Convective Momentum Transport and Its Impact on the Madden‐Julian Oscillation in E3SM‐MMF
Convective momentum transport (CMT) is the process of vertical redistribution of horizontal momentum by small‐scale turbulent flows from moist convection.
Qiu Yang +2 more
doaj +1 more source
EVALUATION OF THE COUPLED MODEL INTERCOMPARISON PROJECT PHASE 6 (CMIP6) HISTORICAL SIMULATIONS OF THE ARCTIC SEA ICE: PROGRESS, LIMITATIONS AND THEIR CAUSES [PDF]
A decline of the Arctic sea ice in response to a warming climate is assessed in the historical sea ice simulations from state-of-the-art global climate models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6).
Watts, Matthew N.
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The Role of Tropical Cyclone—Ocean Interactions in Future Changes in Hurricane Katrina
Abstract Tropical cyclone (TC) intensity and precipitation are projected to increase in the future. However, some projections are based on atmosphere‐only models in which sea surface temperatures are prescribed, whereas projections based on global atmosphere‐ocean coupled models can be subject to long‐term ocean biases.
Dakota C. Forbis +3 more
wiley +1 more source
Exploring Ways to Reduce Biases in a Hybrid Global Climate Model With Machine‐Learned Moist Physics
Abstract In a previous study (Han et al., 2023, https://doi.org/10.1029/2022ms003508), we implemented a deep convolutional residual neural network for moist physics into the 3‐D real‐geography CAM5 and carried out a stable multi‐year integration successfully. However, the simulation has large temperature and moisture biases in high latitude troposphere
Yilun Han, Guang J. Zhang, Yong Wang
wiley +1 more source
Ocean Barrier Layers in the Energy Exascale Earth System Model
Ocean barrier layers (BLs) separate the mixed layer from the top of the thermocline and are able to insulate the mixed layer from entrainment of cold thermocline water.
J. E. Jack Reeves Eyre +4 more
doaj +1 more source
The Value of Forecasters‐in‐the‐Loop in Real‐Time Flood Forecasting in the Age of Machine Learning
Abstract Machine learning (ML) applications in hydrological forecasting are increasingly prevalent and show great potential. However, many previous studies have only evaluated performance through reanalysis or retrospective simulations compared to simplified baselines.
Vinh Ngoc Tran +7 more
wiley +1 more source
Drilling Down I/O Bottlenecks with Cross-layer I/O Profile Exploration [PDF]
I/O performance monitoring tools such as Darshan and Recorder collect I/O-related metrics on production systems and help understand the applications' behavior.
Ather, Hammad +3 more
core +1 more source
Abstract A process‐oriented calibration framework is developed for the Simplified Higher‐Order Closure (SHOC) turbulence scheme in DOE's Simple Cloud Resolving E3SM Atmospheric Model (SCREAM). This framework leverages machine learning surrogates and observational constraints to efficiently calibrate SHOC adjustable parameters across two convective ...
Yunyan Zhang +6 more
wiley +1 more source
Understanding aerosol–cloud interactions using a single-column model for a cold-air outbreak case during the ACTIVATE campaign [PDF]
Marine boundary layer clouds play a critical role in Earth's energy balance. Their microphysical and radiative properties are highly impacted by ambient aerosols and dynamic forcings.
S. Tang +14 more
doaj +1 more source
An earlier study evaluating the dust life cycle in EAMv1 has revealed that the simulated global mean dust lifetime is substantially shorter when higher vertical resolution is used, primarily due to significant strengthening of dust dry removal in source ...
Easter, Richard C. +7 more
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