### Abstract

Reduced complexity climate models are useful tools for quantifying decision-relevant uncertainties, given their flexibility, computational efficiency, and suitability for large-ensemble frameworks necessary for statistical estimation using resampling techniques (e.g., Markov chain Monte Carlo). Here we document a new version of the simple, open-source, global climate model Hector, coupled with a 1-D diffusive heat and energy balance model (Diffusion Ocean Energy balance CLIMate model) and a sea level change module (Building blocks for Relevant Ice and Climate Knowledge) that also represents contributions from thermal expansion, glaciers and ice caps, and polar ice sheets. We apply a Bayesian calibration approach to quantify model uncertainties surrounding 39 model parameters with prescribed radiative forcing, using observational information from global surface temperature, thermal expansion, and other contributors to sea level change. We find the addition of thermal expansion as an observational constraint sharpens inference for the upper tail of posterior equilibrium climate sensitivity estimates (the 97.5 percentile is tightened from 7.1 to 6.6 K), while other contributors to sea level change play a lesser role. The thermal expansion constraint also has implications for probabilistic projections of global surface temperature (the 97.5 percentile for RCP8.5 2100 temperature decreases 0.3 K). Due to the model's parameterization of thermal expansion as an uncertain function of global ocean heat, we note a trade-off between two ways of incorporating thermal expansion information: Ocean heat data provide a somewhat sharper equilibrium climate sensitivity estimate while thermal expansion data allow for constrained sea level projections.

Original language | English (US) |
---|---|

Pages (from-to) | 677-690 |

Number of pages | 14 |

Journal | Earth's Future |

Volume | 7 |

Issue number | 6 |

DOIs | |

State | Published - Jun 2019 |

### Fingerprint

### Keywords

- data-model fusion
- probabilistic projections
- reduced-complexity model

### ASJC Scopus subject areas

- Environmental Science(all)
- Earth and Planetary Sciences (miscellaneous)

### Cite this

*Earth's Future*,

*7*(6), 677-690. https://doi.org/10.1029/2018EF001082

**Impacts of Observational Constraints Related to Sea Level on Estimates of Climate Sensitivity.** / Vega-Westhoff, Ben; Sriver, Ryan; Hartin, Corinne A.; Wong, Tony E.; Keller, Klaus.

Research output: Contribution to journal › Article

*Earth's Future*, vol. 7, no. 6, pp. 677-690. https://doi.org/10.1029/2018EF001082

}

TY - JOUR

T1 - Impacts of Observational Constraints Related to Sea Level on Estimates of Climate Sensitivity

AU - Vega-Westhoff, Ben

AU - Sriver, Ryan

AU - Hartin, Corinne A.

AU - Wong, Tony E.

AU - Keller, Klaus

PY - 2019/6

Y1 - 2019/6

N2 - Reduced complexity climate models are useful tools for quantifying decision-relevant uncertainties, given their flexibility, computational efficiency, and suitability for large-ensemble frameworks necessary for statistical estimation using resampling techniques (e.g., Markov chain Monte Carlo). Here we document a new version of the simple, open-source, global climate model Hector, coupled with a 1-D diffusive heat and energy balance model (Diffusion Ocean Energy balance CLIMate model) and a sea level change module (Building blocks for Relevant Ice and Climate Knowledge) that also represents contributions from thermal expansion, glaciers and ice caps, and polar ice sheets. We apply a Bayesian calibration approach to quantify model uncertainties surrounding 39 model parameters with prescribed radiative forcing, using observational information from global surface temperature, thermal expansion, and other contributors to sea level change. We find the addition of thermal expansion as an observational constraint sharpens inference for the upper tail of posterior equilibrium climate sensitivity estimates (the 97.5 percentile is tightened from 7.1 to 6.6 K), while other contributors to sea level change play a lesser role. The thermal expansion constraint also has implications for probabilistic projections of global surface temperature (the 97.5 percentile for RCP8.5 2100 temperature decreases 0.3 K). Due to the model's parameterization of thermal expansion as an uncertain function of global ocean heat, we note a trade-off between two ways of incorporating thermal expansion information: Ocean heat data provide a somewhat sharper equilibrium climate sensitivity estimate while thermal expansion data allow for constrained sea level projections.

AB - Reduced complexity climate models are useful tools for quantifying decision-relevant uncertainties, given their flexibility, computational efficiency, and suitability for large-ensemble frameworks necessary for statistical estimation using resampling techniques (e.g., Markov chain Monte Carlo). Here we document a new version of the simple, open-source, global climate model Hector, coupled with a 1-D diffusive heat and energy balance model (Diffusion Ocean Energy balance CLIMate model) and a sea level change module (Building blocks for Relevant Ice and Climate Knowledge) that also represents contributions from thermal expansion, glaciers and ice caps, and polar ice sheets. We apply a Bayesian calibration approach to quantify model uncertainties surrounding 39 model parameters with prescribed radiative forcing, using observational information from global surface temperature, thermal expansion, and other contributors to sea level change. We find the addition of thermal expansion as an observational constraint sharpens inference for the upper tail of posterior equilibrium climate sensitivity estimates (the 97.5 percentile is tightened from 7.1 to 6.6 K), while other contributors to sea level change play a lesser role. The thermal expansion constraint also has implications for probabilistic projections of global surface temperature (the 97.5 percentile for RCP8.5 2100 temperature decreases 0.3 K). Due to the model's parameterization of thermal expansion as an uncertain function of global ocean heat, we note a trade-off between two ways of incorporating thermal expansion information: Ocean heat data provide a somewhat sharper equilibrium climate sensitivity estimate while thermal expansion data allow for constrained sea level projections.

KW - data-model fusion

KW - probabilistic projections

KW - reduced-complexity model

UR - http://www.scopus.com/inward/record.url?scp=85067842907&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85067842907&partnerID=8YFLogxK

U2 - 10.1029/2018EF001082

DO - 10.1029/2018EF001082

M3 - Article

AN - SCOPUS:85067842907

VL - 7

SP - 677

EP - 690

JO - Earth's Future

JF - Earth's Future

SN - 2328-4277

IS - 6

ER -