Climate modelling ensembles are fundamental for exploring and understanding uncertainty in future climate change projections. Uncertainty has manifold origins, including: process understanding and intermodel differences, model tuning strategies, parameterization choice and weak constraints on parameters, limited observations of initial conditions and climate model initialization strategy, and questions over the relationship between observations and model variables. These combine with unknowns such as future anthropogenic greenhouse gas emissions to give large climate prediction uncertainties which impact climate decision-making. Understanding them is also critical for advancing process understanding.
These uncertainties manifest themselves across the modelling hierarchy, from Earth system and cloud resolving models, to simple models for conceptual studies and interdisciplinary models for integrated assessment. In this context, this session invites wide-ranging and interdisciplinary contributions on the science, method, and application of long-term climate modelling ensembles, including but not restricted to:
Approaches to ensemble simulations: sampling uncertainty in large models (e.g., Earth System Models, cloud resolving models); model weighting and interpretation of small ensembles; characterising uncertainties from initial conditions & climate model initialization; data science approaches to sampling & emulation; future considerations in large ensemble simulations; uncertainty reduction in Earth system tipping elements; cross-cutting theories of ensemble design; anticipating future model evolution.
Comparing ensembles with observations: confronting model ensembles with observations including Palaeoclimate data; irreducible uncertainties in ensembles; emulators for characterizing uncertainty across scenarios; observational constraints with large forcing, slow dynamics, and internal variability; effects of model resolution on reliability; assessment and evaluation.
Ensembles and decision-making: Post-hoc emulation for uncertainty characterization; reducing generalization errors in simple models and emulators; consistent modelling hierarchies for mitigation and adaptation; irreducible errors in decision-relevant simulations; integrated assessment model ensembles; value of information from ensemble design; effects of scale on uncertainty; decision-sciences based approaches to ensemble design.
These uncertainties manifest themselves across the modelling hierarchy, from Earth system and cloud resolving models, to simple models for conceptual studies and interdisciplinary models for integrated assessment. In this context, this session invites wide-ranging and interdisciplinary contributions on the science, method, and application of long-term climate modelling ensembles, including but not restricted to:
Approaches to ensemble simulations: sampling uncertainty in large models (e.g., Earth System Models, cloud resolving models); model weighting and interpretation of small ensembles; characterising uncertainties from initial conditions & climate model initialization; data science approaches to sampling & emulation; future considerations in large ensemble simulations; uncertainty reduction in Earth system tipping elements; cross-cutting theories of ensemble design; anticipating future model evolution.
Comparing ensembles with observations: confronting model ensembles with observations including Palaeoclimate data; irreducible uncertainties in ensembles; emulators for characterizing uncertainty across scenarios; observational constraints with large forcing, slow dynamics, and internal variability; effects of model resolution on reliability; assessment and evaluation.
Ensembles and decision-making: Post-hoc emulation for uncertainty characterization; reducing generalization errors in simple models and emulators; consistent modelling hierarchies for mitigation and adaptation; irreducible errors in decision-relevant simulations; integrated assessment model ensembles; value of information from ensemble design; effects of scale on uncertainty; decision-sciences based approaches to ensemble design.