Quantitative Scenarios Can Help Identify Arctic Research Needs

The Issue

Policy responses to rapid change must consider complex combinations of social, environmental, political, and economic factors that can determine the future state of the Arctic. Quantitative scenarios offer a transparent and replicable approach to determining plausible combinations of factors that may result in different possible futures.

Why It Matters

Policy decisions need to be informed by science that is not tucked away in the ivory tower but, rather, reflects real-world problems. Planning relevant research in a rapidly changing world—of which the Arctic is the epitome—is important to the well-being of society and the ecosystems. Forecasting is difficult, and best guesses are inadequate, but quantitative scenarios can systematically develop robust, plausible, and consistent pictures of multiple possibilities. This means research can be linked to different potential outcomes and agile policy strategies can be developed to account for more than one possible future state of the Arctic.

State of Knowledge

Figure 1: The future is influenced by many different factors. Precise predictions are difficult. Hence, it is more suitable to consider various scenarios.Figure 1: The future is influenced by many different factors. Precise predictions are difficult. Hence, it is more suitable to consider various scenarios.
Scenarios are pictures of the future that form 'a framework for what if–ing that stresses the importance of multiple views of the future in exchanging information about uncertainty among parties to a decision' [1]. In contrast to forecasts—which narrow possibilities to the most likely—scenarios are designed to span the range of possibilities. In this way scenarios are also not visions of what we would like to have happen, but prepare us to respond to multiple possible circumstances.

Scenario techniques are well established as part of the futurist's toolkit [e.g., 1, 2]. There are many different nuances of scenario techniques, but broadly, these can be differentiated as either being qualitative or quantitative. Qualitative scenarios are generally based on the judgment, experiences, and creativity of one or more people. Quantitative scenarios offer a deeper analysis of underlying factors driving the future based on repeatable research, scoring, and calculation. In the best case, quantitative scenarios will also ensure that biases and opinions of project participants are either completely removed or significantly buffered by designing scoring processes such that the futurist may not be able to merely 'engineer' their most likely scenario. One quantitative scenario method is the Robustness-Analysis [3], an elaboration of the Consistency-Analysis [4]. The Robustness-Analysis relies on an iterative process of collecting data through workshops and research to determine the Key Factors influencing the future state of—in this case—the Arctic. Quantifying combinations of the Key Factors allows projections of the possible future states (Future Projections). This process is open to diverse sources of expertise that allows a wider range of possible Key Factors, their Future Projections and the detection of "wild cards", or "black swans" – the unlikely but system transforming events that may be missed by too narrow of a focus or lack of knowledge. A Key Factor should lead to two to five Future Projections. Quantitative scenarios result from scoring the plausibility that a Future Projection will occur relative to all Future Projections of a given Key Factor and scoring the pairwise consistency of Future Projections from different Key Factors (Fig. 2). For example, projecting year-round heaving sea ice is not consistent with projecting increased Arctic shipping. Thus, each scenario for the future that is produced is a combination of possible future projections. Each scenario thus has a plausibility score, a consistency score, and robustness score, which directly denote the scenarios quality.

Figure 2: Two example key factors (Arctic shipping and Sea ice conditions) with their respective future projections and consistency scores shown. Scoring ranges from -2 to +2 with the former meaning entirely inconsistent future projections – these two futures cannot exist at the same time in the same place - and the latter indicating entirely consistent, in some cases dependent, relationships between the two future projections. Numbers between these two endpoints indicate some possibility of consistencyFigure 2: Two example key factors (Arctic shipping and Sea ice conditions) with their respective future projections and consistency scores shown. Scoring ranges from -2 to +2 with the former meaning entirely inconsistent future projections – these two futures cannot exist at the same time in the same place - and the latter indicating entirely consistent, in some cases dependent, relationships between the two future projections. Scores are combined in order to create possible future scenarios that can be evaluated and ranked relative to each other. All possible combinations of Future Projections for all Key Factors are assessed in terms of plausibility. Thus, a hypothetical "most plausible" overall future would include all Future Projections (one for each Key Factor) that received the highest plausibility scores, when viewed independently. However, this "most plausible" future is not necessarily internally consistent, given that Key Factors are not actually entirely independent of one another. Thus, the method also assesses consistency. Every possible combination of Future Projections (for pairs of Future Projections from different Key Factors) has a pairwise consistency score assigned based on the project participants' input and other pertinent research. The "most consistent" future would the one with the highest score based on all the pairwise consistency values described above. Finally, plausibility and consistency are combined into one score for "robustness". "Robust" sets of Key Factor Future Projections scored relatively highly in both consistency and plausibility. In other words, the "most robust" model output tells a story about a future that is both internally consistent and reasonably plausible in all its component parts.

Because scenarios span a range of possibilities, they then can shape agile research and policy strategies that are not as fragile as narrow strategies based on rigid assumptions about the future. More nimble strategies open to a wider array of possible futures can help shape the distant outcomes toward desirable states. Identifying a range of plausible scenarios facilitates the monitoring of early indicators of undesirable trajectories.

Where the Science is Headed

Scenarios have been used previously to assess developments in certain areas of the Arctic [e.g., 5, 6, 7]. Those projects yielded valuable insights for communities, industries, and policy-makers. SEARCH is bringing scientists and policy makers together to develop plausible scenarios of the future Arctic and the research needed to support policy decisions concerning those scenarios. Most recently, the Adaptations for a Changing Arctic project of the Assessment and Monitoring Program (AMAP) of the Arctic Council completed a three region review of ongoing adaptation processes and scenarios in the Arctic. For the Bering Chukchi Beaufort region report an extensive discussion of different types of scenarios was reviewed [6].

Further Reading

Figure 3: Robustness Analysis: Scenarios for Strategic Planning PosterFigure 3: Robustness Analysis: Scenarios for Strategic Planning Poster

References

  1. R.J. Lempert, S.W. Popper, and S.C. Banks (2003). Shaping the next one hundred years: new methods for quantitative, long-term policy analysis. RAND, Santa Monica, CA, USA. ISBN 0-8330-3275-5.
  2. J.C. Glenn, and T.J. Gordon (2009). Futures Research Methodology – Version 3.0. The Millenium Project, Washington D.C., USA. ISBN 0-9818-9411-9.
  3. J.E. Walsh, M. Mueller-Stoffels, and P.H. Larsen (2011). Scenarios as tools to understand and respond to change. In: North by 2020: Perspectives on Alaska's Changing Social-Ecological Systems. University of Alaska Press, pp. 19–40.
  4. J. Gausemeier, A. Fink, and O. Schlake (1996). Szenario-Management: Planen und Führen mit Szenarien (English: Scenario management: plan and lead with scenarios). Carl Hanser Verlag, Munich, Germany. ISBN 3-446-18721-9
  5. Mueller-Stoffels, Marc and Hajo Eicken (2011). Futures of Arctic Marine Transport 2030: An Explorative Scenario Approach. In: North by 2020: Perspectives on Alaska's Changing Social-Ecological Systems. Ed. by Hajo Eicken and Amy Lovecraft. University of Alaska Press, pp. 477–492.
  6. Lovecraft, A.L. and B. Preston (Lead Authors) (2017). Arctic Scenarios. In Adaptation Actions for a Changing Arctic - Perspectives from the Bering/Chukchi/Beaufort Region, Chapter 8. Arctic Monitoring and Assessment Programme (AMAP), Arctic Council, Oslo, Norway.
  7. L.W. Brigham (2007). Thinking about the Arctic's Future: Scenarios for 2040. The Futurist, World Future Society, Bethesda, MD, USA.