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2025The futures of climate modelingS. Bordoni, S. M. Kang, T. A. Shaw et al. · npj Climate and Atmospheric Science2023ClimateLearn: Benchmarking Machine Learning for Weather and Climate ModelingTung Nguyen, Jason Jewik, Hritik Bansal et al. · Neural Information Processing SystemsClimateLearn is an open-source PyTorch library that vastly simplifies the training and evaluation of machine learning models for data-driven climate science, and is the first large-scale,open-source effort for bridging research in weather and climate modeling with modern machine learning systems.2022Overcoming the disconnect between energy system and climate modelingMichael T. Craig, J. Wohland, L. Stoop et al. · Joule2022Advances and challenges in climate modelingO. Alizadeh · Climatic Change2019Thirty Years of Regional Climate Modeling: Where Are We and Where Are We Going next?F. Giorgi · Journal of Geophysical Research - Atmospheres2022The “teapot in a city”: A paradigm shift in urban climate modelingN. Villefranque, F. Hourdin, Louis d'Alençon et al. · Science AdvancesThis review presents recent advances that are at the origin of last decade's revolution in computer graphics, and recent breakthroughs in statistical physics that extend well-established path-integral formulations to nonlinear coupled models and highlights the potential of these approaches beyond urban climate modeling for the necessary appropriation of the issues at the heart of the energy transition by societies.2021The Need for Urban‐Resolving Climate Modeling Across ScalesAshish Sharma, D. Wuebbles, R. Kotamarthi · AGU Advances2020Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutionsJ. Yuval, P. O’Gorman · Nature CommunicationsA random forest is used to learn a parameterization from coarse-grained output of a three-dimensional high-resolution idealized atmospheric model and the parameterization leads to stable simulations at coarse resolution that replicate the climate of the high-resolution simulation.2015A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challengesA. Prein, W. Langhans, G. Fosser et al. · Reviews of GeophysicsThis study aims to provide a common basis for CPM climate simulations by giving a holistic review of the topic, and presents the consolidated outcome of studies that addressed the added value of CPMClimate simulations compared to LSMs.2020Progress in climate modeling of precipitation over the Tibetan PlateauQ. Bao, Jian Li · National Science ReviewThere is a need for more accurate Tibetan Plateau (TP) climate modeling that clarifies its role in monsoon variations, which influences the simulation of the monsoon circulation through the teleconnection associated with the local latent heat flux.2020Recommendations for Future Research Priorities for Climate Modeling and Climate ServicesC. Hewitt, F. Guglielmo, S. Joussaume et al. · Bulletin of The American Meteorological Society - (BAMS)2020Benefits and added value of convection-permitting climate modeling over Fenno-ScandinaviaP. Lind, D. Belušić, O. B. Christensen et al. · Climate Dynamics2018Sky View Factor footprints for urban climate modelingAriane Middel, J. Lukasczyk, R. Maciejewski et al. · Urban Climate2020Special issue: Advances in convection-permitting climate modelingA. Prein, R. Rasmussen, C. Castro et al. · Climate Dynamics2018The state of the art and fundamental aspects of regional climate modeling in South AmericaT. Ambrizzi, M. Reboita, R. D. da Rocha et al. · Annals of the New York Academy of SciencesThe evolution of regional climate modeling is reviewed, as well as the studies developed for South America are presented.2017Improving the Simulation of Large Lakes in Regional Climate Modeling: Two-Way Lake-Atmosphere Coupling with a 3D Hydrodynamic Model of the Great LakesP. Xue, J. Pal, Xinyu Ye et al.2018Benefits of explicit urban parameterization in regional climate modeling to study climate and city interactionsM. Daniel, A. Lemonsu, M. Déqué et al. · Climate Dynamics2018CLIMLAB: a Python toolkit for interactive, process-oriented climate modelingB. Rose · Journal of Open Source SoftwareCLIMLAB is an open-ended engine for interactive, process-oriented climate modeling for use in education and research and a tool for climate research, where the same needs exist for more robust, processbased understanding and reproducible computational results.2016Added value in regional climate modelingM. Rummukainen2016Coordinated Global and Regional Climate ModelingJ. Scinocca, V. Kharin, Y. Jiao et al.2016Adapting observationally based metrics of biogeophysical feedbacks from land cover/land use change to climate modelingLiang Chen, P. Dirmeyer2000Climate extremes: observations, modeling, and impacts.D. Easterling, G. Meehl, C. Parmesan et al. · ScienceResults of observational studies suggest that in many areas that have been analyzed, changes in total precipitation are amplified at the tails, and changes in some temperature extremes have been observed.2016Lessons from climate modeling on the design and use of ensembles for crop modelingD. Wallach, L. Mearns, A. Ruane et al. · Climatic ChangeQuestions and propositions are identified that should help make ensemble modeling with crop models more rigorous and informative and the use of the multi-model mean or median as a predictor is investigated.2014Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensembleS. Kotlarski, K. Keuler, O. B. Christensen et al.2018Climate ModelingHuei-Ping Huang · Handbook of Environmental Engineering