Articles | Volume 16, issue 24
https://doi.org/10.5194/gmd-16-7275-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-7275-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
18 Dec 2023
Model description paper |
| 18 Dec 2023
| 18 Dec 2023
Representation of atmosphere-induced heterogeneity in land–atmosphere interactions in E3SM–MMFv2
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
Walter M. Hannah
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
David C. Bader
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
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Cited articles
Baker, I. T., Denning, A., Dazlich, D. A., Harper, A. B., Branson, M. D., Randall, D. A., Phillips, M. C., Haynes, K. D., and Gallup, S. M.: Surface-Atmosphere Coupling Scale, the Fate of Water, and Ecophysiological Function in a Brazilian Forest, J. Adv. Model. Earth Syst., 11, 2523–2546, https://doi.org/10.1029/2019MS001650, 2019.
Betts, A. K.: Idealized model for equilibrium boundary layer over land, J. Hydrometeorol., 1, 507–523, 2000.
Betts, A. K.: Understanding hydrometeorology using global models, B. Am. Meteorol. Soc., 85, 1673–1688, https://doi.org/10.1175/BAMS-85-11-1673, 2004.
Betts, A. K., Barr, A. G., Beljaars, A. C. M., Miller, M. J., and Viterbo, P. A.: The land surface-atmosphere interaction: A review based on observational and global modeling perspectives, J. Geophys. Res., 101, 7209–7225, 1996.
Betts, A. K., Tawfik, A. B., and Desjardins, R. L.: Revisiting hydrometeorology using cloud and climate observations, J. Hydrometeorol., 18, 939–955, https://doi.org/10.1175/jhm-d-16-0203.1, 2017.
Dirmeyer, P. A. and Halder, S.: Sensitivity of numerical weather forecasts to initial soil moisture variations in CFSv2, Weather Forecast., 31, 1973–1983, https://doi.org/10.1175/waf-d-16-0049.1, 2016.
Dirmeyer, P. A., Balsamo, G., Blyth, E. M., Morrison, R., and Cooper, H. M.: Land-Atmosphere Interactions Exacerbated the Drought and Heatwave Over Northern Europe During Summer 2018, AGU Advances, 2, e2020AV000283, https://doi.org/10.1029/2020AV000283, 2021.
Ek, M. B. and Holtslag, A. A. M.: Influence of soil moisture on boundary layer cloud development, J. Hydrometeorol., 5, 86–99, 2004.
Findell, K. L. and Eltahir, E. A. B.: Atmospheric controls on soil moisture–boundary layer interactions: Part II: Feedbacks within the continental United States, J. Hydrometeorol., 4, 570–583, https://doi.org/10.1175/1525-7541(2003)004<0570:Acosml>2.0.Co;2, 2003a.
Findell, K. L. and Eltahir, E. A. B.: Atmospheric controls on soil moisture–boundary layer interactions: Part I: Framework development, J. Hydrometeorol., 4, 552–569, https://doi.org/10.1175/1525-7541(2003)004<0552:Acosml>2.0.Co;2, 2003b.
Gentine, P., Holtslag, A. A. M., D'Andrea, F., and Ek, M.: Surface and Atmospheric Controls on the Onset of Moist Convection over Land, J. Hydrometeorol., 14, 1443–1462, https://doi.org/10.1175/JHM-D-12-0137.1, 2013.
Gettelman, A., Bardeen, C. G., McCluskey, C. S., Järvinen, E., Stith, J., Bretherton, C., McFarquhar, C., Twohy, J., and D'Alessandro, W. Wu: Simulating observations of Southern Ocean clouds and implications for climate, J. Geograph. Res.-Atmos., 125, e2020JD032619, https://doi.org/10.1029/2020JD032619, 2020.
Golaz, J.-C., Caldwell, P. M., Van Roekel, Luke P., et al.: The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution, J. Adv. Model. Earth Syst., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019.
Grabowski, W. W.: An improved framework for superparameterization, J. Atmos. Sci., 61, 1940–1952, 2004.
Guillod, B. P., Orlowsky, B., Miralles, D. G., Teuling, A. J., and Seneviratne, S. I.: Reconciling spatial and temporal soil moisture effects on 300 afternoon rainfall, Nat. Commun., 6, 6443, https://doi.org/10.1038/ncomms7443, 2015.
Guo, Z.: GLACE: The Global Land–Atmosphere Coupling Experiment. Part II: Analysis, J. Hydrometeorol., 7, 611–625, 2006.
Hannah, W. M., Jones, C. R., Hillman, B. R., Norman, M. R., Bader, D. C., Taylor, M. A., Leung, L. R., Pritchard, M. S., Branson, M. D., Lin, G., Pressel, K. G., and Lee, J. M.: Initial Results From the Super-Parameterized E3SM, J. Adv. Model. Earth Syst., 12, e2019MS001863, https://doi.org/10.1029/2019MS001863, 2020.
Hirsch, A. L., Kala, J., Pitman, A. J., Carouge, C., Evans, J. P., Haverd, V., and Mocko, D.: Impact of land surface initialization approach on subseasonal forecast skill: A regional analysis in the Southern Hemisphere, J. Hydrometeorol., 15, 300–319, https://doi.org/10.1175/jhm-d13-05.1, 2014.
Huang, H.-Y. and Margulis, S. A.: On the impact of surface heterogeneity on a realistic convective boundary layer, Water Resour. Res., 45, W04425, https://doi.org/10.1029/2008WR007175, 2009.
Huang, H.-Y. and Margulis, S. A.: Impact of soil moisture heterogeneity length scale and gradients on daytime coupled land-cloudy boundary layer interactions, Hydrol. Process., 27, 1988–2003, https://doi.org/10.1002/hyp.9351, 2013.
Jimenez, P. A., de Arellano, J. V., Navarro, J., and Gonzalez-Rouco, J. F.: Understanding land–atmosphere interactions across a range of spatial and temporal scales, B. Am. Meteorol. Soc., 95, ES14–ES17, 2014.
Khairoutdinov, M. F. and Randall, D. A.: Cloud-resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, 315 and sensitivities, J. Atmos. Sci., 60, 607–625, 2003.
Khairoutdinov, M. F., Randall, D. A., and DeMotte, C.: Simulations of the atmospheric general circulation using a cloud-resolving model as a super-parameterization of physical processes, J. Atmos. Sci., 62, 2136–2154, 2005.
Koster, R. D.: Contribution of land surface initialization to subseasonal forecast skill: First results from a multi-model experiment, Geophys. Res. Lett., 37, L02402, https://doi.org/10.1029/2009gl041677, 2010.
Lee, J.: E3SM-MMF code with MAML implementation and data used to produce figures, Zenodo [data set], https://doi.org/10.5281/zenodo.6554887, 2023.
Lee, J. M., Zhang, Y., and Klein, S. A.: The Effect of Land Surface Heterogeneity and Background Wind on Shallow Cumulus Clouds and the Transition to Deeper Convection, J. Atmos. Sci., 76, 401–419, https://doi.org/10.1175/JAS-D-18-0196.1, 2019.
Lin, G., Leung, L. R., Lee, J., Harrop, B. E., Baker, I. T., Branson, M. D., Denning, A. S., Jones, C. R., Ovchinnikov, M., Randall, D. A., and Yang, Z.: Modeling Land-Atmosphere Coupling at Cloud-Resolving Scale Within the Multiple Atmosphere Multiple Land (MAML) Framework in SP-E3SM, J. Adv. Model. Earth Syst., 15, e2022MS003101, https://doi.org/10.1029/2022MS003101, 2023.
Miralles, D. G., Teuling, A. J., van Heerwaarden, C. C., and de Arellano, J. V.-G.: Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation, Nat. Geosci., 7, 345–349, 2014.
Oleson, K., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven, C. D., and Yang, Z.-L.: Technical description of version 4.5 of the Community Land Model (CLM) (No. NCAR/TN-503+STR), https://doi.org/10.5065/D6RR1W7M, 2013.
PaiMazumder, D. and Done, J. M.: Potential predictability sources of the 2012 U.S. drought in observations and a regional model ensemble, J. Geophys. Res.-Atmos., 121, 12581–12592, https://doi.org/10.1002/2016JD025322, 2016.
Pincus, R., Mlawer, E. J., and Delamere, J. S.: Balancing Accuracy, Efficiency, and Flexibility in Radiation Calculations for Dynamical Models, J. Adv. Model. Earth Syst., 11, 3074–3089, https://doi.org/10.1029/2019MS001621, 2019.
Rasch, P. J., Xie, S., Ma, P.-L., Lin, W., Wang, H., Tang, Q., Burrows, S. M., Caldwell, P., Zhang, K., Easter, R. C., Cameron-Smith, P., Singh, B., Wan, H., Golaz, J.-C., Harrop, B. E., Roesler, E., Bacmeister, J., Larson, V. E., Evans, K. J., Qian, Y., Taylor, M., Leung, L. R., Zhang, Y., Brent, L., Branstetter, M., Hannay, C., Mahajan, S., Mametjanov, A., Neale, R., Richter, J. H., Yoon, J.-H., Zender, C. S., Bader, D., Flanner, M., Foucar, J. G., Jacob, R., Keen, N., Klein, S. A., Liu, X., Salinger, A., Shrivastava, M., and Yang, Y.: An Overview of the Atmospheric Component of the Energy Exascale Earth System Model, J. Adv. Model. Earth Syst., 11, 2377–2411, https://doi.org/10.1029/2019MS001629, 2019.
Rieck, M., Hohenegger, C., and van Heerwaarden, C. C.: The influence of land surface heterogeneities on cloud size development, Mon. Weather Rev., 142, 3830–3846, 2014.
Rieck, M., Hohenegger, C., and Gentine, P.: The effect of moist convection on thermally induced mesoscale circulations, Q. J. Roy. Meteorol. Soc., 141, 2418–2428, 2015.
Rochetin, N., Couvreux, F., and Guichard, F.: Morphology of breeze circulations induced by surface flux heterogeneities and their impact on convection initiation, Q. J. Roy. Meteorol. Soc., 143, 463–478, 2017.
Roundy, J. K. and Santanello, J. A.: Utility of satellite remote sensing for land–atmosphere coupling and drought metrics, J. Hydrometeor., 345, 863–877, 2017.
Roundy, J. K., Ferguson, C. R., and Wood, E.: Temporal variability of land–atmosphere coupling and its implications for drought over the Southeast United States, J. Hydrometeor., 14, 622–635, 2013.
Teuling, A. J.: Observational evidence for cloud cover enhancement over western European forests, Nat. Commun., 8, 14065, 2017.
Vilà-Guerau de Arellano, J., Ouwersloot, H. G., Baldocchi, D., and Jacobs, C. M. J.: Shallow cumulus rooted in photosynthesis, Geophys. Res. Lett., 41, 1796–1802, https://doi.org/10.1002/2014GL059279, 2014.
Wang, S.-Y. S., Santanello, J., Wang, H., Barandiaran, D., Pinker, R. T., Schubert, S., Gillies, R. R., Oglesby, R., Hilburn, K., Kilic, A., and Houser, P.: An intensified seasonal transition in the Central U.S. that enhances summer drought, J. Geophys. Res.-Atmos., 120, 8804–8816, https://doi.org/10.1002/2014JD023013, 2015.
Short summary
Representing accurate land–atmosphere interaction processes is overlooked in weather and climate models. In this study, we propose three methods to represent land–atmosphere coupling in the Energy Exascale Earth System Model (E3SM) with the Multi-scale Modeling Framework (MMF) approach. In this study, we introduce spatially homogeneous and heterogeneous land–atmosphere interaction processes within the cloud-resolving model domain. Our 5-year simulations reveal only small differences.
Representing accurate land–atmosphere interaction processes is overlooked in weather and climate...
