Reply to ''Comments on 'A Density Current Parameterization Coupled with Emanuel's Convection Scheme. Part I: The Models'''

By J. Grandpeix, J. Lafore. 2012

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In the original paper of this discussion, Grandpeix and Lafore (2010, hereafter GL10) presented a density current parameterization coupled with Emanuel’s convection scheme. Jun-Ichi Yano’s comment (Yano 2012, hereafter Yc12) questions various formulas of this parameterization and proposes new ones following an approach based on segmentally constant approximation (SCA; Yano et al. 2010). Essentially, Yc12 ascertains an overall agreement between the new formulas and the GL10 ones, the proposed modifications amounting to the introduction of new terms representing various processes neglected in GL10. Yet Yc12 claims that it was a mistake not to use SCA for the wake equation derivation. Although we acknowledge the interest of the new developments presented by Yc12, we think that the very existence of two different ways of developing wake models should be considered as very positive. Let us emphasize, however, that most of the equations put forward by Yc12 have never been published and appear for the first time in this comment: what we are dealing with here is not really a comment but rather a note about new developments of SCA. Our reply to Yc12 is structured in two parts: 1) a first part devoted to a global analysis of Yc12 and of its relevance for the wake model of GL10, and 2) a second part (hereafter referred to as the appendix) that contains detailed item-by-item responses to Yano’s remarks. In what follows, we shall keep the equation numbering proposed in Yc12: all of the equations numbered without a prefix are those from GL10, unless otherwise noted. The equations in Yc12’s main text and appendix are numbered with prefixes 2 and A, respectively.

Subfields: Biomedical Engineering, Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/207269ec60f04dfabc75e79dd82f50f5de1d3331

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Citations in the corpus

• Deep learning to represent subgrid processes in climate models (2018, background): “Similarly, recurrent neural networks might be used to capture convective memory across time steps (46, 47).”
• Presentation and Evaluation of the IPSL‐CM6A‐LR Climate Model (2020, background): “, 2010), and a parameterization of the cold pools or wakes created below cumulonimbus by the evaporation of convective rainfall (Grandpeix & Lafore, 2010; Grandpeix et al., 2010).”
• A Unified Convection Scheme (UNICON). Part I: Formulation (2014, background): “While the details of the formulation differ, Grandpeix and Lafore (2010) explored a conceptually similar approach.”
• The Formation of Wider and Deeper Clouds as a Result of Cold-Pool Dynamics (2014, methodology): “In theory, a complex parameterization for the development of cold pools should be used [see, e.g., Grandpeix and Lafore (2010)].”
• LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection (2013, methodology): “The wake model is fully described in Grandpeix and Lafore (2010) and Grandpeix et al (2010).”
• Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5 (2013, background): “A parameterization of the cold pools generated by the re-evaporation of convective rainfall has been introduced (Grandpeix and Lafore 2010; Grandpeix et al. 2010).”
• Observational Evidence for Relationships between the Degree of Aggregation of Deep Convection, Water Vapor, Surface Fluxes, and Radiation (2012, background): “Some attempts to include features of mesoscale convective organization in parameterization schemes have been carried out (Moncrieff 2004; Qian et al. 1998; Redelsperger et al. 2000; Grandpeix and Lafore 2010; Mapes and Neale 2011).”
• Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the AMIE/DYNAMO field campaign (2015, background): “A different cold pool definition using the vertically integrated buoyancy field from surface to the top of the cold pool [e.g., Grandpeix and Lafore, 2010] may alleviate this issue.”
• Influence of the subcloud layer on the development of a deep convective ensemble (2012, background): “Such a model can either be highly idealized in the form of a model of convection with memory (e.g., Mapes and Neale 2011) or can be rooted in a physical model of the cold pool circulation (e.g., Grandpeix and Lafore 2010).”
• The double ITCZ bias in CMIP5 models: interaction between SST, large-scale circulation and precipitation (2015, background): “IPSLCM5B-LR and NCAR-CCSM4 also show an improvement in the SI index, and it can be explained by improvements in the existing parametrization of deep convection (Grandpeix and Lafore 2010; Grandpeix et al. 2010 for IPSL-CM5B-LR; Neale et al. 2008 for NCAR-CCSM4).”