Meteorological extreme events such as El Niño events are expected to affect tropical forest net primary production (NPP) and woody growth, but there has been no large-scale empirical validation of this expectation. We collected a large high–temporal resolution dataset (for 1–13 years depending upon location) of more than 172 000 stem growth measurements using dendrometer bands from across 14 regions spanning Amazonia, Africa and Borneo in order to test how much month-to-month variation in stand-level woody growth of adult tree stems (NPP stem ) can be explained by seasonal variation and interannual meteorological anomalies. A key finding is that woody growth responds differently to meteorological variation between tropical forests with a dry season (where monthly rainfall is less than 100 mm), and aseasonal wet forests lacking a consistent dry season. In seasonal tropical forests, a high degree of variation in woody growth can be predicted from seasonal variation in temperature, vapour pressure deficit, in addition to anomalies of soil water deficit and shortwave radiation. The variation of aseasonal wet forest woody growth is best predicted by the anomalies of vapour pressure deficit, water deficit and shortwave radiation. In total, we predict the total live woody production of the global tropical forest biome to be 2.16 Pg C yr −1 , with an interannual range 1.96–2.26 Pg C yr −1 between 1996–2016, and with the sharpest declines during the strong El Niño events of 1997/8 and 2015/6. There is high geographical variation in hotspots of El Niño–associated impacts, with weak impacts in Africa, and strongly negative impacts in parts of Southeast Asia and extensive regions across central and eastern Amazonia. Overall, there is high correlation ( r = −0.75) between the annual anomaly of tropical forest woody growth and the annual mean of the El Niño 3.4 index, driven mainly by strong correlations with anomalies of soil water deficit, vapour pressure deficit and shortwave radiation. This article is part of the discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

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To understand the impacts of extreme climate events, it is first necessary to understand the spatio-temporal characteristics of the event. Gridded climate products are frequently used to describe climate patterns but have been shown to perform poorly over data-sparse regions such as tropical forests. Often, they are uncritically employed in a wide range of studies linking tropical forest processes to large-scale climate variability. Here, we conduct an inter-comparison and assessment of near-surface air temperature fields supplied by four state-of-the-art reanalysis products, along with precipitation estimates supplied by four merged satellite-gauge rainfall products. Firstly, spatio-temporal patterns of temperature and precipitation anomalies during the 2015–2016 El Niño are shown for each product to characterize the impact of the El Niño on the tropical forest biomes of Equatorial Africa, Southeast Asia and South America. Using meteorological station data, a two-stage assessment is then conducted to determine which products most reliably model tropical climates during the 2015–2016 El Niño, and which perform best over the longer-term satellite observation period (1980–2016). Results suggest that eastern Amazonia, parts of the Congo Basin and mainland Southeast Asia all experienced significant monthly mean temperature anomalies during the El Niño, while northeastern Amazonia, eastern Borneo and southern New Guinea experienced significant precipitation deficits. Our results suggest ERA-Interim and MERRA2 are the most reliable air temperature datasets, while TRMM 3B42 V7 and CHIRPS v2.0 are the best-performing rainfall datasets. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

In June 2013, Uttarakhand experienced a hydro-meteorological disaster due to a 4 d extreme precipitation event of return period more than 100 years, claiming thousands of lives and causing enormous damage to infrastructure. Using the weather@home climate modelling system and its Half a degree Additional warming, Prognosis and Projected Impacts simulations, this study investigates the change in the return period of similar events in a 1.5°C and 2°C warmer world, compared to current and pre-industrial levels. We find that the likelihood of such extreme precipitation events will significantly increase under both future scenarios. We also estimate the change in extreme river flow at the Ganges; finding a considerable increase in the risk of flood events. Our results also suggest that until now, anthropogenic aerosols may have effectively counterbalanced the otherwise increased meteorological flood risk due to greenhouse gas (GHG) induced warming. Disentangling the response due to GHGs and aerosols is required to analyses the changes in future rainfall in the South Asia monsoon region. More research with other climate models is also necessary to make sure these results are robust.