DOI: 10.6075/j03j3bgf
title: Test data from SPCAM for machine learning in moist physics

description: Current moist physics parameterization schemes in general circulation
 models (GCMs) are the main source of biases in simulated precipitation and
 atmospheric circulation. Recent advances in machine learning make it
 possible to explore data-driven approaches to developing parameterization
 for moist physics processes such as convection and clouds. This study aims
 to develop a new moist physics parameterization scheme based on deep
 learning. We use a residual convolutional neural network (ResNet) for this
 purpose. It is trained with one-year simulation from a superparameterized
 GCM, SPCAM. An independent year of SPCAM simulation is used for
 evaluation. In the design of the neural network, referred to as ResCu, the
 moist static energy conservation during moist processes is considered. In
 addition, the past history of the atmospheric states, convection and
 clouds are also considered. The predicted variables from the neural
 network are GCM grid-scale heating and drying rates by convection and
 clouds, and cloud liquid and ice water contents. Precipitation is derived
 from predicted moisture tendency. In the independent-data test, ResCu can
 accurately reproduce the SPCAM simulation in both time-mean and temporal
 variance. Comparison with other neural networks demonstrates the superior
 performance of ResNet architecture. ResCu is further tested in a single
 column model for both continental midlatitude warm season convection and
 tropical monsoonal convection. In both cases, it simulates the timing and
 intensity of convective events well. In the prognostic test of tropical
 convection case, the simulated temperature and moisture biases with ResCu
 are smaller than those using conventional convection and cloud
 parameterizations.
description: This dataset is extracted from a simulation based on a superparameterized GCM, SPCAM (https://wiki.ucar.edu/display/ccsm/Superparameterized+CAM+(SPCAM)). The SPCAM implements a 2-D CRM in CAM5.2 to replace its conventional parameterization for moist convection and large-scale condensation. The dynamic framework of CAM5 has a horizontal resolution of 1.9 x 2.5 degrees and 30 vertical levels that are shared with the embedded CRM. The SPCAM used in this study has a coupled land surface model Community Land Model 4.0 (CLM4.0). It uses a prescribed climatological sea surface temperature field that comes with the CAM5 model. It is run for three years and 4 months from Jan. 1st in 1998 to March 31st in 2001 with a time step of 20 minutes. The first year and 4 months are for spin up, the second year is used for training and the third year is used for testing and evaluation. The training data from SPCAM is output every timestep.
description: Training samples of the entire second year are compressed in
 SPCAM_ML_Han_et_al_0.tar.gz, and testing samples of the entire third year
 are compressed in SPCAM_ML_Han_et_al_1.tar.gz. In each of them, there are
 a data documentation file and 365 netCDF data files that are marked with
 its date. The variable fields contain temperature and moisture tendencies
 and cloud water and cloud ice from the CRM, and vertical profiles of
 temperature and moisture and large-scale temperature and moisture
 tendencies from the dynamic core of SPCAM’s host model CAM5 and PBL
 diffusion. In addition, we include surface sensible and latent heat
 fluxes. For more details, please read the data documentation inside the
 tar.gz files.