5
Representative Concentration Pathways and Socioeconomic Scenarios and Narratives

John Weyant introduced the session, which was a discussion of representative concentration pathways (RCPs) from the perspective of the integrated assessment modeling (IAM) community. He said that the IAM community and individual IAM teams offer some frames for socioeconomic scenarios. Expressing concern that people outside the community would expect perfect synchrony among efforts, he suggested that a more appropriate standard of comparison is with past efforts. He noted that the workshop has been characterized by constructive comment.

CHARACTERISTICS, USES, AND LIMITS OF REPRESENTATIVE CONCENTRATION PATHWAYS1

Jae Edmonds


Jae Edmonds said that the RCPs were developed to quickly deliver emissions data to the climate modeling community. Taken from the open literature, they were to provide a selection of pathways leading to different degrees of climate forcing to be used as inputs to climate models, story lines, and impact, adaptation, and vulnerability (IAV) analysis. The RCPs are presented in radiative forcing units (w/m2) from greenhouse



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5 Representative Concentration Pathways and Socioeconomic Scenarios and Narratives John Weyant introduced the session, which was a discussion of rep- resentative concentration pathways (RCPs) from the perspective of the integrated assessment modeling (IAM) community. He said that the IAM community and individual IAM teams offer some frames for socioeco - nomic scenarios. Expressing concern that people outside the community would expect perfect synchrony among efforts, he suggested that a more appropriate standard of comparison is with past efforts. He noted that the workshop has been characterized by constructive comment. CHARACTERISTICS, USES, AND LIMITS OF REPRESENTATIVE CONCENTRATION PATHWAYS1 Jae Edmonds Jae Edmonds said that the RCPs were developed to quickly deliver emissions data to the climate modeling community. Taken from the open literature, they were to provide a selection of pathways leading to dif- ferent degrees of climate forcing to be used as inputs to climate models, story lines, and impact, adaptation, and vulnerability (IAV) analysis. The RCPs are presented in radiative forcing units (w/m2) from greenhouse 1 Edmonds’s presentation is available at http://www7.nationalacademies.org/hdgc/ Characteristics_Uses_ and_Limits_of_the_RCPs_Presentation_by_Jae_Edmonds.pdf [No - vember 2010]. 

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0 DESCRIBING SOCIOECONOMIC FUTURES FOR CLIMATE CHANGE gases (GHGs) and aerosols but not including forcing from albedo changes or from mineral dust. The four RCPs are known by their levels of forcing in 2100: 8.5, 6, 4.5, and 3 (in w/m2). Each was developed by a different research group using a different model, and thus the socioeconomic sce - narios do not constitute a set, for example, with population or GDP of the highest RCP being the highest for those variables when compared with the other RCPs. All will start from the same historical baseline in 2000 (RCP 6 was still being harmonized at the time of the workshop). 2 The RCPs are designed for climate modelers and therefore include the full suite of relevant gases, aerosols, and land use and land cover. They are downscaled to 0.5 degree, and scenarios are extended in a stylized way to 2300 to allow for climate model research on equilibrium behavior of the climate system. They are consistent with data back to 1850 for gases and land cover to 1700. They have data for over a dozen sectors. Edmonds emphasized that the RCPs were selected to bound a wide range of pos- sible future forcing characteristics over time, not to bound socioeconomic uncertainty. Emissions trajectories for the RCPs are openly available, but the underlying socioeconomic data are not yet available. Edmonds said that some in the community want to make more data available than went into the RCPs, so as not to overemphasize the RCPs. The research teams are trying to create additional socioeconomic scenarios, called replica- tion ensembles, which would yield the same end states that their models produced. Noting that the drivers are not all downscaled in the RCPs, Edmonds emphasized that many different socioeconomic scenarios are consistent with any of these levels of forcing. Even the 2.6 results can be reached from any socioeconomic scenario—if the requisite policies are adopted. During the parallel phase of the process, while the climate modeling groups are conducting climate model experiments, the IAM community is producing new socioeconomic scenarios, with alternative backgrounds, different technology availability regimes, and alternative shapes of the emissions pathways leading to the same end points—including even 2.6, which can be produced in various ways. An open question is what range of socioeconomic scenarios should be explored in the IAMs. It may be that the 8.5 scenario requires a more tightly specified socioeconomic future than the 2.6 scenario does. Edmonds concluded by noting that the new scenarios process could provide an embarrassment of riches—hundreds of scenarios with dif - ferent combinations of socioeconomic and climate changes. The RCPs 2They are documented in Moss et al. (2010), and data can be found at http://www.iiasa. ac.at/web-apps/tnt/RcpDb/ [November 2010].

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 REPRESENTATIVE CONCENTRATION PATHWAYS provide detail down to 0.5 degree for drivers and are well documented. But because they come from four different models, they do not share a common set of reference assumptions from a socioeconomic perspective. There are socioeconomic circumstances that some models do not cover, and, in some respects, they are incompatible. For example, the 4.5 RCP has an increase in forests; the 2.6 RCP has an increase in crops and pastures. In the models, technology for crop productivity has as much effect on climate as energy technology, through effects on land use. In the discussion, Gary Yohe suggested that if there are many models with many drivers, a decision analyst could determine which drivers are most important to influence, in order to affect forcing. Edmonds pointed out that models would have to be comparable to be used in that way. Anthony Janetos noted that no one has yet investigated whether downscaled climate models are consistent with multiple socioeconomic scenarios at the same scale. MULTIMODEL ANALYSIS OF KEY ASSUMPTIONS UNDERLYING REPRESENTATIVE CONCENTRATION PATHWAYS Tom Kram Tom Kram presented a quantitative comparison of the RCPs prepared by Detlef Van Vuuren, who could not be present. Each RCP is internally consistent, and each could be used to drive climate models as a basis for impact assessment (if information is included to indicate exposure and other impact-related variables) and for mitigation analysis (again, with additional information, such as baselines, targets, and assumptions about technology and governance). A crucial question for creating additional scenarios is how strongly assumptions about socioeconomic change are correlated with outcomes. Kram examined this question by comparing scenarios, adding in newly published material and material from the RCP groups. The results have five implications: (1) There is very little correlation between population assumptions and radiative forcing—any reasonable population scenario could coexist with almost any of the emission outcomes. (2) The full range of estimates of gross domestic product (GDP) is consistent with all the end points for forcing, except that the 2.6 scenario was consistent only with the lower part of the GDP range. (3) Primary energy consumption is related to emissions, in that the only scenarios that result in 8.5 w/m 2 involve burning large amounts of fossil fuels. (4) CO2 emissions are closely corre- lated with radiative forcing—the 2.6 scenario requires net carbon storage by the end of the century. (5) Forest cover varies considerably across the models more than with radiative forcing level, suggesting that this result

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 DESCRIBING SOCIOECONOMIC FUTURES FOR CLIMATE CHANGE is sensitive to model assumptions. Together, the models do not cover all the plausible futures. Kram said that, in order to use the scenarios to study IAV questions, it would be necessary to map levels of vulnerability against the climate signal given by the scenarios. It might be very useful to analyze two or three levels of vulnerability against several strengths of climate signal. A different approach is needed for mitigation, Kram said, and the mitigation community may want to examine whether particular RCPs are consistent with certain assumptions about technology or about potential interna- tional agreements. In summary, Kram said that these RCPs, especially the middle ones, are consistent with a very broad range of futures in terms of key socioeco- nomic variables. This suggests that looking at a small set of socioeconomic scenarios might be very useful in IAV research. In the discussion, the following issues were raised: • he most recent scenarios tend not to cover the full socioeconomic T space. For example, most of the estimates of population are near the UN median projection. • he conclusions about low correlations are based only on a first T pass and reflect a selection bias in the scenarios and the lack of examination of interactions in which some combinations of eco- nomic assumptions are inconsistent with some pathways. • here is debate in the community about whether any 2.6 scenario is T reasonable. That scenario was created as an “only-if” scenario—the idea was that 2.6 could be reached only if all the assumptions in the scenario are met. • he potential to change net emissions with agricultural technology T and increased uptake of carbon in soils may not have been fully examined in all the models. • nteraction between the IAM and IAV communities will be needed I to consider interaction effects and to develop skeletally described scenarios for the RCP forcing levels for the IAV community. Such analyses would need conceptual design, for example, to decide on how to focus on particular vulnerable groups. • An econometric approach might be used to capture interactions. • any scenarios cannot reach 2.6. It might be useful to develop a M 3.7 scenario—a value that is in a lot of other analyses. Second-best solutions are important for reaching 3.7. • s it sensible to put probabilities on the scenarios? This has been I tried with simpler models, for example, using multimodel analysis of key assumptions underlying the RCPs Monte Carlo analysis.