Search

Long and homogeneous series are a necessary requirement for reliable climate analysis. Relocation of measuring equipment from one station to another, such as from the city center to a rural area or a nearby airport, is one of the causes of discontinuities in these long series that may affect trend estimates. In this paper, an updated procedure for the composition of long series, by combining data from nearby stations, is introduced. It couples an evolution of the blending procedure already implemented within the European Climate Assessment and Dataset (ECA&D, which combines data from stations no more than 12.5 km apart from each other) with a duplicate removal, alongside the quantile matching homogenization procedure. The ECA&D contains approximately 3000 homogenized series for each temperature variable prior to the blending procedure, and approximately 820 of these are longer than 60 years; the process of blending increases the number of long series to more than 900. Three case studies illustrate the effects of the homogenization on single blended series, showing the effectiveness of separate adjustments on extreme and mean values (Geneva, Switzerland), on cases in which blending is complex (Rheinstetten, Germany), and on series that are completed by adding relevant portions of Global Telecommunications System synoptic data (Siauliai, Lithuania). A trend assessment on the whole European continent reveals the removal of negative and very large trends, demonstrating a stronger spatial consistency. The new blended and homogenized dataset will allow a more reliable use of temperature series for indices calculation and for the calculation of gridded datasets and it will be available online for users (https://www.ecad.eu). [ABSTRACT FROM AUTHOR]

This paper proposes an optimal method for estimating time-dependent climate change signals from general circulation models. The basic idea is to identify vectors that maximize the mean-square detection statistic derived from optimal fingerprinting techniques. The method also provides an objective and systematic procedure for identifying the limit to which a signal can be restricted in space and time without losing detectability. As an illustration, the method is applied to the Coupled Model Intercomparison Project, phase 3 multimodel dataset to determine the continental seasonal-mean anomaly in surface air temperature and precipitation that is most detectable, on average, in these models. Anomalies in seasonal-mean surface air temperature are detectable in all seasons by almost all models on all continents but Europe; seasonal-mean anomalies over Europe are undetectable for some models, though this does not preclude other expressions of the signal, such as those that include longer time averages or time-lag information, from being detectable. Detectability in seasonal-mean temperature is found not only for multidecadal warming trends but also for cooling after major volcanic eruptions. In contrast, seasonal-mean precipitation anomalies are detectable in only a few models for averages over 5 yr or more, suggesting that the signal should include more spatiotemporal detail to be detectable across more models. Nevertheless, of the precipitation anomalies that are detectable, the signal appears to be of two characters: a systematic trend and enhanced frequency of extreme values. These results derived from twentieth-century simulations appear to be consistent with previous studies based on twenty-first-century simulations with larger signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

The idea of using large-scale information to predict local climate variability is widely exploited in climate change impact studies as an alternative to computationally expensive high-resolution models. This approach implies the hypothesis that the statistical relationship between large-scale climate states and local variables defined for the present-day climate remains valid in the altered climate. In this paper, the concept of weather regimes is used to deduce a relationship between large-scale circulation and European winter temperature. The change in temperature with increased greenhouse gases is, however, not homogeneous among the individual regimes. As a result, the impact of the weather regimes on local temperature changes varies in the future, limiting its usefulness for refining temperature changes to the small scale. [ABSTRACT FROM AUTHOR]

In this study, we aim to better understand the current and future projections of precipitation extremes in Europe in the context of climatic variability over a long-term period from the last millennium to the end of the twenty- first century. The daily gridded precipitation data from five global climate models (GCMs) of phase 5 of the Coupled Model Intercomparison Project (CMIP5) are chosen to investigate natural variability and precipitation extremes during the last millennium (850-1849), the historical (1850-2005) period, and two representative concentration pathway (RCP2.6 and RCP8.5) scenarios (2006-99). First, the seasonal and annual precipitation and extreme statistics from GCMs are evaluated using reconstruction and observation. Second, the spatial and temporal patterns of extreme precipitation from GCMs are investigated from the last millennium to the end of the twenty-first century. Meanwhile, the characteristic changes of extreme precipitation for the five regions of Europe are further analyzed. The results revealed the following: GCMs underestimate extreme precipitation and overestimate mean precipitation compared with the observations from European Climate Assessment and Dataset (ECA&D) ; the majority of Europe except southern Europe will most likely have large magnitude increases in the extreme precipitation and mean precipitation in the future under both RCP scenarios; there is no systematic change of precipitation extremes from the last millennium to the historical period from all GCMs; and larger magnitude increases are shown in 100- and 200-yr than in 5- and 10-yr return period precipitation from both RCP scenarios. In addition, short-duration extreme precipitation willmost likely increase more than longer-duration extremes. [ABSTRACT FROM AUTHOR]

Over recent decades Europe has experienced heavy floods, with major consequences for thousands of people and billions of euros worth of damage. In particular, the summer of 2013 flood in central Europe showed how vulnerable modern society is to hydrological extremes and emphasized once more the need for improved forecast methods of such extreme climatic events. Based on a multiple linear regression model, it is shown here that 55% of the June 2013 Elbe River extreme discharge could have been predicted using May precipitation, soil moisture, and sea level pressure. Moreover, the model was able to predict more than 75% of the total Elbe River discharge for June 2013 (in terms of magnitude) by also incorporating the amount of precipitation recorded during the days prior to the flood, but the predicted discharge for the June 2013 event was still underestimated by 25%. Given that all predictors used in the model are available at the end of each month, the forecast scheme can be used to predict extreme events and to provide early warnings for upcoming floods. The forecast methodology could be relevant for other rivers also, depending on their location and their climatic background. [ABSTRACT FROM AUTHOR]