Abstract: Over the last decades modelling of climate change through the analysis of empirical meteorological data has become of great interest. The standard approach gives satisfactory results only in the climatic zones with extreme dynamics of climate change, thus there is need to develop and apply more subtle methods such as fractal analysis and chaotic evolution analysis of the atmospheric system. The scaling analysis of meteorological time series is complicated because of the presence of localized trends and nonstationarities. The objective of this study was to characterize scaling properties (i.e. statistical self-similarity) of the daily air temperature, wind velocity, relative air humidity, global radiation and precipitation through multifractal detrended fluctuation analysis on data from 31 years for stations located in Finland, Germany, Poland and Spain. The empirical singularity spectra indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating considerable differences in dynamics and development. The log-log plots of the cumulative distributions of all the studied absolute and normalized meteorological parameters tended to linear functions for high values of the response, indicating that these distributions were consistent with the power law asymptotic behaviour. Additionally, we investigated the type of multifractality that underlies the q-dependence of the generalized Hurst exponent, by analysing the corresponding shuffled and surrogate time series. The results suggest that MFDFA is valuable for assessing the change of climate dynamics. No Label

Abstract: The aim of the present paper is to exemplify and discuss the importance of farm scale modeling in relation to The EU Joint Programming Initiative (JPI-FACCE) knowledge hub on Agriculture, Food Security and Climate Change project.In particular, livestock production systems include complex interactions, with non-linear relationships between input factors, production, emissions, local climate as well as natural resources (e.g. soil types, rotational land versus permanent grasslands etc.). Moreover, management options pursued by the different types of farmers and other relevant decision makers are important to integrate. Consequently, results of regional scale impact assessments depend on the farming systems model approach, the approach to upscale results, and the inclusion of the relevant stakeholders and decision makers at the scales considered.Different farming systems models are reviewed, including the existing dynamic and static biophysical models. Finally, procedures for upscaling and validity testing of synthesized model results at regional scales are presented. Based on a discussion of these procedures, recommendations for hot-spot analyses in farming systems with regard to integrated climate change adaptation and mitigation for a sustainable food production are synthesized, and the potentials for integration of recommended policies and farm management options into overarching models in order to assess their impact on the regional to global scales are discussed. No Label

Abstract: The report presented here provides an inventory of reports and conference papers  produced by the partners of the livestock and grassland modelling theme (LiveM) of the  Modelling European Agriculture with Climate Change for Food Security (MACSUR)  knowledge hub. The findings presented illustrate the diverse nature of the multidisciplinary  LiveM research community, and provide a reference source for those seeking  to identify and pull out farm-level modelling outputs from the work of MACSUR and its  partners. The survey of farm-scale outputs from LiveM revealed the interdependent, dual  role of a knowledge hub: to increase the capacity of modelling to meet stakeholder and  societal needs under climate change, and to apply that increased capacity to provide new  understanding and solutions at the policy and (the focus here) farm scale. While capacity  building work across disciplines is time-consuming, difficult, and to a large extent invisible  to stakeholders, such work is vital to ensuring that subsequent scientific outcomes reflect  best practice, and integrated expertise. Long term, sustained funding of network-based  capacity building activities is highlighted as essential to ensuring that the farm-scale  modelling work highlighted here can continue to build on ongoing improvements in model  quality, flexibility and stakeholder relevance.