Articles | Volume 6, issue 1
https://doi.org/10.5194/wcd-6-231-2025
© 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/wcd-6-231-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sensitivity of tropical orographic precipitation to wind speed with implications for future projections
Department of Earth and Planetary Science, University of California, Berkeley, CA 94720, USA
now at: Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
William R. Boos
Department of Earth and Planetary Science, University of California, Berkeley, CA 94720, USA
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Related authors
Quentin Nicolas and Belinda Hotz
EGUsphere, https://doi.org/10.5194/egusphere-2025-6032, https://doi.org/10.5194/egusphere-2025-6032, 2025
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Short summary
Short summary
Heatwaves are intensifying at a fast pace, and how much further they can strengthen is unknown. Our study seeks to estimate a physical upper limit to surface air temperature. We show that, unlike what recent work suggested, the intensity of the most extreme heatwaves is not constrained by the onset of thunderstorms. Instead, the limit is set by the development of a several-kilometer-deep layer of well-mixed air above the ground, and modulated by a very hot and unstable near-surface layer.
Quentin Nicolas and Belinda Hotz
EGUsphere, https://doi.org/10.5194/egusphere-2025-6032, https://doi.org/10.5194/egusphere-2025-6032, 2025
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Short summary
Short summary
Heatwaves are intensifying at a fast pace, and how much further they can strengthen is unknown. Our study seeks to estimate a physical upper limit to surface air temperature. We show that, unlike what recent work suggested, the intensity of the most extreme heatwaves is not constrained by the onset of thunderstorms. Instead, the limit is set by the development of a several-kilometer-deep layer of well-mixed air above the ground, and modulated by a very hot and unstable near-surface layer.
Michael P. Byrne, William R. Boos, and Shineng Hu
Weather Clim. Dynam., 5, 763–777, https://doi.org/10.5194/wcd-5-763-2024, https://doi.org/10.5194/wcd-5-763-2024, 2024
Short summary
Short summary
In this study we investigate why climate change is amplified in mountain regions, a phenomenon known as elevation-dependent warming (EDW). We examine EDW using observations and models and assess the roles of radiative forcing vs. internal variability in driving the historical signal. Using a forcing–feedback framework we also quantify for the first time the processes driving EDW on large scales. Our results have important implications for understanding future climate change in mountain regions.
Ankur Mahesh, Travis A. O'Brien, Burlen Loring, Abdelrahman Elbashandy, William Boos, and William D. Collins
Geosci. Model Dev., 17, 3533–3557, https://doi.org/10.5194/gmd-17-3533-2024, https://doi.org/10.5194/gmd-17-3533-2024, 2024
Short summary
Short summary
Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.
Cited articles
Ahmed, F. and Neelin, J. D.: A process-oriented diagnostic to assess precipitation-thermodynamic relations and application to CMIP6 models, Geophys. Res. Lett., 48, e2021GL094108. https://doi.org/10.1029/2021GL094108, 2021. a
As-syakur, A. R., Osawa, T., Miura, F., Nuarsa, I. W., Ekayanti, N. W., Dharma, I. G. B. S., Adnyana, I. W. S., Arthana, I. W., and Tanaka, T.: Maritime Continent rainfall variability during the TRMM era: The role of monsoon, topography and El Niño Modoki, Dynam. Atmos. Oceans, 75, 58–77, https://doi.org/10.1016/j.dynatmoce.2016.05.004, 2016. a
Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D., Soci, C., Villaume, S., Bidlot, J.-R., Haimberger, L., Woollen, J., Buontempo, C., and Thépaut, J.-N.: The ERA5 global reanalysis: Preliminary extension to 1950, Q. J. Roy. Meteor. Soc., 147, 4186–4227, 2021. a
Byrne, M. P., Pendergrass, A. G., Rapp, A. D., and Wodzicki, K. R.: Response of the Intertropical Convergence Zone to Climate Change: Location, Width, and Strength, Current Climate Change Reports, 4, 355–370, https://doi.org/10.1007/s40641-018-0110-5, 2018. a
Chen, S.-H. and Lin, Y.-L.: Effects of Moist Froude Number and CAPE on a Conditionally Unstable Flow over a Mesoscale Mountain Ridge, J. Atmos. Sci., 62, 331–350, https://doi.org/10.1175/JAS-3380.1, 2005. a, b
Chu, C.-M. and Lin, Y.-L.: Effects of Orography on the Generation and Propagation of Mesoscale Convective Systems in a Two-Dimensional Conditionally Unstable Flow, J. Atmos. Sci., 57, 3817–3837, https://doi.org/10.1175/1520-0469(2001)057<3817:EOOOTG>2.0.CO;2, 2000. a
Colle, B. A.: Sensitivity of orographic precipitation to changing ambient conditions and terrain geometries: An idealized modeling perspective, J. Atmos. Sci., 61, 588–606, 2004. a
Douville, H., Raghavan, K., Renwick, J., Allan, R., Arias, P., Barlow, M., Cerezo-Mota, R., Cherchi, A., Gan, T., Gergis, J., Jiang, D., Khan, A., Pokam Mba, W., Rosenfeld, D., Tierney, J., and Zolina, O.: 8 – Water Cycle Changes, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/9781009157896.010, 2021. a
Espinoza, J. C., Chavez, S., Ronchail, J., Junquas, C., Takahashi, K., and Lavado, W.: Rainfall hotspots over the southern tropical Andes: Spatial distribution, rainfall intensity, and relations with large-scale atmospheric circulation, Water Resour. Res., 51, 3459–3475, https://doi.org/10.1002/2014WR016273, 2015. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Gutiérrez, J., Jones, R., Narisma, G., Alves, L., Amjad, M., Gorodetskaya, I., Grose, M., Klutse, N., Krakovska, S., Li, J., Martínez-Castro, D., Mearns, L., Mernild, S., Ngo-Duc, T., van den Hurk, B., and Yoon, J.-H.: Atlas, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1927–2058, https://doi.org/10.1017/9781009157896.021, 2023 (IPCC WGI Interactive Atlas available at: http://interactive-atlas.ipcc.ch/, last access: 19 January 2025). a
Held, I. M. and Soden, B. J.: Robust responses of the hydrological cycle to global warming, J. Climate, 19, 5686–5699, 2006. a
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. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Houze, R. A., Rasmussen, K. L., Zuluaga, M. D., and Brodzik, S. R.: The variable nature of convection in the tropics and subtropics: A legacy of 16 years of the Tropical Rainfall Measuring Mission satellite, Rev. Geophys., 53, 994–1021, https://doi.org/10.1002/2015RG000488, 2015. a
Huffman, G. J., Stocker, E. T., Bolvin, D. T., Nelkin, E. J., and Tan, J.: GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V06, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, https://doi.org/10.5067/GPM/IMERGDF/DAY/06, 2019. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Janjić, Z.: Nonsingular Implementation of the Mellor–Yamada Level 2.5 Scheme in the NCEP Meso Model, NCEP Office Note no. 436, https://repository.library.noaa.gov/view/noaa/11409 (last access: 19 January 2025), 2002. a
Jiang, Q.: Moist dynamics and orographic precipitation, Tellus A, 55, 301–316, https://doi.org/10.3402/tellusa.v55i4.14577, 2003. a
Kirshbaum, D. J. and Smith, R. B.: Temperature and moist-stability effects on midlatitude orographic precipitation, Q. J. Roy. Meteor. Soc., 134, 1183–1199, https://doi.org/10.1002/qj.274, 2008. a
Kirshbaum, D. J., Adler, B., Kalthoff, N., Barthlott, C., and Serafin, S.: Moist Orographic Convection: Physical Mechanisms and Links to Surface-Exchange Processes, Atmosphere, 9, 80, https://doi.org/10.3390/atmos9030080, 2018. a
Koszuta, M., Siler, N., Leung, L. R., and Wettstein, J. J.: Weakened Orographic Influence on Cool-Season Precipitation in Simulations of Future Warming Over the Western US, Geophys. Res. Lett., 51, e2023GL107298, https://doi.org/10.1029/2023GL107298, 2024. a
Kunz, M. and Wassermann, S.: Sensitivity of flow dynamics and orographic precipitation to changing ambient conditions in idealised model simulations, Meteorol. Z., 20, 199–215, 2011. a
Lilly, D. and Klemp, J.: The effects of terrain shape on nonlinear hydrostatic mountain waves, J. Fluid Mech., 95, 241–261, 1979. a
Long, R. R.: Some Aspects of the Flow of Stratified Fluids: I. A Theoretical Investigation, Tellus, 5, 42–58, https://doi.org/10.1111/j.2153-3490.1953.tb01035.x, 1953. a
Mellor, G. L. and Yamada, T.: Development of a turbulence closure model for geophysical fluid problems, Rev. Geophys., 20, 851–875, https://doi.org/10.1029/RG020i004p00851, 1982. a
Miglietta, M. M. and Rotunno, R.: Numerical Simulations of Conditionally Unstable Flows over a Mountain Ridge, J. Atmos. Sci., 66, 1865–1885, https://doi.org/10.1175/2009JAS2902.1, 2009. a
Nicolas, Q.: qnicolas/windSensitivity: Accepted version, Version v1.1.0, Zenodo [code], https://doi.org/10.5281/zenodo.14541436, 2024a. a
Nicolas, Q.: Data for Nicolas & Boos, “Sensitivity of tropical orographic precipitation to wind speed with implications for future projections”, Version v1, Zenodo [data set], https://doi.org/10.5281/zenodo.11479598, 2024b. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011. a
Rajendran, K., Kitoh, A., Srinivasan, J., Mizuta, R., and Krishnan, R.: Monsoon circulation interaction with Western Ghats orography under changing climate: projection by a 20-km mesh AGCM, Theor. Appl. Climatol., 110, 555–571, 2012. a
Ramesh, N., Nicolas, Q., and Boos, W. R.: The Globally Coherent Pattern of Autumn Monsoon Precipitation, J. Climate, 34, 5687–5705, https://doi.org/10.1175/JCLI-D-20-0740.1, 2021. a
Raymond, D. J., Sessions, S. L., Sobel, A. H., and Fuchs, Ž.: The Mechanics of Gross Moist Stability, J. Adv. Model. Earth Sy., 1, 9, https://doi.org/10.3894/JAMES.2009.1.9, 2009. a
Rodwell, M. J. and Hoskins, B. J.: Subtropical anticyclones and summer monsoons, J. Climate, 14, 3192–3211, 2001. a
Roe, G. H.: OROGRAPHIC PRECIPITATION, Annu. Rev. Earth Pl. Sc., 33, 645–671, https://doi.org/10.1146/annurev.earth.33.092203.122541, 2005. a
Roxy, M. and Tanimoto, Y.: Role of SST over the Indian Ocean in Influencing the Intraseasonal Variability of the Indian Summer Monsoon, J. Meteorol. Soc. Jpn. Ser. II, 85, 349–358, https://doi.org/10.2151/jmsj.85.349, 2007. a
Shi, X. and Durran, D. R.: The response of orographic precipitation over idealized midlatitude mountains due to global increases in CO2, J. Climate, 27, 3938–3956, 2014. a
Shige, S., Nakano, Y., and Yamamoto, M. K.: Role of Orography, Diurnal Cycle, and Intraseasonal Oscillation in Summer Monsoon Rainfall over the Western Ghats and Myanmar Coast, J. Climate, 30, 9365–9381, https://doi.org/10.1175/JCLI-D-16-0858.1, 2017. a
Shrivastava, S., Kar, S. C., and Sharma, A. R.: Inter-annual variability of summer monsoon rainfall over Myanmar, Int. J. Climatol., 37, 802–820, https://doi.org/10.1002/joc.4741, 2017. a
Siler, N. and Roe, G.: How will orographic precipitation respond to surface warming? An idealized thermodynamic perspective, Geophys. Res. Lett., 41, 2606–2613, https://doi.org/10.1002/2013GL059095, 2014. a
Skamarock, C., Klemp, B., Dudhia, J., Gill, O., Liu, Z., Berner, J., Wang, W., Powers, G., Duda, G., Barker, D., and Huang, X.-Y.: A Description of the Advanced Research WRF Model Version 4.1, NCAR Technical Note NCAR/TN-556+STR, https://doi.org/10.5065/1dfh-6p97, 2019. a
Smith, R. B.: The Influence of Mountains on the Atmosphere, Adv. Geophys., 21, 87–230, https://doi.org/10.1016/S0065-2687(08)60262-9, 1979. a, b
Smith, R. B. and Barstad, I.: A Linear Theory of Orographic Precipitation, J. Atmos. Sci., 61, 1377–1391, https://doi.org/10.1175/1520-0469(2004)061<1377:ALTOOP>2.0.CO;2, 2004. a
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008. a
Varikoden, H., Revadekar, J. V., Kuttippurath, J., and Babu, C. A.: Contrasting trends in southwest monsoon rainfall over the Western Ghats region of India, Clim. Dynam., 52, 4557–4566, https://doi.org/10.1007/s00382-018-4397-7, 2019. a
Vecchi, G. A. and Harrison, D. E.: Interannual Indian rainfall variability and Indian Ocean sea surface temperature anomalies, Geoph. Monog. Series, 147, 247–259, https://doi.org/10.1029/147GM14, 2004. a
Viviroli, D., Kummu, M., Meybeck, M., Kallio, M., and Wada, Y.: Increasing dependence of lowland populations on mountain water resources, Nature Sustainability, 3, 917–928, https://doi.org/10.1038/s41893-020-0559-9, 2020. a
Wang, B., Biasutti, M., Byrne, M. P., Castro, C., Chang, C.-P., Cook, K., Fu, R., Grimm, A. M., Ha, K.-J., Hendon, H., Kitoh, A., Krishnan, R., Lee, J.-Y., Li, J., Liu, J., Moise, A., Pascale, S., Roxy, M. K., Seth, A., Sui, C.-H., Turner, A., Yang, S., Yun, K.-S., Zhang, L., and Zhou, T.: Monsoons Climate Change Assessment, B. Am. Meteorol. Soc., 102, E1–E19, https://doi.org/10.1175/BAMS-D-19-0335.1, 2021. a
Wang, S. and Sobel, A. H.: Factors Controlling Rain on Small Tropical Islands: Diurnal Cycle, Large-Scale Wind Speed, and Topography, J. Atmos. Sci., 74, 3515–3532, 2017. a
Wilks, D. S.: “The Stippling Shows Statistically Significant Grid Points”: How Research Results are Routinely Overstated and Overinterpreted, and What to Do about It, B. Am. Meteorol. Soc., 97, 2263–2273, https://doi.org/10.1175/BAMS-D-15-00267.1, 2016. a
Wing, A. A. and Singh, M. S.: Control of Stability and Relative Humidity in the Radiative-Convective Equilibrium Model Intercomparison Project, J. Adv. Model. Earth Sy., 16, e2023MS003914, https://doi.org/10.1029/2023MS003914, 2024. a
Yang, Z.-L., Niu, G.-Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res.-Atmos., 116, D12110, https://doi.org/10.1029/2010JD015140, 2011. a
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and Kitoh, A.: APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges, B. Am. Meteorol. Soc., 93, 1401–1415, 2012. a
Executive editor
How precipitation over ocean regions changes with global warming is grounded in a well accepted theory, but there is so far no such theory to describe precipitation changes over land. This paper provides important theoretical, numerical and observational evidence for the sensitivity of orographically-induced precipitation in the tropics to background wind changes. The result that tropical orographic rainfall increases with cross-slope winds at a rate of 20-30% (which is more than twice the rate expected from simple "upslope flow” theory) has important implications for constraining future climate change, providing an avenue for reliable projections of continental precipitation without the need for climate models that explicitly resolve convection.
How precipitation over ocean regions changes with global warming is grounded in a well accepted...
Short summary
Rainfall in mountainous regions constitutes an important source of freshwater in the tropics. Yet how it will change with global warming remains an open question. Here, we reveal a strong sensitivity of this rainfall to the speed of prevailing winds. This relationship, validated by theory, simulations, and observational data, suggests that regional wind shifts will significantly influence future rainfall changes in the tropics.
Rainfall in mountainous regions constitutes an important source of freshwater in the tropics....