the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
General circulation models simulate negative liquid water path–droplet number correlations, but anthropogenic aerosols still increase simulated liquid water path
Johannes Mülmenstädt
Edward Gryspeerdt
Sudhakar Dipu
Johannes Quaas
Andrew S. Ackerman
Ann M. Fridlind
Florian Tornow
Susanne E. Bauer
Andrew Gettelman
Yi Ming
Youtong Zheng
Po-Lun Ma
Hailong Wang
Kai Zhang
Matthew W. Christensen
Adam C. Varble
L. Ruby Leung
Xiaohong Liu
David Neubauer
Daniel G. Partridge
Philip Stier
Toshihiko Takemura
Related authors
Climate models are crucial for predicting climate change in detail. This paper proposes a balanced approach to improving their accuracy by combining traditional process-based methods with modern artificial intelligence (AI) techniques while maximizing the resolution to allow for ensemble simulations. The authors propose using AI to learn from both observational and simulated data while incorporating existing physical knowledge to reduce data demands and improve climate prediction reliability.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
hiddensource of inter-model variability and may be leading to bias in some climate model results.
global dimmingas found in observations. Only model experiments with anthropogenic aerosol emissions display any dimming at all. The discrepancies between observations and models are largest in China, which we suggest is in part due to erroneous aerosol precursor emission inventories in the emission dataset used for CMIP6.
variantsof the model using an implausibility metric. Despite many compensating effects in the model, the procedure constrains the probability distributions of many parameters, and direct radiative forcing uncertainty is reduced by 34 %.
Our results show that aerosol variability has a large impact on simulating aerosol climate effects, even when meteorology and dynamics are fixed. Processes most affected are gas-phase chemistry and aerosol uptake of water through equilibrium reactions.
Related subject area
Climate models are crucial for predicting climate change in detail. This paper proposes a balanced approach to improving their accuracy by combining traditional process-based methods with modern artificial intelligence (AI) techniques while maximizing the resolution to allow for ensemble simulations. The authors propose using AI to learn from both observational and simulated data while incorporating existing physical knowledge to reduce data demands and improve climate prediction reliability.