Simulated Changes in Tropical Cyclone Size, Accumulated Cyclone Energy and Power Dissipation Index in a Warmer Climate

Wehner, Michael

doi:10.3390/oceans2040039

Cited by 6 publications

(7 citation statements)

References 46 publications

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“…Numerical weather prediction and climate models tend to systematically over estimate R max . Their coarse horizontal resolution can not adequately simulate smaller sizes (Bian et al., 2021; Wehner, 2021). Our framework clearly shows that this systematic large size bias will contribute to a systematic low bias of the pressure tendency (both positive and negative) and thus limit the skill of predicting the TC intensity evolution.…”

Section: Discussionmentioning

confidence: 99%

The Dependence of Tropical Cyclone Pressure Tendency on Size

Sparks

Toumi

2022

Geophysical Research Letters

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Current theories of tropical cyclone (TC) intensification give little direct indication of the role of the TC size in intensity changes, although there are observations showing a relationship. We develop a new model of TC central pressure tendency where the pressure change can be expressed as exponential with a time constant determined by the ratio of radius maximum wind (Rmax) and the column inflow or outflow speed. An analysis of observations confirms the relationship which becomes more important for a larger pressure tendency and suggests an upper bound on pressure tendency for a given Rmax. The dependence of the pressure tendency on size poses a challenging constraint on the accurate forecasting of TCs in numerical weather prediction and climate models.

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Section: Discussionmentioning

confidence: 99%

The Dependence of Tropical Cyclone Pressure Tendency on Size

Sparks

Toumi

2022

Geophysical Research Letters

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“…The size of TCs is typically determined as radii of various wind speeds [39] and it is uncertain whether and how it is affected by climate change [16]. However, some studies find a moderate increase in wind speed radii with larger temperatures [40,41]. We therefore here assume three scenarios A, B and C with no, moderate (5 km • C −1 ) and strong (10 km • C −1 ) increases in radius of affected area by flooding from Hurricane Harvey.…”

Section: Methods Overviewmentioning

confidence: 99%

Economic losses from hurricanes cannot be nationally offset under unabated warming

Middelanis

Willner²,

Otto³

et al. 2022

Environ. Res. Lett.

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Tropical cyclones range among the costliest of all meteorological events worldwide and planetary scale warming provides more energy and moisture to these storms. Modelling the national and global economic repercussions of 2017’s Hurricane Harvey, we find a qualitative change in the global economic response in an increasingly warmer world. While the United States were able to balance regional production failures by the original 2017 hurricane, this option becomes less viable under future warming. In our simulations of over 7000 regional economic sectors with more than 1.8 million supply chain connections, the US are not able to offset the losses by use of national efforts with intensifying hurricanes under unabated warming. At a certain warming level other countries have to step in to supply the necessary goods for production, which gives US economic sectors a competitive disadvantage. In the highly localized mining and quarrying sector—which here also comprises the oil and gas production industry—this disadvantage emerges already with the original Hurricane Harvey and intensifies under warming. Eventually, also other regions reach their limit of what they can offset. While we chose the example of a specific hurricane impacting a specific region, the mechanism is likely applicable to other climate-related events in other regions and other sectors. It is thus likely that the regional economic sectors that are best adapted to climate change gain significant advantage over their competitors under future warming.

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“…Globally, there has been considerable variability in the reported measures of cyclone power on annual and interdecadal timescales (Bell et al 2000) and global averages of power metrics have been found to be of limited use for revealing the impact of climate change on TC power characteristics (Wehner 2021). Miller et al (2023) examined TCs with genesis in the Coral Sea and grouped their tracks over the last fty years based on K-means clustering of the maximum wind-weighted centroids.…”

Section: Introductionmentioning

confidence: 99%

“…They are alike in that each uses the cyclone peak surface windspeeds, integrated over the lifetime of the TC track, to derive a measure of dissipated energy. They differ in that ACE calculates the square of this windspeed, whilst PDI uses the cube of the windspeed (Wehner 2021). It is worth mentioning here that the nancial consequences of TC damage have been shown to be a function of the maximum windspeed cubed (Southern, 1979).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Coral Sea tropical cyclone power and latitude of maximum intensity using climate indices

Miller

Silva

Strauss

2023

Preprint

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Tropical cyclone (TC) wind power, often highly destructive, can be quantified using the power dissipation index (PDI) and in this study, the PDIs for Coral Sea TC tracks, as well as the latitude of maximum intensity (LMI) were investigated for correlation with climatological indices. Multiple linear regression with collinearity removed and an overall Pearson correlation of 0.7 or above was used for this. The results for all tracks showed that three indices dominated for PDI: Niño 4 Sea Surface Temperature (SST), the Dipole Mode Index (DMI) and the Madden Julian Oscillation (MJO). The TC tracks clustered by maximum windspeed-weighted locations were then examined: For cluster 1 (located SE Australia), the additional influence of the Southern Annular Mode (SAM) was apparent, whilst for cluster 2 (located NW Australia), the same indices dominated as for the all-tracks model. For LMI, four indices were identified: the Indian Ocean East SST Anomaly (DMI E), the MJO, the Southern Oscillation Index (SOI) and the SAM. Only TCs clustered in the northeast of Australia had a valid model for LMI, with correlation 0.8, using three indices: DMI E, DMI and the SOI. Overall, raised Niño 4 SST combined with a negative DMI and low MJO amplitude were shown to predict large increases in TC power, whilst a combination of increasing DMI E temperature anomaly with a positive SOI moves LMI equator-wards. Improved understanding of the relative influences of the climate indices on TC power dissipation has direct ramifications for the future risk/hazard profile of coastal communities.

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Simulated Changes in Tropical Cyclone Size, Accumulated Cyclone Energy and Power Dissipation Index in a Warmer Climate

Cited by 6 publications

References 46 publications

The Dependence of Tropical Cyclone Pressure Tendency on Size

The Dependence of Tropical Cyclone Pressure Tendency on Size

Economic losses from hurricanes cannot be nationally offset under unabated warming

Prediction of Coral Sea tropical cyclone power and latitude of maximum intensity using climate indices

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