In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a <q>special report in 2018 on the impacts of global warming of 1.5 °C  above pre-industrial levels and related global greenhouse gas emission pathways</q>. In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from impacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the impacts of additional warming up to 1.5 °C , including a potential overshoot and long-term impacts up to 2299, and comparison to higher levels of global mean temperature change (based on the low-emissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the climate effects based on the same climate scenarios while accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and in particular differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0. With the aim of providing the scientific basis for an aggregation of impacts across sectors and analysis of cross-sectoral interactions that may dampen or amplify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact models across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiversity).

Estimating the current risk of coastal flooding requires adequate information on extreme sea levels. For over a decade, the only global data available was the DINAS-COAST Extreme Sea Levels (DCESL) dataset, which applies a static approximation to estimate extreme sea levels. Recently, a dynamically derived dataset was developed: the Global Tide and Surge Reanalysis (GTSR) dataset. Here, we compare the two datasets. The differences between DCESL and GTSR are generally larger than the confidence intervals of GTSR. Compared to observed extremes, DCESL generally overestimates extremes with a mean bias of 0.6 m. With a mean bias of −0.2 m GTSR generally underestimates extremes, particularly in the tropics. The Dynamic Interactive Vulnerability Assessment model is applied to calculate the present-day flood exposure in terms of the land area and the population below the 1 in 100-year sea levels. Global exposed population is 28% lower when based on GTSR instead of DCESL. Considering the limited data available at the time, DCESL provides a good estimate of the spatial variation in extremes around the world. However, GTSR allows for an improved assessment of the impacts of coastal floods, including confidence bounds. We further improve the assessment of coastal impacts by correcting for the conflicting vertical datum of sea-level extremes and land elevation, which has not been accounted for in previous global assessments. Converting the extreme sea levels to the same vertical reference used for the elevation data is shown to be a critical step resulting in 39–59% higher estimate of population exposure.

Social-ecological systems (SES) are dynamic systems that continuously change in response to internal or external pressures. A better understanding of the interactions of the social and ecological systems that drive those dynamics is crucial for the development of sustainable management strategies. Dynamic models can serve as tools to explore social-ecological interactions; however, the complexity of the studied systems and the need to integrate knowledge, theories, and approaches from different disciplines pose considerable challenges for their development. We assess the potential of Ostrom’s general SES framework (SESF) to guide a systematic and transparent process of model development in light of these difficulties. We develop a stepwise procedure for applying SESF to identify variables and their relationships relevant for an analysis of the SES. In doing so we demonstrate how the hierarchy of concepts in SESF and the identification of social-ecological processes using the newly introduced process relationships can help to unpack the system in a systematic and transparent way. We test the procedure by applying it to develop a dynamic model of decision making in the management of recreational fisheries. The added value of the common framework lies in the guidance it provides for (1) a structured approach to identifying major variables and the level of detail needed, and (2) a procedure that enhances model transparency by making explicit underlying assumptions and choices made when selecting variables and their interactions as well as the theories or empirical evidence on which they are based. Both aspects are of great relevance when dealing with the complexity of SES and integrating conceptual backgrounds from different disciplines. We discuss the advantages and difficulties of the application of SESF for model development, and contribute to its further refinement.

In this paper we compare 10 established frameworks for analyzing social-ecological systems. We limited ourselves to frameworks that were explicitly designed to be used by a wider community of researchers and practitioners. Although all these frameworks seem to have emerged from the need for concepts that permit structured, interdisciplinary reasoning about complex problems in social-ecological systems, they differ significantly with respect to contextual and structural criteria, such as conceptualization of the ecological and social systems and their interrelation. It appears that three main criteria suffice to produce a classification of frameworks that may be used as a decision tree when choosing a framework for analysis. These criteria are (i) whether a framework conceptualizes the relationship between the social and ecological systems as being uni- or bidirectional; (ii) whether it takes an anthropocentric or an ecocentric perspective on the ecological system; and (iii) whether it is an action-oriented or an analysis-oriented framework.