Multi-stage ensemble-learning-based model fusion for surface ozone simulations: A focus on CMIP6 models.

ABSTRACT: Accurately simulating the geographical distribution and temporal variability of global surface ozone has long been one of the principal components of chemistry-climate modelling. However, the simulation outcomes have been reported to vary significantly as a result of the complex mixture of uncertain factors that control the tropospheric ozone budget. Settling the cross-model discrepancies to achieve higher accuracy predictions of surface ozone is thus a task of priority, and methods that overcome structural biases in models going beyond naïve averaging of model simulations are urgently required. Building on the Coupled Model Intercomparison Project Phase 6 (CMIP6), we have transplanted a conventional ensemble learning approach, and also constructed an innovative 2-stage enhanced space-time

SUBMITTER: Sun Z 

PROVIDER: S-EPMC9488062 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature