The point of no return for climate action: effects of climate uncertainty and risk tolerance

Abstract

If the Paris Agreement targets are to be met, there may be very few years left for policy makers to start cutting emissions. Here we calculate by what year, at the latest, one has to take action to keep global warming below the 2 K target (relative to pre-industrial levels) at the year 2100 with a 67 % probability; we call this the point of no return (PNR). Using a novel, stochastic model of CO 2 concentration and global mean surface temperature derived from the CMIP5 ensemble simulations, we find that cumulative CO 2 emissions from 2015 onwards may not exceed 424 GtC and that the PNR is 2035 for the policy scenario where the share of renewable energy rises by 2 % year −1 . Pushing this increase to 5 % year −1 delays the PNR until 2045. For the 1.5 K target, the carbon budget is only 198 GtC and there is no time left before starting to increase the renewable share by 2 % year −1 . If the risk tolerance is tightened to 5 %, the PNR is brought forward to 2022 for the 2 K target and has been passed already for the 1.5 K target. Including substantial negative emissions towards the end of the century delays the PNR from 2035 to 2042 for the 2 K target and to 2026 for the 1.5 K target. We thus show how the PNR is impacted not only by the temperature target and the speed by which emissions are cut but also by risk tolerance, climate uncertainties and the potential for negative emissions. Sensitivity studies show that the PNR is robust with uncertainties of at most a few years.

Complementing CO<sub>2</sub> emission reduction by solar radiation management might strongly enhance future welfare

“…Our climate model is based on linear response theory and although this approach captures many climate feedbacks adequately, it does not capture the possible dependence of the response on the background state, e.g. a saturation of carbon sinks (Aengenheyster et al, 2018).…”

“…We replace the carbon-climate part of DSICE by an emulator of full-fledged climate model simulations (Aengenheyster et al, 2018;MacMartin and Kravitz, 2016). We also include global mean precipitation as a proxy for the residual climate change (changes remaining if SRM is employed to keep global mean temperature constant).…”

“…The coefficients ψ (for values see Table 1) are chosen such that for the standard case of 2.5 K warming in equilibrium without SRM, our total damage equals that of the original DICE model, and the contributions by T , P , and C are 60 %, 30 %, and 10 %, respectively. The standard case was determined by running the climate module of GeoDICE to equilibrium with constant greenhouse-gas concentrations, such as to obtain T = 2.5 K. Following Aengenheyster et al (2018), and approximately in agreement with RCP (representative concentration pathway) scenarios, it was assumed that other forcing agents (other greenhouse gases and aerosols constituting F other ) contribute 14 % to the total radiative forcing. These other forcing agents are not assumed to cause direct damage.…”

“…Several SRM techniques have been proposed (Latham et al, 2008;Ahlm et al, 2017;Gabriel et al, 2017;Seneviratne et al, 2018), although for some of them it is yet unknown whether they will be effective in cooling the planet and whether they will be technically feasible. The scheme that is most likely to become ripe for employment in the near future is sulfate-aerosol-based SRM (McClellan et al, 2010;Moriyama et al, 2017).…”

Predicting Climate Change through Response Operators in a Coupled General Circulation Model

“…Capturing the slow oceanic processes is essential for a correct representation of the multidecadal and long-time climatic response. Encouragingly, response theory has recently been shown to have a great potential for predicting climate change in multi-model ensembles of CMIP5 atmosphere-ocean coupled GCMs outputs [31]. Blending together data coming from different models is outside response theory theoretical framework, yet heuristically justifiable.…”