Articles | Volume 6, issue 6
https://doi.org/10.5194/gmd-6-1941-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/gmd-6-1941-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
08 Nov 2013
Model description paper |
| 08 Nov 2013
The GREENROOF module (v7.3) for modelling green roof hydrological and energetic performances within TEB
C. S. de Munck
Météo France, CNRM-GAME, CNRS UMR3589, Centre National de Recherches Météorologiques, Toulouse, France
A. Lemonsu
Météo France, CNRM-GAME, CNRS UMR3589, Centre National de Recherches Météorologiques, Toulouse, France
R. Bouzouidja
NIDAPLAST, Thiant, France
Centre d'Études Techniques de l'Équipement de l'Est, Tomblaine, France
Université de Lorraine, LEMTA UMR7563, Vand\oe uvre-les-Nancy, France
V. Masson
Météo France, CNRM-GAME, CNRS UMR3589, Centre National de Recherches Météorologiques, Toulouse, France
R. Claverie
Centre d'Études Techniques de l'Équipement de l'Est, Tomblaine, France
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Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-233, https://doi.org/10.5194/gmd-2024-233, 2025
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The greening of cities is recommended to limit the effects of climate change. In particular, green roofs can provide numerous environmental benefits, such as urban cooling, water retention and carbon sequestration. The aim of this research is to develop a new module for calculating green roof CO2 fluxes within a model that can already simulate hydrological and thermal processes of such roofs. The calibration and evaluation of this module take advantage of long term experimental data.
Perrine Hamel, Martí Bosch, Léa Tardieu, Aude Lemonsu, Cécile de Munck, Chris Nootenboom, Vincent Viguié, Eric Lonsdorf, James A. Douglass, and Richard P. Sharp
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The InVEST Urban Cooling model estimates the cooling effect of vegetation in cities. We further developed an algorithm to facilitate model calibration and evaluation. Applying the algorithm to case studies in France and in the United States, we found that nighttime air temperature estimates compare well with reference datasets. Estimated change in temperature from a land cover scenario compares well with an alternative model estimate, supporting the use of the model for urban planning decisions.
Robert Schoetter, Yu Ting Kwok, Cécile de Munck, Kevin Ka Lun Lau, Wai Kin Wong, and Valéry Masson
Geosci. Model Dev., 13, 5609–5643, https://doi.org/10.5194/gmd-13-5609-2020, https://doi.org/10.5194/gmd-13-5609-2020, 2020
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Cities change the local meteorological conditions, e.g. by increasing air temperature, which can negatively impact humans and infrastructure. The urban climate model TEB is able to calculate the meteorological conditions in low- and mid-rise cities since it interacts with the lowest level of an atmospheric model. Here, a multi-layer coupling of TEB is introduced to enable modelling the urban climate of cities with many skyscrapers; the new version is tested for the high-rise city of Hong Kong.
Aurélien Mirebeau, Cécile de Munck, Bertrand Bonan, Christine Delire, Aude Lemonsu, Valéry Masson, and Stephan Weber
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-233, https://doi.org/10.5194/gmd-2024-233, 2025
Preprint under review for GMD
Short summary
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The greening of cities is recommended to limit the effects of climate change. In particular, green roofs can provide numerous environmental benefits, such as urban cooling, water retention and carbon sequestration. The aim of this research is to develop a new module for calculating green roof CO2 fluxes within a model that can already simulate hydrological and thermal processes of such roofs. The calibration and evaluation of this module take advantage of long term experimental data.
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
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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.
Perrine Hamel, Martí Bosch, Léa Tardieu, Aude Lemonsu, Cécile de Munck, Chris Nootenboom, Vincent Viguié, Eric Lonsdorf, James A. Douglass, and Richard P. Sharp
Geosci. Model Dev., 17, 4755–4771, https://doi.org/10.5194/gmd-17-4755-2024, https://doi.org/10.5194/gmd-17-4755-2024, 2024
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The InVEST Urban Cooling model estimates the cooling effect of vegetation in cities. We further developed an algorithm to facilitate model calibration and evaluation. Applying the algorithm to case studies in France and in the United States, we found that nighttime air temperature estimates compare well with reference datasets. Estimated change in temperature from a land cover scenario compares well with an alternative model estimate, supporting the use of the model for urban planning decisions.
Robert Schoetter, Yu Ting Kwok, Cécile de Munck, Kevin Ka Lun Lau, Wai Kin Wong, and Valéry Masson
Geosci. Model Dev., 13, 5609–5643, https://doi.org/10.5194/gmd-13-5609-2020, https://doi.org/10.5194/gmd-13-5609-2020, 2020
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Cities change the local meteorological conditions, e.g. by increasing air temperature, which can negatively impact humans and infrastructure. The urban climate model TEB is able to calculate the meteorological conditions in low- and mid-rise cities since it interacts with the lowest level of an atmospheric model. Here, a multi-layer coupling of TEB is introduced to enable modelling the urban climate of cities with many skyscrapers; the new version is tested for the high-rise city of Hong Kong.
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Geosci. Model Dev., 13, 385–399, https://doi.org/10.5194/gmd-13-385-2020, https://doi.org/10.5194/gmd-13-385-2020, 2020
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The TEB urban climate model simulates micrometeorological conditions from the neighborhood scale to the entire city. It has recently been improved to more realistically address the radiative effects of trees within the urban canopy. This article presents additional developments that have been made to better represent the effect of trees on heat and moisture exchange, as well as on air flow in the streets, and on thermal comfort.
Xenia Stavropulos-Laffaille, Katia Chancibault, Jean-Marc Brun, Aude Lemonsu, Valéry Masson, Aaron Boone, and Hervé Andrieu
Geosci. Model Dev., 11, 4175–4194, https://doi.org/10.5194/gmd-11-4175-2018, https://doi.org/10.5194/gmd-11-4175-2018, 2018
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Integrating vegetation in urban planning is promoted to counter steer potential impacts of climate and demographic changes. Assessing the multiple benefits of such strategies on the urban microclimate requires a detailed coupling of both the water and energy transfers in numerical tools. In this respect, the representation of water-related processes in the urban subsoil of the existing model TEB-Veg has been improved. The new model thus allows a better evaluation of urban adaptation strategies.
Emilie C. Redon, Aude Lemonsu, Valéry Masson, Benjamin Morille, and Marjorie Musy
Geosci. Model Dev., 10, 385–411, https://doi.org/10.5194/gmd-10-385-2017, https://doi.org/10.5194/gmd-10-385-2017, 2017
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In order to assess the potential of cooling of urban vegetation in cities, we need to refine some processes in the microclimate models running on cities as the TEB model. The shading effects of trees on roads, low vegetation (grass), or walls are key processes impacting both air and surface temperatures in the streets by reducing them and improving the thermal comfort of inhabitants. They have been implemented into the TEB model and simulations have been evaluated by a fine-scale model, SOLENE.
V. Vionnet, E. Martin, V. Masson, G. Guyomarc'h, F. Naaim-Bouvet, A. Prokop, Y. Durand, and C. Lac
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V. Masson, P. Le Moigne, E. Martin, S. Faroux, A. Alias, R. Alkama, S. Belamari, A. Barbu, A. Boone, F. Bouyssel, P. Brousseau, E. Brun, J.-C. Calvet, D. Carrer, B. Decharme, C. Delire, S. Donier, K. Essaouini, A.-L. Gibelin, H. Giordani, F. Habets, M. Jidane, G. Kerdraon, E. Kourzeneva, M. Lafaysse, S. Lafont, C. Lebeaupin Brossier, A. Lemonsu, J.-F. Mahfouf, P. Marguinaud, M. Mokhtari, S. Morin, G. Pigeon, R. Salgado, Y. Seity, F. Taillefer, G. Tanguy, P. Tulet, B. Vincendon, V. Vionnet, and A. Voldoire
Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, https://doi.org/10.5194/gmd-6-929-2013, 2013
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Atmos. Chem. Phys., 13, 4941–4961, https://doi.org/10.5194/acp-13-4941-2013, https://doi.org/10.5194/acp-13-4941-2013, 2013
S. Faroux, A. T. Kaptué Tchuenté, J.-L. Roujean, V. Masson, E. Martin, and P. Le Moigne
Geosci. Model Dev., 6, 563–582, https://doi.org/10.5194/gmd-6-563-2013, https://doi.org/10.5194/gmd-6-563-2013, 2013
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Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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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
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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.
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
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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.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
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This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
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We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
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Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
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Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
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We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
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We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
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When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Cited articles
ACERMI [Association pour la CERtification des Matériaux Isolants: CSTB-LNE]: CERTIFICAT ACERMI No. 04/003/323, Licence no. 04/003/323, 2009.
Alexandri, E. and Jones, P.: Developing a one-dimensional heat and mass transfer algorithm for describing the effect of green roofs on the built environment: Comparison with experimental results, Build. Environ., 42, 2835–2849, https://doi.org/10.1016/j.buildenv.2006.07.004, 2007.
ARGEX: available at: http://www.argex.eu/en/products/characteristics.html (last access: 24 February 2012), 2012.
Bass, B. and Baskaran, B.: Evaluating Rooftop and Vertical Gardens as an Adaptation Strategy for Urban Areas, Report no NRCC-46737, Edited by National Research Council Canada, Institute for Research in Construction, Ottawa (Canada), 2003.
Bass, B., Krayenhoff, E. S., Martilli, A., Stull, R. B., and Auld, H.: The impact of green roofs on Toronto's urban heat island, in: Proceedings of the First North American Green Roof Conference: Greening Rooftops for Sustainable Communities, 20–30 May, Chicago, Toronto (Canada), Cardinal Group, 292–304, 2003.
Berghage, R., Beattie, D., Jarrett, A., Thuring, C., and Razaei, F.: Green Roofs for Stormwater Runoff Control, Report no EPA/600/R-09/026, February 2009, edited by: the National Risk Management Research Laboratory, Office of Research and Development, US Environment Protection, Agency, Cincinnati, OH 45268, 2009.
Berndtsson, J. C., Bengtsson, L., and Jinno, K.: Runoff water quality from intensive and extensive vegetated roofs, Ecol. Eng., 35, 369–380, 2009.
BING [Federation of European Rigid Polyurethane Foam Associations]: Thermal insulation materials made of rigid polyurethane foam (PUR/PIR), Properties – Manufacture, Report no 1, 6 October, 2006, available at: http://www.excellence-in-insulation.eu/site/fileadmin/user_upload/PDF/Thermal_insulation_materials_made_of_rigid_polyurethane_foam.pdf, last access: 27 October 2013.
Boone, A.: Modélisation des processus hydrologiques dans le schéma de surface ISBA: Inclusion d'un reservoir hydrologique, du gel et modélisation de la neige, PhD thesis, Université Paul Sabatier, Toulouse, France, 2000.
Boone, A., Masson, V., Meyers, T., and Noilhan, J.: The influence of the inclusion of soil freezing on simulations by a soil-vegetation-atmosphere transfer scheme, J. Appl. Meteorol., 39, 1544–1569, 2000.
Bouzouidja, R.: Caractérisation du substrat d'une toiture végétalisée, Master's thesis, University of Lorraine, Nancy, France, 2010.
Bouzouidja, R., Lacroix, D., Séré, G. and Claverie, R.: Experimental determination of hydrological parameters of green roofs, Build. Environ., in preparation, 2012.
Bueno, B., Pigeon, G., Norford, L. K., Zibouche, K., and Marchadier, C.: Development and evaluation of a building energy model integrated in the TEB scheme, Geosci. Model Dev., 5, 433–448, https://doi.org/10.5194/gmd-5-433-2012, 2012.
Carter, T. and Butler, C.: Ecological impacts of replacing traditional roofs with green roofs in two urban areas, Cities Environ., 1, 1–17, 2008.
Castleton, H. F., Stovin, V., Beck, S. B. M., and Davison, J. B.: Green roofs; building energy savings and the potential for retrofit, Energ. Buildings, 41, 1582–1591, 2010.
Clapp, R. B. and Hornberger, G. M.: Empirical equations for some soil hydraulic properties, Water Resour. Res., 14, 601–604, 1978.
CRITT Horticole [Centre régional pour l'Innovation et le Transfert Technologique Horticole]: Définition et mesure du LAI, Intégration de la résistance stomatique (RS) dans la formule de l'ETP, Unpublished report, 2012.
Decharme, B., Boone, A., Delire, C., and Noilhan, J.: Local evaluation of the Interaction between Soil Biosphere Atmosphere soil multilayer scheme using four pedotransfer functions, J. Geophys. Res., 116, D20126, https://doi.org/10.1029/2011JD016002, 2011.
Del Barrio, E. P.: Analysis of the green roofs cooling potential in buildings, Energ. Buildings, 27, 179–193, 1998.
de Munck, C., Pigeon, G., Masson, V., Meunier, F., Bousquet, P., Tréméac, B., Merchat, M., Poeuf, P., and Marchadier, C.: How much air conditioning can increase air temperatures for a city like Paris (France)?, Int. J. Climatol., 33, 210–227, https://doi.org/10.1002/joc.3415, 2013.
Diak, G. R., Bland, W. L., Mecikalski, J. R., and Anderson, M. C.: Satellite-based estimates of longwave radiation for agricultural applications, Agr. Forest Meteorol., 103, 349–355, https://doi.org/10.1016/S0168-1923(00)00141-6, 2000.
Doya, M., Briottet, X., Djedjig, R., and Ouldboukhitine, S.: Description des mesures VEGDUD du 23/09/2011, Unpublished report, 2011.
Durham, A. K., Rowe, D. B., and Rugh, C. L.: Effect of watering regimen on chlorophyll fluorescence and growth of selected green roof plant taxa, HortScience, 41, 1623–1628, 2006.
EEA: European Environment Agency: Urban adaptation to climate change in Europe, Challenges and opportunities for cities together with supportive national and European policies, EEA Report no 2/2012, ISSN 1725-9177, 2012.
Falienor: Résultats de l'analyse de substrat de toiture végétalisée No 1807676 – 14-F TOITURE par le Laboratoire SAS, Issued on 09/03/2010, 2010.
Farouki, O. T.: Thermal properties of soils, Ser. on Rock and Soil Mech., 11, 136 pp., Trans. Tech. Publ., Clausthal-Zellerfled, Germany, 1986.
Feng, C., Meng, Q., and Zhang, Y.: Theoretical and experimental analysis of the energy balance of extensive green roofs, Energ. Buildings, 42, 959–965, 2010.
Foster, J., Lowe, A., and Winkelman, S.: The value of green infrastructure for urban climate adaptation, Published by the Center for Clean Air Policy, Washington DC, USA, February 2011, 2011.
Getter, K. L. and Rowe, D. B.: The role of extensive green roofs in sustainable development, HortScience, 41, 1276–1285, 2006.
Giguère, M.: Mesures de lutte aux îlots de chaleur urbains. Revue de literature, Direction des risques biologiques, environnementaux et occupationnels, Institut National de Santé Publique, Gouvernement du Québec, Juillet 2009, 2009.
Gill, S. E., Handley, J. F., Ennos, A. R., and Pauleit, S.: Adapting cities for climate change: the role of the green infrastructure, Built Environ., 33, 115–133, 2007.
Hamdi, R. and Masson, V.: Inclusion of a Drag Approach in the Town Energy Balance (TEB) Scheme: Offline 1D Evaluation in a Street Canyon, J. Appl. Meteorol. Clim., 47, 2627–2644, https://doi.org/10.1175/2008JAMC1865.1, 2008.
Hilten, R. N., Lawrence, T. M., and Tollner, E. W.: Modeling stormwater runoff from green roofs with HYDRUS-1D, J. Hydrol., 358, 288–293, 2008.
Jacquet, S.: Performance énergétique d'une toiture végétale au centre-ville de Montréalm Résumé de mémoire, Centre d'Ecologie Urbaine de Montréal, ISBN 978-2-9810129-8-2., available at: http://www.ecologieurbaine.net/node/1214, 2011.
Jaffal, I., Ould-Boukhitine, S.-E., and Belarbi, R.: A comprehensive study of the impact of green roofs on building energy performance, Renew. Energ., 43, 157–164, 2012.
Jarvis, P.: The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field, Philosophical Transactions of the Royal Society of London, Biol. Sci., 273, 593–610, 1976.
Jim, C. Y.: Effect of vegetation biomass structure on thermal performance of tropical green roof, Landscape Ecol. Eng., 8, 173–187, https://doi.org/10.1007/s11355-011-0161-4, 2011.
Jim, C. Y. and He, H.: Coupling dynamics with meteorological conditions in the green roof ecosystem, Ecol. Eng., 36, 1052–1063, 2010.
Jim, C. Y. and Peng, L. L. H.: Weather effect on thermal and energy performance of an extensive tropical green roof, Urban For. Urban Gree., 11, 73–85, 2011.
Jim, C. Y. and Tsang, S. W.: Modeling the heat diffusion process in the abiotic layers of green roofs, Energ. Buildings, 43, 1341–1350, 2011.
Kalzip®: Kalzip low U-value roof system – achieving 0.10 W/m2/K, Product review, 2010, available at: http://www.kalzip.com/PDF/uk/Kalzip-low-U-value-system.pdf, last access: 27 October 2013.
Kumar, R. A. and Kaushik, S. C.: Performance evaluation of green roof and shading for thermal protection of buildings, Build. Environ., 40, 1505–1511, 2005.
Lawlor, G., Currie, B. A., Doshi, H., and Wieditz, I.: Green roofs: a resource manual for municipal policy makers, Report 65255, Published by Canada Mortgage and Housing Corporation, May 2006, 2006.
Lawrence, D. M. and Slater, A. G.: Incorporating organic soil into a global climate model, Clim. Dynam., 30, 145–160, 2008.
Lazzarin, R. M., Castellotti, F., and Busato, F.: Experimental measurements and numerical modelling of a green roof, Energ. Buildings, 37, 1260–1267, 2005.
Leca®: Declaration of technical specifications BS EN 13055-1 for lightweight aggregate, Issued on 01-01-2009, 2009.
Leca®: available at: http://www.leca.co.uk/33755 (last access: 24 February 2012), 2012.
Lemonsu, A. and Masson, V.: Simulation of a summer urban breeze over Paris, Bound.-Lay. Meteorol., 104, 463–490, 2002.
Lemonsu, A., Grimmond, C. S. B., and Masson, V.: Modeling the surface energy balance of the core of an old Mediterranean city: Marseille, J. Appl. Meteorol., 43, 312–327, 2004.
Lemonsu, A., Bélair, S., Mailhot, J., and Leroyer, S.: Evaluation of the Town Energy Balance Model in Cold and Snowy Conditions during the Montreal Urban Snow Experiment 2005, J. Appl. Meteorol. Climatol., 49, 346–362, 2010.
Lemonsu, A., Pigeon, G., Marchadier, C., and Salagnac, J.-L.: Research report for the VURCA project, Scénarios du bâti & simulations, No ANR-08-VULCN-013 VURCA, 47 pp., 2011.
Lemonsu, A., Masson, V., Shashua-Bar, L., Erell, E., and Pearlmutter, D.: Inclusion of vegetation in the Town Energy Balance model for modelling urban green areas, Geosci. Model Dev., 5, 1377–1393, https://doi.org/10.5194/gmd-5-1377-2012, 2012.
Masson, V.: A physically-based scheme for the urban energy budget in atmospheric models, Bound.-Lay. Meteorol., 94, 357–397, 2000.
Masson, V., Champeaux, J. L., Chauvin, F., Meriguet, C., and Pigeon, G.: ECOCLIMAP: a global database of land surface parameters at 1-km resolution in meteorological and climate models, J. Climate, 16, 1261–1282, 2003.
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013.
Mentens, J., Raes, D., and Hermy, M.: Green roofs as a tool for solving the rainwater runoff problem in the urbanized 21st century?, Landscape Urban Plan., 77, 217–226, 2006.
Mualem, Y.: A new model to predicting the hydraulic conductivity of unsaturated porous media, Water Resour. Res., 12, 513–522, 1976.
Nardini, A., Andri, S., and Crasso, M.: Influence of substrate depth and vegetation type on temperature and water runoff mitigation by extensive green roofs: shrubs versus herbaceous plants, Urban Ecosyst., 15, 697–708, https://doi.org/10.1007/s11252-011-0220-5, 2012.
Noilhan, J. and Planton, S.: A simple parameterization for land surface processes for meteorological models, Mon. Weather Rev., 117, 536–549, 1989.
Oberndorfer, E., Lundholm, J., Bass, B., Coffman, R. R., Doshi, H., Dunnett, N., Gaffin, S., Kohler, M., Liu, K. K. Y., and Rowe, B.: Green Roofs as Urban Ecosystems: Ecological Structures, Functions, and Services, BioScience, 57, 823–833, 2007.
Ochs, F., Heidemann, W., and Müller-Steinhagen, H.: Effective thermal conductivity of the insulation of high temperature underground thermal stores during operation, in: Proceedings of the Ecostock 2006 conference, Richard Stockton College, New Jersey, USA, 31 May–2 June 2006, 2006.
Offerle, B., Grimmond, C. S. B., and Fortuniak, K.: Heat storage and anthropogenic heat flux in relation to the energy balance of a central European city centre, Int. J. Climatol., 25, 1405–1419, 2005.
Ouldboukhitine, S.-E., Belarbi, R., Jaffal, I., and Trabelsi, A.: Assessment of green roof thermal behaviour: a couple heat and mass transfer model, Build. Environ., 46, 2624–2631, https://doi.org/10.1016/j.buildenv.2011.06.021, 2011.
Palla, A., Gnecco, I., and Lanza, G.: Unsaturated 2D modelling of subsurface water flow in the coarse-grained porous matrix of a green roof, J. Hydrol., 379, 193–204, 2009.
Palla, A., Gnecco, I., and Lanza, G.: Compared performance of a conceptual and a mechanistic hydrologic models of a green roof, Hydrol. Process., 26, 73–84, 2012.
Penney, J.: Climate change adaptation in the city of Toronto. Lessons for Great Lakes communities. Clean Air Partnership, Toronto, Ontario, Canada, December 2008, available at: http://www.cleanairpartnership.org/files/Climate20Adaptation20the20of20-20for20Lakes2020J.29.pdf (last access: 27 October 2013), 2008.
Peters-Lidard, C. D., Blackburn, E., Lian, X., and Wood, E. F.: The effect of soil thermal conductivity parameterization on surface energy fluxes and temperatures, J. Atmos. Sci., 55, 1209–1224, https://doi.org/10.1175/1520-0469(1998)055<1209:TEOSTC>2.0.CO;2, 1998.
Pigeon, G., Moscicki, M. A., Voogt, J. A., and Masson, V.: Simulation of fall and winter surface energy balance over a dense urban area using the TEB scheme, Meteorol. Atmos. Phys., 102, 159–172, 2008.
Prata, A. J.: A new long-wave formula for estimating downward clear-sky radiation at the Surface, Q. J. R. Meteorol. Soc., 122, 1127–1151, 1996.
RECTICEL®: available at: http://www.recticelinsulation.fr/nos-produits/powerline/#donnees_techniques (last access: 1 June 2012), 2012.
Rosenzweig, C., Solecki, W. D., Parshall, L., Lynn, B., Cox, J., Goldberg, R., Hodges, S., Gaffin, S., Slosberg, R. B., Savio, P., Dunstan, F., and Watson, M.: Mitigating New York City's Heat Island. Integrating Stakeholder Perspectives and Scientific Evaluation, B. Am. Meteorol. Soc., 90, 1297–1312, 2009.
Sailor, D. J.: A green roof model for building energy simulation programs, Energ. Buildings, 40, 1466–1478, 2008.
SILRES®: available at: www.wacker.com/cms/media/publications/downloads/6528_EN.pdf (last access: 24 February 2012), 2012.
Simunek, J., Voel, T., and Van Genuchten, M. Th.: The SWMS_2D code for simulating water flow and solute transport on two-dimensional variably saturated media, Version 1.21., Research report No 132, US Salinity Laboratory, USDA, ARS, Riverside, California, 197 pp., 1994.
Simunkek, J., Van Genuchten, M. Th., and Sejna, M.: The HYDRUS_1D Software package for simulating the one-dimensional movement of water, heat and multiple solutes in variably-saturated media, Version 3.0, HYDRUS Software Series 1. Dept. of Environmental Science, University of California Riverside, Riverside, California, USA, 240 pp., 2005.
Sinclair: available at: http://www.william-sinclair.co.uk/industrial/products/expanded_clay (last access: 24 February 2012), 2012.
SOPREMA®: SOPRALENE® FLAM JARDIN, Fiche technique No DT-10/005_FR CE, 2012a.
SOPREMA®: SOPRALENE® FLAM 180, Fiche technique No DT-09/086_FR CE, 2012b.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106, 7183–7192, 2001.
USEPA: Reducing Urban Heat Islands: Compendium of Strategies, Green Roofs, 26 pp., available at: http://www.epa.gov/hiri/mitigation/greenroofs.htm, 2008.
Van Genuchten, M. Th.: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892–898, 1980.
Van Woert, N. D., Rowe, D. B., Andresen, J. A., Rugh, C. L., and Xiao, L.: Watering regime and green roof substrate design affect Sedum plant growth, HortScience, 40, 659–664, 2005.
Voyde, E., Fassman, E., and Simcock, R.: Hydrology of an extensive living roof under sub-tropical climate conditions in Auckland, New Zealand, J. Hydrol., 394, 384–395, 2010.
Wark, C. G. and Wark, W. W.: Green roof specifications and standards. Establishing an emerging technology, The Construction Specifier, 56, August 2003, n0 8, available at: www.proenviroconstruction.com/pdf/GreenRoof.pdf, 2003.
Wolf, D. and Lundholm, J.T.: Water uptake in green roof microcosms: effects of plant species and water availability, Ecol. Eng., 33, 179–186, 2008.
Yu, C. and Zheng, C.: HYDRUS: software for flow and transport modeling in variably saturated media, Ground Water, 48, 787–791, 2010.
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