Abstract. Projections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. In this paper, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate a wide range of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the same time, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2 • C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. To serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017-2018 time frame, and output from the climate modelPublished by Copernicus Publications on behalf of the European Geosciences Union.

[1] We describe a new method for evaluating the radiative forcing, the climate feedback parameter (W m À2 K À1) and hence the effective climate sensitivity from any GCM experiment in which the climate is responding to a constant forcing. The method is simply to regress the top of atmosphere radiative flux against the global average surface air temperature change. This method does not require special integrations or off-line estimates, such as for stratospheric adjustment, to obtain the forcing, and eliminates the need for double radiation calculations and tropopause radiative fluxes. We show that for CO 2 and solar forcing in a slab model and an AOGCM the method gives results consistent with those obtained by conventional methods. For a single integration it is less precise but since it does not require a steady state to be reached its precision could be improved by running an ensemble of short integrations.

NATURE CLIMATE CHANGE | ADVANCE ONLINE PUBLICATION | www.nature.com/natureclimatechange 1 D espite two decades of effort to curb emissions of CO 2 and other greenhouse gases (GHGs), emissions grew faster during the 2000s than in the 1990s 1 , and by 2010 had reached ~50 Gt CO 2 equivalent (CO 2 eq) yr −1 (refs 2,3). The continuing rise in emissions is a growing challenge for meeting the international goal of limiting warming to less than 2 °C relative to the pre-industrial era, particularly without stringent climate policies to decrease emissions in the near future 2-4 . As negative emissions technologies (NETs) seem ever more necessary 3,[5][6][7][8][9][10] To have a >50% chance of limiting warming below 2 °C, most recent scenarios from integrated assessment models (IAMs) require large-scale deployment of negative emissions technologies (NETs). These are technologies that result in the net removal of greenhouse gases from the atmosphere. We quantify potential global impacts of the different NETs on various factors (such as land, greenhouse gas emissions, water, albedo, nutrients and energy) to determine the biophysical limits to, and economic costs of, their widespread application. Resource implications vary between technologies and need to be satisfactorily addressed if NETs are to have a significant role in achieving climate goals.options, to be able to decide which pathways are most desirable for dealing with climate change.There are distinct classes of NETs, such as: (1) bioenergy with carbon capture and storage (BECCS) 11,12 ; (2) direct air capture of CO 2 from ambient air by engineered chemical reactions (DAC) 13,14 ; (3) enhanced weathering of minerals (EW) 15 , where natural weathering to remove CO 2 from the atmosphere is accelerated and the products stored in soils, or buried in land or deep ocean [16][17][18][19] ; (4) afforestation and reforestation (AR) to fix atmospheric carbon in biomass and soils [20][21][22] ; (5) manipulation of carbon uptake by the ocean, either

Global efforts to mitigate climate change are guided by projections of future temperatures. But the eventual equilibrium global mean temperature associated with a given stabilization level of atmospheric greenhouse gas concentrations remains uncertain, complicating the setting of stabilization targets to avoid potentially dangerous levels of global warming. Similar problems apply to the carbon cycle: observations currently provide only a weak constraint on the response to future emissions. Here we use ensemble simulations of simple climate-carbon-cycle models constrained by observations and projections from more comprehensive models to simulate the temperature response to a broad range of carbon dioxide emission pathways. We find that the peak warming caused by a given cumulative carbon dioxide emission is better constrained than the warming response to a stabilization scenario. Furthermore, the relationship between cumulative emissions and peak warming is remarkably insensitive to the emission pathway (timing of emissions or peak emission rate). Hence policy targets based on limiting cumulative emissions of carbon dioxide are likely to be more robust to scientific uncertainty than emission-rate or concentration targets. Total anthropogenic emissions of one trillion tonnes of carbon (3.67 trillion tonnes of CO(2)), about half of which has already been emitted since industrialization began, results in a most likely peak carbon-dioxide-induced warming of 2 degrees C above pre-industrial temperatures, with a 5-95% confidence interval of 1.3-3.9 degrees C.