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Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Value management and model pluralism in climate science.

Julie Jebeile¹, Michel Crucifix²

¹Institute of Philosophy & Oeschger Center for Climate Change Research, University of Bern, Switzerland.

Studies in History and Philosophy of Science

|June 24, 2021

Summary

Climate models can create unequal representations of future climates due to non-epistemic values. Value management offers a solution by adopting social objectivity to improve climate projection accuracy and uncertainty quantification.

Keywords:

Climate models Model pluralism Multi-model ensemble Scientific objectivity Strong objectivity Uncertainty quantification Value-free ideal Values in science

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Area of Science:

Philosophy of Climate Science
Climate Modeling
Scientific Objectivity

Background:

Non-epistemic values significantly influence climate modeling.
Epistemic inequality risks biased climate model representations favoring certain regions.

Purpose of the Study:

To promote value management as a strategy to overcome epistemic inequality in climate modeling.
To explore alternative social accounts of objectivity for managing value influence.

Main Methods:

Challenging the value-free ideal and inductive risk arguments in climate science.
Applying Longino's and Harding's concepts of objectivity to scientific practice.
Evaluating and designing multi-model ensembles for improved uncertainty quantification.

Main Results:

Value management, supported by social objectivity, can mitigate epistemic inequality.
Objectivity frameworks offer practical guidance for climate model development.
Improved multi-model ensembles enhance the quantification and expression of climate projection uncertainty.

Conclusions:

Value management provides a viable framework for addressing epistemic inequality in climate science.
Adopting social objectivity enhances the reliability and fairness of climate models.
This approach improves the capacity of climate models to represent diverse future climate scenarios accurately.