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In his article Willems (Clim Chang 120(4):931-944, ) proposed a methodology to analyse extremes in rainfall series. When applying it to artificially generated, non-cyclic random variables we were able to detect cyclic behavior. Therefor we had a closer look on the methodology. Here we discuss our considerations, why this method generates cycles, depending on chosen subperiods and their coherence between detected cycle lengths. To verify these relationships some examples based on random data samples are given. [ABSTRACT FROM AUTHOR]

Climate change impacts are already happening through the world, and it is now clear that there is the need for an adaptive response from global institutions down to the local level. Reducing vulnerability to cope with climate variability might be more challenging in tropical countries than in North America or Europe. The ten papers of this special issue were presented during the Adaptclim conference that was held by the Sinergia Project, the CLARIS LPB project, and the GeoData Institute in Asunción, Paraguay, in 2010. All papers, except one regarding the Brahmaputra Basin in South Asia, present studies from South America. These studies are first contextualized geographically and then are related one to another by a simplified vulnerability concept linking climate stress to sensitivity and adaptive capacity of natural and human systems. One half of the papers focus on actual or future climate change and the present-day causes of the vulnerability of natural and agrosystems. Droughts are and will be the main source of stress for agriculture in South America. Increasing fragmentation of forest of the center of this continent is aggravating their vulnerability to dry spells. Another half of the studies of this special issue deal with the adaptive capacity human populations to system perturbations produced or enhanced by climate change. The studies point out inclusion of traditional knowledge and involvement of local actors in their own vulnerability assessment to increase adaptive capacity. These elements of climate justice, giving voice to those less responsible for carbon emissions but bearing their most severe consequences, allow the particular needs of a community to be considered and can direct adaptation policy toward preserving or rebuilding their specific capabilities under threat from climate change. The special issue also made clear that a basin analysis of the climate change problem could provide information, results, and methods more readily of use for the local population and decision makers. [ABSTRACT FROM AUTHOR]

Studies of climate change impacts on agricultural land use generally consider sets of climates combined with fixed socio-economic scenarios, making it impossible to compare the impact of specific factors within these scenario sets. Analysis of the impact of specific scenario factors is extremely difficult due to prohibitively long run-times of the complex models. This study produces and combines metamodels of crop and forest yields and farm profit, derived from previously developed very complex models, to enable prediction of European land use under any set of climate and socio-economic data. Land use is predicted based on the profitability of the alternatives on every soil within every 10' grid across the EU. A clustering procedure reduces 23,871 grids with 20+ soils per grid to 6,714 clusters of common soil and climate. Combined these reduce runtime 100 thousand-fold. Profit thresholds define land as intensive agriculture (arable or grassland), extensive agriculture or managed forest, or finally unmanaged forest or abandoned land. The demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available. An iteration adjusts prices to meet these constraints. A range of measures are derived at 10' grid-level such as diversity as well as overall EU production. There are many ways to utilise this ability to do rapid What-If analysis of both impact and adaptations. The paper illustrates using two of the 5 different GCMs (CSMK3, HADGEM with contrasting precipitation and temperature) and two of the 4 different socio-economic scenarios ('We are the world', 'Should I stay or should I go' which have contrasting demands for land), exploring these using two of the 13 scenario parameters (crop breeding for yield and population) . In the first scenario, population can be increased by a large amount showing that food security is far from vulnerable. In the second scenario increasing crop yield shows that it improves the food security problem. [ABSTRACT FROM AUTHOR]

Adaptation and mitigation are complementary strategies for addressing the impacts of climate change, yet are often considered separately. This paper examines the literature for evidence of the interactions of adaptation and mitigation measures across the agriculture, biodiversity, coasts, forests, urban and water sectors, focusing on Europe. It found that often adaptation and mitigation synergies and conflicts were not explicitly mentioned within a sector, let alone between sectors. Most measures, however, were found to have an effect on another sector, resulting in neutral, positive (synergies) or negative (conflicts) interactions within and between sectors. Many positive cross-sectoral interactions involved biodiversity or water and thus these could represent good starting places for the implementation of integrated, cross-sectoral strategies. Previous studies suggest that adaptation and mitigation are undertaken on different time and geographical scales; this study found many local scale measures which could facilitate integration between both adaptation and mitigation. It is important that cross-sectoral interaction of adaptation and mitigation measures are explicitly recognised if they are to be mainstreamed into policy, so that positive outcomes are enhanced and unintended consequences avoided. [ABSTRACT FROM AUTHOR]

Scenario development methods get to grips with taking a long-term view on complex issues such as climate change through involvement of stakeholders. Many of the recent (global) scenario exercises have been structured according to a Story-and-Simulation approach. Although elaborately studied, conceptual and practical issues remain in linking qualitative stories and quantitative models. In this paper, we show how stakeholders can directly estimate model parameter values using a three-step approach called Fuzzy Set Theory. We focus on the effect of multiple iterations between stories and models. Results show that we were successful in quickly delivering stakeholder-based quantification of key model parameters, with full consistency between linguistic terms used in stories and numeric values. Yet, values changed strongly from one iteration to the next. A minimum of two and preferably at least three iterations is needed to harmonise stories and models. We conclude that the application of Fuzzy Set Theory enabled a highly valuable, structured and reproducible process to increase consistency between stories and models, but that future work is needed to show its true potential, particularly related to the effect of iterations. Additionally, the number of tools that need to be applied in a short period of time to execute a Story-And-Simulation approach introduces drawbacks that need to be studied. However, an approach such as Story-And-Simulation is indispensable and effective in marrying the perspectives of scientists and other stakeholders when studying complex systems and complex problems. [ABSTRACT FROM AUTHOR]

This paper presents a method for identifying a representative subset of global climate models (GCMs) for use in large-scale climate impact research. Based on objective criteria (GCM performance in reproducing the seasonal cycle of temperature and precipitation, and a subset ability to represent future inter-GCM variability), two candidate subsets are selected from a reference set of 16 GCMs. An additional subset based on subjective expert judgement is also analysed. The representativeness of the three subsets is validated (with respect to the reference set) and compared for future changes in temperature, precipitation and Palmer drought index Z (direct validation), and occurrence of the European corn borer and snow-cover characteristics implemented in the CLIMSAVE Integrated Assessment Platform (indirect validation). The direct validation indicates that one of the objective-based subsets (ECHAM5/MPI-OM, CSIRO-Mk3.0, HadGEM1, GFDL-CM2.1 and IPSL-CM4 models) provides the best choice for the Europe-wide climate change impact study. Its performance is balanced between regions, seasons and validation statistics. However, the expert-judgement-based subset achieved slightly better results in the indirect validation. The differences between the subsets and the reference set are generally much lower for the impact indices compared to their mean (across all GCMs in the subset) changes due to projected climate change. The ranking of the candidate subsets differs between regions, climatic characteristics and seasons, demonstrating that the subset suitability for a specific impact study depends on the target region and the roles of individual seasons and/or climatic variables on the processes being studied. [ABSTRACT FROM AUTHOR]

Integrated cross-sectoral impact assessments facilitate a comprehensive understanding of interdependencies and potential synergies, conflicts, and trade-offs between sectors under changing conditions. This paper presents a sensitivity analysis of a European integrated assessment model, the CLIMSAVE integrated assessment platform (IAP). The IAP incorporates important cross-sectoral linkages between six key European land- and water-based sectors: agriculture, biodiversity, flooding, forests, urban, and water. Using the IAP, we investigate the direct and indirect implications of a wide range of climatic and socio-economic drivers to identify: (1) those sectors and regions most sensitive to future changes, (2) the mechanisms and directions of sensitivity (direct/indirect and positive/negative), (3) the form and magnitudes of sensitivity (linear/non-linear and strong/weak/insignificant), and (4) the relative importance of the key drivers across sectors and regions. The results are complex. Most sectors are either directly or indirectly sensitive to a large number of drivers (more than 18 out of 24 drivers considered). Over twelve of these drivers have indirect impacts on biodiversity, forests, land use diversity, and water, while only four drivers have indirect effects on flooding. In contrast, for the urban sector all the drivers are direct. Moreover, most of the driver-indicator relationships are non-linear, and hence there is the potential for 'surprises'. This highlights the importance of considering cross-sectoral interactions in future impact assessments. Such systematic analysis provides improved information for decision-makers to formulate appropriate adaptation policies to maximise benefits and minimise unintended consequences. [ABSTRACT FROM AUTHOR]