New PROTECT publication about sea level rise learning scenarios

The article “Sea Level Rise Learning Scenarios for Adaptive Decision-Making Based on IPCC AR6” was published in Earth’s Future.

In many cases of climate change adaptation, substantial investments in infrastructure (e.g., dikes) are necessary. However, the uncertainty surrounding future projections, such as sea level rise, poses significant challenges. Decision scientists tackle these issues by employing flexible and staged decision-making methods. For instance, a coastal decision-maker might opt to construct a dike with a broader foundation initially and, if required, increase its height in the future. This approach allows decision-makers to gain insights by monitoring future sea level rise trends to determine if higher dike protection levels are warranted.

To assess whether it is economically advantageous to defer infrastructure investments in favor of learning from future observations, which could justify additional expenses for flexible infrastructure, it is essential to establish learning scenarios. Learning scenarios involve projecting critical variables from the current perspective and from various future timeframes. For instance, sea level rise learning scenarios encompass projections of sea level rise from 2050 onward, contingent on the amount of sea level rise observed up to 2050.

This paper aims to elucidate coastal decision-making employing a learning scenario through a straightforward example. Additionally, it introduces a novel method for generating learning scenarios and applies this method to create sea level rise learning scenarios.

Key points:

  • We develop sea-level rise learning scenarios based on IPCC AR6 using a novel method termed direct fit.
Static sea level rise scenario from Intergovernmental Panel on Climate Change sixth Assessment Report for the shared socioeconomic pathway SSP 2–4.5 plotted as pointwise boxplots (yellow) and a simple learning scenario derived from the static scenario (blue). The yellow boxplots show the minimum value, the 25th percentile value, the median (black line), the 75th percentile value, and the maximum value.

  • Learning scenarios provide information on future variable values seen not only from today, but also from future moments in time. The information seen from a future moment in time is an updated estimation based on learning, e.g. observations, until that future moment in time.
Workflow 1f trajectories (left) and direct fit learning scenario (right) filtered by an observation window. The observation window filters all sea level rise values in 2070 lying between the 50% and 70% quantile value of workflow 1f. Based on the filtered trajectories or on the filtered pathways in the learning scenario, future sea level rise boxplots seen from the observation in 2070 are plotted.

  • We show how climate learning scenarios can be applied for improving and justifying investments in flexible long-lasting infrastructure. This figure visualises how sea level rise learning scenarios extend adaptation pathway analysis.
A simple example: adaptation pathway map extended by a sea level rise learning scenario. We choose the probability of transitioning from any node to any of its two next nodes to be 50%. Note that any other combination of probabilities adding up to 1 would in principle be an equally suitable choice, because the value of sea level rise attained at the two next nodes is computed based on these probabilities.

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