This paper introduces a new approach-the Principal Component Gradient Analysis (PCGA)-to detect ecological gradients in time-series populations, i.e. several time-series originating from different individuals of a population. Detection of ecological gradients is of particular importance when dealing with time-series from heterogeneous populations which express differing trends. PCGA makes use of polar coordinates of loadings from the first two axes obtained by principal component analysis (PCA) to define groups of similar trends. Based on the mean inter-series correlation (rbar) the gain of increasing a common underlying signal by PCGA groups is quantified using Monte Carlo Simulations. In terms of validation PCGA is compared to three other existing approaches. Focusing on dendrochronological examples, PCGA is shown to correctly determine population gradients and in particular cases to be advantageous over other considered methods. Furthermore, PCGA groups in each example allowed for enhancing the strength of a common underlying signal and comparably well as hierarchical cluster analysis. Our results indicate that PCGA potentially allows for a better understanding of mechanisms causing time-series population gradients as well as objectively enhancing the performance of climate transfer functions in dendroclimatology. While our examples highlight the relevance of PCGA to the field of dendrochronology, we believe that also other disciplines working with data of comparable structure may benefit from PCGA.

Observed recent and expected future increases in frequency and intensity of climatic extremes in central Europe may pose critical challenges for domestic tree species. Continuous dendrometer recordings provide a valuable source of information on tree stem radius variations, offering the possibility to study a tree's response to environmental influences at a high temporal resolution. In this study, we analyze stem radius variations (SRV) of three domestic tree species (beech, oak, and pine) from 2012 to 2014. We use the novel statistical approach of event coincidence analysis (ECA) to investigate the simultaneous occurrence of extreme daily weather conditions and extreme SRVs, where extremes are defined with respect to the common values at a given phase of the annual growth period. Besides defining extreme events based on individual meteorological variables, we additionally introduce conditional and joint ECA as new multivariate extensions of the original methodology and apply them for testing 105 different combinations of variables regarding their impact on SRV extremes. Our results reveal a strong susceptibility of all three species to the extremes of several meteorological variables. Yet, the inter-species differences regarding their response to the meteorological extremes are comparatively low. The obtained results provide a thorough extension of previous correlation-based studies by emphasizing on the timings of climatic extremes only. We suggest that the employed methodological approach should be further promoted in forest research regarding the investigation of tree responses to changing environmental conditions.

Dendrometers are measurement devices proven to be useful to analyze tree water relations and growth responses in relation to environmental variability. To analyze dendrometer data, two analytical methods prevail: (1) daily approaches that calculate or extract single values per day, and (2) stem-cycle approaches that separate high-resolution dendrometer records into distinct phases of contraction, expansion and stem-radius increment. Especially the stem-cycle approach requires complex algorithms to disentangle cyclic phases. Here, we present an R package, named dendrometeR, that facilitates the analysis of dendrometer data using both analytical methods. By making the package freely available, we make a first step towards comparable and reproducible methods to analyze dendrometer data. The package contains customizable functions to prepare, verify, process and plot dendrometer series, as well as functions that facilitate the analysis of dendrometer data (i.e. daily statistics or extracted phases) in relation to environmental data. The functionality of dendrometeR is illustrated in this note.

(1) German Research Centre For Geosciences, Geomorphology, Hydrology, Paleoclimatology, Potsdam, Germany (heinrich@gfz-potsdam.de), (2) German Archaeological Institute, Berlin, Germany , (3) University of Arizona, Tree-Ring Laboratory, Tucson, USA, (4) Institute of Geography and Spatial Organization of the Polish Academy of Sciences, Department of Environmental Resources and Geohazards, Poland, (5) University of Greifswald, Landscape Ecology, Greifswald, Germany, (6) Johann Heinrich von Thünen Institute, Institute of Forest Ecosystems, Eberswalde, Germany

In this study, we explore the potential to reconstruct lake-level (and groundwater) fluctuations from tree-ring chronologies of black alder (Alnus glutinosa L.) for three study lakes in the Mecklenburg Lake District, northeastern Germany. As gauging records for lakes in this region are generally short, long-term reconstructions of lake-level fluctuations could provide valuable information on past hydrological conditions, which, in turn, are useful to assess dynamics of climate and landscape evolution. We selected black alder as our study species as alder typically thrives as riparian vegetation along lakeshores. For the study lakes, we tested whether a regional signal in lake-level fluctuations and in the growth of alder exists that could be used for long-term regional hydrological reconstructions, but found that local (i.e. site-specific) signals in lake level and tree-ring chronologies prevailed. Hence, we built lake/groundwater-level reconstruction models for the three study lakes individually. Two sets of models were considered based on (1) local tree-ring series of black alder, and (2) site-specific Standardized Precipitation Evapotranspiration Indices (SPEI). Although the SPEI-based models performed statistically well, we critically reflect on the reliability of these reconstructions, as SPEI cannot account for human influence. Tree-ring based reconstruction models, on the other hand, performed poor. Combined, our results suggest that, for our study area, long-term regional reconstructions of lake-level fluctuations that consider both recent and ancient (e.g., archaeological) wood of black alder seem extremely challenging, if not impossible.

Our knowledge on past environmental conditions largely relies on reconstructions that are based on linear regressions between proxy variables (e.g. tree-rings, lake sediments, ice cores) covering a comparably long period (centuries to millennia) and environmental parameters (e.g. climate data) of which only rather short measurement series exist (mostly decades). In general, the corresponding measurements are prone to errors. For instance, air temperature records that are to be prolonged by reconstruction from tree-rings are normally not measured in situ, i.e. where the trees used for reconstructions are growing. In contrast, the variation of tree-ring properties which are used as proxies does not only depend on temperature variations but also on other environmental variables and biological effects. However, if regressions are based on noisy data, knowledge on the noise intensity of both predictor and predictand is needed and model parameter estimates (slope and intercept) will be erroneous if information on the noise is not included in their estimation (Kutzbach et al., 2011). Here, we investigate the performance of the new Sequential Iterative Noise Matching Algorithm (SINOMA; Thees et al., 2009; and Thees et al., submitted) on a variety of typical proxy-data of differing temporal resolution (i.e. hourly (dendrometers, piezometers), seasonally (tree-rings), and annually (tree rings and varved lake sediments)). For each of the investigated proxies a number of pseudo-proxy datasets is generated. I.e. to each proxy variable two different noises are added, resulting in two noisy variables that originate from a common signal (the proxy) and of which the respective error noises and the true model parameters (slope and intercept) between both are known. SINOMA is applied to each of these pseudo-proxy datasets and its performance is evaluated against traditional regression techniques. The herewith submitted contribution thus focuses on the applicability of SINOMA rather than on its mathematical background which we intend to present in another contribution to this EGU session (Thees at al., 2014). On average, SINOMA performs better than or, under specific error noise conditions, equal to the traditional modeling techniques. However, some of the investigated data reveal constraints of SINOMA, which have to be considered in ‘real-world’ applications. Nevertheless, our results indicate that SINOMA likely is a more reliable tool for estimating regression parameters if compared to traditional techniques. Based on the generally noisy characteristics of proxies used typically, applications of SINOMA to already existing reconstructions will probably result in different model parameter estimates, most likely leading to differing amplitudes of reconstructed past environmental conditions. Therefore, SINOMA has the potential to reframe our picture of the past.

The presence of soluble carbohydrates in the cambial zone, either from sugars recently produced during photosynthesis or from starch remobilized from storage organs, is necessary for radial tree growth. However, considerable uncertainties on carbohydrate dynamics and the consequences on tree productivity exist. This study aims to better understand the variation in different carbon pools at intra-annual resolution by quantifying how cambial zone sugar and starch concentrations fluctuate over the season and in relation to cambial phenology. A comparison between two physiologically different species growing at the same site, i.e., the evergreen Picea abies Karst. and the deciduous Larix decidua Mill., and between L. decidua from two contrasting elevations, is presented to identify mechanisms of growth limitation. Results indicate that the annual cycle of sugar concentration within the cambial zone is coupled to the process of wood formation. The highest sugar concentration is observed when the number of cells in secondary wall formation and lignification stages is at a maximum, subsequent to most radial growth. Starch disappears in winter, while other freeze-resistant non-structural carbohydrates (NSCs) increase. Slight differences in NSC concentration between species are consistent with the differing climate sensitivity of the evergreen and deciduous species investigated. The general absence of differences between elevations suggests that the cambial activity of trees growing at the treeline was not limited by the availability of carbohydrates at the cambial zone but instead by environmental controls on the growing season duration.

Tree-ring chronologies of Pinus sylvestris L. from latitudinal and altitudinal limits of the species distribution have been widely used for climate reconstructions, but there are many sites within the temperate climate zone, as is the case in northeastern Germany, at which there is little evidence of a clear climate signal in the chronologies. In this study, we developed long chronologies of several cell structure variables (e.g., average lumen area and cell wall thickness) from P. sylvestris growing in northeastern Germany and investigated the influence of climate on ring widths and cell structure variables. We found significant correlations between cell structure variables and temperature, and between tree-ring width and relative humidity and vapor pressure, respectively, enabling the development of robust reconstructions from temperate sites that have not yet been realized. Moreover, it has been shown that it may not be necessary to detrend chronologies of cell structure variables and thus low-frequency climate signals may be retrieved from longer cell structure chronologies. The relatively extensive resource of archaeological material of P. sylvestris covering approximately the last millennium may now be useful for climate reconstructions in northeastern Germany and other sites in the temperate climate zone.