Midlatitude Rossby wave modulation of the Indian summer monsoon

Abstract

The dominant mode of July–August seasonal variability of Indian summer monsoon rainfall obtained by performing an empirical orthogonal function analysis over India, excluding northeast India, for the period 1979–2014 is highly correlated to the quasi‐stationary midlatitude Eurasian Rossby wave train, known as the ‘Silk Road’ pattern. A new hypothesis has been proposed and tested using observed Indian rainfall and the ERA‐Interim dataset, by which the positive feedback between the midlatitude Rossby wave and the Indian summer monsoon circulation has been established. A Rossby wave train features successive upper‐tropospheric negative and positive geopotential height anomalies over the northeast of the Mediterranean and northwest of India, respectively. The negative height anomaly decreases the sum of the vertical integral of potential and internal energy, while this increases in the region of positive geopotential height anomalies. This is associated with middle to lower tropospheric midlatitude descent and ascent anomalies at 40°E and 50°E, which cause negative surface temperature anomalies over Syria, Iraq, Jordan and Saudi Arabia, and a positive surface temperature anomaly over Iran, respectively. The negative and positive surface temperature anomalies are associated with positive and negative surface pressure anomalies over Saudi Arabia and Iran, respectively, which generate anomalous northwesterly winds over eastern Saudi Arabia and the Persian Gulf. Further downstream this anomalous northwesterly wind combines with the climatological background cross‐equatorial southwesterly flow in the Arabian Sea. The flow further converges towards central and northwest India. The flow also acts as a source of moisture supply from the warm Arabian Sea and Persian Gulf for the deep convection over the Western Ghats, northwest and central India and hence reinforces active Indian summer monsoon conditions.

Prominent mode of summer surface air temperature variability and associated circulation anomalies over the Arabian Peninsula

“…In summary, the mid‐latitude tropospheric Rossby wave train type pattern influences the AP SATs by modulating the upper tropospheric Asian jet stream during the summer. The Asian jet stream and Eurasian wave train impose a surface pressure dipole anomaly over the AP and Iran, and consequently influence the AP SAT variability (Yadav, ).…”

“…The rate of decrease in temperature from the surface to the 750 hPa level is relatively lower than that of the above layers, suggesting the presence of an inversion layer at around 750 hPa level. In general, the summer SAT is high, owing to strong summer time solar insolation (being a cloud‐free region), strong adiabatic heating (large‐scale subsidence), and low thermal inertia of the dry desert soil (Yadav, ; ; Attada et al ., ). The vertical profile of the mean vertical velocity displayed an ascent from the surface to the lower troposphere (i.e., 700 hPa) due to surface heating, and a descent throughout the middle and upper troposphere.…”

“…In summary, the mid-latitude tropospheric Rossby wave train type pattern influences the AP SATs by modulating the upper tropospheric Asian jet stream during the summer. The Asian jet stream and Eurasian wave train impose a surface pressure dipole anomaly over the AP and Iran, and consequently influence the AP SAT variability (Yadav, 2017). Surface heat lows, characterized by winds converging at the surface level and diverging at mid-level, are common over the Middle East desert regions during summer (Blake et al, 1983).…”

“…The extratropical Eurasian Rossby waves caused the positive GPH anomalies in the upper to middle troposphere, which accumulated substantial amounts of atmospheric mass. The tropospheric subsidence enhances the potential and internal energy (Yadav, ). The strong adiabatic heating due to the mid‐tropospheric descent further caused the positive SAT anomalies over the AP.…”

“…Monthly means of horizontal winds, vertical winds, sea level pressure (SLP), geopotential height (GPH), surface net solar radiation, and 2‐m air temperature (2mT) variables are analyzed. To identify the leading spatio‐temporal modes of variability, an empirical orthogonal function (EOF) analysis is performed on the linearly detrended ERA‐Interim dataset (e.g., Yadav, ; ; ; Chowdary et al ., ; Chen et al ., ). A correlation analysis is also performed at the 95% significance level, computed based on the Student's t ‐distribution with ( N −2) degrees of freedom, where N is the number of years in the record, to investigate significant relationships between the AP SATs and large‐scale circulation patterns.…”