Estimating the Human Influence on Tropical Cyclone Intensity as the Climate Changes

Wehner, Michael; Zarzycki, Colin M.; Patricola, Christina M.

doi:10.1007/978-3-030-02402-4_12

Cited by 32 publications

(30 citation statements)

References 73 publications

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“…This technique is similar to the 'pseudo-global warming' approach and has been extensively used for storyline EA of tropical cyclones (TCs). Previous studies conducted a storyline EA aimed at TC-induced extreme events based on RCM and variable-resolution GCM simulations and showed significant impacts of global warming on rainfall amounts, wind speed, storm size and storm surge height [20][21][22][23][24] . However, storyline attribution cannot make a statement about the changes in cyclogenesis rates or the probability because it is conditional on the event's underlying existence 20,24 .…”

Section: Introductionmentioning

confidence: 99%

Advanced risk-based event attribution for heavy regional rainfall events

et al. 2020

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Risk-based event attribution (EA) science involves probabilistically estimating alterations of the likelihoods of particular weather events, such as heat waves and heavy rainfall, owing to global warming, and has been considered as an effective approach with regard to climate change adaptation. However, risk-based EA for heavy rain events remains challenging because, unlike extreme temperature events, which often have a scale of thousands of kilometres, heavy rainfall occurrences depend on mesoscale rainfall systems and regional geographies that cannot be resolved using general circulation models (GCMs) that are currently employed for risk-based EA. Herein, we use GCM large-ensemble simulations and high-resolution downscaled products with a 20-km non-hydrostatic regional climate model (RCM), whose boundary conditions are obtained from all available GCM ensemble simulations, to show that anthropogenic warming increased the risk of two record-breaking regional heavy rainfall events in 2017 and 2018 over western Japan. The events are examined from the perspective of rainfall statistics simulated by the RCM and from the perspective of background large-scale circulation fields simulated by the GCM. In the 2017 case, precipitous terrain and a static pressure pattern in the synoptic field helped reduce uncertainty in the dynamical components, whereas in the 2018 case, a static pressure pattern in the synoptic field provided favourable conditions for event occurrence through a moisture increase under warmer climate. These findings show that successful risk-based EA for regional extreme rainfall relies on the degree to which uncertainty induced by the dynamic components is reduced by background conditioning.

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Section: Introductionmentioning

confidence: 99%

Advanced risk-based event attribution for heavy regional rainfall events

et al. 2020

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“…In addition to precipitation increases from climate change, other anthropogenic influences such as urbanization (Zhang et al 2018;Sebastian et al 2019) and land subsidence (Miller and Shirzaei 2019) are important factors in assessing overall flood risk. As a result, the attribution statements in this study are high conditional on only the climate change aspects of the total change in flood risk (Wehner et al 2019). There are a few other studies completing the chain of attribution from extreme precipitation to flooding to financial losses.…”

Section: Introductionmentioning

confidence: 78%

Attributable human-induced changes in the magnitude of flooding in the Houston, Texas region during Hurricane Harvey

Wehner

Sampson

2021

Climatic Change

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The human influence on precipitation during tropical cyclones due to the global warming is now well documented in the literature. Several studies have found increases in Hurricane Harvey’s total precipitation over the Greater Houston area ranging from the Clausius-Clapeyron limit of 7% to as much as 38% locally. Here we use a hydraulic model to translate these attribution statements about precipitation to statements about the resultant flooding and associated damages. We find that while the attributable increase in the total volume of flood waters is the same as the attributable increase in precipitation, the attributable increase in the total area of the flood is less. However, we also find that in the most heavily flooded parts of Houston, the local attributable increases in flood area and volume are substantially larger than the increase in total precipitation. The results of this storyline attribution analysis of the Houston flood area are used to make an intuitive best estimate of the cost of Hurricane Harvey attributable to anthropogenic global warming as thirteen billion US dollars.

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“…Some recent studies have conducted factual real‐world versus counterfactual natural‐world experiments under a weather hindcast setup (Takayabu et al ., 2015; Pall et al ., 2017; Wehner et al ., 2019). In these experiments, a standard ensemble hindcast is compared against a modified hindcast in which SSTs, initial boundary conditions, and lateral boundary conditions for a regional downscaling model are modified through subtraction of an attrbutable anthropogenic component.…”

Section: Discussionmentioning

confidence: 99%

Benchmark estimate of the effect of anthropogenic emissions on the ocean surface

Stone

Pall

2021

Intl Journal of Climatology

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Investigations into the role of anthropogenic emissions in the occurrence of extreme weather often use a method that compares simulations of atmospheric climate models run under a factual scenario of historical boundary conditions observed during the period of the event against simulations run under a counterfactual scenario of what those boundary conditions might naturally have been over that same period in the absence of anthropogenic emissions. A particular requirement for this experiment design is an accurate estimation of ocean surface boundary conditions for use by the counterfactual natural simulations. Here we use output from the CMIP5 multi‐climate‐model archive to develop a robust estimate of sea surface temperatures and sea ice conditions for use in counterfactual natural simulations, intended as a benchmark estimate to facilitate comparison across climate models and across studies. This development includes tests to ensure that the final estimate is stable from year‐to‐year and stable against other perturbations to the methodology, as well as consideration of the strengths and weaknesses in comparison to other available attributable warming estimates. While this estimate is tailored specifically for the International CLIVAR C20C+ Detection and Attribution Project, it can be used by related projects as well.

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Estimating the Human Influence on Tropical Cyclone Intensity as the Climate Changes

Cited by 32 publications

References 73 publications

Advanced risk-based event attribution for heavy regional rainfall events

Advanced risk-based event attribution for heavy regional rainfall events

Attributable human-induced changes in the magnitude of flooding in the Houston, Texas region during Hurricane Harvey

Benchmark estimate of the effect of anthropogenic emissions on the ocean surface

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