Articles | Volume 2, issue 4
https://doi.org/10.5194/wcd-2-1093-2021
© Author(s) 2021. 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-2-1093-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
23 Nov 2021
Research article | Highlight paper |
| 23 Nov 2021
| 23 Nov 2021
Future summer warming pattern under climate change is affected by lapse-rate changes
Roman Brogli
Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland
Silje Lund Sørland
Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland
NORCE, Jahnebakken 5, 5007 Bergen, Norway
Nico Kröner
Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland
South Pole, Technoparkstrasse 1, 8005 Zurich, Switzerland
Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland
Related authors
Roman Brogli, Christoph Heim, Jonas Mensch, Silje Lund Sørland, and Christoph Schär
Geosci. Model Dev., 16, 907–926, https://doi.org/10.5194/gmd-16-907-2023, https://doi.org/10.5194/gmd-16-907-2023, 2023
Short summary
Short summary
The pseudo-global-warming (PGW) approach is a downscaling methodology that imposes the large-scale GCM-based climate change signal on the boundary conditions of a regional climate simulation. It offers several benefits in comparison to conventional downscaling. We present a detailed description of the methodology, provide companion software to facilitate the preparation of PGW simulations, and present validation and sensitivity studies.
Silje Lund Sørland, Roman Brogli, Praveen Kumar Pothapakula, Emmanuele Russo, Jonas Van de Walle, Bodo Ahrens, Ivonne Anders, Edoardo Bucchignani, Edouard L. Davin, Marie-Estelle Demory, Alessandro Dosio, Hendrik Feldmann, Barbara Früh, Beate Geyer, Klaus Keuler, Donghyun Lee, Delei Li, Nicole P. M. van Lipzig, Seung-Ki Min, Hans-Jürgen Panitz, Burkhardt Rockel, Christoph Schär, Christian Steger, and Wim Thiery
Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, https://doi.org/10.5194/gmd-14-5125-2021, 2021
Short summary
Short summary
We review the contribution from the CLM-Community to regional climate projections following the CORDEX framework over Europe, South Asia, East Asia, Australasia, and Africa. How the model configuration, horizontal and vertical resolutions, and choice of driving data influence the model results for the five domains is assessed, with the purpose of aiding the planning and design of regional climate simulations in the future.
Marie-Estelle Demory, Ségolène Berthou, Jesús Fernández, Silje L. Sørland, Roman Brogli, Malcolm J. Roberts, Urs Beyerle, Jon Seddon, Rein Haarsma, Christoph Schär, Erasmo Buonomo, Ole B. Christensen, James M. Ciarlo ̀, Rowan Fealy, Grigory Nikulin, Daniele Peano, Dian Putrasahan, Christopher D. Roberts, Retish Senan, Christian Steger, Claas Teichmann, and Robert Vautard
Geosci. Model Dev., 13, 5485–5506, https://doi.org/10.5194/gmd-13-5485-2020, https://doi.org/10.5194/gmd-13-5485-2020, 2020
Short summary
Short summary
Now that global climate models (GCMs) can run at similar resolutions to regional climate models (RCMs), one may wonder whether GCMs and RCMs provide similar regional climate information. We perform an evaluation for daily precipitation distribution in PRIMAVERA GCMs (25–50 km resolution) and CORDEX RCMs (12–50 km resolution) over Europe. We show that PRIMAVERA and CORDEX simulate similar distributions. Considering both datasets at such a resolution results in large benefits for impact studies.
Shaochun Huang, Wai Kwok Wong, Andreas Dobler, Sigrid Jørgensen Bakke, Stein Beldring, Ingjerd Haddeland, Hans Olav Hygen, Tyge Løvset, Stephanie Mayer, Kjetil Melvold, Irene Brox Nilsen, Gusong Ruan, Silje Lund Sørland, and Anita Verpe Dyrrdal
EGUsphere, https://doi.org/10.5194/egusphere-2025-5331, https://doi.org/10.5194/egusphere-2025-5331, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This paper documents the model experiment used to generate the most updated, comprehensive and detailed climate and hydrological projections for the national climate assessment report for Norway published in October 2025. The new datasets (COR-BA-2025 and distHBV-COR-BA-2025) of these projections are openly accessible and will serve as a knowledge base for climate change adaptation to decision makers at various administrative levels in Norway.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
Short summary
Short summary
We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
Short summary
Short summary
We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
Ruoyi Cui, Nikolina Ban, Marie-Estelle Demory, Raffael Aellig, Oliver Fuhrer, Jonas Jucker, Xavier Lapillonne, and Christoph Schär
Weather Clim. Dynam., 4, 905–926, https://doi.org/10.5194/wcd-4-905-2023, https://doi.org/10.5194/wcd-4-905-2023, 2023
Short summary
Short summary
Our study focuses on severe convective storms that occur over the Alpine-Adriatic region. By running simulations for eight real cases and evaluating them against available observations, we found our models did a good job of simulating total precipitation, hail, and lightning. Overall, this research identified important meteorological factors for hail and lightning, and the results indicate that both HAILCAST and LPI diagnostics are promising candidates for future climate research.
Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, Giuseppe Formetta, Christoph Schär, and Marco Borga
Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, https://doi.org/10.5194/hess-27-1133-2023, 2023
Short summary
Short summary
Convection-permitting climate models could represent future changes in extreme short-duration precipitation, which is critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite overall good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider the role of orography.
Roman Brogli, Christoph Heim, Jonas Mensch, Silje Lund Sørland, and Christoph Schär
Geosci. Model Dev., 16, 907–926, https://doi.org/10.5194/gmd-16-907-2023, https://doi.org/10.5194/gmd-16-907-2023, 2023
Short summary
Short summary
The pseudo-global-warming (PGW) approach is a downscaling methodology that imposes the large-scale GCM-based climate change signal on the boundary conditions of a regional climate simulation. It offers several benefits in comparison to conventional downscaling. We present a detailed description of the methodology, provide companion software to facilitate the preparation of PGW simulations, and present validation and sensitivity studies.
Qinggang Gao, Christian Zeman, Jesus Vergara-Temprado, Daniela C. A. Lima, Peter Molnar, and Christoph Schär
Weather Clim. Dynam., 4, 189–211, https://doi.org/10.5194/wcd-4-189-2023, https://doi.org/10.5194/wcd-4-189-2023, 2023
Short summary
Short summary
We developed a vortex identification algorithm for realistic atmospheric simulations. The algorithm enabled us to obtain a climatology of vortex shedding from Madeira Island for a 10-year simulation period. This first objective climatological analysis of vortex streets shows consistency with observed atmospheric conditions. The analysis shows a pronounced annual cycle with an increasing vortex shedding rate from April to August and a sudden decrease in September.
Christian R. Steger, Benjamin Steger, and Christoph Schär
Geosci. Model Dev., 15, 6817–6840, https://doi.org/10.5194/gmd-15-6817-2022, https://doi.org/10.5194/gmd-15-6817-2022, 2022
Short summary
Short summary
Terrain horizon and sky view factor are crucial quantities for many geoscientific applications; e.g. they are used to account for effects of terrain on surface radiation in climate and land surface models. Because typical terrain horizon algorithms are inefficient for high-resolution (< 30 m) elevation data, we developed a new algorithm based on a ray-tracing library. A comparison with two conventional methods revealed both its high performance and its accuracy for complex terrain.
Christian Zeman and Christoph Schär
Geosci. Model Dev., 15, 3183–3203, https://doi.org/10.5194/gmd-15-3183-2022, https://doi.org/10.5194/gmd-15-3183-2022, 2022
Short summary
Short summary
Our atmosphere is a chaotic system, where even a tiny change can have a big impact. This makes it difficult to assess if small changes, such as the move to a new hardware architecture, will significantly affect a weather and climate model. We present a methodology that allows to objectively verify this. The methodology is applied to several test cases, showing a high sensitivity. Results also show that a major system update of the underlying supercomputer did not significantly affect our model.
Daniel Regenass, Linda Schlemmer, Elena Jahr, and Christoph Schär
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-426, https://doi.org/10.5194/hess-2021-426, 2021
Manuscript not accepted for further review
Short summary
Short summary
Weather and climate models need to represent the water cycle on land in order to provide accurate estimates of moisture and energy exchange between the land and the atmosphere. Infiltration of water into the soil is often modeled with an equation describing water transport in porous media. Here, we point out some challenges arising in the numerical solution of this equation and show the consequences for the representation of the water cycle in modern weather and climate models.
Silje Lund Sørland, Roman Brogli, Praveen Kumar Pothapakula, Emmanuele Russo, Jonas Van de Walle, Bodo Ahrens, Ivonne Anders, Edoardo Bucchignani, Edouard L. Davin, Marie-Estelle Demory, Alessandro Dosio, Hendrik Feldmann, Barbara Früh, Beate Geyer, Klaus Keuler, Donghyun Lee, Delei Li, Nicole P. M. van Lipzig, Seung-Ki Min, Hans-Jürgen Panitz, Burkhardt Rockel, Christoph Schär, Christian Steger, and Wim Thiery
Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, https://doi.org/10.5194/gmd-14-5125-2021, 2021
Short summary
Short summary
We review the contribution from the CLM-Community to regional climate projections following the CORDEX framework over Europe, South Asia, East Asia, Australasia, and Africa. How the model configuration, horizontal and vertical resolutions, and choice of driving data influence the model results for the five domains is assessed, with the purpose of aiding the planning and design of regional climate simulations in the future.
Christian Zeman, Nils P. Wedi, Peter D. Dueben, Nikolina Ban, and Christoph Schär
Geosci. Model Dev., 14, 4617–4639, https://doi.org/10.5194/gmd-14-4617-2021, https://doi.org/10.5194/gmd-14-4617-2021, 2021
Short summary
Short summary
Kilometer-scale atmospheric models allow us to partially resolve thunderstorms and thus improve their representation. We present an intercomparison between two distinct atmospheric models for 2 summer days with heavy thunderstorms over Europe. We show the dependence of precipitation and vertical wind speed on spatial and temporal resolution and also discuss the possible influence of the system of equations, numerical methods, and diffusion in the models.
Emmanuele Russo, Silje Lund Sørland, Ingo Kirchner, Martijn Schaap, Christoph C. Raible, and Ulrich Cubasch
Geosci. Model Dev., 13, 5779–5797, https://doi.org/10.5194/gmd-13-5779-2020, https://doi.org/10.5194/gmd-13-5779-2020, 2020
Short summary
Short summary
The parameter space of the COSMO-CLM RCM is investigated for the Central Asia CORDEX domain using a perturbed physics ensemble (PPE) with different parameter values. Results show that only a subset of model parameters presents relevant changes in model performance and these changes depend on the considered region and variable: objective calibration methods are highly necessary in this case. Additionally, the results suggest the need for calibrating an RCM when targeting different domains.
Marie-Estelle Demory, Ségolène Berthou, Jesús Fernández, Silje L. Sørland, Roman Brogli, Malcolm J. Roberts, Urs Beyerle, Jon Seddon, Rein Haarsma, Christoph Schär, Erasmo Buonomo, Ole B. Christensen, James M. Ciarlo ̀, Rowan Fealy, Grigory Nikulin, Daniele Peano, Dian Putrasahan, Christopher D. Roberts, Retish Senan, Christian Steger, Claas Teichmann, and Robert Vautard
Geosci. Model Dev., 13, 5485–5506, https://doi.org/10.5194/gmd-13-5485-2020, https://doi.org/10.5194/gmd-13-5485-2020, 2020
Short summary
Short summary
Now that global climate models (GCMs) can run at similar resolutions to regional climate models (RCMs), one may wonder whether GCMs and RCMs provide similar regional climate information. We perform an evaluation for daily precipitation distribution in PRIMAVERA GCMs (25–50 km resolution) and CORDEX RCMs (12–50 km resolution) over Europe. We show that PRIMAVERA and CORDEX simulate similar distributions. Considering both datasets at such a resolution results in large benefits for impact studies.
Cited articles
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Barcikowska, M. J., Kapnick, S. B., Krishnamurty, L., Russo, S., Cherchi, A., and Folland, C. K.: Changes in the future summer Mediterranean climate: contribution of teleconnections and local factors, Earth Syst. Dynam., 11, 161–181, https://doi.org/10.5194/esd-11-161-2020, 2020. a
Berg, A., Findell, K., Lintner, B., Giannini, A., Seneviratne, S. I., Van Den Hurk, B., Lorenz, R., Pitman, A., Hagemann, S., Meier, A., Cheruy, F., Ducharne, A., Malyshev, S., and Milly, P. C.: Land-atmosphere feedbacks amplify aridity increase over land under global warming, Nat. Clim. Change, 6, 869–874, https://doi.org/10.1038/nclimate3029, 2016. a, b
Bosshard, T., Kotlarski, S., Ewen, T., and Schär, C.: Spectral representation of the annual cycle in the climate change signal, Hydrol. Earth Syst. Sci., 15, 2777–2788, https://doi.org/10.5194/hess-15-2777-2011, 2011. a
Brogli, R. and Vergara-Temprado, J.: broglir/pgw-python: First release (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.4890235, 2021. a
Brogli, R., Sørland, S. L., Kröner, N., and Schär, C.: Causes of future Mediterranean precipitation decline depend on the season, Environ. Res. Lett., 14, 114017, https://doi.org/10.1088/1748-9326/ab4438, 2019b. a, b, c
Buzan, J. R. and Huber, M.: Moist Heat Stress on a Hotter Earth, Annu. Rev. Earth Pl. Sc., 48, 623–655, https://doi.org/10.1146/annurev-earth-053018-060100, 2020. a, b
Byrne, M. P. and O'Gorman, P. A.: Link between land-ocean warming contrast and surface relative humidities in simulations with coupled climate models, Geophys. Res. Lett., 40, 5223–5227, https://doi.org/10.1002/grl.50971, 2013b. a, b
Ceppi, P. and Gregory, J. M.: Relationship of tropospheric stability to climate sensitivity and Earth's observed radiation budget, P. Natl. Acad. Sci. USA, 114, 201714308, https://doi.org/10.1073/pnas.1714308114, 2017. a
Chadwick, R., Ackerley, D., Ogura, T., and Dommenget, D.: Separating the Influences of Land Warming, the Direct CO2 Effect, the Plant Physiological Effect, and SST Warming on Regional Precipitation Changes, J. Geophys. Res.-Atmos., 124, 624–640, https://doi.org/10.1029/2018JD029423, 2019. a
Charney, J. G.: A Note on Large-Scale Motions in the Tropics, J. Atmos. Sci., 20, 607–609, https://doi.org/10.1175/1520-0469(1963)020<0607:ANOLSM>2.0.CO;2, 1963. a
Charney, J. G.: A Further Note on Large-Scale Motions in the Tropics, J. Atmos. Sci., 26, 182–185, https://doi.org/10.1175/1520-0469(1969)026<0182:AFNOLS>2.0.CO;2, 1969. a
Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L., Fichefet, T., Friedlingstein, P., Gao, X., Gutowski, W., Johns, T., Krinner, G., Shongwe, M., Tebaldi, C., Weaver, A., and Wehner, M.: Long-term climate change: Projections, commitments and irreversibility, in: Climate Change 2013 the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1029–1136, https://doi.org/10.1017/CBO9781107415324.024, 2013. a
Cramer, W., Guiot, J., Fader, M., Garrabou, J., Gattuso, J.-P., Iglesias, A., Lange, M. A., Lionello, P., Llasat, M. C., Paz, S., Peñuelas, J., Snoussi, M., Toreti, A., Tsimplis, M. N., and Xoplaki, E.: Climate change and interconnected risks to sustainable development in the Mediterranean, Nat. Clim. Change, 7, 972–980, https://doi.org/10.1038/s41558-018-0299-2, 2018. a
Diffenbaugh, N. S. and Giorgi, F.: Climate change hotspots in the CMIP5 global climate model ensemble, Climatic Change, 114, 813–822, https://doi.org/10.1007/s10584-012-0570-x, 2012. 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, b
Fasullo, J. T.: Robust land-ocean contrasts in energy and water cycle feedbacks, J. Climate, 23, 4677–4693, https://doi.org/10.1175/2010JCLI3451.1, 2010. a, b, c
Flannaghan, T. J., Fueglistaler, S., Held, I. M., Po-Chedley, S., Wyman, B., and Zhao, M.: Tropical Temperature Trends in AMIP Simulations and the Impact of SST Uncertainties, J. Geophys. Res.-Atmos., 119, 13327–13337, https://doi.org/10.1002/2014JD022365, 2014. a
Frierson, D. M. and Davis, N. A.: The seasonal cycle of midlatitude static stability over land and ocean in global reanalyses, Geophys. Res. Lett., 38, 1–6, https://doi.org/10.1029/2011GL047747, 2011. a
Good, P., Lowe, J. A., Andrews, T., Wiltshire, A., Chadwick, R., Ridley, J. K., Menary, M. B., Bouttes, N., Dufresne, J. L., Gregory, J. M., Schaller, N., and Shiogama, H.: Nonlinear regional warming with increasing CO2 concentrations, Nat. Clim. Change, 5, 138–142, https://doi.org/10.1038/nclimate2498, 2015. a
Hall, A.: Projecting regional change, Science, 346, 1460–1462, https://doi.org/10.1126/science.aaa0629, 2014. a
Held, I. M. and Soden, B. J.: Water Vapor Feedback and Global Warming, Annu. Rev, Energ, Env,, 25, 441–475, https://doi.org/10.1146/annurev.energy.25.1.441, 2000. a
Huang, J., Li, Y., Fu, C., Chen, F., Fu, Q., Dai, A., Shinoda, M., Ma, Z., Guo, W., Li, Z., Zhang, L., Liu, Y., Yu, H., He, Y., Xie, Y., Guan, X., Ji, M., Lin, L., Wang, S., Yan, H., and Wang, G.: Dryland climate change: Recent progress and challenges, Rev. Geophys., 55, 719–778, https://doi.org/10.1002/2016RG000550, 2017a. a
Huang, J., Yu, H., Dai, A., Wei, Y., and Kang, L.: Drylands face potential threat under 2 ∘C global warming target, Nat. Clim. Change, 7, 417–422, https://doi.org/10.1038/nclimate3275, 2017b. a
Izumi, K., Bartlein, P. J., and Harrison, S. P.: Consistent large-scale temperature responses in warm and cold climates, Geophys. Res. Lett., 40, 1817–1823, https://doi.org/10.1002/grl.50350, 2013. a
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J. F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: New high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014. a, b
Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda, M., Benestad, R., Boberg, F., Buonomo, E., Cardoso, R. M., Casanueva, A., Christensen, O. B., Christensen, J. H., Coppola, E., De Cruz, L., Davin, E. L., Dobler, A., Domínguez, M., Fealy, R., Fernandez, J., Gaertner, M. A., García-Díez, M., Giorgi, F., Gobiet, A., Goergen, K., Gómez-Navarro, J. J., Alemán, J. J. G., Gutiérrez, C., Gutiérrez, J. M., Güttler, I., Haensler, A., Halenka, T., Jerez, S., Jiménez-Guerrero, P., Jones, R. G., Keuler, K., Kjellström, E., Knist, S., Kotlarski, S., Maraun, D., van Meijgaard, E., Mercogliano, P., Montávez, J. P., Navarra, A., Nikulin, G., de Noblet-Ducoudré, N., Panitz, H.-J., Pfeifer, S., Piazza, M., Pichelli, E., Pietikäinen, J.-P., Prein, A. F., Preuschmann, S., Rechid, D., Rockel, B., Romera, R., Sánchez, E., Sieck, K., Soares, P. M. M., Somot, S., Srnec, L., Sørland, S. L., Termonia, P., Truhetz, H., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community, Reg. Environ. Change, 20, 51, https://doi.org/10.1007/s10113-020-01606-9, 2020. a, b, c
King, A. D.: The drivers of nonlinear local temperature change under global warming, Environ. Res. Lett., 14, 064005, https://doi.org/10.1088/1748-9326/ab1976, 2019. a
Kjellström, E., Bärring, L., Nikulin, G., Nilsson, C., Persson, G., and Strandberg, G.: Production and use of regional climate model projections – A Swedish perspective on building climate services, Clim. Serv., 2–3, 15–29, https://doi.org/10.1016/j.cliser.2016.06.004, 2016. a
Korty, R. L. and Schneider, T.: A Climatology of the Tropospheric Thermal Stratification Using Saturation Potential Vorticity, J. Climate, 20, 5977–5991, https://doi.org/10.1175/2007JCLI1788.1, 2007. a
Koutroulis, A. G.: Dryland changes under different levels of global warming,
Sci. Total Environ., 655, 482–511, https://doi.org/10.1016/j.scitotenv.2018.11.215, 2019. a
Kovats, R. S., Valentini, R., Bouwer, L., Georgopoulou, E., Jacob, D., Martin, E., Rounsevell, M., and Soussana, J.-F.: Europe, in: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Barros, V. R., Field, C. B., Dokken, D. J., Mastrandrea, K. J., Mach, K. J., Bilir, T. E., Chatterjee, K. L., Ebi, K. L., Estrada, Y. O., Genova, R. C., Girma, B., Kissel, E. S., Levy, A. N., MacCracken, S., Mastrandrea, P. R., and White, L. L., Cambridge University Press, Cambridge, UK and New York, NY, USA, 1267–1326, 2014. a
Kröner, N., Kotlarski, S., Fischer, E., Lüthi, D., Zubler, E., and Schär, C.: Separating climate change signals into thermodynamic, lapse-rate and circulation effects: theory and application to the European summer climate, Clim. Dynam., 48, 1–16, https://doi.org/10.1007/s00382-016-3276-3, 2017. a, b, c
Lenderink, G., Belusic, D., Fowler, H. J., Kjellström, E., Lind, P., van Meijgaard, E., van Ulft, B., and de Vries, H.: Systematic increases in the thermodynamic response of hourly precipitation extremes in an idealized warming experiment with a convection-permitting climate model, Environ. Res. Lett., 14, 074012, https://doi.org/10.1088/1748-9326/ab214a, 2019. a
Matthews, T.: Humid heat and climate change, Prog. Phys. Geog., 42, 391–405, https://doi.org/10.1177/0309133318776490, 2018. a
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van
Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A.,
Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next generation of scenarios for climate change research and assessment, Nature, 463, 747–756, https://doi.org/10.1038/nature08823, 2010. a
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. a
Pithan, F. and Mauritsen, T.: Arctic amplification dominated by temperature feedbacks in contemporary climate models, Nat. Geosci., 7, 2–5, https://doi.org/10.1038/NGEO2071, 2014. a
Po-Chedley, S., Armour, K. C., Bitz, C. M., Zelinka, M. D., and Santer, B. D.: Sources of intermodel spread in the lapse rate and water vapor feedbacks, J. Climate, 31, 3187–3206, https://doi.org/10.1175/JCLI-D-17-0674.1, 2018. a
Santer, B. D., Wigley, T. M., Mears, C., Wentz, F. J., Klein, S. A., Seidel, D. J., Taylor, K. E., Thorne, P. W., Wehner, M. F., Gleckler, P. J., Boyle, J. S., Collins, W. D., Dixon, K. W., Doutriaux, C., Free, M., Fu, Q., Hansen, J. E., Jones, C. S., Ruedy, R., Karl, T. R., Lanzante, J. R., Meehl, C. A., Ramaswamy, V., Russell, C., and Schmidt, C. A.: Atmospheric science: Amplification of surface temperature trends and variability in the tropical atmosphere, Science, 309, 1551–1556, https://doi.org/10.1126/science.1114867, 2005. a
Santer, B. D., Solomon, S., Pallotta, G., Mears, C., Po-Chedley, S., Fu, Q., Wentz, F., Zou, C. Z., Painter, J., Cvijanovic, I., and Bonfils, C.: Comparing tropospheric warming in climate models and satellite data, J. Climate, 30, 373–392, https://doi.org/10.1175/JCLI-D-16-0333.1, 2017. a
Schär, C., Frei, C., Lüthi, D., and Davies, H. C.: Surrogate climate-change scenarios for regional climate models, Geophys. Res. Lett., 23, 669–672, https://doi.org/10.1029/96GL00265, 1996. a
Schneider, T.: The thermal stratification of the extratropical troposphere, in: The Global Circulation of the Atmosphere, edited by: Schneider, T. and Sobel, A. H., Princeton Univ. Press, Princeton, NJ, 47–77, 2007. a
Sobel, A. H. and Bretherton, C. S.: Modeling Tropical Precipitation in a Single Column, J. Climate, 13, 4378–4392, https://doi.org/10.1175/1520-0442(2000)013<4378:MTPIAS>2.0.CO;2, 2000. a
Son, J., Liu, J. C., and Bell, M. L.: Temperature-related mortality: A systematic review and investigation of effect modifiers, Environ. Res. Lett., 14, 073004, https://doi.org/10.1088/1748-9326/ab1cdb, 2019. a
Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S., Salzmann, M., Schmidt, H., Bader, J., Block, K., Brokopf, R., Fast, I., Kinne, S., Kornblueh, L., Lohmann, U., Pincus, R., Reichler, T., and Roeckner, E.: Atmospheric component of the MPI-M Earth System Model: ECHAM6, J. Adv. Model. Earth Sy., 5, 146–172, https://doi.org/10.1002/jame.20015, 2013. a
Stickler, A., Grant, A. N., Ewen, T., Ross, T. F., Vose, R. S., Comeaux, J., Bessemoulin, P., Jylhä, K., Adam, W. K., Jeannet, P., Nagurny, A., Sterin, A. M., Allan, R., Compo, G. P., Griesser, T., and Brönnimann, S.: The Comprehensive Historical Upper-Air Network, B. Am. Meteorol. Soc., 91, 741–752, https://doi.org/10.1175/2009BAMS2852.1, 2010. a
Stuecker, M. F., Bitz, C. M., Armour, K. C., Proistosescu, C., Kang, S. M., Xie, S.-P., Kim, D., Mcgregor, S., Zhang, W., Zhao, S., Cai, W., Dong, Y., and Jin, F.-F.: Polar amplification dominated by local forcing and feedbacks, Nat. Clim. Change, 8, 1076–1081, https://doi.org/10.1038/s41558-018-0339-y, 2018. a
Sutton, R., Suckling, E., and Hawkins, E.: What does global mean temperature
tell us about local climate?, Philos. T. Roy. Soc. A, 373, 20140426, https://doi.org/10.1098/rsta.2014.0426, 2015. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a, b, c
The HadGEM2 Development Team: G. M. Martin, Bellouin, N., Collins, W. J.,
Culverwell, I. D., Halloran, P. R., Hardiman, S. C., Hinton, T. J.,
Jones, C. D., McDonald, R. E., McLaren, A. J., O'Connor, F. M.,
Roberts, M. J., Rodriguez, J. M., Woodward, S., Best, M. J., Brooks, M. E.,
Brown, A. R., Butchart, N., Dearden, C., Derbyshire, S. H., Dharssi, I.,
Doutriaux-Boucher, M., Edwards, J. M., Falloon, P. D., Gedney, N.,
Gray, L. J., Hewitt, H. T., Hobson, M., Huddleston, M. R., Hughes, J.,
Ineson, S., Ingram, W. J., James, P. M., Johns, T. C., Johnson, C. E.,
Jones, A., Jones, C. P., Joshi, M. M., Keen, A. B., Liddicoat, S.,
Lock, A. P., Maidens, A. V., Manners, J. C., Milton, S. F., Rae, J. G. L.,
Ridley, J. K., Sellar, A., Senior, C. A., Totterdell, I. J., Verhoef, A.,
Vidale, P. L., and Wiltshire, A.: The HadGEM2 family of Met Office Unified
Model climate configurations, Geosci. Model Dev., 4, 723–757,
https://doi.org/10.5194/gmd-4-723-2011, 2011. a
Thorne, P. W., Lanzante, J. R., Peterson, T. C., Seidel, D. J., and Shine, K. P.: Tropospheric temperature trends: History of an ongoing controversy, WIREs Clim. Change, 2, 66–88, https://doi.org/10.1002/wcc.80, 2011. a
Tuel, A.: Explaining Differences Between Recent Model and Satellite Tropospheric Warming Rates With Tropical SSTs, Geophys. Res. Lett., 46, 9023–9030, https://doi.org/10.1029/2019GL083994, 2019. a
Williams, I. N., Pierrehumbert, R. T., and Huber, M.: Global warming, convective threshold and false thermostats, Geophys. Res. Lett., 36, L21805, https://doi.org/10.1029/2009GL039849, 2009.
a
Xu, K.-M. and Emanuel, K. A.: Is the Tropical Atmosphere Conditionally Unstable?, Mon. Weather Rev., 117, 1471–1479, https://doi.org/10.1175/1520-0493(1989)117<1471:ITTACU>2.0.CO;2, 1989. a
Zamora, R. A., Korty, R. L., and Huber, M.: Thermal stratification in simulations of warm climates: A climatology using saturation potential vorticity, J. Climate, 29, 5083–5102, https://doi.org/10.1175/JCLI-D-15-0785.1, 2016. a
Short summary
In a warmer future climate, climate simulations predict that some land areas will experience excessive warming during summer. We show that the excessive summer warming is related to the vertical distribution of warming within the atmosphere. In regions characterized by excessive warming, much of the warming occurs close to the surface. In other regions, most of the warming is redistributed to higher levels in the atmosphere, which weakens the surface warming.
In a warmer future climate, climate simulations predict that some land areas will experience...
