Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-2525-2024
© 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-17-2525-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
05 Apr 2024
Model description paper |
| 05 Apr 2024
| 05 Apr 2024
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
School of Built Environment, University of New South Wales, Sydney, Australia
ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia
Negin Nazarian
School of Built Environment, University of New South Wales, Sydney, Australia
ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia
Melissa Anne Hart
ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia
E. Scott Krayenhoff
School of Environmental Sciences, University of Guelph, Guelph, ON, Canada
Alberto Martilli
Atmospheric Pollution Division, Environmental Department, CIEMAT, Madrid, Spain
Related authors
Alberto Martilli, Negin Nazarian, E. Scott Krayenhoff, Jacob Lachapelle, Jiachen Lu, Esther Rivas, Alejandro Rodriguez-Sanchez, Beatriz Sanchez, and José Luis Santiago
Geosci. Model Dev., 17, 5023–5039, https://doi.org/10.5194/gmd-17-5023-2024, https://doi.org/10.5194/gmd-17-5023-2024, 2024
Short summary
Short summary
Here, we present a model that quantifies the thermal stress and its microscale variability at a city scale with a mesoscale model. This tool can have multiple applications, from early warnings of extreme heat to the vulnerable population to the evaluation of the effectiveness of heat mitigation strategies. It is the first model that includes information on microscale variability in a mesoscale model, something that is essential for fully evaluating heat stress.
Gianluca Pappaccogli, Andrea Zonato, Alberto Martilli, Riccardo Buccolieri, and Piero Lionello
EGUsphere, https://doi.org/10.5194/egusphere-2025-219, https://doi.org/10.5194/egusphere-2025-219, 2025
Short summary
Short summary
We present the MLUCM BEP+BEM model that bridges mesoscale and microscale phenomena within the urban canopy, capturing scale interactions and feedback. The accuracy and low computational cost of this one-dimensional model makes it ideal for offline climate projections to assess urban climate impacts under different emission scenarios. The model's features allow analyzing urban overheating, energy demands, and evaluating the efficiency of strategies like green/cool roofs, and photovoltaic panels.
Alberto Martilli, Negin Nazarian, E. Scott Krayenhoff, Jacob Lachapelle, Jiachen Lu, Esther Rivas, Alejandro Rodriguez-Sanchez, Beatriz Sanchez, and José Luis Santiago
Geosci. Model Dev., 17, 5023–5039, https://doi.org/10.5194/gmd-17-5023-2024, https://doi.org/10.5194/gmd-17-5023-2024, 2024
Short summary
Short summary
Here, we present a model that quantifies the thermal stress and its microscale variability at a city scale with a mesoscale model. This tool can have multiple applications, from early warnings of extreme heat to the vulnerable population to the evaluation of the effectiveness of heat mitigation strategies. It is the first model that includes information on microscale variability in a mesoscale model, something that is essential for fully evaluating heat stress.
Matthias Demuzere, Jonas Kittner, Alberto Martilli, Gerald Mills, Christian Moede, Iain D. Stewart, Jasper van Vliet, and Benjamin Bechtel
Earth Syst. Sci. Data, 14, 3835–3873, https://doi.org/10.5194/essd-14-3835-2022, https://doi.org/10.5194/essd-14-3835-2022, 2022
Short summary
Short summary
Because urban areas are key contributors to climate change but are also susceptible to multiple hazards, one needs spatially detailed information on urban landscapes to support environmental services. This global local climate zone map describes this much-needed intra-urban heterogeneity across the whole surface of the earth in a universal language and can serve as a basic infrastructure to study e.g. environmental hazards, energy demand, and climate adaptation and mitigation solutions.
Mohsen Moradi, Benjamin Dyer, Amir Nazem, Manoj K. Nambiar, M. Rafsan Nahian, Bruno Bueno, Chris Mackey, Saeran Vasanthakumar, Negin Nazarian, E. Scott Krayenhoff, Leslie K. Norford, and Amir A. Aliabadi
Geosci. Model Dev., 14, 961–984, https://doi.org/10.5194/gmd-14-961-2021, https://doi.org/10.5194/gmd-14-961-2021, 2021
Short summary
Short summary
The Vertical City Weather Generator (VCWG) is an urban microclimate model developed to predict temporal and vertical variation of potential temperature, wind speed, and specific humidity. VCWG is forced by climate variables at a nearby rural site and coupled to radiation and building energy models. VCWG is evaluated against field observations of the BUBBLE campaign. It is run under exploration mode to assess its performance given urban characteristics, seasonal variations, and climate zones.
Brian N. Bailey, María A. Ponce de León, and E. Scott Krayenhoff
Geosci. Model Dev., 13, 4789–4808, https://doi.org/10.5194/gmd-13-4789-2020, https://doi.org/10.5194/gmd-13-4789-2020, 2020
Short summary
Short summary
Numerous models of plant radiation interception based on a range of assumptions are available in the literature, but the importance of each assumption is not well understood. In this work, we evaluate several assumptions common in simple models of radiation interception in canopies with widely spaced plants by comparing against a detailed 3-D model. This yielded a simple model based on readily measurable parameters that could accurately predict interception for a wide range of architectures.
Gianluca Mussetti, Dominik Brunner, Stephan Henne, Jonas Allegrini, E. Scott Krayenhoff, Sebastian Schubert, Christian Feigenwinter, Roland Vogt, Andreas Wicki, and Jan Carmeliet
Geosci. Model Dev., 13, 1685–1710, https://doi.org/10.5194/gmd-13-1685-2020, https://doi.org/10.5194/gmd-13-1685-2020, 2020
Short summary
Short summary
Street trees are regarded as a powerful measure to reduce excessive heat in cities. To enable city-wide studies of the cooling effect of street trees, we developed a coupled urban climate model with explicit representation of street trees (COSMO-BEP-Tree). The model compares well with surface, flux and satellite observations and responds realistically to changes in tree characteristics. Street trees largely impact energy fluxes and wind speed, while air temperatures are only slightly reduced.
Negin Nazarian, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 13, 937–953, https://doi.org/10.5194/gmd-13-937-2020, https://doi.org/10.5194/gmd-13-937-2020, 2020
Short summary
Short summary
We present an update to the Multi-Layer Urban Canopy Model by revisiting the parameterization of length scales based on high-resolution and validated large-eddy simulations. Additionally, the inclusion of dispersive fluxes in the parameterization schemes are also discussed. The results demonstrate that updated parameterizations improve the accuracy of the vertical exchange of momentum in the street canyon.
Ashley M. Broadbent, Andrew M. Coutts, Kerry A. Nice, Matthias Demuzere, E. Scott Krayenhoff, Nigel J. Tapper, and Hendrik Wouters
Geosci. Model Dev., 12, 785–803, https://doi.org/10.5194/gmd-12-785-2019, https://doi.org/10.5194/gmd-12-785-2019, 2019
Short summary
Short summary
We present a simple model for assessing the cooling impacts of vegetation and water features (green and blue infrastructure) in urban environments. This model is designed to be computationally efficient so that those without technical knowledge or access to high-performance computers can use it. TARGET can be used to model average street-level air temperature at canyon to block scales (e.g. 100 m resolution). The model is carefully designed to provide reliable and accurate cooling estimates.
Giovanni Di Virgilio, Melissa Anne Hart, and Ningbo Jiang
Atmos. Chem. Phys., 18, 6585–6599, https://doi.org/10.5194/acp-18-6585-2018, https://doi.org/10.5194/acp-18-6585-2018, 2018
Short summary
Short summary
Hazard reduction burns (HRBs) may prevent wildfires, but both generate PM2.5 air pollution. We identify the meteorological factors linked to high PM2.5 pollution & assess how they differ between HRB days with low vs. high PM2.5. Boundary layer, cloud cover, temperature & wind speed strongly influence PM2.5, with these factors being more variable & higher in magnitude during mornings & evenings of HRB days when PM2.5 remains low, indicating how HRB timing can be altered to reduce air pollution.
Mathew J. Lipson, Melissa A. Hart, and Marcus Thatcher
Geosci. Model Dev., 10, 991–1007, https://doi.org/10.5194/gmd-10-991-2017, https://doi.org/10.5194/gmd-10-991-2017, 2017
Short summary
Short summary
City-scale models describing the surface energy balance have difficulties representing heat storage in urban materials. This paper proposes an alternative method to discretise heat conduction through urban materials. We compare the new method with an approach commonly used in urban models and find the new method better matches exact solutions to heat transfer for a wide variety of urban material compositions. We also find the new method improves the bulk energy flux response of an urban model.
Beatriz Sanchez, Jose-Luis Santiago, Alberto Martilli, Magdalena Palacios, and Frank Kirchner
Atmos. Chem. Phys., 16, 12143–12157, https://doi.org/10.5194/acp-16-12143-2016, https://doi.org/10.5194/acp-16-12143-2016, 2016
Short summary
Short summary
This paper is focused on analyzing the coupled behavior between dispersion of reactive pollutants and atmospheric dynamics in different atmospheric conditions using a computational fluid dynamics model. It allows one to provide the selection of the chemical reactions needed that gives the best compromise between accuracy in modeling NO and NO2 dispersion in the streets and the computational time required. The conclusions can be applied to future studies about modeling air quality in cities.
Related subject area
Climate and Earth system modeling
Correction of sea surface biases in the NEMO ocean general circulation model using neural networks
Representing lateral groundwater flow from land to river in Earth system models
FINAM is not a model (v1.0): a new Python-based model coupling framework
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Enhancing winter climate simulations of the Great Lakes: insights from a new coupled lake–ice–atmosphere (CLIAv1) system on the importance of integrating 3D hydrodynamics with a regional climate model
Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol–climate model ECHAM6.3–HAM2.3
Assessing the climate impact of an improved volcanic sulfate aerosol representation in E3SM
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
ICON-HAM-lite 1.0: simulating the Earth system with interactive aerosols at kilometer scales
Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions
Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
Reducing time and computing costs in EC-Earth: an automatic load-balancing approach for coupled Earth system models
FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the maximum entropy concept
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
Linear Meta-Model optimization for regional climate models (LiMMo version 1.0)
An example of how data quality hinders progress: translating the latest findings on the regulation of leaf senescence timing in trees into the DP3 model (v1.0)
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
OpenBench: a land models evaluation system
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
ROCKE-3D 2.0: An updated general circulation model for simulating the climates of rocky planets
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The sensitivity of EC-Earth3 decadal predictions to the choice of volcanic forcing dataset: Insights for the next major eruption
A first calibration of the JULES-crop version 7.4 for rice using the novel O3-FACE experiment in China
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
nextGEMS: entering the era of kilometer-scale Earth system modeling
Andrea Storto, Sergey Frolov, Laura Slivinski, and Chunxue Yang
Geosci. Model Dev., 18, 4789–4804, https://doi.org/10.5194/gmd-18-4789-2025, https://doi.org/10.5194/gmd-18-4789-2025, 2025
Short summary
Short summary
Inaccuracies in air–sea heat fluxes severely degrade the accuracy of ocean numerical simulations. Here, we use artificial neural networks to correct air–sea heat fluxes as a function of oceanic and atmospheric state predictors. The correction successfully improves surface and subsurface ocean temperatures beyond the training period and in prediction experiments.
Chang Liao, L. Ruby Leung, Yilin Fang, Teklu Tesfa, and Robinson Negron-Juarez
Geosci. Model Dev., 18, 4601–4624, https://doi.org/10.5194/gmd-18-4601-2025, https://doi.org/10.5194/gmd-18-4601-2025, 2025
Short summary
Short summary
Understanding horizontal groundwater flow is important for understanding how water moves through the ground. Current climate models often simplify this process because they do not have information about the land surface that is detailed enough. Our study developed a new model that divides the land surface into hillslopes to better represent how groundwater flows. This model can help improve predictions of water availability and how it affects ecosystems.
Sebastian Müller, Martin Lange, Thomas Fischer, Sara König, Matthias Kelbling, Jeisson Javier Leal Rojas, and Stephan Thober
Geosci. Model Dev., 18, 4483–4498, https://doi.org/10.5194/gmd-18-4483-2025, https://doi.org/10.5194/gmd-18-4483-2025, 2025
Short summary
Short summary
This study presents FINAM (
FINAM is not a model), a new coupling framework written in Python to dynamically connect independently developed models. Python, as the ultimate glue language, enables the use of codes from nearly any programming language like Fortran, C++, Rust, and others. FINAM is designed to simplify the integration of various models with minimal effort, as demonstrated through various examples ranging from simple to complex systems.
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
Geosci. Model Dev., 18, 4399–4416, https://doi.org/10.5194/gmd-18-4399-2025, https://doi.org/10.5194/gmd-18-4399-2025, 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 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.
Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj B. Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, and Eric Kemp
Geosci. Model Dev., 18, 4293–4316, https://doi.org/10.5194/gmd-18-4293-2025, https://doi.org/10.5194/gmd-18-4293-2025, 2025
Short summary
Short summary
This study introduces a new 3D lake–ice–atmosphere coupled model that significantly improves winter climate simulations for the Great Lakes compared to traditional 1D lake model coupling. The key contribution is the identification of critical hydrodynamic processes – ice transport, heat advection, and shear-driven turbulence production – that influence lake thermal structure and ice cover and explain the superior performance of 3D lake models to their 1D counterparts.
Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
Geosci. Model Dev., 18, 4183–4213, https://doi.org/10.5194/gmd-18-4183-2025, https://doi.org/10.5194/gmd-18-4183-2025, 2025
Short summary
Short summary
This study represents the primary marine organic aerosol (PMOA) emissions, focusing on their sea–atmosphere transfer. Using the FESOM2.1–REcoM3 model, concentrations of key organic biomolecules were estimated and integrated into the ECHAM6.3–HAM2.3 aerosol–climate model. Results highlight the influence of marine biological activity and surface winds on PMOA emissions, with reasonably good agreement with observations improving aerosol representation in the southern oceans.
Ziming Ke, Qi Tang, Jean-Christophe Golaz, Xiaohong Liu, and Hailong Wang
Geosci. Model Dev., 18, 4137–4153, https://doi.org/10.5194/gmd-18-4137-2025, https://doi.org/10.5194/gmd-18-4137-2025, 2025
Short summary
Short summary
This study assesses volcanic aerosol representation in E3SM (Energy Exascale Earth System Model), showing that an emission-based approach moderately improves temperature variability and cloud responses compared to a prescribed forcing approach, yet significant bias persists.
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., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025, https://doi.org/10.5194/gmd-18-4009-2025, 2025
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.
Philipp Weiss, Ross Herbert, and Philip Stier
Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025, https://doi.org/10.5194/gmd-18-3877-2025, 2025
Short summary
Short summary
Aerosols strongly influence Earth's climate as they interact with radiation and clouds. New Earth system models run at resolutions of a few kilometers. To simulate the Earth system with interactive aerosols, we developed a new aerosol module. It represents aerosols as an ensemble of lognormal modes with given sizes and compositions. We present a year-long simulation with four modes at a resolution of 5 km. It captures key processes like the formation of dust storms in the Sahara.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev., 18, 3799–3817, https://doi.org/10.5194/gmd-18-3799-2025, https://doi.org/10.5194/gmd-18-3799-2025, 2025
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 vital 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 multi-objective optimization of rice irrigation schemes at a large scale.
Carolina A. Bieri, Francina Dominguez, Gonzalo Miguez-Macho, and Ying Fan
Geosci. Model Dev., 18, 3755–3779, https://doi.org/10.5194/gmd-18-3755-2025, https://doi.org/10.5194/gmd-18-3755-2025, 2025
Short summary
Short summary
Access to deep moisture below the Earth's surface is important for vegetation in areas of the Amazon where there is little precipitation for part of the year. Most existing numerical models of the Earth system do not adequately capture where and when deep root water uptake occurs. We address this by adding deep soil layers and a root water uptake feature to an existing model. Out modifications lead to increased dry-month transpiration and improved simulation of the annual transpiration cycle.
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev., 18, 3661–3679, https://doi.org/10.5194/gmd-18-3661-2025, https://doi.org/10.5194/gmd-18-3661-2025, 2025
Short summary
Short summary
We present an automatic tool that optimizes resource distribution in coupled climate models, enhancing speed and reducing computational costs without requiring expert knowledge. Users can set energy/time criteria or limit resource usage. Tested on various European Community Earth System Model (EC-Earth) configurations and high-performance computing (HPC) platforms, it achieved up to 34 % faster simulations with fewer resources.
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
Geosci. Model Dev., 18, 3533–3557, https://doi.org/10.5194/gmd-18-3533-2025, https://doi.org/10.5194/gmd-18-3533-2025, 2025
Short summary
Short summary
As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
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.
Sergei Petrov, Andreas Will, and Beate Geyer
EGUsphere, https://doi.org/10.5194/egusphere-2025-710, https://doi.org/10.5194/egusphere-2025-710, 2025
Short summary
Short summary
This study introduces a new method that helps improve the accuracy of climate models by automatically selecting the best parameters to match real-world observations. Instead of manually adjusting many parameters, the method uses a mathematical tool to find the most appropriate settings for the model. It can be especially helpful for researchers who introduce new physical parameters into climate models to assess their impact on model results and optimize them along with the old ones.
Michael Meier, Christof Bigler, and Isabelle Chuine
EGUsphere, https://doi.org/10.5194/egusphere-2025-460, https://doi.org/10.5194/egusphere-2025-460, 2025
Short summary
Short summary
Formulated according to the leaf development process, the DP3 model of leaf coloring considerably contrasts previous models and allows to set up new hypotheses, e.g. on aging versus stress caused color changes. The DP3 model was as accurate as previous models and a comparison to the constant simulation of the mean date of leaf coloring indicated that noisy leaf coloring data forced the models to resort to this mean, which hinders model evaluation.
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.
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibo Liu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2025-1380, https://doi.org/10.5194/egusphere-2025-1380, 2025
Short summary
Short summary
Land surface models are used for simulating earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes evaluation process more comprehensive and efficient. It stands out by incorporating various factors and working with data at any scale which enabling scientists to incorporate new types of models and measurements as our understanding of Earth’s systems evolves.
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.
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
Short summary
Short summary
We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
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.
Roberto Bilbao, Thomas J. Aubry, Matthew Toohey, and Pablo Ortega
EGUsphere, https://doi.org/10.5194/egusphere-2025-609, https://doi.org/10.5194/egusphere-2025-609, 2025
Short summary
Short summary
Large volcanic eruptions are unpredictable and can have significant climatic impacts. If one occurs, operational decadal forecasts will become invalid and must be rerun including the volcanic forcing. By analyzing the climate response in EC-Earth3 retrospective predictions, we show that idealised forcings produced with two simple models could be used in operational decadal forecasts to account for the radiative impacts of the next major volcanic eruption.
Beiyao Xu, Steven Dobbie, Huiyi Yang, Lianxin Yang, Yu Jiang, Andrew Challinor, Karina Williams, Yunxia Wang, and Tijian Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-4077, https://doi.org/10.5194/egusphere-2024-4077, 2025
Short summary
Short summary
Ozone (O3) pollution harms rice production and threatens food security. To understand these impacts, we calibrated a crop model using unique data from experiments where rice was grown in open fields under controlled O3 exposure (free air). This is the first time such data has been used to improve a model’s ability to predict how rice responds to O3 pollution. Our work provides a more accurate tool to study O3’s effects and guide strategies to protect agriculture.
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).
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
EGUsphere, https://doi.org/10.5194/egusphere-2025-509, https://doi.org/10.5194/egusphere-2025-509, 2025
Short summary
Short summary
The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Cited articles
Abdella, K. and Petersen, A.: Third-Order Moment Closure Through A Mass-Flux Approach, Bound.-Lay. Meteorol., 95, 303–318, https://doi.org/10.1023/A:1002629010090, 2000. a
Agbaglah, G. and Mavriplis, C.: Three-dimensional wakes behind cylinders of square and circular cross-section: early and long-time dynamics, J. Fluid Mech., 870, 419–432, https://doi.org/10.1017/jfm.2019.265, 2019. a
Akinlabi, E., Maronga, B., Giometto, M. G., and Li, D.: Dispersive Fluxes Within and Over a Real Urban Canopy: A Large-Eddy Simulation Study, Bound.-Lay. Meteorol., 185, 93–128, https://doi.org/10.1007/s10546-022-00725-6, 2022. a, b
Ayotte, K. W., Finnigan, J. J., and Raupach, M. R.: A Second-Order Closure for Neutrally Stratified Vegetative Canopy Flows, Bound.-Lay. Meteorol., 90, 189–216, https://doi.org/10.1023/A:1001722609229, 1999. 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
Blunn, L. P., Coceal, O., Nazarian, N., Barlow, J. F., Plant, R. S., Bohnenstengel, S. I., and Lean, H. W.: Turbulence Characteristics Across a Range of Idealized Urban Canopy Geometries, Bound.-Lay. Meteorol., 182, 275–307, https://doi.org/10.1007/s10546-021-00658-6, 2022. a, b, c
Boeing, G.: Urban spatial order: street network orientation, configuration, and entropy, Appl. Network Sci., 4, 67, https://doi.org/10.1007/s41109-019-0189-1, 2019. a
Bougeault, P. and Lacarrere, P.: Parameterization of Orography-Induced Turbulence in a Mesobeta–Scale Model, Mon. Weather Rev., 117, 1872–1890, https://doi.org/10.1175/1520-0493(1989)117<1872:POOITI>2.0.CO;2, 1989. a
Brown, M., Lawson, R., DeCroix, D., and Lee, R.: Comparison of Centerline Velocity Measurements Obtained Around 2D and 3D Building Arrays in a Wind Tunnel, article, 1 July 2001, New Mexico, University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu/ark:/67531/metadc716934/m1/1/ (last access: 2 April 2024), 2001. a
Castro, I. P.: Are Urban-Canopy Velocity Profiles Exponential?, Bound.-Lay. Meteorol., 164, 337–351, https://doi.org/10.1007/s10546-017-0258-x, 2017. a, b, c
Cheng, W.-C. and Porté-Agel, F.: A Simple Mixing-Length Model for Urban Canopy Flows, Bound.-Lay. Meteorol., 181, 1–9, https://doi.org/10.1007/s10546-021-00650-0, 2021. a
Christen, A., Rotach, M. W., and Vogt, R.: The Budget of Turbulent Kinetic Energy in the Urban Roughness Sublayer, Bound.-Lay. Meteorol., 131, 193–222, https://doi.org/10.1007/s10546-009-9359-5, 2009. a
Coceal, O. and Belcher, S. E.: A canopy model of mean winds through urban areas, Q. J. Roy. Meteor. Soc., 130, 1349–1372, https://doi.org/10.1256/qj.03.40, 2004. a
Coceal, O., Thomas, T. G., Castro, I. P., and Belcher, S. E.: Mean Flow and Turbulence Statistics Over Groups of Urban-like Cubical Obstacles, Bound.-Lay. Meteorol., 121, 491–519, https://doi.org/10.1007/s10546-006-9076-2, 2006. a
Coceal, O., Dobre, A., Thomas, T. G., and Belcher, S. E.: Structure of turbulent flow over regular arrays of cubical roughness, J. Fluid Mech., 589, 375–409, https://doi.org/10.1017/S002211200700794X, 2007. a
Couvreux, F., Hourdin, F., and Rio, C.: Resolved Versus Parametrized Boundary-Layer Plumes. Part I: A Parametrization-Oriented Conditional Sampling in Large-Eddy Simulations, Bound.-Lay. Meteorol., 134, 441–458, https://doi.org/10.1007/s10546-009-9456-5, 2010. a
Davis, K. A., Pawlak, G., and Monismith, S. G.: Turbulence and Coral Reefs, Annu. Rev. Mar. Sci., 13, 343–373, https://doi.org/10.1146/annurev-marine-042120-071823, 2021. a
Deardorff, J. W.: Stratocumulus-capped mixed layers derived from a three-dimensional model, Bound.-Lay. Meteorol., 18, 495–527, https://doi.org/10.1007/BF00119502, 1980. a
Finnigan, J., Harman, I., Ross, A., and Belcher, S.: First-order turbulence closure for modelling complex canopy flows: First-Order Turbulence Closure for Canopy Flows, Q. J. Roy. Meteor. Soc., 141, 2907–2916, https://doi.org/10.1002/qj.2577, 2015. a
Finnigan, J. J.: Turbulent Transport in Flexible Plant Canopies, Springer Netherlands, Dordrecht, 443–480, ISBN 978-94-009-5305-5, https://doi.org/10.1007/978-94-009-5305-5_28, 1985. a
Finnigan, J. J. and Belcher, S. E.: Flow over a hill covered with a plant canopy, Q. J. Roy. Meteor. Soc., 130, 1–29, https://doi.org/10.1256/qj.02.177, 2004. a
Finnigan, J. J. and Shaw, R. H.: Double-averaging methodology and its application to turbulent flow in and above vegetation canopies, Acta Geophys., 56, 534–561, https://doi.org/10.2478/s11600-008-0034-x, 2008. a
Frigo, M. and Johnson, S.: FFTW: an adaptive software architecture for the FFT, in: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181),IEEE, Seattle, WA, USA, ISBN 978-0-7803-4428-0, vol. 3, 1381–1384, https://doi.org/10.1109/ICASSP.1998.681704, 1998. a
Ghonima, M. S., Yang, H., Kim, C. K., Heus, T., and Kleissl, J.: Evaluation of WRF SCM Simulations of Stratocumulus‐Topped Marine and Coastal Boundary Layers and Improvements to Turbulence and Entrainment Parameterizations, J. Adva. Model. Earth Sy., 9, 2635–2653, https://doi.org/10.1002/2017MS001092, 2017. a
Giometto, M. G., Christen, A., Meneveau, C., Fang, J., Krafczyk, M., and Parlange, M. B.: Spatial Characteristics of Roughness Sublayer Mean Flow and Turbulence Over a Realistic Urban Surface, Bound.-Lay. Meteorol., 160, 425–452, https://doi.org/10.1007/s10546-016-0157-6, 2016. a, b, c
Glazunov, A. V., Debolskiy, A. V., and Mortikov, E. V.: Turbulent Length Scale for Multilayer RANS Model of Urban Canopy and Its Evaluation Based on Large-Eddy Simulations, Supercomputing Frontiers and Innovations, 8, https://doi.org/10.14529/jsfi210409, 2021. a, b
Han, J. and Bretherton, C. S.: TKE-Based Moist Eddy-Diffusivity Mass-Flux (EDMF) Parameterization for Vertical Turbulent Mixing, Weather Forecast., 34, 869–886, https://doi.org/10.1175/WAF-D-18-0146.1, 2019. a
Harman, I. N., Böhm, M., Finnigan, J. J., and Hughes, D.: Spatial Variability of the Flow and Turbulence Within a Model Canopy, Bound.-Lay. Meteorol., 160, 375–396, https://doi.org/10.1007/s10546-016-0150-0, 2016. a
Hendricks, E. A., Knievel, J. C., and Wang, Y.: Addition of Multilayer Urban Canopy Models to a Nonlocal Planetary Boundary Layer Parameterization and Evaluation Using Ideal and Real Cases, J. Appl. Meteorol. Clim., 59, 1369–1392, https://doi.org/10.1175/JAMC-D-19-0142.1, 2020. a, b
Kondo, H., Genchi, Y., Kikegawa, Y., Ohashi, Y., Yoshikado, H., and Komiyama, H.: Development of a Multi-Layer Urban Canopy Model for the Analysis of Energy Consumption in a Big City: Structure of the Urban Canopy Model and its Basic Performance, Bound.-Lay. Meteorol., 116, 395–421, https://doi.org/10.1007/s10546-005-0905-5, 2005. a
Krayenhoff, E. S.: A multi-layer urban canopy model for neighbourhoods with trees., Ph.D. thesis, University of British Columbia, https://doi.org/10.14288/1.0167084, 2014. a
Krayenhoff, E. S., Santiago, J. L., Martilli, A., Christen, A., and Oke, T. R.: Parametrization of Drag and Turbulence for Urban Neighbourhoods with Trees, Bound.-Lay. Meteorol., 156, 157–189, https://doi.org/10.1007/s10546-015-0028-6, 2015. a
Krayenhoff, E. S., Jiang, T., Christen, A., Martilli, A., Oke, T. R., Bailey, B. N., Nazarian, N., Voogt, J. A., Giometto, M. G., Stastny, A., and Crawford, B. R.: Urban Climate A multi-layer urban canopy meteorological model with trees (BEP- Tree): Street tree impacts on pedestrian-level climate, Urban Clim., 32, 100590, https://doi.org/10.1016/j.uclim.2020.100590, 2020. a, b, c
Kusaka, H., Kondo, H., and Kikegawa, Y.: 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
Li, Q. and Bou-Zeid, E.: Contrasts between momentum and scalar transport over very rough surfaces, J. Fluid Mech., 880, 32–58, https://doi.org/10.1017/jfm.2019.687, 2019. a, b, c, d
Li, Q., Bou‐Zeid, E., Grimmond, S., Zilitinkevich, S., and Katul, G.: Revisiting the relation between momentum and scalar roughness lengths of urban surfaces, Q. J. Roy. Meteor. Soc., 146, 3144–3164, https://doi.org/10.1002/qj.3839, 2020. a
Lim, H. D., Hertwig, D., Grylls, T., Gough, H., Reeuwijk, M. v., Grimmond, S., and Vanderwel, C.: Pollutant dispersion by tall buildings: laboratory experiments and Large-Eddy Simulation, Exp. Fluids, 63, 92, https://doi.org/10.1007/s00348-022-03439-0, 2022. a
Lipson, M. J., Grimmond, S., Best, M., Abramowitz, G., Coutts, A., Tapper, N., Baik, J., Beyers, M., Blunn, L., Boussetta, S., Bou‐Zeid, E., De Kauwe, M. G., De Munck, C., Demuzere, M., Fatichi, S., Fortuniak, K., Han, B., Hendry, M. A., Kikegawa, Y., Kondo, H., Lee, D., Lee, S., Lemonsu, A., Machado, T., Manoli, G., Martilli, A., Masson, V., McNorton, J., Meili, N., Meyer, D., Nice, K. A., Oleson, K. W., Park, S., Roth, M., Schoetter, R., Simón‐Moral, A., Steeneveld, G., Sun, T., Takane, Y., Thatcher, M., Tsiringakis, A., Varentsov, M., Wang, C., Wang, Z., and Pitman, A. J.: Evaluation of 30 urban land surface models in the Urban‐PLUMBER project: Phase 1 results, Q. J. Roy. Meteor. Soc., 150, 126–169, https://doi.org/10.1002/qj.4589, 2023. a, b
Lopez‐Gomez, I., Cohen, Y., He, J., Jaruga, A., and Schneider, T.: A Generalized Mixing Length Closure for Eddy‐Diffusivity Mass‐Flux Schemes of Turbulence and Convection, J. Adv. Model. Earth Sy., 12, e2020MS002161, https://doi.org/10.1029/2020MS002161, 2020. a
Lu, J.: Coherent Structures Sampling and a Stochastic Eddy-Diffusivity/Mass-Flux Parameterization for the Transition from Stratocumulus to Cumulus, Master's thesis, UC San Diego, https://escholarship.org/uc/item/6tc324jj (last access: 2 April 2024), 2019. a
Lu, J., Nazarian, N., Anne Hart, M., Krayenhoff, E. S., and Martilli, A: Code for “A one-dimensional urban flow model with an Eddy-diffusivity Mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)” Submitted to GMD, Zenodo [code], https://doi.org/10.5281/zenodo.10207052, 2023c. a
Macdonald, R. W.: Modelling the mean velocity profile in the urban, Bound.-Lay. Meteorol. 97, 25–45, https://doi.org/10.1023/A:1002785830512, 2000. a
Maronga, B., Banzhaf, S., Burmeister, C., Esch, T., Forkel, R., Fröhlich, D., Fuka, V., Gehrke, K. F., Geletič, J., Giersch, S., Gronemeier, T., Groß, G., Heldens, W., Hellsten, A., Hoffmann, F., Inagaki, A., Kadasch, E., Kanani-Sühring, F., Ketelsen, K., Khan, B. A., Knigge, C., Knoop, H., Krč, P., Kurppa, M., Maamari, H., Matzarakis, A., Mauder, M., Pallasch, M., Pavlik, D., Pfafferott, J., Resler, J., Rissmann, S., Russo, E., Salim, M., Schrempf, M., Schwenkel, J., Seckmeyer, G., Schubert, S., Sühring, M., von Tils, R., Vollmer, L., Ward, S., Witha, B., Wurps, H., Zeidler, J., and Raasch, S.: Overview of the PALM model system 6.0, Geosci. Model Dev., 13, 1335–1372, https://doi.org/10.5194/gmd-13-1335-2020, 2020. 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, b, c
Martilli, A., Santiago, J. L., and Salamanca, F.: On the representation of urban heterogeneities in mesoscale models, Environ. Fluid Mech., 15, 305–328, https://doi.org/10.1007/s10652-013-9321-4, 2015. a
McNorton, J. R., Arduini, G., Bousserez, N., Agustí‐Panareda, A., Balsamo, G., Boussetta, S., Choulga, M., Hadade, I., and Hogan, R. J.: An Urban Scheme for the ECMWF Integrated Forecasting System: Single‐Column and Global Offline Application, J. Adv. Model. Earth Sy., 13, e2020MS002375, https://doi.org/10.1029/2020MS002375, 2021. a
Nazarian, N. and Kleissl, J.: Urban Climate CFD simulation of an idealized urban environment: Thermal effects of geometrical characteristics and surface materials, Urban Clim., 12, 141–159, https://doi.org/10.1016/j.uclim.2015.03.002, 2015. a
Nazarian, N., Krayenhoff, E. S., and Martilli, A.: A one-dimensional model of turbulent flow through “urban” canopies (MLUCM v2.0): updates based on large-eddy simulation, Geosci. Model Dev., 13, 937–953, https://doi.org/10.5194/gmd-13-937-2020, 2020. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa
Piacsek, S. A. and Williams, G. P.: Conservation properties of convection difference schemes, J. Comput. Phys., 6, 392–405, https://doi.org/10.1016/0021-9991(70)90038-0, 1970. a
Poggi, D. and Katul, G. G.: The effect of canopy roughness density on the constitutive components of the dispersive stresses, Exp. Fluids, 45, 111–121, https://doi.org/10.1007/s00348-008-0467-7, 2008a. a
Poggi, D. and Katul, G. G.: Micro- and macro-dispersive fluxes in canopy flows, Acta Geophys., 56, 778–799, https://doi.org/10.2478/s11600-008-0029-7, 2008b. a, b, c
Raupach, M. R. and Shaw, R. H.: Averaging procedures for flow within vegetation canopies, Bound.-Lay. Meteorol., 22, 79–90, https://doi.org/10.1007/BF00128057, 1982. a
Redon, E., Lemonsu, A., and Masson, V.: An urban trees parameterization for modeling microclimatic variables and thermal comfort conditions at street level with the Town Energy Balance model (TEB-SURFEX v8.0), Geosci. Model Dev., 13, 385–399, https://doi.org/10.5194/gmd-13-385-2020, 2020. a
Ross, A. N.: Boundary-layer flow within and above a forest canopy of variable density, Q. J. Roy. Meteor. Soc., 138, 1259–1272, https://doi.org/10.1002/qj.989, 2012. a
Salamanca, F., Krpo, A., and Martilli, A.: A new building energy model coupled with an urban canopy parameterization for urban climate simulations – part I. formulation, verification, and sensitivity analysis of the model, Theor. Appl. Climatol., 99, 331–344, https://doi.org/10.1007/s00704-009-0142-9, 2010. a
Santiago, J. L. and Martilli, A.: A Dynamic Urban Canopy Parameterization for Mesoscale Models Based on Computational Fluid Dynamics Reynolds-Averaged Navier-Stokes Microscale Simulations, Bound.-Lay. Meteorol., 137, 417–439, https://doi.org/10.1007/s10546-010-9538-4, 2010a. a
Santiago, J. L., Coceal, O., and Martilli, A.: How to Parametrize Urban-Canopy Drag to Reproduce Wind-Direction Effects Within the Canopy, Bound.-Lay. Meteorol., 149, 43–63, https://doi.org/10.1007/s10546-013-9833-y, 2013. a, b
Santiago, J. L., Krayenhoff, E. S., and Martilli, A.: Urban Climate Flow simulations for simplified urban configurations with microscale distributions of surface thermal forcing, Urban Clim., 9, 115–133, https://doi.org/10.1016/j.uclim.2014.07.008, 2014. a
Schmid, M. F., Lawrence, G. A., Parlange, M. B., and Giometto, M. G.: Volume Averaging for Urban Canopies, Bound.-Lay. Meteorol., 173, 349–372, https://doi.org/10.1007/s10546-019-00470-3, 2019. a
Schoetter, R., Kwok, Y. T., de Munck, C., Lau, K. K. L., Wong, W. K., and Masson, V.: Multi-layer coupling between SURFEX-TEB-v9.0 and Meso-NH-v5.3 for modelling the urban climate of high-rise cities, Geosci. Model Dev., 13, 5609–5643, https://doi.org/10.5194/gmd-13-5609-2020, 2020. a
Siebesma, A. P., Soares, P. M. M., and Teixeira, J.: A Combined Eddy-Diffusivity Mass-Flux Approach for the Convective Boundary Layer, J. Atmos. Sci., 64, 1230–1248, https://doi.org/10.1175/JAS3888.1, 2007. a, b, c
Simón-moral, A., Santiago, J. L., and Martilli, A.: Effects of Unstable Thermal Stratification on Vertical Fluxes of Heat and Momentum in Urban Areas, Bound.-Lay. Meteorol., 163, 103–121, https://doi.org/10.1007/s10546-016-0211-4, 2016. a, b, c, d
Sirko, W., Kashubin, S., Ritter, M., Annkah, A., Bouchareb, Y. S. E., Dauphin, Y., Keysers, D., Neumann, M., Cisse, M., and Quinn, J.: Continental-Scale Building Detection from High Resolution Satellite Imagery, arXiv [preprint], https://doi.org/10.48550/arXiv. 2107.12283, 2021. a
Stiperski, I. and Calaf, M.: Dependence of near‐surface similarity scaling on the anisotropy of atmospheric turbulence, Q. J. Roy. Meteor. Soc., 144, 641–657, https://doi.org/10.1002/qj.3224, 2018. a
Sun, J., Takle, E. S., and Acevedo, O. C.: Understanding Physical Processes Represented by the Monin–Obukhov Bulk Formula for Momentum Transfer, Bound.-Lay. Meteorol., 177, 69–95, https://doi.org/10.1007/s10546-020-00546-5, 2020. a
Sušelj, K., Teixeira, J., and Matheou, G.: Eddy Diffusivity/Mass Flux and Shallow Cumulus Boundary Layer: An Updraft PDF Multiple Mass Flux Scheme, J. Atmos. Sci., 69, 1513–1533, https://doi.org/10.1175/JAS-D-11-090.1, 2012. a
Sützl, B. S., Rooney, G. G., Finnenkoetter, A., Bohnenstengel, S. I., Grimmond, S., and van Reeuwijk, M.: Distributed urban drag parametrization for sub‐kilometre scale numerical weather prediction, Q. J. Roy. Meteor. Soc., 147, 3940–3956, https://doi.org/10.1002/qj.4162, 2021a. a, b
Sützl, B. S., Rooney, G. G., and van Reeuwijk, M.: Drag Distribution in Idealized Heterogeneous Urban Environments, Bound.-Lay. Meteorol., 178, 225–248, https://doi.org/10.1007/s10546-020-00567-0, 2021b. a
Williamson, J.: Low-storage Runge-Kutta schemes, J. Comput. Phys., 35, 48–56, https://doi.org/10.1016/0021-9991(80)90033-9, 1980. a
Wilson, J. D.: A second-order closure model for flow through vegetation, Bound.-Lay. Meteorol., 42, 371–392, https://doi.org/10.1007/BF00121591, 1988. a
Xie, Z.-T., Coceal, O., and Castro, I. P.: Large-Eddy Simulation of Flows over Random Urban-like Obstacles, Bound.-Lay. Meteorol., 129, 1–23, https://doi.org/10.1007/s10546-008-9290-1, 2008. a
Ye, Q., Schrijer, F. F., and Scarano, F.: Geometry effect of isolated roughness on boundary layer transition investigated by tomographic PIV, International J. Heat Fluid Flow, 61, 31–44, https://doi.org/10.1016/j.ijheatfluidflow.2016.05.016, 2016. a
Yuan, C., Shan, R., Zhang, Y., Li, X.-X., Yin, T., Hang, J., and Norford, L.: Multilayer urban canopy modelling and mapping for traffic pollutant dispersion at high density urban areas, Sci. Total Environ., 647, 255–267, https://doi.org/10.1016/j.scitotenv.2018.07.409, 2019. a
Short summary
This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
This study enhances urban canopy models by refining key assumptions. Simulations for various...
