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The lower Brahmaputra River in Bangladesh and Northeast India often floods during the monsoon season, with catastrophic consequences for people throughout the region. While most climate models predict an intensified monsoon and increase in flood risk with warming, robust baseline estimates of natural climate variability in the basin are limited by the short observational record. Here we use a new seven-century (1309–2004 C.E) tree-ring reconstruction of monsoon season Brahmaputra discharge to demonstrate that the early instrumental period (1956–1986 C.E.) ranks amongst the driest of the past seven centuries (13th percentile). Further, flood hazard inferred from the recurrence frequency of high discharge years is severely underestimated by 24–38% in the instrumental record compared to previous centuries and climate model projections. A focus on only recent observations will therefore be insufficient to accurately characterise flood hazard risk in the region, both in the context of natural variability and climate change. 

This paper presents new estimates of the hemispheric energy balance based on an assembly of radiative flux and ocean heat data. Further, it provides an overview of recent simulations with fully coupled climate models to investigate the role of its representation in causing tropical precipitation biases. The energy balance portrayed here features a small hemispheric imbalance with slightly more energy being absorbed by the Southern hemisphere. This yields a net transport of heat towards the NH composing of a northward cross-equatorial heat transport by the oceans and a southward heat flow in the atmosphere. The turbulent fluxes and hemispheric precipitation balance to about 3 Wm−2 with slightly larger total accumulation occurring in the NH. CloudSat data indicate more frequent precipitation in the SH implying more intense precipitation in the NH. Fully coupled climate model simulations show that reducing hemispheric energy balance biases does little to reduce existing biases in tropical precipitation. 

Prediction of Indian summer monsoon rainfall (ISMR) is at the heart of tropical climate prediction. Despite enormous progress having been made in predicting ISMR since 1886, the operational forecasts during recent decades (1989–2012) have little skill. Here we show, with both dynamical and physical–empirical models, that this recent failure is largely due to the models’ inability to capture new predictability sources emerging during recent global warming, that is, the development of the central-Pacific El Nino-Southern Oscillation (CP–ENSO), the rapid deepening of the Asian Low and the strengthening of North and South Pacific Highs during boreal spring. A physical–empirical model that captures these new predictors can produce an independent forecast skill of 0.51 for 1989–2012 and a 92-year retrospective forecast skill of 0.64 for 1921–2012. The recent low skills of the dynamical models are attributed to deficiencies in capturing the developing CP–ENSO and anomalous Asian Low. The results reveal a considerable gap between ISMR prediction skill and predictability.