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Title: On the nonlinearity of spatial scales in extreme weather attribution statements

Abstract

In the context of continuing climate change, extreme weather events are drawing increasing attention from the public and news media. A question often asked is how the likelihood of extremes might have changed by anthropogenic greenhouse-gas emissions. Answers to the question are strongly influenced by the model used, duration, spatial extent, and geographic location of the event—some of these factors often overlooked. Using output from four global climate models, we provide attribution statements characterised by a change in probability of occurrence due to anthropogenic greenhouse-gas emissions, for rainfall and temperature extremes occurring at seven discretised spatial scales and three temporal scales. An understanding of the sensitivity of attribution statements to a range of spatial and temporal scales of extremes allows for the scaling of attribution statements, rendering them relevant to other extremes having similar but non-identical characteristics. This is a procedure simple enough to approximate timely estimates of the anthropogenic contribution to the event probability. Furthermore, since real extremes do not have well-defined physical borders, scaling can help quantify uncertainty around attribution results due to uncertainty around the event definition. Results suggest that the sensitivity of attribution statements to spatial scale is similar across models and that the sensitivity ofmore » attribution statements to the model used is often greater than the sensitivity to a doubling or halving of the spatial scale of the event. The use of a range of spatial scales allows us to identify a nonlinear relationship between the spatial scale of the event studied and the attribution statement.« less

Authors:
ORCiD logo [1];  [2];  [1];  [1];  [2];  [3];  [4];  [5];  [5]
  1. Univ. of New South Wales, Sydney, NSW (Australia)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. National Inst. for Environmental Studies, Tsukaba (Japan)
  4. Univ. of Cape Town (South Africa)
  5. Met Office Hadley Centre, Exeter (United Kingdom)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1379890
Grant/Contract Number:
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Climate Dynamics
Additional Journal Information:
Journal Name: Climate Dynamics; Journal ID: ISSN 0930-7575
Publisher:
Springer-Verlag
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Angélil, Oliver, Stone, Daíthí, Perkins-Kirkpatrick, Sarah, Alexander, Lisa V., Wehner, Michael, Shiogama, Hideo, Wolski, Piotr, Ciavarella, Andrew, and Christidis, Nikolaos. On the nonlinearity of spatial scales in extreme weather attribution statements. United States: N. p., 2017. Web. doi:10.1007/s00382-017-3768-9.
Angélil, Oliver, Stone, Daíthí, Perkins-Kirkpatrick, Sarah, Alexander, Lisa V., Wehner, Michael, Shiogama, Hideo, Wolski, Piotr, Ciavarella, Andrew, & Christidis, Nikolaos. On the nonlinearity of spatial scales in extreme weather attribution statements. United States. doi:10.1007/s00382-017-3768-9.
Angélil, Oliver, Stone, Daíthí, Perkins-Kirkpatrick, Sarah, Alexander, Lisa V., Wehner, Michael, Shiogama, Hideo, Wolski, Piotr, Ciavarella, Andrew, and Christidis, Nikolaos. 2017. "On the nonlinearity of spatial scales in extreme weather attribution statements". United States. doi:10.1007/s00382-017-3768-9.
@article{osti_1379890,
title = {On the nonlinearity of spatial scales in extreme weather attribution statements},
author = {Angélil, Oliver and Stone, Daíthí and Perkins-Kirkpatrick, Sarah and Alexander, Lisa V. and Wehner, Michael and Shiogama, Hideo and Wolski, Piotr and Ciavarella, Andrew and Christidis, Nikolaos},
abstractNote = {In the context of continuing climate change, extreme weather events are drawing increasing attention from the public and news media. A question often asked is how the likelihood of extremes might have changed by anthropogenic greenhouse-gas emissions. Answers to the question are strongly influenced by the model used, duration, spatial extent, and geographic location of the event—some of these factors often overlooked. Using output from four global climate models, we provide attribution statements characterised by a change in probability of occurrence due to anthropogenic greenhouse-gas emissions, for rainfall and temperature extremes occurring at seven discretised spatial scales and three temporal scales. An understanding of the sensitivity of attribution statements to a range of spatial and temporal scales of extremes allows for the scaling of attribution statements, rendering them relevant to other extremes having similar but non-identical characteristics. This is a procedure simple enough to approximate timely estimates of the anthropogenic contribution to the event probability. Furthermore, since real extremes do not have well-defined physical borders, scaling can help quantify uncertainty around attribution results due to uncertainty around the event definition. Results suggest that the sensitivity of attribution statements to spatial scale is similar across models and that the sensitivity of attribution statements to the model used is often greater than the sensitivity to a doubling or halving of the spatial scale of the event. The use of a range of spatial scales allows us to identify a nonlinear relationship between the spatial scale of the event studied and the attribution statement.},
doi = {10.1007/s00382-017-3768-9},
journal = {Climate Dynamics},
number = ,
volume = ,
place = {United States},
year = 2017,
month = 6
}

Journal Article:
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  • Extreme event attribution characterizes how anthropogenic climate change may have influenced the probability and magnitude of selected individual extreme weather and climate events. Attribution statements often involve quantification of the fraction of attributable risk (FAR) or the risk ratio (RR) and associated confidence intervals. Many such analyses use climate model output to characterize extreme event behavior with and without anthropogenic influence. However, such climate models may have biases in their representation of extreme events. To account for discrepancies in the probabilities of extreme events between observational datasets and model datasets, we demonstrate an appropriate rescaling of the model output basedmore » on the quantiles of the datasets to estimate an adjusted risk ratio. Our methodology accounts for various components of uncertainty in estimation of the risk ratio. In particular, we present an approach to construct a one-sided confidence interval on the lower bound of the risk ratio when the estimated risk ratio is infinity. We demonstrate the methodology using the summer 2011 central US heatwave and output from the Community Earth System Model. In this example, we find that the lower bound of the risk ratio is relatively insensitive to the magnitude and probability of the actual event.« less
  • The annual "State of the Climate" report, published in the Bulletin of the American Meteorological Society (BAMS), has included a supplement since 2011 composed of brief analyses of the human influence on recent major extreme weather events. There are now several dozen extreme weather events examined in these supplements, but these studies have all differed in their data sources as well as their approaches to defining the events, analyzing the events, and the consideration of the role of anthropogenic emissions. This study reexamines most of these events using a single analytical approach and a single set of climate model andmore » observational data sources. In response to recent studies recommending the importance of using multiple methods for extreme weather event attribution, results are compared from these analyses to those reported in the BAMS supplements collectively, with the aim of characterizing the degree to which the lack of a common methodological framework may or may not influence overall conclusions. Results are broadly similar to those reported earlier for extreme temperature events but disagree for a number of extreme precipitation events. Based on this, it is advised that the lack of comprehensive uncertainty analysis in recent extreme weather attribution studies is important and should be considered when interpreting results, but as yet it has not introduced a systematic bias across these studies.« less
  • Extreme event attribution characterizes how anthropogenic climate change may have influenced the probability and magnitude of selected individual extreme weather and climate events. Attribution statements often involve quantification of the fraction of attributable risk (FAR) or the risk ratio (RR) and associated confidence intervals. Many such analyses use climate model output to characterize extreme event behavior with and without anthropogenic influence. However, such climate models may have biases in their representation of extreme events. To account for discrepancies in the probabilities of extreme events between observational datasets and model datasets, we demonstrate an appropriate rescaling of the model output basedmore » on the quantiles of the datasets to estimate an adjusted risk ratio. Our methodology accounts for various components of uncertainty in estimation of the risk ratio. In particular, we present an approach to construct a one-sided confidence interval on the lower bound of the risk ratio when the estimated risk ratio is infinity. We demonstrate the methodology using the summer 2011 central US heatwave and output from the Community Earth System Model. In this example, we find that the lower bound of the risk ratio is relatively insensitive to the magnitude and probability of the actual event.« less
  • The President s Climate Change Action Plan calls for the development of better science, data, and tools for climate preparedness. Many of the current questions about preparedness for extreme weather events in coming decades are, however, difficult to answer with assets that have been developed by climate science to answer longer-term questions about climate change. Capacities for projecting exposures to climate-related extreme events, along with their implications for interconnected infrastructures, are now emerging.