Atmospheric gravity waves (GWs) span a broad range of length scales. As a result, the un-resolved and under-resolved GWs have to be represented using a sub-grid scale (SGS) parameterization in general circulation models (GCMs). In recent years, machine learning (ML) techniques have emerged as novel methods for SGS modeling of climate processes. In the widely-used approach of supervised (offline) learning, the true representation of the SGS terms have to be properly extracted from high-fidelity data (e.g., GW-resolving simulations). However, this is a non-trivial task, and the quality of the ML-based parameterization significantly hinges on the quality of these SGS terms. Here, we compare three methods to extract 3D GW fluxes and the resulting drag (GWD) from high-resolution simulations: Helmholtz decomposition, and spatial filtering to compute the Reynolds stress and the full SGS stress. In addition to previous studies that focused only on vertical fluxes by GWs, we also quantify the SGS GWD due to lateral momentum fluxes. We build and utilize a library of tropical high-resolution ($\Delta x =3~km$) simulations using weather research and forecasting model (WRF). Results show that the SGS lateral momentum fluxes could have a significant contribution to the total GWD. Moreover, when estimating GWD due to lateral effects, interactions between the SGS and the resolved large-scale flow need to be considered. The sensitivity of the results to different filter type and length scale (dependent on GCM resolution) is also explored to inform the scale-awareness in the development of data-driven parameterizations.

Simulating whole atmosphere dynamics, chemistry, and physics is computationally expensive. It can require high vertical resolution throughout the middle and upper atmosphere, as well as a comprehensive chemistry and aerosol scheme coupled to radiation physics. An unintentional outcome of the development of one of the most sophisticated and hence computationally expensive model configurations is that it often excludes a broad community of users with limited computational resources. Here, we analyze two configurations of the Community Earth System Model Version 2, Whole Atmosphere Community Climate Model Version 6 (CESM2(WACCM6)) with simplified “middle atmosphere” chemistry at nominal 1 and 2 degree horizontal resolutions. Using observations, a reanalysis, and direct model comparisons, we find that these configurations generally reproduce the climate, variability, and climate sensitivity of the 1 degree nominal horizontal resolution configuration with comprehensive chemistry. While the background stratospheric aerosol optical depth is elevated in the middle atmosphere configurations as compared to the comprehensive chemistry configuration, it is comparable between all configurations during volcanic eruptions. For any purposes other than those needing an accurate representation of tropospheric organic chemistry and secondary organic aerosols, these simplified chemistry configurations deliver reliable simulations of the whole atmosphere that require 35% to 86% fewer computational resources at nominal 1 and 2 degree horizontal resolution, respectively.

A document by Akhilesh S. Nair. Click on the document to view its contents.

A document by Siddhartha Bishnu. Click on the document to view its contents.

The Tropical cyclone (TC) forecast skill of the eight global medium-range forecast models which are participating in the DIMOSIC (DIfferent Models, Same Initial Conditions) project is investigated in this study. Each model was used to generate 10-day forecasts from the same initial conditions provided by the European Centre for Medium-Range Weather Forecasts. There are a total of 123 initial dates spanning in one year from June 2018 to June 2019 with a 3-day interval. The TC track and intensity forecasts are evaluated against the best track dataset. TC-related precipitation and tropical cyclogenesis forecasts are also compared to explore the differences and similarities of TC forecasts across the models. This comparison of TC forecasts allows model developers in different centers to benchmark their model against other models, with the impact of the initial condition quality removed. The verifications reveal that most models show slow-moving and right-of-track biases in their TC track forecasts. Also, a common dry bias in TC-related precipitation indicates a general deficiency in TC intensity and convection in the models which should be related to insufficient model resolution. These findings provide important references for future model developments.