Extreme weather and climate events and their impacts can occur in complex combinations, an interaction shaped by physical drivers and societal forces. In these situations, governance, markets and other decision-making structures-together with population exposure and vulnerability-create nonphysical interconnections among events by linking their impacts, to positive or negative effect. Various anthropogenic actions can also directly affect the severity of events, further complicating these feedback loops. Such relationships are rarely characterized or considered in physical-sciences-based research contexts. Here, we present a multidisciplinary argument for the concept of connected extreme events, and we suggest vantage points and approaches for producing climate information useful in guiding decisions about them.
Continental United States (CONUS) hurricane-related inflation-adjusted damage has increased significantly since 1900. However, since 1900 neither observed CONUS landfalling hurricane frequency nor intensity shows significant trends, including the devastating 2017 season. Two large-scale climate modes that have been noted in prior research to significantly impact CONUS landfalling hurricane activity are El Niño–Southern Oscillation on interannual time scales and the Atlantic multidecadal oscillation on multidecadal time scales. La Niña seasons tend to be characterized by more CONUS hurricane landfalls than El Niño seasons, and positive Atlantic multidecadal oscillation phases tend to have more CONUS hurricane landfalls than negative phases. Growth in coastal population and regional wealth are the overwhelming drivers of observed increases in hurricane-related damage. As the population and wealth of the United States has increased in coastal locations, it has invariably led to the growth in exposure and vulnerability of coastal property along the U.S. Gulf and East Coasts. Unfortunately, the risks associated with more people and vulnerable exposure came to fruition in Texas and Florida during the 2017 season following the landfalls of Hurricanes Harvey and Irma. Total economic damage from those two storms exceeded $125 billion. Growth in coastal population and exposure is likely to continue in the future, and when hurricane landfalls do occur, this will likely lead to greater damage costs than previously seen. Such a statement is made recognizing that the vast scope of damage from hurricanes often highlights the effectiveness (or lack thereof) of building codes, flood maps, infrastructure, and insurance in at-risk communities.
Atlantic hurricane seasons have a long history of causing significant financial impacts, with Harvey, Irma, Maria, Florence, and Michael combining to incur more than 345 billion USD in direct economic damage during 2017–2018. While Michael’s damage was primarily wind and storm surge-driven, Florence’s and Harvey’s damage was predominantly rainfall and inland flood-driven. Several revised scales have been proposed to replace the Saffir–Simpson Hurricane Wind Scale (SSHWS), which currently only categorizes the hurricane wind threat, while not explicitly handling the totality of storm impacts including storm surge and rainfall. However, most of these newly-proposed scales are not easily calculated in real-time, nor can they be reliably calculated historically. In particular, they depend on storm wind radii, which remain very uncertain. Herein, we analyze the relationship between normalized historical damage caused by continental United States (CONUS) landfalling hurricanes from 1900–2018 with both maximum sustained wind speed (Vmax) and minimum sea level pressure (MSLP). We show that MSLP is a more skillful predictor of normalized damage than Vmax, with a significantly higher rank correlation between normalized damage and MSLP (rrank = 0.77) than between normalized damage and Vmax (rrank = 0.66) for all CONUS landfalling hurricanes. MSLP has served as a much better predictor of hurricane damage in recent years than Vmax, with large hurricanes such as Ike (2008) and Sandy (2012) causing much more damage than anticipated from their SSHWS ranking. MSLP is also a more accurately-measured quantity than is Vmax, making it an ideal quantity for evaluating a hurricane’s potential damage.
This study investigates global tropical cyclone (TC) activity trends from 1990 to 2021, a period marked by largely consistent observational platforms. Several global TC metrics have decreased during this period, with significant decreases in hurricane numbers and Accumulated Cyclone Energy (ACE). Most of this decrease has been driven by significant downward trends in the western North Pacific. Globally, short‐lived named storms, 24‐hr intensification events of ≥50 kt day−1, and TC‐related damage have increased significantly. The increase in short‐lived named storms is likely due to technological improvements, while rapidly intensifying TC increases may be fueled by higher potential intensity. Damage increases are largely due to increased coastal assets. The significant decrease in hurricane numbers and global ACE are likely due to the trend toward a more La Niña‐like base state from 1990 to 2021, favoring North Atlantic TC activity and suppressing North and South Pacific TC activity.
The damage potential of a hurricane is widely considered to depend more strongly on an integrated measure of the hurricane wind field, such as integrated kinetic energy (IKE), than a point‐based wind measure, such as maximum sustained wind speed (Vmax). Recent work has demonstrated that minimum sea level pressure (MSLP) is also an integrated measure of the wind field. This study investigates how well historical continental US hurricane damage is predicted by MSLP compared to both Vmax and IKE for continental United States hurricane landfalls for the period 1988–2021. We first show for the entire North Atlantic basin that MSLP is much better correlated with IKE (rrank = 0.50) than Vmax (rrank = 0.26). We then show that continental US hurricane normalized damage is better predicted by MSLP (rrank = 0.83) than either Vmax (rrank = 0.67) or IKE (rrank = 0.65). For Georgia to Maine hurricane landfalls specifically, MSLP and IKE show similar levels of skill at predicting damage, whereas Vmax provides effectively no predictive power. Conclusions for IKE extend to power dissipation as well, as the two quantities are highly correlated because wind radii closely follow a Modified Rankine vortex. The physical relationship of MSLP to IKE and power dissipation is discussed. In addition to better representing damage, MSLP is also much easier to measure via aircraft or surface observations than either Vmax or IKE, and it is already routinely estimated operationally. We conclude that MSLP is an ideal metric for characterizing hurricane damage risk.
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