5:15 PM - 7:15 PM
[ACG55-P01] Minimal CMIP Emulator (MCE): Update for comprehensive probabilistic climate projections
Keywords:emulator, probabilistic climate assessment, constraining, emission scenario
Climate model emulators or reduced complexity models serve as alternatives to high-cost computing using complex Earth System Models (ESMs). Their primary application is a probabilistic climate assessment with a large ensemble of climate projections. This ensemble perturbs model parameters and external forcing factors. Ensemble members are typically sampled based on multiple ESMs and constrained based on multiple lines of evidence from observations and process understandings. This sampling and constraining is essentially a synthesis of evidence on which climate assessment is based, where the emulators play a foundational role.
Constrained posterior ensembles are directly related to probabilistic climate assessment for emissions scenarios, such as whether they are consistent with the 1.5-degree goal for long-term climate mitigation. Different ensembles from various models and approaches have been investigated in the Reduced Complexity Model Intercomparison Project for the sixth assessment of the Intergovernmental Panel on Climate Change. The author participated in this project with its own model, MCE, and showed that its posterior ensemble has good quality in terms of median and uncertainty ranges of climate projections. Since then, MCE has been updated to expand functions and improve transparency so that the model can be used more widely.
This presentation describes the following four updates: (1) full implementation of non-CO2 forcing factors, (2) treatment of anthropogenic land CO2 emissions in the global carbon budget, (3) generic constraining methods, and (4) user interfaces for different execution modes. Furthermore, it will include a preliminary demonstration as a typical example to explore the impact of recent significant warming trends on posterior ensembles and scenario assessment.
Constrained posterior ensembles are directly related to probabilistic climate assessment for emissions scenarios, such as whether they are consistent with the 1.5-degree goal for long-term climate mitigation. Different ensembles from various models and approaches have been investigated in the Reduced Complexity Model Intercomparison Project for the sixth assessment of the Intergovernmental Panel on Climate Change. The author participated in this project with its own model, MCE, and showed that its posterior ensemble has good quality in terms of median and uncertainty ranges of climate projections. Since then, MCE has been updated to expand functions and improve transparency so that the model can be used more widely.
This presentation describes the following four updates: (1) full implementation of non-CO2 forcing factors, (2) treatment of anthropogenic land CO2 emissions in the global carbon budget, (3) generic constraining methods, and (4) user interfaces for different execution modes. Furthermore, it will include a preliminary demonstration as a typical example to explore the impact of recent significant warming trends on posterior ensembles and scenario assessment.