Geoscientific Model Development

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Combined list of the recent articles of the journal Geoscientific Model Development and the recent discussion forum Geoscientific Model Development Discussions
Updated: 14 hours 48 min ago

Methane dynamics in the Baltic Sea: investigating concentration, flux and isotopic composition patterns using the coupled physical-biogeochemical model BALTSEM-CH4 v1.0

Tue, 03/26/2024 - 17:31
Methane dynamics in the Baltic Sea: investigating concentration, flux and isotopic composition patterns using the coupled physical-biogeochemical model BALTSEM-CH4 v1.0
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2023-211,2024
Preprint under review for GMD (discussion: open, 0 comments)
Methane (CH4) cycling in the Baltic Sea is studied through model simulations, allowing a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source, and two main sinks: CH4 oxidation in the water (87 % of the sinks) and outgassing to the atmosphere (13 % of the sinks). This study addresses CH4 emissions from coastal seas and is a first step towards understanding the relative importance of open water outgassing compared to local coastal hotspots.

MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory

Mon, 03/25/2024 - 17:31
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024, 2024
The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.

Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP

Fri, 03/22/2024 - 17:31
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, 2024
Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.

Comparison of 4-dimensional variational and ensemble optimal interpolation data assimilation systems using a Regional Ocean Modeling System (v3.4) configuration of the eddy-dominated East Australian Current system

Fri, 03/22/2024 - 17:31
Comparison of 4-dimensional variational and ensemble optimal interpolation data assimilation systems using a Regional Ocean Modeling System (v3.4) configuration of the eddy-dominated East Australian Current system
Colette Gabrielle Kerry, Moninya Roughan, Shane Keating, David Gwyther, Gary Brassington, Adil Siripatana, and Joao Marcos A. C. Souza
Geosci. Model Dev., 17, 2359–2386, https://doi.org/10.5194/gmd-17-2359-2024, 2024
Ocean forecasting relies on the combination of numerical models and ocean observations through data assimilation (DA). Here we assess the performance of two DA systems in a dynamic western boundary current, the East Australian Current, across a common modelling and observational framework. We show that the more advanced, time-dependent method outperforms the time-independent method for forecast horizons of 5 d. This advocates the use of advanced methods for highly variable oceanic regions.

A dynamic informed deep learning method for future estimation of laboratory stick-slip

Fri, 03/22/2024 - 17:31
A dynamic informed deep learning method for future estimation of laboratory stick-slip
Enjiang Yue, Mengjiao Qin, Linshu Hu, Sensen Wu, and Zhenhong Du
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-46,2024
Preprint under review for GMD (discussion: open, 0 comments)
Laboratory earthquakes is important means to understand natural earthquakes while previous work focused on transient prediction, lacking future prediction capability. We propose a method and evaluate on data from lab experiments with different slip behaviors. It outperforms state-of-the-art methods in modeling slip moments, intervals and predictions beyond trained horizons especially for challenging slip scenarios, which is crucial for quasi-periodic geophysical process like seismicity.

Applying double-cropping and interactive irrigation in the North China Plain using WRF4.5

Fri, 03/22/2024 - 17:31
Applying double-cropping and interactive irrigation in the North China Plain using WRF4.5
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-38,2024
Preprint under review for GMD (discussion: open, 0 comments)
Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.

Tomofast-x 2.0: an open-source parallel code for inversion of potential field data with topography using wavelet compression

Thu, 03/21/2024 - 17:31
Tomofast-x 2.0: an open-source parallel code for inversion of potential field data with topography using wavelet compression
Vitaliy Ogarko, Kim Frankcombe, Taige Liu, Jeremie Giraud, Roland Martin, and Mark Jessell
Geosci. Model Dev., 17, 2325–2345, https://doi.org/10.5194/gmd-17-2325-2024, 2024
We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that is enhancing its performance and applicability for both industrial and academic studies. We focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. The optimisation work described in this paper is fundamental to allowing more complete descriptions of the controls on magnetisation, including remanence.

Advances and prospects of deep learning for medium-range extreme weather forecasting

Thu, 03/21/2024 - 17:31
Advances and prospects of deep learning for medium-range extreme weather forecasting
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, 2024
In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.

Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm

Wed, 03/20/2024 - 17:31
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, 2024
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts

CD-type discretization for sea ice dynamics in FESOM version 2

Wed, 03/20/2024 - 17:31
CD-type discretization for sea ice dynamics in FESOM version 2
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, 2024
Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.

An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)

Wed, 03/20/2024 - 17:31
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, 2024
Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.

cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands

Tue, 03/19/2024 - 17:31
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, 2024
This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.

LOCATE v1.0: numerical modelling of floating marine debris dispersion in coastal regions using Parcels v2.4.2

Tue, 03/19/2024 - 17:31
LOCATE v1.0: numerical modelling of floating marine debris dispersion in coastal regions using Parcels v2.4.2
Ivan Hernandez, Leidy M. Castro-Rosero, Manuel Espino, and Jose M. Alsina Torrent
Geosci. Model Dev., 17, 2221–2245, https://doi.org/10.5194/gmd-17-2221-2024, 2024
The LOCATE numerical model was developed to conduct Lagrangian simulations of the transport and dispersion of marine debris at coastal scales. High-resolution hydrodynamic data and a beaching module that used particle distance to the shore for land–water boundary detection were used on a realistic debris discharge scenario comparing hydrodynamic data at various resolutions. Coastal processes and complex geometric structures were resolved when using nested grids and distance-to-shore beaching.

HETerogeneous vectorized or Parallel (HETPv1.0): an updated inorganic heterogeneous chemistry solver for the metastable-state NH4+–Na+–Ca2+–K+–Mg2+–SO42−–NO3−–Cl−–H2O system based on ISORROPIA II

Tue, 03/19/2024 - 17:31
HETerogeneous vectorized or Parallel (HETPv1.0): an updated inorganic heterogeneous chemistry solver for the metastable-state NH4+–Na+–Ca2+–K+–Mg2+–SO42−–NO3−–Cl−–H2O system based on ISORROPIA II
Stefan J. Miller, Paul A. Makar, and Colin J. Lee
Geosci. Model Dev., 17, 2197–2219, https://doi.org/10.5194/gmd-17-2197-2024, 2024
This work outlines a new solver written in Fortran to calculate the partitioning of metastable aerosols at thermodynamic equilibrium based on the forward algorithms of ISORROPIA II. The new code includes numerical improvements that decrease the computational speed (compared to ISORROPIA II) while improving the accuracy of the partitioning solution.

Introduction of Prognostic Graupel Density in Weather Research and Forecasting (WRF) Double-Moment 6-Class (WDM6) Microphysics and Evaluation of the Modified Scheme During the ICE-POP Field Campaign

Tue, 03/19/2024 - 17:31
Introduction of Prognostic Graupel Density in Weather Research and Forecasting (WRF) Double-Moment 6-Class (WDM6) Microphysics and Evaluation of the Modified Scheme During the ICE-POP Field Campaign
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2023-241,2024
Preprint under review for GMD (discussion: open, 0 comments)
Researchers enhance the WDM6 scheme by incorporating prognostic graupel density. The modification affects graupel characteristics, including fall velocity-diameter and mass-diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating prognostic graupel density for a more realistic portrayal of microphysical properties in weather models.

Regionalization and its impact on global runoff simulations: A case study using the global hydrological model WaterGAP3 (v 1.0.0)

Mon, 03/18/2024 - 17:31
Regionalization and its impact on global runoff simulations: A case study using the global hydrological model WaterGAP3 (v 1.0.0)
Jenny Kupzig, Nina Kupzig, and Martina Floerke
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-47,2024
Preprint under review for GMD (discussion: open, 0 comments)
Valid simulation results from global hydrological models (GHM) are essential, e.g., to studying climate change impacts. Regionalization is a necessary step, to adapt GHM to ungauged basins to enable such valid simulations. In this study, we highlight the impact of regionalization on global simulations by using different regionalization methods. Applying two valid regionalization strategies globally we’ve found that the “outflow to the ocean” changed in the range of inter-model differences.

Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2

Fri, 03/15/2024 - 17:31
Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2
Pedro Felipe Arboleda-Obando, Agnès Ducharne, Zun Yin, and Philippe Ciais
Geosci. Model Dev., 17, 2141–2164, https://doi.org/10.5194/gmd-17-2141-2024, 2024
We show a new irrigation scheme included in the ORCHIDEE land surface model. The new irrigation scheme restrains irrigation due to water shortage, includes water adduction, and represents environmental limits and facilities to access water, due to representing infrastructure in a simple way. Our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, even if there are difficulties due to shortcomings and limited information.

CSDMS Data Components: data–model integration tools for Earth surface processes modeling

Fri, 03/15/2024 - 17:31
CSDMS Data Components: data–model integration tools for Earth surface processes modeling
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, 2024
This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.

Minimum-variance-based outlier detection method using forward-search model error in geodetic networks

Fri, 03/15/2024 - 17:31
Minimum-variance-based outlier detection method using forward-search model error in geodetic networks
Utkan M. Durdağ
Geosci. Model Dev., 17, 2187–2196, https://doi.org/10.5194/gmd-17-2187-2024, 2024
This study introduces a novel approach to outlier detection in geodetic networks, challenging conventional and robust methods. By treating outliers as unknown parameters within the Gauss–Markov model and exploring numerous outlier combinations, this approach prioritizes minimal variance and eliminates iteration dependencies. The mean success rate (MSR) comparisons highlight its effectiveness, improving the MSR by 40–45 % for multiple outliers.

Lagrangian tracking of sea ice in Community Ice CodE (CICE; version 5)

Fri, 03/15/2024 - 17:31
Lagrangian tracking of sea ice in Community Ice CodE (CICE; version 5)
Chenhui Ning, Shiming Xu, Yan Zhang, Xuantong Wang, Zhihao Fan, and Jiping Liu
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-29,2024
Preprint under review for GMD (discussion: open, 0 comments)
Sea ice models are mainly based on non-moving structured grids, which is different from the buoy measurements that follow the ice drift. To facilitate Lagrangian analysis, we introduce online tracking of sea ice in the CICE model. We validate the sea ice tracking with buoys, and further evaluate the sea ice deformations in high-resoltuion simulations, which show multi-fractal characteristics. The source code is openly available and can be used in various scientific and operational applications.

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