Articles | Volume 16, issue 1
https://doi.org/10.5194/esd-16-55-2025
© 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/esd-16-55-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing coupling between soil temperature and potential air temperature using PALM-4U: implications for idealized scenarios
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Christopher C. Holst
Institute of Meteorology and Climate Research – Atmospheric Environmental Research, Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany
Basit Khan
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Mubadala Arabian Center for Climate and Environmental Sciences (ACCESS), New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
Susanne A. Benz
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Related authors
No articles found.
Hengheng Zhang, Wei Huang, Xiaoli Shen, Ramakrishna Ramisetty, Junwei Song, Olga Kiseleva, Christopher Claus Holst, Basit Khan, Thomas Leisner, and Harald Saathoff
Atmos. Chem. Phys., 24, 10617–10637, https://doi.org/10.5194/acp-24-10617-2024, https://doi.org/10.5194/acp-24-10617-2024, 2024
Short summary
Short summary
Our study unravels how stagnant winter conditions elevate aerosol levels in Stuttgart. Cloud cover at night plays a pivotal role, impacting morning air quality. Validating a key model, our findings aid accurate air quality predictions, crucial for effective pollution mitigation in urban areas.
Kevin Wolz, Christopher Holst, Frank Beyrich, Eileen Päschke, and Matthias Mauder
Geosci. Instrum. Method. Data Syst., 13, 205–223, https://doi.org/10.5194/gi-13-205-2024, https://doi.org/10.5194/gi-13-205-2024, 2024
Short summary
Short summary
We compared wind measurements using different lidar setups at various heights. The triple Doppler lidar, sonic anemometer, and two single Doppler lidars were tested. Overall, the lidar methods showed good agreement with the sonic anemometer. The triple Doppler lidar performed better than single Doppler lidars, especially at higher altitudes. We also developed a new filtering approach for virtual tower scanning strategies. Single Doppler lidars provide reliable wind data over flat terrain.
Claire Gallacher, Susanne Benz, Denise Boehnke, and Mathias Jehling
AGILE GIScience Ser., 5, 23, https://doi.org/10.5194/agile-giss-5-23-2024, https://doi.org/10.5194/agile-giss-5-23-2024, 2024
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-61, https://doi.org/10.5194/gmd-2024-61, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
An enhanced emission module has been developed for the PALM model system, allowing greater levels of flexibility and performance in modelling emission sources across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for PM10. The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in the PM concentration distribution.
Susanne A. Benz and Philipp Blum
Nat. Hazards Earth Syst. Sci., 19, 1433–1444, https://doi.org/10.5194/nhess-19-1433-2019, https://doi.org/10.5194/nhess-19-1433-2019, 2019
Short summary
Short summary
This study aims to identify clusters of landslide events within a global database that are triggered by the same rainfall event. Results show that 14 % of all recorded landslide events are actually part of a landslide cluster consisting of at least 10 events. However, in a more regional analysis this number ranges from 30 % for the west coast of North America to 3 % in the Himalayan region. These findings provide an improved understanding for managing landslide mitigations on a larger scale.
Susanne A. Benz, Peter Bayer, Gerfried Winkler, and Philipp Blum
Hydrol. Earth Syst. Sci., 22, 3143–3154, https://doi.org/10.5194/hess-22-3143-2018, https://doi.org/10.5194/hess-22-3143-2018, 2018
Short summary
Short summary
Climate change is one of the most pressing challenges modern society faces. Increasing temperatures are observed both above ground and, as discussed here, in the groundwater – the source of most drinking water. Within Austria average temperature increased by 0.7 °C over the past 20 years, with an increase of more than 3 °C in some wells and temperature decrease in others. However, these extreme changes can be linked to local events such as the construction of a new drinking water supply.
Cited articles
Asaeda, T. and Ca, V. T.: The subsurface transport of heat and moisture and its effect on the environment: A numerical model, Bound.-Lay. Meteorol., 65, 159–179, https://doi.org/10.1007/BF00708822, 1993. a, b
Attard, G., Rossier, Y., Winiarski, T., and Eisenlohr, L.: Deterministic modeling of the impact of underground structures on urban groundwater temperature, Sci. Total Environ., 572, 986–994, https://doi.org/10.1016/j.scitotenv.2016.07.229, 2016. a
Bayatvarkeshi, M., Bhagat, S. K., Mohammadi, K., Kisi, O., Farahani, M., Hasani, A., Deo, R., and Yaseen, Z. M.: Modeling soil temperature using air temperature features in diverse climatic conditions with complementary machine learning models, Comput. Electron. Agr., 185, 106158, https://doi.org/10.1016/j.compag.2021.106158, 2021. a
Benz, S. A., Bayer, P., Blum, P., Hamamoto, H., Arimoto, H., and Taniguchi, M.: Comparing anthropogenic heat input and heat accumulation in the subsurface of Osaka, Japan, Sci. Total Environ., 643, 1127–1136, https://doi.org/10.1016/j.scitotenv.2018.06.253, 2018. a
Benz, S. A., Davis, S. J., and Burney, J. A.: Drivers and projections of global surface temperature anomalies at the local scale, Environ. Res. Lett., 16, 064093, https://doi.org/10.1088/1748-9326/ac0661, 2021. a
Benz, S. A., Menberg, K., Bayer, P., and Kurylyk, B. L.: Shallow subsurface heat recycling is a sustainable global space heating alternative, Nat. Commun., 13, 3962, https://doi.org/10.1038/s41467-022-31624-6, 2022. a, b, c
Böttcher, F. and Zosseder, K.: Thermal influences on groundwater in urban environments – A multivariate statistical analysis of the subsurface heat island effect in Munich, Sci. Total Environ., 810, 152193, https://doi.org/10.1016/j.scitotenv.2021.152193, 2022. a
Brunsell, N. A., Mechem, D. B., and Anderson, M. C.: Surface heterogeneity impacts on boundary layer dynamics via energy balance partitioning, Atmos. Chem. Phys., 11, 3403–3416, https://doi.org/10.5194/acp-11-3403-2011, 2011. a, b, c
Cermak, V., Bodri, L., Kresl, M., Dedecek, P., and Safanda, J.: Eleven years of ground–air temperature tracking over different land cover types, Int. J. Climatol., 37, 1084–1099, https://doi.org/10.1002/joc.4764, 2017. a
Chakraborty, T. and Lee, X.: A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability, Int. J. Appl. Earth Obs., 74, 269–280, https://doi.org/10.1016/j.jag.2018.09.015, 2019. a
Copernicus Climate Change Service: ERA5-Land hourly data from 2001 to present, https://doi.org/10.24381/CDS.E2161BAC, 2019. a, b
Epting, J., García-Gil, A., Huggenberger, P., Vázquez-Suñe, E., and Mueller, M. H.: Development of concepts for the management of thermal resources in urban areas – Assessment of transferability from the Basel (Switzerland) and Zaragoza (Spain) case studies, J. Hydrol., 548, 697–715, https://doi.org/10.1016/j.jhydrol.2017.03.057, 2017. a
Gao, Z., Horton, R., Wang, L., Liu, H., and Wen, J.: An improved force-restore method for soil temperature prediction, Eur. J. Soil Sci., 59, 972–981, https://doi.org/10.1111/j.1365-2389.2008.01060.x, 2008. a
Gehrke, K. F., Sühring, M., and Maronga, B.: Modeling of land–surface interactions in the PALM model system 6.0: land surface model description, first evaluation, and sensitivity to model parameters, Geosci. Model Dev., 14, 5307–5329, https://doi.org/10.5194/gmd-14-5307-2021, 2021. a, b, c
Glocke, P.: Glocke_PALM4U_idealized_scenarios, Zenodo [data set], https://doi.org/10.5281/zenodo.14529807, 2024. a
Heaviside, C., Macintyre, H., and Vardoulakis, S.: The Urban Heat Island: Implications for Health in a Changing Environment, Current Environmental Health Reports, 4, 296–305, https://doi.org/10.1007/s40572-017-0150-3, 2017. a
Hennemuth, B. and Lammert, A.: Determination of the Atmospheric Boundary Layer Height from Radiosonde and Lidar Backscatter, Bound.-Lay. Meteorol., 120, 181–200, https://doi.org/10.1007/s10546-005-9035-3, 2006. a, b
Hermoso de Mendoza, I., Beltrami, H., MacDougall, A. H., and Mareschal, J.-C.: Lower boundary conditions in land surface models – effects on the permafrost and the carbon pools: a case study with CLM4.5, Geosci. Model Dev., 13, 1663–1683, https://doi.org/10.5194/gmd-13-1663-2020, 2020. a
Hu, G., Wu, X., Zhao, L., Li, R., Wu, T., Xie, C., Pang, Q., and Cheng, G.: An improved model for soil surface temperature from air temperature in permafrost regions of Qinghai-Xizang (Tibet) Plateau of China, Meteorol. Atmos. Phys., 129, 441–451, https://doi.org/10.1007/s00703-016-0468-7, 2017. a
Huang, K., Li, X., Liu, X., and Seto, K. C.: Projecting global urban land expansion and heat island intensification through 2050, Environ. Res. Lett., 14, 114037, https://doi.org/10.1088/1748-9326/ab4b71, 2019. a
Intergovernmental Panel On Climate Change: Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1st edn., Cambridge University Press, https://doi.org/10.1017/9781009157896, 2023. a
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of the Köppen-Geiger climate classification updated, Meteorol. Z., 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006. a
Kraus, H.: Grundlagen der Grenzschicht-Meteorologie, Springer, https://doi.org/10.1007/978-3-540-75981-2, 2008. a, b, c
Krč, P., Resler, J., Sühring, M., Schubert, S., Salim, M. H., and Fuka, V.: Radiative Transfer Model 3.0 integrated into the PALM model system 6.0, Geosci. Model Dev., 14, 3095–3120, https://doi.org/10.5194/gmd-14-3095-2021, 2021. a, b
Kurylyk, B. L. and MacQuarrie, K. T. B.: A new analytical solution for assessing climate change impacts on subsurface temperature, Hydrol. Process., 28, 3161–3172, https://doi.org/10.1002/hyp.9861, 2014. a
Liang, L. L., Riveros-Iregui, D. A., Emanuel, R. E., and McGlynn, B. L.: A simple framework to estimate distributed soil temperature from discrete air temperature measurements in data-scarce regions, J. Geophys. Res.-Atmos., 119, 407–417, https://doi.org/10.1002/2013JD020597, 2014. a
Lund, T. S., Wu, X., and Squires, K. D.: Generation of Turbulent Inflow Data for Spatially-Developing Boundary Layer Simulations, J. Comput. Phys., 140, 233–258, https://doi.org/10.1006/jcph.1998.5882, 1998. a
Manoli, G., Fatichi, S., Schläpfer, M., Yu, K., Crowther, T. W., Meili, N., Burlando, P., Katul, G. G., and Bou-Zeid, E.: Magnitude of urban heat islands largely explained by climate and population, Nature, 573, 55–60, https://doi.org/10.1038/s41586-019-1512-9, 2019. a
Maronga, B., Gryschka, M., Heinze, R., Hoffmann, F., Kanani-Sühring, F., Keck, M., Ketelsen, K., Letzel, M. O., Sühring, M., and Raasch, S.: The Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric and oceanic flows: model formulation, recent developments, and future perspectives, Geosci. Model Dev., 8, 2515–2551, https://doi.org/10.5194/gmd-8-2515-2015, 2015. a
Maronga, B., Banzhaf, S., Burmeister, C., Esch, T., Forkel, R., Fröhlich, D., Fuka, V., Gehrke, K. F., Geletič, J., Giersch, S., Gronemeier, T., Groß, G., Heldens, W., Hellsten, A., Hoffmann, F., Inagaki, A., Kadasch, E., Kanani-Sühring, F., Ketelsen, K., Khan, B. A., Knigge, C., Knoop, H., Krč, P., Kurppa, M., Maamari, H., Matzarakis, A., Mauder, M., Pallasch, M., Pavlik, D., Pfafferott, J., Resler, J., Rissmann, S., Russo, E., Salim, M., Schrempf, M., Schwenkel, J., Seckmeyer, G., Schubert, S., Sühring, M., von Tils, R., Vollmer, L., Ward, S., Witha, B., Wurps, H., Zeidler, J., and Raasch, S.: Overview of the PALM model system 6.0, Geosci. Model Dev., 13, 1335–1372, https://doi.org/10.5194/gmd-13-1335-2020, 2020. a, b, c, d, e, f, g
Menberg, K., Bayer, P., Zosseder, K., Rumohr, S., and Blum, P.: Subsurface urban heat islands in German cities, Sci. Total Environ., 442, 123–133, https://doi.org/10.1016/j.scitotenv.2012.10.043, 2013. a
Nitoiu, D. and Beltrami, H.: Subsurface thermal effects of land use changes, J. Geophys. Res.-Earth, 110, F01005, https://doi.org/10.1029/2004JF000151, 2005. a
Noethen, M., Hemmerle, H., Menberg, K., Epting, J., Benz, S. A., Blum, P., and Bayer, P.: Thermal impact of underground car parks on urban groundwater, Sci. Total Environ., 903, 166572, https://doi.org/10.1016/j.scitotenv.2023.166572, 2023. a
Oke, T. R., Mills, G., Christen, A., and Voogt, J. A.: Urban Heat Island, in: Urban Climates, Cambridge University Press, 197–237, https://doi.org/10.1017/9781139016476.008, 2017. a, b, c
Rahman, M., Sulis, M., and Kollet, S. J.: The subsurface–land surface–atmosphere connection under convective conditions, Adv. Water Resour., 83, 240–249, https://doi.org/10.1016/j.advwatres.2015.06.003, 2015. a
Rizwan, A. M., Dennis, L. Y. C., and Liu, C.: A review on the generation, determination and mitigation of Urban Heat Island, J. Environ. Sci., 20, 120–128, https://doi.org/10.1016/S1001-0742(08)60019-4, 2008. a
Santamouris, M., Ding, L., Fiorito, F., Oldfield, P., Osmond, P., Paolini, R., Prasad, D., and Synnefa, A.: Passive and active cooling for the outdoor built environment – Analysis and assessment of the cooling potential of mitigation technologies using performance data from 220 large scale projects, Sol. Energy, 154, 14–33, https://doi.org/10.1016/j.solener.2016.12.006, 2017. a
Schumann, U. and Sweet, R. A.: Fast Fourier transforms for direct solution of poisson's equation with staggered boundary conditions, J. Comput. Phys., 75, 123–137, https://doi.org/10.1016/0021-9991(88)90102-7, 1988. a, b
Shahmohamadi, P., Che-Ani, A. I., Etessam, I., Maulud, K. N. A., and Tawil, N. M.: Healthy Environment: The Need to Mitigate Urban Heat Island Effects on Human Health, Procedia Engineer., 20, 61–70, https://doi.org/10.1016/j.proeng.2011.11.139, 2011. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A Description of the Advanced Research WRF Model Version 4, https://doi.org/10.5065/1DFH-6P97, 2019. a
Staniec, M. and Nowak, H.: The application of energy balance at the bare soil surface to predict annual soil temperature distribution, Energ. Buildings, 127, 56–65, https://doi.org/10.1016/j.enbuild.2016.05.047, 2016. a, b
Taylor, C. A. and Stefan, H. G.: Shallow groundwater temperature response to climate change and urbanization, J. Hydrol., 375, 601–612, https://doi.org/10.1016/j.jhydrol.2009.07.009, 2009. a
Tong, S., Prior, J., McGregor, G., Shi, X., and Kinney, P.: Urban heat: an increasing threat to global health, The BMJ, 375, n2467, https://doi.org/10.1136/bmj.n2467, 2021. a
Wanner, L., De Roo, F., Sühring, M., and Mauder, M.: How Does the Choice of the Lower Boundary Conditions in Large-Eddy Simulations Affect the Development of Dispersive Fluxes Near the Surface?, Bound.-Lay. Meteorol., 182, 1–27, https://doi.org/10.1007/s10546-021-00649-7, 2022. a, b
World Urbanization Prospects: The 2018 Revision, UN, https://doi.org/10.18356/b9e995fe-en, 2019. a
Wouters, H., Petrova, I. Y., van Heerwaarden, C. C., Vilà-Guerau de Arellano, J., Teuling, A. J., Meulenberg, V., Santanello, J. A., and Miralles, D. G.: Atmospheric boundary layer dynamics from balloon soundings worldwide: CLASS4GL v1.0, Geosci. Model Dev., 12, 2139–2153, https://doi.org/10.5194/gmd-12-2139-2019, 2019. a
WRF Community: Weather Research and Forecasting (WRF) Model, UCAR/NCAR, https://doi.org/10.5065/D6MK6B4K 2000. a
Zhiyin, Y.: Large-eddy simulation: Past, present and the future, Chinese J. Aeronaut., 28, 11–24, https://doi.org/10.1016/j.cja.2014.12.007, 2015. a
Short summary
Utilizing the urban microclimate model PALM-4U, we show that temperature anomalies of ± 5 K at a depth of 2 m in the soil can impact atmospheric potential air temperatures within idealized domains. The impact depends on the season, time of day, land cover, and lateral boundary conditions of the domain. The magnitude of change depends mostly on seasonality and the time of day, ranging from 0.1 to 0.4 K. Land cover influences the absolute temperature but has a smaller impact on the magnitude.
Utilizing the urban microclimate model PALM-4U, we show that temperature anomalies of ± 5 K at a...
Altmetrics
Final-revised paper
Preprint