Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-91-2024
© Author(s) 2024. 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-17-91-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
09 Jan 2024
Development and technical paper |
| 09 Jan 2024
| 09 Jan 2024
WRF (v4.0)–SUEWS (v2018c) coupled system: development, evaluation and application
Department of Meteorology, University of Reading, Reading, UK
Institute for Risk and Disaster Reduction, University College London, London, UK
Hamidreza Omidvar
Institute for Risk and Disaster Reduction, University College London, London, UK
Zhenkun Li
Shanghai Climate Centre, Shanghai, China
Ning Zhang
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Wenjuan Huang
Shanghai Climate Centre, Shanghai, China
Simone Kotthaus
Institut Pierre-Simon Laplace, École Polytechnique, Palaiseau, France
Helen C. Ward
Department of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
Zhiwen Luo
Welsh School of Architecture, Cardiff University, Cardiff, UK
Department of Meteorology, University of Reading, Reading, UK
Related authors
Shuo-Jun Mei, Guanwen Chen, Jian Hang, and Ting Sun
EGUsphere, https://doi.org/10.5194/egusphere-2025-1485, https://doi.org/10.5194/egusphere-2025-1485, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Cities face growing heat challenges due to dense buildings, but predicting surface temperatures is complex because sunlight, airflow, and heat radiation interact. By simulating how sunlight bounces between structures and how heat transfers through materials, we accurately predicted temperatures on roofs, roads, and walls. The model successfully handled intricate city layouts thanks to GPU speed. By revealing which heat matters most, we aim to guide smarter city designs for a warming climate.
Ruidong Li, Jiapei Liu, Ting Sun, Shao Jian, Fuqiang Tian, and Guangheng Ni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3780, https://doi.org/10.5194/egusphere-2024-3780, 2025
Short summary
Short summary
This work presents a new approach to simulate sewer drainage effects for urban flooding with key missing information like flow directions and nodal depths estimated from incomplete information. Tested in Yinchuan, China, our approach exhibits high accuracy in reproducing flood depths and reliably outperforms existing methods in various rainfall scenarios. Our method offers a reliable tool for cities with limited sewer data to improve flood simulation performance.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
Short summary
Short summary
This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
Short summary
Short summary
We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Hamidreza Omidvar, Ting Sun, Sue Grimmond, Dave Bilesbach, Andrew Black, Jiquan Chen, Zexia Duan, Zhiqiu Gao, Hiroki Iwata, and Joseph P. McFadden
Geosci. Model Dev., 15, 3041–3078, https://doi.org/10.5194/gmd-15-3041-2022, https://doi.org/10.5194/gmd-15-3041-2022, 2022
Short summary
Short summary
This paper extends the applicability of the SUEWS to extensive pervious areas outside cities. We derived various parameters such as leaf area index, albedo, roughness parameters and surface conductance for non-urban areas. The relation between LAI and albedo is also explored. The methods and parameters discussed can be used for both online and offline simulations. Using appropriate parameters related to non-urban areas is essential for assessing urban–rural differences.
Isabella Capel-Timms, Stefán Thor Smith, Ting Sun, and Sue Grimmond
Geosci. Model Dev., 13, 4891–4924, https://doi.org/10.5194/gmd-13-4891-2020, https://doi.org/10.5194/gmd-13-4891-2020, 2020
Short summary
Short summary
Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local- and larger-scale urban climate. DASH considers both urban form and function in simulating QF by use of an agent-based structure that includes behavioural characteristics of city populations. This allows social practices to drive the calculation of QF as occupants move, varying by day type, demographic, location, activity, and socio-economic factors and in response to environmental conditions.
Ting Sun and Sue Grimmond
Geosci. Model Dev., 12, 2781–2795, https://doi.org/10.5194/gmd-12-2781-2019, https://doi.org/10.5194/gmd-12-2781-2019, 2019
Short summary
Short summary
A Python-enhanced urban land surface model, SuPy (SUEWS in Python), is presented with its development (the SUEWS interface modification, F2PY configuration and Python frontend implementation), cross-platform deployment (PyPI, Python Package Index) and demonstration (online tutorials in Jupyter notebooks for users of different levels). SuPy represents a significant enhancement that supports existing and new model applications, reproducibility and enhanced functionality.
Fushan Wang, Guangheng Ni, William J. Riley, Jinyun Tang, Dejun Zhu, and Ting Sun
Geosci. Model Dev., 12, 2119–2138, https://doi.org/10.5194/gmd-12-2119-2019, https://doi.org/10.5194/gmd-12-2119-2019, 2019
Short summary
Short summary
The current lake model in the Weather Research and Forecasting system was reported to be insufficient in simulating deep lakes and reservoirs. We thus revised the lake model by improving its spatial discretization scheme, surface property parameterization, diffusivity parameterization, and convection scheme. The revised model was evaluated at a deep reservoir in southwestern China and the results were in good agreement with measurements.
Ting Sun, Zhi-Hua Wang, Walter C. Oechel, and Sue Grimmond
Geosci. Model Dev., 10, 2875–2890, https://doi.org/10.5194/gmd-10-2875-2017, https://doi.org/10.5194/gmd-10-2875-2017, 2017
Short summary
Short summary
The diurnal hysteresis behaviour found between the net storage heat flux and net all-wave radiation has been captured in the Objective Hysteresis Model (OHM). To facilitate use, and enhance physical interpretations of the OHM coefficients, we develop the Analytical Objective Hysteresis Model (AnOHM) using an analytical solution of the one-dimensional advection–diffusion equation of coupled heat and liquid water transport in conjunction with the surface energy balance relationship.
Wen-Yu Yang, Guang-Heng Ni, You-Cun Qi, Yang Hong, and Ting Sun
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-388, https://doi.org/10.5194/amt-2016-388, 2016
Revised manuscript has not been submitted
Short summary
Short summary
Using a dataset consisting of one-year measurements by an X-band radar and distrometer, we found that error corrections greatly improve X-band-radar-based rainfall estimation. Specifically, the greatest improvement is realized by the beam integration. Derivation of localized Z-R relationships for specific rainfall systems is also of great importance. Moreover, wind drift correction improves quantitative estimates and temporal consistency.
Russell H. Glazer, Sue Grimmond, Lewis Blunn, Daniel Fenner, Humphrey Lean, Andreas Christen, Will Morrison, and Dana Looschelders
EGUsphere, https://doi.org/10.5194/egusphere-2025-2064, https://doi.org/10.5194/egusphere-2025-2064, 2025
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Short summary
Short summary
In this study we use very high resolution numerical weather prediction model simulations of the Berlin, Germany region along with assessment of field campaign observations to understand better the impact of urban areas on the near-surface boundary layer. We find that there a clear affect of urban areas up to 15 kilometers downwind of the city centre in both the field campaign observations and the high resolution model.
Shuo-Jun Mei, Guanwen Chen, Jian Hang, and Ting Sun
EGUsphere, https://doi.org/10.5194/egusphere-2025-1485, https://doi.org/10.5194/egusphere-2025-1485, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Cities face growing heat challenges due to dense buildings, but predicting surface temperatures is complex because sunlight, airflow, and heat radiation interact. By simulating how sunlight bounces between structures and how heat transfers through materials, we accurately predicted temperatures on roofs, roads, and walls. The model successfully handled intricate city layouts thanks to GPU speed. By revealing which heat matters most, we aim to guide smarter city designs for a warming climate.
Ludovico Di Antonio, Matthias Beekmann, Guillaume Siour, Vincent Michoud, Christopher Cantrell, Astrid Bauville, Antonin Bergé, Mathieu Cazaunau, Servanne Chevaillier, Manuela Cirtog, Joel F. de Brito, Paola Formenti, Cecile Gaimoz, Olivier Garret, Aline Gratien, Valérie Gros, Martial Haeffelin, Lelia N. Hawkins, Simone Kotthaus, Gael Noyalet, Diana L. Pereira, Jean-Eudes Petit, Eva Drew Pronovost, Véronique Riffault, Chenjie Yu, Gilles Foret, Jean-François Doussin, and Claudia Di Biagio
Atmos. Chem. Phys., 25, 4803–4831, https://doi.org/10.5194/acp-25-4803-2025, https://doi.org/10.5194/acp-25-4803-2025, 2025
Short summary
Short summary
The summer of 2022 has been considered a proxy for future climate scenarios due to its hot and dry conditions. In this paper, we use the measurements from the Atmospheric Chemistry of the Suburban Forest (ACROSS) campaign, conducted in the Paris area in June–July 2022, along with observations from existing networks, to evaluate a 3D chemistry transport model (WRF–CHIMERE) simulation. Results are shown to be satisfactory, allowing us to explain the gas and aerosol variability at the campaign sites.
William Morrison, Dana Looschelders, Jonnathan Céspedes, Bernie Claxton, Marc-Antoine Drouin, Jean-Charles Dupont, Aurélien Faucheux, Martial Haeffelin, Christopher C. Holst, Simone Kotthaus, Valéry Masson, James McGregor, Jeremy Price, Matthias Zeeman, Sue Grimmond, and Andreas Christen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-167, https://doi.org/10.5194/essd-2025-167, 2025
Preprint under review for ESSD
Short summary
Short summary
We conducted research using sophisticated wind sensors to better understand wind patterns in Paris. By installing these sensors across the city, we gathered detailed data on wind speeds and directions from 2022 to 2024. This information helps improve weather and climate models, making them more accurate for city environments. Our findings offer valuable insights for scientists studying urban air and weather, improving predictions and understanding of city-scale atmospheric processes.
Jinhong Xian, Zongxu Qiu, Huayan Rao, Zhigang Cheng, Xiaoling Lin, Chao Lu, Honglong Yang, and Ning Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-157, https://doi.org/10.5194/egusphere-2025-157, 2025
Short summary
Short summary
We studied how turbulence kinetic energy (TKE) changes in the lower atmosphere over Shenzhen, focusing on its role in weather and climate. Using advanced wind lidar technology, we tracked how TKE varies with height and across seasons. We found that heat near the ground drives turbulence, while wind effects become stronger higher up. Our results help improve weather and climate models by providing better data on how turbulence behaves in the atmosphere, aiding understanding of climate change.
Jinhong Xian, Zongxu Qiu, Hongyan Luo, Yuanyuan Hu, Xiaoling Lin, Chao Lu, Yan Yang, Honglong Yang, and Ning Zhang
Atmos. Chem. Phys., 25, 441–457, https://doi.org/10.5194/acp-25-441-2025, https://doi.org/10.5194/acp-25-441-2025, 2025
Short summary
Short summary
The existing methods for observing turbulent kinetic energy (TKE) budget terms can only rely on ground-based towers. We have developed a new detection method that can directly observe and analyze the generation and dissipation mechanisms of turbulent energy at different heights in the vertical direction of the boundary layer. This research result will extend our study of TKE budget terms from near the ground to high altitude, providing a higher and more detailed perspective.
Ruidong Li, Jiapei Liu, Ting Sun, Shao Jian, Fuqiang Tian, and Guangheng Ni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3780, https://doi.org/10.5194/egusphere-2024-3780, 2025
Short summary
Short summary
This work presents a new approach to simulate sewer drainage effects for urban flooding with key missing information like flow directions and nodal depths estimated from incomplete information. Tested in Yinchuan, China, our approach exhibits high accuracy in reproducing flood depths and reliably outperforms existing methods in various rainfall scenarios. Our method offers a reliable tool for cities with limited sewer data to improve flood simulation performance.
Matthias Zeeman, Andreas Christen, Sue Grimmond, Daniel Fenner, William Morrison, Gregor Feigel, Markus Sulzer, and Nektarios Chrysoulakis
Geosci. Instrum. Method. Data Syst., 13, 393–424, https://doi.org/10.5194/gi-13-393-2024, https://doi.org/10.5194/gi-13-393-2024, 2024
Short summary
Short summary
This study presents an overview of a data system for documenting, processing, managing, and publishing data streams from research networks of atmospheric and environmental sensors of varying complexity in urban environments. Our solutions aim to deliver resilient, near-time data using freely available software.
Martial Haeffelin, Jean-François Ribaud, Jonnathan Céspedes, Jean-Charles Dupont, Aude Lemonsu, Valéry Masson, Tim Nagel, and Simone Kotthaus
Atmos. Chem. Phys., 24, 14101–14122, https://doi.org/10.5194/acp-24-14101-2024, https://doi.org/10.5194/acp-24-14101-2024, 2024
Short summary
Short summary
This study highlights how the state of the urban atmospheric boundary layer impacts urban park cooling effect intensity at night. Under summertime heat wave conditions, the urban atmosphere becomes stable at night, which inhibits turbulent motions. Under those specific conditions, urban parks and woods cool much more efficiently than the surrounding built-up neighbourhoods in the evening and through the night, providing cooler air temperatures by 4 to 6° C depending on park size.
Weilin Liao, Yanman Li, Xiaoping Liu, Yuhao Wang, Yangzi Che, Ledi Shao, Guangzhao Chen, Hua Yuan, Ning Zhang, and Fei Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-408, https://doi.org/10.5194/essd-2024-408, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
The currently available urban canopy parameter (UCP) datasets are limited to just a few cities for urban climate simulations by the Weather Research and Forecasting (WRF) model. To address this gap, we develop a global 1 km spatially continuous UCP dataset (GloUCP), which provides superior spatial coverage and higher accuracy in capturing urban morphology across diverse regions. It has great potential to support further advancements in urban climate modeling and related applications.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
Short summary
Short summary
This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Jonnathan Céspedes, Simone Kotthaus, Jana Preissler, Clément Toupoint, Ludovic Thobois, Marc-Antoine Drouin, Jean-Charles Dupont, Aurélien Faucheux, and Martial Haeffelin
Atmos. Chem. Phys., 24, 11477–11496, https://doi.org/10.5194/acp-24-11477-2024, https://doi.org/10.5194/acp-24-11477-2024, 2024
Short summary
Short summary
The low-level jet (LLJ) is common in Paris during summer. The LLJ core height and speed significantly influence vertical mixing in the urban boundary layer, which affects air temperature variations between the urban canopy layer and surrounding rural areas, determining the urban heat island (UHI) intensity. This study highlights the importance of wind profile observations for understanding urban boundary layer dynamics and near-surface atmospheric conditions relevant to health.
Nimra Iqbal, Marvin Ravan, Zina Mitraka, Joern Birkmann, Sue Grimmond, Denise Hertwig, Nektarios Chrysoulakis, Giorgos Somarakis, and Angela Wendnagel-Beck
EGUsphere, https://doi.org/10.5194/egusphere-2024-1907, https://doi.org/10.5194/egusphere-2024-1907, 2024
Short summary
Short summary
This work deepens the understanding of how perceived heat stress, human vulnerability (e.g. age, income) and adaptive capacities (e.g. green, shaded spaces) are coupled with urban structures. The results show that perceived heat stress decreases with distance from urban center, however, human vulnerability and adaptive capacities depend stronger on inner-variations and differences between urban structures. Planning policies and adaptation strategies should account for these differences.
Jinhong Xian, Chao Lu, Xiaoling Lin, Honglong Yang, Ning Zhang, and Li Zhang
Atmos. Meas. Tech., 17, 1837–1850, https://doi.org/10.5194/amt-17-1837-2024, https://doi.org/10.5194/amt-17-1837-2024, 2024
Short summary
Short summary
Improving the monitoring capability of atmospheric turbulence can help unravel the mystery of turbulence. Based on some assumptions, scientists have proposed various detection methods. However, these assumptions limit their applicability. We abandoned these assumptions and proposed a more accurate method, revealing some new results. Our method can provide more accurate three-dimensional features of turbulence, which will have a huge driving effect on the development of turbulence.
Megan A. Stretton, William Morrison, Robin J. Hogan, and Sue Grimmond
Geosci. Model Dev., 16, 5931–5947, https://doi.org/10.5194/gmd-16-5931-2023, https://doi.org/10.5194/gmd-16-5931-2023, 2023
Short summary
Short summary
Cities' materials and forms impact radiative fluxes. We evaluate the SPARTACUS-Urban multi-layer approach to modelling longwave radiation, describing realistic 3D geometry statistically using the explicit DART (Discrete Anisotropic Radiative Transfer) model. The temperature configurations used are derived from thermal camera observations. SPARTACUS-Urban accurately predicts longwave fluxes, with a low computational time (cf. DART), but has larger errors with sunlit/shaded surface temperatures.
Junxia Dou, Sue Grimmond, Shiguang Miao, Bei Huang, Huimin Lei, and Mingshui Liao
Atmos. Chem. Phys., 23, 13143–13166, https://doi.org/10.5194/acp-23-13143-2023, https://doi.org/10.5194/acp-23-13143-2023, 2023
Short summary
Short summary
Multi-timescale variations in surface energy fluxes in a suburb of Beijing are analyzed using 16-month observations. Compared to previous suburban areas, this study site has larger seasonal variability in energy partitioning, and summer and winter Bowen ratios are at the lower and higher end of those at other suburban sites, respectively. Our analysis indicates that precipitation, irrigation, crop/vegetation growth activity, and land use/cover all play critical roles in energy partitioning.
Jinghui Lian, Thomas Lauvaux, Hervé Utard, François-Marie Bréon, Grégoire Broquet, Michel Ramonet, Olivier Laurent, Ivonne Albarus, Mali Chariot, Simone Kotthaus, Martial Haeffelin, Olivier Sanchez, Olivier Perrussel, Hugo Anne Denier van der Gon, Stijn Nicolaas Camiel Dellaert, and Philippe Ciais
Atmos. Chem. Phys., 23, 8823–8835, https://doi.org/10.5194/acp-23-8823-2023, https://doi.org/10.5194/acp-23-8823-2023, 2023
Short summary
Short summary
This study quantifies urban CO2 emissions via an atmospheric inversion for the Paris metropolitan area over a 6-year period from 2016 to 2021. Results show a long-term decreasing trend of about 2 % ± 0.6 % per year in the annual CO2 emissions over Paris. We conclude that our current capacity can deliver near-real-time CO2 emission estimates at the city scale in under a month, and the results agree within 10 % with independent estimates from multiple city-scale inventories.
Joanna E. Dyson, Lisa K. Whalley, Eloise J. Slater, Robert Woodward-Massey, Chunxiang Ye, James D. Lee, Freya Squires, James R. Hopkins, Rachel E. Dunmore, Marvin Shaw, Jacqueline F. Hamilton, Alastair C. Lewis, Stephen D. Worrall, Asan Bacak, Archit Mehra, Thomas J. Bannan, Hugh Coe, Carl J. Percival, Bin Ouyang, C. Nicholas Hewitt, Roderic L. Jones, Leigh R. Crilley, Louisa J. Kramer, W. Joe F. Acton, William J. Bloss, Supattarachai Saksakulkrai, Jingsha Xu, Zongbo Shi, Roy M. Harrison, Simone Kotthaus, Sue Grimmond, Yele Sun, Weiqi Xu, Siyao Yue, Lianfang Wei, Pingqing Fu, Xinming Wang, Stephen R. Arnold, and Dwayne E. Heard
Atmos. Chem. Phys., 23, 5679–5697, https://doi.org/10.5194/acp-23-5679-2023, https://doi.org/10.5194/acp-23-5679-2023, 2023
Short summary
Short summary
The hydroxyl (OH) and closely coupled hydroperoxyl (HO2) radicals are vital for their role in the removal of atmospheric pollutants. In less polluted regions, atmospheric models over-predict HO2 concentrations. In this modelling study, the impact of heterogeneous uptake of HO2 onto aerosol surfaces on radical concentrations and the ozone production regime in Beijing in the summertime is investigated, and the implications for emissions policies across China are considered.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
Short summary
Short summary
We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Simone Kotthaus, Juan Antonio Bravo-Aranda, Martine Collaud Coen, Juan Luis Guerrero-Rascado, Maria João Costa, Domenico Cimini, Ewan J. O'Connor, Maxime Hervo, Lucas Alados-Arboledas, María Jiménez-Portaz, Lucia Mona, Dominique Ruffieux, Anthony Illingworth, and Martial Haeffelin
Atmos. Meas. Tech., 16, 433–479, https://doi.org/10.5194/amt-16-433-2023, https://doi.org/10.5194/amt-16-433-2023, 2023
Short summary
Short summary
Profile observations of the atmospheric boundary layer now allow for layer heights and characteristics to be derived at high temporal and vertical resolution. With novel high-density ground-based remote-sensing measurement networks emerging, horizontal information content is also increasing. This review summarises the capabilities and limitations of various sensors and retrieval algorithms which need to be considered during the harmonisation of data products for high-impact applications.
Andreas Plach, Rolf Rüfenacht, Simone Kotthaus, and Markus Leuenberger
EGUsphere, https://doi.org/10.5194/egusphere-2022-1019, https://doi.org/10.5194/egusphere-2022-1019, 2022
Preprint archived
Short summary
Short summary
Greenhouse gases emissions are contributing to global warming and it is essential to better understand where they originate from and how they are transported. In this study we analyze greenhouse gas observations at a Swiss tall tower where measurements are taken more than 200 m above ground and investigate their origin by looking at the condition of the atmosphere at the time of the observations. We find that most pollution at this site is caused from emissions transported from further away.
Mathew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow, Andreas Christen, Nektarios Chrysoulakis, Andrew Coutts, Ben Crawford, Stevan Earl, Jonathan Evans, Krzysztof Fortuniak, Bert G. Heusinkveld, Je-Woo Hong, Jinkyu Hong, Leena Järvi, Sungsoo Jo, Yeon-Hee Kim, Simone Kotthaus, Keunmin Lee, Valéry Masson, Joseph P. McFadden, Oliver Michels, Wlodzimierz Pawlak, Matthias Roth, Hirofumi Sugawara, Nigel Tapper, Erik Velasco, and Helen Claire Ward
Earth Syst. Sci. Data, 14, 5157–5178, https://doi.org/10.5194/essd-14-5157-2022, https://doi.org/10.5194/essd-14-5157-2022, 2022
Short summary
Short summary
We describe a new openly accessible collection of atmospheric observations from 20 cities around the world, capturing 50 site years. The observations capture local meteorology (temperature, humidity, wind, etc.) and the energy fluxes between the land and atmosphere (e.g. radiation and sensible and latent heat fluxes). These observations can be used to improve our understanding of urban climate processes and to test the accuracy of urban climate models.
Will S. Drysdale, Adam R. Vaughan, Freya A. Squires, Sam J. Cliff, Stefan Metzger, David Durden, Natchaya Pingintha-Durden, Carole Helfter, Eiko Nemitz, C. Sue B. Grimmond, Janet Barlow, Sean Beevers, Gregor Stewart, David Dajnak, Ruth M. Purvis, and James D. Lee
Atmos. Chem. Phys., 22, 9413–9433, https://doi.org/10.5194/acp-22-9413-2022, https://doi.org/10.5194/acp-22-9413-2022, 2022
Short summary
Short summary
Measurements of NOx emissions are important for a good understanding of air quality. While there are many direct measurements of NOx concentration, there are very few measurements of its emission. Measurements of emissions provide constraints on emissions inventories and air quality models. This article presents measurements of NOx emission from the BT Tower in central London in 2017 and compares them with inventories, finding that they underestimate by a factor of ∼1.48.
Helen Claire Ward, Mathias Walter Rotach, Alexander Gohm, Martin Graus, Thomas Karl, Maren Haid, Lukas Umek, and Thomas Muschinski
Atmos. Chem. Phys., 22, 6559–6593, https://doi.org/10.5194/acp-22-6559-2022, https://doi.org/10.5194/acp-22-6559-2022, 2022
Short summary
Short summary
This study examines how cities and their surroundings influence turbulent exchange processes responsible for weather and climate. Analysis of a 4-year observational dataset for the Alpine city of Innsbruck reveals several similarities with other (flat) city centre sites. However, the mountain setting leads to characteristic daily and seasonal flow patterns (valley winds) and downslope windstorms that have a marked effect on temperature, wind speed, turbulence and pollutant concentration.
Yiqing Liu, Zhiwen Luo, and Sue Grimmond
Atmos. Chem. Phys., 22, 4721–4735, https://doi.org/10.5194/acp-22-4721-2022, https://doi.org/10.5194/acp-22-4721-2022, 2022
Short summary
Short summary
Anthropogenic heat emission from buildings is important for atmospheric modelling in cities. The current building anthropogenic heat flux is simplified by building energy consumption. Our research proposes a novel approach to determine ‘real’ building anthropogenic heat emission from the changes in energy balance fluxes between occupied and unoccupied buildings. We hope to provide new insights into future parameterisations of building anthropogenic heat flux in urban climate models.
Hamidreza Omidvar, Ting Sun, Sue Grimmond, Dave Bilesbach, Andrew Black, Jiquan Chen, Zexia Duan, Zhiqiu Gao, Hiroki Iwata, and Joseph P. McFadden
Geosci. Model Dev., 15, 3041–3078, https://doi.org/10.5194/gmd-15-3041-2022, https://doi.org/10.5194/gmd-15-3041-2022, 2022
Short summary
Short summary
This paper extends the applicability of the SUEWS to extensive pervious areas outside cities. We derived various parameters such as leaf area index, albedo, roughness parameters and surface conductance for non-urban areas. The relation between LAI and albedo is also explored. The methods and parameters discussed can be used for both online and offline simulations. Using appropriate parameters related to non-urban areas is essential for assessing urban–rural differences.
Jean-François Ribaud, Martial Haeffelin, Jean-Charles Dupont, Marc-Antoine Drouin, Felipe Toledo, and Simone Kotthaus
Atmos. Meas. Tech., 14, 7893–7907, https://doi.org/10.5194/amt-14-7893-2021, https://doi.org/10.5194/amt-14-7893-2021, 2021
Short summary
Short summary
PARAFOG is a near-real-time decision tool that aims to retrieve pre-fog alert levels minutes to hours prior to fog onset. The second version of PARAFOG allows us to discriminate between radiation and stratus lowering fog situations. It is based upon the combination of visibility observations and automatic lidar and ceilometer measurements. The overall performance of the second version of PARAFOG over more than 300 fog cases at five different locations presents a good perfomance.
Michael Biggart, Jenny Stocker, Ruth M. Doherty, Oliver Wild, David Carruthers, Sue Grimmond, Yiqun Han, Pingqing Fu, and Simone Kotthaus
Atmos. Chem. Phys., 21, 13687–13711, https://doi.org/10.5194/acp-21-13687-2021, https://doi.org/10.5194/acp-21-13687-2021, 2021
Short summary
Short summary
Heat-related illnesses are of increasing concern in China given its rapid urbanisation and our ever-warming climate. We examine the relative impacts that land surface properties and anthropogenic heat have on the urban heat island (UHI) in Beijing using ADMS-Urban. Air temperature measurements and satellite-derived land surface temperatures provide valuable means of evaluating modelled spatiotemporal variations. This work provides critical information for urban planners and UHI mitigation.
Jinghui Lian, François-Marie Bréon, Grégoire Broquet, Thomas Lauvaux, Bo Zheng, Michel Ramonet, Irène Xueref-Remy, Simone Kotthaus, Martial Haeffelin, and Philippe Ciais
Atmos. Chem. Phys., 21, 10707–10726, https://doi.org/10.5194/acp-21-10707-2021, https://doi.org/10.5194/acp-21-10707-2021, 2021
Short summary
Short summary
Currently there is growing interest in monitoring city-scale CO2 emissions based on atmospheric CO2 measurements, atmospheric transport modeling, and inversion technique. We analyze the various sources of uncertainty that impact the atmospheric CO2 modeling and that may compromise the potential of this method for the monitoring of CO2 emission over Paris. Results suggest selection criteria for the assimilation of CO2 measurements into the inversion system that aims at retrieving city emissions.
Claire E. Reeves, Graham P. Mills, Lisa K. Whalley, W. Joe F. Acton, William J. Bloss, Leigh R. Crilley, Sue Grimmond, Dwayne E. Heard, C. Nicholas Hewitt, James R. Hopkins, Simone Kotthaus, Louisa J. Kramer, Roderic L. Jones, James D. Lee, Yanhui Liu, Bin Ouyang, Eloise Slater, Freya Squires, Xinming Wang, Robert Woodward-Massey, and Chunxiang Ye
Atmos. Chem. Phys., 21, 6315–6330, https://doi.org/10.5194/acp-21-6315-2021, https://doi.org/10.5194/acp-21-6315-2021, 2021
Short summary
Short summary
The impact of isoprene on atmospheric chemistry is dependent on how its oxidation products interact with other pollutants, specifically nitrogen oxides. Such interactions can lead to isoprene nitrates. We made measurements of the concentrations of individual isoprene nitrate isomers in Beijing and used a model to test current understanding of their chemistry. We highlight areas of uncertainty in understanding, in particular the chemistry following oxidation of isoprene by the nitrate radical.
Wenhua Wang, Longyi Shao, Claudio Mazzoleni, Yaowei Li, Simone Kotthaus, Sue Grimmond, Janarjan Bhandari, Jiaoping Xing, Xiaolei Feng, Mengyuan Zhang, and Zongbo Shi
Atmos. Chem. Phys., 21, 5301–5314, https://doi.org/10.5194/acp-21-5301-2021, https://doi.org/10.5194/acp-21-5301-2021, 2021
Short summary
Short summary
We compared the characteristics of individual particles at ground level and above the mixed-layer height. We found that the particles above the mixed-layer height during haze periods are more aged compared to ground level. More coal-combustion-related primary organic particles were found above the mixed-layer height. We suggest that the particles above the mixed-layer height are affected by the surrounding areas, and once mixed down to the ground, they might contribute to ground air pollution.
Roland Stirnberg, Jan Cermak, Simone Kotthaus, Martial Haeffelin, Hendrik Andersen, Julia Fuchs, Miae Kim, Jean-Eudes Petit, and Olivier Favez
Atmos. Chem. Phys., 21, 3919–3948, https://doi.org/10.5194/acp-21-3919-2021, https://doi.org/10.5194/acp-21-3919-2021, 2021
Short summary
Short summary
Air pollution endangers human health and poses a problem particularly in densely populated areas. Here, an explainable machine learning approach is used to analyse periods of high particle concentrations for a suburban site southwest of Paris to better understand its atmospheric drivers. Air pollution is particularly excaberated by low temperatures and low mixed layer heights, but processes vary substantially between and within seasons.
Yanxu Zhang, Xingpei Ye, Shibao Wang, Xiaojing He, Lingyao Dong, Ning Zhang, Haikun Wang, Zhongrui Wang, Yun Ma, Lei Wang, Xuguang Chi, Aijun Ding, Mingzhi Yao, Yunpeng Li, Qilin Li, Ling Zhang, and Yongle Xiao
Atmos. Chem. Phys., 21, 2917–2929, https://doi.org/10.5194/acp-21-2917-2021, https://doi.org/10.5194/acp-21-2917-2021, 2021
Short summary
Short summary
Urban air quality varies drastically at street scale, but traditional methods are too coarse to resolve it. We develop a 10 m resolution air quality model and apply it for traffic-related carbon monoxide air quality in Nanjing megacity. The model reveals a detailed geographical dispersion pattern of air pollution in and out of the road network and agrees well with a validation dataset. The model can be a vigorous part of the smart city system and inform urban planning and air quality management.
Lisa K. Whalley, Eloise J. Slater, Robert Woodward-Massey, Chunxiang Ye, James D. Lee, Freya Squires, James R. Hopkins, Rachel E. Dunmore, Marvin Shaw, Jacqueline F. Hamilton, Alastair C. Lewis, Archit Mehra, Stephen D. Worrall, Asan Bacak, Thomas J. Bannan, Hugh Coe, Carl J. Percival, Bin Ouyang, Roderic L. Jones, Leigh R. Crilley, Louisa J. Kramer, William J. Bloss, Tuan Vu, Simone Kotthaus, Sue Grimmond, Yele Sun, Weiqi Xu, Siyao Yue, Lujie Ren, W. Joe F. Acton, C. Nicholas Hewitt, Xinming Wang, Pingqing Fu, and Dwayne E. Heard
Atmos. Chem. Phys., 21, 2125–2147, https://doi.org/10.5194/acp-21-2125-2021, https://doi.org/10.5194/acp-21-2125-2021, 2021
Short summary
Short summary
To understand how emission controls will impact ozone, an understanding of the sources and sinks of OH and the chemical cycling between peroxy radicals is needed. This paper presents measurements of OH, HO2 and total RO2 taken in central Beijing. The radical observations are compared to a detailed chemistry model, which shows that under low NO conditions, there is a missing OH source. Under high NOx conditions, the model under-predicts RO2 and impacts our ability to model ozone.
Rutambhara Joshi, Dantong Liu, Eiko Nemitz, Ben Langford, Neil Mullinger, Freya Squires, James Lee, Yunfei Wu, Xiaole Pan, Pingqing Fu, Simone Kotthaus, Sue Grimmond, Qiang Zhang, Ruili Wu, Oliver Wild, Michael Flynn, Hugh Coe, and James Allan
Atmos. Chem. Phys., 21, 147–162, https://doi.org/10.5194/acp-21-147-2021, https://doi.org/10.5194/acp-21-147-2021, 2021
Short summary
Short summary
Black carbon (BC) is a component of particulate matter which has significant effects on climate and human health. Sources of BC include biomass burning, transport, industry and domestic cooking and heating. In this study, we measured BC emissions in Beijing, finding a dominance of traffic emissions over all other sources. The quantitative method presented here has benefits for revising widely used emissions inventories and for understanding BC sources with impacts on air quality and climate.
Isabella Capel-Timms, Stefán Thor Smith, Ting Sun, and Sue Grimmond
Geosci. Model Dev., 13, 4891–4924, https://doi.org/10.5194/gmd-13-4891-2020, https://doi.org/10.5194/gmd-13-4891-2020, 2020
Short summary
Short summary
Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local- and larger-scale urban climate. DASH considers both urban form and function in simulating QF by use of an agent-based structure that includes behavioural characteristics of city populations. This allows social practices to drive the calculation of QF as occupants move, varying by day type, demographic, location, activity, and socio-economic factors and in response to environmental conditions.
Freya A. Squires, Eiko Nemitz, Ben Langford, Oliver Wild, Will S. Drysdale, W. Joe F. Acton, Pingqing Fu, C. Sue B. Grimmond, Jacqueline F. Hamilton, C. Nicholas Hewitt, Michael Hollaway, Simone Kotthaus, James Lee, Stefan Metzger, Natchaya Pingintha-Durden, Marvin Shaw, Adam R. Vaughan, Xinming Wang, Ruili Wu, Qiang Zhang, and Yanli Zhang
Atmos. Chem. Phys., 20, 8737–8761, https://doi.org/10.5194/acp-20-8737-2020, https://doi.org/10.5194/acp-20-8737-2020, 2020
Short summary
Short summary
Significant air quality problems exist in megacities like Beijing, China. To manage air pollution, legislators need a clear understanding of pollutant emissions. However, emissions inventories have large uncertainties, and reliable field measurements of pollutant emissions are required to constrain them. This work presents the first measurements of traffic-dominated emissions in Beijing which suggest that inventories overestimate these emissions in the region during both winter and summer.
Michael Biggart, Jenny Stocker, Ruth M. Doherty, Oliver Wild, Michael Hollaway, David Carruthers, Jie Li, Qiang Zhang, Ruili Wu, Simone Kotthaus, Sue Grimmond, Freya A. Squires, James Lee, and Zongbo Shi
Atmos. Chem. Phys., 20, 2755–2780, https://doi.org/10.5194/acp-20-2755-2020, https://doi.org/10.5194/acp-20-2755-2020, 2020
Short summary
Short summary
Ambient air pollution is a major cause of premature death in China. We examine the street-scale variation of pollutant levels in Beijing using air pollution dispersion and chemistry model ADMS-Urban. Campaign measurements are compared with simulated pollutant levels, providing a valuable means of evaluating the impact of key processes on urban air quality. Air quality modelling at such fine scales is essential for human exposure studies and for informing choices on future emission controls.
Ting Sun and Sue Grimmond
Geosci. Model Dev., 12, 2781–2795, https://doi.org/10.5194/gmd-12-2781-2019, https://doi.org/10.5194/gmd-12-2781-2019, 2019
Short summary
Short summary
A Python-enhanced urban land surface model, SuPy (SUEWS in Python), is presented with its development (the SUEWS interface modification, F2PY configuration and Python frontend implementation), cross-platform deployment (PyPI, Python Package Index) and demonstration (online tutorials in Jupyter notebooks for users of different levels). SuPy represents a significant enhancement that supports existing and new model applications, reproducibility and enhanced functionality.
Zongbo Shi, Tuan Vu, Simone Kotthaus, Roy M. Harrison, Sue Grimmond, Siyao Yue, Tong Zhu, James Lee, Yiqun Han, Matthias Demuzere, Rachel E. Dunmore, Lujie Ren, Di Liu, Yuanlin Wang, Oliver Wild, James Allan, W. Joe Acton, Janet Barlow, Benjamin Barratt, David Beddows, William J. Bloss, Giulia Calzolai, David Carruthers, David C. Carslaw, Queenie Chan, Lia Chatzidiakou, Yang Chen, Leigh Crilley, Hugh Coe, Tie Dai, Ruth Doherty, Fengkui Duan, Pingqing Fu, Baozhu Ge, Maofa Ge, Daobo Guan, Jacqueline F. Hamilton, Kebin He, Mathew Heal, Dwayne Heard, C. Nicholas Hewitt, Michael Hollaway, Min Hu, Dongsheng Ji, Xujiang Jiang, Rod Jones, Markus Kalberer, Frank J. Kelly, Louisa Kramer, Ben Langford, Chun Lin, Alastair C. Lewis, Jie Li, Weijun Li, Huan Liu, Junfeng Liu, Miranda Loh, Keding Lu, Franco Lucarelli, Graham Mann, Gordon McFiggans, Mark R. Miller, Graham Mills, Paul Monk, Eiko Nemitz, Fionna O'Connor, Bin Ouyang, Paul I. Palmer, Carl Percival, Olalekan Popoola, Claire Reeves, Andrew R. Rickard, Longyi Shao, Guangyu Shi, Dominick Spracklen, David Stevenson, Yele Sun, Zhiwei Sun, Shu Tao, Shengrui Tong, Qingqing Wang, Wenhua Wang, Xinming Wang, Xuejun Wang, Zifang Wang, Lianfang Wei, Lisa Whalley, Xuefang Wu, Zhijun Wu, Pinhua Xie, Fumo Yang, Qiang Zhang, Yanli Zhang, Yuanhang Zhang, and Mei Zheng
Atmos. Chem. Phys., 19, 7519–7546, https://doi.org/10.5194/acp-19-7519-2019, https://doi.org/10.5194/acp-19-7519-2019, 2019
Short summary
Short summary
APHH-Beijing is a collaborative international research programme to study the sources, processes and health effects of air pollution in Beijing. This introduction to the special issue provides an overview of (i) the APHH-Beijing programme, (ii) the measurement and modelling activities performed as part of it and (iii) the air quality and meteorological conditions during joint intensive field campaigns as a core activity within APHH-Beijing.
Fushan Wang, Guangheng Ni, William J. Riley, Jinyun Tang, Dejun Zhu, and Ting Sun
Geosci. Model Dev., 12, 2119–2138, https://doi.org/10.5194/gmd-12-2119-2019, https://doi.org/10.5194/gmd-12-2119-2019, 2019
Short summary
Short summary
The current lake model in the Weather Research and Forecasting system was reported to be insufficient in simulating deep lakes and reservoirs. We thus revised the lake model by improving its spatial discretization scheme, surface property parameterization, diffusivity parameterization, and convection scheme. The revised model was evaluated at a deep reservoir in southwestern China and the results were in good agreement with measurements.
Tom V. Kokkonen, Sue Grimmond, Sonja Murto, Huizhi Liu, Anu-Maija Sundström, and Leena Järvi
Atmos. Chem. Phys., 19, 7001–7017, https://doi.org/10.5194/acp-19-7001-2019, https://doi.org/10.5194/acp-19-7001-2019, 2019
Short summary
Short summary
This is the first study to evaluate and correct the WATCH WFDEI reanalysis product in a highly polluted urban environment. It gives an important understanding of the uncertainties in reanalysis products in local-scale urban modelling in polluted environments and identifies and corrects the most important variables in hydrological modelling. This is also the first study to examine the effects of haze on the local-scale urban hydrological cycle.
Dantong Liu, Rutambhara Joshi, Junfeng Wang, Chenjie Yu, James D. Allan, Hugh Coe, Michael J. Flynn, Conghui Xie, James Lee, Freya Squires, Simone Kotthaus, Sue Grimmond, Xinlei Ge, Yele Sun, and Pingqing Fu
Atmos. Chem. Phys., 19, 6749–6769, https://doi.org/10.5194/acp-19-6749-2019, https://doi.org/10.5194/acp-19-6749-2019, 2019
Short summary
Short summary
This study provides source attribution and characterization of BC in the Beijing urban environment in both winter and summer. For the first time, the physically and chemically based source apportionments are compared to evaluate the primary source contribution and secondary processing of BC-containing particles. A method is proposed to isolate the BC from the transportation sector and coal combustion sources.
Roy M. Harrison, David C. S. Beddows, Mohammed S. Alam, Ajit Singh, James Brean, Ruixin Xu, Simone Kotthaus, and Sue Grimmond
Atmos. Chem. Phys., 19, 39–55, https://doi.org/10.5194/acp-19-39-2019, https://doi.org/10.5194/acp-19-39-2019, 2019
Short summary
Short summary
Particle number size distributions were measured simultaneously at five sites in London during a campaign. Observations are interpreted in terms of both evaporative shrinkage of traffic-generated particles and condensational growth, probably of traffic-generated particles under cool nocturnal conditions, as well as the influence of particles emitted from Heathrow Airport at a distance of about 22 km. The work highlights the highly dynamic behaviour of nanoparticles within the urban atmosphere.
Ting Sun, Zhi-Hua Wang, Walter C. Oechel, and Sue Grimmond
Geosci. Model Dev., 10, 2875–2890, https://doi.org/10.5194/gmd-10-2875-2017, https://doi.org/10.5194/gmd-10-2875-2017, 2017
Short summary
Short summary
The diurnal hysteresis behaviour found between the net storage heat flux and net all-wave radiation has been captured in the Objective Hysteresis Model (OHM). To facilitate use, and enhance physical interpretations of the OHM coefficients, we develop the Analytical Objective Hysteresis Model (AnOHM) using an analytical solution of the one-dimensional advection–diffusion equation of coupled heat and liquid water transport in conjunction with the surface energy balance relationship.
Wen-Yu Yang, Guang-Heng Ni, You-Cun Qi, Yang Hong, and Ting Sun
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-388, https://doi.org/10.5194/amt-2016-388, 2016
Revised manuscript has not been submitted
Short summary
Short summary
Using a dataset consisting of one-year measurements by an X-band radar and distrometer, we found that error corrections greatly improve X-band-radar-based rainfall estimation. Specifically, the greatest improvement is realized by the beam integration. Derivation of localized Z-R relationships for specific rainfall systems is also of great importance. Moreover, wind drift correction improves quantitative estimates and temporal consistency.
Carole Helfter, Anja H. Tremper, Christoforos H. Halios, Simone Kotthaus, Alex Bjorkegren, C. Sue B. Grimmond, Janet F. Barlow, and Eiko Nemitz
Atmos. Chem. Phys., 16, 10543–10557, https://doi.org/10.5194/acp-16-10543-2016, https://doi.org/10.5194/acp-16-10543-2016, 2016
Short summary
Short summary
There are relatively few long-term, direct measurements of pollutant emissions in urban settings. We present over 3 years of measurements of fluxes of CO, CO2 and CH4, study their respective temporal and spatial dynamics and offer an independent verification of the London Atmospheric Emissions Inventory. CO and CO2 were strongly controlled by traffic and well characterised by the inventory whilst measured CH4 was two-fold larger and linked to natural gas usage and perhaps biogenic sources.
Simone Kotthaus, Ewan O'Connor, Christoph Münkel, Cristina Charlton-Perez, Martial Haeffelin, Andrew M. Gabey, and C. Sue B. Grimmond
Atmos. Meas. Tech., 9, 3769–3791, https://doi.org/10.5194/amt-9-3769-2016, https://doi.org/10.5194/amt-9-3769-2016, 2016
Short summary
Short summary
Ceilometers lidars are useful to study clouds, aerosol layers and atmospheric boundary layer structures. As sensor optics and acquisition algorithms can strongly influence the observations, sensor specifics need to be incorporated into the physical interpretation. Here, recommendations are made for the operation and processing of profile observations from the widely deployed Vaisala CL31 ceilometer. Proposed corrections are shown to increase data quality and even data availability at times.
J. Lindén, C.S.B. Grimmond, and J. Esper
Adv. Sci. Res., 12, 157–162, https://doi.org/10.5194/asr-12-157-2015, https://doi.org/10.5194/asr-12-157-2015, 2015
Short summary
Short summary
Long term meteorological records from stations associated with villages are generally classified as rural and assumed to have no urban influence. Using temperature sensor networks installed around two such stations, spatial variations of the same order magnitude as the long-term temperature trend from these stations were found. The potential bias in the long term series therefore warrants careful consideration in temperature trend evaluation also in village stations.
H. C. Ward, J. G. Evans, C. S. B. Grimmond, and J. Bradford
Atmos. Meas. Tech., 8, 1385–1405, https://doi.org/10.5194/amt-8-1385-2015, https://doi.org/10.5194/amt-8-1385-2015, 2015
Short summary
Short summary
Two-wavelength scintillometry, a ground-based remote sensing technique for deriving large-area heat fluxes, has been used over an urban area for the first time. The long data set enables investigation of the performance of the technique and characteristics of turbulent transport processes at sub-daily to inter-annual timescales. In this first paper, the structure parameters of temperature and humidity, and the correlation between temperature and humidity, are presented and analysed.
H. C. Ward, J. G. Evans, and C. S. B. Grimmond
Atmos. Meas. Tech., 8, 1407–1424, https://doi.org/10.5194/amt-8-1407-2015, https://doi.org/10.5194/amt-8-1407-2015, 2015
Short summary
Short summary
Two-wavelength scintillometry, a ground-based remote sensing technique for deriving large-area heat fluxes, has been used over an urban area for the first time. The long data set enables investigation of the performance of the technique and characteristics of turbulent transport processes at sub-daily to inter-annual timescales. In this second paper, sensible and latent heat fluxes representative of an area of 5--10 km2 are presented and analysed.
Y. Zhang, Z. Gao, D. Li, Y. Li, N. Zhang, X. Zhao, and J. Chen
Geosci. Model Dev., 7, 2599–2611, https://doi.org/10.5194/gmd-7-2599-2014, https://doi.org/10.5194/gmd-7-2599-2014, 2014
L. Järvi, C. S. B. Grimmond, M. Taka, A. Nordbo, H. Setälä, and I. B. Strachan
Geosci. Model Dev., 7, 1691–1711, https://doi.org/10.5194/gmd-7-1691-2014, https://doi.org/10.5194/gmd-7-1691-2014, 2014
A. Font, C. S. B. Grimmond, J.-A. Morguí, S. Kotthaus, M. Priestman, and B. Barratt
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-13-13465-2013, https://doi.org/10.5194/acpd-13-13465-2013, 2013
Revised manuscript not accepted
H. C. Ward, J. G. Evans, and C. S. B. Grimmond
Atmos. Chem. Phys., 13, 4645–4666, https://doi.org/10.5194/acp-13-4645-2013, https://doi.org/10.5194/acp-13-4645-2013, 2013
Related subject area
Climate and Earth system modeling
SURFER v3.0: a fast model with ice sheet tipping points and carbon cycle feedbacks for short- and long-term climate scenarios
NMH-CS 3.0: a C# programming language and Windows-system-based ecohydrological model derived from Noah-MP
A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature
Baseline Climate Variables for Earth System Modelling
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
The ensemble consistency test: from CESM to MPAS and beyond
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
A Fortran–Python interface for integrating machine learning parameterization into earth system models
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)
COSP-RTTOV-1.0: Flexible radiation diagnostics to enable new science applications in model evaluation, climate change detection, and satellite mission design
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
Climate model downscaling in central Asia: a dynamical and a neural network approach
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
The Development and Application of an Arctic Sea Ice Emulator v.1
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Process-based modeling framework for sustainable irrigation management at the regional scale: Integrating rice production, water use, and greenhouse gas emissions
A regional physical-biogeochemical ocean model for marine resource applications in the Northeast Pacific (MOM6-COBALT-NEP10k v1.0)
Architectural insights into and training methodology optimization of Pangu-Weather
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
Geosci. Model Dev., 18, 3081–3129, https://doi.org/10.5194/gmd-18-3081-2025, https://doi.org/10.5194/gmd-18-3081-2025, 2025
Short summary
Short summary
We present SURFER v3.0, a simple climate model designed to estimate the impact of CO2 and CH4 emissions on global temperatures, sea levels, and ocean pH. We added new carbon cycle processes and calibrated the model to observations and results from more complex models, enabling use over timescales ranging from decades to millions of years. SURFER v3.0 is fast, transparent, and easy to use, making it an ideal tool for policy assessments and suitable for educational purposes.
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev., 18, 3157–3174, https://doi.org/10.5194/gmd-18-3157-2025, https://doi.org/10.5194/gmd-18-3157-2025, 2025
Short summary
Short summary
NMH-CS 3.0 is a C#-based ecohydrological model reconstructed from the WRF-Hydro/Noah-MP model by translating the Fortran code of WRF-Hydro 3.0 and integrating a parallel river routing module. It enables efficient execution on multi-core personal computers. Simulations in the Yellow River basin demonstrate its consistency with WRF-Hydro outputs, providing a reliable alternative to the original Noah-MP model.
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025, https://doi.org/10.5194/gmd-18-3003-2025, 2025
Short summary
Short summary
We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
Short summary
Short summary
PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
Short summary
Short summary
Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
Short summary
Short summary
This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
Short summary
Short summary
Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
Short summary
Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
Short summary
Short summary
The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary
Short summary
We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
Short summary
Short summary
We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
Short summary
Short summary
We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
Short summary
Short summary
Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
Short summary
Short summary
We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
Short summary
Short summary
The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
Short summary
Short summary
We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
Short summary
Short summary
This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
Short summary
Short summary
Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
Short summary
Short summary
CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
Short summary
Short summary
The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
Short summary
Short summary
A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
Short summary
Short summary
In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2025-169, https://doi.org/10.5194/egusphere-2025-169, 2025
Short summary
Short summary
Satellites have observed earth's emission of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about the earth and atmosphere. We present a tool that runs alongside global climate models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
Short summary
Short summary
The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
Short summary
Short summary
We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
Short summary
Short summary
We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
Short summary
Short summary
Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
Short summary
Short summary
In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
Short summary
Short summary
We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
Short summary
Short summary
The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
Short summary
Short summary
Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
Short summary
Short summary
We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
Short summary
Short summary
Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Sian Megan Chilcott and Malte Meinshausen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-203, https://doi.org/10.5194/gmd-2024-203, 2025
Preprint under review for GMD
Short summary
Short summary
Climate models are expensive to run and often underestimate how sensitive Arctic sea ice is to climate change. To address this, we developed a simple model that emulates the response of sea ice to global warming. We find the remaining carbon dioxide (CO2) emissions that will avoid a seasonally ice-free Arctic Ocean is lower than previous estimates of 821 Gigatonnes of CO2. Our model also provides insights into the future of winter sea ice, examining a larger ensemble than previously possible.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
Short summary
Short summary
A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-212, https://doi.org/10.5194/gmd-2024-212, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with key physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for irrigation optimization to deliver co-benefits of ensuring food production, reducing water use and greenhouse gas emissions.
Elizabeth J. Drenkard, Charles A. Stock, Andrew C. Ross, Yi-Cheng Teng, Theresa Morrison, Wei Cheng, Alistair Adcroft, Enrique Curchitser, Raphael Dussin, Robert Hallberg, Claudine Hauri, Katherine Hedstrom, Albert Hermann, Michael G. Jacox, Kelly A. Kearney, Remi Pages, Darren J. Pilcher, Mercedes Pozo Buil, Vivek Seelanki, and Niki Zadeh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-195, https://doi.org/10.5194/gmd-2024-195, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We made a new regional ocean model to assist fisheries and ecosystem managers make decisions in the Northeast Pacific Ocean (NEP). We found that the model did well simulating past ocean conditions like temperature, and nutrient and oxygen levels, and can even reproduce metrics used by and important to ecosystem managers.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Cited articles
Alexander, P., Bechtel, B., Chow, W., Fealy, R., and Mills, G.: Linking urban climate classification with an urban energy and water budget model: Multi-site and multi-seasonal evaluation, Urban Clim., 17, 196–215, https://doi.org/10.1016/j.uclim.2016.08.003, 2016. a
Alexander, P. J., Mills, G., and Fealy, R.: Using LCZ data to run an urban energy balance model, Urban Clim., 13, 14–37, https://doi.org/10.1016/j.uclim.2015.05.001, 2015. a
Allen, L., Lindberg, F., and Grimmond, C. S. B.: Global to city scale urban anthropogenic heat flux: Model and variability, Int. J. Climatol., 31, 1990–2005, https://doi.org/10.1002/joc.2210, 2010. a
Ao, X., Grimmond, C. S. B., Liu, D., Han, Z., Hu, P., Wang, Y., Zhen, X., and Tan, J.: Radiation Fluxes in a Business District of Shanghai, China, J. Appl. Meteorol. Clim., 55, 2451–2468, https://doi.org/10.1175/jamc-d-16-0082.1, 2016. a
Ao, X., Grimmond, C. S. B., Ward, H. C., Gabey, A. M., Tan, J., Yang, X.-Q., Liu, D., Zhi, X., Liu, H., and Zhang, N.: Evaluation of the Surface Urban Energy and Water Balance Scheme (SUEWS) at a Dense Urban Site in Shanghai: Sensitivity to Anthropogenic Heat and Irrigation, J. Hydrometeorol., 19, 1983–2005, https://doi.org/10.1175/jhm-d-18-0057.1, 2018. a, b, c
Baklanov, A., Grimmond, C., Carlson, D., Terblanche, D., Tang, X., Bouchet, V., Lee, B., Langendijk, G., Kolli, R., and Hovsepyan, A.: From urban meteorology, climate and environment research to integrated city services, Urban Clim., 23, 330–341, https://doi.org/10.1016/j.uclim.2017.05.004, 2018. a
Banks, R. F., Tiana-Alsina, J., Rocadenbosch, F., and Baldasano, J. M.: Performance Evaluation of the Boundary-Layer Height from Lidar and the Weather Research and Forecasting Model at an Urban Coastal Site in the North-East Iberian Peninsula, Bound.-Lay. Meteorol., 157, 265–292, https://doi.org/10.1007/s10546-015-0056-2, 2015. a, b
Banks, R. F., Tiana-Alsina, J., Baldasano, J. M., Rocadenbosch, F., Papayannis, A., Solomos, S., and Tzanis, C. G.: Sensitivity of boundary-layer variables to PBL schemes in the WRF model based on surface meteorological observations, lidar, and radiosondes during the HygrA-CD campaign, Atmos. Res., 176-177, 185–201, https://doi.org/10.1016/j.atmosres.2016.02.024, 2016. a
Barlow, J. F., Dunbar, T. M., Nemitz, E. G., Wood, C. R., Gallagher, M. W., Davies, F., O'Connor, E., and Harrison, R. M.: Boundary layer dynamics over London, UK, as observed using Doppler lidar during REPARTEE-II, Atmos. Chem. Phys., 11, 2111–2125, https://doi.org/10.5194/acp-11-2111-2011, 2011. a
Best, M. J. and Grimmond, C. S. B.: Key Conclusions of the First International Urban Land Surface Model Comparison Project, B. Am. Meteorol. Soc., 96, 805–819, https://doi.org/10.1175/bams-d-14-00122.1, 2015. a
Best, M. J., Grimmond, C. S. B., and Villani, M. G.: Evaluation of the Urban Tile in MOSES using Surface Energy Balance Observations, Bound.-Lay. Meteorol., 118, 503–525, https://doi.org/10.1007/s10546-005-9025-5, 2006. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Bohnenstengel, S. I., Hamilton, I., Davies, M., and Belcher, S. E.: Impact of anthropogenic heat emissions on London's temperatures, Q. J. Roy. Meteor. Soc., 140, 687–698, https://doi.org/10.1002/qj.2144, 2013. a
Capel-Timms, I., Smith, S. T., Sun, T., and Grimmond, S.: Dynamic Anthropogenic activitieS impacting Heat emissions (DASH v1.0): development and evaluation, Geosci. Model Dev., 13, 4891–4924, https://doi.org/10.5194/gmd-13-4891-2020, 2020. a, b
Chen, F., Kusaka, H., Bornstein, R., Ching, J., Grimmond, C. S. B., Grossman-Clarke, S., Loridan, T., Manning, K. W., Martilli, A., Miao, S., Sailor, D., Salamanca, F. P., Taha, H., Tewari, M., Wang, X., Wyszogrodzki, A. A., and Zhang, C.: The integrated WRF/urban modelling system: Development, evaluation, and applications to urban environmental problems, Int. J. Climatol., 31, 273–288, https://doi.org/10.1002/joc.2158, 2011. a
Chrysoulakis, N., Grimmond, S., Feigenwinter, C., Lindberg, F., Gastellu-Etchegorry, J.-P., Marconcini, M., Mitraka, Z., Stagakis, S., Crawford, B., Olofson, F., Landier, L., Morrison, W., and Parlow, E.: Urban energy exchanges monitoring from space, Sci. Rep., 8, 11498, https://doi.org/10.1038/s41598-018-29873-x, 2018. a
Demuzere, M., Harshan, S., Järvi, L., Roth, M., Grimmond, C. S. B., Masson, V., Oleson, K. W., Velasco, E., and Wouters, H.: Impact of urban canopy models and external parameters on the modelled urban energy balance in a tropical city, Q. J. Roy. Meteor. Soc., 143, 1581–1596, https://doi.org/10.1002/qj.3028, 2017. a
Dou, J., Grimmond, S., Cheng, Z., Miao, S., Feng, D., and Liao, M.: Summertime surface energy balance fluxes at two Beijing sites, Int. J. Climatol., 39, 2793–2810, https://doi.org/10.1002/joc.5989, 2019. a
Dyer, A. J.: A review of flux-profile relationships, Bound.-Lay. Meteorol., 7, 363–372, https://doi.org/10.1007/bf00240838, 1974. a
ECMWF: IFS Documentation CY47R3 – Part IV Physical processes, ECMWF, https://doi.org/10.21957/eyrpir4vj, 2021. a
Feng, J.-M., Wang, Y.-L., Ma, Z.-G., and Liu, Y.-H.: Simulating the Regional Impacts of Urbanization and Anthropogenic Heat Release on Climate across China, J. Climate, 25, 7187–7203, https://doi.org/10.1175/jcli-d-11-00333.1, 2012. a
Gabey, A. M., Grimmond, C. S. B., and Capel-Timms, I.: Anthropogenic heat flux: Advisable spatial resolutions when input data are scarce, Theor. Appl. Climatol., 135, 791–807, https://doi.org/10.1007/s00704-018-2367-y, 2018. a
Grimmond, C., Cleugh, H., and Oke, T.: An objective urban heat storage model and its comparison with other schemes, Atmos. Environ. B, 25, 311–326, https://doi.org/10.1016/0957-1272(91)90003-w, 1991. a, b, c
Grimmond, C. S. B. and Oke, T. R.: Aerodynamic Properties of Urban Areas Derived from Analysis of Surface Form, J. Appl. Meteorol., 38, 1262–1292, https://doi.org/10.1175/1520-0450(1999)038<1262:apouad>2.0.co;2, 1999. a, b, c
Grimmond, C. S. B. and Oke, T. R.: Turbulent Heat Fluxes in Urban Areas: Observations and a Local-Scale Urban Meteorological Parameterization Scheme (LUMPS), J. Appl. Meteorol., 41, 792–810, https://doi.org/10.1175/1520-0450(2002)041<0792:thfiua>2.0.co;2, 2002. a
Grimmond, C. S. B., Oke, T. R., and Steyn, D. G.: Urban Water Balance: 1. A Model for Daily Totals, Water Resour. Res., 22, 1397–1403, https://doi.org/10.1029/wr022i010p01397, 1986. a, b, c, d
Grimmond, C. S. B., Blackett, M., Best, M. J., Baik, J.-J., Belcher, S. E., Beringer, J., Bohnenstengel, S. I., Calmet, I., Chen, F., Coutts, A., Dandou, A., Fortuniak, K., Gouvea, M. L., Hamdi, R., Hendry, M., Kanda, M., Kawai, T., Kawamoto, Y., Kondo, H., Krayenhoff, E. S., Lee, S.-H., Loridan, T., Martilli, A., Masson, V., Miao, S., Oleson, K., Ooka, R., Pigeon, G., Porson, A., Ryu, Y.-H., Salamanca, F., Steeneveld, G., Tombrou, M., Voogt, J. A., Young, D. T., and Zhang, N.: Initial results from Phase 2 of the international urban energy balance model comparison, Int. J. Climatol., 31, 244–272, https://doi.org/10.1002/joc.2227, 2010a. a, b
Grimmond, C. S. B., Blackett, M., Best, M. J., Barlow, J., Baik, J.-J., Belcher, S. E., Bohnenstengel, S. I., Calmet, I., Chen, F., Dandou, A., Fortuniak, K., Gouvea, M. L., Hamdi, R., Hendry, M., Kawai, T., Kawamoto, Y., Kondo, H., Krayenhoff, E. S., Lee, S.-H., Loridan, T., Martilli, A., Masson, V., Miao, S., Oleson, K., Pigeon, G., Porson, A., Ryu, Y.-H., Salamanca, F., Shashua-Bar, L., Steeneveld, G.-J., Tombrou, M., Voogt, J., Young, D., and Zhang, N.: The International Urban Energy Balance Models Comparison Project: First Results from Phase 1, J. Appl. Meteorol. Clim., 49, 1268–1292, https://doi.org/10.1175/2010jamc2354.1, 2010b. a, b
Grimmond, S., Bouchet, V., Molina, L. T., Baklanov, A., Tan, J., Schlünzen, K. H., Mills, G., Golding, B., Masson, V., Ren, C., Voogt, J., Miao, S., Lean, H., Heusinkveld, B., Hovespyan, A., Teruggi, G., Parrish, P., and Joe, P.: Integrated urban hydrometeorological, climate and environmental services: Concept, methodology and key messages, Urban Clim., 33, 100623, https://doi.org/10.1016/j.uclim.2020.100623, 2020. a
Halios, C. H. and Barlow, J. F.: Observations of the Morning Development of the Urban Boundary Layer Over London, UK, Taken During the ACTUAL Project, Bound.-Lay. Meteorol., 166, 395–422, https://doi.org/10.1007/s10546-017-0300-z, 2017. a
Hamilton, I. G., Davies, M., Steadman, P., Stone, A., Ridley, I., and Evans, S.: The significance of the anthropogenic heat emissions of London's buildings: A comparison against captured shortwave solar radiation, Build. Environ., 44, 807–817, https://doi.org/10.1016/j.buildenv.2008.05.024, 2009. a
Hertwig, D., Grimmond, S., Hendry, M. A., Saunders, B., Wang, Z., Jeoffrion, M., Vidale, P. L., McGuire, P. C., Bohnenstengel, S. I., Ward, H. C., and Kotthaus, S.: Urban signals in high-resolution weather and climate simulations: Role of urban land-surface characterisation, Theor. Appl. Climatol., 142, 701–728, https://doi.org/10.1007/s00704-020-03294-1, 2020. a, b
Hogan, R. J.: Flexible Treatment of Radiative Transfer in Complex Urban Canopies for Use in Weather and Climate Models, Bound.-Lay. Meteorol., 173, 53–78, https://doi.org/10.1007/s10546-019-00457-0, 2019. a
Högström, U.: Non-dimensional wind and temperature profiles in the atmospheric surface layer: A re-evaluation, Bound.-Lay. Meteorol., 42, 55–78, https://doi.org/10.1007/bf00119875, 1988. a
Hollinger, D. Y. and Richardson, A. D.: Uncertainty in eddy covariance measurements and its application to physiological models, Tree Physiol., 25, 873–885, https://doi.org/10.1093/treephys/25.7.873, 2005. a
Hutchinson, T. A.: An adaptive time-step for increased model efficiency, in: Extended Abstracts, Eighth WRF Users' Workshop, 5 June 2009, Omaha, Nebraska, 4, 2007. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res., 113, 13103,, https://doi.org/10.1029/2008jd009944, 2008. a, b, c
Iamarino, M., Beevers, S., and Grimmond, C. S. B.: High-resolution (space, time) anthropogenic heat emissions: London 1970–2025, Int. J. Climatol., 32, 1754–1767, https://doi.org/10.1002/joc.2390, 2011. a, b
Janjić, Z. I.: The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes, Mon. Weather Rev., 122, 927–945, https://doi.org/10.1175/1520-0493(1994)122<0927:tsmecm>2.0.co;2, 1994. a, b, c
Järvi, L., Grimmond, C. S. B., Taka, M., Nordbo, A., Setälä, H., and Strachan, I. B.: Development of the Surface Urban Energy and Water Balance Scheme (SUEWS) for cold climate cities, Geosci. Model Dev., 7, 1691–1711, https://doi.org/10.5194/gmd-7-1691-2014, 2014. a, b, c, d
Järvi, L., Grimmond, C. S. B., McFadden, J. P., Christen, A., Strachan, I. B., Taka, M., Warsta, L., and Heimann, M.: Warming effects on the urban hydrology in cold climate regions, Sci. Rep., 7, 1–8,, https://doi.org/10.1038/s41598-017-05733-y, 2017. a, b
Järvi, L., Rannik, Ü., Kokkonen, T. V., Kurppa, M., Karppinen, A., Kouznetsov, R. D., Rantala, P., Vesala, T., and Wood, C. R.: Uncertainty of eddy covariance flux measurements over an urban area based on two towers, Atmos. Meas. Tech., 11, 5421–5438, https://doi.org/10.5194/amt-11-5421-2018, 2018. a
Järvi, L., Havu, M., Ward, H. C., Bellucco, V., McFadden, J. P., Toivonen, T., Heikinheimo, V., Kolari, P., Riikonen, A., and Grimmond, C. S. B.: Spatial Modeling of Local-Scale Biogenic and Anthropogenic Carbon Dioxide Emissions in Helsinki, J. Geophys. Res.-Atmos., 124, 8363–8384, https://doi.org/10.1029/2018jd029576, 2019. a, b, c
Jimenez, P. A., Hacker, J. P., Dudhia, J., Haupt, S. E., Ruiz-Arias, J. A., Gueymard, C. A., Thompson, G., Eidhammer, T., and Deng, A.: WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction, B. Am. Meteorol. Soc., 97, 1249–1264, https://doi.org/10.1175/bams-d-14-00279.1, 2016. a
Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C. S. B., Oleson, K. W., Kouznetsov, R., Masson, V., and Järvi, L.: Seasonal surface urban energy balance and wintertime stability simulated using three land-surface models in the high-latitude city Helsinki, Q. J. Roy. Meteor. Soc., 142, 401–417, https://doi.org/10.1002/qj.2659, 2015. a
Kawai, T., Ridwan, M. K., and Kanda, M.: Evaluation of the Simple Urban Energy Balance Model Using Selected Data from 1-yr Flux Observations at Two Cities, J. Appl. Meteorol. Clim., 48, 693–715, https://doi.org/10.1175/2008jamc1891.1, 2009. a
Kim, Y., Sartelet, K., Raut, J.-C., and Chazette, P.: Evaluation of the Weather Research and Forecast/Urban Model Over Greater Paris, Bound.-Lay. Meteorol., 149, 105–132, https://doi.org/10.1007/s10546-013-9838-6, 2013. a, b
Kokkonen, T., Grimmond, C., Räty, O., Ward, H., Christen, A., Oke, T., Kotthaus, S., and Järvi, L.: Sensitivity of Surface Urban Energy and Water Balance Scheme (SUEWS) to downscaling of reanalysis forcing data, Urban Clim., 23, 36–52, https://doi.org/10.1016/j.uclim.2017.05.001, 2018a. a
Kokkonen, T. V., Grimmond, C. S. B., Christen, A., Oke, T. R., and Järvi, L.: Changes to the Water Balance Over a Century of Urban Development in Two Neighborhoods: Vancouver, Canada, Water Resour. Res., 54, 6625–6642, https://doi.org/10.1029/2017wr022445, 2018b. a
Kokkonen, T. V., Grimmond, S., Murto, S., Liu, H., Sundström, A.-M., and Järvi, L.: Simulation of the radiative effect of haze on the urban hydrological cycle using reanalysis data in Beijing, Atmos. Chem. Phys., 19, 7001–7017, https://doi.org/10.5194/acp-19-7001-2019, 2019. a, b
Kotthaus, S. and Grimmond, C.: Energy exchange in a dense urban environment – Part I: Temporal variability of long-term observations in central London, Urban Clim., 10, 261–280, https://doi.org/10.1016/j.uclim.2013.10.002, 2014a. a, b, c
Kotthaus, S. and Grimmond, C.: Energy exchange in a dense urban environment – Part II: Impact of spatial heterogeneity of the surface, Urban Clim., 10, 281–307, https://doi.org/10.1016/j.uclim.2013.10.001, 2014b. a, b, c, d
Kotthaus, S. and Grimmond, C. S. B.: Atmospheric boundary-layer characteristics from ceilometer measurements. Part 1: A new method to track mixed layer height and classify clouds, Q. J. Roy. Meteor. Soc., 144, 1525–1538, https://doi.org/10.1002/qj.3299, 2018a. a, b, c, d
Kotthaus, S. and Grimmond, C. S. B.: Atmospheric boundary-layer characteristics from ceilometer measurements. Part 2: Application to London's urban boundary layer, Q. J. Roy. Meteor. Soc., 144, 1511–1524, https://doi.org/10.1002/qj.3298, 2018b. a
Kotthaus, S., O'Connor, E., Münkel, C., Charlton-Perez, C., Haeffelin, M., Gabey, A. M., and Grimmond, C. S. B.: Recommendations for processing atmospheric attenuated backscatter profiles from Vaisala CL31 ceilometers, Atmos. Meas. Tech., 9, 3769–3791, https://doi.org/10.5194/amt-9-3769-2016, 2016. a
Kotthaus, S., Halios, C. H., Barlow, J. F., and Grimmond, C.: Volume for pollution dispersion: London's atmospheric boundary layer during ClearfLo observed with two ground-based lidar types, Atmos. Environ., 190, 401–414, https://doi.org/10.1016/j.atmosenv.2018.06.042, 2018. a
Kotthaus, S., Bravo-Aranda, J. A., Collaud Coen, M., Guerrero-Rascado, J. L., Costa, M. J., Cimini, D., O'Connor, E. J., Hervo, M., Alados-Arboledas, L., Jiménez-Portaz, M., Mona, L., Ruffieux, D., Illingworth, A., and Haeffelin, M.: Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations, Atmos. Meas. Tech., 16, 433–479, https://doi.org/10.5194/amt-16-433-2023, 2023. a, b
Kusaka, H., Kondo, H., Kikegawa, Y., and Kimura, F.: A Simple Single-Layer Urban Canopy Model For Atmospheric Models: Comparison With Multi-Layer And Slab Models, Bound.-Lay. Meteorol., 101, 329–358, https://doi.org/10.1023/a:1019207923078, 2001. a, b
Lapo, K. E., Hinkelman, L. M., Sumargo, E., Hughes, M., and Lundquist, J. D.: A critical evaluation of modeled solar irradiance over California for hydrologic and land surface modeling, J. Geophys. Res.-Atmos., 122, 299–317, https://doi.org/10.1002/2016jd025527, 2017. a
Lindberg, F., Grimmond, C., Yogeswaran, N., Kotthaus, S., and Allen, L.: Impact of city changes and weather on anthropogenic heat flux in Europe 1995–2015, Urban Clim., 4, 1–15, https://doi.org/10.1016/j.uclim.2013.03.002, 2013. a, b
Lindberg, F., Grimmond, C., Gabey, A., Huang, B., Kent, C. W., Sun, T., Theeuwes, N. E., Järvi, L., Ward, H. C., Capel-Timms, I., Chang, Y., Jonsson, P., Krave, N., Liu, D., Meyer, D., Olofson, K. F. G., Tan, J., Wästberg, D., Xue, L., and Zhang, Z.: Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services, Environ. Model. Softw., 99, 70–87, https://doi.org/10.1016/j.envsoft.2017.09.020, 2018. a, b
Loridan, T., Grimmond, C. S. B., Offerle, B. D., Young, D. T., Smith, T. E. L., Järvi, L., and Lindberg, F.: Local-Scale Urban Meteorological Parameterization Scheme (LUMPS): Longwave Radiation Parameterization and Seasonality-Related Developments, J. Appl. Meteorol. Clim., 50, 185–202, https://doi.org/10.1175/2010jamc2474.1, 2011. a
Loridan, T., Lindberg, F., Jorba, O., Kotthaus, S., Grossman-Clarke, S., and Grimmond, C. S. B.: High Resolution Simulation of the Variability of Surface Energy Balance Fluxes Across Central London with Urban Zones for Energy Partitioning, Bound.-Lay. Meteorol., 147, 493–523, https://doi.org/10.1007/s10546-013-9797-y, 2013. a, b, c
Mahrt, L., Thomas, C., Richardson, S., Seaman, N., Stauffer, D., and Zeeman, M.: Non-stationary Generation of Weak Turbulence for Very Stable and Weak-Wind Conditions, Bound.-Lay. Meteorol., 147, 179–199, https://doi.org/10.1007/s10546-012-9782-x, 2012. a
Marconcini, M., Heldens, W., Del Frate, F., Latini, D., Mitraka, Z., and Lindberg, F.: EO-based products in support of urban heat fluxes estimation, in: 2017 Joint Urban Remote Sensing Event (JURSE), 1–4, IEEE, https://doi.org/10.1109/jurse.2017.7924592, 2017. a
Martilli, A., Clappier, A., and Rotach, M. W.: An Urban Surface Exchange Parameterisation for Mesoscale Models, Bound.-Lay. Meteorol., 104, 261–304, https://doi.org/10.1023/a:1016099921195, 2002. a
Masson, V.: A Physically-Based Scheme For The Urban Energy Budget In Atmospheric Models, Bound.-Lay. Meteorol., 94, 357–397, https://doi.org/10.1023/a:1002463829265, 2000. a
Masson, V., Lemonsu, A., Hidalgo, J., and Voogt, J.: Urban Climates and Climate Change, Annu. Rev. Env. Resour., 45, 411–444, https://doi.org/10.1146/annurev-environ-012320-083623, 2020. a
Meyer, D., Schoetter, R., Riechert, M., Verrelle, A., Tewari, M., Dudhia, J., Masson, V., van Reeuwijk, M., and Grimmond, S.: WRF-TEB: Implementation and Evaluation of the Coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) Model, J. Adv. Model. Earth Syst., 12, e2019MS001961, https://doi.org/10.1029/2019ms001961, 2020. a
Mitchell, V. G., Cleugh, H. A., Grimmond, C. S. B., and Xu, J.: Linking urban water balance and energy balance models to analyse urban design options, Hydrol. Process., 22, 2891–2900, https://doi.org/10.1002/hyp.6868, 2008. a
Mitraka, Z., Del Frate, F., and Carbone, F.: Nonlinear Spectral Unmixing of Landsat Imagery for Urban Surface Cover Mapping, #IEEE_J_STARS#, 9, 3340–3350, https://doi.org/10.1109/jstars.2016.2522181, 2016. a
Monteith, J. L.: Evaporation and environment, in: Symposia of the society for experimental biology, 19, 205–234, Cambridge University Press (CUP) Cambridge, 1965. a
Narumi, D., Kondo, A., and Shimoda, Y.: Effects of anthropogenic heat release upon the urban climate in a Japanese megacity, Environ. Res., 109, 421–431, https://doi.org/10.1016/j.envres.2009.02.013, 2009. a
Offerle, B., Grimmond, C. S. B., and Oke, T. R.: Parameterization of Net All-Wave Radiation for Urban Areas, J. Appl. Meteorol., 42, 1157–1173, https://doi.org/10.1175/1520-0450(2003)042<1157:ponarf>2.0.co;2, 2003. a
Oke, T. R.: Boundary Layer Climates, Routledge, ISBN 9781134951345, https://doi.org/10.4324/9780203407219, 2002. a, b
Omidvar, H., Sun, T., Grimmond, S., Bilesbach, D., Black, A., Chen, J., Duan, Z., Gao, Z., Iwata, H., and McFadden, J. P.: Surface Urban Energy and Water Balance Scheme (v2020a) in vegetated areas: parameter derivation and performance evaluation using FLUXNET2015 dataset, Geosci. Model Dev., 15, 3041–3078, https://doi.org/10.5194/gmd-15-3041-2022, 2022. a, b, c, d, e
Onomura, S., Grimmond, C., Lindberg, F., Holmer, B., and Thorsson, S.: Meteorological forcing data for urban outdoor thermal comfort models from a coupled convective boundary layer and surface energy balance scheme, Urban Clim., 11, 1–23, https://doi.org/10.1016/j.uclim.2014.11.001, 2015. a
Porson, A., Clark, P. A., Harman, I. N., Best, M. J., and Belcher, S. E.: Implementation of a new urban energy budget scheme into MetUM. Part II: Validation against observations and model intercomparison, Q. J. Roy. Meteor. Soc., 136, 1530–1542, https://doi.org/10.1002/qj.572, 2010. a
Rafael, S., Martins, H., Marta-Almeida, M., Sá, E., Coelho, S., Rocha, A., Borrego, C., and Lopes, M.: Quantification and mapping of urban fluxes under climate change: Application of WRF-SUEWS model to Greater Porto area (Portugal), Environ. Res., 155, 321–334, https://doi.org/10.1016/j.envres.2017.02.033, 2017. a
Ryder, C. and Toumi, R.: An urban solar flux island: Measurements from London, Atmos. Environ., 45, 3414–3423, https://doi.org/10.1016/j.atmosenv.2011.03.045, 2011. a
Sailor, D. J. and Vasireddy, C.: Correcting aggregate energy consumption data to account for variability in local weather, Environ. Model. Softw., 21, 733–738, https://doi.org/10.1016/j.envsoft.2005.08.001, 2006. a
Shuttleworth, W. J.: A simplified one-dimensional theoretical description of the vegetation-atmosphere interaction, Bound.-Lay. Meteorol., 14, 3–27, https://doi.org/10.1007/bf00123986, 1978. a
Shuttleworth, W. J.: Evaporation Models in the Global Water Budget, in: Variations in the Global Water Budget, 147–171, Springer Netherlands, ISBN 9789400969568, 9789400969544, https://doi.org/10.1007/978-94-009-6954-4_11, 1983. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Duda, M. G., Barker, D. M., Huang, X.-Y., Wang, W., Powers, J. G., Liu, Z., and Berner, J.: A description of the advanced research WRF model version 4, National Center for Atmospheric Research: Boulder, CO, USA, 145, 550, 2019. a
Sun, J., Wang, Z., Zhou, W., Xie, C., Wu, C., Chen, C., Han, T., Wang, Q., Li, Z., Li, J., Fu, P., Wang, Z., and Sun, Y.: Measurement report: Long-term changes in black carbon and aerosol optical properties from 2012 to 2020 in Beijing, China, Atmos. Chem. Phys., 22, 561–575, https://doi.org/10.5194/acp-22-561-2022, 2022. a
Sun, T. and Grimmond, S.: A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): development, deployment and demonstration, Geosci. Model Dev., 12, 2781–2795, https://doi.org/10.5194/gmd-12-2781-2019, 2019. a, b, c
Sun, T., Grimmond, C. S. B., and Ni, G.-H.: How do green roofs mitigate urban thermal stress under heat waves?, J. Geophys. Res.-Atmos., 121, 5320–5335, https://doi.org/10.1002/2016jd024873, 2016. a
Sun, T., Jarvi, L., Grimmond, S., Lindberg, F., Li, Z., Tang, Y., and Ward, H.: C.: Urban-meteorology-reading/suews: 2018c Release, Zenodo [code], https://doi.org/10.5281/ZENODO.3267306, 2019. a, b
Sun, T., Omidvar, H., and Grimmond, S.: WRF(v4.0)-SUEWS(2018c): Input data for the evaluation at two UK sites, Zenodo [data set], https://doi.org/10.5281/zenodo.7957903, 2023a. a, b
Sun, T., Omidvar, H., Li, Z., and Grimmond, S.: WRF-SUEWS source code for GMD submission, Zenodo [code], https://doi.org/10.5281/zenodo.8137708, 2023b. a
Tang, Y., Sun, T., Luo, Z., Omidvar, H., Theeuwes, N., Xie, X., Xiong, J., Yao, R., and Grimmond, S.: Urban meteorological forcing data for building energy simulations, Build. Environ., 204, 108088, https://doi.org/10.1016/j.buildenv.2021.108088, 2021. a, b
Theeuwes, N. E., Ronda, R. J., Harman, I. N., Christen, A., and Grimmond, C. S. B.: Parametrizing Horizontally-Averaged Wind and Temperature Profiles in the Urban Roughness Sublayer, Bound.-Lay. Meteorol., 173, 321–348, https://doi.org/10.1007/s10546-019-00472-1, 2019. a
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008mwr2387.1, 2008. a
Tiedtke, M.: A Comprehensive Mass Flux Scheme for Cumulus Parameterization in Large-Scale Models, Mon. Weather Rev., 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117<1779:acmfsf>2.0.co;2, 1989. a
Tsiringakis, A., Steeneveld, G.-J., Holtslag, A. A. M., Kotthaus, S., and Grimmond, S.: On- and off-line evaluation of the single-layer urban canopy model in London summertime conditions, Q. J. Roy. Meteor. Soc., 145, 1474–1489, https://doi.org/10.1002/qj.3505, 2019. a
UK ONS: Population, latest available census and estimates (2010 - 2011), in: Statistical Papers – United Nations (Ser. A), Population and Vital Statistics Report, 5–14, UN, ISBN 9789210559881, https://doi.org/10.18356/a7dbb328-en, 2013. a
Van Ulden, A. P. and Holtslag, A. A. M.: Estimation of Atmospheric Boundary Layer Parameters for Diffusion Applications, J. Clim. Appl. Meteorol., 24, 1196–1207, https://doi.org/10.1175/1520-0450(1985)024<1196:eoablp>2.0.co;2, 1985. a
Vilà-Guerau de Arellano, J., Hartogensis, O., Benedict, I., de Boer, H., Bosman, P. J. M., Botía, S., Cecchini, M. A., Faassen, K. A. P., González-Armas, R., van Diepen, K., Heusinkveld, B. G., Janssens, M., Lobos-Roco, F., Luijkx, I. T., Machado, L. A. T., Mangan, M. R., Moene, A. F., Mol, W. B., van der Molen, M., Moonen, R., Ouwersloot, H. G., Park, S.-W., Pedruzo-Bagazgoitia, X., Röckmann, T., Adnew, G. A., Ronda, R., Sikma, M., Schulte, R., van Stratum, B. J. H., Veerman, M. A., van Zanten, M. C., and van Heerwaarden, C. C.: Advancing understanding of land–atmosphere interactions by breaking discipline and scale barriers, Ann. NY Acad. Sci., 1522, 74–97, https://doi.org/10.1111/nyas.14956, 2023. a
Ward, H. and Grimmond, C.: Assessing the impact of changes in surface cover, human behaviour and climate on energy partitioning across Greater London, Landscape Urban Plan., 165, 142–161, https://doi.org/10.1016/j.landurbplan.2017.04.001, 2017. a, b, c
Ward, H. C., Evans, J. G., and Grimmond, C. S. B.: Multi-season eddy covariance observations of energy, water and carbon fluxes over a suburban area in Swindon, UK, Atmos. Chem. Phys., 13, 4645–4666, https://doi.org/10.5194/acp-13-4645-2013, 2013. a, b, c, d
Ward, H. C., Tan, Y. S., Gabey, A. M., Kotthaus, S., and Grimmond, C. S. B.: Impact of temporal resolution of precipitation forcing data on modelled urban-atmosphere exchanges and surface conditions, Int. J. Climatol., 38, 649–662, https://doi.org/10.1002/joc.5200, 2017. a
Warren, E., Charlton-Perez, C., Kotthaus, S., Lean, H., Ballard, S., Hopkin, E., and Grimmond, S.: Evaluation of forward-modelled attenuated backscatter using an urban ceilometer network in London under clear-sky conditions, Atmos. Environ., 191, 532–547, https://doi.org/10.1016/j.atmosenv.2018.04.045, 2018. a
Willmott, C., Robeson, S., and Matsuura, K.: Climate and Other Models May Be More Accurate Than Reported, Eos (Washington DC), DC, https://doi.org/10.1029/2017eo074939, 2017. a
Xu, J., Li, C., Shi, H., He, Q., and Pan, L.: Analysis on the impact of aerosol optical depth on surface solar radiation in the Shanghai megacity, China, Atmos. Chem. Phys., 11, 3281–3289, https://doi.org/10.5194/acp-11-3281-2011, 2011. a
Yang, Y., Guo, M., Ren, G., Liu, S., Zong, L., Zhang, Y., Zheng, Z., Miao, Y., and Zhang, Y.: Modulation of Wintertime Canopy Urban Heat Island (CUHI) Intensity in Beijing by Synoptic Weather Pattern in Planetary Boundary Layer, J. Geophys. Res.-Atmos., 127, e2021JD035988, https://doi.org/10.1029/2021jd035988, 2022. a
Zhang, C., Wang, Y., and Hamilton, K.: Improved Representation of Boundary Layer Clouds over the Southeast Pacific in ARW-WRF Using a Modified Tiedtke Cumulus Parameterization Scheme*, Mon. Weather Rev., 139, 3489–3513, https://doi.org/10.1175/mwr-d-10-05091.1, 2011. a
Zhang, N. and Chen, Y.: A Case Study of the Upwind Urbanization Influence on the Urban Heat Island Effects along the Suzhou–Wuxi Corridor, J. Appl. Meteorol. Clim., 53, 333–345, https://doi.org/10.1175/jamc-d-12-0219.1, 2014. a
Zhang, N., Wang, X., Chen, Y., Dai, W., and Wang, X.: Numerical simulations on influence of urban land cover expansion and anthropogenic heat release on urban meteorological environment in Pearl River Delta, Theor. Appl. Climatol., 126, 469–479, https://doi.org/10.1007/s00704-015-1601-0, 2015. a
Zheng, Z., Ren, G., Gao, H., and Yang, Y.: Urban ventilation planning and its associated benefits based on numerical experiments: A case study in beijing, China, Build. Environ., 222, 109383, https://doi.org/10.1016/j.buildenv.2022.109383, 2022. a
Zong, L., Yang, Y., Xia, H., Gao, M., Sun, Z., Zheng, Z., Li, X., Ning, G., Li, Y., and Lolli, S.: Joint occurrence of heatwaves and ozone pollution and increased health risks in Beijing, China: role of synoptic weather pattern and urbanization, Atmos. Chem. Phys., 22, 6523–6538, https://doi.org/10.5194/acp-22-6523-2022, 2022. a
Short summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban...
