Chatfield, R. B. and Brost, R. A.: A two-stream model of the vertical transport of trace species in the convective boundary layer, J. Geophys. Res.-Atmos., 92, 13263–13276,
https://doi.org/10.1029/JD092iD11p13263, 1987.
a
Couvreux, F., Rio, C., Guichard, F., Lothon, M., Canut, G., Bouniol, D., and Gounou, A.: Initiation of daytime local convection in a semi-arid region analysed with high-resolution simulations and AMMA observations, Q. J. Roy. Meteor. Soc., 138, 56–71,
https://doi.org/10.1002/qj.903, 2012.
a
Couvreux, F., Hourdin, F., Williamson, D., Roehrig, R., Volodina, V., Villefranque, N., Rio, C., Audouin, O., Salter, J., Bazile, E., Brient, F., Favot, F., Honnert, R., Lefebvre, M.-P., Madeleine, J.-B., Rodier, Q., and Xu, W.: Process-based climate model development harnessing machine learning: I. A calibration tool for parameterization improvement, J. Adv. Model. Earth Sy., 13, e2020MS002217,
https://doi.org/10.1029/2020MS002217, 2021.
a,
b,
c,
d
Daleu, C. L., Plant, R. S., Woolnough, S. J., Sessions, S., Herman, M. J., Sobel, A., Wang, S., Kim, D., Cheng, A., Bellon, G., Peyrille, P., Ferry, F., Siebesma, P., and van Ulft, L.: Intercomparison of methods of coupling between convection and large-scale circulation: 1. Comparison over uniform surface conditions, J. Adv. Model. Earth Sy., 7, 1576–1601,
https://doi.org/10.1002/2015MS000468, 2015.
a
Del Genio, A. D., Wu, J., Wolf, A. B., Chen, Y., Yao, M.-S., and Kim, D.: Constraints on cumulus parameterization from simulations of observed MJO events, J. Climate, 28, 6419–6442,
https://doi.org/10.1175/JCLI-D-14-00832.1, 2015.
a,
b
Diallo, F. B., Hourdin, F., Rio, C., Traore, A.-K., Mellul, L., Guichard, F., and Kergoat, L.: The surface energy budget computed at the grid-scale of a climate model challenged by station data in West Africa, J. Adv. Model. Earth Sy., 9, 2710–2738,
https://doi.org/10.1002/2017MS001081, 2017.
a,
b
Dirmeyer, P. A., Cash, B. A., Kinter, J. L., Jung, T., Marx, L., Satoh, M., Stan, C., Tomita, H., Towers, P., Wedi, N., Achuthavarier, D., Adams, J. M., Altshuler, E. L., Huang, B., Jin, E. K., and Manganello, J.: Simulating the diurnal cycle of rainfall in global climate models: Resolution versus parameterization, Clim. Dynam., 39, 399–418,
https://doi.org/10.1007/s00382-011-1127-9, 2012.
a
Dorrestijn, J., Crommelin, D. T., Siebesma, A. P., and Jonker, H. J. J.: Stochastic parameterization of shallow cumulus convection estimated from high-resolution model data, Theor. Comp. Fluid Dyn., 27, 133–148,
https://doi.org/10.1007/s00162-012-0281-y, 2013.
a
Feng, Z., Hagos, S., Rowe, A. K., Burleyson, C. D., Martini, M. N., and de Szoeke, S. P.: Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the AMIE/DYNAMO field campaign, J. Adv. Model. Earth Sy., 7, 357–381,
https://doi.org/10.1002/2014MS000384, 2015.
a,
b
Freitas, S. R., Grell, G. A., Chovert, A. D., Silva Dias, M. A. F., and de Lima Nascimento, E.: A parameterization for cloud organization and propagation by evaporation-driven cold pool edges, J. Adv. Model. Earth Sy., 16, e2023MS003982,
https://doi.org/10.1029/2023MS003982, 2024.
a,
b,
c,
d
Gettelman, A., Truesdale, J. E., Bacmeister, J. T., Caldwell, P. M., Neale, R. B., Bogenschutz, P. A., and Simpson, I. R.: The Single Column Atmosphere Model version 6 (SCAM6): Not a scam but a tool for model evaluation and development, J. Adv. Model. Earth Sy., 11, 1381–1401,
https://doi.org/10.1029/2018MS001578, 2019.
a
Grandpeix, J.-Y. and Lafore, J.-P.: A density current parameterization coupled with Emanuel's convection scheme. Part I: The models, J. Atmos. Sci., 67, 881–897,
https://doi.org/10.1175/2009JAS3044.1, 2010.
a,
b,
c,
d,
e,
f
Grandpeix, J.-Y., Phillips, V., and Tailleux, R.: Improved mixing representation in Emanuel's convection scheme, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, Applied Meteorology and Physical Oceanography, 130, 3207–3222,
https://doi.org/10.1256/qj.03.144, 2004.
a
Grandpeix, J.-Y., Lafore, J.-P., and Cheruy, F.: A density current parameterization coupled with Emanuel's convection scheme. Part II: 1D simulations, J. Atmos. Sci., 67, 898–922,
https://doi.org/10.1175/2009JAS3045.1, 2010.
a,
b
Grant, L. D., Lane, T. P., and van den Heever, S. C.: The role of cold pools in tropical oceanic convective systems, J. Atmos. Sci., 75, 2615–2634,
https://doi.org/10.1175/JAS-D-17-0352.1, 2018.
a
Guichard, F., Petch, J. C., Redelsperger, J.-L., Bechtold, P., Chaboureau, J.-P., Cheinet, S., Grabowski, W., Grenier, H., Jones, C. G., Köhler, M., Piriou, J.-M., Tailleux, R., and Tomasini, M.: Modelling the diurnal cycle of deep precipitating convection over land with cloud-resolving models and single-column models, Q. J. Roy. Meteor. Soc., 130, 3139–3172,
https://doi.org/10.1002/qj.503, 2004.
a
Hourdin, F., Couvreux, F., and Menut, L.: Parameterization of the Dry Convective Boundary Layer Based on a Mass Flux Representation of Thermals, J. Atmos. Sci., 59, 1105–1123,
https://doi.org/10.1175/1520-0469(2002)059<1105:POTDCB>2.0.CO;2, 2002.
a
Hourdin, F., Musat, I., Bony, S., Braconnot, P., Codron, F., Dufresne, J.-L., Fairhead, L., Filiberti, M.-A., Friedlingstein, P., Grandpeix, J.-Y., Krinner, G., LeVan, P., Li, Z.-X., and Lott, F.: The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection, Clim. Dynam., 27, 787–813,
https://doi.org/10.1007/s00382-006-0158-0, 2006.
a
Hourdin, F., Foujols, M.-A., Codron, F., Guemas, V., Dufresne, J.-L., Bony, S., Denvil, S., Guez, L., Lott, F., Ghattas, J., Braconnot, P., Marti, O., Meurdesoif, Y., and Bopp, L.: Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model, Clim. Dynam., 40, 2167–2192,
https://doi.org/10.1007/s00382-012-1411-3, 2013.
a
Hourdin, F., Jam, A., Rio, C., Couvreux, F., Sandu, I., Lefebvre, M.-P., Brient, F., and Idelkadi, A.: Unified Parameterization of Convective Boundary Layer Transport and Clouds With the Thermal Plume Model, J. Adv. Model. Earth Sy., 11, 2910–2933,
https://doi.org/10.1029/2019MS001666, 2019.
a
Hourdin, F., Rio, C., Grandpeix, J.-Y., Madeleine, J.-B., Cheruy, F., Rochetin, N., Jam, A., Musat, I., Idelkadi, A., Fairhead, L., Foujols, M.-A., Mellul, L., Traore, A.-K., Dufresne, J.-L., Boucher, O., Lefebvre, M.-P., Millour, E., Vignon, E., Jouhaud, J., and Diallo, F. B.: LMDZ6A: The atmospheric component of the IPSL climate model with improved and better tuned physics, J. Adv. Model. Earth Sy., 12, e2019MS001892,
https://doi.org/10.1029/2019MS001892, 2020a.
a,
b
Hourdin, F., Rio, C., Jam, A., Traore, A.-K., and Musat, I.: Convective Boundary Layer Control of the Sea Surface Temperature in the Tropics, J. Adv. Model. Earth Sy., 12, e01988,
https://doi.org/10.1029/2019MS001988, 2020b.
a
Hourdin, F., Williamson, D., Rio, C., Couvreux, F., Roehrig, R., Villefranque, N., Musat, I., Fairhead, L., Diallo, F. B., and Volodina, V.: Process-based climate model development harnessing machine learning: II. Model calibration from single column to global, J. Adv. Model. Earth Sy., 13, e2020MS002 225,
https://doi.org/10.1029/2020MS002225, 2021.
a,
b,
c
Jam, A., Hourdin, F., Rio, C., and Couvreux, F.: Resolved Versus Parametrized Boundary-Layer Plumes. Part III: Derivation of a Statistical Scheme for Cumulus Clouds, Bound.-Lay. Meteorol., 147, 421–441,
https://doi.org/10.1007/s10546-012-9789-3, 2013.
a
Kendon, E. J., Roberts, N. M., Senior, C. A., and Roberts, M. J.: Realism of rainfall in a very high-resolution regional climate model, J. Climate, 25, 5791–5806,
https://doi.org/10.1175/JCLI-D-11-00562.1, 2012.
a
Khairoutdinov, M. and Randall, D.: High-resolution simulation of shallow-to-deep convection transition over land, J. Atmos. Sci., 63, 3421–3436,
https://doi.org/10.1175/JAS3810.1, 2006.
a
Khairoutdinov, M. F. and Randall, D. A.: Cloud resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, and sensitivities, J. Atmos. Sci., 60, 607–625,
https://doi.org/10.1175/1520-0469(2003)060<0607:CRMOTA>2.0.CO;2, 2003.
a
Krueger, S. K., Morrison, H., and Fridlind, A. M.: Cloud-resolving modeling: ARM and the GCSS story, Meteor. Mon., 57, 25.1–25.16,
https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0047.1, 2016.
a
Kurowski, M. J., Suselj, K., Grabowski, W. W., and Teixeira, J.: Shallow-to-deep transition of continental moist convection: Cold pools, surface fluxes, and mesoscale organization, J. Atmos. Sci., 75, 4071–4090,
https://doi.org/10.1175/JAS-D-18-0031.1, 2018.
a
Lac, C., Chaboureau, J.-P., Masson, V., Pinty, J.-P., Tulet, P., Escobar, J., Leriche, M., Barthe, C., Aouizerats, B., Augros, C., Aumond, P., Auguste, F., Bechtold, P., Berthet, S., Bielli, S., Bosseur, F., Caumont, O., Cohard, J.-M., Colin, J., Couvreux, F., Cuxart, J., Delautier, G., Dauhut, T., Ducrocq, V., Filippi, J.-B., Gazen, D., Geoffroy, O., Gheusi, F., Honnert, R., Lafore, J.-P., Lebeaupin Brossier, C., Libois, Q., Lunet, T., Mari, C., Maric, T., Mascart, P., Mogé, M., Molinié, G., Nuissier, O., Pantillon, F., Peyrillé, P., Pergaud, J., Perraud, E., Pianezze, J., Redelsperger, J.-L., Ricard, D., Richard, E., Riette, S., Rodier, Q., Schoetter, R., Seyfried, L., Stein, J., Suhre, K., Taufour, M., Thouron, O., Turner, S., Verrelle, A., Vié, B., Visentin, F., Vionnet, V., and Wautelet, P.: Overview of the Meso-NH model version 5.4 and its applications, Geosci. Model Dev., 11, 1929–1969,
https://doi.org/10.5194/gmd-11-1929-2018, 2018.
a
Legay, A., Deremble, B., and Burchard, H.: Derivation and implementation of a non-local term to improve the oceanic convection representation within the
k–
ϵ parameterization, J. Adv. Model. Earth Sy., 17, e2024MS004243,
https://doi.org/10.1029/2024MS004243, 2025.
a
Lochbihler, K., Lenderink, G., and Siebesma, A. P.: Cold pool dynamics shape the response of extreme rainfall events to climate change, J. Adv. Model. Earth Sy., 13, e2020MS002306,
https://doi.org/10.1029/2020MS002306, 2021.
a
Lothon, M., Campistron, B., Chong, M., Couvreux, F., Guichard, F., Rio, C., and Williams, E.: Life cycle of a mesoscale circular gust front observed by a C-band Doppler radar in West Africa, Mon. Weather Rev., 139, 1370–1388,
https://doi.org/10.1175/2010MWR3480.1, 2011.
a,
b
Ma, H.-Y., Chuang, C. C., Klein, S. A., Lo, M.-H., Zhang, Y., Xie, S., Zheng, X., Ma, P.-L., Zhang, Y., and Phillips, T. J.: An improved hindcast approach for evaluation and diagnosis of physical processes in global climate models, J. Adv. Model. Earth Sy., 7, 1810–1827,
https://doi.org/10.1002/2015MS000490, 2015.
a
Meyer, B. and Haerter, J. O.: Mechanical forcing of convection by cold pools: Collisions and energy scaling, J. Adv. Model. Earth Sy., 12, e2020MS002 281,
https://doi.org/10.1029/2020MS002281, 2020.
a,
b
Pantillon, F., Knippertz, P., Marsham, J. H., and Birch, C. E.: A parameterization of convective dust storms for models with mass-flux convection schemes, J. Atmos. Sci., 72, 2545–2561,
https://doi.org/10.1175/JAS-D-14-0341.1, 2015.
a,
b
Park, S., Song, C., Kim, S., and Kim, J.: Parameterization of the elevated convection with a unified
convection scheme (UNICON) and its impacts on the diurnal cycle of precipitation, J. Adv. Model. Earth Sy., 16, e2023MS003651,
https://doi.org/10.1029/2023MS003651, 2024.
a
Piriou, J.-M., Redelsperger, J.-L., Geleyn, J.-F., Lafore, J.-P., and Guichard, F.: An approach for convective parameterization with memory: Separating microphysics and transport in grid-scale equations, J. Atmos. Sci., 64, 4127–4139,
https://doi.org/10.1175/2007JAS2144.1, 2007.
a,
b
Provod, M., Marsham, J. H., Parker, D. J., and Birch, C. E.: A characterization of cold pools in the West African Sahel, Mon. Weather Rev., 144, 1923–1934,
https://doi.org/10.1175/MWR-D-15-0023.1, 2016.
a
Qian, L., Young, G. S., and Frank, W. M.: A convective wake parameterization scheme for use in general circulation models, Mon. Weather Rev., 126, 456–469,
https://doi.org/10.1175/1520-0493(1998)126<0456:ACWPSF>2.0.CO;2, 1998.
a
Randall, D., Khairoutdinov, M., Arakawa, A., and Grabowski, W.: Breaking the cloud parameterization deadlock, B. Am. Meteorol. Soc., 84, 1547–1564,
https://doi.org/10.1175/BAMS-84-11-1547, 2003.
a,
b
Redelsperger, J.-L., Diedhiou, A., Flamant, C., Janicot, S., Lafore, J.-P., Lebel, T., Polcher, J., Bourlès, B., Caniaux, G., de Rosnay, P., Desbois, M., Eymard, L., Fontaine, B., Geneau, I., Ginoux, K., Hoepffner, M., Kane, C., Law, K., Mari, C., Marticorena, B., Mougin, E., Pelon, J., Peugeot, C., Protat, A., Roux, F., Sultan, B., and Van Den Akker, E.: Amma, une étude multidisciplinaire de la mousson ouest-africaine, La Météorologie, 54, 22–32,
https://doi.org/10.4267/2042/20098, 2006.
a
Rio, C., Hourdin, F., Grandpeix, J.-Y., and Lafore, J.-P.: Shifting the diurnal cycle of parameterized deep convection over land, Geophys. Res. Lett., 36,
https://doi.org/10.1029/2008GL036779, 2009.
a,
b,
c,
d,
e
Rio, C., Grandpeix, J.-Y., Hourdin, F., Guichard, F., Couvreux, F., Lafore, J.-P., Fridlind, A., Mrowiec, A., Bony, S., Rochetin, N., Roehrig, R., Idelkadi, A., Lefebvre, M.-P., and Musat, I.: Control of deep convection by sub-cloud lifting processes: the ALP closure in the LMDZ5B general circulation model, Clim. Dynam., 40, 2271–2292,
https://doi.org/10.1007/s00382-012-1506-x, 2013.
a
Rochetin, N., Grandpeix, J.-Y., Rio, C., and Couvreux, F.: Deep convection triggering by boundary layer thermals. Part II: Stochastic triggering parameterization for the LMDZ GCM, J. Atmos. Sci., 71, 515–538,
https://doi.org/10.1175/JAS-D-12-0337.1, 2014.
a,
b
Rochetin, N., Hohenegger, C., Touzé-Peiffer, L., and Villefranque, N.: A physically based definition of convectively generated density currents: Detection and characterization in convection-permitting simulations, J. Adv. Model. Earth Sy., 13, e2020MS002402,
https://doi.org/10.1029/2020MS002402, 2021.
a,
b
Rooney, G. G., Stirling, A. J., Stratton, R. A., and Whitall, M.: C-POOL: A scheme for modelling convective cold pools in the Met Office Unified Model, Q. J. Roy. Meteor. Soc., 148, 962–980,
https://doi.org/10.1002/qj.4241, 2022.
a,
b,
c,
d,
e,
f
Rotunno, R., Klemp, J. B., and Weisman, M. L.: A theory for strong, long-lived squall lines, J. Atmos. Sci., 45, 463–485,
https://doi.org/10.1175/1520-0469(1988)045<0463:ATFSLL>2.0.CO;2, 1988.
a
Sadourny, R. and Laval, K.: January and July performances of the LMD general circulation model, in: New Perspectives in Climate Modelling, edited by: Berger, A. and Nicolis, C., Elsevier, 173–197, ISBN 978-0444422958, 1984.
a,
b
Siebesma, A. P., Bretherton, C. S., Brown, A., Chlond, A., Cuxart, J., Duynkerke, P. G., Jiang, H., Khairoutdinov, M., Lewellen, D., Moeng, C.-H., Sanchez, E., Stevens, B., and Stevens, D. E.: A large eddy simulation intercomparison study of shallow cumulus convection, J. Atmos. Sci., 60, 1201–1219,
https://doi.org/10.1175/1520-0469(2003)60<1201:ALESIS>2.0.CO;2, 2003.
a,
b
Stephens, G. L., L'Ecuyer, T., Forbes, R., Gettelmen, A., Golaz, J.-C., Bodas-Salcedo, A., Suzuki, K., Gabriel, P., and Haynes, J.: Dreary state of precipitation in global models, J. Geophys. Res.-Atmos., 115, D24211,
https://doi.org/10.1029/2010JD014532, 2010.
a
Strauss, C., Ricard, D., Lac, C., and Verrelle, A.: Evaluation of turbulence parametrizations in convective clouds and their environment based on a large-eddy simulation, Q. J. Roy. Meteor. Soc., 145, 3195–3217,
https://doi.org/10.1002/qj.3605, 2019.
a
Suselj, K., Kurowski, M. J., and Teixeira, J.: A unified eddy-diffusivity/mass-flux approach for modeling atmospheric convection, J. Atmos. Sci., 76, 2505–2537,
https://doi.org/10.1175/JAS-D-18-0239.1, 2019.
a,
b,
c,
d,
e
Tan, J., Oreopoulos, L., Jakob, C., and Jin, D.: Evaluating rainfall errors in global climate models through cloud regimes, Clim. Dynam., 50, 3301–3314,
https://doi.org/10.1007/s00382-017-3806-7, 2018.
a
Thiam, M. L., Hourdin, F., Grandpeix, J.-Y., Rio, C., and Coulon-Decorzens, M.: Data and scripts used in the study: Evaluation and improvement of the cold pool parameterization in the LMDZ climate model against Large Eddy Simulations, Zenodo [data set],
https://doi.org/10.5281/zenodo.20548562, 2026.
a
Touzé-Peiffer, L., Vogel, R., and Rochetin, N.: Cold pools observed during eurec 4 a: Detection and characterization from atmospheric soundings, J. Appl. Meteorol. Clim., 61, 593–610,
https://doi.org/10.1175/JAMC-D-21-0048.1, 2022.
a
Vogel, R., Konow, H., Schulz, H., and Zuidema, P.: A climatology of trade-wind cumulus cold pools and their link to mesoscale cloud organization, Atmos. Chem. Phys., 21, 16609–16630,
https://doi.org/10.5194/acp-21-16609-2021, 2021.
a
Williamson, D., Goldstein, M., Allison, L., Blaker, A., Challenor, P., Jackson, L., and Yamazaki, K.: History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble, Clim. Dynam., 41, 1703–1729,
https://doi.org/10.1007/s00382-013-1896-4, 2013.
a
Wing, A. A., Reed, K. A., Satoh, M., Stevens, B., Bony, S., and Ohno, T.: Radiative–convective equilibrium model intercomparison project, Geosci. Model Dev., 11, 793–813,
https://doi.org/10.5194/gmd-11-793-2018, 2018.
a
Yanai, M., Esbensen, S., and Chu, J.-H.: Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets, J. Atmos. Sci., 30, 611–627,
https://doi.org/10.1175/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2, 1973.
a
Young, G. S., Perugini, S. M., and Fairall, C. W.: Convective wakes in the equatorial western Pacific during TOGA, Mon. Weather Rev., 123, 110–123,
https://doi.org/10.1175/1520-0493(1995)123<0110:CWITEW>2.0.CO;2, 1995.
a
Zhang, M., Somerville, R. C. J., and Xie, S.: The SCM concept and creation of ARM forcing datasets, Meteor. Mon., 57, 24.1–24.12,
https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0040.1, 2016.
a
Zuidema, P., Torri, G., Muller, C., and Chandra, A.: A survey of precipitation-induced atmospheric cold pools over oceans and their interactions with the larger-scale environment, Surv. Geophys., 38, 1283–1305,
https://doi.org/10.1007/s10712-017-9447-x, 2017.
a
