Rapid attribution of the May/June 2016 flood-inducing precipitation in France and Germany to climate change

Oldenborgh, Geert Jan van; Philip, Sjoukje; Aalbers, Emma; Vautard, Robert; Otto, Friederike E. L.; Haustein, Karsten; Habets, Florence; Singh, Roop; Cullen, Heidi

doi:10.5194/hess-2016-308

Cited by 34 publications

(23 citation statements)

References 21 publications

(15 reference statements)

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“…In later work mean sea level pressure was added as a gridded variable (van den Besselaar et al, 2011). While E-OBS remains an important data set for model validation (Lenderink, 2010;Min et al, 2013;Nikulin et al, 2011), it is also used more generally for monitoring the climate across Europe (Lavaysse et al, 2017;van der Schrier et al, 2013;van Oldenborgh et al, 2016), particularly with regard to the assessment of the magnitude and frequency of daily extremes.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Version of the E‐OBS Temperature and Precipitation Data Sets

et al. 2018

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Abstract.We describe the construction of a new version of the Europewide E-OBS temperature (daily minimum, mean and maximum values) and precipitation dataset. This version provides an improved estimation of interpolation uncertainty through the calculation of a 100-member ensemble of realizations of each daily field. The dataset covers the period back to 1950, and provides gridded fields at a spacing of 0.25• x 0.25 • in regular latitude/longitude coordinates. As with the original E-OBS dataset, the ensemble version is based on the station series collated as part of the ECA&D initiative. Station density varies significantly over the domain, and over time, and a reliable estimation of interpolation uncertainty in the gridded fields is therefore important for users of the dataset. The uncertainty quantified by the ensemble dataset is more realistic than the uncertainty estimates in the original version, although uncertainty is still underestimated in data-sparse regions. The new dataset is compared against the earlier version of E-OBS and against regional gridded datasets produced by a selection of National Meteorological Services (NMSs). In terms of both climatological averages and extreme values, the new version of E-OBS is broadly comparable to the earlier version. Nonetheless, users will notice differences between the two E-OBS versions, especially for precipitation, which arises from the different gridding method used. Keypoints:• An improved uncertainty estimate is provided through the generation of multiple realizations• The new dataset is broadly consistent with the original version

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Section: Introductionmentioning

confidence: 99%

An Ensemble Version of the E‐OBS Temperature and Precipitation Data Sets

et al. 2018

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“…It is fair to say that the United States is years ahead of the rest of the world with regard to disaster-related damage data collection (Tschoegl et al, 2006). For instance, the American National Oceanic and Atmospheric Administration (NOAA) provides a database for flood fatalities and costs caused by weather events since 1903 at a county level, called the Storm Events Database (Downton et al, 2005;National Climatic Data Center, 2015).…”

Section: Damage Data Collectionmentioning

confidence: 99%

“…More recently, from May to June 2016, large parts of the Parisian basin were also flooded. More than 2000 municipalities were affected by this event for a total cost of damage to insured goods higher than EUR 1.4 billion (Van Oldenborgh et al, 2016;Ramos et al, 2017;CCR, 2016).…”

Section: Introductionmentioning

confidence: 99%

DamaGIS: a multisource geodatabase for collection of flood-related damage data

Saint-Martin

Javelle

Vinet

2018

Earth Syst. Sci. Data

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Abstract. Every year in France, recurring flood events result in several million euros of damage, and reducing the heavy consequences of floods has become a high priority. However, actions to reduce the impact of floods are often hindered by the lack of damage data on past flood events. The present paper introduces a new database for collection and assessment of flood-related damage. The DamaGIS database offers an innovative bottom-up approach to gather and identify damage data from multiple sources, including new media. The study area has been defined as the south of France considering the high frequency of floods over the past years. This paper presents the structure and contents of the database. It also presents operating instructions in order to keep collecting damage data within the database. This paper also describes an easily reproducible method to assess the severity of flood damage regardless of the location or date of occurrence. A first analysis of the damage contents is also provided in order to assess data quality and the relevance of the database. According to this analysis, despite its lack of comprehensiveness, the DamaGIS database presents many advantages. Indeed, DamaGIS provides a high accuracy of data as well as simplicity of use. It also has the additional benefit of being accessible in multiple formats and is open access. The DamaGIS database is available at https://doi.org/10.5281/zenodo.1241089.

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“…The methodologies employed in this study are used regularly in the literature and were previously applied to the rapid attribution of the French and German 2016 flooding event (Van Oldenborgh et al 2016) and of Storm Desmond over the UK in 2015(Van Oldenborgh et al 2015. The presented analysis builds upon these methodologies for anthropogenic climate change attribution and also explores the role of climate variability.…”

Section: )mentioning

confidence: 99%

“…In order to estimate the observed return periods using the 3-day annual events found above, we fit the resulting data to a Generalised Extreme Value (GEV) Distribution (Coles, 2001) in a similar manner as previously done for rapid attribution of the 2015 storm Desmond over the UK (Van Oldenborgh et al 2015) and for the rapid attribution of the 2016 flooding in France and Germany (Van Oldenborgh et al 2016). We first analyze the GEV distribution of observations and model simulations to determine if they represent the statistics of summertime extreme precipitation events sufficiently to employ them in further work.…”

Section: Defining An Extreme Event and Its Statisticsmentioning

confidence: 99%

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

Wiel¹,

Kapnick²,

Oldenborgh³

et al. 2016

Preprint

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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 (U.S.) starting around 10 August, 2016. Precipitation was heaviest in the region broadly encompassing the city of Baton Rouge, with a three-day maximum found at a station in Livingston, LA (east of Baton Rouge) from 12–14 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 thirteen people. As the disaster was unfolding, the Red Cross called the flooding the worst natural disaster in the U.S. 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 U.S. 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: 29–31º N, 85–95º W, which we refer to as the Central U.S. 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 (C.I.): 450–1450). The probability for an event like this to happen anywhere in the region is presently 1 in 30 years (C.I. 11–110). We estimate that these probabilities and the intensity of extreme precipitation events of this return time have increased since 1900. A Central U.S. 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, with the regional probability of 3-day extreme precipitation increasing due to anthropogenic climate change by a factor of more than a factor 1.4 in the most accurate analyses. 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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Rapid attribution of the May/June 2016 flood-inducing precipitation in France and Germany to climate change

Cited by 34 publications

References 21 publications

An Ensemble Version of the E‐OBS Temperature and Precipitation Data Sets

An Ensemble Version of the E‐OBS Temperature and Precipitation Data Sets

DamaGIS: a multisource geodatabase for collection of flood-related damage data

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

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