Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions
(2022)
Scientific data from precipitation driver response model intercomparison project
Scientific Data Nature Research 9:1 (2022) 123
Abstract:
This data descriptor reports the main scientific values from General Circulation Models (GCMs) in the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). The purpose of the GCM simulations has been to enhance the scientific understanding of how changes in greenhouse gases, aerosols, and incoming solar radiation perturb the Earth's radiation balance and its climate response in terms of changes in temperature and precipitation. Here we provide global and annual mean results for a large set of coupled atmospheric-ocean GCM simulations and a description of how to easily extract files from the dataset. The simulations consist of single idealized perturbations to the climate system and have been shown to achieve important insight in complex climate simulations. We therefore expect this data set to be valuable and highly used to understand simulations from complex GCMs and Earth System Models for various phases of the Coupled Model Intercomparison ProjectBoundary conditions representation can determine simulated aerosol effects on convective cloud fields
Communications Earth and Environment Springer Nature 3:1 (2022) 71
Abstract:
Anthropogenic aerosols effect on clouds remains a persistent source of uncertainty in future climate predictions. The evolution of the environmental conditions controlling cloud properties is affected by the clouds themselves. Hence, aerosol-driven modifications of cloud properties can affect the evolution of the environmental thermodynamic conditions, which in turn could feed back to the cloud development. Here, by comparing many different cloud resolving simulations conducted with different models and under different environmental condition, we show that this feedback loop is strongly affected by the representation of the boundary conditions in the model. Specifically, we show that the representation of boundary conditions strongly impacts the magnitude of the simulated response of the environment to aerosol perturbations, both in shallow and deep convective clouds. Our results raise doubts about the significance of previous conclusions of aerosol-cloud feedbacks made based on simulations with idealised boundary conditions.Defining regime specific cloud sensitivities using the learnings from machine learning
Copernicus Publications (2022)
Insights from ACRUISE (Atmospheric Composition and Radiative forcing changes due to UN International Ship Emissions regulations) from aircraft, modelling, and satellite perspectives
Copernicus Publications (2022)