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Changes in alpine plant growth under future climate conditions
Alpine shrub- and grasslands are shaped by extreme climatic conditions such as a long-lasting snow cover and a short vegetation period. Such ecosystems are expected to be highly sensitive to global environmental change. Prolonged growing seasons and shifts in temperature and precipitation are likely to affect plant phenology and growth. In a unique experiment, climatology and plant growth was monitored for almost a decade at 17 snow meteorological stations in different alpine regions along the Swiss Alps. Regression analyses revealed highly significant correlations between mean air temperature in May/June and snow melt out, onset of plant growth, and plant height. These correlations were used to project plant growth phenology for future climate conditions based on the gridded output of a set of regional climate models runs. Melt out and onset of growth were projected to occur on average 17 days earlier by the end of the century than in the control period from 1971–2000 under the future climate conditions of the low resolution climate model ensemble. Plant height and biomass production were expected to increase by 77% and 45%, respectively. The earlier melt out and onset of growth will probably cause a considerable shift towards higher growing plants and thus increased biomass. Our results represent the first quantitative and spatially explicit estimates of climate change impacts on future growing season length and the respective productivity of alpine plant communities in the Swiss Alps.
Long-term variations of the mesospheric wind field at mid-latitudes
Continuous MF radar observations at the station Juliusruh (54.6° N; 13.4° E) have been analysed for the time interval between 1990 and 2005, to obtain information about solar activity-induced variations, as well as long-term trends in the mesospheric wind field. Using monthly median values of the zonal and the meridional prevailing wind components, as well as of the amplitude of the semidiurnal tide, regression analyses have been carried out with a dependence on solar activity and time. The solar activity causes a significant amplification of the zonal winds during summer (increasing easterly winds) and winter (increasing westerly winds). The meridional wind component is positively correlated with the solar activity during summer but during winter the correlation is very small and non significant. Also, the solar influence upon the amplitude of the semidiurnal tidal component is relatively small (in dependence on height partly positive and partly negative) and mostly non-significant. The derived trends in the zonal wind component during summer are below an altitude of about 83 km negative and above this height positive. During the winter months the trends are nearly opposite compared with the trends in summer (transition height near 86 km). The trends in the meridional wind components are below about 85 km positive in summer (significant) and near zero (nonsignificant) in winter; above this height during both seasons negative trends have been detected. The trends in the semidiurnal tidal amplitude are at all heights positive, but only partly significant. The detected trends and solar cycle dependencies are compared with other experimental results and model calculations. There is no full agreement between the different results, probably caused by different measuring techniques and evaluation methods used. Also, different heights and observation periods investigated may contribute to the detected differences.
Spatial patterns of linear and nonparametric long-term trends in Baltic sea-level variability
The study of long-term trends in tide gauge data is important for understanding the present and future risk of changes in sea-level variability for coastal zones, particularly with respect to the ongoing debate on climate change impacts. Traditionally, most corresponding analyses have exclusively focused on trends in mean sea-level. However, such studies are not able to provide sufficient information about changes in the full probability distribution (especially in the more extreme quantiles). As an alternative, in this paper we apply quantile regression (QR) for studying changes in arbitrary quantiles of sea-level variability. For this purpose, we chose two different QR approaches and discuss the advantages and disadvantages of different settings. In particular, traditional linear QR poses very restrictive assumptions that are often not met in reality. For monthly data from 47 tide gauges from along the Baltic Sea coast, the spatial patterns of quantile trends obtained in linear and nonparametric (spline-based) frameworks display marked differences, which need to be understood in order to fully assess the impact of future changes in sea-level variability on coastal areas. In general, QR demonstrates that the general variability of Baltic sea-level has increased over the last decades. Linear quantile trends estimated for sliding windows in time reveal a wide-spread acceleration of trends in the median, but only localised changes in the rates of changes in the lower and upper quantiles.
Towards global empirical upscaling of FLUXNET eddy covariance observations: Validation of a model tree ensemble approach using a biosphere model
Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical up-scaling eddy covariance measurements would constitute a new and possibly powerful data stream to study the variability of the global terrestrial carbon and water cycle. This paper introduces and validates a machine learning approach dedicated to the upscaling of observations from the current global network of eddy covariance towers (FLUXNET). We present a new model TRee Induction ALgorithm (TRIAL) that performs hierarchical stratification of the data set into units where particular multiple regressions for a target variable hold. We propose an ensemble approach (Evolving tRees with RandOm gRowth, ERROR) where the base learning algorithm is perturbed in order to gain a diverse sequence of different model trees which evolves over time. We evaluate the efficiency of the model tree ensemble (MTE) approach using an artificial data set derived from the Lund-Potsdam-Jena managed Land (LPJmL) biosphere model. We aim at reproducing global monthly gross primary production as simulated by LPJmL from 1998–2005 using only locations and months where high quality FLUXNET data exist for the training of the model trees. The model trees are trained with the LPJmL land cover and meteorological input data, climate data, and the fraction of absorbed photosynthetic active radiation simulated by LPJmL. Given that we know the "true result" in the form of global LPJmL simulations we can effectively study the performance of the MTE upscaling and associated problems of extrapolation capacity. We show that MTE is able to explain 92% of the variability of the global LPJmL GPP simulations. The mean spatial pattern and the seasonal variability of GPP that constitute the largest sources of variance are very well reproduced (96% and 94% of variance explained respectively) while the monthly interannual anomalies which occupy much less variance are less well matched (41% of variance explained). We demonstrate the substantially improved accuracy of MTE over individual model trees in particular for the monthly anomalies and for situations of extrapolation. We estimate that roughly one fifth of the domain is subject to extrapolation while MTE is still able to reproduce 73% of the LPJmL GPP variability here. This paper presents for the first time a benchmark for a global FLUXNET upscaling approach that will be employed in future studies. Although the real world FLUXNET upscaling is more complicated than for a noise free and reduced complexity biosphere model as presented here, our results show that an empirical upscaling from the current FLUXNET network with MTE is feasible and able to extract global patterns of carbon flux variability.
Long-term trends in the ionospheric E and F1 regions
Ground based ionosonde measurements are the most essential source of information about long-term variations in the ionospheric E and F1 regions. Data of such observations have been derived at many different ionospheric stations all over the world some for more than 50 years. The standard parameters foE, h'E, and foF1 are used for trend analyses in this paper. Two main problems have to be considered in these analyses. Firstly, the data series have to be homogeneous, i.e. the observations should not be disturbed by artificial steps due to technical reasons or changes in the evaluation algorithm. Secondly, the strong solar and geomagnetic influences upon the ionospheric data have carefully to be removed by an appropriate regression analysis. Otherwise the small trends in the different ionospheric parameters cannot be detected. The trends derived at individual stations differ markedly, however their dependence on geographic or geomagnetic latitude is only small. Nevertheless, the mean global trends estimated from the trends at the different stations show some general behaviour (positive trends in foE and foF1, negative trend in h'E) which can at least qualitatively be explained by an increasing atmospheric greenhouse effect (increase of CO2 content and other greenhouse gases) and decreasing ozone values. The positive foE trend is also in qualitative agreement with rocket mass spectrometer observations of ion densities in the E region. First indications could be found that the changing ozone trend at mid-latitudes (before about 1979, between 1979 until 1995, and after about 1995) modifies the estimated mean foE trend.
Sensor-based detection of the severity of hyperkeratosis in the teats of dairy cows
The aim of this study was to evaluate whether the severity of hyperkeratosis (HK) in the teats of dairy cows can be assessed by a dielectric measurement. The study focused on surveying the occurrence of hyperkeratosis in a total of 241 teats of lactating dairy cows. A scoring system consisting of four categories was used to macroscopically assess the severity of HK. Additionally, the dielectric constant (DC) of all teats with milkability was measured in a double iteration with the MoistureMeterD (Delfin Technologies, Kuopio, Finland) on four different days. The Spearman rank correlation coefficient revealed a negative correlation between the DC and HK score (rs = −0.55 to −0.36). The results of the regression analysis showed that the DC values differed significantly between healthy teat ends (≤2) and teat ends with HK (≥3). Thus, the non-invasive measurement of DC provides a promising method of objectively assessing the occurrence and severity of HK.
Reconstructing Late Holocene North Atlantic atmospheric circulation changes using functional paleoclimate networks
Obtaining reliable reconstructions of long-term atmospheric circulation changes in the North Atlantic region presents a persistent challenge to contemporary paleoclimate research, which has been addressed by a multitude of recent studies. In order to contribute a novel methodological aspect to this active field, we apply here evolving functional network analysis, a recently developed tool for studying temporal changes of the spatial co-variability structure of the Earth's climate system, to a set of Late Holocene paleoclimate proxy records covering the last two millennia. The emerging patterns obtained by our analysis are related to long-term changes in the dominant mode of atmospheric circulation in the region, the North Atlantic Oscillation (NAO). By comparing the time-dependent inter-regional linkage structures of the obtained functional paleoclimate network representations to a recent multi-centennial NAO reconstruction, we identify co-variability between southern Greenland, Svalbard, and Fennoscandia as being indicative of a positive NAO phase, while connections from Greenland and Fennoscandia to central Europe are more pronounced during negative NAO phases. By drawing upon this correspondence, we use some key parameters of the evolving network structure to obtain a qualitative reconstruction of the NAO long-term variability over the entire Common Era (last 2000 years) using a linear regression model trained upon the existing shorter reconstruction.
Global gridded crop model evaluation: Benchmarking, skills, deficiencies and implications
Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.