Geoscientific Model Development

The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-55,2024
Preprint under review for GMD (discussion: open, 0 comments)
The Modular and Integrated Data Assimilation System (MIDAS) is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.

Refactoring the EVP solver for improved performance – a case study based on CICE v6.5
Till Andreas Soya Rasmussen, Jacob Poulsen, Mads Hvid Ribergaard, Ruchira Sasanka, Anthony P. Craig, Elizabeth Clare Hunke, and Stefan Rethmeier
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-40,2024
Preprint under review for GMD (discussion: open, 0 comments)
Earth system models (ESM) today strive towards better quality based on improved resolution and improved physics. One of the limiting factors is the super computers at hand and how to utilize these. This study focus on a refactorization of one part of a sea ice model (CICE), namely the dynamics. The study shows that the performance can be significantly reduced, which means that one can either run the same simulations much cheaper or advance the system according to what is needed.

Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2023-195,2024
Preprint under review for GMD (discussion: open, 0 comments)
The satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator highlighting its added value through a case study and diagnostics.

Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, 2024
The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.

A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, 2024
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-flux term. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.

A new temperature–photoperiod coupled phenology module in LPJ-GUESS model v4.1: optimizing estimation of terrestrial carbon and water processes
Shouzhi Chen, Yongshuo H. Fu, Mingwei Li, Zitong Jia, Yishuo Cui, and Jing Tang
Geosci. Model Dev., 17, 2509–2523, https://doi.org/10.5194/gmd-17-2509-2024, 2024
It is still a challenge to achieve an accurate simulation of vegetation phenology in the dynamic global vegetation models (DGVMs). We implemented and coupled the spring and autumn phenology models into one of the DGVMs, LPJ-GUESS, and substantially improved the accuracy in capturing the start and end dates of growing seasons. Our study highlights the importance of getting accurate phenology estimations to reduce the uncertainties in plant distribution and terrestrial carbon and water cycling.

DCMIP2016: the tropical cyclone test case
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, 2024
Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.

Quantifying the role of ozone-caused damage to vegetation in the Earth system: A new parameterization scheme for photosynthetic and stomatal responses
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-6,2024
Preprint under review for GMD (discussion: open, 0 comments)
This study introduces a new scheme to simulate surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal response to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.

At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-36,2024
Preprint under review for GMD (discussion: open, 0 comments)
This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.

Intercomparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1–CMAQ v5.3.1, WRF–Chem v4.1.1, and WRF v3.7.1–CHIMERE v2020r1) in eastern China
Chao Gao, Xuelei Zhang, Aijun Xiu, Qingqing Tong, Hongmei Zhao, Shichun Zhang, Guangyi Yang, Mengduo Zhang, and Shengjin Xie
Geosci. Model Dev., 17, 2471–2492, https://doi.org/10.5194/gmd-17-2471-2024, 2024
A comprehensive comparison study is conducted targeting the performances of three two-way coupled meteorology and air quality models (WRF-CMAQ, WRF-Chem, and WRF-CHIMERE) for eastern China during 2017. The impacts of aerosol–radiation–cloud interactions on these models’ results are evaluated against satellite and surface observations. Further improvements to the calculation of aerosol–cloud interactions in these models are crucial to ensure more accurate and timely air quality forecasts.

MESSAGEix-GLOBIOM nexus module: integrating water sector and climate impacts
Muhammad Awais, Adriano Vinca, Edward Byers, Stefan Frank, Oliver Fricko, Esther Boere, Peter Burek, Miguel Poblete Cazenave, Paul Natsuo Kishimoto, Alessio Mastrucci, Yusuke Satoh, Amanda Palazzo, Madeleine McPherson, Keywan Riahi, and Volker Krey
Geosci. Model Dev., 17, 2447–2469, https://doi.org/10.5194/gmd-17-2447-2024, 2024
Climate change, population growth, and depletion of natural resources all pose complex and interconnected challenges. Our research offers a novel model that can help in understanding the interplay of these aspects, providing policymakers with a more robust tool for making informed future decisions. The study highlights the significance of incorporating climate impacts within large-scale global integrated assessments, which can help us in generating more climate-resilient scenarios.

ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package
Daniel Giles, Matthew M. Graham, Mosè Giordano, Tuomas Koskela, Alexandros Beskos, and Serge Guillas
Geosci. Model Dev., 17, 2427–2445, https://doi.org/10.5194/gmd-17-2427-2024, 2024
Digital twins of physical and human systems informed by real-time data are becoming ubiquitous across a wide range of settings. Progress for researchers is currently limited by a lack of tools to run these models effectively and efficiently. A key challenge is the optimal use of high-performance computing environments. The work presented here focuses on a developed open-source software platform which aims to improve this usage, with an emphasis placed on flexibility, efficiency, and scalability.

NeuralMie (v1.0): An Aerosol Optics Emulator
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-30,2024
Preprint under review for GMD (discussion: open, 0 comments)
Particles in the Earth’s atmosphere strongly impact the planet’s energy budget and atmosphere simulations require accurately representing their interaction with light. This work introduces two approaches to representing light scattering by small particles. The first is a scattering simulation based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.

Impact of ocean vertical mixing parameterization on Arctic sea ice and upper ocean properties using the NEMO-SI3 model
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-49,2024
Preprint under review for GMD (discussion: open, 0 comments)
We study the parameters involved in the turbulent kinetic energy mixed layer penetration scheme of the NEMO model in Arctic sea ice-covered regions. This evaluation reveals the impact of these parameters on mixed layer depth, sea surface temperature and salinity, and ocean stratification. Our findings also demonstrate considerable impacts on sea ice thickness and sea ice concentration, emphasizing the importance of accurate ocean mixing representation in understanding Arctic climate dynamics.

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
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
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
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
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
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.