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Model Development Is Crucial in Understanding Climate Change

Jul 16, 2019     Email"> PrintText Size

Numerical models are a key tool for climate scientists in understanding the past, present and future climate change arising from natural, unforced variability or in response to changes, according to Dr. BAO Qing, a scientist at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), and the corresponding author of a recently published study.

"Climate changes substantially influence human society in all aspects, and climate prediction is a constant hot topic in climate science community," says Dr. BAO. "The Coupled Model Intercomparison Project (CMIP) uses state-of-the-art climate models to provide a physical evidence base for policymakers, such as the Intergovernmental Panel on Climate Change (IPCC)."

Dr. BAO and his model team from LASG are in charge of the development of CAS FGOALS-f3-L climate model's atmospheric model. They recently completed the Atmospheric Model Intercomparison Project (AMIP) simulations in the sixth phase of CMIP and published their datasets of the Earth System Grid Federation (ESGF) nodes as a data description paper in Advances in Atmospheric Sciences.


The earth grids indicate the dynamical core of the atmospheric model component in FGOALS-f3-L, while the clouds and associated precipitation indicate the key physical scheme in the atmospheric model - the Resolving Convective Precipitation (RCP) scheme - which makes the model scale-aware and computationally fast. (Image by AAS

The Finite-volume Atmospheric Model (FAMIL) in FGOALS-f3-L, which is the new-generation Atmospheric General Circulation Model (AGCM) of the Spectral Atmosphere Model of LASG (SAMIL), has been fixed for the CMIP6 experiments in 2017. In this version, the dynamical core and model physics parameterization scheme have been substantially updated. The new model is fast in completing huge computing tasks and overcomes some model biases related to climate sensitivity and cloud microphysics from the last version.

The current version shows good ability not only in capturing large-scale patterns of climatological mean precipitation and surface temperature, but is also good at reflecting intraseasonal events like Madden–Julian Oscillation (MJO) and typhoons, which were a challenge for the CMIP5 models, according to Dr. HE Bian, first author of the paper.

Following the design of the AMIP experiments, three ensemble simulations were carried out over the period 1979-2014, which were forced by monthly mean observed sea surface temperature and sea ice, as recommended by the CMIP6 projects. The model outputs contain a total of 37 variables and include the required three-hourly mean, six-hourly transient, daily and monthly mean datasets.

"Preliminary evaluation suggests that FGOALS-f3-L can capture the basic patterns of atmospheric circulation and precipitation well, and these datasets could contribute to the benchmark of current model behaviors for the desired continuity of CMIP," Dr. BAO explains. "Analysis of these datasets will also be helpful in understanding the sources of model biases and beneficial to the development of climate forecast systems."


The resolving convective precipitation scheme plays an important role in simulating intraseasonal atmospheric variabilities. (Image by HE Bian) 

(Editor: LI Yuan)

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