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

Abstract
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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 CO2 concentration and global mean surface temperature derived from the CMIP5 ensemble simulations, we find that cumulative CO2 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.

How to cite.
Aengenheyster, M., Feng, Q. Y., van der Ploeg, F., and Dijkstra, H. A.: The point of no return for climate action: effects of climate uncertainty and risk tolerance, Earth Syst. Dynam., 9, 1085-1095, https://doi.org/10.5194/esd-9-1085-2018, 2018.
1 Introduction
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The Earth system is currently in a state of rapid warming that is unprecedented even in geological records (Pachauri et al., 2014). This change is primarily driven by the rapid increase in atmospheric concentrations of greenhouse gases (GHGs) due to anthropogenic emissions since the industrial revolution (Myhre et al., 2013). Changes in natural physical and biological systems are already being observed (Rosenzweig et al., 2008), and efforts are made to determine the “anthropogenic impact” on particular (extreme weather) events (Haustein et al., 2016). Nowadays, the question is not so much if but by how much and how quickly the climate will change as a result of human interference, whether this change will be smooth or bumpy (Lenton et al., 2008) and whether it will lead to dangerous anthropogenic interference with the climate (Mann, 2009).

The climate system is characterized by positive feedbacks causing instabilities, chaos and stochastic dynamics (Dijkstra, 2013) and many details of the processes determining the future behavior of the climate state are unknown. The debate on action on climate change is therefore focused on the question of risk and how the probability of dangerous climate change can be reduced. In scientific and political discussions, targets on “allowable” warming (in terms of change in global mean surface temperature, GMST, relative to pre-industrial conditions1) have turned out to be salient. The 2 K warming threshold is commonly seen – while gauging considerable uncertainties – as a safe threshold to avoid the worst effects that might occur when positive feedbacks are unleashed (Pachauri et al., 2014). Indeed, in the Paris COP21 conference it was agreed to attempt to limit warming below 1.5 K (United Nations, 2015). It is, however, questionable whether the commitments made by countries (the so-called nationally determined contributions, NDCs) are sufficient to keep temperatures below the 1.5 K and possibly even the 2.0 K target (Rogelj et al., 2016a).

A range of studies has appeared to provide insight into the safe level of cumulative emissions to stay below either the 1.5 or 2.0 K target at a certain time in the future with a specified probability, usually taken as the year 2100. The choice of a particular year is necessarily arbitrary and neglects the possibility of additional future warming. Early studies made use of Earth System Models of Intermediate Complexity (EMICs; Huntingford et al., 2012; Steinacher et al., 2013; Zickfeld et al., 2009) to obtain such estimates. Because it was found that peak warming depends on cumulative carbon emissions, EΣ, but is independent of the emission pathway (Allen et al., 2009; Zickfeld et al., 2012), focus has been on the specification of a safe level of EΣ values corresponding to a certain temperature target. In more recent papers, also emulators derived from either C4MIP models (Sanderson et al., 2016) or CMIP5 (Coupled Model Intercomparison Project 5) models (Millar et al., 2017b), with specified emission scenarios, were used for this purpose. Such a methodology was recently used in Millar et al. (2017a) to argue that a post-2015 value of EΣ≈200 GtC would limit post-2015 warming to less than 0.6 ∘C (so meeting the 1.5 K target) with a probability of 66 %.

In this paper we pose the following question: assume one wants to limit warming to a specific threshold in the year 2100, while accepting a certain risk tolerance of exceeding it, then when, at the latest, does one have to start to ambitiously reduce fossil fuel emissions? The point in time when it is “too late” to act in order to stay below the prescribed threshold is called the point of no return (PNR; van Zalinge et al., 2017). The value of the PNR will depend on a number of quantities, such as the climate sensitivity and the means available to reduce emissions. To determine estimates of the PNR, a model is required of global climate development that (a) is accurate enough to give a realistic picture of the behavior of GMST under a wide range of climate change scenarios, (b) is forced by fossil fuel emissions, (c) is simple enough to be evaluated for a very large number of different emission and mitigation scenarios and (d) provides information about risk, i.e., it cannot be purely deterministic.
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https://www.earth-syst-dynam.net/9/1085/2018/