Influence of deepening and mesoscale organization of shallow convection on stratiform cloudiness in the downstream trades

Vogel, Raphaëla; Nuijens, Louise; Stevens, Björn

doi:10.1002/qj.3664

Cited by 20 publications

(30 citation statements)

References 36 publications

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“…Hohenegger et al (2019) find similar dependencies of cloud fraction on grid spacing between 2.5 km and 80 km and hypothesize that if horizontal resolution is not sufficient for proper mixing, the boundary layer grows and clouds form higher at colder temperatures leading also to more cloudiness. The decrease in cloud fraction between the simulations with 600 m and 300 m grid spacing is still substantial and not converged, which is in agreement with idealized modelling studies showing that LEM underestimates cloud fraction when the grid spacing becomes as fine as 50 m (Vogel et al, 2019).…”

Section: Vertical Distribution Of Water Vapor and Cloud Fractionsupporting

confidence: 81%

“…December 2013 is a stronger secondary maximum of cloud fraction near 2 km height in the simulations with 600 m to 2.5 km grid spacing. These small stratiform cloud shields below the inversion are often present in both model and observations (Lamer et al, 2015;Vogel et al, 2019) but cannot be found in the WALES data in our time period. The LEM simulations with finest grid spacing (300 m) are closer to the observations in this case.…”

Section: Vertical Distribution Of Water Vapor and Cloud Fractionmentioning

confidence: 56%

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The vertical Structure and spatial Variability of lower tropospheric Water Vapor and Clouds in the Trades

Naumann¹,

Kiemle²

2019

Preprint

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<p><strong>Abstract.</strong> Horizontal and vertical variability of water vapor is omnipresent in the tropics but its interaction with cloudiness poses challenges for weather and climate models. In this study we compare airborne lidar measurements from a summer and a winter field campaign in the tropical Atlantic with high-resolution simulations to analyse the water vapor distributions in the trade wind regime, its covariation with cloudiness and their representation in simulations. Across model grid spacing from 300&#8201;m to 2.5&#8201;km, the simulations show good skill in reproducing the water vapor distribution in the trades as measured by the lidar. An exception to this is a pronounced moist model bias at the top of the shallow cumulus layer in the dry winter season which is accompanied by a too weak humidity inversion at the cloud top. The model's underestimation of water vapor variability in the cloud and subcloud layer occurs in both seasons but is less pronounced. Despite the model's insensitivity to resolution from hecto- to kilometer scale for the distribution of water vapor, cloud fraction decreases strongly with increasing model resolution and is not converged at hectometer grid spacing. The observed cloud deepening with increasing water vapor path is captured well across model resolution but the concurrent transition from cloud-free to low cloud fraction is better represented at hectometer resolution. In particular, in the wet summer season the simulations with kilometer-scale resolution overestimate the observed cloud fraction near the inversion but lack condensate near the observed cloud base. This illustrates how a model's ability to properly capture the water vapor distribution does not need to translate into an adequate representation of shallow cumulus clouds that live at the tail of the water vapor distribution.</p>

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Section: Vertical Distribution Of Water Vapor and Cloud Fractionsupporting

confidence: 81%

Section: Vertical Distribution Of Water Vapor and Cloud Fractionmentioning

confidence: 56%

The vertical Structure and spatial Variability of lower tropospheric Water Vapor and Clouds in the Trades

Naumann¹,

Kiemle²

2019

Preprint

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“…The rate at which cloudiness reduces with increasing mixing depends both on the shallowness of the present-day cloud profile (Brient et al 2016), and on the coupling between convective mixing, surface turbulent fluxes and low-cloud radiative effects (Vial et al 2016). In contrast to climate models, cloud-base cloudiness in large-eddy simulations (LES) and in observations is much less sensitive to changes in the large-scale environment (Bretherton et al 2013;Blossey et al 2013;Vogel et al 2016;Nuijens et al 2014Nuijens et al , 2015.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Shallow Convective Mass Flux from the Subcloud-Layer Mass Budget

Vogel¹,

Bony²,

Stevens

2020

Journal of the Atmospheric Sciences

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This paper develops a method to estimate the shallow-convective mass flux M at the top of the subcloud layer as a residual of the subcloud-layer mass budget. The ability of the mass-budget estimate to reproduce the mass flux diagnosed directly from the cloud-core area fraction and vertical velocity is tested using real-case large-eddy simulations over the tropical Atlantic. We find that M reproduces well the magnitude, diurnal cycle, and day-to-day variability of the core-sampled mass flux, with an average root-mean-square error of less than 30% of the mean. The average M across the four winter days analyzed is 12 mm s−1, where the entrainment rate E contributes on average 14 mm s−1 and the large-scale vertical velocity W contributes −2 mm s−1. We find that day-to-day variations in M are mostly explained by variations in W, whereas E is very similar among the different days analyzed. Instead E exhibits a pronounced diurnal cycle, with a minimum of about 10 mm s−1 around sunset and a maximum of about 18 mm s−1 around sunrise. Application of the method to dropsonde data from an airborne field campaign in August 2016 yields the first measurements of the mass flux derived from the mass budget, and supports the result that the variability in M is mostly due to the variability in W. Our analyses thus suggest a strong coupling between the day-to-day variability in shallow convective mixing (as measured by M) and the large-scale circulation (as measured by W). Application of the method to the EUREC4A field campaign will help evaluate this coupling, and assess its implications for cloud-base cloudiness.

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“…outside deep convective regions (e.g., Muller and Held, 2012;Hohenegger and Stevens, 2016;Wing et al, 2017).…”

Section: Vertical Distribution Of Water Vapor and Cloud Fractionmentioning

confidence: 99%

The vertical structure and spatial variability of lower-tropospheric water vapor and clouds in the trades

Naumann

Kiemle

2020

Atmos. Chem. Phys.

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Abstract. Horizontal and vertical variability of water vapor is omnipresent in the tropics, but its interaction with cloudiness poses challenges for weather and climate models. In this study we compare airborne lidar measurements from a summer and a winter field campaign in the tropical Atlantic with high-resolution simulations to analyze the water vapor distributions in the trade wind regime, its covariation with cloudiness, and their representation in simulations. Across model grid spacing from 300 m to 2.5 km, the simulations show good skill in reproducing the water vapor distribution in the trades as measured by the lidar. An exception to this is a pronounced moist model bias at the top of the shallow cumulus layer in the dry winter season which is accompanied by a humidity gradient that is too weak at the inversion near the cloud top. The model's underestimation of water vapor variability in the cloud and subcloud layer occurs in both seasons but is less pronounced than the moist model bias at the inversion. Despite the model's insensitivity to resolution from hecto- to kilometer scale for the distribution of water vapor, cloud fraction decreases strongly with increasing model resolution and is not converged at hectometer grid spacing. The observed cloud deepening with increasing water vapor path is captured well across model resolution, but the concurrent transition from cloud-free to low cloud fraction is better represented at hectometer resolution. In particular, in the wet summer season the simulations with kilometer-scale resolution overestimate the observed cloud fraction near the inversion but lack condensate near the observed cloud base. This illustrates how a model's ability to properly capture the water vapor distribution does not necessarily translate into an adequate representation of shallow cumulus clouds that live at the tail of the water vapor distribution.

show abstract

Influence of deepening and mesoscale organization of shallow convection on stratiform cloudiness in the downstream trades

Cited by 20 publications

References 36 publications

The vertical Structure and spatial Variability of lower tropospheric Water Vapor and Clouds in the Trades

The vertical Structure and spatial Variability of lower tropospheric Water Vapor and Clouds in the Trades

Estimating the Shallow Convective Mass Flux from the Subcloud-Layer Mass Budget

The vertical structure and spatial variability of lower-tropospheric water vapor and clouds in the trades

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