Evaluating the Performance of Cumulus Convection Parameterization Schemes in Regional Climate Modeling System

In this study, we explored the performance of the cumulus convection parameterization schemes of Regional Climate Modeling System (RegCM) towards the Indian summer monsoon (ISM) of a catastrophic year through various numerical experiments conducted with different convection schemes (Kuo, Grell amd MIT) in RegCM. The model is integrated at 60KM horizontal resolution over Indian region and forced with NCEP/NCAR reanalysis. The simulated temperature at 2m and the wind at 10m are validated against the forced data and the total precipitation is compared with the Global Precipitation Climatology Centre (GPCC) observations. We find that the simulation with MIT convection scheme is close to the GPCC data and NCEP/NCAR reanalysis. Our results with three convection schemes suggest that the RegCM with MIT convection scheme successfully simulated some characteristics of ISM of a catastrophic year and may be further examined with more number of convection schemes to customize which convection scheme is much better.

Empirical values and assumptions in the convection of numerical models

Convection influences climate and weather events over a wide range of spatial and temporal scales. Therefore, accurate predictions of the time and location of convection and its development into severe weather are of great importance. Convection has to be parameterized in Numerical Weather Prediction models, Global Climate Models, and Earth System Models (NWPs, GCMs, and ESMs) as the key physical processes occur at scales much lower than the model grid size. The convection schemes described in the literature represent the physics by simplified models that require assumptions about the processes and the use of a number of parameters based on empirical values. The present paper examines these choices and their impacts on model outputs and emphasizes the importance of observations to improve our current understanding of the physics of convection.

Incorporating features from the Stochastic Time-Inverted Lagrangian Transport (STILT) model into the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model: a unified dispersion model for time-forward and time-reversed applications

The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model is a state-of-the-science atmospheric dispersion model that is developed and maintained at the National Oceanic Atmospheric Administration’s (NOAA) Air Resources Laboratory (ARL). In the early 2000s, HYSPLIT served as the starting point for development of the Stochastic Time-Inverted Lagrangian Transport (STILT) model that emphasizes backward-in-time dispersion simulations to determine source regions of receptors. STILT continued its separate development and gained a wide user base. Since STILT was built on a now outdated version of HYSPLIT and lacks long-term institutional support to maintain the model, incorporating STILT features into HYSPLIT allows these features to stay up to date. This paper describes the STILT features incorporated into HYSPLIT, which include: a new vertical interpolation algorithm for WRF derived meteorological input files, a detailed algorithm for estimating boundary layer height, a new turbulence parameterization, a vertical Lagrangian timescale that varies in time and space, a complex dispersion algorithm, and two new convection schemes. An evaluation of these new features was performed using tracer release data from the Cross Appalachian Tracer Experiment and the Across North America Tracer Experiment. Results show the dispersion module from STILT, which takes up to double the amount of time to run, is less dispersive in the vertical and in better agreement with observations than the existing HYSPLIT option. The other new modeling features from STILT were not consistently statistically different than existing HYSPLIT options. Forward-time simulations from the new model were also compared against backward-time equivalents and found to be statistically comparable to one another.

Sensitivity of ENSO Simulation to the Convection Schemes in the NESM3 Climate System Model: Atmospheric Processes

The El Niño-Southern Oscillation (ENSO) is the most prominent climate system in the tropical Pacific. However, its simulation, including the amplitude, phase locking, and asymmetry of its two phases, is not well reproduced by the current climate system models. In this study, the sensitivity of the ENSO simulation to the convection schemes is discussed using the Nanjing University of Information Science and Technology Earth System version 3.0 (NESM3) model. Three convection schemes, including the default, the default coupled with the stochastic multicloud model (SMCM), and the default used in the Coupled Model Intercomparison Project Phase 6 (CMIP6), are implemented. The model results reveal that the low-level cloud cover and surface net shortwave radiation are best represented over the tropical Pacific in the model containing the SMCM. The simulations of the ENSO behavior’s response to changes in the convection scheme are not uniform. The model results reveal that the model containing the SMCM performs best in terms of simulating the seasonal cycle of the sea surface temperature anomaly along the equatorial Pacific, the phase locking, and the power spectrum of ENSO but with a modest ENSO amplitude. Compared to the model containing the default convection scheme, the coupling of the default scheme and the SMCM provides a good simulation of the ENSO’s asymmetry, while the model containing the CMIP6 convection scheme outperforms the others in terms of the simulation of the ENSO’s amplitude. Two atmospheric feedback processes were further discussed to investigate the factors controlling the ENSO’s amplitude. The analyses revealed that the strongest positive atmospheric Bjerknes feedback and the thermodynamic damping of the surface net heat flux occurred in the model containing the CMIP6 convection scheme, suggesting that the atmospheric Bjerknes feedback may overwhelm the heat flux damping feedback when determining the ENSO’s amplitude. The results of this study demonstrate that perfectly modeling and predicting the ENSO is not simple, and it is still a large challenge and issue for the entire model community in the future.

A Convection Parameterization for Low-CAPE Environments

Numerical models that are unable to resolve moist convection in the atmosphere employ physical parameterizations to represent the effects of the associated processes on the resolved-scale state. Most of these schemes are designed to represent the dominant class of cumulus convection that is driven by latent heat release in a conditionally unstable profile with a surplus of convective available potential energy (CAPE). However, an important subset of events occurs in low-CAPE environments in which potential and symmetric instabilities can sustain moist convective motions. Convection schemes that are dependent on the presence of CAPE are unable to depict accurately the effects of cumulus convection in these cases. A mass-flux parameterization is developed to represent such events, with triggering and closure components that are specifically designed to depict subgrid-scale convection in low-CAPE profiles. Case studies show that the scheme eliminates the “bull’s-eyes” in precipitation guidance that develop in the absence of parameterized convection, and that it can represent the initiation of elevated convection that organizes squall-line structure. The introduction of the parameterization leads to significant improvements in the quality of quantitative precipitation forecasts, including a large reduction in the frequency of spurious heavy-precipitation events predicted by the model. An evaluation of surface and upper-air guidance shows that the scheme systematically improves the model solution in the warm season, a result that suggests that the parameterization is capable of accurately representing the effects of moist convection in a range of low-CAPE environments.