The joint driving effects of climate and weather changes caused the Chamoli glacier-rock avalanche in the high altitudes of the India Himalaya

Ice avalanches are one of the most devastating mountain hazards,and can pose a great risk to the security of the surrounding area.Although ice avalanches have been widely observed in mountainous regions around the world,only a few ice avalanche events have been studied comprehensively,due to the lack of available data.In this study,in response to the recent catastrophic rock-ice avalanche(7 February 2021)at Chamoli in the India Himalaya,we used high-resolution satellite images and found that this event was actually a glacier-rock landslide,where the collapse of the rock-ice body was caused by the sliding of the bedrock beneath the glacier,for which the source area and volume loss were about 2.89×10^(5) m^(2) and 2.46×10^(7) m^(3),respectively,corresponding to an average elevation change of about−85 m.Furthermore,visual analysis of the dense time-series satellite images shows that the overall downward sliding of the collapsed rock-ice body initiated around the summer of 2017,and thereafter exhibited clear seasonality(mainly in summer).Meteorological analysis reveals a strong rainfall anomaly in the initiation period of the sliding and a remarkable winter warming anomaly in the 40 days before the collapse.Comparisons of multi-temporal digital elevation models(DEMs)further suggest that the glacier geometry in the collapsed areas was likely changing(i.e.,accelerated surface thinning in the lower part of the glaciers and insignificant change in the upper part),which is consistent with the region-wide climate warming.Finally,by combining the above findings and a geomorphic analysis,we conclude that the rock-ice avalanche event was mainly caused by the joint effects of climate and weather changes acting on a steeply sloping and fracture-prone geological condition.The findings of this study provide new and valuable evidence for the study of slope/glacier instability at high altitudes.This study also highlights that,for the Himalaya and other high mountain ranges,there is an urgent need to identify the glaciers that have a high risk of ice avalanches.

The insight of why:Causal inference in Earth system science

The utilization of big Earth data has provided insights into the planet we inhabit in unprecedented dimensions and scales.Unraveling the concealed causal connections within intricate data holds paramount importance for attaining a profound comprehension of the Earth system.Statistical methods founded on correlation have predominated in Earth system science(ESS)for a long time.Nevertheless,correlation does not imply causation,especially when confronted with spurious correlations resulting from big data.Consequently,traditional correlation and regression methods are inadequate for addressing causation related problems in the Earth system.In recent years,propelled by advancements in causal theory and inference methods,particularly the maturity of causal discovery and causal graphical models,causal inference has demonstrated vigorous vitality in various research directions in the Earth system,such as regularities revealing,processes understanding,hypothesis testing,and physical models improving.This paper commences by delving into the origins,connotations,and development of causality,subsequently outlining the principal frameworks of causal inference and the commonly used methods in ESS.Additionally,it reviews the applications of causal inference in the main branches of the Earth system and summarizes the challenges and development directions of causal inference in ESS.In the big Earth data era,as an important method of big data analysis,causal inference,along with physical model and machine learning,can assist the paradigm transformation of ESS from a model-driven paradigm to a paradigm of integration of both mechanism and data.Looking forward,the establishment of a meticulously structured and normalized causal theory can act as a foundational cornerstone for fostering causal cognition in ESS and propel the leap from fragmented research towards a comprehensive understanding of the Earth system.