Evaluating Large‐Domain, Hecto‐Meter, Large‐Eddy Simulations of Trade‐Wind Clouds Using EUREC⁴A Data

Abstract

The meso‐scale variability in cloudiness of the marine trade‐wind layer is explored with large‐eddy simulations of regional extent and validated against observations of the EUREC4A field campaign. 41 days of realistically forced simulations present a representative, statistical view on shallow convection in the winter North Atlantic trades that includes a wide range of meso‐scale variability including the four recently identified patterns of spatial organization: Sugar, Gravel, Flowers and Fish. The results show that cloud cover is on average captured well but with discrepancies in its vertical and spatial distribution. Cloudiness at the lifting condensation level depends on the model resolution with the finer one producing on average a more realistic cloud profile. Independent of the resolution, the variability in cloudiness below the trade inversion is not captured, leading to a lack of stratiform cloudiness with implications on the detectability of meso‐scale patterns whose cloud patches are characterized by stratiform clouds. The simulations tend to precipitate more frequently than observed, with a narrower distribution of echo intensities. The observed co‐variability between cloudiness and environmental conditions is well captured.

Spatial Organisation Affects the Pathway to Precipitation in Simulated Trade‐Wind Convection

“…Schulz and Stevens (2023) show that this simulation reproduces differences in the mesoscale structure underlying the canonical forms of trade cumulus organization of Stevens et al. (2020), as well as variability in precipitation, which makes them a good starting point to investigate how the process of precipitation may vary with spatial organization.…”

“…Schulz and Stevens (2023) show that this simulation reproduces differences in the mesoscale structure underlying the canonical forms of trade cumulus organization of Stevens et al (2020), as well as variability in precipitation, which makes them a good starting point to investigate how the process of precipitation may vary with spatial organization. A nested 312 m simulation does not show a substantially greater skill in representing different cloud organizations or rain rates (Schulz & Stevens, 2023) and shows the same qualitative behavior in our analysis (not shown). We refer the reader to Schulz and Stevens (2023) for an in-depth observational evaluation and comparison of the simulations.…”

“…The simulation output is freely available and can be easily accessed via the EUREC 4 A-Intake catalog at https:// github.com/eurec4a/eurec4a-intake as described at https://howto.eurec4a.eu/simulations. Detailed information about the simulations is given in Schulz and Stevens (2023)…”

“…How does spatial organization influence the development of (surface) precipitation in the trades? In this study, we exploit realistic large‐domain hectometer (hm)‐scale simulations of the North Atlantic trades (Schulz & Stevens, 2023) to investigate whether and how spatial organization affects the pathway to trade‐cumulus precipitation.…”

“…To answer this question on a process‐level, we make use of large‐domain hm‐scale simulations of the North Atlantic trades that were run for the period January–February 2020 during the EUREC 4 A campaign (Bony et al., 2017; Schulz & Stevens, 2023; Stevens et al., 2021) and were designed to explore spatial organization on the mesoscale (20–200 km). We follow the method of Langhans et al.…”