Articles | Volume 16, issue 13
https://doi.org/10.5194/gmd-16-3785-2023
© 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-16-3785-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
11 Jul 2023
Model description paper |
| 11 Jul 2023
| 11 Jul 2023
Deep learning for stochastic precipitation generation – deep SPG v1.0
Leroy J. Bird
CORRESPONDING AUTHOR
Bodeker Scientific, Alexandra, New Zealand
Matthew G. W. Walker
Bodeker Scientific, Alexandra, New Zealand
Greg E. Bodeker
Bodeker Scientific, Alexandra, New Zealand
Isaac H. Campbell
Bodeker Scientific, Alexandra, New Zealand
Guangzhong Liu
Bodeker Scientific, Alexandra, New Zealand
Swapna Josmi Sam
Bodeker Scientific, Alexandra, New Zealand
Jared Lewis
Climate & Energy College, The University of Melbourne, Parkville, Victoria, Australia
Suzanne M. Rosier
National Institute of Water and Atmospheric Research, Wellington, New Zealand
Related authors
Brian Nathan, Stefanie Kremser, Sara Mikaloff-Fletcher, Greg Bodeker, Leroy Bird, Ethan Dale, Dongqi Lin, Gustavo Olivares, and Elizabeth Somervell
Atmos. Chem. Phys., 21, 14089–14108, https://doi.org/10.5194/acp-21-14089-2021, https://doi.org/10.5194/acp-21-14089-2021, 2021
Short summary
Short summary
The MAPM project showcases a method to improve estimates of PM2.5 emissions through an advanced statistical technique that is still new to the aerosol community. Using Christchurch, NZ, as a test bed, measurements from a field campaign in winter 2019 are incorporated into this new approach. An overestimation from local inventory estimates is identified. This technique may be exported to other urban areas in need.
Ethan R. Dale, Stefanie Kremser, Jordis S. Tradowsky, Greg E. Bodeker, Leroy J. Bird, Gustavo Olivares, Guy Coulson, Elizabeth Somervell, Woodrow Pattinson, Jonathan Barte, Jan-Niklas Schmidt, Nariefa Abrahim, Adrian J. McDonald, and Peter Kuma
Earth Syst. Sci. Data, 13, 2053–2075, https://doi.org/10.5194/essd-13-2053-2021, https://doi.org/10.5194/essd-13-2053-2021, 2021
Short summary
Short summary
MAPM is a project whose goal is to develop a method to infer particulate matter (PM) emissions maps from PM concentration measurements. In support of MAPM, we conducted a winter field campaign in New Zealand. In addition to two types of instruments measuring PM, an array of other meteorological sensors were deployed, measuring temperature and wind speed as well as probing the vertical structure of the lower atmosphere. In this article, we present the measurements taken during this campaign.
Dongqi Lin, Basit Khan, Marwan Katurji, Leroy Bird, Ricardo Faria, and Laura E. Revell
Geosci. Model Dev., 14, 2503–2524, https://doi.org/10.5194/gmd-14-2503-2021, https://doi.org/10.5194/gmd-14-2503-2021, 2021
Short summary
Short summary
We present an open-source toolbox WRF4PALM, which enables weather dynamics simulation within urban landscapes. WRF4PALM passes meteorological information from the popular Weather Research and Forecasting (WRF) model to the turbulence-resolving PALM model system 6.0. WRF4PALM can potentially extend the use of WRF and PALM with realistic boundary conditions to any part of the world. WRF4PALM will help study air pollution dispersion, wind energy prospecting, and high-impact wind forecasting.
Forrest M. Hoffman, Birgit Hassler, Ranjini Swaminathan, Jared Lewis, Bouwe Andela, Nathaniel Collier, Dóra Hegedűs, Jiwoo Lee, Charlotte Pascoe, Mika Pflüger, Martina Stockhause, Paul Ullrich, Min Xu, Lisa Bock, Felicity Chun, Bettina K. Gier, Douglas I. Kelley, Axel Lauer, Julien Lenhardt, Manuel Schlund, Mohanan G. Sreeush, Katja Weigel, Ed Blockley, Rebecca Beadling, Romain Beucher, Demiso D. Dugassa, Valerio Lembo, Jianhua Lu, Swen Brands, Jerry Tjiputra, Elizaveta Malinina, Brian Mederios, Enrico Scoccimarro, Jeremy Walton, Philip Kershaw, André L. Marquez, Malcolm J. Roberts, Eleanor O’Rourke, Elisabeth Dingley, Briony Turner, Helene Hewitt, and John P. Dunne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2685, https://doi.org/10.5194/egusphere-2025-2685, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
As Earth system models become more complex, rapid and comprehensive evaluation through comparison with observational data is necessary. The upcoming Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project (CMIP7) will require fast analysis. This paper describes a new Rapid Evaluation Framework (REF) that was developed for the Assessment Fast Track that will be run at the Earth System Grid Federation (ESGF) to inform the community about the performance of models.
Owyn Aitken, Antoni Moore, Ivan Diaz-Rainey, Quyen Nguyen, Simon Cox, and Greg Bodeker
Abstr. Int. Cartogr. Assoc., 7, 4, https://doi.org/10.5194/ica-abs-7-4-2024, https://doi.org/10.5194/ica-abs-7-4-2024, 2024
Antoni Moore, Quyen Nguyen, Ivan Diaz-Rainey, Greg Bodeker, Simon Cox, and Owyn Aitken
Abstr. Int. Cartogr. Assoc., 7, 107, https://doi.org/10.5194/ica-abs-7-107-2024, https://doi.org/10.5194/ica-abs-7-107-2024, 2024
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
Short summary
Short summary
The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Quyen Nguyen, Antoni Moore, Ivan Diaz-Rainey, Greg Bodeker, Simon C. Cox, Murray Cadzow, and Paul Thorsnes
Abstr. Int. Cartogr. Assoc., 6, 187, https://doi.org/10.5194/ica-abs-6-187-2023, https://doi.org/10.5194/ica-abs-6-187-2023, 2023
Jarmo S. Kikstra, Zebedee R. J. Nicholls, Christopher J. Smith, Jared Lewis, Robin D. Lamboll, Edward Byers, Marit Sandstad, Malte Meinshausen, Matthew J. Gidden, Joeri Rogelj, Elmar Kriegler, Glen P. Peters, Jan S. Fuglestvedt, Ragnhild B. Skeie, Bjørn H. Samset, Laura Wienpahl, Detlef P. van Vuuren, Kaj-Ivar van der Wijst, Alaa Al Khourdajie, Piers M. Forster, Andy Reisinger, Roberto Schaeffer, and Keywan Riahi
Geosci. Model Dev., 15, 9075–9109, https://doi.org/10.5194/gmd-15-9075-2022, https://doi.org/10.5194/gmd-15-9075-2022, 2022
Short summary
Short summary
Assessing hundreds or thousands of emission scenarios in terms of their global mean temperature implications requires standardised procedures of infilling, harmonisation, and probabilistic temperature assessments. We here present the open-source
climate-assessmentworkflow that was used in the IPCC AR6 Working Group III report. The paper provides key insight for anyone wishing to understand the assessment of climate outcomes of mitigation pathways in the context of the Paris Agreement.
Brian Nathan, Stefanie Kremser, Sara Mikaloff-Fletcher, Greg Bodeker, Leroy Bird, Ethan Dale, Dongqi Lin, Gustavo Olivares, and Elizabeth Somervell
Atmos. Chem. Phys., 21, 14089–14108, https://doi.org/10.5194/acp-21-14089-2021, https://doi.org/10.5194/acp-21-14089-2021, 2021
Short summary
Short summary
The MAPM project showcases a method to improve estimates of PM2.5 emissions through an advanced statistical technique that is still new to the aerosol community. Using Christchurch, NZ, as a test bed, measurements from a field campaign in winter 2019 are incorporated into this new approach. An overestimation from local inventory estimates is identified. This technique may be exported to other urban areas in need.
Greg E. Bodeker, Jan Nitzbon, Jordis S. Tradowsky, Stefanie Kremser, Alexander Schwertheim, and Jared Lewis
Earth Syst. Sci. Data, 13, 3885–3906, https://doi.org/10.5194/essd-13-3885-2021, https://doi.org/10.5194/essd-13-3885-2021, 2021
Short summary
Short summary
Ozone in Earth's atmosphere has undergone significant changes since first measured systematically from space in the late 1970s. The purpose of the paper is to present a new, spatially filled, global total column ozone climate data record spanning from October 1978 to December 2016. The database is compiled from measurements from 17 different satellite-based instruments where offsets and drifts between the instruments have been corrected using ground-based measurements.
Ethan R. Dale, Stefanie Kremser, Jordis S. Tradowsky, Greg E. Bodeker, Leroy J. Bird, Gustavo Olivares, Guy Coulson, Elizabeth Somervell, Woodrow Pattinson, Jonathan Barte, Jan-Niklas Schmidt, Nariefa Abrahim, Adrian J. McDonald, and Peter Kuma
Earth Syst. Sci. Data, 13, 2053–2075, https://doi.org/10.5194/essd-13-2053-2021, https://doi.org/10.5194/essd-13-2053-2021, 2021
Short summary
Short summary
MAPM is a project whose goal is to develop a method to infer particulate matter (PM) emissions maps from PM concentration measurements. In support of MAPM, we conducted a winter field campaign in New Zealand. In addition to two types of instruments measuring PM, an array of other meteorological sensors were deployed, measuring temperature and wind speed as well as probing the vertical structure of the lower atmosphere. In this article, we present the measurements taken during this campaign.
Dongqi Lin, Basit Khan, Marwan Katurji, Leroy Bird, Ricardo Faria, and Laura E. Revell
Geosci. Model Dev., 14, 2503–2524, https://doi.org/10.5194/gmd-14-2503-2021, https://doi.org/10.5194/gmd-14-2503-2021, 2021
Short summary
Short summary
We present an open-source toolbox WRF4PALM, which enables weather dynamics simulation within urban landscapes. WRF4PALM passes meteorological information from the popular Weather Research and Forecasting (WRF) model to the turbulence-resolving PALM model system 6.0. WRF4PALM can potentially extend the use of WRF and PALM with realistic boundary conditions to any part of the world. WRF4PALM will help study air pollution dispersion, wind energy prospecting, and high-impact wind forecasting.
Greg E. Bodeker and Stefanie Kremser
Atmos. Chem. Phys., 21, 5289–5300, https://doi.org/10.5194/acp-21-5289-2021, https://doi.org/10.5194/acp-21-5289-2021, 2021
Short summary
Short summary
This paper presents measures of the severity of the Antarctic ozone hole covering the period 1979 to 2019. The paper shows that while the severity of Antarctic ozone depletion grew rapidly through the last two decades of the 20th century, the severity declined thereafter and faster than expected from declines in stratospheric concentrations of the chlorine- and bromine-containing chemical compounds that destroy ozone.
James Keeble, Birgit Hassler, Antara Banerjee, Ramiro Checa-Garcia, Gabriel Chiodo, Sean Davis, Veronika Eyring, Paul T. Griffiths, Olaf Morgenstern, Peer Nowack, Guang Zeng, Jiankai Zhang, Greg Bodeker, Susannah Burrows, Philip Cameron-Smith, David Cugnet, Christopher Danek, Makoto Deushi, Larry W. Horowitz, Anne Kubin, Lijuan Li, Gerrit Lohmann, Martine Michou, Michael J. Mills, Pierre Nabat, Dirk Olivié, Sungsu Park, Øyvind Seland, Jens Stoll, Karl-Hermann Wieners, and Tongwen Wu
Atmos. Chem. Phys., 21, 5015–5061, https://doi.org/10.5194/acp-21-5015-2021, https://doi.org/10.5194/acp-21-5015-2021, 2021
Short summary
Short summary
Stratospheric ozone and water vapour are key components of the Earth system; changes to both have important impacts on global and regional climate. We evaluate changes to these species from 1850 to 2100 in the new generation of CMIP6 models. There is good agreement between the multi-model mean and observations, although there is substantial variation between the individual models. The future evolution of both ozone and water vapour is strongly dependent on the assumed future emissions scenario.
Cited articles
Ahn, K.-H.: Coupled annual and daily multivariate and multisite stochastic
weather generator to preserve low-and high-frequency variability to assess
climate vulnerability, J. Hydrol., 581, 124443, https://doi.org/10.1016/j.jhydrol.2019.124443, 2020. a
Ailliot, P., Allard, D., Monbet, V., and Naveau, P.: Stochastic weather
generators: an overview of weather type models, Journal de la Société
Française de Statistique, 156, 101–113, 2015. a
Ba, J. L., Kiros, J. R., and Hinton, G. E.: Layer normalization, arXiv preprint
arXiv:1607.06450, 2016. a
Bachlechner, T., Majumder, B. P., Mao, H., Cottrell, G., and McAuley, J.:
Rezero is all you need: Fast convergence at large depth, in: Uncertainty in
Artificial Intelligence, 1352–1361, PMLR, 2021. a
Bird, L. and Walker, M.: bodekerscientific/SPG: Release version 1.0 (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.6801733, 2022. a
Black, M. T., Karoly, D. J., Rosier, S. M., Dean, S. M., King, A. D., Massey, N. R., Sparrow, S. N., Bowery, A., Wallom, D., Jones, R. G., Otto, F. E. L., and Allen, M. R.: The weather@home regional climate modelling project for Australia and New Zealand, Geosci. Model Dev., 9, 3161–3176, https://doi.org/10.5194/gmd-9-3161-2016, 2016. a
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin,
D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and
Zhang, Q.: JAX: composable transformations of Python+NumPy programs,
http://github.com/google/jax (last access: 4 July 2023), 2018. a
Carreau, J. and Vrac, M.: Stochastic downscaling of precipitation with neural
network conditional mixture models, Water Resour. Res., 47, https://doi.org/10.1029/2010WR010128, 2011. a
Castellano, C. M. and DeGaetano, A. T.: A multi-step approach for downscaling
daily precipitation extremes from historical analogues, Int. J. Climatol., 36, 1797–1807, 2016. a
Clevert, D.-A., Unterthiner, T., and Hochreiter, S.: Fast and accurate deep
network learning by exponential linear units (elus), arXiv preprint
arXiv:1511.07289, 2015. a
Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer-Verlag London, 1st edn., 209 pp., https://doi.org/10.1007/978-1-4471-3675-0, 2001. a
Fahad, A. A., Burls, N. J., and Strasberg, Z.: Correction to: How will southern
hemisphere subtropical anticyclones respond to global warming? Mechanisms and
seasonality in CMIP5 and CMIP6 model projections, Clim. Dynam., 55,
719–720, 2020a. a
Fahad, A. a., Burls, N. J., and Strasberg, Z.: How will southern hemisphere
subtropical anticyclones respond to global warming? Mechanisms and
seasonality in CMIP5 and CMIP6 model projections, Clim. Dynam., 55,
703–718, 2020b. a
Haerter, J. O., Eggert, B., Moseley, C., Piani, C., and Berg, P.: Statistical
precipitation bias correction of gridded model data using point measurements,
Geophys. Res. Lett., 42, 1919–1929, 2015. a
Heek, J., Levskaya, A., Oliver, A., Ritter, M., Rondepierre, B., Steiner, A.,
and van Zee, M.: Flax: A neural network library and ecosystem for JAX,
http://github.com/google/flax (last access: 4 July 2023), 2020. a
Hendrycks, D. and Gimpel, K.: Gaussian error linear units (gelus), arXiv
preprint arXiv:1606.08415, 2016. a
Hessel, M., Budden, D., Viola, F., Rosca, M., Sezener, E., and Hennigan, T.:
Optax: composable gradient transformation and optimisation, in JAX,
http://github.com/deepmind/optax (last access: 4 July 2023), version 0.0.1, 2020. a
Iizumi, T., Takayabu, I., Dairaku, K., Kusaka, H., Nishimori, M., Sakurai, G.,
Ishizaki, N. N., Adachi, S. A., and Semenov, M. A.: Future change of daily
precipitation indices in Japan: A stochastic weather generator-based
bootstrap approach to provide probabilistic climate information, J.
Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2011JD017197, 2012. a
Kröse, B. and van der Smagt, P.: An Introduction to Neural Networks, 135 pp., publisher AbeBooks, University of Amsterdam, 1996. a
Lewis, S. C., Perkins-Kirkpatrick, S. E., and King, A. D.: Approaches to
attribution of extreme temperature and precipitation events using multi-model
and single-member ensembles of general circulation models, Advances in
Statistical Climatology, Meteorol. Oceanogr., 5, 133–146, 2019. a
Li, C., Sinha, E., Horton, D. E., Diffenbaugh, N. S., and Michalak, A. M.:
Joint bias correction of temperature and precipitation in climate model
simulations, J. Geophys. Res.-Atmos., 119, 13–153,
2014. a
Li, C., Zwiers, F., Zhang, X., Li, G., Sun, Y., and Wehner, M.: Changes in
annual extremes of daily temperature and precipitation in CMIP6 models,
J. Climate, 34, 3441–3460, 2021. a
Loshchilov, I. and Hutter, F.: Decoupled weight decay regularization, arXiv
preprint arXiv:1711.05101, 2017. a
Massey, N., Jones, R., Otto, F., Aina, T., Wilson, S., Murphy, J., Hassell, D.,
Yamazaki, Y., and Allen, M.: weather@home – development and validation of a
very large ensemble modelling system for probabilistic event attribution,
Q. J. Roy. Meteor. Soc., 141, 1528–1545, 2015. a
Meinshausen, M., Meinshausen, N., Hare, W., Raper, S., Frieler, K., Knutti, R.,
Frame, D., and Allen, M.: Greenhouse-gas emission targets for limiting global
warming to 2 ∘C, Nature, 458, 1158–1162, 2009. a
Meinshausen, M., Raper, S. C. B., and Wigley, T. M. L.: Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: Model description and calibration, Atmos. Chem. Phys., 11, 1417–1456, https://doi.org/10.5194/acp-11-1417-2011, 2011. 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
Meinshausen, M., Lewis, J., McGlade, C., Gütschow, J., Nicholls, Z., Burdon,
R., Cozzi, L., and Hackmann, B.: Realization of Paris Agreement pledges may
limit warming just below 2 ∘C, Nature, 604, 304–309, 2022. a
Monjo, R., Gaitán, E., Pórtoles, J., Ribalaygua, J., and Torres, L.:
Changes in extreme precipitation over Spain using statistical downscaling of
CMIP5 projections, Int. J. Climatol., 36, 757–769, 2016. a
Nicholls, Z., Meinshausen, M., Lewis, J., Corradi, M. R., Dorheim, K., Gasser, T., Gieseke, R., Hope, A. P., Leach, N. J., McBride, L. A., Quilcaille, Y., Rogelj, J., Salawitch, R. J., Samset, B. H., Sandstad, M., Shiklomanov, A., Skeie, R. B., Smith, C. J., Smith, S. J., Su, X., Tsutsui, J., Vega-Westhoff, B., and Woodard, D. L.: Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections, Earths Future, 9, e2020EF001900, https://doi.org/10.1029/2020EF001900, 2021. a
Peterson, T. C., Easterling, D. R., Karl, T. R., Groisman, P., Nicholls, N., Plummer, N., Torok, S., Auer, I., Boehm, R., Gullett, D., Vincent, L., Heino, R., Tuomenvirta, H., Mestre, O., Szentimrey, T. S., Salinger, J., Førland, E. J., Hanssen-Bauer, I., Alexandersson, H., Jones, P., and Parker, D.: Homogeneity
adjustments of in situ atmospheric climate data: a review, Int.
J. Climatol., 18,
1493–1517, 1998. a, b
Piani, C., Haerter, J., and Coppola, E.: Statistical bias correction for daily
precipitation in regional climate models over Europe, Theor. Appl.
Climatol., 99, 187–192, 2010. a
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C.,
Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp,
A., Cuaresma, J. C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S.,
Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da
Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D.,
Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G.,
Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M.,
Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A.,
and Tavoni, M.: The Shared Socioeconomic Pathways and their energy, land use,
and greenhouse gas emissions implications: An overview, Global Environ.
Change, 42, 153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009,
2017. a
Richardson, C. W.: Stochastic simulation of daily precipitation, temperature,
and solar radiation, Water Resour. Res., 17, 182–190, 1981. a
Rosier, S., Dean, S., Stuart, S., Carey-Smith, T., Black, M. T., and Massey,
N.: 27. Extreme Rainfall in Early July 2014 in Northland, New Zealand – Was There an Anthropogenic Influence?, B. Am. Meteorol.
Soc., 96, S136–S140, https://doi.org/10.1175/BAMS-D-15-00105.1, 2015. a
The National Climate Database: General CliFlo Info, The National Climate Database [data set], https://cliflo.niwa.co.nz/, last access: 4 July 2023. a
van der Wiel, K. and Bintanja, R.: Contribution of climatic changes in mean and
variability to monthly temperature and precipitation extremes, Commun.
Earth Environ., 2, 1–11, 2021. a
Venema, V. K. C., Mestre, O., Aguilar, E., Auer, I., Guijarro, J. A., Domonkos, P., Vertacnik, G., Szentimrey, T., Stepanek, P., Zahradnicek, P., Viarre, J., Müller-Westermeier, G., Lakatos, M., Williams, C. N., Menne, M. J., Lindau, R., Rasol, D., Rustemeier, E., Kolokythas, K., Marinova, T., Andresen, L., Acquaotta, F., Fratianni, S., Cheval, S., Klancar, M., Brunetti, M., Gruber, C., Prohom Duran, M., Likso, T., Esteban, P., and Brandsma, T.: Benchmarking homogenization algorithms for monthly data, Clim. Past, 8, 89–115, https://doi.org/10.5194/cp-8-89-2012, 2012. a, b
Wedi, N. P., Polichtchouk, I., Dueben, P., Anantharaj, V. G., Bauer, P.,
Boussetta, S., Browne, P., Deconinck, W., Gaudin, W., Hadade, I., Hatfield,
S., Iffrig, O., Lopez, P., Maciel, P., Mueller, A., Saarinen, S., Sandu, I.,
Quintino, T., and Vitart, F.: A baseline for global weather and climate
simulations at 1 km resolution, J. Adv. Model. Earth
Sy., 12, 1–17, https://doi.org/10.1029/2020ms002192, 2020.
a
Wilks, D. S.: Use of stochastic weathergenerators for precipitation
downscaling, Wiley Interdisciplinary Reviews: Climate Change, 1, 898–907,
2010. a
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P.,
Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen,
P., Ma, C., Jernite, Y., Plu, J., Xu, C., Le Scao, T., Gugger, S., Drame, M.,
Lhoest, Q., and Rush, A.: Transformers: State-of-the-Art Natural Language
Processing, in: Proceedings of the 2020 Conference on Empirical Methods in
Natural Language Processing: System Demonstrations, 38–45, Association
for Computational Linguistics, Online, https://doi.org/10.18653/v1/2020.emnlp-demos.6,
2020. a
Zhang, M. R., Lucas, J., Hinton, G., and Ba, J.: Lookahead Optimizer: k steps forward, 1 step back, ArXiv, https://doi.org/10.48550/arXiv.1907.08610, 2019. a
Short summary
Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Deriving the statistics of expected future changes in extreme precipitation is challenging due...
