Role of land‐surface vegetation in the march of Indian monsoon onset isochrones in a coupled model

Abstract

Although the Climate Forecast System version‐2 model simulates an overall dry bias in boreal summer mean rainfall over Indian land, the deficiency is particularly prominent over northwest India. The prevailing dryness limits the interannual prediction skill of the Indian summer monsoon rainfall and its subseasonal variability because of poor representation of latent heating due to weak moist convection and the resulting circulation. Here, we show that land‐surface vegetation plays a crucial role in determining the dry bias in the Climate Forecast System version‐2 model. We replaced the land‐surface model's existing vegetation type over India with that derived from recent satellite‐based observations. The modifications helped improve the seasonal mean rainfall over northwest India by 6%. The improvements are especially noticeable during the monsoon season's onset (14%) and withdrawal (10%) phases. Simulations with modified vegetation advanced the onset dates over Kerala, central India, and northwest India closer to that observed. This improvement in the mean onset dates is most prominent over northwest India. Such an improvement was possible owing to a substantial reduction of long rainfall hiatus after onset over Kerala in the simulation with modified vegetation. The modification makes the spatial orientation of monsoon onset isochrones more realistic. We found that although the vertically integrated moisture flux is eastward over most of the Indian monsoon region during its onset phase, its intraseasonal components are westward. In other words, at the intraseasonal time‐scale, moisture propagates against the prevailing low‐level westerlies. This intraseasonal eddy moisture transport advances onset from the Bay of Bengal toward the far northwest parts of the Indian land. The representation of such intraseasonal moisture seepage in the model updated with satellite‐derived vegetation types was improved. Our study indicates the necessity of greater attention to land‐surface representations for improved predictions of onset dates.

Deciphering the relationship between vegetation and Indian summer monsoon rainfall

“…Saha et al (2014) gives a detailed description of the CFSv2 model. The version of CFSv2 employed here is similar to that used in Rajendran et al (2021) and Chakraborty et al (2023).…”

“…Zhang et al (2021Zhang et al ( , 2022 have demonstrated that increasing the vegetation cover in the Loess Plateau in China leads to net increase in water yield. Chakraborty et al (2023) has shown that vegetation changes influence monsoon onset predictions by modulating moisture advection over Indian land. Further, Indian monsoon rainfall has declined due to the reduction in forest cover (Devaraju et al 2015, Paul et al 2016.…”

Coupled model biases and extended‐range prediction skill during the onset phase of the Indian summer monsoon with different initializations related to land surface and number of observations

“…Chakraborty et al . (2023) and Menon et al . (2022) showed that the specified soil and vegetation conditions can affect local onset by altering the time‐scales of soil moisture–precipitation feedback via the timing of diurnal convection.…”

“…One of the famous mechanisms was proposed by Eltahir (1998) and Seneviratne et al (2010): increased soil moisture affects the rainfall both by changing the Bowen ratio and the surface albedo, which together influence the moist static energy in the atmospheric boundary layer and the rainfall pattern over a region. Chakraborty et al (2023) and Menon et al (2022) showed that the specified soil and vegetation conditions can affect local onset by altering the time-scales of soil moisture-precipitation feedback via the timing of diurnal convection. In the context of these studies highlighting land-atmosphere feedback and land surface memory on a monthly scale, understanding the role of soil moisture initialization on the extended-range forecast is essential.…”

Integration of a Groundwater Model to the Noah Land Surface Model for Aquifer‐Soil Interaction

“…However, the impact demonstrates significant spatial variability and could modulate seasonal rainfall as well. A recent study by Chakraborty et al (2022) showed that a realistic distribution of land surface vegetation could improve the forecast of the northwestward progress of the onset of the Indian summer monsoon due to improved subseasonal moisture flux and land-atmosphere interaction. Land surface processes thus influence various phases of the monsoon from onset to subseasonal phenomena.…”