Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.food security | AgMIP | ISI-MIP | climate impacts | agriculture T he magnitude, rate, and pattern of climate change impacts on agricultural productivity have been studied for approximately two decades. To evaluate these impacts, researchers use biophysical process-based models (e.g., refs. 1-5), agro-ecosystem models (e.g., ref. 6), and statistical analyses of historical data (e.g., refs. 7 and 8). Although these and other methods have been widely used to forecast potential impacts of climate change on future agricultural productivity, the protocols used in previous assessments have varied to such an extent that they constrain crossstudy syntheses and limit the ability to devise relevant adaptation options (9, 10). In this project we have brought together seven global gridded crop models (GGCMs) for a coordinated set of simulations of global crop yields under evolving climate conditions. This GGCM intercomparison was coordinated by the Agricultural Model Intercomparison and Improvement Project (AgMIP; 11) as part of the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP; 12). In order to facilitate analyses across models and sectors, all global models are driven with consistent biascorrected climate forcings derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive (13). The objectives are to (i) establish the range of uncertainties of climate change impacts on crop productivity worldwide, (ii) determine key differences in current approaches used by crop modeling groups in global analyses, and (iii) propose improvements in GGCMs and in the methodologies for future intercomparisons to produce more reliable assessments.We examine the basic patterns of response to climate across crops, latitudes, time periods, regional temperatures, and atmospheric carbon dioxide concentrations [CO 2 ]. In anticipation of the wider scientific community using these model outputs and the expanded application of GGCMs, we introduce these models and present guidelines for their pract...

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 ...

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).

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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 impacts projections. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from other human drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the effects of additional warming up to 1.5 °C, including a potential overshoot and long-term effects up to 2299, compared to a no-mitigation scenario (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 but accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and differential bio-energy 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 impacts projections from a range of impact models across different sectors (global and regional hydrology, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, health, and tropical cyclones).

Statistical bias correction is commonly applied within climate impact modeling 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. Thereby the climate signal is modified in a way not necessarily preserving the trend of the original climate model data.

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. However, 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 bias correction 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