Sea Ice

The following provides an introduction to the Scenario Process Employed for the Arctic Scenarios 2030.

Scenario planning provides 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 [Lempert et al., 2003]. What if-ing means here, that scenarios are intended to encourage decision makers to think outside the box and to give them an overview of possible and likely futures. Hence, the objective in finding Key Factors and Future Projections is not to just imagine the most likely future developments, but to imagine as many ways as might develop out of the current state.

This document contains the definitions of the Key Factors (KF) and Future Projections (FP) extracted and extended from the notes of the Arctic Marine Shipping Assessment (AMSA) Scenario Workshop held in San Francisco in May 2007.

To further the quantitative scenario process based on the AMSA Workshop outcome we will use a Consistency and Robustness Analysis. This method allows a in-depth analysis of possible futures through a software supported process.

Plausibility values are assigned to each FP of a KF. The higher a FP’s assigned value the more plausible it is considered to be the actual future development.

Further all FP’s are compared with each other and it is evaluated if it is consistent that two FP’s occur in the same future. This comparison is expressed in consistency values assigned to each pari of FP’s ranging from totally inconsistent to totally consistent to occur in the same future. This results in a matrix of consistency values. For an in-depth discussion of Scenario-Management and the Consistency and Robustness Analysis please refer to Appendix B and C.

Definitions for KF and FP are important so the persons evaluating the consistency matrix and the plausibilities for the FP all know what is meant by each item. This will help making the evaluation process more consistent. And, further it will establish a base of knowledge as to what research is already being done on the respective topic.

The objective here is to back the FP as much as possible by peer-reviewed literature. However, this is not always possible as developing FP means to think outside the box. That is, parts of the scenario process are deliberately concerned with what one could call science fiction. This is very important as the unexpected often turns out to be the future development. For example Thomas Watson, chairman of IBM, said in 1943,

I think there is a world market for maybe five computers.

However, the transistor was invented and new operating systems made personal computers possible a mere forty years later.

The above example points out the importance to attempt to reduce bias in the scenario process as much as possible. Here, quantitative, sometimes called explorative, approaches such as the Robustness Analysis have an advantage over qualitative (narrative) scenario processes. The reason for this is, that one only works on very small parts of the project at a time, i.e. one plausibility value or the comparison of only two KF’s future projections. This often leads to unexpected results that can be beyond the scope of narrative scenario methods.

In this scenario process the consistency matrix and the plausibility values will be determined for a future of the year 2050. And the generated data will be fed into the software ScenLab by evolve:IT to extract robust raw scenario bundles. Furthermore, the bundles are the base fro further analysis and the final scenario narrative.