
Joseph
,
2003
:
On breaks of the Indian monsoon
.
J. Earth Syst. Sci.
,
112
,
529
558
, doi:.
Gimeno
,
L.
,
A.
Drumond
,
R.
Nieto
,
R. M.
Trigo
, and
A.
Stohl
,
2010
:
On the origin of continental precipitation
.
Geophys. Res. Lett.
,
37
,
L13804
, doi:.
Gimeno
,
L.
, and Coauthors
,
2012
:
Oceanic and terrestrial sources of continental precipitation
.
Rev. Geophys.
,
50
,
RG4003
, doi:.
Goessling
,
H. F.
, and
C. H.
Reick
,
2012
:
Atmospheric water vapour tracers and the significance of the vertical dimension
.
Atmos. Chem. Phys. Discuss.
,
12
,
30 119
30 176
, doi:.
Higgins
,
W.
, and
D.
Gochis
,
2007
:
Synthesis of results from the North American Monsoon Experiment (NAME) process study
.
J. Climate
,
20
,
1601
1607
, doi:.
Krishnamurthy
,
V.
, and
J. L.
Kinter III
,
2003
:
The Indian monsoon and its relation to global climate variability. Global Climate, X. Rodó and F. A. Comín, Eds., Springer-Verlag, 186–236
.
Krishnamurthy
,
V.
, and
J.
Shukla
,
2007
:
Intraseasonal and seasonally persisting patterns of Indian monsoon rainfall
.
J. Climate
,
20
,
3
20
, doi:.
Lawrence
,
D. M.
, and
P. J.
Webster
,
2001
:
Interannual variations of the intraseasonal oscillation in the South Asian summer monsoon region
.
J. Climate
,
14
,
2910
2922
, doi:.
Lettau
,
H.
,
K.
Lettau
, and
L. C. B.
Molion
,
1979
:
Amazonia’s hydrologic cycle and the role of atmospheric recycling in assessing deforestation effects
.
Mon. Wea. Rev.
,
107
,
227
238
, doi:.
Meehl
,
G. A.
,
1994
:
Influence of land surface in the Asian summer monsoon: External conditions versus internal feedbacks
.
J. Climate
,
7
,
1033
1049
, doi:.
Merrill
,
J. T.
,
R.
Bleck
, and
D.
Boudra
,
1986
:
Techniques of Lagrangian trajectory analysis in isentropic coordinates
.
Mon. Wea. Rev.
,
114
,
571
581
, doi:.
Peel
,
M. C.
,
B. L.
Finlayson
, and
T. A.
McMahon
,
2007
: Updated world map of the Koppen–Geiger climate classification. Hydrol. Earth. Syst. Sci.,11, 1633–1644, doi:.
Rajeevan
,
M.
,
S.
Gadgil
, and
J.
Bhate
,
2010
: Active and break spells of Indian summer monsoon. J. Earth. Syst. Sci.,119, 229–247, doi:.
Saha
,
S.
, and Coauthors
,
2010
:
The NCEP Climate Forecast System Reanalysis
.
Bull. Amer. Meteor. Soc.
,
91
,
1015
1057
, doi:.
Saha
,
S. K.
,
S.
Halder
,
A.
Suryachandrarao
, and
B. N.
Goswami
,
2012
:
Modulation of ISOs by land–atmosphere feedback and contribution to the interannual variability of Indian summer monsoon
.
J. Geophys. Res.
,
117
,
D13101
, doi:.
Shukla
,
J.
, and
Y.
Mintz
,
1982
:
Influence of land-surface evapotranspiration on the earth’s climate
.
Science
,
215
,
1498
1501
, doi:.
Stohl
,
A.
, and
P.
James
,
2004
: A Lagrangian analysis of the atmospheric branch of the global water cycle. Part I: Method description, validation, and demonstration for the August 2002 flooding in central Europe. J. Hydrometeor.,5, 656–678, doi:.
Stohl
,
A.
, and
P.
James
,
2005
:
A Lagrangian analysis of the atmospheric branch of the global water cycle. Part II: Moisture transports between Earth’s ocean basins and river catchments
.
J. Hydrometeor.
,
6
,
961
984
, doi:.
Tuinenburg
,
O. A.
,
R. W. A.
Hutjes
, and
P.
Kabat
,
2012
:
The fate of evaporated water from the Ganges basin
.
J. Geophys. Res.
,
117
,
D01107
, doi:.
van der Ent
,
R. J.
,
H. G.
Savenije
,
B.
Schaefli
, and
S. C.
Steele-Dunne
,
2010
:
Origin and fate of atmospheric moisture over continents
.
Water Resour. Res.
,
46
,
W09525
, doi:.
van der Ent
,
R. J.
,
O.
Tuinenburg
,
H.-R.
Knoche
,
H.
Kunstmann
, and
H.
Savenije
,
2013
: Should we use a simple or complex model for moisture recycling and atmospheric water tracking? Hydrol. Earth Syst. Sci.,17, 4869–4884, doi:.
Wang
,
B.
,
2006
: The Asian Monsoon. Praxis Publishing, 459 pp.
Watts
,
C. J.
,
R.
Scott
,
J.
Garatuza-Payan
,
J. C.
Rodriguez
,
J. H.
Prueger
,
W. P.
Kustas
, and
M.
Douglas
,
2007
:
Changes in vegetation condition and surface fluxes during NAME 2004
.
J. Climate
,
20
,
1810
1820
, doi:.
Yasunari
,
T.
,
2007
:
Role of land–atmosphere interaction on Asian monsoon climate
.
J. Meteor. Soc. Japan
,
85B
,
55
75
, doi:.