The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe

Barriopedro, David; Fischer, Erich M.; Luterbacher, Jürg; Trigo, Ricardo M.; García‐Herrera, Ricardo

doi:10.1126/science.1201224

Cited by 1,374 publications

(1,065 citation statements)

References 22 publications

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“…In the last few decades, Europe has warmed not only faster than the global average, but also faster than expected from anthropogenic greenhouse gas increases (Ruckstuhl et al 2008;Philipona et al 2009;van Oldenborgh et al 2009). With the warming, Europe experienced record-breaking heat waves and extreme temperatures that imposed disastrous impacts on individuals, and society (Stott et al 2004;Fischer and Schär 2010;Barriopedro et al 2011;Christidis et al 2011Christidis et al , 2012Hegerl et al 2011;Rahmstorf and Coumou 2011;Hoerling et al 2012;Schubert et al Abstract Analysis of observations indicates that there was a rapid increase in summer (June-August) mean surface air temperature (SAT) since the mid-1990s over Western Europe. Accompanying this rapid warming are significant increases in summer mean daily maximum temperature, daily minimum temperature, annual hottest day temperature and warmest night temperature, and an increase in frequency of summer days and tropical nights, while the change in the diurnal temperature range (DTR) is small.…”

Section: Introductionmentioning

confidence: 99%

Understanding the rapid summer warming and changes in temperature extremes since the mid-1990s over Western Europe

2016

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frequency of summer days. It explains 45.5 ± 17.6 % and 40.9 ± 18.4 % of area averaged signals for these temperature extremes. The direct impact of the reduction of AAer precursor emissions over Europe acts to increase DTR locally, but the change in DTR is countered by the direct impact of GHGs forcing. In the next few decades, greenhouse gas concentrations will continue to rise and AAer precursor emissions over Europe and North America will continue to decline. Our results suggest that the changes in summer seasonal mean SAT and temperature extremes over Western Europe since the mid-1990s are most likely to be sustained or amplified in the near term, unless other factors intervene.

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Section: Introductionmentioning

confidence: 99%

Understanding the rapid summer warming and changes in temperature extremes since the mid-1990s over Western Europe

2016

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“…Therefore studies have mostly focused on regional analysis of changes [e.g., Barriopedro et al, 2011] or where global assessments have been possible they have usually relied on investigating just the extremes rather than the whole distribution [e.g., Alexander et al, 2006;Brown et al, 2008]. Other studies have relied on model output to investigate distribution changes [e.g., Beniston, 2004;Clark et al, 2006], or the relationship between mean and extreme changes [Hegerl et al, 2004].…”

Section: Introductionmentioning

confidence: 99%

The shifting probability distribution of global daytime and night‐time temperatures

Donat¹,

Alexander²

2012

Geophysical Research Letters

297

202

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[1] Using a global observational dataset of daily gridded maximum and minimum temperatures we investigate changes in the respective probability density functions of both variables using two 30-year periods; 1951-1980 and 1981-2010. The results indicate that the distributions of both daily maximum and minimum temperatures have significantly shifted towards higher values in the latter period compared to the earlier period in almost all regions, whereas changes in variance are spatially heterogeneous and mostly less significant. However asymmetry appears to have decreased but is altered in such a way that it has become skewed towards the hotter part of the distribution. Changes are greater for daily minimum (night-time) temperatures than for daily maximum (daytime) temperatures. As expected, these changes have had the greatest impact on the extremes of the distribution and we conclude that the distribution of global daily temperatures has indeed become "more extreme" since the middle of the 20th century. Citation: Donat, M. G., and L. V.Alexander (2012), The shifting probability distribution of global daytime and night-time temperatures, Geophys. Res. Lett., 39,

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“…This was aggravated by the ongoing political conflict as well as the worst flooding in a decade by the end of the year (Isar 2010). In summer 2010, western Russia, Ukraine, Belarus, Georgia and Kazakhstan experienced a heat wave that lead to historical warm records in several cities and a considerable high number of casualties related to the extremely hot temperatures (Barriopedro et al 2011;Trenberth and Fasullo 2012;Coumou and Rahmstorf 2012). By the end of 2010 and the beginning of 2011, a series of loods took place in the southern part of Africa, mainly in South Africa, Mozambique, Zimbabwe and southern Botswana.…”

Section: Validation Processmentioning

confidence: 99%

Extreme anomaly event detection in biosphere using linear regression and a spatiotemporal MRF model

et al. 2018

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Detecting abnormal events within time series is crucial for analyzing and understanding the dynamics of the system in many research areas. In this paper, we propose a methodology to detect these anomalies in multivariate environmental data. Five biosphere variables from a preliminary version of the Earth System Data Cube have been used in this study: Gross Primary Productivity, Latent Energy, Net Ecosystem Exchange, Sensible Heat and Terrestrial Ecosystem Respiration. To tackle the spatiotemporal dependencies of the biosphere variables, the proposed methodology after preprocessing the data is divided into two steps: a feature extraction step applied to each time series in the grid independently, followed by a spatiotemporal event detection step applied to the obtained novelty scores over the entire study area. The first step is based on the assumption that the time series of each variable can be represented by an autoregressive moving average (ARMA) process, and the anomalies are those time instances that are not well represented by the estimated ARMA model. The Mahalanobis distance of the ARMA models' multivariate residuals is used as a novelty score. In the second step, the obtained novelty scores of the entire study are treated as time series of images. Markov random fields (MRFs) provide an effective and theoretically well-established methodology for integrating spatiotemporal dependency into the classification of image time series. In this study, the classification of the novelty score images into three classes, intense anomaly, possible anomaly, and normal, is performed using unsupervised K-means clustering followed by multi-temporal MRF segmentation applied recursively on the images of each consecutive L ! 1 time steps. The proposed methodology was applied to an area covering Europe and Africa. Experimental results and validation based on known historic events show that the method is able to detect historic events and also provides a useful tool to define sensitive regions.

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The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe

Cited by 1,374 publications

References 22 publications

Understanding the rapid summer warming and changes in temperature extremes since the mid-1990s over Western Europe

Understanding the rapid summer warming and changes in temperature extremes since the mid-1990s over Western Europe

The shifting probability distribution of global daytime and night‐time temperatures

Extreme anomaly event detection in biosphere using linear regression and a spatiotemporal MRF model

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