Articles | Volume 16, issue 3
https://doi.org/10.5194/gmd-16-907-2023
© 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-16-907-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
06 Feb 2023
Development and technical paper |
| 06 Feb 2023
| 06 Feb 2023
The pseudo-global-warming (PGW) approach: methodology, software package PGW4ERA5 v1.1, validation, and sensitivity analyses
Roman Brogli
Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland
SRF Meteo, 8052 Zurich, Switzerland
Christoph Heim
CORRESPONDING AUTHOR
Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland
Jonas Mensch
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
Christoph Schär
Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland
Related authors
Roman Brogli, Silje Lund Sørland, Nico Kröner, and Christoph Schär
Weather Clim. Dynam., 2, 1093–1110, https://doi.org/10.5194/wcd-2-1093-2021, https://doi.org/10.5194/wcd-2-1093-2021, 2021
Short summary
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.
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.
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.
Roman Brogli, Silje Lund Sørland, Nico Kröner, and Christoph Schär
Weather Clim. Dynam., 2, 1093–1110, https://doi.org/10.5194/wcd-2-1093-2021, https://doi.org/10.5194/wcd-2-1093-2021, 2021
Short summary
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.
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
Adachi, S. A. and Tomita, H.: Methodology of the Constraint Condition in
Dynamical Downscaling for Regional Climate Evaluation: A Review, J. Geophys. Res.-Atmos., 125, e2019JD032166, https://doi.org/10.1029/2019JD032166, 2020. a, b, c
Adachi, S. A., Kimura, F., Kusaka, H., Inoue, T., and Ueda, H.: Comparison of
the Impact of Global Climate Changes and Urbanization on Summertime Future
Climate in the Tokyo Metropolitan Area, J. Appl. Meteorol.
Clim., 51, 1441–1454, https://doi.org/10.1175/JAMC-D-11-0137.1, 2012. a
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, b
Boé, J., Somot, S., Corre, L., and Nabat, P.: Large discrepancies in
summer climate change over Europe as projected by global and regional climate
models: causes and consequences, Clim. Dynam., 54, 2981–3002,
https://doi.org/10.1007/s00382-020-05153-1, 2020. a
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., Kröner, N., Sørland, S. L., Lüthi, D., and
Schär, C.: The Role of Hadley Circulation and Lapse-Rate Changes for
the Future European Summer Climate, J. Climate, 32, 385–404,
https://doi.org/10.1175/JCLI-D-18-0431.1, 2019a. 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
Brovkin, V., Wieners, K.-H., Giorgetta, M., Jungclaus, J., Reick, C.,
Esch, M., Bittner, M., Legutke, S., Schupfner, M., Wachsmann,
F., Gayler, V., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T.,
von Storch, J.-S., Behrens, J., Claussen, M., Crueger, T., Fast, I.,
Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S.,
Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D.,
Meraner, K., Mikolajewicz, U., Modali, K., Müller, W., Nabel,
J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H.,
Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida,
U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPIESM1.2-LR model output prepared for CMIP6 C4MIP, Earth System Grid Federation [data set],
https://doi.org/10.22033/ESGF/CMIP6.748, 2019. a
Christensen, J. H. and Christensen, O. B.: A summary of the PRUDENCE model
projections of changes in European climate by the end of this century,
Climatic Change, 81, 7–30, https://doi.org/10.1007/s10584-006-9210-7, 2007. a
Cinquini, L., Crichton, D., Mattmann, C., Harney, J., Shipman, G., Wang, F.,
Ananthakrishnan, R., Miller, N., Denvil, S., Morgan, M., Pobre, Z., Bell,
G. M., Doutriaux, C., Drach, R., Williams, D., Kershaw, P., Pascoe, S.,
Gonzalez, E., Fiore, S., and Schweitzer, R.: The Earth System Grid
Federation: An open infrastructure for access to distributed geospatial
data, Future Gener. Comp. Sy., 36, 400–417,
https://doi.org/10.1016/j.future.2013.07.002, 2014. a
Dai, A., Rasmussen, R. M., Liu, C., Ikeda, K., and Prein, A. F.: A new
mechanism for warm-season precipitation response to global warming based on
convection-permitting simulations, Clim. Dynam., 55, 343–368,
https://doi.org/10.1007/s00382-017-3787-6, 2020. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler,
M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J.,
Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N.,
and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of
the data assimilation system, Q. J. Roy. Meteor.
Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Dominguez, F., Dall'erba, S., Huang, S., Avelino, A., Mehran, A., Hu, H., Schmidt, A., Schick, L., and Lettenmaier, D.: Tracking an atmospheric river in a warmer climate: from water vapor to economic impacts, Earth Syst. Dynam., 9, 249–266, https://doi.org/10.5194/esd-9-249-2018, 2018. a
Expósito, F. J., González, A., Pérez, J. C., Díaz,
J. P., and Taima, D.: High-Resolution Future Projections of Temperature and
Precipitation in the Canary Islands, J. Climate, 28, 7846–7856,
https://doi.org/10.1175/JCLI-D-15-0030.1, 2015. a
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W.,
Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P.,
Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.:
Evaluation of Climate Models, in: Climate Change 2013: The Physical Science
Basis, Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D.,
Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y.,
Bex, V., and Midgley, P. M., Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 741–866,
https://doi.org/10.1017/CBO9781107415324, 2013. a, b
Giorgi, F., Jones, C., and Asrar, G.: Addressing Climate Information Needs at
the Regional Level: the CORDEX Framework, WMO Bulletin, 53, 2008. a
Gutjahr, O., Putrasahan, D., Lohmann, K., Jungclaus, J. H., von Storch, J.-S., Brüggemann, N., Haak, H., and Stössel, A.: Max Planck Institute Earth System Model (MPI-ESM1.2) for the High-Resolution Model Intercomparison Project (HighResMIP), Geosci. Model Dev., 12, 3241–3281, https://doi.org/10.5194/gmd-12-3241-2019, 2019. a
Gutmann, E. D., Rasmussen, R. M., Liu, C., Ikeda, K., Bruyere, C. L., Done,
J. M., Garrè, L., Friis-Hansen, P., and Veldore, V.: Changes in
Hurricanes from a 13-Yr Convection-Permitting Pseudo–Global Warming
Simulation, J. Climate, 31, 3643–3657,
https://doi.org/10.1175/JCLI-D-17-0391.1, 2018. a
Haberlie, A. M. and Ashley, W. S.: Climatological representation of mesoscale
convective systems in a dynamically downscaled climate simulation,
Int. J. Climatol., 39, 1144–1153, https://doi.org/10.1002/joc.5880,
2019. a
Hall, A.: Projecting regional change, Science, 346, 1461–1462,
https://doi.org/10.1126/science.aaa0629, 2014. a
Hara, M., Yoshikane, T., Kawase, H., and Kimura, F.: Estimation of the Impact
of Global Warming on Snow Depth in Japan by the Pseudo-Global-Warming
Method, Hydrological Research Letters, 2, 61–64, https://doi.org/10.3178/hrl.2.61,
2008. a
Hawkins, E. and Sutton, R.: The Potential to Narrow Uncertainty in Regional
Climate Predictions, B. Am. Meteorol. Soc., 90,
1095–1108, https://doi.org/10.1175/2009BAMS2607.1, 2009. a
Hazeleger, W., van den Hurk, B., Min, E., van Oldenborgh, G., Petersen, A.,
Stainforth, D., Vasileiadou, E., and Smith, L.: Tales of future weather,
Nat. Clim. Change, 5, 107–113, https://doi.org/10.1038/nclimate2450, 2015. a
Heim, C., Brogli, R., menschj, and Vergara-Temprado, J.: Potopoles/PGW4ERA5: SST climate delta (v1.1.1), Zenodo [code], https://doi.org/10.5281/zenodo.6627081, 2022. a
Hentgen, L., Ban, N., Kröner, N., Leutwyler, D., and Schär, C.:
Clouds in Convection‐Resolving Climate Simulations Over Europe, J.
Geophys. Res.-Atmos., 124, 3849–3870,
https://doi.org/10.1029/2018JD030150, 2019. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2018. 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., 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., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hoyer, S. and Hamman, J.: xarray: N-D labeled Arrays and Datasets in Python,
Journal of Open Research Software, 5, 10, https://doi.org/10.5334/jors.148, 2017. a
Ikeda, K., Rasmussen, R., Liu, C., Newman, A., Chen, F., Barlage, M., Gutmann,
E., Dudhia, J., Dai, A., Luce, C., and Musselman, K.: Snowfall and snowpack
in the Western U.S. as captured by convection permitting climate simulations:
current climate and pseudo global warming future climate, Clim. Dynam.,
57, 2191–2215, https://doi.org/10.1007/s00382-021-05805-w, 2021. a
Ito, R., Takemi, T., and Arakawa, O.: A Possible Reduction in the Severity of
Typhoon Wind in the Northern Part of Japan under Global Warming: A Case
Study, SOLA, 12, 100–105, https://doi.org/10.2151/sola.2016-023, 2016. a
Jung, C. and Lackmann, G. M.: Extratropical Transition of Hurricane Irene
(2011) in a Changing Climate, J. Climate, 32, 4847–4871,
https://doi.org/10.1175/JCLI-D-18-0558.1, 2019. a
Jungclaus, J., Bittner, M.,, Wieners, K.-H., Wachsmann, F., Schupfner, M., Legutke, S., Giorgetta, M., Reick, C., Gayler, V., Haak, H., de Vrese, P., Raddatz, T., Esch, M., Mauritsen, T., von Storch, J.-S., Behrens, J., Brovkin, V., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Müller, W., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A.,and Roeckner, E.: MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.741, 2019. a
Kawase, H., Yoshikane, T., Hara, M., Kimura, F., Yasunari, T., Ailikun, B.,
Ueda, H., and Inoue, T.: Intermodel variability of future changes in the
Baiu rainband estimated by the pseudo global warming downscaling method,
J. Geophys. Res., 114, D24110, https://doi.org/10.1029/2009JD011803,
2009. a
Kawase, H., Hara, M., Yoshikane, T., Ishizaki, N. N., Uno, F., Hatsushika, H.,
and Kimura, F.: Altitude dependency of future snow cover changes over
Central Japan evaluated by a regional climate model, J. Geophys.
Res.-Atmos., 118, 444–12, https://doi.org/10.1002/2013JD020429, 2013. a
Keller, M., Kröner, N., Fuhrer, O., Lüthi, D., Schmidli, J., Stengel, M., Stöckli, R., and Schär, C.: The sensitivity of Alpine summer convection to surrogate climate change: an intercomparison between convection-parameterizing and convection-resolving models, Atmos. Chem. Phys., 18, 5253–5264, https://doi.org/10.5194/acp-18-5253-2018, 2018. a
Kendon, E. J., Rowell, D. P., and Jones, R. G.: Mechanisms and reliability of
future projected changes in daily precipitation, Clim. Dynam., 35,
489–509, https://doi.org/10.1007/s00382-009-0639-z, 2010. a
Kimura, F. and Kitoh, A.: Downscaling by Pseudo Global Warning Method – The
Final Report of ICCAP, Tech. rep., Research Institute for Humanity and Nature (RIHN), Kyoto, Japan,
2006. a
Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, 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, 3425–3440,
https://doi.org/10.1007/s00382-016-3276-3, 2017. a
Leutwyler, D., Lüthi, D., Ban, N., Fuhrer, O., and Schär, C.:
Evaluation of the convection-resolving climate modeling approach on
continental scales, J. Geophys. Res.-Atmos., 122,
5237–5258, https://doi.org/10.1002/2016JD026013, 2017. a
Liu, C., Ikeda, K., Rasmussen, R., Barlage, M., Newman, A. J., Prein, A. F.,
Chen, F., Chen, L., Clark, M., Dai, A., Dudhia, J., Eidhammer, T., Gochis,
D., Gutmann, E., Kurkute, S., Li, Y., Thompson, G., and Yates, D.:
Continental-scale convection-permitting modeling of the current and future
climate of North America, Clim. Dynam., 49, 71–95,
https://doi.org/10.1007/s00382-016-3327-9, 2017. a
Lynch, P.: The Emergence of Numerical Weather Prediction: Richardson's Dream,
Cambridge University Press, ISBN 9781107414839, 2006. a
Lynn, B., Healy, R., and Druyan, L.: Investigation of Hurricane Katrina
characteristics for future, warmer climates, Clim. Res., 39, 75–86,
https://doi.org/10.3354/cr00801, 2009. a
Maz'yai, V. and Schmidt, G.: On approximate approximations using Gaussian
kernels, Tech. rep.,
https://academic.oup.com/imajna/article/16/1/13/724826 (last access: 2 December 2022), 1996. a
Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M., Velders, G. J. M., Vollmer, M. K., and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geosci. Model Dev., 13, 3571–3605, https://doi.org/10.5194/gmd-13-3571-2020, 2020. a
Misra, V. and Kanamitsu, M.: Anomaly Nesting: A Methodology to Downscale
Seasonal Climate Simulations from AGCMs, J. Climate, 17, 3249–3262,
https://doi.org/10.1175/1520-0442(2004)017<3249:ANAMTD>2.0.CO;2, 2004. a
Nowicki, S., Goelzer, H., Seroussi, H., Payne, A., Lipscomb,
W., Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis,
X., Barthel, A., Bracegirdle, T., Cullather, R., Felikson, D.,
Fettweis, X., Gregory, J. M., Hattermann, T., Jourdain, N.,
Kuipers Munneke, P., Larour, E., Little, C., Morlighem, M.,
Nias, I., Shepherd, A., Simon, E. G., Slater, D. A., Smith, R.,
Straneo, F., Trusel, L., van den Broeke, M., and van de Wal,
R.: input4MIPs.CMIP6.ISMIP6.NASA-GSFC.HadGEM2-ES-rcp85-1-0, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/input4MIPs.15848,
2021. a
Patricola, C. M. and Wehner, M. F.: Anthropogenic influences on major tropical
cyclone events, Nature, 563, 339–346, https://doi.org/10.1038/s41586-018-0673-2,
2018. a
Pichelli, E., Coppola, E., Sobolowski, S., Ban, N., Giorgi, F., Stocchi, P.,
Alias, A., Belušić, D., Berthou, S., Caillaud, C., Cardoso,
R. M., Chan, S., Christensen, O. B., Dobler, A., de Vries, H., Goergen, K.,
Kendon, E. J., Keuler, K., Lenderink, G., Lorenz, T., Mishra, A. N., Panitz,
H.-J., Schär, C., Soares, P. M. M., Truhetz, H., and Vergara-Temprado,
J.: The first multi-model ensemble of regional climate simulations at
kilometer-scale resolution part 2: historical and future simulations of
precipitation, Clim. Dynam., 56, 3581–3602,
https://doi.org/10.1007/s00382-021-05657-4, 2021. a
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K.,
Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S.,
Schmidli, J., van Lipzig, N. P. M., and Leung, R.: A review on regional
convection-permitting climate modeling: Demonstrations, prospects, and
challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475,
2015. a
Prein, A. F., Rasmussen, R. M., Ikeda, K., Liu, C., Clark, M. P., and Holland,
G. J.: The future intensification of hourly precipitation extremes, Nat.
Clim. Change, 7, 48–52, https://doi.org/10.1038/nclimate3168, 2017. a
Rajczak, J. and Schär, C.: Projections of future precipitation extremes
over Europe: a multi-model assessment of climate simulations, J.
Geophys. Res.-Atmos.,122, 10773–10800, https://doi.org/10.1002/2017JD027176, 2017. a
Rasmussen, R. and Liu, C.: High Resolution WRF Simulations of the Current and
Future Climate of North America, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D6V40SXP, 2017. a
Rasmussen, R., Liu, C., Ikeda, K., Gochis, D., Yates, D., Chen, F., Tewari, M.,
Barlage, M., Dudhia, J., Yu, W., Miller, K., Arsenault, K.,
Grubišić, V., Thompson, G., and Gutmann, E.: High-Resolution
Coupled Climate Runoff Simulations of Seasonal Snowfall over Colorado: A
Process Study of Current and Warmer Climate, J. Climate, 24,
3015–3048, https://doi.org/10.1175/2010JCLI3985.1, 2011. a
Rew, R. and Davis, G.: NetCDF: an interface for scientific data access, IEEE Comput. Graph. Appl., 10, 76–82, https://doi.org/10.1109/38.56302, 1990. a
Rockel, B., Will, A., and Hense, A.: The Regional Climate Model COSMO-CLM
(CCLM), Meteorol. Z., 17, 347–348,
https://doi.org/10.1127/0941-2948/2008/0309, 2008. a, b
Rowell, D. P. and Jones, R. G.: Causes and uncertainty of future summer drying
over Europe, Clim. Dynam., 27, 281–299,
https://doi.org/10.1007/s00382-006-0125-9, 2006. a
Rummukainen, M.: State-of-the-art with regional climate models, John Wiley
& Sons, Ltd, 1, https://doi.org/10.1002/wcc.8, 2010. a
Sato, T., Kimura, F., and Kitoh, A.: Projection of global warming onto
regional precipitation over Mongolia using a regional climate model, J. Hydrol., 333, 144–154, https://doi.org/10.1016/j.jhydrol.2006.07.023, 2007. a, b, c
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, b, c
Schär, C., Leuenberger, D., Fuhrer, O., Lüthi, D., and Girard, C.:
A New Terrain-Following Vertical Coordinate Formulation for Atmospheric
Prediction Models, Mon. Weather Rev., 130, 2459–2480,
https://doi.org/10.1175/1520-0493(2002)130<2459:ANTFVC>2.0.CO;2, 2002. a
Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Di Girolamo,
S., Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D., Osterried, K.,
Panosetti, D., Rüdisühli, S., Schlemmer, L., Schulthess, T. C.,
Sprenger, M., Ubbiali, S., and Wernli, H.: Kilometer-scale climate models:
Prospects and challenges, B. Am. Meteorol. Soc.,
101, E567–E587, https://doi.org/10.1175/BAMS-D-18-0167.1, 2020. a
Schwingshackl, C., Davin, E. L., Hirschi, M., Sørland, S. L., Wartenburger,
R., and Seneviratne, S. I.: Regional climate model projections underestimate
future warming due to missing plant physiological CO 2 response,
Environ. Res. Lett., 14, 114019, https://doi.org/10.1088/1748-9326/ab4949,
2019. a
Seneviratne, S., Eltahir, E., Schär, C., and Pal, J.: Summer dryness in
a warmer climate: a process study with a regional climate model, Clim.
Dynam., 20, 69–85, https://doi.org/10.1007/s00382-002-0258-4, 2002. a, b
Shepherd, T. G.: Storyline approach to the construction of regional climate
change information, P. Roy. Soc. A, 475, 20190013,
https://doi.org/10.1098/rspa.2019.0013, 2019. a
Sørland, S. L. and Sorteberg, A.: Low-pressure systems and extreme
precipitation in central India: sensitivity to temperature changes, Clim.
Dynam., 47, 465–480, https://doi.org/10.1007/s00382-015-2850-4, 2016. a
Sørland, S. L., Schär, C., Lüthi, D., and Kjellström, E.:
Bias patterns and climate change signals in GCM-RCM model chains,
Environ. Res. Lett., 13, 074017, https://doi.org/10.1088/1748-9326/aacc77,
2018. a
Sørland, S. L., Fischer, A. M., Kotlarski, S., Künsch, H. R., Liniger,
M. A., Rajczak, J., Schär, C., Spirig, C., Strassmann, K., and Knutti,
R.: CH2018 – National climate scenarios for Switzerland: How to construct
consistent multi-model projections from ensembles of opportunity, Climate
Services, 20, 100196, https://doi.org/10.1016/j.cliser.2020.100196, 2020. a
Sørland, S. L., Brogli, R., Pothapakula, P. K., Russo, E., Van de Walle, J., Ahrens, B., Anders, I., Bucchignani, E., Davin, E. L., Demory, M.-E., Dosio, A., Feldmann, H., Früh, B., Geyer, B., Keuler, K., Lee, D., Li, D., van Lipzig, N. P. M., Min, S.-K., Panitz, H.-J., Rockel, B., Schär, C., Steger, C., and Thiery, W.: COSMO-CLM regional climate simulations in the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework: a review, Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, 2021. 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
Taniguchi, K.: Future changes in precipitation and water resources for Kanto
Region in Japan after application of pseudo global warming method and
dynamical downscaling, J. Hydrol., 8, 287–303,
https://doi.org/10.1016/j.ejrh.2016.10.004, 2016. a
The HadGEM2 Development Team: Martin, G. M., 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
Ullrich, P. A., Xu, Z., Rhoades, A., Dettinger, M., Mount, J., Jones, A., and
Vahmani, P.: California's Drought of the Future: A Midcentury Recreation of
the Exceptional Conditions of 2012–2017, Earth's Future, 6, 1568–1587,
https://doi.org/10.1029/2018EF001007, 2018. a
van der Linden, P. and Mitchell, J. F. B (Eds.): ENSEMBLES: Climate Change and its Impacts: Summary of research and results from the ENSEMBLES project, Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK, 160 pp., 2009. a
Wang, S. and Wang, Y.: Improving probabilistic hydroclimatic projections
through high-resolution convection-permitting climate modeling and Markov
chain Monte Carlo simulations, Clim. Dynam., 53, 1613–1636,
https://doi.org/10.1007/s00382-019-04702-7, 2019. a
Wu, W. and Lynch, A. H.: Response of the seasonal carbon cycle in high
latitudes to climate anomalies, J. Geophys. Res.-Atmos., 105, 22897–22908, https://doi.org/10.1029/2000JD900340, 2000. a
Yoshikane, T., Kimura, F., Kawase, H., and Nozawa, T.: Verification of the
Performance of the Pseudo-Global-Warming Method for Future Climate Changes
during June in East Asia, SOLA, 8, 133–136, https://doi.org/10.2151/sola.2012-033,
2012. a, b
Zhuang, J., Dussin, R., Jüling, A., and Rasp, S.: xESMF: Universal
Regridder for Geospatial Data, Zenodo [data set], https://doi.org/10.5281/zenodo.3700105, 2020. a
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.
The pseudo-global-warming (PGW) approach is a downscaling methodology that imposes the...
