Dataset Information

Impact of scale-aware deep convection on the cloud liquid and ice water paths and precipitation using the Model for Prediction Across Scales (MPAS-v5.2).

ABSTRACT: The cloud liquid water path (LWP), ice water path (IWP), and precipitation simulated with uniform- and variable-resolution numerical experiments using the Model for Prediction Across Scales (MPAS) are compared against Clouds and the Earth's Radiant Energy System (CERES) and Tropical Rainfall Measuring Mission data. Our comparison between monthly-mean model diagnostics and satellite data focuses on the convective activity regions of the tropical Pacific Ocean, extending from the Tropical Eastern Pacific Basin where trade wind boundary layer clouds develop to the Western Pacific Warm Pool characterized by deep convective updrafts capped with extended upper-tropospheric ice clouds. Using the scale-aware Grell-Freitas (GF) and Multiscale Kain-Fritsch (MSKF) convection schemes in conjunction with the Thompson cloud microphysics, uniform-resolution experiments produce large biases between simulated and satellite-retrieved LWP, IWP, and precipitation. Differences in the treatment of shallow convection lead the LWP to be strongly overestimated when using GF, while being in relatively good agreement when using MSKF compared to CERES data. Over areas of deep convection, uniform- and variable-resolution experiments overestimate the IWP with both MSKF and GF, leading to strong biases in the top-of-the-atmosphere longwave and shortwave radiation relative to satellite-retrieved data. Mesh refinement over the Western Pacific Warm Pool does not lead to significant improvement in the LWP, IWP, and precipitation due to increased grid-scale condensation and upward vertical motions. Results underscore the importance of evaluating clouds, their optical properties, and the top-of-the-atmosphere radiation budget in addition to precipitation when performing mesh refinement global simulations.

SUBMITTER: Fowler LD

PROVIDER: S-EPMC7970523 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The deep convective cloud-environment feedback loop is likely important to Titan's global methane, energy, and momentum cycles, just as it is for Earth's global water, energy, and momentum budgets. General circulation models of Titan's atmosphere are unable to explicitly simulate deep convection and must instead parameterize the impact of this important subgrid-scale phenomenon on the model-resolved atmospheric state. The goal of this study is to better quantify through cloud resolving modeling the effects of deep convective methane storms on their environment and to feed that information forward to improve parameterizations in global models. Dozens of atmospheric profiles unstable with respect to deep moist convection are extracted from the global Titan Atmospheric Model (TAM) and used to initialize the cloud-resolving Titan Regional Atmospheric Modeling System (TRAMS). Mean profiles of heating/cooling and moistening/drying of the large-scale environment in TRAMS indicate that Titan's deep convection forces the environment in a manner analogous to Earth: Large-scale subsidence of the environmental air warms and dries the environment, but clouds can also moisten the environment through the detrainment and evaporation of condensate near cloud top. Relative humidity profiles and characteristic convective time scales are derived to guide the tuning of the deep convective parameterization implemented in TAM, as described in a companion paper. The triggering of convection, the dry convective mixing of the planetary boundary layer, and the entrainment of environmental air into rising air parcels are found to be critical to determining whether a deep convective cloud will form. Only profiles with relatively large convective available potential energy (CAPE) and well mixed planetary boundary layers with high relative humidity were found to produce storms. Environments with low level thermal inversions and planetary boundary layers with low relative humidity or rapidly decreasing moisture with height failed to generate deep convection in TRAMS despite positive CAPE.

Dataset Information

Impact of scale-aware deep convection on the cloud liquid and ice water paths and precipitation using the Model for Prediction Across Scales (MPAS-v5.2).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets