Predictability of weather and climate

Abstract:

The North Indian Ocean (NIO), which includes the Bay of Bengal (BoB) and the Arabian Sea (AS), is frequently impacted by tropical cyclones (TCs). Notably, in November 2020, the simultaneous occurrence of Cyclones Gati in the AS and Nivar in the BoB presented a rare and compelling phenomenon worthy of investigation. This study delves into the underlying mechanisms driving the genesis and intensification of these two Very Severe Cyclonic Storms (VSCS). Key contributing factors include elevated sea surface temperatures (SST) and Tropical Cyclone Heat Potential (TCHP), both crucial for the development and intensification of TCs. Notably, the BoB exhibits higher TCHP values compared to the AS, which facilitated greater intensity for Nivar over Gati. Despite their concurrent formation, the genesis processes of Gati and Nivar were distinct. The early November development of Gati in the AS was modulated by the Rossby wave and the Madden–Julian Oscillation (MJO). Subsequently, the eastward propagation of the MJO triggered the formation of Cyclone Nivar in the BoB, enhancing convective activity from the AS and coinciding with the arrival of a downward-propagating Rossby wave. This study provides a comprehensive analysis of the simultaneous formation of Gati and Nivar, contributing novel insights into the complex interactions between oceanic and atmospheric phenomena. Such insights are vital for enhancing the predictive capabilities for destructive weather events in the NIO.

Abstract:

Past studies show that coupled model biases in European blocking and North Atlantic eddy-driven jet variability decrease as one increases the horizontal resolution in the atmospheric and oceanic model components, but it remains unclear if atmospheric or oceanic resolution plays a greater role and why. Here, following recent work by Schemm et al., we leverage a large multimodel ensemble to show that a coupled model’s ability to simulate extreme Gulf Stream precipitation is tightly linked to its simulated frequency of European blocking and northern jet excursions. Furthermore, the reduced biases in blocking and jet variability are consistent with better resolved precipitation extrema in high-resolution models. The analysis supports a hypothesis that models which simulate more extreme precipitation can generate more strongly poleward-propagating cyclones and more intense anticyclonic anomalies due to the stronger latent heat release occurring during extreme events. By contrast, typical North Atlantic SST biases are found to share only a weak or negligible relationship with blocking and jet biases. Finally, while previous studies have used a comparison between coupled models and models run with prescribed SSTs to argue for the role of ocean resolution, we emphasize here that models run with prescribed SSTs experience greatly reduced precipitation extremes due to their excessive thermal damping, making it unclear if such a comparison is meaningful. Instead, we speculate that most of the reduction in coupled model biases may actually be due to increased atmospheric resolution leading to better resolved convection.

Abstract:

While it is widely believed that the intense rainfall in summer 2022 over Pakistan was substantially exacerbated by anthropogenic climate change1, 2, climate models struggled to confirm this3, 4. Using a high-resolution operational seasonal forecasting system that successfully predicted the extreme wet conditions, we perform counterfactual experiments simulating pre-industrial and future conditions. Both experiments also exhibit strong anomalous rainfall, indicating a limited role of CO2-induced forcing. We attribute 10% of the total rainfall to historical increases in CO2 and ocean temperature. However, further increases in the future suggest a weak mean precipitation reduction but with increased variability. By decomposing rainfall and large-scale circulation into CO2 and SST-related signals, we illustrate a tendency for these signals to compensate each other in future scenarios. This suggests that historical CO2 impacts may not reliably predict future responses. Accurately capturing local dynamics is therefore essential for regional climate adaptation planning and for informing loss and damage discussions.

Abstract:

<jats:title>Abstract</jats:title> <jats:p>Flash droughts—characterized by their rapid onset—can cause devastating socioeconomic and agricultural damage. During such events, soil moisture depletion is driven not only by precipitation shortages but also by the elevated atmospheric moisture demand arising due to extreme heat. However, the role of extreme heat in shaping the evolution of flash droughts and their ecological impacts remains uncertain. Here we investigate the processes involved by analysing global reanalysis data from 1950 to 2022. We find that, when flash droughts are accompanied by extreme heat, they exhibit 6.7–90.8% higher severity and 8.3–114.3% longer recovery time than flash droughts without extreme heat. The presence of extreme heat during flash droughts accelerates soil moisture drawdown over high latitudes, where wet soils and enhanced radiation foster evapotranspiration. By contrast, it slows the absolute onset speed in subtropical transitional climate zones owing to evapotranspiration throttling. Our machine learning approach further reveals that hot flash droughts lead to sharper declines in ecosystem productivity, particularly in croplands, thereby threatening global food security. These findings underscore the pressing need for enhanced infrastructure and ecosystem resilience to hot flash droughts in a warming future.</jats:p>