Space-time clustering of climate extremes amplify global climate impacts, leading to fat-tailed risk

Abstract

Abstract. We present evidence that the global juxtaposition of major assets relevant to the economy with the space and time expression of extreme floods or droughts leads to a much higher aggregate risk than would be expected by chance. Using a century-long, globally gridded time series that indexes net water availability, every year we compute local occurrences of an extreme “dry” or “wet” condition for a specified duration and return period. A global exposure index is then derived for major mining commodities by weighting extreme event occurrence by local production exposed. We note significant spatial and temporal clustering of exposure at the global level leading to the potential for fat-tailed risk associated with investment portfolios and supply chains. This may not be a surprise to climate scientists familiar with the space-time patterns of interannual to decadal climate oscillations that can affect remote regions through teleconnections. However, the traditional approach of climate risk analysis only considers local or point extreme value analysis and hence does not account for temporal and spatial clustering of exposure for global portfolios. As multinational enterprises and supply chains assess and disclose physical climate risks, they need to consider the much higher chance that they may have multiple assets that may be exposed to extreme wet and/or dry climate extremes in the same year.

Compound Continental Risk of Multiple Extreme Floods in the United States

“…Importantly, identifying large‐scale climate drivers of such compound risk offers a wide range of opportunities for developing a climate‐informed payout modeling structure at the continental United States level. Utilizing teleconnections such as ENSO and AMO for flood risk estimation and re‐insurance measures would provide a wide range of benefits for the regional to continental climate risk adaptation and preparedness strategies (Bonnafous & Lall, 2021; Lima et al., 2015; Pizarro & Lall, 2005). Decision‐makers and policy analysts at various levels can readily access a comprehensive set of plausible correlated peak flood data which is internally consistent with observational records.…”

Climate-catchment-soil control on hydrological droughts in peninsular India

“…We used daily observed streamflow records of past 50 years (1965–2019) from 98 stream gauges over PRB in a multi-stage framework 37 , 41 (Fig. 1 ) to quantify the contiguity in locations and time of occurrence of hydrologic droughts (the space–time clustering 42 or synchronicity in drought properties) and identify potential KDDs from a wide range of climate, soil, and terrain attributes (Fig. 1 , Supplementary Fig.…”