- About NCAR
- Community Resources
- Visitor Programs
- Labs / Programs
- Events / Calendar
- Education & Outreach
- For Staff
Predicting the future is never easy, which is why future-oriented climate research considers multiple alternative scenarios. Since human activities have been influencing the climate, this means that scenarios must estimate how human behavior and societal dynamics might influence future climate change. For instance, in forecasting global climate in 2100, the Intergovernmental Panel on Climate Change (IPCC) reports were based on a spectrum of future possible socioeconomic realities that range from constant population growth and ongoing reliance on fossil fuels to introduction of efficient emissions-reduction technology and population stabilization. An essential part of developing scenarios for how global society will move from today’s reality to the IPCC’s spectrum of potential future-climate end points, however, requires developing realistic storylines – that is, the qualitative descriptions of a future-scenario. This description, or narrative, is used to guide integrated assessment modeling, which provides the quantitative components of socioeconomic scenarios, such as emissions of greenhouse gases. This process of developing qualitative and quantitative components of scenarios is true not just for the IPCC work but also for any research attempting to forecast impacts of broad environmental change.
One could imagine that many possible combinations of storyline variables are possible. Since the interactions of these variables produce particular scenario outcomes, this makes developing different plausible background narratives complex. Further upping the ante in the case of the IPCC storyline creation is the fact that decision makers around the world rely on these scenarios – and therefore on the underlying assumptions – for developing environmental policy. In an award-winning paper published in Environmental Research Letters, Vanessa Schweizer and Elmar Kriegler suggest a systematic methodology for storyline development that investigates very large numbers of scenarios (thousands, millions, or more) to increase the likelihood that scenarios featured in scientific assessment reports represent a comprehensive and realistic set of possible futures.
The duo tested their ideas using the scenarios featured in the IPCC’s Third and Fourth Assessment Reports. These scenarios came from the IPCC Special Report on Emissions Scenarios, and are also known as the SRES scenarios. Schweizer, a post-doctoral researcher at the National Center for Atmospheric Research in the Integrated Science Program and Climate and Global Dynamics division, and Kriegler, a scientist at the Potsdam Institute for Climate Impact Research, evaluated the underlying qualitative and quantitative assumptions of the IPCC SRES scenarios for “internal consistency.” Internal consistency refers to how well a scenario represents dynamics currently understood by the scientific community, and it is necessary for a storyline’s plausibility. For example, a scenario with strong internal consistency might describe a global society that has high wealth, high education levels, and low fertility rates. A global society with high wealth, low education levels, and low fertility rates, however, is less plausible, and therefore does not have high internal consistency.
To assist in their assessment, the researchers turned to the cross-impact balance (CIB) method, a research technique that evaluates relationships between representative variables in a system under study (e.g., the socioeconomic variables of wealth, education level, and fertility in the aforementioned example), Schweizer says. While the authors were interested in assessing how well the SRES scenarios did generally in terms of achieving internal consistency, they hoped that the systematic approach of their study might inform scenario development for the IPCC’s Fifth Assessment Report and other similar environmental change studies that rely on scenarios.
“CIB provides a thorough means of scenario assessment by allowing us to quantify qualitative data and assumptions, such as how dependent energy systems are on fossil fuels, different rates of growth for per capita gross domestic product, and the influence of policy choices,” explains Schweizer. “We were excited to try out this method, since it is able to compare particular futures of interest to a very large set of alternative futures. Such comparisons can help researchers see if particular futures, like the SRES scenarios, were overly or artificially constrained. We were able to investigate this by also looking for any internally consistent scenarios that weren’t represented in the SRES scenarios.”
The research results indicated that while the degree of internal consistency varied among the SRES storylines, the SRES scenarios overall were adequately internally consistent. However, an interesting finding of the analysis, Schweizer says, was that while many ways exist to achieve high-emissions scenarios – such as, increased use of carbon-intensive fuels combined with strong economic and population growth – achieving a low-carbon emissions future required making particularly strong assumptions about global environmental policy. In other words, achieving a low-carbon future would require environmental policies that made the use of fossil fuels unattractive, for example, or supported development of renewable energy capabilities. Lacking such policies, very low-carbon futures became less internally consistent.
Although this finding might appear obvious to some, it was not obvious when the SRES scenarios were developed in the late 1990s. “The SRES scenarios were designed to consider alternative futures in the absence of explicit climate policy. An assumption was made that, potentially, more general environmental policies could also be directed to achieve lower emissions. CIB offered a methodical way to assess the internal consistency of such assumptions in the storylines used to develop the quantitative components of socioeconomic scenarios,” says Schweizer. “Importantly, with CIB, it’s possible to uncover a system’s tendencies through this sort of analysis, which can have important effects both on the outcomes for environmental change studies and policy decisions based on the research results.”