Abstract. The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added.Published by Copernicus Publications on behalf of the European Geosciences Union. V. K. C. Venema et al.: Benchmarking monthly homogenization algorithmsParticipants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, stateof-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.
Abstract. The method of nearest-neighbor resampling is extended to simultaneous simulation of daily precipitation and temperature at multiple locations over a large area (25 stations in the German part of the Rhine basin). Nearest neighbors refer here to historical days for which the observed weather is closest to that of the simulated weather for a given day. Resampling is done from these nearest neighbors to obtain the weather variables for the next day. The nearest neighbors are defined in terms of a weighted Euclidean distance to a feature vector containing summary statistics of the daily precipitation and temperature fields (spatial averages, fraction of stations with precipitation, and principal components). The inclusion of atmospheric circulation variables in the feature vector is also studied. There is a weak tendency to underestimate the standard deviations and autocorrelation coefficients of daily precipitation and temperature and the standard deviations of the monthly precipitation totals and monthly mean temperatures. However, the underprediction of these second-order moment statistics is not statistically significant if the number k of nearest neighbors in the resampling procedure is small (k • 5) and the dimension q of the feature vector is low (q • 3). A small systematic underprediction is also observed for the quantiles of the distributions of the N-day winter maximum precipitation amounts. The spatial dependence of these extremes and the distributions of N-day maximum snowmelt are adequately reproduced. Long-duration simulations show that realistic unprecedented multiday precipitation amounts can be generated.
Abstract. The COST (European Cooperation in Science and Technology) Action ES0601: Advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies. The algorithms were validated against a realistic benchmark dataset. Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including i) the centered root mean square error relative to the true homogeneous values at various averaging scales, ii) the error in linear trend estimates and iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Moreover, state-of-theart relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that currently automatic algorithms can perform as well as manual ones.
The development of a daily historical European-North Atlantic mean sea level pressure dataset (EMSLP) for 1850-2003 on a 5°latitude by longitude grid is described. This product was produced using 86 continental and island stations distributed over the region 25°-70°N, 70°W-50°E blended with marine data from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). The EMSLP fields for 1850-80 are based purely on the land station data and ship observations. From 1881, the blended land and marine fields are combined with already available daily Northern Hemisphere fields. Complete coverage is obtained by employing reduced space optimal interpolation. Squared correlations (r 2 ) indicate that EMSLP generally captures 80%-90% of daily variability represented in an existing historical mean sea level pressure product and over 90% in modern 40-yr European Centre for Medium-Range Weather Forecasts Re-Analyses (ERA-40) over most of the region. A lack of sufficient observations over Greenland and the Middle East, however, has resulted in poorer reconstructions there. Error estimates, produced as part of the reconstruction technique, flag these as regions of low confidence. It is shown that the EMSLP daily fields and associated error estimates provide a unique opportunity to examine the circulation patterns associated with extreme events across the European-North Atlantic region, such as the 2003 heat wave, in the context of historical events.
The International Surface Pressure Databank (ISPD) is the world's largest collection of global surface and sea-level pressure observations. It was developed by extracting observations from established international archives, through international cooperation with data recovery facilitated by the Atmospheric Circulation Reconstructions over the Earth (ACRE) initiative, and directly by contributing universities, organizations, and countries. The dataset period is currently 1768-2012 and consists of three data components: observations from land stations, marine observing systems, and tropical cyclone best track pressure reports. Version 2 of the ISPD (ISPDv2) was created to be observational input for the Twentieth Century Reanalysis Project (20CR) and contains the quality control and assimilation feedback metadata from the 20CR. Since then, it has been used for various general climate and weather studies, and an updated version 3 (ISPDv3) has been used in the ERA-20C reanalysis in connection with the European Reanalysis of Global Climate Observations project (ERA-CLIM). The focus of this paper is on the ISPDv2 and the inclusion of the 20CR feedback metadata. The Research Data Archive at the National Center for Atmospheric Research provides data collection and access for the ISPDv2, and will provide access to future versions.
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