scholarly journals Pathways and pitfalls in extreme event attribution

2021 ◽  
Vol 166 (1-2) ◽  
Author(s):  
Geert Jan van Oldenborgh ◽  
Karin van der Wiel ◽  
Sarah Kew ◽  
Sjoukje Philip ◽  
Friederike Otto ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Extreme Event ◽  
Climate Change Impacts ◽  
Climate Science ◽  
Companion Paper ◽  
Lessons Learned ◽  
Future Climate Change ◽  
Practical Applications ◽  
Event Attribution

AbstractThe last few years have seen an explosion of interest in extreme event attribution, the science of estimating the influence of human activities or other factors on the probability and other characteristics of an observed extreme weather or climate event. This is driven by public interest, but also has practical applications in decision-making after the event and for raising awareness of current and future climate change impacts. The World Weather Attribution (WWA) collaboration has over the last 5 years developed a methodology to answer these questions in a scientifically rigorous way in the immediate wake of the event when the information is most in demand. This methodology has been developed in the practice of investigating the role of climate change in two dozen extreme events world-wide. In this paper, we highlight the lessons learned through this experience. The methodology itself is documented in a more extensive companion paper. It covers all steps in the attribution process: the event choice and definition, collecting and assessing observations and estimating probability and trends from these, climate model evaluation, estimating modelled hazard trends and their significance, synthesis of the attribution of the hazard, assessment of trends in vulnerability and exposure, and communication. Here, we discuss how each of these steps entails choices that may affect the results, the common problems that can occur and how robust conclusions can (or cannot) be derived from the analysis. Some of these developments also apply to other attribution methodologies and indeed to other problems in climate science.

scholarly journals Attributing compound events to anthropogenic climate change

2021 ◽  
pp. 1-45
Author(s):  
Jakob Zscheischler ◽  
Flavio Lehner
Keyword(s):  
Climate Change ◽  
South Africa ◽  
Climate Model ◽  
Extreme Event ◽  
Synthetic Data ◽  
Anthropogenic Climate Change ◽  
Anthropogenic Influence ◽  
Western Cape ◽  
Event Attribution ◽  
Compound Events

AbstractExtreme event attribution answers the question whether and by how much anthropogenic climate change has contributed to the occurrence or magnitude of an extreme weather event. It is also used to link extreme event impacts to climate change. Impacts, however, are often related to multiple compounding climate drivers. Because extreme event attribution typically focuses on univariate assessments, these assessments might only provide a partial answer to the question of anthropogenic influence to a high-impact event. We present a theoretical extension to classical extreme event attribution for certain types of compound events. Based on synthetic data we illustrate how the bivariate fraction of attributable risk (FAR) differs from the univariate FAR depending on the extremeness of the event as well as the trends in and dependence between the contributing variables. Overall, the bivariate FAR is similar in magnitude or smaller than the univariate FAR if the trend in the second variable is comparably weak and the dependence between both variables is moderate or high, a typical situation for temporally co-occurring heatwaves and droughts. If both variables have similarly large trends or the dependence between both variables is weak, bivariate FARs are larger and are likely to provide a more adequate quantification of the anthropogenic influence. Using multiple climate model large ensembles, we apply the framework to two case studies, a recent sequence of hot and dry years in the Western Cape region of South Africa and two spatially co-occurring droughts in crop-producing regions in South Africa and Lesotho.

scholarly journals Climate change increases the probability of heavy rains like those of storm Desmond in the UK – an event attribution study in near-real time

2015 ◽  
Vol 12 (12) ◽  
pp. 13197-13216 ◽  
Author(s):  
G. J. van Oldenborgh ◽  
F. E. L. Otto ◽  
K. Haustein ◽  
H. Cullen
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Extreme Event ◽  
Initial Assessment ◽  
Model Simulations ◽  
Event Attribution ◽  
Climate Model Simulations ◽  
Using Data ◽  
The Uk ◽  
Precipitation Events

Abstract. On 4–6 December 2015, the storm "Desmond" caused very heavy rainfall in northern England and Scotland, which led to widespread flooding. Here we provide an initial assessment of the influence of anthropogenic climate change on the likelihood of one-day precipitation events averaged over an area encompassing northern England and southern Scotland using data and methods available immediately after the event occurred. The analysis is based on three independent methods of extreme event attribution: historical observed trends, coupled climate model simulations and a large ensemble of regional model simulations. All three methods agree that the effect of climate change is positive, making precipitation events like this about 40 % more likely, with a provisional 2.5–97.5 % confidence interval of 5–80 %.

scholarly journals LPJmL4 – a dynamic global vegetation model with managed land: Part I – Model description

2017 ◽  
Author(s):  
Sibyll Schaphoff ◽  
Werner von Bloh ◽  
Anja Rammig ◽  
Kirsten Thonicke ◽  
Hester Biemans ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Change Impacts ◽  
Companion Paper ◽  
Future Climate Change ◽  
Model Description ◽  
Vegetation Model ◽  
Dynamic Global Vegetation Model ◽  
Reference Simulation ◽  
Managed Ecosystems ◽  
Global Vegetation

Abstract. This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates – internally consistently – the growth and productivity of both natural and agricultural vegetation in direct coupling with water and carbon fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within, and impacts upon, the terrestrial biosphere as increasingly shaped by human activities such as climate change and land-use change. Here we describe the core model structure including recently developed modules now unified in LPJmL4. Thereby we also summarize LPJmL model developments and evaluations (based on 34 earlier publications focused e.g. on improved representations of crop types, human and ecological water demand, and permafrost) and model applications (82 papers, e.g. on historical and future climate change impacts) since its first description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at a Gitlab server, we hope to stimulate the application and further development of LPJmL4 across scientific communities, not least in support of major activities such as the IPCC and SDG process.

scholarly journals Singular Extreme Events and Their Attribution to Climate Change: A Climate Service–Centered Analysis

2020 ◽  
Author(s):  
Aglae Jezequel ◽  
Vivian Dépoues ◽  
Hélène Guillemot ◽  
Amélie Rajaud ◽  
Mélodie Trolliet ◽  
...  
Keyword(s):  
Climate Change ◽  
Extreme Event ◽  
Climate Science ◽  
Structured Interviews ◽  
European Climate ◽  
Weather Events ◽  
Event Attribution ◽  
Climate Service ◽  
Do So

<p>Extreme event attribution (EEA) proposes scientific diagnostics on whether and how a specific weather event is (or is not) different in the actual world from what it could have been in a world without climate change. This branch of climate science has developed to the point where European institutions are preparing the ground for an operational attribution service. In this context, the goal of this article is to explore a panorama of scientist perspectives on their motivations to undertake EEA studies. To do so, we rely on qualitative semi-structured interviews of climate scientists involved in EEA, on peer-reviewed social and climate literature discussing the usefulness of EEA, and on reports from the EUCLEIA project (European Climate and Weather Events: Interpretation and Attribution), which investigated the possibility of building an EEA service. We propose a classification of EEA’s potential uses and users and discuss each of them. We find that, first, there is a plurality of motivations and that individual scientists disagree on which one is most useful. Second, there is a lack of solid, empirical evidence to back up any of these motivations.</p>

Flood hazard estimation and climate change: impacts and uncertainties for Irish catchments using CMIP6

2020 ◽  
Author(s):  
Hadush Meresa ◽  
Conor Murphy ◽  
Rowan Fealy
Keyword(s):  
Climate Change ◽  
Flood Risk ◽  
Climate Model ◽  
Extreme Event ◽  
Flood Hazard ◽  
Coupled Model ◽  
Climate Change Impacts ◽  
Heavy Precipitation ◽  
Atmospheric Temperature ◽  
Projected Changes

<p>In the coming decades, climate change will likely become a complex issue affecting hydrological regimes and flood hazard conditions. According to the IPCC reports, significant changes in atmospheric temperature, precipitation, humidity, and circulation are expected which may lead to extreme events including flood, droughts, heatwaves, heavy precipitation, and more intense cyclones. Although the effects of climate change on flood hazard indices is subject to large uncertainty, the evaluation of high-flows plays a crucial role in flood risk planning and extreme event management. With the advent of the Coupled Model Intercomparison Project Phase 6 (CMIP6), flood managers are interested to know how changes in catchment flood risk are expected to alter relative to previous assessments. Here we examine catchment based projected changes in flood quantiles and extreme high flow events for Irish catchments, selected to be representative of the range of hydrological conditions on the island. Conceptual hydrological models, together with different downscaling techniques are used to examine changes in flood risk projected from the CMIP6 archive for mid and end of century. Results will inform the range of plausible changes expected for policy relevant flood indices, the sensitivity of findings to use of different climate model ensembles and inform the tailoring of adaptation plans to account for the new generation of climate model outputs.</p>

scholarly journals Early emergence in a butterfly causally linked to anthropogenic warming

2010 ◽  
Vol 6 (5) ◽  
pp. 674-677 ◽  
Author(s):  
Michael R. Kearney ◽  
Natalie J. Briscoe ◽  
David J. Karoly ◽  
Warren P. Porter ◽  
Melanie Norgate ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Change ◽  
Future Climate Change ◽  
Climate Data ◽  
Regional Warming

There is strong correlative evidence that human-induced climate warming is contributing to changes in the timing of natural events. Firm attribution, however, requires cause-and-effect links between observed climate change and altered phenology, together with statistical confidence that observed regional climate change is anthropogenic. We provide evidence for phenological shifts in the butterfly Heteronympha merope in response to regional warming in the southeast Australian city of Melbourne. The mean emergence date for H. merope has shifted −1.5 days per decade over a 65-year period with a concurrent increase in local air temperatures of approximately 0.16°C per decade. We used a physiologically based model of climatic influences on development, together with statistical analyses of climate data and global climate model projections, to attribute the response of H. merope to anthropogenic warming. Such mechanistic analyses of phenological responses to climate improve our ability to forecast future climate change impacts on biodiversity.

scholarly journals Singular Extreme Events and Their Attribution to Climate Change: A Climate Service–Centered Analysis

2020 ◽  
Vol 12 (1) ◽  
pp. 89-101
Author(s):  
Aglaé Jézéquel ◽  
Vivian Dépoues ◽  
Hélène Guillemot ◽  
Amélie Rajaud ◽  
Mélodie Trolliet ◽  
...  
Keyword(s):  
Climate Change ◽  
Extreme Event ◽  
Climate Science ◽  
Structured Interviews ◽  
European Climate ◽  
Weather Events ◽  
Event Attribution ◽  
Climate Service ◽  
Do So

AbstractExtreme event attribution (EEA) proposes scientific diagnostics on whether and how a specific weather event is (or is not) different in the actual world from what it could have been in a world without climate change. This branch of climate science has developed to the point where European institutions are preparing the ground for an operational attribution service. In this context, the goal of this article is to explore a panorama of scientist perspectives on their motivations to undertake EEA studies. To do so, we rely on qualitative semi-structured interviews of climate scientists involved in EEA, on peer-reviewed social and climate literature discussing the usefulness of EEA, and on reports from the EUCLEIA project (European Climate and Weather Events: Interpretation and Attribution), which investigated the possibility of building an EEA service. We propose a classification of EEA’s potential uses and users and discuss each of them. We find that, first, there is a plurality of motivations and that individual scientists disagree on which one is most useful. Second, there is a lack of solid, empirical evidence to back up any of these motivations.

scholarly journals Statistical Methods for Extreme Event Attribution in Climate Science

2020 ◽  
Vol 7 (1) ◽  
pp. 89-110
Author(s):  
Philippe Naveau ◽  
Alexis Hannart ◽  
Aurélien Ribes
Keyword(s):  
Statistical Methods ◽  
Extreme Events ◽  
Climate Model ◽  
Extreme Event ◽  
Numerical Models ◽  
Climate Science ◽  
Value Theory ◽  
Mitigation And Adaptation ◽  
Event Attribution ◽  
Modeling Uncertainties

Changes in the Earth's climate have been increasingly observed. Assessing the likelihood that each of these changes has been caused by human influence is important for decision making on mitigation and adaptation policy. Because of their large societal and economic impacts, extreme events have garnered much media attention—have they become more frequent and more intense, and if so, why? To answer such questions, extreme event attribution (EEA) tries to estimate extreme event likelihoods under different scenarios. Over the past decade, statistical methods and experimental designs based on numerical models have been developed, tested, and applied. In this article, we review the basic probability schemes, inference techniques, and statistical hypotheses used in EEA. To implement EEA analysis, the climate community relies on the use of large ensembles of climate model runs. We discuss, from a statistical perspective, how extreme value theory could help to deal with the different modeling uncertainties. In terms of interpretation, we stress that causal counterfactual theory offers an elegant framework that clarifies the design of event attributions. Finally, we pinpoint some remaining statistical challenges, including the choice of the appropriate spatio-temporal scales to enhance attribution power, the modeling of concomitant extreme events in a multivariate context, and the coupling of multi-ensemble and observational uncertainties.

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