Despite considerable advances in process understanding, numerical modeling, and the observational record of ice sheet contributions to global mean sea-level rise (SLR) since the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change, severe limitations remain in the predictive capability of ice sheet models. As a consequence, the potential contributions of ice sheets remain the largest source of uncertainty in projecting future SLR. Here, we report the findings of a structured expert judgement study, using unique techniques for modeling correlations between inter- and intra-ice sheet processes and their tail dependences. We find that since the AR5, expert uncertainty has grown, in particular because of uncertain ice dynamic effects. For a +2 °C temperature scenario consistent with the Paris Agreement, we obtain a median estimate of a 26 cm SLR contribution by 2100, with a 95th percentile value of 81 cm. For a +5 °C temperature scenario more consistent with unchecked emissions growth, the corresponding values are 51 and 178 cm, respectively. Inclusion of thermal expansion and glacier contributions results in a global total SLR estimate that exceeds 2 m at the 95th percentile. Our findings support the use of scenarios of 21st century global total SLR exceeding 2 m for planning purposes. Beyond 2100, uncertainty and projected SLR increase rapidly. The 95th percentile ice sheet contribution by 2200, for the +5 °C scenario, is 7.5 m as a result of instabilities coming into play in both West and East Antarctica. Introducing process correlations and tail dependences increases estimates by roughly 15%.

/pdf/ice-sheet-contributions-to-future-sea-level-rise-from-fsp1affwvz.pdf

Ice sheet contributions to future sea-level rise from structured expert judgment.

Summary
Expert judgment informs a variety of important applications in conservation and natural resource management, including threatened species management, environmental impact assessment, and structured decision-making. However, expert judgments can be prone to contextual biases. Structured elicitation protocols mitigate these biases and improve the accuracy and transparency of the resulting judgments. Despite this, the elicitation of expert judgment within conservation and natural resource management remains largely informal. We suggest this may be attributed to financial and practical constraints which are not addressed by many existing structured elicitation protocols.
In this paper, we advocate that structured elicitation protocols must be adopted when expert judgments are used to inform science. In order to motivate a wider adoption of structured elicitation protocols, we outline the IDEA protocol. The protocol improves the accuracy of expert judgments and includes several key steps which may be familiar to many conservation researchers, such as the four-step elicitation, and a modified Delphi procedure (“Investigate”, “Discuss”, “Estimate” and “Aggregate”). It can also incorporate remote elicitation, making structured expert judgment accessible on a modest budget.

The IDEA protocol has recently been outlined in the scientific literature, however, a detailed description has been missing. This paper fills that important gap by clearly outlining each of the steps required to prepare for and undertake an elicitation.

Whilst this paper focuses on the need for the IDEA protocol within conservation and natural resource management, the protocol (and the advice contained in this paper), is applicable to a broad range of scientific domains, as evidenced by its application to biosecurity, engineering, and political forecasting. By clearly outlining the IDEA protocol, we hope that structured protocols will be more widely understood and adopted, resulting in improved judgments and increased transparency when expert judgment is required.


This article is protected by copyright. All rights reserved.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.12857

A practical guide to structured expert elicitation using the IDEA protocol

Expert judgement is often used to assess uncertainties in model-based climate change projections. This Perspective describes a statistical approach to formalizing the role of expert judgement, using Antarctic ice loss as an illustrative example. Expert judgement is an unavoidable element of the process-based numerical models used for climate change projections, and the statistical approaches used to characterize uncertainty across model ensembles. Here, we highlight the need for formalized approaches to unifying numerical modelling with expert judgement in order to facilitate characterization of uncertainty in a reproducible, consistent and transparent fashion. As an example, we use probabilistic inversion, a well-established technique used in many other applications outside of climate change, to fuse two recent analyses of twenty-first century Antarctic ice loss. Probabilistic inversion is but one of many possible approaches to formalizing the role of expert judgement, and the Antarctic ice sheet is only one possible climate-related application. We recommend indicators or signposts that characterize successful science-based uncertainty quantification.

Expert judgement and uncertainty quantification for climate change

The inclusion of expert judgments along with other forms of data in science, engineering, and decision making is inevitable. Expert elicitation refers to formal procedures for obtaining and...

/pdf/expert-elicitation-using-the-classical-model-to-validate-34p3oggk17.pdf

Expert elicitation: using the classical model to validate experts' judgments

Cross validation for the classical model of structured expert judgment

The effect of the number of seed variables on the performance of Cooke′s classical model

Systems engineers are routinely tasked with facilitating the delicate balance between cost, schedule, and technical performance in acquisition programs that are continuously subjected to various outside influences. While there are several quantitative methods to estimate acquisition program cost and schedule performance as well as identify their risks e.g., Earned Value Management, the estimation of technical performance and technical risk is generally heuristic in nature. In order to monitor the progress of the technical aspects of an acquisition program, the systems engineering discipline utilizes the process of tracking Technical Measures to gain insight into the design and development, to assess risks and issues, and to evaluate the likelihood of realizing objectives. However, with the diversity of so many technical programs, the estimation and risk analysis of technical performance in technology acquisition programs rely on the opinions of experts because the identification and application of relevant quantitative data for constructive modeling is not practical. The Expert-weighted Technical Risk Index methodology proposed in this article introduces a well-established method for mathematically combining expert judgment into the realm of systems engineering to develop predictive progress plans for technical performance estimation and risk analysis. © 2013 Wiley Periodicals, Inc. Syst Eng 17

The Development of Progress Plans Using a Performance-Based Expert Judgment Model to Assess Technical Performance and Risk

The rapidly changing environment and asymmetric threats currently encountered on the modern battlefield requires the timely delivery of effective weapons systems. Unfortunately in fiscal year 2008, according to the US Government Accountability Office, research and development costs of the United States Department of Defense major weapons acquisition programs increased 42 percent above original estimates, and delays in initial operational capability deliveries slipped to 22 months. While there are several quantitative methods to estimate acquisition program cost and schedule performance and to identify their risks (e.g., Earned Value Management), the estimation of technical performance and technical risk identification is generally heuristic in nature and based on expert judgment because of limited quantitative data for constructive modeling. The proposed research in this paper expands upon the Technical Risk Index Distribution method developed by Lewis, Mazzuchi and Sarkani by incorporating a performance-based method of mathematically combining quantified expert opinion for technical performance estimation and risk analysis.

Justin W. Eggstaff

Papers

The effect of the number of seed variables on the performance of Cooke′s classical model

The Development of Progress Plans Using a Performance-Based Expert Judgment Model to Assess Technical Performance and Risk

A Performance‐based Statistical Expert Judgment Model to Assess Technical Performance and Risk