Precipitation Extremes Under Climate Change

O’Gorman, Paul A.

doi:10.1007/s40641-015-0009-3

Cited by 524 publications

(355 citation statements)

References 100 publications

(196 reference statements)

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“…In this context, Westra et al [33] analyzed the relationship between maximum daily rainfall and the global near-surface temperature, finding a positive relationship between both variables. Similarly, worldwide studies done by O'gorman [34] and Asadieh & Krakauer [35], found that the maximum daily precipitations are growing faster than the average annual precipitation, implying that rainfall intensity is generally increasing, which is similar to what Sarricolea & Martin-Vide [27] and Sarricolea et al [28] found in Chile, through a CI analysis. Therefore, the objective of this study was to characterize in time and space the behavior and concentration of daily and monthly rainfall in two climatic zones (arid-semiarid and humid-subhumid) of the country.…”

Section: Introductionsupporting

confidence: 66%

Spatial and Temporal Analysis of Rainfall Concentration Using the Gini Index and PCI

Sangüesa

Pizarro

Ibáñez

et al. 2018

Water

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This study aims to determine if there is variation in precipitation concentrations in Chile. We analyzed daily and monthly records from 89 pluviometric stations in the period and distributed between 29 • 12 S and 39 • 30 S. This area was divided into two climatic zones: arid-semiarid and humid-subhumid. For each station, the Gini coefficient or Gini Index (GI), the precipitation concentration index (PCI), and the maximum annual precipitation intensity in a 24-h duration were calculated. These series of annual values were analyzed with the Mann-Kendall test with 5% error. Overall, it was noted that positive trends in the GI are present in both areas, although most were not found to be significant. In the case of PCI, the presence of positive trends is only present in the arid-semiarid zone; in the humid-subhumid zone, negative trends were mostly observed, although none of them were significant. Although no significant changes in all indices are evident, the particular case of the GI in the humid-subhumid zone stands out, where mostly positive trends were found (91.1%), of which 35.6% were significant. This would indicate that precipitation is more likely to be concentrated on a daily scale.

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Section: Introductionsupporting

confidence: 66%

Spatial and Temporal Analysis of Rainfall Concentration Using the Gini Index and PCI

Sangüesa

Pizarro

Ibáñez

et al. 2018

Water

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“…When scaling future changes of extreme precipitation by seasonal mean temperature changes [as conventionally done e.g. by Kendon et al (2014), Gorman (2015), Ban et al (2015) and Prein et al (2016b)], the true scaling rates are-in our view-only approximated since the temperature for days with extreme precipitation events will differ. Wang et al (2017) showed that scaling unconditional extreme daily precipitation (defined with no regard to temperature) with mean seasonal temperature may yield a spuriously low scaling rate (2-5% K −1 ), that is not directly related to any specific process or to C-C scaling.…”

Section: Discussionmentioning

confidence: 99%

Evaluation and projected changes of precipitation statistics in convection-permitting WRF climate simulations over Central Europe

Knist

Goergen²,

Simmer

2018

Clim Dyn

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We perform simulations with the WRF regional climate model at 12 and 3 km grid resolution for the current and future climates over Central Europe and evaluate their added value with a focus on the daily cycle and frequency distribution of rainfall and the relation between extreme precipitation and air temperature. First, a 9 year period of ERA-Interim driven simulations is evaluated against observations; then global climate model runs (MPI-ESM-LR RCP4.5 scenario) are downscaled and analyzed for three 12-year periods: a control, a mid-of-century and an end-of-century projection. The higher resolution simulations reproduce both the diurnal cycle and the hourly intensity distribution of precipitation more realistically compared to the 12 km simulation. Moreover, the observed increase of the temperature-extreme precipitation scaling from the Clausius-Clapeyron (C-C) scaling rate of ~ 7% K −1 to a super-adiabatic scaling rate for temperatures above 11 °C is reproduced only by the 3 km simulation. The drop of the scaling rates at high temperatures under moisture limited conditions differs between sub-regions. For both future scenario time spans both simulations suggest a slight decrease in mean summer precipitation and an increase in hourly heavy and extreme precipitation. This increase is stronger in the 3 km runs. Temperature-extreme precipitation scaling curves in the future climate are projected to shift along the 7% K −1 trajectory to higher peak extreme precipitation values at higher temperatures. The curves keep their typical shape of C-C scaling followed by super-adiabatic scaling and a drop-off at higher temperatures due to moisture limitation.

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“…Also, the response to radiative forcing may be nonlinear: thermodynamic and/or dynamic changes may be different for different weather systems (O'Gorman, 2015).…”

Section: Introductionmentioning

confidence: 99%

Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change

Wiel

Kapnick

Oldenborgh

et al. 2017

Hydrol. Earth Syst. Sci.

138

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Abstract. A stationary low pressure system and elevated levels of precipitable water provided a nearly continuous source of precipitation over Louisiana, United States (US), starting around 10 August 2016. Precipitation was heaviest in the region broadly encompassing the city of Baton Rouge, with a 3-day maximum found at a station in Livingston, LA (east of Baton Rouge), from 12 to 14 August 2016 (648.3 mm, 25.5 inches). The intense precipitation was followed by inland flash flooding and river flooding and in subsequent days produced additional backwater flooding. On 16 August, Louisiana officials reported that 30 000 people had been rescued, nearly 10 600 people had slept in shelters on the night of 14 August and at least 60 600 homes had been impacted to varying degrees. As of 17 August, the floods were reported to have killed at least 13 people. As the disaster was unfolding, the Red Cross called the flooding the worst natural disaster in the US since Super Storm Sandy made landfall in New Jersey on 24 October 2012. Before the floodwaters had receded, the media began questioning whether this extreme event was caused by anthropogenic climate change. To provide the necessary analysis to understand the potential role of anthropogenic climate change, a rapid attribution analysis was launched in real time using the best readily available observational data and high-resolution global climate model simulations. The objective of this study is to show the possibility of performing rapid attribution studies when both observational and model data and analysis methods are readily available upon the start. It is the authors' aspiration that the results be used to guide further studies of the devastating precipitation and flooding event. Here, we present a first estimate of how anthropogenic climate change has affected the likelihood of a comparable extreme precipitation event in the central US Gulf Coast. While the flooding event of interest triggering this study occurred in south Louisiana, for the purposes of our analysis, we have defined an extreme precipitation event by taking the spatial maximum of annual 3-day inland maximum precipitation over the region of 29–31° N, 85–95° W, which we refer to as the central US Gulf Coast. Using observational data, we find that the observed local return time of the 12–14 August precipitation event in 2016 is about 550 years (95 % confidence interval (CI): 450–1450). The probability for an event like this to happen anywhere in the region is presently 1 in 30 years (CI 11–110). We estimate that these probabilities and the intensity of extreme precipitation events of this return time have increased since 1900. A central US Gulf Coast extreme precipitation event has effectively become more likely in 2016 than it was in 1900. The global climate models tell a similar story; in the most accurate analyses, the regional probability of 3-day extreme precipitation increases by more than a factor of 1.4 due to anthropogenic climate change. The magnitude of the shift in probabilities is greater in the 25 km (higher-resolution) climate model than in the 50 km model. The evidence for a relation to El Niño half a year earlier is equivocal, with some analyses showing a positive connection and others none.

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Precipitation Extremes Under Climate Change

Cited by 524 publications

References 100 publications

Spatial and Temporal Analysis of Rainfall Concentration Using the Gini Index and PCI

Spatial and Temporal Analysis of Rainfall Concentration Using the Gini Index and PCI

Evaluation and projected changes of precipitation statistics in convection-permitting WRF climate simulations over Central Europe

Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change

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