Articles | Volume 8, issue 3
Earth Syst. Dynam., 8, 707–717, 2017
https://doi.org/10.5194/esd-8-707-2017
Earth Syst. Dynam., 8, 707–717, 2017
https://doi.org/10.5194/esd-8-707-2017

Research article 09 Aug 2017

Research article | 09 Aug 2017

On determining the point of no return in climate change

Brenda C. van Zalinge, Qing Yi Feng, Matthias Aengenheyster, and Henk A. Dijkstra Brenda C. van Zalinge et al.
  • Institute for Marine and Atmospheric Research Utrecht, Department of Physics, Utrecht University, Utrecht, the Netherlands

Abstract. Earth's global mean surface temperature has increased by about 1.0 °C over the period 1880–2015. One of the main causes is thought to be the increase in atmospheric greenhouse gases. If greenhouse gas emissions are not substantially decreased, several studies indicate that there will be a dangerous anthropogenic interference with climate by the end of this century. However, there is no good quantitative measure to determine when it is too late to start reducing greenhouse gas emissions in order to avoid such dangerous interference. In this study, we develop a method for determining a so-called point of no return for several greenhouse gas emission scenarios. The method is based on a combination of aspects of stochastic viability theory and linear response theory; the latter is used to estimate the probability density function of the global mean surface temperature. The innovative element in this approach is the applicability to high-dimensional climate models as demonstrated by the results obtained with the PlaSim model.

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Short summary
The increase in atmospheric greenhouse gases (GHGs) is one of the main causes for the increase in global mean surface temperature. There is no good quantitative measure to determine when it is too late to start reducing GHGs in order to avoid dangerous anthropogenic interference. We develop a method for determining a so-called point of no return (PNR) for several GHG emission scenarios. The innovative element in this approach is the applicability to high-dimensional climate models.
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