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Julien Cattiaux and Aurélien Ribes


Weather extremes are the showcase of climate variability. Given their societal and environmental impacts, they are of great public interest. The prevention of natural hazards, the monitoring of single events, and, more recently, their attribution to anthropogenic climate change constitute key challenges for both weather services and scientific communities. Before a single event can be scrutinized, it must be properly defined; in particular, its spatiotemporal characteristics must be chosen. So far, this definition is made with some degree of arbitrariness, yet it might affect conclusions when explaining an extreme weather event from a climate perspective. Here, we propose a generic road map for defining single events as objectively as possible. In particular, as extreme events are inherently characterized by a small probability of occurrence, we suggest selecting the space–time characteristics that minimize this probability. In this way, we are able to automatically identify the spatiotemporal scale at which the event has been the most extreme. According to our methodology, the European heat wave of summer 2003 would be defined as a 2-week event over France and Spain and the Boulder, Colorado, intense rainfall of September 2013 a 5-day local event. Importantly, we show that in both cases, maximizing the rarity of the event does not maximize (or minimize) its fraction of attributable risk to anthropogenic climate change.

Open access
Aurélien Ribes, Soulivanh Thao, and Julien Cattiaux


Describing the relationship between a weather event and climate change—a science usually termed event attribution—involves quantifying the extent to which human influence has affected the frequency or the strength of an observed event. In this study we show how event attribution can be implemented through the application of nonstationary statistics to transient simulations, typically covering the 1850–2100 period. The use of existing CMIP-style simulations has many advantages, including their availability for a large range of coupled models and the fact that they are not conditional to a given oceanic state. We develop a technique for providing a multimodel synthesis, consistent with the uncertainty analysis of long-term changes. Last, we describe how model estimates can be combined with historical observations to provide a single diagnosis accounting for both sources of information. The potential of this new method is illustrated using the 2003 European heat wave and under a Gaussian assumption. Results suggest that (i) it is feasible to perform event attribution using transient simulations and nonstationary statistics, even for a single model; (ii) the use of multimodel synthesis in event attribution is highly desirable given the spread in single-model estimates; and (iii) merging models and observations substantially reduces uncertainties in human-induced changes. Investigating transient simulations also enables us to derive insightful diagnostics of how the targeted event will be affected by climate change in the future.

Open access
Stephen J. Vavrus, Fuyao Wang, Jonathan E. Martin, Jennifer A. Francis, Yannick Peings, and Julien Cattiaux


This study tests the hypothesis that Arctic amplification (AA) of global warming remotely affects midlatitudes by promoting a weaker, wavier atmospheric circulation conducive to extreme weather. The investigation is based on the late twenty-first century over greater North America (20°–90°N, 50°–160°W) using 40 simulations from the Community Earth System Model Large Ensemble, spanning 1920–2100. AA is found to promote regionally varying ridging aloft (500 hPa) with strong seasonal differences reflecting the location of the strongest surface thermal forcing. During winter, maximum increases in future geopotential heights are centered over the Arctic Ocean, in conjunction with sea ice loss, but minimum height increases (troughing) occur to the south, over the continental United States. During summer the location of maximum height inflation shifts equatorward, forming an annular band across mid-to-high latitudes of the entire Northern Hemisphere. This band spans the continents, whose enhanced surface heating is aided by antecedent snow-cover loss and reduced terrestrial heat capacity. Through the thermal wind relationship, midtropospheric winds weaken on the equatorward flank of both seasonal ridging anomalies—mainly over Canada during winter and even more over the continental United States during summer—but strengthen elsewhere to form a dipole anomaly pattern in each season. Changes in circulation waviness, expressed as sinuosity, are inversely correlated with changes in zonal wind speed at nearly all latitudes, both in the projections and as observed during recent decades. Over the central United States during summer, the weaker and wavier flow promotes drying and enhanced heating, thus favoring more intense summer weather.

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Julien Cattiaux, Fabrice Chauvin, Olivier Bousquet, Sylvie Malardel, and Chia-Lun Tsai


The evolution of tropical cyclone activity under climate change remains a crucial scientific issue. Physical theory of cyclogenesis is limited, observational datasets suffer from heterogeneities in space and time, and state-of-the-art climate models used for future projections are still too coarse (~100 km of resolution) to simulate realistic systems. Two approaches can nevertheless be considered: 1) perform dedicated high-resolution (typically <50 km) experiments in which tropical cyclones can be tracked and 2) assess cyclone activity from existing low-resolution multimodel climate projections using large-scale indices as proxies. Here we explore these two approaches with a particular focus on the southern Indian Ocean. We first compute high-resolution experiments using the rotated-stretched configuration of our climate model (CNRM-CM6-1), which is able to simulate realistic tropical cyclones. In a 2-K warmer world, the model projects a 20% decrease in the frequency of tropical cyclones, together with an increase in their maximum lifetime intensity, a slight poleward shift of their trajectories, and a substantial delay (about 1 month) in the cyclone season onset. Large-scale indices applied to these high-resolution experiments fail to capture the overall decrease in cyclone frequency, but are able to partially represent projected changes in the spatiotemporal distribution of cyclone activity. Last, we apply large-scale indices to multimodel CMIP5 projections and find that the seasonal redistribution of cyclone activity is consistent across models.

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Pascal Yiou, Julien Cattiaux, Davide Faranda, Nikolay Kadygrov, Aglae Jézéquel, Philippe Naveau, Aurelien Ribes, Yoann Robin, Soulivanh Thao, Geert Jan van Oldenborgh, and Mathieu Vrac
Free access