Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions

Helgeson, Casey; Srikrishnan, Vivek; Keller, Klaus; Tuana, Nancy

doi:10.1086/711501

Cited by 29 publications

(26 citation statements)

References 95 publications

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“…We adopt a well-studied, state-of-the-art, yet still relatively simple model. This model simplicity provides the advantages of transparency and the ability to perform careful and exhaustive uncertainty and sensitivity analyses 37 . These advantages come, however, with several caveats that point to fruitful research directions.…”

Section: Discussionmentioning

confidence: 99%

A tighter constraint on Earth-system sensitivity from long-term temperature and carbon-cycle observations

et al. 2021

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The long-term temperature response to a given change in CO2 forcing, or Earth-system sensitivity (ESS), is a key parameter quantifying our understanding about the relationship between changes in Earth’s radiative forcing and the resulting long-term Earth-system response. Current ESS estimates are subject to sizable uncertainties. Long-term carbon cycle models can provide a useful avenue to constrain ESS, but previous efforts either use rather informal statistical approaches or focus on discrete paleoevents. Here, we improve on previous ESS estimates by using a Bayesian approach to fuse deep-time CO2 and temperature data over the last 420 Myrs with a long-term carbon cycle model. Our median ESS estimate of 3.4 °C (2.6-4.7 °C; 5-95% range) shows a narrower range than previous assessments. We show that weaker chemical weathering relative to the a priori model configuration via reduced weatherable land area yields better agreement with temperature records during the Cretaceous. Research into improving the understanding about these weathering mechanisms hence provides potentially powerful avenues to further constrain this fundamental Earth-system property.

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Section: Discussionmentioning

confidence: 99%

A tighter constraint on Earth-system sensitivity from long-term temperature and carbon-cycle observations

et al. 2021

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“…The simplicity of the model permits millions of model evaluations without requiring a reduced-form emulator, allowing full statistical calibration of the model on historical data and insights into the dynamics of the full model. This level of simplicity provides epistemic benefits by making full uncertainty quantification computationally tractable (Helgeson et al 2021 ), much like the statistical approach of Raftery et al ( 2017 ) and Liu and Raftery ( 2021 ). The mechanistically motivated model structure allows us to incorporate theoretical insights about the dynamics and structure of the relationships between interpretable model parameters using prior distributions drawn from the literature.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic projections of baseline twenty-first century CO2 emissions using a simple calibrated integrated assessment model

et al. 2022

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Probabilistic projections of baseline (with no additional mitigation policies) future carbon emissions are important for sound climate risk assessments. Deep uncertainty surrounds many drivers of projected emissions. Here, we use a simple integrated assessment model, calibrated to century-scale data and expert assessments of baseline emissions, global economic growth, and population growth, to make probabilistic projections of carbon emissions through 2100. Under a variety of assumptions about fossil fuel resource levels and decarbonization rates, our projections largely agree with several emissions projections under current policy conditions. Our global sensitivity analysis identifies several key economic drivers of uncertainty in future emissions and shows important higher-level interactions between economic and technological parameters, while population uncertainties are less important. Our analysis also projects relatively low global economic growth rates over the remainder of the century. This illustrates the importance of additional research into economic growth dynamics for climate risk assessment, especially if pledged and future climate mitigation policies are weakened or have delayed implementations. These results showcase the power of using a simple, transparent, and calibrated model. While the simple model structure has several advantages, it also creates caveats for our results which are related to important areas for further research.

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“…These processes are recognised as complex and difficult to model with reducedcomplexity models (Montaño et al, 2020;Vitousek et al, 2017). By using the empirical model, our objective is to reproduce the observed trends and modes of variability without trying to model the physical processes explicitly, while keeping a low computation time (see Helgeson et al, 2020, for a broader discussion of this approach). The term n • Tx is the product of the number of years n since the reference year and the multi-decadal linear trend Tx derived from observations after subtracting the effect of sea level rise.…”

Section: Setting Up Shoreline Change Projections Within the Extra-pro...mentioning

confidence: 99%

Deep uncertainties in shoreline change projections: an extra-probabilistic approach applied to sandy beaches

Thiéblemont¹,

Cozannet²,

Rohmer³

et al. 2021

Preprint

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<p>Global mean sea-level rise and its acceleration are projected to aggravate coastal erosion over the 21st century, which constitutes a major challenge for coastal adaptation. Projections of shoreline retreat are highly uncertain, however, namely due to deeply uncertain mean sea-level projections and the absence of consensus on a coastal impact model. An improved understanding and a better quantification of these sources of deep uncertainty are hence required to improve coastal risk management and inform adaptation decisions. In this work we present and apply a new extra-probabilistic framework to develop shoreline change projections of sandy coasts that allows considering intrinsic (or aleatory) and knowledge-based (or epistemic) uncertainties exhaustively and transparently. This framework builds upon an empirical shoreline change model to which we ascribe possibility functions to represent deeply uncertain variables. The model is applied to two local sites in Aquitaine (France) and Castell&#243;n (Spain). First, we validate the framework against historical shoreline observations and then develop shoreline change projections that account for possible (although unlikely) low-end and high-end mean sea-level scenarios. Our high-end projections show for instance that shoreline retreats of up to 200m in Aquitaine and 130m in Castell&#243;n are plausible by 2100, while low-end projections revealed that 58m and 37m modest shoreline retreats, respectively, are also plausible. Such extended intervals of possible future shoreline changes reflect an ambiguity in the probabilistic description of shoreline change projections, which could be substantially reduced by better constraining SLR projections and improving coastal impact models. We found for instance that if mean sea-level by 2100 does not exceed 1m, the ambiguity can be reduced by more than 50 %. This could be achieved through an ambitious climate mitigation policy and improved knowledge on ice-sheets.&#160;</p>

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Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions

Cited by 29 publications

References 95 publications

A tighter constraint on Earth-system sensitivity from long-term temperature and carbon-cycle observations

A tighter constraint on Earth-system sensitivity from long-term temperature and carbon-cycle observations

Probabilistic projections of baseline twenty-first century CO2 emissions using a simple calibrated integrated assessment model

Deep uncertainties in shoreline change projections: an extra-probabilistic approach applied to sandy beaches

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