Water scarcity severely impairs food security and economic prosperity in many countries today. Expected future population changes will, in many countries as well as globally, increase the pressure on available water resources. On the supply side, renewable water resources will be affected by projected changes in precipitation patterns, temperature, and other climate variables. Here we use a large ensemble of global hydrological models (GHMs) forced by five global climate models and the latest greenhouse-gas concentration scenarios (Representative Concentration Pathways) to synthesize the current knowledge about climate change impacts on water resources. We show that climate change is likely to exacerbate regional and global water scarcity considerably. In particular, the ensemble average projects that a global warming of 2°C above present (approximately 2.7°C above preindustrial) will confront an additional approximate 15% of the global population with a severe decrease in water resources and will increase the number of people living under absolute water scarcity (<500 m 3 per capita per year) by another 40% (according to some models, more than 100%) compared with the effect of population growth alone. For some indicators of moderate impacts, the steepest increase is seen between the present day and 2°C, whereas indicators of very severe impacts increase unabated beyond 2°C. At the same time, the study highlights large uncertainties associated with these estimates, with both global climate models and GHMs contributing to the spread. GHM uncertainty is particularly dominant in many regions affected by declining water resources, suggesting a high potential for improved water resource projections through hydrological model development.F reshwater is one of the most vital natural resources of the planet. The quantities that humans need for drinking and sanitation are relatively small, and the fact that these basic needs are not satisfied for many people today is primarily a matter of access to, and quality of, available water resources (1). Much larger quantities of water are required for many other purposes, most importantly irrigated agriculture, but also for industrial use, in particular for hydropower and the cooling of thermoelectric power plants (2, 3). These activities critically depend on a sufficient amount of freshwater that can be withdrawn from rivers, lakes, and groundwater aquifers. Whereas scarcity of freshwater resources already constrains development and societal well-being in many countries (4, 5), the expected growth of global population over the coming decades, together with growing economic prosperity, will increase water demand and thus aggravate these problems (6-8).Climate change poses an additional threat to water security because changes in precipitation and other climatic variables may lead to significant changes in water supply in many regions (6-11). The effect of climate change on water resources is, however, uncertain for a number of reasons. Climate model projections, although rather ...

Abstract. Statistical bias correction is commonly applied within climate impact modelling to correct climate model data for systematic deviations of the simulated historical data from observations. Methods are based on transfer functions generated to map the distribution of the simulated historical data to that of the observations. Those are subsequently applied to correct the future projections. Here, we present the bias correction method that was developed within ISI-MIP, the first Inter-Sectoral Impact Model Intercomparison Project. ISI-MIP is designed to synthesise impact projections in the agriculture, water, biome, health, and infrastructure sectors at different levels of global warming. Bias-corrected climate data that are used as input for the impact simulations could be only provided over land areas. To ensure consistency with the global (land + ocean) temperature information the bias correction method has to preserve the warming signal. Here we present the applied method that preserves the absolute changes in monthly temperature, and relative changes in monthly values of precipitation and the other variables needed for ISI-MIP. The proposed methodology represents a modification of the transfer function approach applied in the Water Model Intercomparison Project (Water-MIP). Correction of the monthly mean is followed by correction of the daily variability about the monthly mean. Besides the general idea and technical details of the ISI-MIP method, we show and discuss the potential and limitations of the applied bias correction. In particular, while the trend and the long-term mean are well represented, limitations with regards to the adjustment of the variability persist which may affect, e.g. small scale features or extremes.

The Inter-Sectoral Impact Model Intercomparison Project offers a framework to compare climate impact projections in different sectors and at different scales. Consistent climate and socio-economic input data provide the basis for a cross-sectoral integration of impact projections. The project is designed to enable quantitative synthesis of climate change impacts at different levels of global warming. This report briefly outlines the objectives and framework of the first, fast-tracked phase of Inter-Sectoral Impact Model Intercomparison Project, based on global impact models, and provides an overview of the participating models, input data, and scenario set-up. multi-sector | climate dataThe Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) fast track took place between January 2012 and January 2013, and was unique in bringing together 28 global impact models from five different sectors (Table 1). During this phase, a common modeling protocol was designed, simulations were performed, and the resulting simulation data were collected in a central archive. Based on these data, an initial round of analysis was carried out, the key outcomes of which are assembled in this special issue of PNAS. The fast-track simulation data will be made freely available for further analysis by the wider research community.The ISI-MIP fast track pursued several specific goals: (i) a quantitative assessment of global climate change impacts at different levels of global warming in a consistent setting across multiple sectors; (ii) basic uncertainty estimates based on the quantification of intermodel variations for both general circulation models (GCMs) and global impact models; and (iii) to initiate an ongoing coordinated impact modeling improvement and intercomparison program, as well as an impact assessment effort driven by the entire community.The central motivation for the project can be summarized by the question: What is the difference between a 2°C, 3°C, and 4°C warmer world, and how well can we differentiate between them?The project builds on earlier climate change risk assessments at the global scale, such as the UK Fast Track project (1), the Climate Impact Response Functions (2) initiative, and the more recent investigation by Arnell et al. (3) covering climate impacts in six sectors (water availability, river flooding, coastal flooding, agriculture, ecosystems, and energy demands) using a coherent set of climatic and socioeconomic scenarios. However, all existing crosssectoral impact studies use only one impact model per sector, and are thus unable to formally assess uncertainties beyond those stemming from climatic and socio-economic input data.In contrast, there are sector-specific multiimpact-model studies, such as Cramer et al. (4) and Sitch et al. (5) in the biomes sector, WaterMIP (6) in the water sector, and AgMIP (7) in the agriculture sector. In this context, ISI-MIP is intended to address the lack of a cross-sectoral multimodel assessment of impacts of climate change.The project serves the dual purpose ...

Future climate change and increasing atmospheric CO 2 are expected to cause major changes in vegetation structure and function over large fractions of the global land surface. Seven global vegetation models are used to analyze possible responses to future climate simulated by a range of general circulation models run under all four representative concentration pathway scenarios of changing concentrations of greenhouse gases. All 110 simulations predict an increase in global vegetation carbon to 2100, but with substantial variation between vegetation models. For example, at 4°C of global land surface warming (510-758 ppm of CO 2 ), vegetation carbon increases by 52-477 Pg C (224 Pg C mean), mainly due to CO 2 fertilization of photosynthesis. Simulations agree on large regional increases across much of the boreal forest, western Amazonia, central Africa, western China, and southeast Asia, with reductions across southwestern North America, central South America, southern Mediterranean areas, southwestern Africa, and southwestern Australia. Four vegetation models display discontinuities across 4°C of warming, indicating global thresholds in the balance of positive and negative influences on productivity and biomass. In contrast to previous global vegetation model studies, we emphasize the importance of uncertainties in projected changes in carbon residence times. We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151% for non-HYBRID4 models. A change in research priorities away from production and toward structural dynamics and demographic processes is recommended.errestrial vegetation is central to many components of the coupled Earth system, in particular the global carbon cycle, biophysical land-atmosphere exchanges, atmospheric chemistry, and the diversity of life with the numerous ecosystem services this engenders. However, vegetation is very sensitive to climate and levels of atmospheric CO 2 , the primary substrate for plant growth. Therefore, it is imperative that we are capable of anticipating the potential responses of global terrestrial vegetation to future changes in climate and atmospheric chemistry. However, a comprehensive, consistent analysis of impacts, taking into account uncertainty in both climate models and impacts models, has so far been lacking. The recent availability of representative concentration pathway (RCP)-driven climate model simulations, with bias-corrected outputs produced within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) (1), allows such an analysis.Vegetation biomass, productivity, and the competitive abilities of different plant types are all influenced by climate and atmospheric CO 2 . Higher temperatures will increase growing season lengths, metabolic rates, and rates of nitrogen mineralization at high latitudes and altitudes, there...

Abstract. 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 "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". 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 longterm impacts up to 2299, and comparison to higher levels of global mean temperature change (based on the lowemissions 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).