Philosophy of Climate Science Part II: Modelling Climate Change

Frigg, Roman; Thompson, Erica; Werndl, Charlotte

doi:10.1111/phc3.12297

Cited by 32 publications

(18 citation statements)

References 51 publications

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“…Radically advancing the skills of climate models is key to nearing these goals, and, indeed, efforts aimed in this direction have been widely documented (see the related chapter on the last report, of the Intergovernmental Panel on Climate Change (2013)). Intercomparing and validating climate models is far from being a trivial task, also at a purely conceptual level (Lucarini, 2013;Frigg et al, 2015). Difficulties can only increase when looking at the practical side of things: how to choose meaningful metrics to study the performance of climate models?…”

Section: Introductionmentioning

confidence: 99%

TheDiaTo (v1.0) – a new diagnostic tool for water, energy and entropy budgets in climate models

Lembo¹,

Lunkeit²,

Lucarini

2019

Geosci. Model Dev.

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This work presents a novel diagnostic tool for studying the thermodynamics of the climate systems with a wide range of applications, from sensitivity studies to model tuning. It includes a number of modules for assessing the internal energy budget, the hydrological cycle, the Lorenz Energy Cycle and the material entropy production, respectively. The routine receives as inputs energy fluxes at surface and at the Top-of-Atmosphere (TOA), for the computation of energy budgets at Top-of-Atmosphere (TOA), at the surface, and in the atmosphere as a residual. Meridional enthalpy transports are also computed from the divergence of the zonal mean energy budget fluxes; location and intensity of peaks in the two hemispheres are then provided as outputs. Rainfall, snowfall and latent heat fluxes are received as inputs for computing the water mass and latent energy budgets. If a land-sea mask is provided, the required quantities are separately computed over continents and oceans. The diagnostic tool also computes the Lorenz Energy Cycle (LEC) and its storage/conversion terms as annual mean global and hemispheric values. In order to achieve this, one needs to provide as input three-dimensional daily fields of horizontal wind velocity and temperature in the troposphere. Two methods have been implemented for the computation of the material entropy production, one relying on the convergence of radiative heat fluxes in the atmosphere (indirect method), one combining the irreversible processes occurring in the climate system, particularly heat fluxes in the boundary layer, the hydrological cycle and the kinetic energy dissipation as retrieved from the residuals of the LEC. A version of the diagnostic tool is included in the Earth System Model eValuation Tool (ESMValTool) community diagnostics, in order to assess the performances of soon available

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Section: Introductionmentioning

confidence: 99%

TheDiaTo (v1.0) – a new diagnostic tool for water, energy and entropy budgets in climate models

Lembo¹,

Lunkeit²,

Lucarini

2019

Geosci. Model Dev.

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“…For instance, a model may adequately predict within a specified error margin the GMST increase by 2100 relative to certain initial conditions under a certain forcing scenario, but it may not be adequate for predictions of changes in precipitation patterns in the Mediterranean area between 2050 and 2100. There is widespread consensus that projections are more reliable for temperature than for precipitation and most other quantities, for longer time averages, larger spatial averages, low specificity, and, all other things being equal and for absolute changes, for shorter lead‐times (Ref , p. 970). The mentioned criteria for specifying a purpose are crucial to determine whether a model that is adequate for a particular purpose is also adequate for another purpose.…”

Section: Adequacy For Purposementioning

confidence: 99%

“…If a model does not account for these processes and feedbacks, it could fit almost perfectly even to data about past and present climate not used for calibration but still be biased for projections. Success with respect to past and present climate alone is thus no assurance that the model will also be successful in projecting future climate (Ref , p. 828) . Some climate scientists conclude from this that it is hard to tell how relevant past data are or that they are not relevant at all for evaluating a model's adequacy for climate projections (Ref , p. 2146).…”

Section: Non‐deductive Inferences From Empirical Accuracymentioning

confidence: 99%

“…The first assumes that the analytic opacity of climate models does not preclude evaluating representations of individual processes in the context of the full model as well as in isolation (Ref , p. 754). At least in certain cases, it seems possible, by comparing individual and combined runs, to achieve an understanding of the effect of components or sub‐models within the model and to attribute success or failure to certain components or sub‐models (Ref , p. 967). Unfortunately, a parameterization that performs better when tested individually does not necessarily improve the performance of the model as a whole.…”

Section: Non‐deductive Inferences From Empirical Accuracymentioning

confidence: 99%

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Building confidence in climate model projections: an analysis of inferences from fit

Baumberger

Knutti

Hadorn

2017

WIREs Climate Change

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Climate model projections are used to inform policy decisions and constitute a major focus of climate research. Confidence in climate projections relies on the adequacy of climate models for those projections. The question of how to argue for the adequacy of models for climate projections has not gotten sufficient attention in the climate modeling community. The most common way to evaluate a climate model is to assess in a quantitative way degrees of 'model fit'; that is, how well model results fit observation-based data (empirical accuracy) and agree with other models or model versions (robustness). However, such assessments are largely silent about what those degrees of fit imply for a model's adequacy for projecting future climate. We provide a conceptual framework for discussing the evaluation of the adequacy of models for climate projections. Drawing on literature from philosophy of science and climate science, we discuss the potential and limits of inferences from model fit. We suggest that support of a model by background knowledge is an additional consideration that can be appealed to in arguments for a model's adequacy for long-term projections, and that this should explicitly be spelled out. Empirical accuracy, robustness and support by background knowledge neither individually nor collectively constitute sufficient conditions in a strict sense for a model's adequacy for long-term projections. However, they provide reasons that can be strengthened by additional information and thus contribute to a complex non-deductive argument for the adequacy of a climate model or a family of models for long-term climate projections. © 2017 The Authors.WIREs Climate Change published by Wiley Periodicals, Inc. How to cite this article:WIREs Clim Change 2017Change , 8:e454. doi: 10.1002 INTRODUCTIONT here is now broad scientific consensus that the Earth's climate has changed significantly over the last century, that much of the observed large-scale warming can be attributed to greenhouse gas emissions associated with human activities, and that these trends will continue in the near future. 1 Less clear is how exactly the climate will change in the more distant future on the global and on regional scales if greenhouse gas emissions increase, stabilize or decrease in particular ways. Climate predictions that are conditional on forcing scenarios are called climate projections. They tell us how one or more climate characteristics would evolve if greenhouse gas concentrations and other external forcings were to follow specified pathways in the future. Projections are currently a major focus of climate research, which is undertaken (and funded) in part with the aim of providing input to policy decisions for mitigation and adaptation to anthropogenic climate change. To project climate change, climate scientists inevitably have to rely on complex numerical climate models. But to what extent can we trust model-based climate projections?Climate model projections cannot be directly evaluated. Most of them have forcing scenarios...

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“…Climate change threatens many species and ecosystem services (Thomas et al, 2004). Uncertainty associated with future climate projections complicates decision making associated with the future management of natural resources (Ahmadalipour, Moradkhani, & Rana, 2018;Thorne et al, 2017), and quantifying that uncertainty is a growing area of research (e.g., Allen, Stott, Mitchell, Schnur, & Delworth, 2000;Frigg, Thompson, & Werndl, 2015;Shiogama et al, the uncertainty and errors of the global circulation models chosen to drive them (Pourmokhtarian, Driscoll, Campbell, Hayhoe, & Stoner, 2016).…”

Section: Introductionmentioning

confidence: 99%

When portfolio theory can help environmental investment planning to reduce climate risk to future environmental outcomes—and when it cannot

Ando

Fraterrigo

Guntenspergen

et al. 2018

CONSERVATION LETTERS

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Variability among climate change scenarios produces great uncertainty in what is the best allocation of resources among investments to protect environmental goods in the future. Previous research shows Modern Portfolio Theory (MPT) can help optimize environmental investment targeting to reduce outcome uncertainty with minimal loss of expected level of environmental benefits, but no work has yet identified the types of cases for which MPT is most useful. This article assembles data on 26 different conservation cases in three distinct ecological settings and develops new metrics to evaluate how well MPT can reduce uncertainty in future outcomes of a set of environmental investments. We find MPT is broadly but not universally useful and works best when multiple investments have negatively correlated outcomes across climate scenarios; a second-best investment has expected value almost as good as the value in the best investment; or multiple investments have little uncertainty in ecological outcomes.

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Philosophy of Climate Science Part II: Modelling Climate Change

Cited by 32 publications

References 51 publications

TheDiaTo (v1.0) – a new diagnostic tool for water, energy and entropy budgets in climate models

TheDiaTo (v1.0) – a new diagnostic tool for water, energy and entropy budgets in climate models

Building confidence in climate model projections: an analysis of inferences from fit

When portfolio theory can help environmental investment planning to reduce climate risk to future environmental outcomes—and when it cannot

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