Interaction between dry and hot extremes at a global scale using a cascade modeling framework

Abstract

Climate change amplifies dry and hot extremes, yet the mechanism, extent, scope, and temporal scale of causal linkages between dry and hot extremes remain underexplored. Here using the concept of system dynamics, we investigate cross-scale interactions within dry-to-hot and hot-to-dry extreme event networks and quantify the magnitude, temporal-scale, and physical drivers of cascading effects (CEs) of drying-on-heating and vice-versa, across the globe. We find that locations exhibiting exceptionally strong CE (hotspots) for dry-to-hot and hot-to-dry extremes generally coincide. However, the CEs differ strongly in their timescale of interaction, hydroclimatic drivers, and sensitivity to changes in the soil-plant-atmosphere continuum and background aridity. The CE of drying-on-heating in the hotspot locations reaches its peak immediately driven by the compounding influence of vapor pressure deficit, potential evapotranspiration, and precipitation. In contrast, the CE of heating-on-drying peaks gradually dominated by concurrent changes in potential evapotranspiration, precipitation, and net-radiation with the effect of vapor pressure deficit being strongly controlled by ecosystem isohydricity and background aridity. Our results help improve our understanding of the causal linkages and the predictability of compound extremes and related impacts.

A multi‐satellite framework to rapidly evaluate extreme biosphere cascades: The Western US 2021 drought and heatwave

“…Cascades and their propagation are a common feature of hydrologic anomalies (Farahmand et al, 2021; Mukherjee et al, 2023), though their connection to the biosphere has not been investigated. We note that biosphere cascades are readily observed in other global locations and time periods (Figures S4 and S5).…”

“…Namely, it involves detecting carbon cycle anomalies and associated climate anomalies, conducting a cascade analysis to pinpoint specific environmental drivers of the anomaly, and conducting a time series analysis to establish more specific linkages between variables informed from the previous steps. The workflow and analysis is more detailed compared to global attributions (Mukherjee et al, 2023; Yang et al, 2023) in order to evaluate complex sub‐seasonal interactions between a breadth of climate variables and terrestrial biosphere variables. These interactions might change substantially in time and space.…”

Future Amplification of Multivariate Risk of Compound Drought and Heatwave Events on South Asian Population

“…In the recent past, SA witnessed a series of extreme drought and heatwave events in the past few decades (Mukherjee & Mishra, 2021; Ullah, Ma, Ren, et al., 2022; Xu et al., 2020). Previous studies have extensively explored the conventional heatwaves and droughts (Mishra et al., 2017; Shahzaman et al., 2021; Mukherjee et al., 2023), along with their physical drivers (Wei et al., 2017; Hina et al., 2021; Iyakaremye et al., 2022) however, assessing future variations in CDHW events and its multivariate risks over SA have been underexplored. In this study, we investigated future variations in CDHW events and its multivariate risks over SA and extended it to explore the projected changes in the future population exposure to monsoonal CDHWS events in SA under the moderate (SSP2‐4.5) and high (SSP5‐8.5) emission scenarios for the two future periods (2021–2055 and 2056–2090).…”

Higher Exposure of Poorer People to Emerging Weather Whiplash in a Warmer World

“…We investigate the influence of four hydroclimatic anomalies, including convective available potential energy (CAPE), vertically integrated moisture divergence (VIMD), vapor pressure deficit (VPD), and PET, on the drought‐to‐downpour weather whiplashes based on odd ratios calculated by fitting a logistic regression (Li et al., 2020; Mukherjee et al., 2023; Varga & Breuer, 2022). The logistic regression is fitted using the binary sequence of the last months of drought events as the independent variable ( X ) and that of months when downpours occur as the dependent variable ( Y ) with Z as the confounding variable.…”