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Abstract
Drought in spring and early summer has been shown to precede anomalous hot summer temperature. In particular, drought in the Mediterranean region has been recently shown to precede and to contribute to the development of extreme heat in continental Europe. In this paper, this mechanism is investigated by performing integrations of a regional mesoscale model at the scale of the European continent in order to reproduce hot summer inception, starting with different initial values of soil moisture south of 46°N. The mesoscale model is driven by the large-scale atmospheric conditions corresponding to the 10 hottest summers on record from the European Climate Assessment dataset. A northward progression of heat and drought from late spring to summer is observed from the Mediterranean regions, which leads to a further increase of temperature during summer in temperate continental Europe. Dry air formed over dry soils in the Mediterranean region induces less convection and diminished cloudiness, which gets transported northward by occasional southerly wind, increasing northward temperature and vegetation evaporative demand. Later in the season, drier soils have been established in western and central Europe where they further amplify the warming through two main feedback mechanisms: 1) higher sensible heat emissions and 2) favored upper-air anticyclonic circulation. Drier soils in southern Europe accelerate the northward propagation of heat and drying, increasing the probability of strong heat wave episodes in the middle or the end of the summer.
Abstract
Drought in spring and early summer has been shown to precede anomalous hot summer temperature. In particular, drought in the Mediterranean region has been recently shown to precede and to contribute to the development of extreme heat in continental Europe. In this paper, this mechanism is investigated by performing integrations of a regional mesoscale model at the scale of the European continent in order to reproduce hot summer inception, starting with different initial values of soil moisture south of 46°N. The mesoscale model is driven by the large-scale atmospheric conditions corresponding to the 10 hottest summers on record from the European Climate Assessment dataset. A northward progression of heat and drought from late spring to summer is observed from the Mediterranean regions, which leads to a further increase of temperature during summer in temperate continental Europe. Dry air formed over dry soils in the Mediterranean region induces less convection and diminished cloudiness, which gets transported northward by occasional southerly wind, increasing northward temperature and vegetation evaporative demand. Later in the season, drier soils have been established in western and central Europe where they further amplify the warming through two main feedback mechanisms: 1) higher sensible heat emissions and 2) favored upper-air anticyclonic circulation. Drier soils in southern Europe accelerate the northward propagation of heat and drying, increasing the probability of strong heat wave episodes in the middle or the end of the summer.
Abstract
It is of fundamental importance to evaluate the ability of climate models to capture the large-scale atmospheric circulation patterns and, in the context of a rapidly increasing greenhouse forcing, the robustness of the changes simulated in these patterns over time. Here we approach this problem from an innovative point of view based on dynamical systems theory. We characterize the atmospheric circulation over the North Atlantic in the CMIP5 historical simulations (1851–2000) in terms of two instantaneous metrics: local dimension of the attractor and stability of phase-space trajectories. We then use these metrics to compare the models to the Twentieth Century Reanalysis version 2c (20CRv2c) over the same historical period. The comparison suggests that (i) most models capture to some degree the median attractor properties, and models with finer grids generally perform better; (ii) in most models the extremes in the dynamical systems metrics match large-scale patterns similar to those found in the reanalysis; (iii) changes in the attractor properties observed for the ensemble-mean 20CRv2c are artifacts resulting from inhomogeneities in the standard deviation of the ensemble over time; and (iv) the long-term trends in local dimension observed among the 56 members of the 20CR ensemble have the same sign as those observed in the CMIP5 multimodel mean, although the multimodel trend is much weaker.
Abstract
It is of fundamental importance to evaluate the ability of climate models to capture the large-scale atmospheric circulation patterns and, in the context of a rapidly increasing greenhouse forcing, the robustness of the changes simulated in these patterns over time. Here we approach this problem from an innovative point of view based on dynamical systems theory. We characterize the atmospheric circulation over the North Atlantic in the CMIP5 historical simulations (1851–2000) in terms of two instantaneous metrics: local dimension of the attractor and stability of phase-space trajectories. We then use these metrics to compare the models to the Twentieth Century Reanalysis version 2c (20CRv2c) over the same historical period. The comparison suggests that (i) most models capture to some degree the median attractor properties, and models with finer grids generally perform better; (ii) in most models the extremes in the dynamical systems metrics match large-scale patterns similar to those found in the reanalysis; (iii) changes in the attractor properties observed for the ensemble-mean 20CRv2c are artifacts resulting from inhomogeneities in the standard deviation of the ensemble over time; and (iv) the long-term trends in local dimension observed among the 56 members of the 20CR ensemble have the same sign as those observed in the CMIP5 multimodel mean, although the multimodel trend is much weaker.
Abstract
Both climate and statistical models play an essential role in the process of demonstrating that the distribution of some atmospheric variable has changed over time and in establishing the most likely causes for the detected change. One statistical difficulty in the research field of detection and attribution resides in defining events that can be easily compared and accurately inferred from reasonable sample sizes. As many impacts studies focus on extreme events, the inference of small probabilities and the computation of their associated uncertainties quickly become challenging. In the particular context of event attribution, the authors address the question of how to compare records between the counterfactual “world as it might have been” without anthropogenic forcings and the factual “world that is.” Records are often the most important events in terms of impact and get much media attention. The authors will show how to efficiently estimate the ratio of two small probabilities of records. The inferential gain is particularly substantial when a simple hypothesis-testing procedure is implemented. The theoretical justification of such a proposed scheme can be found in extreme value theory. To illustrate this study’s approach, classical indicators in event attribution studies, like the risk ratio or the fraction of attributable risk, are modified and tailored to handle records. The authors illustrate the advantages of their method through theoretical results, simulation studies, temperature records in Paris, and outputs from a numerical climate model.
Abstract
Both climate and statistical models play an essential role in the process of demonstrating that the distribution of some atmospheric variable has changed over time and in establishing the most likely causes for the detected change. One statistical difficulty in the research field of detection and attribution resides in defining events that can be easily compared and accurately inferred from reasonable sample sizes. As many impacts studies focus on extreme events, the inference of small probabilities and the computation of their associated uncertainties quickly become challenging. In the particular context of event attribution, the authors address the question of how to compare records between the counterfactual “world as it might have been” without anthropogenic forcings and the factual “world that is.” Records are often the most important events in terms of impact and get much media attention. The authors will show how to efficiently estimate the ratio of two small probabilities of records. The inferential gain is particularly substantial when a simple hypothesis-testing procedure is implemented. The theoretical justification of such a proposed scheme can be found in extreme value theory. To illustrate this study’s approach, classical indicators in event attribution studies, like the risk ratio or the fraction of attributable risk, are modified and tailored to handle records. The authors illustrate the advantages of their method through theoretical results, simulation studies, temperature records in Paris, and outputs from a numerical climate model.
Abstract
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.
Abstract
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.
Abstract
The relative contribution of the synoptic-scale circulations to local and mesoscale processes was quantified in terms of the variability of midlatitude temperature anomalies from 2003 to 2013 using meteorological variables collected from three French observatories and reanalyses. Four weather regimes were defined from sea level pressure anomalies using National Centers for Environmental Prediction reanalyses with a K-means algorithm. No correlation was found between daily temperature anomalies and weather regimes, and the variability of temperature anomalies within each regime was large. It was therefore not possible to evaluate the effect of large scales on temperature anomalies by this method. An alternative approach was found with the use of the analogs method: the principle being that for each day of the considered time series, a set of days that had a similar large-scale 500-hPa geopotential height field within a fixed domain was considered. The observed temperature anomalies were then compared with those observed during the analog days: the closer the two types of series are to each other, the greater is the influence of the large scale. This method highlights a widely predominant influence of the large-scale atmospheric circulation on the temperature anomalies. It showed a potentially larger influence of the Mediterranean Sea and orographic flow on the two southern observatories. Low-level cloud radiative effects substantially modulated the variability of the daily temperature anomalies.
Abstract
The relative contribution of the synoptic-scale circulations to local and mesoscale processes was quantified in terms of the variability of midlatitude temperature anomalies from 2003 to 2013 using meteorological variables collected from three French observatories and reanalyses. Four weather regimes were defined from sea level pressure anomalies using National Centers for Environmental Prediction reanalyses with a K-means algorithm. No correlation was found between daily temperature anomalies and weather regimes, and the variability of temperature anomalies within each regime was large. It was therefore not possible to evaluate the effect of large scales on temperature anomalies by this method. An alternative approach was found with the use of the analogs method: the principle being that for each day of the considered time series, a set of days that had a similar large-scale 500-hPa geopotential height field within a fixed domain was considered. The observed temperature anomalies were then compared with those observed during the analog days: the closer the two types of series are to each other, the greater is the influence of the large scale. This method highlights a widely predominant influence of the large-scale atmospheric circulation on the temperature anomalies. It showed a potentially larger influence of the Mediterranean Sea and orographic flow on the two southern observatories. Low-level cloud radiative effects substantially modulated the variability of the daily temperature anomalies.
Abstract
We have analyzed the record-breaking drought that affected western and central Europe from July 2016 to June 2017. It caused widespread impacts on water supplies, agriculture, and hydroelectric power production, and was associated with forest fires in Iberia. Unlike common continental-scale droughts, this event displayed a highly unusual spatial pattern affecting both northern and southern European regions. Drought conditions were observed over 90% of central-western Europe, hitting record-breaking values (with respect to 1979–2017) in 25% of the area. Therefore, the event can be considered as the most severe European drought at the continental scale since at least 1979. The main dynamical forcing of the drought was the consecutive occurrence of blocking and subtropical ridges, sometimes displaced from their typical locations. This led to latitudinal shifts of the jet stream and record-breaking positive geopotential height anomalies over most of the continent. The reduction in moisture transport from the Atlantic was relevant in the northern part of the region, where decreased precipitation and increased sunshine duration were the main contributors to the drought. On the other hand, thermodynamic processes, mostly associated with high temperatures and the resulting increase in atmospheric evaporative demand, were more important in the south. Finally, using flow circulation analogs we show that this drought was more severe than it would have been in the early past.
Abstract
We have analyzed the record-breaking drought that affected western and central Europe from July 2016 to June 2017. It caused widespread impacts on water supplies, agriculture, and hydroelectric power production, and was associated with forest fires in Iberia. Unlike common continental-scale droughts, this event displayed a highly unusual spatial pattern affecting both northern and southern European regions. Drought conditions were observed over 90% of central-western Europe, hitting record-breaking values (with respect to 1979–2017) in 25% of the area. Therefore, the event can be considered as the most severe European drought at the continental scale since at least 1979. The main dynamical forcing of the drought was the consecutive occurrence of blocking and subtropical ridges, sometimes displaced from their typical locations. This led to latitudinal shifts of the jet stream and record-breaking positive geopotential height anomalies over most of the continent. The reduction in moisture transport from the Atlantic was relevant in the northern part of the region, where decreased precipitation and increased sunshine duration were the main contributors to the drought. On the other hand, thermodynamic processes, mostly associated with high temperatures and the resulting increase in atmospheric evaporative demand, were more important in the south. Finally, using flow circulation analogs we show that this drought was more severe than it would have been in the early past.
