scholarly journals A dynamical adjustment perspective on extreme event attribution

2021 ◽  
Author(s):  
Laurent Terray
Keyword(s):  
Atmospheric Circulation ◽  
Extreme Events ◽  
Western Europe ◽  
Climate Model ◽  
Extreme Event ◽  
Dynamic Component ◽  
Western Asia ◽  
Event Attribution ◽  
Climate Model Simulations

Abstract. Here we demonstrate that dynamical adjustment allows a straightforward approach to extreme event attribution within a conditional framework. We illustrate the potential of the approach with two iconic extreme events that occurred in 2010: the early winter European cold spell and the Russian summer heat wave. We use a dynamical adjustment approach based on constructed atmospheric circulation analogues to isolate the various contributions to these two extreme events using only observational and reanalysis datasets. Dynamical adjustment results confirm previous findings regarding the role of atmospheric circulation in the two extreme events and provide a quantitative estimate of the various dynamic and thermodynamic contributions to the event amplitude. Furthermore, the approach is also used to identify the drivers of the recent 1979–2018 trends in summer extreme maximum and minimum temperature changes over western Europe and western Asia. The results suggest a significant role of the dynamic component in explaining temperature extreme changes in different regions, including regions around the Black and Caspian Seas as well as central Europe and the coasts of western Europe. Finally, dynamical adjustment offers a simple and complementary storyline approach to extreme event attribution with the advantage that no climate model simulations are needed, making it a promising candidate for the fast-track component of any real-time extreme event attribution system.

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 Can the Issuance of Hazardous-Weather Warnings Inform the Attribution of Extreme Events to Climate Change?

2020 ◽  
Vol 101 (8) ◽  
pp. E1452-E1463
Author(s):  
Dale R. Durran
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Extreme Events ◽  
Weather Forecasting ◽  
Extreme Event ◽  
Probability Of Detection ◽  
False Alarms ◽  
Event Attribution ◽  
Hazardous Weather

Abstract When extreme weather occurs, the question often arises whether the event was produced by climate change. Two types of errors are possible when attempting to answer this question. One type of error is underestimating the role of climate change, thereby failing to properly alert the public and appropriately stimulate efforts at adaptation and mitigation. The second type of error is overestimating the role of climate change, thereby elevating climate anxiety and potentially derailing important public discussions with false alarms. Long before societal concerns about global warming became widespread, meteorologists were addressing essentially the same trade-off when faced with a binary decision of whether to issue a warning for hazardous weather. Here we review forecast–verification statistics such as the probability of detection (POD) and the false alarm ratio (FAR) for hazardous-weather warnings and examine their potential application to extreme-event attribution in connection with climate change. Empirical and theoretical evidence suggests that adjusting tornado-warning thresholds in an attempt to reduce FAR produces even larger reductions in POD. Similar tradeoffs between improving FAR and degrading POD are shown to apply using a rubric for the attribution of extreme high temperatures to climate change. Although there are obviously significant differences between the issuance of hazardous-weather warnings and the attribution of extreme events to global warming, the experiences of the weather forecasting community can provide qualitative guidance for those attempting to set practical thresholds for extreme-event attribution in a changing climate.

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.

How to combine climate models and observations in event attribution?

2021 ◽  
Author(s):  
Yoann Robin ◽  
Aurélien Ribes
Keyword(s):  
Bayesian Method ◽  
Climate Models ◽  
Climate Model ◽  
Coupled Model ◽  
Model Data ◽  
Human Influence ◽  
Scale Parameters ◽  
Event Attribution ◽  
Intercomparison Project ◽  
Climate Model Simulations

<p>We describe a statistical method to derive event attribution diagnoses combining climate model simulations and observations. We fit nonstationary Generalized Extreme Value (GEV) distributions to extremely hot temperatures from an ensemble of Coupled Model Intercomparison Project phase 5 (CMIP)<br>models. In order to select a common statistical model, we discuss which GEV parameters have to be nonstationary and which do not. Our tests suggest that the location and scale parameters of GEV distributions should be considered nonstationary. Then, a multimodel distribution is constructed and constrained by observations using a Bayesian method. This new method is applied to the July 2019 French heatwave. Our results show that<br>both the probability and the intensity of that event have increased significantly in response to human influence.<br>Remarkably, we find that the heat wave considered might not have been possible without climate change. Our<br>results also suggest that combining model data with observations can improve the description of hot temperature<br>distribution.</p>

Seasonal predictions as a high-resolution large ensemble to study extreme events over recent decades

2021 ◽  
Author(s):  
Timo Kelder ◽  
Louise Slater ◽  
Tim Marjoribanks ◽  
Rob Wilby ◽  
Christel Prudhomme ◽  
...  
Keyword(s):  
Extreme Events ◽  
Climate Model ◽  
Risk Estimation ◽  
Seasonal Prediction ◽  
Horizontal Resolution ◽  
Ensemble Member ◽  
Model Stability ◽  
Model Fidelity ◽  
Large Ensembles ◽  
Climate Model Simulations

<p>Large ensembles of climate model simulations may be used to assess the likelihood of extreme events, which only have a limited chance of occurring in observed records. In this talk, we discuss how the ECMWF seasonal prediction system SEAS5 can be used to generate a 100-member ensemble over 1981-present. SEAS5 is a global coupled ocean, sea-ice, atmosphere model with a horizontal resolution of 36 km. We introduce an open and reproducible workflow to retrieve Copernicus SEAS5 data and evaluate the ensemble member independence, model stability, and model fidelity. We illustrate how the increased sample size may help risk estimation, detecting trends in 100-year extremes as well as analysing drivers of extreme events that are difficult to discern from limited observational records.</p>

scholarly journals Seasonal Drought in the Greater Horn of Africa and Its Recent Increase during the March–May Long Rains

2014 ◽  
Vol 27 (21) ◽  
pp. 7953-7975 ◽  
Author(s):  
Bradfield Lyon
Keyword(s):  
Atmospheric Circulation ◽  
Climate Model ◽  
Southern Oscillation ◽  
Meteorological Drought ◽  
Tropical Pacific ◽  
Horn Of Africa ◽  
New Findings ◽  
Climate Model Simulations ◽  
Greater Horn Of Africa ◽  
The Tropical Pacific

Abstract This paper provides a review of atmospheric circulation and sea surface temperature (SST) conditions that are associated with meteorological drought on the seasonal time scale in the Greater Horn of Africa (the region 10°S–15°N, 30°–52°E). New findings regarding a post-1998 increase in drought frequency during the March–May (MAM) “long rains” are also reported. The period 1950–2010 is emphasized, although rainfall and SST data from 1901–2010 are used to place the recent long rains decline in a multidecadal context. For the latter case, climate model simulations and isolated basin SST experiments are also utilized. Climatologically, rainfall exhibits a unimodal June–August (JJA) maximum in west-central Ethiopia with a generally bimodal [MAM and October–December (OND) maxima] distribution in locations to the south and east. Emphasis will be on these three seasons. SST anomalies in the tropical Pacific and Indian Oceans show the strongest association with drought during OND in locations having a bimodal annual cycle, with weaker associations during MAM. The influence of the El Niño–Southern Oscillation (ENSO) phenomenon critically depends on its ability to affect SSTs outside the Pacific. Salient features of the anomalous atmospheric circulation during drought events in different locations and seasons are discussed. The post-1998 decline in the long rains is found to be driven strongly (although not necessarily exclusively) by natural multidecadal variability in the tropical Pacific rather than anthropogenic climate change. This conclusion is supported by observational analyses and climate model experiments, which are presented.

A consistent multi-method global extreme event attribution framework

2020 ◽  
Author(s):  
Karsten Haustein ◽  
Benjamin Strauss ◽  
Sihan Li ◽  
Friederike Otto
Keyword(s):  
Climate Model ◽  
Extreme Event ◽  
Global Climate ◽  
Global Climate Model ◽  
Return Time ◽  
Event Attribution ◽  
Temperature Thresholds ◽  
Scaling Methods ◽  
Future Warming ◽  
Cold Extremes

<div> <div> <div> <p>In order to streamline observational and global climate model based extreme event attribution techniques, we propose a multi-method framework which drastically increases the robustness of rapid attribution studies, hence further facilitating the communication of extreme weather related risks across the globe.</p> <p>We use advanced observational datasets for temperature (Berkeley Earth) and rainfall (CPC), together with CMIP5 simulations and the large HadRM3P ensemble from the weather@home project (W@H) Recent (Climatology) and current/future warming scenarios (1°C, 1.5°C, 2°C, 3°C and 4°C) are juxtaposed to pre-industrial (Natural) baseline conditions.</p> <p>Two scaling approaches are applied to the observational data to estimate the statistics of future warming scenarios. One in which percentiles of the metric of interest (Tmax, Tmin, Precip) are scaled with Global Mean Surface Temperature (GMST) and another in which the mean is scaled against GMST. Model subsetting (similar to the HAPPI experiment) as function of GMST is applied to the CMIP5 data in order to assign the warming thresholds. W@H scenarios are prescribed to achieve the desired warming threshold. We analyse the results in terms of classes of events, using percentiles, absolute and return-time based thresholds. Before the subsetting, model biases are removed means of quantile-mapping (both for CMIP5 and W@H).</p> <p>The results between both scaling methods and model subsetting are mostly consistent across many regions and virtually for all temperature thresholds under consideration. The percentile-based scaling method does, however, reveal that the tail of the distributions (highest Tmax, lowest Tmin) has potentially widened with warming. Overall, we find that historically rare extreme events become increasingly common in the future as far as Tmax and Precip is concerned. In contrast, cold extremes become increasingly rare.</p> </div> </div> </div>

scholarly journals CO2 Sensitivity of Extreme Climate Events in the Western United States

2006 ◽  
Vol 10 (15) ◽  
pp. 1-17 ◽  
Author(s):  
Jason L. Bell ◽  
Lisa C. Sloan
Keyword(s):  
United States ◽  
Extreme Events ◽  
Climate Model ◽  
Heat Waves ◽  
Regional Climate ◽  
Western United States ◽  
Climate Extreme ◽  
Climate Model Simulations ◽  
Atmospheric Co2 Concentrations ◽  
High Elevations

Abstract Based upon trends in observed climate, extreme events are thought to be increasing in frequency and/or magnitude. This change in extreme events is attributed to enhancement of the hydrologic cycle caused by increased greenhouse gas concentrations. Results are presented of relatively long (50 yr) regional climate model simulations of the western United States examining the sensitivity of climate and extreme events to a doubling of preindustrial atmospheric CO2 concentrations. These results indicate a shift in the temperature distribution, resulting in fewer cold days and more hot days; the largest changes occur at high elevations. The rainfall distribution is also affected; total rain increases as a result of increases in rainfall during the spring season and at higher elevations. The risk of flooding is generally increased, as is the severity of droughts and heat waves. These results, combined with results of decreased snowpack and increased evaporation, could further stress the water supply of the western United States.

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 Atmospheric circulation patterns associated with the variability of River Ammer floods: evidence from observed and proxy data

2016 ◽  
Vol 12 (2) ◽  
pp. 377-385 ◽  
Author(s):  
Norel Rimbu ◽  
Markus Czymzik ◽  
Monica Ionita ◽  
Gerrit Lohmann ◽  
Achim Brauer
Keyword(s):  
Time Series ◽  
Atmospheric Circulation ◽  
Western Europe ◽  
Wave Train ◽  
Heavy Precipitation ◽  
Circulation Pattern ◽  
Flood Frequency ◽  
Discharge Data ◽  
Western Asia ◽  
Instrumental Period

Abstract. The relationship between the frequency of River Ammer floods (southern Germany) and atmospheric circulation variability is investigated based on observational Ammer River discharge data back to 1926 and a flood layer time series from varved sediments of the downstream Lake Ammer for the pre-instrumental period back to 1766. A composite analysis reveals that, at synoptic timescales, observed River Ammer floods are associated with enhanced moisture transport from the Atlantic Ocean and the Mediterranean towards the Ammer region, a pronounced trough over western Europe as well as enhanced potential vorticity at upper levels. We argue that this synoptic-scale configuration can trigger heavy precipitation and floods in the Ammer region. Interannual to multidecadal increases in flood frequency, as detected in the instrumental discharge record, are associated with a wave train pattern extending from the North Atlantic to western Asia, with a prominent negative center over western Europe. A similar atmospheric circulation pattern is associated with increases in flood layer frequency in the Lake Ammer sediment record during the pre-instrumental period. We argue that the complete flood layer time series from Lake Ammer sediments covering the last 5500 years contains information about atmospheric circulation variability on interannual to millennial timescales.

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