Your browser doesn't support javascript.
loading
Gauging mixed climate extreme value distributions in tropical cyclone regions.
O'Grady, J G; Stephenson, A G; McInnes, K L.
Affiliation
  • O'Grady JG; CSIRO Oceans and Atmosphere, Melbourne, Australia. julian.ogrady@csiro.au.
  • Stephenson AG; DATA61, Melbourne, Australia.
  • McInnes KL; CSIRO Oceans and Atmosphere, Melbourne, Australia.
Sci Rep ; 12(1): 4626, 2022 03 17.
Article in En | MEDLINE | ID: mdl-35301336
ABSTRACT
In tropical cyclone (TC) regions, tide gauge or numerical hindcast records are usually of insufficient length to have sampled sufficient cyclones to enable robust estimates of the climate of TC-induced extreme water level events. Synthetically-generated TC populations provide a means to define a broader set of plausible TC events to better define the probabilities associated with extreme water level events. The challenge is to unify the estimates of extremes from synthetically-generated TC populations with the observed records, which include mainly non-TC extremes resulting from tides and more frequently occurring atmospheric-depression weather and climate events. We find that extreme water level measurements in multiple tide gauge records in TC regions, some which span more than 100 years, exhibit a behaviour consistent with the combining of two populations, TC and non-TC. We develop an equation to model the combination of two populations of extremes in a single continuous mixed climate (MC) extreme value distribution (EVD). We then run statistical simulations to show that long term records including both historical and synthetic events can be better explained using MC than heavy-tailed generalised EVDs. This has implications for estimating extreme water levels when combining synthetic cyclone extreme sea levels with hindcast water levels to provide actionable information for coastal protection.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Cyclonic Storms Type of study: Prognostic_studies Language: En Journal: Sci Rep Year: 2022 Type: Article Affiliation country: Australia

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Cyclonic Storms Type of study: Prognostic_studies Language: En Journal: Sci Rep Year: 2022 Type: Article Affiliation country: Australia