We present a new method to determine the monthly mean air sea temperature difference (ΔT = SST − Ta) from satellite observations. The satellite observed parameters viz., vertically integrated water vapour (W), sea surface temperature (SST) and wind speed (U) are used to derive ΔT. Genetic Algorithm (GA) is used to find the optimum relations between the input (W, SST, U) and output (ΔT) parameters. The input data consist of 6 years (January 1988–December 1993) of monthly averages of water vapour and wind speed from SSM/I (Special Sensor Microwave Imager), and sea surface temperature data from AVHRR (Advanced Very High Resolution Radiometer). Surface Marine Data (based on COADS observations) of SST and Ta (air temperature) are used to develop and evaluate the non‐linear empirical relationship. Largest seasonal dependent differences between ΔT derived from the new method and in‐situ data are found over the western boundary currents (such as the Kuroshio and Gulf Stream). After removal of the systematic biases, ΔT can be determined with accuracy of 0.40 ± 0.11°C.

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ERA5 reanalysis data shows significant correlation between Siberian snow depth in March and following Indian summer monsoon rainfall (ISMR). Whilst the Siberian snow depth is negatively correlated with seasonal monsoon rainfall over most of India, it is positively correlated with the monsoon rain over north‐east Indian regions. This relationship has significantly strengthened during the past 2 decades. We show that the strengthening relationship is more likely related to recent atmospheric circulation changes owing to persistent global warming, particularly the changes in the atmospheric circulation over the North Atlantic. There are indications that during 1999–2018, the extratropical climatic variability (e.g., Arctic Oscillation [AO] and North Atlantic Oscillation [NAO]) significantly influenced Siberian snow in the month of March. In contrast, during the period 1979–1998, the influence of NAO and AO on Siberian snow was negligible. Analysis indicates that Siberian High, a dominant atmospheric circulation system that exerts strong influence on Eurasian weather and climate, has also significantly weakened during the past 2 decades. We hypothesize that the March Siberian snow influences the ISMR through a delayed hydrological response, in which the increased (reduced) accumulated snow during springtime provides wetter (drier) soil during the following summer. This leads to the alternation of the meridional tropospheric temperature gradient. The heavy (less) spring snowfall in north (south) Siberia led to changes in summertime meridional tropospheric temperature gradient, which resulted in the weakening of subtropical westerly jet and Tibetan anticyclone. The reduced intensity of tropical easterly jet connected to Tibetan anticyclone resulted in the weakening of summer monsoon Hadley cell, leading to subsequent suppression of rainfall. This association between Siberian snow and ISMR provides seasonal prediction potential.

Climate modes like ENSO (El Nino Southern Oscillation) and IOD (Indian Ocean Dipole) produce an impact on the monsoon rainfall over India. Monsoon rainfall is extremely important for the agriculture of our country. The impact of these climate modes on monsoon rainfall thus in turn affects the rain-fed crops (Kharif). In this study, four Kharif season crops namely Rice (Oryzasativa), Maize (Zea mays), pulses and sugarcane (Saccharum o cinarum) are chosen over four arid/semi-arid agro-climatic zones of western India to study the effect of the climate modes on selected crops. The detailed analysis has been carried out to show the impact of El Nino/La Nina (phases of ENSO) and IOD years on the crop productions over the mentioned zones viz. (Central plateau & hills region; Western plateau & hills region; Gujarat hills and plains region; Western Dry region) from 1966-2011. Rice production has been largely affected during drought years associated with El-Nino. The production of Pulses shows marginal improvement during the neutral years or non-El Nino/non-La Nina. The Maize production seems to be better in La Nina years as compared to neutral years and worst in the El Nino years. El Nino years provides a minor impact on Sugarcane productions in different zones. La Nina years are well suited for sugarcane production in any zones of our study. Positive IOD years are associated with poor crop productions as compared to negative IOD years mostly in all zones due to the co-occurrence of positive IOD years with El Nino. The correlations between positive IOD and rainfall are much weaker as compared to the correlations between the El Nino and rainfall in the years of co-occurrences over the zones making El Nino much more in uential than positive IOD events.