scholarly journals Interpreting climate model projections of extreme weather events

2015 ◽  
Vol 10 ◽  
pp. 10-28 ◽  
Author(s):  
Stephen J. Vavrus ◽  
Michael Notaro ◽  
David J. Lorenz
Author(s):  
Sarah E Perkins-Kirkpatrick ◽  
Daithi Stone ◽  
Dann M. Mitchell ◽  
Suzanne M. Rosier ◽  
Andrew David King ◽  
...  

Abstract Investigations into the role of anthropogenic climate change in extreme weather events are now starting to extend into analysis of anthropogenic impacts on non-climate (e.g. socio-economic) systems. However, care needs to be taken when making this extension, because methodological choices regarding extreme weather attribution can become crucial when considering the events’ impacts. The fraction of attributable risk (FAR) method, useful in extreme weather attribution research, has a very specific interpretation concerning a class of events, and there is potential to misinterpret results from weather event analyses as being applicable to specific events and their impact outcomes. Using two case studies of meteorological extremes and their impacts, we argue that FAR is not generally appropriate when estimating the magnitude of the anthropogenic signal behind a specific impact. Attribution assessments on impacts should always be carried out in addition to assessment of the associated meteorological event, since it cannot be assumed that the anthropogenic signal behind the weather is equivalent to the signal behind the impact because of lags and nonlinearities in the processes through which the impact system reacts to weather. Whilst there are situations where employing FAR to understand the climate change signal behind a class of impacts is useful (e.g. “system breaking” events), more useful results will generally be produced if attribution questions on specific impacts are reframed to focus on changes in the impact return value and magnitude across large samples of factual and counterfactual climate model and impact simulations. We advocate for constant interdisciplinary collaboration as essential for effective and robust impact attribution assessments.


2017 ◽  
Vol 30 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Oliver Angélil ◽  
Dáithí Stone ◽  
Michael Wehner ◽  
Christopher J. Paciorek ◽  
Harinarayan Krishnan ◽  
...  

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 and 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.


2020 ◽  
Author(s):  
Peter Watson ◽  
Sarah Sparrow ◽  
William Ingram ◽  
Simon Wilson ◽  
Drouard Marie ◽  
...  

<p>Multi-thousand member climate model simulations are highly valuable for showing how extreme weather events will change as the climate changes, using a physically-based approach. However, until now, studies using such an approach have been limited to using models with a resolution much coarser than the most modern systems. We have developed a global atmospheric model with 5/6°x5/9° resolution (~60km in middle latitudes) that can be run in the climateprediction.net distributed computing system to produce such large datasets. This resolution is finer than that of many current global climate models and sufficient for good simulation of extratropical synoptic features such as storms. It will also allow many extratropical extreme weather events to be simulated without requiring regional downscaling. We will show that this model's simulation of extratropical weather is competitive with that in other current models. We will also present results from the first multi-thousand member ensembles produced at this resolution, showing the impact of 1.5°C and 2°C global warming on extreme winter rainfall and extratropical cyclones in Europe.</p>


2018 ◽  
Vol 4 (10) ◽  
pp. eaat3272 ◽  
Author(s):  
Michael E. Mann ◽  
Stefan Rahmstorf ◽  
Kai Kornhuber ◽  
Byron A. Steinman ◽  
Sonya K. Miller ◽  
...  

Persistent episodes of extreme weather in the Northern Hemisphere summer have been associated with high-amplitude quasi-stationary atmospheric Rossby waves, with zonal wave numbers 6 to 8 resulting from the phenomenon of quasi-resonant amplification (QRA). A fingerprint for the occurrence of QRA can be defined in terms of the zonally averaged surface temperature field. Examining state-of-the-art [Coupled Model Intercomparison Project Phase 5 (CMIP5)] climate model projections, we find that QRA events are likely to increase by ~50% this century under business-as-usual carbon emissions, but there is considerable variation among climate models. Some predict a near tripling of QRA events by the end of the century, while others predict a potential decrease. Models with amplified Arctic warming yield the most pronounced increase in QRA events. The projections are strongly dependent on assumptions regarding the nature of changes in radiative forcing associated with anthropogenic aerosols over the next century. One implication of our findings is that a reduction in midlatitude aerosol loading could actually lead to Arctic de-amplification this century, ameliorating potential increases in persistent extreme weather events.


2020 ◽  
Vol 163 (2) ◽  
pp. 669-687
Author(s):  
Nathan P. Kettle ◽  
John E. Walsh ◽  
Lindsey Heaney ◽  
Richard L. Thoman ◽  
Kyle Redilla ◽  
...  

AbstractUnderstanding potential risks, vulnerabilities, and impacts to weather extremes and climate change are key information needs for coastal planners and managers in support of climate adaptation. Assessing historical trends and potential socio-economic impacts is especially difficult in the Arctic given limitations on availability of weather observations and historical impacts. This study utilizes a novel interdisciplinary approach that integrates archival analysis, observational data, and climate model downscaling to synthesize information on historical and projected impacts of extreme weather events in Nome, Alaska. Over 300 impacts (1990–2018) are identified based on analyses of the Nome Nugget newspaper articles and Storm Data entries. Historical impacts centered on transportation, community activities, and utilities. Analysis of observed and ERA5 reanalysis data indicates that impacts are frequently associated with high wind, extreme low temperatures, heavy snowfall events, and winter days above freezing. Downscaled output (2020–2100) from two climate models suggests that there will be changes in the frequency and timing of these extreme weather events. For example, extreme cold temperature is projected to decrease through the 2040s and then rarely occurs afterwards, and extreme wind events show little change before the 2070s. Significantly, our findings also reveal that not all weather-related extremes will change monotonically throughout the twenty-first century, such as extreme snowfall events that will increase through the 2030s before declining in the 2040s. The dynamical nature of projected changes in extreme events has implications for climate adaptation planning.


2021 ◽  
Vol 118 (49) ◽  
pp. e2112087118
Author(s):  
Nicholas J. Leach ◽  
Antje Weisheimer ◽  
Myles R. Allen ◽  
Tim Palmer

Attribution of extreme weather events has expanded rapidly as a field over the past decade. However, deficiencies in climate model representation of key dynamical drivers of extreme events have led to some concerns over the robustness of climate model–based attribution studies. It has also been suggested that the unconditioned risk-based approach to event attribution may result in false negative results due to dynamical noise overwhelming any climate change signal. The “storyline” attribution framework, in which the impact of climate change on individual drivers of an extreme event is examined, aims to mitigate these concerns. Here we propose a methodology for attribution of extreme weather events using the operational European Centre for Medium-Range Weather Forecasts (ECMWF) medium-range forecast model that successfully predicted the event. The use of a successful forecast ensures not only that the model is able to accurately represent the event in question, but also that the analysis is unequivocally an attribution of this specific event, rather than a mixture of multiple different events that share some characteristic. Since this attribution methodology is conditioned on the component of the event that was predictable at forecast initialization, we show how adjusting the lead time of the forecast can flexibly set the level of conditioning desired. This flexible adjustment of the conditioning allows us to synthesize between a storyline (highly conditioned) and a risk-based (relatively unconditioned) approach. We demonstrate this forecast-based methodology through a partial attribution of the direct radiative effect of increased CO2 concentrations on the exceptional European winter heatwave of February 2019.


2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Michael E. Mann ◽  
Stefan Rahmstorf ◽  
Kai Kornhuber ◽  
Byron A. Steinman ◽  
Sonya K. Miller ◽  
...  

Abstract Persistent episodes of extreme weather in the Northern Hemisphere summer have been shown to be associated with the presence of high-amplitude quasi-stationary atmospheric Rossby waves within a particular wavelength range (zonal wavenumber 6–8). The underlying mechanistic relationship involves the phenomenon of quasi-resonant amplification (QRA) of synoptic-scale waves with that wavenumber range becoming trapped within an effective mid-latitude atmospheric waveguide. Recent work suggests an increase in recent decades in the occurrence of QRA-favorable conditions and associated extreme weather, possibly linked to amplified Arctic warming and thus a climate change influence. Here, we isolate a specific fingerprint in the zonal mean surface temperature profile that is associated with QRA-favorable conditions. State-of-the-art (“CMIP5”) historical climate model simulations subject to anthropogenic forcing display an increase in the projection of this fingerprint that is mirrored in multiple observational surface temperature datasets. Both the models and observations suggest this signal has only recently emerged from the background noise of natural variability.


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