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, sta- tistical 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 spa- tial organization: Sugar , Gravel , Fish and Flowers . 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 reso- lution, 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 pat- terns whose cloud patches are characterized by stratiform clouds. The simulations tend to precipitate more frequently than observed, with a narrower distribution of echo in- tensities. The observed co-variability between cloudiness and environmental conditions is well captured.

Spatial organisation affects the pathway to precipitation in simulated trade-wind convection

“…Here, we analyse a simulation with 625 m gridspacing that covers the western tropical Atlantic from about 60.25 -45.0 • W and 7.5 -17.0 • N, spanning about 1650 km in the east-west direction, and 1050 km in the north-south direction. Schulz and Stevens (2023) show that this simulation reproduces differences in the mesoscale structure underlying the canonical forms of trade cumulus organisation of Stevens et al (2020), which makes them a good starting point to investigate how the process of precipitation may vary with spatial organisation. A nested 312 m simulation does not show a substantially greater skill in representing different cloud organisations or rain rates (Schulz & Stevens, 2023).…”

“…Schulz and Stevens (2023) show that this simulation reproduces differences in the mesoscale structure underlying the canonical forms of trade cumulus organisation of Stevens et al (2020), which makes them a good starting point to investigate how the process of precipitation may vary with spatial organisation. A nested 312 m simulation does not show a substantially greater skill in representing different cloud organisations or rain rates (Schulz & Stevens, 2023). Focusing on the 625 m simulation in this study, but repeating the analysis with the 312 m simulation shows the same qualitative behaviour (not shown).…”

“…150 vertical levels are used, resulting in 70 m and 85 m vertical resolution at 1000 m and 2000 m, respectively. For more details on the setup and performance of the LES, please refer to Schulz and Stevens (2023).…”

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

“…To answer this question, we make use of large-domain LES of the North Atlantic trades that were run for the period January to February 2020 during the EUREC 4 A campaign (Bony et al, 2017;Stevens et al, 2021;Schulz & Stevens, 2023). We follow the method of Langhans et al (2015) and Lutsko and Cronin (2018) and decompose the formation of surface precipitation into two phases, (i) a production phase, in which cloud condensate is converted into rain water, and (ii) a sedimentation phase, in which the produced rain water falls towards the ground while part of it evaporates.…”