European Climate Assessment Dataset Research Articles

Köppen climates and Scheffer index as indicators of timber risk in Europe (1901–2020)

Timber heritage faces numerous risks, with the threat from insect and fungal attack particularly sensitive to climate. The Scheffer index estimates the deterioration of wood by combining average temperature and days of rain. We determined the Scheffer index from twenty sites in Europe across the 20th and early 21st century using meteorological observations, largely from the European Climate Assessment Dataset. Results showed that Scheffer indices across Europe loosely align with Köppen climate classes, which are also defined in terms of temperature and rainfall. However, within a given Köppen class, a wide range of Scheffer indices were found, thus representing very different threats to heritage. This suggests that specific heritage climate parameters, such as the Scheffer index, are needed. Trends in the Scheffer index provide a fairly robust guide to management strategy by identifying locations where the risk for timber is increasing. However, a simple index cannot necessarily describe the complex interactions between biological pests and climate; such complexity is better handled with well-constructed models. Nevertheless, the Scheffer index offers heritage managers an easily calculated estimate of the spatial distribution of risk to timber and the likely direction of future change.

Near-optimal selection of representative measuring points for robust temperature field reconstruction with the CRO-SL and analogue methods

In this paper we tackle a problem of representative measuring points selection for temperature field reconstruction. This problem is a version of the more general Representative Selection (RS) problem, well-known in computer and data science. In this particular case, the objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a reconstruction error is minimized when reconstructing the monthly average temperature field. We use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer (CRO-SL), which is an evolutionary-type method able to combine several different search procedures within a single population. The CRO-SL is combined with the Analogue Method (AM) to identify the most representative points. This approach exhibits strong performance from experiments with gridded and un-gridded temperature field datasets (European Climate Assessment & Dataset (ECA) and ERA-Interim reanalysis (ERA)). Different aspects such as the error assessment and the comparison with alternative approaches, are discussed in the experimental analysis of this article. We show that the algorithm performs better than a greedy approach, i.e. the best solution for N points is different from the N best individual predictors. The solutions obtained with the proposed methodology are climatologically consistent and include points from Scandinavia, Central and Southern Europe, the Black Sea and Central and South Western Asia as the more representative in the case of the ECA dataset; similar areas are selected for ERA. We have found out that once the number of stations/points goes over a threshold, the improvement in the model is obtained by increasing the density of data in the given zones, instead of adding data from different zones to the algorithm. The method proposed may have direct application in Palaeoclimalogy, where there are a large amount of distributed proxies with scarce information, so the proposed approach could be useful to select the most important ones to reconstruct a desired field.

Extreme value and cluster analysis of European daily temperature series

Time series of daily mean temperature obtained from the European Climate Assessment data set is analyzed with respect to their extremal properties. A time-series clustering approach which combines Bayesian methodology, extreme value theory and classification techniques is adopted for the analysis of the regional variability of temperature extremes. The daily mean temperature records are clustered on the basis of their corresponding predictive distributions for 25-, 50- and 100-year return values. The results of the cluster analysis show a clear distinction between the highest altitude stations, for which the return values are lowest, and the remaining stations. Furthermore, a clear distinction is also found between the northernmost stations in Scandinavia and the stations in central and southern Europe. This spatial structure of the return period distributions for 25-, 50- and 100-years seems to be consistent with projected changes in the variability of temperature extremes over Europe pointing to a different behavior in central Europe than in northern Europe and the Mediterranean area, possibly related to the effect of soil moisture and land-atmosphere coupling.

Estimation of persistence and trends in geostrophic wind speed for the assessment of wind energy yields in Northwest Europe

Wind climate in Northwest Europe is subject to long-term persistence (LTP), also called the Hurst phenomenon. Ignorance of LTP causes underestimation of climatic variability. The quantification of multi-year variability is important for the assessment of the uncertainty of future multi-year wind yields. Estimating LTP requires long homogeneous time series. Such series of wind observations are rare, but annual mean geostrophic wind speed (U) can be used instead. This study demonstrates a method to estimate the 10-year aggregated mean U for the near and the far future and its uncertainty in Northwest Europe. Time series of U were derived from daily sea level pressure from the European Climate Assessment Dataset. Minor inhomogeneities cannot be ruled out, but were shown to hardly affect the estimated Hurst exponent $$(\hat{H})$$ . A maximum likelihood method was adjusted to remove the biases in $$\hat{H}$$ . The geostrophic wind speed over the North Sea, the British Isles and along the Scandinavian coast are characterised by statistically significant H between 0.58 and 0.74, (H = 0.5 implies no LTP). The additional affect of the parameter uncertainty is estimated in a Bayesian way and is highly dependent on the record length. The assessment of structural changes in future wind fields requires general circulation models. An ensemble of seventeen simulations (ESSENCE) with one single climate model (ECHAM5/MPI-OM) was used to evaluate structural trends and LTP. The estimated $$\hat{H}$$ in the ESSENCE simulations are generally close to 0.5 and not significant. Significant trends in U are found over large parts of the investigated domain, but the trends are small compared to the multi-year variability. Large decreasing trends are found in the vicinity of Iceland and increasing trends near the Greenland coast. This is likely related to the sea ice retreat within the ESSENCE simulations and the associated change in surface temperature gradients.

Hot European Summers and the Role of Soil Moisture in the Propagation of Mediterranean Drought

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.

Updated and extended European dataset of daily climate observations

AbstractThe European Climate Assessment (ECA) dataset of daily observations, which has been widely used for studies on climate extremes, has been updated and extended. It now contains observational series of 2191 stations located in Europe and the Mediterranean (average inter‐station distance: ∼75 km). About 1200 precipitation series and 750 temperature series cover the period 1960–2000. For a small number of stations (<15%) air pressure, cloud cover, sunshine duration, snow depth and relative humidity series have been collected. All series are quality controlled and the homogeneity of the precipitation and temperature series is assessed. About 50% of the daily series are publicly available for climate studies through the website http://eca.knmi.nl. The main potential of the ECA dataset follows from its daily resolution, enabling studies of impact relevant climate extremes and variability. To guide these studies, climate indices calculated from the ECA series are presented on the website too. Besides, gridded versions of the daily ECA data are available for easy comparison with climate model simulations. A trend analysis for the diurnal temperature range (DTR) demonstrates the utility of the dataset. Seasonal and annual DTR trends were calculated for 333 homogeneous temperature series in ECA and a Europe average trend was estimated. In spring and summer, the DTR increased from 1979 to 2005, whereas in autumn and winter the DTR generally decreased. The European average trend in annual DTR was 0.09 °C decade−1. Copyright © 2008 Royal Meteorological Society

Improved confidence in climate change projections of precipitation evaluated using daily statistics from the PRUDENCE ensemble

An ensemble of regional climate modelling simulations from the European framework project PRUDENCE are compared across European sub-regions with observed daily precipitation from the European Climate Assessment dataset by characterising precipitation in terms of probability density functions (PDFs). Models that robustly describe the observations for the control period (1961–1990) in given regions as well as across regions are identified, based on the overlap of normalised PDFs, and then validated, using a method based on bootstrapping with replacement. We also compare the difference between the scenario period (2071–2100) and the control period precipitation using all available models. By using a metric quantifying the deviation over the entire PDF, we find a clearly marked increase in the contribution to the total precipitation from the more intensive events and a clearly marked decrease for days with light precipitation in the scenario period. This change is tested to be robust and found in all models and in all sub-regions. We find a detectable increase that scales with increased warming, making the increase in the PDF difference a relative indicator of climate change level. Furthermore, the crossover point separating decreasing from increasing contributions to the normalised precipitation spectrum when climate changes does not show any significant change which is in accordance with expectations assuming a simple analytical fit to the precipitation spectrum.

Signals of anthropogenic influence on European warming as seen in the trend patterns of daily temperature variance

AbstractSignals of anthropogenic warming over Europe are searched for in the spatial trend patterns for the variance and skewness (expressed by the 10th and 90th percentiles) of the distribution of daily mean temperature. Comparisons are made between these patterns in the station records of the European Climate Assessment dataset for the 1976–99 period, the patterns associated with natural variability in the observations (which were empirically derived from the observations in the 1946–75 period), and the patterns of future warming and natural variability as simulated by the National Center for Atmospheric Research Community Climate System Model in the Challenge ensemble experiment.The results indicate that, on the basis of the patterns for the variance, a distinction can be made between temperature change due to natural variability and temperature change due to changes in external forcing. The observed variance trend patterns for the spring (March–May) and summer (June–August) warming 1976–99 are clearly different from the patterns for the change in variance associated with a warming due to natural variability in the observations. This led us to conclude that a change in an external forcing has to be invoked to explain the observed spring and summer warming. From the evaluation of the greenhouse and natural variability patterns in the climate model simulations, we infer that the observed spring and summer variance trend patterns contain imprints consistent with anthropogenic warming. The analysis of the variance trend patterns for the winter (December–February) season is inconclusive about identifying causes of the observed warming for that season. Unlike the other three seasons, the autumn (September–November) is for Europe a period of cooling in recent decades. The observed variance trend pattern for this season closely resembles the estimated pattern for the change in variance associated with a cooling due to natural variability, indicating that the observed autumn cooling can be ascribed to random weather variations in the period under consideration. Copyright © 2005 Royal Meteorological Society

Homogeneity of 20th century European daily temperature and precipitation series

AbstractDaily European station series (1901–99) of surface air temperature and precipitation from the European Climate Assessment dataset are statistically tested with respect to homogeneity. A two‐step approach is followed. First, four homogeneity tests are applied to evaluate the daily series. The testing variables used are (1) the annual mean of the diurnal temperature range, (2) the annual mean of the absolute day‐to‐day differences of the diurnal temperature range and (3) the wet day count (threshold 1 mm). Second, the results of the different tests are condensed into three classes: ‘useful’, ‘doubtful’ and ‘suspect’. A qualitative interpretation of this classification is given, as well as recommendations for the use of these labelled series in trend analysis and variability analysis of weather extremes. In the period 1901–99, 94% of the temperature series and 25% of the precipitation series are labelled ‘doubtful’ or ‘suspect’. In the sub‐period 1946–99, 61% of the temperature series and 13% of the precipitation series are assigned to these classes. The seemingly favourable scores for precipitation can be attributed to the high standard deviation of the testing variable, and hence the inherent restricted possibilities for detecting inhomogeneities. About 65% of the statistically detected inhomogeneities in the temperature series labelled ‘doubtful’ or ‘suspect’ in the period 1946–99 can be attributed to observational changes that are documented in the metadata. For precipitation this percentage is 90%. Copyright © 2003 Royal Meteorological Society

Daily dataset of 20th‐century surface air temperature and precipitation series for the European Climate Assessment

AbstractWe present a dataset of daily resolution climatic time series that has been compiled for the European Climate Assessment (ECA). As of December 2001, this ECA dataset comprises 199 series of minimum, maximum and/or daily mean temperature and 195 series of daily precipitation amount observed at meteorological stations in Europe and the Middle East. Almost all series cover the standard normal period 1961–90, and about 50% extends back to at least 1925. Part of the dataset (90%) is made available for climate research on CDROM and through the Internet (at http://www.knmi.nl/samenw/eca).A comparison of the ECA dataset with existing gridded datasets, having monthly resolution, shows that correlation coefficients between ECA stations and nearest land grid boxes between 1946 and 1999 are higher than 0.8 for 93% of the temperature series and for 51% of the precipitation series. The overall trends in the ECA dataset are of comparable magnitude to those in the gridded datasets.The potential of the ECA dataset for climate studies is demonstrated in two examples. In the first example, it is shown that the winter (October–March) warming in Europe in the 1976–99 period is accompanied by a positive trend in the number of warm‐spell days at most stations, but not by a negative trend in the number of cold‐spell days. Instead, the number of cold‐spell days increases over Europe. In the second example, it is shown for winter precipitation between 1946 and 1999 that positive trends in the mean amount per wet day prevail in areas that are getting drier and wetter.Because of its daily resolution, the ECA dataset enables a variety of empirical climate studies, including detailed analyses of changes in the occurrence of extremes in relation to changes in mean temperature and total precipitation. Copyright © 2002 Royal Meteorological Society.