A Combined Eddy-Diffusivity Mass-Flux Approach for the Convective Boundary Layer

By A. P. Siebesma, P. M. M. Soares, João Paulo Ramos Teixeira. 2007

365 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/179c16f43db2e9e2f1b9f22ab3862546ff77afb4

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Citations in the corpus

Uncertainty in Model Climate Sensitivity Traced to Representations of Cumulus Precipitation Microphysics (2016, positive): “The scaling of «s with height z is supported by results of large-eddy simulations of shallow cumulus convection (e.g., de Roode and Duynkerke 2000; Siebesma et al. 2007).”
Higher-Order Turbulence Closure and Its Impact on Climate Simulations in the Community Atmosphere Model (2013, methodology): “The first is known as the eddy diffusion mass flux (EDMF) approach (Siebesma et al. 2007; Pergaud et al. 2009), which seeks to unify PBL and shallow convective processes by the marriage of traditionally used diffusion schemes in the PBL and the commonly used mass flux approach to parameterize…”
Large-Eddy Simulation of the Atmospheric Boundary Layer (2020, methodology): “This is typically accomplished by including an additional term in the eddy diffusivity formulation (Siebesma et al. 2007), i.e. 〈w′θ ′〉 = −Kθ ∂〈θ〉 ∂z + M(θu − 〈θ〉) (15) where M is the mass flux and θu is the potential temperature in updraft regions.”
Unified treatment of dry convective and stratocumulus‐topped boundary layers in the ECMWF model (2011, methodology): “Note that pure diffusion models typically result in a more tilted shape due to their local down-gradient behaviour (Siebesma et al., 2007).”
A Planetary Boundary Layer Height Climatology Derived from ECMWF Reanalysis Data (2013, methodology): “In particular, ERA-Interim has shown significant improvements of the simulations of marine cloudy boundary layers due to the implementation of the eddy-diffusivity mass flux (EDMF) approach (e.g., Soares et al. 2004; K€ohler 2005; Siebesma et al. 2007).”
A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challenges (2015, methodology): “A combination of the two approaches has been proposed by Siebesma et al. [2007].”
A Parameterization of Dry Thermals and Shallow Cumuli for Mesoscale Numerical Weather Prediction (2009, background): “…∂z = Bu − w2u − P (6) where on the right-hand side (rhs), the first term is the buoyancy, the second term is the entrainment, and P represents the pressure term defined, e.g. in Simpson and Wiggert (1969), Siebesma et al. (2003), Soares et al. (2004) as a linear combination of the first two terms.”
The Challenge of Forecasting the Onset and Development of Radiation Fog Using Mesoscale Atmospheric Models (2015, methodology): “The microphysics are calculated using the ICE-3 package (Pinty and Jabouille 1998), and the shallow convection parametrization is based on the Eddy-Diffusivity Mass-Flux scheme (Siebesma et al. (2007); De Rooy and Siebesma (2008); de Rooy and Siebesma (2010)).”
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, methodology): “They have often been employed to augment observations from local field studies, and parameterization schemes have been fit to and evaluated with the observations and the high-resolution simulations used in tandem [e.g., Liu et al., 2001; Siebesma et al., 2003; Stevens et al., 2005; Siebesma et al., ”
Entrainment and detrainment in cumulus convection: an overview (2013, positive): “Nowadays the mass flux concept is also used for dry convection in the increasingly popular eddy diffusivity mass flux (EDMF) schemes (Soares et al., 2004; Siebesma et al., 2007).”
LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection (2013, methodology): “This approach was developed independently by two teams and since adopted in several groups (Soares et al, 2004; Siebesma et al, 2007; Pergaud et al, 2009; Angevine et al, 2010; Neggers et al, 2009; Neggers, 2009).”
Shifting the diurnal cycle of parameterized deep convection over land (2009, methodology): “The so-called ‘‘thermal plume model’’ used here and similar approaches [Soares et al., 2004; Siebesma et al., 2007] were shown to effectively reproduce the diurnal cycle of the continental boundary layer on fair-weather cumulus days.”
Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities (2011, positive): “In principle this could be treated as part of the PBL scheme; see, for example, the combined eddy–diffusivity mass–ﬂux model of Siebesma et al. (2007), but for the COSMO model such an extended PBL scheme is not yet available.”
A Unified Convection Scheme (UNICON). Part I: Formulation (2014, methodology): “An approach in line with this philosophy is an eddy diffusivity–mass flux model (Siebesma et al. 2007).”
Cloud feedback mechanisms and their representation in global climate models (2017, neutral): “GCMs usually represent cloud-related processes through distinct parameterizations, with separate assumptions for subgrid variability, despite a goal for unification.(98,99) Physical assumptions used in PBL parameterizations often relate cloud formation to buoyancy production, stability, and wind she”
A New Moist Turbulence Parameterization in the Community Atmosphere Model (2009, methodology): “A hybrid new eddydiffusion mass-flux parameterization that was initially developed for the simulation of DCBLs (e.g., Siebesma et al. 2007) is also being extended to improve parameterizations of cloud-topped boundary layers in the European Centre for Medium-Range Weather Forecasts operational…”
A Dual Mass Flux Framework for Boundary Layer Convection. Part I: Transport (2009, methodology): “…features a transporting bulk updraft; the PBL scheme used in this study is the Eddy Diffusivity Mass Flux (EDMF) scheme (Siebesma and Teixeira 2000; Siebesma et al. 2007) as currently operational in the European Centre for MediumRange Weather Forecasts (ECMWF) Integrated Forecasting System (IFS).”