Articles | Volume 29, issue 15
https://doi.org/10.5194/hess-29-3435-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-3435-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Aug 2025
Research article |
| 01 Aug 2025
| 01 Aug 2025
The role of land–atmosphere coupling in subseasonal surface air temperature prediction across the contiguous United States
Yuna Lim
CORRESPONDING AUTHOR
Earth System Science Interdisciplinary Center, University of Maryland College Park, College Park, Maryland 20740, USA
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA
Andrea M. Molod
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA
Randal D. Koster
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA
Joseph A. Santanello
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA
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Cited articles
Abdolghafoorian, A. and Dirmeyer, P. A.: Validating the land–atmosphere coupling behavior in weather and climate models using observationally based global products, J. Hydrometeorol., 22, 1507–1523, https://doi.org/10.1175/JHM-D-20-0183.1, 2021.
Ardilouze, C., Batté, L., Decharme, B., and Déqué, M.: On the link between summer dry bias over the U.S. Great Plains and seasonal temperature prediction skill in a dynamical forecast system, Weather Forecast., 34, 1161–1172, https://doi.org/10.1175/WAF-D-19-0023.1, 2019.
Benson, D. O. and Dirmeyer, P. A.: The soil moisture – surface flux relationship as a factor for extreme heat predictability in subseasonal to seasonal forecasts, J. Clim., 36, 6375–6392, https://doi.org/10.1175/jcli-d-22-0447.1, 2023.
Bolton, D.: The computation of equivalent potential temperature, Mon. Weather Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2, 1980.
Budyko, M. I.: Heat Balance of the Earth's Surface, Gidrometeoizdat, Leningrad, 255 pp., 1956.
Buizza, R. and Palmer, T. N.: Impact of ensemble size on ensemble prediction, Mon. Weather Rev., 126, 2503–2518, https://doi.org/10.1175/1520-0493(1998)126<2503:IOESOE>2.0.CO;2, 1998.
Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N., Martin, R. V., Logan, J. A., Higurashi, A., and Nakajima T.: Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and Sun photometer measurements, J. Atmos. Sci., 59, 461–483, https://doi.org/10.1175/1520-0469(2002)059<0461:TAOTFT>2.0.CO;2, 2002.
Dirmeyer, P. A.: The terrestrial segment of soil moisture-climate coupling, Geophys. Res. Lett., 38, L16702, https://doi.org/10.1029/2011GL048268, 2011.
Dirmeyer, P. A.: Characteristics of the water cycle and land-atmosphere interactions from a comprehensive reforecast and reanalysis dataset: CFSv2, Clim. Dynam., 41, 1083–1097, https://doi.org/10.1007/s00382-013-1866-x, 2013.
Dirmeyer, P. A., Halder, S., and Bombardi, R: On the Harvest of Predictability from Land States in a Global Forecast Model, J. Geophys. Res.-Atmos., 123, 13111–13127, https://doi.org/10.1029/2018JD029103, 2018.
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.
Domeisen, D. I. V., Butler, A. H., Charlton-Perez, A. J., Ayarzagüena, B., Baldwin, M. P., Dunn-Sigouin, E., Furtado, J. C., Garfinkel, C. I., Hitchcock, P., Karpechko, A. Y., Kim, H., Knight, J., Lang, A. L., Lim, E.-P., Marshall, A., Roff, G., Schwartz, C., Simpson, I. R., Son, S.-W., and Taguchi, M.: The Role of the Stratosphere in Subseasonal to Seasonal Prediction: 1. Predictability of the Stratosphere, J. Geophys. Res.-Atmos., 125, e2019JD030920, https://doi.org/10.1029/2019JD030920, 2020.
Ducharne, A., Koster, R. D., Suarez, M. J., Stieglitz, M., and Kumar, P.: A catchment-based approach to modeling land surface processes in a general circulation model: 2. Parameter estimation and model demonstration, J. Geophys. Res.-Atmos., 105, 24823–24838, https://doi.org/10.1029/2000jd900328, 2000.
Entekhabi D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J., Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C., Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman, S. W., Tsang, L., and van Zyl, J.: The Soil Moisture Active Passive (SMAP) Mission, Proc. IEEE, 98, 704–716, https://doi.org/10.1109/jproc.2010.2043918, 2010.
Fischer, E. M., Seneviratne, S. I., Lüthi, D., and Schär, C.: Contribution of land-atmosphere coupling to recent European summer heat waves, Geophys. Res. Lett., 34, L06707, https://doi.org/10.1029/2006GL029068, 2007.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Clim., 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017 (code is available at https://disc.gsfc.nasa.gov/datasets?project=MERRA-2, last access: 28 July 2025).
Griffies, S. M.: Elements of the Modular Ocean Model (MOM): 2012 release. GFDL Ocean Group Tech. Rep. 7, NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, 618 pp., 2012.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P. , Rozum, I., Vamborg, F., Villaume S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020 (data set is available at https://rda.ucar.edu/datasets/d633000/, last access: 28 July 2025).
Hunke, E. W.: Lipscomb the Los Alamos Sea Ice Model, documentation and software manual version 4.0, Tech. Rep., Los Alamos National Laboratory, 2008.
Klein, S. A., Jiang, X., Boyle, J., Malyshev, S., and Xie, S.: Diagnosis of the summertime warm and dry bias over the U.S. southern Great Plains in the GFDL climate model using a weather forecasting approach, Geophys. Res. Lett., 33, L18805, https://doi.org/10.1029/2006GL027567, 2006.
Koster, R. D., Suarez, M. J., Ducharne, A., Stieglitz, M., and Kumar, P.: A catchment-based approach to modeling land surface processes in a general circulation model: 1. Model structure, J. Geophys. Res.-Atmos., 105, 24809–24822, https://doi.org/10.1029/2000JD900327, 2000.
Koster, R. D., Dirmeyer, P. A., Guo, Z., Bonan, G., Chan, E., Cox, P., Gordon, C. T., Kanae, S., Kowalczyk, E., Lawrence, D., Liu, P., Lu, C.-H., Malyshev, S., McAvaney, B, Mitchell, K., Mocko, D., Oki, T., Oleson, K., Pitman, A., Sud, Y. C., Taylor, C. M., Verseghy, D., Vasic, R., Xue, Y., and Yamada, T.: Regions of Strong Coupling Between Soil Moisture and Precipitation, Science, 305, 1138–1140, https://doi.org/10.1126/science.1100217, 2004.
Koster, R. D., Guo, Z., Dirmeyer, P. A., Bonan, G., Chan, E., Cox, P., Davies, H., Gordon, C. T., Kanae, S., Kowalczyk, E., Lawrence, D., Liu, P., Lu, C.-H., Malyshev, S., McAvaney, B., Mitchell, K., Mocko, D., Oki, T., Oleson, K. W., Pitman, A., Sud, Y. C., Taylor, C. M., Verseghy, D., Vasic, R., Xue, Y., and Yamada, T.: GLACE: The Global Land-Atmosphere Coupling Experiment, Part I: Overview, J. Hydrometeorol., 7, 590–610, https://doi.org/10.1175/JHM510.1, 2006.
Koster, R. D., Schubert, S. D., DeAngelis, A. M., Molod, A. M., and Mahanama, S. P.: Using a simple water balance framework to quantify the impact of soil moisture initialization on subseasonal evapotranspiration and air temperature forecasts, J. Hydrometeorol., 21, 1705–1722, https://doi.org/10.1175/JHM-D-20-0007.1, 2020.
Koster, R. D., DeAngelis, A. M., Schubert, S. D., and Molod, A. M.: Asymmetry in subseasonal surface air temperature forecast error with respect to soil moisture initialization, J. Hydrometeorol., 22, 2505–2519, https://doi.org/10.1175/JHM-D-21-0022.1, 2021.
Koster, R. D., Feldman, A. F., Holmes, T. R. H., Anderson, M. C., Crow, W. T., and Hain, C.: Estimating Hydrological Regimes from Observational Soil Moisture, Evapotranspiration, and Air Temperature Data, J. Hydrometeorol., 25, 495–513, https://doi.org/10.1175/jhm-d-23-0140.1, 2024.
Lim, Y., Son, S.-W., and Kim, D.: MJO prediction skill of the subseasonal-to-seasonal prediction models, J. Clim., 31, 4075–4094, https://doi.org/10.1175/JCLI-D-17-0545.1, 2018.
Mariotti, A., Baggett, C., Barnes, E. A., Becker, E., Butler, A., Collins, D. C., Dirmeyer, P. A., Ferranti, L., Johnson, N. C., Jones, J., Kirtman, B. P., Lang, A. L., Molod, A., Newman, M., Robertson, A. W., Schubert, S., Waliser, D. E., and Albers, J.: Windows of opportunity for skillful forecasts subseasonal to seasonal and beyond, B. Am. Meteorol. Soc., 101, E608–E625, https://doi.org/10.1175/BAMS-D-18-0326.1, 2020.
Molod, A., Salmun, H., and Waugh, D. W.: The Impact on a GCM Climate of an Extended Mosaic Technique for the Land-Atmosphere Coupling, J. Clim., 17, 3877–3891, https://doi.org/10.1175/1520-0442(2004)017<3877:TIOAGC>2.0.CO;2, 2004.
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015.
Molod, A., Hackert, E., Vikhliaev, Y., Zhao, B., Barahona, D., Vernieres, G., Borovikov, A., Kovach, R. M., Marshak, J., Schubert, S., Li, Z., Lim, Y.-K., Andrews, L. C., Cullather, R., Koster, R., Achuthavarier, D., Carton, J., Coy, L., Friere, J. L. M., Longo, K. M., Nakada, K., and Pawson, S.: GEOS-S2S Version 2: The GMAO High-Resolution Coupled Model and Assimilation System for Seasonal Prediction, J. Geophys. Res.-Atmos., 125, e2019JD031767, https://doi.org/10.1029/2019JD031767, 2020.
Pegion, K., Kirtman, B. P., Becker, E., Collins, D. C., LaJoie, E., Burgman, R., Bell, R., DelSole, T., Min, D., Zhu, Y., Li, W., Sinsky, E., Guan, H., Gottschalck, J., Metzger, E. J., Barton, N. P., Achuthavarier, D., Marshak, J., Koster, R. D., Lin, H., Gagnon, N., Bell, M., Tippett, M. K., Robertson, A. W., Sun, S., Benjamin, S. G., Green, B. W., Bleck, R., and Kim, H.: The Subseasonal Experiment (SUBX): A Multimodel Subseasonal Prediction Experiment, B. Am. Meteorol. Soc., 100, 2043–2060, https://doi.org/10.1175/BAMS-D-18-0270.1, 2019.
Reichle, R. H., De Lannoy, G. J. M., Liu, Q., Ardizzone, J. V., Colliander, A., Conaty, A., Crow, W., Jackson, T. J., Jones, L. A., Kimball, J. S., Koster, R. D., Mahanama, S. P., Smith, E. B., Berg, A., Bircher, S., Bosch, D., Caldwell, T. G., Cosh, M., González-Zamora, Á, Holifield Collins, C. D., Jensen, K. H., Livingston, S., Lopez-Baeza, E., Martínez-Fernández, J., McNairn, H., Moghaddam, M., Pacheco, A., Pellarin, T., Prueger, J., Rowlandson, T., Seyfried, M., Starks, P., Su, Z., Thibeault, M., van der Velde, R., Walker, J., Wu, X., and Zeng, Y.: Assessment of the SMAP level-4 surface and root-zone soil moisture product using in situ measurements, J. Hydrometeorol., 18, 2621–2645, https://doi.org/10.1175/JHM-D-17-0063.1, 2017.
Rienecker, M. M., Suarez, M. J., Todling, R., Bacmeister, J., Takacs, L., Liu, H. -C., Gu, W., Sienkiewicz, M., Koster, R. D., Gelato, R., Stajner, I., and Nielsen, J. E.: The GEOS-5 data assimilation system: Documentation of versions 5.0.1 and 5.1.0, and 5.2.0, NASA Tech. Rep., Series on Global Modeling and Data Assimilation NASA/TM-2008-104606, 2008.
Roundy, J. K., Ferguson, C. R., and Wood, E. F.: Impact of land-atmospheric coupling in CFSv2 on drought prediction, Clim. Dynam., 43, 421–434, https://doi.org/10.1007/s00382-013-1982-7, 2014.
Roy, T., J.Martinez, J. A., Herrera-Estrada, J. E., Zhang, Y., Dominguez, F., Berg, A., Ek, M., and Wood, E. F.: Role of Moisture Transport and Recycling in Characterizing Droughts: Perspectives from Two Recent U.S. Droughts and the CFSv2 System, J. Hydrometeorol., 20, 139–154, https://doi.org/10.1175/JHM-D-18-0159.1, 2019.
Santanello, J. A., Peters-Lidard, C. D., and Kumar, S. V.: Diagnosing the sensitivity of local land-atmosphere coupling via the soil moisture-boundary layer interaction, J. Hydrometeorol., 12, 766–786, https://doi.org/10.1175/JHM-D-10-05014.1, 2011.
Santanello, J. A., Dirmeyer, P. A., Ferguson, C. R., Findell, K. L., Tawfik, A. B., Berg, A., Ek, M., Gentine, P., Guillod, B. P., van Heerwaarden, C., Roundy, J., and Wulfmeyer, V.: Land-atmosphere interactions: The LoCo perspective, B. Am. Meteorol. Soc., 99, 1253–1272, https://doi.org/10.1175/BAMS-D-17-0001.1, 2018.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil moisture–climate interactions in a changing climate: A review, Earth Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
Seo, E. and Dirmeyer, P. A.: Understanding the diurnal cycle of land-atmosphere interactions from flux site observations, Hydrol. Earth Syst. Sci., 26, 5411–5429, https://doi.org/10.5194/hess-26-5411-2022, 2022.
Vitart, F. and Takaya, Y.: Lagged ensembles in sub-seasonal predictions, Q. J. Roy. Meteorol. Soc., 147, 3227–3242, https://doi.org/10.1002/qj.4125, 2021.
Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C., Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H., Hodgson, J., Kang, H.-S., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi, P., Mallas, I., Manoussakis, M., Mastrangelo, D., MacLachlan, C., McLean, P., Minami, A., Mladek, R., Nakazawa, T., Najm, S., Nie, Y., Rixen, M., Robertson, A. W., Ruti, P., Sun, C., Takaya, Y., Tolstykh, M., Venuti, F., Waliser, D., Woolnough, S., Wu, T., Won, D.-J., Xiao, H., Zaripov, R., and Zhang, L.: The Subseasonal to Seasonal (S2S) Prediction Project Database, B. Am. Meteorol. Soc., 98, 163–173, https://doi.org/10.1175/BAMS-D-16-0017.1, 2017.
Wang, L. and Robertson, A. W.: Week 3–4 predictability over the United States assessed from two operational ensemble prediction systems, Clim. Dynam., 52, 5861–5875, https://doi.org/10.1007/s00382-018-4484-9, 2019.
Yoon, D., Chen, J.-H., and Seo, E.: Contribution of land-atmosphere coupling in 2022 CONUS compound drought-heatwave events and implications for forecasting, Weather Clim. Extrem., 46, 100722, https://doi.org/10.1016/j.wace.2024.100722, 2024.
Short summary
To better utilize a given set of predictions, identifying “forecasts of opportunity” is valuable as this helps anticipate when prediction skill will be higher. This study shows that when strong land–atmosphere (L–A) coupling is detected 3–4 weeks into a forecast, the surface air temperature prediction skill at this lead time increases across the Midwest and northern Great Plains. Regions experiencing strong L–A coupling exhibit warm and dry anomalies, enhancing predictions of abnormally warm events.
To better utilize a given set of predictions, identifying “forecasts of opportunity” is valuable...
