Parameterizations for convective transport in various cloud-topped boundary layers

Sikma, M.; Ouwersloot, H. G.

doi:10.5194/acp-15-10399-2015

Cited by 5 publications

(3 citation statements)

References 35 publications

(56 reference statements)

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“…Based on Cuijpers and Bechtold (1995), more 25 representative estimates of this convective cloud cover have been proposed (e.g., Neggers et al, 2006). However, as discussed by Sikma and Ouwersloot (2015), these have to be further adapted. To accurately represent convective transport, it will be important to include these updated parameterizations.…”

Section: Discussionmentioning

confidence: 99%

OESbathy version 1.0: a method for reconstructing ocean bathymetry with realistic continental shelf-slope-rise structures

Goswami¹,

Olson²,

Hinnov³

et al. 2015

Preprint

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The convective transport module, CVTRANS, of the ECHAM/MESSy Atmospheric Chemistry (EMAC) model has been revised to better represent the physical flows and incorporate recent findings on the properties of the convective plumes. The modifications involve (i) applying intermediate time stepping based on a settable criterion, 5 (ii) using an analytic expression to account for the intra time step mixing ratio evolution below cloud base, and (iii) implementing a novel expression for the mixing ratios of atmospheric compounds at the base of an updraft. Even when averaged over a year, the predicted mixing ratios of atmospheric compounds are significantly affected by the intermediate time stepping. For example, for an exponentially decaying atmospheric 10 tracer with a lifetime of 1 day, the zonal averages can locally differ by more than a factor of 6 and the induced root mean square deviation from the original code is, weighted by the air mass, higher than 40 % of the average mixing ratio. The other modifications result in smaller differences. However, since they do not require additional computational time, their application is also recommended. 15 25 be parameterized (e.g., Arakawa, 2004; Kim et al., 2012). Useful tools to derive and 3118 GMDD 8, 2015 check these parameterizations are large-eddy simulation (LES) models that operate in smaller domains with a higher resolution (e.g., Bechtold et al., 1995;Siebesma and Cuijpers, 1995; Ouwersloot et al., 2013).Here, we revise the parameterization in the convective transport scheme (CV-TRANS, Tost et al., 2010) of the ECHAM/MESSy Atmospheric Chemistry (EMAC) 5 GMDD 8, 3117-3145, 2015 Abstract GMDD 8, 3117-3145, 2015 Abstract GMDD 8, 3117-3145, 2015 Abstract GMDD 8, 3117-3145, 2015 Abstract GMDD 8, 3117-3145, 2015 Abstract 8, 3117-3145, 2015 Abstract GMDD 8, 3117-3145, 2015 Abstract GMDD 8, 3117-3145, 2015 Abstract GMDD 8, 3117-3145, 2015 Abstract 8, 3117-3145, 2015 Abstract 8, 3117-3145, 2015 Abstract 8, 3117-3145, 2015 Abstract 8, 3117-3145, 2015 Abstract 8, 3117-3145, 2015 Abstract 8, 3117-3145, 2015 Abstract 8, 3117-3145, 2015

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Section: Discussionmentioning

confidence: 99%

OESbathy version 1.0: a method for reconstructing ocean bathymetry with realistic continental shelf-slope-rise structures

Goswami¹,

Olson²,

Hinnov³

et al. 2015

Preprint

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“…Here we will assume that 𝑤𝑤 𝑐𝑐𝑐𝑐 is much smaller than 𝑤𝑤 𝑐𝑐𝑐𝑐 , and therefore we neglect it in equation where 0.91 is a constant estimated from a suite of numerical experiments of shallow cumulus carried out using large-eddy simulations (Sikma and Ouwersloot 2015), and 𝑤𝑤 * is the convective velocity scale of the most energetic turbulent eddies defined as…”

Section: A5 Representations Of Area Fraction Cloud Core and Mass Fluxmentioning

confidence: 99%

CloudRoots-Amazon22: Integrating clouds with photosynthesis by crossing scales

de Arellano,

Hartogensis,

de Boer

et al. 2024

Bulletin of the American Meteorological Society

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How are rainforest photosynthesis and turbulent fluxes influenced by clouds? To what extent are clouds affected by local processes driven by rainforest energy, water and carbon fluxes? These interrelated questions were the main drivers of the intensive field experiment CloudRoots-Amazon22 which took place at the ATTO/Campina supersites in the Amazon rainforest during the dry season, in August 2022. CloudRoots-Amazon22 collected observational data to derive cause-effect relationships between processes occurring at the leaf-level up to canopy scales in relation to the diurnal evolution of the clear-to-cloudy transition. First, we studied the impact of cloud and canopy radiation perturbations on the sub-diurnal variability of stomatal conductance. Stoma opening is larger in the morning, modulated by the cloud optical thickness. Second, we combined 1 Hz-frequency measurements of the stable isotopologues of carbon dioxide and water vapor with measurements of turbulence to determine carbon dioxide and water vapor sources and sinks within the canopy. Using scintillometer observations, we inferred 1-minute sensible heat flux that responded within minutes to the cloud passages. Third, collocated profiles of state variables and greenhouse gases enabled us to determine the role of clouds in vertical transport. We then inferred, using canopy and upper-atmospheric observations and a parameterization, the cloud cover and cloud mass flux to establish causality between canopy and cloud processes. This shows the need of comprehensive observational set to improve weather and climate model representations. Our findings contribute to advance our knowledge of the coupling between cloudy boundary layers and primary carbon productivity of the Amazon rainforest.

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“…Since we are also interested in the properties of the clouds during the experiment, we make further use of a three-dimensional conditional average over the grid, similar to that used in other studies Ouwersloot et al, 2013;Sikma and Ouwersloot, 2015). In this case, we mainly focus on the conditional averages for clouds (q l > 0) and for the buoyant part of clouds or cloud cores (q l > 0 and θ v > θ v ), where the overbar stands for domain average properties at each vertical level).…”

Section: Conditional Averagingmentioning

confidence: 99%

On the interactions between clouds, radiation, turbulence and vegetation in the atmospheric boundary layer

Bagazgoitia

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Parameterizations for convective transport in various cloud-topped boundary layers

Cited by 5 publications

References 35 publications

OESbathy version 1.0: a method for reconstructing ocean bathymetry with realistic continental shelf-slope-rise structures

OESbathy version 1.0: a method for reconstructing ocean bathymetry with realistic continental shelf-slope-rise structures

CloudRoots-Amazon22: Integrating clouds with photosynthesis by crossing scales

On the interactions between clouds, radiation, turbulence and vegetation in the atmospheric boundary layer

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