Geoscientific Model Development

A Fortran-Python Interface for Integrating Machine Learning Parameterization into Earth System Models
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-79,2024
Preprint under review for GMD (discussion: open, 0 comments)
Earth System Models (ESMs) struggle the uncertainties associated with parameterizing sub-grid 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.

In silico calculation of soil pH by SCEPTER v1.0
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, 2024
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.

Modelling water quantity and quality for integrated water cycle management with the Water Systems Integrated Modelling framework (WSIMOD) software
Barnaby Dobson, Leyang Liu, and Ana Mijic
Geosci. Model Dev., 17, 4495–4513, https://doi.org/10.5194/gmd-17-4495-2024, 2024
Water management is challenging when models don't capture the entire water cycle. We propose that using integrated models facilitates management and improves understanding. We introduce a software tool designed for this task. We discuss its foundation, how it simulates water system components and their interactions, and its customisation. We provide a flexible way to represent water systems, and we hope it will inspire more research and practical applications for sustainable water management.

A Unified System for Evaluating, Ranking and Clustering in Diverse Scientific Domains
Zengyun Hu, Xi Chen, Deliang Chen, Zhuo Zhang, Qiming Zhou, and Qingxiang Li
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-82,2024
Preprint under review for GMD (discussion: open, 0 comments)
ERC firstly unified the evaluating, ranking, and clustering by a simple mathematic equation based on Euclidean Distance. It provides new system to solve the evaluating, ranking, and clustering tasks in SDGs. In fact, ERC system can be applied in any scientific domain.

Implementation of a Simple Actuator Disk for Large-Eddy Simulation in the Weather Research and Forecasting Model (WRF-SADLES v1.2) for wind turbine wake simulation
Hai Bui, Mostafa Bakhoday-Paskyabi, and Mohammadreza Mohammadpour-Penchah
Geosci. Model Dev., 17, 4447–4465, https://doi.org/10.5194/gmd-17-4447-2024, 2024
We developed a new wind turbine wake model, the Simple Actuator Disc for Large Eddy Simulation (SADLES), integrated with the widely used Weather Research and Forecasting (WRF) model. WRF-SADLES accurately simulates wind turbine wakes at resolutions of a few dozen meters, aligning well with idealized simulations and observational measurements. This makes WRF-SADLES a promising tool for wind energy research, offering a balance between accuracy, computational efficiency, and ease of implementation.

WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, 2024
This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.

Implementation and evaluation of diabatic advection in the Lagrangian transport model MPTRAC 2.6
Jan Clemens, Lars Hoffmann, Bärbel Vogel, Sabine Grießbach, and Nicole Thomas
Geosci. Model Dev., 17, 4467–4493, https://doi.org/10.5194/gmd-17-4467-2024, 2024
Lagrangian transport models simulate the transport of air masses in the atmosphere. For example, one model (CLaMS) is well suited to calculating transport as it uses a special coordinate system and special vertical wind. However, it only runs inefficiently on modern supercomputers. Hence, we have implemented the benefits of CLaMS into a new model (MPTRAC), which is already highly efficient on modern supercomputers. Finally, in extensive tests, we showed that CLaMS and MPTRAC agree very well.

GNNWR: An Open-Source Package of Spatiotemporal Intelligent Regression Methods for Modeling Spatial and Temporal Non-Stationarity
Ziyu Yin, Jiale Ding, Yi Liu, Ruoxu Wang, Yige Wang, Yijun Chen, Jin Qi, Sensen Wu, and Zhenhong Du
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-62,2024
Preprint under review for GMD (discussion: open, 1 comment)
In geography, understanding how relationships between different factors change over time and space is crucial. This study implements two neural network-based spatiotemporal regression models as well as an open-sourced Python package named GNNWR, to accurately capture the varying relationships between factors. This makes it a valuable tool for researchers in various fields, such as environmental science, urban planning, and public health.

An improved and extended parameterization of the CO2 15 µm cooling in the middle and upper atmosphere (CO2_cool_fort-1.0)
Manuel López-Puertas, Federico Fabiano, Victor Fomichev, Bernd Funke, and Daniel R. Marsh
Geosci. Model Dev., 17, 4401–4432, https://doi.org/10.5194/gmd-17-4401-2024, 2024
The radiative infrared cooling of CO2 in the middle atmosphere is crucial for computing its thermal structure. It requires one however to include non-local thermodynamic equilibrium processes which are computationally very expensive, which cannot be afforded by climate models. In this work, we present an updated, efficient, accurate and very fast (~50 µs) parameterization of that cooling able to cope with CO2 abundances from half the pre-industrial values to 10 times the current abundance.

GEOMAPLEARN 1.0: Detecting geological structures from geological maps with machine learning
David Oakley, Christelle Loiselet, Thierry Coowar, Vincent Labbe, and Jean-Paul Callot
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-35,2024
Preprint under review for GMD (discussion: open, 0 comments)
In this work, we develop two automated workflows for identifying fold structures on geological maps using machine learning. In one method, we identify map patterns suggestive of folding based on pre-defined rules and apply a clustering algorithm to group those from the same fold together. In the other, we train a convolutional neural network to identify folds based on a set of training examples. We apply both methods to a set of synthetic maps and to real-world maps from two locations in France.

Learning from conceptual models – a study of emergence of cooperation towards resource protection in a social-ecological system
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-57,2024
Preprint under review for GMD (discussion: open, 0 comments)
Social-ecological systems are the subject of many sustainability problems. Because of the complexity of these systems we must be careful when intervening in them, otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation, and simulated an intervention measure to save a forest from infestation.

Autoencoder-based feature extraction for the automatic detection of snow avalanches in seismic data
Andri Simeon, Cristina Pérez-Guillén, Michele Volpi, Christine Seupel, and Alec van Herwijnen
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-76,2024
Preprint under review for GMD (discussion: open, 0 comments)
Avalanche seismic detection systems are key for forecasting, but distinguishing avalanches from other seismic sources remains challenging. We propose novel autoencoder models to automatically extract features and compare them with standard seismic attributes. These features are then used to classify avalanches and noise events. The autoencoder feature classifiers have the highest sensitivity to detect avalanches, while the standard seismic classifier performs better overall.

Comparison of the Coastal and Regional Ocean COmmunity model (CROCO) and NCAR-LES in non-hydrostatic simulations
Xiaoyu Fan, Baylor Fox-Kemper, Nobuhiro Suzuki, Qing Li, Patrick Marchesiello, Peter P. Sullivan, and Paul S. Hall
Geosci. Model Dev., 17, 4095–4113, https://doi.org/10.5194/gmd-17-4095-2024, 2024
Simulations of the oceanic turbulent boundary layer using the nonhydrostatic CROCO ROMS and NCAR-LES models are compared. CROCO and the NCAR-LES are accurate in a similar manner, but CROCO’s additional features (e.g., nesting and realism) and its compressible turbulence formulation carry additional costs.

Simulations of Snow Physicochemical Properties in Northern China using WRF-Chem
Xia Wang, Tao Che, Xueyin Ruan, Shanna Yue, Jing Wang, Chun Zhao, and Lei Geng
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-37,2024
Preprint under review for GMD (discussion: open, 0 comments)
We employed the WRF-Chem model to parameterize atmospheric nitrate deposition in snow and evaluated its performance in simulating snow cover, snow depth, and concentrations of black carbon (BC), dust, and nitrate using new observations from Northern China. The results generally exhibit reasonable agreement with field observations in northern China, demonstrating the model's capability to simulate snow properties, including concentrations of reservoir species.

The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): Mercury modeling to support international environmental policy
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Terry Keating, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-65,2024
Preprint under review for GMD (discussion: open, 1 comment)
This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed to inform the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic and multi-media mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases in the environment.

Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)
Felix Wieser, Rolf Sander, Changmin Cho, Hendrik Fuchs, Thorsten Hohaus, Anna Novelli, Ralf Tillmann, and Domenico Taraborrelli
Geosci. Model Dev., 17, 4311–4330, https://doi.org/10.5194/gmd-17-4311-2024, 2024
The chemistry scheme of the atmospheric box model CAABA/MECCA is expanded to achieve an improved aerosol formation from emitted organic compounds. In addition to newly added reactions, temperature-dependent partitioning of all new species between the gas and aqueous phases is estimated and included in the pre-existing scheme. Sensitivity runs show an overestimation of key compounds from isoprene, which can be explained by a lack of aqueous-phase degradation reactions and box model limitations.

Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.

VISIR-2: ship weather routing in Python
Gianandrea Mannarini, Mario Leonardo Salinas, Lorenzo Carelli, Nicola Petacco, and Josip Orović
Geosci. Model Dev., 17, 4355–4382, https://doi.org/10.5194/gmd-17-4355-2024, 2024
Ship weather routing has the potential to reduce CO2 emissions, but it currently lacks open and verifiable research. The Python-refactored VISIR-2 model considers currents, waves, and wind to optimise routes. The model was validated, and its computational performance is quasi-linear. For a ferry sailing in the Mediterranean Sea, VISIR-2 yields the largest percentage emission savings for upwind navigation. Given the vessel performance curve, the model is generalisable across various vessel types.

Investigating ground-level ozone pollution in semi-arid and arid regions of Arizona using WRF-Chem v4.4 modeling
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, 2024
This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.

Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
Geosci. Model Dev., 17, 4383–4399, https://doi.org/10.5194/gmd-17-4383-2024, 2024
There is relatively limited research on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPUs, have distinct advantages in energy efficiency and scalability. The air quality modeling system can run stably on the MIPS and LoongArch platforms, and the experiment results verify the stability of scientific computing on the platforms. The work provides a technical foundation for the scientific application based on MIPS and LoongArch.