The effect of climate–carbon cycle feedbacks on emission metrics

Sterner, Erik; Johansson, Daniel

doi:10.1088/1748-9326/aa61dc

Cited by 19 publications

(19 citation statements)

References 34 publications

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“…Note that emission metrics can also be estimated with more complex model simulations (e.g. Tanaka et al, 2009;Sterner and Johansson, 2017), with the strong caveat that the approach lacks the simplicity and transparency of the IRFs. Now let us illustrate the typical formulation of the simple IRF-based model of the climate change induced by a given species (x).…”

Section: Impulse Response Functionsmentioning

confidence: 99%

“…However, this is at the expense of the simplicity and transparency that are characteristic of the impulse response functions. For the climate-carbon feedback, Sterner and Johansson (2017) recently proposed a first model-based estimate. Their results show the same difference in physical behaviour when compared to Collins et al (2013) as ours, therefore strengthening our conclusions as to the need to update the IPCC metrics' estimates.…”

Section: Conceptual Aspectsmentioning

confidence: 99%

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Accounting for the climate–carbon feedback in emission metrics

et al. 2017

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Abstract. Most emission metrics have previously been inconsistently estimated by including the climatecarbon feedback for the reference gas (i.e. CO 2 ) but not the other species (e.g. CH 4 ). In the fifth assessment report of the IPCC, a first attempt was made to consistently account for the climate-carbon feedback in emission metrics. This attempt was based on only one study, and therefore the IPCC concluded that more research was needed. Here, we carry out this research. First, using the simple Earth system model OSCAR v2.2, we establish a new impulse response function for the climate-carbon feedback. Second, we use this impulse response function to provide new estimates for the two most common metrics: global warming potential (GWP) and global temperature-change potential (GTP). We find that, when the climate-carbon feedback is correctly accounted for, the emission metrics of non-CO 2 species increase, but in most cases not as much as initially indicated by IPCC. We also find that, when the feedback is removed for both the reference and studied species, these relative metric values only have modest changes compared to when the feedback is included (absolute metrics change more markedly). Including or excluding the climate-carbon feedback ultimately depends on the user's goal, but consistency should be ensured in either case.

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Section: Impulse Response Functionsmentioning

confidence: 99%

Section: Conceptual Aspectsmentioning

confidence: 99%

Accounting for the climate–carbon feedback in emission metrics

et al. 2017

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“…The amount of atmospheric CO 2 increase due to El Nino events has been the subject of several investigations using observations and climate models with an incorporated carbon cycle, including expected El Nino changes, in some models, under an increasing atmospheric CO 2 (Keeling et al 1995, Meehl and Washington 1996, Jones et al 2001, Richards 2013, Christensen et al 2013, Cox et al 2013, Taschetto et al 2014, Kim et al 2016, Sterner and Johansson 2017. Most of these analyses assume a single El Nino type.…”

Section: Introductionmentioning

confidence: 99%

The carbon cycle response to two El Nino types: an observational study

Chýlek

Tans

Christy

et al. 2018

Environ. Res. Lett.

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We analyze monthly tropical near surface air temperature and Mauna Loa Observatory carbon dioxide (CO 2 ) data within 1960-2016 to identify different carbon cycle responses for two El Nino types: El Ninos originating in the central tropical Pacific (CP El Nino) and El Ninos originating in the eastern tropical Pacific (EP El Nino). We find significant differences between the two types of El Nino events with respect to time delay of the CO 2 rise rate that follows the increase in tropical near surface air temperatures caused by El Nino events. The average time lag of the CP El Nino is 4.0 ± 1.7 months, while the mean time lag of EP El Nino is found to be 8.5 ± 2.3 months. The average lag of all considered 1960-2016 El Ninos is 5.2 ± 2.7 months. In contrast the sensitivity of the CO 2 growth rate to tropical near surface air temperature increase is determined to be about the same for both El Nino types equal to 2.8 ± 0.9 ppm yr −1 K −1 (or 5.9 ± 1.9 GtC yr −1 K −1 ). Our results should be useful for the understanding of the carbon cycle and constraining it in climate models.

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“…However, we added more elaborate information about the CO 2 uptake, which was given the same attention as the emissions. For the first period of the graphs (i.e., 1900-2015), the CO 2 emissions and uptake values were produced using a simple climate model (Sterner and Johansson 2017), which simulates the carbon cycle response. For this, widely used "historic emissions" that give a realistic impression were used (Meinshausen et al 2011).…”

Section: Methodsmentioning

confidence: 99%

Knowing how and knowing when: unpacking public understanding of atmospheric CO2 accumulation

et al. 2019

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It has been demonstrated that most people have a limited understanding of atmospheric CO 2 accumulation. Labeled stock-flow (SF) failure, this phenomenon has even been suggested as an explanation for weak climate policy support. Drawing on a typology of knowledge, we set out to nuance previous research by distinguishing between different types of knowledge of CO 2 accumulation among the public and by exploring ways of reasoning underlying SF failure. A mixed methods approach was used and participants (N = 214) were enrolled in an open online course. We find that ostensibly similar SF tasks show seemingly contradictory results in terms of people's understanding of CO 2 accumulation. Participants performed significantly better on stock stabilization tasks that explicitly ask about the relationship between stocks and flows, compared with a typical SF task that does not direct the participants' attention to what knowledge they should use. This suggests that people possess declarative and procedural knowledge of accumulation (knowing about the principles of mass balance, i.e., what and how to use them) but lack conditional knowledge of accumulation (knowing when to use these principles). Additionally, through a thematic analysis of answers to an open-ended question, we identified three overarching ways of reasoning when dealing with SF tasks: system, pattern, and phenomenological reasoning, providing additional theoretical insights to explain the large difference in performance between the different SF tasks. These more nuanced perspectives on SF failure can help inform interventions aimed at increasing climate science literacy and point to the need for more detailed explorations of public knowledge needed to leverage climate policy support.

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The effect of climate–carbon cycle feedbacks on emission metrics

Cited by 19 publications

References 34 publications

Accounting for the climate–carbon feedback in emission metrics

Accounting for the climate–carbon feedback in emission metrics

The carbon cycle response to two El Nino types: an observational study

Knowing how and knowing when: unpacking public understanding of atmospheric CO2 accumulation

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