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.

Field-scale experiments simulating realistic future climate scenarios are important tools for investigating the effects of current and future climate changes on ecosystem functioning and biogeochemical cycling. We exposed a seminatural Danish heathland ecosystem to elevated atmospheric carbon dioxide (CO 2 ), warming, and extended summer drought in all combinations. Here, we report on the short-term responses of the nitrogen (N) cycle after 2 years of treatments. Elevated CO 2 significantly affected aboveground stoichiometry by increasing the carbon to nitrogen (C/N) ratios in the leaves of both co-dominant species (Calluna vulgaris and Deschampsia flexuosa), as well as the C/N ratios of Calluna flowers and by reducing the N concentration of Deschampsia litter. Belowground, elevated CO 2 had only minor effects, whereas warming increased N turnover, as indicated by increased rates of microbial NH 4 1 consumption, gross mineralization, potential nitrification, denitrification and N 2 O emissions. Drought reduced belowground gross N mineralization and decreased fauna N mass and fauna N mineralization. Leaching was unaffected by treatments but was significantly higher across all treatments in the second year than in the much drier first year indicating that ecosystem N loss is highly sensitive to changes and variability in amount and timing of precipitation. Interactions between treatments were common and although some synergistic effects were observed, antagonism dominated the interactive responses in treatment combinations, i.e. responses were smaller in combinations than in single treatments. Nonetheless, increased C/N ratios of photosynthetic tissue in response to elevated CO 2 , as well as drought-induced decreases in litter N production and fauna N mineralization prevailed in the full treatment combination. Overall, the simulated future climate scenario therefore lead to reduced N turnover, which could act to reduce the potential growth response of plants to elevated atmospheric CO 2 concentration.

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.

Summary 1.Recent findings indicate that the interactions among CO 2 , temperature and water can be substantial, and that the combined effects on the biological systems of several factors may not be predicted from experiments with one or a few factors. Therefore realistic multifactorial experiments involving a larger set of main factors are needed. 2. We describe a new Danish climate change-related field scale experiment, CLIMAITE, in a heath/ grassland ecosystem. CLIMAITE is a full factorial combination of elevated CO 2 , elevated temperature and prolonged summer drought. The manipulations are intended to mimic anticipated major environmental changes at the site by year 2075 as closely as possible. The impacts on ecosystem processes and functioning (at ecophysiological levels, through responses by individuals and communities to ecosystem-level responses) are investigated simultaneously. 3. The increase of [CO 2 ] closely corresponds with the scenarios for year 2075, while the warming treatment is at the lower end of the predictions and seems to be the most difficult treatment to increase without unwanted side effects on the other variables. The drought treatment follows predictions of increased frequency of drought periods in summer. The combination of the treatments does not create new unwanted side effects on the treatments relative to the treatments alone.

Crowther et al. 1 reported that the best predictor of surface soil carbon (C; top 10 cm) losses in response to warming is the size of the surface C stock in the soil (i.e. C stocks in unwarmed plots), with soils high in soil C also losing more C. This relationship was based on a linear regression of soil C losses and soil C stocks in field warming studies, which was then used to project C losses over time and to generate a map of soil C vulnerability. However, a few extreme data points can strongly influence the slope of a regression line (i.e. high leverage points) 2. Of the 49 sites in Crowther et al, only five are in the upper half of the C stock range. This paucity of high-soil C data calls into question the robustness of the overall relationship and raises the possibility that this relationship could be substantially altered by new data from sites with relatively high surface C stocks. We obtained information on soil C losses from published and unpublished data from 94 additional field warming studies worldwide, and thereby tripled the data set used by Crowther and colleagues to a total of 143 studies (Table S1). We performed the same mixed-model regression analyses as used by Crowther et al. to examine spatial patterns of soil carbon responses to warming, by linking these to standing soil C stocks, climate data and soil properties (see Methods for details, Table S2 for study-specific data on soil properties and climate, and Table S3 for Akaike Information Criterion results). We chose the same predictors in our models to compare our results directly to theirs. Our new