On determining the Point of no Return in Climate Change

Zalinge, Brenda C. van; Feng, Qu; Dijkstra, Henk A.

doi:10.5194/esd-2016-40

Cited by 2 publications

(3 citation statements)

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“…The second method consists of deriving the linear response function from a step response, i.e. the response to a Heavisidetype perturbation (e.g., Hasselmann et al, 1993;Ragone et al, 2016;MacMartin and Kravitz, 2016;Lucarini et al, 2017;Van Zalinge et al, 2017;Aengenheyster et al, 2018). We call it here step method.…”

Section: Second Complication: Nonlinearitymentioning

confidence: 99%

“…Reick (2002) and Lucarini (2009) used a weak periodic forcing to derive response functions in the Fourier space (also called susceptibilities). Hasselmann et al (1993), Ragone et al (2016), MacMartin and Kravitz (2016), Lucarini et al (2017), Van Zalinge et al (2017), Aengenheyster et al (2018) and Bódai et al (2020) identify the linear response function using step experiments, where the perturbation is a Heaviside-type function.…”

Section: Introductionmentioning

confidence: 99%

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Comment on npg-2021-9

2021

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Existent methods to identify linear response functions from data require tailored perturbation experiments, e.g. impulse or step experiments. And if the system is noisy, these experiments need to be repeated several times to obtain a good statistics. In contrast, for the method developed here, data from only a single perturbation experiment at arbitrary perturbation is sufficient if in addition data from an unperturbed (control) experiment is available. To identify the linear response function for this ill-posed problem we invoke regularization theory. The main novelty of our method lies in the determination of the level of background noise needed for a proper estimation of the regularization parameter: This is achieved by comparing the frequency spectrum of the perturbation experiment with that of the additional control experiment. The resulting noise level estimate can be further improved for linear response functions known to be monotonic. The robustness of our method and its advantages are investigated by means of a toy model. We discuss in detail the dependence of the identified response function on the quality of the data (signal-to-noise ratio) and on possible nonlinear contributions to the response. The method development presented here prepares in particular for the identification of carbon-cycle response functions in Part II of this study. But the core of our method, namely our new approach to obtain the noise level for a proper estimation of the regularization parameter, may find applications in solving also other types of linear ill-posed problems.

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Section: Second Complication: Nonlinearitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comment on npg-2021-9

2021

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“…For 205 example the case ∆T max = 2 K and β = 0.9 corresponds to a 90% probability of staying below 2 K Then, in the context of (11), the PNR is the earliest t s that does not result in reaching the defined 'Safe State' (van Zalinge et al, 2017) in terms of ∆T max and β. It is determined from the probability 210 distribution p(∆T 2100 ) of GMST in 2100.…”

Section: Point Of No Returnmentioning

confidence: 99%

Risk and the Point of No Return for Climate Action

Aengenheyster

Feng

Ploeg

et al. 2018

Preprint

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Abstract. If the Paris targets are to be met, there may be very few years left for policy makers to start cutting emissions. Here, we ask by what year at the latest one has to take action to keep global warming below the 2 K target (relative to preindustrial 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, 5 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% per year.Pushing this increase to 5% per year 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% per year. If the risk tolerance is tightened to 5%, the PNR is brought forward to 2022 for the 10 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 and to 2026 for the 1.5 K target, respectively. We thus show the impact on the PNR not only of the temperature target and the speed by which emissions are cut, but also of risk tolerance, climate uncertainties and the potential for negative emissions.

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On determining the Point of no Return in Climate Change

Cited by 2 publications

References 10 publications

Comment on npg-2021-9

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Risk and the Point of No Return for Climate Action

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