The weakening relationship between Eurasian spring snow cover and Indian summer monsoon rainfall

A Physical Mechanism for the Indian Summer Monsoon—Arctic Sea-Ice Teleconnection

Significant changes in the Arctic climate, particularly a rapid decline of September Arctic sea ice has occurred over the past few decades. Though the exact reason for such drastic changes is still unknown, studies suggest anthropogenic drivers, natural variability of the climate system, and a combination of both as reasons. The present study focus on the influence of one of the natural variabilities of the climate system, the teleconnections associated with the Indian Summer Monsoon (ISM), and its relationship to September Arctic sea ice. Using 50 years (1951–2000) of National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) NCEP/NCAR reanalysis data, APHRODITE precipitation data, Gridded Monthly Sea Ice Extent and Concentration, 1850 Onward, V2, and HadISST sea-ice concentration data, it is shown that during many strong (weak) ISM years, the Arctic sea ice increased (decreased) predominantly over the Chukchi and Beaufort Seas. The ISM plays a significant role in causing a positive (negative) North Atlantic Oscillation (NAO) during strong (weak) ISM years through the monsoon-desert mechanism associated with monsoonal heating. Simultaneously, the NAO during a strong (weak) ISM causes weakening (strengthening) of the Beaufort Sea High (BSH). The strength of the BSH modulates the Arctic atmospheric circulation, advecting cold air and the direction of the transpolar drift stream, both leading to the generation of more (less) sea ice over the Chukchi-Beaufort Sea region during strong (weak) ISM years. The study illustrates a new atmospheric teleconnection between the tropics and the Arctic.

Analyzing Variations in the Association of Eurasian Winter–Spring Snow Water Equivalent and Autumn Arctic Sea Ice

Because Eurasian snow water equivalent (SWE) is a key factor affecting the climate in the Northern Hemisphere, understanding the distribution characteristics of Eurasian SWE is important. Through empirical orthogonal function (EOF) analysis, we found that the first and second modes of Eurasian winter SWE present the distribution characteristics of an east–west dipole and north–south dipole, respectively. Moreover, the distribution of the second mode is caused by autumn Arctic sea ice, with the distribution of the north–south dipole continuing into spring. As the sea ice of the Barents–Kara Sea (BKS) decreases, a negative-phase Arctic oscillation (AO) is triggered over the Northern Hemisphere in winter, with warm and humid water vapor transported via zonal water vapor flux over the North Atlantic to southwest Eurasia, encouraging the accumulation of SWE in the southwest. With decreases in BKS sea ice, zonal water vapor transport in northern Eurasia is weakened, with meridional water vapor flux in northern Eurasia obstructing water vapor transport from the North Atlantic, discouraging the accumulation of SWE in northern Eurasia in winter while helping preserve the cold climate of the north. The distribution characteristics of Eurasian spring SWE are determined primarily by the memory effect of winter SWE. Whether analyzed through linear regression or support vector machine (SVM) methods, BKS sea ice is a good predictor of Eurasian winter SWE.

Capturing transformation of flood hazard over a large River Basin under changing climate using a top-down approach

Existing flood modeling studies over coastal catchments involving different combinations of model chain setup imparting complex information fails to entail the needs of policy or decision-makers. Thus, a comprehensive framework that pertains to the requirements of practitioners and provides more perspicuous flood hazard information is required. In this paper, a novel approach translating complex flood hazard information in the form of decision priority maps derived using a rational combination of models (physical and statistical) is elucidated at the finest administrative scale. The proposed methodology is illustrated over a highly flood-prone deltaic region in Mahanadi River Basin, India, to characterize impacts of climate change for a 1:100 years return period flood event under future conditions (2026-2055). The modeled flood events are further analyzed to capture the transformation dynamics of flood hazard classes (FHCs) in near-future, for prioritizing areas with greater hazard potential. Interestingly, the results capture a high transformation characteristic from low to high FHCs in agriculture-dominated areas, which are significantly greater than the areas experiencing flood hazard reduction. The results show a significant increase of 12.5% and 27.35% in areas with high FHCs under RCP4.5 and RCP8.5 scenarios, respectively. Moreover, a notable climate change response is indicated under both climate change scenarios, with approximately 22% (RCP4.5) and 25% (RCP8.5) in villages showing a drastic increment in flood hazard magnitude. The results thus highlight the importance of identifying and prioritizing the areas for flood adaptation where a relative change in flood hazard potential is higher due to climate change. Therefore, we conclude that this study can provide an insight into the implication of new approaches for effective communication of flood information by bridging the gaps between scientific communities and decision-makers in appraisal for better flood adaptation measures.