2017
DOI: 10.1073/pnas.1618082114
|View full text |Cite
|
Sign up to set email alerts
|

Quantifying the influence of global warming on unprecedented extreme climate events

Abstract: Efforts to understand the influence of historical global warming on individual extreme climate events have increased over the past decade. However, despite substantial progress, events that are unprecedented in the local observational record remain a persistent challenge. Leveraging observations and a large climate model ensemble, we quantify uncertainty in the influence of global warming on the severity and probability of the historically hottest month, hottest day, driest year, and wettest 5-d period for dif… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

2
313
0

Year Published

2017
2017
2023
2023

Publication Types

Select...
8
1

Relationship

2
7

Authors

Journals

citations
Cited by 520 publications
(362 citation statements)
references
References 51 publications
2
313
0
Order By: Relevance
“…We first remove the linear trend from the observed time series, then calculate the observed event magnitude minus the detrended event magnitude, divided by the observed event magnitude minus the detrended mean. The resulting value represents the percent of the magnitude of the event attributable to the observed trend. The contribution of the observed trend to the probability of the event (Figure a herein and Figure c from Diffenbaugh et al ()). To generate a sample of return intervals for the given event in both the observed and detrended timeseries, we fit a Gumbel distribution, a variation of the Generalized Extreme Value distribution, to a bootstrapped sample of each timeseries 200 times.…”
Section: Methodsmentioning
confidence: 88%
See 1 more Smart Citation
“…We first remove the linear trend from the observed time series, then calculate the observed event magnitude minus the detrended event magnitude, divided by the observed event magnitude minus the detrended mean. The resulting value represents the percent of the magnitude of the event attributable to the observed trend. The contribution of the observed trend to the probability of the event (Figure a herein and Figure c from Diffenbaugh et al ()). To generate a sample of return intervals for the given event in both the observed and detrended timeseries, we fit a Gumbel distribution, a variation of the Generalized Extreme Value distribution, to a bootstrapped sample of each timeseries 200 times.…”
Section: Methodsmentioning
confidence: 88%
“…The first two metrics focus solely on trends and events in the observational record (using NCEP/NCAR R1 reanalysis data), while metrics 3–5 use climate model data (CESM‐LE) to explicitly distinguish anthropogenic forcing from natural variability. The contribution of the observed trend to the magnitude of the event (Table herein and Figure a from Diffenbaugh et al ()). We first remove the linear trend from the observed time series, then calculate the observed event magnitude minus the detrended event magnitude, divided by the observed event magnitude minus the detrended mean.…”
Section: Methodsmentioning
confidence: 91%
“…Nevertheless, targets and actions for reducing greenhouse gas (GHG) emissions are core components. However, the 2°C agenda of the Paris Agreement is not likely to be enough to reduce climate change to levels that would ease the dramatic increase in global impacts from cyclones, fire and floods as well as the entire loss of coral reefs from ocean warming, despite much scepticism about these issues (Diffenbaugh et al, 2017;Dunlap, 2013). Hence the IPCC has agreed to gather the research for a new agenda that would enable a mechanism to ratchet up the reduction of global greenhouse emissions in a way that ultimately leads to no more than 1.5°C.…”
Section: The Paris Agreement and The 15°c Agendamentioning
confidence: 99%
“…Similarly, investing in an infrastructure that is truly climate resilient will depend on the understanding that incremental changes in mean climate belie far greater changes in the frequency of unprecedented extreme events (Diffenbaugh et al, 2017;Huang et al, 2018;Pendergrass et al, 2017;Swain et al, 2018). Similarly, investing in an infrastructure that is truly climate resilient will depend on the understanding that incremental changes in mean climate belie far greater changes in the frequency of unprecedented extreme events (Diffenbaugh et al, 2017;Huang et al, 2018;Pendergrass et al, 2017;Swain et al, 2018).…”
Section: /2019ef001242mentioning
confidence: 99%