4.6 Article

Human activity and anomalously warm seasons in Europe

Journal

INTERNATIONAL JOURNAL OF CLIMATOLOGY
Volume 32, Issue 2, Pages 225-239

Publisher

WILEY
DOI: 10.1002/joc.2262

Keywords

detection and attribution; regional climate change; temperature extremes

Funding

  1. Joint DECC
  2. Defra Integrated Climate Programme - DECC/Defra [GA01101]
  3. EU [212250]
  4. DFG

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Seasonal mean temperatures averaged over the European region have warmed at a rate of 0.350.52 K/decade since 1980. The last decade has seen record-breaking seasonal temperatures in Europe including the summer of 2003 and the spring, autumn, and winter of 2007. Previous studies have established that European summer warming since the early twentieth century can be attributed to the effects of human influence. The attribution analysis described here employs temperature data from observations and experiments with two climate models and uses optimal fingerprinting to partition the climate response between its anthropogenic and natural components. These responses are subsequently combined with estimates of unforced climate variability to construct distributions of the annual values of seasonal mean temperatures with and without the effect of human activity. We find that in all seasons, anthropogenic forcings have shifted the temperature distributions towards higher values. We compute the associated change in the likelihood of having seasons whose temperatures exceed a pre-specified threshold. We first set the threshold equal to the seasonal temperature observed in a particular year to assess the effect of anthropogenic influences in past seasons. We find that in the last decade (1999-2008) it is extremely likely (probability greater than 95%) that the probability has more than doubled under the influence of human activity in spring and autumn, while for summer it is extremely likely that the probability has at least quadrupled. One of the two models employed in the analysis indicates it is extremely likely the probability has more than doubled in winter too. We also compute the change in probability over a range of temperature thresholds which enables us to provide updates on the likely change in probability attributable to human influence as soon as observations become available. Such near-real time information could be very useful for adaptation planning. (C) Crown Copyright 2010. Published by John Wiley & Sons, Ltd.

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